From b4cf759f59f5c166075e9330686f814e3c9f82a9 Mon Sep 17 00:00:00 2001 From: Chabname Ghassemi Date: Tue, 24 Sep 2024 08:55:16 +0200 Subject: [PATCH] import MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Co-authored-by: Mathieu Bolteau Co-authored-by: Lucas Bourneuf Co-authored-by: Clémence Frioux Co-authored-by: Loïc Paulevé --- .gitignore | 20 + LICENCE.txt | 145 + MANIFEST.in | 6 + Makefile | 29 + README.mkd | 369 + documentations/developers.mkd | 0 documentations/fba.mkd | 170 + documentations/flux.mkd | 12 + documentations/full_network.mkd | 103 + documentations/network.mkd | 1 + documentations/network_analyse.mkd | 193 + documentations/options.mkd | 11 + .../FBA/fba_avoid_accumulation_seed.png | Bin 0 -> 22583 bytes .../network_analyse/multiple_objective.png | Bin 0 -> 4850 bytes .../network_analyse/objective_found.png | Bin 0 -> 3598 bytes .../network_analyse/objective_not_found.png | Bin 0 -> 4525 bytes .../warning_deleted_reaction.png | Bin 0 -> 8239 bytes .../warning_reaction_backward.png | Bin 0 -> 22787 bytes documentations/target.mkd | 2 + networks/objective/e_coli_core_target.txt | 1 + networks/sbml/e_coli_core.xml | 9898 +++ .../toys/graphs/toy_paper_import_rxn_visu.png | Bin 0 -> 91784 bytes ...toy_paper_no_rev_rm_R2_import_rxn_visu.png | Bin 0 -> 81259 bytes ...oy_paper_no_reversible_import_rxn_visu.png | Bin 0 -> 87849 bytes .../toy_stoichiometry_import_rxn_visu.png | Bin 0 -> 46924 bytes networks/toys/sbml/toy_paper.sbml | 135 + .../toys/sbml/toy_paper_no_rev_rm_R2.sbml | 139 + .../toys/sbml/toy_paper_no_reversible.sbml | 139 + networks/toys/sbml/toy_stoichiometry.sbml | 108 + networks/toys/seeds/p_seed.txt | 4 + networks/toys/seeds/toy_stoichiometry_seed.lp | 3 + .../targets/toy_stoichiometry_targets.txt | 3 + .../analyses/01_compare_s2lp_netseed.ipynb | 860 + notebook/analyses/02_s2lp_sol_analyses.ipynb | 514 + notebook/analyses/03_iCN718.ipynb | 567 + notebook/analyses/04_supp_one_sol.ipynb | 767 + notebook/analyses/05_supp_precursor.ipynb | 664 + notebook/analyses/06_supp_iCN718.ipynb | 715 + notebook/run/01_get_sbml_BiGG.ipynb | 167 + notebook/run/02_get_objectives.ipynb | 5572 ++ notebook/run/03_run_seed2lp.ipynb | 2443 + notebook/run/04_normalise_network.ipynb | 41173 +++++++++++ notebook/run/05_run_netseed.ipynb | 369 + notebook/run/06_run_precursor.ipynb | 610 + notebook/run/07_run_seed2lp_iCN718.ipynb | 2223 + .../run/08_run_seed2lp_one_solution.ipynb | 1531 + notebook/run/09_run_scopes.ipynb | 58329 ++++++++++++++++ notebook/run/10_get_data_for_analyse.ipynb | 1050 + scripts/01_retrieve_bigg_sbml.py | 34 + scripts/02_get_objective.py | 91 + scripts/05_format_results.py | 66 + scripts/10_1_scope_analyse.py | 255 + scripts/10_3_iCN718_metabolite_analyses.py | 49 + scripts/10_4_get_supp_data.py | 183 + scripts/10_5_get_exchange.py | 90 + .../01_job_retrieve_bigg_sbml.sh | 20 + .../slurm_cluster/02_01_job_get_objective.sh | 20 + scripts/slurm_cluster/02_02_get_targets.sh | 26 + .../slurm_cluster/02_02_job_get_targets.sh | 22 + .../03_01_execute_workflow_search.sh | 51 + scripts/slurm_cluster/03_01_job_run_s2lp.sh | 62 + ...03_02_execute_workflow_search_exclusive.sh | 36 + .../03_02_job_run_s2lp_exclusive.sh | 63 + scripts/slurm_cluster/03_run_s2lp.sh | 85 + .../04_job_run_sbml_normalisation.sh | 21 + .../04_run_sbml_normalisation.sh | 27 + .../05_1_job_run_n2pcomp_netseed.sh | 25 + scripts/slurm_cluster/05_1_run_n2pcom.sh | 83 + scripts/slurm_cluster/05_2_format_results.sh | 44 + .../05_2_job_format_netseed_results.sh | 25 + .../06_1_job_run_n2pcomp_precursor.sh | 25 + .../06_2_job_format_precursor_results.sh | 25 + .../slurm_cluster/07_execute_s2lp_iCN718.sh | 20 + .../slurm_cluster/07_job_run_s2lp_iCN718.sh | 61 + scripts/slurm_cluster/07_run_s2lp_iCN718.sh | 78 + .../08_execute_s2lp_one_solution.sh | 20 + .../08_job_run_s2lp_one_solution.sh | 62 + .../slurm_cluster/09_job_run_scope_iCN718.sh | 25 + .../slurm_cluster/09_job_run_scope_netseed.sh | 25 + .../slurm_cluster/09_job_run_scope_s2lp.sh | 25 + scripts/slurm_cluster/09_run_scope.sh | 42 + .../10_1_job_run_scope_analyse_iCN718.sh | 32 + .../10_1_job_run_scope_analyse_netseed.sh | 30 + .../10_1_job_run_scope_analyse_s2lp.sh | 30 + .../slurm_cluster/10_1_run_scope_analyse.sh | 53 + .../10_2_job_run_scope_analyse_concat.sh | 25 + .../10_2_run_scope_analyse_concat.sh | 26 + .../10_3_job_iCN718_metabolite_analysis.sh | 28 + .../slurm_cluster/10_4_job_get_supp_data.sh | 31 + .../10_5_job_get_iCN718_exchange.sh | 26 + seed2lp/__init__.py | 12 + seed2lp/__main__.py | 582 + seed2lp/_version.py | 2 + seed2lp/argument.py | 517 + seed2lp/asp/enum-cc.lp | 50 + seed2lp/asp/flux.lp | 67 + seed2lp/asp/maximize_flux.lp | 2 + seed2lp/asp/maximize_produced_target.lp | 7 + seed2lp/asp/minimize.lp | 8 + seed2lp/asp/seed-solving.lp | 174 + seed2lp/clingo_lpx.py | 235 + seed2lp/color.py | 34 + seed2lp/config.yaml | 26 + seed2lp/description.py | 497 + seed2lp/file.py | 151 + seed2lp/flux.py | 260 + seed2lp/linear.py | 429 + seed2lp/log_conf.yaml | 25 + seed2lp/logger.py | 116 + seed2lp/metabolite.py | 60 + seed2lp/network.py | 909 + seed2lp/reaction.py | 201 + seed2lp/reasoning.py | 825 + seed2lp/resmod.py | 68 + seed2lp/sbml.py | 246 + seed2lp/scope.py | 122 + seed2lp/solver.py | 301 + seed2lp/utils.py | 183 + setup.cfg | 49 + setup.py | 32 + tests/fba.py | 147 + tests/full_network.py | 166 + ...ometry_cycle_import_rxn_network_render.log | 16 + ...ichiometry_cycle_rm_rxn_network_render.log | 18 + .../graph/toy_example_import_rxn_visu.png | Bin 0 -> 89849 bytes .../network/graph/toy_example_rm_rxn_visu.png | Bin 0 -> 76000 bytes ...oy_stoichiometry_cycle_import_rxn_visu.png | Bin 0 -> 73133 bytes .../toy_stoichiometry_cycle_rm_rxn_visu.png | Bin 0 -> 68879 bytes tests/network/sbml/toy_paper.sbml | 135 + .../network/sbml/toy_paper_no_rev_rm_R2.sbml | 139 + .../network/sbml/toy_paper_no_reversible.sbml | 139 + tests/normalization.py | 201 + tests/pytest.ini | 3 + tests/target.py | 286 + tests/utils.py | 181 + 135 files changed, 138733 insertions(+) create mode 100644 .gitignore create mode 100644 LICENCE.txt create mode 100644 MANIFEST.in create mode 100644 Makefile create mode 100644 README.mkd create mode 100644 documentations/developers.mkd create mode 100644 documentations/fba.mkd create mode 100644 documentations/flux.mkd create mode 100644 documentations/full_network.mkd create mode 100644 documentations/network.mkd create mode 100644 documentations/network_analyse.mkd create mode 100644 documentations/options.mkd create mode 100644 documentations/pictures/FBA/fba_avoid_accumulation_seed.png create mode 100644 documentations/pictures/network_analyse/multiple_objective.png create mode 100644 documentations/pictures/network_analyse/objective_found.png create mode 100644 documentations/pictures/network_analyse/objective_not_found.png create mode 100644 documentations/pictures/network_analyse/warning_deleted_reaction.png create mode 100644 documentations/pictures/network_analyse/warning_reaction_backward.png create mode 100644 documentations/target.mkd create mode 100644 networks/objective/e_coli_core_target.txt create mode 100644 networks/sbml/e_coli_core.xml create mode 100644 networks/toys/graphs/toy_paper_import_rxn_visu.png create mode 100644 networks/toys/graphs/toy_paper_no_rev_rm_R2_import_rxn_visu.png create mode 100644 networks/toys/graphs/toy_paper_no_reversible_import_rxn_visu.png create mode 100644 networks/toys/graphs/toy_stoichiometry_import_rxn_visu.png create mode 100644 networks/toys/sbml/toy_paper.sbml create mode 100644 networks/toys/sbml/toy_paper_no_rev_rm_R2.sbml create mode 100644 networks/toys/sbml/toy_paper_no_reversible.sbml create mode 100644 networks/toys/sbml/toy_stoichiometry.sbml create mode 100644 networks/toys/seeds/p_seed.txt create mode 100644 networks/toys/seeds/toy_stoichiometry_seed.lp create mode 100644 networks/toys/targets/toy_stoichiometry_targets.txt create mode 100644 notebook/analyses/01_compare_s2lp_netseed.ipynb create mode 100644 notebook/analyses/02_s2lp_sol_analyses.ipynb create mode 100644 notebook/analyses/03_iCN718.ipynb create mode 100644 notebook/analyses/04_supp_one_sol.ipynb create mode 100644 notebook/analyses/05_supp_precursor.ipynb create mode 100644 notebook/analyses/06_supp_iCN718.ipynb create mode 100644 notebook/run/01_get_sbml_BiGG.ipynb create mode 100644 notebook/run/02_get_objectives.ipynb create mode 100644 notebook/run/03_run_seed2lp.ipynb create mode 100644 notebook/run/04_normalise_network.ipynb create mode 100644 notebook/run/05_run_netseed.ipynb create mode 100644 notebook/run/06_run_precursor.ipynb create mode 100644 notebook/run/07_run_seed2lp_iCN718.ipynb create mode 100644 notebook/run/08_run_seed2lp_one_solution.ipynb create mode 100644 notebook/run/09_run_scopes.ipynb create mode 100644 notebook/run/10_get_data_for_analyse.ipynb create mode 100644 scripts/01_retrieve_bigg_sbml.py create mode 100644 scripts/02_get_objective.py create mode 100644 scripts/05_format_results.py create mode 100644 scripts/10_1_scope_analyse.py create mode 100644 scripts/10_3_iCN718_metabolite_analyses.py create mode 100644 scripts/10_4_get_supp_data.py create mode 100644 scripts/10_5_get_exchange.py create mode 100755 scripts/slurm_cluster/01_job_retrieve_bigg_sbml.sh create mode 100755 scripts/slurm_cluster/02_01_job_get_objective.sh create mode 100644 scripts/slurm_cluster/02_02_get_targets.sh create mode 100755 scripts/slurm_cluster/02_02_job_get_targets.sh create mode 100755 scripts/slurm_cluster/03_01_execute_workflow_search.sh create mode 100755 scripts/slurm_cluster/03_01_job_run_s2lp.sh create mode 100755 scripts/slurm_cluster/03_02_execute_workflow_search_exclusive.sh create mode 100755 scripts/slurm_cluster/03_02_job_run_s2lp_exclusive.sh create mode 100755 scripts/slurm_cluster/03_run_s2lp.sh create mode 100755 scripts/slurm_cluster/04_job_run_sbml_normalisation.sh create mode 100755 scripts/slurm_cluster/04_run_sbml_normalisation.sh create mode 100755 scripts/slurm_cluster/05_1_job_run_n2pcomp_netseed.sh create mode 100755 scripts/slurm_cluster/05_1_run_n2pcom.sh create mode 100755 scripts/slurm_cluster/05_2_format_results.sh create mode 100755 scripts/slurm_cluster/05_2_job_format_netseed_results.sh create mode 100755 scripts/slurm_cluster/06_1_job_run_n2pcomp_precursor.sh create mode 100755 scripts/slurm_cluster/06_2_job_format_precursor_results.sh create mode 100755 scripts/slurm_cluster/07_execute_s2lp_iCN718.sh create mode 100755 scripts/slurm_cluster/07_job_run_s2lp_iCN718.sh create mode 100755 scripts/slurm_cluster/07_run_s2lp_iCN718.sh create mode 100755 scripts/slurm_cluster/08_execute_s2lp_one_solution.sh create mode 100755 scripts/slurm_cluster/08_job_run_s2lp_one_solution.sh create mode 100755 scripts/slurm_cluster/09_job_run_scope_iCN718.sh create mode 100755 scripts/slurm_cluster/09_job_run_scope_netseed.sh create mode 100755 scripts/slurm_cluster/09_job_run_scope_s2lp.sh create mode 100755 scripts/slurm_cluster/09_run_scope.sh create mode 100755 scripts/slurm_cluster/10_1_job_run_scope_analyse_iCN718.sh create mode 100755 scripts/slurm_cluster/10_1_job_run_scope_analyse_netseed.sh create mode 100755 scripts/slurm_cluster/10_1_job_run_scope_analyse_s2lp.sh create mode 100755 scripts/slurm_cluster/10_1_run_scope_analyse.sh create mode 100755 scripts/slurm_cluster/10_2_job_run_scope_analyse_concat.sh create mode 100755 scripts/slurm_cluster/10_2_run_scope_analyse_concat.sh create mode 100755 scripts/slurm_cluster/10_3_job_iCN718_metabolite_analysis.sh create mode 100755 scripts/slurm_cluster/10_4_job_get_supp_data.sh create mode 100755 scripts/slurm_cluster/10_5_job_get_iCN718_exchange.sh create mode 100644 seed2lp/__init__.py create mode 100644 seed2lp/__main__.py create mode 100644 seed2lp/_version.py create mode 100644 seed2lp/argument.py create mode 100644 seed2lp/asp/enum-cc.lp create mode 100644 seed2lp/asp/flux.lp create mode 100644 seed2lp/asp/maximize_flux.lp create mode 100644 seed2lp/asp/maximize_produced_target.lp create mode 100644 seed2lp/asp/minimize.lp create mode 100644 seed2lp/asp/seed-solving.lp create mode 100644 seed2lp/clingo_lpx.py create mode 100644 seed2lp/color.py create mode 100644 seed2lp/config.yaml create mode 100644 seed2lp/description.py create mode 100644 seed2lp/file.py create mode 100644 seed2lp/flux.py create mode 100644 seed2lp/linear.py create mode 100644 seed2lp/log_conf.yaml create mode 100644 seed2lp/logger.py create mode 100644 seed2lp/metabolite.py create mode 100644 seed2lp/network.py create mode 100644 seed2lp/reaction.py create mode 100644 seed2lp/reasoning.py create mode 100644 seed2lp/resmod.py create mode 100644 seed2lp/sbml.py create mode 100644 seed2lp/scope.py create mode 100644 seed2lp/solver.py create mode 100644 seed2lp/utils.py create mode 100644 setup.cfg create mode 100644 setup.py create mode 100644 tests/fba.py create mode 100644 tests/full_network.py create mode 100644 tests/network/graph/logs/toy_stoichiometry_cycle_import_rxn_network_render.log create mode 100644 tests/network/graph/logs/toy_stoichiometry_cycle_rm_rxn_network_render.log create mode 100644 tests/network/graph/toy_example_import_rxn_visu.png create mode 100644 tests/network/graph/toy_example_rm_rxn_visu.png create mode 100644 tests/network/graph/toy_stoichiometry_cycle_import_rxn_visu.png create mode 100644 tests/network/graph/toy_stoichiometry_cycle_rm_rxn_visu.png create mode 100644 tests/network/sbml/toy_paper.sbml create mode 100644 tests/network/sbml/toy_paper_no_rev_rm_R2.sbml create mode 100644 tests/network/sbml/toy_paper_no_reversible.sbml create mode 100644 tests/normalization.py create mode 100644 tests/pytest.ini create mode 100644 tests/target.py create mode 100644 tests/utils.py diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..32adc67 --- /dev/null +++ b/.gitignore @@ -0,0 +1,20 @@ +*.pyc +*.vim +.cache/ +.pytest_cache +__pycache__/ +build/ +dist/ +seed2lp.egg-info/ +repo-data/ +todel.png +venv-*/ +venv/ +viz-nor.png +viz.png +/results +/tmp +/analyses* +/tests/tmp +/tests/results +/data \ No newline at end of file diff --git a/LICENCE.txt b/LICENCE.txt new file mode 100644 index 0000000..2b5b4ca --- /dev/null +++ b/LICENCE.txt @@ -0,0 +1,145 @@ + GNU LESSER GENERAL PUBLIC LICENSE + Version 3, 29 June 2007 + +Copyright (C) 2007 Free Software Foundation, Inc. + +Everyone is permitted to copy and distribute verbatim copies of this license +document, but changing it is not allowed. + +This version of the GNU Lesser General Public License incorporates the terms and +conditions of version 3 of the GNU General Public License, supplemented by the +additional permissions listed below. + +0. Additional Definitions. + +As used herein, “this License” refers to version 3 of the GNU Lesser General +Public License, and the “GNU GPL” refers to version 3 of the GNU General Public +License. + +“The Library” refers to a covered work governed by this License, other than an +Application or a Combined Work as defined below. + +An “Application” is any work that makes use of an interface provided by the +Library, but which is not otherwise based on the Library. Defining a subclass of +a class defined by the Library is deemed a mode of using an interface provided +by the Library. + +A “Combined Work” is a work produced by combining or linking an Application with +the Library. The particular version of the Library with which the Combined Work +was made is also called the “Linked Version”. + +The “Minimal Corresponding Source” for a Combined Work means the Corresponding +Source for the Combined Work, excluding any source code for portions of the +Combined Work that, considered in isolation, are based on the Application, and +not on the Linked Version. + +The “Corresponding Application Code” for a Combined Work means the object code +and/or source code for the Application, including any data and utility programs +needed for reproducing the Combined Work from the Application, but excluding the +System Libraries of the Combined Work. + +1. Exception to Section 3 of the GNU GPL. + +You may convey a covered work under sections 3 and 4 of this License without +being bound by section 3 of the GNU GPL. + +2. Conveying Modified Versions. + +If you modify a copy of the Library, and, in your modifications, a facility +refers to a function or data to be supplied by an Application that uses the +facility (other than as an argument passed when the facility is invoked), then +you may convey a copy of the modified version: + + a) under this License, provided that you make a good faith effort to ensure + that, in the event an Application does not supply the function or data, the + facility still operates, and performs whatever part of its purpose remains + meaningful, or + b) under the GNU GPL, with none of the additional permissions of this License + applicable to that copy. + +3. Object Code Incorporating Material from Library Header Files. + +The object code form of an Application may incorporate material from a header +file that is part of the Library. You may convey such object code under terms of +your choice, provided that, if the incorporated material is not limited to +numerical parameters, data structure layouts and accessors, or small macros, +inline functions and templates (ten or fewer lines in length), you do both of +the following: + + a) Give prominent notice with each copy of the object code that the Library + is used in it and that the Library and its use are covered by this License. + b) Accompany the object code with a copy of the GNU GPL and this license + document. + +4. Combined Works. + +You may convey a Combined Work under terms of your choice that, taken together, +effectively do not restrict modification of the portions of the Library +contained in the Combined Work and reverse engineering for debugging such +modifications, if you also do each of the following: + + a) Give prominent notice with each copy of the Combined Work that the + Library is used in it and that the Library and its use are covered by this + License. + b) Accompany the Combined Work with a copy of the GNU GPL and this + license document. + c) For a Combined Work that displays copyright notices during execution, + include the copyright notice for the Library among these notices, as well + as a reference directing the user to the copies of the GNU GPL and this + license document. + d) Do one of the following: + 0) Convey the Minimal Corresponding Source under the terms of this + License, and the Corresponding Application Code in a form suitable for, + and under terms that permit, the user to recombine or relink the + Application with a modified version of the Linked Version to produce a + modified Combined Work, in the manner specified by section 6 of the GNU + GPL for conveying Corresponding Source. + 1) Use a suitable shared library + mechanism for linking with the Library. A suitable mechanism is one that + (a) uses at run time a copy of the Library already present on the user's + computer system, and (b) will operate properly with a modified version + of the Library that is interface-compatible with the Linked Version. + e) Provide Installation Information, but only if you would otherwise be + required to provide such information under section 6 of the GNU GPL, and + only to the extent that such information is necessary to install and execute + a modified version of the Combined Work produced by recombining or relinking + the Application with a modified version of the Linked Version. (If you use + option 4d0, the Installation Information must accompany the Minimal + Corresponding Source and Corresponding Application Code. If you use option + 4d1, you must provide the Installation Information in the manner specified + by section 6 of the GNU GPL for conveying Corresponding Source.) + +5. Combined Libraries. + +You may place library facilities that are a work based on the Library side by +side in a single library together with other library facilities that are not +Applications and are not covered by this License, and convey such a combined +library under terms of your choice, if you do both of the following: + + a) Accompany the combined library with a copy of the same work based on the + Library, uncombined with any other library facilities, conveyed under the + terms of this License. + b) Give prominent notice with the combined library that part of it is a work + based on the Library, and explaining where to find the accompanying + uncombined form of the same work. + +6. Revised Versions of the GNU Lesser General Public License. + +The Free Software Foundation may publish revised and/or new versions of the GNU +Lesser General Public License from time to time. Such new versions will be +similar in spirit to the present version, but may differ in detail to address +new problems or concerns. + +Each version is given a distinguishing version number. If the Library as you +received it specifies that a certain numbered version of the GNU Lesser General +Public License “or any later version” applies to it, you have the option of +following the terms and conditions either of that published version or of any +later version published by the Free Software Foundation. If the Library as you +received it does not specify a version number of the GNU Lesser General Public +License, you may choose any version of the GNU Lesser General Public License +ever published by the Free Software Foundation. + +If the Library as you received it specifies that a proxy can decide whether +future versions of the GNU Lesser General Public License shall apply, that +proxy's public statement of acceptance of any version is permanent authorization +for you to choose that version for the Library. diff --git a/MANIFEST.in b/MANIFEST.in new file mode 100644 index 0000000..1d2d9d1 --- /dev/null +++ b/MANIFEST.in @@ -0,0 +1,6 @@ +include LICENCE.txt MANIFEST.in README.mkd +exclude Makefile +recursive-include seed2lp *.py +recursive-include seed2lp *.lp +prune networks +prune test diff --git a/Makefile b/Makefile new file mode 100644 index 0000000..1569b26 --- /dev/null +++ b/Makefile @@ -0,0 +1,29 @@ +NET=toy_2_small2.sbml + +run: + python -m seed2lp network networks/$(NET) -v viz.png -vr viz-nor.png -vd viz-dag.png + +t: test +tf: testff +ts: testspec +test: + python -m pytest -vv test seed2lp --doctest-module --durations=0 +testff: # --failed-first and --exitfirst argument + python -m pytest -vv test seed2lp --failed-first --exitfirst --doctest-module --durations=0 +testspec: # --failed-first and --exitfirst argument + python -m pytest -vv test seed2lp --doctest-module --durations=0 -k orbidden_inter + +black: + black seed2lp + + +.PHONY: t test black run + + +# release cycle recipes +fullrelease: + fullrelease +install_deps: + python -c "import configparser; c = configparser.ConfigParser(); c.read('setup.cfg'); print(c['options']['install_requires'])" | xargs pip install -U +install: + python setup.py install diff --git a/README.mkd b/README.mkd new file mode 100644 index 0000000..17bb093 --- /dev/null +++ b/README.mkd @@ -0,0 +1,369 @@ +# Seed2LP + +Seed2LP is a Python tool that searches seeds in metabolic networks. It uses answer set programming (ASP) and provides three approaches for seed detection: +- [**Full network**](#full-network) mode, that activates all compounds of the metabolic network. +- [**Target**](#target) mode, that activates metabolites of interest. +- [**FBA**](#fba) mode, that randomly searches seeds ensuring a positive flux into the objective reaction + +The notebook directory of this repo contains scripts that reproduce the results presented in the associated paper. + +
+
+ +## Install + +**From github** +``` +pip install git+https://github.com/bioasp/seed2lp +``` + +**From repository source after git clone** + +``` +pip install . +``` +or +``` +python setup.py install +``` + + +
+ +## Requirements + +Requires Python >= 3.10. + +For below requirements, installing Seed2LP will install all needed packages if not already installed. + + + +| name | version | +|:----------:|:-------------:| +| [clyngor](https://github.com/Aluriak/clyngor) | 0.3.18 | +| [clingo-lpx](https://github.com/potassco/clingo-lpx) | 1.3.0 | +| [cobrapy](https://cobrapy.readthedocs.io/en/latest/index.html) | 0.26.0 | +| [pyyaml](https://pyyaml.org/wiki/PyYAMLDocumentation) | 6.0 | +| [menetools](https://github.com/cfrioux/MeneTools) | 3.4.0 | +| [padmet](https://github.com/AuReMe/padmet) | 5.0.1 | + +
+ +## Command examples +The following examples uses a toy described on Seed2LP paper. + +> Get all solutions in all Target modes (Reasoning, Filter, Guess&Check, Guess&Check with Diversity, Hybrid lpx), in all optimisations (subset minimal and minimize) +>``` +>seed2lp target networks/toys/SBML/toy_paper.SBML results/toys/ +>``` + +> Get one solution in Full Network in reasoning and check flux +>``` +>seed2lp full networks/toys/SBML/toy_paper.SBML results/toys/ -nbs 1 -so reasoning -cf +>``` + +> Get one solution in FBA only in subset minimal +>``` +>seed2lp fba networks/toys/SBML/toy_paper.SBML results/toys/ -nbs 1 -m subsetmin +>``` + +
+ +## 📜[Network analyse](documentations/network_analyse.mkd) +The first step of the tool is to read the network, analyse it to be able to perform a logic analyse of the Network, each reaction must be defined as follows: + +$$ Reaction: Set\{reactants\} ⟶ Set\{products\} \quad with \quad bounds = [0,\infty]$$ + + +For each reaction, multiple factors are verified and correction is done if needed such as swapping set of reactants and set of products if the boundaries of a reaction are negative or deleting reactions with null boundaries. A warning is outputted when changes are done. + +📃 **Output example:** + +``` +WARNING : + - R_R2: Deleted. + Boundaries was: [0.0 ; 0.0] + - R_R7: Reactants and products switched. + Boundaries was: [-1000.0 ; 0.0] + +``` + +## Search mode features +It is possible to run Seed2LP for all the 3 modes ([**Full network**](#full-network), [**Target**](#target) and [**FBA**](#fba) by changing the command, and for each one to use specifics arguments. + +There are also 2 additional features: +- [**Network**](#network): allows drawing the network from the ASP definition and checking the network ASP decription by comparing with the Cobrapy description, or write into an SBML file the corrected network (exchanging products and reactants or delteing reaction with all boundaries to 0) +- [**Flux**](#flux): Allows checking the flux with Cobrapy from Seed2LP result files, or any file having the same json structure than Seed2LP. + +
+
+ +### 📜 [Full Network](documentations/full_network.mkd) +This mode searches seeds with this goal: "activate"/"produce" all reaction/metabolites of the network. +> 💻 **Command:** +> +> ``` +> seed2lp full [network_file] [output_directory] [arguments] +> ``` + +There are also two submodes: +- 📜 [Reasoning](documentations/full_network.mkd#reasoning): Use ASP solving with clingo + - **Classic:** Use reasoning (logical, Boolean abstraction of metabolic activity) search only, without calculating flux into objective reaction + - **Filter:** Use reasoning search only, but for all results the flux is checked with [cobrapy](https://cobrapy.readthedocs.io/en/latest/index.html), and only the solutions having flux are returned. + - **Guess-check:** Use reasoning search and directly interact with the solver during solving by adding new constraints. Flux is checked with [cobrapy](https://cobrapy.readthedocs.io/en/latest/index.html) for every solution proposed by the solver and new constraints are derived out of it to guide the remaining solving process. All outputted solutions have flux natively. +- 📜 [Hybrid](documentations/full_network.mkd#hybrid): Ensure the resulting model statisfies both logical and flux constraints using a hybrid ASP-LP solver. + +
+
+ +### 📜 [Target](documentations/target.mkd) +This mode searches seeds with this goal: producing a set of metabolites, or if a reaction is given, ensuring the production of the reactants needed for its activation. +> 💻 **Command:** +> +> ``` +> seed2lp target [network_file] [output_directory] [arguments] +> ``` + +There are also two submodes: +- 📜 [Reasoning](documentations/target.mkd): Use ASP solving with clingo + - **Classic:** Use reasoning (logical, Boolean abstraction of metabolic activity) search only, without calculating flux into objective reaction + - **Filter:** Use reasoning search only, but for all results the flux is checked with [cobrapy](https://cobrapy.readthedocs.io/en/latest/index.html), and only the solutions having flux are returned. + - **Guess-check:** Use reasoning search and directly interact with the solver during solving by adding new constraints. Flux is checked with [cobrapy](https://cobrapy.readthedocs.io/en/latest/index.html) for every solution proposed by the solver and new constraints are derived out of it to guide the remaining solving process. All outputted solutions have flux natively. +- 📜 [Hybrid](documentations/target.mkd): Ensure the resulting model statisfies both logical and flux constraints using a hybrid ASP-LP solver. + +
+
+ +### 📜 [FBA](documentations/fba.mkd) +This mode randomly searches seeds by ensuring flux on objective reaction. + +> 💻 **Command:** +> +> ``` +> seed2lp fba [network_file] [output_directory] [arguments] +> ``` + +
+
+ +## set-mode features +It is possible to choose what kind of set of seeds to search. + +### Subset minimal +The subset minimal (submin) mode finds a set of seeds which satisfies all the constraints, then from this set, it tries to eliminate seeds until it finds the minimal set that will satisfy the constraints. + +This method of search **does not guarantee minimality** in terms of size of the set. But it can be a **faster way** to have set of seeds the most minimal possible from a set already validated. + +> 📃 **Example:** +> +> Considering all the followings sets of seeds below, that statisfy constraints for the objective, which of them are subset minimal and will be selected? +> +> | Sets of seeds | is choosen ? | reasons | +> |:--------------:|:---------------:|:-------------:| +> | {A,B,C,D,E} | no | {A,B,C} is a set of seeds included into this set| +> | {A,B,C} | yes | There is no other set smaller included in this set| +> | {A,D,E,F} | no |{A,D,F} is a set of seeds included into this set | +> | {A,D,F} | yes | There is no other set smaller included in this set | +> | {A,C,E,F} | yes | There is no other set smaller included in this set| + +
+
+ +### Minimize +The minimize (min) set-mode finds set of seeds of minimal size which satisfies all the constraints. + +This method of search **ensures minimality**. But it is computationally more demanding. + +> 📃 **Example:** +> +> Considering all the followings sets of seeds below, that statisfy constraints for the objective, which of them are of minimal size and will be selected? +> +> | Sets of seeds | is choosen ? | reasons | +> |:--------------:|:---------------:|:-------------:| +> | {A,B,C,D,E} | no | There is a smaller set of size 3 existing | +> | {A,B,C} | yes | This a one of the smaller set | +> | {A,D,E,F} | no | There is a smaller set of size 3 existing | +> | {A,D,F} | yes | This a one of the smaller set | +> | {A,C,E,F} | no | There is a smaller set of size 3 existing| + +
+ +
+
+ +## Results +For each mode, a result json file is created. This file will write all models for all submodes reasoning / hybrid and **set-mode** subset minimial / minimize. + +If the check-flux option is used, `-cf` / `--check-flux`, a tsv file is created with all the fluxes calculated with Cobrapy, and if hybrid submode or fba mode is used, the LP flux found (calculated from ASP). + +The results are also written in the terminal. + +
+
+ +## Additional features +### 📜 [Network](documentations/network.mkd) +This feature can reconstruct a very basic picture of the network from the ASP conversion of the source network (in SBML format). +- It can create the picture with the reaction names, or without the reaction. +- It can also create all the reactions description / formula from ASP and Cobrapy and give a difference of both contents. +- It can write an SBML file derived from the SBML sources after correction of the network + - Deletion of reaction with boundaries [0,0] + - Exchanging set of reactants and products when boundaries negatives: ie [-1000, 0], [-100; -10], ... + +> 💻 **Command:** +> +> `seed2lp network [network_file] [output_directory] [arguments]` + +
+
+ +### 📜 [Flux](documentations/flux.mkd) +This feature allows the calculation, using Cobrapy library, of the flux of the objective function from a result file of seed2lp and save them. This can be a validation of the solutions provided by Seed2LP. + +> 💻 **Command:** +> +> `seed2lp flux [network_file] [seed2lp_result_file]` + + + +> 📝 **Notes:** +> +> It is possible to do the flux calculation using Cobrapy directly after the seed search by using the argument `-cf` / `--check-flux` on each mode. A tsv file is created with all the fluxes calculated with Cobrapy, and if hybrid submode or fba mode is used, the LP flux found (calculated from ASP). + +
+
+ +## Main 📜 [options](documentations/options.mkd) for [search mode features](#search-mode-features) + +### Data given by user + +| option | short| default | description | search mode | +|:----------------------:|:----:|:--------:|:------------:|:-------------:| +| --targets-file | -tf | optional | List of metabolites⭐ and/or
an objective🔆 reaction as file | Target | +| --objective | -o | optional | An objective🔆 reaction
as command line | Full Network and FBA | +| --seeds-file | -sf | optional | List of metabolites⭐ known to be seeds and will
be in the initial set of seeds| ALL | +| --possible-seeds-file | -psf | optional | List of metabolites⭐ among which the seed selection will be performed
be in the set of seeds | ALL | +| --forbidden-seeds-file | -fsf | optional | List of metabolites⭐ that can not
be in the set of seeds | ALL | + + +> 📝 **Notes:** +> +> **Target** search mode : +> +> - If **no target file** given, the targets will be the **reactants of the objective reaction** found in the SBML file. The flux calculation will be done **on the objective objective reaction** found in the SBML file. +> - If only **Metabolites** given in the file: the targets will be **these given metabolites**. The flux calculation will be done on the **objective objective reaction** found in the SBML file. +> - If only **Reaction** given in the file: the targets will be the **reactants of the objective reaction** found in the SBML file. The flux calculation will be done **on this given reaction** +> - If both **Metabolites** and **Reaction** given in the file: the targets will be **these given metabolites**. The flux calculation will be done **on this given reaction** +> +> **Full network** (**hybrid** submode) and **FBA** : +> +>If no objective reaction given, the objective reaction will be the one found in the SBML file for flux calculation. + +> 💬 **Comments:** +> +> ⭐ One metabolite by line, must be prefixed with "M_" to fit the ID of the SBML file. +> +> 🔆 One Objective reaction only, must be prefixed with "R_" to fit the ID of the SBML file. + + +
+
+ +### Set mode + +| option | short| default | description | search mode | +|:-----------:|:----:|:--------:|:------------:|:-------------:| +| --mode | -m | subsetmin | run different set modes:
minimize, subsetmin, all | ALL | +| --solve | -so | reasoning | run different submodes:
reasoning, filter, gues-check, hybrid, all | Full Network
and Target | + + +
+
+ +### Set of seed restrictions + +| option | short| default | description | search mode | +|:----------------------:|:----:|:--------:|:------------:|:-------------:| +| --targets-as-seeds | -tas | False | If used, targets found are given,
allowed to be in the set of seeds | ALL | +| --topological-injection | -ti | False | If used, the exchange⭐ metabolite
from source file are set as seeds directly | ALL | +| --keep-import-reactions | -kir | False | If used, the exchange⭐ reactions
are not deleted during the conversion to ASP| ALL | +| --accumulation | -accu | False | If used, solutions with possible
accumulating metabolites are allowed | Full Network
and Target | + + +> 💬 **Comments:** +> +> ⭐ An **exchange reaction** is defined as a reaction having **one or multiple metabolites** as reactants or products (while the other set must be empty), it represent import or export reactions, and can be reversible or not, written forwards or backwards: +> - R ⟶ ∅ $\quad$|$\quad$ R ⟷ ∅ $\quad$|$\quad$ R ⟵ ∅ +> - ∅ ⟶ P $\quad$|$\quad$ ∅ ⟷ P $\quad$|$\quad$ ∅ ⟵ P +> +> An **exchange metabolite** will be the metabolite involved in the exchange reaction + + +
+
+ +### Flux + +| option | short| default | description | search mode | +|:----------------------:|:----:|:--------:|:------------:|:-------------:| +| --check-flux | -cf | False | If used, the Cobrapy flux calculation
from seeds will be executed and saved | ALL | +| --maximize-flux | -max | False | If used, the flux calculation
with ASP will be maximized | ALL⭐ | + + +> 💬 **Comments:** +> +> ⭐ The option is used only in [Hybrid](documentations/hybrid.mkd) mode for [Full Network](documentations/full_network.mkd) and [Target](documentations/target.mkd) search mode features. + + +
+
+ +## Configuration file +The application has its own configuration file located [here: seed2lp/config.yaml](seed2lp/config.yaml). + +It is possible to use another configuration file by using the option `-conf` or `--config-file`. + +But all these configurations are overwritten by the arguments if used. The config file is used as "the default configuration" of your app. + + +
+
+ +## Output files +### Result file + +
+
+ +### Flux file + + +
+
+
+ +## Troubleshooting +### GCC version +Seed2Lp needs a gcc version >= 7. + + +> 💻 Linux Commands: +> +> **Check your version:** +>``` +>gcc --version +>``` +> +> **Install latest version (here v11):** +>``` +> sudo apt install gcc-11 +>``` +> +> **Link to the latest version (here v11):** +>``` +> sudo update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-11 20 +>``` +> **Check your version:** +>``` +>gcc --version +>``` diff --git a/documentations/developers.mkd b/documentations/developers.mkd new file mode 100644 index 0000000..e69de29 diff --git a/documentations/fba.mkd b/documentations/fba.mkd new file mode 100644 index 0000000..bfbee7e --- /dev/null +++ b/documentations/fba.mkd @@ -0,0 +1,170 @@ +# FBA + +## Objective reaction +
+ +> ⚠️ **Warning:** +> +> The objective reaction is **not used for the seed searching**, with some [exceptions](#description). It is mainly needed to **calculate and ensure the flux**. + + +### Objective given by user +The objective reaction can be given by the user by using the option `--objective` or `-o` and the name of the reaction prefixed with **"R_"**. The tool will first check if the reaction exists in the source file, then apply this reaction as objective reaction for the flux calculation. If the + + +> 📃 **Examples:** +> +> +> +> +> +> +> +> +> +> +>
OK ERROR
+> +> 💻 **Command:** +> +> ``` +> seed2lp fba [source_file_path] [output_directory_path] -o R_R2 +> ``` +> +> 📝 **Notes:** +> "R_R2" is an existing reaction and will replace the objective reaction written into the source file. +> +> +> 📃 **Output example:** +> +>``` +> Objective set: +> Objective reaction from command line +> +> Objective : R_R2 +>``` +> +> +> +> 💻 **Commands:** +> +> ``` +> seed2lp fba [source_file_path] [output_directory_path] -o R2 +> ``` +> +> ``` +> seed2lp fba [source_file_path] [output_directory_path] -o oops +> ``` +> +> 📃 **Output example:** +>``` +> ERROR: +> R2 does not exist in network file networks/toys/sbml/toy_accumulation.sbml +>``` +>``` +> ERROR: +> oops does not exist in network file networks/toys/sbml/toy_accumulation.sbml +>``` +> +>
+ + + +### Objective found into source file +When no argument is used for the objective reaction, the tool will find from the objective list of source file the objective. The description of this automatic objective search is described in 📜[Network analyse](documentations/network_analyse.mkd#Objective-Reaction) + + + + +## Seed searching + +After analysing and converting the network a seed resoltuion is performed. Please read the 📜[network analyse document](documentations/network_analyse.mkd) to understand the technical terms + +### Description + +The FBA mode aims to **search randomly** sets of seeds by **ensuring flux** on the objective reaction (found or given). Thus **all metabolites** can potentially be seeds, **except**: +- Transported⭐ metabolites are not defined as possible seeds to reduce combinatorics and avoid very similar solutions. +- Metabolites that are never consumed as written in the source file. Indeed, the [flux rules](flux.mkd#flux-calculation-rules) creates an **import/export reaction** for seeds, and the programm can **by-pass the accumulation🔆** by adding these type of metabolites as seeds and using the **export reaction**. + + +> 💬 **Comments:** +> +> ⭐ A transported metabolite is a metabolite that is **transferred from one compartment to another**. The formula is therefore generally something like: $\quad$ $\quad$ M_A_e ⟷ M_A_c ⟷ M_A_m +> +> Because it the same metabolites on different compartment, the program tags **M_A_c and M_A_m as transported** metabolite, therefore **only M_A_e is selected** as seed. +> +> This restriction allows to **avoid similar set of seeds** such as {M_A_e, M_B} or {M_A_c, M_B} or {M_A_m, M_B} +> +> 🔆 Some source files can create **reaction without using the product of this reaction in another reaction**, even export reactions. This is an accumulation **already existing** in the source file. + +> ⚠️ **Warning:** +> +> However, as the main solution may result in **an accumulation**, one or more **seeds can be added** to the set in order to **use the export reaction** generated during the calculations and therefor avoid accumulation. +> +> If the program does that, these specific seeds are written in red into the terminal and written into the output file. +> +> 📃 **Output example:** +> +> ![](pictures/FBA/fba_avoid_accumulation_seed.png) + + +--- +### Application with options +We will discuss here some options, it is possible to consult [all options](options.mkd#fba). + + + + + **Target as seeds:** $\quad$ `--targets-as-seeds` $\quad$`-tas` + +In FBA mode, the main purpose is to **ensure flux into objective reaction**. To do that, we need to be able to **produce the reactants** of the objective reaction. But **one solution** of the set of seed could be **all reactants of the objective reaction**. This is not the purpose of the program. + +By default, the program **does not allow** the reactant of the objective reaction as seed. But that can be allowed by using this target as seeds option. This can be usefull if there is no solutions without this permission. + +This option is at `False` by default, and if used becomes `True`. + +> 💻 **Command:** +> +> ``` +> seed2lp fba [source_file_path] [output_directory_path] -tas +> ``` + +> ⚠️ **Warning:** +> +> With this option, one / multiple / all reactants of the objective reaction can be present into the set of seeds. + + +
+ + **Forbidden seeds:** $\quad$ `--forbidden-seeds-file` $\quad$ `-fsf` + +It is possible to give a list of metabolites (as file) that are not allowed to be seeds. This option could be interesting to use with the **target as seeds* option, to avoid some targetted metabolites to be seeds, but not all of them. + + + +> 💻 **Command:** +> +> ``` +> seed2lp fba [source_file_path] [output_directory_path] -fsf +> ``` +> +> ``` +> seed2lp fba [source_file_path] [output_directory_path] -tas -fsf +> ``` +> +> 📝 **Notes:** The order of the options does not matter + +> ⚠️ **Warning:** +> +> This option implies upstream knowledge of metabolites that are not desired as seeds. It may be necessary to first run the command without this option, locate the metabolites not desired as seeds and then run with this option again using the file containing this list of metabolites. + +
+ + +
+
+ +
+ +# 📃 Full example + diff --git a/documentations/flux.mkd b/documentations/flux.mkd new file mode 100644 index 0000000..a46667b --- /dev/null +++ b/documentations/flux.mkd @@ -0,0 +1,12 @@ +# Flux + +## ASP flux calculation rules + + +## Cobrapy flux calculation rules + + +## Flux option + + +## Flux Feature \ No newline at end of file diff --git a/documentations/full_network.mkd b/documentations/full_network.mkd new file mode 100644 index 0000000..02c02b4 --- /dev/null +++ b/documentations/full_network.mkd @@ -0,0 +1,103 @@ +# Full Network + +
+
+ +## Reasoning + +This mode search seeds by ensuring logical constraints and can be used with accumulation constraints of a metabolite. + +> 📝 **Notes:** +> +> There is no linear calcul, so the stoichiometry of metabolites are not involved into the algorithm of seed searching, such as the boundaries of a reaction. + + +### Logic Algorithm +After analysing and converting the network a seed resoltuion is performed. Please read the 📜[network analyse document](documentations/network_analyse.mkd) to understand the technical terms + + +#### Seed searching +The Full Network mode set all metabolite as Target. The targets must be in the scope of producible metabolites by the network. + +A metabolite is an **external seed** when: +- The metabolite is a **reactant** of a reaction and **not a product** of any reaction (not exchanged reaction and not reversible reaction). The metabolite is **not a transported⭐ metabolite** +- The metabolite is **not a product** of any reaction **except his own exchange reaction** +- Specific case of **reversible transport reaction** involving a **transported⭐ metabolite without any import reaction**. The reversibility prevents Seed2LP to select one of the metabolite as seed because it becomes a product of a reaction. This case is taken into account and the classic metabolite is chossen as seed + +> 💬 **Comments:** +> +> ⭐ A transported metabolite is a metabolite that is **transferred from one compartment to another**. The formula is therefore generally something like: $\quad$ $\quad$ $M_A_e ⟷ M_A_c ⟷ M_A_m +> +> Because it the same metabolites on different compartment, the program tags **M_A_c and M_A_m as transported** metabolite, therefore **only M_A_e is selected** as seed. +> +> This restriction allows to **avoid similar set of seeds** such as {M_A_e, M_B} or {M_A_c, M_B} or {M_A_m, M_B} + + +> 📃 **Example** +> +> Using the reaction below, M_A_c is an R_A is an exchange reaction and the the metaoblite M_A_c is set as **exchange metabolite** and will be chosen as seed because it **complies with the seed external definition**. +> M_B_m and M_B_c are both product of the reaction (or the reversible). One of them will be taged as classic metabolite and the other as a transported reaction, then while searching seeds, the **classical M_B** metabolite will be chosen as seeds +> +> ```mermaid +> flowchart TD +> None-->C{R_A}; +> C-->M_A_c; +> M_A_c & M_B_c-->R; +> R{R_1}-->M_D_c; +> M_B_c-->B{R_B} +> M_B_m-->F{Rev_R_B} +> B-->M_B_m; +> F-->M_B_c; +> ``` + +There is a set of impossible seeds, which are metabolite that are products but never reactant. + +#### Accumulation + +SBML source file are sometimes are written with **reaction without using the product of this reaction in another reaction**, even export reactions. This is an accumulation **already existing** in the source file.. This type of network can give an unsatisfiable problem. In order to prevent this, Seed2LP forbids by default the accumulation. + +The accumulation is forbidden with a constraint: + - a metabolite product wich is in the scope (producible metabolite) must be consummed at least once by a reaction + +Because most of the time the network doesn't result to an unsatisfiable problem, it is possible to allow the accumulation by using option `--accumulation` / `-accu`. But it is possible that the solution does not ensure flux while testing it with `--check-flux` / `-cf` option in a second step. + +This prohibition of accumulation is not needed when using filter or guess-check mode because the flux calculation with [cobrapy](https://cobrapy.readthedocs.io/en/latest/index.html) is tested for each solution while searching them. + +### Classic +For the classic mode, no other constraints are added to the [**Algorithm**](#Logic-Algorithm). All solutions are selected and outputted without any flux verification. + +### Filter +No other constraints are added to the [**Algorithm**](#Logic-Algorithm), but for all solutions the flux is checked with [cobrapy](https://cobrapy.readthedocs.io/en/latest/index.html), and only the solutions having flux are selected and outputted. + +### Guess-Check +This mode directly interact with the solver during solving by adding new constraints to the [**Algorithm**](#Logic-Algorithm). Flux is checked with [cobrapy](https://cobrapy.readthedocs.io/en/latest/index.html) for every solution proposed by the solver and new constraints are derived out of it to guide the remaining solving process. All outputted solutions have flux natively. + +## Diagramm + + +
+
+ +## Hybrid +This mode search seeds by ensuring logical and linear constraints and the accumulation option is not taken into account if used. + +### Logic-Linear Algorithm +the Logic part of the algorithm is the same as the [reasoning algorithm](#Logic-Algorithm), and add some logic constraints in order to complete linear calcul. + + + +
+
+ +# Application with options + + +
+ + +
+
+ +
+ +# 📃 Full example \ No newline at end of file diff --git a/documentations/network.mkd b/documentations/network.mkd new file mode 100644 index 0000000..05db335 --- /dev/null +++ b/documentations/network.mkd @@ -0,0 +1 @@ +# Network diff --git a/documentations/network_analyse.mkd b/documentations/network_analyse.mkd new file mode 100644 index 0000000..39b23c5 --- /dev/null +++ b/documentations/network_analyse.mkd @@ -0,0 +1,193 @@ +# Network Analyse +## File description +The input file are **SBML file** (XML like file used for networks). + +**Compatibilities** + +| xmlns version | level | version | +| :----------- | :---: | :-----: | +| http://www.sbml.org/sbml/level3/version1/core | 3 | 1 | + + +## Network correction +The network is read from the SBML source file. To perform a logic analyse of the Network, each reaction must be defined as follows : + +$$ Reaction: Set\{reactants\} ⟶ Set\{products\} $$ +$$ having \quad bounds = [0,n] \quad with \quad n ∈ \ ]0,\infty[$$ + + +For each reaction, multiple factors are verified and correction is done if needed. + +### Reversible reaction +A reaction is reversible when the lower boundary is negative and the upper is positive ( such as $[- \infty, \infty]$, $[-10, 100]$). These reversible reactions are then divided into two reaction: + +> 📃 **Example:** +> +> $$ +> A ⟷ B [-10, 100] ⟹ \left\{ +> \begin{array}{ll} +> \ A → B & [0, 100] \\ +> \ B → A & [0, 10] +> \end{array} +> \right. +> $$ + +The SBML file give a tag "reversible" which does not systematically correlate with the boundaries, this is why Seed2LP does not take that tag into account and only analyse the reversibility of a reaction on its boundaries. +- If the upper and lower bound have the same sign or is 0 the reaction is not reversible : $[-1000,-10]$ , $[-1000,0]$, $[0, 1000]$, $[10,1000]$, ... +- Else the reaction is reversible + +### "Backward" reaction +A reaction written "backward" is a reaction with negative boundaries. These reactions are modified by swapping the set of reactants with the set of products. +> 📃 **Example:** +> $$ A ⟶ B \quad [-10, 0] ⟹ B ⟶ A \quad [0, 10]$$ + +> 📃 **Output example:** +> +>![](pictures/network_analyse/warning_reaction_backward.png) + +### "Deleted" reaction +A reaction having the eupper and lower bound null is not activated an has to be deleted to not be considered during the logic analyse +$$bounds = [0,0] \quad ⟹Reaction \quad deleted$$ + +> 📃 **Output example:** +> +>![](pictures/network_analyse/warning_deleted_reaction.png) + +### Exchange reaction +A reaction with no set of products or no set of reactants is considered as "Exchange" reaction. These reaction can involve one or multiple metabolites. The metabolite is also set as exchanged. +> 📃 **Example:** +> $$ ∅ → A \quad [0, \infty] \quad \text{or} \quad ∅ → A+B+C \quad [0, \infty] \quad \text{or} \quad A+B+C → ∅ \quad [0, \infty]$$ + +### Import Reaction +An import reaction is an exchange reaction import a metabolite, meaning the metabolites are the products. +> 📃 **Example:** +> $$ ∅ → A \quad [0, \infty] \quad \text{or} \quad ∅ → A+B+C \quad [0, \infty]$$ + +> **OPTIONS** +> +> **Default option of Seed2lp** +> +> By default, the import reaction are "removed" to not take into account the environement predefined into the source file. +> +> **option --keep-import-reactions / -kir** +> +> When the otpion is used, the import reaction are kept and the predefined environment is taken into account for the seed searching. + +### Transport Reaction +A reaction is set a "Transport" when: +- Only one metabolite is involved into the reaction as reactant and product +- The root of the ID is the same (M_A_e and M_A_c) + +The reactant is a classic metabolite and the product will be set as "transport". If several transport reactions follow one another, only the first reactant is set as classic metabolite, all other are set as transported. + +This specification allows the logic programming to reduce the combinatorial in order to have more different solutions. + +> 📃 **Example:** +> +> Two chain of transported reaction suh as : +> $$ A\_e → A\_c → A\_m \quad \text{and} \quad B\_e → B\_c $$ +> and we suppose that A and B are seeds. +> +> All the will be : +> $$ S = \{A\_e, B\_e\}, \{A\_e, B\_c\}, \{A\_c, B\_e\}, \{A\_c, B\_c\}, \{A\_m, B\_e\}, \{A\_m, B\_c\}, $$ +> +> When the metabolite $A\_c$, $A\_m$ and $B\_c$ are set as transported, they are exclude as seeds and the the new set of solutions becomes: +> $$ S = \{A\_e, B\_e\} $$ + + +### Objective Reaction +When no argument is used for the objective reaction, the tool will find it from the objective list of SBML source file on the "listOfObjectives" xml tag. Several cases exist: + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Cases 📃 Output example:
+ No objective list or no objective into the list + + +![](pictures/network_analyse/objective_not_found.png) +
+ One objective found into the list with coeff 1 + + +![](pictures/network_analyse/objective_found.png) +
+ One objective found into the list without coeff 1 + + + ![](pictures/network_analyse/objective_not_found.png) +
+ Multiple objectives found into the list with only one having a coeff 1 + + +![](pictures/network_analyse/objective_found.png) +
+ Multiple objectives found into the list with multiple ones having a coeff 1 + + +![](pictures/network_analyse/multiple_objective.png) +
+ Multiple objectives found into the list without anyone having a coeff 1 + + +![](pictures/network_analyse/objective_not_found.png) +
+ + +### Metabolites +| Type | Tag | Impact | +| :--: | :-: | :----: | +| Exchanged metabolite | "exchange" | possible seed and needed to take back import reaction for linear calcul if choosen as seed | +| Transported metabolite | "transport" | Reduce combinorial| +| Classical metabolite | "other" | possible seed | + +### Targets +On target mode, if no metabolite given by the user, the reactant of the objective are set as target. + + + +## 🔧 Conversion into ASP instance +### Reversible reaction +- The backward way reaction has his id prefixed with **rev_** +- An atom `reversible(R1, rev_R1)` is created + +### Removed reaction +ASP atoms are prefixed by "rm_": +- `rm_reaction(...)` +- `rm_reactant(...)` +- `rm_product(...)` +- `rm_bounds(...)` + +### Objective Reaction +An objective reaction found are given by user creates an atom `objective(...)` + +### Inputs +when a target, possible_seed, seed or forbidden_seed is given by the user, the corresponding atoms are created \ No newline at end of file diff --git a/documentations/options.mkd b/documentations/options.mkd new file mode 100644 index 0000000..d15e692 --- /dev/null +++ b/documentations/options.mkd @@ -0,0 +1,11 @@ +# Options + +## Full Network + +## Target + +## FBA + +## Network + +## Flux \ No newline at end of file diff --git a/documentations/pictures/FBA/fba_avoid_accumulation_seed.png b/documentations/pictures/FBA/fba_avoid_accumulation_seed.png new file mode 100644 index 0000000000000000000000000000000000000000..644fffa0191cb28fd7d3cfc342eaf745142e3468 GIT binary patch literal 22583 zcmd43WmH_3(!=a$ap=2e*G&~GXyM!EoBVfcd=exLorlO{zhE3fIrpp#%W)J)- z_^F_#=}(8cUzo22`3Sx+Sur)@6QM2!H5oHQ4& zKm#BzPJ=1~8BlW^wiE@DW9tw>##2HS@c+KDXghu?KtdE)%LN%{LQiM<=M4uba-kyv zB#f;f<2pnfx_@65A1N*j`wlvycNR%zu#U^T67RzN>%8xHjh2VDT=wpf)xFFE&Hg9Eg<(Zjwh}peUxc zw)5W361tboofXoG>2h(YRNySFJ?f6gIZL^hk5cmW%-7`+HGznLuI-CGx_#fgj`mE` z@@XZMI_47nGw}k$Y&(|ovL-2N>2hbe))N=K7w;Uo8p*v%PPsuKuB^dqze(g-1erh3>QUw%M0Dtwmfoe4crd#K-PQHuIg4g*QbK-fKo|kyvy*;=b8<>@Q+S8 zey!68{+~GrJZiC z3Y}P5i)a?a;+JWlF|Q=-n60^I@nC|Fn}!r;s-#7mH(Jb8xw4Rr|HVl}$Y?xkap~JNO+XL>vf?p`4q8C^$ieq)#STEfk z6&8}q&gA;i!q|#aJw^JdSc(ZZI%8i^?)f8w@hH|iKQDfAY>N#7>Ybnja12f*k!iB) zY*jXfq5HWVUB0`w(w5-%0V@3@RJG!acgXWNb%;M=6ka+p zu~#iGU|K22>n>}fVN6K;wS>UBTbq#H^*k#7MpeDQs&?^+tWF6ZSbY~1bg`F*OX@uO zQh47~hC4TU9_*q>FFuO81nTF0`)Sc9{zTM;uX$LvU51R7PjH;J>!~^Dm=rR)O**ge z%Zki};BNPV-|77}^+eYD15Pi#s*9$-x|`yw4yq_~Pt)@jO*P%8fOdoVE^b zo=XnbRBVDEIOl=0(|TEF(h)%Y4wvY+deaTO56;55z;4`N(nccN@y%y~VCa}(#k2lP zIHMB`XV@@H4S&C)PvxQy3N9Hfb6-`bhzoZ6pe2>m(?s|Rt3Z;0@wrkzyn+7V&tYM> z9VhfSiHROKPIFtAbt&*fu4hL(M$Rq!EW&VoXzwJV$irl$R#-*ZVQ|q#YPPtTEst z6b$QmhXxdHhf@Q0g@ubBzdUa@vpnzK($XC`((I*9oga1BZ>Yi~Mom$QP*)$%+nXo< zG$K7aN{7KkmZ00WH&Mfq18$d!Zl>js&6f|WhS^ctbzYzEZ?}j%^G^l*(mRTtINc|@ z{=ybiF92Y~DjXH|f0XEXd*pnfb(NgTNBwv{K)3o+h|wo`-0l8X$&~g3d@lTt*M=Yd zYJf_)djypl9-Vlx^<`zaaXgUN_jfuHZ9UV%*prD&D~r5F zeYDgpPzg%Xoahmm^*u;~DMdzcsc_M>OW<)-!uF5*gh>)~8&J6xY?9smAwkDj0DFZe8*UrO}6<*yzLgl5Smw zNH)(7nXlSRQtA&x4%f0~@F{egDi~}nB>G8GVGfxIS%5JbE(aBG6ARPyV`wC+b#Rt0 zA4XISZ^W$AhFte~m4d{u(GJ*^1FiZ_%8<4n2X=f&gC2-#o)0|)FhbjiCL4bjQ$wThmv@ z^v|z=^Y`3C(L?ec;jUvf}fjC(cRg5_K zrp;=JeJB`>!&xvYDX6QyyJqifFLfBh8$a_+Ulh;NCnVz`reNo))Cdb&(O8mvDEVCc zB_lVLpHcDhTd`$|ZICjzP1j?=k0}hJN$ap&i}s(CtW9^41oB6Cbhd_C*-Rk*@?1}u zrRiHK#xMAz>)6%xokt)r`~{~LfK}zv8s%l%m6p-KrFlqx#t5&OmhUp$P($l%%j440 zE_3SFTR$0rjcHhyeBmbv8X6REP4Q)tPET?H=F_x;`NT4^EMo}A*(Bb{lYcS<0&AMrUUYZl(PtQ7uS+C@X^skQn-%^=l|lA@_! z)vA{0wO-Y!@wwa<=jN{L79)7rY?=TC`){YSg(@fqvP1KfrR+|ub+)|f)w33P(k*lZ zD1(RB^$J3NfLSY<%Z}c-ddVAjB|78s*~iq`#iqCdUHO)8gvpiK0%mb$mEfI(m87mj zyj)*h8$~#@rBplt!0C57?iI7u_cr3NnE{cKSsovj-y@LjAC;9drt-;kMGB$Fd%yF_ zNon;%f4Cvu#%KGm`KCDB6Z$L2cH|uHsfI7kA};ug=mMBC#35$@M_#o^yQJdQqbPZZ z2tBFa;8kXTVTlX5Z=$xFRZXh?cmV* z_zlR$!5T>$WiU&{v_$e)Q}6voVaSeRTrI3^pf$O0U0kwt6*_aYF?yxPL{wFU(mCA* zN`?A#Q$J`QIojy@iH|S&l&rr~%p^sWAkjEFqYy2Z!Q45=u;az__jwL{eL#<0$laLs zJ&y+xSP(Tw;Vv!zF!)6pk}ZUMVDstVcl~qXXv3a>9J<{hnaHsMLYm{<&@K1yxrf?Q#uWR%X<;rW`W~R52+|m}QscsN-M32v-%(?$7NzWo$ zFor9FO&_Y3fkLpjH?G?)mCLmdX`EGz8}e$Bh=JAIf+Jqaq<_JNi_|ppgBK-OZ}dxY zYtge-k@*T3$>uzsxJnrBjCI(ctMItL!4#Lq#Z8vpK5X>HvY?O(&yG?jwzCv}3W3y& z*ToN7Jp|Lm?R*pc5zM&uTX;?=#Um;hwLMT*yoCUNP_5KOUvXNwCw78_gKEY!^|%za z9r-2E@3B!(hB3J|kxW9@>USu=i>FpeAdid~>k?Oia<1^I_5;GMC56d3T;fl^ZRH5! z{y1Z-eOh+0*M}+HC&Hk*Z_lB7dlOCAwImgJp;kE-p$wNNqvNEARZ1?oo9pC1hy>OH zgi!X!W^=xcy4S<30&6J>c;t$b<=^xKG=4=sNAB5o6=QavMVijH<>z%J=dON+y;aH@ zuCdqi?7~(jBBn4c^KmN(#6?~G<4WG%A;`k_R*ze4)bYGJaLZV7`Xv??^Y|4(z2&VR1llBJh8=+Ux{SUy+vhAcA7wh9Faz^;eZW+vdz9Ip9SF(w}eJz0J-5=9+kM z5s4aHM z2~vWOO?YXKaEy}*ZxAAb4X`5Z6wSpp2qwed<<5Xk_%EqSJifpxhuq&!>t7H_zdli8 z9|U`<0DSLF7$t&%6E2&R9Ap?Md!o7)J}w51fk=ta59aS#Xp|+m^a4vchTj9ue>WD0 z*lH-dO`aC3n$`S~Rbs#@7j?fQtW^O-*7qW7+(?f0xgVMtu5n-l1})Vhlw87B`dj^( zWyS+RH=ARI73A~DdKa}uXK4d)M4C~U_Xc(oTSKE{ia|3Zj0vz>4h*^7zgrO{G5+vf zpxR3rFuD-Y+M=T313NJqe7WUr|=eaLC5+96LD!S5H~#Q4!6@RcM-^@UI> zTpoVZvS5zu)g)CoRo6cd*4ps6um52ju(J`i&;V*A zKw{Gsi%lDVC@W`-z4r?I7$|BC5VzDIful=M`4BdG^=~nT3fixf$cMAgs650;_%n+x zdES^*c>mfv?gq-=PzD_`*-+Vx=D^+=x2Br)E%hbD`E+}Za|45^fY=_?kZl$OO-$?7 zVqA6a{`mZG#q0M-*pFWzYAUFY{22vKpa6G39wMraxbOn%wk)rOhh~?H^+uoXSao1X zRSut1>P9G)QD%=CmJ4uWg^M2= zrC>R#1xsMC0YCnSB#P;>tUPh|pwZ*_L*y|UfS-{3jK!F8D%Z|U4Ab6OHcO|fX^ve$k2lbcPC0fi2;=QK zJb}mj({!2LfYe`EM1bvG^VOj|0yT_xWbds4q8${LHk+*FI_bi>-n=v7;Wkk0?8&*D zi#y;46$Q2qL14DQs$0la!OYO4vW3}KZ9$XNFV`|>WTL_6Hkq>P-LcKTD)UF%iPTb3 zAvOSaaKO>-;pbs_hQAs}PXeNQT9zx+Xd-%3uxm8|=|k~3)xo>qU1Rs{g!yE(f?%`{ zNqf>CFrMb^@ac)?PhUB3dhuv<$a0ej#E)Bum(|$~Blk>4Q2qpbIZ^TBp{sPHNN3_g zDs?tt{dU5tD)ob`)GN9EMb2q7kWoL6d(NXhXJ>SNeONWksdzuF`zKlRBEf7!z1z{S zRb(z9OJ*n8xJvo0%<0*CP^h0J>2c(~^)**tjcQ(zy&vXS`;puB@=OaO>zolHwBDcz zXt966!_tb*QE5Rn+V0prX})vWZO?m89p!RT;}5S;O*_4#UN+HSxmmw99d7=d6JqAa zV;557t;cBL=MN>3;w~=kUNU!oeh9mT`jWG^|5#ViUoa>Xi<4S*{;pB;SEJn_True` z6OSZSx)2`DFpq|OWal6JT(@4AOLH6jXZF;5!>xTR#-OE(e{E9+>h_cwF3YY2+?M*O zmldloy|5p5Et$3SVNubjOvSxFw%fon{k0uQXL5QcDgx6*B@Yz1v(u1Re&~pF%?sCU zItRWlh~ma*NZrN&pp?G~uY_Dwy~5t745CD?w=m__hnV4FFRYw6NJ?Ut?yX8HdJakr zsm#wJR-CW?ReuHF;o`E$Za+fQ-{HbC9(xl{!39T2$iD8tFpB}RS!6EugzXyVXseZZW1+paVF|Hd>Z(_$i;Jx{05(hblwMj5@jhw;zk}PX9EoH{ z@L1+f)xCm1S&bg0wa!`H8MxTUq%?&PD}5a;L^=nB^X*3tF`^Od@ekjFetwy_p35`A>ifW{jq2!p1i5 z_>zEJasfA(%B?Zj3t~@4mZSILGau$`D_V;GUStmgOmpN;D%q>po7Y=<_wxhi%M;ng zT$srIL(FfCk}IqQP7(C8nnTb?jx%C`=hRv0o*oZ$R2-rQD5yx|XdnD95=wgd2o2>-Z==_*5O%;#SwUP+p~DA1R7WJ{%PIe!^jDf+3b`$*$P#{8T*BiJ>fV&=Q;-z z8QKXc9`dl2+x6+_*tNE}vE%cZd6qdF z2%EJ@vW1J7Qyl2LBIOJio^poi|KwGjwFL-c<;k8(TjWf?>+OtKv}*iE^cLkWTHP)e zks-jYK;Z<}@|cd_VebnWuWE=Oa{kz0Swq1zAPQyRAL2?yXg2y$hp^H9K!=7m|3DSP ztl{=eDBbK6&zh3fKacocVXRQYG++KHt_A!$1xB(*V^D7j+YSoMvH- zhbPLW91|o(&aj=2Q;z(gM!R+?DsgWrw`&551a~*Izy+>Uu{c<#e9&~*-sJ-+?1oV| z4(oAoBT`OQc%re+Km)Z7JBUHa&xZb`mjGAnw{jsZYyl9}tiNw_Ig)Qq-6q4dxHKLk z%9xiPTt;j_8o0OIMjF<9CL6aS${yeOCBf&bC2m*h#|DbXw#M+n^+aQR^T;?IPBm?k zKeI9Bej(npV#cAIBES7TJ7Zx)hPMf8yz1(Xgl!sA0ybpX=&Lh**?q>9SHG@ZBcFX9 z$~Zk_738c5$ldMRTq9`-w)(j}R&C3RGw9eW6=Y%n=)yPwS8S)b_HG^Lq28e^HRNM( z2N1d}5yKGomNd?V49yFP-_=poLMzD5VQ5Ctfervml&vFEO>yc zJVYTDpYi=FDmxMVVYRsDQJkPtN}dV9&@lHR6kP}MX5^$AriJ9S&^Fa;i;4$cZKW{v$>yi%$*H@wbZq|LxOJKYXWEFFZ4Q4f(pN_JP*Zi%59#`GmhLRA|;clMj*4 zBE|P(Z^HJl2!<@$ss>Wc*%zx^(ew+V3B2;ld1?>LfM)}*3Im+qh)k@JjB=?2&|a3E4f{u9AOeybC|MW|9ssN`=Z*7Bh^#y>gvN(Z3~N&hcu` zeOM@O`PY$B1AV$4DYSSPBRQwD&HOkDnYZv9ADgMPz{@AYU2Elns3~ym8&Zuir?RyI zrGbqS3C+!_Lu*nnC;1@gM1_mP>_9fLJ*nq^_titii*^NH9P*h%Cpu*+9&kajtvQ7w z7#V^@WaW(fzxZU9L|o1=}~rD=_0)J#(EOL+`)tHuO(%ypS_F zebE~Z@#aHacZO9oi(GZ$riz3{g(VeT$eY{Z+yo>Xfzz* zH~gd!mj?yh8QwPOUNMrP$Vbm!_(FM2eg}5mKRlAcUO@y)v_Y5A!$Ph;46h+_u zr=%q>C4;S-6^}Z=?MC^*(sHpkFNOPFrK)V;v^L>r@%#qiqOMF-75^f+;$OacI_f}q z?t<(NBdgGFkqRTK!4~Td^64fc6|8PR(oZv`Hh>kZ;=JP-J*9B?#6SsNCt+oeifn_n)*N& z9!L3q4+E+gx#3F?R($E$%*pyvUw|A(=C_1geW``!8Lq~V*TQ~c(pn&iX2JmUbRhqp z%+kI42Wo{{2m6{)R;=n9{#p)$d!s9ZI3Bw4DomO;tZy(yysd9EvW7UlT{YG+yUY8o zI+)&L9?y&n0{j0iWb+T|EwwYGW}%Ck3zUl$#T;cS}_+A5b*Lixrp19gw!?>SDNE=d^d z$OpYNT)`rp0p-A6b|I0*p?PVN_L$bAwGMaZ%T+SJ>>6J#r{0NUU5+Le1JYFXh=+~6 z2sAIQ_iaZP)3%>(uS%7#pK3(r{MEFt0qx}1N(xtBajGsf!H~vLrts7d0nye5)AFLo zNTgcjnE(ivS5YNTy|~gTsx!hl^4@#i>kz9EF=>K5U27^JcaV z+B)>cno#IA@y$VWiupe+_0bEH?vA4DrCQ<&I8J!S?3k#$DN4ca+F>oWg!1$&Bm(yF z_qURqVI$hn<&2$P1!zV?t_+FpheQPI1ALbzP9HLG+e{Vpt=nIAG?T0H(Y{dZy;e8_ zRES1*ZCFV0#n5Pn93LFpDdMDMRm*TCSh8_v9CRcSbyasA4SFaz_{V9T4&PR`AilH# zzj7=%QZs&ahnxM)~DCLM9Id^9uoaZN!o)+w;)MsjQS zUets6Rd96b4DHi+gJlmsAr#XY$pu#Ytgc!a+sN`$OsjCYHp^Z&lJXn>!#BUufcWO8 zfUv~J^e`86Tl>bK5^GxVKjx%kaodchpR0Hc6Y>q?6#JXA!Qz2hi^{Cd$uL5>GnTHIDMjOazcijOpdC6{~mF0ck2T77V9ggbeyVEI*O zZ~%`*CDT?~&_H15h&(OdbSI)#5|*3xao(>=h5NAeev!jYqWd2s{p+1cjRYEUseiea zjI!r?not~b-o-mG&Pun2cadA_CHA+uX}x%f?>^{%4S7z^pKeF0bdt;Q#ovalqJNLs z5Z_vqf9fe-E|Viz4ke1ro?Q@fQzaExRqL`9T})>Ex3AtyjTH`rZihH-Ah9y+$W+VY>M+=EWn@0QYkSio?2RC+lTqN!Z85e zlD!A#cWwWvS;J81{YUWUC02$WR9f4aJ+{UkQ_BA4U7mi)bt9j+7`D1Uj#%hrpZrR& zCKEkO=_9X4HBR{iRYHRm>!vAOk1ZZwP%8|nJ!UnkH&lZvTqY4Wr=Qw<9PDdB^`Sn6 zfleM1WD&e63VgvF$Eu|VRY=X>)Nh{Sj}H!K?!}YZ1F3&>F!Yf|aKbdV8EX0Ixw#NF zv%b{>MxH;x;d`OT;O?-#v#|nfSqEMb6@MZg`r@AXZS4%Pvq!sQMtPooIYJCo$MN89Gzj>OTG&`Sm9LYpeaV*GhroXE6DPvEbBE8_NZ z61Cj|g})UIf}7`m`0+4l$ibwcWR^2#?m`x(j_>S0@)r1a_K}paQuU;#mE0N0`KcqA z2jRioMh3r@953)0WEo9|iKM=Fj^>w3eSR-pw}gyP+PXwxsp2O2z?F2b^Wg9Qob$@# zAa%#a>yrQ;UudWo!Zbb{CXEQxn6!uVDh@giow~!H8J+Jm30(4cja?4rq`ls*&c~o{ z_IN5p(1xSh5-ffMDKmKa`IRJye0*QAj1Xt}YSF};H&XlEM+y%uj|uTNW$)J@BCX~F z6H8Wc?YHVtd$`0xZ~4*l@RIRHb~pfitfuwT9vFq@P1$6PQF+86K4^>A0!%&HpHY{1 z@o*b@oJQ2ed_YfXZYC%eX<0*GIVS;eBrA{F?QOm^5Xn!9-1pz#BY+r#*o8`+W zr*NW=(joE8+WC4rGK&$KXFiPwocU5v?^($X#^$}a2g2iSLQ zER+3>*5T2TQeBISBKg0hiN^A464|%Bcw;=bdlWLSn8l5hQ)|*oDS$iu_YJ>oZ@j+e zTF?W*H%%GBvYEr2vX(?lh#LojCIiU6``HgtLDQpCp7-mC8M$NZWLf@d$~Zl!8P=4# z<7c`vEs42%TVAx!MVFa-Ghm}WR|58kVZke-6!ozY%G$mu2dt1#t#g z`hFsaQ$@*h*GIM~t6Ks4F5UmsH${BR`Tf#xUgx(*u+~yE`qij^y1ie(Jx}V^lR~N$ zhi#l!?pzw#kgD?PVIQBEaEZk&Ig_}N5t-3y6Xt;lX&TSICvfST7Q)>%7|$_|eP8g| z8|R+0D@blm2BXs3Bw2b$E_W*8`H|z3;hz;twMFDNsZ;wfvatc%pHmw})zZ)V&9hfs ze{McG0|etPl?Uid;n+hxLWdLM1#_XSiAHJI_tl6IjzbLir_u$#puC1`@Rs+~ZNe8{ zIyMr@HpdV`{J5V@DVVfqY~14~=7|6^=eS`Gb|GQwKa%E5uM$~s-EP*^Km0iS09E^T zi^cW}e)@No1M&mJAC9L~I1xTQytf|;3MO#WyRX#beKcF_j;_gm_VC2>Y&D=Y0VXEi zIu7K?(y*SbZh`7Ay*ZJ6POl$karN%oZYxmKcPG~0B%h52ya!MfKeXIWbc{D%u2Ro_ z&`r)|y0`wOdAuhTLk#gwXxZSD8PVSYE{d#otAkbJb4z$I4 zW*%myN}p-@PSyn|wp9;LJa{sc)@6PBIOy$VMj0v2=#5B_7#3_49}mywcBC*?$>?t* zX8((-MC5@%VHDgyb;pB!Cid;fvGe0KPzp;gJQFFJOyuHyg$?y`6Q*|LRyYtxdWd6+ z=R;sFeC~e6mCL;KYX|*v%Vp5Y$kgd4$@$y*&I+4n%k*2_kT=EcQj^u0sU}=RYyGo| z%iyTXEHG9v*7-t({Y5Gn+GPKc*Z=@Hq$5`=-y@!b1WeuO#iSMq&qQ%2-<33Pe|E6M z77zFF$Jv6GxZm1NeNvY2i#H9w%OuAj5>zOoTn9Q4*P=bA?k!8yT9et$F%FB{#o{#b@YT__VNA!=b*? z`y~!fxe||zbh1q#zj++(gF_bTNg*o)SJPeVI&u$TQw}n7b!Oe%zWugW5Jjq4_w}W$ zcIXwSa8^ysFqlITVn}v6d*RMhz1;gL{;>X7SqAQ1Vq(1-d}haBI%#4Nxf&MaJkN5+ zd-kORb+VzydJ$BUO^VAhVO4k7DVAp1Y;@^HWmI&jpHkp-$BuDf?pKPXJ371 zFEX@F_FaVhvW7aXJWXEH$S8GF+wPPmqD3H98F!Q@!Y&K;Tg}AxnqZ_CzDr837XE&h9>vRPp9fI_O=pQ@vB;~)<3`KWZm6e(A3NK z&#!eI9af<%R^kZ|mun}P5u9Ro0s#WrH`dLV_VfH}d=gol1H`-cg@43%IhCHU05`lk zRt}q-^eeqo@P0XVo$D?8R!xvV@8XSkKRG$ z1`nR2QQB@`p#r3x$qo)rZMwJpGF!@yTNBdNuYA5wO7~NgzCmPTZ3@OAH_&x03KWWl z%>GhS(zu?4h<30_2hH;c9oJc(V#f$vEof7JM_Inoo~O$;XSbiqI7;+_8=zOiT9?Fj zbHd3k-zBt=gRuD=2$)J@6}CjS zri7p2!^1ZBre!!D31Wv!$ALO^;jFNrAVZ z*#|xYdvv{s_HobKZCyxa8xb4xpL=CS_FPPC3Hv8FT;@XT7H(@S4 z6)}RY+t$*!ucn+FI~0$-2F|Sw2ssU#?$S%t32FEKQONFoiQV z0^Ud8W!zj(?DM6q?~x*v7Q$fr?3G1=88$nS)hKBF`BlwnljXfXwXVAJX3YqcBggLL zHczQFq`TgIP6eP$Z0=k)1ko08cJTv=9KF&;kxykw86Rl6GL00GR*ExmGdqN4_1WD3 zd9>(DK&!mn~?$nlZ zy8@ruJpuF>emb8ZhRlM4e)m5hC;Uiu3@`IP zeO(>_wO$1G))T^axUf`eGf6!Y&+L0EjS=tdfZf1WCkt0rLy5P3hy*8%HHrM(37|g@ zo42UC8pf%UU~kvGiIr4GQ?I9YklWM3Knswj)kIwSWX$fS2X&13+f39BOSe!dlwRRj zX<}-xl_219N9~ra)d!9}d^qr^c0UKMJMOan`(!7luaf(UD9LWq_qZ@q*8x2VEm9`f zw>>Z3<>HokAK4pwBLA*qds_dj6n`jot#_&tw#04@w$vNn!eoVgpH?qD!q5x{G*_2m zg-dhEot~UJoOU;Sretfqh40h%2r|N)6TO`7C#G-_JFW$GBgiv^a?Kca-r901Zih&p z7csUJ{6$R55U2NN;~dsR|4=q#L)YdD!LWn{g}On_e$`bv2W}TjG-g9nQ|Za>Fq~aS1Q-WAG7I8`JcMOd!jiHEZ5UMYrIT& zUR*HXL_B9&>FSHSvryE(V#IA_YoN;~d)6Cm)H z)zrCGJLX(PedXO2Yci#S_G`f}CoKhiPL94>kR|DeNx*C}!Jl&jr`_#{Z`Yy?vfmL# zb(RRur5l?Mo>UiisT2MrJBgP7<86ynHH>KdrTJyy@}iU0TinvUW#6&pyu3-oq$+)V z@sE!HwY{-!za10>{DUkl12@GmNKIchn)%!rgLgX1v9M+&uSyTH z2`vjjHlgJ%?gW4=oEs@5QsCAdK^6Qia7`S7KlLT@chZCuSG5J~(d_v-AnQgAN9{2{ z3K7uv?7!4yF9zl2FliX!8zq&)Zt;o{R$WO~EzWG!pItv#5s#}ec%c3Gc8r`4^IyxU zb`|L&%jBp(*c>U+~F4Noo@ZHQ#Z8364q0Kt7z_9h88T6iKF#6Kdw_%qb;4!X< zI3I*XZv-vU3I!Jm`@}ulF%_!O+ZBuEiJ|zO;Cq4X3h0hYrc;wDIqj50$gPcI>sXil zG)D|)mRQ$Z5&x66)D*w1ImSfA+vynOQ{|%;o>+M5!!3pIcOE;CAiYWB+cT_(J!bpV zCHUjSHY`r>_o(oYrWItE@}FTA57YUm;m92iC2k@$rD;>QM6M9mI zjWukyitWmNB7}rP0#7N(l|goxda}B}i_h2|H>99UNi7r_Y>$2<8{636smu$X5Zz4^ zc;&K;5ZAHHndG~_eYzX$c=6;L_9R>?7$XuGr!w%uhc;XtV68BWKt8Yj2+BlEudmLsj-)J;h1c-J^nMK5z={Ns^0h{PBEZzy&6}Lf;zcZC+9vW zUl8|Q?!6XVD8tn?=1-uu=Q{?A*urVhbgLJ_dVNG!HeT+&p1CnAz}K0zSFB&+`iwK3-8S z$U(Tb<>N!9<>D!eMI#Sb-m1mIgT+6)BM|P*tMDPZFlXO<)Hqj4v!-fb!P`^HR|NX*=x zK}2sig>gBrX#`SRoN~D^QIF0KRFSq9-qfEaAMY*j<)+#%vrAK%9a>&<5KMZI|j$F^52;R-clktm3?Ew&mAt}-}sr2 zqhm+Nc(Z889$#EK>BoeoFjd?@xG0|JmF_<3N6`8Ifx`jjQVFSH1i}N#w|T>h>9*}u zo1O)HU!OezA}`S>sY(6cf~Ys4 zLniI%iHD*H6z0&A(gvTbWbG&}$7)=hpA=rlIPlm9_%9#m-YcqA>l9j#SMy{1rHsR- zQ?ciO5gqtKBB{ug%#cE49S)`>k}`;3tM;mvjiqLZY`RiLbwB(7_4lF0Z?&$F)Kbmj zs~tpsc>_9UWWb- z>*C}heoajXS<^Ssfsif-v5f}(cM0QFq-BO!1v44tB{3&Yr&@tanPWNn8L?2Jj2|n! z22!V=6Y9m*&R98-^-UNWE%mzCBgA;KBZcsGigj30s(LZPn=$s>gX?ura=w}8Lxxbj zDfIm#IKIB{E>tF+YHxNJqE|Zvt|;src}YR_D4fOsLg=->#vU;~n^^l&2r*ix#%7h= zPdL^VM;V98HA#PU2k~-W3tXw#av2qtBKyU_Q2Sna>ntj^B#!@!EaQh97 z<+YeAVc5T+xC3^S!O2=oDyfzQ;>gN&Yp%l?ByXkf$F93vQfKSNIn=1$l<7{%EU-fD zl5Hw?b&};mE7CvPMWn#i2=yH-CZF8b7LC9cWS8j@IYQ?m$ZTo z@E+Gj`w=SMvE9FcJ_6`5LN6co%Rmg2YhDK*A2$v>A&fxMuwHGOPozdJGoc|dYQLYu z`6NgV+(Y2K00jm}Q^z7QsKSBk^I740&pxACE>XY=p^%fvNTAYKOH>9pGA5crsgo3G zFjl66Dl(?IjH-|FAgn9i6r0xTgPSB|#_2kvtrR^Mt3NLnS9&U5dXFSy6PV95;f;O! zUxb`Q?>~gxZu38cT+cd6tZkfGz+9=&o(v>Abh&D}^qC1SaLy`AX2Ew}by<~HwwP{n zXi{+^LT4_BSBRLGbcXZRLjhI+tJ%vA;DcV)g0Rf-rjS1e=jnm!Tce?)s@>`bTS-5mHwCR7uP+BF&Co&m^^|J!#Wx`W+?i#;BF9FpzUXDYN>#V$PF z;>rf38@B7iFP{9Po8(4uq!q1COH<4I=OLaFuCXC4-*Z!~k}BRYg{wAbsZH)F1RJzx z%wUKx3#@GT``%;pQ<7#74$Q~svLD<9&g5ra605Xce0#`zH;9?fL@2_F3xdF%S^xf9 z___+_Wz!zI5D}sj52u*`vVn3j>PdSd)+g$F4@%dAa zK|5gz!X3ND40}l%SMAH!n5I4e$VFJz40>WvYiXXZ#B~ncJY#3(pHH;ib96Tri}f9E zrE({A{N{;j?(eDI|f`aFlc@SRjlr>j) z_9@ptm$vC!SAta^G+DkSs>%O%Lhfr^p`KjS=12rDa;T)BUJ#yh)%GrkTLjZb413^( z%Zr1Yp`MK!fHqkb&&aJ)laCdrnC>p36864&eWM?X$n@2&{snD<`Bkc(Wfi8FFt zKK~cip#G-;)pJ=k7LGybD#G$g47VdGj-H=giR5hk>11a;f`-z8WagLA+Fi?OE@a%F zmch3JX`s8DV=Ibs)O2T!vejX%X)> zH639v%bO~GXiOM7v^OQt&j5RZR!35M`F-RW&TrZxO9+)Wt7ze0PC)JvlcIyx*(tn* zPFxGe9UvxHHXtQt47;q~QIk1&lSYrB)KK_#{JvX5?^IU^98a91@Q+I0!&*KBfSfeP ze*Z0&x`DMH#D#yECUDKApxz6nP9442&qgo7uz^D*j4`nw(p=~Z)kuMa@zUR818eM& zxPQ(^nqd;j*3bSS;1KxW5N`hGLE}sVQ@?5|3S(98-pW-dg!>G{|AIa z(^qZpM4IkjNTx@dG1a@wM`d*^@w{cyZS!WcDMW;e1_3i*$ ztUcmq(1VjocluZ(-K5a+pr~Du2q-9m#lQa-Kt9r}@Uzza@>Tj|Ahy8D(GnPFhhH}kmP%yzvc3#SroMqr@%AQ9Hd+dYu0@J;F?*GW~uDGc_XodHCE>9`rPqv zGR|?XCBM_~tZkLUaB=I*D zAJt>h^Eczu*(!`3Z7E^s`a1sKk@Ml$+{<|QUt%sT6kyp_y6f!lFU>)SSb|?V2mViy zbFG|#te}&Bvv7%n))lf ziDo_OX0w%cUOT*%`6b|PRG-#?Jbe3~oK#Ta_#~(Q$(w^-KV!~*C`O}a)hEMTeMU`% z$Fz+>?f+NWTv1F<+Of5@wAf1(!oZDYjd#e*k2N{?CWUNhory80^nauOEoT__U?}FO zvt=EXOVS<5<9UtJuoD+FtMp7UO@}e|*o0ZNeU|78GnJDKpVfR9kSR>?9r4P22!r@P zYO3x44Aqxz@mC#%2O;D_Lwh&&_tW9OFStc=L~YjAR8KCVkEh}^81d+GBb2V(J~1DA ziYE#3<_%k+T+v!4-L%qSf#=FxF?o(D!2Q;~f2vCrh?KS!!@CB_tabBL4|0TGKe$_; zZU1*2HF1?z?DMdM}HkmIqj*^PppV5gfki87t5u9d_qEf8jB?Xv8=1iTuddRS@R&T-;m1v@V{#R1gQE%>5JP zntLq0Lq?uxshsv6z9-HB?&V$C%{0XqOu9@nl#otqSPzF?%3OQCoC!wBxUcD~w7Wi` zyz2q}u~c{FA93^_SaILEAT`;U;_`QK)~u5c{vY$|gn#qu=qR)P;hKf`0r6WCz zgh4V4l6?>1xii)KKHYoY_uO;tIrpA3f6R8y%sIa~bAB_Q&*w`-X^M!WD#0@L>Q%^! zayM%R4Yd!mYANf%8vMO%9YHEG5nAx|y*8ovCs&XZ(+(x1upIv<^V2F$bL-%p=Z>uJ zBK>mT&+`6Biq8@g(UX6}Z4_sR5_v_c-H+HPzcFHRx-QxOVX+!>1%yq4I>kU#SBZz? z!(A)3?97z~Rh?@w@1FD3VvK{ITh0SL*X!HyGP$C9A)OLED}F3&A-%HhqIxUi_akWV zwmTnQ`PJL^`KEsRGF49&7Ba_z>13af4LfSJ_f8R>u^BW^Q+{c7=GM3SBd@Wn{HcF8 zqBbkYMaZJp>Yl>yrXAGqq$JBv(JmLACMnMB5Lcnpc{V-z%_`krW7Qjru`U-ptl|u} zDhq(i>vK)y18!(7M=aiLS#Wyy`ebn*uKX!NS#(Dc@RfV~vm3RMajXq&-7p_9iS4`P zw+tK#r3tPX)OD3a--lKm@#M^QTzKGq`%MhVsQ&0LVr%j=HOt=IVFJ2rf4Jb>i{0Uu z>pbumFo-e#ad428@Tv1UL|!+PNE!v~CDQ`0VWF0t@^o3IoIb%|DoM!0H(W=YS;_7yRye?&}A+*x1y@vq4njCRK&_Ne|gd?8@|4w3bm+IMm0qa8k&t9GKb1+F$_J7S9poQsh-j zObH$HYVVf6WUx}gnvvMkXuG1;-Pg3~^4_wEaeIMdh3_9Z`$@~?6ygPz_>eO5w>sau z#2^nq_+v#0>#Fny>l$g=Mv2QH;(XY4P*ZCP{Z4X6gU*5 z3^%&cDP)Z1_GFB_yA|jwQ5Qah=<6z4xTz8(UMd{oczd9_pPIYi>m~ho?Niywdw={@ zEeVb?_@&}$Ko=44I1l5u5|C@3W1)**2`))(ORC#k^`*yo#hNTfiuD~k<15VxhcY?B z*D~4%S`wJ>6RD2yt&%F~P#z#smz>1+8w>aRoy1r2@t9UY4FJdHP0X(bqIRB_*6_>z#_i|>=Q-S$m$Pch`qHIsIcE>wT5Eo0r3<=Pfx8~anaNjQZP~@)qpnt<>(RX zycbWWx2=}{5h|HJmm~V>LyKh3T27T;{|XKxrH7kSJv@LbtB@7FtJ!Ut#$_YX^4$5r z>8%S%p*+q84yMrPX5i3_O-(oQ<8aiS0_hyTTee!o-FJ9$c3(hjw4mZ)?hSj&pBJYp z5NQE=!F36ws}^j@Z|v)OiljYU0UZ^5G2ceLr(9+oi@Q-S`$iM*1YwXluLnG0S}z7C zL66K*_`0fc=PXiFLv=8d>Fz9%EBzN$jSr51rU%fQ81C#vyYO?7LLTqG`9_g8sz;F> zJ!-~)^B?c{dyLL~Cy@Z#cposw^kMXBXbjsMv7u`F=dWASEE}MDWvxs{B)!~sr_PRf ztwd!aY26nzxVp=-6 z)T3H>BB}Htd4J%dmPS#ikA1dQP~+VB09^O{U*AdJwlU#~O);(W@oBEc35Jyh8Qm_N zLB~}N8cRzlpis#X3s19AcSqOF2{~}yq0nZwHO4sZROg8z^`Aea9838mrnHjz(_bAK zpvCccywau~Er@-BxY2vLw}D!TOGR)>VaI55H5dA$;SXneNq6s2pGDK9SK97}IvD98 z#kyLW!c&2wUbS1x_+X8@|cL5q|FrG|hgBf60G~ zf2!A81-j3=Qo;5S-ro7|;5F5a+P_^C^hlQDqv40WvvfGdfQ*V80QpuNXAbs{60cj@ zFk73dvzqSzjL4*x}hS zwAh&ElQ9Ph9p>*}nAxUpFok~|NMu)lUc{A_Zs$*hV;DGQgd7MNJA;-x4yP>PtJN4% z_tbJsJtdk=}j+&MJeCwdSFpDnom+QZjh+-jeuD@ zoh)+-3Q{_m#HM%5x_dg>D*}^dG11vOs32{dRg2MLH=URKeSckFQQ%K`~>3R+0ZZYo2# zI~<67y5dY#zPAdU#_jD?pRHdzt!G?p=U_pU&)u?%aWzI(EDPTHAj+Q}&IIU|d4O?- zY2>l!Cc_aY3LFWRCl<<+*Qk1X zzb4xsJeKeE!%j>T&VbK7F0?&+d*EA?Vqu3XdZWo$!4#)76t0eg0>yj<47MdnMN@GMI+={*9V=h?5Xz2~2I6WLkpP6VABn$!q8&)P zgS5*Cjd$-dtR<=@VW*|oVhW{*3|3(&{_DI4nU}Lv+}62Auu3i99oa&wpTx(eRvm+tMRSFf0j2V? zCt<{5?eQz$f18Vv0R1$%5z&eG+aY$Z(iVi0w$GGQ{c$8F~YWOsNpEY5C3voH^7FOLSlpCX zQh%Z0VD5ns2l)4hg4-d}5pynT`s7-B6{?L&J@BI%Zp_A1G|9Nr~sMK(&j zA4d4+p)wt{Fr{lPQOI+g9$|d@;(nH;unX#)Sec{YR_?ctHDj$a%oNCM!sR3t_yCvSuLK==p zEzcZ@vO&f8jv=WgBC&_>!uIJQrs^LD$0DmMXy_L<3vU$ZSmuhl-=MxjmuU!Ppj{po zRJE?7rMc2w?X$0Wgigq6Y8)^#J5A=~g6sYqSSyxscEiD}M!lWy96O&*G`+r_{QcNY zB9Xm1O;`A2_BF8UXubV3+N}ZkFY8UX$gP?Boo1bC`M21=M7iWS<-4dNniBip zo4qSeK^q>Fk)tXkqB!A9IMW4keXNiu*m_RljfNT_H&{5RIBh`y#MEcyl_OIGsl7f{ zaB59o>anEZx?R=Ss^)Xpd6nU*S93+A?gkE zjLi>KJ|#!bMsP_C^Oa5Vl4yMv8O93txiyW}J=ny)4(%)5Rt!UGPck(hP-5lLt5_>P z0;;pcJcJzkpTLwAaZX~Jp=Mi76Noj$P4NzOa};SCy;#!W6?9MTMG})E(&Vu*j@OgE ztle4jyPIYa;9+9f3=8jMF5jSLsCtagxWXc)^0T2PCh@c;_Z_^xS(u~Hzlr^$whI1R z?60{e-kL(SJYh=)Q6NH5&vz}@x+Q1cR`thw|Jivj34zr>a9I(pfve$s zG)Zv0aY!;3*u0psPE1c;ypE+Cl$YSvPw#)?zgORHSq z(6#WWYG-HH%}Mt;r##1go(p7Zz#ofh-2C^+zl&ImeUn79ILwORd8w}>`;rQ=wd0D} zC=AHN%hyN-FO=rL_6O0hMLTB0q64=*|3&kUuAJ5>UoBd-kj;B;#^&q%rDR^G!u@tm z9up6o*z2$*9Yxn>g-?vEI#ExcvYpQt4}2y7@IkI2%F53Qq|iRPmV11~GsL+T$@!R= zTSZ=w%A{`IrlT<+mR2rY=e;M*Jj@Ye)5SRcN%Oin7EtBOt!as@f(i}2`1rgwoy%dv<)|Jx)g5$0Kt>e zS4~gT>_6Bx>72yCx<*1|doPp+NTCsjXu~241o&#gv-(tj8CUM*!QlkRp8ME`j3i3%e+A(GaoXYHd}P8NxwMZh+0Fa;>ccE@`7zbp zqFjGdjpUx31~S#y{7E$=U4h-xIHNC$B|C=mnsPo0c_cAf!@Km>a?<|g`t4^`&b{dO zOE5aPBUivACr=xCp$p1?=kuusqn!Y`p@Wsd%JqTbJ1+{I4FlZ4--o$9)+@8&Z6;T9 zZw?Z)gHXfDK}qhDf$Fzc7A6oGU%T)N4AV6bIOydn>}^{)XeRYlTXr+We49Qmq>uz;H2u9qU7hE0I;l^2Qk<un z{prZazctigqZ&3i|38%f)QqQJo=!3fw`T9IIK^yDWB=Ovbsket^@-ks=FcnyDwR2D zdpE;L#Xx=oa=V|Vq)t{+uD;_8+=ti5d1`pl63(-3EXBELwMsO#b(sX_bDNk^P|DYm z8q7xqmVclAkN%$iJC;}OR`7-NFx@QqA$|GNdIlyrzbSX|rko%LNtiN6jL qJ{kIm-24AM6~Ny8|Mc|3zrx}rD@I;ABgsp^RQkHcIq);!OfN7fHnuid^zM@PqMW@_{o z9UXlPEq}~(g*Kz#>iW_GBh0|ej)^wlOkS^PYrYU;rx07HPe}NaU~f7w1p1G+Jj^rL z+ZzJ&g@){1LhI4dam1M!J+O;-zl)3xw6o9YLQv8}R9G5bh>rX#>h6RxG3i?9GMFo7 z1as#*$wFN2sQI&6+o(Kl)lPO=)sAnfPt3WVk2LPG7i+)4YcZ}_^1OjT_L31FABsLs zMP$4cE|nf)AQ??e!%tJ%C;sdmJL*NOJBEJD2;b8=nS{vk`}@cmjyXi> zL>2d&&cS;Q=@ZUgdiY<*Yc;uW0MMYA}?G5+y7$7{NgqEL~F!Ui%LDTkrfD zpK46G9s0C#U|hTMAfCM6f@Ki-<}e$IN%MwpW~+ElLN|do_boa1k@l%PEbGbdxd_vh~($o z28zA(84v}93|Cmlp!)md-df#>nT?r{iVa+VMu^lH$dJySJGd~zR)iy_Zd2B)6tz;)P{p|C>1D}sWWc+5|2H|*wl_c>arwiH zEum1Y+(DkYvjQS^nwpaCLY@Yb0x`uKbr|9S8pNftm9fXLfO?5%3wt<>b@TLxy0aMYRYT*b1q{(&C-@u|B?O7^Mz$Rdz_9RCQ_Ed16`N=b>S}3Lf(wP@%|vtQKJ_i zG0ISm>f8Mo3FO)dTfi=W)`uCYXZ>ugdND<8+8EEHjc?!Us+pF5aL!@Y*%H?eU>h*hq797~j!u zT~^@~Xy2OjE;@Ra2o)j+2UvwynP8x9sJQl77nOACO*- z@RJ4o-RAv2p?B}$A5RWkrV>G4l%U+p78d!W5*hC5PwRvJ2?<-(7h@OWdfjV?&Nfm#JUplD6(5an{36bTi|(}zb$Tdn;mmSR7_4&toWQG2-G z8KHLrQX-Hmo9Mw7#lpbd@eDtQorLW>iq+rMhS(N_=tjYg-lN+j!-Ru<<43Tqz|_L~ z(w`Eja<%te6|YjFM3ZXUej?_Cvh;@){%$#vj>MrI9pKa8s0Uta4cOI%++C5M!u|7q zq>{>6)3U7oOi3VXCkJZh^~5TSOLqxV8#hE7#ITc>R2DB->(7@tgC84d1L1P#TaJSm z1=+-@d`(Bes1BRd5JqD%$b2|wGI%?$)zuK-Dyaj2vMF|I4A9=NfK$v{#0>V<<3`|T z{Q$orR=$=>`Mw&L+^P>BW-|0N{)KuGW&HebA}p@lSX-F_XOfCNBqdcuDf)Sz?oowFY_2gyeHutzCIp_ZJ2 z;;E0gjBNy7z{;P|BA5Op#MpPDN9TkJV1U}d zceFJMeJ;s)zP4%zpmUAKSooZkh zc8@JUbf79^-8j6H9Mv4CPLj-y4m+DJs-%lCpx5|0cuHzh$LrO}T#Z`JbsM^}V4_VL zDg6bN!2)?;%#W_yEw4RE^eq-W_?c$6daFy76!C^`c(eSP1~6`T8mkG2ic^St>QZg| z;d?wKZnRB&+`Z6BF0ESmm=)ZCXedLUSD>-EtUU2TKGTwIfa;-dorr)>448g#gSsp+ zX`Er~jxh1zy$XVvuAuX3Xv7Lv9iaSa`J=FfrqIs6GiLKF&Gq9%Dtkns@z|;!(a;DZ zsfw^#~L;_H$fu&yEBgdTL{^Bc7XRb z&f!`V2^KW7-gbZ+`OG@Wt}6_1nE3%>sJ{d)Wvf^s7eT2`Dd-@?uZ7gcqK$b=vC5j+ zXA`>lLwZ#gd_=wz8*Sq3er-0Qx^Mb*?Y_s1uRR1zUiyAvRb;(TdrhZZ!E7N-QR8al z;pHcAUMHjw6pShm$>)tl%fM~2Zk|0-{ykv401UPM!u#zTJm5(y$OJLmbIjd0X)zUJ z+#16Qk!AzM(JGfRd4B6P>*IT^W!Jr!A?%5wdpb%xSi{(?mv#x1Y+nJ6S>OK77J@ll zk==4eeIUQZ(hlYqnsz+9Td@5)0=$j>z?M{&ZIExK#+x&2qdo?*SI7691_^!k@hkuQP>5p|JPGLOyc1E@9rE>e9VQj=iR^iDri?G}Qg_OB zKJ6DHY9n_zE2g++mWLTgrT5{T_K~QH@a4_%vD5u99}W_jl_Q%){3!x6-Aur-DM=*P{^_ALNfZAmdst#S+6y6;%hi~D=53U#+TSy zl$sp6lBDn=Z=OkC)Szti_;i>MC@fgo54mmT1Sm`joZ!t(?TEdr>dE~x&SV0yC*tA> zqn{t@RN;pe%>JN*8@zow7a(eSOQHR(DXKky0!{ya-8luW47_$OmY!j{EnH}4b8l`tgxD;3+ zAK)`9Q>cXgo%rNZ)nMgP8NeGQHOS3Z3#ic*URg?;ySxE2vf7w8-SBH$7q$nmxi0|D z!=~}{Rqqb(ardNz*u?W_8s2hK|CwDLO*AE%rkf-F(cHkGR5_TUAa8cj&PtzrOAGQ_ zrLPo<>Wd5hTR@>~JsTZEQ#LP#v3eKBO!7po+{&06J9mS#RIUcS+Nvcm5mO!Q zxPaJW|c1kqCQDoNJ-Si`*C*R>N*R2;icNwL7 z4aF7ApJjCmzp(7d{5*1_iR+Od1flKb(5IU%;ZMbFt{KezS}cD5V@Cel=Io)Y~3AeWqSf%@?X+F=AN0TyrW@GV2ZFU>P_iKm5+|E5uH~ zx>}1jSa?>huO@fnn9DV}NhfCTESKfHnp}Vn8#9^)mM3+DQ9NHu`0?MB``)W^O`7Q| zC`jKnEupwIkK^PYW1Fn`2e4zQuO`gJwXM?&qrP|to23wGwYGP*WTh=_VzXt?)B{c{`8XYbGG8w4%a-( zLy|YyiZvJQfZ_`8q_0oQCPoHpxJ5g>)Is6yr*LT3=Kb}BEJkiAJ!P5m-}I3-u(+aQ zlB|)eYsj8AukIi;eRY}N_%5E+{f*nM&jrzA62U~tzz|$r0cVjdC@C#(HwBbp7!?&7Ejl@2sn(yj%pyyI`J3J|G-xq#I~tSa{;QKkd6A5=1*xP zUD!4Hi=sRIJQKxaD0}e|moB4e+-wf?#RwId7#;Su5e;M-{5Opl6btFR@DdQ=Z75_= zvOJ-A85vha(t9C#dtNItxDfTz#+0yx1rJ}+SNh6%p0_s~PMdevwt-~`6*rk~Y65rf z)jqX68q%~pGpg5V`R+=TbvE=jL~qCIv~kz8YV&>-{MVqEy!gV^nNujZzdvY3Uqs}$FyqoLp; zDprsZF8iox!RRQ4qH~C$l2v#;2)mII#i`bfQvt|9>$1us`{AsUa%bU@~j1$BlcC z;Y&>FEMDPUGyxvOO;`+nD~q_=ehj?g&B3&JuDqdIxVCFuve{voO~X&Hc}vgNv<)0f zMiFVZcB(V~`Ziqbtoq&aZdHuw8pS`@{Uds-tk!!7GDvCb*b-$JkV*4xmR%It!xd8Y{e{e z=+b#7(~FlRo{I(NZps;4yj$)r`Lzpw2OkQIQ)R7@wMJ^a}JN}BNUuX~b16vsI zvu)~G5uKb>8AMN;$%MIO=j zhu^44a=nf>6n01SDnJP_*N^JthpntQ@^l%E@uM^2e*5&rY7^H)M&VM7n8t|3xXoDe zf5OlF+Ez|8iw%|?^lv`$qTgI2MS%=Njvm0}`QU8{<+97FWRJ9s~~8?>?ab1U{vrh=CBg=YRG zKFsIDor>y^2NA8MFR|!f1J@T7-pnlPkED|^BY#@;Zt{38q?bRy9{xSkX@zK$Hp>vI zO7bsO(us%=uO)lQiKa;}$D6+S#Sx1d_08gW^mheSj8^Lt_3o2I#?OLPE?B0Et_dT{ zqOs>>hif|QSiTG90qo>Hg|5v5+|VuRmuCY`P3P5ZgI&6Yz;ubB$WRm8Ra9ZwF1H%S zM0kCT4wu+E%~0#lqK+>wNC@-GMOZfab>#L~Kj%P1F~=avh^#sG0U6Lz*hn6@xlT}x4>cS&>#s2!;zf{}6UfK8W=9o|&R8ui%L*M4!4V$3X&ifxNjo7CcfZ&P@SnX8jUPg7 znM%_$*`46^zJsExZQD0_Y_x|t8-L!lU)`5wHSoRl=|o#<`$@Q$ojS{M%_rj2sZ2)@ zrgJd-OZVA?plx8k`qjV-pDjj)2uq>xH6I_=))P8E9Z;z!X}9XIW)0SLYckZ#UZdSg zhJ%VVhjnA^!(Stdgo=jq2(Q}HuHZ*je!Jhf8nO@VvLBXT)IR^3z%CC-Y3#;)&*(dm z9kX+lyQnt1w8<{U|(SRxBc^h^aHy-+0X=)Z=KNT=ga<9TvDRu71quUP02u z2^d4AsuaB55@JiM#dX>E#${9u%|f5zwDPwKUCZzC8~d_aJ-()jOF#_Q{k>YCdshrv zRuidNS`6y3cL}p-F5O)W=zZ|Kj%LmKCAs}XEw4>pMxa59`_~wo(jzd0bu|TAA}OXl zf&cDArqb+uW49i)`1owL`^SD#aTR5kW}yb%EqvpOwC<@AE@F-$%&1kHm6_N8J0+gU zFUR(d%Q{aGr-07}vg&f=`K_(QFa{Ngz8&yj-V!gcDE@aFccoq0_{c~h=nkEzTAcU2 zCpEFygU!Fq+#%2Wih{OFg|Ypm5Iu2i**|DJ?nJMnoI{+?`b zXaF1caanTt%PK?)JYSD7 z(5N&Vqnk&me%jYugsYu!_*P1$4v9R?;`r$-ZD$o!^E63()}ExP%$_b@o#s|7s^cgs z_%JwQ|F?B9XCR)bW9t(F^t#pk_LW5Ap|@IIfoQ2l6I<*e1+%{QON^^0-Y>&$IEB(g$lGbQtBm1i~M3AF!;u!w9l?h=Ps048k8^?M@Hr zvxq}yKjhCuR`u~Vh0#JbJ+i_~<`Ewu+vQI>KYk_G-$SfP-}7SvXG<~9J$^G!JTr*& zP+o-HNb7;??lIK1aAv$x!b8jpe0u#2g<<-%eNgaM-)YJAnjCi{Zl^yD}c+U z`-@9x3S*!4=S^B*H=-kx4vb#pXuQy{3*UD(S@?yl_)FfG3%FNCr7Yi&%@I)Z=s400 zar35J=O&h0HSG1it&lSp9P#EviV%SeVmo)Y)m&XRL6Qzdyppei&kxKm`;l|w*0Thg z*qsJ3J5Mjbvf!ljILm4Fe^&z)6rxSA#)Q{Xzhc>yJ-+c|@OOTgPlBdsBRel?iKV4b zq(B69z&b5aMQ+{y#4>GMkU#xFSITXR&O7sMacIoI^^FqtQzc(a^~3nkeT?8eq7w z3!1lg-~J%~zh@g4Pa393v;Zfo?lAk=l}HOXDlvwtE<%^i{_IH5x~&cmIPvq76`VGG z9|jz7`JQzWe%!Bzm4^FI8=6%G8mQE#OTZ&sNIgMLjWwdV`p?S2IZAzBr4~?wvQkD_ z>rXIjdgpb7F6nC#=HHXsMJc~O1I`#nFL+vD{*uk$+R@wz-;NL#R(;BkrL92^{i7Urh6IXF1s z>^X#&huwFCyLzz)uCN;x_Pp#C!wY@JUY~)39N~6BzVJt$p*|e<1A|~b=fk{1eS8AL z{DR;cM~OfV4q;;p)9dz;1=PaWVEa4SZHTGDaLsvn#0puKB=Bh@I=QBH4FiTm@NPVFM+AX(=~J=e}; z__@ehDzHxk?wb+Uy!UG3EvZSsbZDP}>6?3{m z0E%Y+HJhggXKVCPYGI9SqI&Cy`ud#FwAB7 z!p_b29F3`y^NR&kCQgME--|N%%R`uk||kFV%^QvKnjwr+_; zWkx{8Q8uw93yBTQu#5TgJ1~MeE|RS$R3wn?$^!J5*sSB`5JNWOL)Ulu(}OqOeUWRM-iVQ}n3Jj+PM_b^yXzt=U6fc}^36bMxn0wf z?M0hZpr_^K^p>Ar2CF$-2{w$?`pc8IZ*!p6y;FEYmmaw+w7y&^>`A0@5 zvCnp5jybskHQs)bSwGZ%DA6|v1VNtaoXUP&k1F`7#2;ccyy^Jnr!4y~%mu4o&!Ow# z&*0Ko;bBT#=|6NW$;ibD(p7Tv=Ih?s9O&|wKJ87?#QdA=?{W#mUeZwMmu2I6Q1-VX zIWd>0e8V_d!)_f}<(+(PFf z3Gp$Uyc{NpVsdLeiDn_MszfgoM9tQ8^gc<^%GJ#oc6M_mQ<-!9|ku_X&Ch%d) z<8&kWzrh#^uC!FUTrrN+HTs!!<~v3gcF(qwJokHNd=6}Nb6OdeA>ZQ+~{tCLb4n zwAsKJJP4ZJf002;%c1-{<*}8;cjfk;D~T9);!=@5pl;>>_Xg3aKP|=G#yD-Y*}~$H zY&zV5eR2AfVme(FlnJ8-0~Qe2QHfV;kAMf=^;7U`+Z>GuiuK zcQ(U&t~>O)=To|+^PV!e_PiHZW8p|m$>BFqIs($xg+|Hu5Vp_yK3|#b6iPcH3;6X3 z`mj7^*M*=rVm)DStZ-Xq4CK8NbqfmwF01y?Wr{a4G4@e*0#R_1XmwGX6_}ifpp^@YQD_{??KluQoo-H+c|}f zO5LOfb$3BnSOic~)N6M@Fv{%N4R<*j0r?seS0V%R6F^@Y@*_a_jhuBHPYM>LV?KG_@sHW6#%B|9ftrpqeq|R#Q*Or+yxmUk(C&oE~QRY1v)Qq!w5f=6R+)$d9 zuH{^c^1{bwaO{CC{c=?{*6p6S{(Z|jLs~RmihBInE-+^V=0BgEnd_=!?4-qZb{|FG z=gtG z=}s$bTXKGw>;C8BMoSqAm0DWh#KWx4y!dGa$e2rc|LZTe=~d%b@`CBT>E!K#T6#_3 z*0IGaSDm4wvBnYyhqe$1!xGuQXYz0O?C*Q8D#i|qH5ViX$g-%A*&cs*ppi8FLgWNJ z>Bd)F(UbOd>{UbFWL}WY%o!bnah2k`ozH~%L(ct{d~?in*qlpJBNffO>p?Mz8{V{w zbn*>*hzpIEa4G1J)O4RD?=7Dka`*Y}l#-%*TzlkZ<=FN3oTdKMb>_U?;z=&Ic5+6| zQoy9`2G(-My?2qPNnb{06xwH#g-_pgU?oT{{W_hG-wW_Lwp1Zu47w&5xQ=^yx2c15 z>)f3v#Xs(mQPRfhOJ&FP(w!D01y=5-Dn^c73+mqTNfen4Piw!PX!?)`kI?)eZLS*w z3Tm``*r8s|AA+=1=(4zLyYo`(@v}I}9Kh{Co437Nx*1rQnQ6n%{Q+%QwijXH>#asH zA*?C8C*4rRr~R1C(3R6#*Z8c6`;%&`5ZCfSL)W_`vHUGjJcl>=gJ=V9vK+wD^~)pv zaR-GP5Epk2ZK0fxwl6=%Eg3L2Q*BjXfF!&o&#Fila;m%d=gj6M(+s}&QvYclA~N9)*OI?e#Ny!*(U<+YlJ{1Nnv7_pz+5xn-% zqNOvJXG^WWZBK#IsD4tq#l%a92Hy6jt_A%wWkP7VS+u60C6&9zX*7SUuCYR6tpzk2 zvkLxYkEr$8k)~A~DevBkLlVr4iW=kH9(;HA)E`xCfrt(J&m(1OhG#Oh-@RD4C|1Cf z11puFNO4u7kA}Z$m8^jcFUZz2PXMnc7U0;O?OJY9mhQ6qE5rcgBxK8x{EH%( ziz@A&I64@_s-~>V;5LO^i@gID&hy1>U$OWQA2=TOVPge44W^aGgbHMEppmPc4-N`H z_Z`A}lwDcit+s+ChoBFed{ef*IKNK66I0oq1xL3+CF2OmsFc1%@PW>D*(4u&@7>9} zps#B?>DIi;!lK<8jc!T#zjHsfTXW^Y8c}Ukgn@wlE!~bEPx*;Czh-Y3noq9h%s9UC zi3+Ua+kM7yxsh@`Ud?4oZkEd(OvlKtncMc5B8;SeXP6biT8S8G*?p$>iZOk)FU2;u zB$)+g>X~ZqchC5yz^KI{OG{?%c@sI22i6w8h^9I88J1`ODAXCRg1at zvouHPNR`}45t~yyTMgb4b#r*YF=7%n`nb5&qsG&>Up2@-e;)EvxUsaL!vaz1n1Jn? zAIwT_*`Ulnh<*+JB%3q!9-hBck!o0+qDC77;2YNhuxDH?(r`oW_gw|TmV8m+8gZ+* zz^*R}1dS;v^b3{;T9pM(LbAZ0$%fIG6qFjR@u*(TkjROZ2Qb;G_<_`a43T+bG^ITi z)F;WiGbQ>K$0~;0v(skVt|r|!%%Z?$&Yj%W_rvDPKfOL(MNlWv`$GmFMncxg(n$at z=dUALHEM~)K8Hd0GAB?xr14D$NMCw{LdZwQF8?Yg9??S->&0B;0GJsqLmLo7+gNzW zL*v1q@rz7e&J;ddsqc@<_8^GYJL#t>^dM!HU@e~~C+AIPn{_YhdHf`x~K2R;et3NK-Nk~oj zmlD-mk)oOPa(9z-Z!&G6M0aiCspy2L@vW5!s?<`c-%=QNhDdnxdi!L(c%~_;^du4X zHu*^-cX@Ap%gk7p=FeBctspI^_xrjP8TDFzi;^i$WPo0+)~lolIfaP_uPj;OH~3eR zLyy-C&mqT?!<6=v3t@?snE{aD&j|nX(|?_r-)~%>EcH{{&vtg+JBVDkOxtTk^}#28 zU8`5jAN$u+%uR#$3J)3tIwq=32+db2VbcX!8J8`-Sl(Izu^s2W$3H7_`kscZva#d;g2np-{|S~-o&!gk?11-iH27bL zPz2DQ9YAkgQsJCZ#o{dpi|oz&m#}pfKq^#chHnui^92!HqC%pWbvUr_FW7wO81nM( zGHDWnAXA)@Eb)acl_NxQ2EBmlp6QCWXFMsS>F3Wsjv$#)p+vjb`}^ocE8RXEOsy6} zWv2-C0t$QA-o^}q8A;ZExpC3ozm`j#!fAznn0`Y*GbG{9$Bk|Sj++F#Q`;*lJlBC#%Jz`91ZaYfw)&LZlHJP;-_6MZ;oA)x^?;R}*_-tpY` zWe2m+OfZymXbz+j%2^E6Ga_6;Jo3R1=t9Mg)3h}N(1mP7Zrd|T_nToCA%vL(&D6Z# zt6*QE1=Idtb->v+6Q<**0%D&tM@ V%=4BznO$$;umFKgt8aKb`5)tX1xEk? literal 0 HcmV?d00001 diff --git a/documentations/pictures/network_analyse/warning_deleted_reaction.png b/documentations/pictures/network_analyse/warning_deleted_reaction.png new file mode 100644 index 0000000000000000000000000000000000000000..30b0de42e45f5882957b660944afa3a32fdb7973 GIT binary patch literal 8239 zcmbt(XE>Z)*Y@bW_f8O^i!gfcL=PS_QV=Ijbc~Fwg+Vtu4SD4H(CGQ2U>{(7O-vvj z|Ib6~x4ZA+yQ2@1*XPN*oq4-Cp21@P0b+DH;&2k0->WEpM@ceiPS~N$*hLCIBAKy6 z1;qbcpU{zYObQ0DapS3aRlUO*@IzpmHMwt~S zYX0~?>;&>eeZDf$G^BP3e1!oZ_8V^Q>q>QQYFgVS0%&n7ufcAJ8$@^%jT6vso=!jb zg}0&zDIg8^8Z59$HC>6;)pNQuqr)q>pOQL;3%Sf94u-f?LHo~3zc^>CZ)V3DYG_+) z$B_z_u|Om$r^kZQh^xQR7dPjhyHZ*DSkO{F)T1UhZXCDnXEJ0)Iwv@{-`CFK+}xqA zdz4Vd1$&}eR@9wdcG!)&k$EKMBOSCBCHG^;;T>yal3ukuD$z}-Fi(F;;ZAp|5B1NG z{NT6xPXZ$tLP2l47gr6s4}gi8=du#KbK9IPzv4a1E@AE4&(xy~tqi*byhCu=&t3)% zj3)f?x7IOvt2AyX7W5Saz-5mWqD%G!tq95zK4o6#g^cy6N1NSX{D5u`BWfA*o!Y}; z`E=ev65|OV31k2}dXSFrzF`zF3MuI>G}L`OX;k%DtdCE~+Y)efDu? z5hFjJR2eAIOV&3XubIhxa1dbz@RrIC)=#d<4t5()I~;mSd3f6!3iVp&KK?K zm~{Wx@#lyPbPcNp~J3w((Vyzf1;-73G3JXRpsYkn|Zv?KTS7a(N6BR5q0>!!ojuo^G+@kV}}>6xp>WCwfmO$=?jQH zfA_5G75}tKz}BF(*SoM5*>jvAB_P5}IyNQs3w^ocxuyFTdsv zc?V&C$qmvL0T4;i5eB`hz($!$h2-m?4keH!ceqAgWxbh={mzV4*{Dx6^AG{AsREr6 zY1tFTabavUoAet>)eHY)^r1h~sxr-n*~Xjl4FgZjp^iZB*sUK%%3}C-1zvL5KfoEB zP-?$S#kzNyc?3*02Dn>xuhuM_Fzl3f9=-G@3V}a_RT2O5Cj>@SC_Oe z2KC}bLb{t>e$+N2i8I-q&J6=?9lB=RD2{Qm)-lwjE7TKRt^f@mzp@89$_*E@=<$&$ z_b4jZz6DQ=Pv5&S+-g1{NZU*N*&J6;`Q2*0*FUpBaQzuRgHz(}%UuU>?QuGF|BE1d zF>$<;=XdKqRp`#bq-<4F<|C?1k+*2FHnB{|Kg|{em`wM!sEHU$G1zm{RVWq;1|hBRl-Fgw2uh| z1$$A}n;ms?35CJhol~jXM3ct$Ei17YqYd(_)Zo@fJAqB~cI}JHV4888A!z;XePw2p zYB!~GcK-?i?ZTxmw{p#`@(bfTi#EC(L*s;SXQF!O=F;eV`~cxm>T1&Za4Ld< z&bw%Io;sOzW9K>aYnOdjbgiQmC4*$zyQqa4 z*Xk(2S;`*iOZurI_y9QK@FBSG*UkFbZft)1Xn18X8bXeu2Vx9PWyl}1JG+?6a3baE zGaM_`wogs)3m20x5z9Z;zOtrb9eU}M4Qu{kH-PB8llXSEMv6^$8l>i(YJ}$Cs|L-o&_}QhR$W$7$zH>} zbDfxU+h&ex_xMOLPfytD*p$wbVryFNqG;3R5+-!E@~Fk|oWb1p$Kd~%tY7K7Awaa& zxk$%8;n?It&ZW4(X3e4B{t(w>I(SN;9r+SB@zjN5WDoMy(HZ7n{ZSu;X9K(4@4 zzFHymWHAEFu3pwePY4@*`VHk6W|F&H7RCKTXVy?fe&d;3Ehzj-a3;* z82hBlaNK!ML0CG({MRoi%j@&z(BQ8G^e8ZY0;O&qZbYI1Kkn1@8vZ@}MmISeUkMRQ z0!ya|Z}m0)m}%Bm4Ig7ixDYGIZ)ozI*Z0~GX{dfNAwlk-jJ7#UXyBvyH|&d=eu%b4 zWq=Ql6L&ToZ(@52E6UaKx3aHO24NJk9tyt*!tPj%4ewuUzc2FrWN~z-2S1LpUfZ<9 z>Ebx9**{r1>z)$Kv~VgfWTO_vA`P^J}gt~plMY4Frz^ws^_G~%#q{jXeK z`xdpKRsq6K$KMT7^rDl_d!QVNC&L9( zZlo)8B04wJkaL+iHVH>cyhnkn@M34K?mPuKQHTIjnBfotKbMIw_!|PJSD#sm4%r^` z?q#ls&>|ZKILtC-(f+t{^*Es*>S)JgH5ko!%4Yd#P0)4q_Z=|p1EY)_F~hI_%(e8g z|DToulXh^5ThP5D=Q?)xfcK4>8=2oQSi)C@o%L3NenW;_U4*_rEy8>6*uI=A_--Tf zk+9-~2H-E&)oy?;6vvheE`hpahihb&&lu!#+nx981S<&o+#7aD5h4E#T$Q(Vihc}B zAt0i?T9z$v$ko~z=NRa6w% zE;^9Zczn6PvD(^O{dY{CJFxQl%DUN+y|PNVHwENfbw>AjV+e_%f3fDy?vTx=R=2$B zz_G~+UBX8?d!r}J|BKvZYd_p^oFUznS<8vCvI1BI3JqP7;aZzu%LG_Ul z-wldUC@bNPAmy4tQo+S~FPn_RINyMZq1BSkpo0Xs|E@<)w#$rt=am`U#0T!Z>{E2U zTl2TY8jo10A+-%(!5z6BMfz*#nsZY-O=Dj7N3_b7YgBw?{p3hVRQ&u}yzB?DGvV}< zoU;&rqb>F**V=%;h#-iP`_H`+YZxa#W7GVN09)}8qn`!4K! z?S{T6?Y!QL8AAbcF-koQh5*y?t1$TC>dzVvGjYkVMAowfslcz*PcIbUb7ES4+-vBg zJSjCi>$F;E-5>;6xJ%nZxik$&opLm9tB`{`Y@ljQF6J8G`m@g1iQ5r;jJ;VzdD zMJ|^P(vJWfcWKC$482w^NHT^9^f*(h(uct$MVaR>{x-#PFkqre4dd`kSsx56ECc3J z9}uApK~dpk;kAV}ePMQn+;zfJI8+|hC@6g&FS4*^|JkMOaOLYr$?WTa1i__0X=%-T zGfLThvmE(-vS@w!N#PR0n4YHv{LPh?DQOgP2E%O`0s#P@=JyY1XnwBhVEW&q_fRb; zA7Z@5WiOcITb~O1$eku|(msOS_#rN2_ijM)LLD_HTy??UFx#Hk0gb5h_rc+OK}9oR&i4} z!A?XpF~(U@bte7-hGLC67~&QQ_5LKS7O7ZE-uZpLco}9tgbndf^Jt5uf;jAqk`{JD zHMAqD^Ac+>^sRh9$@6*3PnAz*?zl!&2|jDd&tQ?Ru0R<3KAd0f_P%chj;I$)Q9cNz zBiDu@YafvlsP&m+Iya!8*64$$gnI49`$jv$QbtWDj1^-6T5~hg5bg48w?OENaVfDZ z->T_O7H+1*07PKV3(Ow+)Imt7N zkU-*SE*{k`lJ`J&P~G!mg;eLOTSs}6dgF6a897PZ+lL3omoNDa|47j3)n?WS%$^Q3 z1SM@+DzDBbUzSda&FCcp^U1zET*dy76}vWpU3N<|k07f5Xss*)R`z3QzXEZ%3lY=TwjVO^!AI zv%`fuQlwa)mUxZXc-}HjBzIc8no0m5cW%RHTGbyl@}~_jN{dz_=vlD5KPg9oNjP9% ztx}uw!{#8)@Cr~N;PnefCDUZIS)=l(gU4P!7>N73qdHZg7*9EQ^r{C5^m24I*#U#p zI=1#r%7^3`LsjpZ?r#b`*ucY0iecHAY!>X$kQlb?Plq0VRf5*v{!S|%B*lF^wLBqD zQ89H~7#>2jZURlHLagTnMjMq6DFMlq6e{1A`aJPwJX*4M5*0_f%DV%ATtz4o?T~Qo z&aa9Qc|Xf)DPUPTF<~;Zn-?G3qL(~KF3BouhKsQr;+~;wXrmW=*Np{@9jI0`wfw&n zagr-0&)-_|sS`!?&nc<|Lp-vH6RkGvnX$!m1PAqGog;bmUBD-wG?gDSl{^Yq*_*AS zt>RN-NLj0aJ~zTQ^}($T6xu|KlLCT2eVx$>21IMZ72X$-k7S?2$IfcS=a@vKrKw-a z1l}Ua4l^d|^N&vAWdBvpw(qOVIxFOi>Paon=^IN4gRfmjZ<)qH>x@6J4ARg>b)oy~B`-|^@x-s5!qg{hI_6uyvezp(`qY5_Kofrv zB(_`FeGHGMWPcZ#ffJ9Cu|DBF(RDjy5D4-`Elz zg>Z##S9wl$G+Bj%j^7wOv(wd!xL;fr`S`+h9QqR@jPbFJf)(<z* z*fOk=$OQGzXdXF_HjPlY&R?iGf-eGDK2E}G^U~WXzLN`fB)tnCcXkF+D^RgyTI_)6RbE31v+0Sg}Id zO%nC;&4(6v=Wh9^pI8vjjpI4=0r~*5?C_(x=j%Hhb8ELC!Pqfsv&7GlG6|$sZmGCP zdf|7sujy31(`DMM?tu$;mqr|INs0;z>F@r2cAIGwn4sZ02=jmW*Ut9V8&tbMPWiW6 z=z#7W!57Mlbw@bJjTbnS(NqQqAjDX==n2HPW7rh(3TnA;-QbA+fNJpmIr0!CK5^f< zhy;llNes_h6K2P6msha;hvS^*u2;K1y)wL(@f%$PUOu)8Tq3YT_ay#H7#b3kkw8Pu zpe}+&V7>o2v&C#YV8HW|vX-p{M{=tox2-6Jqb8Kif;ch?M# znz=?!CoK4s;#V`w0T^8p!O3u7w;;gNooTfa(Z;_7l3NeuUoM^t)T{Ph^Xl(J?#Un< z!~)Az0`%JwdGfSKs>GGXjlG&&mfQ#Rj-ICm8Vs;AIyEVz zIdJ7flL`vmLwPVG_?^aE!lP*__kFrmu~pwx%E+_bCW?x?I$Z#%BrT8OSxyR7HqIf7 zK&5lFHr3NOMaTehyuHVB!X;%uySc@Wy$@ zyqF0T5pi7rYl$K#*BzzdDG0 zj(404E6=@eT^PDf<3%6bG98}DDkoL$eI?LA$tOhyz3V|-p4dXf_!*GQg5=#XqxA2& zV4%OSiN9EmKfmOq7t%4D-04?0=ae$q#wKbf`lqjoP@q}qr|{Zfw#VZOpTH7iV?#}+ zE+R?Y=^lFZd2Fof_RCh)Gw1Y`xqODWg9(k=eF*P2N)hX*E&7-AJn#cjjz2OiDBaej-iOytzKAF308l-zQv{Zz}1*c-ihY zrX*St5UY<#E-cfTGcv5l%5(tlbuH=s&Q~P!u9WMI{Sw1n3$c1|V9kv@1E~5pr?M7f ztXv(4UP3qN43|Af+JmZ^V`)Ri2aSgBRg~r|?hu2SJr9X>YnYhLC2tMgg!cVkPDZl_ zCqp;aye-nwD?}#X=o5mJHY&64cwaF3sh;X8FT?V+Kg08R0%iB0IU*v@|7v4?jjL5= zajFAV&}XE~EaO;l_fdUi)3F@zo{{d`3x}4qqPbo@ZI0I-<9;Wt!eti7L&1ZC=dUhH z$P^wd*ASY_L7@U7X-cpR0Ql*QNO{|GZ%h^CmGcV}Ig6kU%MLrFbFsekgX3{Cwg zTQJb5GbvxxL53^vHu$TVhWY;fMT$`bX{rjI4x=&3H4GQf{ZhhIiq-n&|8f)gMf;8d zwLhXFuG^AAdZPIGY=h>pG*0m|)^@>t!o{n@_ZgH|kZgPXeE94rM zj`7%kc5S04+9oQ3J0DG2POg*<%NmBh1pk|y3(A`x#<;*XRUR0CS0HXT7nK*F_UhTu z|3$wssCT9?{%}nhKbQfgT1!26PQPjx>#3c%vkc70`*Fg0o=mwLcgNpW)M@l#l%aT- z7(OSlKJ{~b%2==|kC3B{i$r9cHsDEwAp+GCS+uQ=HFlHd2J3?r9)v~bjZtwazJV`w z%4QXj;`s9J^m=ai_S4%zxL#}l&uD-Yevs6xe= V*n9?zkthBDH6<;@Z}JwQ{|68?=kWjl literal 0 HcmV?d00001 diff --git a/documentations/pictures/network_analyse/warning_reaction_backward.png b/documentations/pictures/network_analyse/warning_reaction_backward.png new file mode 100644 index 0000000000000000000000000000000000000000..6433e940933da61a02b8ed4fe5ca0af89b44773c GIT binary patch literal 22787 zcmcG$WmsKXmZ-h4ph1GWyE_DT2<`-Tg1f`UgF|o#7J_?#;2st~eMS?eyUqjXu0$5;87ed&?! zevih;(ZEnSe(r5!k270*r5N+Ri6c&ui=ma`^XFO_SS)mv77N?GYZ=}jimcZjE0)NY z{KaEIzO7{)jb$zSEbh57XdtLC@jR6mLU;bepyyY2@NVLtROqmfK(EQ4FH%U^=)ffm zI#3A#xQHi+)4~FmJkTdh=;yC5_<jaJ5t{yL}AJr9uRwZ<%C^-lb>CokY1bIEx&prjAtK?-$+n%8_tpb@bg7o zYsxP*rf-*oql2#(0+~q{B@wuNZNRbhIgk9w>Y!(Ekgl=<*^Yex4J#{rd&L1T*>K^_RHr_oGt@h?N|`1 zAJ6P#0?BVh{5T#E6z6*w%OlW{5;oALj%TTp-0dNuW+&ED#N)lTZjPj8SjZJB{#U!qX;WqxcTLzLzu!ErxUON) zoNpa(Z}%++U3Dct76XLgI@0ie7Z;t@xsYx_7XA-qZ3OdFm~Cn;$8Nj zKyA4FVgX+f;~K-{tDu*&k(y8j;CT!h4RxcWox&SEmf4jn^1{M8K4{U>To->$ z3f>hf?M)Se;bo2VRgvKHZVumm+XPRwagt=oq|YU7P2KG5`JwY&#l$w8h@U}dq%HB9 zb;VMTK^O#o+^IzYAy?KT={pwf9V8G~P7pTcw-hyUJ3GY>u7xB2QQ9#=Eki zgoLZJo?A`eeEbv5doR|oGJw==uRuEbp{s17tl`f&Sv1Sb<`=qgy=nPv{T`N|MljR#^=#--C9Xa zMB2s`snPTN6hmss0M<^5LC!I3>r%li{R5B=0r`S!S4r5!N~jPD9KThrz`y&CZRFC5GHRQ zHXLFy#i2S>@*Ag$FRpKV@C_4j=TW^b-0==>i7$=f6%Z7_2zIJljUdoInzD>FGid=c z^fz&mu>k&9_s8U`s#zPPkc0~~S_RGt^}%Z9me=4aeRRa+h9yz@+;1=EMX(fiYZzgr zMPPAC&h!wJPo;v(u{$D%LxJs^>YpB7TSJ5J^Oso)DagToIHYST3Y0Os39 z(wQ632W~mT0x>$W^)v6y*7#gX-Z1F1usZtkyB>8vxd(ocT1M_b!HO+R6Y}_jS2`p9 z{g&Gr;w+;StaA4WU!^{@jMTjzn3ESMeiPTp9wRu0s<9*DetMeNCc6NI);w;6jyNc` zvi5p6pz5KA3oJ64D{s?rBQdk>uOL?>D>~NB7o`b#e#<`QG;ck;UYEW?!n)JO-Pov< zRIcBUSUPnkHhK`;?f7vd67eWdoM`C`d;4=B*d0HCzN27Of)+Y33uB1)s;cF-;LgZF zcUwChoh-vVYB}{u?If`ct3$Sh4?HqEB&jbPZ>MK2A(#~-Yy4G+9tTM_INV$%UQa0X zz<(r1@Yr5DYcX~xxV`H6E1B4*kD1O@q!3C)wq*>j68&fdulUcd15I3<=^zoZnCj1q z416wwLED8Q!3$Y(dM|UCf=J5GYJ8Zi%h!?)yHm}BL!dHhjA3sfm(CRn@wR?P;Y*10 zs|o3Edm{F;@;2|+jm76FxIUuG>cQSRS7Aem+I>D8{G}^A$JSy?G<%TsM)qZHa@HA* zpsxgUAgs*XD^RZ(!MH~dx3dc`jcsBZLPBINy)X|ZblT(gyoka^qZu^cx3r7#aQ-^% zj_SSj6aIO{ z$ys@mx#yIe+;kf;vZAOh@?*2wg}0uMO)zAsstub(7vIV6zw+MhG5d13r<3eMhG{~V zu*uJ?xL0||O9UPb+|RN1j6VKWwh?w#eOt7cruW*|f)=4)5Jm2)?s>JwZru+(d1*2z zpym$EhpQm=V+HLWWW+BnbfzlzZ{nh0sca8#svgP*5-n@qV}2`q>$32QL1J)}+rYv@ z0im23tC|dxK|;3qyVrh1Xa0D`x>*)_)0N1;qX{Q?)Raip>{jq>E^jw<5ru->lB0&B zcU=A~(J{c&%tgI~@9`@!>3wTsKys!2Jtr~g%;{8a(y$dmoUg$aNlZuS+QthWFam?g ziC)Rd6M6ufNbC>Ks6DS`E|dC&E{R~n(mv$r zH5Z{DF&JTxNn*-PUVnKj&VOM?m~<9z?Rd&SWUTWbAj0TMq}M1RJeOpA zU?nxKCn3ADW{fpDWec4vna+pBHo9eF#`e|5f{5Wa#$Dy)-?8`JIH`+%QDN$16t`eP z&}xh<*%SF^f4hp|ZE*)qXc-86w`v3Wcl@whXNQ~f#8Wm@x&4%zi)253sW-yKaYx7q z_9^})a;>E12E7A?Pt8|ozitkVgF?(LvcsPcR(6iIGtjl*4a?&qtoof6A; zcd?dADJAC;D({QH$RMZcJnU36g|8!|7RWg@P++-It=U54PT1y+aeHZ{I=n~lG=s>o zt!*$S>B=tAK{_Xv5(gNg)HCOk{~j9?yJd$>l8~*bUIz^wV`s?o0`!Y)ne5Foaoxsu zy%6%y&d6FHvF~rEVW&|_e*0UXo5;gvUE_UFG{i+w8{Re9LttN9N=yP-zr{fDX8z+R zt|$8P8%ae|yup3}E4l->*(c(6QzA^jr`VRh85Y(^u-3@{bCugNT={c`3T&}s>Zfp; zyqink!(Nr2ogSy$bISb#qUE#Q$K?)2STb2_{KCu)3V-5&LWT+N9vwhoylPn=J^d}q zuiiIb-R%;C==U3?Po5ohaQQdXLoUOKbYmS}_ARxWz+-wfGW%n`^$mE6&x;Sgn{g7F zu*12JCCD5dXR@*D`nF0>|G2NYTlFf&MxGq}gLc;6jRwa%!z3yR7P|DoNuV+l9`g#txiBSuUD->ke{vD35DV7*|qS zzDz+mzUJZU7XP(z{zY`UKk~$h;~Nl(b*8Jr18B8shwtKZNsTXbW$bWudWM$p7?axA zww)P0Nc{&HIwmi4zuw1p4c8AU_ z&kuER#yG&5z$$*9-3bLEG27e#%Dt&Rn5HdSJ@)VcotFR7KwUV)zw4Nb0b$?x+5~Tm1Ir++Huj$XKAGg5TlxL zeNrPs1o6ju6+cXe@L`5q#%9mPMbS%nGt~cuJEaQDG1Q>BCVV{fG@E#ipRVOOuPK6XJL~YWUC(dvAxE64H zKXcTyM=jMA>~S~NoS5?)@~y_UYD?5C^2)bG;j4l=}}$aPtam9s8SfS=0!>q6j79!|j$V zRl4Nvuz32%+SwQCGP^yC-JoMfkM4-`sad73`t_Fr;#&;)IqR|QRR}7-j{3rnH$u!k z(2jx3Qp!vLg-vAnWlg?<3Bsp~ZkBzhknrQC(k-&D*Cd{<6j$a8 zPVg`9#3;{~T+S81TN@gU{WwGeLk1M{h(kyR8ZCYzeQ6&mL^(!89YqhU6oAL&i@dO=YO$d7nT>2TsViSf1P2vK1gN_$o7Ky8%O`Ui5Hx4=%z= z;>`R?k}+4WxEG!`iWtrVCeq)Zp%TeP2OC+dEOYFQHBFhVIobshm1@QI@K_POJqQHu zm~Z^6M31@;D9!^gToZ=uNCa>LtkEX6*|0q!eO`~zyE+u@X~*DMVk5 zvueE#7SKIbo)h$KnB;|Dm8!DSD$)3jWQV^hl1zU_Re-_6G%2H0MQBlmns z^xxVSEr|k{(bA;x$QLD4uP7b*rj!z@frpMcwk{+yDdEb2WLa1@Hp{p2hI>u8VX>qq zYD=yBCw@iorZFjoU(qS6`YK^(el-M$8E%0K9pW>>B`P3%nr z@9wmKY!eCC;_}jXpE~m3a4>xbv@>lV{E0;hkMX`@if`oIpQJJ5Ngp&Jx1trVi+;cc zy%6$Eb(+|>%DYAtEhOpGo!yypz2pq^5s4kGwp^{q$7h)e5Be&#N+=;gm5O+37kFUR zSLczQhNP-_i8B6Gf3vSIKE(Cmbwn=5$<8Hj%02SvWuyHiXa;tGwR#M}WUrnwha`8H{V*hV+kTsb_a$+$T(jH?&9dF6{>u;bnL?wW z+Pk@(Ihgr-e7CO6Vsa+T{7qE>bc<+o=mHvTw33HFm-hRLR^?S9t}@%6tUQDd-qMAL zm_%I1o{D?NyEpVO(P9GRczOigs}dC_7X)HHUN1_BIV6S{C{DF(WL2RP|K zbI+-+(5u>9n@I&AT~$@1GPY+bw0;mG7O_}mf9Ht*GWM&CO@RvVz0;3A)$V|=c$C(*(O7BeCoX0tgmzs)AZFLB^OO=z!bjk{2cD>CW{$L|LzAGrN zlW~DB_X|Ri%f9}M9&NmEyO@?;pQ^C0Cv0mB%B~H6QoSGWp{~Z=WKD_Tvu)9@f za<`zQ!`)5vK1FfxwHv7zp#!kMHglRmtBAk(zL>psiG8V(Vi;Y*TlZn9=#Mn5elIxmMc}HD2LR^7a#~PF_{sg7kgHS-DPD#9UhD#P z6us~8hkcggs}3kRVHl4XzQxs)s&B$me|{quDv1`~buWQ%|M6@l9)@Hhyv5`58PF}=YqBF=&fXKC}pomjTNPt%M>qb7^aEuF=148 zJ;aJ_PkmpF9V!ESkNUOIh)>VY3UGz*NHqnH+J(z1`l=+ zyj2HJ`B(XNyq3bz2I-1ntFt6yhFsexr&u(VW6$p7 zZ-0#%q$^2ZOx}C5^$l!Zch^7p-rb*DKk9pTVv?#S(!Uz?)h~(6-Mtq3J1q#=y$%|= zF_q0iHin3ut?;LCMNH zJ+$)4ctapE9ii0cH~q2)<@J-}UsUq@jWO8@y<6vl4N^S4Jwp+&(hEtZd4W`3>5Tnz zhtUPmS5{>Ki+eTZ$9h+r-j0!GvwwjUW_B5HNbZSleY@D2`LMu}1(BtYxJUkzwhI}j zOipX{36Zj4pr+T~a1}XFl7))z{Z1gQzl}n@f(ws1fs^+$RCJzVFy6Hi0c474FOxHU zHuV0d!A93Jm$A7i*i7bKnwnIKaGZds(P54X3gB}Se$@j6cA(kZ6dowygkS-()>7tMSm4P ze8zu;HuEPIuur=u(Tdk>`lgWv>=SII2!IQ05@s}2 zD5^(vsc6TMIT+__%4hAlN4q^w-u@$d5`^oT_mwfjd#a@rA^@7 zQ!0K1(E~Kz1A<#%3vh4brjWvntY}q^Y(oz`t`U$Xt-uksu##!)O$~ITa%??W+k{fJV=#lc)6p(p%pbiWPLMtLHf){XTt zo?q+HYZl^wn6xLo$V(%y9rGb;_;XAl=fQp~dGYGiP0lSN!Ab};*9j#kd4I|_O8>sD zBzGvD{BjKQt1@x_u$gZ6R;<+R$4?ay)UTJWt5~GSoya_RO@pj6ZtiR{^ztIm^?QN;>R3ixr(#D~3Zegtu> ziUA@(iT-ub(l1#xT3h7fuyA}N6wV9Okm|;_R%b-4&7bCKmM~weEFEWwsT;MukLF}L zxwQ3ojpzyfOsnJ@D|s-jSK`5SR1q-YH1v|F3Jj$Hh9xeUD4$7pI2_jy$tUee6^DFM z{&K&gz;wpZgGEbul7EQ7DZgy`^-~922l9Y>L*}9f@r_W)pFuZ%=r|ag1ZQxb4}(NW zTIb6Q_Q?F@$#L%+t+yEv!Sr>ri~H5tC!vRjn#hJ8sN;m$uXhX+S0a)-kuDVizoXHG zgksmV!&v$UVq%qm(jO(s|N9r#emDv9GXSnFoEDBA%T%|Q`nlX2ObUv8e9#OIhl|TW z6W3*^=6twjRbOqI!M2S>IoWpS-4Yh|)Fv+q*o&GnYwFZJJc6!ppCC(YH(JREXVtit_(`cXK9=epVf}p6Mi@p*3VA*U`H5qRfaO`t!gVnLS1}Op@ZQ zorL3#4EpgkqX|=Q3&P6OVrud&@x~yl+SuI zYF9VMhS83keuHvcY$u31lfLd& zA>gvo5*NCjI=1mJp-&1lu57S68Qj=~r?-XreXU@euV}mKN$=>n9W0S?lkq<6J8XiVmvn}@P&(RMobA6hzrqN z;bGc3=in&Ew9N#5x=f{_?z(Fy@m{r1h>kkrl6J(SCD!wvAMKvq`#xYH73P|m2Hu`J zL=ambNq`D*NleztweMdufF{YppPSigJIs`SYjTS}0oC-*->T`#6;G}4X-IQ6$3j+G zzuF5d;+$ciGedhzdHS(X9f&fuBh(#5(Kg z(R|+1qF;YlN|Xf!)?olCgvXtt2F?A@z2e3715-NwJ<+|%P3zxX0Ed~g`mu5Bs!Pr- zW&cxx3l!zmbexee`iD$mqrJ%m`YNh)L7`PKfPY73#^b+j8_k%E#_l`!VV3Kwb1TrA zwY3Hk9T(Sp%i2DWERfcU*Fp=V(5xsYXjdE_-{b$y#H;%=t$#dO|pl{VxU+Z2I`@T>BIHP;a~~XtH3&)~ct*(LDu4DGxF8 zKdCxFYVjJT<=6yuVzxWe6AG#g!4jM!6*SKWNU+6Q5xBzVhdgHt_XG{ zL~WJG3Dp)UXEk?t&lCUsxaERr(y{k5toXbH*8~e1*f+Lzg7#rlJC%&5>$Suez{j28 z-!OGU^)pE9T5-m$L-Qi*} z49h%wU7x_iLKt6G9rb0+jrnMc$Bb448CM3+XZonT^E0uFzq*EY0CSxx1|7^Y4Hvv@q2d zdc-626fq7sKrv57#GuRXd(}3B{RsPo$c%&R>fcw^Gavt{vc48Xz78~({7=R8l+PKa z{%oWX)ZWF9w>ufFgl5;?M!*f8oiv?A{vv8Pf#Kq0iegF+jc$zMc33E zKT#NwG8}?1jgrppmw}0UmGQkU?lOlT!hYXr706k%JDOvGI;Qzf8!)ws%rCr_6nuVH zTu$@vtd&nCZI@`HvE5kf+e!8o7#SZK2da)EydkOID3J|(|LSMg?6|0X5}tZLUOra` zO5CXhC{PMV;)lwF>v~vAMQ8O!@da_n6;htS_{ZMBlug4*T_JC_{l4PbO=|dg(*<_!+*BJ5D%Q(+=HqoRjK05b-zZ;3XyiV2e3U9$9M^sNpQ&T zhbp;0ie+Q=ceJo(@{|B>>j4UD$U9#y033zJ-pR*XBQxvw{jQwF&QEUaDef`Tl z{I|wo)yuW;B2IWXQVW#6neZ3!n#K1=k-L;!f~c7peZ%Fo8zebs;8VK=7uN(yX~Gow#mkD+32A@HwM- zAqx=LcgU2J2Gwc5l4Shw8^%aOgsy*YoR?YgUAtq{iVu9L67ck9nWpwx2OdNyd%7RM zH3ZZSTG{tTMoF=o_Bmd6Rx(@~+-qlJ6#8zBYU-Z_fpCJcJ%4%YZ@)GRzuGNw+;!x5 zP35zHSvh+OU}@!asE`(|MfvJJkJ4oysU$p-{p>Fk%P)H(Nf@eUtP8Zq-|spKm`uUC z;%V`a@Bi?JwQmyobX9Jl4HrzCzR?x8JIKu=s_Dc0dHK7zN2cYWm8~@_SABkyn#6!O za39kF_i@4aOveNlTT;U@`Hx5^3G=dFb3EnNErY}1WAhIPxA^61ejOJIp_I5 zupfjv4Bw}1hBd&D_!GbIeWCJnIT-3xCM2%Z@M+xzWNP`YD8#hYjY`P~S<==FZ{Fr+ z@OB!eEg=zJhex0|80!T6b1@|Qj`9$rsoAn^vEhLN%E2KE0VHdE990Q`bh#&|37wBY z$Svf(fb5jzl*RH*`&_n)dcG3kGFGntCPaOZ#n^U zF&heG-T$I4)%rFwIP8m#M%0RzR(OIzZ8c8D&>=<#M7m66?v~H*1hpt6Kxw1 z@a^PGo9-Q3OF^S!rDSQAYt+^vi46#o203AK6A!G1Sq%J-{cL|%3FuobK07sx=RH9= zJiwHYcIN&bmm0+l*tz=>PtPF*o#m@4%_5-)0f{Q0d^tFA0|F(|LwWS+sn%yJ?fU>o z);DHS_W^h(LL>j!F(U`QDH5?-qq=>_O2D?p1XCQD$ej+`Hqe;%euMJHJPCM1p;#xf z5jigKCBTkwvE&5}f)hTSQ;_ADOQp*2up5}^#nz)=nYQQvF{{BOWP_k0bDAkaN%KsejbXB_g1X`ocLy*2OI zsd6^1b7dgs_EU2i&OAAIi3oZ{WFZeihqo47fSc|eT1u~wJEM>U0KrW<%15B-KJ#co z(0Rg64K(xl|K=RXxi{jTO!lq(?Ck8|OaOvxQeR|F=-@@QQoqctU~Qz89L*^Ho2#W9 z#u=dPJmBtk>HKUR%ojQsaFqUm6lgjp5BH6Jg?l_2k-IKN?{s{!)txYew>ZatR|Y>wJIy z*g*lhls-A-Nm(~wK?U)C|3kdOZCp}Fc+L1<`X9{mu`h|ysu_z~>$qtJqiIwKd(o>Z#b zjG2cW*wjhuCZ6HA> zJWmzW{TpX~8;UU;*bP7Fmk#FO;c}tgoUp?EJcuTxIB`A$4YI!F`&tVI^FFx z*0l469XBk}OakxC#!j7&0$83`0~#HuZl;hoP973ZXNArM$0A!yEigNE8XjCTRr6^% zANk_Ya78;FjsqOGQPIqdmX9XQ1L z55fSW+a2~FghB6_Fo+=iXTs14ss|H36NZgv!tg8tJn$GQoPGPI7kgrq0(Gwp*ebI& zuR1w^60yi@_+mVH4R7YB5@2zx-AwcyhqLTv`?58U-q7XaRD-!vrUrN&+arq^4-y_r zk#nwjdVmOlZoP1r^<9IM1xjLV-+np+D372(zMMAf)|HfxIF>JgTBl-mKk9>rP}ZC3 z!|x9K{jkEW@7D|Q;3JnGl(D@eu9&v$VB!RA#4iZr=peg6y=@(YWfz;=MSAyax7ciV z)OPf}Xm_x!@ngLdtegtY9MB^3B!sc*fPhHh z7zLR=)H_oEa@H5)YR1(1{k4_Il!{u8TYCWg?hJI-{3(0X{}qi`xHQBVxzWsvK4mw{ z$27Hk*<()1c@XW~jh+Dk0*!~??!mQ0_{DtY@ZWsKIRk1yUI`Sj_#|x{-OEM7adp!r zT#qv%lH6?1IS-478~h=4cweE^*AHlY=kV#Su!hcuh^6u1!ykYG z{G7Jg5+x*aqFHE5oYusaN`sO0H%6ZOIouRPjL1@oCTtK#-p>VZ>o$Bp>I6-)=>a~1 z^ScJ-D@hAeUu=Qnjel_fG{zi5*gI}G?3lfRfgCg+$bM9{NhsyRAI9qgCgY4q)wAN< zWsdY_5>yr^yxWXE?r;2H(f%6?x@#NoI<_M_BFTiy?}cxgVtP?9&Y#|q+k$80FPAGa zcT_#(TwVUn=GDh{NXn;&*TQgIyKG^)AAmlA-4FFnoBit#C8-HWMB>a}taiQhZ_7%m zpM~@G$bYK@a0l-Eont7r;{3re?x(-|T7MMj@FWiMU3qx1-E(k+tWKA_f=Kfso%Hzq ztZES(tcuSX22Mwrm;CKD0{0;BUjRNoLxAG*3qa7{LAyvcyqidPhmM3^czHj$H}_FQ zyOfP!>smRyn8P}{OYU*712z0J<%_LQbnRD*TQX#tt`P84QwOU2buyCk)iylco|xvL zUgp^+D=%ngv^nNxK(-|$Uu$zLCa|#Jr5*uy=E8BB<@VT@ek|p=ZT?mdE`k6%Ed=Oh z6a6yf=O_&LwJo3RBLHO0NyYGj5xJS{&gblu(T)5`KF+YVdA!-QrF=7@=$(m*dgt37 zNH1&>OjjE|20nLuDt_<{*TbL7g(j->!iRt*i zv+vU`SULkWX()7PP%y{|HE&WLW6VfQPp+7PW0N|yoL$I@28=sRX$&7X-*t6A z5%w{zHQm1p8IeToA!W+UnQ0MKgjKFba^M$&-!?&Q?pM0xeEC1&$?^Yhcrt~|`)}b1 z^PljfU|tSL2Syc0$(3Fq|G37TCHGwr>NNsKwMED3;N-^RWNde!0f|UG>+yy{5yG`g zrZc2oP@ljg)Co;$F1`s{TR^G}JoofL^WCYVlJtD5x7YnUZkMC_^_kH7S(kiN##H(2 z>>`v9us@?xuA``R2A+1jNPqtl+AOI648Dap?C`sG*kkuFk%S%jm!S^|I+=in(18sU zXW}}8J&71EN*vz>_4O{Fj{epSFd9(a6#(puA6`4c5TWsJLS*nQ`Mrq~X@mA1gIn7Wd~h!De{N$@n&zMx$MCWjOWA&|rWN!2ABa zEEA^3YkB}oR(MULo~x0UmF28uq(-+T-oE`P@?}Qzf8;hyyxnhh{js23u05`b_P=l& zsJ}%-Gi^Tb_`It6OSwTC%~I%DZjhfTB>h8fm;&Sm?0+XW2>mHHP?@y|ThT$A=crnL zgM}ZxH%PkkeGOFdrT~%aPnAA#<$Mo-R6zEdCE-ItD?pn_zu3dS+`%`Vke^8*4Pr>Y zSZv-~5Cj>cRFY98E`&w zYSW4jJt!vNPTDZk28Lc{<@#+^1C$2<>VyJt0tw2)1O|5tOf7D5TPuuIum(u9Oxw{k zFcfAvqJd3(>C%V4-KRIrWFNvl>*^>hTIpR0X$- zTdulpIo14h$vr2#{5!HZz(Z0(6O-kb>6?@%BRBOhW)df%bCp5v$_!4NKZpc!j(YIu zq5OHJ|e*~DAB3rnL z1!gVklFX0y3otgFx*nQjUcvmR2kCM8W-uCsGZcHHOy-T&)8K|wX;F27C`N8KrcK?> zIKV1Wxbr(?RD|KQz9)fJJW$h@bLzY{_;gGT>6icrlH|{qzl5FPtWkP!&c%Fd<~W-O zL##|f1A3Qt_hpkO@+`hO$)fFjR`DVJ+rtbLa+AmL>^N-x=Q)ctCFR?ZV?ah8EnVjOFMHR8Hr;5u4 ztty>(LTr>Bv8Kr}FQ(cs$iIo!%2=;+GH{L%r_k3;JDR@sgIeh-w}4-V!fxQH%?Yj} z82M(uFg$%SZ>w$;zt}J%H0E+Xef-cJdR`Q{5)|^OrRe}Gw|M&h7!{$a)Xv!NL2HZiR*EeJ?aR5XN=j2de93j4@tZYZ zA_AzS=TZnn0mKsd<%5g|V#Stl{`4{Q-otC4t9Nz|)D?f|v0xylSsw2lNH&jxJtM)f zS>LAiPMD_T9IEa4C6}V|=|?9ZbUIS|lo69EfT{HRanmLl$I&5avEf32MFYAeJY}of zq~MV2T$(PwZUiR3AEEy7RN4q>i?QTRY%=|MD;mEI_H1M43m8%ym+$63t~*4vIVj%s zSfx|sKVjYm6Cm0(9&4AQb7t+j}!z+`=>>$UHzrZQSeI z2>6Aehyq7sXMwytF-JV88aD=Px9&R>!Yt3ZYcw%k?O;bIKYN>EP%RgHYMTr>z7}5!x6~%kSUh!VDj`)SVL_Y z&@IW`(f0Yu0UDKe_fikr>KsVqhO;%ZF@R|qPdVc935Z;GxDo-W2WHOlN&he^^ex-m zdslvLE1a?${?G8o!e8JI{lwuJdE)Dq&nGF7i2)z~5BTF~7!A~`LAw9S$|UYPxP9Kk zFHtpwk=(3itA4-h1^gBqadnU1lX+Y1IH9eAo155IA0~0q#`Qk^$gA-jJQsu;%~Nv z=7!#aQqL%n&MSAOJES@NO89lF`k`|PyZwn=P z7VH#qI)|FhEX}5}|q=FLf_Y@cn2@bW&VvFzS5*Qwkwltto{V zd;8RvCx&?N4?k)6mT{IZgNi)-jLkwjwHx;~Z!%Q0g_BmG7(3xoJ1ce5#^K_?FZDOf z&gzUMt_fq_wBPXE7`Vy&x@o$2hJP3PA8NbA&WNv0NTKu{lUL^A2dd{cTzRJzpR6P2 z)h%g+d>c0Vm&6`F*AR@{a3li5n8#Hg7Ptg9@RiaK*C;hPH2cwZ>%aVtmyXCeJ3qe& zA73Rhr)(UZna1**ph``zzSE)fRVNV?9RDV<*Wz&_$BDzDR^?T8bd$3C6L2?fnIJ$H z2nU$J6}z7_sSqr{)?d^~u0|XlJ{HyMe4%t&b5ds<@{h5L+6zLJ{h4kGAV8p1J*)(2 zJ@iaeZ~Ra74Fd>xa6*8!Xp3hfKSsCh?&qXosl)knrdP{1lHJ^vRc}Ym?>cPI2@M=D zmix^5wLHDuNrqnDS8O-m@uNiggu@TjL;`n?141k|y$c- z_h&GyR{Tng>J{HxG^L%;@I)@IoNTtlzEXuLn0i_+4hSv%(zJks_I^m%au#RnH;smW-(B%$_7*A@nfZ(0Wuwryptt;IB&SbsqCHehD|? z2SK9wtc&8G?v=?Vl*jvI$jwyL7}~3Et_+xjbYhx~yvQEXMAi>YB+wteVG~(9gx@1? zu(j!gGA{W!Bt5A|@`%{l;T8GO9Pe(VF?Ytu3j=2g;Q2qF3jmfU4;CH{IrmlnkTWhb z@A>FumP}vu<^k0Z=#bT>9iDzojX%>9bGol0!qwYkRj0h|G|g=m?r1{~h1llZ&@7^+ zXr5_D9Xm7!?WOg9-f`xW)sx)rGFKNS)JQRU)>sK-sJ6aK%uT8I*_o9kJg=_Hb9xzz zFfc5xs*Z>9v=B?D>LRaBtc=O{fxt5Q!cQ1=w4pDjM8U4wv=OP=XVjia@NFC}s@KNR zl!{#=d&>E~aSUq*iXL*#vz1#yk0L#pGB7w{^5l9*TE8(ffK_a^lyE=oPKDLzMsl3? z`;PDHh-UsDpsPmsn0;A*Frsz9vYYM{%7Hr?YS$ID=(w1r9~ajLGQ_RO^>Z=>aOLjX z$DtHoEBvQHi#@bI1}(6nSWemdjBpl=_vrl71@KlMMp^KjI|vpcQ_CBC5)3`Lz0Il_ zp(nq}pRV2CkY#-I1|SMlR3y~zfmU*lWO@{+WJ9meuxi7@{SqCqmoCraz0iTfMt`*E zfI|`PV1x=iJKU&%@l{Fd2jQxVCV1aG75AuT0B$%{fVok)#7~S67x4&2 z#G^CJchP}=s3E0Z;l^a@e;>4PuK~tzlW=*=LjG_1&{TZ?+ps7$_A*2XHap?_e&sl#HymdKA zjz3##leBm7J7bcfd2hGwlu@NTjaAc++8!g6>+iD`vw(a4_gRaXf10&OINkWbuw&!; zEicyS#mpV|kTPxPiZ(%VV@%6)E9r)ikW8kGx~G75`~{28o!!MORLSJX!0Brpf~>&h zC=J1%lt?w~yEU#;bu%yWBB2CZcF$1?9%{Y#(FT^1S?t03cD*TW#W`(eo>;2hD-LUv>wclbx^->dqC#$Ltm`XUpFq2+ud+{wvYN}{g-hy#l^M@xUC|BKGzl?wfoh}Ue3ax`V0x_JT2Q& z0cLdJzf5zojmp-gyaAN`)?r7Ko=gr2`}1;qzWsOau$o(`?JzWj z9$(0iH))yQR>%X@=ho)(H~-!o3<{V4mk@(}7ZQ83xr;B9`G4sO*87K6ibGdQ$?b0_*ugsjP-^GdfhW*I4QZ(&)<5I0vxh$A|xjFSs z{8&E?3_ZMOmCuXz?wI!nR8sEyFA6chPZmvP7DxE-Q6F#vV1_Su>W+kiTz`~Qy>xv~ zA^ICXg+{^YN81W_X=m|9F$&Z3#EhLatO&0eKq7-k?c)!F@=rwX$YSLjb5xacplI4% z4Q>%jg5~0~%X5S%kttN_)&u6A zV1SN=AJI1IfbFI1Ah=AG6YyRBurG$({@K1z)8_s!^2Paol`k^?O1?;GEu^RXzhhq< z{Dpl%;`^`mh1LHX`(ow)wtazhbdj7JR<(0{ddUH_zrPZn?Ci76{)&E>p~U=FdWFD0 z7aTPL6_L?x2v1!o1f7;P!n&bQg3|qA`K5)dK zJhCJET;WznA*R0))e?vi!I2nxe6uHsQl0n{HR>-9oS)0u31Ud|LjBkJ$t?K=7e%!#2)S695<0or=Z-x z8YYh4w&TaV53F9|!$*S|p3~SA`B9TlmW9F*LL&T*(*?lop4QrNulQ*&x4tju_gt4bMX~ff(3QVvYDpy zcDB7`el(p3MQ2bR;Fo2ufU_em!8i)^fK%{%azLvZ%wUhMFb2-qAWKmFqSfS`foQuC zygJZ|CDFeJhCSF~SCoM&_deq$Jy9V+bh$@d@!y~P5Hb`WI&!m~=(B|%{8W4T1^&l{ z4)T;VHU!e+c=GrQHk}nOC=Bi6m*a#@q_xDqAD)x>|CMo`(QtKb7#<~hC()zxDp3-> z42Dz$uPCF7-lB$4M)&H31o7%gh(0<|GDI1@MT;<6qKrB-%3$zKlJCd2*7xgP>+E0W ztY_bQo&DU;HPoR4tv1YkR)QI|u(2>&)JvbMejJXYF>*Kx=eMcVvKtam#TS#Y3g_6> zja+svILYKid$18=Wt|uM{$4i1a1!}2>bMMIMxogrn~hRi^DZTA=Na2~)iQvthu5UP zQl`(7*p|oI0BSwVz7=^ML#CUo-OqrgaIzY-%o(`0n}2{z0NGA!u_LHE??awW-ce`g z-{MpBmu;7h18TaDP{lXlChw-9f zV;T-H28aOd!1ztJRw=WtO?2PX+WxEjqJ1>b!U|u3E1j@|uu_6ETVtwNClP)flVtpZwg8+7P!a z{+|d2jZDB@#_Vwco#f-0{$g*FLCk;A4}qCe8LrrV6U#Q$v-$kj|#IV)l|C&{6F$TpHO)R$lxQv zWjp3o4zc?Ec=*Pe(}O$K2?H96xhXM2N7@cRhjxM_4K z^AWT$bSnOdHEC};izb7UAseYfM9yk{d$@NbyX^)tCnP8b2QApd_pBnZ?VEWy%Znjn zmtp8a*w9homcQJs8`iz*FXq0?ntb4V_i~*PQ^bCq+2nrBb-oHLjR{DY6iv%qSD@Qu zz#uAH4Xo25Jii|51V|SIPL5(gNBhSoQao@HI(V$c0b}h*b_ z*|qKYPv-E-fa_rnL)T)tYtpea*N7L_%N2X5@R}WF*RY&K>rSHR;1gAsw)dI~H)*}^ z)AiXu#hpd)aG5${fuB#DtS-avZiOOvxWo%^N|*{PZ9 zQ+2+0N+leso4=!Bkz3?+C;W{g%|GYa!v_)B%f$A<9vzoly7!B0q9<^zcnRDDl*A5Z z9ZHOsY_sw9;XL)27t8Th6}-=)VBw)xrv^SM-F`85l=CCvc|jNn*`+e17x8nG&mXTM?HUR> zf2oQc)zwBC@OgVGs{#)vzrFX;#dq@c;|~jp<$y?m738G8<(%^mJk$DxR_;}hjNWW~ z{ERJ0Plxg((c{gV%08&C?-l-iaXI`muxq=ewi;k#epeBK zqLj#WrT3C@kLicl?pRt;PktW~Ce?0RC-m{vt|g-iqd#M*37>|L@hQ9^g@4FzkJUOn zt?$ne078mKiLal^9&Q;?OTy`DaEh0zq^a-;^ydP(YX#1NzJhNQJV z#nyQw;$9duUPk@)@@{OkAGgeXx3u>bAMM`GqAlkTfnfAbqBO+Rbap zVujR9`!SvEG8#Xv=P1wzo{TR7B~;=_d$~Qw6bdT6esG8me&;*yhy2{MeEk$HLzxRp zAEVn@;lbqCQ$p-?%BybBhwXWmb^l<$Jnqg*M7#=z<^IXy^;(OgOU_bk&g`ECgqOZgmrd~aOm{Q!T-E|UnQD0}h7u34v;`)5y=YHObVj^C z)Kq+%6+f0RxQP#)2)vs5#{>r}O!le|Cl4~AK=ODxzxlO!9K;Hes=i~dIb&(qJaG99js_So(WV4votCOl~l%v+@J|ABoX7!dX=6G z;_mlollfko1ub$|z|g4j^9s{Xn ztpSLQ<|q?+hA}KNjI)#LH3H8#J>18SDctE>JbyqFrmUKolSPq7%Plvt%D^up$PDr@ ztQ~V-M@7Zro>yOJ#0dfW+$?eDx2Y}+SaxueW!_sjEf#I56Q3l@luU;$8#Plg*}F z4ZFBso?I^%)nbsl6?=0IMUNJf8BSe1JHJm%`=!N)4wxL&{4%6JHgj3K( zKvTX_sHa3>oSZNCJd|+pu{AS{*mhHVJl4gpe52`>ZrXh7(R?iGTW-{X3Gwy5TcOi4 z*yu>Pz^m-(lF>AWo01C67~p3TqszN7%~B@#m@^p%=BS+mo_$NL){9jd7LUR0H(B%e zFWP?rC42k*j0dvio`<`TWmr|!t$wqX6Ack*qN*HgNm^ZXk*Qg*SQCW>__Xio2=n>{ z0@uQ7w4T;eN%j7G_t<^u6dCz<2hNak%x03;W;Xt-)W(Vkt9#7F>6^>=lra>`YMXym zo~eru?5QRyS83kAg819nD9%Tpve^pu;D~CLYWh24){s-)#b>fUjEO^jSi~s#0U91a zH}%tv)YY|#ln1TJ8~qdw@3tO~t*j<};&|`DK_T7CZjkB$e`I|rr2r3#XW6}YkKP~O z+x6=;QQDNm;F76$Xwch3q(7I<3QdHTSAj^>(NWM=9mzyxwig317z?^2vz^ypua?_x zmHu{HU6eK2HKEH+TwHKyrcB{=u=R9Hcd;35?7Q2?!Y?pPYuS9QP{uWpk+iUIqoCS} zGLHvh;yX~aOL>Ns#Cy3NwPK>3FSWCPE@vTVlY3iVlFQBVFy~p6|D9c9}!@`vYLZ609>x8aY z<(RfQ!tbKNJUSS=GW%MAg1*yF=8`nny)X@_YTLG~L2enw;T9yGxZgrlxpc@v^1-r7 zx-z&ZQ0+Xq!(7)&x|Bc>q(g93%Zb?^<~iV7IlqPxLMCrnwdDEc6~Xq+Fxt?P6N(hH zM(70FqCM+W>(PeN@eieyDHYaXeGMMV;9b!1t`c}HIn4&E;qr<=@__LAwWv!lKdCGJ zFsX_HA8k(}pnp^7z2zL!PKkz1-#$Q`+hL4_P8??0XY^jD;fl(nM_dLWpZ5r$ieGie z!%mxq(V*I?C^eDgwGZdZI_RK{n8|I)v>iu-+B~LxrS_F!o^VN zC6U+xiphhlS?+IC?AH2ukpyoRoY8xvQ=^d%^K-r&-nr-Fb4oLPX?NF+g%%eS&X;kS zN1JDuP1ySkJ715lA6k6zVd?6GFMWv##C$)V&m5gUZC034<%#>y#(w5%9PfNQufu7+ z3h$<#^!d!zZC12v-eeej5(@En%{m*b+*MIU#4hrr)^=U5p+aFG@^8`*HtIj6?dp91 literal 0 HcmV?d00001 diff --git a/documentations/target.mkd b/documentations/target.mkd new file mode 100644 index 0000000..f46f384 --- /dev/null +++ b/documentations/target.mkd @@ -0,0 +1,2 @@ +### Targets +On target mode, if no metabolite given by the user, the reactant of the objective are set as target. \ No newline at end of file diff --git a/networks/objective/e_coli_core_target.txt b/networks/objective/e_coli_core_target.txt new file mode 100644 index 0000000..bddb9f6 --- /dev/null +++ b/networks/objective/e_coli_core_target.txt @@ -0,0 +1 @@ +R_BIOMASS_Ecoli_core_w_GAM \ No newline at end of file diff --git a/networks/sbml/e_coli_core.xml b/networks/sbml/e_coli_core.xml new file mode 100644 index 0000000..b1ee5c6 --- /dev/null +++ b/networks/sbml/e_coli_core.xml @@ -0,0 +1,9898 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/networks/toys/graphs/toy_paper_import_rxn_visu.png b/networks/toys/graphs/toy_paper_import_rxn_visu.png new file mode 100644 index 0000000000000000000000000000000000000000..5fa943b9264d4cef637025741ef1c245ae7d83ac GIT binary patch literal 91784 zcmcG$cR1H?|2O{DP{_MBZ1+K%TI#O!+Y{j%cTO==o3niQ&RKizg5;FgsWm@TI`8bd{dhyoCYp^(;p{`g+kZW3 zT9Y5Je3HbY`+NYe3 znfys!#V+ypUna1;@ndK=ed1~~ z6u!VM>^^2cJ8rR#M^3v>QuWw6&Yf!y9Xh1oe@IC!VD`q@z}ctPvcr9fYMd%+#`&y) zAt9`CtF-JsKhxllTP2`HRV$-?s#d0_(oSVrr%AT+5r+WzqeUZT^!MUaH>W%C|NX1T z;!eN6f2F-5_<#O~niSTYV7uB^N0pP4vw8UQmoLhTbAP0qMordn?Ow;p$tittzG!Or zXYk$a2BklL^4#RzFY-3caimp0*Fl)l-fUA;{v}l}asKohOYh?1V(CgD_TUW9We@HH zZX0g$$sfLaS^Vg+W2P4`2ERO$7iW-lIq6K^+sT>Q+M|zT?N-Fb#yX9*Q;m<0n_RdM zsQcoy>EQQ_^vuldSK5@6bT&?W@SpgQCvKD`ibDO=Qjdic!X z&peKAX~rsQYP6G+lP0F7-qiG*#U&+FkL9lVWM*E?X z>{%fJK|#BT-eZHU1zb07+|bXqIQwkL({1KG)sd=4jtEard;h)F^}*%(g>HNH?b~N!WfervwYz=1r$*LoM)F+kJ@3Im z>)L2}zRQ;{GX*O3czjqc?^F)#>+6d-_4=IttFtsackRkBuVgrT_UyI&%CuXG$ne*?tb{4(uM{W7Z;cGw{I&LaAP1COms)UgER5VTg`H-1p%_uEC+9 znxCJ#DsNIz=s9<8vnxtX{q5S^`{AMgo-4iiSwFviWyf6v1P5PN5)gJ2Q&Q44)cGVs zY5)B@*siPMkh*$mi#v;suUu%}y?Yz1f4o|~{q&pSm_ygq_8mTXa{c-(N4GgPCz_7M zbz_B{ZE8)@jK`~t)tX+OYPOM-mR8W$-*EiNzQ7&lKW>tf)6bw0?v^7yIL6Fbm#d3kA(DY!h*L-s`X zXAODfvu6{I9uPW_M>Bu^%aGC3I(ZO$s*OJt=c)kHtTFWK}mcHR-=TNAGa;s%WE8^V>_NmoJBn zj@mjoJCiLsGx+*bO!BFv+*w>4#cQN+{-r_&6LS^I#Ic@jR|@%i%I);_yyA6~x!gjb z?7Y;Zeb?0ER#Rvn?|zp}($Xx)j~}PBw)*U}{x11Q?ka7bbs=f)4xX)8@kOuX=5%Brhrqr}gYe$O!P=*})Y%*D>+vZ764Sx3Ab`ykVH zP>kKYGK{CZsqpG=_S?5_i~OmX%(KbgeW}T0w7o=f*RFEZn9-gnW8LgCG2J@bu&qu$ zz4H588e{t_&YktBv$liZrD`ATS&wxf>#;Di<`W)b zr7z9;xs$)njYpF!Eg5c z%#%4DeIRskep)a4Lv^n6#Fpca<@``CnTwmNLhj$cA8B<`Pmcoywrt=_Dl;!{7|Ln9 zM)U!EUgh&suWhkWSU%N!_~7^K*;dZo)}${?jLS0%Z|{9Le)6fCVoG4w%Z0OhhONG& ztfrI~+KfM)Dx%R%)g3ChG&?){{^Q3R`eHZY%_q$FVb@|v=A3xUnfvdmi z+ovb{%jB0Au6wUsQ!S=TM@L6WNLtdrj<}nN(?MY3ttOLM*1F_Pu%Y zuGEk2R5nw%mU*G@@yK!dWbv@PPS*XyO_>+hP@HB)P5nNm1&GWx$Dl@9H6~GQ-@d)2 z&^@nl&4NQ;-65CbBdrCd78ZWE{!Q}oeAu34C|D;7T&~`)c>3&_f7P9>iUQ%ujp0J`swV1AkWr(=WW}z6~D5m z334Ck=MC{Bye|=%lF*s;AC>(S8^y$&j(Suf2B5!Tl!q#s+Zt^aG z**@??dHHJ6FaaUd&x~ihy1~FR*IH17y`OF0L+j?|meBnC`EyD4IR}c3gy=a7i^x>I zKqa5X#zw8+&^QgA2fHrnTF>WR{XIG}X8zo$Ah%T1go#;rWiMCHN122tvcs%9h6k`A zBqSu1l$H5qWwYOzKRMtY6ce*4J2$tqqT=bJv~G8uw@&w^Eoh&=crgY%)6tNqj)ijg z@L_5nAD?uOMVFbGnS95aU3kPMmX-m#E`E(sxyhTJo_%7Ta zSC_r_ICET(; zZuQ)s-&^-j)x|367kTU-Zppch6;$Y$BO@;UQY$SoKsEfHO%Pe)1HoM{Wu3$JV&jHz z$uJZa7Ltne`uwL8Cr)@JBnT~xcrKTrcGaUC%*@SIcV}9vMl5Ld%ufxK;nSHnZanq0 zYosOTq*RwtJ=cAGSCJY^-Um8Cdwcr{vw2b7=LZiSq%d&Hp6J;1 zVy{D=S4&HaS+mRZaA=}>6npb-mr2W(eCPF)x~In;PV1IzyV#s~3aA4&di}|MR{>$+ zBe_4Ts%Wq^sJ&Re9bDtJamP86?)Tw*LyBVTf&v07NQc8$hTpp76%oPO_xpGJq3hIr zUs5>$cVg1gB=(nnJGY!4=i-A-QPc1+nn+<&vAyk!25A)MCFXCj;-T&<2>j&jxP z{Q2p6^ZfjLNxM#^M5{c3i0!-pbiT7sWqY}_R$*KFayp6`omG7@eNm*jAf}p~ky%rx z)J-wLiQBQ-J1dlY7Cy{mOyTe6i+KOvoaBFh zf`jQEJ$|hD`rI+)8)`~QA|e0zMgOeOT4K(Mw~ zt|&SBhTTSp zBe3}i>sv}n3FG%{0=oUXQaknYlkQQiq>eEvZ~>6L{`&O}mi6n~_MRK>u99`=W1CE^>0#Nc1FVK;&MO z6?F-huPSe9;=FtJZjwRP%SjDvf}NLJHdItpNDoB?1hC%3bCGIjXaJmExzBlgm7SfP z)6&8oGjsE?!5=$>wr&;NzMUSxKiNSZuU=A0%KQHPjRTFz*IoV$#Cj~wff^qs>;av& zG0oVRI!$#?hEw?KH*a>LmbZU=ycbZ8pZ9=U0Pq6;?%le%PqMtdsl>K#H$8Wb8u0So z{re@gwd-&_k~?>bO(mwKF|J&>((+ayd zBQHZxP!RM+eYx@TXVGhH<#lyT9O8xtp&W$Yxg+>^%Xh0=rE5tSpthYeRK8SI&R`{(UWv9J6L*3A(T>79K$jAEV`OLYmdA|JSxhc$a`PDu)Tcw5+CiF;cp=#$}Z8v%EsmwuX3|%dPo=%~>{Gpl;x6{(gR|QC-JClJLoy)f(c0 z0wNN<4?FMKsJ5cfL6Gw;aGe%Fv3dIRsbyQ?er|4V@_sT;=gLY*FlJr;SsE#-XA51e zCC`x&c)agh8Xq+y?>ap_y~OQj_4D0)l>|ya*fY+5;$5|RHUFMH8&UFzK#+TFCK^Np3=D{1m^d~o32cLcWC}(_ zP%(MnDeQa*@sa`ROq~1`P}B)^>1>we8~q+ANchb%M&C~lC*%Fv_6s6!wZ;@=<#ydezbr9MK7@_Szqwt#xpQ+p2 zr+MTYhs7N9MTj!&xiq^qjC+p{xM1gWn`b57?y4L$oy1^{a8=(kdDl$w1e|8aH@E!! zIrit9aXeU;g1@fCSqlrW!VRbl3N9{kpbk=3dXJ(k(~xhjm26vkbM~oK_51h508ts& zhE9L~@uQ@|XK-P})lI-6rSHKdal2Vm)R*HFydVH#2Td0is1m ze}9#R>8c3qGzg=nHFvjTk5<|gE#62z`Anp|AQIT1#4X>4in@lg)pGaH|;bR{j?Ajn{Lc!COeWuKS z-ieh#35mpv4N~>sBvRujPfSfoC^L*Jr46z zmySN%O%KTyn?MnQh#x~>K;QmanUtHehp(2udlv(p5d5eV1U-l8It7mhs&@#u4w3r> z4~N`Rm$y2TuPVgitNv*K5a*e{@aN8End=i}sl7U-QWTDn3N;EsI(I zB-xslG<~rONRf7seD--PYyifFuIKmS#dcD`F8|DpMgM5%f^2Ws`Hsf-#touuLPx%y zn7Em=bE49J{ra}noen*p#>mKs;uXvx0kKJ;9gfiw882e`I*?}!#>);UKJdeAty(hI25{Q0|{buZfEl~$Z>BP0Jq+pMR{Q3PY%EcNR z4E+E&;W}1>8GMzVVVmCC`=oYl(RdPqzl?)yO)#9Tt>kW-R?@l+^PMzZ7GRVR`vmJH z1}zI%=^Yve5%9?(nw^aWyz24nSmE?%fUR)0my2*Dppy2@?gCnzwK6v4<>jW3;sS2o zRB?0LXK!z>{nCjFPa#lAz&kH*Z)sT>xt|g=_oqXmE-iuOX82IkUmqWD*k#3vf}sf( zg+kHMQRYu1>7}K48;VUDtJQ@II)i>y;dq4+!Y3h3x-UR%PV{)MscE%WaA;<%i+E(c zpQ0tQl(dvIG#KGRRiOJbv9ak4*g;%--WZdMXAZD6`bE$9ul0o%{p1NRQKHBah0n10 z$*6}sQgN?HYwJ@(VFts_{X@F2dVr`5QAy(^Uos`qBz zJCTuoFay44@y|KX6k<=h?01+K0 z=I}XF=(idgX{GKWg3uhXw|4^umNhpU47&@PwG6ct@tI|0H!`jXA{aCvBt(0Tkp>&& z2DXza^j7IhdP6}`*sj(;bJ?!1VLXgV%=R@6bP3kLO0Y3>7q(_n#Ub^YpeaNS#A5|W zLU?y_l7;GIyEu0>puCed^Xho_5lS)X*|8I!!DvuMi-G4ZHN80mtzOEania$>Mqwy2 z3d%XzoIu%rlPXG+#si6^8<{5#(d&^ckPyA4`QzV{`&ouMR3c! zfPe_iMCgK4+ldRfBwIMLw2QnaHOF}!tE#Ff6m#<~wak;Z0r5=9N5cLjKEqh2@?dio zBYI~g`3Cgts(cN~f6N+Ei~inb&FsW4BD3C4vpzvgMp7@(9+nm+G$=MBl`R?ODt>9U z3wqdKcJN(@uLkQ2^}7s(n=EB8gf7$xzJ2oh_w!)!9>fNfa-ABaE&nfzs6oa8yNa|w z^cXS2>~w{pyLx!3cCh{YCYArbLcWhOuZr;&1A~Hm^u-Q8+ob809!gHj$0GY4 zwS1?s72p^O0M*1MAjI?Y=aVn#D1H6?vC6^h*{;(sYn!z5IyVz_YHJDu%zp^oN~)@B zLBKL>+75t7oPHWbxq9_#Jy0{Wc245ZmDe2|!zXtG`0JxTHyOp52cMUwj==;8B2olpuI*mSxoyYo=;)g;+9@;b*Zm=DQ>&}j(EicXg$WLNf%kc` zfAgL|)m>6;5sz%n`pw=rAS5Wb643(M5xpl}plsL3%|Zy+_BoYb+58B40UVF0Q+XCT z@rUW}vrAYx_LMg^vSJ1MR7je=Tf2diQvjYLVTP!077fEtR1VCn^Jd_Yqd4U`{PHA~ zboJ`h`fsgIWqc8aCU1*&C0!Ylni>p4_aJy^mQCAXNHc-P${FuZLKK<&^XI;5fGSh1 z%;sNQr%VfD8E3oiXxw`6K;-=wN$1c!e#IO3HTB#itqZ!(^}S);KxTP*bZB5;pcE|M z+1c4?aV}F#VS5xjhQ=2Ldgq3qmN&ezP;V*nG_KnW^@?5!ZU8R56ie&ekeU!n!qcaB zPg}F@mraI=TK*Q+D+3|S;D@oPsRsSBkWp6SQRC4UWZ58H0YItyp7GB7^hi9`r2UA0 zK$1|c%m}X6yfC%_U&;X~4>}L+h=EbpO9(Kd=wio;jL15~MU!PR$?O(O@=A%}20b%mx-!?GfWZC65 za63L;5V;HDROU>5j#XTPwy3teIQuKw9JiXtYXe84^=-80(nb&wiIbjy&5&Ybuk@}X z{2nF|c&T9_x4j#F)#ofV#^ieET@ssP6p$?{B_`C(sjT&zZsR?Rq ztuQI`3dD0_4KL}u1MwlG<=(w}YWtaj0+OWRZ+)8GD=h!N28FjVP7Zv&$6MMLid ze^BsORMOcn5$XaAbs2`$AuKIsCMGXH1R5TO#YzAZA|+(!<(UsPWlCSv+aAn8>HGYQ z5kVhjR#u_cYBwXw(B|w$+YdqashZ?fgJf({sEOuV{RtkFl#~>(%JWgR6d^wBgEB~m zV#ZbxPz6r1L&NkxPLsEpTBnWu^gNFU9s@_SND{Z*vj4F@%h>Y5DTHy zkOoe!A7TuLT!P3}tdS>AowI<_L;opuzopdD|7&trxP%8(+qZJntm4P@E3mwyH!iXZ?n480ahlu1rZ zAWj;uf~QtyKp91zYqxcAzVk%7Svl>|C;K)+aaHh7{7U#TUJ{Bi7(f@KFOs=4oPW-e z6}qU09)~bcI2IH3B5~o9bYDEEGi7c)?(%1PS_Q%>_&YrxUo`Fh09ZU5p}s)<`Ew28 z**d-h+-N#5hi_;d@7BADRjZ$FLPg2~tb+$=Z(5ivr=_Ju3InhgQ5oeIC)acCwA=)p zM`G*NH9)2BpzJ|s@ke(Qb_n+sG00~A1m!NBOa0;0j{?w+_I}64ZaPL zUD1V!x&=3EeK7m>P7!{kguC}_e9PQUX8r^w0Cy&8Gwxv}L@T1^_oZteM|zP+ArMx& zdwK|9ADx=gomog1L7>waz2qxmFr=2C+hZ?mKrRB}OPK|Wg&2BANlD4~B2Rf@lMs4W z9U%;Zib-y#fItrF2sb1(0hU0rR0%H3$jBgD1d+ZC5a3Sq)kPSAqmuvvmKY%}aKUHB zD)}^_=^yGe9UJFEN1nlkNm-@;w_7|JfVW>SF9&NRg4$170jO+#V9mC@)V`yO{&=V$;r@ ztXq#iDu%jfdj9+x+(O6(sTELz75vwDm8;(eCMK~;h)A;61~;Je`5+WhiiLv?wx{Xs zB|4Zsv1P1==gw_`p5h1W*NK(sG&d;>N2;T<(}!4)p36%~NRQN+viyw_y)rH2Kji`^ zi0nW}4V`G9>91b-KxQK34b3`fv6GYNmdGD5v$Nm8YQFd2ff=cDy$@~bl22}pEpufI zj-m|+3#&kpU;?NBOTkv9W@l%&{QQ(2>tv;?tLx4S4N5QC1`+!L(yb&aKGrCCoxn*j zE)N0yp;}WTY2~&w`>PIFMma+Pfyj4mWp0W=Y*6RHO!&8LTMOh(aPsJn^U)=^cR&** zycX@Hl%sHRF1KWRi|V~>M@Kn~=n0y?q)9QQf4f8VzPtMQ+FEFKB6>670F+C3tkK5Fb z^M56HhkhR-8i5V;-3kn7WcX)PKmlaJ10&)&l7Gp`$xtT_!Ve-F8kjR4-!%hV8gVUy zz^4VYA+Tn+X{)fu!XqM9ojG#`acnG))!1<0qA#jhEfB9bibVu@hHT->ZH1vglpSd2 z(2`bAAn?=w8TfKNGjkV!r$1gG^syCqSPCyrXPTTl_jFW71B>JKojc)=9)$q<<-5(Y zU^_OXH8yHfykOW34h|+nPyJ|ajt8l9YlkvSslII!WDn;>vjC3 zYHI4grV&~bGA6Nj3Rpa)Tch1pT)A>3=jLm;V?shg?Qkw2Nmbt2s=>5={q@S5d|e;~ z6be8%32YT%Gp4=D(tYKmd5bulc-DXYp#3hskh?ly11ldrx(>x9q4I*2Rq4BTtFW>l zQ~JO-P0>qZfO$yLJh+y+h6a(T8#{LHj9>@>JX927p>moRK0_?Ui3z;5P%s;!gCj5c zAk-jYhymx3{|ki8T6kB6nK@EJv+xvDKa^K$M4k3Q zLMU*HPfT2mTg8^p-G(5y67dTT{un=IZ!e7+DtPJlPq&G=KNm>^^2aL+f*2;~lYQ~N z0ggI=OtL+f4-B^!1OvIZV}~`gm18v`@U|L4D6HTkMcBHUFHY|w-c4?dueZ1NaH*kzZ_XHoo#KFmX?GLAZNh$K?d$vgGT)C#^ed3!oaYw!zjD;h>IdWuV5k1yk$k} z0oGl(KVn`_4Q@n+{1(68^n4CSk3J(Kc6a&$< zuD;$oFp!2M^%2oq%*)KooTR;oHVPkfQ@6U_T6PJwvyjH`?Q@n!yQkT^cdy^%EHtKe z;5sIbC0|%@U!NRi*=McY#x5$MT}%0@PRg{&?IRX@#hB5Ew^Mr63DbCjd;?khTUzbtMDuzP0=J@4x>>JwXH} z;v*|D(G#mlCKj)eL^@fUhyb_S`ny+M-3cgLCt}^#GlvfnGmm^16hy(S($y3+t1V|( zNl3T9AyN8bGeR@`{QTK&v(o5t;st&;0+QASb8NQls-R!BYLy8x{XY8&-oQ)P05an- z)3N64`SYUro&o|Xk5;Z>Fo7^}4|7xG)BDctfZ@`d}vwx(53sO9#xgTMr-7A<$o9wn-pve6Q2!CX&Cwjx~koWj`be z+zR?xPD=~n`?a%#vfR}HUpOK}fts0Wwt=Q>(R6@0S)5&BMRU68O7fzyKo9ZRo;`Q& z2BfaCPoEe;1%1kq->~37P8$-B>11C$439gmo=|U{ZD_iDd{#U^lgAFuCq0x3r+O^| z!(l9ktaH_ChLBgg|6>it_5@y)E0&yJfyqMe!h zot9zObvVb9q6?sc!3Wi7dgRRb-Ft%(>Zvi?ffO@7zXH~nl=Jx6`&k-FDgNcbUcL_! zj5)Eoty{90A(SZ|Jm^)|!Nja7EPf`}7yaJk{Vje-SFc;5+vzEIX^6s8k526|LmNi< zhTKhZuxJ#c*pT<9oAVrP7NC!V+^oc=6`7a{RL#VF1k~M`R5wT8!i9HWFGOq+2DIYU zduiy6u>>NEZ8vzkEy0$m^?2jAh6W!b)q%q_VcNo_JB0FpFM@o8%@S@ncg@kU5+>+s zJaE_??-LmXwCKf*R>g?nJGy@Vrb8|S3Y$8K*7fno*t9A^b0<+Nm@q2215x=1&2$1q z!8G&@3uD0Ayg`g>@MT#H4#4?8fd>e#T;tG_;Rxow7H`WtI+~AcoSex+O4SIBQ6emE zdt4xKV|jUbEZKI59EABQQ80$oJ~$Wx^@%Wi6bnuLHu3?;Qs1~i4U$YC5d?nu@F|L!IZW?@Ep8os_*CM?phu&}ys-zvc> zBkW(QA3-O_&1Togv zM`@01?QhF>K50SAK>@Ze$GwwT2|Uqw<023Es!9n{B!Gi?9rQv$F|nh!P%?@15)%_M zO@8U?SMjUIyTJ-kc1RK$%e7|LVN@?x%x^rjDfGs6A%*nYQh=&H7Rx~$ES9GLc7*j8 zE-$%)$(_8_%pMSs5(In%B|RQbmk@Y#vDJYJnZrH=>`#rIK%6s3v z{|rv7i49R7uTq9p3u>XKTBMZV^k^cElCtwi(M}S3CQ)v9cVwwpi6UBpIWI3*eXubw z3nQ*ChTV(F0hQ$Ws}2qa^z$hv`uxuTLRX-glHo4y{Vv+3i!9-0NQCXsd5D4pU`OT& z0O`w6vWd&3I)olw0-FZ239_zJV#rvn3jJlCCF?m@9UON7AfT;{ugz_O9?LNRm70!+ zg}I6LL4V$kk86qe^ESh=e_(*f zfLKgQsDE~dlE*3v#Hr!wnGfv7a)CIch<_nvTDmH+;F1?q6E-$+NH((0<3hkJf)1)| zakS-hqi?-_Ko9_(s6*vO6iK7+RzMPqe*XJ=xyKroZ;`qMu;f=%R8$wsgsD_$<0z$_ z3$qh{KPmI#R(#S^k#ai9h7B9qvHC2(KHo@8J0!%&7182~6bIyRMGz$_5TcR7bPBSO zhJCgH0TB;Wg-Xfv+GL+5_a6IIJaP_rm1>%ac$Egl=y5tGG zNs{$|gtQyEUjecm)IizI;rNr*iycr8@*k_1vJ>PlGVjuN;8x_sNOS-|mC!_TW%g!c zT8CjtEVX-FjmBO8{({X$y@r9i7@r{TG>FOp$0k1JJuVc3dL{1y*CXu|fUQ|pRduBA z)>(xwFY{U9BW!`VNCi}m4G)3dh&(}V{rm{9(G)YP76Md5 zlNYly{#!XjMMZ02_(0Aq!2?m>cLOs5$-#>L4DOTn5CmI|4OP6gl8>&s6{+V<#x`{|1MBwpMi6WRu1 zWcetj1@1a_xJx`lGSEr#Tms?wSM7r%+GKCCi;>?<`~k7B&L>Z`4&pOOxJ zCooE_dOSRt0he=Pr$!gPu>7sw7ZiZ{#>RJO6l5?A0u*a=)Pn~>@SHKdoiL6EO|yT0 zf!1`Ie#Qz+#pq{U=7HdvT>PFjwNFvx*s(9mI&sLFlavp{DKf%>oyc-?hoodN0Hn^G z)p{Bm?A8tP9&Ukpn#f?pzIbuQ+5m)zWaU!zB#ON@uzNtQF`HIU0eEuC>)gN`o+5%c7f3Q3I)lz7?DUe#$~8+%OQVyM z+O`fso7sbPmQrf+9_+l}gVoWFE(8BSBap6+$QLBx=Q#YRbEQybJ+vjLy75q~M%*QN zIXLtNr=NkiX6NMa%gH^snK-jdc0>IaAIIP*lve^dh!1|kRQLtbVKrc@n4C?on z8eR$s&^`sLUhCvZO+c>csY@t`0N>>pha~eXRBeYp8Q$pKxssPCG>~7(L^L#BGS3r4uXQBDN%(qPXAjUD#S8s_ zAhbh3Lh>_*gF-^Y{xrN65fu|tLh(8GOW5asWdV}sFMfSa16cFiFoy+De&wjAp3$pU zuWmhg^757}+RY_6<`jyeVwqVpATmj$n0A!<5s8{9R``*=m~OB^#ZoGC8xj^kfs+Eh zHJeY-Nu{QI&$5X=-Rr3Jh$w*ndkEo5V3FRjC;Lw2%0isE8K zLmYUsN++^Kt?IS|2LbNMEFn@wp~SMm?M5DN5gRALLnI*J zT4F%16jzftpN++l97pof5-cM|&OdUlY{jT&`sJT;1nwt|vK~h;pLG`Vwj|$+w~Q`t z;KHR*zw8P^%)oa*>})_nCmD)ZT?Nyx?cv2;sQJ&0i+Cv{wuNm)%koUxY;2sF=l~x+ ztR9_O^9PP56YMK8)c}Et%vM5lAuAaEN-?B0KR-WNq(x2_)yrYx3oK?60_R9L62)v3 zBMK%+G$MQsfti{p;3y!^PiLyR9MM)(yiQ6}0toFvA(lfYPoA7pw%6_sGBa1VK-3lu-VRIJc$)~4)nw|AX!pwGqxnZ0ZcFJGPw<~$GCH`Zr~|& z7cwk&f5&-}f*|=lgl=vmCky8~j)V~^oSIfxipF#+I-0S4XnGWuaw+w3C2h0`s#~f; zP|_42B@!Et08^%bzo>5e;E)|~x(tmMG3MkLRW=o#m-(Rh7}le~?v zwQ;tPYJs|xiAj02-6o%}-abBYT6QsPzpZtY44Cyqbu@n)WyWP{Kf3xRtSOh{w5EaG zSHEi8Sg}-}m9YoUR^5(VSQ=F+OcXB`+A1{tD>u7oOSk(-aaq5!{YU3dON)g64%#8s zj2EV{QR+zOEcowT9*LjN#1>DGorDUTGVXbPe2dF;mgQS$Ac{z+DJv^m+xrJjZI2g z3$Tb}q!*4I0RAQkXCfFPj{}y4O{|Cs3o>PbJp+HH@I=jgK~h;b2M`FDRu_s`2^30G zYwLCRo5q)CxBwrLbW+*>NoheSD}?$+6ueDHSVh7Fh%T>kTciEuOPVnsaVVw`nc1tJ z_~yRU?%iK!7HhtlPK+~P!K4j2L4+fm3jTwAf3D{&@@xcoLLK-^!oPm?=rLPDRDv!d zEFj6A zP3BW^Z6rlVvW-8cF_BcgWc(EHVSc}QqL$L(!z8?m&CfEh{T3&7CN=1WPOlX3DH>JH z%hz$s9d=2Mu#hzG3$8`V0hF8^dx2cWkL-N1zc7t{9s4UDL*$@~+b7%<6nw0^G?Wqs zpfa86Rg7vF(zCOZg6e!iOKU~4HZ2rJ>!vqkl!ZhSzvS9cHO4#7W)Y4}%&LVMDah)6 zc$*-4heRUd;PPQfq%9PwV{yRuX5S{2Ob3I-VWgEupnf?_*b>|VHr9%#T4$_{TKUDr zk6-J?0zjyh6Mg0Szc}@(5E+HkE;h0pK*YU3P81eZ>glz^A@FB?^*+13xEMV z^mQ_ROXfuYr`m^x=;2&>-xi6=-6fF-xtf^lMES()K*=HQ10{dlBRD7sta?iCqvWQP zBq$p1#We_hejhFfWscug9v+xjE3hBP6~i>lhH(te?i;3Kf!J0Dew0ufDBu?K zd=gDt<5FU>vo|13D)!n;fCA^BoitW}7DzIyR8&;>&1n_RJdE<(AtOU3=n=W}N1rf< z0#^6=^L16B+wPm9pceyq(LRbuk%QCp#$Dw|kB^4~J1hzSU+0OO+=8Pzl(S4o`NP|jT|DBSWM3!!N`(?i!qc1aL*PF3~9 z%swQjg-PzPz1@VM)we+{w>PVAVh?6JP?S3}TEPfEyP=p+)mgtGl?$azHHB|Nbk4AfWdB_g{sm7zL=VwI=5kC6(G=x$+~Q zDZC-W8K)M-(bm+|bX_o4N^tnkzjo^O3-B%e_v!z~@BR0`faZ_D)N1^SYQNI>h6FGi zj7fM7T_IU?SZEk`C7m4`qw3P&aubLfuG^20b`%1OC-Zn=ByvD0<}h~ihQ;{f3P#_M z89kV@k1aLoOpm`c9ixZe(bbNh%U^Wk06nF-HhJjAWB+v4mJzS!0O* zg#!+Vu86ZOqFgWQY}MYLf5|#o8v~eYak=Dh5abVF038ON0qgfhFil66j#=lC$Ys8z z?T{9M^RbAFVM2)REP7g1Q?t@b#Jy?5^qJPek#U=WrL5QsnI}XiVkgAZ{`Jcr zw!wQmB9Dz+Ts21PL8OsmBt{Q~fW9h^4odpf-@3#xA&(W`D6&8oEn~y`S6Az+Uy13^ zLogby4Sx5ARo3ax=TVHrZnfJofOGq=Uu%f+y5EHr+DQc>$NohaoWsrrM=|vC6i-nL zIPLW9JM3{EBzG{C>Wf7wh^Iutzn~eCHm&=x+6NZfWSI-@;2BXi@$*x`2CcX$zZ3u{ zJMDUhQvhxxg#bYBJKDT|AJhuVk8L-ee(`}gK&}GHC0;(!Qc;#ffOBsko+F5a2pkq= zh~t`wk#tNyx1`;F=_pzPIn)P(3#iLCjb=CP8v(opMQAs}M#a)IlG z0P88bo{a`7Dr;bGD*(h`!s{kbgc-ge2|5OJCV!-sa?bL{1+90P$MdCrbE%mfNK{zEXP@5rR;?b|F1I8qA19J~OOYLN+jJe&@!8*mP{<;Ce&m5U1tgqq{a z-}-e@K*R6@1i$V1a;l%fuzzdWfa>SzGnA!E5n9hq zlNniZ>JS(v=EN5>+v4$#(hBENO3g)s8zjDe`}QsBw-fR?K=OTD(Z;|Pa9M48Yd4|~ za6&FDLz@kS2cu(E2yI}c)2Xv*p9|ex0nef?K3`aHBfA)06Q}bBzx<1_ad2$Tca3#b z!u++kUzVvPw9n!)AM2i|T&-V%5^3?t^WqE5PC7bieyBc&Ab4b$ZvXQ17#H?f%Gs`z zh~6H+HsXB1?FCzp3lX*;feP0tYf3T9P>K15&!1BpouJQ?K{#@F6@-&^Tp0L6BPIjJI3=XQw%_ZA(c#NJejJXh^k27e{OdP& zn9oER2Og`(lfjeLmAeif4QR{{AcPzzMtnx89P^Cc~SoHtHINDb$m5Wf0+H3o2FtoU|C zx=xJe8XpCmwu1mMZFKnqL~kS$MJMh>Md{@jUBqEFXzYeX9z2-r_fJi|n0N&nio}OP-hQ0F33qnG)Kn5tLav_)Z<;8E z3W|ztd)G5ZMyFinOLA)*dLJzEHAG}6&ywY88c9GDWh`V?&StP3w#s0v_yQ`d(17OKNK95SJnG0F?7BS?hq$N%sA&gmDDPLp&@cT$;f>Ar163)YFX>-_WV!Z;qb4akXfwD&H+hg+E?cf+T4wdOw?5| zS_+*P*H+`X7zACejhgP)$vA_on5Vf$IvC&`>DaM^`qH)e6m)OvJ<65S?8 z0L$?VFdR4-S~FRY1`E3bTC)Xj9$p+KvB*@$4|6a@!g9Q#{C^_rB)apawurKe%N*Ck z(1w@!c%T-%%kU)0OdXUZ9T&f~S7Y9vcKZAsDJ$Y~8qYsyNaTlCwf&ozlD3@!_Jw`p z(3^~J9ffp}D0!sm;><0aZx;&J^qa>gxP-P4707@viaMLy)}Ut*y(Hj?u1HM*Y(Fe4 zbG8rVx0?D~bQ?KO2yGCVx%c*r4J3d5>sOTXAh0%>53b&~+p2LJNx=Qpuh6S?tp;UI zVQ`Wd>f}fv(6OnRB0Wc({ZRp6GE;pKW>P48lENztQ1x-oLPU zddCzw1q7vp3^oyqeqAbqS24hP?&G5>o=pvw1L^MHA?4xd%C(5n zehd5k`?o;{qs&gzc@y+YbcFZA^89ZwOwfs3g?^@$PL6SzdsNm)9_7}28>Gn}KRRGI zVI?PxUaYx3pV7mCtRG1tqLGko3>WP0L^*Z`fEOs_i#n(3?-HxK8}n~TYG*jtvpz__ zqA)4mav=Z77HdB2w+u`zV1XR&+eQ)j_V0)IXHV!(#O5zEG+B%(Dt8`o z=%lLX)Wjdr9y*_9<;;2{y;u3mM`i8IeGh!)T?Jg$U*^-+==Xc~;Cp`3>HR6Ed5^_e z$-CkgRQExN4hX0|CdN*f2%t2(94+&OP5&6nWhCAOZ{{+ETbWFdLy~$bKqGwL{pAwX zp}0(%z<<}u^^gNcP2e9G`O12x5gi9+@RzTY|NGhPQc~gM$8Y@madj|mEGY76tN!tx z>dn|RTFDXr{X;muf|3qSQ$qd$BkRb~qajJs(#+?R4b$@!mqZ zs&w`UjZ^c9Hr7U1P|2fJ{fhwfM?0>NQDV%hYt5jA{jr^oIfbt!XZ*#Q=aVBKvPTr8 zpRYFEW?rPNE)uE6c7QIN8wNgwLPXW$3QCk)7{w-OjOs{HGB5%{u=>&^(~pOHrw;8w zq)*ag!KwD~UcJ6;N(r$}Uj{$%Losm7~Ft80F}lc{Pwk7JrkIb(^%(fe7G-JRfPHlBpE`4A^;x76rmANi^+#- zf|&h%)Q85vSD6cRpfjMb)hDWRkO46P0iV!d>L3j9!ajnF#!nu!$-Z$c37bhAwm=kF zqQ;GYx!tuSP>7OLj~o$kEy<10RdsiFZ`!phl2B+{auW$1us+D_ z7fD5eNJ6O1uM1Z#$Nyc~E?otf%8Lcqa6&7Yl|*lVy>JQ;3772_pF9nN2--AGyv?lB z3{Wj29K7Kmv@?tv$z$w=oMG7kkZ0NSCb=#{RT*b`l_3L5=)4NQ0Da}>ihzKfNB^a? zZjyKn@)3P4Ij_n0<4-KWHQ7ghpWm@?_~c};0f*+05f}85vI@Uy%nee*C}5SNW@f$# zUEzWc1ZNb+^{^dawD6HAhl5ax0;UP2K*(dwnV8924wn-Z7WOVGk|!2hlBOU6pc!_t zv{molA4E#b`qCv*i{1%BxfmG0DkSq>7<|1ecFG60bjaXg^eIgY0l%YA$Qy|y`hqGw z5127@c5*@!8v5TuE(A5%;NH+dRso5o<$=b5h0C0L2Mdr4?E`fYw-|%qKeFpw14BcJ z`;S8$H)DwjkjJjYL0~D^EP%=AuV~LW=vfLX0=tSMD*{v_M{y&XY7hTU^KynPhMX!!7p%w5d_#;zYLK+$3@|4=izX89u{8w~c&mquzMK^oWBg zV9Id!?%m`KuP7#OI3r8u{TpT*A6J3xCSd3!okNTd+> zM&=<9*hke(x8wKALsR`LMIKTTA{A4$T>0?fIG}9oku{Kn{SaLuCTX930u^)Y-D0P6oQ&O|Qswkaxnf)7dYLpLxXX)34^7gXmlZ&C-L@s@Y*8(Ki1wmtjcrk;@sE;8;WAXf(@`>FW7OT zhz$_LiUlhgD<(?pfPftZu`5jhJBf)HR1}riOYAW=M6pCc!HP1!g*n$XGuO;tGv8n5 zJ15!f{l3q0mvyhT?#GPUpr6M8VBOnK4q&SLB%!QFT+q^`k|F@7oS@DYJ(kL?v{j!t zV)yWr!P`ST%r{%Q+cMoE{iQM+@Sc0=_H7wd?eFvr+`C?3E%cN0S^xpNPu&{9{3gN#O^rY?i?W_;PviMVPo01ym%SRd*q;U(go zY<>8ubegg;d4J~3-=I$rrpkW@#`}ks4)s1X?tB$y3G2Unp@c6D8ZT}a1;0)fIFH2! z*UmhMn!s<4P-w)AjEbpX9&zGnkVk*!Y&png!1f=Xw?BT0`p)~)HheoZEF=b^Zg}k5 zzJ5IP3n~2}!rbY2+zS-|{>i5)bA{OqPo61vQ7=cAywraKW@h^{5%Zkl?cAW>#+Jot zfw)aFGDeTk@aJZ*k$BSG>40E1)UvaX_uxK}XWKVPcXNE9RKxY1`YlB-2@3qmut z);+O2=)oJP@xv!EO{e;B?~UJ>!U+YUn}V|kfqa&BVaZ#k@iz=3uMHfv8F8**=K>beKTs;F~S1%|EBj@84`b;?{7;!$pO%9?XC;XBF5q+>%yIt*yvC;0OJ9`6WHAGYh-N>aX`_4gDV%K;}i$)#H@ozvr=yF*;Q3>8|`9-sYPUx4AMV$Ng zH-ZgbMTO?(la4_2{PWug>o7TQ(6|b9gcOwnY?lTDJvDGsz~3c59icy^=q!{oaY>F= z;iSz~lig`<(yJx+$jH%!Aij0kJQ9?+z)6+&8Xo>~xl3Bf+Vd8H5|_cmHPvrZus#gP zlt7{yCAX(_RM83%&{IITN&sHT4U8a)uF7EZ&UOc?pTbdfX2k?x+~BYOBhbit`yXn_z;EnWkEaj%_3bP3IR33#FjOZufY; zx3eu&MzS>UylB)F8hZs!F7-Sgif0{QkWsb?i%w6wfgn#j()1X1?_cR169D7K*<|j|a zcKZ2!nbh-}reIWuuh~j{i~odd$WYFwtX4lq&6jldi#@&ho%uao9^i{o2`vdc7XyRN z*1p%Bzjb|R*V8R8n{M4;!1#H8xW8*JvQrJLt=4SZm~gOcP4k2CBplNNTt5{iL>l=e`#MZs59L<)zUQ-{Dq4h2^qxU<@8z&t$(!WbefzHq6x$2>wE zZuKBz-I(h13&n>uCTUbR7BtpZYaE;#Wy-|xq`U|Pq$AR&*{6}wZMbq|_T)Q}^b(`` zxi~R5*w=dN z&g%iVZo<%%NE%o*mBB?;{F}WzvyX4j+aH|%$Wc|pQ7S3_UBn)l#t`7ubb!)u->+iB zB6>L0FZaaUVdwD7%*;o;ky2Q3bFe9Z6aXOM8-V(m4zo#<#<|m5- zNzoU@$6z?*6fw#Bdx@|`!*h)6T=?FDZt4R-9Y z+&^UHN*R2qHIYs<11D{9Ux1D8%#F2kK-Qc> zdrJ=KNy^|H+%Sn|<`Oh?`SPmOiA%S<>JxCN#q7RAB$jG<+j-~ad7#{10!BU!M7kTpFz*o9L9Iy8|2HBMOq)if3GPZL3cWW z9@y5SfYkhcwQE4DCN!41Z`712E2L%Nn|QuGvO!}wBy)mgk!`NxRG&yo>ygpb(|$kk zT7+6=*3#31QtPZ2n&M52IO#v9t7lX#O>}s>sz+oMM+p1QBq9vj&pLK4^)GT z;rj44&pVGBJ64~jIWaB=fxP7?{L~!N?JSvQ%S?iXOQ+~R3B#dImHSsCCJCC>ViZo4 z4BmRKsOmOu)!3w3Px?f>d?al@cS-NO@U>LpNw5P*l%vPg;o|aBHO=k3d`kmpMu=x5 zbkMs8Es$}EW4eC~LmeN!q6WEa09Xd6X!k#+Gab*KJ10*?jb7wxNucofwAnMMmPN11 zFHhXLQw?8JQCoL(06Gk~xO`~`K3NtERHaS4Q>qr4G_DtW*s_E=0MbFJ+hql zYkLkgdr8JA8iFzljJpzc-5q(5Mka{oHBh0q4!siQy*zJ6%|!R|NK2cyYL)gMQ?h$Y zy5zvmO;QHD`E^dt7$Nt}PqmTb_FA7B6RCU8~#dJ!&IE|kF`y_ob( zl%|Jc61%}r(3Qs-uf@bGb$C=I6LasFzwDmu5myUS+VY{sB23%kF84EfRO|FM+Qh}h zMeYdn!DE;`<@@!kBhmn85em{uSS4oue;s@Lc#OD7a(mNw9zH_p^-h&|9%TscCt(of z&{CCJU|->8z=6O$!FaAc2D0N?d)?h)G`U7eQB4ZG1Ni_(zGz6Ypo_8Vb~B&7l~zn1LKn!4^vG}Y#MSiAe+CXe-hIP zmyHlmYR!4#bn)WWj+R}&eXQ4Z>b+9T@RC<-jEwwn&UOMEF4<%q>o}K~9Uola58dqZhd1Tv zU}634a`!3K{Z8I=Hb`l|4|%EwU$1hP+U8-M;$IisczzTx!AzWdj@^@+hYgDuwZah+ zZS;;0zsdjQWK=a6$QTT2b@-ne|LRk*R;^~6FEbWZo&^kGDT)bIvg!=te_Fc-^8y4; zJRV^?fGeEy@9z)BPzbm@ICz)a%yi9!HYWL1e38U~<>lMJp{FY}F-U?;-nIMuV|XGr zTslule1KeS<}V=97dW5~G{~zO+;Vqr`*S;A`Aag0<%Tpxw)2`rCE`eh&$C@t zn@Y6`sBb%q_a`1ad8``**UBw}*W?)tRLn_M-3(f~GGoTUd(XmlX|{^2K$0iw{qVN? zkKB|-gsNuj^0MJ=TY3hyd|4OyFqGLrp+8>)La^Ly*`^IV6WpleoMF1P7erD})uM(w zjqVqC`j`7a>aM&glgaMVqenZ-{h5<#<$PHZg;o57+Rj29>fTqmu@gD%+D#wO?}exwm1zwzZaOs8-qTuiIsI14=c5zSmt zL-+nC5^R`hiF*g#w{fkm3-(+|hV3GFN&h9)x9QeCm0v{V_J4$ST&nZio!*WaJC>1S z!-u=JFJNMrc=Fl6Xa2g~*b*$^u0~N?Yn>Do;eTIR>5m=^Hh^c{3|1Wh|i z3V}iA=E?D{)%Y^8?#~fq-sg5ryJ%gLMmTwP&0^X8?z%AtUZq4uNhcXL@)NFqtjpX_wWav`HO#W!##<|3Bp zbU2mbJ&1yH=k%$!J7Z!rl(dVRI|GAgR40zlY~wEY%Tb()S4AAmq%q`d8{{7Zltt8Z z@h3A%dz#U@Gxy$n@=QUy*?WWe12Q$H(l;2`FH{@A1*ip2V*jA@N{Lkrr*HNE4~H3` zA%!PM%JmgA7Jd7!xHqVrojolUi@6}Hi2G@h{nkH#+IkFjkuDeAt0}lLCTamW%kSmQ zvfM6dxe)ar>x;gUOtLh$vf6vasRVT}t<;VKIR$+-=4HQu*Jy1$9a~98o+YVb)A@pS z^NRKVbEqsJ`G+kWO71w8r-wd1_#P@#aH<~UgKv~urmGu`ZE9GyJ6N91c!iPg)_%Ed zG%h>y)afE1DGVXkx{W&=JaM}55nYhM3p*=Aj@lt)7N$TXE zoJkQO;DF~EJ)EqF53YiI%I(jOIN`{(g1=w6<+shH+{%OPO}w(h1YA6XEc=+4UDAYR}HbY-QZ2 zchTdC*CyQ_Sw1?pidYVUk8^G|NaJWi@tfKEm!8wO*|38WEVE1GA6O93;=q-%e6sK< zph-zXP}Aj~k&YS8?JphmGAeiH&nVB1JmP20W1nw)|two+}Q@w*-!OLfs)%6BWtj!5tVJ{Abo%F*YxSDNJ$rtBGc6XPu{v$) zj_n-2ZK+q-=Jlo{q!?qg9K;po3#R*v5v&FS>DC?Fz=CgfogLl7V(cAn71^=DpKW68 ziFn~jgNvGnSs$3|){0-=3lukO!v>o_^9f3F$K;PkEVw?71Swk=_(0I42Swe29$v{% zp=QnebF;XHrx{gcuFaXvsihn)3$@H<>^#mXeJ$$^de_Z3zH&{TMqh?*;t=IJ)SWDI zeg4+KtT8oS3@(qLtQ)igCZm7f>e7{6mf%rY`~Nf@?RZmPRf<3At#QjYQ^U34E`eeE zBg@YdPYYh`-j7+&+cQ?B=Yr!)8UtLCbJ}v`LmB&Fc>eIsn~{$?_FVWi@0QM(C^R%w zvIt||Cd1!TV-7lA{^9s{#_qlYIg2CP)V+SRS-2-mL(iv(d$N(E*2A+07>9XQ;+3k7 zH`Rz+c$+Qyd_(rU_x-DTP=H`}6bx>^1^2dl8Ky(b7UjQ0%TQGORE8%{+BP%~OPbWX za`Gwj(qrKC@mVK9Of7y4IEGL^JUQf7aAY|Bf7LktppXj&I6w0BUw?e-SUdae&Tz#6 zK%|;hR#GefN`1fO%|lOBc1h#R1XrLQ2C0mO*Q30PG{Ua)>%WmkX!(^G=)}c+YdS27 z{Ha<2`p_BpaRv>K-)ov@NZF&!6|#{h3Z0<`0?OMvbLFRLj@KP=fRXTyn!q+cycYFG zczAfr^P?ea9Z%}k!`=N_;}0p+DQ&Ti%D#E9PiZ%^MPpvbE?px7&=;MDxks)OVY~Q~9q*p7jQvOqI zX6y2P4!Y!2`2z|axthTDHJvuxa5Errpi!&wK(06JT4C-MkzUWpkW6f$md%Dw>yGd~x}vjx=MU>}ZS*U(x++iJt46XlU?wmq{>dE7&YTgIpFV3_67!70?+bNKKT(~f;Ap^8ke61}?FTG<$p>`#1`5NHtk z`heX=N=p6hrEBsUhY2=;g$pkm&6@N=`_KZ;l3g_kx5ZGaIMK<|Hewo zSCixAgu|%;AwC`3dmY}QCd7UIco4vz1bwt;M&kTIOz#T!E)Slj$ufwSoE6|Rplxjk zniIGxt_trWNDBq{aUUNA*QPz*`}S7&0nmeud1Br%hs>Zd$fBWhd|Joay=`0j>e4-^ zaig8)do4Au*E2OOE#QM8K9-i6B4srBM%Hn1Tp(meBd&Gq)%bTk82|^)gUT0hgL}y+ZsqG#_0=cpkjToS%9ceFH9B0D(0x=%^sPhhLopPQ6`hyI(oyP%e{=TO&HZZCDkjON?Aq^ zBqAnr>O!RCs)sv727Hu4s9PBniRK3xrYB>Yz*P;~(GknD3Fp&59?%JBicsYr`_3+q z)dyMx$)dE;L^L6o7NU+i_FC8YW{*$tj-99A*`rlrRGex*;x6_v#!j5#omH73lPGm8 z23P?$5DhAzb)x(M4RcJb2Dtu*kLtZMFCn8zpo!U1?9exLwD(0DNT>)j2|XOAKhN`lIu3A6^USgo# z)_%q~fA~>)mV6sA7g2M{XS~bjta^SguJ_(sqy58iKD99rG5;fsIYVQuYJ#f{h z`wSH5C09V)s)y1WAqz9TMU2eoN{aE93YB!`Z{9EvUCPg`*?zhKK*Gofbm(tWLV0-q zL2}KtO7Q8=XSu>c`1#@#tRmb~N=j+doUOkS`2YLUmGgc6dQq3ZMYrpD)?ZNt0kyW= z2}wL_wb?SUImrOr?;#geK~4TmprUF)0l<248&xEg@AA<=P^jeade~QLD|u~$shBf2 zqv>Yibpqin*oPBt#o1ST)n=>7s@-`hPE-%{{q`N>u$uc>a!5D@qcG6{<&hTQ?%ko| zync};olFWhOLtgGNbV;{rcwih04y7>ny~En!d}C(i^^}aG z4FzbmSAaAbHRGF^{xsP(NLJ}fx*FeJiOaGtTf z%E&v6FU4522D6@1jO&`ueB#XIJ$SBmZ`Ufb_muQ(L-yXnh9o=#o*n%o#JMTFks!^9 zaqNZ$CK8mfSFa4Nhkgc~qd|I1_Mze5h8tb)jrKr$7(1W6RP7c==TNI7(I@}M1@OIL z&}7rb&aO!I+bv(H^Psmpq*Rx~5$}aQsVo>j%wVDQ2Bu^IwDoChhDQVAs38}|<@86U z03=27!&~>_7|U@`!cQ;eb-DQRv(07{u*@E}3r}*Erp3QIEZEWvc ze;gZ&3bt_G`+98n0_(|G{2{}Xrl0N{(&Q3^xYR4+*{C{)rXnIb1%jAQT>G?}HwPWq zZgO;wC|?xj4`td#9M*1DN~$_ciV=kHx7U0HH?M@=mbrvU_EUenQyq-f=Ds)ggu+3H z_=LltKB)11JJ+G3ee-NkPQVM4f)~2`U5KR;jVEnx_h#!R{9A!i@0n( z*V9;k06EACM3fZP{ z2Rn7*I-#d3(#X6gj*vKdXuA_`yWI*OZ_NFd_bPr}4rB*{qrFtr5F@kNSAWU5mP-J! zcK4KwBHH?>?qlD`F zoiq5yg!yTkFJ+&#U(J;*$EJe^8A=LYUp0JCQSWH&uj5Auwr@tzXR`N=)qDipiUnnF9{mhc5W~8sDx* zAFO|uIR4D(voRLxgjvs)D%hoGy;b#!AU|kvv z=EX!?OhSl@n;>eQrr6R{?9TkDtivQ@T$?D5Mo}#o-bN;OsCD)-IQR^_Gc?g@*bpMC zTcUPcl6H9Bw2iG~@CFeAcJK95U|m-kf6x1r87V%Zd-b;Vu~!ezOB$mcLZTzGYqU^^ zTeCYg5ocDUBsvuh+LNzY9sQjG_8}i49nZ8iF2?FXJorw{w(qIR2fzw6@~9}l8vE5e z(Sh9`62GvEtwfos2A&5V(@>TZchK}}pAt-dSG6PGbWzdrO8rIqo&!k>_OUN~W zt70l@ojiby(y;(kut{pq27XNv$Eb9NSxR40T2#sBumit#ZMMBhS5m+t8dso;a>(qX zGUB&T8K^#2;HB?_bPc}6G4kxjK#T)sh9Mx@b6Q|N=GWMJed?`EhK|H+ZYd|;2R(pe zdcs;x`y&Vlm=%fbzISR*+i4X;jXzGvC2NnOZN?Kf>dL{y*lP_A7Zo2px;--{gl64H z#87Xe_FlYPJJk8z^z*%P?T2^y^p$=Bv-_$#KQ$tM63d#2<_xUs6a+Q|8EC;i(L$*rr2E+U!q!bzgVR?47#d#$p=TBD53_3 zAmW%_*ScaEIK}=eKYpB}eRa4#4L<6B=*o=5V$@HR2%7lO^;kOekI?QN&JoA8gv$0p zVwFdO_;-_hcXdXh)sMng^J-Zs{_=IwP-& z4y(@DLSRKND~g`VjEwj=-k7$u30!72!M31POkN2R6)}o2A=L~|ULBG(QXkiY+O`Gr zoMzket5c&p5v3@*O-OHiPE#H%SMf0!K;}+7L~TpsbBa*1m)4dD>is!yYFkGygdFQp zj`aWd?b!mJd08#`!W0uh_($H=f5C1qWbO@SVvf3}Iy70ozTC2k2^}nYo+){w?fE}l$}KP3VM?X8PY#?NozOP; zjB88dlMSX@XFHY)-Qw;N=5pkZg$q9(SvYx2xexP?@BWfEuhZ+2uhYKxA44Qa zFMXTzY4Cuyhqj^@g`i-Kc$Ugqv3c72(MHSY$XvfqPZ-d7K93ymjj$7QTSRPTxS@NN7^#m5YiYlII0On^1UfU0DgT>+ zF$i#j6cr#s*e=TTtCZDp@2k|*;z!CpngAN9_wl0Xddq09ST^9GOc`h!IRH!5;3G#I zq%Dq$xpI8)kRf&91d21m)Is(fNl7iEe6IG9J(-LI74(_LZ7Te*HhkDE%DLTs=Npxz z@n<|Kx)Aq^^gLU>dyEO>LniBYY&i%`{5(tcC<(xa?3n0RdnweOa{8QHyYr1ABO)-x zbM1cCyB})-`*fZ_>etEADH>9KJKFsbVB|$J>2=LbS^=qLlb7;LTM?F}QL^TnP&asa}j<&@Maa zd?SoKkKSJ&Nuh1I&oz(fM_slf(^OMZEG@F`>SlL0H#N;nB3<3wOjvIWU~=^B4EDCz z2m9^c38Rrg=Kimr;XM=0&Njcgc?jDhXi5%}zY2RL_Whg~MMOh~HkE~|^B=uEo0ps- zG82%(?so@)q=mB(cLLxUE?F10N9Lk{vZ%vlszvUiC?EMb@B8fRNX<&$eka$D%6WXc zU~cI65s8V3=&OVIul}riXSW&lh0I!2J2G;k@-rICnw~ccD+B2!W%{Ql{4_+Ay%&wO zAH**MM0pGo;&P|=bXqdw7n@sq)lLW3+vxvt@9zg#&;>d25zJc#do@6zbKLj0Y18I3 zVX64Y>M4|3`b}}4I`Utt2BSuLvf)+czyJJUN$Vi#wlCd=yp>!5 ztR*T`ub#Lge0B|HFi;#v(Pm;UzK`&J(YkZF0K!b+^IkGX6)6)xJ)r_NbmMeaxVr5nKg^KbcWwvBi13gkFoYCfX1?-CJY9_Ha8w$B@}Q%? z$Drz@*~q_e(Nk$tzIGQ6*K-r_p`M4&iX`U1wnJYYq;s3_{?HImcWSKw!M4!O!`!w@ zj|CVf-_pT<{J3|=5YA=G^_JGvHsJ^gOr{0~jK24qg+t4|_&X(%G-61_Uq9|cWeW=} zMc2kuHrcygbvB_nUlSH)!n!JrLxJiqA8mbshMUKn3jAxaCFwVS=<0AZ<>ZSEc=o+# zKhWY!%FTPyPP^2=%jeFX6&M5oRxY^;tebdmb$xhms-58T3a*585_W$f)IdZ0Pw%{$ zXPI2T|5BubGmQKXH%VG66}ILT5@`ML=2#lU#khqd3lgYj3+voPjNgY=bGXOqDr+8v{Es7l=WefF-tbu824o8|n_8h~FavC>o9A9$o>kQ`3 zdfpOwfQ|?kV~vDi5xu3;>HGoITV&dz2EugA&YdN+!US>>?=kn}xUhF`-)d93lT(8Z zl|qdKcPFZZW+RW4ffmHC-G6hIn7iOxQLI?L)S=O`Tu2n%`t>WzftS$2_vn5^D#8h9 zVfZ$qF{oIQnWD8Tc`r-K0gTO9mpXJX8gqRMbHdJ@4Q0d5&`18-#<63cwEEIZVVa9{ z8dZpY?a)fsoxs!yYEGy#SB}#G!7{EP^0I!#-ruIn%!{k!Sq9|34o25~+_>!mA<$p%tfS^2!#)fz5r+ySAFeb;N7f}h~d zd&lMv`)_>tLS+&CQ~8IhncFLoY_ieO5{m?KAq4vpXb?LK#xO+==b zJ>1=wFbWjj3ca`|HIK!%#Ub%6bSF)?B_M{6xnn&rcG=r6mrdTWUna&UCc?*0xYYa1 zvYU9IP+IpX$iF;b&*DZy*gpbV8bZ-#wr0(XqM%7cwb5|ygz$j}#!h$%|C)W0<4#;^ zq6s^bl)di`(qW9oT+8(0EE--hQ0M{lbwm+!=wcq$k_5Lliqb=w40e`b(a>=ZZ80u& z@S5e`no1TfXTioVPS#<5?cQ_eXAbdl95=23w@RCrp@X^l59&W+gcW>Ox%Xwxb~_uJ z3Vuc2(8*n;P9FM!sLcJGmv_U9>Mx_1PlK5tpY*1WxTV@%NhvmiU0ed(ILe@{+j5hA zeSHsIYJzH^4mwaMjgH1%RD=*PY?|8hze#e&%w)%bGGfN!{5WJtHBo>-Zo7^4fm~FB zVBUdmSygIP3j7;Gy@zD={-WBQVl8grAWzRAhz*pO_WxWCsD6CYrs|BMA5wJ*78Z=O zapRC@)zY4shZ?D%jQ;*d>P&bp?;)O%i&y?+;95$H}yCHk_?dyqBhKw2XvEmLOxe+DJ$+xRvEi?8z<@3y z{H_W6fR!$1&dqVi)NabI+@Rot?{-R)>tnJe^fT;J0GKvvNYm?rpKY3gYc5jb2rv zI5l?*NR{xL|0`SMyo?IW&D)e}B7-xd@p_#DGB*4FU@YpE0cyOeZ>K>y)5_jB zbM1x=Aq?9MTAHk~h9?udbEin`LO3W@a>($eKfZ!uhw0+yi1bE@D5UZ> zU!~m_Yx=k<+?wJ8y}Wu+@5;}dXr{HHl-Z_Tf5Z?QqfaEn_17H?ePI^$z(<-z4k~X< z-VqyD^f4L-@Tf{_H#KbRK-}$dakp47h4!5OKAIw=rPb#6V-;R>{tb-n@E?EB3-;T| zgx~zUncGp$Zo(BG*wYV0Pv;;$Y1_O4h*QjMuTDaP*hX6YPsG% z_~u@60EJlwLueCn%c9|_~FJ@^9ERkAXQ+p8hQT>!W1mAof?h|aDCIl+Ins6ZFB3oAD{_Bp_fJ@ z&KV>L2N9!AuIh}7km%Snx-F}%tG)|9Yh8eCZeVePma_4V#f8Ps$JFyiqX#N9cNbVa687T-(u!@Bm z-o(I&u(T%DxP=@98`6>*gY+C4Bg58!-mxwBws?*l2et@&y!+|J`NA)-yzmQb@3WU6 zX+fnS$>G6S&-w>hk*WR`&!0A_SktC^+trE;??eCfGNpho;!3b%>FM4tm=KgE)3B(s z96~h{15qvw>vz$%d-o2_P1d45Jaq0)T=Mh$Ijhah!}1TNRInY)InSYW>`rvjKY^7} z<(aQ|x8V^HqrupC8e#YGN-X;D?Av_<-rz~HZb(DlnlJ29c%U|o8*>25g6{i%J=(HG zixbRyObmRrb;ri@!{hht3E#Fd`Nw~&(}JqX+&HfCi_Sfb#pOZ_hy9sf zao;nmF97T77Wn61{+puT&}n10uerxavS(XYYSSN2UA@Zd*RSKmH|W`e{ajLlK<|;Q zsP>phJ)%Kyq?%EGLvea`UQVgH_kE1U`U?p`MP~T<1tOEwvT3LcC1y>DURWLCDq`P!87cmIOz^=2z z3_cGGO$&XsB)$$Wn&RH3X;ZB)*4{@j_z6#wq?6{>={^xUv|H_bgBlO`k}Fp)3oLkC z@#`*n6cmcMRK1HE=>rWOZ-b#9v>S%=w14&k4IMUZGBv1~MXY2o^A*8CzDXNJ&8fk> zsNiy$0rSzWFgs0nH`wDi$#E1^Bl|;%Ua-ryPB6akFCV^sy)|4nu6PeBb^4eRNE9Vb z0Lj_&?&IuN$OIKBkDE9i?;Uh;r@Y?pE5~JK|t;QdyV{X1U zGbk%ArT<*FDoyOmrtc27Ozg^23i;O6yDe{;|NB4X0|{HN7JPU!BIV`d%*-G-Kelzs zhBbfksmnvIK>TP5!^B?gcP({3_NM5ubXv-`{|_JX{~@VNYtp&16*@}R3(3!h6g7rV z%RM8(C$ad%xpSucuNbq13up<6H>XpJZukD%mp444qSUX_7ekjTi~l*ltQ(=rYBm30 z_0q)swHsCI0ydy0a{`vq>e`dvON$y#5_~7f^LY3y$fL@&a_cn8uUeHjXFF$F!v=fbBk6yiYT}o$g$dN8WYT#e> zfv_n988K{uMb&(sm_!jfBCAyHvX2ETLb3`5hP7V5+CTJ(4TF|KG%x;lal8oL1@rEU z7=QsebO~UsRE9gukd45y7Hig0Z3V9CHf_;^eW4*b|G%^UQdflTM0%11pE5D6IQtJ6 zA_9sU&)Jg(0wVS(l_h?eZ%&K=?Cf^d!@B$uz zTA!ih9W`o{0U;X+!mRN5zOd!_E*3OQgNjMOwe#J>e{kBzoJ^<+bQwwK+}$feW|cuR z0^z8P!ath!)-iSzEi_VsQ=nGcl9zsE5?Caz;(D4QaEIydUk!Q1iev)jVz^&^H(D}v zv0juJzyZOcxFoXve)wd4J2*b3p@eF zEg{qiE`63!kmkOaX_70Gh@Kkp|5%9S;KQ;vCJjzX1La=IfCn=^^=( zA`hb6E)C#8=AT*=|HoR@OO$L@hzA$*oG9Q9i6g$zLUIhYx0T>X4Lqt_0G z9f^r9qdU~7p)3ZgkJwTBEzo;+`W5u zZWm4%K|1@w@-p0qG;nscDFAg8Hd-b&bxv=hK`9-tH57yJs-Ta!zWfH9kG^|Lrqj}G zQ|DBxU3=u@v_9awC~a-NF5RJ^t4ChsUz%VLX!wW=pB_|KpO`qW&$+a}erZOfC@m^W z;8rsXtYc#nvb;VedYd}=y+fTB)3HiV%$e)@Zo~5BW|vF+IRv7Ro-wN_3nwu<`-V-O zGsN4wS>dbrT%NdQx|TMMQVd?*%}*$Tq;)8V0JuH3HeS;?58!>k;?Q`izH4Re8_~tl;Pz>kk}~_{`oedVHPWL# zgUqZ7#+4&x&u+u2y7JJWI3?N1muadFy$`5++CTfDiE2S00$IvKtP5Ah0{25=C}O0Oa?M2imSGtzT?Ua=k6l+5Mst43lQcbg)0`Gvb4#< z+uc}J&w7iNKvH}g8AyA0&JEg`WZSrL8bZ9qN;7mZX|YL;*|;9S_m576LjHNqNW}=Z3}%=1qag`l^ABg({>~eiCMO6nJJ{TnQ49S zsPm#XuJ1z^s9;6(b`jf?Ox>NZm(~7y>Obh5A@qor zYS*~25*1}(H)@i?m$>BN7XCG2%?XBL*$B~+4zKBpwM!~(pE#+6q!q`p_aM|RVM8O* z)F6d~SXD_aXTG}K0+4IQG|RQg|7CjYt$TuEA=yB)-DHISM&k7lg5%jgfoMnn@IzV3 zw5S-@dU<|n=i!o!;eyMopW$!p?)66#7)U%KjkR=k;TP4`-*6o?Xepr0tZA0+A-igL z!$xuWu&f#^azYntq~G7I;SE?e1`ZJIY|H+A`S)t*-(z^Qj2lO`F|!H^zd%5R1I9U6 zM6jeT8~JnEdCAP^n`Y{dSDNxIj>w`QG1a%v z`^<;SnoCkK66}*L>)A4SBl`k4ubg7280Ma>dc#Z{O5IsVm<%LG)6K}}jI4>*_S^0I zZ(X^PN?Wh3_tt6c-bab#4(nfx1IEY><=mPg-uj4!m0VsPWRg=J3^mu#d>Og*K@oeq97EsNeuyCDI`^4#erVbd&!18Ne5f0**r&D!&PfEt|NtQ+cM-#AbuPdWssbnV&_pC7h&c)C9uDi2oT3V#o%U zw|4w+;T>vj(}!n=Y`~KvCOX>jY8XFk+lljMLhh$hnTq8gcL#=SjI8}9;edLELPjGg z5wFp$4#v@zm}p|4YGPJ;7&0)erQ!l#i9T|}&I@k<(qiGjWX7b^o#CJ@l~UJPdkh&;`r+9-ay5I*6;yK!!;MMwY7L() zEb4UaTAL!gJF%9;U}k`G&2^G<(`ZPOls1&P(lhM;+D5B?x${SNQaTD$5ofPg-L(lM zY>}}b#{?Oz7gOI8j|~Q{Z+ol-*dg*|dpeP*J53i?b14XFJ)G`v014!-q02E)CrWT5 zbAOB+V;gTN{Rjl+FzAJht!TmfEIi8p!w1U2P`X*rT*<|$(B~%Du!Ee)>fJ*H2}|yI zp@qhs?TsyGvvy0fN4fVxFP+?ZXJXyS(`nEhy=NgV?p@u*jqrN}w8dssSEHuAwL(=Xe))#|?g%cdP-_bnvNn6sUo zI#WYMx;g&$Q=pGIXB%ywKD!m=HYkF>6X?_&kPrbf51Bk-#BsH8LSo|DZwKb14SW8z zpou*-Jpk6aZ^`lT&Y!=?Z;wKZLAYSopWhq}eskP ztIco>UzvyF#xYPl3GTiOhPyU)LD!K)kI3U50%3X2u;0rGQ&Ig!bBNyr2GiZLCg!3;-eT29T&Sg-eUu8)w#IN=2wujv}|oA_p6a z;=|4zj`|svIkZ@c;9|_=_b|wibR}|FLu-F00`>lf{DX{)9@_a_N4WfBSspUfQw#kdX~TBsUhdM!B_zvBu*rYIkpjH)Xq{Oe*}T&^{s-^|9?bus6@$`2iCs#+ z<(TgKtCSo1e|asZ5o!W96!k8J(b@xv#(Zs%U}>q$dP`rw#NE49!%%yThKEzN<=Bub zVc{o-O*<-8SSn*#f?zM^$;^t9+m$iDYNjg|3sy#P`|huaz8<6v_<=ImkoPt{P8El> z*HlobAeJ3I__&FPa-N>vRl6HKEc@SX6?;J<%Q*YyC>GqnnK9?w}&TaBCtk!?y& z&bjmFDR4%-JVs=|aOqXh{$U`(;GOlqzGMFd;*(GAK0^z~B9G&?$=!O|*F$c2iX8)aSs>yBPOqW27wMNH1*l~4M<_64ZF;N&gH z=OXADnj=kR7@KBn- z?8K|QiVg#w@mV9Y8b&lrfLDt!sEs(UHQy8e+F4ewgVN(!mp7D$nn4e5*;)%-19tCj z+;G6Q6?~4Uk@5GT;ABMX_N_5g1I-oY5TekEli40zcuZi;(c@~SrM zx%qYKKU!aeHtBJz)v(7agZMAzuih)19U~H(+40z(HY~n_{-kY}5P}LH`hJed*I>1J zlntUO>AdRkUIFs`bM2ob>ZMknCQ)LDd?g z{!N?(U5G62I0aCI3pZ&+&VQ48Np1#k*k#@F0T_BF>uU zgJL;?aNmc#W%Im{XUoFDz|c$-Pr zBup!`v4OJ&W!;BP!L-`X&?NTfZ=$YXh$4J|!X=_dVgaRHE2?b}c*j0{UdDM7f+0RF zmf({ZCRkqr-wur$Si9HcQ9dIs%U6yHvyuMrq#KEq5p{@M)8)MuD)F&((RQf?ZQFLx zoTZU*G_D+0{;K+%B^Dc&EGYu6Bo$o2rxXYZ@?q06;qJ*&g>)u6BvTh8eN|JYtjOxX zjKp>L@C$uViNgm8ptOU$9+!O(^Enx~QiY_U(SDS1JdwF{Q3H+0_PJ9yQiFe+vT=C& zamLx=!|_QaqJ8f4DZ-)vU*>03EUkUR=R6I{>cFI>6kJub zR&?ObI{_KU@-b^x)-CvfEg_OkoJ^l@#i{6lVx7WdDKSa`nR=)co&D(1QTLvupyqRq zoOm4Rph_vYLeNKO5Q-344y~U|xz>|z30UySCd`hU?ZA+%{YpiDCe4}>wjdDD$A|TW z%dDNbS}!)d;p<^ThTPaR_CEd-u(iJ>UvAf;#oVXI(Dfh7wHsYftGq$O0}9_rv-~2+ z(Bu_La+Wqtj9oDT7zwcC+;+W+}GV|1hK-4Vdm zhGFaK<)wrh{TVDez8tskG(;J#Tq#q#%W&F>mee;v{K$tmsV&%h_Er6b@>Alak34Kn zd1)u1I`QWE%TMs9l86W%!6c4RU_JPrXoC&)|JbQKPSqVG`zvgijLNvubvTyQ@>r*& z)BC4$WZYGR4r+auUf_UzeyukTfpUh3Ch1EHZ%8(gI$S$y2ZI{KDa5cd2@HLuyU zw6MJ;-wB&};}twY28ks_1YgRuatO9d=9pv&@B{yw_T;1AOLtL;I>hDUa+ElhcUY@nGvC$}9?r z??S@-9ErZSt~&o|_n(xX8Ef&|lsii}hmRH(e5p;KDe37s)nunLPfzP2-@pE6Il3RM z+5C97~C5N!xiZM$F2@+j-ZrsRW zBt?oNl_`Xm#msBj`jOctwwVC9L`(W?;fWDJ7iff0%N6zif}} z?vw_xXqJfqkEx*7Pr4*xGBhjfM`b?A2pw{Y8YOfpVlfh+Sq8B7`C+4Rw%$ST|9Y|+ z871fy(H>a`{Q*y1XMkiNx;+D{C*|W>suG}G038(5R{KDSHaR`hL9|XW^pMYlJTa%w zm=Wf7cS2=0PZ4aykPre%t=fZ)HT?B!Eq%t1KXZO+sB<%&Lg}W|bb;?Hv78_P$<~mp zH4(E{?OlOr6y?*#luIb9C|a(AY?C1upUVx-r|0a8T{i?l8Ago#@t$Qeze9hJ-Zg{j zg0YrtV6GltxKIw({8+i!S6&+wQoSo_ZK3>xQT`slGJwQgdLJ#OS(PTK$U#ZcrXczB zm+va&-Ks#R1wu^iQB+x$H4-vHOVkBu;SP9Gl zYC^-)Eox&({0u$!u1xhAGE~+Ec*x7trt$Zg#GHcYtx*u>Dq~PiQGGm199s}YcY$eR zH(*rBim9bi@wFH|!N}<0q0>7;tyDZf{=vlf7?`BB=VhBc<%V!3F(tX-FD&)6cbg$3R0|MaaGyJwp8hYSXNR zO}?o&ZXCL5+13e$0JpK@0cPF9*?~rfqd5u+k+gziM$&09!@{q5mSo9b+LaOoMXQ8W z5VMTO_?WMwqzbNH;X6(N_tRYQ7c4`6|5nWn5YdUaBY*4*7c}l5 z!h%q0GY7;kYao3NMrX9VK^$lXa09ZkRxZUJg%RpO)yKw~Hdb$hu&nYw$QB3~il?C}e*9*@Q3a#gZU%Yrh zBrakQ8Uu7>*!F7dNTLaboiS`b5X$@Q80Z`IR>UgCHN}MqvOeV6X6Qrgr+Ry>&nKoK z;zz)KpyrheviIlX4UVP&YvHzIvhdrdMs$5whE{uV6EGrK_woZ ztriaOV;JD_=U=VbGDOp|L4dB6ec0eF61wj zkH8GLIP&bl1a0uHM>JwaD@PJuw$}AXTBe~x(%}k6yNw$c=l9bba43y4X|dAb4PAFN@a?4cKoa|2RD$w?L!vg=vPju;H!IGYyvBt z^Zui~xQs4M3D+Q^&8YjDFrM*`0+8erRJ&4*$ld`qJVirw7p zPipM};Y;z5iy;LNU@zL{$xW~JGv?JeZ)SUl`F76Q7SRQ8isk*zkVzUl+_i@P=~zy; z79j#CfUHjtb_f3Q%dEZgCpa&l-6@3yfp$JnCrQP})5@VZo7umNTT!O3F02|ery*h; zQrHd-WDC-b+&{(7R(coZAZzeSevrU|gJ|`nll?wl3%PPIT;hcT$Tid-$=DXOhU$JX zLA*3`XK%W+@SFT#;fSQE9^~C6@RSfJE~Y!tx*S5Y7QVhodAI7y)$nDg!uhjuSJ$MC zsU7H3C5QFbq!ZGDFZhj&bhS`M-e*$#&oCdas` z_u7kJ$erXD9p?+L-?y(Bj}|A%l`s!PNqF#Jdt?h+00(n^;SAh3H93$54jU_CMG$k| z0zIk5^e7C)3SnI8a|UD3LrU=)HA+?P<`uU7;jTy4#A5QK($Un4O4t+lxRm2yTRNw9EP%q^mW$$~)n1u~R90hUn6*m1X$y#%fTpg;y{c(Gj|fzUQv zV7V_YM_f3?KZOQ>aV|zL0yM=?qK56pR9axOM6CWtlE(oj3J%GAabx-BVWx7UWcDjl zR>m&|z6N?s=ys*7RysdFkg~=dx5g#hQgt67{%X7lF-u{H$f5GH zO$N6M#B+lXN9D`jAqR06@j2!HnzK-mDq8ME2*TZ2A08Xd>yR@!wzWc7o+6l$qZThy zsX!X_!kgV>?P6|yp$`|RJx)WXE;cQG1$Me-Yc%SN7y{Vo2;?ZcZLRzL~ ze)M#`X~T-4*^DOZwrNwDDGxmK31Riuw&IIAh}}jndxkJUC1O7aTJ2jQG5juMzjl-$P&zw2{GUgZ(XY+GrO_)=>24rUd zir6nF^#`|H7*M|l2f|Opc(4J#GSOZQsR|kQaNHSRJ*K}UJqN07y&7MO?{R{uzdSyk zKV8oe;HcSQ+9UP6gk%~J5sROkSzkB^a|zB2svga_G=>ERg_jxv7vYR|i=oF0TMg#s z%&9|D#~X~8I`tC*#;6$EvK$h*P{W!!BWd~Q(I+bQu3wbz>7n}g`Lzb}JtnT&$I%Gs zQbS)udZTfiF12v(iPsG>TH-c{VF&>-NJbaDAAMueto9rbj+q57M+95SDm1=3v%28xyLZteAlvJasOvq3)f}f8CgXZ zj#RLa0P6WQi7pXJI&c{@NHg%!ym$`UGn81V%KtQT1WyYD04m0aO7LsS-u~-iQ;N^k z2nVN5O&G-wF1!(3aNSMDP^F~V)+h6D!(DjYq&JcxcyzRb>KVeg6kV!%pzg2#VQqpe z)G1B^sK4Z`0Z7iOw0>EGUD7zR5?OHpsGP(pzyhOcFBao zP-s9_!mTMb=*S69&@;;*P$O|T3o@*@f(kh{E>4=#Q~3qy_qPV8&v{c&dtv`U)?vlX z51MUqigmA8wtH;+-uIb<$Bf+F*y%lmbqqH?=xw|lmQ^rxn)h)HQAV{9M5s$V1ps4LhxIl- zFn%_Yy>xW(bz<^==U!4cwMh#yzE*lJ+-YJ?s zg-U2Ek-^rgcd4xnPcOG=h4;Hj>@z2e&4iIEpF4m_ae4y~(h%0m!(?y-mvgDJwL;K` z7&4<4R$U1M1RR^)6xx> zUKJgmIpF&S#>*vlr>JEnA}oUrGmhnLap4{z(YOG~ z!Wv!vDJe7 zIs9rq!uMD@KG6`a&eJ2z1;Vmz*rp71$S4X*(2ZVI`d4_oP(Lw{D~V#UZfE=GKLIV$ zX(FGKIJd3tI`?fLBbhB&%aAZG(Zzkte|Nb>t5%IOV;wEJQf2=8bUiM3^xYXjn}_|F zb!9OKGeyl&_x_0mygUN^E_$w%cb7UQ6|AaIu$^Dm7X?IvR;>p5dmF>xNNxlmD+*`k z?`O5Tvc;Yn8yAD^AP_Uz9K*Bg({X2o2Cq@3KcQT~j359CA#T~fbn>#Cj2WTx9gw1_)pk>|M5tp8>ClH^2MgK=~Cimsn^utGvxU>A(z>Q$gl;l1mZ#_ob z_?NWuO!N?DQ@>$DJMNm(+2ebn`-%^e;2Ko~OwFa2x&I5w-1?Lp+#hPGJx2@Re8Z7& z!-{4ZC8l}Jntcf~7JcoE&k=oMRj>j;`z>EqHlLKGhgSc@M1{7869>Yvc1iC%FHYGV z_~6vbFpky{HT2g{7rs-Jd;^dA&yMpc)%g&&3AVF5I?rCZw3C1U0H~l(bTfi5B*Snc zM|(IVR#-+fehftR3}qvT=QSWzSl|O_A*DgzJ7`@*#A&<`KH}RLv1)gu@Adc-pgHPL zY3vqXn-S=`La$g;0M?7SIxM}?gYnRKekRpU(aZN^A-Y{-4L9g~s!D!dWW0OavXi}o zT)p`VpJ*>-XblxQ_0F9Rw_IDm*#s7H?$^(=dEB?R+1V1v!?8}9nduUjQ7WpGUT~ex z_K!UY4l5x8PxKmi?$V|4aGIDuVPEL%|Nd3NbjP}WG$Zpxb0UI{;~3rj!Yxe(R>!>5 zwfWWsFX;k!J`;d=L5l}JZ|@lH5b<=}AAL)YHn3Ps;K@bY`V;&tqIv=SY4O&tn{yI8 z>{?eZ%cM!$#F#2`UJ0qxeH)1uc3TFQ8rOOL=WrqjwhMhZT(GIeGMPs~>bt-8oSQ{w z;SNjstlWRk_UpcFxCzg=n3U~4)28kBt6$e;r5>gr=dJ~x%2P(TIxM?Vi8c;x=PYbP zNy*!O9vCaqco2gPh#lSW<+ljie({Hm#o6kEohL z(Z-&PI%#K&QL=boxcX$s;nZKgTEf#k6ttYSi*xUh;9eXx6jpu*nebh^mektE%*_rm zNIJhYboJ_I0ObtE*KOOjYUtjZwPLPLbWQM>+odbvm50VSYv9nZva1-DQNlE+Uq6tk zjIC9#5chhW?N2^rX|6loh*FJGMk3SG5~xcn zlln_t1}5VHiARkcdy1$uZf?#zx~~+!gB#_EjT8KcE; z*TXKNzr_YVtZ_uYue3%k`86F2EIvHgPNRX?CAs1A56}OSdpy>}_Osf4!~geaw{#A+ z*aiTYa&#DyG`4r?MwJ|gymhHt<~~ZM1doJ@4I8@eULM)1Wy@=!C!Io)jHZ#%Gp0|M zd5;!kF%ERrC9;`&sKWn2+nWc}yngNf+dSl)qfi+_DJnx`ED0%+u|djErVeE+88THA zDMggbG;GNfkukHRa+D4tb4A4o$(;0kt?hiC>7Uze-RJx;qGQQ^c z|BUx-u4~7ka`ibgtL<{anp!nnGoH72@#pv1(ymW&f0V#!7bL$jT>d>IWg7QSKUf8k z=w-o@VGW1qriXQ3%GdDiOZ>*y`ZLXBfr6R(#-LSflN;F*LJGEF0pB-Ywb$flTDiMa&ndw8Fm@! zaSu3&n@nIS(QKSju#HPIok{0wuUWYPu(?J$HX+kGaAmX1rmy7QTa4x_tfIQH{|LD0 z1m0V8%&M+p6IXE(a#1n%=p?h?E!;;_WiqRRVj+r9X)K>4_c#l5KtVr~(JpJb*OkW6 zp%}VvKqrR&M?NWXyP?jYkj+t!X`JrHyAo!R>%uqZR1(yCdgUJ-#VIMMmvQvL2L*ll z_D!Tt!gnVeOE9FiAFq~-+SML962N^YGZ+-~qP%?f zn{N`5B{_I@jpXGU%CA)SG26Nak||p(c?={b*2(ol0~*^pMg_1d(KNf8XA@ ztUNVeC^-aZH}2f2h~B7WFf3{I%e_KN(-8oG+Md}V`O`^(`@m;5n8H-DUH!bRsJ|VKd2G=eXRzFK7H%yL(+nhtq~U1j7A#)e?cSpRnLEy&5Jnf^1E*6nvbm0KMe<%@FHfgp%g4JXbM3rV39ny1*Vmo(m)6N z;WTG%yMe2A0of|L)3uI{`fFZE^@#3L+zTZtHn&ec@lq7XtnQw1{bO02!SZpVw~H*FZ`L`}2$E@|?z%UbQ*m zd}7ueJ?TW|ASY)ORVUUu@&^ph7N`3%wCQwQ{Ca+0b+m>(vfD|+Cn30)7)HVqXUndt z|JT^im>;aQ!him`1!+%uA-1vUr*vI|8@E_m6y8_%c%94Sf7H;>>*Ei*Tp_{O#z&EI z?I-LoW6AOOG#fK2^oB_`OLLj3_j?Uudb+N>iDi{3Kl2!d`R=pOJg@|PIGqK@1V33;P>*0 zLh|&%RW2kxkO?y=c#?bdeMb`DK{dMqQQAm6xM^9Vuea;F3wBH;%mhrFsl5(}sB!R;~tqM~bCtp*8L7TfVay6^6cn zfvEV(tI-O{)XX#-(1R_%efygDR((IH@1LQp_~Ph5qR*n! zhuP2hV?$7`mYnB*8ZLBe9Dn9n0F+e+; zLF+>VE_#$ke)CdJQT`JwlUz@{8qpK+yFjP}BK$~off2~gzMd^I9(S;=#qv5kVo=Ka zu#p&uaFw%W(SH3pu)*nDc>d7ZCtn%=;!D@Dq-#DqHlY zhetZ_)!xnrvQ}^IQtEBpPhQJttT%Jyr`gFDKIe&LK*P(N%X^m7!M#CxTJFrkqT%JjZyb`>shr)G zMSvc`gIH5<2h6llx9B|UXXf93Z{ZC$)6~3i#=A(+XRlQAXQ$Mbr&;z#=!dwbU?PYE zk2->y1;|wE#|UBA`?1>G3O9W}Ut5b5yrt^6RJUKh zGcG-w2Xh+8?xV6PfgvTv6-G;p+Yzf}f}E2AIGF5*ri7~uvhf+E+3hgJkz>``JC7di zq;HU>giMm?OeE9eo-g)s?r(ha)jqRk+lz{dDp#bDp*tOPTF)s>1dPAnk4gX#)gCcO z%$^ST_EKzZarY^IIyiY*n&oUJ zU~y7&Ue|j6tmbD7rGF()L*!=VUcS6l35#S@=04#YlRFSYv8p*PeFVj&6Bb(0&rHLTrArvr_u2E}eQ`sN;wQ)G4| zrgo5m;)YIPENxy=1=yHB9lJrhhs@HpCm~^|k7O#yKws{hf03@62!sbkGR%G+>bElJ;k(2`W#2hLIGw}vB)%&ZZ@sZgECHHAgs6F5`8OP-ld$zD}Dx-VO z>a|155_#pjN$K}kbTwHedE*Se_3t+4k=z5+8L-mWi!1yn-mk#{)5|;)*&_LItgT;Y z7`j}7?G&WKCe^Jqth7{n`F7AhHBa_hKdXMXb3aw@d+ui;Dmsn?`@jUs1jk5$`uP50TeMb-{A_z<+ph~#E)q%pDR%%E!?QX|lZKoOh0$bWQ? zEzb^0ks!zi(`bT#`z4>|{n*k0%IRugYMNr3(RwukpO=0$pkhQu(OjH;n>^{PDO?R) z;&#~HS~4e;zCq&ZtxsQ4P({fw*5k)3I+t`lGFvHXm~2{JuP_&|_GQf5s89Tqrx3^n z{zyInDkPX2IzhLthua!vKJ)fY-CbF*ke8r0)3_VY(~?XTQg|$Dt@(}@Y4OeCnxvdQ zJ(GCH(1tC&XWvg-F#JW_tb&t93UmM_cPr|-hIg8&w+fQ`!+;xCudXIBj3wA|tm5=I z_u}%iKOE6SQSfm(9xjZ<7^sOn`75WKJ%9=pu(OFqp)7V37WXqfjS~7gO`0YwFylV;5w@;6P zAySklz6>7lG?(ozV@(0D(7}A0SG=uqVc^XExBK_&cMS0kJdEVcWrU>`#!iO!=P138 zq}@dM<%vfchf-acrA1WwfDhHhyu=SOtCn*L6?NA8y*0_i=;VUMx7!Tr-~aryzZ{)} z0+Sxf_ZnZVN*i;!bXQ1o3BW6HrTF}e`S!(!>u!D4J=XsqbG6y7ncL-v=E(?-JkiL8?j}=RT=MRH^A_08v8jJ7hwG|nOB`XSF z|MSleyNsB6^DlhTM5gOEdWt&-tZbCjj0o-;5s!5J9VVIOs!fr!YGgX+HMH?B)Qmh1k-THtj8TIHfGNr*kOqC=1WCC3Vxyq z`|x=7Fd&72*ZuCSYhx#64KYf(MGc z+4#o~3Ki+HmU@gwzMgg5v*#9Y0&nb@`3>pU!4z;GySSd$LP|TD_zz!h{k@z#95Hcm z)l%Rp?E1YkXAB+D1aLw)h@0yn_9rBq7?!c7ApVjaD zn?XLX4TwhQ-FDO0-qSNLq2yJH*qO83kcHTB1f^D*g-#@{n^ZE^Swu7VBZrkN+XCG( z3UTFGgaVz*S6Jc@i${c*mVG1XzQ(8deJE~38YwUbhru4{T(<$fBhBLPTEZE!8@ruqlYZs?{jH?8S-Cx8mpHX5o2^-&W-387#+0me z;_3ZI=$Qa%Jj#Vn`l2)9^imsyOY5GASCaP4Etzrh*fDvQCuiMRO}bFr z>hq@jO5XQP(CCFvfIkgrFu#TD95Gr3nHBh@bXy_=fB)DgzGlv86jXqNuXa9r@}&A~ z6(jF%W8{(QP9yWzv+4B_UPgn zBIoRNy)>P8@?-|D$or)aGIc^YAQ(}Bgy;?wQ@O*7dgJo^{ol_q`UrGGdFk@JBBajHIsKFgOc0|T7 z;?;$Q7xdBvqS3W(c@IVj)BytD9;Bq+G{LE?GjGoBc5@(0CxY}b#;TQW=zjg0J&VdG z+)1@qJ80GZZ?iFWfm){ZG>qA+0wJYEH8-)+Hr$@kzRB?6KL@-iNR6BA*Cg=A$jX^s zUUrns56>=4zk6o884HV6uSLy`$`KJH;{4oiv4R#Tn*HR+{tKcK>mZHZ` zo`8Wp-#G9mNNrOSh^k-DBGDm9n%5fq-sXS)JV;bGAcG`WQ4xC97ytd3riiPyY~6YT ziQ51A@!ca6wWxMgfLC+f4+g(JJV*iOF=Clp(C96{ZpTVN%FuOyiB%%$4$q^ZZotg+ z5u7xX8V+2QpQ*ihKvR!T+&|0hMvd|ScIT{+Hb`VU@n3UX=lMOe z%Y*xDQT-G)OMAewlZ69|e$3-9y58CAzY3?HM$A7ier%NnHfYsw_dU9&D8?J9sX3a5 z8q)DIRq7##d`w|)ay}uH5;k@X+6X(&KM z?WByLrn&x-5MjCtH9W1Tu_Tsu85$lGsQD!0pn!wQ49zo`w>D^*d(dcEjj?}&N&)iVcHmlO zSlGgLepv1}L@72(2dt|Se4FofWTGa$2KMQfMQRP|AKI4O4)$1f;-p_6O2ZFK zfk)}Kh)8}fS$;3uHF(t8h(?ovNt;@g5UTFrTx{g#8^V3A?3k^8X&2`rL<0+Qx9WFH zI7XXlYVP;lU^-^m#2{UvQoC%Kq@X5y31-AS>S@V?SQbmH0Gp7@RmMlTa<@%>-K|jo zy#@<~K%Cw%GE5f61+hzpNdPg~mS>;UrfjbI+M!#AciYCZ6c331{>8DeR`oaoJf4m) z+VWI$Y6!QmI%z_xG3}!E=4+dtrB)8gZQXUk$xVJHN^7n!Hp{i6>oPaDqi_?C*>Gjr zR>i6lbUyd70J|WRIZ+YQa(TkqDZ4y2D@$I!M9f2Lr#d;dGfQBCoNgc2A{U{4eyM`q zdf0L|S+!j|o_)^mDiXfflr~r0b3C;bhxm}S5zz{6!Xua`v%oApY-EAfIWQ^RAUA5^ z$l+D#w#z13yLQOd*P5iXi&iZC`aTx&^y@&?SJ~bEGn&m@X1I(8Z0)KqzXezVDAb9b z0=B^{Id^h9)29Xu@;Oy9k1@n64m z;`s5%JzBAjs&yfZsfhC{-5L+)kuB{rVt+*Xcmmmj$M#~YlvAg+sy-ILK6VLP?JG$$xdaN1(Y!BRpDiAn)p807 zb`Vo}sj;df_8o6xpFf_(PJxs>1qTOu9NnBqP zK=g^bwS!&KK=dl z;Q-*m6LWbYo>ou==00o@EdQ_nj~>WT>Y2OoI1SV2#jPdL4xCmZ{_Qtcsq8!%Whlln z>FG9A1-c&)0p%LVHul{Eln2Jr6LBps|2tHRcprPsvga5AXaA=*LejjE~z1Na1 zIq+;f#4yz1L{BDhebc&6a@`7*6%z*EPGE@n7y| z)|}EHXVmU7tuA*DzG3-g=pb1Ft02NoExx!GE?v_U1v5in`{t)ks$y#e9Zx_ds>Qg9qClJaB2%Yt|sP1;~H;s?oYxn~^&UsLWC& zH$_BJJIDF$a!Pmw9FiQZc(d1$$VYz4VPHe-mK<<%#iyZY&8bqI$w5^4g0BNX2JXJ_X8V!t(<#=ayF%h-Y+ zl$^uIjLGk2yom|fyhAcpB?3Gi2V6-IOf7kM0!U%O&hYS2v6mX?Hd#M3ULFUY+dk-6 zFba?^QK(AFx&CAgjLvX7&<6vGOb(<}cX-CO9KG;@L(!c2Jb~LhF!A)!k9i6#1e^}L z*#GCy9ym~b>T*^Ft&yDudg z5kc_bxIy>^T&Rsw=wTI$w5m241Y&Az4#u`jKdaBhX_+0f}LySyea`2p55t>n9k#Wr`V8^ z6AE806=V`n=4RrBtNT2AwF2+GMtmdqz9f?KYQ!xKc$|owC>$7bQag1@)cfsCFSH9F zNs{d@cL!^9|GEoD2~rBtkFO66mFaV|kUU}d6je|X7%AnIMcu3f`{nf^xB)~dIcHK_@d&%Hw} z=F-P-c41bdOERz84 z%#yjuz&)`H6HD0`X6T4Yjq=z7^@BV-td!Y`qR?P+A06^G$!cHGZPD$0i`b5}E7+~3 zq&$`@zvm`7b|L^rN4U%Y1V#~%LR{mlRwCX}u=E1A6e@iOEPWq9b^HOTq$t&>UfzHC zlE^xun75uea9r0$>?5Q_Awm*rY7Rw3FQ_sN;IV9gpP#DOTkcUlZ<_r^s{uGwVvI`3F8m68U+W7R-Wh20!6vUu9ozh2? zNybN^BLFf_X;FfHsqKiUm zFqIjN9O02b{9M73HQV0U??OU|8eFe?_f0G|v2J@0eyX{&g^fWqk``G|#HNRL9ZNT) z^i)i4Xg`D2cM-sX7l~Aa0T5XjDJCrNt0zg&+b27b)J2oF2}yu7zKFRDPdPS@3R zkl;|G?nZ2DiVTMtxuYPI5*B2x0rTSi|AfRr)wLG5tP(keWa7&B3r;ImSfDC&IP)t# zDO9pCK-#Z?deNVcJFg5y!~Mi=*x$<`i|I~@=8+EFpnV0!D?N3{dqD0?dt_71AaZ4MZt2Uz0{Nhw*1eubEOcFbjx(s8S)D@NyB@j4f*#> zTIp%{-+>xvJ(ZxLPWE%cLr*-jDf(r+rvQ**)yD3#8`iA$($7XK*v8+WKey8-OK}zt z2EjY)Pfs*mJFCUPA5{(K%4Oj4sEFu9_RT@3q&O;G6|xhVES^CxUJRzTL-8QuE4H<< zPWC=Cs4M=|@>_8;9$z>pDk!i(Uz32OIoMvZ>ao6yAn!oX|Lzm7j<8a}Yw+|xbxum! zFezz^%Mc5YL#w0>99)@Ak2@qdBxyrF037KX8;63;scUH^`gXA&D*fh=)tGubWHbYU z2fJaW+MKWi;g&>Ak;E@9-;o#TLuXtcZt=g6pd9&qShF}3flLt@wAIr2CNhp2L^qUi ziNHjU)BGIEY~wb3gMqH7tc*uq0n70E)7r3eS+E~8$1tQE~49 zqPb6IQN~acBIFG0meTJZC48W#Af8NK?mlJ8bG5x&xDX&C1704rga0Jo7VN=%c>SYW%*Q1bG3nk^isi9#u%=4mVX&9NU)SJ*UH_5u2 z|0N)!#JJm%Ue&rRd_5AIEI50IxP_DG8I)2^0g-sKavLY!aW;cq3r$p9^aeCWR!&da z&UfDIw!1qA7z2Hm(ZT|2rbl(nRRkc~+|5@Cj}!I4%M46Ex(axP!BM5{fJ9_-49aJt!Be{fB)$D6Z|GR>)WkXsM`TPC-bLT+=GXY9!;M! z=kU!pgNI-E^4# zAQevtOe<;XF`mF6E#+9lD_6bUysWEXW989L?BVDvpL>2X5ei{P z=X*Ajf(#9#_n*mdY#2L)eKZt3Na)H3n_|&8;SmzZq02DP91XK|F)>h=X{Tg5 z%*71W?7$_@&>*^bn(UwnNMr-ZJP|@zdZo4^9oV&N7x-};3bf>2b@4P<&n8!K_)pHS zqcK8q6gsbzq)x%}lJ^bW*me}JNmzWlf7-H~{U4J*ag5&qlU&7UP2>q<|0%z83vk8{a2$F2h@`y}Lt+R9+P#e28h1magaJe| z#!9ei^SrGssm~iVZ1|jfNXZqF@YiP;%X)MKrf5+}B4d4bnR?WPydk_-I|YXsqIBys5-~qZdiMBs&6E`F!IFfL zpcp#P$YW+=>grx7$f5YNh@N$4_#C*GXLure~yke zuoL;2pWW|CfCS+X43ZpLxon>OXYv^7G3R));uFg5AK94~xySMD>~UJjpP0BJU4$oi zRu0c*cN%tk-S~6I1|MkZ7ing4bzkPLn5!f3@nYr-84juW@e?N;%^kLD0k)u%yiUZR zzhynoy`?G6HX^CP*_A#-yNn*QAZldJnSjb1+ry5xL8^mw&vuR>8f&JR1>M{z>~`@q zdOt{KuxHY5-+s{XYWM3sS)yr#Nut#{g8hWDesLSNV;=~BHI05&Lt^(8Vks1(5Dn0-mSSCO*#yUy2><`^zHoyiN=%W6x{2IXH z0&i(q*4ZuwA1e`*k~7Pv@;^p8nSfy>d;e85N^5m`awXYjE^viS&xRFx@--ivXlo=1 zkW!xNZCd7LgXFZ<7YuQodfOdqTWR>ET`?P2?WN<`SZ(_0>A|pKLrTbR)TBvN?3wgI z@pq2Vw`2MBIigGL%P6y(n@obH0Lto;yP~~rlmvY}cR)Ako*;ux!T$r8_9mqlZOg~V z7^zibDH-YZ_QvIhxJ_!W3$E^gBAe9~#V(a1O*8_&T`nvFj+1^>a6zHiDP`}juTGWI ziL?NC18s($yWE}|iGw{)T$5cQrWN?P%%_WzFC8laLT+Q*Kz~a(YO=kaAIcJQ&CG^m zfZj;bEB&Qz+pGZTrQ3y!dDEs%mnrlqk2d5TKPeMY?3BzB83prMER3dgAQ|LBDoqZL zbf6lsjAvy^zcJO|Y%~F~_9^ih2PNvG?f5$ZLyR}y(bR7lgvaPHfDPP(3IP~sop2<_ea-?FGDC-U4k+1wuOL?HO?q#8vZTbVADrSAeQMHv zmUrjlTCi7?_X1Bqi&cIsa}+cL0b~2UOB-Jho zcl0~h(>^O;6rXD}{^+i}J9}{D(cSVX__pX!>}P@e$ngS-O=DRO53G#R0H9_nNdugU zk&b<*0rbps{QQkzpA$BmqN2ecZS`q|ac&Zio1kDl-w9g5yo&%IGfGMrR%7NS8UJlrc8iyGXl_;XWdv09#40DC(4McaKh< zST;+E)4^6wo=RAb1z`6ey0_Q0TV+cz9Ubx6fy986Hw9Xa+FZK3MYE#D&4uD+xr08M9vP#;=p_3-yZ;nh^!Zk{Y7fN zo1=L|({J!O5|E-=0B-`9@x2Xj*kh8V3LrtyhIUu9$mp8wGPP?^$pcurRLBeYZ z@!}W2%?_aw#*J^B`TnNLZajb9TMAMkN&z}~l`sJS8d7Hnc=on8b|1nSVziLOIzeQ4 zBP%PE3pa&2p>F?}a@voABn^WY3oNrxU(@*;X+mTg%u-vz0$hiPat#^gF2pXAB3C@|wOp$c2#W$Su+?)U*+H=SuWzj1Q;CEgIDxWUw5G<@BX^$252JEYh z{ZEmRSC)TdwKnR0zFogpF9N4ZPFhwE>e~|*w57tgPvf0`)7G!Cklm8F^QOhxPn2A2dHHyaxk3?riLQ*p?Jt`iCDp(B=o_ z$B7_y+XI|2&wEnM-pI(oK0c--@~{E#(Z7P+Bgrb_aTfHUzOV*jp=uRllV1(mNi+r; zRnG{@9Yc9(Fk)+WDs|e89+K06OMGiv0|&^uW@yZF770sZAxJdveJxs(Nv$1u0src6JP4b%GhQ?!@#`gU*Zq=$btkDP2bT&?AZ*x0DWa}#gB`y>Bs ztJplV99PSC`G@j1oUq90w8ybz;l*bqRXBy~VV8%=VqP+gA1lVze+*++#Apkp;~+@; zgbqUvT70SmyPS3RfclvGos@WZ8Hc~L>5XSlA^0=W9$oIOqByge#q!Thzy|Yb{-hh6 z89e;A!fjHfxrxaim&5w@WLAelfm5PTETrdap!ukTrT{=!dqEOFaZZ^!HIPGM;cG4X zuAJ~wKNM%mAbOU=T7=^V@0SGfQ(XfCo9~~#R!rx=Ul9FR3D-&D$1)5aCo=IrkB*MT zWKogJ62wq^h`vTFRHAq_$~T6G%PeGA*;r~l0HCV?7Gf1EmWUvPN3t*d9=rr)FT6GA zNDGQ78S>K^Zzs<#Ca=S5ep-K6!oZacI{h_q7bTgqJ3g}e`@%YZppfO!=~`IepY8LB zoO(Lz2K-OUyz!m>M$de-IV}%w@t=058E-3%$JqF(Hxe>j7g%>%fQ#5NVas z{-EYoEsE!ZsXU%IiX-T|>!cGB$CPQeGe-cq4#EnQajaMfon4B%4vF?nY^H|z`Jbch zBXSB?3+mDU&s_21hP)cTK53X}NW@K0=9f@zSYC2^8_ph{%!seLN;`HbWYmlFs= z`75BeWR23$t=y{dleQ)7H+ph_H`cLv#kR$8vD0L)9kzEE7|h?L?#7ByY(d$S!UvGB z4CX>~4viN7SPuLLux8v>P(%kui)X=6zp3VdUvR`(-}$hM&=g{|%%e4~-oNDLCS3{p zq-7m0;tfywoNSH9H+43!jv-eSWN-~QNajy4k@vLMvl$eC6-fE=p zjt3BfG8KVn7EdyR`Bg(q=4Aa6z(nXCTXc6lOAc$l<}eQo06D0xy5!zpAc1*&(|Ctp zIk;tJAngQ=bwi0-9YZqL7oxLU3z*BJ&6jXG|s^EtpTMc62+5yaLbJ$okGRB|2p^#Xp zxw`{jN*XiY<;?@4Aie)VEDtpuWdXVvaS)E3-t=CpUl)58k`t`$}peu=~>5{Kj18UA`hx~zNg0Fd9LRM^m}w_T~N@n3lLQTU77 zK7M&Y5tYusO1by%ze+b6p7iS(2jGLt8k$a!*~WBjDZ^mB8tRPbDxe4nz?w@qUD`i2 zmWd?GKg*;THm;Xy=jRvSzrNL*P9owJ=#n73>)JSJ|*OgRl;@}Fa6&&H9iH|@04wius2)R9C9TN4xN`Z~cRLSsQ{A%B zc69Sdw_kuLaEk-`jT$)^Bbz#^riQZ%pM5{GX;V`(gYSDmQKnxGsKu}wpJ#pTC*w(} zr(8^RGI;NpkCqT5lZsj#lxhayUOc$=nN`*1iM5QH;-V{D8Tl&~@i$wy(_(xyBQQ=+ zqKpyaE8NSCPp{O-lqG7EN<$+r^4{ySxbkngYBJaEU{?lRwrX{+YTT}vmEu?jkxvij-XbHWn7JMiL)igYc8)U7K2iMbM9M9)bCe6n%p>w*6yV-APRtJ2@x zg2(M319`5YOJXF^g!mE)w?EK#u4UfHhRy01o+HFEty7rNWo`Et9>Hw5jD3UG zxK-&pV)IJ^zPwz@Ui!~8BuFQ3vrYz63yhC1n%IsREwXrETk0U{zRdM}D?6(pJQJIy zg6R^4#Qf;DVWFY5B{j@67%_Z!BZ)b5h`4mC>ae-D+~hJSjA4XJu#k|rIK8rRQWI>~ z4}W~XQbNzwM8YYE?n|BMhKdDvvyzm?bKcHhF%kiU<VF*;Ufa5?&vsCv9@5BFt1~r6dG)riOo5=aMLE6NZrREldom49~s~4%`Cn>k8S|f>*a%^1R~;N^WeLCITTUHU#)< zDd=qHUt--TzJ;gZQ`a0GgUJ`0vVVrm2p`yJy{$o7mk$0vDjGmTR1{Qm*)lCpTqj8* zK}RzC>!}B5so4m4MJZ_mAa4BRi_Z=h_;?vV%%g$wciflou^X%ED#;h)I-Oruv)N@d z`TLFjKkDG_dGqGY%WMc`PIPZ}GXJ$OOFOB#JoQGLlXA$m$FZMtRxr9{?t=%{k_k#s zTMC&Qy)>m~BRxE=m(4kM=8V(1cN9?CSQ?|w7JeJu>^=${tLq6~Da`bUi1-~6W-Tyq zA%){ovQTA)>?q`752zxcmXxRSeIp|a#7CPR)fihK3Ra!*HMD08`GQ1~S-CPYU2z#! z7oaHs6K@vi^sFeO+MNs*rIP6~kZQrHxGl zt0H0irr&@2t)8On_dpeu-!5Qxk zlHP&~s%_i!G+)k+O`F>9)^K5126G`~rYhh7p)SB$uh#o%=Cc_NbSs6SgGHRc%r=O6 zXc-bjM71{dAhzU1M#s$8Pz;(lv9%cSNgIMwFNf*SGNiGWK01B@PUK!&BAZMH7wSI$ zsl_Vuqk33CMu>F)g%V>?sjk<4tP^!<%gfnh<8qfrzPkJM89p47!-di?tnZv*E7IuS56m?aV&fRsYKEdcs)QZoRj`~f{wJ|0zTyuiI zW>`Auh0WBdy0}@mdIpVU4m!i}*sX)W+U4+OD`;x>ThM-^yFu|k|7;*zoJLc5ulodc)vEWDEy& z5o5^)$()gtm3q3~#lay?c?bsB&J7}M>oWKlZ3RZIcXL6r7+I-TeNty>#r_sc?GL`W zcW50TMm$MCDP%g&q=J6$hAm=d%tFQ@Hd`WI!AGDLdOFkAetu`Ow3BDr91J1NdI~Z) zEPDTW!<|G?8E=W62S0#gx!zuL3CLE%4ybz!oJT@flEYw>&35VO02iEova6^HWzeDL z_Skc=r5VrQ_N15+vs3Bv8asCE?s)@y_H0Igf&X^dJ%3_bSf3j<^-GUfZyw+2kw%gz zvuFYhT<2Gw_M@u9RHH`~*+s9)VfdAIJto|#`06-s#me0C?~B^%Efc{`Y=^rOaIaA1 z#TzCx`t7&ijT<-S_V60DePe6a7okfc)sx3Q@=zRvP`?(Lj(R-`wLv~#>)Xk~qH zV|ws`S6>g3Nb`RJ98kzwTiXSyYM+phsM@N^Ojtoj{?#;QD$LbX8mi_xb<+psulXnnrqgFCOgQWa59c0E>qLFhzf%dfZ1b0LR@6c2X_U)@kOWpXbq? zU%`nMGVU_RIROIJiQw^@B0w@NlFqy;vidNVO1`0%0<#%4IoZBWPcb+MuE?951fjLn`0-j0(olw0_@aE9Z)ci#AbBGvJpk~j ztdeE;;pXR`9_ku$a7_mjYo|nUU}hUC0?PS7P{0AH-@vy_2b_6(AHvz=Sd{ zf;b=tAZ*%nmLE382P?iD?iSQ<-oX>)q!Z#)gn`*wgn7am@F=7R5*ZE+%T=qM^|SA7 z`1;KnMxU8j>uVJh6cBCGk(02qYr!{T`JCnPjLw=X42e{v8A>B?UKK^7YUT$-3>{c4p*$=-LCV;5NH-nH6-_yH!@bz#l(?! zq~dd#{|vv!X`YX`E5@DN;EVxa+G3X@N@{@&NYC$iYQ_~bE&McCU-AZxk&`dQ?u8Yh z0{n5KN{Ym*WQ`>C)-EpE;(!k~h;zz$=c@ip?WKOf-ZkK~yXLRlxow-cmtvHJ5w zg2Gfd`_w%@o9XEMra*c&YVwT)mksCigyhJ8qdfaOL;rm&4T0bDtReyxYcGll`j?<;?|GIDCI0K~{2z4wN=LlgNvnNk;3fKisbx z7T#){XT;KR+shWESj_WEDaVkT@S7#`V@WpZDrBq{4k=fN_%dhJY~ic4@f{tyut
S96gILBXi%BfUG zK?qGqf2l-^rBDE^e0;jy_Bh0dlizY&9eFumF%!xb!xGVm(Sd0dPzs6ww(?c-UofcX zRhDq>4IoC6b;k6LHB8=ky5jwNg#vo`^M#)3)jCAAO!2vLPIe2IJ|F50px48dD}j%u z)6O+Ntp+?@&N;E5gyWRSA=a!(WJnVaRWD-npkisYX3Nbly(^E-?TelJpnHGU4&Sgr zq2Qwqov=vIcH4S?eDv0>am)oH>BptcZ*JD>?8zrPbWBYCtn(ku zUEB7ZU$7W$B{La9sVVot^5iWflTf7fYZq`=xcjkmmAt&#UTh;REG&#K+@~VIeKMR! z@d62r%$x1L>c`UUGny5CVv^exxy?8-OoG%DvQpGBiW@*BLLW>z5s2aq8S z0%%}b*D-pB=>~eSWsK#E%!RfW zbI@|-@wBwP1!;|x=i*1dw3+<=ukHVkS-Y)RId;*40&PiX|7X@afkEMV->X{eKJ)>w zVme|OAKUn)eh0jMEYb7mls$c0@*fW4B)jTovvcRsS^wuBcJ*Os*#F;&HHnM--~Y(a zPX_%y$U4*nCc3ntml5r3r48Q&XZ-aBEh%|#o!IFI>b3RHT9^FC!Qc7gWu16ku^l#P z6nZgf++&~F{OUYl983`SwRQDXfYxj^x(RDSCRfq$rAXkh7g$CDjFl z|N17$M=-R&MgUlzzgJNw!Q+{BD`nsE<*=bL-8`hO;6~PsO_+W~D*@p0F2L;&+d-PLYB$1)BHE4MSMum5#lR~!HD$K{|#QKLGp4%byb(Ui^@p1M~_Iy8zCVFG~{`2K2KA_>8I_QHdVmG zvrMv$WMkI(^>1XL7b`A;O)y943gSOjVMG!SNySq}KE_R2PggfKs|v0^a_iQ$kj$B8 zE%`mA*~&q~FqesZfxA5?AXggsA868V2p@zI+9V2LjyO)ABah2hh$!`e*+9cT|3E`wSr31oa7gd`BI6)HYrvf?s)@THoc z{i!oMEttU0tJ_Afguy>n+VlX^1;SxW7Ql2fq?K0*_7oX)0yVgwIMwzUOtaqLA@LuQ zW2qZiQ}Kv<#an}!igoRV@j6t8N8PD&=KLsV^i}F#WCg&<2_@HoE-t9v=W0f%Z{72# zcA8-~{u(>bppWL=IkS_6T+tg$eI+xO75kLRVESrBrO&Um=+~0vWc=%?a4O&k$S>4G z;V-xnU^T;S^z`c8P;Ooya=;ntQzlj7!AUAC3J4%SMF11CEc|~?`u+fzX+&a3ZxLU- z$&ZOR$NabF%n6cu7nW^2;;VZf|3*O#d-#Yv2t7J5 zWhsy3Lr0EUdPs-R`^Oj5JMPki2Z5whtmPKKtjZPjcr%G2o#(ZHGnKtok2-!e3K@(h zHvmrx0cbIQZHR3qT&o>CHt73WaFH_44Jl2+>wl#cSK)S9S5g>55Qahw#UkOb%5c>7XIcXB zRpK<~{1r^s#gvmY@$$kM1nY+XKFV<%Yhsci8dSP`>q?GDZkjyW{Wp+gibVxRQ@>MZ ztN29CuH0lK;|@bgb$;b(n-0?deGP1C0kd`>%%)%uLx*6%zRs(S4zZg!p{8+=@m_^a z#{6F_S7j_?$kTvJ%&Um?T_<^EGJ_ZBL9E!iqopMvsDYK- z;K6}O>Z%?{ZT}*6`56_ygnC+V2Rg#)*L?A>S;y0;DBQ3pfpl8vLs00oJmDgSrwoJA zXUt!F#uDm%Z!JeeJ*KhOquU!B4ISQ71}{;1fKQyf84?&+OOAMHp-~`!U+rK0#(mdf zJL`l&swB=j8Gi99K?dIEJ)cdc?OJNE-7=+$Jw} zNCS-Xv;fd#k|M{AzOTNXUg{0vU_yI|06e^D;6wuMojm!}|Ic@z8h0qj?cC93zFYB> zW!8DXOdiO+r->&{RL-vYSXp`KqxO;|U-H|YAU^I`wcH0%P{7Dpvt~^}6NJ-vQ*#dZ z0y%yi!~{aR#bo3r+x*_c;osm?2GMbY8>{kkHOqJYO7QpJNjen@*4I$}3D(iRd?hbv z+_`}uhk!UDC5Ud@44{JNq6olq0sNr{YDcekL!KgaJ4Sz#=ud?Q8dKOwPlFY#9$9-E z&9nTg3>3`#=Ui8Y;tHfJdIV^&zLxXAi@W0)I=sHe{ga-K!Usw$L(R*KU+aXe@?7gO zI7yeT?WtvX$^-DVw?4X4gXBXN zt3Cu_nklVS!ZesZ%V#+VzXs#Qu<)wXsyel6%N#Wd%aN~KqwW#FB%<7O3fCZ-1%E z6FXWgu<0bdO8eHIRl0g{S^Zdq`uniJID6?EGnv!reP!bYq8Vzbpl`YfON#w=Vf2?z zKB>nKUUWO!$GJ5H{ZQCh*>Pkl@$B7y=iUb*`@8>Q$=Fm-$T%Q$sKvFnzK{&mTvL4w z)Lz4slzgi6JM>nF0f>Y&7M+02kj9qcCi!rU0o5~Jk)2a3+zZ7`u3%St0 zA4A%R5;1e-B^Gf2H%Bqte=Qq-S!UmBpFP+SHLb3Z?ySIUsV z3nRa{quN5GaTQo{zv?B(Iqm6r!DVdn;lm@J>sya|!@&X+)a7sG%4dI~?UO1BXKcfp zKWERT>p|ob)N9_j-;rKR;zCHjK?kMBs@vIMs_i3fWhrleOCe<3Y`7@hbVFvmL{f1X z-G&Swzv)-c`rQYg*LR>sM69&^A3-00H}VF4L8^gdf7OW5(S2zmGnbFoJqJPAnj{>MZop@b*+YlepJ*Ow+^_#mPYB(jo z2ofS~gU~MmBHNVO))hw!kufrSY$Hm8#uUA_Mw70;t@DPaD>vCyey15 zrAVf1;ebE>@T9X2rMA$fJXUW`bj9Vqs}g*LhX&TDCk!G2EooUHlA#)V$?*0m#4l0z z(}EI2U-@*dOVaze8bVa`{^aC86cV)hHL@>S#JKbtMUepZ z$lITzq?}Z`CdBp%@5pYss~ccsz0p=q%YnuK!YCr^!oYQ(^PoH6ousW+!yK0ZB@4Di zqmYbTClI#>{f2nR^Gu|Y?DuOMdP;gm{-zga=((EVNxB6F6RZ}fTnV>_Y~|#3;JSk zn`_F}0quw!*@B~my7W@@=hpOq)lrn*KhheONDU#l9E7{ZT1rKR1P2Y9Oe%-1TuTv$ zCr5%~o=Y>v*SCHUn0XKkrQTk!FZX-|?iN6{lol!J#AOc%jz@QD*W zF=EH($dDkAa4pWqHlS@A|FjEjEAOwvf?aW(y zdOxH9K~LWXTVF9U570`Eq1v*&MD%G4-bU$tg*KsX*$uQ>w0OZ5Ls35rFEV*&C=#Vi zmBq(pBUt6KWdyb4`BZ#?*j@DMbta^vThF)0|Dy#^k2Eg2sg{&R0mE#xdSrzFYMaAY z9@9wsC60)-i?}8L>&{_}z5mIV7hWh+o7 z-07xyl`m_;(x*{-w^5)gTg&^CYK~qA#GDk-F+cse4J4w=2oAN9T;NOmx-? z)l3RKKHq;4d+#}Uh;a@qF@KD*f3q1cgKh=M_h#XDjfkFJK$jyWJHQcOBGHA9Q9eTK zByAbO5`c?JHIKxfHgKgoAodW7zw6VE`$nBeOPln)29;Evo+ORbHiJ>`AUK-!_)HJs z%V*AP0OBB1g*iJYOxY*($X8##HQ}&`Vvu%H8VNW}auZ9ntxcnOT6Fpr@}u{kKBeQL zv+u}}>ZgzHp<<%uEja8X09N>xHN#&bN0qW&?xzv=BRXG{U|b1Scj9{dIza}$8nuc@ z+mzL_M~}gT1rU(0Ya^WO$iD&iLs=P|v>HT)NxyFk32|yQ%+2;?%hs(CmC@ca0>F1s z+~LDbs5FLfy2g@{BKo88AcYT(%#wv*K}WAb3OMex?&u*OS22qtki7Ld%UnbU;=u7T z5J+t9`@U_HEWq>>&;3ji?z%;sl9mXOUIC}bh*XehYp>uACJ7&SIF#g>XsSD#n#!mS z8>~r(O$0eSQgOOnk_DxxcB@u;*fes1y&Wb^5|=+tv9_X@Vqg9GRY=~uwPQwf^jJuJ zj*WB+DThVS!92Dm0*Jc+AFVFTSdySgQ;Qkj7K{3tvu3DpT@b2FdnPsAoerwg5WK7R z6+&2jddIFqTSqc4ktjT!1jTBY!1{*#Mk)%O(0_!QM7)fk=?-je*(^YG^>NfWKx+lX7u!X{dt^-d2|=J3($`!h{|CZi2)agaU|;8?dec zGK%Fh@AQAaOUpqG)QHV6%+0MWXd1PMS2qrNR_`z%BN5$G`Zd-eQ?NB!!1K78?b%P# z$bOBIPLSn_c7U4JngWA!_OiSICmHFj>9$pCv`&Xu;SI&M092phs4~P3u<6%DCJ`1V zh)@Bk56{jgX3G|t3oD)wu&IMFNQ5+%!qqw895&tCg!CQe#GKClo6d(p{gM(FU#lm=Q_Byg`e}lMUTa3gfMA#w%*%0z8a^6l- zI_pJW=UumEyV4wG?d^R$*WT8~!tX;})VU~-t`Zu^8GqBHXwz#+dXdWXka_^2_}j(C zZ)!|_u$D$us=u)6)hZ~%L~LG4=_%{oXk)V5xrDO5HBUgJmlCtA0h=I ze+mm~8;)fZh`EG`*y*h7B=pk`bT7PC!UWvN@iQxcJXlVOUc@Idkfy8eqD3B?HgVE< z!7bdiqu(KN=gu%-A#P@a&9GtlU!Q%W%Hs`Xkj=~dv;WuCnLyRNuYG(QDvnvvQ8MKy z8M>lDX42r0cB83X$Jj_Hky1EjMT4Ouq|&H59HBJen99^v88Vb1vZ?4s6uO^Z&VAo^ zy?5QU?$X`EfB%MO_&(q7^9WrGX@S8EaV`w6@ElcDs|x!DNxflZ0}jy^Y-LtI7vLW) z@4}*@O91aCR;K6E8I zwrFo>cnvSuQ-LgQ+<4Km5x+-suCCw+#l^*wQ4mosxF~LC!eD-44H%mcD@?r>Yr3Cy zpBS8Wl8H^#@%L+C7H&fxEci97%5!2^DyxzHGuhC9Zd~j&2s?#K6ZBQBEsA1wm%xGF zP}hDLi306+vIr4jb6PO*Q|U0U{UAHg(9aB)Jjl1hpmjM&pG7E2HdwFGPfzf71V^2c z7@RSvI=c`#KO^I#JB$7pLqpLiUT~H*G``;4iV2!4LYi^F?gFW0yysk*{bZ@M2bMt@ z=>tqGQD>m?z+(N)RTR|?Vqp7qT|0}Sk~#-%9GU_EcXLKR7fX}7iZDZ1b&LtVsU2Qc zU?C9qFBg3OsLwpW*21Uw5%wx1I;9cB*dT07<{0j)7SPRj`Qyp7^b_obH*1jpi0K8mteYvw#!_&InTT^v zxXdPvcnA;*;RjX9>r}fL9*87_%mXMvc0Ln7^8=E;T%mG+F14;mU+9$dkBZH$ICf+1 zLTZHa^_66zzaop<$fk-8WR#4Ky|C$mY`;6MfB-(AW+F@H=Hc;!2|iiqB@P)|AcCue zp-;Z}Pc$UNO9LjbjAgxX@gp9Vm$#L$1HE!`sm*oNnlg~6y({TCJTO97WeLo|OQ4sO z4;a@e`mu=65aZ(J{=q_va*AaJ$?lnz32Sr%nkilE95Nj7D#SqBuqTcX)qqd7BNq^rR_?PQ_qm)2TH7d51P11c zvM85TgDvrfhjbBL`|CXM!l4~GPo9(ta4>47%KW8=Lpx$ILxOf|O)8{=YY6aGaV zIH1jJjggPe5D7;{+U>hT^Aa=a1C;+3B+V5!&yZ65v+3)kXR9Lo=<0i_23pl`cN45K zCnQppLoXqCI7{|#=C$^nfPh1&(F{au47p3ybmvUrQE`h~0N1FbDLq+()R*Y=sfnIg zj0S&-;cKNbj24Xweh(X@idO-s*#tWm=7CU^unZl~c;nB(dqU$!@+N!j1$xmPE~xYm zl#o*ifRjpZwTYkTVe3Zpbo`O%b3h~_(A{Eh7pjZMX+9$os~DytR!|+h{~IxqDjJk6 zVbWzX=V!x6Y5NB?^q|zUlIeFB&uVq=9zv%#0c_SCCg|K=s&b=~JXp}#fJ~=e+uIS7 zEcm``(cf$^kdp3$GyH8O=n-YOvbo9}9b4?YSxXtcQE5sbY64R&!aD+03OQuY9SM%= z5hv2TRob^lG?H>#y)%{4L1h%w8(ctt_gW4nb?9Nb^CsgM{|}3-D!&Gqh)6L(iqcAg zuub6sm8lPfUE(9C(P$NJuM&TXpS0rdLH3@Pe(f9h?5c`0mV+;m=6dG3@XO*3V8k1CQUe)42G<&Vx7vg1 z6#(`!N^0#nzx#5Y3cC}YinWb%8skygfsZwZp)T}xzJ0UV;3wny|NDQ4MXjf3DXSbD zU!Of;4aR_v0-X}9iyq;dD59rs!UO!je*zy+%>T#!H0kqcdi2Af zV31rEQ7FD)XIRyiOr@=ic+KK%jF3tqr>jKIv8*^ z{-U;Yrq-T-ILD~Th9jt{?jDlf1RtJWDk)457Mf>aPCs?_}qvk>lSwJkBX2!kFG0@{`hi}o_u6C@H08CiZZ%keEin|txq67H;28F50tE535cbn ztn7jH0Ktutuk7bR<(o2>6mX%6pKsDW%q=8<{#g4+y5n*9@$uu;#9~!$H%V=y7;0)d z4=T+l)2q`~riQo+;Is>*L&9*ZmCU2JBH<)MAkr`ji;zERB!6>TMw zzP+lHl69cZO*c0$f9dz*M{B>RR$kkl1kH)=h+Z{IgIbqDJ3d#k#}#Rx-tgh}j9`*W zJx2Mpkx0VAwEI@}82_=p!+r#5Vm|OfN9wVl0mC{->XrHOc$u{QZTaS%xee7iy!+AMIFAi_mXTy!ft?M_}Mi!!c zZuO0qS65Ly-Ijk_?6rw%*qRV!K?;Na7l)ZJpDRZB<{;H+=d!HzJ33Y=xrI_#Sqsi~@#=q82Hb;&uRHmA1A zzo7XrB$2PhDb(;1NdeN2sBoq&qtq|_glCUJuRP!A#PL(7Y$H4}*V9>14y0zt5m}!& z5@ruccsLolAHxa0G&RjL5#sVix z%j?c|2e=!0%p0R$Q3)ihENY^hn0bbLd&meM>{4Zie(T?jf==nl@&qt7BmGP*q4HoW zs@&>E%ku*)$j-?r@-n({?V1=lUY_HZc5~@p{1>C%fSj(HX5pvH^RX6ZSnxT8 z067rsj!~7|vURJ`+6lX^45IWN)N(YLevPSVX;rUY4dDW$Gy9k(3106vh)t(m|lhRvr7271Lue6(DITsw4FRT1e-NfUq7?Fo>@Vulsw$Q78KC>N?MNb z-CpheHQOnOOgc3YQWLc%L6mYn=QHKYmoGWY6xg|Qr#+Dm38``&UAaa?7ABAur-GOS zJlmq)5a0isCEBM_+5}{vL$lUwCysJ`+s76t8A1FKSzlcp9SfK{MYnD_@W;W0iw%Y{ z4H{V5fhQ?Lu#@E`p9+D5(o-b86ev8reDh{#WZ~d91xXveeEw|NROh`n6CTt34Gs79|JRD*~Sxmm+)~u zGx(UhFmibCku^{A=8qjaw!Wdk9?C{94UJUtLbUQJtcbZqI@6;x=xk$7U|n?crm(tn z?HKmMTzk-oWTJ)9H~G z5D-p)8|!InPlLWDY&r5A0zlo+KBLX?5C*1$B$CN_HnPh1Fg;GgZPff#>Vu~x?|IO*K~Th6^j$gbOsC}w!)eL1I457j9=tCp1Y$GmwVwD zl#=<7xuS6!NzkKc9z2yzzYhaLSO?$nhwP-%W1K%X;91k4rqh&V6VYy_5W<*Sa1_s} zR4H}6xbUPK+Ck<5WDp@I&{FUXk}F5RgWX|Ejp0#=Dfe$+Z0y{zLyoGK`o&V;;XJ~; zDD?+<)Yd&vU=>}zUffAPH4s)+SSgxXHwGTOwG^7F6*caYfT6;&PH2Ajm-#i8&pNEW z?RmH-V?(vZIl^wZ2U_^?ucO~~d(P9i$jYCl!{ULhQj4T6XF^+=gx59rp4)PAd^oh6~hTG^&T7*Mx?(#{+k-Qd)zMdM+hZ8=45C zNf}0P7Oy=v6y(Q^AI>HGyql z_5BeG&e`w@so%a~K{KEvmAT)cfSiJHv#$FFV%4i>PurwMBOIN72qq-%ZKLkEr?%_JW6M0(Ryr625`gc@QST{c}Ad7{5q}%766~IkH(xh)zz@T?ov)_FC z63GqL)&G+^n|o7pMN3A-*H^{{(KAEO>|yn&v<|YuVm}P3SdGgT9*P6F{B&P`u;xcE zhc0R%pI&fhLSqc`4$?tgf7Zeuo3ttF8%@deqqj_5?`*;Ha%I}>$ny08j3X_1PAsK(}ASjsQbrR_WDhCY~Ijhn;%-7!(5FD0t8$ID;NRV>2xNBCC6oxzkXJ^hS-6@)fg;{JywwJjysvijRi;yvV^?F>Irx9xDNre2I}u#uq}F0a6WOgqT%T{ z2ya<|Use&P&tA0q5;s99+sbT;J-!*Gi47?#^}1uY{rjHjIWsIR0b~yf)wV8LQOV;e z>o(3>Uz2tJG;Mk?(8sM-MDNZS8~FK8u5=t6^tab0eLvZ1L^j9gUPyG#UNxP9iW8P) z;Qb?&7YSwoC`IJq{UoQX+63d-(dSktuX#kHVp{PxW*1aV>+XFM01qi@Rd0_P3l?_bt(dWPKvQMIGIQ@P zZpP!hHpfPD_@(Ul5`*Z|_-szurD{v-w{SZH821)+@L)2RK(p1<^sv_4?2LS?)xtDMZ8)K-D1wLrudm;(rA^&0CsVr`)C!qlMmCA};>C-7`VOmuAZWQ8^|S0{wMnIs^o&~I4kVwg61w*$QvV7|o@HP9+V(JlD%X2_Gn zot!rKa$gd!8S4M}D}e<^=ROJ$wSN6F{GYC~Aov(%y8my6H5?F)!=XaClay${+-~(8 zsxJy=|8_tm62!j>m{A;<-c1)rHR z*GBe@!m4O*Wfehzzz_~3+>1UjH@`2)@r4IsHN)E4k)9nq=n00BXlQYzCif&Nc}Y(7 z2WJnW_#mX;sDu>Qylfoma^EqB{Ap{K4mqsl^R-E(%wfl@S)kUA0_u>M&@BQ(2QWJ5R^EJo7;r+6ri**0i0nW$T*M-w{U^!D&djgu90}pM`|gC9G=MQMJu;A=hn)8qBt)j!Ig8kv6}Ory>Vn6@ylw2;B*-yRZL~6z~qcg zYlG$v@6ExnBCi)wI1mw$u{~aoELIN)hF?##C>6Ls7&}@#AWFTm1=#B?HFFUvvO>pU zy2AaM5y6;^bV|^v0Hy&@4MeB_#eiR7PSO+l#|pN}@VG3{H)B(A&4~(K|g2YgXQPaOM!K zeqlw03(9%=re;F~2OiR4sK@!DTZdg}QJV&4a5s4NlC&TBBafI!uvC7Hb#nj=An~;l z(3-f3oQsdOPv)Z+vA49`PXBg|JTt8M&!Hfjn^3;}lQ3rO5jlHQR5gqwJuHfq)ecHW z=@gY%PJ(>mKC1{y2vxNDvLXSBDqI{v1c&ggl%iXf*F#&_`mo&0&4&rm8 zt6VpCsf>d!d7xyy9e*xyRoB%Q|9%IK#Q%gi`y7b!^5qmsB%cSis0_c_{`IQ1CTqmM Nt)^R>WlVM5^dC2ClRp3e literal 0 HcmV?d00001 diff --git a/networks/toys/graphs/toy_paper_no_rev_rm_R2_import_rxn_visu.png b/networks/toys/graphs/toy_paper_no_rev_rm_R2_import_rxn_visu.png new file mode 100644 index 0000000000000000000000000000000000000000..5e414840623ab3571d388c4ddce4298ee0cd24bd GIT binary patch literal 81259 zcmeFZc{rBu`ZxMCD+);{LsBV3h9()xm?1J`N{Gmqgp#p=422{aGK7RAnUYMEDMMt8 zlrdw5e)FasnJ-ZALJDQ=d8+mJ$;I|Kcl^(KVADOn^hmGrV*~TE1Ui}-D)luVE{ z(n(Qq%Iv+I~P%c_+PomFKkp*5gvf#&Pw6GJCsvdu4yV zcMJ(oJ7DwUo!TH(+}6R4qM~~GEV*fu?iewfqD#BDa|YAgm*&%NPM9T__$V3|-`gBw z>8%&d%*?!>?Ud&35Q`jv1;H`Z=(mQCxC)(LD~0m&>1CZBWRAOI)$}r?o}Q<`i<+rE z+e#b1zKKsvEOH~mzWr>{zb{Gdi|*51*c<*|m-j!o;{U}zA`9~Mg|?wv$nfxRV*6!F zOT_}`sex~49IIBX!X291w0Y#XFFEV$>ks_QI~por=ssL{<>&i!qmQd8?QSy{0@zNx z$W4>Izumg!O>9eeqvheJM{Ui@1K9L)uXzm*TU$0JooaY-X7Fq58nzSHOFn+=O0M8WZ*TjqPwTGP+o$K`B$`$|S-XCH zeU?Roao|B7cDIy3`I^Y%#q(3YkK1*xO-@b@3lG0|`Lg`d{FFu8+k*xM2FFB2pE%`t zaLc>OCR|&bwL4<-eeKCal_;P4_qq7_12hs<(y#x~<2!gTT=?>q`%D7O-`}n*n_5^T z->hj)JwNv+rz6RrK$K$NU(=MYLs=XxrsX^T_JQ-%kX?6)@75w6*YOJO>S*z&HBl!r zZQ71t1B` zj>T12cN9i_{(QP{wwt=Urw98^a>EwZ{bM7tnym9$9G2M;SXIGGpXsoKAN?44XP0I= zTGLH7FmS8v5f%>C%{1%k@0Y)L@uIIsUr}(G_P{y@28M{kR%MHG6M`Zlm!^iAk~Cim zv9vWeKT(yKTkAA?XY8Aqu()^>?pw&JnXmfG7p1J9!k264wr`h0VSrlv2O)Ebi0G2UDwPXB0R&IhCVCL+pCW>YkliqkQH}dUkfgg}m#oGe2)Q zxwu&Uxg3Nm5BZ*LrGbyyL{04*82GJ%Sw4XEV`*t`dfVdS;y`0EgN20!R{XsELgaD# z(TR!TFJDqida%RR-NHVQoLYW;Ax~mz8uztz+cwXjAT{6eZE<`9E>Z5wbN$LO5=*{q zPmkF6g@?2F`ud_=gdDq{#LA=Lrk{7+pTlD@X!}9E&XJLo#&YHzJA{l&e74#y=sq7~ zbgrtdR!(W#DeADlEyvcQZz;xYnx*OGIg^q0g5lOo?=>5?&~b3oCaJ|M9*#~;JvchL zZph+$mW8y8ObJ7d)k6PvsnE%6m6&}ePCWhbLq9SqN=Yn7LsNyZK;npP`vbjH_rBW^ z@_IQ}&gkn$YHexxTwxQ}vW4~72X7Adq`Bp9h9137Z40|v(oG@)&u`(7c;N59d1j>J za;YEV;&hws9&T%^C%VG$8eEM3^y z*&AQ!1}7!$mkVH3Ff!UDP_!s?@7}n(R*XVaVc58SqRaH~s?Ls;eZ{M zXlXAGH73i*%BElIRn+8Rl)gvPH96RzqpM5L#N?fmbM)T5d!Ali^sKBjw}OJ`?dob=w?YVvXw!=`PZ$&US1I~epib`_MK_33( zh82zQA(6v}LrU*6HRRZK^s}=u)b5)a^Qe?|`%&P+M6t5^t!^9t9Q!0;`?hT*ylMG& za&wQre*HR9!fnUSojafK=}M`qZ^dyfDKFnZO-+sGulvSmm8`66b-1v6rvHuhn;WwA zz6>>`aI{@_a1g1es1TT3z49&Y`i&cjhDGj;NosfhwB%`By8)ys9ZlR^6z5e${1-9cL&Pi@=?(mP?V1?j)UB7=<;ZzuyFT`~Ri>A&x zMm%{^=0nfr)ElB5hKfjSN*@?Jf3{HKmQd7tF8{Rs*-s9h#~E5$m@zRoKkdDRjXkfx z>`R1*Wup%&pGeJETxrPJc*L8prm9n z{;MKV(!*e9o$R{3j(XR6lT|V^GdFJ8^5SCgz3Qh&RbL2Mgg44pSY%3AHoaUo+A?zH z+w(gz$>);Qdwtu+$HzZcSIeYoHQ=VXCFE{Sc1J&a`0QrQn~TMtD=O+OF8B;{i^xx(q}x0!KRdN!XvenmE8@hlBVcRG;E^VY;A4T z)z!mJ+<4tA#LU9dH8@y-8oQB(2H%%?W5giU#!ypCJuxulw4} zNJ&dqR8?{9*|XNe!{en^>dPi;Gcz;S*)DHVyezv$n6lA-zrVFf(G9$ci`3E68~>eF zbZy{k>{eR=QPBwWB#DV-`Jx^sarM1C9LWurc~u?f=lWxUV`6GQc~NtmupB`1u(r1L z&GhLGns+*4^GzO4PEbgwKJwihpP}0nBX%3|~hTnV6hw@)A$4&tZaOoSD60rPmYn5ts>wQ1J z?$1G7%3+62*`NROJ)1?+U2JqTKJLMT2V4C$G&M^KTxOzk*SI=}kZQK~`|Gc?>WRI1 zDxn9@mxb_Z>%4ccNUjZIO5mPSJ2It)V}ROJ(VBU=^}XY74UUQa7>^8#hJEj^|53cV zezW3iu$Th5aFg~)QulJ}A>Jy+q-jvjJYnEu0OSQy#T zqJz^EGn*tXC^+Wjt~KVW&JoyP>b7>n1_l%p6bRjXC&7&yH~#AH4~~qi#^FSJevX4( z9eG@XSMvU;ApK=o+o|8*0w*UO(7!Zam6w;5NldX>>+Anu$Vt}ZDO_20AHo+hlgWdH4Bao!G#A78~`q?OH$(^2m;V*-@bi&zqe;jE7i{qr9Ub( zq8j7FpR}+;IHP5;O;n?^4Id?M`5!Wd{Q+93dg3#JT=D_G0Kx>c+xY#*pUC@kKN59| zY)qczO%5zSaE_yX)^Agn1vk5MH!?g9 z;Pi9~i1Ans$A5M4uOFYt;9%H$@vNq%=5q%s&dL=3okxX?K2V-3d+zU;pBbSQd!@@c z88n>cC*L=_>7QFG*yW%0&)t5duK)jk^ws6t>kAr_b69t!GiPKI)I;XyctZRd8#NE^ z+Zs1qu{>0&(8D{~9xd*I=i-nYoo8ISsnIg~3ByLtuzbpZ;!)cN7|o;`aW58V9yEv^5{#7}7 zl7hlr>K>q%YWDddCjgY5cZKKvof$>Wc6dSs+NpYn-#ZN6iHYGrD|Q){FflO!1Bh{| zSzcOTIbzL>3VJXlOlm+Jbef?G?r7G?#94D^xp#Ub@b`+Y@t5HQg zT57rvIzq+%m$e79roG?8t)Rkt6g3v{EFRyi{ zPMtChJ4^MGTrDVw&&o(0buq95)s`*p-OG<3ZzI(TT|eo34nG(%&u<^xKQAw@+ymxQ zYHIW&1vBixK`z~bAYe2*k35Kr+YMOlm7SfvTEXipTdQG6G%j+{!{&QdtWDcnP-(iY zTR#EX9-c8ZGpkQfVp?9DP^AEW`ve6AN#S>FP0Aj8{QQ{;kPuJs`1RqizCx^gU5>3G zco90YF$m?ReT-5^FL|7EUJn>4tLRM^m*4W%W+xhFvug(=hEK)CH+%e#9&;F=o12>h zj7B$_Y-F(Tq%$?aCRqeQ1xo|>wYz?OeP?9|FF1pdkE3fLq|}oY zDLR|dM9`n7=h|hx9Y8AEwr|JN|AaCTCVV;I&H*nv0KD$rP{Z}?(Ua5W(eb{%8}X$S zz&#EpC#U^S)Tk~zS~@rQS#tyD@~5v~9|lS;GfNkHCtNf#qCC?*nw6B~Ln|?RGTl|m z$6HKHY;4Tt-Ti~i%xZHM);$@zdr%i07IMh zZk1Y$x_x^U5WVa05ou}ZeP<$`&$Vd#RI1=N$n~j0h$w%^DJ?P**Bh_FS(z9s?QLvg z5hVGB4YGm7if6}q4(#9m0lf}D8-P8dQ5?9yWbqpmz-^I*6oLi7 z7p$UDFz$l;9653XoJD#BY6_d7+F+6<5C4VtCFmGH#OraeyUP4`{wQ>Ny7TB2^Bq6I zty2T6z%{Zho8F|Se}wel;^KnB0{~WBS4WTkv*WO(40IjA!-t!OTBI~(j$ZlE36Kuq z$Z}{B@=L_XNJo*Xv$H68j~?4MaBPi~>({RzJARxfLSQ2hkjg76e1R;~6IIw!19(d$ z6;7YtB_R=wLKz$uc3MhmH6(!_`8R`cC}MU+Ace0a{iJX_%G2$~^_qkr|v6@-fwr08wYk!y9h1Azadf8etKA!+$kRXmhazy(jyd0hh2aL$=;# zu}JBVkPulFl`Y}n;g`(J9t8yfUDKXT)`X<=C?Y};_!H>rN3Om1t5Sf2bqDwl{uPB*Ku;~(@|^p^ zpQEDJhi6+7L&@6UQlg^+6cg$fkAkp6tFbEl`y)4guFYdGArzc{9X~(+yZ7(A2L}}h zU@uJwOc-Ni{M3}9?fc-tFO;QS;7owz5Fns8G^U{qT_K~LSGs;c@u$2Jfh;8TUy{^&b*RzvuZ)zsW3 zC@2V$cXfI3=J>>fAPyG@Z3LH+hj_gv6qtl~$-cio^HduL(a>_yX|R`3tG>p`Qvdkz z)M05DCA>jDJw9RW=EeQ8J=c$){9q7j3i}f$g>?9lp6b;(!-4|o8PXm$m)bphc1@!pXddSJv68^z##XUv;lh4*zZrjIW3A3q*GJL`n4bkfyTY;0_75!@2g zxGuv~Nz`HB7K9t{1D+da?tC@G{XpNbn;pHld-v{RxMp2l-IkV?ulYCa&qP6qcnk?o zdp2Gc7=`F@5M(WfrogX%6nVseoY%j#Iq&M~3TnFs+AfOY0XE(JyRe38l_vbN>}Depql10iWBbaNq0G3Uxp zbE49#-3JR$Rc7A@m`MfPmg{hYAC8>e$5Wt{t^`lPih3pRwi9PL=mv`dt-@j;( zWCaTJzTOu5Pjh7eVJ14Pk_kkUQx z-Fx)t(Vh5suIT7!^6#eqv}xc>B$!x!0S+K4oO~dAFL`>X3|QQT%*(3yAE;?L_4F}> z6NjN*ytVD9f$Z9vW6Oc2{qEhn&Od+r0|RMsRO%a-WVN)m<5TwT-hBt8`ASEjKgt+N zvB}qGr?PC?w9+Crt6hI>SX2Y*O|^OR-Xll0L*W9Y#a{T>*7h{iz*%9?r&kdI7~qYJ zjLcOC@fjHz*w5r6)~;Q9nnB+;QrzVUN>L_Y8IB&fXW6%JkPhB!hHg35=e0OzLF>c%T2C4%Hx*Wtr0!me5BpiM>pBd>` zyPc7do`In_t!P;h@&GRP)-4K9Vri8?(JKeBlh|-_Xx}Gr%l6bAMLeo0h|@`IYW)TMjK6>UtXH zlhD$8eVzvZhrThb+J3~ky%!C7^ym|gfbLss79bPAuP_O>c_a8d4k88Q zjv*Da0y{enHFizZN?yjcytYA%I4;5Ik0a zS09Ikz}r9r>Xpi~f4>nV>FS8X)WB%~uaGK=^pllODJUqs(n-IAZ4rXr*V>su~m=Tnunkg@T7$x`;y7(%O1}Ev3G#z~$(g^)w$o ze!PuuKMR#Af$N;}3RYvb`@XAyd#pv1u>jmZ~& zO~K8DAGhDPFf(!)kMaSFu-xEjsdANQv5KPQDFJ0=kaBi$-B?>GNEspe|@$JB{Aeh&T5z^ zoFXFI0B!K|miLaJc{~Y3>$$yVLp`<(4*1?Pwk#=tG_dVph1H`+5|$d}zZYIA0h${) zI5=oFZ^n@d*OH9*Tv_Sdz2_<5;#Zso57+iM+51!*H`ckXeB8i6$I6;7ux|3dWdZbu z?VA4Q-mq(5Yd$?aJ*56Xz$_f73&Vm{cZGui zHXVsI>8p)VMMFhx08Baeu8fI2TA3^kPSFP(k7Nhd*u5yQFR+_t=jH?wFa%a*5r z_=$o6r^j`wNn6jbEbY~AXuO$DW5)1SFW=k3ricO#sdozt3yXffQxJ6eYm)bO zMgGl+mdwk6^>&`y!9v$>VLc7qJ^YAGgi7GA&>7J`h%%64haC?n*wfYZ5x)SrgWO|6 zjYcyK4Gk2h^}w4ntj8WgU`J7KBIgV1UJ`OXGNF|hX^0@(Z` z`D(;MN=nDMKmku`Si@v`jsu!yPdSIja~K%23k#CMRd5a!#pQJIIh)0uCygKi{rdIG z+_4SY-k6t%#~8dHn8zPTx1hRg>08mV7kFgR()3Oy0Rz|WW%;V5{Liil^75|(S&xr5 zF0^G?Oq7Uhcvc@p5xhBm2?~TxzEgU=S_w5oP!Tv2WNCEHpAW4F>&^Y|4Rqcg`O{DU zgz-!6vt1jyySpC;Xk2ugZoTYu^JYi|{nw17z%Nga#%ra-+4UoyapSdVX=CH#u3~Si?d>ZmfW`m@HzDvDnA5;rwQPQ+ z01`*E{}NvYztn42SC-uh7sr*r9{hmi*+d7>9y^;5bP(!PqyWL*OX0efWS>E3z~b3i^H%* zrF7J4sJ{0}yFm%X-3kF<8@_q-=0jCgN_API<3FbQN$!~O=g*(h(b1g*U`Bc02LgiQ zJzmM1cF1u^6ET#eDZy3fHUw-#QmXrY1f97Q0IR4j-9%ot?1-?i3AiFeP6BCG)23Nx zA-SW@kv@0g|HdlM5~`8Z&A7A?iayj;T?b(zS)H8nM? zq7J9~O*a0y@9z&uf9$(eTF6`rW2_45JF}qi+O+)fO@OPs=kAT4O{An1Oz%K!BXn>} z!c{m_h*DKLyw}rh-0&>;Xg&e{M{V0L!Toj^ZV5tbCl%!C)i)}Me&C#H@RR2z`m^7j zv2c}5hXVU3EG%iDcjubbt4T-+EwB-UgKj zhV_p}nyu%mn(49Pjs_0OUu=@|4G9m>5xc)Xj;9t*xIMu(mAoz^rOArSL@8)r`eD&3 z`zW)^mkH}Y>@EDLtwFNtJgLC zd`tJeLvl@-u#nKFh6V=U9s}pWy;`sJiT2@my(xtVpTfec%)h<uNe3eG0i7x<(*S&_E;&Dz4F%DK9QbwanAE~c?KH$q z0#sCv9C$shlSrrmI2oV*yV3N_)}_ToAAkRY*YAyMCLTY29IJXd^$9{fYoIOqJxEQN zC$1(``C4(wo|Lr0PfblBzTdE{&L^d8|f0)1!EXSZzHwCUZA5q9vL|C|@?9?N2&6^(<(q>k#nwN?OC1D4R>lfIeKfZ!fsSx@*c_$4O~Qupttb=+jW z%wd&cSI-SWW$%Fl3PI_P=QuzaOn!Y1BIp>EL}E(NV|k7najiJqjtJmuOPhO^RCU7> z>S3f3C;DpzE)4V&V;Oo8lsavzw#TS<2*nh%006=Y^6MisBd5t^f=#_Em@P{r(pTFaoh~^Zk%@#GFQNp_Ls%gzs;AK}zQF zLJ-tXm6e;cwY5`i8lENXzK|iepNEt4HcI@HitImd`_E-$W*+W<3mnbJt0guuma3QS z-2L`|#*0H&J3kovCu*e}N8?5QuoGnQ2Q+%rZye2J!y*Y1`~XSi=k)oAo*pOEtHNJZFiX=Y(+Y_Nj>}@wtaDyN8JTZ}hkuhN+-SY~c zL4FtknJ7f42yvNqJW^2sI}ZEdH&%MC19+V81}}4r2ni{L2v_q;H**#VOh*X=)8MC1 zi8_rhlfceyu3$F(f8hDIo9XFA^w};$J0YSYR8KrRBI_&W7j!ct*#~FG)6>)3<|#q& zg8+u<>FL%#-lf-H$X12|KL^K&ZpRKiGwM9*KX7mp)Z*nx_z{{0FPr?phdb-2TMFGy zpkjs`FH8`*_UrWZ;g*lkTrPQNZDEsikHiDs!Y-E7)XQS}^6vhimBoI^A9*(daXJpL z$(rjL7_gIb51l^vMb4ef?Cf3~W@DTQNP>zuR#otibn~tsRC>Y}^kQb6D*}y>O3bIs zNBceOx!a8>?x^;2Ln&#*qkDCi z_i+vs3Eb9aP^mZB*-b-l$<2iT_k=<0irB04L@eFM<>q+SaSe~7Sd(^-6}N)VVKRwHW|l$(f$Mn)eV?N!>Qn`M~)4)&+S z;;1z4A6@}p=w>}xM*u*Bo4t&H2RhJ}0IIY_AGM3RFCK#$W7hKKqUclg1NI=yFnvd% z)1!MOc)2o%NNzej*aR9Lbxo&~^xUiqo4 z>>hTzVnd24L@bi9Ldyahe%-S5^88=iFL_WTP=_@iux15HNCp!doBC!*7S!B)|H|@A zQMR+VP3#GLl?j4ah$z{ioEn1upRs;+0OuCEqyh>kOqhXjLdkEQc+( zLCgXKlS);C4oubpS206GLa{BWtqp{dH)OraSxpEM4NmdOMeHRQ%Xe{bo$=}B53#oWVRJQwpf&Ia%5*MMWl2j|aa0-x4 zg2`Y41Pnmk&xA*yK)|u??+KJF`}9d_&^(T{CBu|j=z1iWGVn5mAX#vJsj;lH@b(E`ewwMz*$T4kBtb zK;4ApO3eLYfH$CmZyTGL5eTmOyXhF@&;JYt0ESHrCdO=KX7)j;t*={4``ZKu-*vK1 zoBpZt)_4>X%B?>5!Uc|iNocFu0EZso7_7mz9%vjOo<>kfShAW?l z;%XkE2oM4!MwVLKW!lKZL>e~^SL{)|Q+F|^R z4|i4$K#RRyTwHt?+WJFiV?&eA+0~p8?yHWGjDcODk)X(k&Uz05l$Ms3qCD@+YyI3$ zK<+KM_S^)`K!7J{IQU@)9?64hni11(ytir&dI#9W$DcphBqb#gx!FyfUUUoFGy{^> z^^x{7Kqm-Z6RHZM;P`ufMFQes+!6Tz$Brc7-QC^6bSr{Bt3x~9g};9D<`4?;X#^YG z<|i*!hY9+zPHjyfnL(&=vp~~#^-_rEma@cQWFp}va~o~^)L=!s6ITOO8adR-; zi8zXevq#{Alog^gE${5OfZ=)(`2>}3!0!`lFf=q&fBoEX_#R}S!Pm#96OiV4LIPZ~ zQbf1O_)W7wDNm&eoEeM32KBXee;G0n+_4C&uh$lO+Tu0{Ec#0bEQ#x)wAp zp@MniSg0p$he*}7knr#dJWnF=BdgAd-HM0S4U;P|ksAiwxZ2jMOY@Gf+=vH^YlKjQ zyg>;P_X8<%eRHJpg~vQcY(q-z%DKR@PPs<39nZbC>8FqSGWAN$69C4 zet;HD+&5x)$;%_pDgz-1s{95B%nzG{d16sK(UwcGYe{PZ1pD;u8x!0lWKTi@5;b6y z5}$aq^Ay>J=uD8GId|>41!W?%icx{svvMe3_;}*X;JE|Scq5MjJ_=2s^Y7mP7y>}~ z$&F_Wu3X`Vk=70M=RejWS`Mt|^0u~}*m~`*6K8ZiwDSNl5J$;31So zBATMsk>?8CUcXEA5I-N^FC?E>Ph5WhIb{?A6OwJx_qQJ;^G4u|iIAHV%NON=#IEt~ z-Rp(*f@Mr}L4Z<7TOeWznPy+mE;gcI#6yh2raSjJ@B&ow-0OdKK-wqjFN|pz-ikO8 z=$JSxb+OVL@U&`4LLYgF`XDbSM4*{OvUkwae?<3Qb@l4iXCQG9iXTG}Bt|;qNS+@X z^})u7?*{GGYDffDB9752+O+xJf1OeUYZGw|o4B_t^5V{Ljl_^Z&AIyPGd-++GQ9-s zg7vJ2;j`oD75yt6yI=_-l2wB52fN#gDvMOcBcwe1cN`v_nkxN||Cw~a4~wvgiyu-3c@`_dS?_B zH$gk@#z_Ekzssxr`s&osIpp)?(5jKqAkh+FPs>80rhJL~`RGxZwx&6W5Wrl*^Rk~C zHzj!-l6runPOg!gTYf?Z8UY5~h;)J{Qwq+6NFT}MWMn|gUyHx}qIQmpiwj|wO=7Ng z7%6D9{tYXX2rqa@kY@jl;*58GTm#=H7ql95Jn+K%*3X}PpflBhU*I$!Hazq6NC;bE z(j>c>6N&vF&UVHHeVIWML67`m0*^)IpEZSco1EN=SO79{w-OQ(%*w6I%wAYL!Ie~e z`Lb>~?FvHkr&U$IH4Xs<;(GQVvjvK%$rEB`jR+c4@nlF+O#J#rqn#fe{{CROeED*O zh(+AZGj!du@Qk8B8ZmFiLQ+3y=UTFb{E+&*ZLZYq`0&q$hSi3=K`ZId;qY zM2|eMH3CubSeOW==Ma3YmaP!2QxYhUfy_w)IXJUKb|nTj$^=l`xxON5gC~0O-_%${8q`=jkO7!Pq@d&GxIy6YeNaZ= zCPYLav)Zh@1bd_S)2BN`+5;HDK?l}ePqK0^&r!n!$KAo^W7x7~i?iLGmw-36{_Nrr zq>OZZ@)8PHe~6S7YPrbf8`v=p5-;dNDp1ySvn?6Fr5OqYj{(w4`zgC#_<7h)xpw7{eQY3}8ZO%4Ljk87}+go$Uq{btq074amwY} z<8~`4D0Bg}5}n#q^fe}2&;lV%_z@K{B0}#)4>e~N$U$tV`{I~9Sl71PPbz2tBrJeu zVFp}gDp5$sSciWvh*%mXk4z=5qu680gBC>MLv8IyI>QAoI>!50s1cGnVm)^C7C3gP z5SdPZU?m7%L3EiaP8nH}`v7LymiLH^GqmL05Wtcpf~!Y_aC!dcGr%Rz!f+5;`8+wY zPc^#w#7KTIXw`Ad1jXrk?yto}D{kgozFAaM)J9rbZY0G3&c19qB<15LjXTlCKY*kJ zFn8uk*5S?)*(0gl@~CJ z3wjME!sG|{e<;3hY5ntk2s$c3Q}Pczo%|3pM8!XirFtB&`Xm+={w*&FuHX#7{E>Qm z3+s`#INK8l{@Q?GQ9`_Vp{J&hY2;Fvn%WeO{M|gdDwb4f}Tpj61QyJ zk3k{?r?o^M(RCjOPH`rH9w9b(A!!be6NefSX(5N30Gx_g)XM~!KK0yh`&hs1)iE+$ zh)Pc+9T=Uk?)S4PP>s?4^ATtTRM2{igu*zEX3mMk6SC53DpHevu3FJdlqSzyru(;R#WMNx%O^KA(ve zD|{6rkRlje4D|G|4y>Fd!ep<1A{NT`tOJm-&WTpqW6sPAe+2sacx%;~{d|0y5WsNoHbx8cB3>FH#s+zW}S`^XJc`#4E}l2!hue{_r6k=-Ovc zlxMi&O8uMqWy3e2dAH`@6b3?Y7_9e#V@$4N`Jd}Z)z5u|=!`mHit&$ifN;aaZVHiw zhj^$i8bD{-S0`k~crl{51MW0AIB1I`ybr|m&xLr9ijIJl6}*q6A2E&7s=!KKm>B^3 zLeo75t?IGwcp+|t4346F;bPa|grjGXm6D)%3IDe&z-p)zduPCCjgiuY;ujL2Zi$`_ zsc$`666V>sPMnQp;`t4*@YesvB%Q@>2OUw9LzpoR*XjJ5AN3^LuF83 zKLKcjNw~eo$@m)Xm-Rr`?*DLb=}7b*mtjjTBNc*pJ%mw3$Ei%9m5HA>Is{#&t>Gbt z1aNt(Xl9vK`^DqHZ=qF=8doUwFwr~G_ zALx{rG)zV&JnQ9de(cl2LL#F0K_(*cIx?`zi_=no0+RIDeSVM|dMz}Pw0boMV{{Jv z_YS^*_x9+sL(QoqTAtO7CmiEGpG^J*(oP^e_zo`&6@UDQeYI684M}~q1523XgY8X+ zJYPcmfG5o9*w|QPnd?)u1>mocrPI=!#9a~99!L7_OovAd?hJA$Rl#mti~|uHP!YTg z@^X$r4<@_`b@wuqv1^|mByHm8xt zio;_eOrR7a@_~uh9a!44*yjmatxp79*ijD(7yh2W1|{_X*cS^BS5LOYeq8HH`NC0~ z?=pxKqQ>#W%k)Yk?gY+qX?Cw`R#8d1p@E1@AQ+x24tMG+8D7G1pZ(7CxcmnybG(HOw4h9*$!wn((7T_JQU z{a7yy^N39>SH8z-LZ*PIFi0blP#mCqsdGC}twW@;PbCo-V#z-Mz)5VsG1e`MNdKJ2 z@Xwq2XXaKmjDGub{l22i-k{y;ns3cd`R-XTpwTd4X)ULbzRTiriLLCJSy{b~y0W>a zBDI=sTq(bTj9ymSO#8$_an8b1Ik~!me*TgEo;nk=nu30NgPEHC$c4gw_nLk(gZL@= z`Q3~Rq19{F#J+wVKypnP8RL>M9IUK-v6npaO)g#X1VQ>Xr}IU`%ulGgs{`KfJ%CYw z)-~`Fr40<)<-Z+w9D2O=5xvEu=;)fE`SDwu40mf_=GqRmLzLqLQj3TvQzNjnG4T;Q zb^)2I{M%6!2#tAOGUh4b3>!}ER{Q6@5*RyOl$D}?A-KQ{Sb~x9*sQE=@D$_~6e>I< zRzS`~p5DI$yG~kNy`DQY5B9^3?)5ybz7Oge8%scxf+8dJt&YdkmV@p4LfW`n)tm8H zMHGc}9rEfQAV8+WwW5_s!{;KVv{hMdTa=&QCI?5y-(tU0_BVvNDMQDjXJ-#Uj+3;y zmAR@fz%&mZKHT-u!P5qc-A4=}E!%%SOD5mxSXee1!4L{Uw@U9LXxpW-pLi@`5m>_bH)k{?+0H)myCT^48^ zBdNq3fRtb~)?NC@??6-1=w6|O7o*~M4liH6l&OM>yV=%ugx3A(m#Ql1OP8KYE%A}C zxG@r)Bn1P_r`;w0-8*K8G8~I%_sS3xR!vRqHlC2r6J!T>@$#nBwCaF45wT`;&UN7T zZzUm~6to&*LPLr$f#K!Ip64mWNs5T1A#WINqX!BIGYTV6tZdbs>#_@zTzjG0PRvW4 z3S<{Ix_p^?FiG8)>>bNZ9pIsL;nf&YfdIG)JJ4ffuC%liz6Htn7+G0SZ}H};%eQU= zpd=|WoVubL_fqroSrM!xb1F)MKD&o7M04v#=EKIwbWu2*%=ePdwP4=Rt-K0d;|RvoY*bp6#r-kEe9( z#EE$99}v{gtBQlNUIIY}4tohu$lDw!6b}y%b93{?c_WaY5;R&u34tYm>X`|hbTW85 z<|J098jH;#=jfO{kdEy~A?#AOLT;PJnXdC#X*8NuPTI4}_D8${m;i~{NN8Tf;*pex zIW3FOS~z?k00Ty$$TT`XDDPQC*?i;d$-@gJ2pOQ;%(eHR#bV+`3IY-?!5dkt)1q;c z##BLqJUl!!6gdS2-r5;yD~TL>k@+4hV`EZr%`Gj3C}HS?LU<843Kb1j#Kf)OOY(n~URVmHSD*|D}ilXeqAhX*UOX?7L3EJr^LT{RZSch?CgunvpuwO*aeeb)ksx= zvYVcGtx5bka!G)%hiHlqVavoV9Rn2Yb&#otgD<%q-vrRiSF}2AKRm4fOau9t@pO(k#f|mr^D`kkRsu=5^}&8Sn&Msj{9Di{ zzcw`7ef^r>!NK8KS{m)<&Hj3|PbJ($5x3pw=;(+ezMk?GY8ys^co)U`{LGL}B>ASb zt5+RkRY`4?d>j%22p@YkGq!O9W1&nR;mO!(BM1Ah%*;t&Wd$fnFJBr%NOPJVW+Agsa2jtFclo@yws514nu3H^ z86=B6Y$5xTpb-}xo#Df8!_i}WKd`R-=S@2(Ku$Qs?n_hJm=@T0?%X-2sUb!%eNu&4 zk6&B6`Zfg@88@|j{4TW`tEHu-&bzB!E-@~y5*#q1FCGYSi2oG&ITE70w8G#@-jy?T zJL%}o{=R#%E;%X52<^aWe)0fp2`X%-{Ra*-&1ob7D}a{koL1PT24BePi+E+v*dZ7O z)@^V7OEv2gK;6TR+OlKl#S_#B9>{uBg|ljEt72;9aqLoO-n@Um1A2^u^p15|kVf=Y zQeVBI!3rY5lJWTTdo^-$VC~74?!?Vvv@&^Mi5538Iy(9RE(czdVF9Fcr?{(eetg%q zO@q^=GRBsD)!SM2Z!zYz~XceO+e?H2Df2#V9N2zmZFt$v^X3K!mS=nPd;t@ht(f4{Zs zkt1&>!VYCVc6C>qorLeP8D9D)FVwiyFX3Gg*?pAfMc+MutLWNX4DA!y=7arckt~zW@MBWSxMMv9 zk-^iKPH;>Nw+a&$%_hyG(7nk*_dBg5894;+ZV4Ducs}=T7abKGhc)8j;(~jOIsSOB z926DRGpn6ev@D9lerCNX{!D2NkF)Rd&z5z(f8OcyvvDzoz1m*zNQNng{-3usc%AeT z_~$JLYE*!2|0{n~X}w`rMlvAReh73Eg6OG8hw+*y(^gYmT>@ig48uHJ0sWIuz4{lMFkoz2cZ}5-J*el3$t`1M*K!n zSv(=;cp z51+(&0kpk`j?jtMe0_jqAi3hDv`V&()=P>L(pvzEODrM{M&-tS>UNLj@M$VGrDZ0$|;mu@0pNHa#X0V@vR!6M);vf!ol&l9H1TS6(;u zI@Yleh+rBcNljBmwq&gE9f7m(=E_e%pEs+}+$_%KktecwGpU_`eKcgI6|n!%p+g_w zBoFa9uPXh!fimUg0Vj*~#hIt;!LuOetwnddgSkOKcH|Dm1_d0KH#(Xf_wnofXey`b!bO+e67_kHr&QMlRnf))$apw2&&PkQ0p#HWp7BCJ7F^>4 z%tf>BPc~Q=N{Wh#k}wm+2;M=QB;GQmYMBP1{A+V_87xvkjQYWcTO$?wMv$xO@DQ~l z)S)#LypwMsxH(j|{;D!R_9xgDc`qcMQE^*ao|^q>-kaZPZQJuVQVt0UT4X(VB$p}r zP_DFpV(eE11CEX~cqd+yWS(U^*=>EEU1WLzA;ERj)c0qesD>TtlKi$)*o=nXAm0b} z-v@YgfRjtVXn;n@)_Njf7=X(!DJzS^X+a3h3-%%O@<|1g#$RkxUbIzJRmDI*f_TSM zV08AQ4fWTQr{S;y?@}o6JwD=aeMPELu7WcaJF&W=Vig7LcQLrlXeVRgugs=wVE`Cr z9PPQ`WaX09w_<=B;pE>m;qz})oJk;D~`h_1Ob3h)?sj` z6Ph*2z7VS(iBltlm%wCOxN`&qk_24y1-5P5W`u!~0Vw2<@Df#PI7tN^9vt*Vn6DGu z?%1_oROBTgn0@`&&@hoXetM^D3+<}UlXRRrhE7VfNhazLDgYbwD#$yuDfO3aPd3!g zE<^iCV@3Sd4Wzr+ATcn#CRr zW=lnZO>U7TRxigsBMoDh@&U(P!otns*)bQ%AZD}}G9YQh+!wrW*eu27=7;HpOeAX% zEcEHS3Jv?Ph=>T?)~(|T99KbL?%lmxyn$l`sWXb}w64re8Mm&LRFsIOrY5{{<$P*N zHEMkTa@p%I2OX|=QhuHLPEmd0dxmWLv^0{qS9A62*2QsTK)3`#-7PM@6nyPI137Nj`?9TGGoj~3&)H&4u{CoCVKMnoT)a#g-G+Jk`l zN67A|XmH1w_rd1uhP?*A=JmvJOcxVr6Dq79o~y`chz|$3L~v@j3se*|*30oKTLo&~ z_O9AFq6%USY=0N+So*?+f@TwxI#0;Qc=hKR4vrXAOAqi$(m{YJ&z?Kyg+LX|0xE<% ziOP?cnhcaVv0Rg{TlfSg`i*Ue1TZ2Jk2`?kvX>F(m;9Ywx%F*81<{+){UA2WtQ6tx zhQ38B%R9;8A9%2K{3MnaA}b_F28^$g6OLYcduYg#a6gZwDJHz9Z513&IK!{g4OHU0 z4{09WpO1J4;x|dBX3mfYip_0x5V$DWQSvGU1mPsc+$hg5JB<#?WYq>M6+@M$qW-+r z&3y53gD!b@<1xF{ND6P(ODlxp+11x~@A2cu&S_kOGt1J1@S?q4glOqB(dTbICz&P& zC;VPi)K8b+{74@fA`~Z|!?|q}oN!OfRp4lLV+}t;M8jJZ(1_MT;>7&tp8|_lJMuUv zBb)f4o2*^8?y%F(`G;Ohqtef_1<(~ZFb}?5dmWnBCcJscxu9l;gMN}>8|+D0bPuqr zhjam{ZdRk4*~}#u4=D_LuRM;3@IwlNyocrUmoHu~U-IC^nM4MJ zy>X|!=Wvp^-H7&K@R^vbc-~DGi?_IjAC}STf7&9-|Hd15d~fon(0ii)zRK*sL&6UI zM|KN3{(sed9Lbh^z&z~<(*jRiBq|NfE>!)C`_|Gcqz)9p=$|A6c`I$YKN3qO*GLmc=I z75OBKwLtLW^srPvA)QO6y57c!u!X6 znAHQzdq{>5thNlw7#V4U2~L5wUJg6(7D%~=dfsGv)q0$Q^tY-DoO9f0*zrpbUAJsO z0cqo;N`{O;1#kDWVh*HN(h}dmVnD@mU0IqTatU7eQAzG3Ifvlc#{Z49H-W0T@85s7 zIVCDGMA9~A4Bh4_8<8oBOi`KV$Q+8wR*6uVDRonZ${b2cMTQ2mC`y#E63I~K_0IGB z*6+X0I%l1=&b^-Xtox?+{tlndd$_LaeSID_VE=AGdz9&c_7JqRWsP1I+JJJ9o9deyn?s&FBXl;D*rja2aj66DUZsNKt=oW)OVPM@df&&7Jd6Rh1_xI zt7+^ooLA~FYrz6dAlO!|Ti-}a(;;&VgWVDP26+%w6slpvD1#eHEtP4dq1)HQrM91R z=3wd~qXntB0bXQ^Uo+eLtl@T@;8)yA*Li6zc&@B0aZYeY!~YcuE%4@>)oMXAO7gD3j$TfN7TXY9=?KD>u=4O~2-laog#T91ui`*%p|q0Of4^q&~G^8U+0U%i@(uBP(G-Eg0Qk3onRY3QM|jownH_s!SKRF?)1 zz|sOarI+keM)7bDlBXYu?q8F-V&2OuW^4z)UUEf_()9G0uO}28Ij;tU6$&WNz`#bV zb$R&qRqpX5o9tXVB=Eml0G?_)1;uIl;+O=p?EX&^TtQAP*% zow_usOoy2Y&^9}z!GG3|JkuV#AfW_sm#e3LHCEjO-QdSjwxdIBkGm7p)mEaUES>>5 zjtP5s#?`?(OFldqYrajxZvczPJnI3kiXfu||EBe6j7JM@1)Evq5@d?2pgK!NLO$Tf z8twu)AH|dL4QqFcMzozbLysRncV$4>K*W90jG$;aaq5%`?YD?_sJ_n0M|5EaJ;hd| zmeKK|7<}+oA%M$TFiTHS{0-V9P zTeq|K{}jU{3HrPJOKZ&?aB@!nAwIE1NI&}Re>0ygjdI_-asPkJq6&ZwT7#c4dC;w* z`9)GN;Bx0f)-Ak*Cw8K5MhPTcz2A`|Gd`H|;DEueUAhlD4teTE^JHn#rp$GJW{Yh6 zx~U=iB5D`62k8Q+)U-2;G_2;ah zgcZ?&apuYlgp2{CtWl?TU(3+8oci_xDXj3lq9Y(0fV#UVzz5JzBNcRI>k7QeBQ7j1 zHb+t@UuwaZ&TL$h-uJ|Xewe+9qu`e^;9~LeK&h#qc^Q$|%H50wFp(8*Pw#yhAumP` zg*WD}fj@l>Eu4`t9xLH#@$E-!ptPMyGq$GqO>J@HLAHm7B6Q{P$AJds>L2?K9H^!! zFaNbvdU!=)etJvEml+vnAFnt@x}}!Ez|#?gMlW@wMpHJ;sj&95K!od!9h+L(Je5)r zfg-(G`kCo{LFR{L=#Hp%0vvqFmTUC;Ov~wFuPgdNFu`ZO!`0@*H8%kN#-6%{j(kJf zsma~bn;_EF!}6jL)P(1_b~lMsH}aN0^BJwaDQgZZ`x>~WlV?O=^teV79*4YNBDq2V zEP1TjIiGnrdPwDj#FA45^r1FI6Y)2=4~Tv__E2qiouTo$MQO|$P(^34w@BL#3_Zaq zT?Q^iA`=DkPk{_=dkoy5Be;do-z|zMG*(b3)!U^G)|4u$sSo! z$F{AtLysNX0(@9WNi%YUB}Y9=OG|2XH~YOcFRf;ST>0U-b6q}zgy6^gPjCuYTqc3( z-$mt#kV~;O!Hq7Mn3K3_nt3a)1ecVyI>y^o?Ha^Ceaic8a>2{El3`QOZ?l}^anwrO z6Jkm=%MiMn$OVr~;1|NPK4AYS_KD0TiI-X9-FnJQ@I`kb9Wd6&^OB7rCum&9 z!5#wCzJ~a87-{}=@LZSLCuwA4N(~}s`lL6@dMR4`9C6UTaYx%o*9$IS5&QSYXJ;G1 zya`Im8W(dRR?owQ8~8^A$4spB$Zls`af6{H>ypQHbaTI!oj7x*CVK#Z`k_%RG`%%v z#9x8y8|nPz$EL&I@`~1oxeokY>ck$(n(PJGmzyMbf&S3SIABuy%^*qkF#1n1>(R3q zsa_Pm?^%@}6}vV5a#QetGA3TIzjkn>yg!0P0^skB+@b}tGnr+pZ5gJ%{OtL2Ww&m6 z2ol=0Yv+m{m6%n;UDP`xLM+PDnQ$TiGzY6c&{Ot>vri)sPPs!0ft1r_g09gyT?yWP znjqh&zGXuw0rR*l%^IQH1b@3MH%VIpBDf-Q9R=FvuU zY}H0}#i>^_9%t;O-;kP8M`{t{o3q8kj{a{>>59F_W+xbUdQzgL%+zAtA!~;b=kRaW zgfor1+WrgfFJGFvIzxJk&+Qm3sgF z?L%IJ0LZwK1OcN!3AWKUv(eF@f zKJ^;giX>Er0$q>?;rl2%n@bx;FCPUUZ9QqymW0 zcIZvFupU2tGabL;%aVdqXU^Dt$Tl-G3knUD1b*d~+Qpi(7J-O2HLQ@*{F)+bdYx%s zJ#^NrE>%%%DB`5z#cp`e*s=9sf2mUDwC-!1;i)+z;{=mQg`lwgHkCI+?9Xl;G-pm5 zyI7ZPtZ&L~m&Y%!E?`uo0g6~x8WSqt^wAl_Q*pj#s>1AmgOXBF|QSbOUz@JYe$6bAR_2PTF(Ue z0frBvY-J;>DBHs#bft$cqv2^SqRAyMF1vx!Db39EQCbh;8M?0SZiYe!xTW5@b?cm z&tPuohsywkkl|v4M69hz_+-i`;qG0Z`;oUJdB_+AH4#i>?b_)4VQr}?CG1PhDx|{& z=D{pQRKs*gJms_>kOqB-*H1q>s}uD;6ch}mqk6b$VR=|kkQ=|@m1GT0Z_e6`yQ;hr z99%W->baXtTD0rbX((O%>Db?I?=`hDVpP+N3|Hma)wBo1V1g1GdI(!?=HkVpAN;<( z7i|jJbI8L~X70V2+&zkpC+@1_9A?09zY<0=n46MuuoF8T{e}jYGYo=PfhnrK9u4&O z-}ZActS^BM>l$9Ao! znVmM7kcHfWcnTP4VPO$RRUH%(QXP`ts^SyFiClr~1*<9@=2MWfb>gtb6|g^jAWj~z z?&$!hz^!qRJ0czj$x`)nHU79BI+5BJM6^1}$5&JaR7WhiV8_|O&8mu7UmJyQ`hJ15 z&-{km2oo{2j-+m7`;nHbQQeAQl?7n*B3Z+aMtfHuHkC0g3K9|(-81h@i$8&@WD=t) zg?!}s^X@P>nN}Z$LMQQs7GuT?!%vqoP#_IL)=^Gi5q|)Xb+IQ|WxaWI#dWwP4<3=! z(u{3Tb-AL!LKsvOl+X~>0fUN>b5onApQX`y#VoJ}azolP?2V3|KCR6{x0>!G@Z1pF50&qP&OCU~2TK!PW*n#;1AQWQL*w$)H+&q6V;Ra$zOzNgj&-`sh*c4n z7ko1Xm87dYXLuoto>R`Z!4+6GFLHx5WyP)8jxxfhT90;x%r4KL$|^LpHB)u6>PXdAeT#+M zAO)3TRxQ8T5A@?$&CP(3RKS@KDs+Z}0u* zHJ=PSJ8%B{C_^jzhttCBy4U=EBzI;-?=ewtdz*E4m&Ai6t{s5xpcbx#JKV%HP$RH$ zI@}%Af@~rKX8&QSj-pZF?ZN!jMb9+G@x{RU$X8IUA`zmCslrdw+|{F}cZ-RMx%+#0 zT4Q)qgg2ra#k9v5P>M0}ANV6c5_X<2_tLhUqw$jl7nQm2#_Zv$nHuz9a3^VpQN2cu z0&C7_)@o^EPXP!ZE;d8CYj~K8;*;z#oS&u`FK>c?vS%fle>Btu*83<#*n>lN<@8Mf4Nd=J*+#YMKH zHGFicOLLOg{G_+kPhRfTz+&VeRTy_5@n6%rd7YQp*iHJt7Zc?HbY$@E^`!e zomM-pO6){V7yTT#nLV!CJaI=Dg+<9qO&`q}HO8%IdmcrnPt%@t#WLzpUYS@5VRBcG zN^Yw_uoSW+WG?K*$+F__Qqi~2k*07jew+&*Y`=X4&X`4XgC|75kW&LFN5Tu|zTv+_ z%OGvsl`B^`=BP6KN>LqR`)wSZd1v8r)26AW-ad zs-nzXl)OueHiLH~tssbB5yMdS3<>ZTQ)y~`aQJ`|j)SB2_oz)Z9nz)g_K?U(9cqpw zYGqSS6=4KN+^!cTJ0yaV6=J0H8lEIr>wNy0IomtnG0b@YNNkfR8w#1Txc#V32h~rj z*By?N<1}Tm_4+0wc2Ob=u|fGL-(HMuI`F<^bXmH09_fQxvs?Z`klJ;{+fhVY!L3X3 zMvszb$Tupxbg4&bzsYNP>f6ecP6Iez)-Qf)EJ-qjv zO)c~Wdqf-*UrL%7Tz)q*?6FO=M(q?5G1&;8*3D?lJpM#qHU;Yw22HjnOIXy{7hAU9 zBU%#-4CF=0ySx4A$73j`cv2Fz{tt$b(m+r6!Gk~#r=~^gQKAn)v`B@lWF_P}9=jUksBV`*@w#>80kVh=O3&ZB0qrYID z>_UJcT1NQ#iqvnStU>Itt$aOpZiOW!zeDQq4dU)0;&aBf#x59=!Q7@S4B!EjOSc~_ zKHOUD2V^yaF{JS|sY`Zj-}r(PIf$E2%Jz~CDF038+-)rGt>IcJOuI^_CI%)n;5Szt z9EM)EDoqu;LFOXo?FoAk5_*J2kk+%Z2nI$YmhXT#dHiK)q}skQb3*zc(2xY~Lb&*; z5AMop**WYIViZhPn&Tx9$H@JId7r{T+*-GmgG_>FL$Ow*t^{QTZq`tU{}n~Wfav@h zt~{=40{BwZe82t4Vi(eXVgI3K5&B0O4*!?Q>;_zRBJyB^(EJ)t3zC_CX|y;MSrenp z79&Os#^Ig#S4AN|gg` zfhVIsyul#}Ua~IRlj$W*GT@QI@K#%C@L2uGHbm9xhgHL>R?6e-pK9{#q;~oAsnf-C z!zoMCo;(q=brSlo8}H2CK8hyBK-KC`Pn&#+qeB6o6h-h@S$7}z_) zor~uwt^Q3UI&PxHz$ZlnG9iCz+0ihG91K|-vRLCxC-uSL_(@6F8tV)HEWkAVXw+gx zPiiCcsjf(Ga&l9L&YhD`ICe!VH|*J40Xt5e@Rx%$cQhPaHq#{Y6v zgRp=@hqT2}YT9MovkV+w288@_nog2No}H1g84KE{7a!R2o<&(Cc2JdgtkZyf5fNQT znezUkmRcJ8Y*M&HL&jzlgXVails1uNDXt)WHT{OL^&?rUxzLb$SF29s!LZnCC3NM0 z(39W5+nR{Zj!~g2Ru^q-ZPhRw>8*YebZQiyXga_5Xop#R}SbhoQh14jIUd)T;-5N3^Dm`%cc7qyZ z#(mANtiS<+i5!bJcWNH^^D%ot(w(-gTbr$!r`>dewt^{RA37blFx8^W7G4alG+Fij zVS5nt8~5*zamYLfg(9MH&Nu6~;ivWaevr&$#i4}DrnCEk?+bP$gHZ|zU*J?yrNfl; zyc|RKEY%-B1KKRAF`=Kt^_*eaV69O%IaIXkBbiM`Z%{DlNxT(sN9GyUGC9Ko7tPp9&5&S?b$n=onTGQkCU%u?1 z>g@S^mCQ%XUF)`bVC}xWdJSr!)1p z{lgx4&7U>yHp}94o|#AC@w=ae5mcP`Ix~jS&;_59WficwI zr6{)J=Z(`Ebq@5Vx^h57(|CYOFEm>Q{RWyH*f1H?W$oIvMhB1C?q}}BeCpJu{hE&r z&YXSh$e*bUCMpI^o*X?mq2JSw3l#!D00pU6HdPgk<59CvZKHc{7Q_k2gtncMQ6Kia z{8aS1icYfpAEMMW~VzpxQ68PiLT>lY^Q84mF4G;P6YUT7T_veXy z6D}A}c*%RtMxKOD*3i&!+tObv_}X*-+&_G8W19w=x;i=vMesAj_gg;2*1Yy%DJ8Vy z>`c{0S*|kuM3$U*5k{>Fem{I~kN3~Kf%3uja<;5kgp##++*R*vgXKeuXkV%*CY_nK z_DhtAJScvS?7X>KNuwvpm|-XsAWYg*cHE0SNW(!6 zBapLGal1BiV}{rh$x$LS*b@+dyGK)G zdV|J{@wH73PrP|kU@J041LDIW@cV@;dUzi*+N2)TVh|5kDqa$$D79a{j2XN*@)`AE ztPe%7*Xa#TXQQIztZx#>y_D~*AEi>U@p5$a^*#8^*Qg^JqZ~%DPETj>u;GCXr%#;F zK(Np)Gh1Df(=}sby&=Ve!Hv=$JxWB(C4RqAi;kgyztDMB!IOyWO<9|=;^T8-X2YBP z$RE21D*D2|tInQ3@A4)12(Jncfb);f!hb#bwdEATU$g=uwE$mZ?;*ADV3#JOrA68; zcG0BeN0pbu0I@@+=P9k)wQtC9O~Zz50=!?edbDHU`51#{b!!1&NE1kfT#FUJ`7&qi z7MIs=x}l7&M6e+apXuON7LaKF`$rK8?B1sG>lMv1(o&%hNNr*UgF%}X<9|9YzyFyK zx$*@uzm>?vV1{WKM=i=U_S#DL?@1CohzOsu#y|i3oRE!D>7$WT;_r2T+V8CdUQl^^ zjMQzT9BO1bSr|$sjQSNgY{<}|9-n$00XM5oZE=IHHosBh_7jocF#&J`P0X0aJBJM) zVmRvsE81$>v{AKcwWoC#zi*#ry?1$K?LfNXhC@BNwxSH#$#oekK&7MHO5Nr}x)ddoShHOjym^nD*#b`) zMPqsK*sbb3Z${?oQ$jcVaC+Q`1Y{qgh0TAoqjS!M|7rnF20rz)9+5%XF62VewOgy5 z#bJlqTd1xpk=xZZv{&w|Mq7rBLp-}k4wYdoo;57!zjFKRUzdSd>0*Ey|SY9I1KMCri<_Z|pomgk_i5eY9yxM(p65Su?) zXntVbNP)=FcJikeb8j6arDGH#Pyud`PFo^WZMVY_p$V%dezmO6h0|S3>d7f4kfb@- zr~oAIkn(oZoBg!Y;mZ} z82RGgLV^QD!L>Yu0U(Z%G65OJ1N~0TmyZKontR&Ihf8OQR#(VL{x&me)?>QRWfV5D zvQa2BeEE2Q5G``$Xosls?{~2!HXS8C8V*>bxG_YdD8zCbOeLYOzq&5Ip81wyipyYQ|Z53>FJv z3p_Ivi@i+0H(9(e4$Kp8Abb*P%VdAwUADAkLe_wS#Y@#s(280$)p2M7f^|Au@r;yd zCg#9Gi+?syZkp#4anPZ0&8Zg`$%z~iU?{jUdd@c$lO;|hl3oYh=;kC%ufdGdvJB8w z$S4S{L@gHK?7jih+IQ%%g_4&c*VpvxG^o;c05P0^M3YKRM){yMO`UR7KdW|VL#cvaWphEaaG6Y z#IAK{6F!bKNte-o%5+Mxh**-AN#)O}2ijrHM?t5I0Ma7mJpP8cd9n zFFM{9o9*9JW}NxqCTeh!11Nr^p?AB+CNBD}aOp&&LQ1@<3T|HXSYor*vF+~uSd7T9 z3iT%vDcKxYE=3ty+Kb5>NmyVl+HzBRaTH1I76pCp4Hz8-v@TO+9GW8N)t`sI2ktN$ zm_#?c1vDW6PC-yluJW00>vNKLbaY&?6Ri*IBPYpYd-lwCXoCjeeQ)?9anY2AK!t=X zq#9jp$giVMh9EZ+xjLw(r~okripOoGcIUu5t9p1i6CzNg7zo5e`A6MT0|w>i{GTn! zbeOcgdiQQC_E>m{G?VE)!p&3PHvY^gMQ!}Ejzi$(61&g2>w4jW*@pD+<1%3=s3UL` z@5KW(Li)JY@qKu7Y2ZEr$w^~Gm5dLd8`Lu5g=fBPY^cidJaxnKNahZ}Jd;>`@^I5` zz8{}M-nV7^m@k#|GYTVnAg(PJFaDF69wJ9%QC~Z;Cy@newjAaY2a5sNh-!5o`oL|Z zH9>=6of_ks4cKd$aNvF1yRW+Z9UI>mz+I{b8ZLP?KmGkIYH+o-7;gGD>%EyC#Tfnp zBv9B3B;}eY1TN-AnYU|@^PeJIU~-}riAN7pGVy%R_X*yEFkDv_1 zpwW51q;^jkN~Zr|^CWScMSh^E(gx*#!jzT!Yey)bZQ zf!C>G0M8&pj)8*+$IhWdzLog0-@^&?o=jbf?;m;-b6QVe8dp*#lBG0`#Jkwi%LeC0 zvW&br@;3d7jF_><5*WlQ@R{0@0~&_|rAf<*YDK2KnAe0O+e}Yx?&IINOP6F8EppT? zLKF1t*@)J23piu!&-Wc@%p(o`cJ@I*xFGDznIse=()WQqU?^@xVwUL`oU_I6fRugC z@as7)px?&djR~?$j3$sur3e6ok^w^rljM$Pz9R-qAn(YE;Upa1fNSbIXxch5pvMPt zh%QCWGvBhwBP}h334m5&k>_HUhuQ}0PGC@$W82ci0hw$^CtT>jpHYeFQRIm*kbNKz zi2g0Qv7XiB$=iUjBv=WlGjgQ)%9UnUR(}y|5eLQYO@%q5u2Wxo*ulRhTBt1dFD=_*W1W%nt z+Y7Y4!qa?fUS8g7&`VgHM2hB}d-hcKv#5@~5HK!|&t(qJjZh4a>ft-}40(Hmh3`X779es}qpq!0D{}vv9RBiSHGQsMngp7ElLHw4S%}mOqS`VZ=ppVq+74 zy_$T(_Kg#qxLmq$C&Z=GVqKb0x+eidweTNl54;Vv8HClNu<{Tj?!K1R?K{R=+tY%| zku1#Dhy4z#D6{dEBMcd{!Y#HY@+?n=u8=;;qv3eY6FmZ_bxy{vn1-mDP_Zg5YL$|p4{2G0D1a4(?117neiqjI zPhu^C2VqJk+1u&E<4z=lTms9^+p|PRORV10{RyAE?rNJE4``W{ET!XP4vpXFLOH8A zN;f6WVObcYZoxNN{u{t;{Mw5sWkpz3&uD;4!G!>hR)pjpWa4kG^gAm0iOh?gB;~5HfOwII-^DT9%Jou zq++s(zsw}YoqM*GTcu~lz@--SV#-uRCe+Ku{v)d>lztjC!J;mfcZZxo4dx4{zqFQ{i;JzYk&M8heK^JLwxL7e+1(c3nFl# z{p6R0NuYab*Fck1p4W3pl0)Tu*S>D_q#d`j7duVu-yIJw%CCkB@)T5gEXu~daf zX!hg_a44_NL|;srQ+|I6uYn@_V03pg)oyXsx~i%?K4r$Zia%dAnbJK&VsIAc)cjs0 zbrBbdD3Y0rTHt>!xyF8Rwl3S$J@%Lf$q9^=D~c10j;*eG^sG?lMylwG4 z^?AeWj4`T^AV%q#7p#eJWhPaad2-N5d8bsbR=oRW{nqRZ4YU{%iOOJpSB`I%$zs74 ztgZK?Ii08qI4rsr0Ucl!$s*>%mBAG|EDhD_^KpD^B)uBd__jvcX*1ap5{cLm;#Cg< zS=(@%+AdC>MGf$MNvb^-J=MXti}_@oK6){-t1z8Yu_L&433x#*b<;ase(0vQ&_Y0G zPZ=ap7Z}jpcSr;Yb}&kKd92{V0d`95#3nP8%1*^JC64SSnc2h*x|MUY_;V*M%W)Ss zhdGv6aAqpxlNVOrr5~k<3#T{jypsnUwiS7PnqSHOQEkL>oWY4gIylBBhv!?UN@`Af zKS&ia6p3LW)Y@x;I&&r z;yERsyb_koJ;<@^OeHnoh!OuZCw0DI<)!zwdo=o+_>u^p2QHF8$K&BUMD;~o`LdgF zaZ|)ppG;T;j~i8@H@~-BS%HS#qtov{WyLgwJ(m&`IiWGvy>4vBSe2^J=1D70PGR}x zGs0K1UUMIzH!UsH&WgZ3I{*1yX)1+N3< z6a5RX#f;cb1B_UDtei^GF-Fx#ofxRLsMY^^cqZv#t<5D(vY;nki4f~jXA_oWJzKlV z<8W>q@&5ZN2^0YQ!u;c#lmJ)Hq6tw}-gigJJ!xVH$`S7;`qH=Gz&M;LU510Hpxk{` zDwCU`b$LX00q|)aM6nhu~ zFF2jd5aLN=|41qg>F=N(8t|;A)ie)jE1{w!4jwlxWJG;|BSBvy`j2U|TNEh^MonBP zvZ>v>zdp_dF6PW)Bgy?JZ{Sa?hxAcqRF`jDcQkm?%B$;3JL9f$I%$^8To%UCmzz%^ z0U&%)DE8E;U%wj48ne)r%F&fuy)82ma^9$@#wigxmhL629vryI?%wV2Bx>(0TNe_N z)Vg$jxzOMvo_$z%a*`_Z1rJQXGhih`_{&|ii8(zR7j zH201296Rt&10)qv?}6VwI=EBaql$MIUBBjQGi_dCd92OPFmY8SBjzggGD7XkFOuIZ z?=8Fn*AG;ui4&963U0SS_^y zPBCvc$o#**a{|#B-SiEsxeDN)Y{xJ@IRSqr*W+7A! z;aQFUp6sYpW@uG#c$TPMfDxowSCmh30b6f9>-@-PFkOm0?PEpU;HBR|2elEbkcRGd zb&oo8dAZIH0SJU5exAI2`}QatFbGAJ>CK>L~`v5i+Hz z2YS|%R*q+xhB7=TN7v8OULL*`$y3;4G=4e_8dN$wAQS4M|D5pbfNJjtX9{q1_VH_% zhcZ@-_|(|CMAI8ltYqN|{p|l*LVyu4jc78fpE9FsO-(#9oHdln@rGg6Ui^fdTSKal z@(&JJ2L70U)B*jFb1oUbIC`*11ehq$n3+*7``J@VLxXhGfPx5uSQSq6 zb4KTNfQzfM&B-nlB#HEM8cj-1gRqtc1}vU6d-e^AO?LNIMop-}?U`TP zrm9NbrAax)LZY+Vf-%Rnn3&1ywv|-A=aQYH+9aSuP(zCv;9{CG`9Y(}wzlGp?&_?c zOBF*CH@nOXx0X?DCIpl=qWrg~`GW9rDHy#>6*8kT6xcF<1#K-(DeAU;i58d0(;FUb{Ekp|~m zh>D6{iDIRN-nMa^=`ENm*m@$}Sy$jd%7NO3yIJ~`d1EKCDpuyZnEQT#fm=anWoDfX zm_%?4Z#Vssc-_Q;`YHxda!a!cD$ZTKIi0$gGa}d*$KR_djpV^NThPKIifZp%XZr>A z2%8@J~rq;t~H8)M!YpB0F9lg-R9DNWNUHw&)_cOIO#= zPQoAm#QgUI-2R_Fz#*ubj;>!MtC6DB_Om0}a)P#s+W^ntk+ygm-4=Bc;!o+p8+V^G zDs;JyZk2B+GHUUBkmRqEaiJ5=cOxI5ywX+wNd^_ln1aB&}A!~ka?TkB1~Q| zaP_=-?Wj4C8vimeJfJSACy&KuuS#k4AsQ{RU=eJgutn398AdKEn(FGtqywijfL28& zXfIzr@#zK4-c62p67`@pl9iFkt(AXn%9rKs8W}3$R~hVyvh@K2z5!0R_#J~1U(#{F zJr4jjGO3kPDs5#bz%dy4pU__%=CYYa7Sp7`i{E8|L6Z@)=eMoR>wIm%9gknyvyrjNKB{&#UeaBU-vcoO;r)9Epep(fRuPD?3e7j<2df^afuRO~;Q^eTA z$G#h4RUy~wH27V#Uu`j0W*RAMxPSdVUP(@?S0@9ku2JMH;uwTY)JZ4<TI_La@ji9H~HK`nLsns*&X{~9!eo%i+*3TzM` zkpQNprJ3}NppWIO#q@|71G$Ys9p4_TqZ?N937=%;aSDTMR6e{v2meD8Koa0j6(Tqn zEOC6;`IP(r-C*nF90XDM(4wl)!=C(jP5SS0@8J|NzyfltabBR>x-zh2y+`|_BJB#J zW72==m@)S)HwE6F^_1gv2K*_RaBJ3C6GLyc)U@iX6JRiuM+tN+aA{LR-vQEeUU7u9 zLL{Kjx?60ivXSMX&dR6zNdgR_6@6$ju=Hf&leEHT6#<6>vtgai{?@bdmqH=niKn66x|U;B_I2cUZ6Kt>2~I zzdv!wjEPgGXre`x76`UXp@^R5%ZE8&*qkzcl&a*6{Njme-S*Xiib{g_ef{Q5$SzY5 z1#*N8$B2%MT0qDxJRk+6g~qF@5MzXZ8On7_Wm#CCAw38M0ncU9v?W(5`&h!paN$h1%NFw!k(D6LhxC7tfF?;j`or+%+0vr-i8 zdqNv20Kbg&w(+KQ2JSTD zOrBZfHBnn^l4RyLp0iVBNbSO1J{%~EI&S>i^05QTs?(*5`^%$UTht}hBm=akN2*60 zAzeDU+15mn+K z0@7(<7@gPRsVumyQeD}8mG)9s!Etm(2yun3dD!i+wTXvCtIck1Hx?R2Z~%)G2rhrV zJ^A|!($-PN*9LGHI&a>I=SdB`6C*msq#D)J&{&iEnF!^7L*NE;C?cuzdE6R?d9CR_ z1kjNwa9F~iTphoFTuC1NS3}8uWT1jrs@rE|6I9S*(aTf>w;j{7vcHW+C7VmHPVfJ_ zX<&1W+0ey!CMEOI&AJ)s&aWr6MTT&~7&o^Ek!q+Wg zPEhcJ&NE^;HSgXbEduj!9#zzwdj=Ai8$NjPWjXP6JYy!59HK=%md@LzTo7&pJml|B zef;9gPj#aR*b2JH%tSlqJ(o1=)*Z$I0jBowHCIjXVKrFPBX!x+3ZlL%@?AguzgmEX zHy!R}ze+~#2ciqzcR(Vl_uq&LgT#C-(Lm62$Wc@(wO40of1Yxhd z-%3;<8K+;t#Q}|%?@JatKI@YcwGlMvwRrZ6_9TvoDVSH~W30sUOexeA1MgH`jO0y=c@RVq_!P0xroI3y5u=69vunu{-9qtFmL0@((Zl@Qflw!!S88izl9Rw!_|6j7*n zs9fK;At=YD3Dy{}D9cv!?7RMbW;s4zydk?iDN`4IvdOfYO~XVhxR-TcQn?e8`(;1E z=SHb})4^1%fjdq)ui@!=<=f(c2vCJAf=qAe+(4O8^~yKHooe+sRvIk~6*sNJE8rf| z&gHZy>tO0!jJXF3?90_^Et>>lsLgUIWD=0J^)=N!JcP}edtxIGC#4V{!szVJUk6UL zwT;6N4DG(oHkFKA5oSTX$Yu3Fr-BA_@~^*5C3h;|D&rxw4Y#U$G%F>vjgO+q7JCEY z67E96;|q?Bz(c4x+U^_l^CxF@gp0}hXAaB2ld6j?Er>o~agPNLHRye8x&{0z+5$LF zabhITX~;J;=1p1YLq;~|bSMu9WO4iv)(4e6o4bkv?gjC*;esb;v=RGboh#tEfcraO z!$Bw9C}tFclz#kAv1VfMk%-s+w`U2@!P*5Xz?60igoz|+hp5KVo6s`*Vdh15Uv15* zCXRB2G{Ts~p~+AIb->tliMu}W5j)oniIBW5a5>xjUaF8A68EzFAmRv*%YGEvL&^%P z3L>2L*$)U@!yr{xv4P1lN&aS_Fsa3MQ19f|V_gZYD$&;t5ehl5Az5 zIPY_vpfLKKs#B*(mDQldW4l9|m0w~{NZ8DT+CcTA}Gx+A15AWEL*$G_Q zL_UPT3{F60AZ6=`W|W8&o1*{JZQ4|eGa8&fKGo&YBsHV7XydH90F4tbr2{q*g4dvy zn&3L)~BAR53uFDLfBY?xdwDpFcnb z=CFy7EZ*b>kuvD`0=}BOAV1c_%UHH=t64U*D?j<9EgHuk|>6t(|RMWyUlr+mI)aGp2LzHxpeL zz?}#1f}jL)s|AkAnE}ZXIr_bwzwcGr0wU!MT#yTH92cU*YMCXdN&d?~*Ox>(E>e_aCGF z7G#_@>QSH0y7Oxh|1vsU$Nxo0PBc-h+FXwLbOSj`i*ojasFm3x`BR2V#iSOr|MbP8 z^(Fa1FScz~=iA5=Y6STr^ZOkBCHg@Pc( z?J%j#Ru!`0a#En$nW`}n&O?ShQR^P%hs=j(bfBucffMHr0iflOnzF3xye%ml#9qRm zmHB)twOPmmFfGa~lHnL8X}m%w$y7ihOEMD47RrjjodZugUju9b&KIjE>jI*|25F@G zbmFSeB;2IwuRP`9U59q<2C*e(TvLLu|zNJ}&W?G(K@3}$&hjwfZZB4GW(msh^^bs6^l z0X5g}U)dE&xUY#O%742f8J1Yo@}GYu&UV!CTO~!(p*^@@%XPDu@LstzGKr2#4(i~~ zmgS}RKWTo6ERV`8tksr_1@K2a$gcMB)k#NTiw+WlUh!T?);7-?^UMe{7HvH|J*9eL zs@0?^vSB&?r@BI1C0{toNIiwMBa71HPSFd%)bOY~&Fp$$0EK!1SKO(y9c7V>Ju)DX zWM}hFP2KrMvI=`kY)9eV71|6GGR*mubTSOCi)s=YDWzX5x?F)#fKyHGbj1;rENn6R zUBm+F898+%k~&;Eq}Gl-mb_@pPoeubI+Di!`8J}L^L@JTnTr;k3NUOY+w0lfBZGs+ z%_PEi>CXwZuF?_H6PctIongaAafX4Yas@A^>KRb+dj$uKH^nc*r0FHDokvI%2~0^6 zP(xY`_y2wcO1I+|Ep=+utjYQ0HL0dS`mM6A|1}*ALK&n~`;H~xi&&AT)Q_@U)R)*SB*dJPV|LucS&pqT8RZCvz`F1qcVB&v+0k8aV>n|vGeJ!)&6CRIT>#p;}C_<7IIhdu9Z zy$7a(hJk|3hthO#H%ZhKrzdPq2JmvBO6c}{2#l3aV2eBfFPd8l8l2oBzDM1=d$%hG zq_ALZ-lr@w2f@}-?73R=AFw~bl!*mDnBX4HkwYLU*8g}yIZ!~d4*!+cx4!cA6=A$g z!mQB*CMyc2d^mP5)tg-JCT%mAPb0x(_O&CoHFhbD$1JY$^_ ziK{FA2R^e2MJp#538bCX?E^1>vF+9kd5o{R8Ub(7h2*POuLedhWsXiiYy0a*MT$69 zD+ugq+gCkhj+k)hM1cggrbEdo1}D(#is8ePO9xbZfWth7oE9zWj+D}=hcOzb=*(I( zdA>5Eo+N}7fFZ9iFry@ldC>H_#Wy-`8HRj?#13`a!~$EXg#ecsEvt6n%lE;m{T=`V z7Vog;axDK{Cf##oMcr&&BFKe|**h|0WYQ%j?%c{)rJqG)UKdqJ@5=Z+qI2LefM+V= zBndVbPhL(xfV$WoH%#O=$T+TGaMk{8mC1*SE*)A} zsFWElMIKY`H2kxJ-{WvKfyh2)UpE4dvia~se0_j5WaN|ef5CUOYX6pn#M9$cKH7M( z3mJz^tXA~2&d#l3S_a-JI#ThwmLv?W=#kwzx49o&@YRmKA83(End5g~3tcArOK?u{ z4@kLVu=_m*0Wt+G{t=8x`=Zy>*p)!Z4fXJ5W2|bOFKf8O9G^Q%IB&jmCKh0*vV+t7 zoU>5bOC>1cFCnLk(+ib+#=pRoBRvH_YWsG{3XtA5&927O0+;e*HQGSgsIpmvmMS<;9)Yg$t#R z9JNGf5#gXk~J53R#9olJ^N*OnjvnJ{RwdTeG@hGW1k`w_StcTx<{N8-m4 zZbW-2Ju{f^zXAKK5IG**iX!#Q&NludsVlshTLA4csW|B8SCrw~0JKi}VD`fvG*>+_ zp`q|Q`ToeLmi-~?Cf^Fv4kONM@?!@;CE`BCE zAB~^>nvL`GXdE;Tx~m^bm8vm_aTp0<(v5QyiAWl^6Le}DdRvDuRYA1K0W|EN(DRb6 zUlucr4aD&jfJ8(oMjJ{H*;#1+sM%BbCqcT6shS>DX>QypJPsZ@Br!R_u73t2u{gzy zgnkbhCh0m5I7o};0^cGzap4CW%9g5ZV&K>nE zsLjOXl>$do9+UghB`uXPbR_!1*-=l*(H|TI%^g+a^oeGSLz*YIP@28=Auk-kWWkK6HTd)&-;>3X&&*as0NNU?b;f@(7>$8P;5ct+KK*4)~~h1 z+4Ctxe6cuT@Y#W*3}lVibGoE+#|#H;gC4Fb5MR1Tr@$c@dpT%gY`+S2Esz!HdhL-d+5fzv6YzKhGkb ziOllyig$HUz<849G!3^o?-T&^Q)XM)yQXkGe#6GMYapYRcm#-JD&6wgl3gj%4x;@8 zZV@g+Uz+0Wy8)bJ`XBlklCX^oHu2?bGiO_FUv??@s4NdnCzi+jJ*XtaV4XaYL_<}? zVMts+i7OYNctv%Fe7<2XZQU?UBx4pWMaPT$3aD)>JC8ZUxD3k$G8s-ErQYXDI+=u=ZSOh-O$9F>L?;N%tN0PbX1R(fC}olc{P z@L)&g$58Ov{6r_xTaIXx;{qikQVz%0GIBY(+4;FK8I8aF^7}XO9lg~=d*vT-7-pzP z;+eICsH~~XwuSl*WDZyAH);VoB}V`Pso1Uhe8Qh>3;XQ)gL=B+r-7vfeWUfxQJavE z=a7eFbIP*~E~Q)FOf#uz*kSi=(X0qc2+k>NEr}o~$NjnZ%VHZ5{?P6$Kq@5~I7_tV z?t)rC*$lenQqonvDr&so(r$kmC0{S z>F(&n{G%(pyTry(C+g_v9Q9TAxre{EqSpt@kOX;Hhs61Yju0D&b-7OTxaNdqY*x0B z!=)fYH9c;^$WO)%y-n8^&jL;oe^Zx&DC$?C)||5$I*elIR%Q?#OnVlrox#fA3Vi&e zOs_x)g>`KykO(_sSO{G*?XxKfn?0>5)e-`**My_r{=cWn_Mjhz-D}7x7=hq0033Js zn`vQlhJ8hdLH+lg!C0vV&i(lBCyVJ4TZ&G`=JUr-_(F*Fg81TK6*Y|Z0XV6KMfh|G zW>ZXDky6N)hWcXNIqOh?F#ZASYZcw4%xRSu; z6aMa_=a-i5pzh4!mQfZ*=$Kx33>5_pgKAT%AR)tzEOD7*?@~swLQ!k9nmPVuLZ^Fb z=ca@j~Kpzd1l3$OXT6vRM&&1K`_Y`X&Fv<$Fr6pt;(hRy24+5Vm$SGaL4oVhIyOf zw$JAW10Vc&dthnSl{?j94jn(<7K1$nVt1E!Z+{ndT~0b@(uM->~jteIxYA_^^`Dfm_|(HJXf` ziymTd&^S5at8#Pvx7YW3+W{(#k(j?q+T(w8#SpvOE{W~a;FZnmi9l)OS2zLT8V!Lo}Y?_(}w$LW`y9N}$(S-SI{ zMjFdZ6A&(g9`&gIWSEWW3|-I8Bby(aqKE>T@5SP%yAiV~n{f zJdOmq?j+f{2YA$7|Aeez9UC4)lqOnonO_EIrUe@_wEfBnFvzSAZ#oEtjI2;-*W%y~ zx7`JusSG<}*Z>94u#cv$jRgPnsT;t{@?xJQgGln-DbKP=1A>4f3J|MT-pAT>uhLe2 z7J@N~a60Z>&qilU!ZGAAB+xor@5`Yloi4qX`7Gm3-DrjV4+cgIytfquPTQd@IQ35o81eoXf!Ye zyDe?9Yq`&vJ2#o?t>8pAOGALZuFGBx3M$?iFz%6Nli2bT-6TlR-<|mQd)n|{bFN$& z-x)m&LI8mq$(bj-VtT#o3cA-qF+w%idR5pEn9n{db_Ph_42St|vD1&A59&~#uv$)V zNKcwo3Z!`Wl%z6pR$9#Z)farewMmU=^Y+b~R`co(+Q1b#P=)=cvbA;vkjTG);D1kO>wB`cM4+#gHS; zQ+vB5hyMF-F!K^}vMcPD-TDE5!BlPz5Qj2tNOww0??%CL45|s=OHPLS)7L2Cp8L8^ zv7cT@!$FF_vtbFnJrT&nf2a}fx1deT-lG2W8k}NSMZr8VYfi~%mmG;J=PODQ8@Ooz zcJ6kw-);QHfBx$-1}P2RVVZ1IMI7nFALL9VAiuLK{uuGRc~%7@1MRpxF*d-ygLXe2 zg}OMfiprRrE3`3FE^fGLipJi*{Gi!(of+AZrs@0?uHvcy;JS1}*2&P-a(W!K5AqD5 z0b~oyh7cr=n+k$X>f)~tRzgK+lB%V^#Ui)qSHfdU&Vf^4U)M#5m8t8hyn^;uD8%>x zvOJ>yJFGUhBhv~(Wg)DS_yFUQqz&6F=MjKS{IE-2*xlI9+s0{$$sMFziGdci3WgSK zFogT3TXw)?3`so5-rna~i3=&AsLBlD&gC3{;&74a6`8q4&T2(BH1f!Lu^43PL=3p) z03UH0E*#@*86pKMR`v}3vL9RV|?n2OG^wlmY2;H z@&Np&CS0Zl+IPx+;R@->PfR#oSDp*AJ^pKB`E;-_k@d0quB|!TY39Y#FN{)p zXHFf!GXSsGpu!B_Yu70I9jR~}a{%BSz!yVWJA$@J{xNj0dIO+IsaXG6n z_t=gB=78ZQ{l`!X$Snz$!}R!?PfjR4dbiRW5tyv{2CN@N4=WC^!23A&AHADPZV-1cMZ39&XM97lhp|PB`M@`_X6XKYfIC)E zSXo)oF3k2_KcGG~4jXMy2xNa)GyQnVli)tFI|}M|UfCjJbjR3;6Ysl$fQ*A1`gLK) zrqY+IiC2S|Fv9Xc^aY~2pb}hjyh!{&qo{)OpCZ68E{PvKc{VEL1mc00JI(a}vACS_EcSV2yNBThe zg_PA-=FXbcg4E36igA~+H+thUs&)N$u@;&Y8LFb^kH>`t(1PdbMt8vG@n3pVcbiI3 zM}U$j8Y46iA2%EuLf20rOReN)+c60xv)#J-Q)bhpgp6|9^s;Z*j1qabgfTh81k`#5 zndjOHPZ7ZU$iDFQ+dh-)vEn_!9dL*RFRbBFW4D6FRI~aFb9U|_3R{Z(stU{yv=R7|gL2v2T?`j4Sl|UV8xJs! z18E!%5n|^dCQRUb0F7T(UON;1{z=Z&gMA5TGV(;2|O~js?)K7A@t^g)9KvHSSN#4}1#DWf~0(~&{g9Cev^Jf`pqYM9X zp>ttkaRvjgZ1iACT#;pySH9kVS0g3+ksKAQzwQhgOV_#Q+;9u|rN6z*gaB>x) z4g;`qTHV~Te%OZ(>jB2ZZzLa$uju8(n9hIQil(k37tj56#j*AO!K<#hnAMA^3`);4 zX);scf9_h`x!FzOQi&L9FE5i7*JueS2ey(pB&b0eBi%3}pQhn<05?z(3-#S2zq7lUxa!uv0Lk`Te%!fy2K(@@4%UgTkHNA`2BOgGfCH+ zjDD<~a(jOL5h3jko0}%7Z)y1H3`;TClGJcco6LlMj!9j2+^CbfsE(tN51D0zUj%_ievZKIL*Iw&+EGn)ohX0CZ{t3Snu_nN(76ZVBN3x#^!5OM3-)xE-x z;nk%GFK_;5dZMGr;jewr8n|=q+4ju_ouMlf-xc|`37WmEP%d8(gXN>F~-d-uQTj~;FKRQs?h zBgAcWvis`(MO8(Wp_#1x}ra zoxM!{GRMt1Sznt5dW4^5gWi5SCUy)Jn3z1_Ce#vDPycl#a+(FKN%54$5rja+QQZ$; zANzNUy&hLlv46(3HBrNMMBLw3s^9i>Olj77n*mAq%~QHj72H4+11U4* zl#(@HVoFUu?dDdhE2wCc|NQfPyQ5grVJv|)TpV>GSz9Qg=>B%~+mWvWqdJry{rPTB zL#1+b=pGwG`e;NFbvO=h+VzbwDc=2@3o?d&Z$`BY(Jv6U_^(h}zG9#d@amS?0b&~` z-f4$fo7{^3!bVoXz_U5)DdK=KK~V?7DS42Ff3(u;QN&9>w-Ui}LH9S{D!RM_CppVPKZLE-ZURLsI; zjT?JScECEHO*s#VHMX}&QP-7QSR8YYrVpoic4J$j17AOEHR1! zOYC65E{fPd&vh@&9{WH4_{Ztv?D6gKWgA({HRpWa_j&I2w12k^cgfMS%QviEJ^0?* z3n8(gVPRpr{SW@u|L&Ni1hU);-(24QuyJJfQFCV>6q17fDgpiApJ&JEMxJi4*OOTVCd zNZ}DA%)YaNW5XL^w%g6;utQYzf)9DkBcDNzV~Z*d?9i7}5s&n6&h0@|$Fxyj19CYy zdiHPd^2c*)Qh{_@^GYv9UlGGyR_OkbPlXexPQCHw!GA>zDQ{!rR(JWw?0OSE8|-d5 zBqknLXE@U?f9m=3r%xZ_U77UqZZ~*A4XWzZtC#Kyx9alcuuq5rrQ?^zdW#y*s|&>B0OzZ+x`$(SrTJvtmMB)L{L027+=j8X^sp=Er*-b> zH8-HyEia;VIJ!iuP+q1XTx@t~Si$}WB!WoQ6*+zh(MKo?#KyFrnP0YK$&x!z?_g=y zsABh3o8CXzryt~iPx$lslP3jd#v$)G3I5>o>E}t4X52e@kQ;t*r+)|zR048>q26zU z9LhmXehG7g`T!xJIg2L45_RbBPx@V}6dcE17mRQbGIYRQ#ib&+TV8lcd!_;kkom{vsW^VZ{^Fw0qLf{pxXKgbhVM=bDr!mM^ zcdf9m>NV~d3Wi4^QH|eTgp1MDRp}}x3jU&-oCbT zJ9gQ|EaK&sskv@<+xKx>x(0G^uLbO zGA5YA7*_=lnpfYop$R!Lix~LDk@}yj`Cop3zuOH-cSqf?-HJo8+@`UCi5>mdpaSKF z)t}YwjQ_c<=5u-mxejw1JnO-qt7HGHVT^q7+QpQ%xoCnnU+!Rs|GiSf( zE!-Y%yj~$C;ac&7l&T9kzT$sikN|wsZ0eyW%;z z5Rp?3zhMtD*Ucf!7qS*j-*k)RpjmFNKvL3SFnr1zLX6REig+m*)fgnBA6X#wsl;6H z6*M+W;WLaM^Rs(jy~z;v!nnhHz%r^iG`UK;GZE))@;*(x+ME~ob!yhu7@2i7S)P4+ z&>{cb7bfkh`T3*g4$bVmC=~8dyCZwa1$YNJLvma_;z8_z1a9vf#a-|XOL0#*2ewL1 zyky@56ez+d+IDng$f&d1hbA8%b(W`=QG>5O513gDF8C~yYAQN}4ZLIej9(X|xR;}9 z8A-Vz;)}J7;-Bxa?WpO8TYkMH@z*Vbvfb?}DV)51qehh-mUqqm{Ih{6?x7<5l;^;C zCMBz@T_t|M-RFC-deE%QrC&-|HFK1LPHE|FhVEI)nw&DptyHTFNK?UDRQO%NAL#n* z{Om%%{OaobYD?Z;;4ih@I$ULcaaiv0<@bm4`)6ZAR$HpN?5Si12~nQZIP%-4$n;gzd-gX zukHRV_(sC(Hax9_Mz*%ape=XLnS;EOL*wo*Jfl~9;rbD-Cg%~J2E3uecb~a~$1if( z_a75htU0?XBH@o6JB|vvL#LMUHktZebPcnrq62)4i0)F{7m#(GjjSz@^GQ1Z$kcZo z=1?1U?_Sr7S7%msA(xtw((d`e1&BzPm3Q!;^OQw;rD1zE9@Xc}OL0H3`j=k>y>XRF zy`%r`chCDgPJh_rI=^i_z`0l3+B*8ohvcyq@#dudQ&n4SK$qF@O>oLc5+dBke*#!k zOubfz0%hzZVAW&{nWFS%ni7vW-KX971C(j)V_?G+A8T1HTK%LR? z;LB%53^Qa2HoUP14ORhvsKP5RZ~FG;X6e_sXThj3CBdidSEP|w@)1Vy^ot+x^!~eN zbPsTpiPxL3Q9*jjgTn989q3BX0h(1M&?;u_-9AvD;e^QwuEqPl@|R^nuCtTg`kJ9z zQc?Go|B{osr~8{o7)AqH*MVDCj9Iu0eighj=J(&9M1G(RoUe~zdFxk%fWWiCm*LD| zPT=K_-|~_w2L@bZByM8T4Xe~{U>5OyL~dqtiTs!#MIiGeylPjUR=JmiehtLDYW-p7 zbc;a#6aO4?le0%#sG^+oD=u=Im?Zf%#t0yzJAD0}ALOoqN{i(#kOL{Fa6mWiK6^`5 z5BzuH`am9R3HCJDuX84sxXFNBmTdvc0-~;h2n0mc%b_H8C$0Ef4hkEe_AwrRBqbdK z9btTDIERF|X=DeQX-tkXBB$uhn*({iGvnN8PN^M4EGBhxK$A~Vdvx-iC_f8R&66t-mfSs>W!*v@8<+rIc zOT2g)w>Mqjm4X=k+G7 zii{kAPRl8ULvYC2M;q@TLC_c^Roy#B9f3Uv)ZTgQSP@`$Ctu%34c*7du88S%VbIXV zoW1+V7~6CWxIivJs>|DKDBz#VZ~QoJT&V0Ph^x%-4MbW+Z5fD&5#7iFuI+03pDCp& zxA~DRd46_N)8fk`3r=zGp<$IOH6&!GD!TfVnN}Cl(>sqI9si_S!xt~3#&qu<(tZ2f z4}ccjrh)r=l4h9S`o*n|;S@e2Sz@?Lp}ywrjIES;l_({JzF&RpL92|u>;%PhiC({5 zrIu`I{(!~JPC5dvL8s0f=zi>ZP?ep@X$1D|H@LWgDg#=@b>prHKV#Z;zyD%YR8;J~ zeS>~3{wHygz)--FC;3pG^tckmi;Hv0p8(maM))Y_`lBT&?q?y125AH~rp4Jx>l%igA`pQPCX^;@HYJ$kli z;hQS9u-CQQz4!I;DNYIzV+_xBxcQs*gkI~GZ)^8EOA*8XR47Zt9ni#>SNCf7E6r{v z1tL;hToO#2s9?Ym6XlpnZX*z$*1nUsv_gP{tPoLfnRaQ`)?e z&u-rvwUkaOodiMkByj8$J}SOA3FKb~496?`A#&^H%vHcv@Kcbd(gCUWqJa!Vn5c;+ zf{y_Tc|+!blBjS1$Av5G+vHlkewEo8@80#fPOAqkC4*c|9NYYfhM?rTWM5RC$6+Dw(#wTyY%cn?PY(6L*fNQZj<0P4&p7Sbb$(aaSc^Jp1(l;o z#3~KBN;WBJ7;j7)o{xOK&**!_lLj?fGa0>T{pyMI$XqHuRcB;r@@S$P1r1cANZ#-l ziz&yo;o#btjXp=HvYO;Qlb430cIAf@8~ZEVi_xG1B!CysGH3rV)!s^ z!(wi*{XQw$5Fab>0K$n*8jy<52h~Z#+2tMr&M2dAA6{C(OkIZ_I5iz7pyVA=&geb@ zezdnQ3$5KVCF?=%afg4k09&28=cEbsmGDJsa8L!No|csgxm~+hzBVegRl{yOS!s(U zNm$wsWpf}v%i_F6j#1?1+)uMvEXU8DUDDs$^%;vp$<3-Q>1w*tx+_vsZdYJvLKe*- zmbUG5>CbtnOGYU@6eJF|RiRWUS+E?{mYNfgCdz8+rPj2-+8j?1L8}21(sJ>OX;R9& zxVS)<#z0C=hed1k>{Jfrw}yKnN;H!n7M(DUAecKZ4iqYcw}^J12^u07zr98Nc}A38*bh(Cxp$Rj8)GOrbC1qpp8WzAz>{T7TpoSj2pUp zjvwTvWCpBQN}os0XrQBmOEvK2`aRy8(5(ZJPDywqaqlbkmMpBY7|{~)R$Ls=6WxM- zGhCW(80VQtJodmkel3$R2r{Ns87!wr@ntlI?y$7gR>#P$UWNaJATYmCeV3P$_wiw5LI(G<&sla+QG%#mSoZZ}y@9HUz2qQQn z)T)q7adC^y=C4HXkfop(N9H+deuXVwNV-dgkZX(h`wr~}Yy#WY#-s72xscsThg7Of zkHi{!OVA_O!dYvC4)5;>@>{55om^~7@J^n9LclE;24}BtrJ0pICbS|OY$U#75ldqD zH97%6@aG6ifXWlLhkZ5O8_2fGswTri}ITPFN%rO|b(dbLtbkGMVyzy%0A6^G1> zqN|oG9_H(+jTA`3)Pu8`wOYE8oW&{vn%v?4+Sl{0u@^$MP`Nv)H`~l9Xu~Gb*e}k* z^!ii0Wnt+k%NIhSHiDDkn^wM+dn8ucJ>gbOEbe)83N^?2AczFi- z-=jn<4IDZjJgPl|6?j7wC+fO5#eI<+3oIqgo`XA1*yGYGV;FC+sZK&dJ==qZ-wb+y2HF-Vq*b zx%4Ws4y6(SCn&E@@#tRXw`=jfLLQG9>fi8wpZ6a=bn)`4r6~|ldrEFa$3r%28OA%z!)^{? zIObz;gJ4V;LW>|inL$*U+Q`Wz#?k}r44)(wEx)kSFNKeIhju=lF@1V%w8rH)={sP$xfxiq@^83_3WO1_0{QS(O(bIGi%tQR2v6?lL9T&WWnnd}w+ zHHH&bRt3#o#KB_Am0X+*%vP6nhvwo0U5#qLMdLWYN@0^JY9J}BY2xhf2@T_IdNiQ> zisj4oS|A1+n1AWcV`RgF$6e|A?>H4wf0)#OF{zGq`ZP=f%s)}nD;27id$Z^0|c z;y4$3*l5?Ls#ld)XPX|;+yXe4GPf?V-4UT4jB?M`+WJo9hiRy@ST>aY8UEE-&GR5E znHY2HZ5!Yrxn9|ScHdmpJef(-_!?wYywG=xr4DY=8YDb+a0n?(+Pt)|`wb0%rP9~- zLZx3%-x>v<{e9l%1vInhi>n-NtQ0OKJa^wjX2I-DA4nBQ!hsJN&;12AHUvPiWsF|? z9q&&nN3YmdMJ&vIpQl6{WkhhUG#q;XPWL3G7vll8>=?IfmaKp9|Iz&}jTo3Zr%r`4 zHz)Y#KhZc{0&sdi^ENkgtF|PuOgB0fjw<-(O}oa00Zme^YdfL$^V!S(TAh&M_*X+u z!PgZFxZdl+%o}g{MZ7=7HARRGSrMrarZqc!c+z6g`iQ6G!>Wx!D_7=P$>0$2hz zy@)+jrSc)K%*cu#;M~fqLaNbbVyFLHMzAIl@aI&Qo5uaktHxrJJ)E#0gC=<^G==bs zScmXWdoo`#%;V0JCyhP;&I}Aq^&+K01~zK_+mJ&pY)4cI^B}|O7v!vGG^a*)Qxi4t zYd2sT8Cbl+hc52foKu*vec?Y7Wa+_Yv?!EpJ4!2iHp~*@>yaY#+uM%JIK=SQfKxc@ zjnGz@;_fzgB%Pwt(X)HqyeN_!75mNe9n)$Kay`Sr{HWLXx|KNV5jtcT?EuwQh06QO zC9GjP19&*&x#UdDsn5S$qD$H_{>7rD|Nedg`$r}XdU%bH)Z?6p?D(eXIP9Uj_jgKp ztP|W&d1r?S?7(z$EZ!hd$g>L`VKL}BaNw%i*M56MgHyz?>G_b5>xqw?P8H&^yGt*+ zX*^!PqCE7ZJ$rAr_YBfz*m*1y9^&3l@!@O(f&hv(* zZQqXbuH}TALzj)~|C;P7Bn$4XZw*~tdAC>QqGcb>QR9ISE#Z`HIQ4+h2utI}-6=vKa)Jhz`Fsr#)xG5K5fSMohFgPoU3ncuy2SuW``U1v90yS>EzpPz;Rie_UKV0vQE z#l0Qr{x!DsS$4@1C8B9J*k_MPGQ(rmz(;5p1t_3^Lyl3N`;hTA=nB2+Qc4P?9pY(M zVJ#auO6J>eFlE+dQ>83{JF?uc%;n5wL>Q{Id3(QHTt(j_$rJC)@3(5DtxZCRtNrsg z8{&J(b_DtBO?y+u;s^nb7`&Djl3o35!u3^u{PC*c-o^y8QyC@XGFSL;D0`0FfShiu z2{kg%4;)vD%qBuUBDpjID+35#5>iDm(9+cqvVZR>W9In6_(tz2<#m|bR` zCTD{><0M<^bpO5m%p>m~1i<`7Nlw6BV;m3;-2uUpq@^od_=oPi6Z>RRPNqNV$Ka?yv^GG0$j4Eb;l_+R-AXqCK{O+6DF^buCanf;)&I(1|n z1S*G^Dv|i*MpPdm3REM2?2{No>&?Lek9s1RoI{U`f zf96bK&Gwniy~Jw-tv%61Dg?naK- zobJt=FK4oihoy!D2OndxDA4h*MfGeVk7aE=o|l)mgF)j;x=`XSWcBppYYf7Dp--d1 zB}LHzN2uhZE(+vb6;JUCc5#y<)Gl-iEZG+=k}CXLR@SCZ&Vrd%PGAAa6erj*gqa^} zGUoSj_D%Myx|iO#4WnV$BNv4^PZl0b;i<_qxCmwBzB{MexN)Gr{~4Y}3=j&@({QEs zi&ETs7B5la4!4<5A&o$Ta-EUqK96mA9tyePJOgC}QJ+nZC{r$$4+nb4BH7_6JI1#> z1ffDXgM1&e8EEjgX1?qC(4|DSm;R86#LGSJ2sof4n5uA62&kFo3ykqxdp87n5)ZfEJz;TF6!z zMAkMS7c!3*+#1CcJGt~9vwHRFw>0i|n34@IAW5atosN8b-84EjqLLC~NTXM-zFhfG zBQ*dth!i?=fB|ws$Jkw^zT{pokY(!yerLkTtgXLE=!!f*{WWznLa7v&TC*3@`oH|i zt(+zX(NcG%f7pTDzR}>F#bJHB(7j(scRekTSV9*H7hy9f;9(iwx!fBg$_8pmOb9QF zKn}gR)S%`LmS*YI=_T7jeh9e7RVJ}Fe-R*A*$)5$rLB6`VW`s`-XD3uwAhr)fjV0- zYP}7!=JV9Pwu&fz_sJ6|$glXyCE3h%=@PDzN<};SwN>wC%tvAcY5J(3YL zKPKwiCVq=lxbV}`&I`U_H3_~!iOe$#f#+8)glt<#O}+)Iznb}VtJPHdhT1J#4!CV+ zw1a`ROaPCg*A~?YYvM+N z;=1CD*N%oLtC$Ax(m*LP09lwu!Q@PztjwIX9cEh!m7-UXI-YZHB=TjCKf+q!8~p(e zvM4W^z6mR3BGMdc%@aU+aE8j!VYRrbWkHF9}(r!9LLZ$5}C; zJWgqLt=v65!*~yrWT3j33MPI3)G_-Qdqs=^=ZII83FMMUN^i>#a!v{tqPP>lzKgaOo0a{c@#E;*1^aS5 zsK-W%cpTMV-+=>5(gA_l#crzq!{Pk6k0VogFV zM*Q$YNuD8VuwVPR)#a>LFYfk_MI4O|4Pc01e40erbJg$D#B_aZ37PxwK z@XtmM^WWMuDD!!VZ1QD#^>K=PQ5CW-RjAosImKi@`+Pcw{GXi@c0_EZq0^i>ho4m2 zjk0m<_Klz+ep9F7Whl=YV%$)l{9A~>Xt}E@-jc>ReM?{4V;v^g%s6}M)KTixQMG{W zoq;PbC*&5qo5I(Y)(+gB_K|ckXK+G;CZ};k1(L7?S5mfIIT4;hVd(562-mat@yAjk z7O!4qAiLA;^MeYJQ3;OfSBB~fEahrbbDM+?I9}kCP>U-7i|lHdJ}az0`NntU*>_Eulu*npVwO~1@ogj$TV6p{v?^i$kvuCpOGymQb z-Hrdgd7)Zjr& z<0FCA@W=45&&E8TJ0y1)C5~OC;Fv5kv(bX&F-{6>bQ6=W1O z!%0X5YJ=U~-)xGD+xYwM-{(Ikg_a^!iNdZCzWpEo9F~1x6@hpq@sbu(fvE-~o{o32>i=g9Rrhf;;S7agDQ}0%#R#{9*=x zu=+l_K33r5H~3T~p(Wx?XB;Gk!5};j)4$KzCgS{0{bIB~4bTD%#%@vS#pK)ZH6y?PD4pEz;HlWNmS@WFI7e_YHe zFyz?v5A$kv6j2^j>b{Bg~wN+81AH@$XowlUKjpW*g4uXCZN&h#o- z!t>*uHWO~Phe;^&ONdFuv)oCyH^1sOu(kxC>?gpCzx%)30&p$;ibcmMuW}RNkmB8z0)+ap8c@M801z^F0UrDE4{D zlCQM`RI3Ig?JJ~07!UTtO?@6* z>|xguOqgL6W}}O**W?YOQO66S#-RT%G`~)zYNv=cr)u_`xdBTHkpbUAUdl*C65e?4 zPk&2`L1vip=eX?1XRltqJkH`TVxSO81y}WXgP*x7M%G~$PuC8C9#MBIIgB182 zt5jf;X#$pCdOM}AfA#46->%#ag{%5OZ9w$icqzTVbI}O$-n0nJ&O1^ zM06B=1|cEcLwh8$3i+u!jE=)_pbQSQVqMrH1%L2XBIhbe76`RkV(x?Q^IAJSnUZO7 zfW89+{y5N*(m~fUTV3%FV39DFxiCSQPtEI*Qq}0NM^~r|iZF>duqNodz!p(|)HUji zw{MPoDtCKS?zvN^mfeT~Ap2r7=56o{P5X$%Op`0bkjbLVyU%TbWnT)7=8eGf7beJD z?@%q~QNOz#+}9;BZ)pe{w_-*GzX|fBP8x}AnQmcO4{QZfK`J&;vtOWKqSMo9QRZqY z=7tNj*4_8cm^t$VtA#oVS0&vrnrT!Bl3ovAtg)kJtNom2eg+mfs%XjIVWS>88)|PqnZJ>;A!0Q z^5dt12OSCMmYGlC$O_Y=iAw>S@!fq0RF1@MoeC3Xtwq2Go8r|V+j!=;Qyq_f?2u3R z3TW7c`2O`4jSCNQUH9T{>&Eihs?|XMKu6R4#?NTnzGVZ_&>m@ifNLaW%y>WUGN7eF z>WI4*D_pl%PCoC}irc%lh=JN$@jj@76nrh2FFU^Q8mi-azb*=Ljm!;7Ttzi}g7;Q0 z0o(70JI5DA_Hx?FCG|RX^=k4(1*qJ7e^AbP8aLV$wOg1%hCD7yGg`{SEvx~F^ZFA!ogdYFYmp0FzxcFy36bFm|9sJ zpm9UyqM-i;FaQRXAt1&OEwywJKtq`g%*`V9-!}7V{Nl~M@c+Sb=d$B_1~a#rlvP|o zk_JT`K|-Y6Z=V0R>8?`dGMhKazb>WPnr+ZPW}i)aoe1VM7~r~pT-Sn~ z8{t!9Z+Ip-K-qsaLUHKOwb%Ee@@!q!qIukz4|vN>ZdL+0K#wWKO3AImws6^ZCeNXy zVf*>X^*!Kwcgi>SyOa%l9EkjxtP_m6!V=6t8{ zLGvHc=PpHtM?+fywaq@ihm{a6Y+N=@+ZQJPEq>QfcPhDy&B5j8HIb23NMtB-aP7-w zQBm%(Plc(onK&VwXbov_FB|#gn3hc<+BcZIVXN<0Di=5ejah*Fk6b^6bHvc}K`C>y z(YjrADg`%V3cSHIfNFAFOFBc?wi2?ID)i*yY#1705TGczIQ=uGKOuTTRN5f;AY~1! z7lk^K#(|QP5~$>7dd^{JT2%4qQ?s`xB$X>$)-<1zHWSb$g&vLz*NnCTBnGhLnly2D z)q)_`%;30L69zRVeVKjq(Y9)%w_Ny_C1%LL*Ko_f`(48U#HL-$F#W;0X>5mkJVO85 zfK-7C9kut<>`{vdK?2M<^zu3wvfS;N|MXfU-#9oN%FnL;{tlG7(-5n*J)IXUbuQ*xOI6WV5_plOKPJCZTuewzP3K_PdSn{xl3EGV+Z@_3D?m z3_lo*&L76tCMqe&*QrW=azg4ebYRf?X_#H8*QeO&uY+`Tz4C9(9R0G5)WcfE=TX^?7}R z*aT*IdE8@NIJxv`%f7%4ZKir?+m5#h5p0sO`4Gc$(GnlFnZx|B zS~Lom(dDR>&|8+a>eAJ&5}I)1rsgbk+rMb|_|N~b5$XT-mrw)DaK8WNR;d41uApd- zUcIX3y)zpB(XcPq60-KWk3?&;6PUD1(kbgZLZ+DrK73 z@>qbJQgPGuFrg@QLH2>~kDgs|H6UtHH3K!8R9xtY)4nt7+cURbJuVg|IF)69Jhk?L zZ82XhNf}A5S?7&DXq6QX=2`+XW9+V7yDpAHW`x3{E+GiTyE|j-Ha)CmG#}9WZVyAo z0T04AR%}+qEW%%)N58&Yd(^O`o>Ym{C<}n^kH+I!(o~v=5LC7buptDk5A`w9@Bxe? zHv0B_{ZcIxJ;NL@(m&f9dAble z>SK8B8c0{=A^|}t&)^lwU=Y%JZm4M%@sFaD6XfwD^$TH$P-i78D&iLl!qpl#q;g{1 zJ({6~}|AT}&I$F^b zwwamVNYeq3W1L-o8N^u8xTv*cHv(tSTy;f^o%wW}d1b3zJ(AkkVRh+&jz-zVJdtcf zLONt1P$?BJfW2#)42V8FuaRnL1gT3qi*X@=llq-SKa(O7D6 z$7n9Mg-yInBB|(;IyT#)fPN_k0Gi54i3NzQ!Ijbnn^+k8Sdu&07MR zqPh613&ZNUvzpQS5+6E{ho<8l+HNDpN!}Kjb{PZ6GFlk6`62~|SPB4J$+s~=rozN9 zIqS#h*wZEcb3H5870!KnW7>d~mt$JL_%Wyaf&j}n@6NWFS2~%s46*FC)UtG$sx3zb zKeZbifBBac{WI=mKF(ab=1T7!(Lr_XEvq<}3o7#MH(inzm=z!0FD0UR3#QiHJ?Y(c z)#|JL*WdE^{pg=(Z-)QiT=UkGY0oVc;6MSoh_%US@a?25fx$5`0@Q? zERQCS8YrRk;ws`ezMa!-ZTt;vG!}~xGs9??;>~wK{HKIc+9FQy^z^b!ggD0L0df)u zWtu2iE(%K$-5Wwl_SOE}{3rX!GN|*M@XkY;n?f*F^zJZ^8P+*JmF#8{F&y`&$T5+r z$SrOoRP5^J7Mb&t(a0C^+4tztBih8pl4**xR>TcJ(~_`Jm4ORnee~w7fY(_7+{I>R zOtZ1E)qvKLvV$fbes3>1tdc4D>G{E>Kb`t}4*`#}Xo4lOV3we)%;^z9r-E`f8_X=$ zavNxEqWAB&dg#x0C!Y_YU!;P+{{A%?Am1X3+x%+_THMLW1@kUA+Yogyxt9e=9oLv*VotM&Ai#!q2Dpd>Qx3QXb>Pn z9#n7c^A$8r&H9H7l9R)T6)}b1^3&@Y9(seUU0pH1(4bC}qBscQT2~FGE1Sx0t$X*o z=OK?+mvTcqJdR(zY60DYT43grsc^p_aj>r`k*;8!j(QClp_4#Z-)=pgpfVXyVvSh=UUx1_0wztg(71}tW65Jg_+*3 zuk>-=tX-;?z6H+njScWE6Yp7uWB@;KfVQ3+J6tw0{V22&|nX7RQ`z|T{N{$_rsPh zRZ1RY#X{IHfF@1iK0?r_X0@y8}mQ6@VSomj8V=4?zP~Li`#34^# zbgm=Tb{2_J`}o!Pn8SB8IuGZrIRR`pKmGJm#pTh+(IMWQ?$4Ua%9Uj3hkMkeDL*S zKt`nyh9COa_geUkj>DeKxL+)Q{KASP?2RdOKv~MEU14a8F_#+Ql(-##-!SJ^+BCLI zVKbb$>{sEO$r=u2d8qnKvf^fuo}z}oN7F-bNi)``Nm)$kUGL)u25{@Km$b%u5wE)+(!CDHbbQ@J@aDRKyK% zrqqhOPP8~7OO~`cy!h=5;;kb_=(Jxz)&7FPf!3%vStpgO0@vT>5H>*)g?WEp`umw* zIZomcSrI8lpJtTKi&~AjG@La-CX_SzhB>a`c`aQkoL}9d6lYoH!?x>PoSjSDocY|5 zefkNqVsAha{Oqy7~K`hOB3oB7YGXb&B+?)6L?!_Up2l@9=jGT zZWNkjWMmYFYfdNg`W(@9L1q5}AT~oA(6R7g?<+^<9?5A_T%`pob6#+8X+*$;%hz+b zXr3F!MPIyeWW2eVV?F4IvH$EU&1U{KYe`e>1E4^!%CE1jU^7CbOQYmL-D{vIHMe@$ zk^9p8d#yX2n71d){ZL)9bq1Cw{F&6xYF1~rkV;nYbi&*dc)tX{;8uO?{ICQYZP_Y1 z#&>}`W^we$CBLw=xv-p;ty_nLje3DYP;+77N})A%%bobIG0oo>GVkxL2aS1t+O2QT zz`Mv^tiWw)&X!uWt12y&)!EvbxI%5L05Q5!O-khVrzfrN*1x~Gx(#fp>7=Y{&c~_x zI3vB;^riUnoLe|mVf=b?z_(UXOiuTI4rMoJ-$ZL=Lokzq?gQg`5pGmWtm7ZQ{%Q#d zSEqR=l?DUwRT07G4}8ZK_qC8M$f7_<{wbwTPLXOu4_)FhT1ZlmT2LOy2$r6{e#q3R ztqeH@f0-Nep4i#1f7;F1^$7%*iYT^XYXotCSGIT)n;t3Hne&swR>}byRnrI_;MP3A zJW%(O3WbVrHKxrRf&M~=B+BzU_wU#JcSMF+0D=WYIB36T4MFlPz<4c%CnAt)R=xMV zfA6nv*tV?&5DL>ZlLSsX)GhHkoP*A@ct;SLo^2E zS}tOLTNXPqehpaw`EpLn2mvFdW-JCsAi$DKZRT^TqD%@LK{)RHjG+=o0hy;~WcXB^ zwj(&li)R#6vrBXzIY@8Keq$xi6p|JYGMzp$nSH?z%*{_s9~wiA1)6+UV z#Hdl5a%Zuvu{U>ux<-km_$K;jO<5%FM008l27X4vcPVbs&IHn1eN(vq5;(~>JWeTt zH4jARg3;F|>Z~kHD%TYh4=SzyNlH|gCQ*Ame1MJ5g z@-9d_|M<}(kF+gYw=Te_kD%nS%k0^XRK|S<4-STqyHps=0I=K43}x%E5G?~-?K$V& zDCNT-ukx~d;i$~5=vn_=jX}F^^L!;uN2NFZ=D}c|^Z++Eir@XOH)LjJ{xp7k z*Ky;>q)GF@54f#&=n`61 zxEn7oBdCIg6Y1%%9^cf-)~0zyURuqp0Dk>+-TV5jA6R1l)UVmxd{azJ)}oJ&W)aVt zFLj9s4qAG&uyEm$kdWf_z8wq3?LQ-T5SaarX1j@RAB-|%K+2g1bEK9)4CiYr#tUGZHnm?i*w-%Gsm;+ddD~mK6vQ; zPCiGql=6ZwWLZ|PuKXK^kj_~(>CH^2Q24~AUgzwaG`Y{Q_~j$ig+{o(4Op`nUELB6 zr~oz656v)Rp~c|(0){pbXoYCbjypM8<3%FY-&ys;uYLRUF>LA=3{e-tOK*8xSM(#7 zZ;M9<`2ufhRb9Sj1G=8yci_M)E+37pCGe4Is{i^hE3b1z3_fViMX5H6`w?8Q5A!4M z-~S=KZ`tldd(huGGhv~eHlm9ysHKA!O4;~=6Yh?HtBWn(R7ko#xzy{v{JTbt zeAuxknx0rHI1B3*)vq7HQ7v1w(nG^h#6$BYE(|Fkem-n$rSup+UqRr$KNk3-;3x<6 zE}5xTlqs~^KWOH=gle_M+BPq5`s|=-voarRKme9pm1845|7nW*A=bX+6`<3jO;}`2 zsHiSDKfyE`I)vQZnwaRqKmZ&u&J-%;OkbeOn>Q~H&3N_OZM*k`qgAkv44F17c&Tc| zh?; z;8G2Di3p>=lHW<%bYw||(QlYVH~fbm@{f)=ckbLb9p|*?*$V3z=bSl&%ON=N-|GQI zC{&s(wr|^p4Ij^YBLOlZ*HWGz&U$^EPn~7mzWvZO-@YEmch%;#H_+DRglT1D%lS!f z1m~hD?%NU>_!mp((yCP}Ljaj!>I@ApE)AJb`gFns9t`p+9QihSCr$FNu*Xm(kS9fn z=te59$-IV-Q-hp&V)V3#GE4C0Q=cEZc5R4{&mnn%K+~3lbvAmK)~xDxE`PiNdoB0_*|yRWw|Q z)`mcU>?5?*zRgZxr2+xMb_V6${io9+t29o4p?h4nLPEU28yc6gy9(f^G9J|IT)JD$1wKCpeN#^NX6to=bZ}D zi4f7ZkA0+2Y`P#b?N&CiO1{{?-+^ni)uJ4h(h`E}D|Axh5;qEg!xj7*1`U(O9o?BIEr7g0qBm&T z^>hGoF2*N$&A1o&?$~ z?}0Lq5{RG#dfvT9RkMAEqk>H5{krU&oFI@1re@rx^Y*Z~hS~yJY{uO;lJ#?+Gp~8i znfu}Cckswk%xVs_YtȮqKwlyiZ$q{1)^l>rF3XBED_Yy zu7mm)ijZguQwj!dVHJmbig;$%zrAwc*n5@J2-d#5RF+e@XD;ttdHeG^F(1@ux^ATB<~4mj?=rMCNvm@xL`qw=Bh3I}X!@&j-+9#! zbR`H`0bHOEsu3QVfS^h_;q4m8Zy|pxDjK!ucf3KyM&L>V!f(3Pc_rk6T+>ivdSvBI z2aFj9jAEsz0V*=2lLaxt`xZ&^9f=bnxNYN}vNp;)r{~Cc{IZwvkx94Q1)wIE#*0$X z@v^n~Bg=O158Up)A5KjDC$3-oL?59DYN0DfFe#QOEyUnj|p&b_8l ziJv-t+*v|RQKSq0P~GaH*Y@Ezm`E_I8VqE*l_0$;Gl^#PYhX`Wf!*gCI>EGPW)M71 zFt56UiB~<#0;SO{a@(t2(^+J6RT#?4JDAle%Nz>psGmDk13@7k(c2zyXFw>e}Fd0NgN-L_T z|N2ufwg@y^fSCG#Ql?$y7>ZRV%1d?S|MjODtjK0dOV&^?RBc^Ib5jZnjVZ!tea{7^ zjidH}fdi$Cri?L$tAsX|yo0_NlJ1_q!XVR5RkbZkc^vlSD=5r=7eF!HldeAy?Aa%NfU+m8?k%cQx9&&)0{jcwy7NpmXGGHO z-OBdJJwNwjf4`O0XeJ}puKjaVd1GFN?)}mWR3GTW=YdKDg}uCvmBgCKSiTwcb+x^< zWGtdNQY2;aFI;vF3gZGZ2Z-ZY!b9-a@;LPsV87Bth=%Cw?H!xb=KSx;$rfY~8mCX+ zr>9Iq#Ba(_6d{M1lKo<0+yB9)6qAq;l2+L~y~Uj(42kVZK~olbQB%)z>Yw)knUlkg z!c2(!Lo-3s%}2ugr4eWdH*M!|rhk9?NOHgK(suZpy5+88VTD9%!f@8*-)q1m9XO8g zHu^&6O<(9+^j^xi23(nc`1e^KaO>Z}oK@4&U#-8tNQzUZ6h=pDj$PKw>Rv}c52@xT z8Pca{eKeme1=Qul4W3UPLdguST(xQz^@i$@yo!3otn1b-$-n5_vu9{d{oX{RG?Bvz z5L|CmP4z%FY*XZ$)$Y*2({E*3Kl@1K&4N$pm2xz#0yXrYSyTO!H8WU=)TpdYmuPzV z4F!j<-(lG(7v9-rMjSgr3}oh)qdU)3J`^#?uSZ#6O)iDsN>Uwn4d=z9%``SuR~KY3 z(C2B?DgAu zOC&|5=gg%KRvEk#0tY}rjFp>DzjLVl_?P_h~|K%?bA#Tp0 zab@SIB{}sCbG+`0r~UUW)5PbXn$)$6q4fD8RZ0H+X%z^V5PM!;rzsU9M(E&)36xb< zzDqdRMfa}|{Q`n>)Bkbf=(a(pnz;_JkL>RE75W`Vjw_`<5$|i=s0;fMub$udsM0nU zd#mO!hPPzenkJIExw-W|xc#@^y4t>SXOlu@cKzJEpbWhtK{Xq|K+}}+BQu{}Z;Onf zzwIlD6ZtbN4lBE+*0agh(Pj*{TXFgY=#MuQcKD&Xd;@@i%u@xp!c0)$iqkEdcQCW4 zr7hC`cWxG@f@~0(899f3`>ihbdraKhRm_cV+&J_=+lmn$+U(N3l?CkS=^5Vs^f(?V z;{aOIKxK4zPR+~wrX`m>XZC#RZ61-Umo26qp=g#E+RG;o{dn~87p#^*`b5GOOLW?^SsP&Qet#A|zNK{Bj8sa6h73m|m2Qt?`;MJoXJI1k<*`c+W z#do@T+aH;%MLA=Qun?WZ+??4s#uU7d0e)NJxSIzpcy_jrXKMNjiUwxl_0cs>GF78; zY9jt5nCuv6=0s+3m%15_r;mp@30oO$lb~-y#^J+z=~li_e3=eS%*;$V*q?5JP0Kfj z;_&#jYt<#C20Jwr%$e^2;H+`sw2iFPq6WIqj9;x0p7DHtX*qhiM_L`5qI!>WD#%yQ zfvA`RO}wQ#fW6Vk7+Xj%&3YgfLw$>ZbH@E~fdZQ~Pvp^~giDixKNpVgzhm#$$W?Ch z#(wd#>Q0|ALnACHvQARFreD8amhOG`_T0uqc9DBk%h%Q>RQpOypxRvyf^GNQ!BQ#F z48uph{19G#YPDOuGo1;$VT1Fnz9J|m+;qJWh(6iTHEEbqFq)0mzW`TEiF)-TuRO~suy-iG->C0@sNauIS zJA{9q`E%MEEIL#m$3YS@mn{#<$+crS)^RA#c^ocv4AhM?ppwpcw&INm31yh}$NwFD zK5bHUOG`t+$BNf$RoG9?VIX4HF972AYulJHQl7hA>E&bVFD+EKKq3LAfPBO|97kd< zEmKz3NG2%8z#2AwrFcBVzyOc1`2hzGw4+wNegFPqc~PJ}pVm8R7BNXHr}<AYUYl8QlTZ!iF7;k(kD`i~z!(&a4#-KMDAU2wqJ_+D_) zsZ%oPa;__>ZQ1AJTW!jwC{HJrv-GiKHdmpWa3*?=-Jqe}X4rnfA-q@<5sg)@TzS`= zCQ)Z$2QKWL-~|X?3juB_k_?X}-nBg1miucsgkqrA!yPjOU^W zl+f{8wrz8UO#Cuv*lEJEoWu1YJ&>ZvvX!2?VrZDHQGt?IzhT=vWw#4FdoSq5)~DdGpM8hFb9<-TjCA;5Iao72(`Tg%!ZzZNHNlK*6Bg7HZ|`$WoICycoo=_AqCouXdui z6A~23FwX*FvP&n7ibj{_)0KLD>(j?+jNiQtx2WApxjYotIzlMd+%mtQ^O+w;a$6M*FB3I-|0k#^=F zLvehoyW2M5qXwJ6=Wg?Two3AJ8cZ%W32E6scNb1)8^_}fM9BLToWoGyEyy>0D zPgV!YZ?(Mh2DFTK=6-xIcZWjL(Ky9qS4{ubb|C16Nt`N>-6RG18)fa3tWdKo zPg{Wtq-$Kt=@;SdIip0GMMYVd8CPl`@;3d7jc6YEk{Rz0a(%??o}j%W7B`4q6KhE< z&&uQc%SJG)H|%YzX!XA@8OAt2f;GJDjW_UzK0Zth?fLpZ@)0U(X26NijV|IV*qw%B-GA zED}hTZrc87&$quCvIIkDr@AYmX=yp+J=PuAw5bFo8(d-fql3n{nx-$L)CJysr~9i; z`>+7)QDa?tmj5vwu>vCu@BH|FJ=l&x+^2d1jE?RHt)|r z%tkQw%8nxFJ30ZfsO;guR-zvi^7)pR{th9q90?;5!QUftgI)hHE&Y>7n!iLyY#TqO zYwvm#pYl~P8{KpEYq#`CtRU0o9rW_$WI9s?k2qcW=VEq)$nF|Y!X9*j!yA4f=E2EU z+8B?MJ&wh(Es1z1i&gUb6Sakka>oDk1CjGDV!~>>sNI_Ns;AGWQ5v;n+PxoIhkJbZ z@caul^(f=Y`NxQnO=rUR8xFfS{N!T%$MiCJCv4rCp6JyPg{bV20-kIwwxRh}Qmf)o zv9;@hs%aLV(Lqtyk~s_;W~J!55kuBm96%E=9N~yGdGadpHC@Y@?#D7xBbmHYj=fk~ zNTg(Zb~(;-076rmEZe>roSFnWN&o)PN)+}I`cv^BGFN!k?ME%oCB(-wa^veAm~a)q zqqxZ6(sKt?vM94%r}rdIZ{Dypq9jyV%|aAq>XZkStcRYPlP&;e=I5vXECQpJCek*gnBfMn3rY64&@j# zz&?qCN%<^>0N!zhstPS$`~4>s4-ioT&q>PBSXqQj!JNRBrV}_2F*t+UCaZFyOHd=1 zOOwh(kgk3$*GW2!N?2%(^saoJW~0tjHc0@MIJDHlQLS@VL?Zp1pQnWBtA}=?Bn7)vp z3d>RgDeVvPIn5zq1C(D3{ez8zjag~k6`ze{p)h=Ol1#6ZWfY-wSdcH@1i*msmXu=e ztZema(U+1})4kXuPzcF!Pt~r0ykO#iP-Qv47~^d9`YjD`iayHnSGIKxNB_#038avq z@}(*+g#ey{I-0I|$L`%#tlmU$fI+`be0Z@AZJ8}~0i3Gz^DMbQYDaH2wQs&1S)7i= zNZ)E@y6lC#_6ykdsYr|Ing}Z4v@{r+9?GkvxH`DTjj&jhf{T$ok{uL8VdUE~imm`c zTN+@FQaLe@JV~UY934i9bJTByH_5qmWbR`Pc)9$fQu^mk(hvZ_qP#sMF(M;iU=_`d z3S_sZZDe;cBFlcBpCHdA3SKos^v$*Er@rYpVf5%@l>T%@1x`((&{@R7dGlzu)nb}# zSGp85rv?L1phO|O5isl;$VW|xm((S@#M+>;<^tIq`*!J4OsQhPW|zVf66^=KyJrJr z@dk8-!{$l$?cX2GlbBvuE<*si9iQeF1`Yd$fG5R(W>VozSd}G3$)TQ|bbw%J8m zAJ^84A3HsuF`!|sa?NCg=+YlqCC?PdC3$k@>l^;!;vuMB^wFb93{H*RIh~THD*hf`W8n z7S^v@7akEoXJTR^%oeeHX7zdeF=i*9OViZQP^G;v8*+|CNK{gCg|f1;&%_X~latd( zZykfUxOn&6%%sGzdqYmPkB_-pPkg<@#2Mpn_EOAr(8{28ygoF zm-OCy0vDpA`xn$yRYNN(w7k8&DU>s3&a}RMO+AvGcqb!+YD)F`Tu<#v->DI*ty{Mm z85`>!JEjsJA3yT-C4bqOar%7w%4a!y^G7Cr?xO@+%7^FWt@+-bv-0fOv+A0fHP4^Z zCCKgN>b|3|r>B=*VVN0mKGH}wK~z?jUTVk9sxSubqu<&jHU`WKbar+Y6cz^I?e1@~ zg08E1)VzJm)qZ#PrK?V^+jj1(e|=}?Z`{X5pOMY;U3YdS>&hmi8Yjpe{9LACU34($ zq34HG$J+2#+?Ti<)+0{S0nbY|bp4Uf-K{BG)X)5p78Mg?<>p?wcI{eqb@ki+bAPk! zE49AA&12ZIWlKsL7jBLIa92h2jrB)FH8uJ0Mm=BOjW1rle3i0grT1Vf)vXPu!XH0= z+-eqqCul+6q{{kgFL_8U+6#J?mOL@5H_;=CR<2xGQc`mJ^w`%d+b8Raiq!^sXj_st z)n@OybM)^YH=8FXnXIg=jvhOvJN&u4r@xm5C+ zgbPl|$zeEf-~jph=H_PUU6~hJSrbIptYN|mJc|=EJo@zougm`Zsawu5^Jp9sFZKRb z^S&0>m-U8(>)+Ut67^iG2P-_@=2k~>E4HTUGn71eQd?J-@rGk?Ha#=5_f7V$jt2)= z6zs~*-`$<#^z=L{kCv~xBDIvLXsY=+To7%t@t?*7xx$05L=U>TrQnN3e*eZ}%4>aP zVQyl=c=Bgw*ru}+!kab;;&=L+H&YJKEWSAeZ#FJ|`c&`5O%3+y>S`qwm6|{*>Q?L* zb8UKC)9mbQS_Xz%p@7+-%a=JWtlq>|IXlXLuafdsB^xaacaUnnRq~SK>&)$ms%~uJ zY;@r)LNRvbXXqvdWlvm9N{Sg7Iht$xgsZf))Uv>V>hJiE`g#xY@SiQdci`LWJA+-7 z+Re$@EIW7Zydb!N>)Eqsy7g_eh-Ax$hj>r^`WW#xZ=aZ|DhDEsU6YG!qzGs7y{?nL zKJloy(Bk4n7QA}X8CpRS4Ox0_%^`@3ip{v( z-lq|R?K#{wwzh?yAJ%cX(cIm2hZ*r1zRt6=>eDAdDk`e(1CPjl*s89wU5`&uPL5$q zIAb+lqICSLNy+2Kbb{Fy?<)heGj}G(%+Jj@e=0rUyG{)k=eB$?^>AzzjjQ_bf&Uz{ zV9jWM)2sWg2%!YcPft!6hS75>Io2#M3s_ja;p9-@!*z-m$YPE^i*plgJBY8gEcKRo zd(W1^(6HV~Xt=LF7NOW!cxdeF%Y&`AtGOrll^hjWcle|9!WfJ8Y#^(c0mtCYm)F&{ zxVyUtJ!E2J)XTRkr!UI8Z;SjQW^kcu>UW>=sb4lJeI#XaGMrm+-pJYW{e2pwAwvfT zhX@WgiuAsR@~LTQtgBZm*grfo{$26crNqSZ1qDi!z#BLC@f!x4bgFLL$mm$H+v^pt zUcEZ)ujba4PTLhXs(Sb%&%#XCg3^)C8;3dyw+yOd;q>Zi$im5JX|2|t9ihf^KX=de ziR>2vq^>NR$12T9o1#8EIx_I&Q|9&?$avWo8NX+qo%}U2G4U)>*?HvGrw#wEmlwO( zExoF0^Z7cb3%qL$90yw8zI}F4WDA9YJ@fqdjiIxl*p0ab0`GNobuA{?h8IT1#zKt5 zYw+sO7+qY8g^T=ay>u_TO8;mzkGBrENF`NOi^0cdCWh!37#MD9o}ix`?rJIaZ@RMP z;HOfx+}vD_D~`=KH4@h!NveslZ)0R+RC0eKoon-$?a1dcR!wFSg>eNY>~YWNSXj>8 zyLZogbx=l5PLxi_3LV7P&(30vw|=uztot4vt}Ae?Iu0nO8+;@{upC--@Lc<8CG`4-FD^Ydpk z%AiToM&1L>H+7H#udnlrbbFooTx$ES<(oEbLLQXm z&PscGd!r2V+Kg;(UQf(KfXokzbsKkDTb z6cj{dvVFU<(^J|a_x8Jb#|JWy@JmkoxKh)y1>ccQOP+(7mjCSe(9q=;xt3yZLKXRDw+;x0su={TUypdA^y%iiH5uaBKKMN$>g@?9lfi=uQpEsgs4@#8wpJIMzLEZ(f1D69OM zHNSxtGLk>EwyvUH!5Em5B4TiXC*{u=_ns#we^MxTHqVQjU%zJ8(b1vbw16^Tnr$v| zUQaYE=^Tj!*2PG`v+Dly?RD_YvtJtGN{;p{<6Cz~x?!53uU;rXFF|(qDh`fNl;FPF zb1Xh{f7SNCydH+j59?ZsN9E=_`RNq>rj=OcxYUEsT~w~MiU8xZ1yQeTeQ;o>z@{@6 zBJ6S49P;()R%`Y>d+<^INp2 zs3WPmMm!Fm_KwAJ+4sdAdZZV>e|={;E~ceJ3FU1C(xrjPE8La6Bs%4xt!nj$w^L4f>6vNy0LH;?l(7` z<+8K01K6r5J3ZEVTZcMD%&q9hyL_@$kw6WQ3joSQyv50Cv(+}0MP$7>*nXGu@W+y% z6-;~%-Bm%BWj@O0yLN@eWO$J+TNlOcJkni#)Y}`Igk$M41!7Ft&9cQVJzpgtZ~>8# zdg%TAk;zFzl)ccH4AvL{jbIuEE@7ad;{(lg=UK(nzwwjX69>4KEoNh58zFaI7s+|_ z=+P?V^zPo?n$f+>xfSi60seHEl|cQq{)@*1msmKqStn^`YlJtWZ^LEwyXoWmZj5d|sRb!SM9LD7|MVhN@!( zH;lZq4-f%_nV6XP4;V*5u0>59OspG4>+rGcG&{F~O%)!o*YM{}gM))rb#*j|pQEQv zy_;fLg^)i_9|^RWb@6kBUlg`r-|xQq2J}W~e?-_K96UT2fT{^--*HPb%dT7#8#gTG z#+?fn7*VNcPyPB>jT>VX6pY3L(-n=oy(iClu%l2+(|eGD=Y~W|FL;%tx_&%eR?Zp# z`PMr-g*N*AQB7j+Rzd)uaQ-*?TTJnh9C9-yW%* z{^fNffxY>5&nxku(9o^o=a1^>urv~98~pJj&8zR(DC-1S(_{UO^e-2i9=gQ^lg6%0 zGlRA;RI!$y_wV07iWT|z__$R4J7m_lD_11Dnm>LN*uQ`O|FmZ4A1&pt z>^zx?0^sN8N7?M|E~~P3Efqyb+h1?b9`sUZ}6D>-qWN(dhASxEhmd zDn?tk>Y$GI{P+-1#{Dz9$4WgpU=I7@VHI7T71B~>}Mf1~O z819Tl^n1J9)B5^8K+DkuNl8i3wQJdBWMn8>(MoqRGnY+SL_RPqioS3`m@CSx!uPaA zmZ?bn)r<^@+`K$TS63Q2Il06eH^kJ{d64wNuUz3m;SR%+k`Bbh--zVP3`PX#D{hkN zD=I2hMd)>uonCAG=BsVOxzHIcp-wp~?7K}$uU#2GD*&hIs)+3B%@2M^da_e6MnEKx>Dp$NZAdyFEF{{+0ILQ`#WV;vKL z1fr7o`nbl$US$~2`5@ka4m1+o2|E^R_te_kVQmPq>9k@LFKieyAd8zDPqQ8W`a%c2 z4MBP@UYNW{+QeV%-mdH8Q&N?^*w?b)aU_5bVZ^8C=j_QdEkAQYPhUR;f7Puw&kASa zZ#2wA9%BU{klLBl-4G|1sO}{?@#|C6qa$6t*uAt&O#1i+Wz_qU($e0EpC6XpA?s`| z`1r_8F;Q8Xq)lg62nh+xidW?JGK1kb9}z)CDLT@%`tRSr zJZkP7nx}p-q6ek~f*Szs@v1T)fRd~bQh8=vR$gA-;qYMvOMiU4=Fvv;pYbz(3poLCkuW2^yyW`HtyNE$*zohM%2lYBGeQyF)ecU}OtW1LMR|FiG5v=RA0B*{FNEh14tj@<6~TDk z$aAqVKbLKc4i3m%i}s&s%o`s3f*!(t@V@w_bVTF|Xqx9;)VHMXT3=Gh#VB)6OU zE0M2NTWqGjp^-b-Q6O^ob6Es-f~bAi$A`3!+{H{!&e{u(!fd; zzqkWdVh6NIgVLZyZ-~k~^7By_p?C7Ei`g`TYy0EWS5VZJ>6 z(*L}hCss(?082?19NlbKbCk=`}_Or8k)P=^Icn^^=WUp&u^z(yK+91vt3Rq zT3S5nM}=3gtiAx`&~xKR1uHJ~!TtNX01K5R+q)|KUIDnG9}z&ZGd4CLVX>OvWKUZ_ zHFQ|#FI;$vP%rfUW1jo--ifzxOOB@8JizVt`+R=Q_v0tx(m{lE3+??5^JjJv^Kd3MJm( z*wDs?#w_+rY~<4VGcq%4T3T4j&x{A787Hs>&zD{>$HEJsY4BCr@7FuENlDSI7r`2` z2_QXW7oq#B1+|xwlG0J(w*imA5g5oj*WB%Gt`$fs#>^eJWW71aqE{E9k2Q{e?m~D6 zzV(M0ckSAxXJ}a4)U<8TGUIw&94nW+Wgy_UARxl3#ojmW_Tq&-t*c8xhYP@0Phj#; zhq6VU_3lB-<%IbV+;9?sBnGBcW)aehR+AlItZ{OPhfnojAl}ylW(y%1ilx`&nybeG z{~oSfaO!VN&^27V6H<4wLy-fQuUL`lTpvU5DKbJUAyb^qRuc$Cb`hGMdVsXB#WfSB z#VWIXM3SYJl9D220ssOm))q9GVKMslO0JC@iHV8bSMB{-HR(x)7AE&%iiJAZmQIIE zP5?$v?KuX)2>Q+T_ChEP2Ecds>?;+P?jwU_bSI=9xj$Nf;e#I@DXw4^I8Q(kV8Ybb z8%H=U?%0HqEG8q9-7q61At8dEbve7AEeYBZFXyS58M*N?5_@+8QDP#0Rs~UqbY zdgk4Js6J?#NsmAy*_8a?!CLSl5p?X5!g6xkE;5qq)CE(U>hv4H9+6vFY70`av&bz3 z0P;n$wh&OJ*k9L zaAik{SDFJauJ`=qXtD-F2q408f>oZV`$-r}gJ?di+iJ7|p-Az#*UOj9{Rjudzn&RS zyZ%V!+!beAFv39I$K+>_(+Yg2kCERx6{JESW>>Jtax@Z_LmydF z2|E+@Aq6<}^x;d_uhVO5Ya{nny?n{E_nr;w&@q=2%7;3X08E{}zsc?>aNJVyfpBKK z?(EP9wpVs(SS2C(;sg3|Ai{8v(wQUh|qsI3lO_!;wmTLJ4qkhW+~Vgn@OJC)!SC z(KuR^70?0Ic+Hm|fM}thYIQ{8HjA@4f)!gP`SLju4O9o<%*<9Z@_0B1;muT z0nzoIofZ`v(aqh`JW&Hspyd3FQDDPK#@%-ru6#jcM)G_;FQ`6XJJXmrJ{YtfBv4->+5|^I4=$;c69Vd;*o1DFb zY`3{!cDv=?{o4D6MXNK-_Q#z)dt_v@!F?_+N@Ds)uc+4fCP zP>`l6ksC-9Uw-?tc7pd?Ny1p#pZif8R5Z z9I!u5fr{{SpGu3%jH2qCaP8As-Y9{)fiMEWE+Tmqdi5Eg1+@Qn?X|!LvIRh%;D;f~ zSa$;4fpY3bt3=DpY=GTQvm?ujdG*HgphCYs{smRyawXSY1GGbU3?O-XAKbwfHgYBp zHVDtW=JV&(JCimB>50b0Cced5%uEg&F5-nOFT@PUD54vIqN4-?xC3~3EzJ8R`0tqn z>80dQMZrR87ZewRHbI_SV$GLXp{bYLejN}!-s4tG3^VYVX|en6H@Q~qkbw*U+0F1* z3lu$~WPvh4b{2ko2I*SRAVxq&@il@7)Xp;~HZ(gXT=8^BSBWCHC*N+(5_iK^k4O>^-n?LzWQ4I6vt$PYHP)bHIXXF|hFXy9 z+8CE(f_GJ;;AU-1m*hBciqaKe!LxexYIH4oJAUsz9jv(9^kNtF^XJb2^PZ87cjCK+ zL^`S7=FDFHav!Uk&~~Sp zH;+pou&5{<9UZjSsii*-Q-N2yz@xkjJw#n79lL3!2`|l^MYzoTdF^(+X|||3S*!GI zV*LF43DRx)%7;Iyi%Uup&T6Sv%PIrz>7|>J7($>EI5aI*L3Z04LT|9j*g$rgpPRJD z{n9R`$}Bz7>c`cz-?QZ>=>(ls*b-YWiX4heIy*P#MdY|LAG1MevQBlRwCNeY%G}Pc z>hYE_ifawqVg)xV#D-ywYr7z%y&75f8QS!JpCrF{QL0HvNf0ip<0ZEJ(*yqf9H9(U zLC`YMM*lCpfmNm*@tu;NAB#?yVd-)k7OyIsNCDutg`7<~>*+S=l z!ypB+_B~QSbMw2qIz-?HNutwZ1JqOA+C7W;8-*zZD2qduoAU9=DHe!HRakza`cjhT zsrEsTR@czzA*BF%1jF2>9KIAgdyxM4D)joRiAebH;RN^O4K#;9Fp19Z0V0VYX7l(M z&5^E(gn_wVPlj9b?X^)UpFf(L`Yi^E_1IA5>9PH)2V1+Nx@ytBkt8^s95B_h@`9Vq zvxooj0`Mq{zB{=ySxf)jJzLTNfK>i+c4M|hB&Z#yh50$3sSy#7i)JahG^E#ubP54M z2iPyFtXz}*d-+tc^9=fJ&;zG`{pjlp2@DL>b(N7pPoxVp#w?&7NTj{^AVgSJmT_If zpZ=z+kgu322VZA05h)8?6IvlhL@2oXr#Cf@7x?_{W!o?@U>_bHKI%6y^gaZ2GsSm$ zloHtGKeOE~oyq|q$+Fm;6D^+?L807kxL$B#fWi!#nwmtwgeVjOqPp)N9H*fX??SN0GOj%_1_4W1t0CxpepYg?YH3pPynLmYlb1j1aXSz`&pP>kv zJvr$KzP-Akffl8y7NVPy&qz1hjGaY-sJ%TkHT8e=K?=pu(XpYiQFdgWh~0qtF;Jp< z?j-x`VjHADZ^G(8Fpjflm9KJ6RCe&ybt}VSoCzJaBt0X=!ckuNG?| zTL<;k@yHRD?uE$lfy19lfx>8g7v?5;Hu@;Z?#ZPk@or`1i0<=oq?hBPcNUSpid=s> z&>ppng=nQ1WIULjndt-93yC20wg$;q^f?w+za2s+r?hr$2*@Gy5@{EY6XF#Gg!iOrAUnEu<@ z(Lter>xCg@X4y}6cJ`3&ZU<=B*!*i-Jh8xtbPBO@pyzy9Y=`;=;hBmuO*=jCxiTP@ z?DxTtOOS52$a6XW{qv0yNWGGIvuNCxPKC2iHD_k#ZO~3f6_;o3`f}Ecx3Zq2`u%&} z9XobBzV}O*l#3JcuajlI1%ot;g7(<|WFC!BP+_4moD5!n2F*w_u`oNv0=%~?eeCDQ z$L!|We{v-1@N}W3ow^sC|IPUCxZmg(I!e{kr^`{YX*#9kIG9YZltt+t;-Ho}ZsN zLJ7Qe%QnK*;#8!MpEl$Xl5DVQ!~imTY&f{VKN?Kxc)RBV`+7i`uhw_|mpuG<)bmp* zC%UPb?;6Ca1BU*&RN(jb52ONj(##wO@`Bm4y75h6j~=bZ znuH@KHylupiHZs#KY&(4_9J>&1Tr-)svBvSfBoSqJbUtPG~Y$V#XyoG$?{vp*+k&L zVd7J*g1l64@KtKBfgy-pG#lp-zW~c7w=~u2^&G=aKyR`UTJdFl$ghC$!B^BUI~nNe zE(2W@j)KOaQHz~OMM1We8&y^fL&iYY&d8%g;pOFpDyIMe#J;=Fr6t$y;ydZH@ukQQ z8{eHp69J|xE-ntuMV#+6sJvIXR%^l7_vH*AE;a+)nPwPsQZ}Rbh;T&_7y%p5myU<% zyDZ_JfL7o&F=WNKdLxf#XHhkfKW-}sJtnedw(;L`%gfHV3}DKU z!=LzEo37|1+Yx*OV(7)SwTAu|@Gx~@xKdSBtuKU@8Ut#dsR) zh2Kmo)=pH2yW#A*Vf=#gpuLBO2PN5H4UnAG7ewlD&l(gE{&rc-5xbW~NO-k~wE@mdCx{;n* z2Ja#Gg&GXgf_7fbeNs|_^a44M`tie4;3vRykhE6>8wkL=y3dI4vSrJ3>D^~IU%U9rczqoAe;Q$!&RD6ZTLM^t`=QA0Fwl zD=LuPVU?e6B(96zS?SE5gWliT^!+6abih+F|Res{c3DQ%_?|aBN(0r>FXy(=9;|B|XCOTHh~R^#A)O+c%ktdIfaW?eiD8+{;rpaDg=tsKF`7?Y;LDHnF(j2kF|64z#Q$1&qAr_o-vx z6)1tA^My4v|3=lLgCfQ{Q`1$Tb{PS$AR@ATuw$1VEw{Ysd?3JRY`T9|mv_TSd1UZ& z2>2ylWiaQm%8f_&?kQY=kOfJPlQUu9!44o8vhSz0&_4tNjXga4DH4j~p^qg%f~(>2 zkbZRtir@(BK%i5=uWkF&K|{74SSSH7y~`*>LIxaEKia)H&VcA|G@hQG;BlvNuKHeG z(dgs%AfTB>aQlx>Pu7DOM2Z}(`-$c~91*-I^JW+)RFrsD`p-EWJcz_Y3-W;T;FSr` z-2xkZR^3?V**G@rjJTlsB5(_C3QZduo6$}CC%SruO0hZ>d?^8FHdakT2{Tai1hb+aG z&xx#q=t-iA7Ga8VrSmX^mX-s}O)e_aD>d!^R&9BY?(a7U65sLKMNe;vOrFWYR9`H- zwKaq_0xK&DD-ZZrqU^ySgx4Sc#PGAHhYDU%(w%vJc$m<|V$+7K{^e>h+>@3vVx_}f zm9V@C?{B_&&7r&72eRuez-hQsDBOZFV?dp%%RgQ4Ih5@g|(5 zB{-00(flhtJ%5`2zUii4Rsr@y(XOlt#f)fwX!Ed-xO^61$#VejK(xrOEy=MY_{GGi z@!j?C8_>X2RpvLtMSKHbTNu1wpcCjtBQ<@cujS{bC)~e&{hGd)Z}mVDX0BN5br5Xw z^`)SN5e&<4ojL{vE9}aAg23oG0dW(cS&G&m_orHdXuvz)Zym%_3_!~YY>6x12w1c$ z)1;b6>L{kr=r3Ew^xoZ@zZ&9u;D4FR!KfmaY4M7du_-j-q$dMuyZg%lsr6{h(HU!8Ir04s9dK$(;UNS@ z2}nB1ZGf=H``Hx-3&+?YB%vs!!MHNoBp0;v5*@$@W7VDbIS{#DJ15Z<#M__TXG-n` z^><{lE1(bGcF{P4sP05##KuQc^IYhhg(=wYpvub18m*(!Lo+O^zK*xIwdtarB1~4% zq4#XyQyaXp582IZN6-TH&eM2B--kqYv`p_@>^$u|8l0Hr#FV z`T%r=$Jg+qpICROLwMmqNXV9Z&tKyS#Cg2+g;qi!^ZLUdtMG)5{_IpEj%#390s{^m zIz%i$FlgL{&`+UcTUckI2ZZ}nY2CW!Q{I*0kfcakM&lnGc)u%(Mq>gM!t47R8RYzK zR1@Okf-p?_8~i~v%GliXAwcHu8hKotwo{9u5am) zuBAn$e}<{LU#}!>OC7X!&Xde|RcfzkCoDpGdNgYOvp&eq)G)O#^@WY>gsRCFICA6& zG3WU+FfP_*RtCY_>Z7GXe~v>nsDZMu`+J9eiu>s|f&oeCvQu_1ZXhu~L)QVg3QFq!hrR?Bx!--UXr>lQ!9>PqbImH zIR(RY=7?LxvtFWC8iPPVpSO5h;?+ko6fvtU5keY^3{vA=uq2%iJ)^(?EO9l`MDY|g zck6o=T?-o)F>s`$VTZClUnGi*e<0gn1H_=>(Jj&TgycSNasA=Lb)@Tr(GdkqXNlIO zEb=}~B;0{_5HvTIh;ho|m-E3A7k2L4%02gNWR~PK$|C0^39>W-P6I%9{iTat{PAk* zhle7-H#q?VF0J|E%)&U4!wE8i;hcRDw7k>U@rgKcmi2F4OM*#4f${QoKpHs>C|D9Eh{Ti!pFQw zREAqQUB}7U8CDnxe2m3|{o6^#K;>TI78gGz6GR{qRS&40k@0cPp`8ITjE^uHW>($?>DBS*h>&(7vDWDnm@vQ*$%ihd9v4qM`nU+zPcV+*6ZKI; z;Zk4b={bKQHNGzHo8k}H(%W9Y_QDD-hOC*)(||dKbi9^yLqOV7EIMi&{QL~i20J=C zozM)!8qBWA=Eei*2fWe)lK>q^L|ATixEy ziLR>_&t_2>7H`{(zSn5`cGqk3^swR!s2>fydWIdiysglLL0(-wkv%>;4wV23A|2`; z7CI~otwE`mW&zkht_Jv(ZF&i?&N;#C%h9d=Y#rCk+Ped4er#4<=f*i&UCrY?L7 z&M`|{0RiUdV5`W#2UVkz=cF!j&HdUsPrg0i$WYL3gI?CHPcaV_dmZ)~4wW6c#HV0f zxAe;ImKDAl=rKcJEQNy)p=i-qV8Xovor2eow`(9fEaf>1;{uZ$;T=YjWq{Px5a?hN z#^A&GRxJ5r7OGPQEBII+}*EWAh#(Ftwy9 zJ`zhL`V5C_d%W<7PZL)H2w5^bA-5+NE`bCmJYS%FFnRHPx>yHrYE-ak+E9j&!YlR*x*is%T!rBeaDe&&ZLHTd3tDfCnfwgu* z=+z*Q(I67>82)+kKy8o~6n`r$g9L~sX&KzrOGcRT=vxivu?&cJpGci-eREX)57OBST8q=lGW8fv%!}6e!6J!F|*8UXOt3`;|2T~gq@$v8^Fp2rjlMT!YIbIbPyJ4>~&8Jel2D5^qzlj zsen2PX_Ppz$X5V3;48$h0(%0TT=^!d0{b~ihS%KR6N@Vl7w&v*&lX(UAQA8V{JikM z;ItM3=3>=kGh;?tK+Ds&+@-F2aF9?_bb@52Kvkfr=z4Fci~idGS5&9k+#eqAZ}5JF zUf?|RQ8b-Lv0N9}<2jqr>k~^o$P*gEKWZ|YCBZfYO>HG)x0JN3JQU3yNE6uL#f!z! zD5TINM`MT49wnmG6Yd?1IMM^sq_q9}OR;WlZghhBo*J5(%P5A6nr8|Y!e%1WMbWLHzZw@7Z$#~nLQrq*q$z%gLo)u zI)8xRKKNJ{zv3nzx|@NeNbBM#1;&IDG# zq>md*4BlaM1-4Oyo{XX;9dh|*YwJ$#M~?#*v>>4p`=pq-`0~z9-y4_Q!8|d+#c?d5 z7o#jfl2ZH0|(B*7AvBxyrie4r*x>On&?Q0Z!`1yZ1tWch`7D9NpQ#vrLcR?;uJBRKKaI-)X+5CmtNe z4Jud^>Eeq)LoN@z>1vG&Mpp})=QDDH&dy?{x~VnUn4*NHMMc4G=qUEMI$LN-M@L8W zHXk2#3b8`y7U(xr24+M2X z83`cY=|rTcu8K`?zlVa=rR9ywY6Zr+jnT+8Yt|5&6@eFqHHP+0dL*fd<=VAt#3_h|WR;Lm^1!+!w7^R&_NY}y{bOd<#!Hij82XfsuL;6WUvpq5SdH^Duco99DFqhkvItY6c`a?1G@g8 zZuaZ*QP6orfN3S#<;ciL!>LLP34AO+yVpNJJ?`!+04&7y@r}Q}S0cTmL1NwJ?4s>- zA#E|k(~_xbNpD(N4YW3d-q`;lF$h3H;0N~JIdF(#va+)8M)8RUJ#xMtkb_W(9>XXm zz6X;M=tzMhd!W4#1Of+37~rRvhzJ#I3Z(pzju0t|Au(yNX~#|VAn*hImXcgi%5dNj zcad?n<0G87_w`O zncfZbiK&pyFyh}k0f!J4GH$2I3JQo_9xx`vP=yf^*|oA}z({Pc%ry$_#k6!7en>3P zNI0w1Z=JbhB#!h^hq~MI?c0)3u}$f&2FKPq0{V#w4tWHIY)E8QRYK?(G2Q|9{C%MSa9+!55_ z^TvGGUTdLCUzXWL<`a;;tKp`bkDWLKY>nV!m+rYr6e8T%<10~mqEJ{J!NH@mTd_1N z+hWtjAE!wgn>fs}V^j?+NiW(RZq4{-=t*6dA{@g&87~mWg-9M`$QO@!lsA-#k@4c? z%i;mE7IttHl37baB7zRafOUK<XI&K!ga4h}as$FpN-j zaC8)Dk>-kuOGqFS1Q2~!5ScSO+bu4DORo|5GD6Kg<3C=2Fhu=y*NveM3TW`4CsX;J z+5Z=V#&}s6SS(_5LTH}5c8wQdC*|T>Jn-eqa>T=O8k(j4T$QoOTZR~li1Q!Z9yTr5 z0XVug!7TK9=>1yAjf|i%&PPYr!HxYJcZ335sMqZ2;6Q=KihWtt*hv5O?b|Hmn!9`Q zA`#nZ2uPCU{h@0z3TkpJx#Q3q*TQH0Dtnhy!Y%n|<78Yr+^}Tc#RnsgWN3_d>VbC7 zM@3!Y{+h}LjBA>*j12B$stuo83nmHQM2w)OcXkMBo%_oGfBXfK*04vHx_+iw728HQ zyn3-gN{;o@6XBY$1(p7`c9-j%l!+?~BYm;AVcSBX4a6(O#w$9{d)Oj|q+ zoqJb&>$|sYkBr;pEM)w)?fz+Olq0h{^0K(JggEOHBk=~=`wd!aE461;j_fMB|KjGg z=W~zmJ#M^tZNS%X;M&^BjV-5{>FMdeJQ^B0gd+7RyBXvl^#BzuQg#bdVfs~}lAg(pm=#Q;G2cSMW?%%%xh!Sc`EhZ%~ z_eP8H+NODMP251W;o5Z(Aaby5cKL?pfMsq~86KND{ zh~iKmS>W^{S{Hooy8ixJNNRE&S@~FTu&@YauYqPk;M*0PoSY@6hF6oh30NM~H*Bbd zlk`Z%v?P+Y#>*QVvX~Pk!}G+Mg8dF!7aD1i$2(@i#lVvfyU|KAP=TUvoR@e1J{x)n zqIr?y4xmtVqnP>3PANZiQig;ug3w9@%D}Mn;esAbU_j(TG-XBqb5BaRpz|>?&j=~m z`;?9xHGx!8@aT~tKHTS{fM0k_?4;}!wg#2qJp+kmZDOz3M=JfVDku(MD zN2WQU!jM>Ds4i}E#ixcjje}z z9!+MpwY7zy(UUO`Vx1<2^rl^Ri;7eU8I1h!6r!U8OuNYGWE=?o3*D=RvgB9|6%{so zvLR3jJgY?dbwZOp_$K>OYrl-%>C+kr5HE~wAmwZ^G+asSY2YMapc3{zOS zllkXOXZ}IPx`Cva2un@7CgLL@=?Vn7mj9R<} zVgRTO0;;ctiofvpUF9k^w$!(;PiCJ4+#u5_PSBMaT3YJi-(JPTL%VO^zLah3mocf0 z&i>@^=V*irG0VZ}p$if;R3Xof^zp=_X(T?I3*IgDOr26*Z(~!_;%!0GkdTl7bw{j? zmn)7HlpW%U9Rb z=nme0V{K=*njCEb_cqa8iQ!@eBag7RRl4x|_wV7wBDN-CGKX1wT~BD4Ezf$k+nJCs8RWJk6{mL5*X}(H-exYal;_;<}WLDkTB)a0Y=hx)q#; zuo@IxTvC#Pw^gyZ8lSC=jr>sw(s;S?WbS@}9L)g^M#Nw2q$6(w2goE0~Q**q8kVTDa?2Dxs>izy%VN( zcFYLET+6~`*dos`jpvA#%-hO659z4m$tgtvLBUs0$A&tKw++U-hdM$IIM1Ok?@hHF z;BooNmCkt7QzNx3Jp%($-dmX>G4HN5Gqjd)E+DFxuGsWCAZ`Tbnv?3<0cggcF}P z@-XI27;N8ax4nJ4Vr2Hg72H{tma+-MAv9U)-h->ivtIw%C(W)?UO~a%F>(q;tQGmI z((g0ufpASTPj`icguo{7hi`ZRfQ58sWEVi?rUb(GjO31D&VmUKGGQ!x`sH2-NVDkR z>xl8d!y^jjJ$hTV@^GO2J`_5FjJ``4F}ym5#>f=o8$T<+DD-?n8*XZUAV+38(ct{>6< zg1tY}@0_gV7fD6E;x=|AIlBcyfqR)Y7FV%iY8kw;n_);MA`Nc;c3BxQ^TGu{K?l2> z(t(3rVBv9}thp?Ve7yiRq7YvP@IY_NJQs~&VwAR?^2yIRliBr+zqah283SL0t8lk{ zl)8J*9%Am0mXTSUvKWxaNxgYfK-H~nxtW>S&1sSw#v_kg(!>M&qQ!%0*-k{KG!kAM ztRuk&>FMcvwGLxS5J!7h-I^GOKH`KEJuG1%3!tD3sF&h z$3(7%l|sH0=;!#1eJL|&9?4Jy6$PsG_%@+kmX_zyVQSKR0%q`qO9ZX$SXYvv2KqT% zQTrL~Mh?tzWAM^}fh9H9RytzW-T-)&_N<&{KXgUfyHia%W3+p% zc5D69=m`iBfS!~nDukROEgdg!#Qpm!VME6;RfUf;#PF_e=bcV$+toPqfEdWbhPUzh zWob!2iN%Z&anWGF&aeEDtv$~o6Jo}bNprNjit5jBm;JoNPF|3qlafOawY>TyE(^a7K)_HFtm$Y+W)AvwUw_J#+Wl3eWzA|ch!Pdl^U@D?$ZhXi9pKQ z_HCg7YX$ji;8Y)sz&R|OTq$i(C+ zmd&Gm9;unqosmL}#`OE1^HPZPfX9V?O!knF#c@!SM0-pq-KjsZcpV@QYM=?b8fF$3 zB?)~^H_!f;h#V95+`n7k27&dDA3qL*ft4Cuz{1>cK;iL$9WWP_+jaf|a~L>~7B-%T zETgZl54kQRF3zm{(ZWvfzZ44asBzc&a%&x(ptdfS$e}GKM7)KRRaDSzRJ2WQ!`=`V z4|kLBcz<7l0*^&6ASH&O`yj(3qPLXi-Q5I-COk|5*6 z@N*IRDKav0o^jU3EA1 z=7v+uKyNS|?lGFds_We~7Vl%LGX|HcMJ7v-y+JpbHfSvsEzyM;3?)aPVX7ScjaESM z<0FhS16F4~o2dXxLz)m|33BWPmKh`!vFhWkN-hl-t)rbbp@{E$ct{8SOe_bC@j)Os zok62wUMtV*Y`&PHA`50gI&jjBLG0?hi+-j*ak+%VK;>1&aVOTpKjogV%$*3JkcrT; znO{%x_bP>c69X`Wb9jr3OS*1nJ`1owPfriv2=`LIAk6rBnY65|F3=3o(J}G7bMV;U z5wdKgJ4G#CZq*Sn{~1RHkZIAO50CH>MuW%rZX)Mj5YS+QcNz!C1-AB=vDK1;YGjtR zT;mEvt4tJ6a>$&7gg!oz6Rk0sb;PhkypFa08gMrMEHa@A89JSSyP%21J7na@LhCxA z@*uC0F%gMvS8nI#n&2Z88yg#m5erScBkC!sXRVM>QG}h)Gnc7eiob6%-b?mP z*9fc;=}|pG1+|Ve-_UaEF->Z5?E3enX6m^dFuYF4)UxmWJ>k6d9jRWazGR6D#G{h) z?6NqHoe1ugoNrh$JGw35hv5mo*c>OJ=M4>km_Q}Q5cm;gJu8dr+!oaaS}f)UU@sh( z6N(WDa!idZ_i{cy;98uV1_7M{A`jN;N39aCJTO+akwiIq0D>IYO9i6$n3@J+(qc0_ z{+O)BsZc4c5_4jU0M~qZ^GkN{TXO|4Qv!bXp>@-9bV$#7*%Jmz4DCk5k$R4 zM?)A87^zuHiRD35$IHc9AB9VZoH+*B9{MCvNdcP;&|2VcsQgibg9lf*sjY(!Nsgu> zJSOp>e(CS0vyC@^(NCNBZ}ClHnwoq#4h$e3f5g?p-8kFw_N^y;229>H0GI_(O~R>Y z&R`^g&X z5lKdK+W2@ValwckXZ(f)2;q@Y)iESXYkU0}`b#1-lOJN5OBlx>fSd^>K8OsnOsR1FuOY|H1j3Zxxx~So91G zoDjDS&!1NVBo!j5?bz$NISF7IL4ByBfa>eluP_CyKn|jZ!k)5i7cb(d1BVQtUn9&7 zIbcCpc(a(oyZR+)SEZC^0CKYM%(y#rS|+DL9`GvR016}rc)Q7dL(mc*IXRva_#Dn- z$DW!nG^h!!8F8D@*M*Wc5fGTr{Xp#m3xR+3b_;%tL`(Pp&?G7F7ETSO#gfU$=)WmJ zOC4O0qcszm3nAw?5$Xn3&l;RBM+U58V_9IvlJPzrY`kq-E#b*#r@TPG3ZezB!CW^P zaa47G1IEHgKmE>~z9z-Wri16MyH?_@L}Iudw@ z8Ae^QQ9;e0!=F}(czVdOyyTWKs@Dfj?qFw;3^^nNp1Bm18@~1cbZ_D0$P7F@f}2q- zKl{&kVjP1L^+fmT9n=3_4__*H#>%+~egp>bvA7&KP>X3D(4{!`$&j2t3aY1o1>$nIMXJEkaq|Ja!v^mcNKPjTY}82JemA~qocEkuhSFUUJYFmXvz&&>ir1 zASR$$-fsk;=yPm!R1}cw`d~T%&`ePQ zgM*jh^3!m>8`21}jt){Dh15j!W}&bGpb-c29}9^P&_?HHW=H&2Sw+Qq5EPha2m~ia zPKiJdfpSzhtDKpcNsc&!n~aDFsB{0s1frTG+~2-72OOGdm<^x}z!ijj+S9 z#}xQlFqUqJnuid%c=hTzP{G1Lax^qF(u#^#V+F1zB%~mIDL6Jr*q*fmlT|nvmsm){ z!f41|!39IBB4b1cf%jnpBJ>H4{ZpG_RW-K_==;~*W)j7XUMX$ zQSO{x{MGvZzkap3UG)f*qa}_lwbOxo$!04nUNrbA8FjIasROw+ww#-*g+n z8txZRLX0Sj8$j=S^x|JKGBRv@FAyjdO(+Apz(u3fe}4PFUh3-Vdh;t`HqxJwtgzhF z+PV?vL*S3vGn7Sf$`0ap7BvTxiLdQeCDj>c0RMmeY7rg&dmZ5olQ5kB-|rxYQ~l4U z{NFGAe|%d5f$n6rGv8eS7DGX`#_YPb5Ta<@k~QvoDMnSWJ?$RPCD(YP)QYo3h#HgA z08-CXDQ(Yxi6gu*@uP=uM~q-M0U4mLm%Xhb+Wz{U?FNv|6)LOXUaL?sfsE@6WeG=4 z?s17A>uRx*@wJ7Rf`S6irZe8r1}{M21P2F`T}HFVAl;hS-H{G)YGj%BSu3me+gh$E zC@P)EF@E~^-eoAUzX zVPb~A0G%)QLp88)@b=x*V?G37i!mMSMMmEd>i^%ITtWZ(mgbU2R7k=&V-`;*2C7X3Q5; zQLxp3IY-?-gX%?3Tm?N9hx#(Xt}3#P#82uwJP@`0z^uG$_miWoi9HBM?%4ahKoL;} zye4CYpaju@F0(KeF9T4hM&+rit0NbW-m^byYWJGW4{?wX=^-cuI4`T(U1IU0f!~VR zKjlz`YLeh2g5mF``CB=K~Yi=(dToJ)hYO792}0S^4xP*i6z&_|8uc zP$;Lqi0cXe#|v=k6ggO3_EC#wurEb}i{%;L*yWNLe7T2WD93Jvt zxiBXwz1J8$sNVbgic>#F9x`d@{-WGE4`QH2WcQkz-)WtDdr`c37yaNV|ExMmg*k`4p>e&CW;FdDeU${`)Fi zY5gOcEx*c>*2|zr$juV&xjE_zM~O34&uPE|vVo8qY()vrMfJja_iop$G5~6iv$4SM zaaNW;#STqvrzuV~m#Zz0!5;eajwx>N)9QzWU&fE6mk1y z#ep4T_HX?&+8d5CfQnpo@;bBF4`p5LN2glMsC>QTkW(P-Iv;Rt&nVN+l&3s10X^5e ze?q-R^V`v@wSmpU{Ib-0C*QyP6!bo%bD8H+6Z5Ydl9Q@vT+^j-rK-^6<55)*b2$IG zOQx~MTs8!=UBgs&hK1>SsVEM(%{Hc*hZ9jhr;%1{)$9GrRNb7Hg@yd1YT1+7;Zs@3 zd=-y>sKdid{L(UK&H=`YAMn0SDqGSp`2&*H4BNCQmopwt0j9cQoBG&0cL}i9UGhkmfYEMUiH^mu+V<@#8`wNU97)oxq#NoZr9AO=|kKm=dAe*IO**rAvCs6+*AWy@_-eSX`I zsV3zWzICL4J_wcbt0*wq+u1qp`yyBoo8xND0tG0Q+tTUs{N5m#*RMv@t>eRf1Y@Y$tUDOTN z@Ye={M`{28(MBHm>X^++P*UJzv<;uxJZGa$#iN8=LC+PJB*~PN7+y)xLRQKpLU`zv zK?o*r!J<_Og6zeSQ=U&NyP3*NEVQL@2T7_Bn22Uo^e~VFx*+g;MYKxkR4~#?0$4(c zCb(44P=?im9QHq1Z1IL#^K)QJ-(l!jD)9Wr1*OJS)&OsOyz0?&YaNAkG3 zdT85s0?L8TTFXwg>aD{%Vf_e%oyLY(YIvA7n$p^cbr6J%xS4znYRzp32ss#HBWgwscO5pFWHW^ArUU!TGm>6Ua zrQ8!Y3e_SQsD0jbNL@8{X&iP=b1T>uBs??aHxvsDd#C#PqE0E zIk3pd^sliVV?uwZ#5MHwcBftLCZZY$c|m{)jd}0WLBLj&Tdrp>Aia>ypQ5ohq%nU4 zsz9|BpHqcpy@?{#*GR=W*ylrdE=5O!l2V8E0)i-(;byv!V;&tJ?>qj-32wiJrlu** zhUd>K1G*k?avP89y5Rn9GDBcN9oDqgmaQnbGQ^3__)zV|4 zK>u{*H5i>d1uRALkksEn8V|OA`?KZRpd4sx5B) zBTvI}I66AMIB|byLjU+d%Sxu8h0H#q72o<>h_geCui5)GQ(dlprFNBjO&#ER*2r&i z{d)CM7u?H`;4DZEvRd7I-Pl8gF;v_?d{;wD>(Lw&*p;v$%lp7nZvs@lSh}$JhUgJR z&2!e(?8e{l)IkzpktMi&T+txHMH(o+@Pn>%we7ngXr>ig{?5!Ccz(^+tz%QBuf;^o z@hbrLKg35-dYMm|a?s4_40l9Jg`(FoNqc`v2 z79N%6p8Khl0+e(;7^@Bk5Ta?X2@yRLVt=UQe=XXE~kK7aUd?Vde_;k$A7ZU95_sG_z2bZDc;0D&%j zD*y3Pqpdm>De*qDVqJcZ9{ShV z_~|C@17Q(5It})8%=zE{Ol-ufTF~(SXuJ5o?MtPHblOES5%7W9&D|`UQEx`8i76^3cjax z(Jomw@{6~yT%2fXlN+dCSZfIhwYH~!U~Ghl^I$tskRT;k|+z)VyRQ6=f|*oK=HDTVJb%cYnRdklFw9C#zi;zkaY* z-p33xfM)A!i$z`*XjGSX3U0oz7Iy#j6@IscwuoCJOvl@|kAJ|@l=F}$W^4?sTF(WI z{Vj#K21%e5?P z9VMq5@OAJD@qmAQ@01D}Zum!E=Fuc4e^M*EkJejeDaG1F?Xc77S3~`Ny+b*Ij_?{) zSu)sw$5T9DwoHd8O5>~(;+BNGh(5RVbToT0HBcrfp|prU~zQJw)Msj+x`A^Kku zcVAjjeNOQN_(Hn4Ncwrn327G7Jb0CC?( zsT9{&{`#wxh`MQVT+7c0`HZlcO8*)VO6Yj)el<5#H51-TYA)<`bQ`Bu21l5L_pPM- zn2L;CA-bbsHk&%!v}kPSG_VTEZ7-m`mZvbHNoI!7lAyUV*ly-7QUI`tNYM`_&l*Xm zvi^Q$x-?R#YxB)(YEQNvl=2= zxz@-Cg0pMvbf35T{}iJxuz$ICBa6nN3|N9CV!+*+jT==ZLk5#40WR11N=UGjlE*hP zF}b*_=Zle!`4%;vQgsW9Jsw;$o}kVw>}%7Kf3CE|&BZH^3a-9(0)hmnY$*iH$5ASagI2NaE z(Req5=jh7|OkKKb5v+Q9WDHlPb+i?}h7ExlH*ljNy;;i(DQ+BIIF9o>$Tmhjc;j-U zO9durmCLN@HsnksU2rzmxcTDMLfqRXV#2Y@-9&uz&r zcf~Nn;*0;qqr$QKWNA2^K2AG5li~iBwWrdQkI&AQ2H26+ zFYP(+qcTZTJ+2M?9PQyvzm=~~Iz{ekYvMZuE8^dN1N!wFP8GjO)6QJ<+R^pM*OMeh zJ{Z)=#da+obfc~vn>Hl^#vVr$!ZFJNfvoiOEYcKDXqbfY_L`7=Nhp9oO9wOqXuY&+ zxp5h(j8{iV0i0Cw<%# z7Nte33&|E_Brv8snMP7>nZox*iV7atl$e;X-Mi7vTg2`%i?)Tz#bGoOOP{KmnjNAZ z$kg(0CT=#l*RPN8om5`|jsQkS$gM@5GcMCm7ZdlPuAI4r{rKPlq=~-ex1lQoaebi= z(gmDDyn-z;;oHnJJ$XPyfB-@&7$)5)(QPc|T9$yKo+eR#b9dSv|tj)0=^P3fO&;lvhyQH*Xun zt>ra{xxBqK!l|<0envJL3($+4558Z&el74xM@1@gZunIx)8)nBGEp{|bB?}Waxwa> ztoo{G@}98mrz5M!ExeY)Q$*mvBXvqv_C5$AcM!A`bbot3hzw$1gNwVv!iE8!RdqYV zlxY}Eq9g~Tetk4C$Nx_|J9WHW0z&awP_iCG?;+{nXbw4kWJpZ4zg>Qu43czM0 z7q7Mq$Qy>0^X&IqZV+KU=eghv3Xn-^Vu_?_5r^cWMT`F4n#`aAPIL(a0BMr;Ub36P zAL-Z&!dn2~LnG#l*!A_x7a0Wrpz%8Kbe@@=UGUG1!m(n48$m3QNKzaf*U|@y%e1!C z7>*(Y8{0NeNL-G9H+7d)ofVxM?;nLSP{!C1i!KB%7Q zDe>ohHVWrB!)KHSd_i+mM`!~{xUWmygEVYHRo(s2CsJE*MFqSP7cI|ly`%fGhf{(e zuv4Znm)1|wgF4F=_kDcmAq zQ(~A(Eq}9DM8D(j^qt+-fFj<*7H&hNW|9&mPEHC6**+RJY*?PinOrMNd}PTj7OflC zTf1vl6)vmTf6)`5%96x<$@_=Qgd*2EHz{UX*r7e5M;jA9RNOzPgB=0h`PO-9oQQTO7sJ zo)&=7z)dKXAyb!ie(dWs+UAlA~sV>)jN2K0j z;}>}sNB#?-dcnGJc9S8+*&?yQ;P*C}6O5x*vgJzFuVft4e2R*U)bQ1tHmM2jOeirZ zLcdstEn>g~cKuhwM~rEs#*GQF2*S2Gm2tW`T2{JuuX3`riQ_Z@l@X5`>Vx`}9%7#o zUk@tBoQ}?S(%6N@92l~J%1`b*Qba-94~^RG&z(}4eBes2Q4eMKi7s$AWnMZby;Ml7 zAMnphTwwU5MjQQL{x{1^t=iNAag(!o$rK`)fey0uhpke)_P=gsX5}^kDiQcvu0PXl zkxX*1Oe$>dbPM1}A|%4XTFC=0hafs+VDa7vs2j{}Ea!Eb0`{QqKRzUhn~&q`GA231 zg%T=~WK!PcUw<%^tqu* z)f3is0oq0BZ3tbVj8kH%H^Vp!-}ia@(l-7SK1ODw_K4$(TXGp45`>y5gmI0*e00HN z^t-f`5^F5>E)J*~DX~l#jLA`3_ikMxD}*COl9*+h8xmYm@`}2kuGjApZ!6uRfPk<{ zYsq<*`R+Uf@s-PuH@?drD3d`~R(^eqy-$$g-VhOZ0pqg$z2D>R7Uj|RcZb7e<bidW7w0-f{t?q^bB_iSGL+prTkJUA_;}FFUYi^BUe{{+;i;oW zgwJYae$r}lv+MSKc5cnQQF`~$2$dc?PA&HJ`|jo2CLn82*#y`78BrPLZ=bzQb8TO? zB>Zz)@rShe;*oy+V8xW)4A3iL1D0VG@>EduO+hmwQ_9xQ8F!hJPrX$uF%F0?lK+(UjBu*m&I8b5%cp4>d4qMF@7Ib`3n(K3#l6{y- z#6SUbF8lXSyuKWZEsWbZ)!D?@zQJU3cZ2-mDA)>|KAkLtV_0`&_7*+xWl>k*^o@ve?*uQxqbkLsI3uL3r(`|8ZJg5cb4#v593Ep74Eh53 z0kK-VO>FaGH`k61WYQ`cEY!t9S(5>x+}-9gklmmGGJexSOBZP*1&bh-tS+gQ61E@~{EWy&smngq zq58Vc=MvIaRy-Rt|DsxQhp~dGXihF+1>m?a<=j~Fa-2dbJv{3fKb6z zC^@7EloMIF1o<4t01iX7i2vj;m2y`MnUZx8eA0z(uLMLyMu$oA7``m>0A1L0RNyJo zMdrbn9+uPQT`OW$fLf0bsVee7@i>UT**?y(aD1C?1RvnY{{CW3(_?cV_oNE5F0}j- zgdUtdK$%falpVT5hD<87bZpWDIclgTv2Vz{&YV9#odEKj(wTz{4U1DgT$QK-TEq*V zmPi5ug>S({G3gO2z5H~+Wgoz6jh zfCMHg%RFt+orx#7!dLV1enStyW0Yh3(=M>GZporE-rSLI#ra#g7rGDw2t!8Dn#iPj zIgH83DlJ&NXpzLswj;9uXPKN8)Cp41%5F!ql|J}%k7)L{>8y-aw6T1`Wy7NPv`2$c zTfDA1efrOoyIF}nEB|(a8yF8?B6%)kqW(GGL6g-fD^Lz_7?eyfxCq?`-NwrVOPI^w zB+!QoLiNx%$2TDe&Q*>Ph0G+H0_dUHb1y}lP%kvNoRg9e9{=nB$X&sy z8^R3|maAs&X2ye7{EJw>WSBXinEs6FBk$3gh@TJETxxV0UGB7p^QgLpUA9idl`h8& z;YE2NB&LQ9P>+iSl#Ar*z_7+#`M5le>X|;6eBT%J9GX4LSl9<)XH;()%gVOvUl z_88UjR2l_jAFEccR>G%x45tw{5VKOmt>ruJ@|1H=#GR_HsoBY^-Ii_p_iy1)uhBuV zzi4_NbjYc=Nxdzl3SkddZ`_EzRiwtCkINXdxhol*guoHHUV1+|$g#ButsAcuz=J5p z;HgEjcSx)Qi4Tt!?YepQ?r^~3&ZEK(9&7jh2$f2!{(rRM^)sG6-)6?H)`-n~Fu}I5 zJ1UY4=2kDjYN;3oA9vd)4MR}U@25Dnx=%R%cS^>@lXIlEp_xy}>9K@eU6vwdnFAHU z9wL8%8Rs*OoZ2wTCT@|5(kjsm!I}X*JI$Zy#K%J6Cc-QjM+T2oHkGq%5aX_Tuy$6F zfMSr^gN|Kh^GSin?(VuG?aLz#j&ardkw?kN-vD6v^UK4riZ80lOAQ|RN5E%d!^|7@=-uDJAOO9X`@Kfe&r^UPvL(O)JG`3Hf#>JE!oC0?Cw%$A10l~w>Djh27nQ@G8`ajy#cig{EdvMp zjwT2IDYE37QI22L=MJ><JmEoZa1+BC9KJNT%{-R4)tO+-d#u+fz@ZUDwh7g!XX{fr|cZ$g)dw z4=2R3$ixeI{`~o@k)300OqRRFXO2}&A$(Y*ptj@3$L{zRJFPpjc4G)duixI-%hqEr z>@0vEQ}NQ}$|){^;~zuDX%PN0E|8DLLbzQy2&G@>;|kd(C{R&8G1#OfQV%7r86u$J zT~GKj_@fPg2?~q&D^GQb7NZ}L4sqM5(3-m|A=eUqBA}bj5qK1=@7rq<+1hZ`BDJ>|pr$}GsNR>kIkhNN@;!5B_h|1wS&K{_RsSQXsnf?UIm4ZPSYgm39rlT{qo;zcP^k~z$UebCkgn&^kZkXr+v?}R*$T0cUHvR>@Na^5Au0J>2$bl1^f{@C% z-tB|`RH7m=MG7g!E~5n%BAXLJ1++AD!cinhB-)rV0Gk-%jRq~VVk(*`zO$^lP5MYj z84Gd|n!C)I!Vmb>VNTmU@7Zy{G911)%G0#V@wK1q7#Gf72q2xD6stHu-bv^*(lmIU zOmlyOHvuJ-M}QsrDn6BM?LuS7Hs+fFR7%pHe*saaB;ABgDGHfI6!TgHA1_Xivd^wU zcJOhF`CpVkbrc18&P$PNisu^n>aAx#GSG(F@0!HBN?HrV(=J1|qb2q`vbt~F!AjvU z?3UVoG+6ICo;`i9D>qNurPqE|sa<&-r?}ZE)jTkwcxKGG|H*koGSFrS(+>O+y`X?*FS4qQD1_o1|xBlc-5PEt#jiD<)&(lcp+jet?h^qq9i>awd z*CXKd^BKe?VsNBmXE_yLSS$ipO3ki+HoweC(Ky`a+wLo~pX|Ij5OI*OjE*OusRb;kHFZnz>Ef5A*0sxdT9}Wwc(WtSQ4u zW|ok*C1o_Uzkb+5(3V z3?ifxJxLV!WT=a?lVre%Q5-@{BX8%SQp^?%SrsESkefDHKKfHqc>pFWI(H-SG)LGnEj=VMHM1=OvLNhM7B7g*TkW{Yu7G@PAJJpZYXy^ z$n}p$B`9jzofBwV*;fVCFdG?hTK_`QEQ~^eFGjrH)}Vr&OoY1M%gae2jh}!IP67s~ zT$8u?j-?)#)HYFcaZMRp5&wh}07AVdBzSPZ$&EGT%V9o*K#ADTX3-iE@&Ba(v2ub1 z(~vO9is>h9?f_%4dauevOx6Zz=kXgj5vfDOS1RdU?MLd0L7FGAHLUHb+G!Z^H?h$R zMUNVJjSE5i65Yp(ymaOPMZus)Yt16#@PLzN<#Cx@M!dVqSZQYs{ z`RJFF-4;#~btJ$r2dv98p z>C(hBl$m@h#^CAU_`hRsu!wqdo?>O4Lf`8@<6!BhJrtgZ3?`J`9KvKB8Op7^FVGsX zqAI1SWG0nxz^k@@yJgwkle2PoWMv8{ z%C!{uA@7{+Xc?{{N^#CO9`yH*61#cndDmaRoYe)~WDXm`u}0eC8oE1`_8SzE4h3Z| z1Ul6bD=MoGys4!Bq2i}dXi7muI{^gIN%jWvFr;n5lmfzH3Bn`c`!Wn#;@W0gIhzX- zk6z7=v8%*E`Ujj;ohC)!UC0UkNoL`ZpRj;miqLgWglm$zf;A*JT)kPdOHUqIiAn)c zfi6Sof+Rxnz>u@nW#c**&@Z-<@j3hw&V&QHY8pqVLdf}C2jVSIr6?$UU(W1~1 zZ$4{)ps01kZMZgwh^%?U`VJV7!9y&6Eio8!W2t)92E{nZF+h&ORKO_V5|U{{B6QVd z1R)<>5IH=UG9Y-zjvdPFH{GHTT`gc3l`|a5WtzxLNt~SSUY`sxw3;3MN?qO;U5|{a z*O)X;JR` zS;E^jD&@WHK+9H%3*`H?K~dqf*G3C~0u~%|fC)#G%BQ*X~hb z=Ye}^zY_(h1tCX?nK3)uXPtE80O4jC_H#K1*yFEDtu00XSOD-Gn(36x2l1OvET1c3 z$D)Zve@_lk0O&p)Cy=M1m(i9o_9B|8jx<~t7DQMt*E}@!Ld2;M>li|Ku8N8lZk7Z5 z64Ok4TQ;^oLUOp6X_2r{r2Upw`E81R{Do}MIwH;f#h_UDArK$)^!f?-f z_r?^?@ra?Npgz9>-XvpRNK%}JUl5#SP3D6BZdZ0M&ToZ4%tyc zz!VC305l<;n#@4sVX&i?1}LA1_L*5#GNR86VAqFao?^YM)6NT!v%Bw4c{gad*m>N+R zpws^TJ|RiMw#2HgD5;-p4LdFpa zs)Cy}AjmRjt=x?x5^5gT0eJO! zj6_HU7AgkX_6}3Wk8)TpVntanU;f^D%- zXpDu{@69YK$snTk+c2WoL|K?&c0ox)lprF`7P*z^*fZHg#9sR0z6%hrE?bS<5zZ`D zZpT_r@7475`rQAK^iOMptW>3kJb8uVbs^M&#Ld%(BV!RfOeTT=trIIUX8OHb%nj*C zrZdRq;2kW7f69qZa0nY7BAgD4`GIg_sjT{h(4s4e|XKg9( zGH8Qj&@?Pd6u7=@SI7n=?N&@rz;K$zAKMBI3!y0hl1cT?HS9*fpdAHK_Q;`rtV=;% zR{~`Icw%38#-EBUG!$lu8k}LJLa56(L=(aUwaplfBk9k@fP{=k3Gh6nUFjYUKo(m~ ziaRoTcCZa#Ao9HN24=tXe4_Fc@}*`v(I|sL(4*3{=8UK3M->XTD~V}FWnYJVKpb+) zfTbyWXAn`K3>&~Rv!g6TWF+kRidEFg5nO1P_>3;$EB#^jl=p@0*dnX{0#D3K;7r+w zm=Um*kYrs51BHm`n-2XxtZF(fFVIgE6)Zy|gY4IxDqu_EWhJwOHsfRd#jWyfc)Cb; zkt`7jV;}>_`NR;42V|Fd>0%%!!KP#&$@}CVKB%!`i4-qEi zA=i(W1-h2#6)Gz#h;9$ZvIGy7HhgCFiDO+oR3dk%%J=*{GMd#dH+f);Rycr>y!f>ni+u9r(jz`%r=FdO+GoR1OuCOyn0_}NU zb9&7OzXUN7{zKW%^tFT6{}73r5$Bol-ML-d$iN(fb1nQ zGr_N_9Um1uK!vgx1XIUvG#~Lnv{efsCBe3K|2&M2v3mo{mj_Jzg6R2fR#;D)7WuP` zC!T`1{5YqstfW*wB%QY_M!kC*&p!QgSTguXs=|;o9YCH{)%l*Z$UhgSoD7#e2(%tF zMgMcTfz~F`vcYc%NCr<;nwG_rusmGPTa)7{(ps(td*4DbilPr467WyLJ=y%+D!QWA zlIy!9rdn%Ldg&Epn^8O(qNqVZrsjP zKM?-c;7^3bGOo#tzxw(0ez?gMeuY1 z_sh%^S`x9OLrIQ#SF94(5~{+&T2E_}gcn1VOe)lOS(9$mb^EB!Yfyebuq9<1=F{&f zGiil~ZIIqxqdmt@f6ZDmU3N?=dt`1=CH<{@K20Oyr&AUaR>FME}C2PZa-Y(1C)?Ion{_{^~kMlftayTK!LtXIq<7q7_sR8*| zui%*Pk|6I-#WRfsEP)dpTk|vgosQ*Gi(AdKv>MK$SZr;QR6JB!6$(RCWIZT`t zX%uM>#)i`)-6;6SCR!;j4N)wcIW|>yUPb3pUFKIU$|2RAt1W|f{C=%M0=3BT3xd~~ z3Qg)iqhrqXnlCl*&R)w4lJ|$_TsYy>;gesKkII|tPvg=f~ zV!Z@0u*>Wo?JYY|SM~d&TEr!xiW)e0@M}QpYi!4CarN%%HsA1qmJKt3>EdQx&7ep* zlF`l#h=`g(hWhyAY4wH5gO38eP?hRB+JbME?Z6cirjRO8?C#*?W+Uq;5F^#Zcv)ID zGgh@rQKjf5VGh)C9$!iF{iH!gQ^af4KpD{%?cR)z_T@B`ZVr8^gzeUho7+B#gAOU< z%@Z@D?W~(N>$*a#`1Gf*UjvY9Ny0omg?u2vL!vE)^c@}TN^PF@{gv82R6ywRBx@I_ zVI8h%3X}1pLPn^dr=YKv$>|YMJG5EAgc`^Y4%#HNh)lRs+02O--2BrWqhRj|=;A41 zW!eaOGUfJ%Cn3Bmy@^!Fl$>ii*81VQ4i3TPkL5y=N-3~;pnG`fHE;G`>*woBvJk*; zaPN2EwrU!W2@tXe)_=h`&n)Gb(<>ZOwDXes5akRRDa8zBJ}m@;{pww*x%UXEm(AK^ z*;s~+g<&dBtFBtHx$S^4g#$f#0(&Ff)_NGNP8}^XVW>6L+81n*h#Uz8kSYzK6q7e# z`RmkPD4?O)hc-AqgDI1WU*I2FB`tbOQ$TtL9&ld z-aGrcWe-AOUsD@-^yAx!3I>6^m}+PNnj1Iis`OtAA?Gue5N*aP(EE&p&Ikx{m`MlU ziuecANQ}vJbSE`Hcy_LluGbC&21tp{O_B{j4p2Z&F0_oEQoEx(=1JCK3V}cuG`Yf9 z1O$D~V@WRj_-qh$4!B~hy?jYpN(=`PQK>D7$d;i3q=m?>q7CWnrDxyqKVNk^fZF&| zf3!w@qYShW@!C<1?}D;jKpRL}@7W;tqS3;%tnIbKLylNtKR8~dgN+OcJbJlymjMoo zTgUK&slZu+PkhYh=cp!7&MgTt8@yr2}I2Uy>jt|5i%651uA}?BtTgn^=Squ?T`gcU>MDEy> zTRXZUR6WMj(>B6o*y{imHy=SuB907@D6x7`Y?zI-*GGma!ZGR+cF_lIK`3QWv)7o7 zw2P%Q{ZLqZtN^-cS^s8I=>`Qo z3Y(CcZ-E$r%}FF8s55FFe!chx|9L%65KK89$q%TBHEkCIio2FA_}8+HC@EPWJRJH0 zKAS3s0n~@oLxuSlP1w-%z(~jb=jG;-Pa{;G?7VzTfNSdljq-1A9jVj9+EQOt|c=f-V_JfM^E6Ym<_c)Cf zRIJEt`~Q*o*KYs(8SpOn_LjZ2Ee2EAvOjRk+Ok8z>LT)GD(kp!ciE6-A_L7gqOWU% z+t0YTs9cteT1@qxP@+3E1m_<$#01mS-N)fcN0uYsWSD zRCO9__e+&}X-9zJU2D?p^(Ru=6&HFtF1dAUaXY(t=$EA(@>Cy(GEV_S6jd>~d$wop|` zK9E!bTA-evmn~fQY3_-=AH{4(N(J|oiJ*~p!Yn+x3>F z-tinxLm|T8hrY87e(w@&d=^HG7TUG#C#ne1noc^iKnd)HoOJmn4bTgccU*ZV$m3~3@IR46zG%j5`;voAz#+w@ zJ9ioZ*2ewpKO6{mdsEGsv~v_%vePRrft>%{LK$;kqc%pCY0oGPHh|w;Awh_Sd(Eaz zn{0#YL-DWYITL7DKw#-6&t_G+E?98P_ET)Tnh!q%-m=tC9m-iUuK+_ZS8SHb+}QZm z?8>UL1q*b%CzHvw>UjDWY!HEvDz8zQVZdo-u*Vt+j|v}KwpwbjRshvB9D~92q=5dF zcm~N-I*n`)mlxU5@2s2tCHgux9Zufw+;JRa>p*@YdG3W@0U40w8VmyCUR=9E&wiD~ z^@*$&sbrvg6s{l3xE<&>KIN{Ruh*_-#YZAo#zE77f&%hixNV*`a4 z)L>%k9PVU455Nidt)XJMb}MwLgx=ga@cUlf!BvO3Pa>P4rP~JP#WODh-DPuvsAT_# za8y$m-fsiL;r!xS^gANu8_~0%fk%+`67^gmz7^DDDu90NP}eJjw`3BP^NW-5jKr!V z@(y5lE)ZEfgn+AJC~e=qJsC4R~HR=7=|yNVaMo@Hv=s$D0R?K<#;Dt?!td9;4xw*A*0 zJ=znaD%_5I8Ie@+4TrO3^J`c`Mg15N6|cb;Sj#vpT(y$wmzodIdc*KZ$CvMv+v95t z*}Z4aG`cXErAn)WT878*?-LX&ZKrdL);X+a?~|0*{18w%PsY>GI*j5U<{oKI?E)2*%-1CG$F>T`JY6cm0*&#gBS?uVdP6`u`!KUqldBVVtG^L|?Fx$px3V1D9@BRj@zn z_U-FX)kA_s_c$kg%_b*^25bt=kE4-JiVW_g;pj7amgK^V9 z7j?4|XPQuNL^x3xTp}UId6r4%<{QY|Zo=mmU#7U;-;pfDjf0*L zv$=lgd-u$PN|KSuCL)KkKOijI0T$@Fx{Ua?Uht*R4)2djSXgaO_~~-P4nPrgsc;ml zAOOVHQs*#3&Dlz*B)c!}WnE*2mN}`?A#l7LV*ug3!pTZJ*GyHw20m=)zEfw;ggqV= zVow0@t)%rkE%oTo8eovR`SiHcL8?K)5(FQDO`+|ryXt3!c^s7^MYM`~u9M z1$~#D#=t6^NoH`MV;*}c7T>0d(!L281~X)(f)!jskBjocY=om4KrUeQ2qLNf4jDGV z#RanUr09PELeT<JsKJ)o^$4~U3IA$`?cXLBjkR#; z!okd9Fw0<{$&CfK`0~$_7PNb28N@$LoVa-Wkv0x7MHAJLCl^%DhY5f>dT?sq#6XM4 zKY!@N6&6i&`tj-f9>SDxe{MsY1Zt$i1662g-H*m7Lf3JxnDtnDg{_LOq2U7ITmN5a zld&cJ`9epJ*|c8!S8)(k0Y)+I!!?oZaKWTpCyKzuWWcqj`lXA9=~a|%RxoF<^T2_v z=!aSwue()>C6hqj%-$L!^qLcXV;PlZadG3*vb|lsD;}MCS{^%i^p9K!9osz5?~OIi z=D+oIzQ9YZ0%)+D3n8r5UP&R-LLn2C+t*cKGVJ74FTQj9aUbWysG0tL^Cl9o(aHP5 z@l(ZAI}!8}*6s=C0B1dN`aKktD3xMBkX?~@BYZ2pBc`W@9zT5=!LLC~`-rmhY}Z7R zSs+-fcQ@jeM&%%(vn+2q#%$|szl7r1#qVjm;SUa(?OB^qG+M}q36^S`>;5AdBK$Gf zz@}95$F7r<`!#>!_2iVR`4cMWLd~Dp?NU6|{Bfdcto_MIYf)j;{Bg9BL#5`=|LcoJ z>v}14beys&a3FAZRYj3;Bv((%tJRXj;i}>&_^cTA4=H>d$&VM zGGGT{qC+OQju5jTAvKyQVE(BHwDFxS+g$*1y;ZZ@IQg(F04BZG#MDzo$(+^i)U*bb zX_?iBH~MrCPebp;Z$?SZ6MDE$6_vpT8-1$p`gE|pUnK0lT_>piND3I4WC{%=S}%H) zn8fpuO>e>{wvfEqKmSbH**$vy+Pt(*%~eEP4Tx{<=GK9Ol z@U$||>5Ps5W>73E5BQN|m<$CYcn;2}CUxtsu}&H-?^}! zF*nN6vV|u=4f&=w%_KWV$8O!!WWMsxPjVZOz*C~CCXFWUOPhE@rhX0F=At(2c@q=$^O=I6KmEEH7_jeyYbes8H9A`)q z+$O%E5tB$LrXNNeYZrZCM~aWL9)f|g;{1^aUvD+C%WjZUnYY5*n{6QS%G-+-9|zNn zy@EJL*KkEPS6LjOc}^bp!7hBgz-;8Wjx zmON`ByNnYwQPUFYQ62`79Qq$!L2bx2@(q}sxWT|i^Hed#w#1XsHLgN#z@RL<#6gas zI{6i+k>)gEy);zFvi{qI_2lFdj(35~4G}4bn@zlPWEN$e!~LdEP|zOD`ta^~tXcl! zasO1j-*AFa2~I-Q0(~Y=OIM{T6OUTX(o;taJ|Ni@{tzVJp+{@W^KrMnUBJHjM_y`c zMJ50ow1YY&Uc%0H@$u*KD?}+mDUQv#0F1d6B72JHxlL3Cqmy z%q1Duu63k#;b<-nU2}4m!TfxIcoAf6qZp>$0XD8n&Sodv)upb>9k=M@uK=c*)1D`MhdGvzd zP|^KQxNd)Mms2#6GEst<(^y&JCl(PrZEF7D5CglvSGYMbaQg}klMJI%EE}Y4J7&zi z6^Rc2UxoYY#@|Uw%&~H+Nb7H#UN$oHN1E|V*A0QO%|#~x(s(sDw+;B`Rdn6;CYJFH z={Y98?9n;Uz$WxmvGZ3tc9G)7dEAcj&?wK_m76@hAvbx_%g?aF-*I(_=@RW#Bqx~6 zo)O1dk6)^M>3u}Eg?x;E^_h2;7z-)!N!Pqe+A}i9u=sC2^Dd<&<(-*Lj=3I=B@i|> zN@18_|HKtYdn6{K%c2MmB%);r)?F^!J-Xl=5P74lYO=^5pPy4-|A-MW9TipzW z4frtHUn-)kecYbc?00oQcqM~^6CL@P7Y!*VB>?)a)Ru0kOE8N(wF( zJ-YQ|W|a>_Rl}g7@$-s27#;t64b^0xsGQ<65(HA@?&N0F3g8={P8HH=&hv9$C(e$_ zskd=uakuSz_o~VGW8xAi$4BgbJ7mZZp$bL6%3X7EkvK>|Ef6B@YCk;FgAu8>l({ez zXWI?iLLs3>k>~}nO4H;_eA8vnpz;e<5R-Zq=V)x6fjzhH`COM=RJ;lc@ z7AcguJn_=p%fU-aDb$gibUr?n9==_6jD1pE`Bbn)UER8hUbxTu^i_IGQA(XrI+GB6 zVZPAlTzFXYJ@>TdR^IUieZv)TCR2?L@%<^|xjBY<%X~kuuyc2wyv?ex3Yiu=EuW0- zhG^^q7^AUcg4?@qA6~zXQg4{`S6TkEGNsspn!~527V;7gcFOwvTX<}YD1LgzbA zOb+wiA3r}|#tZ}489YB^Y!^^g<;0eeR3bPgwY9VN{upWqxf0<0sh{Wi0=^lX9_e{l z^{LzP4HPdFb}419Ii=gCeX5=~F#cO!g3E7Ca)QS0 ziG8|cXs-X&)P#noJzvJ=u1NA&ZkhbOx<@;w6~#_Hwi^6;U#MC(>W%U{fktg$r5PEc zBaf5L7NA1@DOy=Jb<3#Mty(eUXl!9C`8(8eE!2ZYu+y4@ie~oaV_IIV{jG5zs7zXN z{BJ)fez^a<3#`=t^e!~j+ElfaEU;Xh<&A}R4K#8=?*XQUrL%{Z27jH^vGzm3U#KYt zW=j%hyvRwokTpfWrMlPJCc`YEyVI_y8~fi{&ahI@(SV=`TPZ&ee(LMJxWaco9S-HN zerjWHlRZEDzA)wLn~&0`3(h_ptJ(=c>oT4EEVGfeRy3zAjs4AX041g+kAHoC)Z&Vs z;eAdOi>lY}=cn~oo@)O%&Bk=RSS_&lg17D8Z&yi{rN*hHIy&BWfzD7cNno3oMxtPs z*fpn6ia>fSiRt3D(mpKZP9N{)^ zy4z3x&wFqc!4+TY&9I2pqW_VZ3`hZlN&@Xy0*!9-3 zP|{S(30l=L@Jo~1A1l@yR&44tV#o6nmsTE~Yq(?by=P}$KdBhnclPxs6S|M-oZ8!N zl$w9X*xg}o5`96!Q40kIyPGyM%A=$_l?q5iwx7MXKLHG z_uYwYrmg7C&+aBz`svfQ1VGT(WQ2Be<}ggjp6WDb+QL3B=WUqg(yX58T@)JEj67A4 zOi{h+E@|c8QQ45bueSi985!#-oQ_i+h(;AMc7JyJkTz37Jh&F?AcwGj1P6a`D%uYu0TSq&YLIcZR9G{>(}C8 zY77IhMkC^Z^|W8Fqioix3~ep2@NMdVn>-TbIA`DmcN%wpS=~Z@)#lgd9&so}@>>)r zh`KD)^Y7?53kTx$n>WEelu?M@2d#-|Vzq3;+m>BUz!UMShRxDXc2xG?!zF72!V`nv z>s1<`LodQsUlI~X7ePk108bVwnwTaw-pxSZd`@rawg4?WJ1)PyY_!~;{J`4#bM3}SFEWq}6t~PY^xvL zBqpotlizGMk5ThKs48hQDC#ixiJt{gPLCy){u?)>-!vFd`$$ZgYKGB!R3^O;k zaC{81ej@60)z_awPe%c>hp*A1S+ilN!{xnolLt;H__b~x=H%3G=5V1CqR`uei&M?X zmB@}z_O~@f=Phg>NEr+OUC;*Xuc`LY2|-zrHj}y zBWweQ->7G*pMpm7bz$KRx?{oJ!F_ib>qWHV8Frdf`zxz?7eAkmOAalke%`r!)D;Kn z&C5KhqrUo@lm4;2Dz&4fY0ZOa4tLIk@V8_rvD7Ww@3ajH3N0q_jegk*GTBP8hqUS^X)~r|YsHJJv4fI%rjC ze74iE<2I_kv)C!#F#<;4P~#zNk)+CiOT_ohwzfJ_Sh63FIb&D58~5+q*wM*NkrJ-u6}uDAhDi z&#j0wIU-lQVNB_n+4HC3`xa#jUwaetWgMSJsMe8gD(UxrgXtCEG2xEInkrf|bp3V< z`AA<+Z3}bl;V)KaGmfJ5!XIGk6maXW3N8 zvHC0E)Y#BzS7MsPJloTEb`x+s7``?9-o-Ed(7nhNrUc-cS@3vfw~g+oAc|1tAvria ztX??}NELve4rv3uXdmcMvJ$hK#n|q?aQD>=?vRXH4ls*&r5~_My=GHZl>sQ2$rqB# z%P}|7)bw)Sgyk)@v|hb?cat*o0V5Hf9*NyRZG}{}`kdvyl6>@+mLRZJHa1a|VsMKW zO7_s<$QG>NlEi#$R$plqI349qd3X}T(qdR|xXlCE1;w4x&!UU+*b7CkW{5&WHL69` zmfEXyGVPoqw^GxVEVoQOQG}FIKBznxMkzOyj-Z{on}W0=`5=7Y3hAdYPYTQS@#cYVf+MFpN-k_Y@2_ur2WNA#?mVL zWa}f|9Pwi`9(ul8({$)Tp5Yq>lH(FuR^gd@asv=hx8jPu4CL7{RWv05&XIHY|+6xFR|TGy`8HuGo7K+k)3n$=sv%tN{0xDj6OHe-xK zIFGg*tq={Dcy9~s9&?URV0@4{4lU)LX_c?7ckOV^drD+2dquosTF<&D3hu>IQX%(t)#XKez^1y7rNU zxCj9#WqW1T=i?pcZ08n{`(}x!gI)1fdV3qLew_t>-%y94>^eLwbbD=?_IG&H!%kdq znb3Ca#{POdi`1Si)4f`Ylh>{ADfdmLkASyZYJ}SE_7S68VWDKn!niBME>Bqn(7zZ% zCL%t=g**IhjhWPl9c?WL@n_7Fur3y3#xyDMn$hOKJ!M8D{!n{6!?d5px6Tw9o%!92 z2M<2&Ua=pI1s`E8bB`^0Pi}yq7YOD<-xbkwd93L2Ric_3B!D?L$J$5+L+21VbJ^%G z^PsdIv`9~)GuS@t*LPL+yhaNJJj7m%0d&jpOG_6mvOjur5rzuThuLxFi5j(A@(8iG zV!tqOiWlt=yKmQpj-k77NTHmAJhE(WeRcp_RKcw;oD~wbM44{cF4Xo3SeNW0`-*gT z-JZZQV}{364$vbLPk-D!v3b3SRF9{pdH`Gq>BTcd(lSQ;%Ka%v&dfslS8m34xbsP2UVM*xSgc-hnaY{f&&|v|tz-7OT>% z|BJUbkLr2v{=YwtIme;M5E+u843SJ7qEsqFq%zMkCn6%1DJ3dVk|}9W86rfeOcmis zGK5n|N`@j+D&3ErT;FTm_xE1w_s6|%Yh7!di~4-t!|SzQdq1Dg{oL<$HplT4`t1A&?L^i|6Up<1eJbfeq-{8vLEvdbjDSw-fu+!9b}v6miXV`? zaqJcZlkzM5`UTQ!g3>4OE*Yv;)e?*-rWW1;hX-LLhvp~?*RcMn_VKGGnM zQ@szUd`sP&^+us|5&DA*V03*0_>=2{%J3zfgKxNpW%gHhJl&SYF zG}@?ZgEde!MFQr{PJcNC8_6*KlZMB}E(s z$V$Kv1lmQ9Vyhk|k2FCMU_?6txhAP-Q+4$_Y+KL`zF?^q+2hH0hEFLu;Jz6nlJTK1q3;RS}X_oByzZTWN*O)Tqoyl*;7 zvKd~Xu#Ie$^tor)wfpzC#c!Mbtqzpss2TC@mJ*uhM{dFRI7z|RA88*zxkU0T*3X17 z>DYeU_iXdm{~Wl5cnf$VNWb}6b4$wtj?#27Y`sGI5_t5gTB}whB3ly@1|dTfb63s) zyO3UMNP5O4u64>^?z4S+$+vGus1VR1Xjg?0nwXw8Y~H-cILrC-=IuOqaEzm4T$}?! z4jGv+`2ADKl~Yr;#n%hM;Qfh*;?nx$1wgx5EeE0tTxThq1=bKaI#AJ41d|4{UEKIIJLas zgd@E9qm&%RtB9s`o=!Rz8u2{3=O3#TJPL;!O>%!UCQUivpdXOF z%J~)+RvOz<|LmDFkD&;P6Tjv3&7EN5Hr}yTIE%;X)qv`%<$D#O8{WMD;uvR6m9}k1 z;lWQqL?YRM#w#|R5zNOaHZ)sl=d^xw26pJuWed8o74-GzKY~f*6NF7d5sEexhehAS z#LC6f0#9uJSy5^UPc;ewlgH>24mp=@;+i}f!3DY;@QeB$MjHVASZ@e)?xQTrpTMHI zIlFRxZW6(U?Zze#!9{|4l=M%)4{BL4YscB(y1|u*fPMRJQPYW`DQGLsWFD+>K%jtv zb30c|0}bRxcPuGSqT@+6&buavW%J0!Xs94w4ks5w9{!pm9`^~YBj-tH_X(JZ^)y}t zFp;YBCNG>M;mN)7OH1UQSae~DRvU54!Y606coh*uHYAw)S!MzNpG^fFMz-(*hlpoM zt|X)%mZ1bgp)UeO^3e#&Cn+?zh6(8})hs5=+_!JvX}ByT7A9`vQ{@mdiug*x@$<)9 z9dWA0--%pSjzi{@({)F!T*C&)Z#nLdEtr;Iu$2=X{=pBa|{_LZdEIHB9b3N(&OdbsPx#8T138eSnPe4&61nkipjd+yjV$aw z##N+Z(+n)c@%SB!VM#?TeSoCy(X@(NJtURMy zAgDkazXFvR2&5%NBieUd{KPEPHmXT>rnvBMl9S9&EgEZK(q)$nTqNI|H=?0sE9a<2nT066rhcu@xBBa0uZL(g?WX2C~ES2i+>3a@5a(gB82v($}MfEC+bS zN$%>yhw+)uJ_y-JC?B}ylP02Tq@}xnZ9@32Ol0oS*T!olO#O$=T?QsI(EaCNC?Z+7 zNK%6klv}7Dw1Am?SzLUVCnP76MWD8fU=mL)Rkv-dqQRYQ+&T$FrhNv9i`)nG8N(5n z)aM~h6t7>~E;N|=b2Rg6*%bNoGN9QNj}lr?Z-<-j(*9Sk1fBaWw*A`9AP?;k&M@kS8qF1j)-nQ%=O{aP%j5zV7ukG|HuFPSu;#0K_xDG`hY zTVWg^S$niBI2*Uq-~S4{i3sKSXn94H95T)*dU*Ho8p&m=i*7qqhbE7J{rhiY|8ZL; zUxE$r{$%(zLpst}Cf03HaUbRL8Mjo?i9v4TAh! z{Q9UEwzDDfV5@?HCIr9#cd9?3hI}>~3vu_V$^mQ>aqVG|Moe|M!Vu2u{P?6x&V9ti zVC`C6+VdYh8FNfye7EKVcQIy`ui||mnmWS1xAOdIYwR{&KjxpB2SA$g4_K54{CJl0 z;jX~kj%Ch>aGi!010NAjdM|w_9K@AIY;L6ZTo2u#(~Fi(Z`!h@*tb7U zSkaaIAfIO8xwvJherUJAMuMiGlMKT{aD%vP43diQzux*F!rH`}Sx(%B*1si7>|NAF z?dvHir{KuZ%#5P*b`tJFfL7Y8TTbL}ZsQ^(PFes-bI>EAp0ZHio!YQ*<9xavWr#IG z;Pu=|&_(;N#%`sntOKhd{mn`Ms`5BMWk#b=;w+sFO_jg)jG+Y*W6?HKn^NtRf*j}eQ^(3N^-Mm<49i;%&-n!Fm zI*x!<<+)YWO>1Y*+JV;*TMdg&rXMFgqi?}yn6 z;?og0ugkrsV(`s3vmH+2K1O5YiI3x-bF{{%>nB%3arx7E8bA%N=_sYwJ%HYiiKCYH z=){5$qw?|e^J=HG8Pp0V zG#oEbsL0n0dA(nF{ReI8RFTTTqf$|aeEj_RSVqh9Sz5ZfJ4xqNTDQK*Avy7q^QqK- zD#T%hqXOMw2!X=lwpq6b6vNzf_W9+VsFmiEv`_)fC^|Xa;?;nd@wUGLcDF}Nsc(X# zt|e#wqzfw-dQ6M9Uc||POmjWCqfF2~a^y%>$G`-t6;#?5ac%IArD7m$tc+ zSfSluV~G;KMM&9Cq!VKs&NwZD1=iGWwt|R}kBKuR<;O^qi!G+?G~;QTwHVICB4}N4 zo}>{ai1oY8XmScr)K!#Z+41&m@ISqRnNFrb$tBD<^jE#RA^SdQ-5_333k?34$kLai z6{NugF^>4Gmz9~TwuQ`?a)hjF#siR&ytx-vAFee}Uvt>2 z0YzWq%Y0zB&Ydf|+6*ScMK^dm{xv%&TRx!|1B|(@bT)BoWafhh5&RO3&3@=n#i0U_ zDfZs|*(1ZK4n+)0j7H;=mE%Sx2A0&7vYiik`TBL2$dZ_GaO1G@ni?7#=>^(C1GE^m zna^2U@?5<8J9X{qNd=bo>Xqig!GRw5JTm=3Q(u3t+=QHS7t-B1soDQ|bc=KNoVaTL zQL{7WJ9&i(9v?Y5IRHF+_*}CVem5M#KlZO#U=zp)T4T?TjmvD}R{pBaIiKDgSe?V5 zo-Mo`T0Xo9x*l3TP&Z2PLyWLmd=Xe9-Txz(Z~M4Bv)=&}*ZjfM)Qf~ktNcQ9q?r<5 zK}`fC$1cbGWz;b;>NogB(ZM8qxV^p4fddDgaxJ8}NsanB5ir&86wrSI~DeZLz4EW{|xD1K&&yWd` z6OK<#;i&|;lZ)z8@VXuE59)6@eR|H~#8t2oJJ&@e`_ft_M>sooQv8zOtQP@Wp$qBB zY(N`DR=m2*6d)Q=M#;P0ZGLfYW-MhEBYCc7X6|Iy$Zqq=NxQh&DD*id&UUmh?x)k3 zWbJHx2<+ZeoHK}w{Jd2K*bm46-Nj7~#Mpa%r=q1uAri4~wm&J~=;zl& z#FQf81Zh0(cjSXxN!(Wj=wSAxHKlLs9yg_1Uu^rPCas(Fz97bXNA6%9>a?Gg<#5z= z==_~i*vhKgXt%J0`MnIAZZ6>D+9VS-KtXiN4%MP@r31Aze7bh>H=cIVr}^|Xkr+2o zaHe!(s=Rf3J=Bm^@sE(A(YrGJ2lu;nQ?8jS`EMFy5r*QOhhp^{kEtYFrF__pjnF+k^UZfmx1-hAZMmL!Le+D{d?Phiq~u0 zf0lJESs7A{MlJE<@~q=}H9Mmv>p*-WIA*24PkW&RZ*oHw=v-st>&Z!DnoTGVOs7de z##>N6V*xk1Xj#r=LiIvs$suJNKhgyb`8hIZ(fMibOYLr#Od%Y}Rjqvc&_`m3P>FCo zq%UC~8$DjQc=2gYG}uS6B6#rd18oP=TuBjYmA%HaXvG0?VBJX`N0Lv|fm^yN-NJkZ zZu%4zZ3t3W)C1JLph|Qt#+8Zbi4vfwiqiziHO_=@`7PpqC=zR;6zY4ebaAH;+S->SucXdmgd0 zD;ZQ(tOz`A9;#J2>^^W%?^W+6BHlK%u<%ZQdNiu+4pk96b~C-MwuH#QWOG{GRAQ9ZVRLJKJusGfYRwVIkP z*^VIexUu(e8t8C`#0U^PYH-2~q^UfFQ6UD?BHpi`_j$3izUx}%S1IyeKNMY|Xgsq{ zO@B*x5|?m2*OwG8m+wC~9r5gGr4tFfygmY6_@{Wbnza7IgO6MI_uh;qdiN-qVCb$q zdNd*8z4fb#*PPVkx0%Goy#pM^Kz;&W*f}n7T65qRth?Y6TmA5nBVtHkowJ)TElMjT zkCz@z?g3kiN|H(}>eIj+ndv}I+f(MrT)DCVFaz~w9lQqrv~jo zN%~G$>;XyPi)HaN8{CO+u^p)#YF>F^?)5L6!4U_abT`SZC;LJ`phTAmcJ`O=&y8n` zAxNLXgE`~M4h$029mg-_e$4(4(&d9R7qr2)|1+F)_e1;M7zi> zw(*~T7G|G5l0Njl_9C}kR5UD`09R*3443c!Q9jz->1S2!f8Bc~Pn*$@4>EH|)uKU* zDMJNQE~})=9lz^xU3D$#ECg`5JD7_7YigPcZdQd6@6tQXTIP>j~5KQ*9PfQ@XPY`K8lVjxc_=G}JyDq`(~$8N`!tiIZV(97_I z%%7mg8>aS(J?Z)~q~HQa+X^aJa{p`8ERr^l95T`{<|iE{mIOKLC!%>?_#j3Vy|?z_ zyb!(0XB{3OyIs5uqC;ET!p`10t9sq1-&^3kw!0_Lw{Xe8I9X}CK2Q!pTQR3qf9!dRSnpi5xZ;Gt_??yzTQ30`(l7=8*uvlprnE7 zRaF1hM{P&jm+uSOao7@!b}SM_DHKHz25w=ISpRrHL|h)|eoW=Jofq0yw297LjFV)bYoyJucZIwA^H;Av-|o4j^OQf)*#&{vJ~B9% z<*Jx|;Fq?{#}*Wm{4g+VQW2PRHx9soye(4RN8jyZs6a_WuuWbM@_Y7N6`%@Lz~S8~ zyR7fvGQi73Ar`SSkEA6CPCc+~d9qn{Y`)_lDwaRSo+zr+5C8DA7-zN78+J)Bxte%DM4CT@` z6nRi1lpRkSojb|%)Ecp4WMBQBU#eC+JJp;gdy(_fmZd#y(3jj+ty|NMHjx|AOfVX{ z?t6(3ZwfCDRhv zk8K5-T)RmVKdZ`PtTaqHq>b#|cR1A}Fg}z%foDHDa5JDT3P_!RqJr>uO+XW@7WSD& zzoEsWCeuw~zUh-!3EQYK+3vW;>j8jSDNqzHF6BW6LrJ5azxl&1V$y~w&HkyX+FkQj zknVTM&e=@M4^v!9W(;kmcZarR3-VdPyi6koK2mMja@UR>mvxdWMEi@S;a^Buv>x;cFKPmEPOCCFUF6QZ+K7$l zF$j(8*Jip&v!SJICZ;r)0Xkd%a2R**(>|ezdBBx#O9A#%9zNU+dv7)E*zRAU<4p6; zrHktzSIuG*x5gHSkvrBmjnqj8+~2QVGhEsbWTfI?P&!N-a5*EEj6h0fln!wAZ|>S=CT(oTCZ5{J*TgV4 zByJU1-)K5CVED(~{Mawl^74H*+?<9H;H6!T10p3W>+@OPGD{9xc#2a?|J8?1u#P`4 zPWS)_y{Aw2QAz}reSGk56ASEb{DXtbOrH+5_&6O@{{VKg_|l*8-YF>VkkcuYZ~r#h zrqo4K^U}#LGhqFvmmiL^E~u)i2~DX?Hw)+W$q7NBJyPAbM1_u_jvYFzhwfohhyQit z_IW0m;Y`Q2O0Re<4YBv{@8WS&&P;fn)-v4`Pk$t%;$@-T-EpKmRdN$C>(9SBb>nNC zb>{=9329E)n-pQ+xZU7AlzOK@o~-V^%&`zWq4{@+7Tnw%#t&EzA^?-_0fS`*KgBk zp97E4a!j~%+fOIJNAN)4;}$y8k9~@V9fPO9)cB*at5I!u#I10C0uu(FJ4?DlCCxm+ z@oT2UoaFyWQ(AuRcq=Q+DIa(pkDs2#SDNvfp%kJlo+_95%yX$p=K>nB=tc2Fi`+@; zZMF?!0z*RXvI>_kTQ(Ce!OC;|Yp%=O!UDsBsT@;4tula_LwCZ>j~?gPj$7&cqfUyj zXxYB}2t!MKq4>5LgwEnH&pxxTnc#B-=do)A-HxNMJ)A#jQkz+vAa3vD);Ce_x z^R^G%|E6tN+{$VBx3_-~WTBCKZSG~Eu?_V!1&P3@J&(a3t~OXqLknNy4m5Vk-z*uNLgSv+xW z6Z64k@8ff|K)SBF6eo@#gD+!OPIs|<7bgn?wCpa3%PEXCOO0 zYM-_d5rLROpu%HXTR&c2<^T6Hg<-0h+EbV zQ(<7K<@<_nA5E3oi$^#QflL9quA4A*45TSs_#(eq(EaETec%<$pAGF9$`wbBIbZ>f zBdv-A{NwNI*Q*C~BvYPjRvB74Yop+n>1z<4dZ5z$uA8*bHe{Ms3j7!G5;a+)EKQjt(PYi$o zo}*t|;grI>9#yJwBLq?mSO)cJ&7>V&wE7J57P^OsG|uHujb#6veZP3@&}XMGdC$o? zDDWzzD9#er@g`eKj-mbp!oCBLA(Q}0TPx3SM~+pAjs$IyXat^thrdI?gm>6;mWwH> zJwmF#S@a|x7}YzDD?3psk0)8XIh(aR3lf;@`ZU6E%2mF&nXA!g3K9|W0Z$Z=Mr!}N zwjdePl`SZIPN9@U21LWbxLb*r0#g7oz88l1(WvO}tlwk-;(%)<(jY@KT)XyybY1}2kYDv> z#WTqVW;AH7@sBmKmib;O^q6TOdK(%in$odz+e^8V)SkJyGct`XYzEyew*T}?d%iRQ zy(At69CFvrFV7R4;pVMdyY!DKA-xVFD?JEu(3FQwfi(Tihqr`x9eTUQjhw>s?%J)J z7txWq3ewT^xQn?5EU|b<3TMJO3JD>s(Ma{(Nc&q*jTKNch^zp~uqXtbCaPL7XASsp zF3Jf;BW&l%&jvYmnrOlLEPw#OutArpGnin7{}ug=A@|arISZadNO~0JSRwV`r!|tt z`NiG%#Q-Ic>WejlbW{)_)0kAHx!0}`+?jg&6K=R-^CXgcPCazu??rpeRZuWkjdUU4 zcSKZ(@?RE@sc+kx5WZQNtL(6FxD5{9ifPoOiBl8T2J$l_)gLB1IyvPd7$!D&!u<51 z(W%{FXGHa?M&?FC$jvAV6P!P4fR|<7(E0+9jSNKk!wCxMd|>+fhm)*ed+bEaN~16! znK$*}93&%76Lp%F`78>A}F}#|^D`)+i9`9qV39S1LREE=O-069v z#lnyJvXj%q2uEy=xX_p$C&;FHycv?1Zv&UgQ=JdfM@$eVK`5N~0)+; zv1s6>OM}7GQVAH*I9q3h22LhL6d)If4^IMgkxFe?xv> z`||ygqactdr8mj^NF98k_QK;V`}%GYZYkI4lO^Fj^yFJ|&n83?j`exO0HL}Gh@uPQ zxkdxNX1xe(NGSyNI+65;3@HV{4ya8gwavJ(L%RNgmmR6`!3Naj_*zOPQ&=XYd-wgy64{OGO+*eCKTI&FF{d zYhvL>S_tN0fvsHJ3_8|LIu%!qZ;6##sFyPcbey9?!1-H zS+|o7?TA?u#Ig+2kR*Uj8Mx||wt>Mx(cEzRWOpUzQ=cB}g3L%pL=)IB zdb&p+1#ezNZ{1DWcgyZ7gd$-DOz3#z^0tcCCPg*WbOe@5cPXI-b>khG zPM8n`eLx?CFV5$t$DhO~u@8}WIl>+j@I=GHQ6ls`4e&O5{Pk^62VSMAi94`R_qJju z((V0%kS}o+_~t(FqvWq-$=y>DKR|bj;)--yjw4w*lg}-crr6f_bT(Gg+o5lK?CK%U z-&%mP<}?Rv(@C1i8x><;QWX@QAa-3rkwi-lBNctkI+{j3M>tjzC>cwWl@%Zg2qfun zc-@?w7ct588Wq7~5+zA^$OlPIUUHK@KBGOfk1<*JJmHWD^fJ>!ni=qmB1tp@ACCCT zvM1;4v>HuE1SBaP0GHWNgAkBG-T}P6gjs@}0rUgdvQU3Lg%YwO@+Zy$rhuGZQ`!YF zxl}DwfqfTb98#*PHRU10U~jOEncDT>M)`YsV+Y}2!L!XLY7@m~(g6slF7q{Va*~Wj zl{1F}z)E^dNd>W?K9x{c z)Y`~a(GQ`dXe3AP>Vi5X-uO=>K<8l{)RlQG2~&L$!% zoxlEqRIS&7Y!dsd@X=rD5MvF+reo(e5`x`avL$8B_|Mo6G}-L zg9OvKrr^M88DB%1n9r#$eH2>bBNxo=6ZHn;Z7O9beS}r@qpz)r0H9_CV=LPn(3Vq4 zBk0ybJo0#JU>x*IZK5m5^!?F_OUP?y&z_Biljh*T`;pJG^Dt>(Vr~%ZiL4s*M4Y!) zo<~=KufxgLgAk#AU=KY(s0(h6Y-zg<7b5gfa$g7ZO~@X|pygY@9SE;~5OI>s5=c z)opHryAo+In$MT^1r8X3Eeo}l%|P{x<_J4~ ze&V1aYsb1zva^HXwEnPcsm&r%r3Xcoohtq2!L(w}|M8Wzh8CPg2*v^W(UnV4eh99W z6LteTDrRv1(Kcvyi0qRvvEva#o*V#HPyAZmIp`LLw)^X;t5F3)Tk%??ejy^ueglW1 zSmo8I2!PshGC(c4p3KA*rLKEb(x<7?i2HnDh!Y0E24MKd z;M&wH`=X-Cd;+Rl6iV82hSGtmsjK@bIE`lxP%$Ct#IK$Q$&WZWLMtZYsV<=BwhaO+ z4_Xuu6eLYrC7+8fBK_w$r>>FK0(bfXY`gd?2n5pQ46mTo9tB4@WP4TcR1sKGTkhn1 zJ(ueydZF|mA;*l$sXky%MOlfa@gWTCR&Q?^>@6tCv$fq zQhoPF?SfJubGRKcGz5GbGANC~#OZUb4NyCah`UoKxej(JrcNsD+gqYLQV?#T@5Vwg z*By_J))Fm0(Jm6v z1(I2LHd`BQ&}NviXrv|fczUj&HbFZs(@Q_~>$fON77P+(8DoWzf`Xf#p~0mXfE zCCIe{Lv|4;XbIzn$51VOTES3X4@64*TP=zpLx^Awq}lW5AKBlpW4vp37oA%efoOoW z`05VW@M%}BnPIiG-{A!bKEf^KmMuph*yHQi5ZDezR_x`IdDXGH4BmV^frd~}vSDqH7N#mP< zPPmavV}g}um->D zLv{vfAv1}1I>=YkW2vNTIzPrHySbh^d#gJh^WIe3&D7M=5id@}AbzsacH%Jmj_AVZ z>MeLA<6aQx+K~9-ARGsrRglR>}H7yROM<>;g%t1j-|I8RmE_mZ>WDo`g- z^hc;|M4|E7sh_u(m$Z!hZ2LU;D<-CInCWcDRz$dV;Xr0bS;|FFjB z1d&j5JECiAEyV|?`8=9KtURrv!Pv#!M1<2QY6da`m}B|yjuzUD0mCWDE(0cDKIpH% z-(nVoJEU(AG0My6!mkdt4hqqIq2@s4G#9`hkmFsjN9g9N`>`{I(lkbG7@*%gkTR+o zf51pkmvFk#!<*GP>q0Vt`k;?s%F_S1O#2KOoQyC;ux3uN z48A;7`TiyPJU6!#5h-=WVVdnZbAZh}vpO!^59KREdKhxq%c+~T6yDN5im>*&_7iOx zkj5w7B!c!CFkn7?pQ7rVdcCRk5U@IAL^spZUvd)8zHm;7ZWoa*(G?{Q?5Q96#75(m zK-Xr;Mk0kuyLMwpWF-&g+1Z|Ws5Lkr4G>W~;Aw)jCjGXr2;E}CeRzj$C;=DhpYE!8 z6bAB&0l)hIi;XjD^oQjU7l-UPNf1SSW~M&ILyxJ+s1R-wX-zs-)~oqe3o=uwJw##7 zi^+q?aWYeriAi#P0!?m%gfLeh6TBDmSIJ_*{jHBrw&5Y}C-FiCxCt>5$eyS^Xj;F)tzP#u|H?N9A0rF}p ztDI2_p)1`+xKRe`2k7n!2rve$5^F{Al+Ai!-Hj9(@4H}j3$I&dK??s&fVT4N(m-u= zub}%l7ZbY9uPck&(`S4=dYhQ~D7mp{qCmfwZWZXc!Buz0z!VTb1d!nHeaW;&Itu}6 z907A*00Pnh5ItP0@6n&_T~@c7Fy}{AlM1IX`y%2@+uz#U%DCgE+Z|fR=``-#{$ii> z{_~Fc^wHR2p?AY7HDJm3j37re z1~1F~mT=Q$bAOtazzHc~eU6HC>@P3Ac`_(3#tW`~h90>VV^`1YrJlyWCi zG3uisli?9w>?U!!lUFsrT{kmEPEc7j;PFd015o37XaE>-4`0*pail$fy0$Yh=;ZKd zq3>(@6zqVo;=#Fkm}O#=cCXr*$*dPg18z-X-i{V_6RGkGfOexa6=Tl-43VUpYVK=p6mJs_*nmz7`S7?G`T|^-=ye#9Mm)=9rBuVBuD8*l(aG;u2 zr%ts=c&OcVQQk0=!zWFAq+a1K-5aRndTQ!nY4}232w<}@E>1TzEczK0=_x9v>Vb*t zfI!+pC_YF0jNkBvjOBW^fOBG)NV3BO^(I11psA>yOlcba5f5f6(PcEj1NIm zeQ(nXRDP^^G4uf4CjU~QQ)cR#J!V&Ey7~i99YFCVz3e>XZtU|E_92VluFhyx8JMZ; zs&Z=LcFt}wR$|(kAtfg5J)s{voo$~^XCM(at1vnT5>foj!3?%?+1x=Gs22Us4RK-? zw-qYT64bYFAf|;2j8%8j8G}^YN1i!k56@hBnPjoJReT&lXTW$CUp;YzvhqSP`kazy znUhlr=MFwMKG2Qa^k(WJYM1(>U7x%n^e~=uJ(t-~-DD28iK?$Imu5_X#1tGaWIk7O za@sLbq0re_buVUmAUX2Hz@HkZk9cpcsUl-bkxE{ND%$KjKD647%;Vm}hpE7ulr28Q zr0l+X`klQ|F&3nS2^9BkXn7JYzP~@0E6Q`|c5eGUrz%WVZL$`LNpNAyc|}655Xp?v zI>qRT!#XBJ)nG_h?QWv_K#a0bOdm*wWvZ0MPTSA!FPbX*cfcP0q*yMlSrZEVDdU!9 zNL}b0EuY}v)`&S8IXF0khTX7(U)?j*-2f_88e?U+IW-rpu%d|-M=AbIW=;LP7GO5B zcD+A`A!?^ybv8&cg#5?%3yN$bj7%f-BUi=t%GkmQOitV!q1&XS4A2_C**Cnom@U#M zMow^~Pp1G}*1!K-xDD0KWxR>ZaR&TVQqNCJPfr&eFopR7 zm$0GjEyP)^*$+GH$+H`xPyq0DAqhF-Z4_=D;oC8cwq*ROBn zWH3dGO*Gpg^B3^?VAA5&j8n~1@7+^IOX2*pA|Yd3&SIc2lEYMXfrszm{-6soWtLf+ zhsm>LN}XiaX3Z9^XZ?1d#}kRc>5Or8XkXlpK_ZQ%;*8Opl_gV;ysXpd_y_(IRVP!q zHvx0KL{gxlzuQQranYGSoE`+eHu~7HKPAm4bV|omXkLR}KsgZDrl4G**c!m0(`np5 z%+^PAI@~0SwFk0&4KfyBtf{{5A-5lIx)KLwlSMS$HtB;_GxrO|#+ot&E01Lln>v8i zBgUKJIj{b97sx>?(a=gmCxfoW^EzeRI8-dB&saG%up5wmG1`i8r=W&wi$J=$zM8V@ zX6|$r?8Bac%QZ;#J$yTPVTL4;P|OYxL9nS`d^|`*QJKoel7ZEu;HJ$?8V!v`{8#`S z&QT`A9i@+$!DkGHIZX1p0GqHUEi2P^BE=ygRV754K!3?NE+lB7a~NfVW2PA5G1Ftc zpPygGa%*kMd8o@x>{0}b-B#|m*dQ-lb0EI~qbK=-U{JsTv?h00dCX52e-zp!@`&R` zBi4k05_iB27&4)`zIVFa{}#^Tc65muhPI~!;rA^1Io}$%;l*zo%%<(kSa4EKlpj}r6JVIsKn$=^Bdu_%z>n@lhi>)4~ z^30ht*Mo$}92PvFc+VQ0WmE>I84v{j*kNVch;q1?-jGD#W~GgcRA1iU*oao;US$&l z(O!nSX-FvWnW$cFLv3`G-BLYXFThboLsRQn^wplmcxetJoDwcGm4jQqPSMe4@xvZ1 zGL2QA@xG|2y|@wr{2L&NkJu#5LR{V>+-7_AX4=hQ4h*X{B4j^=2Kh1F8B_{9+$L0k zo8Y$QkdCw(>UJq(`CQ=Tzj*uPhQ@{6ENrZ-{-B+aE~4gC!!nD2zTdGxB%xu-{z~9T zHZNI$BX{3j6|Az+^S;w!vLAG|p=NPijEkB0K#*sX<~8+F^qAHVCn! z$L>-Pum0f26fU~NJlyIX{eO>I)(w5y4pI^2089WgT<(8s7rWFl0896;E`HgNljLzF zsQ6FsXrZV+Di^Z|O#nOzGIg;`S@-3^1fb-|j8i%2`NoNy4XwRRW^Vb14L`p;n!)7G zO{62yntm~OQ5}MK1^zS^2pPD7fJcQqk;tuih#KthnKPdg?n|?B(&#UzMaS{*Y45dI$t^cX*=AulNo!> zE!nQEXCrX`|0C(cUpb77*UaZQsR0QhPr)tkCpi9D?MM(gAK(+Qe~Q-`G^jO~u^xtk zyX-Yge{J+7J51$b7ck`woTFo4azrp9WANa_(M@oX-)d9;V( z#CY~>4PdXjX5TJw8YvV2!08Nq)s#pNL`56Dwz1w?MwH{d!WI?F&wh3^3) zC|>7Bq~mA%;7=1#aG#_OdLNdq>Pn}A_D9d3Z-tr?r}v%qE;KuVhw_<9{$}A>KLa~c z(Ll$_NN*W0#ZC}7bKP!j<(u}%Gh|j0%(1Uv2T$P_6cH6^8%b04KjZ4y1& z1=>joz;e@2OWY_a^}@lR@5BL|?^~0~2z8n>=+iUKbjrm>6rL7$`%`TLHwAQe41AHl zApPjw9e9Tt5cm2}Kb=b|CKDDwcTv2bKQ_)1HyJ8Knjm!nIUQ!`e}E*0<|+GcnM@C< zvBtf{jdan;Db_#1&Tf!y<)S|7OqwN4B)Md|l2O!x&q`8dSa+ldR|VM-{R?24OxC7% ztk@@Q@{}pH73`>EpC2Sbu5CknAS9c``-1!e=dx~rP(4haXdo+pahd^BoGA`v-a5vy zA`Z;x!9iPD=>J{5)7kklQ?fzO7>LtRj(9S6$|B3qL0h9fiAo;cF}*yjmlax`t8&oA;6@E0f4o zhxcfKiA`NJITm;I;1OVyad)&_=nA}AOF_;oM)DNrGP*rKa(D>u3j$RpketL+k9J*Q zUP1@)EW~d=;YeT*X)aWaa55ANQW3IiW`P&s*%4#ZS7lrZB--iK2KuK++NA>l?SVIt z3YidLw7G(O@o3B*hc7Z;8^7txRi=?a)RF8%(vjpZt{g%#%J9jvZ_`>!8FE-DttMlG z?~|a2(UIVD@yW0(aPGBBW^Xv*p1Uh@0S^ZE+?#D$CXNe$SF7#3Jmf1U=SjK>MOe|T zM~}_q?-ZT0?Cd;bI#1hyO14@t>Nj@l_9arhVg5A2Ce8K6ICdy zS+Ahq?eR|T1m>quFuiWg+rE2M^Y=L<8Dd8u9xa(k9JyqHj0MIzc^p?-A;U*m41gJ$ z<({u-trq2@hmIKo^~Cams-a2J(7_rSfAICgLCUu@Y5x~zsNUj-qe2tBqCQ!enTZ7* zf}e&c%P9av!X-28MWHT%mz>QQxdq*r4Qaw3#&DgkT}NchA5abqz!??bv-=Tj4e6@9 z0&sF4?)3#VVw}##DQ6aJAo)7Pa@lBi;!HRJGw?D`C4#ytkn-1hEuyuBq-NHP#I9vl z%k0~`?69n#jIYp9-D;c(ToZ9-1~-{Ke@&ST;QnLBA_QAbPEHZTR*N@Nv;!?B9Glp& zZig>lzPJ{jg0dX)aF@@Esi9O0MjuaOo5Wb$6kn1_vr7p}Nl4z|fy)ytX zeiLRrME9m@YTEluyE5pQc0BRsIbOv{Eyhbc1y8Fd?t|`>+fY>E8fQxKEZthTyLO8x z#vnvStr6Ob---3t|5|}q+%#eQ>DJy0CcXdqTMHlx91dWzW9GwdWMn#3Dfze;nleSz z#VtIfhDi;wDSTzHefjD5_*Du029QRQEPBGVRM2iSb^o|wlG8*9E1JsnioTNwax;FU zrV51AEC#_@yqyw!cYm**Jzeu=|Ba;$TXHy3RT*FH@wF_IBCj{%FVY`DoPE9x%4L*1 zrJf{-1a$GOhBC$qYMfTEm)FnZq-2u30t5T9MTV0S*xA{|MQ4fg#tU}Z9}(+5e!Is> zly2`5JdS^cgJJ4KH%X=0>`F2*%SA5PDE-bI6H0wT+}v2WF0n+z>cVBtN39my#7|a+ zz-tA=EzV-x49hVT7*=WV03Dqsu$=kvWN~z^H>ZDa81nVz4<8++G?vIgUdY{4kV3XX zu(QM+wps?t$p~ZAXd7wJNqCU5)oXJ}McE2M{Y=`YcUVN^n{KFeh#ZPMbWewD7-5Ra zZH`ajjXU_8$*ezl-b(KKJGu_8K8R*XG)JVxy%mlaeV7;w+G)_ zfl^FqG3;wIhL-2Y_hfibciUrQpA;z8vrYw$An_6&1%&!fcmf%GCPE(yeN=bb(Q(cu zr?`BRqW+7aa2f!BY@GY#;%Xg02ttWnBNH^bv(QE}C&{NnaN+y! z=hT#yZCXK4L5>fnMThU%&ikYg79)fOxk-udl5`n{2G=xf20Jswzm zt0SCqWthtlP@A zXE6}iOw^R2MIwq=xG;LjqPcnU@*G(v62fC$vW4T!f9g7e7~%&>?4mek zo`CLk)7IWdXeT`_VP^=ez+~)kliOKY@>^=FfA07c{qo~#(vZTZ=VFbXX!q=y!ov>e zf2&~H^u!52#*>52Lf@>ka9fT{t|xOV|7MYsPTsMR_nH{5vX)v((V=J0#$qLj{H;Tv zg}{M=1F@|*2Mx)6Y1O2Gkqq&@ zyTp}zmq~Tty-lcx1q6`viF!uNJsW;h25WD=MJ`4;@%}B72+)Ow5W_ML6=}2~D}-XC zu^@b3-yThlwK=-`-M6aggAQ)vV4zFIsC=G=$0aI0Xa~)NA!`pK`^5@lmX0e%# zslLenFa9$8*DD0LoOk$uOapYT%oK>6#k!+-(lo0>B1jikVmdTbZ zY$oNEG*Kz=PlC3WnNZ@#4n!)-6`7Xx^y!q7@vzfgB&j$49Xigg>-DnA;vf+!4?%4p z^9$uQDX0X3R5KL<5X|9PSc`@$SDtz1afU}Ad?N0mP`hQ z0jNtd&-_GcKn=_u^X;(+xdtrGx7eEH8izkCEq8?*+(dgp!^_)d>v!)yGG2$rgz^U} ze-OQO8mpgz$YHZ`nNy-S45Q4VK&2s#S2Cyp?IaECqi2Pf{M*3w-*{B{05@aN%^+l{ zOEdg@;>}-1pLw5=F;;2^w!OvMAg}3TKbH+bgNU2a z^W)|2OxuAu7m$;>T5z4EIhVHLuj_8@F7t5!j(`_(*Wg>c@pj z2Vx1rOH;O8evQnnAV4_Z;AEZnyU?>M4n>~sC-cq)Es^wIAD_9!Q5hm;G6lNIUM=&ENO)BA!`{vVZyhtCl?V?DxO3CPnbK(M>ET*e6&nN zRv>crHd?V`cJ;y-U@2y+c6qj%LOWm8=A`gj(m;=MD ze!bTkYFME_>+`*^zutk2NAKOi&Nd>Pp|JRw)4I0ipMuRQYu&iz-ZZ(0T0)XbgR(xl zpUxygBO+e-7G`_6vr0O&{5BwwS_&EXCM~hDAw*I`W_5^!O|hOVQQjiQiVi)U9jBaK zOREcTh7kw88x8@Jo%zmSd?d)vrQ$Npw?c(&PZJG&k)> zdHEcMYKN>0iPgPZ?B_(TL_st&=h7O5LJU7%y;^5Ig=9}L_CtYf>!@o^<+;E^07{!7 zWv^VjHoX7HO<7H60Xcj%c>iu4eCkBK_%yY#!$8o&r3aDqZo=R;*`*xn`Aj|0DFo z(^zf75l>JY-K1T}|>UqdX)Ij?Gv|HIzik7k7aDxZoLgJ&wH_elTj z_>>m*$c$0O-jPR-He&H|whJNdP~rYJCkN@p?!+S;V^X^yG?9E9JYyz32TC)%EQ1Ta zc}_%fiDYE%T~~IJ@ByGYn^B0Q|9cRs1&Qk1M$;2vwHD8uHERQPtzY`_55%|RMdWC( z!5eVqNeFH(oFblxMx_}?s%JHY8KfYCF3HI~<*?)!Acx%Yle`pzI6nL$Y z*%wx$oS#}L-pZUMyjwMzqjH^bYFgctqzTo_VTb@nH3FM^KO_8Z{bTW|9K$!EAH!HzPl0 z?~0X9vfB{+jXe0DR_A?93m8A$OMrj0fQkK|7$BJ<9q2e@h&n{9frukea} zK?F7hX^kJ42pQ^+O}Y1f{{p`qQ@Mj zi_?U+c24=?hsQR^Jc$qgmfyQO#`a5DWq)8jaSajQFU*#2#V21-`o8br*~SXw32D^& zL!&~1NS{tI9TxN)m8lF#?cJ|m3Pv`-b_%%QgeT*_ei^Q*a^=>ox0@y`o{;u79(w`8 z;Fo{m>5XmJsL{4>^PG{F{YhUye#_$))wyJ0Rr#LT7vh+`ReY_~GspB_?>FfiQ_g%^ zzIOn}AAkJ!S0^d*pEu2?6eW)t~pD$;1EsCI7$uWzF3bX;L0= zyiwd_nhkb0>qh3Ls=x8ci@U~U)wiCg0qe~(Q`N8MZ~C%qxKTE393nnj`De^rW2#n#B;^hr$}wk|ay$nYM6a8UA|H0iQU+ zGw5z%yIXSEp#ybguAOmsDiAqUn9Ns3mw*bxNUCKKe*iXxhV7{LYXF{c`0M`zrBuhp z)#w|at2PV<8(v4+&QWb{2Z3myZk{J>HN}SrJ<%L(_8qR~D+FrwFR2*KprGU#{#iMMX+g53Ss6Pl6@H^>%}dw2O`RZWF44^pIgQfBEoP00 zT4M5KHEdn4>$EqU_4}n?LmAwZn8SzA$rX^XJnPB-Xli^s^t77Syl%ZL0t|DTwj*9^ z;q5>zreIal)G`wg&nPWa=~tyo;Vqtb(n!FZgA-#bbwH557affqJ9)A z%CcWk#ed5pdO}dp3KRbVMLt*C9f(Hc)|h-wGTUWUox-w=u&&O2LG48c4@YfM#_~F= zNp3hHW|6+_!SFC+dDO<53FP4~+8L~C07WKeBTR}aath!#l7|_GSp8DwU7tNBwR8US zs!6|I-U&c@j+HH_CZ#QPWn05aFn_u&Q{edJGOAG@FR!SLtqj%;ME@h%BpE#Vr+kh% z+8M;FMcSn0SyY!P%F5&F*36+3Qj>y=n~T15Y490`+;%jXULj{?0R<`whLCN6gEa+9 z79E)E3W^OS^$`&yWisHPT0rBW-LN|AEG=C?gH&ga&Ez;4|w8 zp~pC8r7o!PSg#%j4ye^{!!_GWX3yi~=gzYpXCo*e>+cN!+;wH!n7K6@)Y{OV zG03cfJv3Cf_$4B;|5V%c5Y9&5+Litf1Fqj_p~`=*I)*wEnQ4k3c{ETamritAyFZHx zL5TcwAA&3*L|#abfd|x6Oz+nBs%pY%%w@_6CDxd1jN%?+{eX3W3{<~G;Gix#`rp6&oU^|P;fGu)`O6+aY&sR9dOvuY!hwEv4}vO@vz6c#ATGsJ?u3z7>(^Vs}sZ~7ZrRK-aU{WK=6G-bOD%_^`ywa=XYH$ta2pnqD zqHY8hjsDK=DfPKRL1&G!_TEtxgzKwRzbmTq-@0`}f#yUrHuk@Mf2-)lKwPhL7yf~P zb)UO?yydmg>!>q#aCYH$7BkylNg*w#B2DJelWR9@?PpdKG{)7nB0P{eCzal_A{t4j zI$Iau)%zSXIH`7Rc9z;xWi?O;{6H*QMoTa?+-1&L{?+|$IXV^EJx;{zG5-BdQ~<06 zl!IUTdom;u>0Bq=dc_??eLMn{CE0OxIpG*zqysk4P{G4fS*c9*tvjiO!pqMujXG8O zM{&Wp8K2za2g`RmAV3&pqom7(qOQhEV#5FX+_$FYp)irCS4QW8GV*BErg>uj#sUAg8|wwcMWRNhaj$col}UH}xB zodgxeRE;CgKI%-k-@=Fz=3UvnyCFk80kJ69?6w_-bCHqed~+(={fTC~4*h4%gP2?c zg(dLSBD=_rc?@@zFNEfZRjcCpTLc-^9BiM%xcvPQZLP%11QL^h?IKKExw5%t`+G-! zf1C!D>Q&akDSFWnL|y8y+cVIj?v%|vx^$^mmDI>aQzaDKNfab94GRj=FiyF$&F`HP ziK)2g9`HAiIjQ71HzVB{tI)`5)KHjs?jmmJ+rq-jmoF=Fa&p9_oZ-i@pGFxMAN~cH zB8wBWXgS3^&LQez<3IsC*4B0lGUfs8X7C>n#m|YQn8cVsv!aAlDCqBr)5;T6j&vs> zw~dI;)7AcncQIox`klG!l{G=zz@3*kx{5;3*s zRBl}7`$FZ!*8f@N8vf5I(naY+m=<7puyctlc6=5$@cSb32GgM_xp|1|*Vfl(e_Y&X zm(~<>^M<6aA95165nSmqOm`!Q^y05X+lrusc41Y4zaYtO4Jq1Y7WjK<^@hX}_h%8X zz)VU0#~Pt;aXp79X^xooRjX9LY(j@AF`ER%grG^`DY1}oT5t@Q;kTfD%M-XaV_dY# z@4#D&cwa~6R-i#$PD47=b7>j7Oh>M*t*xMf*I#;<_735l=ArzRURp^-QBhG#b;ByrrrE1 zLs)ot9sXUKpGop^&aWMK@5t{=Y8Fu3b5yuM#)H>=Zwinnxx}&K$GeZNRoS0|M3O0> zgXIKd8Al?rV;mNaY}jQ~O=6%cY6_W#xU@E*zqY?w9XhQfEkopOG-CWnx=2D+D79)HNIqbvMZ{wYHCBZu!Clf~4yT|YcY|Na%ocov@s&2DYY%owygk~-nzZ+* zrfsOSF0e{)OW5ZAv#dVKb&*oA-aUM6I%3E-2U}Y)oI>X%Q)$;RvrMKZUlXA)8S5QE zCd8Ffl%{}`hfyRLjfmMHaRyt3)=(deG)Uu2@bEQO3x7Fyfp7KiT{Dlc63UIM96?Lq zNtbU+TVz_G%ikg#Co}@W0+~2B@S$0I@0y%!k+7ZeU1$@008nOOsmXU)!`L@KUciir z7Wj(2I34{a@r(!Kx%p$A9T*sRh0GK)mn$S~ADP_MP(3=cweH*oiyobrxupDqLeXNL zxv`=WDp0E-BcTQz-i}~{l{5}j1q=cvo zN=Ww6P~9V|c}xURYRPz^A;j409v&;ljrQU5qaVf*kCC|qILU_N3&sa9hg!-9 z@-o^;kYLOB6M5wXWYwImB`@7pkO)MawcP7d6A^^F6u+y$FR*f+=;jSI{AY3~D%OK* z!`RJE571J2J3>=M>)7!iY&-|Z@q`GHen6p#WC9ThrIxGVX$s3u1_slkU3bj;9c*W_ zduU>4$Q+*7OgEA}={0X_u&xzRl0dhGcxMzjuTAUL2qe{~+#`Xir*P=8e}Mgy%`_uw zU%DH_X~3!DfhIBHi{aiH1kK+&|J5tS`plN?%Fl5~&w?P}bxKGka@J#md7P=FG*(=0 z({f;Wb5ghwB%m1T;e77o5ntJnZgOCB^3UU+lFSopcA!-sjy9z_Re-VTk1W}m!Z268wCcKpTNX5aYx z-pHEt-Fg%bUlhblb_z`|fB5K;q~p-Iu_wnZRKUkxVcm&{180e(kAf?AzIU4W4m$^%>` z`jP`v6cMs?zch^p0}Fl?=`A(Gl>xcyh%;)E#X%2=*SpwPv#oAE_<%vydbGn=(cS|_ z42QAt=R6{XW{TVJ=x?J!^qrK3xO}yqTpphAU-iP_^w$J`u@57=hzanxp^uPf>Aho& zm{5Wrc6@ zUqmLs6^C|GmYuSl;EMuAqOGxpe}=zVk0n@(a29<1Ge2XlALZy?_vhQ|fRGa^+I5GU{S0;k!lV=jrSI`E~bdLU@t| z^nqL<9~6aFZ#}){`nC}?xckV--m=%!%RG$U48`D!>OI#JdjSoNek!V}9$bJCNFjVa z3Xo>{Rvs;yHLHz|NeF-958>5lght~lHJHqzaG(a}VQZ6EdeVxEf}xE>fwY)qw75Ti z|K5j1E0g@F)@OAb?f634M+734qz8l*+Z!+0@{r`%8L8^qdXV#pi4FSQSv=llj*c%6 zJ7^B5CUlIA7ri3`liQ|)Sx74(TtKIL{4}_VQ@3=DB43m2ja(^eLhQkeQ)a_|r+EtQ zdObEYGR9Yo4``Vs=xv#J`|km{CS>jCK!ggRWvkwwt=7{!c!*d_PfX+L{!sFzO%NJ$ zpvEL3|11ru5~_^`;o)Ri9=?&bB%-Jwy*ZLz5^fOBb-)!zAiW0aKH3dxZa@vXa0(}* z2eb+`*zZ;^R&|u^@x2)z>i|mlQ5b2!e!$FbMCyv1r2$*L8V+~1zEXoEWzLiihK3EV zNR>)=qJRm-IqH1-7JLuMk9w{&jzC)4P$UnqN%Ys#`TeI9qG&04Ltcp~NTHXtVa@Z2 z=>W7KH$cli9#kqIVp8+NABqhYMl50QU`A;bnpEPH5Gk^{$*F;$hB3O5mmtYL>Kg5e zmJPz&{Vq?2QAG}Ps~8Auqf9c-Vu37ZL~bn=3Um9A=4PB(IG*8|O6uQyWRp|>NaKJg z)-ZL!LSNeQ5L$eG|Fk7VfV{G5cr|+G?<%FC9ATJE?n;!BkfVjD9?HoF>m<=utZ^s@ zZ%ya?wYXzLN=;#x{g(8BK2O8{tG%lYhdN#3V{^$xF*awjG&s{_yS%JOWT&I?mX&BL zLy6HA$Ls9orG&~XR!*BRD{I?|CY{P0X30yF@v@~QDr;ucdI{$+$x2FyB+d}MgICSF9>{xAUhPzpDv6amPF(gB4GumUTR z4j2r{Gw;Ge)*h7*v&Z*jA>$MvpiyveS`nZS zdZithlSnA7)8GGX=#DBI$kZor9}4<}mIvRbD^_UJcqG_|*~=C!j!x&VSd56H`L-EYvT zHZd)>C7pCjExze}PG!qI=MUxktFHPvu8GqB>xlz?F4KadfQ{8t`>HhUpCz{B7@IBE z&X-5Kspk)D>i9P40q0c1!onWig}Cz%TrboY`kxrRIX?P^c5!9H^Al-2Adn!yIs&)( z^Ds`|ZOEx0o}lGwYdW!3M!~=L?2)DX>4OhS(C!(ClW;^&+6zTsU!>c7NZpecK|Z3^ z*53XIv2+zY!0?lEK3C59495+Ukn?gf*_*V{X*_luhTUL=WA zsG>L6)bx~|J!@DRDBfAk^{_Rk`l&v@1s!$z71=dxz_I>@h0D#yFop%72^li|O5jp7 z&D3vY+b+i{8e5p-l1u1Vwhq6FK*mADA}%nAsEQl}cg3sON?<`yM_Q}p`PGZ^qfUj^ z-GJIrD3ewQk9GtEoW=eJHg9v#jf2j}e!gRr2G#n(#dpHp>YJE|Fca)@aj~1D4K+Zu zH0Rl&Z@0&z?2%Xp*rQJ!RPOlXxVt;}vZcZj<3mOPzsru@X*#KTlBrU;yg1wG&8O1M z)@Gy6pXW2ypgu1YdhsWbT>~o#?N-fn?e=X;uqw?~Aav|7v$~It{zr6=AEfr8dme&} zy;hpJ_}rZ>tgOcB=#L*i9(yf@(>mM1`|)4)Uy_@0el>fvwWZ}3bRhmLdSu~QVd;fJ z5J2y_p?h}tXr?dq|GOGKrhdP|62$G9EKD)DR#}-x&no!&jPjj|6bKZzLii~;xt0+D zwi7A|Wk_sH6d|mcgQ14_W+`iW!KX0TezxTiSKn`t; zd=n+r0Yn0b+|7I)hr;bvZ|_d<6xq9XuWhSvsh)12Z94K9NHB6hcn*Q<-ynuk0GrMB zh+NEpqePC7#{d&!1Q9~4I1v9)x4D3#)Czvp0vDK!xomiHmE*;ni;Ri_pfANeWE|*u z98Es_p~JPEC^9o2b#+~vn23e2n!(BlPXvG8Aqy@7&m|m*6ySWjFxw8)1UocY0W|Z# z7SriUP~&7MZ@me*e8vcd3Gafs5|r-r^8aFJWV8X{YC}2tkC;g-gwQnVK_j#TabRm( zn+#GL;gao9Q9@YH6*Qc%`5Imnpk-zibW?tW+o-RyGp~)SPUn>Q`}!6GKP!oU2Mq#g z?RdQqHN;WmV@O}l;c^!dL@%uh4hlLCZ--t4C$SeHk-WF}o>iZw#Ra8_?f(^#R6Ha* zu<fOc~_Zw*5(q#F*5WhICEhF&ZPY~TDrUk-WZY7G$8UensU^3^bDhl2;t)%K~ zT2CBiQzlxo8yp;jV(}gXm}~&55DW@QML!gamqH?D62eeh`FO&e-~h-2(4@^}9Ey)m zw=|(zF5U?mmL(@oYQc7&!izmb7sIvQgUc;N-DXNU3-D?L46ge|%q2h??4WQ2L1ESx zHO#B?z|vWJ5*p5LM@{pT+naSN77nE<@(i4FiBPz@K84LbgT0ZI@;VVwN$e-*KS6(y zVCx2_kUbEm4F6m=aRXslIEuuPT<&sn2dbuOWw(i{FLY_1f-V^u9lfm07##yNKj()W zrh(DET3)^bUa#%|K<_db=$iSs+M4D;o9>T*1w=tahC4a*cIM?uV=@AT2DUgH6fAfB zTQk;xYPrj}E1NI|3In1UaTZ@c8k1tY40qTNv;?ShacX z*?KFEWVb@0z)pOKRO=f0Xb_Ub;L!u<@#P{%@I{pB!~ejBObeI`1%jmn#maD!Z$?Fp(iZw_ zc*&cL(P9`8$3YIga^Fv>yy;m|3mbv`%V6+R7#ZleN(Z)V0e&RvIN-dk3lP=UbMTP# zX4^HIG?b6^g@%olOmlgw-L{w?9LVY@87kLIF{n^Ee^v+z*uO0plSyL7&}P>Iu12Bu zp0je_^x1+x(Q$fU*t>0sdaBZ^Q2v*0`wty|hzC=-+7u{4?5$iaIW+Ub?K}hpM>}-L zK{#NNAL`D$wKwbcLn-aEC%pQv!v5%FsV-iv`cn!kc*5c38yBag!Y`f3H9XyY+{#?` GrT!b34053W literal 0 HcmV?d00001 diff --git a/networks/toys/graphs/toy_stoichiometry_import_rxn_visu.png b/networks/toys/graphs/toy_stoichiometry_import_rxn_visu.png new file mode 100644 index 0000000000000000000000000000000000000000..9a6847ebab55b6f2f023bb4805e9fe2f71fe3279 GIT binary patch literal 46924 zcmeFZcR1Jm-#4xuDJ=~t5hWy~p^Q@5*?VTMkiF9&Q7D_tBr7|Vkx;hmjO>}cLiYW5 zpXYh~?(6;?*L~gB_xk-l`F=gUJWO10J&DYbJmV&fG8td0%d<6$t%^4!&(KYc(kyv8)8#mIR_$rxz`#IVvdU$ADSaSVZD+vC zv|Z8r_uJ6Q@)xtZiU_s3Ns^N0?PdC(zt;6X{-#f(HDar)tBfoxPNN( z>zi-U5R9ppRr*<&{b0>O{lSv%`tLYC^ZC){vcUPN?nnmFKoU01;;nuC{dcO`KNyxQ zcDrZ{g_3XI^7@jA*2=;JJp+T3qM}gA)7^&6iIkmrwsC?!C+zI({6FzX*=Oip_KOkp z$uRF1AlbZmvyhk=Jv;j=SJ$m{baeMEEa`t;M~ z`LQk2^@21ZkN2cG%o_!sQ+XN}cc#qyII}|CrlIPPl47@ABh5*NRdP%jczGkN$A0uq zOw_p3JScy*?^NZQrDx@~th}b?qG-1l)kUrEVO(EI z()9K8LIVc}2iaZLop}HBY%yw1luNOxjo=a0(V@TTzDKv?qxr|&+&9fe^&?hdAsn|K zr|c!obGvV88DrjG>K_Qw&fY?V*Jf;->pqlXVkEG#S(Ki-cXnV+7% zzdr5G*VErGGx@;R*O$bg{lkb%(BfE2YKMturC*xL$z*wz*a@!_JfS8(vw19+^6lsD z+t}zWPIivBXFdP*%Yaoa_p8RnatqmfOT5&np5F68aU)F$2X;`LcrW?$O&|UEp}wJ- z*Bu3phtHfj6MEUq^v7onyWVYj-@{11;4$6N*t2I3rBNzwhtsqp82{O~e}8ehb{X3d zayg}pcoME-scF8x`%}~lMC|OY*x1;(y1DV#&?V<%*$V#5Hi@)b%W6?~oE&~iew1HU z_8@-oTbVfV4~C8JiZ5QddNsppL|U&V^!B}bEa~a#ocCH^;)?4E9Bm!uN988xNZ!oS zkCWLcpLATDtjRX{SvO)8V07)|$%nPPE92=c0zN0y-qkudI9N>g-0-I1`~KN^W#`VF z@%N7(ziM?CZ+yJtlOMLn{olW&Y{pxQypLat*C^!G)YK$D!WLCL@qE^=({9_Uh26cW zu7clKumz5b8HSCgzZr=L3Ejo3tPW=1@^EQx*e@tZA>q<-YU(50c5IQ(3rF4e9M{4! z$HzPj3!~Pnji_v=}To^ac)-h#z0?ccxZttQ$7aCb9UuyRVfv$M1RtgH~LuGH(8d(qvyc$IQLKL0V< zZ?h|^*(l_yHIqP0I=`P0lW~PFgVkh5NQzqC=4ovA-hqLyK78xt>uW1jom~}vXGZE{ z$}R-0EG=!4b*R1J@8jCZ|9j%+VKXx`Y{y709XX{_0xx>vA|E)6XW_+~50vlgdaz>B zXl!a~>iYEQW(wQkxw*NjW}kWkdS>S57xce#TQ9{F1YuVMC8Uf=%Z zOr+Ceu87Kj$jI96jdj~YEQ;5|uUel_xpc3U?W*l$b@(+~#SaEjZtWDicQbHs_+GP} zlvGs}6A;*L+>)aE&65%f;%U*79SYxkXe)&x=H)jPILyD1iV+xeSZ!=%m0uckJLB`> z=!K`){UlD#&P&*jOic@!Eb95eo3?Bx%HL$iCvAtdApIF!^it>4gT=4<`rpGUUYmVh zdhL9Ke7krg?;R^EPEu0Rj$G^K)zxCyueTfD-5F_&qlgjkVp^R38B@krk=e!c^`vI% zLr+&V*$CGHY4VI?=l`jcAvg4kA}&qLxA61x+l?jXR7-jdHlL~#EH*9Lf8am>YcG4C zybmKyo7cb9@c&IQ{m&|HibGP=vCF1=DJf&EjwVV=N=8|cUJP|D*vv__+uYn7E#R!0 z=U&~@i%~Kml+5ze->*$^FR<7|lUYz)rEU5btNMR-A^%!`_;;TqUuwYV8y2RTP^(e+ zpkS12|Dl`3rngMTo|~SILe(HY!5xg6HrkY+t*mEe=Hu>Ob|A7X-~NK% z841+&3qve&!LRPMCCeHZFrts>{;G@qHSf?k=1>sv;>C-yF2|lPn*_Y6h40=y+11rG z#3~o;;o2#ZW!4>ms%taR{_R(N3_F#Vgmd>N%i$xO27AAK`&J#s)mSY@X6c)zk}lY* zR``I&a;Vx~Ysu@_<(=f@^n84gmrUC0GW8`cC@@wFd$%Pk7Cu-rXv<&-I=Zl+%!3Pj5&&^=Dg~l(h8Y@bKfky}c+^ zH>1hiLXHdscUBr^DZjh(`e}G)$*D`njvd3(XJBCXQWE{KaCPv!NFZbNt>To;w6wIX z?d_ZIvK;F-U^Omw={KQj{4e#gkSzY`Bnl$0y(pdV-2M@1!K zXqYhKi2qY}oVW-qK>1!I*w0msAwNGKkL{)5SW3%S2(NQ)u^cHLTv9o9Zr#|t0Vh?i z)yU(*!omgy(hI)A7v&kxoN*r?zn77b0c0_N{r{yz^UC@2=TXxHQ25$1^(m&OGg{Hm zW%6uHs5lL-11a3`GjpAPX}&=9^)B1Xu(0aEsvzMzcNor{^J;36m6nz+FE2lN;esE^ zGWqtKu`3P-ofH%ltgar9h=}-QbrpY5S6BaF+DWCaKg{$e!8N4(EtP0MP*54V+7+Ae zBg3_k!yTV2RX^T;R9wtYGWsL=`RmumCMPHHGWteGMb?(*&24Om2X*Dzwf^yOv1H|} zc)6rQ)A#A_;E&I|ykb$)ItpF7n=~Fg;B|9z8yFmX_4>8(@&#NKFo*frk8`G7`4vDr zceHs!|8Bsuka5 z>lqpnm>a4wtbenOxD$i=$7rBZIi_m2M>&s>zm1FY^Y{OnW!xepB0_ietVfbUsA=^ZyF>p3O zLA{88oYGZq-@bKye)zm^aInF$h`9JW>=ksbClA(^+3$WKQO+`owMsBz(7YUn_MYar zXnM)Cqa1CbqPA8ruCboz<_qH+6ddJsx`JNx z@|bRLkZUKkc-WO9PfBKNB%W>Cwpoq0zWVaz`sOWL3_CuiHw=cj72Q2aOZ!MI&n6v@ zX0of`xsMNp-|0JjwJz(KTJ8)?begHG*txfE-AZ#>vOv!-6+K_@)i||4p!>74eXrYL ze^v>p?N1){m-%Emuh`($(=s!)o8qO!BDh0>G#)68%)SqBEl8w1d^po)LK)5M%f!zd z0x@Fsm2`L0cnQQ<*&iB4rzP;k)aMm%JYz83tvs;yiKefdeJ{bv@CSdfIwhJk zdKE1Y@2gXS95&;M%~OdA{^-R(xaU9rdF=0hkZ9M%#l`ontYmXPgo+odAH`}z>&~zk z6j67cm&4blXJ@5zS&}4iB`)i8G{C|FxCx~^o8*qYnHKcpl-BF3_D?Bjz7zFq*RFmF zmp@M$7?fghY3cWS`GMlfuo*%wn?;NWP`^>d52OQ*m%cc9Gf6%fExleQGL0_r2X^RE z8Q*#l+U~XNdu?>KEob>i9qp6V3qpH(?h>E>UMW+DSzF-nd3BxHfeKW`1C*3<+IokT zcjEh;)|ZCG!mkFre7P6B)t^-`prNrbgwt@pa&q979ec_R1lxpLt44uU`U=;_3WGUx z$@)vZl6)`iA|s3V_~>g+_XAO|iO7{tv7$j4cxR1^XbE}^Z-sRyTkf)@0n%lgb&E?& z@5cioK&E`M@?XpH_b<(Q_tPjf{e>GVh4wSw4tscemlhS>81XgQOzNnFS3}Cj$L9d_ ziI6$3ti^e}`+bKGAJ)>+`slPYd!XtR9o?@V$;YmSrxZJL8a3^FcuLTl+uA-(Je=jh z;q@S5;eL6_w9KYuXFpoylgNIVw=R&mnX_4oludNl>B1b?OgWN z;{bEBAaJ)q$47SjhmM}!RlCevu7YQbBqK9(X}W}Bq$QQ+^XJc2Q(fWU7v?j4-n4o+AlVafn1f<)}}Tz znjb%YTq)P;Ea33h(ozo7jw>Lc!90$aUcGwt?pl3d3x33YZNA0B(^CuJllrRFvx~+* zUcP=^6)zQY#d+B(cJx66YQ$Wl1fP_oq@;2|+ zU%Vg(PZQ2D?L-B5d;9qz7Nv}>G4(=NF9A*KoOlu_?xEiWpT+s5@d+9kCDLVnPLg!(t$YX11 zOsBf6JxA4@YtxUEQ*rs5>-0}e`Q+tY!-CYrKGgjzsA_JWK9b#}mTP4{w1R$~s9XA4 zKIt3ycvx5%liAPE^1wB&j?T_cj*BUD&Kpa$E~OO}9_8giD4t5XImTD*XX$wye%lRM zVCRDI=4M#jzaM4VnG3LE0qj^d$+l9JDrlmRh3@m)f#*&rXF}jYvYYD~7r?>Mqene| zPk>7Wvui_SU}}+V?bhO?LjCRo!jw>3H{h2T`;lDLY#Pby%y{nH;6R0+1dB?h-tI?_ z9yMP&1eCGo%k%S}70*qm?857|8vEgO;EXu;!oWdGvORl-!1kLtsP+Ow;T70Uc0_;t z7>pPBBSozWJtlC|DI7!Ax$(HL{y^acgO7e{$L2{q*V6y^fEsfOh5+9sL;q?XLVgVSK>}F+>{`L ziCEBc)j%KKGwIx~mCg01x?$5_6@@3*Rp96s5g~r- zmg{Za#9xMGvxSGCQ>d5~=x41EWRy6xBB&EpO6ds8Gt02 z>QOqo2Q-qxls_00^_IBLOi%ZCUGBPzcP#BJZl(WO&pSz3dIB-8l2vjzK(j3@Ej^w;PijagF*k51hii4pI|o4 z%_x)sD#sKa;j!&KapF6g2Z1fPxm8~=?@fLd=9NkYj-*=^$PTR{EIizPuI4iIJ^rL- zCN*U*0#7PvIN!#C<>lpN(fAyOzsZecynp0cz|{HC{0Rl$>dVy6%bweJQ?a2;Lh7OO zImY?~zihf$K_et3#$~b7miWAwdPcR-te0sD?!SLq9XhA{b+kDtJu6EWo%6*J_Qyf$ z4#Fc1EFwg~vz^odPzmF*e2TX>JDE4dVf9Ort5JQPe!TfCGjo+y+php-h3T1@XwXdP z8VXqy41mAX*KA&(*Jc2OlI_}c^r{tOQBhHCLBi8`PSQNCBVGbn=}I}K$01Giqc;Cp zUT%n!pvkqFSgzi2N%1-G>;2U~=ICRs;2t3%A@~Lkivb}l+?aL^C%w~}mvg|t02u-1 zw(zb0Ic0Be&uQ4;`aS$wW31@mt2X08KqdjKnbIO6A|LH$=+I=Sxhz}(N2FC%UjV71 z-;mSrhT-lR)!wxWwFZE*6xYWHwq+R~#vbWwP1pAK_pf^^d;n|Ac-dhjHi*ThwE|ox zBH}wb0y+z)er?1cDlfO)Gzq%y^1_5DC@ty_U=_1kN~mz_IboDv{A#dC^kqROCvMye z2^dU6zP%+_n!%6vM+r)km)AcuBtKEO6HUl=s*B;&sVA>5oB855H76+ui;JHIfx!LV zGciF!-HnnQZ8`iM0#*OS#5w)ObMCI44WPu~bHlZkz>%z~AHQH}eR5jjK(%_eNYn{i zTU#us1clTS-@kug0iv*Xc)=D!os^MD`RLrP#(-JEZ*FBvYkMJPj??{h`O-_Z~!VT_#xP= zxvg!`Ps?Vp{We*sncJrF=e$Rzug5=Ctz~sp{;zB1|4tKup8wy_g8p|BOlKw5v9F&W z^ZXJ@nu6MmVlAQ8p{v7+4;;c9MBfbNQk^-v>yxa5e?! zc>C^MAKv_(08)mwvuY3H9ZsA$VKv%B@Q?$?kKciKCss8+F@eft*LML+LJy!Fo3S)3 zmh6J>xx4_f-Ma^&mXII6dayv<%t0-+#qZUtnN~*%>MLK+;z-HKzyAFBvjgA#DorH; zG~&SCym8&?H;9woko=A26Q!0ccZi}Ht^SYiObBuYw3Teofv7ViVbKdY3; zw#92kX~njf38nn0$AL3iY)9%0IDd~cx_|t51*DS;Fu|eV1dww>Lql3ljzX@glrtDH zvx2PbEzrsG>T0KP>ucApVI5)_o`TzO^nxCvY&6?}C)AlLT*>lP5G~U(9qqpAX=^`$ zdh_<}R=X<)7lR5{Z&&Ph3lVCSJ4sK!RYgUGgnavxk!t)-9u%XdBn63zSy$rAQNTqn zX!zDWv@M<#eL=6JOcV)z4IPF5=FPKMz0PQ|wcxF=FXA5mEv2{y1s%p?<1p!*ci=$- zQh{MZfvyWW6v(6swMr*Sh)J8qIyol#k!8p5&!=GgZTnjU0XVYo(63!XjO3G}#= zf@0WDf;T~O1tOj%$kA^w8}!fpG&Hrj)??EP3qvIod^%sB?UP&T-+2L=A1OpGZf>5jF64hW9Zl^d=tsm)J8hz7UvB2Tt zmhI#xq5GjxSmF&ZEt9%(;p$zVKi@^;G`$HNHnng)6m3RMIPX7nLmIN#&XeX*)*EF( zGN6p9SBw_Ect#@PEfn8c)yd)E9oHd?xfvgXi>DF-L#S_J;*OKk>IL_@h;NXl0|LIG z;6&uE)_3s($TO?OvGL(iW09>+n7M4}!TN}%lUPK7`%jGu>$YO@0VOjsAeh5YN|{>v zBO`2R#WvR0lrnTGv{SwQSF_?lrU|w z)$_$0JnO;{^Y0k#f2iECgwDoMVAh#yJ!tx~v~;_Mh6e19sx>9eC7sLa{}{R%$5J;J zM(8ebLeb5i|DmE6FTi*k0+Njj{DNO^6wRku1)3N)YE&{5W91Zqn!P}VlDr7>&3s| zWHZBM2bf7|8cx+HRJYzn`kYoXhXXf2^We`T_zkkZ=73e-5=>Y%AT7W?`}n_Q0k)za za034U(iTGlteXD>(CHf*>iPP$%0;=;{yK41o8FAt+u7A6$eh7C7lXqDm4y9VW=>97 zNlCv)?Y*x25QzDxH%Bc4nW}}d$K{zgIsM^l6M*)&L&1o3fx3i%K&kc=&;1?}=p^)P zY(hb&U^v9&M(m*0m}wOF z2rkPZ?(|i>F^C1ez({b~f3;=Cg)ko+qAwele-F6);a;0>RnYlNgL*pfXlx`B9bMgh zd-vYG>T&qcp?5k7#pIl&`V-xxNojgDRHwzmo)z7_9#dZjq>T4XR7sFtI4b~6!lI%j zU0v!U7Psfw0YX#(-NC16FI<>QZotxk^zq>F8__lS&4w+PS9)_v| ze|=fNi;Dfh>Yp>-6}z`@fjJ}>&!lO(-zH$^?%lB;J~aJ?X~L6Qus{VuPA1P#EnJ+k zA9Vqbt^9*^%hVGg>N-Ay8(glGR&>5W1-2zmmnS#U^ih@K@GALJrDB=7RT)DED>#314F1lFZ^gdcIdKM*T7_F-aB@OaB=sX zoXe^Cvt)qHCAGCa_zX?$Q-bb?go;hi0)(QBQ@B&moMvV977b$6WIICS8|>$XAPsLO z?&O2PjZD3oc$J(KI%_KO?a+0FMMV1GbwK-k1!4)Fah8dxxUKCdnm?>!F}Us{@CK>( zD{d#vlLj1U%d-i0D!4|3Yg$@b z60;vef;xrr48e5I32wZ;JunIhy0-6>K)Dx3wP>;Fo-^WMci~uLBSk!ZyqREpf0Hi+ zBos$xtKrbReEE{Vx&(zJx_4QbJ7k)fVZIHe^pgFQuhJBPuyYY&QT*)0g^)sjY>;{% zQW7y{-JkQPi+70x#WA8;%=DMFeRjTvTSq%5Wa85j5!IkU@2%{Ah!m?gdp>^-9seBE zfF9s$0?A^@c*sYu+&@WZ$0M!jF%7Xw$4DLJ05J#g zc{=N>OW6PH&=8>LJWonWYKU##31NWNOA|wC`O8Iu|0Nt{=oW``8sbnCRuCA^x zgM-Tf_m5t)c|U5~%v@=}3CLA)Si|XthzOZk;o2ocEnp}?MYwZDGxY^l8=$SyCkqD1 z1x)ywoxS~8wLm#SN8{UAqJTIZgPVA-mM3reOo?m%g6CD2b#6jPLOnyWWCjTtNb~Q3 zKo=2_p#^5qhFSt?rPk`y9w}D*KYyq@)lFp2+;z^?wSb#W)*;${A4Wsb0G-E#YRDGq=(6AlyV$!#&Q7ZBQZ?qBFm)2%uL z@g?}8v3%AfYkO-e+k;iRhx(=ovj}ypuS_vpA3@RBdL7*(GB+PLMrr!915u&dJ|}pB zuUe04+kU5%IHS3-d#Z4Q7eMn%OG{4s$}-;lGvG^@j;b(iJO=me+Xr2CceuDhFE7cJ4Xh8r4==G;LI2x?Zw9n?+PMrXlz6!&lI)djls$Yg?$#%jS#LKIK^1&pxI7h^u zA|EV!fNU9!l~4)6i^0qL^XE_S{B}?HZ;+<6*^c~~tvdh6-S#69yh$A2^x_|0z@qx` zC3qHuTePPgDD-q}Y%lR?2^}WubCDbLJUroO5REZ{6cFBTg69Oo)PpXO?3)|;t)(Rh z>%DPl=Zw|07hI@~l!VdCCOY#D_aqdlqpF&HUeXI_TI2K)I0JT{m`q`OQq#p7DicUx zbyq5$8?FVF`Y%TUJv6|$LCn6by}fU0suoo5=w(xC7{oAmf|llBCBx09NtPFdDw3G| zT_1p{_2*9{4z(9P8}En44;jsJaZ>H}GI7r@{QM5!fdgY@RGPL}%?=2o7tK@;!hQcz zqG6>w*wgb6-*Wr*?bX$}+JKbweB}L*j)02zb=yBQ;!NH-3R>Ca*J_@W2KF~+i3H-t9^qtT& zxYBzd>eTXWR09Do^*o{Yh?SUiB6F&{FcRNT2YrQDIOQFGhM~WeVlg7+83}n!PLu{W z{Z8=erd9aj`skUpPA-3C0?)Cqyug+VOyK)JlcINm0s(?(#HfjMSFeVmP&PuZ!(RU@ z*J$LtXZ0oR%_AH-J84{2&2i7$2Ka-!Ih_977Z1Ps4SQm+-Fnvt;}%u`&>Q^x#b)Z~ z;w>y_tId{v#rR;&=|Zj*38H%t8z27+@ws3uchS14>u@+BlcJ4DSJ$n|Ez zr{JPhxUqkMYD8w$cG zv%u=mJBs0A904@wX#Yq?d-tHS8uZF@R zEGfBHIaz+B!6@wQTl&w-qs(-tP62Od;s)}UN0UT?bg{7IM(}c-{`9&#%{MD>8q_^R z%VpOo-)rA-|0wax2txuJ7)o^w3acnSlgdkAq~X1rYr$27WC-$uBI1>td=3c%Lh{M~ z-5@Fwq$fobvrm?1pnCPd@x$W|1cVBLcu^U$kO&kb4nFX%N+4DB(__f_s8s}ROLS(8 zF1p);Do7ar_`NcUisg0DH<77&FnQ$NEt{dnLc zk|$w&E)fJR>Mh=e=u2yz@F1*&Qv_nz*PvLxHg%u> zH3f|JcR))NmI>UPjyzi*BAzNOz4;-Mb^qkE5N&UaG->~k-daGhNvPF0Wp&j_T1Mud z;28+T%YcB}_!S^gI{Ny4_zMw{BP(cVkOp+w1bz9k+B+2(t41v;UMTDTkVV3QfNRz> zI_fDNM>riLBOZBqc^wf+ddSVyz!A0^4!0F^-@$)zz`^GK20Z_7!1MoC0Z+X&Xu%d5 zkL1-tFB+GEXn@%>qp(WmQj-voz~|4OH|Q$z9M!-41gWUM=ULJe9T&!<0g^?6G$%06 za@h}z@b7t+?I=S4x`4GQR%id8(H6FWq|>a2(obqbN@%!$dHgS6Yn;HVHHViuc?^ zMe$IZSL&t*T-@0Ri7qcr)go!p_QBBacFsAL=lD&?r1w6d;JuCz1waFosZUh(*^&s( zZ*Tbb`n*WiIe!Mt)M!}M&ru5hHpSnTemLiU3jYe_&1L0hH_4v?KZ*FtI6FE0pZvsl z0WxWV=-bmS`qlc?!EhJRIt38@`gp&0Gvp{@dZM{&0X9Aqp>qIQgP^fU`Zq2jYJOAK z<(`z6&aWE&Va;g4zQu1(gPap9N+`0R zxPjpG(+E~Dsd+P=B36L?oE{-cA7MM7EPUX+zBc7T#B&M!7hAQjmR7}P z7ZFw^kUyMgfZB+L!PR%5mve}g-S7KK`d1E_t4|o$=mA_lv$#ksbD&&pGqY4H;X>i3 z{VWmVr7CZL#|bGBH*0jO?#9!8L_PD|fSz@M?^i+L-XaNuL=P(mwSJq<<*vty4J@B3 zU-n*kPM@#O@%rP7?C1-+iBbVjZmL05^p2l zt$XD=f4ONsKj7#u&@``528E*>)NxnZyc9(4g#G;&ZqmHXh=(oVB`y7KlrZjK`Q+)_ zdu6b~G(SYC>hHgNE3bR~sQ|nk0_v;aV8#A3wX&|{WoHGv+iEI8aWDS(i(vM+9#S9T za2cS_Y=sUdlS}=Li0=Yjrh`3wGRwEe)o>}!{rxC1ahH~9LrN9_v?b<4*xGtV!3+vg zh1A|E1rAvagNi(;8v;hJ@o#vFhmfI?x@_9fgE!?g=?K%J0=z^>NJz3~AjFfg_I?IR` zkIrsTcao@xrIki08S2M_0s~9InW_~-<`Fmgf)E+R+y_Gz#-D=1KHY)*5*2llNHJrM zLZ;giTNDW^;a27zXkw~(2nkg>fvSV6Kkj!R7k%Zy>c^0zj6I-2_~R9j!PVssJpoe#Z(v1!!qxmsO5JMdP9UY97}04B;POH z-I3reQ7vP4;AuTLe%<>g^YvnNaTuXnQCwd7`fmP$UrO*|SWTt1wa6U2#~=w0ky%2b z<-4{-`W;cxEj@6{MFQlt5wFHvpv;vkSMHjbNkF`X>hXf=A{h-$WH+_m%AjfCLkKCu zthQEG^hiQru#;#Qgnh-bhQWzysW)=zGPw^!32zga7sd@74!y2?d({Gm0EC~k^cP(= z!VogUpDm!@V4~p#S}c)ZNVBM#gSYZ|W8IOMa{vv-HrxfR!R4Ov3>YLq#&8dM_yUSM z31U8#oX@3JA4~a*f=9e~xQ`73fK(>;?!7>aHkSR`wVxlg%l7mWdxlUWy_A!G>^$-_ z%UB;OPGN(t3dDevqZ^o0J-R7IKItNq(6%eurR4qKCSG75r5FwCM}X7dUUOo=NVj@S z{Sm|*60Jrq3MaT0P!rAxV$$&;IgR56@CgHpep&2~zd*uVi=RA`&$pj*;F)ex%-D0KyZ3J$Q8R zqF-R76Xf@uSjcQ^N`c*Hv|YmX;a&SJLrkQ2)s!^Pq2}Z1&ZoOnf!lEjD~Ru3u=q1? z{;B9L+wNU$t*t$vzJ%wFxEq2VA^}PpaD-RNT2M;zaJ-rAh1g{Gb7qJ5&NDzg+ zWj;fS>$+-9<|Q!KY7E;AZqgi)+eHblN5ai5F_RU0LJt|Duq)<|A+lfyP)uc6@I?6A z1G=X)&lB;Q#=ws6-}iV4lwhFtqr*HC@u1qBd?TRcd9^jMI)bnU<#F**O$|kuK#LI}BDe}>%DTL>N;Y&@(HjIy#Pl3Z?w)xUrqBi+~0(Q)70ya)!FzP>)v2JV>T!MzsM*VkL| zC&ROTxA77YJ+z63?keMOq`)_cARWpg^iBkVKS2d13_TzP%<)X)!EWj4=^>iC-O^wX zk?TVW_A4p`t6}o#S```+RaMpM&%6W^$Gu0DzNe^kFZl4mpHLQs zCrXIh`hut~cF^3$diRFRK#l>zC>S|YMK};?ElYG;c+-D{3h&%O3ZrjiMELKZ63{en z{%j?)UTWq$=BZ^O_xM410+qF<7y80QLubj;<51UD(cXxWW;k^(!^6EG=A%HwK}ja` z8BE{>d&jV-fxrXYVL--WVO)ur_k?VSl;iKQ79X^dhC&w?jF3fHB^(m|u_KdBqm`IN zLZ}sliKJ>!_u8bbL00?dX!{$K zq{y|EMa;r{!%ihR0y_rUAwp_pM)ITGbYmqq$&x9W4=kXe1fJLUj@M7TO~^~b5-!Vr ziR(8b2l3f3g)#x&pz_`TaDe2b2j#6PRYPNBHqq@pMXg@xe6&$X1X?2B`V0je7dI#n zSn_&My$~ywl{+lYz{M2;caOk;CE3rKWhDRP4l=l}Nq+{%|Fxtc%l%Ce5f z#>NsuDc~Q(2rdk`n@`DKF&8lX8jf@sB5Xhe+N04_2`Dx|+-HMsK*)Ovxg^gPcTe{< zlu2|ZPqdCXm=XJ_sa3UeZUqjZLOQL^l&wx}te=PT6TfdUDri1>S+;&zGCeI-CC59b z%K>X%nGMtlgYN2Gr*9EqbT%ZO(V%hNgfSx$^jkKAH&~FN#eJ5iH63?>O??5wSHuVt z*v9~0HkI|Sx<~LOFqMB3^JE}X3b}8~%9rn|2dL59+kn!&QNUv6G~Kg_@De6VwNp zrNrBxhz}sdB0Mxyqgn)o4kq3Hv13q9$C**4wcBCr9KMsVgW+~3T17+DLQ0o!q5VIZ zj2dl(osSW*IMznY7&$m_Ks-f#(@j^2$C@Hcbg06Ajm}|w^I$>=)emti-Y;A~ci;>l5?XuuL5T4lO;yut-VC69@&^n~T+pYb)Iwa3zu} zS5m>FQ0oQ&w71V8uiX-T2{tFPQgz)t^k3-C?XogD^B~hO(bOkNqj^X*sB? zMiX&tfgl>iyUw%W908{Db+Qk=yhhT6eX`RE12Oat$DhPs=)6qSKpc&mbl9>EdP(sJqs+(Q3B~9}tKrdlg-Lb7p5dV?SOW3%? zBnJGt#tdCijtdvKVEhJrFk#hIqKP=VK!^}T$N-9ktjZ%FEJ)mu;tPTP1NxMhruzdz z7<|QCE92Y-pI5a)|8VQ+fr6oy_1nKQ;5;ReDe-zb1~s>Ggr0uM0q5{ z2dL*U*(Wzurvw=0SQxO~K6k3&{V}eSd*XZcKEC_?*)zr`)pY%Bg?y{#DLd(ULb&yv zBisdf93!(dxThQ!J}fA|d-ra(WNA5ZRUXzNBazg>(F!;@!V7Xlr8~Fu2W&+2#!6%u zG-5w&Fq+i3qrDNzHKwf|x@&MwPw$F~ZhEzA`nQvvR=T^JTkqIdQU-G)EGR`46-($h zu(JnJr_e+YZRsB!O-wU6&g*mpxf^7@XBHOpa6k!8<(Nf^8dG~;9CCsX6$D<#4nG5w zA0N+%?7|49+Q5$dG=!u%MMXuC=}l5?l|F>4fw=){>Wa!rf?3utb#*T>bnz-YTy1Kh zDS)`D_!+;X`1l?~0IWtDZW})0`Tq5*D~Ji5Uj;Gh@yLC+7PY@R^rC3=Plw+|PH42K z9@?1jBV1NuJ_plG45tC&U5$ zqoboi&-Woy6l>D>5rx<#*twDx_wnX08<=`Gxo#6 z4ad1&B+vmg*bM5Tm{JuEx!g>tOuBG5YQZT!y>)JO_Mv-DtStNhau*jD9gd^K=o{1~ z$9Il#^Kx9hH^s!n!n^UJMUZYfefl)h#>Qr1;16^J?}o|bSbHc1^ub>2SUC819w-lw zj8vfcZu$ZfWz@Rq?c_f<2coj|M1)IxJN&A&w6v4l+|;a;DTPK@Wz4`Wvo1$w_V}lB2Ex7A8%zxe~koczBWuvTaxpo6Ld73-7>Si6{ngN zm6d04E=HR5m^@&-|FKv}Gl07OFORl3Esw-89WCb{c%qji%G-2xyBgxzn0!Ei$i$2= zIXSuiq4$!u=$^zVIAA3~asx~h@coI}yxh)TD*pVjL)_#Wd_JgR9(|{r1ull?@tDGu z$GEOSOuH##h*&8+%nDR<_+;<%U&kf>GU7GRV!d848*Lg>_4Mge4)eZSa8je1%Ea8- zD|9$8`dfj#%6C}LBV7e~%6)s6qK=M+P-I-)EqW2BC*cW&@VW3VEiF|?@@Y(LIWZcV zk-O%YOJcJLyADP^xq^Ay)PL zazNoYH}@|^3GA+Mci>4qg5eA(f&xgd0(ld26}!pgr%n?TM95v)^_C!l=C zpyXX%?iK$UY#KPqgc?Yg^4Dxlv)I45#re^DKo`gWWQ9t9p12Mmw6yGOd0T^dy4f4- z{x2!!Uymi9hR%2f!@>wY)O|G9CXTFtUx^&Hz$TeOZHS195{CA4PZ4n>4-8-3d(!=s2M_K&db9$i zjhIcjbmZCM28%$nL=r5t^7IRgSE) zmXqNnPRQu*KQ((}Mpk9qr?RGoNYE3hBbSW_Xcmv6qiI3MW*=>!3|~hVW#@cvDaaRq zR4gsBZ(qOO!cPN=XWUczp-ih=9cp^^A5VEkya3Z;m!U*x0o7lwHuD!;@Gpu>UEdwY< zRHUWpX!@%mckwov;&Ry7*rKr7I8EC9aiBPKO+C?xw!77k<9-4vvHVW4KK~Oyb-E;$SsY#DwfJZEF^qh=LvA zC!6Gu0Ra*xj?aNi<1ySLCw4F|$_i71h*f%Eyfgque!$FEiPMbO`mLeb@yO#J03SVg z^tUxMA~AUrAZ9?@BsSi*EtZj$^)&Dq)PhDF!-IhOt>~nX*34aGwg?2QuB{<&tOcG* z7A~P?a3x*;@Ie9Jzd|Lj5xudD@mk*0U3|u9bAuLz_9)bEaA<9u+(R%hJi(-EEWP{s z*wpRxfIBpS=ZSGoqL!fNS-aCWT_10A+wKUu2c)czseSTemxz$|bN-gQTD7d4!}v+M}+n8p5Uk4LU2+*T#>d(-09q|EQG{yI7y^}y1g4QGmU!WiO=)k z$(z1~q!%GEqia0aShK~@QsU%GSN)R<^TtyE!YCzwU3Ck`o7Qc}kR9Q&pvS}OhZ8uAdP>aG z$k-}Ae(X_VcRU9*0I?-+4-ZmY4Kc9WNlPk1w=jM+F+*boNf|sR3hdkiZYhIbBh+?; zL>%u@g)ygKZu<;0ELl8k>u2=BP77`Nzma^yQ$^nRcJ$a@sqw3H3=G?Fuma%7 zS11rQSPwWN$1S&HzpM~->DWW!u|f35P$MC@A?Yt=EA*HOcY3G_T?;w|p+aFAF9HEd zPyQB7t=g1WFjdUb#X&*Ta9ImL20Z>6{f!!2UX_1P5Rs5t{yp*prvqW1ys6uUi^~$h z86%rEZ3??)>kUAfXx(t@#+*TO#2P5atfAd8ATmFI#BBmiY-s264NFeBb+1;CNtSUTkFDs6?ms(Yh94{aFE(Vc+>molJd}@F+iX+Jdg8)Fe=Xp!gvPF z9{*g2IY|bAKdW_5oh0e;AlO!iT<{_~k0ReHtvqn|#-@j+Oa3N_nlwKGUCKf1| z+xJk%zhN}ahOK5a)BM(=;VTFzu|2RC38X5@W6`XIU;+4|iqjL9g~AAJD?(YDIFth^w=C?o2#qPL)gIdorNwMUQ$Nx!?ZO1LV3^E5bxiAa?`BYq~Jyh)N{zHcT8p=VTj^E z&T8Szz5se*UjT};S*D%JNC~|DfZd$+EzX3KhsIwl@41>UeF98fUmQ8IdNXMCZ>;Aa)k?p#yeyJq)-7-B)Gp*VccbD%Ve3tP{()g5pAE#+ zq)p7#aOQzX^lQw>;1rLSh-cF~VhS)>7m%^ zQ&+Ainy{~de^Xz%e|>!lEEy~S!kjMV@kqc62+fCxXF+j>;Q4q}0+1KYyqR*{^3H*| z8*;z#WSWkutNJ-|P)H9pys(9R>|=Nr|*3^r>{{V5XPHffy)D8T_#QbVUltC7pa4`SJ+ zgB);j$_}y)#CfRx&8wEFz%WS8C0rv9*taBk4bL1n!@oJ-XitA3@Cc+4x%32OwU75VVFVh6 zchhk~Ss;#j8r^gA+$~<&I8UcK56cCSzQ=*njA~UoCa$p`+!`%_UUEb5nbzU0$IZXTB#8ZXn(mv>zOiBm{qe zs=Ifig6WhzIN53Fh3SC(A>G+71!Y%swSf`Wnsy~Cj}E8~S9_Zz6D^Le~_wU3Cn#Q5;?0|8+;&TaRq zWKee5i=$5p3;Dnnf=$Nt`Pxr&0*g5*!?SALrIt-R%(fdh6|E zfYW->^ZgCUpus= z$g_K*C$T;Voei^IF!_nonFwb9<^le3O>BnoiNI7FO~G-(+a`uZ;pX5tH5rwk6eqZe zX=_lU49rp{Q+KP$7;5o;DcNNSrM?eVWD(X8%*4ld!usCA^%1YdvLNgLW?zA=2#9SC z6dRDE2CRrP9ny1h2!j2lu2~3A=QIqL9fX5V_FPaZBI({mc6P$p6!L9~5=hv2U9ZqST|NWZ~97td?hx1>{zT#(bSS>TNvvij(1p*F&WF00Biu+*$U{ATKLS`N7v4s2E zjv+|M-6TNj#HxaQ3&2qUrv)gkrTnPv#gtaqjcRf8m~Ve4WwNU>I$?>BzcJ9GGG&t3 zXlZ-o{7(Lozlu+^x%WADA+Sa)9KKbZF~{bK<4ybUIVA)cXT4wnLL#!l{S5*WB?omjqu zN7$p$rz_8v0!J+9Ufk#~#>6;Wa#T@WT#k6f(n%SkbmLG^G92kq3dybMxM$QRD;64^ zzOa9-fd1D@?f=K`l&Ad5Nd)wK(jAFTuc;Zm`PBX<;enfS%5vj4Mmm3GG7lKLWu*Xv zGDH{>6@uZ`>G&t*?uj1~Tj%HJi37udnUh;qDl03GU|7f99a$dQF2#u`f+s91Loc_=xw`Xp_m)Z5d zQxl#oibfJk6&$+w`1nd=!kdEN0wsPgV5i(uruc{D?t((fjQ?*83RUDKO2WB~MKs>C zn|za#lX1Y$8*j;NlzW#>3j5N_8FWS*KcOBBQYd)Zj~#+0(t-b@y*H2QaSz+Re;Gr` zJhU|!BSWSPNrNKVhN47dD3r`YiBP1F$dqlagi=I?%u^c3rcy~|C`qYM5g~d%r@f!& zUh7`Z^T&O^>sjx5*LwEy&)#;wuHSWiuX8xg<2cU9GZ!FPi8-61O`qvo=t(2*wX!jP zbI;6vYMElm&8jwVT=oV9^+VSpJ=)AR&6}Gd_9AJ!y2|`3qur^H#+|fGChYDq=7nkZ zZB%inavR*q$x-Lj^y=Lku5B3f2#OM#d!b>4z8ziPHnn2y&iQUJ7Ut%__1X#*TwT6S z>nTNG3YCG&i))t*KeTv|G5#*7_g^BN!h{{0<6fO5XyZLFVK zwKlcHu_4UEla+ZVZrqsuYtf9!lM|UvWwt*U(%{-*HT!k#)JPQma2P1nDzm7yaHxFE zz}q!otW`se#|_^b1vKnCXwba;INx^d+TEYI=fNDHzo>q?rVcBIYF%8P(sXBVa9~(i z*p16JjB|l*qANEvxn^eZ39FMb1>$q*(c86eKYP}!JN)KLKYd!ehDLo0u~m!1R|)cf zumXt6cu-i}I!#LDm^rz+`ooPv7I^30J%bf1R)}*4fc|{mG(SK4s#nJ@Bqx_Xd&=^F ze|7{ULu2d+o&s4DYw*ouyVZ?+>%%{WTFftKkM4oP9aa1OJttkc02uJ%ULKYJti+cJ(;hVp8biQ)3GN&$rF&W6^K>wix{ui!#V=Q3Q^XJpodpyNU zJ{tWC;+cK>_HkPkR^%tLkr>s{*xIhAFB?5!f)4x{@Fyr+yhIAkqeYisYtXagQFfuU zewP~RGrmL9$Q%$_Y09Z6YQPS-A`GO*jvQH^Ll1B0b#d_)lo;??5kf!s6w+UhVzwWh zwAQ8JJ+x@J5Rf0EN2kCOEqh9efGxCguO$Zn*CThq&6egu6Lh4Z|T|DBj7_7Bt&AETcbEPySwDSemw%TS;m%7Y$0(( zbbVYK{@qcpM-SmwK|yDPsYK5SO1kr7$Ki&C<)1&l;b?g*KB}|t!5m1A+ogK_ZIJVZ zi5>5BI+GeWRhx(%Kvk_gf2q?&9)d9F!+YP1fWwPJXcn6&a)_y_nO(6J=}as z;Wyu!X=zP>st8338DTr>3NilYRkCfrk9Q2WC-7kLuoTE2rVcPfv1Ub7+R6F?MRaN!@6ufms9yfl?+s z9FALZ&S~zp;&L7h#q_Hx(7XOJwr&DLiGc!z9s#FIN<|7^d}zMuJs(= z79|(-USs(sj6sAy1d7GYxL6tc3Jz2@dUtF?RkQd6QcS@( z0GIPg=ATk#l#$Ja4?~eY-l!}PEAU7?Bg3y$gVmT?LPwiP3o={W5)bN^UcC|W7oHTT z_m~w-gKpiGvka`TpwMbUvK9I|u$OFP$IP%r-LE}l$d#PYSE@cQOaMel zW{LKzLdRtil!V_P!nyfb(HbJPAe-5)n&KuuQc%RSWmko9?es9u z_dt$~Ks1?gG*VX9?%ut4>R0o)+J_j6@;Vd(cj+-#hk4#x_1(EiwT`vi41QAsik8#U zukL2Hk+~{t#PAMAYxmPiYLPR9F}wXngYyVNna_($Ikd-&lC&8aJt&j;By0E*+@tn= zXPK;fIpb^iOWp~LZssso;(4E=;;=5(6&}-+DecHSA#joYY0t+*-?$-eXMJ(8?KXD* z9m-|%(4M;h2gvz|4cqfxBc60P`}L1rUTeo6R%bTfUnq}@auex#3j-TAj!Yv*pi=as zZ>66M2jQe3ntJBQc(T|5EGcjQ`rcDm{}6(o9t^)X@BpM*@FN-Cf_Y0;#ah^5x#^mc zpgmxh%zqN-xbQBkE7ZKL_7QC<#1Qpvz(9sypab7g?{>|^-Z645j`7MO1(e3``PB>d zuT$spYfkb^XchMYz#=M$3cpo!D!~YYRq8J}eUgnv)aebX_vHjH;EwR4ZDd#p=_t8Y z0HOhO6$Xng&?9DQZpdWz9T!)JMjcE0IL5)uW!Ak|-$=hbh6%!trGAmDf$4UzZs2TE zk%W6b>k0L_^Wq5_-^wExcrn)zH>xX;`Un=Jw={-};#~qvi()Z))%D~-(6Ru*;yGTj zYa{X4$Zf6;x1TmyDQif0nu6GddDZ^%-w{L<%oqPFxFFA{HCkpp0>@&8hK5ois3U#yk)>#h`CZ#v1oe|j#F$_K7HxhDtqqSF(V}B4x zE}%>nH;fr;-gWhjJyWt4s6iTT0+CQ&VbuFTfMI?<{pxaGUKt!q>zcssO8m0=8D1u& z$3h`ha7`2J-#=(u%v8`6Dhq?NYZI9-GaL|8VP*%mC*Q1oPEc(9GI$2#OKKX@s5~Wf zrgK145WBZi_5AzxL6WsRZ81VNHQittdjQ!nW(y37qvM*~+^zd5A1?PSaw=bK?E2$# z0ERAI(kH*op)DZB6&;8TZ46>B3n+LJPYt{uw%qnptG2o!?!{sf)I%w`Zfce@UrmvD zGUJR5cGPsFc)o;8beei@mXZ+4$hZVGEz1}CI&oq9aS_e<5xLN;e#3@N3l>CW9JaDG zk|Zvo8)%9&gV4sR4Qiu4~%PdPvx1Xcp+rQEc9ryQ`VL7+Y@fBC>lS54M3ta%24zKF&#h< z01S7xjX@$0I(3xW6xpQ>5T=4yR(QcT!?tj6DHu5Yx#Jygr5j&z7U2J8XH>T9*mjJ( zX*t|KzC5${OV6L6=~B-X6oh5G_jaw!Cs8N>F^wk;n{aH$^mfMUrRb`GL3w7`>g@@d z!LGhJ~dYjiZ8P4k8K#yl;80 z#SDsc#j_6pH9rxbOHe|C7WKLFE+z(~X{OnL#jYHrrpo{r1Asa zH>V=m8@oN}l&;p^7_&*veTb#swqCV5^9*U8BBTHAzbOy|pXCcc4i~RMRl1XH~HIDi{kB{Z=h!sZ#9&{9}VWrN?{D$7W#Opul~Rf3J<`FuLpD z!Cw#Lt~qS50uH{o&ShRXZ68xqSf~_HXl=^`i2>;@;6hR_ocuGUN4gx9T}^zG~7uBi0wS4oPg{oid{3a`CbzmAgKS zZ0%Xty;G3q5ZkD=-HiUBUint#L^a{&QJ*Q^*cFz>J$u%UBu0I8)T67-0dg~K-{hQg zGbtSvl`%hT$4vbuyTfP^hfpe=DP0GoDY7xd))}W9#@(;KU_q(+$Nd>o%D#X9PNh1n z>)qjxfovHbaoR&GZ+^#ga1<2Ae|=zPd_1%Ed0)bNPJgVXu)^u=8$V?a_S2{!_qtcm zM9;$resu4lp{LivxStKDM<)4%ii|7c;TakK{4AvDlT_+%i>DpAx)Vz;I#Ac?liv@? z1eT}xTuLXrM(4&c2r#FP5Ab2UKvse>ByKwds&)hBsj!RdcFDT@d2qH4;~~kbr_56j z_@0tYD6~-HY)V$=Q0D}#|FaBhs7|`N{s5Xi+=l5}Pc{-G7um9bl8-8*{V?}lLT{nr z5npxs`n(Sx9#hVjW=0#V=*Ukpw9L42I`LM85bZ!Q@+sFWOFL84_Yk2cmHC9}uhP>m zgci6LB*lIlNf{=x#B5=mU387c`&fG63X^a;sA{rTcW z^q~pF!E8K4C*D2`a%zcm(_%*l>*T5qwng{CXf?#ahssxlS{1Z273mBGf+$hcKM%QV zusomA0q6gHr0OROVUyZs8BQEGZ(cWmQgIH~E&aRGz`v2yNaIRovV8x3KUD%SKVi^O zWHq@vQkM$9RnT<8s=mg7r#0FEG^u>GLX2gol7=M*PITER&eY6^W7rgNV%37!ix=OB zCWIsbC!~klv=Kich3uf|DSsCpY*f+e^VYyCZQ8a?4>fDZ(2a^aEb*)AQ+;r3te)ya zEdkKCJ^zTFA1@>bNOD&@=?pZd#FnxH_`EFl^2aeznMQeY2=8*==~hJemAv(H`^=Re zzFd)p05Ey`!rL7w(y*`TCUbXjoWJosQiZla{Jp43@Rn>5wCHm5-fyetuj#`HYNMkQ zt`q$|BW(wJ+)P&oMuff{9{)bqm4vui>ZG zrM!2-oP)ncbJTnJhy=PKP@uOoPOKxTgo|wazV`&Jr7dKM0f@o?L`aY=_;}xmN87YG z3O=6I{!;i{#I?gimW_DtJEI3x_^b6lw!;y^&G0_lyiASVPq(q^ z0}4DM7rs!5+e*8$zkfctITTWYao0_m}1Iy7CgX`Nqz$t_xZ|%n@%?n z9Bb0av9l>9^o=%kG(1EXET&?dd`elo_MUp~-q&D4*%eqz`+7~?*kue1zF=C5tz+u6 zI!;MU6Df<~(Bs_U;vWNHXJ|}5L#O($9nYPoPxtYcMULl2jnf&^g2G@fv|oU+a~xel z_a9F(EkN)F_Cd>d21`aca z1tRSb$eAQKB^KI&8zKC<`0Lzw%5;citWE@-1hr9xYE4v50w&0KQg{3jvW7Uvvmhjo z@5Bu7$=R94vY6%a<;zdMzG4|x9@i=G1x{2jB`T__y7D&gn0u~ppHtZ8M;Khhp&sSh z+|wGqJnkjbtRm89`Yt|lDOE#VyAmcy{bD`O3Kv;~0Y|NXB^liD_Ec^7*EV0(#~a0J zCGs-CWzgPwZ@&3SS{Z?#t~`Petkt$!zTz zbS8NY?@kMj2XqPHLc;!L%Y}+i^#QWV%S9STRcyQ2&Uts2UFe#qjZqWl;fXIPn+8t+ z+}rp+vc!!zo>84WJF_>rn4|HnhwM7)J8&R&H5&hr|LOed(yZ7#=rvN6g=!<<@Nj+c zNlAOS^ZW~N8$L-KfcAWx-p$V~IFb|HJW6_eNg9r24|EyCW{-dZ1hHcB?=i&wBsd`| zdRoB7BxgpeOQ9YnCXRaNd7tu(caecoQ#g8E!4O0s$7mEJ(Sxxm#SA4ewngIrZ@~AW zvx+Z{a1%xt!(m34&2b0fhs9YnH5C(qwyVcOjLrii{Rgm3HdJY#*=8k>Flky&J>&~9 z{^ol?yBO7N4`crEB!gm1VQcQ&X5L$xWir73W;c?uK*4}qLK0>Il>lOm2GXqC9woIT zcaD?T5^xOCk?4I3yn1yg1##szV_`6w#Sww6c+(1h+vya^zDx_UA7n=OuoxXro$E=N zf+MaEu{j-QA#t@O_FVckebNgKVByfEj5+zmqv3CIC6({?e*XL!$R`2MW0(az&VUmq zx`OV9QVQ8F&r7@Awsq?V;pjy?XUsTisHI!`_x<}3{ljNK%Ewwrl!1Vv_dngmlc?p7 zgD#6>jK@r!+8zW=pmS<#%zG%B#fTbCBq~X;qEJpJZb-kCls?OB#vhwS@uB6KMW4?k zksKxC08-ta+IRoxCtX;B`6mW3(5S(vN) z*t|7EqDYLW%s&Ew<&D$J;Rn_DPIvFz39;0)`Gryr%G5@Z3O+?CPwtM?IC|UBE}U9O zz)@KdLs|^|uyo~QD8K??b7<#)JG|a@d9kae&-jR`&<>`uvPk6RCC^<)yaxag?=k?+Z6?lrQzYGn)h)3V#xS#;GkAOv=+hNQHNaXzaDO-Xj95}iGg zwT(A!JaO`5f6p%qDWZBdzNr*{^yY>^OJ4rX2?J=f$t}0KX3jW^mzhd_2+Vhb50i{P z#@D~*+z}b}afTQj*_(*l3-7b0@!dz4(zHjCwn5ed*@kaq)#7zC!}+x};tIki9i`VRGj$WtQ4& zvh)M^X&bnrQ_VkK``+?$_FhXaZ(Fg0oiWjM;j3FmKmaDUy0#nEVGBOUZO_4jmIPEx z5qDp4J7vgEX7iIde57vYrUgNZ$iDQ;{ZXgKe|2>edvsL4YjJ%$mKp?Mb~m3fq1)mM zr}l9%&C{+&>vENae6h$aFWqeAQ*<6@+m8Gk{X>ht7!GeSLDw@2#?cl?Fj2!g1aXNO z+`*`OGzIMc(Ka%g5xypiA9U^7v>-EIgq(DhMZf=3NmqXdRhUnbC6!qo1BK{Ce#9oe z@p`U%drA6r@!|q$wja4G(}8{oPV1$Y2K2_6Ebs>COd}(cfk*LrUn-PP*{9F=0?p%60D=Q0Izs(}j4V|a#n5TL;!R0g z9kUb@N*(@U&P1HbIy=*D-O@`e>}gEGW_hOs!lS79O6|P;0FT?FR)yA#iuyoxv^bK8(>9GP_C8iZ6p-diYS( zwbatmyee`c*^0wtOZp5EOVjIouBu%%8PLC%)OEmi zDP+LHv#;0{%~AxnO%4gv(enWfYF>#YG8HG?&z*{PocJwf{V|(i(&3v$3Kikb>sPQIR9=1 z9retss|J_P^x_v8ENk59adxm-Nv}bRdaiWn^j{x-%zdP``exQ1zwb!?kt1^bv$JF=wP?4nV_J{^U=+G|n(%yq9@ZzO~Igza){?4l;acq<}Eb z*p^4B&B%z)ab%!JT~07K5|;y6Us_wm@A&6%{mZG3o;OnY@Dw@D0d3EI^tp8TnmPuCvU8D;WItFhY@t#i}a7M2tu=^JSN{eHhMcJ~+yc)Z%5fpzT zas~|^TmTi2~UaNxyc?L8C35aHI(FVmPD3IT7)sWCuX=;Xwcd&5J&)8i}I$Lv7>ud2v zJJqQ(BP?6}7|@qE<>E;#s}O*Wl=$@Ba7K1qjG=zo%^Nplkr8b~Mn;C~LmKhz&?kF64Wi2Rta)@1C5vUm@Hn}9p0jf5%8F|?hQ0-wmcxVTt7SDgIdP^-?6U+g(t zoMTZE_3G72LgI*=Ub?!%)uS(SWbJ|R;j`PQV(TDKI20aT`r$*<48y7m4E)Gy7QfXv zQLYv1sB74^{pnQhebyZS8i@apM|IaaYPS`dAlANg&yr}sWqD&792om0KDPAwEvq07 z2Fj!&X1B5clkmfi!0%&Q>N6_+->SO}F0-#n$@|2K!bw?|&Y!Qt#^?));?WE`^}o=H zf6rLNlNYizV6j3hvUnB}65%!LmL5ECK%hj(F5=4uEv7DHnfVqrIX1 zPT4O(cp&`<{o%J_Yf^6DjO~HjwmDJ9vrTI+8{R5$)Cil=wm4+H)4k#I9XOr3yXt=Vt8CuSK<}6J6d#oJRpg{w)!~zI`CXFJ-p80xGYwm&tO$=}J z{xUGA)aF)rHxR^llxz^CInFX-;cQe+^3IW=AFUM}5}U%RryPm2XM$> zB-qV)II!v&Bt@L7ay!ud%NpL8|H+xvce5VqY~he%tTT4MBx`13XWiI&I)%7XOd?`3 zaVdHj*d@oDRZ{rZ-W;Fa`>((L`t;-DZHXG+(o(~W877)!-uyw2j`-zO%iJ;+OelbM zRPFzHRS-ZtiFO!9n5&APW|DHO((>x&%@#+VuKIW8aKj_Ua>*47***z zi|5YG!l3|`dB*Cq>)+gO&fokthI4vOD(^kv7KU!gzCie(vv)#bz^Qg}c21%fE3Nq@78f}S&c?WA>Kgs3b`r7? z`e-WR4rFfT7qUBvjFkTs;B$#C8i|Q zl`@(F>4Yjp`bs7M2>*&odhN+7-^~}-M-OI`fI+uYAAOA>BuhCLz<{C+~U7_U*?2CL%`zZCZ|jJE-qw6oJxJL#o0+ z1zuod!!o~V!BwS^%fIU^3N9UoO!3po6VDRIMf?h-^fiF%q%}A8~wL9W=%w{7e`bDRb(Hw4TJGz zHyJ0fbXyr53xqw!KdhC_m{!-@MD=<};ZQ+Hwwop{H3VOf@eptfabrkm-Z-+$6~Eh7p|bl&p&%PUba0ahs#Xx47` zUovNoIdOm?af!+VtL8{Bp&K8hda)U%L&q&<1>>w6MRxOdu#l}}6e0I(7tNUy4>g7P zs(y^Px9!fkcODM9_xDG&z!jawDIF+k|MuIB#}yGNhHNicx#mY% zO2O-mY*9a_v9<4(K9SaDjxp_^(YUy}j_~vxW-x4+lA(dB^P)u}rlno#IDELqov_ZI zNt>x@X&bn|bb%o!P9(eyG}-I@_<~wLMlfKtrm$Iwx>nGpp#s?0*%eTryUd^e?$Qz? zD+2=qNQ@g{jcyvzQOaPvSWLUOilf*PzU8YQsUzBEMnvw|(Ud(@FkKa}=$dUc=RP*D zpEga&P&W+!5h}^q^X4^Rm>keyXbVte^xRf8eVQq^I`QVyx74RkM*#%Fn5ZbvH%st< zmR?*|wrNVQQ41FIjCbtEA9Z#0rR)sK$jl6ljcuc44y@U7HrNcCsCxZJlKf6B%` zGEx^&D@o>8~ zGOOnXz>4^NEdc7=0YDu^sMUS<45N3&#U{&^_2V*5R@=^;sYX)@1dK0T6He_0;(D(2 zRsZD27a_^z(d#nP8L?dg$mhWR%30^I=ATibi;!u}-RuG&D5m1Me_^P0h@dRX|RNYi=G5_>(n*>zJ1e z)b8KE1)>5!P5`!Hvgw&08^e!D)%)e+D>VptHDb2K7$$qiiKY={dL@+cT!Bw)>>%8B z^YH3%?{XX1;v6gI_FGEgp;mMNUVvaZcmU_dIt; zvi{D|U4}5H76V!Lv8g`2Fa^74%&+zilW3*srAbY{!6%`^9at)+0~yg;FVk zNqm&nxc_kvEv9TOaik@7S+XQa_@eH|JNhQSv>-+xMe*f~fy4~%9=<6jH#gYBJD$bZ z<z7RaJQ&cvDbNw|DQ})Y^G$0lSAf@7a!l zb5iyF*J=OtP@K<0uU>5r3q$?lirXAU-h9sP#IaJ~4ET#^j1C zZ?tRl@#?~lAJr6aB}(;Qt=v8P15py7N!mq6ikt5byB8n?<#0dDChX zZzUyIT?Jz^W4EI`a72$CfUNI&CnD1L@X1X9)TVp6QLU$;rWza`^SN`oN`oF-pwh_Z zOHno4khF}9`1`l=K|r^W<2&~3*@RiigthBG7h8*n_WtvN=!Ns<&6A_i3@6G3UyVY} z8z+e)7=7cOUDsnSUc7h?f1>+^%OPBx&2oPK{{0a5vUbRMLO*w?oA%_%Mv8giU0`w6 zi7w0+ys0y`0S*ohmVXT9723XiJ8L&=a@v(_O#Y>E>Cz>6O`yFxIeq`}16_PO@Wzo- zryg)z1Fwiy(5CX&S4Lm6NLxc7w*0CH^vVO(VTu(G2DugHM~|l6b$u9?QQ^oW?!@-c zKHNJi8=HCu#`>{qrSG7VCfX*XQJ}tCKRygo1b(PBTK;BB@-1(X#SM;(isD0S>e4AE z;iftZ{vy;if*K$)?Da1ijv z;HK;nnmy2B=G-}R6n7?V!(4PTZQ{&R1!%29ak4q(6j4|A$AB#fj>RP<{f(l@Hyn8t zt`pR_4R8MB?9K(~f<3CsJJzeCwBYN}(s%DdPn_@#irL(P9|W=s13a~waV>Qwh5E+m z=sxu7KJ25J1=tthT-=0H&PN}MifY0py4ooZtO(P%s7+4PwB)7;P(`+H=;r1Y5O^jk zO1K^-4A$mml>{=1X`%p~@zpW zkZeV!1l>!w@CR5hH#W5T2SkIaze@`=z=rnY#pQeF&Ykt!5AEK)k>@6exvTuU#{s)DrLYYGA&g@0JmO?t)Q(3V_5+${FJ7{lVGmD7_J!!vY#(IhVQ1 z?rXuCk-G$4&~cc5WZ3zyz8pf`o;^X;O!Nj=bri-vAGr)abc&D|4U5bN#kJ3koev#O0k@0=q$mLILw^vMtku!Zjv)UGf(R|~PGG&1qL0IJ0zL*p za^oFr3A;qJ^TV1Jtz5N=mDwFoc@06?)B0v`MA=X6X3FTD1MrFH*sGU{U^&BDI7?y~ zGNc`Jr$qYCcXK<`HJSTS$0#@uw6JrhPIZ`FCNNl(VN|bptWp|mgFRwI#^dDB)v~DYpoIDY0bNho>(6a zT|b7JvdmFa`P;@f%ZIN2{!Ry{*(O9O;p#QR+|~UwFPM?oNp1$C2t9pTg=jw#%LZWj8U~Vr-9T6tT#$;4b=M zQey_STBeE{eSBE5^U&SUZPZ-nWX5bU3`$`G96Nu$1%(`4{U%Z&D%q%~3u@lJormmS z3g5PUrhA)rx=T?L13QX-*=*XhL+`gtH<{~vaC#pLYC`6|yLjRyC9b>(aSt9e5)C$y zap;?wb;ut|yGCz9P{zlJb+Z(pD^{2xYm~8d!x#H4<~rx%(gDH4k5`1Wh3u~`d;_=? z-k_|QaLKYOv08i|F`;XarBptMc(#~#5~2*bgZc+a#s=OU>Mx9&A6HLWGe(cn^$7(Q zb)YsQ5-Ic^JsN3i+giaVHIv1ZqXIQmp&Rq0s1Z8QLR#wWve6y6ZryOa^#TtbRHu~* z;S`W*?{ciPgDfd2U|=XyMG7NOzN9Sb%$Rdf*KG=TOJ6lMBb-w{$45`hDTpF9=O%~m`8`nl#uiuX)j+kjhUhnGbP~Z z)5#ZOV~^dsrTI^xuFxDl?rCyzW7*XRCMlw3)CAajYu4G|onLEkTq~l?%$A4GFwlDH z)UC-!CGGihmAtg~p^FwRQfLN!skrbK^Mj`R8?jXw zC-j!mtkCH&x$D@fF@GvJ^I}D5s2O-E_>fAwpd$;IOK#&_f63{uec0sdg_d!xZQMr; z*_b?D*}^KjQNFV7u#O!}=jv?g98tDuX}zE$&31&GZyDG8|Lp%{H(>eI4m84^p@ong zhIwq*k{ohAs=a35W)RIhLStQfBLf3<0*}@k8}rsy*_l?z{b7k)+u3EEErho&t|4?- zUAuN(`1*o|w~7%1Hc^vFJb)}RDxh6x_1H<1TDED^W90CZj!ptx3yBR5l#kir#m0QeRDJ|29tgP$ENCBb@oxg1By5$rmOsCK7_3yhq`Ehe7TAWbp8V7Da zp=~ql(QT)c_e7L+n7wF`Ds!%c9EVQ3WrJgWU{ITHD=wxymR>uc&5?+v z|J~Q%`b)od@XA)MTxnP}fT{|>fW_5X08#YG+C?2dSpD^3#IAq9`lus?c zq#azhMdZWq%oGY9I(-Cxy7%fOoX=3oEp(D3zNlBPZo-L4_`21+6Cu5>0!qf3abLfD z(aZ?m+SeyJy4yf~eW5u3`fCV2Kue_EW?S}-kv7VU5~JGldO+?DAu?eAvUK_KlJw=w zqtr-kIKiqVBOsmjUaQfBZak8SVk46s>>Q z%!awjt9XUu8Do`0Co${ZN;sVKHIBntSogDW*YDI!S@xUXFRWB@urbT{gHwzRb{v}v zKNdfn${+jD;R|g~;#WN)75k1vw?LebWosMN0(wp8xy8$^( zRVrY&O(;(EGP#P$!Dh&2+G}W9hG|)XXG|p0kCKEXceppr9jLwo6MZ~N=a{m zP0KU?Gddos=un;%Rdy6$Qjmjc z{RR!6b?Jwt%~^m zVx-A@9e62mj!m34Z}}+(VAHRy-0|_OpF+_lVT`${O(U=YAM;MuWB(f6CF0bnclt}% z2?)MJa8iP>dNQnqA4f6+rmn1!pr}D9V%p~~V>c)yotPSiT)+MYK!i#-LHm4EOj6-E z5E%w>N2_muNs%607A)|=sQ}s5-K8Vqx)4;IptcEp8uVG5MQOrl;uQ+g3Q~~hAzR={ zCd{*mQyi^HiT$|69xaW6epue zl+y{{xKi(+H%WM(^RhUCe&}zFUd;Qkzy(Sf75;wM8S+chYiQ65_DUASouD zc6lciz`dw1EcRh7W;b)N3G)NjPksJ8;N;0}M9z>47h2v4OJB-|K-&=;Kj0!I!)v|W zp~AE?S~~I2G1|m+GgVQeu7rS4p0~L62!ux*%LjGx@oVs2BT`%UdZcJkxe>DQTIzC) zR6$@tU8Il(ofI?|emE(^jBYCEFn3h)`gGKusu{sk1Ti>gjzE&$=xvM6X?B=X9U*5 z2Y&mim#Z5J(#iG4yAO0To2%7}2K{&M2pmfAf^VS46jjbqZkk`ctJ8OoBI!!SAy+y_ zkd(fxmyHcO)r4Pf{9de8?T!7LZFNsv!OE1Tr6O&m~8wE^gMexFDF^hIWX6Tv<*Yx8VqmtDZwhcVI=JywuB~hUF7$^ zSnkAjK6L^lO`G}~FSUEjiAA)M;4@_OZT|mGSOOznySw1*O+U28##O34=Pd2orAyw- z+V5?J9YM>LmhSdF5R{k0Zb8Y2!?rigW4gVcDa0oU-SjJ*H-kCReQcWBeEB6(j~Mbl zSPOo5d*2T^Y~$dt9dJ##ULAVP^z0qC0A4at?+4t*F;LI~Xi7e&-yW04bu_gFIG$Bg zU6C?woV4 z?v~K_0Srkw&rN$8C^WB1KP2k%#si;D%xOpcO=p64jj6SDTJYnq z!L>D;Uj{ctn9zW3{MqFtT3`eGU>B?G-~!Nw`^m|1U@t+}u1180Zh&a3B$D4?g9+27 z{jo#K*eLJJHZVl6uDkL2vV-S6wcY{!?h|j&u65t{R5E6x=_P)DrAjl@qFCGZek7Wa+64ReN ziG%9X@vn#M3%xZX_|oOe1>iYf zlq_)5@V1z6h4qZT0bU%cMvNP$ihQu&qMU&|eDq#zfXR10EQCI5T~ZA%--Ndd`+%^2 zKbEfj-14BOa%SMw3lWMcM(}24VK!gfQ+T_<^zN|lBx@ziA+k2Y;C7TemVeY%WJ})< z1l_c_Yx9)UoSeoK-ib+>X=!(%$o&?gLHQP1Zw!D|;)>5BSFO7IdRGQaZZCtGt5;ur z67AknxgKqw`JaDon|N!c1Lx!}jXxN~>42Nq6n8LKsqFmt@P??UsAQvPJQSP2JCR`t zv#Tjr?HG*K2cBYL91u8sZ|c070i;RDXy^LJf|bEgs>1~Z8NWiLwGjPB9!$guYuynr z5{J828G5+6=_~S(#B!8Nyg3+Bb@Rz8sujX^2h9>GGrmm$xIEafxZ3x_Uzg7I%vG29KPo8 zPv1QnHr%XplEG`ozJ1TlEI4`mxM5Z0EA*C1!|Pb~Y9sV5S=deEClEHZ9!?Xjka7I{ zb?aAoRh2KMI=5;n|2(l{Kzy3Rk`ysgsCiZUQ;Tk__@!HGOe_4hY*A*+W%PBi>5j1L zFONEd8&YT#yE&^{t8!liS!|9e;mD7m^ef=6I01@F5Cisl?B1br7-J?G=wjlZYwcGL zRMzLihs7AFG$BDVHH)yk6Y6QI-amI&#taZRSi(Di<;AG8>3!8^LF*I%Xc9^&#>Ey| z!t{c_G9w3&=_nWOk^Y#@0rKg3y*sBHR(GWy#a({Sl%Jmjn{IQ%j(?=XT<eo#~vB3Z}9+tm2Ev|X7R0}(Zy05%yhvQPxgu^1Mt~P?X`kDLVfZPZcO-9@i;!V^{~gbwVK!$OY>=kb-j>pL{`!p5IvA@}IUlfeCQhix;XC zSVF;~N@Kh}cUZS9tL*G9L6*xD7#3dDq zITxsF8h|f#<=yo3mUK}_2)#kTa+h5VNRGCIL$M1_Io1gYt_>titE*5@!xKEkKo1d@ zAOAPy0|bkcpS_g%4FEV+lP3A(5S&2kusV>rO82w7s;vJHGwsoi+O1_z@J zz=A)}s$y6_3M^PQBJeVWfyhy-_Y=kqzasSPS+#$#;GVz!F8b8^@|#Kz!-U|jEw;X1 zHYKq3ITLcQUnU>||ME8BWM{%_L9wjxPALN95d1>8x;2a0dV7AFRy{?~x?*q^vws;V zfWNRXs0=0G@l6>`i~;~4PPbZBjGbr5nKP<_3!b_%&W7`j=$)9+rW9>9tK`M`g~ zSNpOUGc+v{haW?MJ+3YYLwD@_%v*Fr@P5&?G`+q^-|Nnr#byHuw*iOE!dOg-1oNNzG_#; z%?6o9q4j$*d*YSoTHH5v-2NqdC?pA|(QS_081mo8ecQdJf~BquUoWiz(pZU+%*BD} zeZIW#+Pib7H>8f&OV~G0fB2$wz>+0PptsiXSh;yBqO{ioXcu5+)Leb_v8V`c_--TT ze^@C?gNtOxgE41QKW%_>bt!9nKbj-wQK%!p)vcC z0fcj=ka6Y6v<()QKYpO&BR0TyES{96l`AdFGV1I9=vcn~aPC@{`Q8Dr7_mKtAW)Z> zI)uIDP|-R&amRbURJx4VV}{;tmqm+YEmeh}xJ(##8Jb@Otc(LBL4r(^`7UeTQm;2} z-#YRsH%~q~FrXpuHgkB>l`AhguHlkl=GL{aNLs(0VI(T@6r)Fc{-1}+RRGySlJsVF z=mlVlmhW473cZF3V2e0wK&_#qGiQ^jQBC4laaNy~qbHk4Qz@@SHmEg%~cjMY>q=wwnxxLle zujK+!vo{v8GE_K-@mpxfO|=SO$WgZbOi#(p&5glpXl>;K9p%Q2HI8+z!#0pSWTcMR=V*nY`A~UqBw-LJ!5S7Xj-Hq( zqiwoue2oFg{Fw)f-pGGG#W*DUhUG3RZk;(ZuE%QS3;gWHq=I}b(Ca8Qn{9CcZlDg&94oiNn{+XV>c=ETO+jRna!@}Tx zD7*oPgbi5t`v>C-td0In4$@!HrlhpAb-I^VtJ{1&*upx;Y##BA+Fw7`sW((eg^SCO z&dH*pA~Oq%@RV1do>5B5-lsKLxp(fvWb3>0{GPTJbG?{*1^f@}+QBqD?x=fqsg|#w zpCbm}D#xvF8XouJTaE3~B1R*$t?fgz@0$k|{J$8FUVk~m1U)M_=d&Gm+XJyKv}pGP z9-g;mV7%d)KOfym8&IsA-(XDxjgHg*WBjUR(jvE`az{&@S}#ZSE(51e>*A{5pD`oH Ln@5kBwekM}8WElw literal 0 HcmV?d00001 diff --git a/networks/toys/sbml/toy_paper.sbml b/networks/toys/sbml/toy_paper.sbml new file mode 100644 index 0000000..58a1680 --- /dev/null +++ b/networks/toys/sbml/toy_paper.sbml @@ -0,0 +1,135 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/networks/toys/sbml/toy_paper_no_rev_rm_R2.sbml b/networks/toys/sbml/toy_paper_no_rev_rm_R2.sbml new file mode 100644 index 0000000..8f750e9 --- /dev/null +++ b/networks/toys/sbml/toy_paper_no_rev_rm_R2.sbml @@ -0,0 +1,139 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/networks/toys/sbml/toy_paper_no_reversible.sbml b/networks/toys/sbml/toy_paper_no_reversible.sbml new file mode 100644 index 0000000..6d647be --- /dev/null +++ b/networks/toys/sbml/toy_paper_no_reversible.sbml @@ -0,0 +1,139 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/networks/toys/sbml/toy_stoichiometry.sbml b/networks/toys/sbml/toy_stoichiometry.sbml new file mode 100644 index 0000000..ba75fc4 --- /dev/null +++ b/networks/toys/sbml/toy_stoichiometry.sbml @@ -0,0 +1,108 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/networks/toys/seeds/p_seed.txt b/networks/toys/seeds/p_seed.txt new file mode 100644 index 0000000..554913a --- /dev/null +++ b/networks/toys/seeds/p_seed.txt @@ -0,0 +1,4 @@ +M_E_c +M_S1_c +M_S2_c +M_D_c \ No newline at end of file diff --git a/networks/toys/seeds/toy_stoichiometry_seed.lp b/networks/toys/seeds/toy_stoichiometry_seed.lp new file mode 100644 index 0000000..6fc740b --- /dev/null +++ b/networks/toys/seeds/toy_stoichiometry_seed.lp @@ -0,0 +1,3 @@ +seed("M_S2_e","exchange"). +seed("M_S1_e","exchange"). +seed("M_E_c","other"). \ No newline at end of file diff --git a/networks/toys/targets/toy_stoichiometry_targets.txt b/networks/toys/targets/toy_stoichiometry_targets.txt new file mode 100644 index 0000000..56e5175 --- /dev/null +++ b/networks/toys/targets/toy_stoichiometry_targets.txt @@ -0,0 +1,3 @@ +C +A +G \ No newline at end of file diff --git a/notebook/analyses/01_compare_s2lp_netseed.ipynb b/notebook/analyses/01_compare_s2lp_netseed.ipynb new file mode 100644 index 0000000..9c6eb8c --- /dev/null +++ b/notebook/analyses/01_compare_s2lp_netseed.ipynb @@ -0,0 +1,860 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Compare Seed2LP / Netseed\n", + "This notebook presents the comparison between Seed2lp and Netseed, analysing the satisfiability of FBA in solutions and the scopes achieved by the computed seeds.\n", + "\n", + "To run correctly this notebook and have the same results as the paper, you must first download the raw results: [https://doi.org/10.57745/OS1JND](https://doi.org/10.57745/OS1JND)\n", + "\n", + "This notebook is written with the hierarchy of downloaded files, if you want to try it with the test from the run notebooks, it is needed to first restructure your data to match the hierarchy of downloaded files.\n", + "\n", + "We suppose here that the downloaded files are in a directory named \"analyses\", this directory path can be changed to your directory path where the data are saved." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Requirements\n", + "Modules *numpy*, *seaborn*, and *scipy* are needed" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Requirement already satisfied: numpy in /home/cghassem/miniconda3/envs/test/lib/python3.10/site-packages (2.1.1)\n" + ] + } + ], + "source": [ + "!pip install numpy" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Requirement already satisfied: scipy in /home/cghassem/miniconda3/envs/test/lib/python3.10/site-packages (1.14.1)\n", + "Requirement already satisfied: numpy<2.3,>=1.23.5 in /home/cghassem/miniconda3/envs/test/lib/python3.10/site-packages (from scipy) (2.1.1)\n" + ] + } + ], + "source": [ + "!pip install scipy" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Collecting seaborn\n", + " Using cached seaborn-0.13.2-py3-none-any.whl.metadata (5.4 kB)\n", + "Requirement already satisfied: numpy!=1.24.0,>=1.20 in /home/cghassem/miniconda3/envs/test/lib/python3.10/site-packages (from seaborn) (2.1.1)\n", + "Requirement already satisfied: pandas>=1.2 in /home/cghassem/miniconda3/envs/test/lib/python3.10/site-packages (from seaborn) (2.2.2)\n", + "Collecting matplotlib!=3.6.1,>=3.4 (from seaborn)\n", + " Downloading matplotlib-3.9.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (11 kB)\n", + "Collecting contourpy>=1.0.1 (from matplotlib!=3.6.1,>=3.4->seaborn)\n", + " Downloading contourpy-1.3.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (5.4 kB)\n", + "Collecting cycler>=0.10 (from matplotlib!=3.6.1,>=3.4->seaborn)\n", + " Using cached cycler-0.12.1-py3-none-any.whl.metadata (3.8 kB)\n", + "Collecting fonttools>=4.22.0 (from matplotlib!=3.6.1,>=3.4->seaborn)\n", + " Downloading fonttools-4.53.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (162 kB)\n", + "Collecting kiwisolver>=1.3.1 (from matplotlib!=3.6.1,>=3.4->seaborn)\n", + " Downloading kiwisolver-1.4.7-cp310-cp310-manylinux_2_12_x86_64.manylinux2010_x86_64.whl.metadata (6.3 kB)\n", + "Requirement already satisfied: packaging>=20.0 in /home/cghassem/miniconda3/envs/test/lib/python3.10/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (24.1)\n", + "Requirement already satisfied: pillow>=8 in /home/cghassem/miniconda3/envs/test/lib/python3.10/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (10.4.0)\n", + "Requirement already satisfied: pyparsing>=2.3.1 in /home/cghassem/miniconda3/envs/test/lib/python3.10/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (3.1.4)\n", + "Requirement already satisfied: python-dateutil>=2.7 in /home/cghassem/miniconda3/envs/test/lib/python3.10/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (2.9.0.post0)\n", + "Requirement already satisfied: pytz>=2020.1 in /home/cghassem/miniconda3/envs/test/lib/python3.10/site-packages (from pandas>=1.2->seaborn) (2024.1)\n", + "Requirement already satisfied: tzdata>=2022.7 in /home/cghassem/miniconda3/envs/test/lib/python3.10/site-packages (from pandas>=1.2->seaborn) (2024.1)\n", + "Requirement already satisfied: six>=1.5 in /home/cghassem/miniconda3/envs/test/lib/python3.10/site-packages (from python-dateutil>=2.7->matplotlib!=3.6.1,>=3.4->seaborn) (1.16.0)\n", + "Using cached seaborn-0.13.2-py3-none-any.whl (294 kB)\n", + "Downloading matplotlib-3.9.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (8.3 MB)\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m8.3/8.3 MB\u001b[0m \u001b[31m44.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[?25hDownloading contourpy-1.3.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (322 kB)\n", + "Using cached cycler-0.12.1-py3-none-any.whl (8.3 kB)\n", + "Downloading fonttools-4.53.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (4.6 MB)\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m4.6/4.6 MB\u001b[0m \u001b[31m48.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[?25hDownloading kiwisolver-1.4.7-cp310-cp310-manylinux_2_12_x86_64.manylinux2010_x86_64.whl (1.6 MB)\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.6/1.6 MB\u001b[0m \u001b[31m49.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[?25hInstalling collected packages: kiwisolver, fonttools, cycler, contourpy, matplotlib, seaborn\n", + "Successfully installed contourpy-1.3.0 cycler-0.12.1 fonttools-4.53.1 kiwisolver-1.4.7 matplotlib-3.9.2 seaborn-0.13.2\n" + ] + } + ], + "source": [ + "!pip install seaborn" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Variable to change (if wanted)" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "analyse_dir = \"../../analyses\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Initialisation and functions" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import os\n", + "import seaborn as sns\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from scipy.stats import kruskal" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "('0.13.2', '3.9.2', '2.2.2', '2.1.1', '1.14.1')" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import matplotlib,scipy\n", + "sns.__version__,matplotlib.__version__,pd.__version__, np.__version__,scipy.__version__" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "s2lp_results_reas_dir = os.path.join(analyse_dir, \"results\", \"s2lp_reasoning\")\n", + "s2lp_results_hyb_cobra_dir = os.path.join(analyse_dir, \"results\", \"s2lp_hyb_cobra\")\n", + "netseed_results_dir = os.path.join(analyse_dir, \"results\", \"netseed_formated_results\")\n", + "s2lp_scope_dir = os.path.join(analyse_dir, \"results\", \"scopes_s2lp\")\n", + "netseed_scope_dir = os.path.join(analyse_dir, \"results\", \"scopes_netseed\")\n", + "s2lp_supp_data = os.path.join(analyse_dir, \"results\", \"supp_data\", \"seed2lp_supp_data.tsv\")" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "def get_fluxes(directory:str, mode:str, optim:str=None):\n", + " flux_all=pd.DataFrame(columns=['species', 'biomass_reaction', 'solver_type', 'search_mode',\n", + " 'search_type', 'accumulation', 'model', 'size', 'lp_flux', 'cobra_flux_init',\n", + " 'cobra_flux_no_import', 'cobra_flux_seeds', 'cobra_flux_demands',\n", + " 'has_flux', 'has_flux_seeds', 'has_flux_demands', 'timer'])\n", + " flux_all['accumulation'] = flux_all['accumulation'].astype('bool')\n", + " flux_all['has_flux'] = flux_all['has_flux'].astype('bool')\n", + " flux_all['has_flux_seeds'] = flux_all['has_flux_seeds'].astype('bool')\n", + " flux_all['has_flux_demands'] = flux_all['has_flux_demands'].astype('bool')\n", + "\n", + " for dirpath, _, filenames in os.walk(directory):\n", + " for filename in [f for f in filenames if (f.endswith(\"_fluxes.tsv\") or f.endswith(\"_fluxes_from_result.tsv\"))]:\n", + " # By default in this notebook we want the no accumulation mode for seed2lp results\n", + " if \"_no_accu_\" in filename \\\n", + " and ((mode == \"full\" and \"_fn_\" in filename) \\\n", + " or (mode == \"target\" and \"_tgt_\" in filename))\\\n", + " or mode == \"netseed\":\n", + " file_path=os.path.join(dirpath, filename)\n", + " current_df = pd.read_csv(file_path, sep='\\t', lineterminator='\\n')\n", + " current_df['accumulation'] = current_df['accumulation'].astype('bool')\n", + " current_df['has_flux'] = current_df['has_flux'].astype('bool')\n", + " current_df['has_flux_seeds'] = current_df['has_flux_seeds'].astype('bool')\n", + " current_df['has_flux_demands'] = current_df['has_flux_demands'].astype('bool')\n", + " flux_all=pd.concat([flux_all if not flux_all.empty else None, current_df], ignore_index=True)\n", + " if optim==\"submin\":\n", + " return flux_all[flux_all[\"search_mode\"]==\"Subset Minimal\"]\n", + " elif optim==\"min\":\n", + " return flux_all[flux_all[\"search_mode\"]==\"Minimize\"]\n", + " else:\n", + " return flux_all" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "def get_scopes(directory:str, mode:str, optim:str=None):\n", + " scope_all=pd.DataFrame(columns=['species','run','mode','optim', 'accu','model',\n", + " 'is_equal_union_species', 'missing', 'percentage_missing',\n", + " 'is_biomass_included', 'missing_biomass', 'percentage_missing_biomass',\n", + " 'is_exchange_included', 'missing_exchange', 'percentage_missing_exchange',\n", + " 'is_seed_included_to_exchange', 'missing_seed_into_exchange', 'percentage_missing_seed_into_exchange',\n", + " 'is_exchange_included_to_seed', 'missing_exchange_into_seed', 'percentage_missing_exchange_into_seeds'])\n", + " if mode == \"netseed\":\n", + " prefix=\"netseed\"\n", + " else:\n", + " prefix=\"scope\"\n", + " for species in os.listdir(directory):\n", + " file_path = os.path.join(directory, species, f\"{species}_{prefix}_compare.tsv\")\n", + " current_df = pd.read_csv(file_path, sep='\\t', lineterminator='\\n')\n", + " current_df['is_equal_union_species'] = current_df['is_equal_union_species'].map({True: 'True', False: 'False'})\n", + " current_df['is_biomass_included'] = current_df['is_biomass_included'].map({True: 'True', False: 'False'})\n", + " current_df['is_exchange_included'] = current_df['is_exchange_included'].map({True: 'True', False: 'False'})\n", + " current_df['is_seed_included_to_exchange'] = current_df['is_seed_included_to_exchange'].map({True: 'True', False: 'False'})\n", + " current_df['is_exchange_included_to_seed'] = current_df['is_exchange_included_to_seed'].map({True: 'True', False: 'False'})\n", + " current_df[\"percentage_similar\"]=100-current_df[\"percentage_missing\"]\n", + " current_df[\"percentage_similar_biomass\"]=100-current_df[\"percentage_missing_biomass\"]\n", + " current_df[\"percentage_similar_exchange\"]=100-current_df[\"percentage_missing_exchange\"]\n", + " current_df[\"percentage_similar_seed_into_exchange\"]=100-current_df[\"percentage_missing_seed_into_exchange\"]\n", + " current_df[\"percentage_similar_exchange_into_seeds\"]=100-current_df[\"percentage_missing_exchange_into_seeds\"]\n", + " scope_all=pd.concat([scope_all, current_df], ignore_index=True)\n", + " # in this notebook we use only reasoning and only the non accumulation mode for Seed2LP\n", + " if mode == \"full\":\n", + " scope_all = scope_all[scope_all[\"mode\"]==\"reasoning\"]\n", + " scope_all = scope_all[scope_all[\"accu\"]==False]\n", + " scope = scope_all[scope_all[\"run\"]==\"full\"]\n", + " elif mode == \"target\":\n", + " scope_all = scope_all[scope_all[\"mode\"]==\"reasoning\"]\n", + " scope_all = scope_all[scope_all[\"accu\"]==False]\n", + " scope = scope_all[scope_all[\"run\"]==\"target\"]\n", + " else:\n", + " scope = scope_all\n", + "\n", + " if optim==\"submin\":\n", + " return scope[scope[\"optim\"]==\"subset_minimal\"]\n", + " elif optim==\"min\":\n", + " return scope[scope[\"optim\"]==\"minimize\"]\n", + " else:\n", + " return scope" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "def get_sizes(flux_df:pd.DataFrame, nb_total_meta:pd.DataFrame):\n", + " list_mean_size = flux_df.groupby(['species'])['size'].mean()\n", + " mean_size_df = pd.DataFrame(list_mean_size)\n", + " data_size_df = pd.concat([nb_total_meta, mean_size_df], axis=1)\n", + " data_size_df[\"percent\"]=data_size_df[\"size\"] / data_size_df[\"number_metabolites\"] *100\n", + " data_size = pd.DataFrame(data_size_df[\"percent\"])\n", + " data_size=data_size.reset_index()\n", + " data_size=data_size.rename(columns={\"index\": \"species\"})\n", + " return data_size" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "def get_total_nb_meta(supp_data_file):\n", + " table_all = pd.read_csv(supp_data_file, sep='\\t', lineterminator='\\n')\n", + " union_all = table_all.loc[table_all[\"type_data\"] == \"Union\"]\n", + " num_metabolite = union_all.groupby(['network'])['number_metabolites'].first()\n", + " return pd.DataFrame(num_metabolite)" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [], + "source": [ + "def plot_compare(s2lp:pd.DataFrame, netseed:pd.DataFrame, col, labels,\n", + " is_precursor:bool=False, y_label:str=\"\"):\n", + " np.set_printoptions(precision=3)\n", + " plt.style.use(\"seaborn-v0_8-colorblind\")\n", + "\n", + "\n", + " nan_spec=s2lp[s2lp[col].isnull()][\"species\"]\n", + " for nspe in nan_spec:\n", + " s2lp=s2lp.drop(s2lp.loc[s2lp['species']==nspe].index)\n", + "\n", + " nan_spec=netseed[netseed[col].isnull()][\"species\"]\n", + " for nspe in nan_spec:\n", + " netseed=netseed.drop(netseed.loc[netseed['species']==nspe].index)\n", + "\n", + " del_spec=set(netseed['species']) - set(s2lp['species'])\n", + " for sp in del_spec:\n", + " netseed=netseed.drop(netseed.loc[netseed['species']==sp].index)\n", + "\n", + " n = len(s2lp['species'].unique())\n", + " \n", + " s2lp=s2lp.assign(Tool=labels[0])\n", + " netseed=netseed.assign(Tool=labels[1])\n", + " \n", + " concat_table = pd.concat([s2lp, netseed])\n", + "\n", + " scope_tab = concat_table.groupby(['species','Tool'])[col].mean()\n", + " scope_df=pd.DataFrame(scope_tab)\n", + " scope_df=scope_df.reset_index()\n", + " plt.figure(figsize=(3,4))\n", + " sns.set_theme(font_scale = 1.5)\n", + "\n", + "\n", + " s2lp_means= scope_df[scope_df['Tool']==\"Seed2LP\"]\n", + " netseed_means= scope_df[scope_df['Tool']==\"NetSeed\"]\n", + " s2lp_gen_mean=s2lp_means[col].mean()\n", + " netseed_means=netseed_means[col].mean()\n", + " print(\"S2LP global mean: \",s2lp_gen_mean, \"\\t NetSeed global mean: \", netseed_means)\n", + "\n", + "\n", + " # KRUSKAL WALLIS TESTS\n", + " # Get the p-value from Kruskall Wallis test\n", + " kstat, p_value = kruskal(scope_df[scope_df[\"Tool\"]==labels[1]][col], scope_df[scope_df[\"Tool\"]==labels[0]][col])\n", + "\n", + " sns.boxplot(data=scope_df, x=\"Tool\", y=col, hue=\"Tool\", fill=False, linewidth=1.5)\n", + " plt.xlabel('')\n", + " plt.ylabel(y_label)\n", + " plt.title(f\"kstat = {kstat}, p-value = {p_value}, n={n}\")\n", + " sns.despine(bottom=True)\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "def get_all_same_validation_fba(table,type):\n", + " count=0\n", + " total=0\n", + " for _,line in table.iterrows():\n", + " if line[type] == line[\"Total_flux\"]:\n", + " count += 1\n", + " total += 1\n", + " else:\n", + " total += 1\n", + " \n", + " return total, count" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "def create_one_plot(s2lp, netseed, labels):\n", + " plt.style.use(\"seaborn-v0_8-colorblind\")\n", + "\n", + " _, s2lp_true_count = get_all_same_validation_fba(s2lp, \"True_flux\")\n", + " _, s2lp_false_count = get_all_same_validation_fba(s2lp,\"False_flux\")\n", + " s2lp_missing_networks = 107 - (s2lp_true_count + s2lp_false_count)\n", + "\n", + " _, netseed_true_count = get_all_same_validation_fba(netseed, \"True_flux\")\n", + " _, netseed_false_count = get_all_same_validation_fba(netseed, \"False_flux\")\n", + " netseed_missing_networks = 107 - (netseed_true_count + netseed_false_count)\n", + "\n", + "\n", + " s2lp_tab=pd.DataFrame([[s2lp_true_count, s2lp_false_count, s2lp_missing_networks]], \n", + " columns=[\"all_true\",\"all_false\",\"missing\"])\n", + " s2lp_tab=s2lp_tab.assign(Tool=labels[0])\n", + "\n", + " netseed_tab=pd.DataFrame([[netseed_true_count, netseed_false_count, netseed_missing_networks]],\n", + " columns=[\"all_true\",\"all_false\",\"missing\"])\n", + " netseed_tab=netseed_tab.assign(Tool=labels[1])\n", + "\n", + " concat_table = pd.concat([netseed_tab,s2lp_tab])\n", + "\n", + "\n", + " plt.figure(figsize=(1,3))\n", + " sns.set_theme(font_scale = 1.5)\n", + " fig, ax = plt.subplots()\n", + " fig.tight_layout()\n", + " groups = concat_table['Tool']\n", + " ax.bar(groups, concat_table[\"all_true\"], color='#1e73be',label='flux', width=0.2)\n", + " ax.bar(groups, concat_table[\"all_false\"], bottom = concat_table[\"all_true\"], \n", + " color='#ef3340', label='no flux', width=0.2)\n", + " ax.bar(groups, concat_table[\"missing\"], bottom = concat_table[\"all_true\"]+concat_table[\"all_false\"], \n", + " color='black', label='no solution', width=0.2)\n", + " \n", + " plt.ylabel('Number of GSMNs', fontsize=25)\n", + " plt.xlabel('Tools', fontsize=25)\n", + " plt.xticks(size=22)\n", + " plt.yticks(size=22)\n", + " #sns.despine(bottom=True)\n", + " plt.tick_params(bottom=False, left=True)\n", + " plt.legend(frameon=True, loc='center right', borderaxespad=-10)\n", + " return " + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "def create_table_plot(table,column_name):\n", + " new_table = table.groupby(['species'])[column_name].agg('count').reset_index()\n", + " new_table=new_table.rename(columns={column_name: \"Total_flux\"})\n", + " new_true = table[table[column_name]==True].groupby(['species'])[column_name].agg('count').reset_index()\n", + " new_true=new_true.rename(columns={column_name: \"True_flux\"})\n", + " new_false = table[table[column_name]==False].groupby(['species'])[column_name].agg('count').reset_index()\n", + " new_false=new_false.rename(columns={column_name: \"False_flux\"})\n", + " new_table=pd.merge(new_table,new_true, how='left', on=['species'])\n", + " new_table=pd.merge(new_table,new_false, how='left', on=['species'])\n", + " new_table=new_table.fillna(0)\n", + " new_table=new_table.fillna(0)\n", + " new_table['True_flux']=new_table['True_flux'].astype(int)\n", + " new_table['False_flux']=new_table['False_flux'].astype(int)\n", + " return new_table" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "def get_mean_std_deviation(table, tool):\n", + " mean = table[\"Total_flux\"].mean()\n", + " std = table[\"Total_flux\"].std()\n", + " print(tool, f\"mean = {mean}\", f\"standard deviation = {std}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "def get_fba_ok_nb(table, tool):\n", + " nb = len(table) - table['True_flux'].value_counts()[0]\n", + " print(tool, f\"Number of networks with solutions satisfying FBA = {nb}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Get data" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_9270/396657918.py:24: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", + " flux_all=pd.concat([flux_all if not flux_all.empty else None, current_df], ignore_index=True)\n" + ] + } + ], + "source": [ + "flux_s2lp_FN_submin = get_fluxes(s2lp_results_reas_dir, \"full\", \"submin\")" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "flux_netseed_all = get_fluxes(netseed_results_dir, \"netseed\")" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_9270/3453808174.py:25: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", + " scope_all=pd.concat([scope_all, current_df], ignore_index=True)\n" + ] + } + ], + "source": [ + "scope_s2lp = get_scopes(s2lp_scope_dir, \"full\", \"submin\")" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_9270/3453808174.py:25: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", + " scope_all=pd.concat([scope_all, current_df], ignore_index=True)\n" + ] + } + ], + "source": [ + "scope_netseed = get_scopes(netseed_scope_dir, \"netseed\")" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "# Get the number of all metabolites per network\n", + "nb_total_meta_df = get_total_nb_meta(s2lp_supp_data)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "data_size_s2lp = get_sizes(flux_s2lp_FN_submin, nb_total_meta_df)" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "data_size_netseed = get_sizes(flux_netseed_all, nb_total_meta_df)" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "s2lp_flux=create_table_plot(flux_s2lp_FN_submin,'has_flux')\n", + "netseed_flux=create_table_plot(flux_netseed_all,'has_flux')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# PLOT" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Fluxes analyses" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Mean and standard deviation" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Seed2LP mean = 888.6095238095238 standard deviation = 281.6013444856349\n" + ] + } + ], + "source": [ + "get_mean_std_deviation(s2lp_flux, \"Seed2LP\")" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "NetSeed mean = 848.1682242990654 standard deviation = 349.1733495520286\n" + ] + } + ], + "source": [ + "get_mean_std_deviation(netseed_flux, \"NetSeed\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Number of networks satisfying FBA constraints" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Seed2LP Number of networks with solutions satisfying FBA = 104\n" + ] + } + ], + "source": [ + "get_fba_ok_nb(s2lp_flux, \"Seed2LP\")" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "NetSeed Number of networks with solutions satisfying FBA = 11\n" + ] + } + ], + "source": [ + "get_fba_ok_nb(netseed_flux, \"NetSeed\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Plot number of fluxes validating (all solutions validates) or not (none of solution validates) FBA" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "create_one_plot(s2lp_flux, netseed_flux, [\"Seed2LP\", \"NetSeed\"])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Set of seed solution and scope analyses" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "> Notes:\n", + ">\n", + "> On the above plot, \"global mean\" is a mean of means per network (all solutions, up to 1000, are averaged by network, then an average of those is performed)" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "S2LP global mean: 89.35444227204987 \t NetSeed global mean: 30.58497220996268\n" + ] + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_compare(scope_s2lp, scope_netseed, 'percentage_similar_exchange_into_seeds', [\"Seed2LP\",\"NetSeed\"],\n", + " y_label=\"%\\ of all exchanged metabolites\\npresent in the seed set\")" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "S2LP global mean: 28.184406328240268 \t NetSeed global mean: 11.503983496802025\n" + ] + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_compare(data_size_s2lp, data_size_netseed, 'percent', [\"Seed2LP\",\"NetSeed\"], \n", + " y_label=\"Size of seed set\\n(%\\ of GSMN size)\")" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "S2LP global mean: 100.0 \t NetSeed global mean: 69.39226789644138\n" + ] + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_compare(scope_s2lp, scope_netseed, 'percentage_similar_biomass', [\"Seed2LP\",\"NetSeed\"], \n", + " y_label=\"%\\ of biomass reactants\\nreachable from seeds\")" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "S2LP global mean: 100.0 \t NetSeed global mean: 68.64459957577657\n" + ] + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_compare(scope_s2lp, scope_netseed, 'percentage_similar', [\"Seed2LP\",\"NetSeed\"], \n", + " y_label=\"%\\ of GSMN metabolites\\nreachable from seeds\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "s2lp", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.14" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/notebook/analyses/02_s2lp_sol_analyses.ipynb b/notebook/analyses/02_s2lp_sol_analyses.ipynb new file mode 100644 index 0000000..1ed99a3 --- /dev/null +++ b/notebook/analyses/02_s2lp_sol_analyses.ipynb @@ -0,0 +1,514 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Get the main Seed2LP solution analyses\n", + "This notebook presents the global data of seed2lp, such as number of networks having solutions, number of networks having at least one solution validating FBA, number of networks with all solution validating FBA.\n", + "\n", + "To run correctly this notebook and have the same results as the paper, you must first download the raw results here: [https://doi.org/10.57745/OS1JND](https://doi.org/10.57745/OS1JND)\n", + "\n", + "This notebook is written with the hierarchy of downloaded files, if you want to try it with the test form the run notebooks, it is needed to first restructure your data to match the hierarchy of downloaded files.\n", + "\n", + "We suppose here that the downloaded files are in a directory named \"analyses\", this directory path can be changed to your directory path where the data are saved." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Variable to change (if wanted)" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "analyse_dir = \"../../analyses\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Initialisation and functions" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import os" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "s2lp_results_reas_dir = os.path.join(analyse_dir, \"results\", \"s2lp_reasoning\")\n", + "s2lp_results_hyb_cobra_dir = os.path.join(analyse_dir, \"results\", \"s2lp_hyb_cobra\")" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "def get_fluxes(directory:str, mode:str, optim:str=None):\n", + " flux_all=pd.DataFrame(columns=['species', 'biomass_reaction', 'solver_type', 'search_mode',\n", + " 'search_type', 'accumulation', 'model', 'size', 'lp_flux', 'cobra_flux_init',\n", + " 'cobra_flux_no_import', 'cobra_flux_seeds', 'cobra_flux_demands',\n", + " 'has_flux', 'has_flux_seeds', 'has_flux_demands', 'timer'])\n", + " flux_all['accumulation'] = flux_all['accumulation'].astype('bool')\n", + " flux_all['has_flux'] = flux_all['has_flux'].astype('bool')\n", + " flux_all['has_flux_seeds'] = flux_all['has_flux_seeds'].astype('bool')\n", + " flux_all['has_flux_demands'] = flux_all['has_flux_demands'].astype('bool')\n", + "\n", + " for dirpath, _, filenames in os.walk(directory):\n", + " for filename in [f for f in filenames if (f.endswith(\"_fluxes.tsv\") or f.endswith(\"_fluxes_from_result.tsv\"))]:\n", + " # By default in this notebook we want the no accumulation mode for seed2lp results\n", + " if \"_no_accu_\" in filename \\\n", + " and ((mode == \"full\" and \"_fn_\" in filename) \\\n", + " or (mode == \"target\" and \"_tgt_\" in filename))\\\n", + " or mode == \"netseed\":\n", + " file_path=os.path.join(dirpath, filename)\n", + " current_df = pd.read_csv(file_path, sep='\\t', lineterminator='\\n')\n", + " current_df['accumulation'] = current_df['accumulation'].astype('bool')\n", + " current_df['has_flux'] = current_df['has_flux'].astype('bool')\n", + " current_df['has_flux_seeds'] = current_df['has_flux_seeds'].astype('bool')\n", + " current_df['has_flux_demands'] = current_df['has_flux_demands'].astype('bool')\n", + " flux_all=pd.concat([flux_all if not flux_all.empty else None, current_df], ignore_index=True)\n", + " flux_all = flux_all[flux_all[\"model\"]!=\"model_one_solution\"]\n", + " flux_all = flux_all[flux_all[\"model\"]!=\"model_one_solution\"]\n", + " if optim==\"submin\":\n", + " return flux_all[flux_all[\"search_mode\"]==\"Subset Minimal\"]\n", + " elif optim==\"min\":\n", + " return flux_all[flux_all[\"search_mode\"]==\"Minimize\"]\n", + " else:\n", + " return flux_all" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "def get_all_same_validation_fba(table, type):\n", + " count=0\n", + " total=0\n", + " for _,line in table.iterrows():\n", + " if line[type] == line[\"Total_flux\"]:\n", + " count += 1\n", + " total += 1\n", + " else:\n", + " total += 1\n", + " return total, count\n", + "\n", + "def get_mixed(table):\n", + " count=0\n", + " for _,line in table.iterrows():\n", + " if line[\"False_flux\"] != line[\"Total_flux\"] and line[\"True_flux\"] != line[\"Total_flux\"] :\n", + " count += 1\n", + " return count" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "def get_separate_data(table_reasoning, table_hybrid):\n", + " table_hybrid[\"solver_type\"] = table_hybrid[\"solver_type\"].str.replace('REASONING GUESS-CHECK-DIVERSITY', 'REASONING GUESS-CHECK DIVERSITY')\n", + " table_hybrid[\"solver_type\"] = table_hybrid[\"solver_type\"].str.replace('REASONING GUESS-CHECK', 'REASONING GUESS-CHECK')\n", + " table_hybrid[\"solver_type\"] = table_hybrid[\"solver_type\"].str.replace('REASONING FILTER', 'REASONING FILTER')\n", + "\n", + " # CLASSIC\n", + " table_reasoning = table_reasoning[table_reasoning[\"solver_type\"]==\"REASONING\"]\n", + " \n", + " # FILTER\n", + " table_filter = table_hybrid[table_hybrid[\"solver_type\"]==\"REASONING FILTER\"]\n", + "\n", + " # GUESS_CHECK\n", + " table_gc = table_hybrid[table_hybrid[\"solver_type\"]==\"REASONING GUESS-CHECK\"]\n", + "\n", + " # GUESS_CHECK_DIV\n", + " table_gcd = table_hybrid[table_hybrid[\"solver_type\"]==\"REASONING GUESS-CHECK DIVERSITY\"]\n", + "\n", + " return table_reasoning, table_filter, table_gc, table_gcd" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "def create_table_plot(table,column_name):\n", + " new_table = table.groupby(['species'])[column_name].agg('count').reset_index()\n", + " new_table=new_table.rename(columns={column_name: \"Total_flux\"})\n", + " new_true = table[table[column_name]==True].groupby(['species'])[column_name].agg('count').reset_index()\n", + " new_true=new_true.rename(columns={column_name: \"True_flux\"})\n", + " new_false = table[table[column_name]==False].groupby(['species'])[column_name].agg('count').reset_index()\n", + " new_false=new_false.rename(columns={column_name: \"False_flux\"})\n", + " new_table=pd.merge(new_table,new_true, how='left', on=['species'])\n", + " new_table=pd.merge(new_table,new_false, how='left', on=['species'])\n", + " new_table=new_table.fillna(0)\n", + " new_table=new_table.fillna(0)\n", + " new_table['True_flux']=new_table['True_flux'].astype(int)\n", + " new_table['False_flux']=new_table['False_flux'].astype(int)\n", + " return new_table" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "def get_sol_FBA_data(table_reasoning, table_filter, table_gc, table_gcd):\n", + " # CLASSIC\n", + " nb_networks_reasoning, all_true_reasoning, = get_all_same_validation_fba(table_reasoning, \"True_flux\")\n", + " mixed_reasoning = get_mixed(table_reasoning)\n", + " \n", + " # FILTER\n", + " nb_networks_filter, all_true_filter, = get_all_same_validation_fba(table_filter, \"True_flux\")\n", + " mixed_filter = get_mixed(table_filter)\n", + "\n", + " # GUESS_CHECK\n", + " nb_networks_gc, all_true_gc, = get_all_same_validation_fba(table_gc, \"True_flux\")\n", + " mixed_gc = get_mixed(table_gc)\n", + "\n", + " # GUESS_CHECK_DIV\n", + " nb_networks_gcd, all_true_gcd, = get_all_same_validation_fba(table_gcd, \"True_flux\")\n", + " mixed_gcd = get_mixed(table_gcd)\n", + "\n", + " df = pd.DataFrame([[\"Reasoning\", nb_networks_reasoning, mixed_reasoning, all_true_reasoning],\n", + " [\"Hybrid-filter\", nb_networks_filter, mixed_filter, all_true_filter],\n", + " [\"Hybrid-GC\", nb_networks_gc, mixed_gc, all_true_gc],\n", + " [\"Hybrid-GC-Div\", nb_networks_gcd, mixed_gcd, all_true_gcd]],\n", + " columns=[\"Solving mode\", \"Nb. of net. with sol.\", \"Nb. of net. with ≥ 1 sol. FBA\", \"Nb of net with all sol. FBA\"])\n", + " return df\n", + " \n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Get data" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "flux_reasoning_target_submin = get_fluxes(s2lp_results_reas_dir, \"target\", \"submin\")" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_38830/2993539488.py:24: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", + " flux_all=pd.concat([flux_all if not flux_all.empty else None, current_df], ignore_index=True)\n", + "/tmp/ipykernel_38830/2993539488.py:24: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", + " flux_all=pd.concat([flux_all if not flux_all.empty else None, current_df], ignore_index=True)\n", + "/tmp/ipykernel_38830/2993539488.py:24: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", + " flux_all=pd.concat([flux_all if not flux_all.empty else None, current_df], ignore_index=True)\n", + "/tmp/ipykernel_38830/2993539488.py:24: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", + " flux_all=pd.concat([flux_all if not flux_all.empty else None, current_df], ignore_index=True)\n", + "/tmp/ipykernel_38830/2993539488.py:24: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", + " flux_all=pd.concat([flux_all if not flux_all.empty else None, current_df], ignore_index=True)\n", + "/tmp/ipykernel_38830/2993539488.py:24: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", + " flux_all=pd.concat([flux_all if not flux_all.empty else None, current_df], ignore_index=True)\n", + "/tmp/ipykernel_38830/2993539488.py:24: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", + " flux_all=pd.concat([flux_all if not flux_all.empty else None, current_df], ignore_index=True)\n", + "/tmp/ipykernel_38830/2993539488.py:24: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", + " flux_all=pd.concat([flux_all if not flux_all.empty else None, current_df], ignore_index=True)\n", + "/tmp/ipykernel_38830/2993539488.py:24: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", + " flux_all=pd.concat([flux_all if not flux_all.empty else None, current_df], ignore_index=True)\n", + "/tmp/ipykernel_38830/2993539488.py:24: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", + " flux_all=pd.concat([flux_all if not flux_all.empty else None, current_df], ignore_index=True)\n", + "/tmp/ipykernel_38830/2993539488.py:24: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", + " flux_all=pd.concat([flux_all if not flux_all.empty else None, current_df], ignore_index=True)\n", + "/tmp/ipykernel_38830/2993539488.py:24: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", + " flux_all=pd.concat([flux_all if not flux_all.empty else None, current_df], ignore_index=True)\n", + "/tmp/ipykernel_38830/2993539488.py:24: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", + " flux_all=pd.concat([flux_all if not flux_all.empty else None, current_df], ignore_index=True)\n", + "/tmp/ipykernel_38830/2993539488.py:24: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", + " flux_all=pd.concat([flux_all if not flux_all.empty else None, current_df], ignore_index=True)\n", + "/tmp/ipykernel_38830/2993539488.py:24: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", + " flux_all=pd.concat([flux_all if not flux_all.empty else None, current_df], ignore_index=True)\n", + "/tmp/ipykernel_38830/2993539488.py:24: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", + " flux_all=pd.concat([flux_all if not flux_all.empty else None, current_df], ignore_index=True)\n", + "/tmp/ipykernel_38830/2993539488.py:24: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", + " flux_all=pd.concat([flux_all if not flux_all.empty else None, current_df], ignore_index=True)\n", + "/tmp/ipykernel_38830/2993539488.py:24: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", + " flux_all=pd.concat([flux_all if not flux_all.empty else None, current_df], ignore_index=True)\n", + "/tmp/ipykernel_38830/2993539488.py:24: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", + " flux_all=pd.concat([flux_all if not flux_all.empty else None, current_df], ignore_index=True)\n", + "/tmp/ipykernel_38830/2993539488.py:24: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", + " flux_all=pd.concat([flux_all if not flux_all.empty else None, current_df], ignore_index=True)\n", + "/tmp/ipykernel_38830/2993539488.py:24: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", + " flux_all=pd.concat([flux_all if not flux_all.empty else None, current_df], ignore_index=True)\n", + "/tmp/ipykernel_38830/2993539488.py:24: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", + " flux_all=pd.concat([flux_all if not flux_all.empty else None, current_df], ignore_index=True)\n", + "/tmp/ipykernel_38830/2993539488.py:24: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", + " flux_all=pd.concat([flux_all if not flux_all.empty else None, current_df], ignore_index=True)\n", + "/tmp/ipykernel_38830/2993539488.py:24: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", + " flux_all=pd.concat([flux_all if not flux_all.empty else None, current_df], ignore_index=True)\n", + "/tmp/ipykernel_38830/2993539488.py:24: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", + " flux_all=pd.concat([flux_all if not flux_all.empty else None, current_df], ignore_index=True)\n", + "/tmp/ipykernel_38830/2993539488.py:24: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", + " flux_all=pd.concat([flux_all if not flux_all.empty else None, current_df], ignore_index=True)\n", + "/tmp/ipykernel_38830/2993539488.py:24: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", + " flux_all=pd.concat([flux_all if not flux_all.empty else None, current_df], ignore_index=True)\n", + "/tmp/ipykernel_38830/2993539488.py:24: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", + " flux_all=pd.concat([flux_all if not flux_all.empty else None, current_df], ignore_index=True)\n", + "/tmp/ipykernel_38830/2993539488.py:24: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", + " flux_all=pd.concat([flux_all if not flux_all.empty else None, current_df], ignore_index=True)\n", + "/tmp/ipykernel_38830/2993539488.py:24: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", + " flux_all=pd.concat([flux_all if not flux_all.empty else None, current_df], ignore_index=True)\n", + "/tmp/ipykernel_38830/2993539488.py:24: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", + " flux_all=pd.concat([flux_all if not flux_all.empty else None, current_df], ignore_index=True)\n", + "/tmp/ipykernel_38830/2993539488.py:24: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", + " flux_all=pd.concat([flux_all if not flux_all.empty else None, current_df], ignore_index=True)\n", + "/tmp/ipykernel_38830/2993539488.py:24: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", + " flux_all=pd.concat([flux_all if not flux_all.empty else None, current_df], ignore_index=True)\n", + "/tmp/ipykernel_38830/2993539488.py:24: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", + " flux_all=pd.concat([flux_all if not flux_all.empty else None, current_df], ignore_index=True)\n", + "/tmp/ipykernel_38830/2993539488.py:24: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", + " flux_all=pd.concat([flux_all if not flux_all.empty else None, current_df], ignore_index=True)\n", + "/tmp/ipykernel_38830/2993539488.py:24: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", + " flux_all=pd.concat([flux_all if not flux_all.empty else None, current_df], ignore_index=True)\n", + "/tmp/ipykernel_38830/2993539488.py:24: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", + " flux_all=pd.concat([flux_all if not flux_all.empty else None, current_df], ignore_index=True)\n", + "/tmp/ipykernel_38830/2993539488.py:24: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", + " flux_all=pd.concat([flux_all if not flux_all.empty else None, current_df], ignore_index=True)\n", + "/tmp/ipykernel_38830/2993539488.py:24: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", + " flux_all=pd.concat([flux_all if not flux_all.empty else None, current_df], ignore_index=True)\n", + "/tmp/ipykernel_38830/2993539488.py:24: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", + " flux_all=pd.concat([flux_all if not flux_all.empty else None, current_df], ignore_index=True)\n", + "/tmp/ipykernel_38830/2993539488.py:24: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", + " flux_all=pd.concat([flux_all if not flux_all.empty else None, current_df], ignore_index=True)\n", + "/tmp/ipykernel_38830/2993539488.py:24: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", + " flux_all=pd.concat([flux_all if not flux_all.empty else None, current_df], ignore_index=True)\n", + "/tmp/ipykernel_38830/2993539488.py:24: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", + " flux_all=pd.concat([flux_all if not flux_all.empty else None, current_df], ignore_index=True)\n", + "/tmp/ipykernel_38830/2993539488.py:24: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", + " flux_all=pd.concat([flux_all if not flux_all.empty else None, current_df], ignore_index=True)\n", + "/tmp/ipykernel_38830/2993539488.py:24: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", + " flux_all=pd.concat([flux_all if not flux_all.empty else None, current_df], ignore_index=True)\n", + "/tmp/ipykernel_38830/2993539488.py:24: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", + " flux_all=pd.concat([flux_all if not flux_all.empty else None, current_df], ignore_index=True)\n", + "/tmp/ipykernel_38830/2993539488.py:24: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", + " flux_all=pd.concat([flux_all if not flux_all.empty else None, current_df], ignore_index=True)\n", + "/tmp/ipykernel_38830/2993539488.py:24: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", + " flux_all=pd.concat([flux_all if not flux_all.empty else None, current_df], ignore_index=True)\n", + "/tmp/ipykernel_38830/2993539488.py:24: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", + " flux_all=pd.concat([flux_all if not flux_all.empty else None, current_df], ignore_index=True)\n", + "/tmp/ipykernel_38830/2993539488.py:24: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", + " flux_all=pd.concat([flux_all if not flux_all.empty else None, current_df], ignore_index=True)\n", + "/tmp/ipykernel_38830/2993539488.py:24: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", + " flux_all=pd.concat([flux_all if not flux_all.empty else None, current_df], ignore_index=True)\n", + "/tmp/ipykernel_38830/2993539488.py:24: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", + " flux_all=pd.concat([flux_all if not flux_all.empty else None, current_df], ignore_index=True)\n", + "/tmp/ipykernel_38830/2993539488.py:24: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", + " flux_all=pd.concat([flux_all if not flux_all.empty else None, current_df], ignore_index=True)\n", + "/tmp/ipykernel_38830/2993539488.py:24: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", + " flux_all=pd.concat([flux_all if not flux_all.empty else None, current_df], ignore_index=True)\n", + "/tmp/ipykernel_38830/2993539488.py:24: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", + " flux_all=pd.concat([flux_all if not flux_all.empty else None, current_df], ignore_index=True)\n", + "/tmp/ipykernel_38830/2993539488.py:24: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", + " flux_all=pd.concat([flux_all if not flux_all.empty else None, current_df], ignore_index=True)\n", + "/tmp/ipykernel_38830/2993539488.py:24: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", + " flux_all=pd.concat([flux_all if not flux_all.empty else None, current_df], ignore_index=True)\n", + "/tmp/ipykernel_38830/2993539488.py:24: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", + " flux_all=pd.concat([flux_all if not flux_all.empty else None, current_df], ignore_index=True)\n", + "/tmp/ipykernel_38830/2993539488.py:24: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", + " flux_all=pd.concat([flux_all if not flux_all.empty else None, current_df], ignore_index=True)\n", + "/tmp/ipykernel_38830/2993539488.py:24: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", + " flux_all=pd.concat([flux_all if not flux_all.empty else None, current_df], ignore_index=True)\n" + ] + } + ], + "source": [ + "flux_hyb_cobra_target_submin = get_fluxes(s2lp_results_hyb_cobra_dir, \"target\", \"submin\")" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "flux_reasoning, flux_filter, flux_gc, flux_gcd = get_separate_data(flux_reasoning_target_submin, flux_hyb_cobra_target_submin)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "reasoning_flux=create_table_plot(flux_reasoning,'has_flux')\n", + "filter_flux=create_table_plot(flux_filter,'has_flux')\n", + "gc_flux=create_table_plot(flux_gc,'has_flux')\n", + "gcd_flux=create_table_plot(flux_gcd,'has_flux')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Number of networks satisfying FBA constraints" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Solving modeNb. of net. with sol.Nb. of net. with ≥ 1 sol. FBANb of net with all sol. FBA
0Reasoning1077310
1Hybrid-filter71071
2Hybrid-GC90090
3Hybrid-GC-Div98098
\n", + "
" + ], + "text/plain": [ + " Solving mode Nb. of net. with sol. Nb. of net. with ≥ 1 sol. FBA \\\n", + "0 Reasoning 107 73 \n", + "1 Hybrid-filter 71 0 \n", + "2 Hybrid-GC 90 0 \n", + "3 Hybrid-GC-Div 98 0 \n", + "\n", + " Nb of net with all sol. FBA \n", + "0 10 \n", + "1 71 \n", + "2 90 \n", + "3 98 " + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data = get_sol_FBA_data(reasoning_flux, filter_flux, gc_flux, gcd_flux)\n", + "data" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Reasoning - nb of networks with no sol FBA: 24\n" + ] + } + ], + "source": [ + "_, all_false = get_all_same_validation_fba(reasoning_flux, \"False_flux\")\n", + "print(\"Reasoning - nb of networks with no sol FBA: \",all_false)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "s2lp", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.14" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/notebook/analyses/03_iCN718.ipynb b/notebook/analyses/03_iCN718.ipynb new file mode 100644 index 0000000..80219d6 --- /dev/null +++ b/notebook/analyses/03_iCN718.ipynb @@ -0,0 +1,567 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Get iCN718 datas and compare\n", + "This notebook presents iCN718 data in Reasoning / Filter / Guess & Check / Guess & Check Diversity.\n", + "\n", + "\n", + "To run correctly this notebook and have the same results as the paper, you must first download the raw results: [https://doi.org/10.57745/OS1JND](https://doi.org/10.57745/OS1JND)\n", + "\n", + "This notebook is written with the hierarchy of downloaded files, if you want to try it with the test form the run notebooks, it is needed to first restructure your data to match the hierarchy of downloaded files.\n", + "\n", + "We suppose here that the downloaded files are in a directory named \"analyses\", this directory path can be changed to your directory path where the data are saved.\n", + "\n", + "> **WARNING**:\n", + ">\n", + "> On the paper the Venn diagramm was computed with http://bioinformatics.psb.ugent.be/webtools/Venn/. Here we use a python implemantation of Venn diagramm to incorporate it into notebooks" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Requirements\n", + "Module *seaborn* and *venny4py* are needed" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!pip install seaborn" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Collecting venny4py\n", + " Downloading venny4py-1.0.3-py3-none-any.whl.metadata (1.8 kB)\n", + "Downloading venny4py-1.0.3-py3-none-any.whl (4.5 kB)\n", + "Installing collected packages: venny4py\n", + "Successfully installed venny4py-1.0.3\n" + ] + } + ], + "source": [ + "!pip install venny4py" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Variable to change (if wanted)" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "analyse_dir = \"../../analyses\"\n", + "metabolite_dir=f\"{analyse_dir}/results/metabolites_iCN718\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Initialisation and functions" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "from venny4py.venny4py import *\n", + "import seaborn as sns\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "metabolites_file = f\"{metabolite_dir}/metabolites_occurences.tsv\"\n", + "file_exch = f\"{metabolite_dir}/iCN718_exchanges.txt\"\n", + "\n", + "iCN718_1000_reas_dir = os.path.join(analyse_dir, \"results\", \"s2lp_reasoning\", \"iCN718\")\n", + "iCN718_1000_hyb_cobra_dir = os.path.join(analyse_dir, \"results\", \"s2lp_hyb_cobra\", \"iCN718\")\n", + "\n", + "\n", + "iCN718_2000_dir = os.path.join(analyse_dir, \"results\", \"iCN718_2000\", \"iCN718\")" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "def get_metabolites(file):\n", + " metabolites=pd.read_csv(file,sep='\\t')\n", + " metabolites.rename( columns={'Unnamed: 0':'metabolites'}, inplace=True )\n", + " for index, met in metabolites.iterrows():\n", + " name=met[\"metabolites\"].removeprefix(\"M_\").strip().removesuffix(\"_e\").removesuffix(\"_c\")\n", + " metabolites.loc[index, 'metabolites']=name\n", + " metabolites=metabolites.groupby(['metabolites']).sum()\n", + "\n", + " return metabolites.reset_index()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "def get_fluxes(directory:str, mode:str, optim:str=None):\n", + " flux_all=pd.DataFrame(columns=['species', 'biomass_reaction', 'solver_type', 'search_mode',\n", + " 'search_type', 'accumulation', 'model', 'size', 'lp_flux', 'cobra_flux_init',\n", + " 'cobra_flux_no_import', 'cobra_flux_seeds', 'cobra_flux_demands',\n", + " 'has_flux', 'has_flux_seeds', 'has_flux_demands', 'timer'])\n", + " flux_all['accumulation'] = flux_all['accumulation'].astype('bool')\n", + " flux_all['has_flux'] = flux_all['has_flux'].astype('bool')\n", + " flux_all['has_flux_seeds'] = flux_all['has_flux_seeds'].astype('bool')\n", + " flux_all['has_flux_demands'] = flux_all['has_flux_demands'].astype('bool')\n", + "\n", + " for filename in os.listdir(directory):\n", + " if \"_fluxes.tsv\" in filename or \"_fluxes_from_result.tsv\" in filename:\n", + " # By default in this notebook we want the no accumulation mode for seed2lp results\n", + " if \"_no_accu_\" in filename \\\n", + " and ((mode == \"full\" and \"_fn_\" in filename) \\\n", + " or (mode == \"target\" and \"_tgt_\" in filename))\\\n", + " or mode == \"netseed\":\n", + " file_path=os.path.join(directory, filename)\n", + " current_df = pd.read_csv(file_path, sep='\\t', lineterminator='\\n')\n", + " current_df['accumulation'] = current_df['accumulation'].astype('bool')\n", + " current_df['has_flux'] = current_df['has_flux'].astype('bool')\n", + " current_df['has_flux_seeds'] = current_df['has_flux_seeds'].astype('bool')\n", + " current_df['has_flux_demands'] = current_df['has_flux_demands'].astype('bool')\n", + " flux_all=pd.concat([flux_all if not flux_all.empty else None, current_df], ignore_index=True)\n", + " flux_all = flux_all[flux_all[\"model\"]!=\"model_one_solution\"]\n", + " flux_all = flux_all[flux_all[\"model\"]!=\"model_one_solution\"]\n", + " if optim==\"submin\":\n", + " return flux_all[flux_all[\"search_mode\"]==\"Subset Minimal\"]\n", + " elif optim==\"min\":\n", + " return flux_all[flux_all[\"search_mode\"]==\"Minimize\"]\n", + " else:\n", + " return flux_all" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "def get_data_1000(table_reasoning, table_hybrid):\n", + " table_hybrid[\"solver_type\"] = table_hybrid[\"solver_type\"].str.replace('REASONING GUESS-CHECK-DIVERSITY', 'REASONING GUESS-CHECK DIVERSITY')\n", + " table_hybrid[\"solver_type\"] = table_hybrid[\"solver_type\"].str.replace('REASONING GUESS-CHECK', 'REASONING GUESS-CHECK')\n", + " table_hybrid[\"solver_type\"] = table_hybrid[\"solver_type\"].str.replace('REASONING FILTER', 'REASONING FILTER')\n", + "\n", + " # CLASSIC\n", + " table_reasoning = table_reasoning[table_reasoning[\"solver_type\"]==\"REASONING\"]\n", + " nb_true = table_reasoning['has_flux'].sum()\n", + " \n", + " # FILTER\n", + " table_filter = table_hybrid[table_hybrid[\"solver_type\"]==\"REASONING FILTER\"]\n", + "\n", + " # GUESS_CHECK\n", + " table_gc = table_hybrid[table_hybrid[\"solver_type\"]==\"REASONING GUESS-CHECK\"]\n", + "\n", + " # GUESS_CHECK_DIV\n", + " table_gcd = table_hybrid[table_hybrid[\"solver_type\"]==\"REASONING GUESS-CHECK DIVERSITY\"]\n", + "\n", + " print(f\"Total number of network\\nReasoning: {len(table_reasoning)}\\t\\tFilter: {len(table_filter)}\\t Guess-Check: {len(table_gc)}\\t Guess-Check Div: {len(table_gcd)}\")\n", + " print(f\"All FBA validation\\nReasoning: {nb_true} / {len(table_reasoning)} networks\")" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "def get_data_2000(table):\n", + " table[\"solver_type\"] = table[\"solver_type\"].str.replace('REASONING GUESS-CHECK-DIVERSITY', 'REASONING GUESS-CHECK DIVERSITY')\n", + " table[\"solver_type\"] = table[\"solver_type\"].str.replace('REASONING GUESS-CHECK', 'REASONING GUESS-CHECK')\n", + " table[\"solver_type\"] = table[\"solver_type\"].str.replace('REASONING FILTER', 'REASONING FILTER')\n", + "\n", + " # CLASSIC\n", + " table_reasoning = table[table[\"solver_type\"]==\"REASONING\"]\n", + " nb_true = table_reasoning['has_flux'].sum()\n", + " \n", + " # FILTER\n", + " table_filter = table[table[\"solver_type\"]==\"REASONING FILTER\"]\n", + "\n", + " # GUESS_CHECK\n", + " table_gc = table[table[\"solver_type\"]==\"REASONING GUESS-CHECK\"]\n", + "\n", + " # GUESS_CHECK_DIV\n", + " table_gcd = table[table[\"solver_type\"]==\"REASONING GUESS-CHECK DIVERSITY\"]\n", + "\n", + " print(f\"Total number of network\\nReasoning: {len(table_reasoning)}\\t\\tFilter: {len(table_filter)}\\t Guess-Check: {len(table_gc)}\\t Guess-Check Div: {len(table_gcd)}\")\n", + " print(f\"All FBA validation\\nReasoning: {nb_true} / {len(table_reasoning)} networks\")" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "def plot_venn(metabolites):\n", + " reasoning = metabolites[metabolites[\"nb_reasoning\"]>0]\n", + " filter = metabolites[metabolites[\"nb_filter\"]>0]\n", + " gc = metabolites[metabolites[\"nb_gc\"]>0]\n", + " gcd = metabolites[metabolites[\"nb_gcd\"]>0]\n", + "\n", + " sets = {\n", + " 'Reasoning': set(reasoning[\"metabolites\"]),\n", + " 'Filter': set(filter[\"metabolites\"]),\n", + " 'Guess&Check': set(gc[\"metabolites\"]),\n", + " 'Guess&Check Diversity': set(gcd[\"metabolites\"])}\n", + " \n", + " venny4py(sets=sets)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "def plot_metabolites(metabolites:pd.DataFrame, mode, nb:int=0):\n", + "\n", + " with open(file_exch, 'r') as file:\n", + " exch_list = file.read().split()\n", + "\n", + " match mode:\n", + " case \"reasoning\":\n", + " col=\"nb_reasoning\"\n", + " case \"filter\":\n", + " col=\"nb_filter\"\n", + " case \"gc\":\n", + " col=\"nb_gc\"\n", + " case \"gcd\":\n", + " col=\"nb_gcd\"\n", + " \n", + " metabolites[\"colors\"]=\"internal\"\n", + " for index, line in metabolites.iterrows():\n", + " if line[col] == 0:\n", + " metabolites=metabolites.drop(index)\n", + " elif line[\"metabolites\"] in exch_list:\n", + " metabolites.loc[index, 'colors']=\"exchange\"\n", + " \n", + "\n", + " metabolites_mode=pd.DataFrame()\n", + " metabolites_mode=metabolites[[\"metabolites\",col,\"colors\"]]\n", + " metabolites_mode=metabolites_mode.sort_values(by=[col], ascending=False)\n", + " if nb!=0:\n", + " metabolites_mode=metabolites_mode.head(nb)\n", + " labels=metabolites_mode[\"metabolites\"].to_list()\n", + "\n", + " size=len(labels)\n", + " count=metabolites_mode['colors'].value_counts()\n", + "\n", + " print (\"exchange: \", count[\"exchange\"], \"/ total: \" , size)\n", + "\n", + " if nb==0:\n", + " x_limit = size*0.7\n", + " y_limit = 20\n", + " size=\"full\"\n", + " else:\n", + " x_limit = size*0.4\n", + " y_limit = 2\n", + " fig_size = (x_limit,y_limit)\n", + "\n", + " plt.figure(figsize=(fig_size))\n", + " fig=sns.barplot(data=metabolites_mode, x='metabolites', y=col, hue=\"colors\",palette=[\"#003380ff\",\"#dd8452ff\"],hue_order=[\"internal\",\"exchange\"])\n", + " fig.set_ylim(0, 2000)\n", + " if nb == 0:\n", + " plt.ylabel('Occurences',fontsize=30)\n", + " plt.xlabel('Metabolites',fontsize=30)\n", + " plt.xticks(labels,rotation=45, horizontalalignment='right', fontsize=30 )\n", + " plt.yticks(fontsize=30)\n", + " else:\n", + " plt.ylabel('Occurences')\n", + " plt.xlabel('Metabolites')\n", + " plt.xticks(labels,rotation=45, horizontalalignment='right' ) \n", + " sns.despine(bottom=True)\n", + " plt.tick_params(bottom=False, left=True)\n", + " plt.legend(frameon=False, bbox_to_anchor=(1, 1), loc='upper right', borderaxespad=-10)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Get data" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "metabolites = get_metabolites(metabolites_file)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "iCN_1000_reas = get_fluxes(iCN718_1000_reas_dir, \"target\", \"submin\")" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "iCN_1000_hyb = get_fluxes(iCN718_1000_hyb_cobra_dir, \"target\", \"submin\")" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Total number of network\n", + "Reasoning: 1000\t\tFilter: 149\t Guess-Check: 116\t Guess-Check Div: 111\n", + "All FBA validation\n", + "Reasoning: 465 / 1000 networks\n" + ] + } + ], + "source": [ + "get_data_1000(iCN_1000_reas, iCN_1000_hyb)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "iCN_2000 = get_fluxes(iCN718_2000_dir, \"target\", \"submin\")" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Total number of network\n", + "Reasoning: 2000\t\tFilter: 2000\t Guess-Check: 2000\t Guess-Check Div: 2000\n", + "All FBA validation\n", + "Reasoning: 381 / 2000 networks\n" + ] + } + ], + "source": [ + "get_data_2000(iCN_2000)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# PLOT" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Venn Diagramm" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAkwAAAJDCAYAAAD5MksWAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjkuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8hTgPZAAAACXBIWXMAAB7CAAAewgFu0HU+AAEAAElEQVR4nOzddVwU+f/A8dfSDVIiiGBTClggJihiYGOf3XrWqXfq6dmn55156tlxtmK32IKtiIUtGBiE0hK7+/vDL/zkQBEFlvg8Hw8eyOzszHvWnZn3fFIil8vlCIIgCIIgCJ+lpOgABEEQBEEQCjqRMAmCIAiCIGRDJEyCIAiCIAjZEAmTIAiCIAhCNkTCJAiCIAiCkA2RMAmCIAiCIGRDJEyCIAiCIAjZEAmTIAiCIAhCNkTCJAiCIAiCkA2RMAmCIAiCIGRDJEyCIAiCIAjZEAmTIAiCIAhCNkTCJAiCIAiCkA2RMAmCIAiCIGRDJEyCIAiCIAjZEAmTIAiCIAhCNkTCJAiCIAiCkA2RMAmCIAiCIGRDJEyCIAiCIAjZEAmTIAiCIAhCNkTCJAiCIAiCkA2RMAmCIAiCIGRDJEyCIAiCIAjZEAmTIAiCIAhCNkTCJAiCIAiCkA2RMAmCIAiCIGRDJEyCIAiCIAjZEAmTIAiCIAhCNkTCJAiCIAiCkA2RMAmCIAiCIGRDJEyCIAiCIAjZEAmTIAiCIAhCNkTCJAiCIAiCkA2RMAmCIAiCIGRDJEyCIAiCIAjZEAmTIAiCIAhCNkTCJAiCIAiCkA2RMAmCIAiCIGRDJEyCIAiCIAjZEAmTIAiCIAhCNkTCJAiCIAiCkA2RMAmCIAiCIGRDJEyCIAiCIAjZEAmTIAiCIAhCNlTye4dnz0JSUn7vNTN1dahf/+vWtba2JjQ0NMMyTU1NLCwsaNCgAWPGjMHGxiYPoiycQkJCKFu2LA0aNOD06dOKDkfILYXx5P2EXC5n586d+Pr6cunSJd6+fYtUKsXIyIgqVarQuHFjOnfuTOnSpfMg6IJh5cqVLFmyhPv376OpqYm1tTWenp4MHTqUMmXKfNU2Dh48yObNm7lw4QJv3rxBJpNhZmZGzZo16dChA+3atUNZWTl9/bTrp1wuz6vD+iZTpkxh6tSprF27ll69eik6HKEQyPeEKSkJ3r6FmJj83vP/09MDU9Ocv8/LywszMzMA3rx5w+XLl1m9ejWbNm3iyJEjNGjQIJcjFYQCpBCfvKGhobRr147r168DYG9vj5eXF+rq6rx58wZ/f3+OHj3Kr7/+ytq1a+natWtuR65wo0ePZt68eaiqquLh4YGenh6XL19mzpw52NraZps0REZG0qlTJ06cOAGAnZ0dXl5eqKqq8vTpU3bu3MmOHTuoXr06V69ezYcjEoT8le8JE3y83r56BSoK2Htq6sff35IwjRs3joYNG6b//e7dO1q3bs25c+cYOHAg9+7dy50gCzkLCwuCg4PR0tJSdChCbiuEJ++bN29wc3MjLCyMRo0asXjx4kwlwikpKezatYtp06bx5MmT3Iq4wAgNDWXBggVoaWlx/vx5HB0d01/z8/PL9lxNSEjA3d2dW7duUb16dVatWoWTk1OGdV6/fs2sWbPYuHFjXhyCICicQhIm+Hi9dXbO//0GBubetkqUKMGcOXOoXbs29+/f58mTJ5QrVy73dlBIqaqqiirKoqyQnbwDBw4kLCwMd3d3jhw5gkoWyZ6qqiqdOnWiTZs2PHjw4HsjLXAuX76MTCajQYMGGZIlAE9Pz2zfP3HiRG7duoWNjQ2nT59GR0cn0zpmZmYsXLiQDh065FrcglCQiEbf38nBwSH932/evMnwWlhYGCNHjqRSpUpoaGhQokQJPD09OXLkSJbbOnDgAP369cPe3h4DAwM0NTWpXLkyo0ePJiIiIsv3vHjxgpEjR2Jra4uOjg56enpUrFiRzp07c/z48Uzrv379mhEjRlChQoX0mBo1asSuXbuy3H7Dhg2RSCSEhIRw6NAh6tWrh66uLnp6enh5eWVZ9B4SEoJEIslQGgdw+vRpJBIJvXr1Ijo6mhEjRmBpaYm6ujrlypVj8uTJpKaVImRxnD179qRkyZJoampStWpV/vnnHwAkEgnW1tZZvk8o3u7du8e+ffsAWLx4cZbJ0qfU1dWpUqVK+t9TpkxBIpGwbt26LNfv1asXEokky7Z6iYmJ/Pnnn9SoUQNdXV20tbWpXr06ixcvRiqVZlo/KiqKyZMnU6VKFfT19dHW1qZs2bK0bt2aHTt2ZFg3Pj6euXPnUq1aNYyMjNDU1MTKygovLy+WLVuWaduqqqoAPHv27IvHn5X379+zfPlyAObNm5dlsvSpunXrfva1DRs2UKNGDbS0tDA0NKR9+/Y8evTos+tfvXqVzp07Y2FhgZqaGqVKlaJbt25fLM0PDAyke/fuWFlZoa6ujomJCbVr12bWrFkkJiZmc7QfzZ07FyUlJcqXL//F+ITiRSRM3ynmk/Ycpp9UFVy+fJmqVauycOFCZDIZzZs3x8nJCX9/f5o1a8aCBQsybatXr15s3boVXV1dPD098fT0JDExkXnz5lGzZk3Cw8MzrP/ixQucnZ1ZuHAhKSkpNGnSBC8vL4yMjNizZw9bt27NsH5wcDDOzs4sWrSI1NRU2rRpQ/Xq1Tl79izt27dnzJgxnz3O5cuX4+3tjVQqpUWLFlhaWnLs2DEaNGiQ46rI9+/fU7t2bbZs2UKtWrVo1KgRb9++Zdq0aQwcODDT+iEhIdSqVYt///0XdXV1WrduTcmSJRk+fDg//fRTjvYtFC+HDh1CLpfj7OyMnZ1dvu03IiKCOnXq8PPPP/Ps2TPq1KmDh4cHz549Y9iwYXTs2DFDI+i4uDhcXFyYNm0aUVFRNGzYEG9vbywsLDh58mR6wgIgk8nw8vJizJgxhIaG4ubmRps2bbC2tubq1av89ddfmeJp2LAh+vr63LlzJ8vXv+TkyZMkJCRgaGiIl5fXN38mEyZMoE+fPujp6eHt7Y2BgQG7du2ibt26WT4QrlixAldXV7Zt20apUqVo06YNlpaWbN68mZo1a+Lv75/pPcuWLaNmzZps3LgRXV1d2rZtS40aNXj16hUTJkzI9FD7X3K5nDFjxjBmzBicnJw4f/48FSpU+OZjFooWhVXJFRWHDh0CwMbGJr06LjY2lrZt2xIVFcWyZcsYMGAAEokEgPv376df7Dw9PbG3t0/f1ooVK/Dy8kJbWzt9WWpqKlOnTmXGjBlMmjQpw9PjqlWriIiIYOjQoSxevDhDXO/fv+fx48cZlnXr1o3Xr18zYMAAlixZkv60fe3aNRo1asTcuXPTL9T/NX/+fPz8/GjUqBHw8cIyZMgQli1bxh9//MHatWu/+jPbu3cvLVu25OrVq+ltJx48eECNGjVYu3YtkyZNylBiNHjwYF69ekXXrl1Zu3YtampqwMenz7R4BCErgf+rxqtWrVq+7rdPnz4EBgbSt29fFi5cmH5OR0dH06lTJ3bt2sWKFSvSHxB8fX159OgRLVu2ZPfu3Rl6mSUkJBAUFJT+99mzZwkICKBGjRqcPXsWTU3N9NdSUlK4cOFCpngMDAwYPnw406dP5+eff8bCwoIuXbp81bGkfYbOzs4oKX37M/aKFSu4evVqepVgcnIyPj4+7N+/nyVLljB58uT0dS9fvsyQIUMwMDBg79691KlTJ/21AwcO0LZtW7p168ajR4/SS8/Onz/P0KFDUVNTY9u2bbRv3z7D/k+fPk2JEiU+G19KSgq9e/dm06ZNeHh4sHv3bvT09L75eIWiR5QwfaO3b9+yfv16xo4di46ODqtWrUpPitauXUtYWBj9+/dn4MCB6csBKleuzLx585BKpaxcuTLDNtu1a5chWQJQUVFh+vTpmJubs3PnzgyvpZU4NW7cOFN8BgYGVK9ePf3vM2fOEBgYiImJCfPnz89QNVG9enV+/vln4GNilJXhw4dnSE4kEgnTpk0DyPHQAWmf16cNTStVqkT37t2Ry+WcOXMmffmTJ084cuQImpqaLFy4MD1ZAqhRowZDhw7N0b6F4iWt5MLExCTL19O6lH/686WS1q8RFBTE/v37sbe3Z9myZRnOaX19/fSkP61KGf7/XPbw8MiQLAFoaWlRu3btTOvWqVMnQ7IEH6ve6mcx5MKhQ4dYsGABbm5uqKmp0aNHD/bs2fNVx5PdZ/i1pk2blqH9lJqaGpMmTQIyX0Nmz56NVCrl77//zpAsAXh7ezN48GCePXvGwYMH05f//vvvyGQyJk+enClZgv8vZctKXFwc3t7ebNq0iQ4dOnD48GGRLAmZiIQpB9zd3ZFIJEgkEkqWLEmvXr1QV1fn6tWrGU7qo0ePAh8ToKykXdAuXbqU6bXQ0FCWLl3KyJEj6du3b/pFPDU1lYiICN69e5e+blpCNH78ePbv3//F+vmzZ8+mx5RVj5i0LsXnz5/Psh1R8+bNMy0zMTHB0NCQsLCwz+43K9WrV89QfZkmraH4p9tLK3b38PDA2Ng403u+9ilZELISEBDA+vXrM/z4+vp+1zaPHTsGQMuWLbNsM1WqVCkqVqzIrVu30s/ZtHN5zpw5bNmyJUNV/385OTmhpKTEmjVrWLFixWfbN6a5fv06Pj4+2NnZcfLkSXx9fZFIJHTq1ClTe8rz588jkUho06ZNTg75q2R1DcnqnJfJZPj5+aGiokKrVq2y3NZ/r6FSqZRTp04B0Lt37xzFFR4ejru7O8eOHePHH39k69atGR7MBCGNSJhywMvLi549e9K9e3caNmyIsrIyb968oXPnziQkJKSv9/TpUwCaNm2anmB9+pP2pPbfC93EiRMpX748Q4cOZeHChaxZsyb9Iv727VsgY5upnj17pjeAbNWqFfr6+ri4uDB+/PhM7YpevnwJQNmyZbM8tlKlSqGhocGHDx+IjIzM9PrnBrXT1dUlOTn5i59bTrYFkPTJ4IhpF9LPvedrB9sTiqe0JPu/7f/SrFq1CrlcjlwuJzg4OFf2mXb+z549O8vzXyKRcOfOHWQyGVFRUcDHB4KxY8fy5s0bunbtiqGhIY6OjowcOTJTx4qKFSsyf/58kpOTGThwIKamptjY2DBgwABOnjyZKZ6xY8eSmJjIggULUFdXx9vbm23btiGTyWjXrl16ogGkfwafNtzO7jP8Wlmdq1md8xEREcTFxZGamoqOjk6Wn19aT7y0a2hERAQJCQno6+tTsmTJHMU1YcIErl69Srdu3fj777+/q9pRKNpEG6Yc+O84TMHBwbi7u3Pjxg3GjRvHokWLgI9PSACtW7fGwMDgs9v7tMTE19eXmTNnYm5uzrx583Bzc8PU1BR1dXUA3NzcuHDhQoaGosrKymzcuJFx48axf/9+Tp06xfnz59MHo1uyZAmDBg3KlWPPzYuIuCAJ+cXZ2ZmNGzemD1iZ29LO9ayWubi4ZDu8Rtr5DR9LlwYNGsS+ffs4efIk/v7+LFy4kIULFzJ+/Hh+//339HWHDx9Ohw4d2LdvHydOnODs2bOsXLmSlStX0rVrVzZt2gR87Kl36tQptLS0cHV1TX9/27Zt2bRpE127dqVVq1YcO3aM2rVrp5eufFpy6/y/ISRu3LiBTCb75vP3a9+X9vmpq6vTuXPnL67r4uLyTbF8qn379uzZswdfX1+6detGs2bNvnubQtEkEqbvYGtry8qVK2nVqhXLli1j5MiRlCtXDktLS+7fv8+oUaO+evTvtGqAtN5o//Wlrq0ODg44ODgwfvx4kpKSWL16NT/++CMjR46kS5cu6OvrY2FhAfz/0+9/vX79mg8fPqCuro6RkdFXxZwfzM3Ngc93h/6WbtJC8dGsWTPGjBlDYGAgwcHB2Nra5uj9aVUzsbGxWb6e1ffP0tIyfd+fNmT+GuXKlWPkyJGMHDkSqVTKrl276NGjB7Nnz6Z79+4Z4i9VqhQDBw5k4MCByOVyTp06RceOHdm8eTPdu3enadOmxMTEpD9k/TfZ6dixI8nJyfTs2ZNmzZoxceJEjh8/zrBhw9KvF/Cx9EtLS4vIyEiOHj2a5wmFsbExGhoayGQyVq5cmd6oO7v3aGlpER0dzdu3b7Os8v+cpk2b0qtXL9q2bUvbtm3ZuXMnLVq0+J5DEIoo8aj/nVq2bIm7uzspKSnMnDkTgCZNmgCwe/fur95OWtF82sX2U35+fl9dHK6urs6QIUOwsbEhKSkpfRC+tDr/Xbt2Zag+TLN+/XrgY0PS7MaqyU9pVQOnTp3Ksqpw27Zt+R2SUIjY2tqmt4MZOnToZ8f5+py0hP3+/fuZXgsPD8+y5Crt/N+7d+93zZ+mrKxMhw4dcHd3Ry6Xc/v27c+uK5FI8PDwoGPHjgDcunUL+NjO0NTUlISEhAwNpNP88MMPrFy5kpiYGMaOHUu5cuUylGTBxw4kAwYMAD5OrxIXF/fFuAMCAnJ0nP+loqKCh4cHycnJWcacFWVlZdzd3QE+O2bWlzRt2pR9+/ahpKREu3btOHDgQI63IRR9ImHKBdOnTwc+DsoWGhrKwIEDKVWqFIsXL2bhwoWZLtJyuRx/f/8MF5a0ovslS5ZkKOZ//PjxZ6vV/v333wzdjdMEBwfz9OlTJBJJ+kSi9evXx8nJifDwcH766acMMQUGBjJnzhwARo4c+Q2fQN4pV64cTZs2JSEhgZEjR5KSkpL+2vXr1zMNpyAI/7Vs2TLMzc05deoUTZs2zbKtklwuz7I7ftrArRs2bMgwAnh0dDR9+/bNsuSpZs2aNG/ePH1YgbSHoU89ePAgQ+Py3bt3ExAQkCnBevXqVXq3/rQ2QCdPnuTo0aOZBr+MjY3l3LlzGdZVUlJi2LBhAPTt2zd9GJQ0MpkMLS2t9MEow8PDsxxXbebMmVSpUiVDM4T/Cg8PZ/To0bRu3TrTazk1adIklJWVGTRoUJYD/X748IEdO3bw4sWL9GXjxo1DSUmJKVOmsHfv3kzvOXPmDNHR0Z/dp6enJ/v370dFRYX27dunD3gqCGkKTlFCIVanTh2aNm3KkSNHmD17Nv/88w/79u3D29ubkSNHMmfOHBwcHDA2NiYiIoLAwEDCw8OZP39+eu+64cOHs379elauXMnp06epVq0aUVFRnDlzBldXV8zMzDh//nyG/e7atYuePXtiZWVF1apV0dXV5dWrV/j7+5OSksLo0aMpVaoU8PEJdPPmzXh4eLB8+XKOHTuGi4sLkZGRnDp1itTUVEaPHk3Lli3z/fPLzj///IObmxsbN27k7NmzuLm5ERkZyenTpxk8eDCLFi0SvVqEzzIzMyMgIIB27dpx4sQJ7OzssLe3p3LlymhpaREVFUVgYCCvXr1CVVU1Q/sda2tr+vbty6pVq6hWrVp6Se3ly5cxMTGhdevWWd6cN2zYQLNmzVi7di2+vr44OTlhaWlJXFwct2/f5smTJ7Ru3RofHx/g48184cKFmJmZ4ezsjKGhIREREZw7d46EhAR8fHzS2+vcvHmTUaNGYWhomN7jNDo6Gn9///RBYT/toTt+/Hju37/Pxo0badGiBba2ttjZ2SGVSrly5QovX77ExsaGzp07M2XKFJo2bcqZM2cyjBGnpaWVXuV38uRJnJ2dsbe3x8bGBhUVFUJCQrh69SpSqTRX2hW5urqyYsUKBg0aRLNmzbCxsaFy5cpoamry/PlzAgMDSUhIIDAwMP2hsG7duixcuJARI0bQpk2b9KYK0dHR3L17l9DQUJ4+ffrZoQUAGjVqxMGDB/H29sbHx4ft27fnSY9BoXBSWMKUmpq787rlZL95YcaMGRw5coS1a9cyceJEatSowa1bt1i0aBH79+8nICAAmUyGmZkZ1atXp1WrVhnmXKpQoQLXrl1j/PjxnD9/nr1792JlZcX48eMZP358liPs/vTTT5QpU4aAgAAuXrxIdHQ0ZmZmNG7cmKFDh2aqh7e1teX69evMnj2b/fv3s2vXLjQ1NalXrx5Dhw7NcuySgsDa2ppLly4xceJEDh8+zJ49eyhfvjxz586lTZs2LFq0qEC1uyryCuHJmzYK9s6dO9mxYweXL1/m8OHDyGQyDA0Nsbe3Z/jw4fzwww/pN+A0//zzD5aWlqxfv57jx49jYmJCp06dmDlz5mdLZA0NDTl37hxr1qxhy5Yt3Lx5k4sXL2JqaoqVlRW9evWiU6dO6ev36tULNTU1zp07R2BgIFFRURgbG1OrVi369++fYV1vb+/0h6m7d+9y5swZSpQogY2NDT169KB3794Z2v0oKyuzYcMGfHx8WLlyJZcvX2bv3r3o6+vj5OTElClT6NGjB2pqaiQlJTFr1iw8PT05d+4c5cuXT9+OkZERJ06c4MCBA2zevJkLFy5w6NAhZDIZpUqVon379nTu3DlXSpjg4+CfLi4uLFiwgJMnT3LkyBHU1dUxNzenTZs2tG3bNtPo7T/++CO1atVi3rx5nD17lp07d1KiRAkqVKjAkCFDMDMzy3a/DRs25NChQ7Ro0YKOHTuyZcuWAnttFPKXRP49lezfwM8P3r79OOm5oujpfZzw/CvmnBQKuM2bN9OtWzcGDx7M0qVLFR1O0SZOXkEQirF8L2FSV/94vctBJ4Y8i0MoHFJSUggODqZq1aoZll+/fp2xY8cC0L17d0WEVryIk1cQhGIs30uYBCGn3r9/T4kSJShfvjyVK1dGW1ubp0+fcv36dWQyGSNHjvzslC6CIAiCkBtEwiQUeMnJyUyaNInjx48TGhpKdHQ0enp6ODk5MWDAgAztOwRBEAQhL4iESRAEQRAEIRtiHCZBEARBEIRsiIRJEARBEAQhGyJhEgRBEARByIZImARBEARBELIhEiZBEARBEIRsiIRJEARBEAQhGyJhEgRBEARByIZImARBEARBELIhEiZBEARBEIRs5Pvku2dDz5KUmpTfu81EXUWd+lb1v2pda2trQkNDv7jO7t27adu2LT179mTdunXpy9etW0fv3r2ZPHkyU6ZM+Y6IBUGxzr5/T5JMpugwUFdSor6BQY7fJ5fL2blzJ76+vly6dIm3b98ilUoxMjKiSpUqNG7cmM6dO1O6dOncD7oAWblyJUuWLOH+/ftoampibW2Np6cnQ4cOpUyZMl+1jYMHD7J582YuXLjAmzdvkMlkmJmZUbNmTTp06EC7du1QVlZOXz/tGlrQJpaYMmUKU6dOZe3atfTq1eu7tnX69Gnc3d0zLFNTU0NPTw9zc3OqV6+Ot7c3rVq1QkUl61tvQf2cvkVRvPfle8KUlJrE2/i3xCTF5Peu0+mp62GqnfMZ1728vDAzM8vyNTU1tRxtKzdPVEHID0kyGW9TUohJTVVYDHoqKpiqqub4faGhobRr147r168DYG9vj5eXF+rq6rx58wZ/f3+OHj3Kr7/+ytq1a+natWtuh14gjB49mnnz5qGqqoqHhwd6enpcvnyZOXPmYGtrm+21KDIykk6dOnHixAkA7Ozs8PLyQlVVladPn7Jz50527NhB9erVuXr1aj4cUcGjra2Nj48PADKZjPfv33Pv3j3Wrl3L2rVrsba2Zv369dSv/3UP7EVNYb735XvCBBCTFMOr2FeoKOf/7lOlHy/235IwjRs3joYNG2b5WkJCAsHBwejr639PeIJQoMWkpvIqKQkVpfyvzU/9X+lWThOmN2/e4ObmRlhYGI0aNWLx4sXY2NhkWCclJYVdu3Yxbdo0njx5kmsxFyShoaEsWLAALS0tzp8/j6OjY/prfn5+aGlpffH9CQkJuLu7c+vWLapXr86qVatwcnLKsM7r16+ZNWsWGzduzItDKBSMjY0z1DKkefDgAePHj2fXrl14enpy7NgxGjRokGGdEydOkJKSkk+R5q22bdvi6uqKsbGxokPJNQpJmABUlFVwNnPO9/0Gvg7Mk+1qaWlluggLQlGkoqSEs45Ovu83MC7um943cOBAwsLCcHd358iRI1lWh6iqqtKpUyfatGnDgwcPvjfUAuny5cvIZDIaNGiQIVkC8PT0zPb9EydO5NatW9jY2HD69Gl0svgOmJmZsXDhQjp06JBrcRcVlSpVYufOnfTu3Zt169bRo0cPHj16hOonDwDly5dXYIS5S19fv8gVIIhG37nk9OnTSCSSrypitLa2ZurUqQD07t0biUSS/vPfJ5MHDx7Qr18/rK2tUVdXx9jYmNatW3Pp0qVM2w0JCUEikdCwYUPi4uL45ZdfqFChAurq6rRp0yYXjlIQCpd79+6xb98+ABYvXvzZtiNp1NXVqVKlSoZlU6ZMyfLcTNOrVy8kEgmnT5/O9FpiYiJ//vknNWrUQFdXF21tbapXr87ixYuRSqWZ1o+KimLy5MlUqVIFfX19tLW1KVu2LK1bt2bHjh0Z1o2Pj2fu3LlUq1YNIyMjNDU1sbKywsvLi2XLlmXadtqN+dmzZ1/8DLLy/v17li9fDsC8efOyTJY+Vbdu3c++tmHDBmrUqIGWlhaGhoa0b9+eR48efXb9q1ev0rlzZywsLFBTU6NUqVJ069aNe/fuffY9gYGBdO/eHSsrK9TV1TExMaF27drMmjWLxMTEbI72o7lz56KkpET58uW/GF9OzZ8/H21tbZ49e8bOnTszvGZtbY1EIkn/+9atW0gkEipXrvzZ7V2/fh2JRIKDg0Om144fP06rVq0wNTVFTU2NMmXKMGjQIF6+fJlp3XXr1iGRSJgyZQqPHz/mhx9+oFSpUigrK7NgwQLgY0nsqlWrqF27NiVLlkRDQ4PSpUvToEEDZsyY8dntfXp8X7r3Xbp0CYlEgrPz5wtTDh48mH6fy28KK2Eqznx8fDh+/DhBQUHUqVOHChUqpL/26b8PHDhAx44dSUxMxN7enpYtW/L69WsOHjzIoUOH2LJlS3pd+acSExNp0KABjx49okGDBjg7O2NkZJQvxyYIBcmhQ4eQy+U4OztjZ2eXr/uOiIigSZMmBAYGYmJiQp06dVBVVeXixYsMGzaMU6dO4evrm36DjIuLw8XFhUePHmFubk7Dhg3R0NDg5cuXnDx5kvj4+PSSG5lMhpeXFwEBARgaGuLm5oaOjg5hYWFcvXqVx48fM2jQoAzxNGzYEH19fe7cucNff/3FmDFjvvpYTp48SUJCAoaGhnh5eX3zZzJhwgT+/PNP6tWrh7e3N1evXmXXrl0EBARw+/btTNU3K1asYMiQIUilUqpXr06dOnUICQlh8+bN7Nu3j8OHD2dKzpYtW8aPP/6IVCrF3t6e2rVrEx0dTXBwMBMmTKBLly5YW1t/Nka5XM7YsWOZO3cuzs7OHD58mJIlS37zMf+XgYEBzZo1w9fXlxMnTtC5c+fPrlulShUcHR0JCgri8uXL1KpVK9M6GzZsAKB79+4Zlk+aNIkZM2agoqJCzZo1sbCw4N69eyxfvpw9e/Zw5syZLBOxBw8epCf49evXJz4+Pr26tkePHmzduhVtbW3q1atHiRIlePPmDXfv3iUgIICJEyd+8dizu/e5uLhQrVo1rl+//tnjTUvc//v9zg8iYVKAv/76iylTphAUFES/fv2yLJUKDQ2lS5cuyGQy9uzZQ+vWrdNfu3TpEk2bNqVPnz40bNgw00Xm8uXLVK9encePHxep+mNByKnAwI9V8NWqVcv3fffp04fAwED69u3LwoUL0dbWBiA6OppOnTqxa9cuVqxYwcCBAwHw9fXl0aNHtGzZkt27d2foZZaQkEBQUFD632fPniUgIIAaNWpw9uxZNDU1019LSUnhwoULmeIxMDBg+PDhTJ8+nZ9//hkLCwu6dOnyVceS9jk6Ozuj9B3t11asWMHVq1fTqwSTk5Px8fFh//79LFmyhMmTJ6eve/nyZYYMGYKBgQF79+6lTp066a8dOHCAtm3b0q1btwzVWufPn2fo0KGoqamxbds22rdvn2H/p0+fpkSJEp+NLyUlhd69e7Np0yY8PDzYvXs3enp633y8n+Pk5ISvry93797Ndt3u3bsTFBTEhg0bMiUQUqmULVu2oKSkxA8//JC+fNeuXcyYMYMKFSqwe/fuDKVPy5cvZ9CgQfTq1SvL78mWLVvo168fS5cuzVBdGBISwtatWylTpgzXrl3LcG+RyWScOXMm22P5mnvfkCFD6NevH8uXL890vC9evODQoUOYmprSrl27bPeX20SVXA64u7tnKEJM+0krrsxNCxcuJC4ujsmTJ2dIlgBcXFyYNGkSsbGxn21cuXjxYpEsCcVeREQEACYmJlm+ntZT59OfnJS8fE5QUBD79+/H3t6eZcuWpSdL8LFtx9q1a1FTU+Off/5JXx4eHg6Ah4dHhmQJPraRrF27dqZ169SpkyFZgo9Vb1n1wDp06BALFizAzc0NNTU1evTowZ49e77qeLL7HL/WtGnTMrSfUlNTY9KkSQCZqjRnz56NVCrl77//zpAsAXh7ezN48GCePXvGwYMH05f//vvvyGQyJk+enClZgv8vZctKXFwc3t7ebNq0iQ4dOnD48OE8SZaA9GtzVFRUtut269YNZWVltm7dmqlB+LFjx3jz5g3u7u5YWFikL0+rHvv3338zVdUNHDiQli1bcvHiRW7cuJFpf0ZGRsyfPz9DsgT//51zdnbOdG9RUlLKNKTCt+rSpQsGBgZs3bqV6OjoDK+tWrUKqVRK7969c9wzPTeIhCkHvLy86NmzZ6afvCjqP3r0KMBns+i0C2JWbZlKliyJq6trrsckCEVNQEAA69evz/Dj6+v73ds9duwYAC1btsyy3VSpUqWoWLEit27dSm9TU716dQDmzJnDli1biIn5/NArTk5OKCkpsWbNGlasWJGe0HzO9evX8fHxwc7OjpMnT6ZXBXbq1IkjR45kWPf8+fNIJJI8affYvHnzTMvSOsuEhYWlL5PJZPj5+aGiokKrVq2y3NZ/r4FSqZRTp04BH9vH5ER4eDju7u4cO3aMH3/8ka1bt+bpDTltnKVP2yt9jpmZGY0bNyYiIiLT/1VadVyPHj3Sl719+5bAwEBKlSqVIcn+1JfuH40bN86yjVrlypXR1tbm4MGDzJkzJ8t2ULlBS0uLXr16kZCQkH588PH/d/Xq1UgkkvRS2fwmEqYcGDduHOvWrcv006RJk1zf19OnT4GPF5OsSrVq1qwJkOWF0srKKtfjEYTCKO1JOO3p+L9WrVqFXC5HLpcTHByca/tNO39nz56d5fkrkUi4c+cOMpksvZTBw8ODsWPH8ubNG7p27YqhoSGOjo6MHDky05hGFStWZP78+SQnJzNw4EBMTU2xsbFhwIABnDx5MlM8Y8eOJTExkQULFqCuro63tzfbtm1DJpPRrl279EQDSP8cPm0blN3n+LWyGhhTV1cXgKSk/x/QOCIigri4OFJTU9HR0cny80trz5V2DYyIiCAhIQF9ff0ctzmaMGECV69epVu3bvz999/fVe34NdJiNjQ0/Kr109onfZpAxMbGsmfPHrS1tTOUpoWEhADw6tWrz373xo4dmyGOT33u/qGnp8eaNWvQ0tLil19+oXTp0pQtW5YePXqwd+9eZLk4qO3gwYORSCTp7ZXgY2PvFy9e0KRJE8qWLZtr+8oJ0YapgEr78nXr1u2LPXuyGsrgv0X0glBcOTs7s3HjxvQBK/NCVjeKtGUuLi7ZDjeirq6e/u85c+YwaNAg9u3bx8mTJ/H392fhwoUsXLiQ8ePH8/vvv6evO3z4cDp06MC+ffs4ceIEZ8+eZeXKlaxcuZKuXbuyadMm4GMnkFOnTqGlpZWh5Llt27Zs2rSJrl270qpVK44dO0bt2rXTS1c+bd+U1mvpxo0byGSyb04ovvZ9aZ+furr6FxtFw8fP+Hu1b9+ePXv24OvrS7du3WjWrNl3b/NL0tqEZdWzLStt27ZFR0eH/fv3Ex0djb6+Pjt37iQxMZEffvghQ5Vv2mdnZGSEt7f3F7drb2+fadmX7h8dO3akcePGHDhwAD8/P86ePcuGDRvYsGEDDRo0wM/PL1NV3reoVKkSjRo14vjx45w/fx43NzeFNvZOIxKmAsrS0pJHjx4xc+ZMUWIkCN+oWbNmjBkzhsDAQIKDg7G1tc3xNtKqZmJjY7N8Patu+paWlun7/7Qh89coV64cI0eOZOTIkUilUnbt2kWPHj2YPXs23bt3z3AMpUqVYuDAgQwcOBC5XM6pU6fo2LEjmzdvpnv37jRt2pSYmJj0KqD/JjsdO3YkOTmZnj170qxZMyZOnMjx48cZNmxYhjYxHh4eaGlpERkZydGjR/M8oTA2NkZDQwOZTMbKlSu/6iZsbGyMlpYW0dHRvH37FlPTrx+cuGnTpvTq1Yu2bdvStm1bdu7cSYsWLb7nED7r/fv36VVrjRo1+qr3aGlp0b59e9avX8+OHTvo169fltVx8P/fPV1d3c8OhfE9DA0N6dGjR/p+04Z9OHPmDKtXr861hGbIkCEcP36cZcuWUbp0aY4cOYK5uXm2SWBeElVyCpJ2EU79zDQTadV8u3fvzreYBKGosbW1TW8DM3To0M+eb19ibm4OwP379zO9Fh4enmXpVdr5u3fv3u+aF0xZWZkOHTrg7u6OXC7n9u3bn11XIpHg4eFBx44dgY9j+MDHhtqmpqYkJCRkaCCd5ocffmDlypXExMQwduxYypUrl6EkCz72sBswYADwcXqVuGwGEQ0ICMjRcf6XiooKHh4eJCcnZxlzVpSVldMbHn9LotC0aVP27duHkpIS7dq148CBAznextcYNWoUCQkJlC1blrZt2371+9Kq5TZu3MjLly85ffo0FhYWmZIuCwsL7OzsCAkJybJRd26rUaNG+ncj7Tv3Jdnd+9K0atWK0qVLs2PHDubMmYNMJqNfv37ZjqWWl0TCpCBpF+HPtZsYM2YMOjo6TJw4kc2bN2e66KampnL48OEvXkAFQfg4Lo+5uTmnTp2iadOmWZ5zcrk8yy7W8LFnlUQiYcOGDRlGAY+OjqZv375ZljzVrFmT5s2bpw8rkFVvqAcPHmRoYL57924CAgIyneuvXr1Kr8JJawN08uRJjh49mmnwy9jYWM6dO5dhXSUlJYYNGwZA3759OXToUIb3yGQytLS00hv6hoeHZzko5MyZM6lSpQrBwcG4u7tneTMODw9n9OjRmXr2fotJkyahrKzMoEGDMjV2Bvjw4QM7duzgxYsX6cvGjRuHkpISU6ZMYe/evZnec+bMmUw9rz7l6enJ/v37UVFRoX379umDnuaGhw8f4uPjw7p161BTU+Pff//N0c3f3d2d0qVLc/bs2fTegF27ds2ymjNtsMhOnTpl2bA7NjaW1atXf/UgnvCxGtHX1zdDWzP4OBSDn58fkHUbtf/K7t6XRllZmQEDBvDhwweWLFmCsrIy/fv3/+p484KoklMQLy8vNDU1WbBgAbdv36Z06dJIJBL69OmDm5sbZcuWZfv27XTs2JFu3boxceJE7Ozs0NPT4/Xr11y/fp3o6OhMY2wIgpCRmZkZAQEBtGvXjhMnTmBnZ4e9vT2VK1dGS0uLqKgoAgMDefXqFaqqqpnGJrK2tqZv376sWrWKatWqpfcwunz5MiYmJrRu3TrLm/OGDRto1qwZa9euxdfXFycnJywtLYmLi+P27ds8efKE1q1bpw8+e+bMGRYuXIiZmRnOzs4YGhoSERHBuXPnSEhIwMfHJ729zs2bNxk1ahSGhoZUr14dU1NToqOj8ff35/3799SuXTtDD9vx48dz//59Nm7cSIsWLbC1tcXOzg6pVMqVK1d4+fIlNjY2dO7cmSlTptC0aVPOnDmToY2LlpZWepXfyZMncXZ2xt7eHhsbG1RUVAgJCeHq1atIpdJcaVfk6urKihUrGDRoEM2aNcPGxobKlSujqanJ8+fPCQwMJCEhgcDAQEqXLg18bKi+cOFCRowYQZs2bXBwcMDBwYHo6Gju3r1LaGgoT58+/eKUHY0aNeLgwYN4e3vj4+PD9u3bc9RjMCIiIn18IZlMRnR0NPfu3UtPtsuVK8f69eu/OBp6VpSUlOjWrRt//PEHS5cuBTJXx6Xp0KED06ZNY/Lkybi6uuLo6EiFChVQVlZOL3lKTk6mffv2X93mNTQ0lA4dOqCjo0ONGjUwNzcnPj6eS5cu8fr1aypUqJBe0vQl2d37PtW/f3+mT59OSkoKLVq0SP9/VhSFJUyp0tQ8m9ctu/0WBKVKlWL//v1MmzaNCxcuEBcXh1wup27duulfmmbNmnH79m3mz5/P0aNHOXnyJEpKSpQqVQp3d3fatGlD48aNFXwkQnGTKpN987xu37vfb2Vtbc3Vq1fZuXMnO3bs4PLlyxw+fBiZTIahoSH29vYMHz6cH374IcuL8j///IOlpSXr16/n+PHjmJiY0KlTJ2bOnMnIkSOz3KehoSHnzp1jzZo1bNmyhZs3b3Lx4kVMTU2xsrKiV69edOrUKX39Xr16oaamxrlz5wgMDCQqKgpjY2Nq1apF//79M6zr7e1NVFQUZ86c4e7du5w5c4YSJUpgY2NDjx496N27d4Z2P8rKymzYsAEfHx9WrlzJ5cuX2bt3L/r6+jg5OTFlyhR69OiBmpoaSUlJzJo1C09PT86dO5dhfjMjIyNOnDjBgQMH2Lx5MxcuXODQoUPIZDJKlSpF+/bt6dy5c66UMMHHwT9dXFxYsGABJ0+e5MiRI6irq2Nubk6bNm1o27ZtpmFdfvzxR2rVqsW8efM4e/YsO3fupESJElSoUIEhQ4ZgZmaW7X4bNmzIoUOHaNGiBR07dmTLli1ZjuuUlfj4eNavXw98HBNLX18fCwsLevfujbe3N61atfrmaqXu3bvzxx9/AB8b4n/pYXnSpEk0btyYv//+m3PnzhEcHIy2tjYWFhZ0796ddu3a5WiuN1dXV37//XdOnTrF/fv3uXjxItra2lhZWTFy5EgGDRr0Vdv7mntfGjMzM+zt7blx44ZCG3unkci/p4L9G/g99uNt/Ftikj4/xkhe01PXw1TbFM/y2U84KQjCR35RUbxNSSHmG9oB5RY9FRVMVVXx/Mru2IIgFF7379/HxsYGa2trHj9+nOfDPWQn30uY1FXUMdU2xVT763sw5FUcgiB8PXUlJUxVVTHNhW7D3xuHIAhF3/Tp0wEYNmyYwpMlUEAJkyAIgiAIQlbOnz/P6tWruX//PgEBAVhbW3Pnzp30CYAVSTT6FgRBEAShQHjw4AFr1qxBW1ubxo0bs2jRogKRLIEoYRIEQRAEQciW4isFBUEQBEEQCjiRMAmCIAiCIGRDJEyCIAiCIAjZEAmTIAiCIAhCNkTCJAiCIAiCkA2RMAmCIAiCIGRDJEyCIAiCIAjZEAmTIAiCIAhCNkTCJAiCIAiCkA2RMAmCIAiCIGRDJEyCIAiCIAjZEAmTIAiCIAhCNkTCJAiCIAiCkA2RMAmCIAiCIGRDJEyCIAiCIAjZEAmTIAiCIAhCNkTCJAiCIAiCkA2RMAmCIAiCIGRDJEyCIAiCIAjZEAmTIAiCIAhCNkTCJAiCIAiCkA2RMAmCIAiCIGRDJEyCIAiCIAjZEAmTIAiCIAhCNkTCJAiCIAiCkA2RMAmCIAiCIGRDJEyCIAiCIAjZEAmTIAiCIAhCNkTCJAiCIAiCkA2RMAlFysaNGxk4cCA1atRAXV0diUTCunXrslz3xo0bTJgwAS8vL0xMTJBIJDRs2PCz205ISGDu3Ll07doVGxsblJSUkEgkhISE5MmxCML3ysn5sGnTJtq2bUv58uXR1dVFR0cHe3t7Ro0axcuXLz+7j02bNlGrVi20tbUpUaIE3t7eXL9+PY+OSBAUR0XRAQhCbpo4cSKhoaEYGxtTqlQpQkNDP7vunj17mDVrFmpqalSqVImIiIgvbvvt27eMGTMGACsrK0qUKEFUVFSuxi8IuSkn58PWrVt5+PAhrq6ulCpVCrlczo0bN1i4cCHr1q3D398fe3v7DO+ZOXMmEydOxMrKikGDBhEbG8vWrVtxc3PjxIkT1KlTJ68PURDyj1wQihA/Pz95SEiIXC6Xy2fNmiUH5GvXrs1y3du3b8uvXbsmT05Olr969UoOyBs0aPDZbcfGxsqPHTsmj4yMlMvlcrmXl5cckD99+jSXj0IQckdOzofExMQsl69atUoOyH18fDIsf/DggVxFRUVeqVIl+fv379OXBwYGytXV1eW2trZyqVSaOwciCAWAqJITipTGjRtjZWX1Veva29tTrVo1VFVVv2p9HR0dPD09MTQ0/J4QBSHf5OR80NDQyHJ5hw4dAHj06FGG5WvXriU1NZVff/0VfX399OVOTk506dKF4OBg/P39vzFyQSh4RMIkCIIgfNbBgwcBcHBwyLD89OnTADRp0iTTe7y8vAA4c+ZM3gYnCPlItGESBEEQ0m3fvp27d++SkJDAnTt3OHr0KGXLlmXatGkZ1nv48CE6OjqYmZll2kbFihXT1xGEokIkTIIgCEK67du3s3PnzvS/a9SowdatWylbtmyG9aKjozE1Nc1yG3p6eunrCEJRIarkBEEQhHS+vr7I5XLevXvHyZMnUVVVpXr16pw8eVLRoQmCQomESRAEQcjEwMAAd3d3jhw5gqamJj169CAlJSX9dX19/c+WIMXExKSvIwhFhUiYBEEQhM/S09PD1dWVly9fZugpV7FiReLi4nj9+nWm96S1XUpryyQIRYFImARBEIQvCgsLA8gwBEeDBg0AOHbsWKb1jx49mmEdQSgKRMIkCIJQzMXGxnL//v0sX1uzZg2XL1+mYsWKVKhQIX157969UVFRYebMmRmq5m7cuMGWLVuwtbWlbt26eR67IOQX0UtOKFJWrVqVPljerVu30peljRlTt25d+vXrB8C9e/eYPXs2AImJienLevXqlb69/867NWbMmPQpVNK2P2bMGHR0dADo16+fuEkIBcbXng+RkZHY2tpSo0YNbGxssLCw4N27d1y5coXr16+jp6fH+vXrM2y7UqVKTJkyhYkTJ+Lo6Ej79u3Tp0YBWLlyJUpK4plcKEIUPdS4IOSmnj17yoHP/vTs2TN93VOnTn1x3axODysrqy+u/7lpJwRBEb72fIiLi5P/9ttv8vr168vNzMzkqqqqcm1tbbm9vb181KhR8ufPn392Hxs3bpTXqFFDrqmpKdfX15c3b95cfu3atXw6QkHIPxK5XC7Pl8xMEARBEAShkBLlpYIgCIIgCNkQCZMgCIIgCEI2RMIkCIIgCIKQDZEwCYIgCIIgZEMkTIIgCIIgCNkQCZMgCIIgCEI2RMIkCIIgCIKQDZEwCYIgCIIgZEMkTIIgCIIgCNkQc8kJxZJUKuXVq1c8e/aM58+fExkZSXR0NDExMbx//5730R9/YqJjSE1NBSBtUPxPB8dXU1NDT18PPV099PX00dPTQ1dXFz09PfT19TE3N8fc3BwLCwsMDQ2RSCQKOV6h6EpJSeHVq1e8fPmSsLAw3r59S0xMDLGxscTGxhITHU1sTAwx0dHExcUhk8nS35v2fUz7rampib6Bwf//6Ounf5fNzMwoU6YMZcqUQV9fX3yXhWJHTI0iFFkymYwnT55w8+ZNbt68yYOHDwgNDeX58+eEvQxDKpWipa2FqbkpegZ6aOpooqWr9fG3jhbautpo6miiovKf54pP7hOpyakkxCWQGJ+Y/vMh4QOJcYkkxCYQ+SaSyPBIZDIZ6urqlCxVEvNS5pQuXZry5cpja2uLnZ0dNjY26Orq5u8HJBQKcrmcFy9eEBwcTHBwMPeCg3keGkpYWBgvw8J4+7/JoLU0NLAwMcFUXx99LS10NTTQ09REV0MDXU1N9LS00NHQQPmTCXHl/9lPYnIy0fHxRCckfPxJTEz//Toqihfh4UilUnR1dChTuvTHBMraGjt7e6pWrYqjoyMlSpTI509IEPKHSJiEIkEmk3H79m0CAgK4ceMGgUGB3Ll9h4T4BMzLmGNVyQozKzNMzU0xKWWCifnHH21d7Tx/UpZKpbyPeE/U2ygi30YS9TaKqDdRvHn+hpdPX/Ls8TOSk5KxKG2Bja0NDnYO2NnZpSdTRkZGeRqfUDBIpVKePn1KcHAwd+/eJfjuXe7evs29Bw+IjYvD1NAQW0tLbEqVwsrEBAsjI8wNDdN/62lp5ct3OSwqimfh4TwLDyc0PJyQt2+58/IlN588ISY+HksLi4/Jk7Mzzs7O1K9fH1NT0zyNSxDyg0iYhEIpLUE6ffo0J0+d5OyZs8TGxlLRviJWNlZYV7bG2sYa60rWaOloKTrcL5JKpbx9+Zbnj57z/MlzXjx+QdiTMJ49fkZ8XDxGxkbY2tniWsuVunXrUqdOHYyNjRUdtvAdZDIZd+/eJSAggIBz5wi6cYP7jx6RlJSEZcmS2FlaYmtu/vG3pSW2pUtjpKen6LC/SC6X8yw8nKCnT7kZEkJQaChXHj0i9PVr7G1saOjhQUN3dxo0aICJiYmiwxWEHBMJk1BoxMfHc/jwYXb47sDPz4/YmFgqV62MbQ1bqtSqgm01WzS0NBQdZq6Ry+VEvonk+ePnPHv4jIe3HhJ8NZjwN+FUqFSB+nXrU7duXerWrUuFChVEm5ICLDExkatXr+Lv70/AuXMEnD9PbFwcThUqUKdSJaqXL4+dpSU2pUujo6mp6HBzVcibN5y+fZvTt29z6s4dnr1+jYOtLS3btMHHxwdnZ2fx3RUKBZEwCQVaTEwMBw4cYIfvDo4cOYKuni4uni7UaFAD22q2aGoXrZtLduRyOW9fviU4MJjga8Hcv3Gfp/efYmRsRMOGDfFs7Enjxo0pV66cokMt1pKTk7l06RLHjx/nxLFjXL52DXVVVWrb2lKnUiXq2tnhUqlSkUuOvkbImzccDwpi9+XL+F2/joW5Oe19fPDp0IFatWqhpCQ6bwsFk0iYhAJHJpPh5+fHP8v+4fChwxiaGuLq6YpbEzcqOVYSF9T/iIuJ417gPW5eusntC7d5FPyIMtZl8GzkiaenJx4eHqIKJI/JZDJu3br1MUHy8+PsuXMgl9OwalUaOTjQ0MGBKtbWqCgrKzrUAuV9XBwHrlxh56VLHLl2DWMjI3r07En/AQOwtrZWdHiCkIFImIQCIzw8nDVr1rBs+TLCI8Jxb+2Oe2t3KjiI6qaciI6K5ubFm9y8eJNbF24R9jwMh6oOtG7Zmk6dOuHg4CA+z1zw4cMHjhw5wo5t2zh27BjvY2KobWtLIwcHGjs6UqtSJVT/28NS+Ky4xEQOXLnCyuPHOX3zJs28vBg0ZAjNmjVDWSSaQgEgEiZB4S5fvsy8+fPYtXMXZW3K0qRTE+o3r1+k2iMp0uvnr7lx/gaXj18m8Hwg5SuWp0unLnTq1AlbW1tFh1eoJCUl4efnx7YtW9i7bx+aqqr4uLnhXaMG9ezsimUVW164/+IFy48eZd2pU+jo6jJg0CCGDBmCoaGhokMTijGRMAkKc/HiRSZPmczJkydxb+VO085NqViloqLDKtJi3sVwwe8C54+cJ+hiEDZ2NunJU8WK4rPPSkpKCidOnGDb1q3s3r0bFYmE9rVr06lOHRo4OIjSjzyUmJTEjoAAFh48yMNXrxg+fDijfvpJDLUhKIRImIR8d/78eSZPmcyZM2do3K4xPgN8MDEXbWzy2/vI95w/dp7zR85z6/ItqjhWoUunLnTs2LHYNxpPTU3l9OnTbNu6lV27diGXSmnr4kKnunVxr1JFVLXlM7lczqGrV5myfTv3Xrxg2LBh/DR6tBheQ8hXImES8s3t27cZOWokZ8+exbO9J+37txeJUgER+SaSC8c+ljzduX4H5+rO6clTmTJlFB1evpBKpZw7d45tW7ey09eXpA8faOPqSqc6dWjs6IiaqqqiQyz25HI5h69dY+r27dx9/pyffvqJX8aNQ0urYI+1JhQNImES8lx0dDSTJ09m6dKlNGzVkM4/dsaklEiUCqrwV+EEHAngwtEL3Au6R02XmunJk7m5uaLDy1VyuZyAgAC2bd2K744dxMXF0apWLTrVqYNXtWqoiySpQEpLnMasX0+CTMaCRYto3bq16Mwg5CmRMAl5Ri6Xs2nTJn4a/RMGpgb0n9gfGycbRYcl5MCbF2/wP+LPhaMXeHz3Ma3btGbE8BHUq1evUN+c4uLi+Pfff1m8aBGhoaF416pFJzc3mlWvjqa6uqLDE75SckoKiw4cYOq2bdStW5dFixeLtnhCnhEJk5An7t+/T99+fbl16xbdRnWjSYcmonFsIff0/lMObTzEmf1nqFipIsOHDadr166Fqjrk4cOHLFm8mLVr11JSX58fmzWjV6NG6BWiYxAyexkZydj169l5/jy//PwzEydNQk1NTdFhCUWMSJiEXCWXy1m1ahUjRo7AzcuN3j/3Rq9EwZ4DS8iZ2Pex+O304+jWo3yI+0Dfvn0ZOnRogR1oUCaTcfToUf5euJCjfn40rVGDYc2a0cTZWQyCWsScvnWLfkuXYmBqyqYtW6hcubKiQxKKEJEwCbkmIiKCvv36cvrMaQZPGUydpnUUHZKQh6RSKVdOX+HwpsPcvHST5i2aM2L4CDw8PApEdV10dDTr1q1jyd9/8+b1a/o0bszQZs2oUMTaYQkZxSYkMHL1arb6+7Ng4UL69etXIL6PQuEnEiYhV/j5+dG9R3fMrM0YMXsExmaiu29x8uzRMw5tOsSpvaewsrJi+LDhdO/eHR0dnXyP5e7duyz++2/+/fdfypiYMKxZM7q7u4tBJYsZ34AABvzzDw0aNmTl6tViCALhu4mESfgucrmcGTNmMH3GdLoO70qb3m1EW6ViLD42nuO7jnN0y1Fi3sXQu1dvfvzxRypUqJCn+5VKpRw4cIC/Fy7k1JkztHRxYVjz5nhUrSpKF4qxFxER9Fi4kIfh4ew/eBAnJydFhyQUYiJhEr7Zhw8f6NO3D4ePHGbc3+Owq26n6JCEAkImk3H93HUObTrEdf/rNPFqwojhI/Dy8srVBObdu3esWrWKpYsXE/3+Pf0aN2ZIs2ZYlyyZa/sQCjepVMov//7LsqNH2bR5M61bt1Z0SEIhJRIm4Zu8ffuW1m1a8zriNROWTsDM0kzRIQkF1MunLzm85TAndp+gYoWKzJg+g+bNm39X4vT+/Xvmz5vH/PnzKVuyJMOaNaNrgwZoiSEBhM9YdewYQ5cvZ8bMmYwZM0aUPAo5JhImIcdu375NC+8WmJQxYcy8MWjrais6JKEQiIuOY++6vez/dz/29vbMnDGTxo0b5+jGFRsby6JFi/jrzz+xNjFhWufOeNesKW5+wlc5dfMm7efMoU27dixbvlwMPSDkiEiYhBy5ePEiXk29qN+yPn3H9UVZRbRXEnIm5l0Mu9fs5uDGg1SvXp2ZM2bSoEGDL74nISGBJUuWMGf2bErq6TG1c2faurqKYQGEHHsYFob3zJmUt7Nj5+7daIrOAMJXEgmT8NXOnTtHs+bNaN27NZ2GdBJP9cJ3eRfxjl2rdnFk6xHc3NyYOWMmtWvXzrDOhw8fWL58ObNmzkRfQ4MpHTvSsW5d0bFA+C5v37+n8ZQpmJQpw74DB9DWFqXkQvZEwiR8lbNnz9K0WVM6DO6AT38fRYejcBf8LnBoyyGe3HnCh8QPlDApQWXHyvQa20vMk5dDkW8i8V3hy7Edx/Dw8GDG9BlUrVqV1atXM3P6dNQlEn7r0IFuDRuiIhKlHNt46hTn7t7l2uPH3AoJITk1lbUjRtCrUSNFh6ZQkTExeE2bhk7Jkhw8fFgkTUK2RMIkZCsgIIAmXk3oOLgj7fq1U3Q4CiWXy1k6eSlHtx/FrIwZ1epWQ1Nbk6i3Udy+cpvRf44WvQW/UXhYOFuXbuXE7hNoqqqhp6XF1C5d6NWoEaoqKooOr9Cy7teP0LdvMdbTQ1tDg9C3b0XC9D/v4uJoPHkyBhYWHDh0SFTPCV8krkLCFwUFBdG0WVPa9W9X7JMlgP0b9nN0+1Gad21O/1/7Z6oakqZKFRRZ4SaVSrl95Ta3L95EX1sLF0N9VELDiAi8zitbW8qUKaPoEAutVT/+SEVzc6xMTZnt68v4f/9VdEgFRgkdHY5NnkyjyZPxadeOvfv3oyKSc+EzxDdD+KxXr17RvEVzGrVvRMdBHRUdjsIlfUhi65KtmFma0X9C5mQJEI3gc0gmkxFwJICtizYR9y6Gno1r07ZeNTTUVIl4E8G5Q+cYPnw4devWpUuXLlhYWCg65EKnsRis8YuM9PQ4+ttvuI4bx6iRI/l78WJFhyQUUCJhErIUHx9PC+8WlLEpQ6+xvRQdToEQGBBIXHQcjdo2QiaTcenYJcJCwtDW08axtiPmVmKOspy4dekWq2YsJ+p1BN0bueLToAaa6v/fzdu4pDFte7eljlcdzh08x9ChQ2nUqBE9e/ZET09M6CzknpIlSnDg119x++UXKlaqxPDhwxUdklAAiYRJyEQmk9G1W1dik2IZN2ec6JH0P4/vPAZASVmJ4a2G8zLkZfprSkpKtOrZij6/9FFUeIVG5JtI1s5ezQW/i3Rt7EKPQZ3Q0fz8gJOm5qa079+eV89fcdz3OAMHDqRHjx40adJEfDeFXGNfpgw7fv6ZlmPGUK5cOby9vRUdklDAiEFMhEx++eUXLly6wK9Lf0VTWzSCTBMdGQ3A3nV70dLVYu6OuWy7to1ZG2dhbm3OnrV7OLTlkIKjLLhSU1LZvXo3Q5oOIuX5GzZPHMCQVu5fTJY+VcqyFD+M+oHGHRuzcfNGfhr9E/fu3cvjqIXipImzM4v696dzp07cuHFD0eEIBYxImIQMdu3axeIli5mwdAJGJY0UHU6BIpPJAFBRVWHC4glUrFIRTW1N7GvY88uCX1BSUmLP2j2KDbKACroYxIhWwzi8djdTe7Ri/uCOlDE1zPF2JEioUrMKgyYPwrSsKePGj2PhwoVER0fnQdRCcTSwaVP6e3ri064dsbGxig5HKEBEwiSke/HiBX379aXPL30ob1de0eEUOGlTwFRwqJApmbSqZEVJy5K8fvaauJg4RYRXIEW8jmDOiNlM6z8VT9tybJs4kAaOlb970FN1DXU823vSb3w/Hj97zMBBAzl48CBSqeilKHy/OT17Yqimxo9Dhig6FKEAEQmTAHzs1t21W1dsa9ji1clL0eEUSBZlP/bQ0tHVyfL1tIQq+UNyvsVUUKUkp7Bz5U6GNB2MLCycrRMHMNC7ARpqqrm6H1NzU7qN6kaTjk3YvHUzo0aNIjg4OFf3IRQ/qioqbB41ip07d7J582ZFhyMUEKLRtwDArFmzuPfgHvN3zxdTnnxGFZcqADx/8jzTa6kpqbx69goNLQ30DfXzO7QCJTAgkJXTliNN+MCMXq2pV7VSnu5PggSHmg5UrFIR/0P+jJ8wnvr169O7dx9KGBjk6b6FoquCuTlLBgxg0MCBuLq6Uq5cOUWHJCiYKGESuHjxItOmT2PE7BHolRDdtT+nVJlSONdx5lXoK47tOJbhNd+VvsTHxOPa2LXYjsUUHhbO7GG/M3PwDJo6VGDrr/3zPFn6lLqGOo3aNaL/hP6EhIUwaNBA9u/fL6rphG/Ww8MD7+rV6dq5M6mpqYoOR1AwMTVKMZeSkoKjkyO2tW3FeEtf4dWzV/zc5WeiI6Op0aAGpcuV5knwE25evImpuSl/bvuTEiYlFB1mvkpJTmH3mt34LttOjcrW/NTeEwtjxX4GcuTcvXaXEztPYKBvwOBBg7G3t1doTIqy6tgx/O/eBeBWaCjXHz+mjq0tFUqVAqCunR39mjRRZIgFWnR8PA4jRvDTuHGMGjVK0eEICiQSpmLur7/+4q8Ff/H3/r/R0NJQdDiFQvircDYv2sx1/+vEvo/FwNiAWu616Dy0MwZGBooOL19dP3edFdOWQVIyo9t7UrdKRUWHlEFSUjL+h85x6eQl6tatS98+fSlRongltL0WLGD9yZOffb2nhwfrRo7Mv4AKoZ3nz9N78WLu3b+PubkYoLa4EglTMfbixQsq21Rm5B8jcW3squhwhEIkLiaOVTNW4H8kgF5ebvzQuDbqqgW3SWTEmwiObT/G62evGTxoMPXr1xdt9YSvJpfLaTZtGiXKl2fLtm2KDkdQEJEwFWMdOnbgWfgzfv3nV3HzEL7ajfM3WDRuASV1tZnygzdlCsl4XXLkBJ0Pws/Xj2rVqjF0yFAxxYrw1R6GhVFl+HAOHT6Mh4eHosMRFEA0+s7Gxo0bGThwIDVq1EBdXR2JRMK6des+u/7Dhw/p3bs3FStWRFNTEwsLCzw9Pdm3b1+W6ycmJjJv3jyqVatGiRIlMDAwwNHRkZkzZ+bpYHzHjx9n//799J3QVyRLwldJSkxixfTlTB8wDR9XR1aM7F5okiX42JvOyc2J/r/251X4K4b+OJQrV64oOiyhkKhobs7PbdsydPBgkpPzZuiQnN5vPvXkyRN0dHSQSCQMGjQo0+sJCQnMnTuXrl27YmNjg5KSEhKJhJCQkNw9iCJMlDBlw9ramtDQUIyNjdHW1iY0NJS1a9fSq1evTOteunQJd3d3UlJSaNWqFRUrVuTt27fs2rWL6OhopkyZwuTJk9PXT0lJoV69ely6dAknJycaNmwIwKlTpwgKCsLe3p7Lly+jpaWVq8ckk8moUrUKju6OdPmxS65uWyiaHtx8wPwxc1GXyZjavSWVLM0UHdJ3kcllXDl5hVP7TuHe0J2+/fqhpSmmARK+LDEpCbvhw/lp/HiGDRuW69vPyf3mUzKZjIYNG3L9+nXi4+MZOHAgy5Yty7BOSEgIZcuWBcDKyorY2FiioqJ4+vQp1tbWuX4sRZEoYcrGqlWrCAkJITw8PMus/VNTp04lMTERX19fdu7cyezZs1mzZg1BQUHo6uryxx9/kJSUlL7+7t27uXTpEm3btiUwMJD58+czf/58bty4QevWrblz5w6+vr65fky+vr68DHtJ616tc33bQtGSmpLKpoUbGdf1FxpWtmbd2N6FPlkCUJIo4dLIhb7j+nL7/m2GDfuRO3fuKDosoYDTVFfntw4dmDVzJomJibm+/Zzcbz41f/58Lly4wIwZMz67jrGxMceOHSMyMpKQkBBq1qyZGyEXKyJhykbjxo2xsrL6qnWfPHmCRCKhWbNmGZZbWVlRpUoVEhMTiYuLy7A+kGl9gBYtWgAQHh7+raFnSSaTMXnKZFr3bo2WTu6WXAlFy7OHzxjbYTRnfY+zeHg3hrdtVKAbdn8Lk1Im9BrbC5saNkycNJE1a9bkWXWLUDR0d3dHS0WFlStX5vq2c3K/SXPv3j0mTpzI+PHjcXJy+ux6Ojo6eHp6YmiY8zkchY9EwpSLHBwckMvlHD58OMPyZ8+ecevWLRwdHTEyMsqwPpBpfYCDBw8ikUhwd3fP1Rh37NjB6zevadGtRa5uVyg6pFIpu9fsZlS7kVQxKcGm8X1xrlBG0WHlGWVlZRp4N6Dn6J74X/Bn5KiRPH78WNFhCQWUirIyk3x88qyUKSekUik9e/akYsWKTJw4UaGxFAdF63FRwWbMmEFAQAA+Pj60atWKSpUqpbdhKl++PNv+0x21RYsWtGnTht27d+Ps7JyhDdPTp09ZsWIF1apVy7X4pFLpx9KlPq3R1BbtNYTM3rx4w8Jf5hP2+Dl/9PehjkMFRYeUb8ytzOk7vi+n9p5izNgxdO7UGR8fH5SVi+fI7cLndWvYkBk7d7J8+XJGKnAMq1mzZnH9+nUuXryImpqawuIoLkTClItsbGy4ePEiHTp0YNeuXenLjYyM6N27N+XLl8+wvkQiYefOnUyYMIE5c+Zw48aN9Nd69uyJp6dnrsbn6+vLm7dvaN6lea5uVyj85HI5fjv9WD1zJW72FZg3oT/6xbDKVlVNlSYdmlCxakUObDjApcuXGf3TT1hYWCg6NKEASStl+vn33xk0aBAaGvk/6G9QUBDTpk1j7NixVK9ePd/3XxyJKrlcdPnyZWrXrk2JEiW4du0a8fHxPH78mB49ejBixAi6dMnYIy0hIYG2bduybt06tmzZQkREBBEREWzdupUjR45Qq1atXO3yuWDhApp3ay5Kl4QM3oW/Y8bAaayftZoJXZrze5+2xTJZ+lTZymXp/2t/tI20GD5iOAcOHEAmkyk6LKEA6dqgAWpKSuzYsSPf952cnEzPnj2pUKFChp7XQt4SCVMuSUlJoXPnzigpKbF7926qVauGlpYW5cqVY968ebRp04YdO3YQEBCQ/p7ff/+dffv2sWLFCjp16oSRkRFGRkZ06tSJ5cuX8/btW2bOnJkr8d24cYOrV67i6ZO7pVZC4Xb+6HmGeQ9FKTKazb/2p0mN4jnfWlY0NDVo2aMlbXq3YdOWTfz222+53glDKLxUlJUZ0LgxSxcvzvd9z5o1i1u3brF27VrU1dXzff/FlUiYcsm9e/d4+vQpLi4uWY6blNZ4OzAwMH1ZWmPvrBp2Z7X+91i6dCmujV0xKkQDDQp5JyU5hWXTlrHg53kMblaPhUM6YWogRr3OSmXHygyYOIAkSRLDhw/PtXNSKPz6NWnC1evX8/07ERgYiEwmw9XVFYlEkv6Tdt9Yvnw5EomENm3a5GtcRZ1ow5RL0roif+4JNG35p08Dn75HV1c32/W/VXR0NJs2b2L8kvHfvS2h8It4HcGcYbOIC49izdhelDc3VXRIBZ62rjY+A324fOIy06ZPo3OnznTo0AElJfHMWZyZlShB29q1+WfpUlbkwTADn+Pp6YmxsXGm5a9eveLQoUPY2NhQp04dnJ2d8y2m4kAkTLnEwcEBPT09AgICOHbsGE2aNEl/7fnz5+kZf4MGDdKX16lTh9u3bzN16lTWrl2bfvGVSqXp9dK5MazAhg0bMC5pTFWXqt+9LaFwu3nxJn+NmkNVK3OWjO2Njmb+N1YtrCRIcGnkglkZM3av3s39B/f5adRP6OjoKDo0QYGGNG1Kixkz+POvv9DX18+XfQ4dOjTL5adPn+bQoUM0aNAg00jfwvcTU6NkY9WqVfj7+wNw69Ytrl+/Tp06dahQ4WN367p169KvXz8AVqxYwcCBA1FSUsLb2xsbGxtev37Nrl27iIuLY/To0fz111/p23727BkuLi68fv0ae3v79AkdT5w4wd27d6lYsSKXLl2iRIkS33UM9lXscWvlRqserb5rO0LhJZfL2b1mN5vmb6S/d316eNYWcwh+h5j3sexevZukuCR+nTAhfcoJofiRy+XYDx/O0LFjP5vIfK2c3G+ycvr0adzd3bOcGgVgzJgxREREAODn50dYWBjt27dPT/r79etH3bp1v+sYijKRMGWjV69erF+//rOv9+zZM8PkiH5+fixcuJBLly7x7t07dHR0cHR0ZMCAAXTr1i3T+8PCwpg1axZHjhzh2bNnSCQSrK2tadWqFePHj//uZCk4OBgHBwfWnl1LCePv25ZQOCXEJbBw3ALuXrrFzN5tqGkjbu65QSqVcmLXCW4E3GDo0KG5PsisUHjM3L6dYyEhnDl37ru2k9P7zX9llzClzVX3OV8zb11xJhKmIiwhIYHx48dz4MQBmg9sTtS7KBLiE4hPSCA+PoH4+Pj//R1PclIScj4+Lf2XRAIa6ppoaWmhqamJlrYmWppaaGlpoqmlib6+/scefoZGGBoZUqJECVRVVPP/gIVMnj9+zuyhM9FVUmJ2n7aUNMyfKoPi5PaV2xzcdBDPxp707dsXVVXx3Vc0uVxOTEICLyMjCYuKSv/9Li6OmMTEjD8JCcQmJpL06ZQ4/yt9TSuFVVZSQldTE30tLQy0tdHX0qKElhZ6WlqU0NFBWUmJ0WvWcP36dapWrSoGOy2iRMJUyEmlUh49esTNmzcJCgri9u27hISE8vz5M6KiIkAC6qU1MalUGm1tQ9TVtVBX0/74W+N/v9W1UFP9pC3LJ1U1EokEuUxGcsoHkpISSE5OJDkp8eO/UxJJTkogITGa2NhIYqIjSU7+OLmwrp4+hoaGGBsbU8ayNKUtS2NZ2pLSlqXR0RZtPvKD/2F/Fo1fSHOXKoxq1xi1IjYPXEHyNuwtO1fuxMjAiPHjxmeYAknIGzKZjGfh4QS/eMHdZ8+4++IFD1+/5mVkJK+jokj48AEAQwMDjIyMMDQ0RFtXFw1NTTTTHv4++beamtrH693/bomf3hqlUimJiYkkJKQ9aMaTkJBAYnw8cXFxREZEEPriBXKZDBUVFUpZWFCmTBkqlC+PY9WqODo6UrVq1SwbaguFh0iYChG5XM69e/c4c+YMV69e48aNm9y9e5sPHxIpXboCVlZVKV3agZIlrTE2LoNMJmXKFC9GLFyHjkH+VMclJSUQGxtJbFwUsTGRRMeEExX1gsjI54SHvyAlORkDgxJYlLbEysqSMpYfk6gylmXQ19cX7WpyQWpKKuv/WseRLYf5pXNTWrg6KjqkYuFD4gf2b9hP2JMwxv0yjipVqig6pCIhJTWVx69fE/z8OXefP+fO8+fcffGCBy9ekJiUhLGhIWUsLTG3tMTM3BxjY2MMDQ0xMjLCoESJfCvx23L2LEFhYQzo3Zvwly8Jf/GCV0+e8Cw4mKe3bxPz/j1m5uZUqVKFak5O1K5dm3r16onJcAsRkTAVYHK5nPv373Pq1ClOnjzN6dOniYqKoGJFZ8qXd8Ha2hFr66pYWTmgqZm51Gb79t/xv7qNHybMUED0mcnkMmKiw4mIfEFExHMiIp4TFfWc8LfPSUyMR1tbmzJW1jjY22FrZ4uNjY0ojcqhd+Hv+HPkH0Q+e82cfu2oZGmm6JCKFTlyLhy7wJkDZ+jevQdt27QRDwE5IJfLeRgWhv/du5y7e5cLDx7w5NUrUqVSSpmaYlmmDOaWlpS2tMSyTBlKly5dYHopPo+I4McVK1i/fj0G/+ktJ5fLiXz1iqe3bxNy9y6ht28TfPkyEa9f41C1Kh4NG+Lh4UG9evW+u92qkHdEwlTApKSkcOrUKXx9d7J37z4iIt5SsWI17Owa4uDQEDu7umhrf107lJGjnKlQuya1mrTM46i/jxw58fHviQh/zqvXj3n5MpgXL4KJi4umdOky2NvbYW9vh62tLaYmpuIG9Bl3r99lzvDZ2JQyYWrPVuhpiSlwFOXp/afsWb2HqlWqMmLkSLQ0xf9FVpJTUrj++DH+wcGcCw4mIDiYqJgYyllbY2NnRyUbG6ysrLCwsEBdAfO15dTQFSto1bEjXp8MK/M5crmcV0+ecOv8ee4EBHA7IIDIN29wrl6djj4+tG/fPr13nFAwiISpAEhKSsLPz48dO3zZt28fqakyatVqhatrOxwcGqKjY5DjbcbGRvHDD8YMmr0Uo1KFb+JQOXLeRb3i2fO7vHgRzIsXd4kIf4mhoRGOTo44OTni6OiIYQlRnC2Xyzmw4QDr/lxLL6869GlaFyUlkVQqWvS7aHat3IU8Rc6vE37F0tJS0SEpnFQqJfDJE44HBXEsKIjzwcFIJBIqV6pEZTs7bO3sqGxjU2BKjXJqlZ8fUSoq/PzLLzl+r1wuJ+zJE676+XHp4EHuXLqEnYMDHX188PHxwc7OLg8iFnJCJEwKFBQUxD//LGPz5s0oKani6toWV9f2VK3qgaqq2ndt+8KF3SxdPpAf568qMiUy8QnRPHt2h5CQIEJDgoiICMPCwhJnZ0ccnRxxcHBAW0tb0WHmqw8JH1g88W8Cz1xlWq/WuNmLJ9KCJDU1FT9fP25fvs2I4SOK3Rg3aVVsJ/6XIJ2+dYv4Dx+wt7PDwcmJqo6OlC9fvsj0LLzy8CF/HznC+n///e7rbtTr11w8dIhLBw8S5O9PZVtbBg8cSPfu3TEwMMidgIUcEQlTPktMTGT79u0sXbqM69ev4uraBk/P/lSt6oGycu71Ylq+fBjP39+h1cBRubbNgiY6JpyQkJuEhNwg5GkQ8fExVKxYmbr13KhTpw7GRkW7R0rU2yimD5iKcuIH/ujbDnMxzlaBFXQxiCNbj9C+XXu6dOlSZB5isiKTyQgIDmabvz97Ll0iLDKSSuXLf0yQnJyws7UtFNVr3yI+KYmuc+fy999/UyYXSxSjIyI4u2sXx/79lzfPntGlc2cGDx5MzZo1c20fQvZEwpRPXr58yV9/zWXdunWoq+vQuPEAPD37YmhYKk/2N/RHOxw9G+PUwDNPtl/QyJETEf6cR4+ucu+ePy/DHlG5si0NGtSjjludIteQ8vnj50ztO5lKJQ2Z2bstGmpF4wm9KHvx9AU7/tmBq4srQ4cORUWl6AzzIJfLuXj/Ptv8/dkeEMC7uDhqubjg6uaGo5NTprkyi7LR69bh0aIFLVq0yPVty+Vy7ly4wLH16/Hfvx8HBwdGjRxJ165di9T3qaASCVMee/78ObNmzWb16lXY2dWlZctRVKvWLE8HNouODqd7d1OGzl1BCZPi2Usq6t0r7t715/49f16/CcHO1p76DepRu3ZtDPQNFB3ed7lz9Q4zB02nSXU7Rvs0QUVZTABbWES9jWLrkq2UKV2G8ePGo1mIG4PL5XKuPnrENn9/tvn7Ex4dTc2aNXGrV48aNWuiUURLkbKz7sQJXgHjx+ftZOfvw8M5sWULB1asQE9Hh0m//soPP/xQZKo3CyKRMOWR58+f8/vvs1izZjUODg3o2HEydnZ18mXf/v47WLlmOEPnrciX/RV0EZEv0pOnt+HPqeJQlfr16+Fa2xU9XT1Fh5cj/kf8WfDzfPo1ryfmgyuk4mPj2f7PdtSU1JgyeUqhKv2Uy+XcePKErf9LksIiI6lRowZu9epRs1YttLS0FB2iwl17/Jh5+/ezYdMmlPLh/ExKTOTov/+yZ/FidLS0mPTrr3Tv3l0kTnlAJEy57P3790yePIVly/6halV3OnacjI1N7XyNYe3asTx8fYU2g0fn634Lg/DwZ9wNPkdwsD9RUa+oWtWJBvXr4eLqUqDHfJLL5exdt5cN8/5lYrcWNK0lBkUszJKTktmzdg9RYVFMnTq1QPegk8vl3A4NZZu/P1v9/Ql9+5bqzs7UrlcPF1dXtLWLV0eL7MQnJdHlzz9ZvmIFpczyr4Q/+cMHjm3YwK6//0ZbQ4O5f/5Ju3btxENVLhIJUy6RyWRs2LCBMWN+xsjIij59FmBr66aQWCZOaoRxZUvcvNsrZP+FgRw5b9+EcPeuP8H3zhEdHY6TUzXq169L7dq10VAvONUJUqmUNbNXc3Lnceb0b0+NymLy3KJAJpNxdPtR7l27x8SJE7G3t1d0SBk8DAtj85kzbPb35/HLlzg7OVG73sdqbZ1i1CbpW/RbupQ+AwfiVjt/H5bhY+J0aO1atv35J64uLixZvJjKlSvnexxFkUiYcsGNGzcYPHgowcH36d59No0b90FJSXHtSn7obkKL/kMpX7W6wmIoTOTIef36CXfvniP47lmSUxLx9PTEu0ULSpYsqdDYkj4kMX/sXB5dC2b+4E5UsDBVaDxC7pIj5/yx85w7eI6fRv2k8GEHZDIZh69dY+GBA5wICsKxShVq16uHm5sbevpi4uavNdPXF2snJ7p17aqwGKJev2b9tGkE7N3LyJEjmTRpUqEd36qgEAnTd0hMTGTcuPH8889SPD370a3bDHR1FTuQ4rt3r+nZsxQjF63Pt/njihKZTMqjx9e4evUAT58GUb1aDVq28sbJ0Snfi7Zj3sUwc9B0kiPfM39wJ0qWKFztrYSvd/PyTQ5tPETPnj1p3bp1vu//fVwca0+c4O+DBwmPjaVxkyY0a9GCUqXyphdvUbfp7FlCPnzg14kTFR0Kdy5cYOX48XyIjmbFsmV50nuvuBD9EL/RjRs36Ny5K8nJyvzxxwUqVCgYpTlPnwaho2cokqVvpKSkTKWKtahUsRYRkS+4euUAM2fOwtjYiJbe3nh4eORLz6bXL14ztc9kzLQ0WDyqOzqaBaeKUMh9VWtVRVdfl40rNvL27Vv69u2bL6XUd5494+8DB9hw+jQlS5akmY8PDd3di20Pt9xS1tSUU2fOKDoMAOxr12bu8eMcWLWK9j4+9O3Th7/++qtQ99BUFFHClEMymYwFCxYwfvwEmjYdRI8es1FTKzgXl5075+B/bTtdfp6s6FCKjKSkBIJunuDatQMkxL+nUaNGeHt7Y25unif7e3jrIdMHTMW1kjUTu7VAVSXvhqAQCpY3L9+wbck27GztGP3TaNTV1XN9H1KplP1XrrDwwAHO3b6Ni4sLzVq2pGrVqqKBcC559e4dA5csYfOWLegUoEbxIXfvMn/QINSAbVu24OjoqOiQChWRMOVAWFgYPXr0IjDwJsOGraN69aaKDimTufO6kaSZQOMufRQdSpEjk8t48iSQa9cO8OjRdRwdnWnVyptq1aqhJMmd0oCrZ64yZ8QfdGpYg8EtG4obWDEU8z6GrUu2UULXgEkTJ6GnlztVsZExMaz282Px4cPEJCZ+rHbz9sbUVLSLy20yuZxOf/7J1OnTsbO1VXQ4GSR/+MD66dM5un49s37/nZEjRyq0zW1hIhKmr3Tp0iVatmxN2bI1+PHHNRgYFMyLzNhf3Chb05EajZsrOpQiLSoqjGvXDnEjyA8DAwO8vZvTuHHj75rL7uj2o6yYtpyffDxpV79gVPEKivEh8QO+K3xJik1i6tRpmH1H54Ogp09ZdOAAm86cobSFBc1atqRBw4Z5Unol/L8fV63Cp1s3GjZooOhQsnTtxAn+Hj6c+nXqsHHDBjE8xFcQCdNX2Lp1K7169aZt25/p3Hlygc7Ge/expHH33lR0FnMM5Yfk5ERu3jrF9WsHeR/9Fg8PD1q1aklpi9JfvQ25XM7mRZvYu2YPM3q3oV7VSnkYsVBYpKamcnDjQULvhzJl8hQqVPj6iZWlUim7L15kwf79XLx3Dzc3N5q3bImdvb0otcwnU7dvx87VlQ4+PooO5bMiwsL4vXt3dFRUOLBvHxYWFooOqUATCdMXyOVypk6dxuzZs/nxx9U0aKC4LqJfQypNpX17DfpNn4+ppbWiwylW5MgJCbnJ1asHePjwCnXr1qdrl87ZtnOSSqX889sSLvtdYN6gjthbiwuW8P/kyDm99zRXTl9h0sRJ2bY5kclk+J4/z29btvAmOpomzZrRtHlzjI2L9kTUBdE/hw8jMzZm6NChig7liz7ExzN/yBCe3rjBwf37qVatmqJDKrBEL7nP+PDhA7169ebYsZNMn34y30fr/haRkS+RyaToGxfM6sKiTIKEstaOlLV25M3bEM6d28yQoUNwb+hO5y6dKWmauUpFKpWyaNxC7gQEsvKnnpQxVeyQFELBI0GCe2t3tHS0mDZ9GhN/nYizs3Om9eRyOXsvXWLS5s08i4igTfv2tGjZUvSEUiATfX1uv3mj6DCypaGtzS9r17Lx99+pW68eGzdsoF27dooOq0ASCVMW4uPj8fZuRUjIG/788zKmplaKDumrhIc/Q0NLB3VNMZ+TIpU0tcan/QRevX7MuXObGTRoEI0be9KpU0eMjT4+6aempLLg53k8uHKb5SN/wNxYDAMhfJ5LIxeUVJSYPmM648eNp2bNj1Xucrmcw9euMXHzZh6EhdG6bVsmtmkj5nQrAEz09Ai/f1/RYXwVJSUlekyciEX58nTp2pV/li6lTx/Rcei/RML0HzExMTRr1oKIiERmzDiDnp6RokP6am/fhmJgrNiRqYX/V8qsPB07TOLly/ucPbeJAf0H4NW0KW3btGH1tFWE3nzAshE/UMrIQNGhCoVAzQY1UVZWZtasWYwZM4YELS0mbt7MrdBQvFu1YuyMGWLKkgLE1MCAtxERyOXyQtNurFGXLugaGjK4b18+fPjAkCFDFB1SgSISpk/ExsbSpElTYmLkTJlyHB0dA0WHlCNRUWHolig8CV5xYWFRmS6dp/Hs2R3OnP6XIyv6oy/R4J9RIllKk5SSyj97TxH8LIwX4e+IiU9ER0uD0sYlaFXHiWa1qqCiLMajqla3Go8j39NyzhzilJXxbtWKYb/9JqYtKYCMdHVJSkoiMTGxUJX41fLyYsK//zK6Vy/kcnmBb4OVn0TC9D/x8fE0a9aC6GgZU6YcQ0ur8E1DkZAQjXohOjGLm1JmFVB5rY6Rij7u5hpsXb+WmrVq4Va7NpoaxbutSWJSMrvOXcPOypw6DhUw0NEiNuEDF+48ZsaGA/hdvcuCoV1QUiocT+p54dbTFyzbf4agR8+xr1EVCapUq1lTJEsFlPb/RkuPT0goVAkTQDUPD8avX8/oHj1QVVVlwIABig6pQBAJE5CSkkKbNu0ID09gypTjhTJZAoiPj0ZdNPIskJKTPrBj7jRSIsP5rVNv9LS0eP3qMXfunOHalavUcnHB1dUFDfWCM2p8ftLT0uTE3LGZRjVPlcoYtmgTl4KfcP7OI+pWqaigCBUn+Nkrlh04w5Xgp7g0cOWXQT0xMDTg+ePn7D+wH6lUirOTk6LDFP5DU00NJBIS4uOhEPZSdHZ355c1axjWuzf6+vp06tRJ0SEpXLFPmORyOT/+OIy7dx/xxx8XC1013Kfi4qNQ1ylcTzLFQXLSB7b9OQWi3zOhfTd0/9cov1SpCpiVKk/Yywfcvn2GK1cuU7t2bWrWqoW6qppig85nSkoSlJQyV7mpKCvR0Kky1x+G8iL8nQIiU5yHL96w/OBZzt9+SI06NfllThcMTf6/yt2yvCVKyhIOHz6MXC6nWha95wTFUZJI0FRXJz4+XtGhfLManp4MW7iQnr16UaZMGWrXLvi9xfNSsU+YFixYwObNW5kz5yL6+iaKDue7xMdHo2cq2jAVJClJSWyfOw2i3zO+XRe0/1P1JkGChUVlzM0r8uLFfQKvn+HixUs08vDA0dkRJQruIKn5QSaTc+HuYwDKmxfu8/NrRUbHsWjPSfyu3sbZpRpjZ4/HuGTWx25hXRo3LzeOHDmCRCIRJU0FjLaGBvEJCYoO47s0aN+eV0+e0LJ1a65cukTZsmUVHZLCFOuEad++ffzyyzh+++0QpUvbKDqc7xaf8B4TzTKKDiNH/v6pH9ERb7N8rYyNAz0m/J7PEeWelOQkts+bhjQqggntu2VKlj4lkShhaWlLaYvKPA29yfETJ7h+/TrNmjfDvFTeTPJbEKWkSll3xB85EB2fyNV7Twl5E4l3bUdq2hTtC3WqVMaOM1dYceAsluWtGDXjZ8wszLJ9n7mVObWbuHL48GGUlJRwrFo1H6LNG8nJyWxYv56HDx/y+tUrYmNj0dbWxqxUKZp4edHQ3R0VlcJz29LS0PhYJVfIdRozhrDHj2nu7c3F8+fRL6bt5grPNy+XBQYG0qVLVwYOXIqjYyNFh5MrEgppGyZ1LW1qNWmZabmBSeEdIiE1ORnf+TNIiQhnQvuu6Hxlo26JkhLlyjpR2qIyt++cZe3atTg6OeHh7o5WMRhfKyVVyqpD59L/lkigW2NXhrT2UGBUeS/wYSh/bDvK+8Qk2vfrQtWajjnqim5hXRrXxq4cPHgQiURC1SpV8jDavPMhMZHDhw5RsVIlavyvQXtcXBzXr15l0YIFnDt7lslTpxbo6ak+pa2uXuhLmAAkEgk/LljAZB8f2vv4cOTw4UKVuOaW4nfEfBw+oH37DjRpMpAmTfoqOpxc8yEpHtVC2GhYQ0ubBu0K9rQzOZGakoLvgpl8ePuaCe26prdZygk1NU2qOXtRtqwjgdePsGTJUjw8PHB2dkJJUjhuFt9CS0ONS0snIpPJCY+Oxf/WQ/7Ze4pbT18wf0gXdDSL1oSx4e9jWbj7BKcCg6nftCGNWjf55klxS5crjUsjF/Yf2I+SRIKDg0MuR5v3dHR12bJ9O6qqqhmWS6VSfvv1VwKvX+fa1avUrFVLQRHmjIaqKkkfPig6jFyhpqHB+PXrGePpybRp05g2bZqiQ8p3RffK+wWDBw9FTc2QHj1mKzqUXFdYBkgrqlJTUti58HcSXr9kfLuu6Gl93wzgJQzMcPfoiaNjE06dOsPq1Wt5GfYyl6ItuJSUJJQsoUf7+tUZ3605Nx+/YN0Rf0WHlWtSpVI2Hb9Ih6n/8DwxhZ9m/kzzji2/OVlKY1neEhcPF/bu38ed4OBcijb/KCkpZUqWAJSVlXF1cwPg1atX+R3WNytq12M9IyNG/vMPs//4g3PnzmX/hiKm2JUwbdq0id27dzN//g1UVDKfmIVaIZ1HWZqSQtC5E8S+i0RdUwvzchWxKF9Z0WHlmDQ1hd1/zyb25TMmtu+G/ncmS2kkSLC2roq5RSXu3D7D+vXrqVqlKh6NPIpFNZ2LbTkArj8MVXAkuePagxD+2HaU2OQUOg7ohkONqrl6Yy1ToQxyuZw9e3ajJJFga1P422fKZDKuX7sGgJVV4Ziqqqiyc3Ghw6hRdOnWjVtBQZQoUXymdSpWCdOTJ08YNGgwAwcupVSp8ooOJ08UxieauOh37F+5MMMy83IVaTN4DIYlSykoqpyRyaTsXvIn7589/Zgsaevk+j7UVDVwdvaibFknAgOPsGTJEtzd3alWrVqRrqYLfx8HgLJy4T7Gt+9jWLjrBGdu3KdBc3c8Wnqipp43w0dYVbRCLpeze/dulH18qFSxcI1flZKSwo7t20EuJyY2lps3bvDixQsaeXriWJh6AkokyAvpg+yXdBg1iltnz9Kvf398d+wolPedb1FsEqbU1FS6dOlGjRotadjwB0WHI/yPY71GWFa2w7S0FWrqmkS+fsmlI3u5FXCKTX9MYsDMRQV+MmG5XM7R9ct4+/Aekzv1wEAnb+fzMjAoSUP3HjwLvc3p08e5HhhI86bNKF26dJ7uNy89eRWOuZEBGmoZS30/JKewcKcfAHXsKygitO+Wkipl66nLrD50DuvK5fjp918wMcv7IRKsK1kjk0rZuXMn3X/4oVB9P1JTU9m6eXP63xKJhLbt2tGjVy/FBSWkU1ZWZuTSpYx0d2fNmjX07Vt02gJ/SbFJmJYuXcrTp89ZtOhIkc2GC+OTTP22XTL8bWZVjtYDRwFwK+AUgaeP4dqsjQIi+3oB+7YTfOEsv3XsiaFO/owSL0GClVUVzM0rcefOWdb/+y9VqjjQqFEjtHOpKjA/nbh2l80nL+FY3hJzQwO0NdV5+z6GC3ceEx2fiFMFSzp7uCg6zBy7cu8pc7YfJT5VSqdB3bGv5pCv159ytuVJjE9ky9at9O7dG2OjwjFOm6amJvsOHkQmkxEVFcXlS5fYsH499+7dY/LUqYVuqpGiyKR0aQb88Qc/jR6Nt7c3JUsW3l7NX6twl3F/pVevXjFx4iT69FmAtnYRHz+iiCSD1dy9AHjxsGA3XL1x9jgB+7YzunVHLIzyf/oDVVV1nJw8adKkP69fv2fJ0qVcuXoFmVyW77F8j7pVKuJZ3Z43UTEcu3qHTccvcuHOYypYmDKua3OWjOieqfSpIHvzLobxq3Yyauk2KrtWY8zs8ThUr6KQhzW7GvZYlLNg06ZNxMbF5fv+v4eSkhLGxsY0b9GCocOGEXz3Ltu3blV0WF+taFyNP69+27aUd3RkzNixig4lXxSLEqbRo8dSsaILbm7tFR1KnpJIJMhlhetG+Tlauh9LapKTCm6X3EdBVzm8dik/Nm9LJXNLhcair29Cg4bdef7sLmfOHuP27Tu0btMKQwNDhcb1tWytzLG1KvwDdMrlcg5duslf249hXbk8o2eNw7ikYucRkyChWr1qXDh6ni2bN9OzZ8/v7o2nCM7VqgFw+9YtBUfy9eRyeZGt0YCP95z+s2czomFDBvTvT7169RQdUp4q8iVMp0+fxtd3B/37Ly7SX1wADU3dAp1g5MTLxw8AMDAumMW8YU8esvPv2XRv4EmNCgWjR58ECWXK2NPUaxAqKvqsWL6Ca9evI6fwVdUWRlGx8fy8wpc/d/jRumcHev/UX+HJUholJSVcPF2RKcnYvmMHqVKpokPKsajISACUC9GAiYkpKWgUwsGEc6J0xYq0HjSIQYMHk5KSouhw8lSRTphSUlIYPHgobduOxcKikqLDyXOamrokJxaeUWUjwl6QkpSU5fKT29YDYF+7fn6Hla2oN2Fs+2sKzZxr0cixuqLDyURNTRMXl7bUrNmaEydPsXnzFmLiYhUdVpF2Jug+nWes4HWKjNG//0L1OjUK3AOaiooKdZrWJep9FPv27UNWANs8Pnv2LMuBHpM+fGD1qlUAVK9RI7/D+maJSUloFfGECaDjTz8RGR3N33//rehQ8lThSdW/werVq4mKiqFDhwmKDiVfaGnpkZSYqOgwvtqdi2e5dGQvZSrbo29sipq6OpGvw3gUdA2ZNBW3lj5Y2RSs0Yrjot+x5Y/fqG5dHp/aDRQdzhdZWtpiYmLJ1asHWb5sGc2aN8fBzl7RYRUpcYkfmLvDj+PX79K8UyvcGtUp0NN2qGuqU9+7Pid2neDEyZN4NipY00L5nzvH3t27sbO3x9TUFC0tLSIjI7l27RqxMTHY2dvTuk0bRYf51RKSktAsBgmThrY2vadNY+qoUfTp0wcDAwNFh5QnimzClJSUxPTpM/HxmYi6evHoUaGlqUfSh8KTMFnbVSUy7AWvQ5/w/MFdUpKT0NLRo4Jjdao3ak75Ks6KDjGD5A+JbP9rKpa6+vT2aFbgShCyoqGhQ526HXn65AYH9h/gfvA9mrdojuZXzm0nfN7V+yFM3bAfLQN9Rk0fi0kpU0WH9FW0dbWp16Iep/aeQk9XF5cCNM1IzVq1iIqM5N69e9wLDubDhw9oaWtjbW1Nvfr18WzSBGVlZUWH+dUSk5KKTY++2t7e+C5YwLx584rstClFNmFavXo1MpkyjRr1VnQo+UZbS593ia8VHcZXs7JxKHAlSJ8jTU1l56JZqCcnMax9N1QK0UVbgoRy5ZwxNS3LlSv7WPbPMlq2akmF8oVzXCNF+5CcwtJ9p9h59hqebbxo2KJRobqJA5QwLkGdpnU4cfAEOrq62NvaKjokACpWrEjFQjbI5pckfPhQLEqY4GMD8E5jx7Jg6FBGjRpVJEcAL7hlx9/hw4cPzJjxO+3b/4qqat6MpFsQaWnpk1xEJnosSORyOQdXLSL25XPGtOqIRiH9TunoGNCw4Q+Ur+jK9u3bOXjoIEkpyYoOq1C5GxJG99mrOXs/lGG/jaJRq8JV4vGpkhYlqelek7179hASWjSmnSlIUqRSpFIpmsWkhAmglpcXpcqVY+7cuYoOJU8UyYRp1apVgCqNGvVSdCj56mOj78JTJVdYnNqxgadBV/m5TafvnkxX0SQSJWwq18bTsz8hT8NYsWwFz148U3RYBV6qVMqKA2foN3c95ZyrMHzqGCysC8/I2Z9jVdEKh1oObN++jTdv3yo6nCIl4X8dWopDo+80aaVMCxctIioqStHh5LoilzAlJyczc+Ys2refWPQm182Gvr4pCTHRig6jSLl6/CBXj+1jTKuOlCwkYxp9DX19Ezwa98a8tAMbN2zk+IkTpEpTFR1WgfT0VQS9/1zH3it3GPDLELy7tEa1EA2imZ3KTpWxrlyWTZs3ER0To+hwioz38fFIlJTQ1c3bqZIKmhqenlhUrMj8+fMVHUquK3IJ0+7du0lOluLu3l3RoeQ7IyMLYt5FKjqMIuPelfMc37yaES3aU86s8A+q+F/KSipUcWiIu3tP7ty5z6pVq3nz5o2iwyowZDI5W05eosfsVRiWtWLkjJ8pb1P0Ju2WIKGqW1WMzIzYtGkTiaKUOldExsZioKdXaKtsv5VEIqH1kCEsW7GC5OSiVeVf5BKmJUv+wdOzf7Fqu5TmY8IUUSjnlCtont2/w55/5tK3UXOqWhe9m+SnjIwsaNKkPwYlrFm9ZjX+Af6FbmqV3PY6Kpohizay5tgFfhjWG5++ndHQ1FB0WHlGSaJELY9aqGgos3X7NlJSRWnj94qMicHIsOiUSueEa/PmyCUSdu3apehQclWRSpju3LnD+fP+NGkyQNGhKISRkQXS1BQSYkWx+veIeh3G9nnTaetSl7p2VRUdTr5QVlalmrMX9ep14dLlq2zesoWEQjQIam66ePcxP8xahUxPn9GzxmHnVDzGrlJWVsataR3iEuLYs3ePGB/+O0XGxmJUSCY7zm0qqqo0/uEHFi9ZouhQclWRSpj++WcZtWp5Y2Ki2Hm9FEVf3xRlZRViRbXcN0v+kIjvghnUKFeJljXdFB1OvitZsiyejfuTkCBn5cpVhL0KU3RI+UYmk7P60DlGL9uOR9um/DCsN9q6OooOK1+pqalRt3ldQkJCOH/+vKLDKdQiY2MxMi4YU+MoglePHly8cIHbt28rOpRcU2QSpri4ONavX4+X12BFh6IwSkpK6JcoKRKmbySXyzmwciGaMhm9PbwKxcCUeUFDQ5sG9bpiYWHPuvXrCQwMVHRIeS4mIZHRy7az7dx1Bv4ylHpNGhTb/39tHW1qe7py+sxpnjx9quhwCq2o+HgMi3HCZGxujouXF0uXLlV0KLmmyCRM27dvR0/PBCcnT0WHolBGhuYiYfpGFw/v4dmdIEa2aIdaMeth+V8SJSWqVm2Eq0tbjvr5sf/AgSLbi+7Bizf0mL2GN8lSRkwfQ9nK5RQdksKZWpSkqktVdu7cyfv37xUdTqFUnKvk0nj16sWGjRuLTEeCIpMwbdu2g7p1uxboeZzyg4lJGaIjwhUdRqHz9G4Qp3ds4MdmbTDWM1B0OAVG6dI2eDbuS8jTF6xdu473Me8VHVKuOnTpJn3/XEfF6lUZMG4oegb6ig6pwKjkWAnT0qZs37FdNALPITnwNjoa42JcwgRQtX59VDU0OHbsmKJDyRVFIruIiori5Mnj1Knjo+hQFK6MpT2Rr14qOoxCJToinD2L59DBrQH2ZcoqOpwCR1fXiEaN+qCmbsjKFat4/OSxokP6bimpUv7YepjZW4/QcUBXWnVri7JK8er+nR0JEmo2rElSajIHDx4UjcBzIDo+nrj4eCwti2d72jTKysq4tmjBtu3bFR1KrigSCdO+ffswM7PG2rp49Gj6EktLOyLDRML0tVKTk9m5cCZ25pY0r+6q6HAKLBVVNVxd22JrW49t27Zx1v8cMgrn0ANv3sUwYP6/BDx4xrApP+HkUrAmeS5IVFRVcPNy4/6D+1y9elXR4RQaLyIj0dbWpoSBgaJDUTi3li3Zv38/Sf8b+bwwKxIJ0/btvtSu3aHYNtL8lKWlHZGvXyBNTVF0KAWeXC7n8PqlEB/HAE9v8f3JhgQJlSq50KDBD1y+dJVtW3eQmFS42iZcvR9C99mrUTE0ZNjU0ZhZmCk6pAJPV18Xl0YuHPM7xrPnzxUdTqHwLDwcy9KlxTUFcHBzQ0VNDT8/P0WH8t0KfcL0/v17jh8/hpubqI4DsLCohFwuJ+r1K0WHUuBdP3WE+5fPM8rbBw214jfQ6bcyMSmDp2c/omMSWbWycIwOLpfL2XDsPCOWbKFecw+6D+9TpAeizG3mVubYVbPD19eXmNhYRYdT4D2PiMCyTBlFh1EgKKuo4NK8eZGoliv0CdOhQ4cwMSlNuXKiWB1AVVWdkqXKEh4mJlT9khcP7+G3cSVDmrbGrETxHI33e2hq6tKwQXdKmlZizdq13Lx9U9EhfVZcYhK/rNzJ+hOX6DtmIA1bNBJP/t/AtrotJUwM2OnrS6pUquhwCrTnkZGUEQlTurRqudRC3nmg0CdMx4+foGrVJuIC+IkylvZEvBRF558T9/4dOxf9jncNN5zLVVR0OIWWkpIyTs5NqFmzJYcOHOLwkcOkygrWBfHJq3B6zVlDaEwCI6aPpaJdJUWHVGgpSZSo2agWMfExRabXU155Fh4uSpg+YV+7NomJiYV+TLdCnzCdPHmKKlXcFR1GgWJVxkE0/P4MaWoqu/6eRTkjE9q61lN0OEVCmTL2NGrclwcPnrJ+/QZiCsjUPMev3aX3nLWUcbBh0K/DMTA0UHRIhZ6amhpuXnUICgrixs2CW6qoSLGJibyPjS32PeQ+paahgW3Nmpw6dUrRoXyXQp0whYaGEhr6FAeHhooOpUCxtLQjIuyFosMokI5vXk1ixFsGe7VGSZRK5hp9fRMaN+6LkpIOq1et5vWb1wqLRS7/OMXJ1A37adPTh7Y9O6CiqqKweIoafUN9annU4tChg4S9Em0l/+t5RAQaGhoYF/NBK//Lvk4dTpw8qegwvkuhTphOnTqFtbUdJUqUVHQoBcrHhOkZMtHOIIObAacIOuvHKG8ftDVEg9/cpqqqjlvt9pS2rML69f/y5OmTfI8hVSpj5uaDbD59hQG/DKFG3Vr5HkNxYFnekooOFdmxYzsJRWQU59zyPCJC9JDLQpU6dfD39yclpfD24C7UCdPJk6ewtxfVcf9VunRlZDIp796Kp780r0Mec3jNEvo3boGlsamiwymyJBIlHB0b4+DQkK1btxJ0Myjf9p3wIZnRy7Zz4cFzhk4aiXVFMQhpXqriUgUtfW127tyJVFY4x+TKC6KHXNYqVauGTCYr1ON5FeqE6dSp06L9UhbU1bUwNrEk/IXoKQeQGBeL78LfaVy1Gq6V7RUdTrFQsWItXF3bcejQYc76n0Oex+NER0bHMXD+Bl4lJDF08khMSomkOK8pKSnh2siV8MjwQt82JTc9i4wUCVMWVNXVsatVi9OnTys6lG9WaBOm8PBwXrx4RuXKYnTmrJQrV42wkEeKDkPh5HI5h9cswVRTi051PRQdTrFSurQNDRp04+KFSxw8eBBZHpVChL6JpM9f61A1KsGACT+iq6ebJ/sRMlPXVMfNy41Lly/xNCRE0eEonEwu59GrV5QrK0o3s1KhenUuX7mi6DC+WaFNmIKCgtDXN8LQ0FzRoRRIDvb1efngvqLDULjb50/z5NY1Bnu1RLmYT8ysCMbGlnh49OLhoxC2bttGUkpyrm4/6PFz+v61jrJVbekxoi/q6uq5un0he4YmhjjUdGDP3j0kfvig6HAU6kVEBPGJidjY2Cg6lAKprL09N4Lyr5o+txXaO8iNGzcoV85RNKz7DDu7urx8cp/UQtzA7ntFR4RzZP0yejb0wljPQNHhFFt6esY08ujN+/cJ/Lv+X+Lj43NluycDg/lx0SbqNfegXa+OKCuLyXMVpbJjZTR1NDl8+LCiQ1Gou8+fU9bKCk1NTUWHUiCVtbcn5MkTYmIKxtAjOVVoE6bAwCDKlHFSdBgFVrlyzigrq/Dq6UNFh6IQcpmM/cvnUcXSmjq2VRQdTrGnoaFNw4bdUVLWYfWatURERnzX9raevMxva/fSvk8nPFp6igcnBVNSUqKWRy3uP7jP7du3FR2Owtx98QJ7BwdFh1FgmZUti6a2Nrdu3VJ0KN+k0CZMN24EUbaso6LDKLCUlVWoVKkWzx8EKzoUhbh0dB9RL5/Rp1EzcTMtIFRU1Kjr1hFjY2vWrl3H8xc5H41eJpOzYKcf/xw4Q++f+lO9Ts08iFT4Fjp6OjjXcebQkcOFtgThe9198QI7e9Gx5HOUlJQoZ2dHUCGtliuUCVNSUhIPHgSLhCkb9nYNePHgnqLDyHdvX4Ryese/DGjcAl1NLUWHI3xCoqRE9RotqFjRlY0bNxF8/+sT+qSUVH5ds4vD14IZMnE4lRwq52Gkwrcoa1sWk1LG7N23D5k8b3tGFjQRMTG8jYrCztZW0aEUaGXs7blx44aiw/gmhTJhevjwIXK5nNKlxRfzS+zt6/H84V3kxWiMlNSUFPYt/Yv69k44lq2g6HCELEiQYGdXl+rVm7N7124uX7mc7Xui4xMZumgTwW+i+HHyKMzLWORDpEJOSZBQvUENXr95XajH2/kWd58/p1TJkpQoUULRoRRoVra23Cyk1baFMmEKCQnBxMQCVVU1RYdSoFWq5EJSYgLhL4vPeExnd25ClhBHl3piCIGCztq6KnXrdubkqdP4HfdDRtaJfVjke/rNXU+iqhpDJo7AwEjckAoyDU0NajSswfETxwkPD1d0OPnm7vPnojruK5QsU4ZnzwrnPalQTrAUGhqKqamVosMo8DT/j727jo7qWvs4/p2JuxskIUKQBBIguLtrcSttaUsNqrcuty637i3QQgUt7poQI0Lc3UPcXWbeP2j7tkWKzMyZmZzPWnfdEiZn/0KSmWf22fvZRqZ49B5MXmoi9i5uQsdRuvy0JCJOHeblpWsxFLiYDk1NJL24gLyyUgqryuns6uKB6fMY7/P328idXV3E5mQQk51BTlkJVQ31SJDQw8aWcd5+TB44GKkWt0NwdPRg8qR1BAXvpK6unoULF6Cr8/9PS2kFl3ni6924e/dh2QOruuWZcDKZjLBzoUQFRVB+uQypVEqPXj2ZOGsyPkPUc4FxT7ee9OrTiwMHD3DffevR7QY7GBMKCrhrxQqhY6g9OxcXSktK6OjoQE9PT+g4t0Qjn4nz8/OxsRE7qd6MIYNmkJ+smTsSbkVbSzNHvv2YecNG09tJ+Ns1v4UFEpAYS2VDHZYmptd9XHldDZ8f3celrHQcrWyY6jeU0f0GUNPQwPbzJ/jk8F7kWr4WxNLSgalT7uVySQW/7thJ+++9mmIz83nok58ZNG44Kx5a0y2LJblczs9fbefgz/tobWll+PiRDBo5hIrL5fz46RZCzgQLHfG6Bo0eTHNrC0FBQUJHUbqqhgaKyssZNHiw0FHUnl3PnsjlcoqKNO+AeI18BsrLK8DW1lPoGBph0KBpHDryKV2dnejoauS3+6ac/vl7rAwMWTB8jNBRAFg/dQ6OVtbYmltyJDKUPaHXPjrCUF+fdZNnMtbb92+zYis72nln78/E5WYSmZnKiD7eqoouCGNjCyZNWkdQ8K/s2LGTPv6jeX7LfqYumsGkOVOFjieYhKh4EqPicfNyZ8NzD6Onf+VnZPbSOXz62kcc3XUI70HeWNvZCJz0anp6ugyfPJyAQwH07t0bVxcXoSMpTVxuLj179MDWRv2+D+rGyNQUCysr8vPzcdewjugaOcOUl5ePnZ04w3Qz+vYdiVwmoyQnQ+goSpMWFUZqRAgPz5inNlP/A3p53FSzTGtTc6b6Db3qFqKhnj6zhowAIK2bnAmob2DE+PGrySpv5plv9zD1ru5dLAEkx1xZHDtl/rQ/iyUAEzNTxs+YSGdHJ1HB/75oXii2jrb0G9yPQ4cO0tau2C7v6iQ+L0+cXboF9s7OGrmOSSMLpoKCfOzsxDVMN0NPzwAfn/HkJmtm34t/01hbw/EfvmTluCk4WWnXuzsdqc7v/6+Rv6a3JamwkIDCOnr7D6BdV0Z7e5vQkQTVUHeln5G1rfVVf/fHrFJWino3p/UZ6oNUT4fTp08LHUUp5FyZYRILpptn6+xMfn6+0DFumcY9E8tkMioqyrC2dhI6isYYPGg6eVq4jkkul3Ns82d42jsyxXeI0HEU7kJyHHBltqo7iMpM44tj+5m0YAkzF64DuR7nAwJobe++55OZmJkAUF1ZfdXfVVdUAVBRWq7STLdKKpUyfMoIEpMSSc/QvpnugooK6puaGCB2+L5plo6OlJSUCB3jlmlcwdTY2IhMJsPExFLoKBrDz28qRVmptLU0Cx1FoWICTlKSlcaDU+doXTfv8wkxJORl4+3ixqBu0E8qPD2Zr04cYMri5QwYNhKpji7ePuOQSg0JOBdAazc91LWf75Vec+ePnqWj/f/PhWxqaCLo1AUAWppbBMl2KyyszPEb4ceRI0doVNBZguoiLicHr969MTEWm+TeLGMzM2rr6oSOccs0rmCq+/0f2djYQuAkmsPNbSCmZlbkp2lms7BrqS67zNkdW1k/ZRaWpmZCx1Go2JxMfgo4ia25BQ/PXCB0HKULSUngu1NHmLFsNd5D/v+oE6lUl/79x6GrZ8L5gPM0t6h/YaBog0f549nfi9z0HD566X0O/LSP337cw4cvvoehkSEAEolmPI17DvTEwtaCo8eOok37PuPy8xk0RPtmuJXJxNxcLJhUoa6uDolEgrGxudBRNIZEIsHffzaZMeq7OPRWyOVyTm//lsHuXgzz0q5u73G5WXxx9DcsjE14YfEarSsG/yk4OZ6tZ48zc+Va+vpd/aIjlerQr/8Y9A3MOX+++xVNOjo6PPDMBqYvmolEIiE8MIzESwn4DBnA3RvvBa6c4aYJpBIpQycOJTcnl/T0dKHjKERzWxsJubn4+/sLHUWjGJub/zn5oUk0bp95XV0dxsZmWt3MTxnGj1vJBx8uY+a6DejoalazsH9Kv3SRwowUNq7bIHQUhYrLyeTzo79hamTMi0vXYm+p3R2tQ1MT+eHcCWavWoenz8DrPk4q0aFf39Gkp4cRcD6AyZMnYWRkpMKkwtLV02X6oplMXzTzbx/PSr2y2NvFXXO265uYmuA9zIdTp07h6emJnoa3OonIyMDSwoK+ffoIHUWjmGhowaRxVUd9fT2mpuLtuFvl5zcFHakuOYlxQke5I+1trZz5ZTOLR43H2lR7Zhn/KJZMDI14cclaHCyv3hWlTcLSkthy5hgzV669YbH0B4lESp++ozAwNCcgMICWbjbTdC2xYdEADBqpWbeD+vr2QaorIThYfZtu3qzg1FTGT5igdWsolc3YzIy6+nqhY9wyjSuY6urqMDERC6Zbpaurx5jRS0mNDBE6yh0JObgLU11dpg8a9u8P1hDxuVl8fvQ3jA2uFEuOVtpdLIWnJ7P59FFmLF+D1wC/f/+E30klOvTtNwo9PTMCAgNpbeseC8FbW67+OuMj44gMisDFw5WBQ30FSHX7pFIpg8cNITw8nMqqKqHj3LaGlhZis7MZN3680FE0jom5OQ0aOMOkcfOhzc3NGBiIuxFux7hxK3jzrbl0tLehp28gdJxbVllcSMTJw7yweJXa9yYKTIwlvaQQgKLKK9u+LyTFklp0pfdI3x4uTBw4mJLqSj47speOri76u7hyMf3qhfm25pZXnUGnqSIzUvnu1BGmL1tFH99Bt/z5UokO/fqPJi0llIDzAUyaMglDfUPFB1Ujn//3YyxtrLDv4YCuni6FOQVkp2ZhY2/D2sfu0cjlCfY97HHxdOHEiROsWbMGTZyfuZiejoO9Pe5ubkJH0TgGRka0NGverm2NK5gAcfrzNvn4jMfQ0JSs+Gj6DxstdJxbIpfLObn9G0b186FvT/Xv8p5eUkhISsLfPpZRUkRGyf+fnzRx4GDqmhrp6OoCIDw95ZrX6ufsqhUF06WsNL45eYhpS1dec4H3zbpSNI0hNTWEgIBAJk+ahIEGvgG4WX4jBpMUnUB+Vh5dXTKs7ayZOn86E+dM/nOnnCbyHeXLiZ0nSElNxae/5m3eCE5NZZx4O+72aOi/mcYVTNp+EKky6ejoMG7sclLDQzSuYEoOD6IsL5tn1j0kdJSbsmHGfDbMmP+vj+vv4sbPT76sgkTCis5O56vjB5myeDn9Bt35jiKpVIf+/ceQkhJCYEAgkyZNRF9Li6YZd81ixl2zhI6hcEbGRgwcNpBTp07S29MTA339f/8kNVHb1ERCbi4PPPWU0FE0lia+lmveXK7ojowfv5LMuCiNamLZ1tLMuV+3smz0BCyMTYSOI7pFCXnZfHnsAJMXLf1bn6U7JZXq4u09FiT6BAReoKOzU2HXFqmG5wBPDAwNuHDhgtBRbkloaiq9nJ21+kBhZdLUWTmNK5iuVKWa+Y+tDvr2HYmFpT2ZsVFCR7lpQft+xdrYmMkDNWs3kAhySkv47Og+xs9diM/QEQq/vlSqi3f/cXR1SQgNCaFL1qXwMUTKc2UB+GCioqIor6gQOs5NC05LY9zEiULH0GjiDJNI7UkkEsaPW0FqRKjQUW5KWUEul84d595JMzRycWt3VlZbzYeHdjNo7Hj8Ro1V2jhSHV28vcdT39BEZGQUMmRKG0ukeLaOtrj1deP48eMa0QG8sr6elPx8xo1V3s+0thNnmEQaY9y4lWQlXKKlqVHoKDckl8k4te1rxvsMwtOxp9BxRLegrrmJ9w/sxLWfN6Onz1H6eHp6Bvj4TKS0tIzEBO07aFrb+Y7wpbyijMRE9f/ehaSm4uXpiaOjo9BRRCqmcQWTVCqlq0tcq3AnPDwGYWffi/RLF4WOckMJoQFUlxSxbMxEoaOIbkFrezsfHtqNqb0DUxavUNm7SUNDE7y9x5GZmUVGZoZKxhQphoGRAQNH+HL6zGla1Pyg5T92x4lun6yrSyPvGGhcYjMzM1pb1XtmRN1JJBImjF+l1rflWhobOL/zB1aMmYSpYfc5BkPTdXZ18fmx/bRKpcxefS86OjoqHd/U1Jr+/cYQHxdPYWGBSscW3Rn3/u6YmJkQEBgodJTrulxTQ2ZxMWPF23F3pKWxERMzzTsnU+MKJnNzc5qbG4SOofHGjVtBbko8TfW1Qke5psC9P+Fobsk4Leg/1F3I5XJ+OHecovoa5t+7AX0DYbb5W1o50ttrGOEREZRXlAuSQXTrpJIrC8BjY2K4XFoqdJxrCklJwbtfP2xtbISOotFampowEwsm5TMzM6O5WfPOoFE3rq7e9HTpS1pUmNBRrlKSk0lc0NkrC701dHFgd/TbxQtE52Wx8L6HBH/3aG/vhqvrAIKDg6mrqxU0i+jmWdtZ4+ntwbHjx5Gp4S6q4LQ0xou34+5Yc0ODWDCpgrm5OU1NDchk4k6YOzVh3GpSI9SrYPpjofcUX3962YuLKjXFufhoTsREsOCeDVja2gkdBwBn537Y2bsTeOECTc1NQscR3aQBIwZQU1NNfHy80FH+pqCigvyyMkaPGSN0FI3X0tiIubnmHZ6ucQXTH1Vpa6v4BHinxo9fQX56EtVlJUJH+VNqVBi1ZZdZPEo80FJTXMpK5+cLp5mz5l4cXdTp2BoJnu6DMDW15cKFINrb24QOJLoJ+voGDBwxgIDAALVqRno+MRE/X18sLcTD3++UOMOkIn9UpS0t4m25O+Xo6MHgIdO5dPa40FGAKzsngn77hbn+IzE20NwzsrqTjOJCvjpxgCmLluHe11voOFeTSOnTZyQ6UgOCxcaWGsOtrzs6ulKio6OFjgJAa0cHp+LimDt3rtBRtEJzQwMW4gyT8v1RlYrrmBRj/rwniA86pxZHpSSGBdLeWM+0QYo7PkOkPMVVlXx0eA8jJk9XShdvRZFKrxzW29LcTvjFi8jk4u18dSeVSvEe6kNISDBt7e1CxyEwMRFTU1P8hw4VOopWaGlowFycYVI+XV1drKxsqKlRz10UmmbQoGlYW/cgIfi8oDm6OjsI2b+DBcPGYKCnJ2gW0b+raWzgg4M78fIbwrBJ04SO8690dQ3w9plARWU1sbGxyDWip3T35tLbBQNjQyIjIwXNIQeORkczd/58dDSwd5A6qisvx8nJSegYt0wjv/s9evSkqqpY6BhaQSqVsmDeE0SfPY5cwIX0cUFnkXR2MGngYMEyiG5Oc1srHxzchbWzCxMXLNaYYw4MDIzx8ZlAXm4+aalpQscR/QupRMqAYT5cvBgmaDPLhLw8ymprmTp1qmAZtE11aSk9e2re6Q0aWTD17NmT6mqxYFKUSZPupqm+juzEGEHG72hvI/TALhYNH4Oerq4gGUQ3p6Ozk0+O/Ibc2IhZK+/WuG69xsYWeHuPIyk5iby8PKHjiP5FD/eemFqYcfGicKcSHI6KYsqUKZgYGwuWQdtUXb4sFkyq4uIizjApkpGRKdOmrufSaWEWf0efO4GhVMrY/r6CjC+6OXK5nK3njlPR2sz8dQ+gq6cvdKTbYm5hR58+I4mKihIbW6o5CRJ8hvsQERlBY5Pqd0ZfrqnhUmYmc+fNU/nY2koul1MpzjCpjrNzT2pqxIJJkebO3Uh2UgyVJUUqHbe9tYWLh/eyeOQ4dFV8jIbo1pyNv0R0Tibz73kQQ2MToePcEVtbF1xdfQgNDaNZDTY8iK7P0cURaztrwsJU3zPu2KVLDPbzw1kDX9zVVX1VFR3t7WLBpCo9e4oFk6I5OnowdNhsLp05ptJxI08fwcLIiJF91HBLuuhP6UUF/Bp0lpkr12JpYyt0HIVwdumPmbktoSGhYrsBNSZBgs8wHy5dukR9vep2R7e0tXEmPp55CxaobMzuoOryZXR1dbG3txc6yi3T2IKpslIsmBRt/rwnSQg5T2uTag43bmlqJPzYPpaOGq9xa2G6k5rGBj47vp8Rk6erZ6+l2yahj9cIWto6iIkRZv2e6ObY97DHvoc9wSEhKhvzfGIiVlZWDB4sbkRRpKrLl3FwctLI53zNSwy4urpSWVlCR4fYuVeRfH0nYW/vRlzQWZWMF358Pw4WVvh79lXJeKJb19nVxWfH9uHg6sZwDWgfcKt0dPTw7jeW/PwCcnKyhY4juoEBwwcQFxdHTW2t0seSyeUcuXSJufPni+dZKlhFURHOzs5Cx7gtGlkweXl5AVBSkilwEu0ikUiYP+8JYs6eQKbkWxRN9bVEnTrCslETNGZbenf0c+BpatramL58NRINfEd4M4yMzenjNYLo6BiqqquEjiO6Dmt7a3r0cuLChQtKHys2J4eapiamTJ6s9LG6m8L0dAZ4a+ZMtUY+AxoYGODu3pvCwlSho2idSZPW0NbSQmZslFLHCTvyG73sHRnYy0Op44huX1ByHCFpicy9ez0GhkZCx1EqG1tnevbsS2hIKK1twvX8Ed2Yz7ABJCcnU1FZqdRxjkRFMW3aNIyMtPvnXgglWVn4+PgIHeO2aGTBBODj401hYYrQMbSOgYExM6Y/wKUzymsxUF9dSfS54ywbNV6cXVJTOaUl/Hj+JNOXrsbGwVHoOCrRq9cADI0sCAsNQyZgE1fR9VnaWOLi6UJgYKDSxiiqqiI2J4c54rlxSlGQno63OMOkWgMGeFNcLBZMyjB79qPkpyVSXpinlOuHHtpNX+de9HPupZTri+5MfXMTnx79jcFjxuM10E/oOKojkdK3z0gaGpuJT0gQOo3oOnyGepORkcHlUuUcj3U0KoqhQ4bg5Ng93iioUkNNDVVlZWLBpGr9+/enqEgsmJTB3r4Xw0fMV0qLgZryUuKCzrJ01HiFX1t057pkMr44cQBzB0dGz+h+77B19Qzo338sWVlZ5BfkCx1HdA1mlua49XUjIDBA4dduamvjXEIC88VWAkpRmJGBiampuOhb1by9vSkoSKerq1PoKFpp/rwnSAwNpLGuRqHXjTh+gAG9PPF01LymZd3B7pDzXK6vY9bKdRq57VcRTEws8eo9lKjIKGrraoWOI7oGb39vcnNyKSsrU+h1T8XEYG9vj6+veOqAMhRmZNC3Xz+NXYqhsc+I/fr1o6urk8uXxa3AyuDjMw7P3v5cPLpfYddsbWokIeQ8swYPU9g1RYoTkZ7CmfhLzFl7H0Ymmt3J+07Z2bvh4OhBSHAI7e1i+xJ1Y2JmgrN7TyKjFLc5paWtjX3h4SxbvlxjX9DVXWF6OgM0dME3aHDBZGxsjIuLm7jwW0kkEgl3r32X6PMnqK9WzI6UuKCz2FlY4u3ippDriRSnsLKc788cZcqiZTj0dBE6jlpwdx+Erp4xF8MjkCEuAlc3vQd6kZiUSHNLi0KudzgqCitra8aNF5cLKEtxRgY+Grp+CTS4YAJxp5yyDRgwHh+f8YQc2nPH15LJuog+fYQZfv7iuzc109TayqdHfsN76HC8/YcLHUdtSCRS+vUdTXV1DclJyULHEf2DrZMtFlYWxMbG3vG1GltbORAezpq1a8VGlUpUmJGhsQu+QcMLpis75cReTMq0ds3bxAWdpbrs8h1dJzM2ivbmJkb3G6igZCJFkMnlfHPqEHoW5oyfs1DoOGpHT9+Qfv3GkJaaRrGKD6YW3ZgECb0H9iYqKoquO2wDceDiRXr07MmIESMUlE70T80NDZQXF4sFk1C8vb0pKhLf+SlTnz7D8fefRcjB3Xd0nUunDjPRZxAGenoKSiZShEMRwWRXlDJ79b3o6OoKHUctmZvb4uE5hPCLETQ0NAgdR/QXrr1d6ejsID09/bavUdvUxOGoKNbcfbc4+61EhRkZGBoZ0auX5raT0eiCaciQIeTmJtHa2iR0FK22ds3bJF28QEVxwW19fnlhHvkZKUz181dwMtGdyCwp4lBEKLNW3YOpuYXQcdSao6MHNjbOhIeHi00t1YiOjg4ePh5ERITf9jV+CwvDw9OTwYMGKS6Y6CqZMTEM9PVFR0dH6Ci3TaMLJh8fH4yNTcjIiBQ6ilZzcxvI6DGLCd6/67Y+P+r0EYZ49sVWfFFWG63t7Xxz6hD+Eybh7O4pdBwNIMHDcwjNLa2kporrJtWJp3dvSkouU3L51pcNVNbXczw6mrXi7JLSpUVEMFHDF9RrdMGko6PD6NFjSEkJFjqK1lu96k3SYsIpzbu1Ng7NDfUkhV1g5iCxlYA6+TXoDFIjY0ZOmSl0FI2ho6OHl9cIUlJSqa6uFjqO6HfGJka4eLoQGXXrb5x3h4YyYMAAjd7qrgnkcjkpERGMGzdO6Ch3RKMLJoDx48eSnh4idAyt17NnHyZNWsuFfTtv6fPiLpzB0dqGPuJWdbURk51BSGoiM1asFdct3SILC3t69OzDxYvhdHaKTXPVRW9fL1KSU2hsuvnlGaW1tZyNi2PNmjVKTCYCKMvPp7K0lNGjRwsd5Y5ofME0btw4UlMvih2/VWDF8lfJSYqlMPPmdibKurqIPnOUmX5DxeluNVHX3MSWs8cYM3NetzlUV9Fcew1AjpS4+Hiho4h+Z2tvg5WtFTExMTf9OTuDgxnq70+fPn2UmEwEkBweTj9vb2xsbISOckc0vmAaOnQonZ3t5OaKT17K5uDgxowZDxB0k7NM6dHhdLW1MrKvON2tDuRyOVvPHsO6R08Gj9bsqXEhSSU69O07ktycHC7fxroZkXL0HtibS5cu0dnV9a+PLays5EJSEqvE2SWVSI2IYPzYsULHuGMaXzAZGhri7z+clBTxtpwqLFv2MkWZqeQm/3uBeunUYSYPGIy+eNtHLQQlx5NaXMi0pauQdNNz4hTF2NiSXm6+RERG0iYenaIWnD2ckSEjLfXfZ8B/DQpizKhRuLu5KT+YiPTISMZr+IJv0IKCCWDChLGkpYkLv1XB2tqJOXMeI2jfDuRy+XUfV5qXTWF2OlP8hqgwneh6ymtr+PnCaSYvWoqZhaXQcbRCzx5eGBmacykqCjnX/10QqYaOjg6e3h6ER0bc8HHZpaWEp6eLs0sqUldZSX5GBmO1YIZJK976T5w4kW++2YxMJlPLE9bvv9+N8vL8a/7dgAETeOedwD//fPlyNgEBP5OTE0NWVjTV1SXY2/diy5Y81YS9CUuWPM/JU9+RFXcJr+scpBt1+gjD+nhjbWqu4nSif5LJZHxz6jDu/QfQVyxgFUcixavPcGJiTpKfl4+bOFshOA9vT1JiUikqLsa5Z89rPubXoCAmTZxIzx49VJzu76ouXybk0CGiz52jKDOT2vJyTC0t6T98OHdt3Ehf/7/3rdvxwQfs+t//rnu9zdHROLi6Kjv2LUsMCcGlVy9c1TDbrdKKgmncuHG0tDSQmxuHp6d6viCYmFgwb94TV33cwcHtb39OSQlm167XkUp1cHHpT21tqWoC3gJzc1sWzH+SC/t20NvP/6rbO031tSRdDOLlpeI7OHVw9NJFyhrrWXP/w0JH0ToGBiZ4evoTHR2Nnb0dJsYmQkfq1oyMjejVuxeRkZE4L1p01d+nFRURm5PDt88+K0C6vzu6eTP7vvgCRzc3Bk+ciLmNDZdzcgg/cYKIEyd4+ttvGXeNr2Hy8uXYX6P4MLFQzz538UFBTJ86VSs2/mhFwWRsbMzIkWOIizujxgWTJatW/fdfH+fjM54PPriIu7sfBgZGLF5sqPxwt2Hhwqc5euwLUiJD8Rn59wXE8UHncLFzwNPx2u/wRKqTV17K/vAgFt67AUNjY6HjaCV7+17UVJcQHh7BpEkTkUrUb5a7O+k9sDfn9p+jacYMTP7yMy8HfrpwgRnTpuFgby9cwN95DRnCOwcPMmDMmL99PPniRV5ZvJhvnn2WkbNno2dg8Le/n7JyJQP/8TnqSi6Xk3DhAvfdYGZMk2jNb/aMGVNJTDwrdIw75ujoQb9+IzEwMBI6yg2ZmFiwYvmrnN/9E+1trX/7u5SwQMb3H6AV7yg0WXtnB1+fPITfyDG49ha3TiuPBE9Pf+rrG8jIyBA6TLdnbWeNuaX5VYu/Q1NTyS4tZfmKFQIl+7vRc+deVSwB+IwaxcAxY2isrSXvJhawq7PSvDwuFxQwefJkoaMohNYUTFOnTiUpKZi2thaho1xTR0cb585tY8+edzh69EvS02+8MFETzJ27EQtTW0IO/f/BvJXFhZQVFzDMq7+AyUQAu0MC6NCRMmbmXKGjaD1dPQP6eA0nMSGR2rpaoeN0e869nUlKTvrzzy1tbWw5e5Y1a9ZgZWUlYLKbo/P7IeXXOnct+eJF9n3+Ofu//JLw48dpaWxUdbybFnfhAr6DBmFrayt0FIXQiltyAP7+/hgZGZGaGsqgQVOFjnOVmppSPvvs3r99zMtrGM88sxMnJ808y0tHR5dHHv6Wl1+ezMAxk7Dr6UpyRDD9XN2xNDEVOl63llyQy7mEaFY88iS6evpCx+kWLK2ccHD04OLFcKZPn4aOVHMPGdV0rr1dOR55nPqGBszNzNgVEoKFlRWzZ88WOtq/qigqIj4oCGsHB3p5e1/19zvef/9vfzaxsOCBt99m8vLlqop40xKDgpgxbZrQMRRGa2aYdHR0mDRpMnFxZ4SOcpUpU+7lzTfP8dNPZezd28Snn8YyadJaMjOjeOWVKTQ3Nwgd8bZ5e49l3PgVnPrpyi7FtItBjBZnlwTV1NrCt6cOM2rqTOx7Ogsdp1txd/ejo1NGYkKi0FG6NTMLM6ztrUlNTSW/ooLDkZE8/Mgj15yxUSedHR18/MgjdLS1se7VV/+W193Hh02ffcb3ly7xW2Ehm6OjefDdd5FIJHy2cSMRJ08KmPxqXV1dJAQHM00smNTT9OlTSUxUv4Jp5crX8PObjKWlPQYGxnh4DOLJJ39i0qS1lJfnc/r0ZqEj3pF77/mQ8oI8Lh7dR1VFKUO9+gkdqVvbHngaE2sb/CdMETpKtyOV6tLXaySZmZlUVFYIHadbc/Z0ITEpkW9PnWLSpEn066fez0symYzPNm4k+eJFpq9dy6Rly/7296PmzGHqqlU49uqFvqEhDq6uzL3/fp7buhWAX959V4jY15UdH09bS4tW9F/6g1YVTHPmzCErK46ysjyho9yUGTM2AJCaGipwkjtjaWnP3WvfIfTIXrxd3TE1VO8F69osuSCXyIwUpi1dpZY9yboDUzNrevbsy6WoS8hkMqHjdFuuni4kllwmt7ycdffcI3ScG5LJZHy+aRMX9u1j4tKlPPLhhzf9uX7jx+Po5kZ+SgrNDepztyL82DEmTpqEkZH2vB5o1TOqq6srw4aNJCRkj9BRboq5+ZWFcK2tN3/CtrqaPv1B6OrC+Qbdv0XK1dnVxbaAUwydMBkrO+G3TXdnLi7etLd3kJEp7poTilRfj0KpFF8/PyzM1beB7h8zS+d372b8XXfx+Bdf3PKbHfPfD7Vta25WRsRbJpfLCTt0iFUrVwodRaG0qmACWL16BWFhu4SOcVMyMq7slPtn80pNlJ0djUwO0oI8aivKhI7TLZ2MiaBV1sWwidqzZkBTSXV0cXcfQlJSMs0t6rlzV9udiErCxMKUy6Xqe0DyH8VSwJ49jFu4kCe//vqW11m1NjVRkJaGobHxn4WT0DJjY6ksLWXBggVCR1EorSuYli5dSlZWHEVF6UJHAaCoKI22tqur/qKiNLZvfw6A8eNXqTqWwgUH7WRsn+H4D5pKzLlTyMVbESpVWV/HgYhgxs9fjJ6+uCtOHdjY9sTCwo7Y2Fiho3Q7RRXVhCdnM3v1XAryCygqLhY60lX+uA0XsGcPY+bP56lvvrlusdTc2EhxdvZVH29raeHLp56ipbGRMQsWoKMmB50HHzjArFmzsFDT7uO3Sz3+dRXIycmJceMmEhKymxUrXhU6DkFBuzh06GN8fMZjb98LAwMTSkoyiI4+TmdnB0uWvMCAAf9/inN9fSU//PDMn3/u6uqgvr6STz+958+P3Xffh3/ezlMHXV1dhATv4pnJ6xjl5svX3zxEbkoCHgMGCR2t2/jlwhlcPPvg6T1A6CiiP0lw9xhMbMxJSstLcbR3FDpQtyCTydkfEsvgsYPx9PHAvY8bIcHBrFCThpV/2PXhh5zfvRsjExN6enqy++OPr3rMyFmz8Bg4kIbqah4ZNQqvwYNx9vLCyt6e2ooK4oOCqCwpoZe3N/f+97+q/yKuQSaTEXroEF99+qnQURRO6womuHJb7p13PmH58lcE7zbt6zuJoqJUcnJiSUkJpq2tGXNzW/z9ZzN79iMMHjz9b49vaWnk/Pntf/tYa2vT3z62cuV/1apgSk0Nobm5njFewzDUM2DqlHs5c+4HnD37oq9FC/7UVUJeNvF5Wdz91AtCRxH9g5GhGc7O/YmOimHmrBlibyYViErPobKhiaXzJwHQz78/AYGBLF++XPDXg78qLygAoKWpiT2ffHLNxzi4uOAxcCBmVlbMuvdeMmNjiT53jsbaWvQNDXHp04e5DzzAnPXrMVCT59qU8HCa6+uZM2eO0FEUTiKXa98q3crKShwdHfn44xjc3X2FjqP1vvn6YTqL0nhr4VPAlXcY27Y/i5GNGf5TZgmcTru1d3by/M/f4TVsJCOmzBA6jugaZLIuYmNO0Lu3B97XaEQoUpymljbe332CKUtn4DfqynN/c2MLnz7/KZ988gnubm7CBuwGvn32WUybm9m9SzPWEt8KrVvDBGBra8uUKdMICdn97w8W3ZGurk7CQvcw3fv/z0SSSqXMnvUIuUlxVJeWCJhO+x27FEaXjo7Yc0mNSaU6eHj6k5ySQlOT5u+IVWfHIxOxcbLDd+T/v1E2NjXCs78HwcHBAibrHro6O7l45IjW7Y77g1YWTACrVq0gNHQXWjiBplYSEs7T1dHOSI8hf/u4o6MHQ/xnEXNeXACuLOW1NRyOCmPigiXoqsliT9G1WVk5YW3tSHRMjNBRtFZ+WRVR6bnMXDWbf9556+/fn6CgC+LrgZIlhITQ1dHBzJkzhY6iFFpbMC1cuJDKyiKysi4JHUWrBV3YycR+o9DX1bvq7yZOWE1rYyPZSeIuIUWTy+X8FHgaz34+9Oqj3h2MRVe4uw+mrKyMkhJx1lXRZDIZ+0NiGDZhGA49r+5B1sevL9XV1WRlZQmQrvsIOXiQRYsWYWBgIHQUpdDagsnCwoKZM2cTHKx991HVRUdHO+EX9zOt/5hr/r2hoQnTpq4nKSSQ1mbxVoQixeRkkFqUz7h5i4SOIrpJBgYmuLr4EB0TQ2dXp9BxtMrF5GzqWtsYP2/8Nf/e0MiA3j5eBIm35ZSmo62N8GPHWKlmuxEVSWsLJoCVK5cTGrpbPJ5ASTIyIpDJOhnqdv2F9QMGTKCHkxfRZ4+L0+EK0tbRwU+BpxkxbSZmFpZCxxHdgh7OfQApKampQkfRGpV1DRyNSGDG8lkYGF5/ZsPL14tL0eIdB2WJDQxEV0eHyZMnCx1FabS6YJo3bx6NjTWkpYUJHUUrxcWdYXCvgejqXH/9jEQiYf6CJ6ksLiI3OU514bTYocgQpIaGDB4zQegoolsklejg6eFPemoaDWp07pem6pLJ2HE+kv5D+tN/yI1vTbv1c6O4sIiq6moVpeteQg8eZOnixejpXb08Q1todcFkYmLC3LnzxNtyShIfc4rhvf69UaK5mQ2zZz1CXMAZGmrFJ6s7cbm6iuPR4UxauPSWj1AQqQcLSwdsbJ2JjolBjjjreifOx6ZS19bG9OX/vsjYwsocWyc7EuLjVZCse2lrbibi5ElWaunuuD9odcEEV3bLXby4ly5xzYBCNTXVkZEdzQiPwTf1eB+fcfTtO4Kok0fFXXO3SS6Xsy3gJH0HDsLZo7fQcUR3wN19EJWVlRQVFQkdRWMVlldzNjqV+fcswtDo5hYZu/d1I0Y8qkbhLp09i5mZGePGjRM6ilJpfcE0c+ZMOjpaSUwMFDqKVklMDMTSxBJ3W5eb/pyZMx+mtbGRtEsXlRdMi0VlppFdVsLYOdp1oGV3pK9vRC/XAcTGxNLZKb6Zu1XtnZ38GhDBsEnD6NXH9aY/z62fG/HxceJ6SgULPXiQ5Uu1f9Zb6wsmQ0NDlixZwrlzW4WOolXi488w3N33lo4aMDIyZcH8J0m+GERNmfqeIK6OumQy9oQFMnzyDEzMzIWOI1KAHj28QKJLprjV/ZYdDU9AamDAxAWTbunzevVxo76unsLCQiUl635qKyqIOHWK1atXCx1F6bS+YAJ47LFHCQ39jaoq9TuxWlMlxJ5mhJvfLX+eu7sfQ4fOIeLkYbo6OpSQTDuFpCTQ1NGO36ixQkcRKYpEiqvrAFJTU+kQfxduWnrhZSJSc1iwfiG6urc2o2FoZICzuzNx4jomhTn500/4+fkxfPhwoaMoXbcomIYMGcLw4SM5efJboaNohcrKIgpLMhl6GwUTwOTJd6Mn1SchNEDBybRTR2cn+yOCGTZlOnr6+kLHESmQna0z+vpGZGSkCx1FIzS1tLErIIqJ8yfh0NPhtq7h2rcXMbFix3VF6Ghv5/S2bTzx+ONCR1GJblEwATz55OOcPv0d7e2tQkfReMnJQTjbumBvbnNbn6+rq8/CBc+QHR9NaX6OgtNpn8CkOLqkUgYMGyV0FJGi/T7LlJaWTnt7m9Bp1JpcLue3kGisnGwZMeX2ZzPc+vQiNTWVLnHzyR0LO3IEKbB06VKho6hEtymYFi5ciLGxodhiQAFSkoMZ7Hxnx3E4OXkyfvwqok4fpb21RUHJtE9bRwcHI0MYPmWGeF6clrKx6YmRsTmpaWlCR1FrMRn5pBeVMf+ehUilt//S1cOtJ22tbRQUFCgwXfd0fPNmHn34YfS7ycx3tymYdHV12bjxUY4f/1zcIXGHUpMv4Ofc/46vM3r0XVhb9iDm/EkFpNJOZ+MvoWtohPeQYUJHESmNhF6uA8nIyKSlRXzzcC3VDU3sD41l5opZWNpY3NG19A306OHag5TkZAWl657So6PJTkxkw4YNQkdRmW5TMAHcf//9FBSkkpISInQUjdXYWENeYSqDXL3v+FpSqQ4LFzxNSU4W+WlJCkinXVra2jhy6SIjp89CquXbdbs7SysHzM2sSRWPTLmKTCZnV0AkHv09GDh8oEKu2bN3T5JTxILpThzbvJlly5fj4HB7a8k0UbcqmGxsbFizZg3Hjn0udBSNlZoahrWZNc5WTgq5npWVAzNnbCD2/Cma6+sUck1tcTI2AmNzC/oMHCR0FJHSSXB1HUh2djZN4kHVfxOUkEF5QxOzVs/hFrqY3JCrhwvJySni3YbbVFVaSsihQ91msfcfut2iiMcf38TgwYOpqCjAzu7mG56JrkhJCcbPuf8t9V/6N35+U0jPCCfqzDHGL1qB5A7WJ2iLxtYWjsdEMH3ZGq3898hKSiA+PITykiI62tsxMTPHybUX42bNx8zS6s/H1VRWEBlwhpK8HBrq6jA0NsbG3oFBo8fj6f3vx/JoEnMLOywt7UlJTmHYMPEWLMDlqlpORCWy9KFlGJsaKey6zp4u1FRXU15RgYO9vcKu212c3LaNYcOH4+/vL3QUldK+Z+J/MXDgQMaOHc/x418LHUUjpSZdYJDLna9f+iuJRMLcORtpqKokMy5KodfWVMcuXcTKzgEPLSsK5HI5Z/fv5sgvP1BXU01f3yEMHjOBnm4elOTnUV9b8+djLxfk8ctnH5AWewlbxx4MHjMeN6++lBUVcPinLVw8q31r31xdB5Kbl0tDo3gwb2dnFzvORzJo9CB6+3gq9NomZsbYOdmTkpKi0Ot2Bx1tbZzevr3bzS5BN5xhgistBtatu48VK17FwMBY6Dgao6OjjczsGJ4do/gtpCYmFsyb+zi//fYu9i5uWNp1n/vi/1Tb1MjpuCjm3n2/Qmfy1EFsaBCJkRfxGzmWifPvumq3k6yr68//Dj93is6ODuavXY+nz/+vXRk5dSY/f/o+ly6cY9jEqVq1e9DUzBobmx4kJSYxalT3biNxPCqRNomEqYunKeX6PT16kpKczKSJE5VyfW0VfPAgRoaGLFq0SOgoKtftZpgA5s6di6WlBYGBvwodRaMUFaUhk8no7eCmlOt7eQ1jyJBZhB3Z161bDRyJCsOhpwuuvfsIHUWhOjvaCT93CgtrGybOW3TNreF/XdxeV10FEgluff8+o2luZY2NYw86OzroaNO+3kWurgMpLCqkrq77rumLyyogLDmbRQ/chZ6+cgpip16O5OSJfeBuhVwu59jmzTz2yCPo6ekJHUflumXBpKOjw6ZNj4ktBm5RQUEKzrbO6Oko7xdl6tR7sbJwIOLEYeTdsLFcVUMd5xNiGDVzrtbNLuVnpNPW0oyn90BkcjmZSfFEBp4lPjyU2sqKqx5v4+AEcjl56X/fOVZfW0NVaQl2Tj0wMjFRVXyVMTa2wM7OhcSkRKGjCOJydS27L0Qxe/UcnFwclTaOraMdRYVF4mvALUiNjKQgPZ0HHnhA6CiC0J657Ft033338fLLr5CQEICf32Sh42iEwsIUPGyclTqGjo4ui+96gc1bNpEcHsSA0ROVOp66ORAegotnb3q6eQgdReHKiq8ceCqRSvnl0/ep+UuRJJFIGDJ2IuPnLPjzY2Omz6YkP5ejv/6IR/8BWNnZ0dzYSFZSAhbWtsxZtU7lX4OquLgMICbmBNU11VhbWQsdR2Va2trZdioMv1GD8B2hmBYC12PnZEdLcwvV1dXY2NzeqQXdzbHNm1m9ahW2trZCRxFEt5xhArC0tGTdunUcPy62GLhZRflJuNv0VPo4pqaWLF3yImmXwinO6j5nbJXVVhOcEs/I6bOFjqIUzU2NAMSEBKJvaMTKR5/i0dffZ+mGjVjZ2hEdHEB8+P/3SLO2d2DlI09g18OZrOQEogLPkXwpAolUis/Q4VjYaO+TtpGRGQ4O7iQmdp9ZJplMzo7zkRhbmzN1iXLWLf2VsakRJmamFBQWKn0sbVBRXEzY0aM83g0Xe/+h2xZMAJs2bSQy8iilpblCR9EIhYXJuNu5qGQsZ+d+zJjxIBGnDtNQXamSMYW2LzwYz34+ODprZ7uLP2596OjoMP/u9Ti6uKJvYICzuydzVt+LRCIhOjjwz8eXFuaz6+tPMTQyYtXGp3nsjQ+49z8v4z1kKIFHDnB850/CfCEq4uLiTXlZORXXuF2pjc5EJ1NYXctdDy5FV1c1jVrtethRKBZMN+Xkjz8yZuxYfH19hY4imG5dMPXv35/Jk6dy+PDHQkdRex0d7RSX5uBuq5qCCWDI4Bn49B9H6JF9Wrm4968q6+uISEtmxLRZQkdRGgNDQwAcerpgav734y1sHZ2wsLahrqqS1pZmurq6OLbzJyQSCfPWrsehpwt6+vpY2tgyYe4iPL0HkpkYR7EWL9o1MDDBwcGD1BTtP2MuJa+E83FpLH5wKWYWpiob19rBWjxT7iY0NzRw+qefumUrgb/q1gUTwGuvvcKpU99TUSH+0tzI5cuZIJfjqoJbcn+QSCTMmvUQxgbmRJ05qtWLM8/GR+Pi7oGdUw+hoyiNle2VBoEGRtduQPjHxzs7OqipKKO+ugpHl17oXeNgTxfP3gBUlBQrKa166NHDi8ulJTQ0aW9fpoq6Bnacj2Da0um4eCp3jeQ/2TnZkl+Qr9IxNdHh777DxdmZBQsW/PuDtVi3L5jGjBnD5MlT2LPnTaGjqLXCwlR62PTEQFe1p1Lr6uqzdMmLVBYVkX7pokrHVpW2jg4CkmLxGzNB6ChK5eLpBUB1edlVf9fV1UVtZSV6+voYm5jS1XmlH1PL7+ue/qml6crxITpa1IPpWoyMzbG2ciQzM0voKErR1t7JtlOh9BncD/9xqu8abetkR1FhoVa/GbtTDTU1HPrmG95+661rtgLpTrr3V/+7t99+k3PntlFSop1PSopQWJiCh61q3/39wcLCjiWLnyMp7AKl+dp3CyYsLRF9IyPc+/sIHUWpLG1s6eXVl9qqShIj/178RgWepa21BU8fX6Q6Otg4OqFvYEhJfi75GX+/JdVQW0NCRBhIJDi7K7YDtDpy6tGH3OwcOjo7hY6iUHK5nN0XopAaGzFr5WyFnRN3K+wc7WhqbKKuvl71g2uIA19+Sf++fZk3b57QUQSn3W/PbpK/vz9z585n9+7/8uSTvwgdRy0V5ifhYa2623H/5Obmy+TJ6wg6vpNpq9ZjYmEpWBZFksvlnIyLwnf0uG7x7m3ywqXs/uZTzu7fTXZKItZ2DpSXFFGYnYm5pRXjZ88HQFdXl3Gz53PuwB4O/Pgd7v18sLa3p6mhgaykBDra2/AfNwkrO+0/B8zK0gE9fSPy8nLx6u0ldByFuRCfTublcta/8IDSmlP+GxNzE4xMjCksLMTSwuLfP6GbqSkr4+iWLRw+eFDr+sLdDu1/hr5Jb731BsHBu8nPTxI6iloqLFDdDrnrGTFiAZ4e/oQd3UdXR4egWRQlpTCPyro6fIaOEDqKSlja2LLqsafx9h9OWXERsWFB1FZW4DdqLCsfewoTM/M/H+s7YjR3rX8Yt779uVyQS3RwIDkpidj3dGbm8jV/69mk1SRSnJy8yEjPRIZ2NHPNKi7jRGQSi9YvxtJGuEJFIrnSj6lQXPh9Tb99/jnDhg5l6tSpQkdRC+IM0+98fHxYunQ5u3a9xnPP7RM6jlqRyWQUX87CzWa9oDkkEgnz5m7ihx+eJvrcCYbNmKfx73pOxkXR338Yhkbd50xDM0srZixddVOP7eXVl15efZWcSP05OLiTn59A2eUynJychI5zR2oamvjpTDgTF0zEo7+70HGu7JQTWwtcpaK4mJPbtnH+3DmNf55VFHGG6S/eeOO/REQcJisrWugoaqWuroKOznYcLOyEjoK+viHLlr1ESU4W2fGXhI5zR8pra4jPyWTQ6PFCRxGpOR0dXRwdPEjPyBA6yh3p6Oxi2+kwevVzZ+RU9Thc2NzKnMrK7tHr7Vbs+egjJk2axNixY4WOojbEgukvevfuzT333MuOHS8LHUWtVFcXo6uji6Wx+b8/WAWsrXuwaOEzxF04S0WR5k6ln4m/hFvvPljbOwgdRaQBHHv0pqyslPoGzVygLJfL2R8SQ5tUyry75wmyyPtazCzNqKqqEjqGWinJyeHczp288/bbQkdRK2LB9A+vvPIyCQnnSUkJ+fcHdxNVVcXYmNkilajPj4uX11DGjl3GxeMHaGnUvB41re3tXEiO1/pWAiLFMTI0w8a6B5mZmUJHuS0XU7KJzy1i6UPL0DdUbXuSGzGzNKO6ulroGGpl94cfMmfuXPz9Vd/qQZ2pzyugmnB1dWXDhofYseNlsTfH76qqirEzV7/DKceOXY5Lz34EH9itcZ3AQ1ITMDI1xa1PP6GjiDSIUw8vcnNzae9oFzrKLUnNv8yh0DgW3rsIGwf1OkzYzNKUutpaumTasaD+ThWkpRF84ABvv/WW0FHUjlgwXcOLL75AZmYUcXFnhY6iFqqqirEztRI6xlWkUil3LXoWIwMzwo7uQ9alGX1qZH+2EhiPpBu0EhApjqWlA4YGJuTmaM75l4Xl1fx0Joxpy6bTx1f92iKYWZgjk8mora0VOopa2Pn++yxdtgwfH+3uC3c7xGfra3B0dGTjxo3s3CnOMgFUVRVhr4YFE4CengErl79Ge2MLUaeOIteAd4lJ+TnUNjbi7T9c6CgijSPBqUcfMjMzkcnV/2e9sq6BLSeCGTFlBEPHq+ftHSMTI3T1dKkW1zGRFR9PxMmTvPH660JHUUtiwXQdzz33LMXFaURGHhE6iuBqKguxM1O/W3J/MDY2Z/WqN6goKiQhJEDoOP/qVFwUPkNH/HkYrUh0K+ztetHW3s7ly5eFjnJDDc0tfH8sGE+/PkyYP0noONclkYC5pYW48BvY+d57rFu3jt69ewsdRS2JBdN1WFtb8/TTT7Nz5yvINGDWQpnq6yqxNlbvLriWlg6sWvk6OYmxZERHCB3nui7XVJGYl43f6HFCRxFpKOnvLQYy1LjFQFt7J1tOhGDV0445q+aqzY646zExM+72x6OkREQQHxTEq6++KnQUtSUWTDfw5JNPUFNTTGjoXqGjCKq5pR4TA/VvrOjo6MHSJS+SEBpAYXqK0HGu6UzcJdz79MfSVvieViLN5eTkRUV5BXV1dUJHuUqXTMb2M2HIDAxY/MASdHTV/2VG38iAlpYWoWMIRi6Xs+Pdd3nwwQdxdXUVOo7aUv+fZAGZm5vzwgvPsXPnK3Ro2K4URWppadSIggnAw2MQ8+c9TsSpw5QX5Akd52/aOzsISknAb4w4uyS6MwaGJljb9iArW70ODJfL5ey5cInK5lZWbFyhVu0DbkTfQL9bF0xxgYFkxMTw0ksvCR1FrYkF07947LHHkEo7OHz4E6GjCKa5tQFjfSOhY9y0AQMmMGniWkKP7KWuokzoOH+Kz81CT18f197iUR+iO2dn24uCgkK1WjJwIjKR1MJSVmxahYmZidBxbpq+gT7Nzc1CxxBER1sbW158kWf/8x8cHR2FjqPWxILpXxgZGfHll5+ze/cbVFRoblfp29XV1Ulbe6vGzDD9YeTIhfj5TSPowC6a69XjtkVYegpeAwchFVsJiBTA2tqJrq4uKiorhI4CQGhSJsFJWazYuAprO/XcVXs9+kb6NDc1CR1DEAe+/hodmYznn39e6ChqT3zmvgnz5s1jypQp/PDDk0JHUbmWlitdtDVphgmuHNQ7fdr99HIdSNCBXbQLPN3e0t5GfG4WfQYNETSHSHtIpbrY2vSkIF/4N3KJOUUcvhjP4geX0KOX5h0ObGCgT3NL95thKiso4LdPPuGrL77AyEiznuOFIBZMN+mLLz7j0qXjREefFDqKSv1RMJkYaN4vk1QqZeGCp7EwtSfk8F66OjoEyxKbk4mxiQlOLr0EyyDSPja2rhQWFdEl6xIsQ87lCn49F8HsVXPo7eMpWI47YWBkQFM3vCX3w8svM2PGDGbPni10FI0gFkw3yd3dnZdeeoktWx6jvb1V6Dgq09p6ZZraSF8zewbp6uqxbOlL0AHhJw8J1tjyYnoyXn6Dxc7eIoWytHJADpSVCbNWr7S6jh9OhjB2zjj8RvkKkkER9Az0aW3pPs/rAFGnTxN34QKff/aZ0FE0hvjsfQueffY/GBpK2b//A6GjqMwfnc7V6eDdW2VkZMqqVa9TX1ZObMBplXdvb2ptITEvhz6+4u04kWJJJTrY2jiTX6D623J1Tc1sPh6Mz/CBjJkxRuXjK5K694lStLaWFra8+CKvvvKK2EbgFmjuq6AADAwM+PrrL9m3711KS3OEjqNSmn5EjLmZDatWvUFhRippUWEqHftSVjoWllbY93RW6bii7sHW1pWSomKV3pZraWtn8/FgHD2dmbF8hsYXHBr+9HbL9n3+OWZGRjz11FNCR9EoYsF0i6ZPn87cufPYsmWTxhcR3Y2dnSvLl71CSngwOUlxKhv3YkYKXoOGINH0VxWRWrK0sEOqo6uyo1LaOzv58VQoeuamLLx3kdbs+uwuv54lOTns/+ILvvnqK/T1NaNPlrrQjp90Ffv0049JSgokIuKw0FFEt8jV1Zu77nqWmPMnyU9NUvp4dc1NpBbk0cdvsNLHEnVTEik2Ns7kq2C3XEdnF9tOhdEok7PskeXo6esqfUyR4sjlcra8+CILFy5k8uTJQsfROGLBdBucnZ15/fX/8sMPj9PWpt07K/6YFZGjPbNpffuOZOGCp4g6fYSC9GSljhWVmYaNnT22Dpq31VqkOexsXSkpKaGjs1NpY3R2drH9TBg17R2sfnItRiaat3P2urTn6e2GLh47RlpkJJ98/LHQUTSSWDDdpscffxwrK1P27Hlb6Cii2+DtPZYFC54i8uRhCjOUd+7cxYxkvPzExd4i5TI3t0FPT5+SkmKlXP9KsXSRypY2Vj+5FhMzzWpke1O0/J5ca1MTP77yCm+8/jo9evQQOo5GEgum26Snp8c333zFwYMfUlSULnQcpfljhkmmheu1fHzGMX/+E0ScPExRZqrCr1/dWE9mUaF4O06kfBIptrauSrkt19kl4+dzF6lobmHNk3djaq45R57cLLlcpvVrDHd/9BF2VlZs3LhR6CgaSyyY7sCECRNYunQZmzc/prULwA0NTQFoadfOgykHDJjA3DkbCT9xiOIsxRa+kRmpOPToiZWtnUKvKxJdi52tK2WlpbR3tCnsml0yGT+fvcjlhhZWP3U3phbaVywBtLd1YGiomb3mbkZhRgaHv/uOb7/5Bl1dcd3Z7RILpjv00Uf/IysrktDQvUJHUQpjYwsAGlu1d62Wr+8k5sx+lIvHD1CcnaGw64b9vjtOJFIFUzMrDAyMKC4uUcj1umQyfjkXzuX6JtY+tRYzC1OFXFcdtba0YmKsncWgXC7n++eeY+XKlYwZo9n9soQmFkx3yNHRkbfffosffniS5uYGoeMonJGRKVKJlMY27T6Y0s9vCrNnPczFY/spycm84+uV19WQd7mEPr7i7TiRqkiwtVPMbbkumYwd5yMorKlnzVNrMbM0U0A+9dXW0oapiXYWTEH795OXlMT/Pug+DZeVRSyYFODhhx+mRw97du58VegoCieRSDA2MqNRy3cDAgwaNI2ZMzYQdnQfxdl3dnsuMiOVHq69MLfUrFPbRZrN1taV8rIy2tpv/7bcHzNL+VV1rH1qHeZW5gpMqJ7aW9sx1sKCqbGujm2vvca777yDvb290HE0nlgwKYCuri5bt27m+PGvSUq6IHQchTM2NqexVbtnmP4wZMgMZs96hIvHDlCYcfsLweMLcnDr76PAZCLRvzMxscDA0JDy2zxbrrOzi5/OhFFc18jaZ9ZhYa39xRJcmWEyMda+nX+bn38eLw8PHnzwQaGjaAWxYFKQoUOH8tJLL/HZZ3fT1FQndByFMjE21/pbcn81aNBU5s7ZSMSJg+Sn3Xpzy7aODjKLC3H16quEdCLRjUiwsHCi9DYKpo7OLradCaOssYW1T6/DohvMLP2hraVN62aYgg8cIPLkSX795Rd0dHSEjqMVxIJJgV566UVcXBzZsmWT0FEUysTEstvMMP3B13cSCxc+TdTpo+Qmx9/S52aUFKCvp499D/HsOJHqWVo6UFp6awVTe2cnP5wMoaqtnTVPr9PqBd7X0tbSiokWFUxVly/z3bPP8tmnn+Lh4SF0HK0hFkwKpKury6+//kxY2G+Ehv4mdByFsbZ1pryhSugYKuftPZa7Fv2H6HMnyEmMvenPS8rPw6V3H605Y0ukWSwt7WluaqKxqfGmHt/W3smW48HUy2SsfWqdVvZZ+jd1NXXY2NgIHUMhZDIZX2zaxPhx47jvvvuEjqNVxGd0BevTpw8ff/wR33yzgepq1RyGqWx29u5crq8UOoYg+vUbxdIlLxAbcIqsuKib+pzEwlxcvPooOZlIdG16eoaYmlne1CxTS3sHm48H0SLVYc2TazE21aLjTm5Sa0sbrc2tWrMo+vjWrRSkpLB1yxatb8apamLBpAQbNmxg9OiRfPnlfVrR0NLOzpXS+u43w/QHL69hLF/+CgnB50mPDr/hY+ubmygsL8W1t7h+SSQcSwsHykpLb/iY5rY2Nh8LosNAn9VPrtGus+FuQV11HRKpFGtra6Gj3LHCjAy2v/EGP2zZojUFoDoRCyYlkEgk/PjjVnJyojhx4luh49wxe/telNbd3q4bbeHhMZgVK14j+WIw8cHnkMtk13xcSmEellbWWNrYqjihSPT/LC0dKSsvRya/9s9pTUMTXx4MAFMjVj2+CkMjAxUnVB/11XVYWVuhq+ELozva2/n0kUdYu2YN8+bNEzqOVhJ7pCuJo6Mjmzd/z+rVa/Dzm0LPnpp7i8bevheV9ZV0dHWgp6On9PFOJAYQV5BCWmk22eV5dHR18sq8x5nrN+Wqx454a/6/Xu/wxq04WNz58SRubr6su/s9du76Ly0N9QybPg+dfxwzkFSQi0tvzf1ei7SDuYUtXZ2d1NbUXjVzUlxZw5bjwTj3dWPBPQvQ1VP+y0DisfMUxCRTmppFeWYeXR2dzHv9CfwWTFP62P+mtqoOOzvNP75oz0cf0dHQwCeffCJ0FK0lFkxKdNddd7Fs2XI+/XQN774biq6u8osNZbC1dUUul1NRX0UPK0elj/dd4K9crivH0tgcW1NrLteVX/ex949bcc2PF9Vc5mTSBdxtXRRSLP3BycmT++79iB07XyP4wC5Gz1uMvuH/38pILS7Ef8ZshY0nEt0OqVQXM3NrKioq/lYwpReWsv10GEPG+zP5rilIVbTGJfDLn6m7XI6xlTmmttbUXb7+77Sq1dfUYW+n2bev0qKi2P/llwQGBGBq2r12OKqSWDAp2RdffMaAAb789ts7rFjxmtBxbouxsRnmplYU1ZSqpGB6cc5juFj3wMnSnu2hv/F1wE/XfewDE1Zd8+MfnvwOgPmDFP8O1tLSnnvv+YDde94iYM/PjFu4HGNzC+qaGimvqaKnm7iNVyQ8czM7Kioq6Nv3ynq6qPRcfguKZsqiqQyfPEylWea8tglr155Y9rAn9Ic9BHy+XaXj30htZR0Deg8QOsZta2ls5LNHH+U/zzzD6NGjhY6j1cQ1TEpmbm7OL7/8xN6975CRESl0nNvWq9cAssrzVDLWcI9BOFne/ju+ts52TiVdQE9Hl1kDJykw2f8zMjJjzeo3cbL35NyubdRWlJFRUoSFhSVm4nEoIjVgbmFHRWUFMrmMM9Ep7AuKYeF9i1ReLAF4jByMZQ/1nMWpKC7HrVcvoWPcth9few07Kytee00z35BrErFgUoHx48fzxBNP8Omna2jV0AaQbh6DySzPFzrGTQlMu0h9ayPj+gzHysRCaePo6upz16LnGDhgEuf3/ERsWiI93D2VNp5IdCvMzG1obWtn57lwgpIyWf3EGvoP7id0LLXS3tZBZXkV7u7uQke5LZGnThGwZw87fvkFfX19oeNoPbFgUpE333wDKytjtm37j9BRbouHx2AyKjSjYDocdwaABYOmK30sqVTK9Gn3M2nCWhJyMjG1UF6BJhLdCrlcSk6LLumllaz7z724eIqd5/+pvLgMPT09nHr0EDrKLautqOCrJ5/kfx98QP/+/YWO0y2IBZOKGBgYsGPHL5w9+wPR0SeEjnPL3Nz8yCvPo6OrQ+goN1RSU0p0XiKO5nYM9xiksnEHDp5Ggxxa62oozEgBLei/JdJcza0t7A4LoEVHn17D3LF11I4u1opWVlyGay9XdDSsK79cLuebp59miJ8fjz76qNBxug3N+inRcAMGDODdd9/hyy/vo17DOme7unojB3Iri4SOckOH488iR85cvylIJar78U4qSsfI0JhZsx+gJDuTrIRouE4PHJFImWoa6tkREoCuhRUDp06lpFQ7ThxQhrKicjzcNW+TxtkdO0gND2f7tm3iEUwqJP5Lq9gTTzzBgAH9+fLL9ciu0/xQHenpGeDi5EVWWa7QUa5LJpdxLP48UomUeUrYHXcjCUWpuLn3x96+F1On3Et9RQUpEaHIOjtVmkPUvZVUVbAz5Dx27p6MXbAcR0d36upqaGzUzLWTylZZXIGHhq1fKsrKYuvLL/PtN9/Qs2dPoeN0K2LBpGJSqZRff/2Z7OwI9u17T+g4t8Sr3yjii9KEjnFdF7NjKG+oZLi7H44K7L10MzLK83ByvrLg29zchqlT1iNrl5EYGkhHa4tKs4i6p8ySQn67GITX0FH4T5uLRCrFyMgMA0Njysu7d6f+a+lo76Q4v4Q+fTXnGKPmxkbev+ceVi5fzvLly4WO0+2IBZMAevbsyb59e9m163Wio08KHeemDfSdzKX8JKFjXNcfi73nD1b+Yu9/yqksxMHR9c8/GxmZMHny3ZgYWZIQfJ6WhnqVZxJ1H7E5GRyPicB/6my8R4zlr+0oLSxsqajUrCUAqlCUU4S+gT4eHppxS04ul/PFpk3YmZvz1VdfCR2nWxILJoGMGzeODz/8H598sorS0hyh49yUgQMnUVRVRJkarr+qaaojJCMKK2MLxvcZrtKxO7o6KK4u+VvBBKCnq8+4sctxcuxNfMg56qsrVJpLpP3kcjkXkuMISUtm7MLluHn7XvUYU1M7KirU73dWaPkZeQzw8dGYBd/7v/iCtPBwDuzfj4FB9z37T0hip28Bbdy4kYiIKN5//y7eey8MAwNjoSPdkI1ND5ydehOdl8hsX+U0hAQ4FHua+MIUALJ+7/10KPY0MfmJAPi5eLPgH7NIxxPP0ynrZNbAiSo57+6vCqsvI5eDnd3V6wmkUilDh87F2NiCpNALePoOxqGX2KtJdOfa2ts4HhtJWUM9k5evw9LO4ZqPszC3pbwiRcXp/i52/ykKY5MBKM/Ku/KxA6fJv3Tld9plsA+D75qh0kwFmQVMm6j62ejbERsYyI733+fM6dM4O4vtIYQiFkwCkkgkbN78HaNGjeGrrx7gySd/QaKis51u1wC/KUQXJCm1YIovTOFYwvm/fSyhKJWEotQ///zPgulw3FlAmNtxuZWF2Ns4oqd37cZxEgn4+IzDwsKeiIhD1FdX4+k7GKmO+Osnuj0VdTUcjrqIvoUlU1ffj5HJ9c8PM7ewIyWlArn8ys+iEApjk0k4cu5vHyuKS6Eo7v8LOVUWTO2t7RTlFuO7caDKxrxdZQUFfPTgg3zw/vtMmDBB6DjdmkQuFxvGCC03N5chQ/xZsuQ15s9/XOg4NxQSsoefNz/BwUe/EzqK2tgStIvw6hzW3f/Kvz62ob6akNA9yCUy+g0fjYGxiQoSirRJSmEeZxOi8fT1x3fsFKQ6N76l1NrWxOFDn/Dkk09iYiL+vAFkJ+dwePthfv3lV5UdQHw72lpaeGHuXIb6+LDj11/V/g21ttOMm7dazt3dnV27drJ9+7MkJV0QOs4N+fpOpqy2jNzKQqGjqI2cykLsHFxu6rFm5tZMnXYfluaOxF44TW252CNHdHO6uro4lxDNuYQYhs9YwKAJ0/61WAIwMDBBX99QXPj9F1nJWfj6+qp1sSSXy/nmmWcwlMvZsnmzWCypAbFgUhMzZszgv//9L//73zIq1bg5pLm5LQO8x3I+NVToKGojp7LgqgXfN6Knq8+oUXcx0GcSKREhFKQni00uRTfU0NLE7rBAcmuqmbLqPlz63PxRGBLA3MKWSnHhNwAyuZz0uHTGjRkrdJQbOrZ1K9GnT3PwwAFxZlBNiAWTGnnhhecZP34MH3ywhI6ONqHjXNfoccs5lx4hdAy10CnrorCqGAfHm5th+oNEAn36DGPixLWU5eWSEhFKZ7v6fs9FwimoKOOXC2fRtbZh6ur1WNjceo8xMzNbKirEXZoAJbnFNDc14z90qNBRriv54kV+fO01du7YgaenuElEXYgFkxqRSCT89NM2ZLI6Nm/eJHSc6xo16i5yynIoqCoWOorgSmpK6ezqxM7+1gqmP9jZuTBj+gNIu3SIDzpHY12NghOKNJZcTlRmKgfCg+kzfAyj5y1FT//2tpNbmIutBf6QGpOKv78/RoaGQke5pqrSUv53//288vLLzJo1S+g4or8QCyY1Y25uzqFDBwgJ2cnp01uEjnNNVlaO+PQbLd6W48oOOVsrBwwMbv/J18jIlEmT1uLS05uE4POUF+YpLqBII7V1tHP4UhhROZmMv2sl/YaO4k5WsJhb2FFRKc4wyeWQHpvOWDW9HdfR3s7/7ruPMSNH8tJLLwkdR/QPYsGkhvr168dPP23n++8fIyMjUug41zR63ArOZYi35QqqirGzv/PznKRSKYMHT2Pk8AVkJ0STHR+NrKtLAQlFmqaqvo4dweeolcmYtvYB7F3c7viaZmY2tDQ30drWvW/7luSX0NjQyPBhw4SOck1bX36Z9ro6fv7pJ/FQXTUkfkfU1KJFi3j66af54IPF1NaWCx3nKqNHLyarNIes8jyhowiqvKEKcyvFnVvn4tqfaVPX01hVTVJoAG0tzQq7tkj9pRcXsCP4LLYefZi0bB3GpuYKua6RkRkADd38iJ7EiAT8hw7FyMhI6ChXObtjBxf27uXQgQNYWFgIHUd0DWLBpMbefPMN/Px8+PDDZXR1qdep99bWTowYNpd9GnQWnjKUNVRhbmGj0GtaWNgxbdp6zI1tiQs8Q22FeHCqtpPJZAQmxXEqLoohU+cwZPJMpDo6Cru+VCLF0MiEhvoGhV1T07S3tpMYnsTsmeq3LigzLo7vnnuO7du24e3tLXQc0XWIBZMa09HRYdeuHdTVFbBt23+EjnOVWXMe40RSIE1t3XcWpLyhCgtLxRZMAHp6Bowes5T+/ceQEh5EUWbqlQUYIq3T1NrCnouBZFaWMWXFvbj1V073aWNjMxoaGpVybU2QFJWEpZUlfoMGCR3lb2orKnj/3nt54vHHWbx4sdBxRDcgFkxqztramkOHDnD27BaOHPlc6Dh/4+s7GWsrJ04mBQodRTAVDdVYKHiG6Q8SCfTvN4oJ41dRkpVJSngw7a0tShlLJIzs0iJ+DjyNxMyCaavvv+55cIpgZGjWbW/JyeUQExTL7Fmz1apZZUtjI2+tXs0gHx/efvttoeOI/oVYMGkAPz8/DhzYz/btzxISslfoOH+SSqXMnPMo+2JP0x1P2OmSdVHdqLyC6Q/2Dm7MnPkQBjomxJw/SUVxgVLHEylfW0c7J2OjOBYdQb/RExizYDl6d7DT8mYYGJpR39A9b8kV5RRRWV7JlClThI7yp86ODj584AFMpFL2/fYbOgq8BStSDrFg0hDTpk1j69YtfPbZ3Wp1fMrkyesorComrlDY09CFUNNUh0wmU3rBBGBkZMK4cSsY7DedrLhLpF+6KDa61FCFlWX8FHia8rZWpq95EK9Bw1Ry7IWRkXm3XcMUHRTN2LFjMTczEzoKcOXYk6+ffprK3FxOHDuGqen1D08WqQ+xYNIga9as4fXXX+eddxaQn58kdBwAzMysmTxpHdsv7hc6ispVNFShp6uHsYlidjL9G4kEPDwHMXPGBuTtcmICTlFTJp5Fpyk6uzoJTIpj/8Vg3AYNY/KKezCzslbZ+MZGZtR3w4KpuryalOgUFsxfIHSUP+147z1iz57l9MmTODgo7zasSLHEgknDPPvsf7j33nW88cZMKirU4wDcpctfIionjsSiNKGjqFRdSwMmxmYqPxTT1NSSSRPvpn/fMaRGhpIdfwlZp3rtohT9XVlNNT9fOEtOTRVTVt6L94ixSFTcZ8fAwJjmbtimIvRkGP5D/fH08BA6CgAntm3j8LffcuLYMXr37i10HNEtEAsmDSORSPjkk48ZN24Ub7wxk8ZG4Y/SsLNzZeq0+9gcskfoKCrV1NaMoaGxIGNLpRL69RvJ9On301LbQGzgaeqrxE7O6kYmkxGWlsSukPM4ePVn6ur7sbJ3FCSLrp4B7d2scWV1eTWJkYmsXrVa6CgAhB8/zpaXXmLfb78xTE2bZ4quTyyYNJCOjg6//vozrq52vPPOAtrbW4WOxNKlLxGTl9CtZpma2poxNBCmYPqDhYUdU6eux62XH0lhgeSlJIgdwtVEVUM9O0LOkVRSxPjFq/GbMBUdXV3B8ujp6tPe0d6tulOEnAhl2LCheLi7Cx2FlIgIPtqwge+/+048I05DiQWThjI0NOTw4YN0dVXzySdr6BL4RdLOzoVp09bzffBuQXOoUlN7i2AzTH+lo6OD78CJTJq8jtrSMuKDztJUVyt0rG5LLpcTk53OrxfOYNbDlel3P4i9s6vQsdDTM0Auk6ldE1xlqSqrJikqiVUrVwkdhcKMDN5Zs4aXX3qJe+65R+g4otskFkwazNLSktOnT5KbG8HWrU8IvrV/ydIXic1P5FJuvKA5VKWprQV9Q/U5YsHWpiczpj9ID/vexAed+b3ZpUzoWN1KfXMTe8MCicjOZNS8xQydPhc9fQOhYwGgq6cPQFs3uS0XeDiQYcOH4S7w7FJVaSlvLF/OyuXLxQN1NZxYMGk4Z2dnTp8+SXDwL+zf/4GgWWxtnbnrrmf54PRmOro6BM2iCk3tzRiowQzTX+nq6jLEfybjxq2kLDeXhJBAWpq6384olZPLSS7I5afAU0gsrZmx7iF6uHsJnepv9HSvFG5t3aAdRU5KDpmJGdx3732C5miqr+fNFSsY4e/PV199pfINIiLFEgsmLeDj48ORI4fZteu/BAT8LGiWJUtfpA0JOyMOC5pDFZrVbIbprxwd3Zk5YwNWZg7EBZzmcm6meLSKkjS3tXIoKpTA5HiGTJ3DqHlLMFDDw12lOrpIpFLa2tqFjqJUnZ1dnNpzmqVLl+HkKMwCe4COtjbeW7cOe3Nzdu3cKTam1AJiwaQlxo0bx6+//sKXX95PTMwpwXIYGBjxwENfsTVkN6V12r1rq7mjFX195XZnvhP6BoaMHLmQESMWUpiWQkJIgLi2SYHkchnxuZn8eP4ETbr6TF+3gV79BqCucwgSQFdHj44O7Z79DT9zEV2JjqDnsslkMj577DFaKis5dvQoRmpYQItunVgwaZHFixfz8ccf8cEHS8jKihYsx7Bhc/Dzm8bHZ7YKlkFVJGr78vj/XFz6MXv2o9hauhAfdIbsxBixS/gdKqmu5Negc1zMSmfwlNmMX7IaY1PVNDC9E9p+S6i2spbQk2E89NDD6OvpCZbjx9deIz0igtMnT2JtrbrmpCLlEgsmLfPYY4/x2GOP8uabsyktzREsxwMbviA8J4agjAjBMoj+n4GBEcOGzWbKlHtpq20k5txJygpyxUXht6i5rZVTsVHsDQ3A2sOLmfc+otazSv8k9MYQZZLL4dTuUwzxH4L/kCGC5Tj4zTec37GDUydO0KtXL8FyiBRPLJi00Hvvvcvs2TN4/fUZ1NSUCpLB3r4X6+75H28d+4rKhmpBMiibRCJBjma9AFlbOzFlyn34+U4lPzmBhJAAGmu18/ujSDK5jNicK7ffKmVdTFvzAH4TpqnNDjgRXLoQxeX8Uh7a8JBgGQJ/+41f3n6bgwcO4OfnJ1gOkXKIBZMWkkgk/PjjVnx9+/Laa1OorS0XJMecOY/i1W8U/z3yGTJxJkNtSKUSPDwHMWf2o9hZ9SIh+BzZ8dHibbrrKKmq4NcLZ4nIyWDI1LlMWLoWCxs7oWOJ/qKsuJxzB87zxOOPYyPQLbDgAwf44vHH+eXnn5k8ebIgGUTKJRZMWkpPT4/9+3+jTx9XXnttCnUCLMCWSCQ8/sR2sqqK2RF+SOXji25M38CIoUNnMWXKfbQ1NBN97iRl+dnibbrfNbe2cDImkr1hgdj07svMex7Gta+3xtx+uxZNmxG9GR3tnRz68SDTp05j+PDhgmQIPXyYTx97jO3btrF06VJBMoiUTyyYtJihoSGHDh3A07MHr702lfr6KpVnsLCw44mnfuHbC7+QejlL5eMrkyYs+L4Z1taOTJl8L4P8ppGfkkxCcACNNd33Np1MLiM2O4Mfzp+kGjnT1zyI3/ipWnP7TdsWfp/bfxYDHQPuvU+YnksXjx3j44cf5oetW1m5cqUgGUSqIRZMWu6PI1R69bLjv/+dSoMA64kGD57G3LmbeOnAR9Q116t8fGWRSiTIZNoxGyOVSvDw8GPO7Eext3EjIeQc2fGX6Ohmt+mKKsv5+cIZInOzGDpjHuOXrMHcxlboWAqlTeVS8qUU4i7G8+x/nsVAX1/l40ecPMlHGzaw+fvvWbNmjcrHF6mWWDB1A0ZGRhw9epiePa3473+n0dhYo/IMa+9+F5seXvxn3/u0d2pHHxhjfUPaW1uEjqFQ+gaG+PvPZOrU9bQ3thJ97jiledp/m66ptYXjMRHsuxiEvZc3M+99GBev/lpVXMjlMjo72tHXkpmyopwijvx8hMc3bhJkN1rU6dP87/77+ebrr1m3bp3KxxepnlgwdRPGxsYcO3YER0czXn99Bo2NtSodX1dXj+df3E91RxvvHP9KK7Y3mxqY0NraJHQMpbCycmDK5HvxHzyTwrRk4oPOU1+pfY1IO7s6uZSVxo/nT1AnkTDj7gfxHTf5z3PXtElH55UO3waGml8w1VTWsvfb31iyeAkTJkxQ+fjRZ8/ywfr1fPXll9wn0K1AkeqJBVM3YmJiwvHjR7GxMeCNN2bS1FSn0vFNTa14+bXjhOXG80PoXpWOrQymBsa0tTQLHUNpJBJwc/Nl9uzHcLTzJOniBZIvBtFQo/q1cIrW1dVFXG4mW8+dIKYwj6EzFjBu8WrMrGyEjqY0nR1XCiZDA80umFqaW9nz9R6GDB7CyhUrVD5+bEAA7917L599+in333+/yscXCUcsmLoZU1NTTp48jpWVriC355ycPHnhpUNsC93LqaQLKh1b0Uy0eIbpr/T1DRgyZDqzZz+KlakTiSHnSYkI0cj+TTKZjMT8bH4IOEFkbhbeYycz655HcfHqp1W3366lvaMVJBL0BFjroyhdnTIObDmAtaU1jz/+uMoXsEedPs07d9/Nxx99xIYNG1Q6tkh4YsHUDZmZmXH69EkcHEx49dXJ1NdXqnR8b+8xbHp8G28e/YLgjEiVjq1IpobGtGrxDNM/mZiYM2zYbGbNegRzQ1vig8+RFhlGc32t0NH+lUwuI6Uglx/OnyQ0M40+I8Yz695H8RgwCKlO93ga7OhoQ1/fAKmG7pKTdck4+ONBmmubeeWll1V+9MnFY8d4/777+OLzz3nkkUdUOrZIPXSPZwrRVUxNTTlx4hi9etnxyiuTqKkpU+n448evYMNDX/PC/g8IE/DcuzthamBCSzeYYfonU1NLRoyYx6yZD2GsZ05s4BnSL12kpUH9dkDK5XLSi/LZHnCKC2nJeA4bxez7HqO3nz/SbnZ6fEd7KwYaejtOJpNxaNthqooreeeddzA3V+25fSEHD/Lhgw/y/Xff8cADD6h0bJH60BU6gEg4xsbGHD16mEWLFvPKKxN5/fVz2Nj0UNn406evRybr5LnNj/PuXc8y1muYysZWBDNDE5qaG5DL5Qq/NRB96Ty52ckUFWVxuSSPrq5Olq98gmEjpl312GeemPOv13v5tW1YWim2O7WZmTWjRi3C23ssSckXiAk8hX1PV5z7emNkYqbQsW6VXC4n63IRYenJNLa30W/YGLz8hqGjp9qnvNzAM0R9/ekNH2M/wI+Jr76j9CztHZpZMHV1yji8/RBl+WW899772Nqodp1ZwN69fPnEE2zftk3ss9TNiQVTN2doaMjBg/tZunQ5L788gTfeOI+dnYvKxp85cwNSqQ7Pf/soby58ikn9Rqts7Dtlb2ZDe0cbLS2NGBsrtkA4eexnamrKMTExx9zcmpqa6x9vM23Gqmt+vKqyhJjoQBwcXRVeLP2VhYUdY0Yvoba2jITEQGLOn8TBxQ2XPt4YGJsobdxrksvJLishLD2F+uYm+gwbhdegYYI1nbR088B7ybW/P0URodQX5uPop5qDYpub61U+M3OnOju7OLD1ALWlNbz/3vvY2an2SJozv/7Kd889x84dO1i8eLFKxxapH7FgEmFgYMC+fXtZuXL1n0WTg4ObysafPv1+9PQMeeWL+3h8SjVLh81V2dh3wtbMGh2pDjXV5QovmJau2IStXU+sre05f3YPx49uv+5jZ8xafc2PH9j3DQDDR0xXaLbrsbR0YPy45VRXXyYxMYDoc8dxcPXAuY83BkZGyh1cLievopSw9BSqGurp4z+ScUNGoCfwjIqVmydWbp5Xfbyrs4OsU0eR6OjgNmGKSrI0N9dhaWmpkrEUoaWphX2b99Pe2M57772v8jPiTmzbxtaXX2bvnj3Mnz9fpWOL1JNYMImAK2fP7dq1g7vvvoeXXhrPG2+co0cPL5WNP2nSGszNbfnfB8vIqyrmyen3oytV7zUmOlIdbM1tqK2toKfz1S+Kd6JP38F39PkdHe3ERAeio6OL/zDVHgRqbe3EhAmrqKwsIikxkOizx3By642zV1/0DBVcOMnlFFaVE5qWTEVdLb0HD2fU0JHoK3ocBSuJDKe9oZ6ew0ZhaGmlkjFbmutwc1N9g8fbUVVWzZ5v9uBo78jb/3sbM1NTlY5/+Lvv+Pmttzh08CAzZ85U6dgi9SUu+hb9SVdXl59/3s7cudN5/vnRpKdHqHR8f/+ZfPC/cELyk3hq91s0asCCakcLe2qqr3+7TCiJCWG0NDfiM2AEpqYWgmSwtXVm4qQ1TJiwmraGZqLOHCMzJlIh7Qg6u7pIKczj1+Bz7A8PwbyXB7PXP4bv2ElqXywB5Jw/BYD7FNXM/sGVGSYLc2F+Fm5Fbloe2/73I0N8B/PG62+otFiSyWT89Oab7HzvPY4eOSIWS6K/EWeYRH+jo6PDli2bcXZ25pVXJvH007sYMUJ109Gurt787+Mo3n1rAeu3P89HS1/E2dpJZePfKidzuxuuLxJKZPhpAEaMnCFwErC3d2XypHVUVRWRmRlJfPA5zCxtcPLoja2TMxLpzb9va2xpJi4vi8T8XKS6engM8mfkgCEYGhsr8StQrKaKcsoT4zGyscVxkL9KxpQDTU11WFiod8EUExzDqT2nuWfdOubPn6/SPksdbW188fjjpISGEnThAkOGqGZtmUhziAWT6CoSiYTXX/8vrq4uPPzwUtav/4TZs1XXd8TCwo433wngqy8f4O4fnuK5mQ8xY4Dqjz+4GU4WdsRWlwgd42+qqkrJzkrA0soOrzu8tacoEsmVGSdbW2cGtTSSnR1DVtIl8pLicHDzwKmX5/Vv18nlFFVVEJuXRfblYmydejJ42hycPfveUrGlLnIDziCXy3CbMBWpim47d7S30NnZobYFU2tLG6d2nyIjPoMXnn+e4cOHq3T8xro63r/nHloqK4m4eBE3NzeVji/SDGLBJLqu9evX06NHD5YsWUplZQFr1ryDVEUvUHp6Bjz+xHbO+07h3W8fJSwnhv9MfxBTQxXvuvoXPS0dOZtzSegYfxMVcQa5XM6w4dNU9v26FUZGpgwYMJ7+/cdQVJRKRkYkURlHsXVywcnT68/jSTq6Okkvyic2N4eapnp69R3AtEkzsbRzEPgruH1ymYy8wDMgkeA++eoWEcrS0FiDVEcXE1P1+v0BKMgu5Mi2I9ja2PL555/j5Oio0vEriot5c8UKetjYcD40FGsVLy4XaQ6xYBLd0KxZswgKusCsWXOorCxk48Yf0NNTzc4jiUTClCnr6N9/DJ98tIo1W5/i9fmP4+firZLxb0ZvBzculxXQ1dWJjo7wv04ymYyoyLNIJFKGj1TdC/Lt0NHRoVevAfTqNYDq6stkZEaSGBKA1NSMWkMjsmur0TMwxHPQUMYOGKQRa5P+TVliHM2VFdgP9MPUXnWFQW1tGXa2dkgl6lNAy7pkBJ8IIexUGEsWL2b5ihXoqriZaG5yMm+uXMmEMWP45eefMTQ0VOn4Is2iPr89IrXl7+9PZGQ4xcXRvPHGLBoba1U6fo8evXn3/VDGT1vPo7++wpfnt9PS3qrSDNfjYeeKTC6jvLxI6CgApKdFU1dbSZ++g7Cyshc6zk2zsnLCxs2POgd3LjbUkVlVgQwJrv0H4NpvgFYUSwA5564s9vaYrNq1ZXW1ZTg6qc/MXFlRGds//onUqFTefvttVq9erfJiKT4oiBfnzWPtihXs2b1bLJZE/0osmEQ3xc3NjfDwMIyN23nppXFUVBSqdHxdXT1Wr3mTN98KIKgwlWXfbeRcaihyuVylOf7JQFcfF5uelBTnCJrjD38s9h6uBou9b0ZbZzthWdH879T3/BC6F2MnLzY8+CX/eW4v8+c9TsPlao5t/ZKLxw5QUVyAsN/tO9PWUE/JpXD0Tc3oOVy1DVpr68pwcBC+YGppbuXUntP88P6P+Hj58MXnX+Ddv7/KcwTs2cObq1bx1htv8PHHH6vlrWuR+hH+HoJIY1hbW3P+/FlWrVrD88+P4uWXj+Pu7qvSDN7eY/jksziOHfuKt3a8yoHY0zwz/QHcbJ1VmuOv+jh4UFKci/9QwSIA0NhYR0pyJCamFvgMGCFsmBto7+wk9XIm8YVppFzOxMzUiqHD5+PrNwWjv6xR8+4/Bu/+YygtyyUq6ihB+3diZGKGc5/+uPT1xtLOAU06RjY/6Dyyzk56jZuEjgoPjpUjp7a2DEfHSSob859kcjmJ4YmcP3geJwdHPvjgA7x691Z5Drlczm+ffsqejz/m119+YcmSJSrPINJcYsEkuiWGhob89tsennrqaV58cRzPP78fPz/VdCr+g66uHgsWPMH48SvY9uMzrN68iWXD5nL3qMVYmah+F1BfezfOFiUo9JoRF0+Rm5MMwOXLeVc+Fn6a7KxEANw9fBgx6u+zSJeiztHV1Yn/0Mno6qr2JPd/09HVSfrlbOKKUkkuzkDfwAjv/uNYNXktzs79kd5g+7ijgzvz5m5k6tT7yMiIIDkliLM7fsDU0gpnr3649vXBwka1R2bcjtzzV2b/3KeodvavqamOzo527O2FmWEqyCog4EAANRW13HvPvUydOvWG329l6ers5Pvnn+fi4cOcPXOGsWPHqjyDSLOJBZPolkmlUj799BN69XLl+efn8OijW5g0aY3Kc1hZOfLkU78wY+ZDbPvhGfZ/9SCL/WexZuRCrE0sVZbDy8Gd7REHFXoIb25OMpeizv3tY3m5KeTlpvz5538WTP/fe0l1zRBvpFPWRUZpDnGFqSSXZKCjo0e//mNZPn4Vri7et3wbxMjQBD/fyfj5TqalpZG09IskpwRzOmoz5la2OPfph0sfH8ytVXs4682oykqnrjAf6959sHR1U+nYNTWlmJlbYKjiY2LyMwsIOR5CYXYBM2fMZOWqVSrv2P2H1qYmPtqwgcvp6VwMC6Nfv36C5BBpNolc6EUgIo22d+9e1q69m+XLX2Xx4udV2mjur+RyOXFxZ9m141Vyc+NY7D+b1SMWYGOq/GMn6lsamf7xav7z/LfYOwh3a1AddMlkZJXlEVeUQmJROhKpDn37jcbbZxxuvQYope9QU3M96WlhJKUEUZCfgqWtA859+uLaxxtTS3GLeGzcGfT0Grlr0SKVjJeXkU/I8RCKcgqZOWMmi5csUfk5cH9VU17OO2vXYgScOHYMRxW3LRBpD7FgEt2x4OBg5s9fwKhRy3nwwS8E3V4vl8uJjz/H7h2vkZUdw+yBE7nLfxZ9HNyVOu6arU8ycOxMRo+ZrdRx1JFMJiO7ooC4witFUpdcTt9+I/H2GY+7ux86KjwTsLGphtTUMJKTgygqSsPargfOffvi0scbE3NLleVQJ2fOfM+o0cMYMlh5TUw7O7tIi0klJjiGkoLLVwqlxYsFLZQAirOzeXPFCgb07cu+337DVKAZLpF2EAsmkUKkpqYyY8YsevQYyNNP78JQ4AaTcrmcpKQLHDvyORGRR+jX04vFg6YzxXsshkroI/XZ2R9Iaa1izT3PK/za6qilvZXs8nzSy3JJKEqjQ9ZJnz4j8PEZj7vHIHR1hF9D1dBQRUpqGMnJFygpycTGsSc9PPrg0MsdK3sHJGrUk0hZ2tpbOHTwIx559BGsrRRfvFRX1BAbEkvCxXj0dPWYMWMms2bNwtpKNQcK30haVBRvr1nD4oUL+fbbb9FT4UJ7kXYSCyaRwly+fJnZs+dSV9fOc8/tp0cPL6EjAVBVVcLZsz9w+uS3tDY3MHvgRGb4jMe7h5fCbiGGZEbx+rEveOXNXwS7LalMHV2d5FcWkVGWS2ZFAUVVxZgYm+PuMYh+/Ubj6emvdgvN/6quvoKUlFAysyIpKkxDV08PO2c3HFyv/M/UykajdtzdrMKiNBITTvH444+jqB/LttY2spKySbiYQE5aDgN9BzJn9hyGDRum8l5K1yKXyzn1889sefFFXnzhBV599VWt/J0UqZ5YMIkUqqWlhYceeph9+w7w+OM/MXLkAqEj/amrq4uYmBOcPvkdMbGnsTa1ZlLf4UzuN5qBzv3uqAtyY1sz0z5axVP/+RJHp14KTC2Mzq4uCqtLyK0sJLMin9yKAnSkerj1GoCbx2Dc3f2wse2JRAPLjI7OdgoLU8nNjSMnN5ay0lyMTc2xd3HDzqUXdj1cMLG00sCv7GqxsSfRN2hj0cI7+z1saW4lMzGT9Ng0slOyMTc3Z8L4CcyYOZMeTupzOHZbSwvfP/884UeP8vNPP7Fggfo8/4g0n1gwiRROLpfz/fffs2nT4yxY8DSrVr2Bjhq88/yr5uZ6oqKOcTF0L9ExJzA1NGVinxGM9BjEIBcfzI1ufa3Duh+fod/IyYwdN08JiZWrqa2FvMoi8qoKya0sprC6GIlEinPPPri5XymQnJw8VXZYrCq1tDSSl59Ibm4cefkJVFcWY2Rihm0PF2x69MS2pytWdg4aedDv6dPfMXbsSAYN8rulz5PLoaqsiryMPLISMslJz8XWxoYxo8cweswYvLy8BGkNcCOl+fl8cN996Hd1cfDAAby81GOGW6Q9xIJJpDSRkZHcddcS7O378vTTOzE3txU60jW1tTUTHX2S8LDfSEg4T01dOV5OvRni4s3QXgMZ5OqNmeG/F1DfBv5CeGU26x96QwWpb49cDvWtDZTXV1FWX8Hl2gpyq4ooryvHxMQCVxdvnF18cHH1xsHBXaULttVFS0sjRUWpFBSmUFCQxOXL2UilUmwcnbFycsLC2hZzGzvMrGzQVeN1MU1NtRw79iUbN23Cwtz8ho/9o0AqyMwnPyOf/MwCmhqacHN3Y6j/UEaPHo2Hu7va3tqKPnuWTx5+mNmzZrFl82ZMTNTvkGGR5hMLJpFSVVRUsHz5SlJSMnn22d/w8homdKQbksvllJRkkpgYSFLCORITA6mtr8DN3g0v+170setFbwc3vOzdsTG1+tsLSEZpDvf88DSvvfUrxsZmgn0NcKWzcm1THaX1Fb8XR5WUNVRRXl9Ba3srRoam2Nk4Y+vQi57O/XFx8cbKykEjb7EpW2dnByWXsygqTKH4cgaVFYXU1JQi6+rExMIac2sbzKysMbexw9zaFnMbW/QNhD+XLC09nIryVNavv/dvH+/qlFFVVkVZcRnlxWVUFFdQWlhGY0Mjbu5u+A70xc/Xl/7e3piqeeEhk8nY8/HH7P3kEz768EMee+wxtS3qRJpPLJhEStfV1cXLL7/Cxx9/zAMPfMH06fdrzJOaXC6nuDidzMxL5OXFk5cdQ15+IjV1FViaWuFp70ZPCzuczO1wMLfl6wu/MGLqYsaNnYuuEmdnOro6aWhppLalgfrf/1fb0kBdSwMVjTWU11fS2dmOmYkVtnYu2Nq6YmPngp2dC7Y2LhibmIvF0R2QybqorimlqrKQisoiKirzqawspKqqmM72NoxMzDC3tsPYwgIjUzMMTUwxNjXD6Pf/GRgZK+13QA50trVy/uxPuDn3wMneibrqWuqrG6guq6b8cjlymQynnk64u7nj4e6Bu7s7ffv1U/sC6a8aa2v55JFHKEhKYt/evYwZM0boSCItJxZMIpU5dOgQa9fezciRS3jwwS8xMNDcE+hrasrIy0sgPz+R8vI8KstyKS/Pp7Akgw5jQ/DxwdTQFEN9Qwz1DDHSM8BQVx9DPX2M9Aww0DX4S7nyl/+SXLk90iHroLWjjbbODlo62mnr/P1/HW00tzfT3NoEgIG+MWamVpiZ22BqZoOZuS3W1k7Y2bpiY+uMoYGxyv9tujOZXE59feWV4qmykNq6cuobKqmvr6KxsZqmxlpkXZ1IpToYmpiib2CErr4+evr66Orro6P3//999Xqx/3+qlnV10dHeRmdbOx1tbXR2tNPe1kZHWyutLc10trcCYGVtjYODA/b2dtjbO9DDyQl3d3dcXF3RV+Pbif8mOyGBD+67D09XV/bu2SM2oxSphFgwiVQqMzOThQvvor1dj2ef3Yejo3IbSqpadnYMz/xnJHd/cY62zjba2pppbWuira2ZtrYmWlubaWttpKO9hSu/ef//6yf/y3/r6Rmib2CMgYExBvpGGBgYo29ghL6+MUZGZpibWWNqZoO+EnpKiZRHJpfT0lxPQ2M1jY3VtLU209beQltbM+3tLbS3Nf/+M9OMTNb5l8+8UlT/MSsllUoxNDTFwMAEQwNjDAxMMDC88v9GRmbk5MRSUnyJTz/9WICvUrnO7drFd88+yyMPP8x7770n9lcSqYx4lpxIpby8vIiMDOf++x/kmWf8efLJX/H3nyV0LIXx8BiMtY0zVVmJ+E5eJnQckZqRSiSYmFhgYmIBSuw+HxiwjUmTtetw2Y62Nra+/DIXfvuNn7ZvZ+nSpUJHEnUzmrdPVqTxTExM2LHjF95663XeeWchu3a9gUwmEzqWQkgkEiaMX0Hi2V1CRxF1U1XVJRQWpTN2rPYUTBXFxbw0fz5Z4eFERUaKxZJIEGLBJBKERCJh48aNBASc59y5b3n77Xk0NtYIHUshZkx/kLzEUCoLM4WOIuqGYqJPMHjQEBzs7YWOohDxQUE8PWUK3u7uXIqMpH///kJHEnVTYsEkEtSYMWOIi4vBwKCRZ54ZSk5OnNCR7piDgxtDhswk5sR2oaOIupmOzjYSEs4yZ47m3+aWy+Xs+/xz3li5kpdfeIHf9u7FzEzYdh2i7k0smESCc3R05Pz5syxfvpBnnx3JgQMf0tXVJXSsOzJn9qMknN1F+++72UQiVUhOCsbExIQh/v5CR7kjlSUlvL5sGce++47Tp07x9NNPa0wrEpH2EgsmkVrQ09Pj448/4tChg5w48QmvvjqJ0tIcoWPdtsGDZ2BmYkXyhQNCRxF1I7Gxx5k1eyY6GniMC1yZVQrYu5dN48fjbGFBcmIiEydOFDqWSASIBZNIzcycOZPk5ER8fJx54gk/Tp/egiZ2vtDR0WH2rIeIOfajRuYXaZ7ikgxKy/KYNm2a0FFuS31VFR+sX8+W55/ny88+4+CBA9hryToskXYQ+zCJ1Nbu3bt5+OFH8PIaxSOPbMbaWn1ORb8Z9fVVrH/AjUXPfU/vYZr5IqZoief3UpAcTmlWPOV5qXR1tjPvic/xm7byqseWZieSEnKI0qx4SrMSaa6vwnXgaO5+75AAydXfnr1v4eJszuOPbxI6yi2LPHWKr596Cl8fH7Zv24arq6vQkUSiq4gzTCK1tXz5cpKSErGykrFp0wBCQvYKHemWmJvbMG/uRoJ3fCjOMv0u8Od3iT35E3XlRZhaO9zwsenhJwjb8xn5CWGYWIkzDTdy+XI2WVnRLF+uWb2/mhsa+PKJJ/jf/ffz2ksvce7sWbFYEqktsWASqbUePXpw4sQxPvzwPb788l4+/ni1RrUfWLTwGaoKM8mKOiN0FLUwZ9MnPPZjDE/tTGPIrHU3fKz32Pms/+wsz+7LY/VbmlUsq1pwyA4mT56sUUeEJIWG8sSECVSmpxMXG8umTZuQaujaK1H3IP50itSeRCLhgQceICEhnvb2AjZtGkBs7GmhY90UMzNr5s/bRIg4ywSAx+AJWNq73NRj7Xr1w6m3Hzq64tEXN1JyOZOsrBiWL9OM2aX21lZ+ePVVXlu2jIfWryc8LIx+/foJHUsk+ldiwSTSGJ6engQFBfLss0/w9tvz+fbbR2jVgG37CxY8RXVxNpmRmlHkiTRLcPBOpkyegoPDjW9xqoOs+HiemTaN1MBAwkJDee2118Sz4EQaQyyYRBpFR0eH//znP1y6FEVhYRhPPjmI1NQwoWPdkJmZNQvmP0Hg9rfo6uwQOo5Ii+TlJ5GbE8cyNV+71NnRwa4PP+S52bNZOHMmsdHRDB06VOhYeYWOyQAAEuFJREFUItEtEQsmkUYaOHAgly5Fcvfdy3j55Yn8/POLdHS0Cx3ruu6661m6mpuJOrJF6CgiLdEl6+L0qW9YtOgutT4GpSgrixfmziV41y5OnzrFJ598gpGRkdCxRKJbJhZMIo2lr6/PO++8zYULF4iO3suzzw4nNzdB6FjXZGhowoP3f0rwrx9QX3lZ6DgiLRAZcQiZrI1ly9TzIFqZTMaR77/nqcmTGe3nR1JCgtiEUqTRxIJJpPFGjRpFQkIc06eP4emnh7J161M0N9cLHesqI0cuxNt7LOe2vip0FJGGq6+vIjh4FxseehADAwOh41wlMzaW52bN4uDnn7N71y5++OEHzM3NhY4lEt0RsWASaQUTExO+/vorgoODyM+/wKOP9iUg4Be12pkmkUh46MEvSb94gty4IKHjiDTYmbNbGOg7kBHDhwsd5W/qq6r46qmneH7OHKaPGUNGWhrz588XOpZIpBBiwSTSKiNHjuTSpUjeeus1fvxxEy+/PEGtbtM5OXmyZPFznPzqWdpbGoWOI9JAGZmRZGZG8dCGB4WO8qeuri5ObNvGo6NG0ZCTw6VLl/j888+xtLQUOppIpDBiwSTSOjo6Ojz00ENkZmYwYkQ/nnlmKFu2PEFTU53Q0QBYuvRFzA3NOfP9y0JHEWmYhoZqjh79jPvuu1dt2gikR0fz3MyZ7PvoI778/HNCgoPx9fUVOpZIpHDiWXIirRcZGcnDDz9KXl4hd9/9AZMmrUUikQiaqagonSeeHMLcJz/He9wCQbOoUuypnylMjgCgPC+V0uwEnL2HY+3kDoCLzwgGz1gLQGVhJmF7PwOgs72VlOBDmFja4ek/+c/rzX/qSxV/BcKRyeXs2vkaVtZ6vPrKy4L/DNdVVvLzW28RsGcPjz76KK+//rq4Tkmk1cSCSdQtdHV1sXXrVp5//gV69OjPgw9+hbu7n6CZTp/eypYfn+b+LwNuuvu1pjv88WMknNt93b/3nbL8zyIoLyGUX15YeMPrvXysQpHx1FpY2H6iow/x5VdfYCFgYdLV1cWp7dv59d138Rs4kG++/poBAwYIlkckUhWxYBJ1K1VVVbz00sv88MNWZszYwKpVb2JqailIFrlcznvvL6W4tog17x1EqqMrSA6R+iu5nMX27c/y6quvMnjQIMFypEVF8f3zz9NQUcEnH33EihUrBJ/pEolURVzDJOpWbGxs+PbbbwgLC6OsLIpHH+3D2bM/IpPJVJ5FIpGw8bHNNFVcJmDbWyofX6QZmprrOXDgfebPmy9YsVRbUcHnmzbx0oIFLJg2jcz0dFauXCkWS6JuRSyYRN3S0KFDCQ8P43//e5dffnmWF18cS3Z2jMpzmJpa8fKLB4k5vo2Ykz+rfHyReuvs7OC3vW/j6tqDtXevVfn4XZ2dHN28mUdGjaLj8mXi4uL48MMPMTMzU3kWkUhoYsEk6rakUinr168nMzOdCRMG8+yzI/n224eprlZtJ25Pz8H855mdnP7meXJiL6h0bJH6kiPnyNHPkNPE888/h66OjurGlsuJDQzkmWnTOPLVV2z57jsCzp/H29tbZRlEInUjFkyibs/a2pqvv/6KiIgImpsz2LDBgx9+eJra2nKVZRg+fB7r1r3P/nfXU1GQrrJxReorKGgnBQXxvPbaq5gYG6ts3KTQUF5esID31q1j6dy5ZKSlsXz5cvH2m6jbEwsmkeh3gwcPJiDgHCdOHKesLIINGzz46acXqK+vUsn48+c/zsTxK9nz31U01qiuWBOpn4TEAMLD9/Pqq6+o7GDdtKgoXlu8mNdXrGDKiBHk5uTw7rvvYmpqqpLxRSJ1JxZMItE/TJo0idDQYA4e3E9e3nk2bHBnx47XaGysVeq4EomEDQ9+gYfLAHa+tITmOtUUaiL1kpoaxrFjX/D0U0/Rt08fpY+XGRfHmytX8vLChYwaMICc7Gw++eQTtWmMKRKpC7FgEomuQSKRMH36dCIjw9m1awepqUfYsMGdPXveprm5QWnj6ujo8sLz+3C06smOl5bQXF+ttLFE6ic9PYKDhz5k06ZNjB49Wqlj5SYl8c7atTw3axZ+bm5kZmby1Vdf0aNHD6WOKxJpKrEPk0h0E2QyGQcPHuSVV16juPgyixY9x+zZj2BoaKKU8dramnn9zblUNpSz8u3fMLGwVco4IvWRmhbGoYMf8cgjDzN16lSljVOQns6uDz4g/PhxVq5axX9few0PDw+ljScSaQuxYBKJboFMJmPPnj28+up/qaqqZdGi55k16yH09Q0VPlZbWwtvv7OQ4sp8Vr3zG2bWjgofQ6QekpIucOToZ2zatIlJEycqZYzi7Gx2/+9/BB88yJKlS3n9v/+lb9++ShlLJNJGYsEkEt2Gzs5OduzYwWuvvU5jYytLlrzEtGnr0dMzUOg4HR1tvPfBMjJzYlj231+xdxO3dWsTOXIuhu0nKGgHTz39FGPHjFH4GKX5+ez56CMC9+5l3vz5vPH66+JRJiLRbRALJpHoDnR0dLB9+3Zef/1NOjpg6dJXmDTpbvT09BU2RldXJ1t/eJozZ39g4bPf4TV8usKuLRJOZ1cHx49/RXZ2FC+9+ILCi5iKoiL2fvIJZ3fuZMaMGbz15psMEvBYFZFI04kFk0ikAG1tbWzdupU333ybjg4Z06ZtYObMDVhbOylsjBMnvmXz5seZdM8rDF+4QeyLo8GaW+rZt+9d2ttree21V+nhpJifE7lcTnJYGMe2biX8+HEmT5nCW2++yfDhwxVyfZGoOxMLJpFIgdrb29m3bx+fffYF0dGXGDNmCXPmbKRv35EKKXDi4s7y7vtL6D9uATMeehcdBc5kiVSjoqKQvXvfxNnZkeeff1YhfY7ampsJ3LePE1u2UJKby5rVq9m4cSO+vr4KSCwSiUAsmEQipYmOjuazz75g9+5duLkNYNasjYwbt/yOF4gXFaXxxlvzkRoZM/8/32Dr4qWgxCJlkiMnNuYUZ85uYcqUKTz44IN3fNxJaX4+J378kXO//oqlpSUbH32U++67D2trawWlFolEfxALJpFIySoqKti8eTNfffUNzc2tTJv2ILNmPYytrfNtX7OlpZHNWx7nQtBOpj7wJkNm3i3eolNjzS31HDv2BUWFyWzc+Ngd9ViSy+XEBwVxfMsWIk+fZuKkSTy+aRNz5sxBR4XnzYlE3Y1YMIlEKtLZ2cnBgwf57LMvuHgxlFGjFjF79kZ8fMbddrETFraPL758AOcBI5mz6ROMLWwUnFp0p3JyYjly5FN6ubnw1FNPYmtze9+jlsZGAvbs4cQPP1BRVMS6u+9m48aN9O/fX8GJRSLRtYgFk0gkgPj4eL744kt+/fVXevbsw6xZjzFhwioMDG79kNXKyiI+/vRu8gtTmPrgm3iPWyjONqmBltZGAgN/Ji7uDHevXcuChQuR3sb3pSQ7m+M//si5nTtxtLdn08aNrFu3DgsLCyWkFolE1yMWTCKRgKqrq9m6dStffvk1dXX1TJmynpkzH8LR8dY6L8tkMo4f/5qff30Zx96+TH/oXexcxaaEQpDJ5cTHnyUwYDs9ejrx6KOP4OHufkvX6OrsJC4wkONbtxJ9/jzTZ8zg8U2bmD59OlKpeKKVSCQEsWASidRAV1cXR48e5bPPviAw8Dx9+w5j1KhljB27DDs7l5u+Tm1tOdu2P8eFC78ybN4DjFv1HwyMxdPmVaXkchanT39HXW0p9913D5MmT77pWaWuri5Sw8MJOXSI8KNH6Whr49577uGxxx7Dy0tc2C8SCU0smEQiNVNQUMBvv/3Gjh27iYmJwtt7FKNGLWPMmKXY2NzcwahpaRf5+ttHqKotZdzqZ/GbslxsQaBEtXUVhITsIjExgJkzZ7J69WpMTf79nEGZTEZaVBShhw5x8cgRmhsamDdvHitXrGDGjBkYGir+yB2RSHR7xIJJJFJjubm57N27lx07dpOQEMuAAWMZPXo5o0cvwcrK4Yaf29XVxZkzW9i15y1kEhi1/AkGTV0pFk4KVFtbTmjYXuLjz+Hn58e6dXf/6+03uVxORkwMIQcPcvHIEeqrq5kzZw4rV6xg9uzZGBkZqSi9SCS6FWLBJBJpiMzMzD+Lp9TUJPz8JjJq1DJGj16MubntdT+vo6ONs2d/ZM/ed+iUyBm97HH8pq1EV8Hn3nUnNbWlhIbuJSEhgEGDBrFq5Qr69Olz3cfL5XKyExIIOXiQsEOHqC4vZ9asWaxcsYI5c+YopHmlSCRSLrFgEok0UFpaGnv27GHHjt1kZaUzaNAURo1axqhRizAzu3bTwo6ONs6d28buve/Q0dXBoFlrGTR9Dea2iju+RZvJkZOfl0Rs7AlS0y4yZLA/K1etwKt372s/Xi4nLzn5SpF0+DDlRUVMnzGDlStWMG/ePMzNzVX8FYhEojshFkwikYZLSkpi9+7d7Ny5m/z8XAYPnoaf3wwGDZqKi4v3VS0GOjraCQ3dy/GT35CRFk6fETMYPGsdHoMnIhF3YF2lpaWRhIRzxMaepL6hiokTJzB79uxr3npra24mJSKC+KAgLp06RUluLlOmTmXlihUsWLAAS0tL1X8BIpFIIcSCSSTSEnK5nPj4ePbt28epU2eJiYnC0tIOX98p+PpOxdd3ylU77vLzkzhx8lsCAn7GyNwa3/9r7+5+mrrjOI5/WgpFBZSKhdW2SDTSU4Yx6AUPZYwMNTqzeLu/bxe72cXuVTYebAISn6DtoLqWp1YEhQJF+nDaXZgwk7n84h7iBu9X8rs5F+fk3L3zO/l9z7VvZUW+0Wn/h3dNjoqyXVY6/VTx2Lji8Un5fGd16+ub+nJoSMeP/z4ryy6XlXzyRE/HxjQ3Oan49LQaGho0PDysWzdv6s6dOzr9FwdVAvhvIZiAQyqXy2lsbEx3797T3bv3NT8fVyBwUd3dXykc/kLhcOTg9yz7+3mNj3+v0Z+/UyI+KW/goi7231Zo4La85/64S3UYlcpF/frikX6Zjyq58FA1NQ719vbqxo3r6uzslMPhULlUUmpuTvGpKcUePNBsNKpysahIJKLr165pZGREly9fZlYScAgRTMARkclkNDo6qnv37mt8fFKp1HO1tQUVCkUUCkVkWQMKBru0vb2hqakfNRn9QXPPflJzW7su9t5U+6UBBbp65T7e+Klf5R9RVVWbm2taXJxVOvVYyeSM3PVu9ff1qX+gX93d3Sru7Wl+ZkbxqSktPHyoxMyMqratnqtXNTw0pJGREfX19XH8HzgCCCbgiHr58qWi0agmJiY1MfFAT58+Un39CVlWnwKBSwoEwvJ4fMpmn+vJs3uKzY0rv7sp34XLCnT3q/1SvwLh3v/NYMz3A2lpaVZLi3PK5TYUDJ5T96VudV04r/pqVavJpFaTSb14/FgvYjGdbG7WQH+/BiMRRSIRXblyRW43JwyBo4ZgAiBJyufzmp6eVjQa1exsTLFYXMnkvAqFfXm9fvn9ITU1tcm2y3qzmdXi4qzy+S21+C7I22HpTEeXWju65O3oUlOL75N+xivbJW1srOjVWlpraymtb6S0tpZWfmdLn3ladLb5lBpqnLJ3d/UqldJyMqm93V2daW1VKBRSl2Wpp6dHg4ODB5/jABxtBBOAP2XbttLptBKJhBKJhGKxuGKxhObnE9rZ2VZj4ymdPOmVw+HUfrGg3d03epvPyX28UafPnteptnM61RpQ0xm/TnrfraYzftWf+HtH6iuVivL5TeVy6++tV8ptLOvNqyVtri1LhYIa3cd0otYll11WaWdHrzMZlYpFBdrbFQqF9Hk4rHA4LMuyZFmWPJ4Pj2QAAIIJwEerVqvKZrOKx+NaWFjQ6uqqVlZWtbKS0eLiorLZVe3t5SVJTpdLTqdL1WpFdqkkqSqH06maWrdc7no56+pVU+dWTV29atxuOVy1B8+oVG1V7LLsSlm2XZZdKqpceKtKoSDZthyVipySHJV3965WKnI4HPK0tCgQDCrg98vv88nv9ysYDMqyLHV2djIoEsBHI5gA/Cvy+bwymYxev36t7e1t7ezsaGtrS8vLy1pfXz+4XiwWVSgUVCqVVCgUVKlUDu5RW1ururq6g3Xs2DE1NzfL4/HI7/fL6/WqqanpYLW1tcnr9crlcn3CNwdwGBFMAAAABgwLAQAAMCCYAAAADAgmAAAAA4IJAADAgGACAAAwIJgAAAAMCCYAAAADggkAAMCAYAIAADAgmAAAAAwIJgAAAAOCCQAAwIBgAgAAMCCYAAAADAgmAAAAA4IJAADAgGACAAAwIJgAAAAMCCYAAAADggkAAMCAYAIAADAgmAAAAAwIJgAAAAOCCQAAwIBgAgAAMCCYAAAADAgmAAAAA4IJAADAgGACAAAwIJgAAAAMCCYAAAADggkAAMCAYAIAADAgmAAAAAwIJgAAAAOCCQAAwIBgAgAAMCCYAAAADAgmAAAAA4IJAADA4Dct24oRgf4MqgAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_venn(metabolites)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Metabolite occurences in solutions" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "exchange: 1 / total: 10\n" + ] + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_metabolites(metabolites,\"reasoning\",10)" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "exchange: 4 / total: 10\n" + ] + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_metabolites(metabolites,\"filter\",10)" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "exchange: 4 / total: 10\n" + ] + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_metabolites(metabolites,\"gc\",10)" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "exchange: 3 / total: 10\n" + ] + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_metabolites(metabolites,\"gcd\",10)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "test", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.14" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/notebook/analyses/04_supp_one_sol.ipynb b/notebook/analyses/04_supp_one_sol.ipynb new file mode 100644 index 0000000..d9b47f0 --- /dev/null +++ b/notebook/analyses/04_supp_one_sol.ipynb @@ -0,0 +1,767 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Get computing time data to obtain the first solution\n", + "This notebook presents computing time data to get the first solution with Seed2Lp, with a timeout at 45 min, in full Network or Target mode, without accumulation allowed, only for subset minimal optimization.\n", + "\n", + "\n", + "To run correctly this notebook and have the same results as the paper, you must first download the raw results: [https://doi.org/10.57745/OS1JND](https://doi.org/10.57745/OS1JND)\n", + "\n", + "This notebook is written with the hierarchy of downloaded files, if you want to try it with the test form the run notebooks, it is needed to first restructure your data to match the hierarchy of downloaded files.\n", + "\n", + "We suppose here that the downloaded files are in a directory named \"analyses\", this directory path can be changed to your directory path where the data are saved." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Requirements\n", + "- Download and Install *R*: https://cran.r-project.org/\n", + "- Type `R` in your console, the R lanuage will run.\n", + "- Execute the command: `install.packages('IRkernel', repos = 'http://cran.us.r-project.org');IRkernel::installspec()`\n", + "- Execute the command: `install.packages(\"reshape2\")`\n", + "- Execute the command: `install.packages(\"data.table\")`\n", + "- Execute the command: `install.packages(\"ggplot2\")`\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Install the package rpv2:" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Collecting rpy2\n", + " Using cached rpy2-3.5.16.tar.gz (220 kB)\n", + " Installing build dependencies ... \u001b[?25ldone\n", + "\u001b[?25h Getting requirements to build wheel ... \u001b[?25ldone\n", + "\u001b[?25h Preparing metadata (pyproject.toml) ... \u001b[?25ldone\n", + "\u001b[?25hRequirement already satisfied: cffi>=1.15.1 in /home/cghassem/miniconda3/envs/s2lp/lib/python3.11/site-packages (from rpy2) (1.16.0)\n", + "Requirement already satisfied: jinja2 in /home/cghassem/miniconda3/envs/s2lp/lib/python3.11/site-packages (from rpy2) (3.1.4)\n", + "Collecting tzlocal (from rpy2)\n", + " Downloading tzlocal-5.2-py3-none-any.whl.metadata (7.8 kB)\n", + "Requirement already satisfied: pycparser in /home/cghassem/miniconda3/envs/s2lp/lib/python3.11/site-packages (from cffi>=1.15.1->rpy2) (2.22)\n", + "Requirement already satisfied: MarkupSafe>=2.0 in /home/cghassem/miniconda3/envs/s2lp/lib/python3.11/site-packages (from jinja2->rpy2) (2.1.5)\n", + "Downloading tzlocal-5.2-py3-none-any.whl (17 kB)\n", + "Building wheels for collected packages: rpy2\n", + " Building wheel for rpy2 (pyproject.toml) ... \u001b[?25ldone\n", + "\u001b[?25h Created wheel for rpy2: filename=rpy2-3.5.16-cp311-cp311-linux_x86_64.whl size=261792 sha256=88a2022583d7fb3ab79ea21354bf08cc3e5e137e1ebc0e57556ab42091e4874e\n", + " Stored in directory: /home/cghassem/.cache/pip/wheels/da/60/76/3bc67cbf19cb7dd4806c73262e7588dfada92f80fcf3558fc5\n", + "Successfully built rpy2\n", + "Installing collected packages: tzlocal, rpy2\n", + "Successfully installed rpy2-3.5.16 tzlocal-5.2\n" + ] + } + ], + "source": [ + "!pip install rpy2" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Variables to change (if wanted)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "analyse_dir = \"../../analyses\"\n", + "data_dir = f\"{analyse_dir}/data\"\n", + "\n", + "# Directories to create for R analyses and plots\n", + "timer_tables_dir = f\"{analyse_dir}/results/timer_one_sol/tables\"\n", + "timers_plot_dir = f\"{analyse_dir}/results/timer_one_sol/plots\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Initialisation and functions" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import pandas as pd" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "if not os.path.isdir(timer_tables_dir):\n", + " os.makedirs(timer_tables_dir)\n", + "\n", + "if not os.path.isdir(timers_plot_dir):\n", + " os.makedirs(timers_plot_dir)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The rpy2.ipython extension is already loaded. To reload it, use:\n", + " %reload_ext rpy2.ipython\n" + ] + } + ], + "source": [ + "%load_ext rpy2.ipython" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "result_dir = os.path.join(analyse_dir, \"results\")\n", + "one_sol_results_dir = os.path.join(result_dir, \"one_solution\")\n", + "one_sol_supp_data = os.path.join(result_dir,\"supp_data\",\"one_solution_supp_data.tsv\")\n", + "sbml_dir = f'{data_dir}/bigg/sbml'" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "def get_timers(file_path, mode):\n", + " table_all = pd.read_csv(file_path, sep='\\t', lineterminator='\\n')\n", + " table_all=table_all.fillna(-50)\n", + " table_all.loc[table_all[\"minimize\"] == \"unsat\", \"minimize\"] = -100\n", + " table_all.loc[table_all[\"minimize_opti\"] == \"unsat\", \"minimize_opti\"] = -100\n", + " table_all.loc[table_all[\"submin\"] == \"unsat\", \"submin\"] = -100\n", + " table_all.loc[table_all[\"minimize\"] == \"Time out\", \"minimize\"] = -200\n", + " table_all.loc[table_all[\"minimize\"] == \"time out\", \"minimize\"] = -200\n", + " table_all.loc[table_all[\"minimize_opti\"] == \"Time out\", \"minimize_opti\"] = -200\n", + " table_all.loc[table_all[\"minimize_opti\"] == \"time out\", \"minimize_opti\"] = -200\n", + " table_all.loc[table_all[\"submin\"] == \"Time out\", \"submin\"] = -200\n", + " table_all.loc[table_all[\"submin\"] == \"time out\", \"submin\"] = -200\n", + " table_all['minimize'] = table_all['minimize'].astype('float')\n", + " table_all['minimize_opti'] = table_all['minimize_opti'].astype('float')\n", + " table_all['submin'] = table_all['submin'].astype('float')\n", + "\n", + " timers_all= table_all.loc[table_all[\"type_data\"] == \"Solving (sec)\"]\n", + " # No accumulation mode\n", + " timers_all = timers_all[timers_all['accumulation']==False]\n", + "\n", + " if mode == \"full\":\n", + " return timers_all[timers_all[\"search_mode\"]==\"Full network\"]\n", + " elif mode == \"target\":\n", + " return timers_all[timers_all[\"search_mode\"]==\"Target\"]\n", + " else:\n", + " return timers_all\n" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "def get_fluxes(directory:str, mode:str, optim:str=None):\n", + " flux_all=pd.DataFrame(columns=['species', 'biomass_reaction', 'solver_type', 'search_mode',\n", + " 'search_type', 'accumulation', 'model', 'size', 'lp_flux', 'cobra_flux_init',\n", + " 'cobra_flux_no_import', 'cobra_flux_seeds', 'cobra_flux_demands',\n", + " 'has_flux', 'has_flux_seeds', 'has_flux_demands', 'timer'])\n", + " flux_all['accumulation'] = flux_all['accumulation'].astype('bool')\n", + " flux_all['has_flux'] = flux_all['has_flux'].astype('bool')\n", + " flux_all['has_flux_seeds'] = flux_all['has_flux_seeds'].astype('bool')\n", + " flux_all['has_flux_demands'] = flux_all['has_flux_demands'].astype('bool')\n", + "\n", + " for dirpath, _, filenames in os.walk(directory):\n", + " for filename in [f for f in filenames if (f.endswith(\"_fluxes.tsv\") or f.endswith(\"_fluxes_from_result.tsv\"))]:\n", + " # By default in this notebook we want the no accumulation mode for seed2lp results\n", + " if \"_no_accu_\" in filename \\\n", + " and ((mode == \"full\" and \"_fn_\" in filename) \\\n", + " or (mode == \"target\" and \"_tgt_\" in filename))\\\n", + " or mode == \"netseed\":\n", + " file_path=os.path.join(dirpath, filename)\n", + " current_df = pd.read_csv(file_path, sep='\\t', lineterminator='\\n')\n", + " current_df['accumulation'] = current_df['accumulation'].astype('bool')\n", + " current_df['has_flux'] = current_df['has_flux'].astype('bool')\n", + " current_df['has_flux_seeds'] = current_df['has_flux_seeds'].astype('bool')\n", + " current_df['has_flux_demands'] = current_df['has_flux_demands'].astype('bool')\n", + " flux_all=pd.concat([flux_all if not flux_all.empty else None, current_df], ignore_index=True)\n", + " flux_all = flux_all[flux_all[\"model\"]!=\"model_one_solution\"]\n", + " flux_all = flux_all[flux_all[\"model\"]!=\"model_one_solution\"]\n", + " if optim==\"submin\":\n", + " return flux_all[flux_all[\"search_mode\"]==\"Subset Minimal\"]\n", + " elif optim==\"min\":\n", + " return flux_all[flux_all[\"search_mode\"]==\"Minimize\"]\n", + " else:\n", + " return flux_all" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "def convert_timers_table(table:pd.DataFrame, optim, list_all_species, dict_species):\n", + " column_conversion = {\"REASONING\":\"Reasoning\",\n", + " \"REASONING FILTER\":\"Filter\",\n", + " \"REASONING GUESS-CHECK\":'Guess-Check',\n", + " \"REASONING GUESS-CHECK-DIVERSITY\":'Guess-Check-div'}\n", + "\n", + " \n", + " set_complete = set(list_all_species)\n", + " set_reasoning = set(dict_species[\"reasoning\"])\n", + " set_filter = set(dict_species[\"filter\"])\n", + " set_gc = set(dict_species[\"gc\"])\n", + " set_gcd = set(dict_species[\"gcd\"])\n", + " \n", + " diff_reasoning = set_complete.difference(set_reasoning) \n", + " diff_filter = set_complete.difference(set_filter) \n", + " diff_gc = set_complete.difference(set_gc) \n", + " diff_gcd = set_complete.difference(set_gcd) \n", + " new_table=pd.DataFrame(columns=['network', 'Reasoning','Filter',\n", + " 'Guess-Check', 'Guess-Check-div'])\n", + " new_table = new_table.set_index('network')\n", + "\n", + " for row in table.iterrows():\n", + " network = row[0]\n", + " if not new_table.empty:\n", + " if network in new_table.index:\n", + " new_table.loc[network, column_conversion[row[1]['mode']]]=row[1][optim]\n", + " else:\n", + " new_table = add_row(new_table, row, optim, column_conversion)\n", + " else:\n", + " new_table = add_row(new_table, row, optim, column_conversion)\n", + "\n", + " for net in diff_reasoning:\n", + " new_table.loc[net, [\"Reasoning\"]]=-1000\n", + " for net in diff_filter:\n", + " new_table.loc[net, [\"Filter\"]]=-1000\n", + " for net in diff_gc:\n", + " new_table.loc[net, [\"Guess-Check\"]]=-1000\n", + " for net in diff_gcd:\n", + " new_table.loc[net, [\"Guess-Check-div\"]]=-1000\n", + " return new_table.reset_index()\n", + "\n", + "\n", + "def add_row(new_table,row, optim, column_conversion):\n", + " network = row[1]['network']\n", + " current = pd.DataFrame(data=[[network, row[1][optim]]], columns=['network', column_conversion[row[1]['mode']]])\n", + " current = current.set_index('network')\n", + " new_table = new_table.combine_first(current)\n", + " return new_table" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "def get_separate_data(table):\n", + " table[\"solver_type\"] = table[\"solver_type\"].str.replace('REASONING GUESS-CHECK-DIVERSITY', 'REASONING GUESS-CHECK DIVERSITY')\n", + " table[\"solver_type\"] = table[\"solver_type\"].str.replace('REASONING GUESS-CHECK', 'REASONING GUESS-CHECK')\n", + " table[\"solver_type\"] = table[\"solver_type\"].str.replace('REASONING FILTER', 'REASONING FILTER')\n", + " \n", + " # CLASSIC\n", + " table_reasoning = table[table[\"solver_type\"]==\"REASONING\"]\n", + " \n", + " # FILTER\n", + " table_filter = table[table[\"solver_type\"]==\"REASONING FILTER\"]\n", + "\n", + " # GUESS_CHECK\n", + " table_gc = table[table[\"solver_type\"]==\"REASONING GUESS-CHECK\"]\n", + "\n", + " # GUESS_CHECK_DIV\n", + " table_gcd = table[table[\"solver_type\"]==\"REASONING GUESS-CHECK DIVERSITY\"]\n", + "\n", + " return table_reasoning, table_filter, table_gc, table_gcd" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "def create_table_plot(table,column_name):\n", + " new_table = table.groupby(['species'])[column_name].agg('count').reset_index()\n", + " new_table=new_table.rename(columns={column_name: \"Total_flux\"})\n", + " new_true = table[table[column_name]==True].groupby(['species'])[column_name].agg('count').reset_index()\n", + " new_true=new_true.rename(columns={column_name: \"True_flux\"})\n", + " new_false = table[table[column_name]==False].groupby(['species'])[column_name].agg('count').reset_index()\n", + " new_false=new_false.rename(columns={column_name: \"False_flux\"})\n", + " new_table=pd.merge(new_table,new_true, how='left', on=['species'])\n", + " new_table=pd.merge(new_table,new_false, how='left', on=['species'])\n", + " new_table=new_table.fillna(0)\n", + " new_table=new_table.fillna(0)\n", + " new_table['True_flux']=new_table['True_flux'].astype(int)\n", + " new_table['False_flux']=new_table['False_flux'].astype(int)\n", + " return new_table" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "def get_list_species(table):\n", + " labels = table['species'].tolist()\n", + " set_labels = set(labels)\n", + " return set_labels" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "def get_all_species():\n", + " species_files = os.listdir(sbml_dir)\n", + " species = [sub.replace('.xml', '') for sub in species_files]\n", + " return species" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Get data" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "timers_FN=get_timers(one_sol_supp_data, \"full\")\n", + "timers_T=get_timers(one_sol_supp_data, \"target\")" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_11027/2993539488.py:24: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", + " flux_all=pd.concat([flux_all if not flux_all.empty else None, current_df], ignore_index=True)\n", + "/tmp/ipykernel_11027/2993539488.py:24: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", + " flux_all=pd.concat([flux_all if not flux_all.empty else None, current_df], ignore_index=True)\n", + "/tmp/ipykernel_11027/2993539488.py:24: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", + " flux_all=pd.concat([flux_all if not flux_all.empty else None, current_df], ignore_index=True)\n", + "/tmp/ipykernel_11027/2993539488.py:24: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", + " flux_all=pd.concat([flux_all if not flux_all.empty else None, current_df], ignore_index=True)\n", + "/tmp/ipykernel_11027/2993539488.py:24: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", + " flux_all=pd.concat([flux_all if not flux_all.empty else None, current_df], ignore_index=True)\n", + "/tmp/ipykernel_11027/2993539488.py:24: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", + " flux_all=pd.concat([flux_all if not flux_all.empty else None, current_df], ignore_index=True)\n", + "/tmp/ipykernel_11027/2993539488.py:24: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", + " flux_all=pd.concat([flux_all if not flux_all.empty else None, current_df], ignore_index=True)\n", + "/tmp/ipykernel_11027/2993539488.py:24: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", + " flux_all=pd.concat([flux_all if not flux_all.empty else None, current_df], ignore_index=True)\n", + "/tmp/ipykernel_11027/2993539488.py:24: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", + " flux_all=pd.concat([flux_all if not flux_all.empty else None, current_df], ignore_index=True)\n", + "/tmp/ipykernel_11027/2993539488.py:24: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", + " flux_all=pd.concat([flux_all if not flux_all.empty else None, current_df], ignore_index=True)\n", + "/tmp/ipykernel_11027/2993539488.py:24: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", + " flux_all=pd.concat([flux_all if not flux_all.empty else None, current_df], ignore_index=True)\n", + "/tmp/ipykernel_11027/2993539488.py:24: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", + " flux_all=pd.concat([flux_all if not flux_all.empty else None, current_df], ignore_index=True)\n", + "/tmp/ipykernel_11027/2993539488.py:24: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", + " flux_all=pd.concat([flux_all if not flux_all.empty else None, current_df], ignore_index=True)\n", + "/tmp/ipykernel_11027/2993539488.py:24: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", + " flux_all=pd.concat([flux_all if not flux_all.empty else None, current_df], ignore_index=True)\n", + "/tmp/ipykernel_11027/2993539488.py:24: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", + " flux_all=pd.concat([flux_all if not flux_all.empty else None, current_df], ignore_index=True)\n", + "/tmp/ipykernel_11027/2993539488.py:24: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", + " flux_all=pd.concat([flux_all if not flux_all.empty else None, current_df], ignore_index=True)\n", + "/tmp/ipykernel_11027/2993539488.py:24: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", + " flux_all=pd.concat([flux_all if not flux_all.empty else None, current_df], ignore_index=True)\n", + "/tmp/ipykernel_11027/2993539488.py:24: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", + " flux_all=pd.concat([flux_all if not flux_all.empty else None, current_df], ignore_index=True)\n", + "/tmp/ipykernel_11027/2993539488.py:24: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", + " flux_all=pd.concat([flux_all if not flux_all.empty else None, current_df], ignore_index=True)\n", + "/tmp/ipykernel_11027/2993539488.py:24: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", + " flux_all=pd.concat([flux_all if not flux_all.empty else None, current_df], ignore_index=True)\n", + "/tmp/ipykernel_11027/2993539488.py:24: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", + " flux_all=pd.concat([flux_all if not flux_all.empty else None, current_df], ignore_index=True)\n", + "/tmp/ipykernel_11027/2993539488.py:24: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", + " flux_all=pd.concat([flux_all if not flux_all.empty else None, current_df], ignore_index=True)\n", + "/tmp/ipykernel_11027/2993539488.py:24: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", + " flux_all=pd.concat([flux_all if not flux_all.empty else None, current_df], ignore_index=True)\n", + "/tmp/ipykernel_11027/2993539488.py:24: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", + " flux_all=pd.concat([flux_all if not flux_all.empty else None, current_df], ignore_index=True)\n", + "/tmp/ipykernel_11027/2993539488.py:24: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", + " flux_all=pd.concat([flux_all if not flux_all.empty else None, current_df], ignore_index=True)\n", + "/tmp/ipykernel_11027/2993539488.py:24: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", + " flux_all=pd.concat([flux_all if not flux_all.empty else None, current_df], ignore_index=True)\n", + "/tmp/ipykernel_11027/2993539488.py:24: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", + " flux_all=pd.concat([flux_all if not flux_all.empty else None, current_df], ignore_index=True)\n", + "/tmp/ipykernel_11027/2993539488.py:24: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", + " flux_all=pd.concat([flux_all if not flux_all.empty else None, current_df], ignore_index=True)\n", + "/tmp/ipykernel_11027/2993539488.py:24: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", + " flux_all=pd.concat([flux_all if not flux_all.empty else None, current_df], ignore_index=True)\n", + "/tmp/ipykernel_11027/2993539488.py:24: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", + " flux_all=pd.concat([flux_all if not flux_all.empty else None, current_df], ignore_index=True)\n", + "/tmp/ipykernel_11027/2993539488.py:24: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", + " flux_all=pd.concat([flux_all if not flux_all.empty else None, current_df], ignore_index=True)\n", + "/tmp/ipykernel_11027/2993539488.py:24: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", + " flux_all=pd.concat([flux_all if not flux_all.empty else None, current_df], ignore_index=True)\n", + "/tmp/ipykernel_11027/2993539488.py:24: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", + " flux_all=pd.concat([flux_all if not flux_all.empty else None, current_df], ignore_index=True)\n", + "/tmp/ipykernel_11027/2993539488.py:24: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", + " flux_all=pd.concat([flux_all if not flux_all.empty else None, current_df], ignore_index=True)\n", + "/tmp/ipykernel_11027/2993539488.py:24: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", + " flux_all=pd.concat([flux_all if not flux_all.empty else None, current_df], ignore_index=True)\n", + "/tmp/ipykernel_11027/2993539488.py:24: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", + " flux_all=pd.concat([flux_all if not flux_all.empty else None, current_df], ignore_index=True)\n", + "/tmp/ipykernel_11027/2993539488.py:24: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", + " flux_all=pd.concat([flux_all if not flux_all.empty else None, current_df], ignore_index=True)\n", + "/tmp/ipykernel_11027/2993539488.py:24: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", + " flux_all=pd.concat([flux_all if not flux_all.empty else None, current_df], ignore_index=True)\n", + "/tmp/ipykernel_11027/2993539488.py:24: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", + " flux_all=pd.concat([flux_all if not flux_all.empty else None, current_df], ignore_index=True)\n", + "/tmp/ipykernel_11027/2993539488.py:24: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", + " flux_all=pd.concat([flux_all if not flux_all.empty else None, current_df], ignore_index=True)\n", + "/tmp/ipykernel_11027/2993539488.py:24: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", + " flux_all=pd.concat([flux_all if not flux_all.empty else None, current_df], ignore_index=True)\n", + "/tmp/ipykernel_11027/2993539488.py:24: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", + " flux_all=pd.concat([flux_all if not flux_all.empty else None, current_df], ignore_index=True)\n", + "/tmp/ipykernel_11027/2993539488.py:24: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", + " flux_all=pd.concat([flux_all if not flux_all.empty else None, current_df], ignore_index=True)\n", + "/tmp/ipykernel_11027/2993539488.py:24: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", + " flux_all=pd.concat([flux_all if not flux_all.empty else None, current_df], ignore_index=True)\n", + "/tmp/ipykernel_11027/2993539488.py:24: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", + " flux_all=pd.concat([flux_all if not flux_all.empty else None, current_df], ignore_index=True)\n", + "/tmp/ipykernel_11027/2993539488.py:24: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", + " flux_all=pd.concat([flux_all if not flux_all.empty else None, current_df], ignore_index=True)\n", + "/tmp/ipykernel_11027/2993539488.py:24: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", + " flux_all=pd.concat([flux_all if not flux_all.empty else None, current_df], ignore_index=True)\n", + "/tmp/ipykernel_11027/2993539488.py:24: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", + " flux_all=pd.concat([flux_all if not flux_all.empty else None, current_df], ignore_index=True)\n", + "/tmp/ipykernel_11027/2993539488.py:24: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", + " flux_all=pd.concat([flux_all if not flux_all.empty else None, current_df], ignore_index=True)\n", + "/tmp/ipykernel_11027/2993539488.py:24: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", + " flux_all=pd.concat([flux_all if not flux_all.empty else None, current_df], ignore_index=True)\n", + "/tmp/ipykernel_11027/2993539488.py:24: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", + " flux_all=pd.concat([flux_all if not flux_all.empty else None, current_df], ignore_index=True)\n", + "/tmp/ipykernel_11027/2993539488.py:24: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", + " flux_all=pd.concat([flux_all if not flux_all.empty else None, current_df], ignore_index=True)\n", + "/tmp/ipykernel_11027/2993539488.py:24: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", + " flux_all=pd.concat([flux_all if not flux_all.empty else None, current_df], ignore_index=True)\n", + "/tmp/ipykernel_11027/2993539488.py:24: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", + " flux_all=pd.concat([flux_all if not flux_all.empty else None, current_df], ignore_index=True)\n", + "/tmp/ipykernel_11027/2993539488.py:24: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", + " flux_all=pd.concat([flux_all if not flux_all.empty else None, current_df], ignore_index=True)\n", + "/tmp/ipykernel_11027/2993539488.py:24: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", + " flux_all=pd.concat([flux_all if not flux_all.empty else None, current_df], ignore_index=True)\n", + "/tmp/ipykernel_11027/2993539488.py:24: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", + " flux_all=pd.concat([flux_all if not flux_all.empty else None, current_df], ignore_index=True)\n", + "/tmp/ipykernel_11027/2993539488.py:24: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", + " flux_all=pd.concat([flux_all if not flux_all.empty else None, current_df], ignore_index=True)\n", + "/tmp/ipykernel_11027/2993539488.py:24: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", + " flux_all=pd.concat([flux_all if not flux_all.empty else None, current_df], ignore_index=True)\n", + "/tmp/ipykernel_11027/2993539488.py:24: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", + " flux_all=pd.concat([flux_all if not flux_all.empty else None, current_df], ignore_index=True)\n", + "/tmp/ipykernel_11027/2993539488.py:24: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", + " flux_all=pd.concat([flux_all if not flux_all.empty else None, current_df], ignore_index=True)\n", + "/tmp/ipykernel_11027/2993539488.py:24: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", + " flux_all=pd.concat([flux_all if not flux_all.empty else None, current_df], ignore_index=True)\n", + "/tmp/ipykernel_11027/2993539488.py:24: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", + " flux_all=pd.concat([flux_all if not flux_all.empty else None, current_df], ignore_index=True)\n", + "/tmp/ipykernel_11027/2993539488.py:24: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", + " flux_all=pd.concat([flux_all if not flux_all.empty else None, current_df], ignore_index=True)\n", + "/tmp/ipykernel_11027/2993539488.py:24: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", + " flux_all=pd.concat([flux_all if not flux_all.empty else None, current_df], ignore_index=True)\n", + "/tmp/ipykernel_11027/2993539488.py:24: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", + " flux_all=pd.concat([flux_all if not flux_all.empty else None, current_df], ignore_index=True)\n", + "/tmp/ipykernel_11027/2993539488.py:24: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", + " flux_all=pd.concat([flux_all if not flux_all.empty else None, current_df], ignore_index=True)\n", + "/tmp/ipykernel_11027/2993539488.py:24: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", + " flux_all=pd.concat([flux_all if not flux_all.empty else None, current_df], ignore_index=True)\n", + "/tmp/ipykernel_11027/2993539488.py:24: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", + " flux_all=pd.concat([flux_all if not flux_all.empty else None, current_df], ignore_index=True)\n", + "/tmp/ipykernel_11027/2993539488.py:24: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", + " flux_all=pd.concat([flux_all if not flux_all.empty else None, current_df], ignore_index=True)\n", + "/tmp/ipykernel_11027/2993539488.py:24: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", + " flux_all=pd.concat([flux_all if not flux_all.empty else None, current_df], ignore_index=True)\n" + ] + } + ], + "source": [ + "flux_FN = get_fluxes(one_sol_results_dir, \"full\", \"submin\")\n", + "flux_T = get_fluxes(one_sol_results_dir, \"target\", \"submin\")" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "FN_reasoning, FN_filter, FN_gc, FN_gcd = get_separate_data(flux_FN)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "T_reasoning, T_filter, T_gc, T_gcd = get_separate_data(flux_T)" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "FN_reasoning_tab=create_table_plot(FN_reasoning,'has_flux')\n", + "FN_filter_tab=create_table_plot(FN_filter,'has_flux')\n", + "FN_gc_tab=create_table_plot(FN_gc,'has_flux')\n", + "FN_gcd_tab=create_table_plot(FN_gcd,'has_flux')\n", + "\n", + "T_reasoning_tab=create_table_plot(T_reasoning,'has_flux')\n", + "T_filter_tab=create_table_plot(T_filter,'has_flux')\n", + "T_gc_tab=create_table_plot(T_gc,'has_flux')\n", + "T_gcd_tab=create_table_plot(T_gcd,'has_flux')" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [], + "source": [ + "dict_species_FN = {\"reasoning\": get_list_species(FN_reasoning_tab),\n", + " \"filter\":get_list_species(FN_filter_tab),\n", + " \"gc\":get_list_species(FN_gc_tab),\n", + " \"gcd\":get_list_species(FN_gcd_tab)}\n", + "\n", + "dict_species_T = {\"reasoning\":get_list_species(T_reasoning_tab),\n", + " \"filter\":get_list_species(T_filter_tab),\n", + " \"gc\":get_list_species(T_gc_tab),\n", + " \"gcd\":get_list_species(T_gcd_tab)}" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [], + "source": [ + "list_all_species = get_all_species()" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [], + "source": [ + "timers_FN_final = convert_timers_table(timers_FN, \"submin\", list_all_species, dict_species_FN)" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [], + "source": [ + "timers_T_final = convert_timers_table(timers_T, \"submin\", list_all_species, dict_species_T)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The plot uses R to get the data tables from `timer_tables_dir` variable, so we are saving data (set at the begining of the notebook in paragraphe [Variable to change](#variable-to-change-if-wanted)) " + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [], + "source": [ + "timers_FN_final.to_csv(f\"{timer_tables_dir}/Full_Network.tsv\", sep='\\t')\n", + "timers_T_final.to_csv(f\"{timer_tables_dir}/Target.tsv\", sep='\\t')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# PLOT" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This part uses R to get the data tables from `timer_tables_dir` variable and write it in `timers_plot_dir` directory (set at the begining of the notebook in paragraphe [Variable to change](#variable-to-change-if-wanted) ) " + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.\n", + "`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.\n", + "`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.\n", + "`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.\n", + "`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.\n", + "`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.\n", + "`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.\n", + "`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.\n" + ] + }, + { + "data": { + "text/plain": [ + "data.table 1.16.0 using 4 threads (see ?getDTthreads). Latest news: r-datatable.com\n", + "\n", + "Attachement du package : ‘data.table’\n", + "\n", + "Les objets suivants sont masqués depuis ‘package:reshape2’:\n", + "\n", + " dcast, melt\n", + "\n", + "Using network as id variables\n", + "Using network as id variables\n", + "De plus : Messages d'avis :\n", + "1: Dans (function (package, help, pos = 2, lib.loc = NULL, character.only = FALSE, :\n", + " les bibliothèques ‘/usr/local/lib/R/site-library’, ‘/usr/lib/R/site-library’ ne contiennent aucun package\n", + "2: The dot-dot notation (`..count..`) was deprecated in ggplot2 3.4.0.\n", + "ℹ Please use `after_stat(count)` instead.\n", + "This warning is displayed once every 8 hours.\n", + "Call `lifecycle::last_lifecycle_warnings()` to see where this warning was\n", + "generated. \n", + "3: Removed 3 rows containing non-finite outside the scale range (`stat_bin()`). \n", + "4: Removed 4 rows containing non-finite outside the scale range (`stat_bin()`). \n", + "5: Removed 4 rows containing non-finite outside the scale range (`stat_bin()`). \n", + "6: Removed 1 row containing non-finite outside the scale range (`stat_bin()`). \n", + "7: Removed 36 rows containing non-finite outside the scale range (`stat_bin()`). \n", + "8: Removed 17 rows containing non-finite outside the scale range (`stat_bin()`). \n", + "9: Removed 8 rows containing non-finite outside the scale range (`stat_bin()`). \n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%%R -i timer_tables_dir -i timers_plot_dir\n", + "library(reshape2)\n", + "library(data.table)\n", + "library(ggplot2)\n", + "\n", + "\n", + "# create output directory if it does not exist\n", + "if (!dir.exists(timers_plot_dir)) {\n", + " dir.create(timers_plot_dir)\n", + "}\n", + "\n", + "# read all files in the input directory\n", + "files <- list.files(timer_tables_dir, full.names = TRUE)\n", + "\n", + "# loop over all files\n", + "for (file in files) {\n", + " # read the data\n", + " data <- read.table(file, header = TRUE) # , row.names = 1\n", + " # get the name of the file, no extension\n", + " file_id <- sub(pattern = \"(.*)\\\\..*$\", replacement = \"\\\\1\", basename(file))\n", + " \n", + " # replace -200 with 2700\n", + " data[data == -200] <- 2700\n", + "\n", + " # replace -100 with NA\n", + " data[data == -1000] <- NA\n", + "\n", + " # get long data from wide format, id = row names\n", + " data_long <- reshape2::melt(data, variable.name = \"Group\", value.name = \"Time\")\n", + "\n", + "\n", + " # plot the data\n", + " p = ggplot(data_long, aes(x = Time, color = Group)) +\n", + " stat_bin(data = subset(data_long, Group == \"Filter\"), aes(y = cumsum(..count..)), geom = \"step\") +\n", + " stat_bin(data = subset(data_long, Group == \"Guess.Check\"), aes(y = cumsum(..count..)), geom = \"step\") +\n", + " stat_bin(data = subset(data_long, Group == \"Guess.Check.div\"), aes(y = cumsum(..count..)), geom = \"step\") +\n", + " stat_bin(data = subset(data_long, Group == \"Reasoning\"), aes(y = cumsum(..count..)), geom = \"step\") +\n", + " # style the plot\n", + " theme_bw() +\n", + " labs(title = file_id, x = \"Time\", y = \"Cumulative count of GSMNs with solutions\", colour = \"Solving mode\") +\n", + " # scale_color_manual(values = c(\"Filter\" = \"red\", \"Guess.Check\" = \"blue\", \"Guess.Check.div\" = \"green\", \"Reasoning\" = \"black\")) +\n", + " # increase font size\n", + " theme(text = element_text(size = 15))\n", + "\n", + " # save the plot\n", + " ggsave(paste0(timers_plot_dir, \"/\", file_id, \".pdf\"), plot = p, width = 10, height = 7)\n", + "}\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "test", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.9" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/notebook/analyses/05_supp_precursor.ipynb b/notebook/analyses/05_supp_precursor.ipynb new file mode 100644 index 0000000..260d172 --- /dev/null +++ b/notebook/analyses/05_supp_precursor.ipynb @@ -0,0 +1,664 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Get Precursor results\n", + "This notebook presents Precursor data in Full Network and Target Mode.\n", + "\n", + "\n", + "To run correctly this notebook and have the same results as the paper, you must first download the raw results: [https://doi.org/10.57745/OS1JND](https://doi.org/10.57745/OS1JND)\n", + "\n", + "This notebook is written with the hierarchy of downloaded files, if you want to try it with the test form the run notebooks, it is needed to first restructure your data to match the hierarchy of downloaded files.\n", + "\n", + "We suppose here that the downloaded files are in a directory named \"analyses\", this directory path can be changed to your directory path where the data are saved." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Requirements\n", + "Module *numpy*, *seaborn* and *scipy* are needed" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Requirement already satisfied: numpy in /home/cghassem/miniconda3/envs/test/lib/python3.10/site-packages (2.1.1)\n" + ] + } + ], + "source": [ + "!pip install numpy" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!pip install seaborn" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!pip install scipy" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Variable to change (if wanted)" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "analyse_dir = \"../../analyses\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Initialisation and functions" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import pandas as pd\n", + "import seaborn as sns\n", + "import matplotlib.pyplot as plt\n", + "from scipy.stats import kruskal\n", + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "precursor_results_dir = os.path.join(analyse_dir, \"results\", \"precursor_formated_results\")\n", + "s2lp_supp_data = os.path.join(analyse_dir, \"results\", \"supp_data\", \"seed2lp_supp_data.tsv\")" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "def get_fluxes(directory:str, mode:str):\n", + " flux_all=pd.DataFrame(columns=['species', 'biomass_reaction', 'solver_type', 'search_mode',\n", + " 'search_type', 'accumulation', 'model', 'size', 'lp_flux', 'cobra_flux_init',\n", + " 'cobra_flux_no_import', 'cobra_flux_seeds', 'cobra_flux_demands',\n", + " 'has_flux', 'has_flux_seeds', 'has_flux_demands', 'timer'])\n", + " flux_all['accumulation'] = flux_all['accumulation'].astype('bool')\n", + " flux_all['has_flux'] = flux_all['has_flux'].astype('bool')\n", + " flux_all['has_flux_seeds'] = flux_all['has_flux_seeds'].astype('bool')\n", + " flux_all['has_flux_demands'] = flux_all['has_flux_demands'].astype('bool')\n", + "\n", + " for dirpath, _, filenames in os.walk(directory):\n", + " for filename in [f for f in filenames if (f.endswith(\"_fluxes.tsv\") or f.endswith(\"_fluxes_from_result.tsv\"))]:\n", + " # By default in this notebook we want the no accumulation mode for seed2lp results\n", + " if (mode == \"full\" and \"_fn_\" in filename) \\\n", + " or (mode == \"target\" and \"_tgt_\" in filename):\n", + " file_path=os.path.join(dirpath, filename)\n", + " current_df = pd.read_csv(file_path, sep='\\t', lineterminator='\\n')\n", + " current_df['accumulation'] = current_df['accumulation'].astype('bool')\n", + " current_df['has_flux'] = current_df['has_flux'].astype('bool')\n", + " current_df['has_flux_seeds'] = current_df['has_flux_seeds'].astype('bool')\n", + " current_df['has_flux_demands'] = current_df['has_flux_demands'].astype('bool')\n", + " flux_all=pd.concat([flux_all if not flux_all.empty else None, current_df], ignore_index=True)\n", + " return flux_all" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "def get_sizes(flux_df:pd.DataFrame, nb_total_meta:pd.DataFrame):\n", + " list_mean_size = flux_df.groupby(['species'])['size'].mean()\n", + " mean_size_df = pd.DataFrame(list_mean_size)\n", + " data_size_df = pd.concat([nb_total_meta, mean_size_df], axis=1)\n", + " data_size_df[\"percent\"]=data_size_df[\"size\"] / data_size_df[\"number_metabolites\"] *100\n", + " data_size = pd.DataFrame(data_size_df[\"percent\"])\n", + " data_size=data_size.reset_index()\n", + " data_size=data_size.rename(columns={\"index\": \"species\"})\n", + " return data_size" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "def get_total_nb_meta(supp_data_file):\n", + " table_all = pd.read_csv(supp_data_file, sep='\\t', lineterminator='\\n')\n", + " union_all = table_all.loc[table_all[\"type_data\"] == \"Union\"]\n", + " num_metabolite = union_all.groupby(['network'])['number_metabolites'].first()\n", + " return pd.DataFrame(num_metabolite)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "def create_table_plot(table,column_name):\n", + " new_table = table.groupby(['species'])[column_name].agg('count').reset_index()\n", + " new_table=new_table.rename(columns={column_name: \"Total_flux\"})\n", + " new_true = table[table[column_name]==True].groupby(['species'])[column_name].agg('count').reset_index()\n", + " new_true=new_true.rename(columns={column_name: \"True_flux\"})\n", + " new_false = table[table[column_name]==False].groupby(['species'])[column_name].agg('count').reset_index()\n", + " new_false=new_false.rename(columns={column_name: \"False_flux\"})\n", + " new_table=pd.merge(new_table,new_true, how='left', on=['species'])\n", + " new_table=pd.merge(new_table,new_false, how='left', on=['species'])\n", + " new_table=new_table.fillna(0)\n", + " new_table=new_table.fillna(0)\n", + " new_table['True_flux']=new_table['True_flux'].astype(int)\n", + " new_table['False_flux']=new_table['False_flux'].astype(int)\n", + " return new_table" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "def get_all_same_validation_fba(table,type):\n", + " count=0\n", + " total=0\n", + " for _,line in table.iterrows():\n", + " if line[type] == line[\"Total_flux\"]:\n", + " count += 1\n", + " total += 1\n", + " else:\n", + " total += 1\n", + " \n", + " return total, count" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "def create_one_plot(s2lp, netseed, labels):\n", + " plt.style.use(\"seaborn-v0_8-colorblind\")\n", + "\n", + " _, s2lp_true_count = get_all_same_validation_fba(s2lp, \"True_flux\")\n", + " _, s2lp_false_count = get_all_same_validation_fba(s2lp,\"False_flux\")\n", + " s2lp_missing_networks = 107 - (s2lp_true_count + s2lp_false_count)\n", + "\n", + " _, netseed_true_count = get_all_same_validation_fba(netseed, \"True_flux\")\n", + " _, netseed_false_count = get_all_same_validation_fba(netseed, \"False_flux\")\n", + " netseed_missing_networks = 107 - (netseed_true_count + netseed_false_count)\n", + "\n", + "\n", + " s2lp_tab=pd.DataFrame([[s2lp_true_count, s2lp_false_count, s2lp_missing_networks]], \n", + " columns=[\"all_true\",\"all_false\",\"missing\"])\n", + " s2lp_tab=s2lp_tab.assign(Tool=labels[0])\n", + "\n", + " netseed_tab=pd.DataFrame([[netseed_true_count, netseed_false_count, netseed_missing_networks]],\n", + " columns=[\"all_true\",\"all_false\",\"missing\"])\n", + " netseed_tab=netseed_tab.assign(Tool=labels[1])\n", + "\n", + " concat_table = pd.concat([netseed_tab,s2lp_tab])\n", + "\n", + "\n", + " plt.figure(figsize=(1,3))\n", + " sns.set_theme(font_scale = 1.5)\n", + " fig, ax = plt.subplots()\n", + " fig.tight_layout()\n", + " groups = concat_table['Tool']\n", + " ax.bar(groups, concat_table[\"all_true\"], color='#1e73be',label='flux', width=0.2)\n", + " ax.bar(groups, concat_table[\"all_false\"], bottom = concat_table[\"all_true\"], \n", + " color='#ef3340', label='no flux', width=0.2)\n", + " ax.bar(groups, concat_table[\"missing\"], bottom = concat_table[\"all_true\"]+concat_table[\"all_false\"], \n", + " color='black', label='no solution', width=0.2)\n", + " \n", + " plt.ylabel('Number of GSMNs', fontsize=25)\n", + " plt.xlabel('Tools', fontsize=25)\n", + " plt.xticks(size=22)\n", + " plt.yticks(size=22)\n", + " #sns.despine(bottom=True)\n", + " plt.tick_params(bottom=False, left=True)\n", + " plt.legend(frameon=True, loc='center right', borderaxespad=-10)\n", + " return " + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "def get_mean_std_deviation(table, tool):\n", + " mean = table[\"Total_flux\"].mean()\n", + " std = table[\"Total_flux\"].std()\n", + " print(tool, f\"mean = {mean}\", f\"standard deviation = {std}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "def plot_compare(s2lp:pd.DataFrame, netseed:pd.DataFrame, col, labels, do_kruskal:bool=True, \n", + " is_precursor:bool=False, y_label:str=\"\"):\n", + " np.set_printoptions(precision=3)\n", + " plt.style.use(\"seaborn-v0_8-colorblind\")\n", + "\n", + "\n", + " nan_spec=s2lp[s2lp[col].isnull()][\"species\"]\n", + " for nspe in nan_spec:\n", + " s2lp=s2lp.drop(s2lp.loc[s2lp['species']==nspe].index)\n", + "\n", + " nan_spec=netseed[netseed[col].isnull()][\"species\"]\n", + " for nspe in nan_spec:\n", + " netseed=netseed.drop(netseed.loc[netseed['species']==nspe].index)\n", + "\n", + " del_spec=set(netseed['species']) - set(s2lp['species'])\n", + " for sp in del_spec:\n", + " netseed=netseed.drop(netseed.loc[netseed['species']==sp].index)\n", + "\n", + " n = len(s2lp['species'].unique())\n", + " \n", + " s2lp=s2lp.assign(Tool=labels[0])\n", + " netseed=netseed.assign(Tool=labels[1])\n", + " \n", + " concat_table = pd.concat([s2lp, netseed])\n", + "\n", + " scope_tab = concat_table.groupby(['species','Tool'])[col].mean()\n", + " scope_df=pd.DataFrame(scope_tab)\n", + " scope_df=scope_df.reset_index()\n", + " plt.figure(figsize=(3,4))\n", + " sns.set_theme(font_scale = 1.5)\n", + "\n", + " prec_fn_means= scope_df[scope_df['Tool']==labels[0]]\n", + " prec_t_means= scope_df[scope_df['Tool']==labels[1]]\n", + " prec_fn_mean=prec_fn_means[col].mean()\n", + " pref_t_means=prec_t_means[col].mean()\n", + " print(f\"{labels[0]} global mean: \",prec_fn_mean, f\"\\t {labels[1]} global mean: \", pref_t_means)\n", + "\n", + " # KRUSKAL WALLIS TESTS\n", + " # Get the p-value from Kruskall Wallis test\n", + " if do_kruskal:\n", + " kstat, p_value = kruskal(scope_df[scope_df[\"Tool\"]==labels[1]][col], scope_df[scope_df[\"Tool\"]==labels[0]][col])\n", + "\n", + " sns.boxplot(data=scope_df, x=\"Tool\", y=col, hue=\"Tool\", fill=False, linewidth=1.5)\n", + " plt.xlabel('')\n", + " plt.ylabel(y_label)\n", + " plt.title(f\"kstat = {kstat}, p-value = {p_value}, n={n}\")\n", + " sns.despine(bottom=True)\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [], + "source": [ + "def get_min_max(tab, mode):\n", + " print(f'Set of seed size for {mode}\\n min: {tab[\"size\"].min()}\\tmax: {tab[\"size\"].max()}')" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [], + "source": [ + "def get_nb_and_percent_sol(tab, limit):\n", + " nb = len(tab[tab[\"Total_flux\"]<=limit])\n", + " print(f\"Number of GSMNs having less than {limit} solutions: {nb} networks ({nb/1.07} %)\")" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [], + "source": [ + "def get_iCN718_sol(tab:pd.DataFrame, mode):\n", + " tab=tab[tab[\"species\"]==\"iCN718\"]\n", + " nb = len(tab)\n", + " val_count = tab[\"size\"].value_counts()\n", + " print(f\"Total number of solutions for iCN718 in {mode}: {nb}\")\n", + " for row in val_count.items():\n", + " print(f\"size: {row[0]}\\t count: {row[1]}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Get data" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "flux_precursor_FN_submin = get_fluxes(precursor_results_dir, \"full\")\n", + "flux_precursor_T_submin = get_fluxes(precursor_results_dir, \"target\")" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "nb_total_meta_df = get_total_nb_meta(s2lp_supp_data)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "data_size_FN = get_sizes(flux_precursor_FN_submin, nb_total_meta_df)\n", + "data_size_T = get_sizes(flux_precursor_T_submin, nb_total_meta_df)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "FN_flux=create_table_plot(flux_precursor_FN_submin,'has_flux')\n", + "T_flux=create_table_plot(flux_precursor_T_submin,'has_flux')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# PLOT" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Fluxes analyses" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Mean and standard deviation" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Full Network mean = 664.0560747663551 standard deviation = 465.29006639145047\n" + ] + } + ], + "source": [ + "get_mean_std_deviation(FN_flux, \"Full Network\")" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Target mean = 56.205607476635514 standard deviation = 142.52373111353674\n" + ] + } + ], + "source": [ + "get_mean_std_deviation(T_flux, \"Target\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Plot number of fluxes validating (all solutions validates) or not (none of solution validates) FBA" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "create_one_plot(T_flux, FN_flux, [\"Target\",\"Full Network\"])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Set of seeds solution analyses" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "> Notes:\n", + ">\n", + "> On the above plot, \"global mean\" is a mean of means per network (all solutions, up to 1000, are averaged by network, then an average of those is performed)" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Set of seed size for Full Network\n", + " min: 6\tmax: 285\n" + ] + } + ], + "source": [ + "get_min_max(flux_precursor_FN_submin, \"Full Network\")" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Set of seed size for Target\n", + " min: 1\tmax: 18\n" + ] + } + ], + "source": [ + "get_min_max(flux_precursor_T_submin, \"Target\")" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of GSMNs having less than 8 solutions: 88 networks (82.2429906542056 %)\n" + ] + } + ], + "source": [ + "get_nb_and_percent_sol(T_flux,8)" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Full Network global mean: 11.356633293864068 \t Target global mean: 0.228610193536069\n" + ] + }, + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAA9IAAAGHCAYAAABcT2qzAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjkuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8hTgPZAAAACXBIWXMAAA9hAAAPYQGoP6dpAACgI0lEQVR4nOzdd1QUV98H8O8sLL0pYAM1UQQUK8bee01sMZpYYokxscX0YknyxCQ+scXeu4kttkQxNuyioGIDG4hKEeldYNmd9w/enYeVBXaXpfr9nMM5MHNn7t1l2m9uE0RRFEFEREREREREOpGVdQGIiIiIiIiIKhIG0kRERERERER6YCBNREREREREpAcG0kRERERERER6YCBNREREREREpAcG0kRERERERER6YCBNREREREREpAcG0kRERERERER6YCBNREREREREpAe9A+kxY8bAw8MDy5cvL4nyEBERlUvffPMNPDw88M0335R1UYiIiKiMmZZ1AQqTkpKCrVu3AgDef/992NnZVcg8StLVq1cRFBSE4OBgBAUF4dGjR1AqlWjdujW2b99e6LZXrlzB2LFji8xj3rx5GD58eIHrT506hb179+L27dtITk6Gg4MDmjRpgpEjR6JLly56fyZtnjx5gp07d+LChQt49uwZlEolHB0d4eHhgQ4dOmDUqFE67+uDDz7A+fPnAaDQ7+mbb77BgQMHitxfUFAQTE01TyWFQoEDBw7gzp07uHv3LmJiYpCYmAhBEODs7IxmzZph2LBhaN++faH7jouLw5YtW3D27FlEREQAAFxcXNCjRw9MmDAB9vb2hW4fGhqK7du3w9/fH1FRUcjJyYGDgwM8PT0xcOBAvPXWW5DJCn+fplKp8Pfff8PHxwd3795FYmIibG1tUbNmTbRs2RIjRoyAm5ubxjbPnz+Hj48P7ty5gwcPHiAhIQHJyckwMzODi4sL2rRpg3fffRf169fXmufjx49x/Phx3LlzB6GhoUhISEBKSgosLS1Rp04d6X9eo0YNrdsHBQXh9OnTuHPnDh4/fozExESkpaXBxsYGr7/+Orp06YJ3330XDg4O+baNiIhAjx49Cv1O8ho6dCh+/fXXAtcbcuz6+voiICAAd+7cwbNnz5CQkIDs7Gzpf9evXz8MGjQo33Gndu/ePZw+fRpXr17FgwcPkJiYCLlcDhcXF7Rt2xajRo3C66+/rvNnJNJXTk4OAgICEBQUJN2jnjx5AlEUMWTIEMyfP79Y+y/OOQ4Ay5cvx4oVK4rM5/jx46hbt26+5SVxjikUCgwdOhQPHjwAAJ2/p+DgYOzevRuXL19GTEyMdJ9p1KgRevTogYEDB+bbJiIiAteuXZP+N8HBwUhPTweQe093dXUtMD9dvzu17du3o3Xr1tLfSqUS/v7+OHfuHAIDAxEWFoa0tDRYWVmhfv366Nq1K959990i72+G3h/HjBkDf39/nct///59rcuzs7Pxxx9/4PDhw3j8+DFUKhVcXV3Rq1cvTJgwATY2Nlq38/Dw0Dlvbc8o/v7+uHjxIu7cuYPw8HAkJiYiIyMDdnZ2cHNzQ69evfDOO+/AwsJC6z6Le38pzrEjiiJu3LiBM2fO4Pr16wgNDUVycjIsLCxQt25ddOjQAaNHj0b16tV1/o6MrbjXlrxiY2Pxxx9/4Ny5c4iIiEBmZiYcHR1Rr149tGnTBuPHj4dcLi/5D1XGinPM5HX58mVs374dN2/eRFJSEqpWrYrWrVtj/Pjx8PLy0rqNrs/yar6+vnBxcdE5fbkPpNUX6yFDhpRYIF3SeZQkfQLIwjg5ORW4rqCLsVKpxNdff41//vkHACAIAuzs7JCQkABfX1/4+vpizJgxmD17drHKtmXLFixatAjZ2dkAAEtLSwiCgIiICERERMDf31/n72H//v1SEK0rc3Nz2NraFrheEIR8y1JTUzFnzhyNNHZ2dkhLS0N4eDjCw8Nx+PBhDBkyBPPmzdN6w7p27RqmTp2KxMREALmfWyaT4eHDh3j48CH27duHzZs3o0GDBgV+1rlz50KhUAAATE1NYW5ujtjYWMTGxuL8+fP466+/sGbNmgJv+NHR0ZgyZQqCgoIAADKZDLa2tkhKSkJCQgKCgoLg4uKSL5AODAzUeAA0NTWFtbU1UlJS8ODBAzx48AC7du3Ct99+q/V/d+rUKSxatEj6Wy6Xw8rKCikpKdKD+Y4dO7BgwQL07Nkz3/b79u3DH3/8If1tbm4Oc3NzJCUlITAwEIGBgdi6dStWr16NFi1aaGxrYmJS6PkAAFlZWUhNTQUANG7cuMB0hh67ixcvxsOHD6W/ra2tIZPJNP53f/zxB9atW5evrH///Te+/PJLjWW2trbIyMiQjp1du3Zh9uzZGDlyZKGfk8hQ0dHRGDduXIntvzjneF5yubzQgM3ExCTfspI6x9asWSMF0boQRRELFizA5s2boVKpAOReK5RKJR4/fozHjx/j0aNHWgPpFStW6PVwmZeVlVWR18jk5GQoFAqYmZnB3d1dY93333+PvXv3Sn/LZDLY2NggJSVF+t9t374dK1euRPPmzbXuvzj3R3t7+yLLHxcXBwBo0qRJgZ9v3LhxCA4OBgCYmZnBxMREur8dPHgQ27dv1/pAXlTeOTk5SEpKKjD/jRs34syZM9LfVlZWMDMzQ0JCAvz9/eHv74+tW7diw4YNWl/mFOf+AhTv2FmzZg1+//136W9BEGBra4vU1FTp3v7nn3/it99+0+uFtjEZ69ri4+ODOXPmIC0tTdqPXC5HVFQUoqKicOHCBYwcOfKVCKSLc8yo5X2BJwgCbGxs8Pz5c/zzzz84evQofvjhB62VfjY2NkWec4mJiVAqlXB2di6wgqYg5TqQpqJZWFjA3d0djRo1QuPGjfHvv//iwoULeu/n4sWLem/z+++/S0H02LFjMWXKFFSpUgUZGRnYtWsXFi1ahO3bt6NOnTo61Xxrs3nzZsyfPx+mpqaYPHkyhg8fjtq1awPIvZHdunULly5d0mlfsbGxmD9/Puzs7ODs7IzQ0FCdtuvfv7/etSdmZmYYM2YMvL290axZM1SrVg1yuRwqlQohISFYs2YNjhw5ggMHDqBevXr48MMPNbaPiYnBlClTkJSUBBcXF/z8889o27YtBEHA3bt3MWfOHNy+fRuTJk2Cj48PrKysNLZ/9OiRFER7eHhg7ty5aNGiBUxMTJCQkICtW7dizZo1CAgIwO+//671ZUdiYiJGjRqFiIgINGzYEDNnzkT79u1hZmaGnJwcRERE4MyZM1pv1DVr1sTHH3+Mli1bolGjRqhatSoEQUB2djauXr2KhQsXIigoCD/99BMaNWqU72ZUv359zJw5E97e3vDw8JDe/GZlZeHcuXP47bff8PTpU3z++ec4fPiwdEyoNW3aFC4uLmjZsiXq1asnvSBLT0/HiRMn8N///hcJCQmYOnUqjh07pvGipGbNmkWeDz/99BN27NgBCwsLvPnmm1rTFOfY7dOnD8aOHQtvb2+4urpKL7OeP3+OvXv3YuXKlQgKCsLXX3+NjRs3amybk5MDMzMz9O3bFwMHDkTLli1hY2OD7OxsXLt2Db/88gsePHiAH374AXXq1CmyVQSRoaytreHp6QkvLy94eXlhy5YtuHv3rlH2XZxzPK8WLVoU2XrrZSVxjt2/fx9r165F7dq18eLFCymQK8zPP/+M7du3w8rKClOnTsWgQYPg7OwMAEhISJBqy7URBAF16tSBl5cXGjVqBAAaLy8LM3HiREycOLHA9dnZ2ejUqROSkpLQq1evfDV3OTk5cHR0xKBBg9C7d280btwYcrkc6enp8PHxwcKFCxEXF4fJkyfj6NGjqFq1qsb2xb0/FlWbfufOHQwbNgwA8Pbbb2tN88UXXyA4OBg2Njb46aef0LdvX8hkMly8eBFff/01IiMj8dFHH+HgwYP5XsYUdX/ZtGkT/vvf/xaYf7t27dCxY0e0bNkSderUkV6EJyYm4vDhw1i4cCEiIiIwbdo0/PPPP/lanRXn/gIU79jJycmBjY0NBg4ciH79+qF58+awsLBAZmYmzp07h19//RVRUVH45JNPcOjQoQJbrZUkY1xbjh49is8//xwqlQojRozA2LFjpQqHtLQ03Lt3DydOnCiw1r+yKc4xA+S+lFCftyNGjMCnn36KKlWqIDo6Gj/99BNOnjyJ77//Hm5ubvmeJ2fPnl1ohV5cXJzUenbIkCFaX54WStTT6NGjRXd3d3HZsmX6bqq38PBw0d3dXXR3dxfDw8MrbB4lKScnR+Pvr7/+WnR3dxdHjx5d5LaXL1+WPru+4uPjxcaNG4vu7u7ilClTtKZZsGCB6O7uLr7xxhtiamqq3nncu3dP9PLyEt3d3cV///1X7+1fNnXqVNHd3V3cs2ePdBwX9j2pv8uvv/662Hm/TKVSiSNGjBDd3d3FXr165Vs/f/580d3dXfT09BSDgoLyrY+LixO9vb1Fd3d3ccWKFfnWL1++vMjj+osvvhDd3d3FDh06aF0/c+ZM0d3dXRw+fLiYkZGh5ycsXFJSktisWTPR3d1dnDVrlt7bP3r0SPp8q1at0nv78+fPS9sfOnRIr20zMzPFVq1aie7u7uIXX3yhNY2xj92XLVq0SCr/s2fPNNaFhoaK0dHRBW6bnJwsdujQQXR3dxfff/99o5etMivJa0Jlo1QqRZVKpbFMfd0tje+vqHN82bJlOt8rX2bscywnJ0ccOnSo6O7uLl64cEHs1q1bkd/T2bNnRXd3d9HLy0sMDAzU+zO8/OyQ93mguM9CR44ckfZ16dKlfOtv3LghvnjxosDtr1+/Lm2/cuXKfOuLe38syty5c0V3d3exefPmWp9dLl26JJXv8OHDhZZ/z549euffr18/0d3dXXz33Xf13lYURXHXrl1S/levXtV7+8LuL6JYvGMnODhYTEpKKnD906dPxaZNm4ru7u7id999p3fZS0NR15bnz59LzwibNm0qgxKWP8U5ZnJycqRr4sSJE/Otz8rKEgcOHGjwObN27VrR3d1d9PDwEB8/fqz39kYftfvAgQPw8vKCh4cHlixZorEuOjoav/zyCwYMGIDmzZujcePG6NixI4YOHYpffvkFt27dktKOGTNGo1lHjx494OHhIf2MGTNGWqdSqeDn54d58+bhnXfeQefOndG4cWO0adMGo0ePxs6dO6XmrXnpk0d5pfebEyO5fPmy1Fy1oDfTH3zwAYDc5vMnT57UO4+1a9dCoVCgZ8+e6NOnj+GFRe7brBMnTqB169YFvmEuTYIgoFmzZgByz4uXqZtttWvXTnp7l5ejoyOGDBkCAFqby8TExAAAHBwcCux70rRpUwCQ+qnkFRoaCh8fHwDAf/7zH1haWhb1kfRib28v1WRr+/xFef3116XmmIZsn7e5oL7bHz9+HMnJyQBQ4NgBxjx2tVEfO0BuLUJe9erVK7R/mZ2dHXr16gUAuH37ttHLFhERIV1DIyIi8PjxY3zzzTfSdblr166YO3duvnLrIj4+Xrq/nDp1qtC0S5cuhYeHh/RZ1cLDw7Fu3TpMnDgRffr0QfPmzdGiRQv0798fP//8M6KiovQuFwB0794dHh4e2L9/f4FpdBms7MGDB5gzZw569+6NZs2aoUWLFnjzzTexZMkSJCQkGFS2siCTybR2eyktxTnHi2Lsc2zTpk24c+cOBg8ejA4dOuhUhpUrVwIA3nvvvQKbPxemJJ8d/vrrLwBA7dq10bZt23zrmzVrVmCXMSC3lYC69k7b91fc+2NhMjMzceTIEQBA3759tXZ7Uu+zdu3a6N+/v9byq/uEHzp0SK/81f2GgYLvL0Up7P5gjO2Lc+w0bNiw0K4UtWvXRps2bQAUfu6ox26ZNGkS2rdvj8aNG6Nt27aYMGECDh8+DFEUDS5jUYq6tmzfvh3Jyclo1KhRiXZvKUjee1F2djY2bNiAt956C82bN0fLli0xduxYnDt3rlTLVJxjxt/fH5GRkQCAyZMn51tvZmaGCRMmAMjt8hEeHq7X/vft2wcAaNWqldbxMIpi1DYF69atw6JFiyCTyTB37lyNvn/37t3D2LFjpQdQExMT2NjYIC4uDrGxsQgKCkJKSor0cG9vb48qVapI/V+qVKmi8Y/IeyJGRUVpHKxWVlawsLBAUlISAgICEBAQgMOHD2Pjxo0aF2998iBN6oMaQL7+sWoODg5wdHREfHw8Lly4gMGDB+u8/4yMDBw/fhwAMGjQoGKVNTExEfPmzYOZmRn+85//lOnDnZpKpUJgYCAAoE6dOvnWqx/mC2vWVK9ePQC5gcGTJ080LgDqJsRJSUmIiIjQGkyrX1xp6+P7999/AwDc3d3h6emp02fSR0JCAsLCwgBo//xFefjwoXQtMWT7q1evSr/ru736IfG1117TGEBHzZjHbkHU5RcEIV+zdl2Ym5sDgNSvsqTcunULs2fPRnp6OqysrGBiYoJnz55h9+7dOHbsGDZt2lTgACHaODo6omPHjjhz5gwOHTpUYB86URSlY/jl/8F3330nDTQkl8ulvvuhoaEIDQ3FgQMHsGbNGrzxxhsGfmrDrV+/HosXL5b+L5aWllAoFFK/y3379mHdunVagwfSVJxz3Bh0PcfCwsKwfPlyVK1aVefR4B8/fowbN24AKLlrjKEiIyPh5+cHABg2bJjB99vCvr/i3h8L8++//0rjXxT00l3dJadTp04Ffr7OnTvD398f165dQ2ZmZqEvDvJS319sbW3Rt29fnbZ52bVr16TfDbk/FPf+UlxFnTtJSUmYNm0aAgICpGW2trZITEzExYsXcfHiRRw5cgRLly6FmZmZ0ctX1LXl4MGDAIC33nqrTJ83MzIyMHr0aNy8eRNyuRxyuRxpaWm4cuUK/P39MW/evHJRsVQU9flmbW0Nb29vrWk6d+4s/X7x4kWdx6a4evUqHj9+DMDwF1dGCaRFUZT66piZmWHBggX5LgDz589HcnIyvLy8MHfuXDRr1kzqMxkVFQVfX1+Nk2bFihUao+f+9ddfBdasmZqa4s0330T//v3h7e0t9cdJT0/HsWPHsGTJEly9ehVLlizBt99+a1Aeld3IkSPx8OFDZGdno2rVqmjatCkGDRqEHj16FHkhUCqVRa7TZwAVIPcBXN2KwMvLC1evXsXGjRsRGBiI9PR0ODs7SyMevjyQycvmzZuH+Ph4zJw506CRiv38/NCnTx9ERUXlG5X1tdde02tfiYmJCA0NxcaNG3Hz5k0AwPjx4wtMX9hDWN51Dx480HhQGDRoEFatWoWMjAxMmTIlXx/pbdu24e+//4alpWW+QXOA/92IGzdujBcvXmDjxo3w8fFBREQEzM3N4ebmhoEDB2L48OE636iUSiXi4+Nx48YNLF++HC9evIBcLsfo0aN12l6hUCA+Ph5+fn5YtmwZgNyXNeqah6JkZ2cjJiYGZ86ckbavW7cuunfvrtP2QO5D2ZUrVwBA6kP3MmMeu3mlp6cjMjISBw8exObNmwHk/p9f7j+oC3UgqU/+hpg7dy5cXV0xb948NG3aFKIo4uLFi5gzZw6ioqKkPnwFDXanzaBBg3DmzBmcPn0aKSkpWgeIvHbtGiIiIiAIQr5Aw9PTE3379kWHDh1Qp04dyGQy5OTkIDg4GMuWLcP58+fx6aef4sSJEzo//BrD3r17sXDhQlhZWWHy5MkYNmwYnJ2doVQqcffuXSxYsACXL1/Gxx9/DB8fH1hbW5da2SoKQ87xhw8fYuDAgQgPD4dMJkO1atXQqlUrvPfee8V6YaHLOSaKImbNmoWsrCzMmzcPVapU0Wnf6uuzXC6Hh4cHfH19sW3bNgQFBSErKws1a9ZEx44dMX78+FJ/ptm3bx9UKhVMTEwwdOhQg/aRkJAgPTMU9v0Zen8sjDqQrV+/Plq2bJlvfWJiImJjYwGgwIE+gf+VW6VSITQ0VKcXhunp6Th69CgAYMCAAXq1BMvMzER0dDT+/fdfqbVCq1atChwsTVvexrq/FIdCocD169cBaP/fK5VKTJ8+HQEBAWjYsCE++eQTtG3bFpaWltJL7N9++w2+vr5YuHAhvvvuO6OUS9drS3h4uNQi0MvLC/fv38e6detw5coVaZRpb29vjBkzRuvxZUzLli2DhYUFVq5ciS5dukAul+PRo0f49ttvcePGDfz888/o06dPoYPplgfqgfHq169fYM22o6MjqlatioSEBISEhOi8b/Wgh/b29ga3Hix2IJ2dnY2vvvoKR48eha2tLVauXCk1y8hLXfs2Z84cjWYRZmZmeO2116RqeUPUqFEDCxcuzLfc2toaQ4cORYMGDfD2229jz549+Oyzz6S3Xcamz5QGLzPGdCDFERgYCDs7OwiCgOjoaERHR+P48ePo3Lkzfv/993wPbXlHonz48KHWmrnY2Fhp5En1hUVX6jdEQO6gDQsXLoQoirCysoKpqSkiIyOxf/9+/PPPP/jxxx8LDGp8fX1x+PBhuLu7S03N9RUdHS21oEhLS5Nqh3bu3InvvvsO7733XqHbq1tqvMzKygqffPKJ1rK7uLggNDS00BcQede9/P1Wq1YNa9aswaeffor79+9j1KhRMDU1hYWFBdLS0iCXy9GzZ0/MmDFD63Gr/v4FQcCwYcMQGhoqjdidmpqK69ev4/r16zh48CDWr19f6DQQEydO1DoAXs2aNTF//nyp5qAgffv2lWqv83Jzc8OiRYuKvNE3adJE6oaQl7e3NxYtWqTXG+u//voLoijC1NS0wADeWMcuANy4cQMjRozIt9zExASDBw/G3LlzdS67mo+PjzQKu6FvYHVlYmKCzZs3w9HREUDu8dSxY0ds2LABgwYNQlRUFHbt2qXXudmjRw/pODx69KjW70ddG+3t7Z2vRmXWrFn50puamqJp06ZYu3YthgwZgvv37+PYsWOlVtuXlpaG3377DUDuw0+nTp2kdSYmJmjcuDE2btyId955B0FBQdi7d69eTQb1ndLtZdOmTcP06dMN3r6kFeccT0xMRHJysjSrgnq067/++guTJ0/Gp59+qnd5dD3HduzYgWvXrqFjx4546623dN6/+hpjZ2eHxYsXSwNC2djYQBAE6TPs378fS5cu1aitKUkqlUpq9ty5c2eDpzBaunQpFApFgdfZ4t4fC/L48WOplrOgmrq8+yrs8+VdFxMTo1Mg7ePjg4yMDAC6XZtjY2PRsWNHreu6detW5DNlSdxfimvz5s3SQHvavoN//vkH/v7+qFevHrZv364RBFpZWWHw4MFwc3PD22+/jT///BOTJ0+W7j+G0Pfakvf+f/36daxYsQIKhQIWFhYwNzfH8+fPcfToUfz777+YPn06pk6danDZivLixQvs3LlTo+VGvXr1sHr1anTt2hUZGRk4ffp0vmuPvtNFvayg6eIMpT7nirqeVK9eHQkJCTqf72lpaTh27BgA4M033zQ4NixWH+m0tDR88MEHOHr0KJydnbFjxw6tQTQA6WBXv8krTU2aNIGjoyMyMjKMNmKoNk5OTgb/6FMjYyx2dnaYMGEC9u7dixs3biAgIAA3b97EoUOHpH4/586dw+eff55v27Zt20oXkDVr1mjdf97l6uH/dZWSkiL9vmjRInh6emLv3r3S1AN79uyBh4cHFAoF5s6dq9G/Xi01NRXff/89ZDIZfvrpJ72nGGjUqBHmzp0LX19f3L59W2qmtXz5ctSpUwcKhQI//vijdCIWRD1ViHrkaiC3yeYnn3xS4M1aPYKgOs+XPXv2TKPvlbbvt02bNti+fbt0A8/JyZHSKZVKZGRkSN0aXqZuNn3gwAGEhYVh5syZCAgIgL+/PwICAjBjxgzIZDLcunWryOaIDg4OcHJy0ugq4eLigtmzZ2t9AfOyqlWr5jtHPDw8MHv2bJ2anTs7O8PJyUlj5NY2bdrgu+++Q61atYrcXk2pVEo3ly5dukij477MGMeumlwul64ReY/fESNGYNq0aXrXmIaFheH7778HALRs2dLgGiNdjRw5UutDTP369aW3v+q++LoyNzeXWjxp63+YnZ0t1eroGwibmJhIQay2866kHD9+HCkpKWjUqJFGEJ2XqampNI2RvjMzqKd0M/Tn5VGPyxtDzvG6deviyy+/xL///otbt27hypUrCAwMxMaNG+Hl5QVRFLFmzRps2rRJr7Loeo5FRERg8eLFsLS0xA8//KBXHurrc2JiIjZu3Ii2bdvCx8cH165dQ2BgIDZt2oRatWohIyMDM2fO1OiKVZIuXLggNbs2tMmoj48Pdu3aBSD3Jay2F63GuD9qo66NlsvlBXZFyzumSGE1xnmvzdrGIdFGXTvm6elZ6LSKannP67xBQN++ffHll18WOc+xse8vxXX16lWptnfgwIFo165dvjTq/qzvvvtugTWpjRs3RoMGDaBQKKQWZIbS99qS9/6/dOlSVKtWDZs3b0ZgYCCuXbuGI0eOoHXr1hBFEcuWLZO6gZWEPn36aO3+ULVqValCU1vQq54uytAfY1OfP0Udj+r1up5v//zzD168eAHA8OsVUIwa6djYWIwePRp3797Fa6+9ho0bNxbahKhbt27Ys2cPvv76a1y/fh3du3dHkyZNjDaIUXZ2Nvbt24cTJ07gwYMHSEpK0jrAmLEHHcnLkCmkylLDhg3RsGFDjWWCIMDT0xNLliyBo6Mjtm/fjtOnT8PPz0/jola1alWMGTMGGzduxMWLF/HFF1/g448/Rp06dRAbG4s///wTf/zxB+RyORQKRb7pF4qSt1mWubk51q5dq/E2qlmzZli7di369u2LzMxMrF69GqtXr9bYx/z58xETE4PRo0cbNBiLtim7LC0t0bt3b7Rq1Qpvv/02IiIi8N///he9e/cusAn86NGjpebL2dnZCA4OxvLly/Hrr79i586dWL16db6HhXHjxmHfvn1ITk7G9OnTMWvWLHTp0gWmpqa4evUqfv75Z40m9dryVve3dHR0xPz589G2bVvY2dkhLCwMmzZtwpEjR3DlyhX88ssv+R4a1AN1qFQqjB07Fh9//LG0zsbGBlOnTkVCQgJ27NiB06dPIzg4uMBmkHlr49PS0nDp0iUsXrwYU6dORZcuXbBkyZJCm6n++eef0u9JSUk4efIkli1bhnHjxmHYsGH4z3/+U+gUEr6+vtLv8fHxOHToENasWYPhw4fj448/xieffFLgtnmdP39eGnilsNoCYxy7al5eXtJ1RaVSITw8HFu3bsXOnTtx8OBBLFy4UOeaxtjYWEyePBkpKSmoVq0aFi9erPd5qS9tAw3lXXf48GHcv38fCoVCrxddgwcPxt69e3H9+nWEh4dr1Dqrm3ybm5ujX79+Wre/evUq/vrrL9y4cQPPnz+XaoHyMmSQHkOpmzKGhoYWOthUZmYmAOg9IJouU7pVZIac49pqgM3MzNCxY0e0atUKo0aNwu3bt7F8+XIMHz5cp+aP+pxjc+bMQUZGBr7++mu9+6HmvT6rWx+pn6VkMhk6dOiApUuX4p133kF6ejo2b95c6PQvxqIORJ2dndG1a1e9t7969arU/a5t27aYMWOG1nTGuD++LCcnR+rb2r1791Jv0vzw4UOpu5euLYWqVq0qndeiKOL58+fYuXMnNm/ejFOnTmHOnDlaa5zVjHl/Ka7Q0FBMnz4dCoUCDRo0wI8//pgvjVKplMYGWLFiBdauXVvg/tQvm4r7Eknfa0ve+78oili+fLlGawQ3NzesWbMGffr0QWxsLFasWIHevXsXq4wFyTto3MuqVasG4H/fU15FTRdVWaivV15eXvliIX0Y/BS1e/du3L17F+bm5tiyZUuR/XC+/PJLtGnTBhkZGdi8ebPUP2Do0KFYtmxZsR5a4uPjMWzYMPzwww+4ePEiYmNjIZPJUKVKFekNifpmpn77QEX79NNPpbec2kbI/eyzzzBgwAAAuW92+vfvj8aNG6Nbt25Yv349mjVrJjVb1daPsTB5A6uBAwdqbdJRs2ZNqYbm8uXLGjfOS5cu4a+//kKNGjUMappXlCpVquCjjz4CkHuhDg4O1mk7MzMzNG/eHBs2bED37t3x+PFjfPXVV/lGmKxevTpWrlwJBwcHxMfH47PPPkPLli3RrFkzTJw4EeHh4RrNrl4eGE/dpNjU1BRbt27FkCFDULNmTVhbW6Nx48ZYvHgxBg0aBKVSiZ9//jlfzXTe77+gZreTJk2Sfte1hszGxga9e/fGnj17ULNmTZw9e7bIOT3zcnBwwNtvv42dO3fCysoK+/btk2ovdOHo6IgJEyZgw4YNEAQBq1atwunTp3XaVl1bUL169UKbShb32C2ITCZD3bp1MXfuXHz11VfIyMjAF198oVMzpvj4eIwbNw5PnjyBk5MTtmzZgho1ahS5XXHp0vQxJydHuplfv34dHTp00PqTt+a6ZcuWcHV11RhUTE1dE9WtWzet150FCxZg1KhRUmuLrKws2Nvb56t91RZclxT1/zArKwtxcXEF/qhr1tQBNeVXnHNczdzcHJ999hmA3ONAPXhWYfQ5x/bu3YtLly7By8sL77//vl5lAzSvMe+9957WCommTZtKL7JK4yVKQkKCFHQMHjxY7/lxAwMD8eGHHyIzMxPe3t5YtWpVgfso7v1RmzNnzkgtJgurncr73Rf2PJn3HNVlPAP1/cXc3FyvZv5qgiBIzzsLFy6EQqHADz/8gHv37um0fXHuL8UVFhaG999/HwkJCXj99dexefNmra00k5OTpWbWycnJhV4r1RVpef8Pw4YN03pvmTZtmk7l1OXakvd/3a5dO61N+q2traUugffv39dpznhDFHbcqc+tnJycEsnbmNSfo6j7nnq9LufbvXv3cOfOHQDF7+JmcCDdrVs32NraIisrC99++22RAaqdnR22bduGP/74Ax988AG8vb1hYmKCoKAgrFy5Er1798bhw4cNKssvv/yCBw8ewMHBAb/88gsuXLiAW7du4fLly9IIfuq3LyU5JH5lY21tLQ2moW04eVNTUyxevBjr1q1D//79Ua9ePbi4uOCNN97A7NmzsWPHDunA1neQr7wP4IWNzKkeMTwjI0Pqjw1Aepv25ZdfQhAEpKena/yoAxelUplvma7y1nLrO9y+IAjSA9Tt27e1BuKtWrXC0aNHMW3aNLRo0QIuLi6oX78+hg4dir/++kujhcDLg56pmyN27dq1wO9PPW1ZSkpKvgdF9fliY2NTYDBUo0YN6YKlbw2ZnZ2d1ORxz549em0L5DYNV7/F3b17t97bN23aVBroQ5ft4+LipClXhgwZUuhUDsU9dnXx3nvvwczMDBkZGdJULQWJj4/H+++/j5CQEDg6OmLr1q2FlqssKRSKAh+M8t5E8w4iljeQTkxMlKb10Nas++LFi9iwYQOA3O/wn3/+kbptqO8VhgQ2xaW+9vTv3x/3798v8idvLQlpp+85/jJ9ru/6nGOpqan473//C5lMhu+++w6ZmZn57k/q55ScnBxpWd6aLn2vMYZO6aaPQ4cOScGLvs0kAwMD8cEHHyA9PR0tWrTA+vXri3wYLs79URt17VStWrUK7HcM/O/eCBTeaiXvurzbaJOdnS1dx3r37q13xcPLevfujVq1akGlUkmfSx/63F+KKywsDGPHjkVsbCxee+01bNu2rcBuU3mf0davX6/TtTLv2A6JiYla7y3aamULU9i1Rd9zEyid87MiU58/RVW4qtcXdb4B/zvfLS0t8eabbxarfAY37fby8sLUqVMxceJE+Pn54cMPP8TatWuL7Ev1xhtvSNOKZGVl4cKFC/j999/x4MEDfPfdd2jbtq1ebewVCgVOnDgBIHeEWHUNaV5KpbLAvqDGpOv8j9r069evwjal6NKli9Rn6WXqNz4tWrTQa5+6DtyW98VI3uZb6uY82vp353Xt2jVpOP2VK1eiZ8+eepWzOPJecJ88eaL1zWXVqlUxffp0rQP9qPuByuXyfCNzqkctLKzJYN6Hi4iICI11Hh4eeo20bsgUD+rPn5aWhvj4eL0HBFFv//TpU73z1nf7gwcPIicnB4IgFPmQWNxjVxfm5uawt7dHbGwsnjx5UmC6+Ph4jB07VuMBv6Dp6krC8+fPCxxMTn3TMzU1lWqM2rRpo/NAJYMGDcLKlSulqYCaN28OHx8fKBQKVK1aVWurAfVDYceOHaV+rC8ztHZA/XIlKyurwDTqaXVepn5wLKkHqmfPnhWrD9iECROkF28VSXGvEbrQ9xxLTk6WjoO8U4Rq888//+Cff/4BkHsNUjc/1PcaUxpT8KgfTFu3bq3XbBbXr1/XCKI3bNig85gxht4fXxYTEyO9gBs6dGihXV6qVKkCZ2dnxMbGSqMJa6O+f8pksiJfXJ46dUp6RjXWAJDVq1dHVFRUofeHguh6fykudRAdExMjBdGFBUEODg4wNTVFTk6OQddKY76ALOja4ubmBhMTkyIrZspzpd68efOk88cQxm4B06BBA5w+fRqhoaFQKpVaKzLi4+ORkJAAoOApedWys7Ol62pBc8Xro1gd5Jo0aYItW7bAwcEB/v7+mDRpks6dvIHck7VHjx5S086srCyNgSPyXswKOugSEhKkB5eC2rhfu3atwIcbXfLQVWHNTIr60XcwrtKQnp4u3SgMmUIjODhYCuj0HfSnbt26UhAYGhpaYDr1/m1sbIocWMPY1H11AMO+n7y1HIZMZaN+g92rV698L7DUx3VhN5u8AcPL+avfyKelpRX4FvDZs2fS+V7cz2/IYEbq7Q2dBkif7dUPiW3atCmyP2NpHLtpaWnSg1dB5Y+Li8v3gF/YdC0lobCBXtTrPDw89B4IEMj9ntUv6NTNudXnxIABA7Q2C1WPkVFQf35RFHH58mW9ywL8r/tKQeNwqFQq6cXiy9Qv84KCgkqkKaVSqSzW/ak0m7kbU3GuEer+qkDB17eyOse8vb2la6Yu15iSngIrMDBQyquwWQheVpwguiiF3R9fduDAASiVSshkMp0GYGzfvj2A3C5NBT03nj9/HkBuN5SiBklS31/q1q2r0wCcRRFFUXo5bsixr8v9pbjCwsIwZswYjSC6qFGZ874U0be7hrEVdG0xNzeXKgt1OTcFQSh30+6mpaUV635hbOrzLT09XZoB6mXq8w0oulLz+PHjUitAY8yjXezprxo1aoStW7di3LhxuHr1Kj744AOsX79e42KYk5MDmUxW4Fu+vBeZvGny7qOgN/nq6R5EUcS9e/fy1X7k5ORgyZIlBZZflzx0Zewh30uaKIqFvqn+/fffpRcQ+g448eLFC2kk0oJGDizKkCFDsGzZMhw+fBjTp0/Pd5F99uyZVMPUpUsXjWOnqP/FmDFj4O/vj9atW2P79u351hf13SQlJUkDXdSsWTPfg3lOTk6hfcRycnKkKUvkcrneNfbHjh2Dr68vTE1N8eGHH+Zb36hRI/j7++PcuXOIjIzUmK5MbefOndLvL+ffs2dP2NvbIzk5GRs2bNA6ZdD69esB5N4IXh5YpqjPHxsbi/379wPIbSb1ch+/orYPCQmR+u2//OChfiAq7P/n5+cnjZZd1IPL1atXpem3dK0tKM6xW9RnB4CNGzdKfZu0lT9vU1MnJ6dSr4lW27VrF8aOHZtv4J5Hjx5Jo90XNCCYLgYNGoTAwED4+Pjgvffek15uFTTirvp6X1C/wZ07d+rdTUPN09MTd+7cwYkTJzBz5sx8x9+BAwcKDLL79u2LBQsWICUlBfPnz8eiRYsKPH5VKhXS0tL0av7p6upa4e5PhSnuOV7U9T07O1t6brCystI6erCh55gu/4vu3bsjMjKywGkxLSws0K9fP+zbtw9//vknxo0bl+8aqh6JHMjtileS1IGgnZ2dNKJ+UUoyiC7q/vgy9UjQ7du313qvfNmQIUNw6NAhPH36FEePHpVmOVG7efOm9N0XVYkQFRWFS5cuAch9CVFU6wFd7g/79u2T+nu/fOwb4/5SXOogOm9zbl2nShsxYgQCAwNx9uxZnD17tsDWkEDuc5q+L6mN8fwwdOhQXLlyBX5+fggKCsrX2jA9PV16/mrWrFmpD2xXlPnz55fpdLwva926NVxcXBAZGYl169ZJLyrUFAqF1J2xZcuWRVZ2qK9Xr7/+er59GcIoQ7Z6enpi69atqFq1qnRxzFvDGh0djd69e2PVqlUIDg7W6Nx+7949fPHFFwByb1itWrWS1tnZ2Ukn1/79+7V2ire2tpbe5s+fPx9+fn5SX6IHDx7gww8/xJ07dwp8I6lLHuVZeno6EhISpB/1QAwKhUJjubY+IP3798eWLVsQGhoqfWeiKOL+/fv44osvsG3bNgC580Gq3wjldfPmTaxZswYhISFSvtnZ2Th37hzee+893Lx5EzVr1iywCeU333wDDw+PApupjR8/Hi4uLnjx4gUmT56sMU3QrVu3MHnyZGRmZsLCwsLoc/EdOnQI06ZNw7FjxxAfHy8tz8zMxMmTJzFixAjpgfurr77K95Lop59+wvfff48rV65otNLIzs6Gn58fxo8fL9V8TZw4UetD8YoVK3Dy5EmNbglRUVH4/fffpSbrM2bM0NoSY8yYMQByj4+JEyfi/Pnz0v/o+fPn+Pnnn6W+om3atMn3IsDGxkYajXLHjh1Ys2aNdE6npaVh1apV0o1gyJAh+V6UvPfee1ixYgXu3bunMXp+SkoK/v77b4wYMQLx8fEQBEHryKx9+/bF5s2bNY5NIPfh9c8//8To0aORlZUFMzMzTJkyRWPbZ8+eYfDgwdi1axfCw8M1agyePXuGdevWYcqUKRBFEQ4ODkXOx6u+6Do4OOg8umZxjt1//vkHH330EY4fP65x7KlUKty7dw9z5szBqlWrAOTWTL3chDkhIUF6wHd2dsa2bdv0DqKXL18unZsvN/vXR05ODiZMmCB9flEUcenSJXzwwQfIzs5GzZo18e677xq8//79+0MulyMpKUmahs3Nza3AqWPUU0udO3cOK1eulGpaU1JSsGbNGsybN8/gli3qweNCQ0MxZ84c6bxNS0vDli1b8P333xe4bzs7O3z33XcAcpuff/jhh7h586Z07KtUKoSGhmLTpk0YMGBAmdfG6Co1NVXjPqS+FmRnZ2ss1/YSe//+/dIx+HLLhuKe4wEBARg3bhwOHjyo8XJDoVDAz89Pun8BwJQpU/Jdn41xjhXXJ598AltbW8TExODjjz+War9UKhUuXbqETz75BKIowtHREePHj8+3/cvPCHn/BykpKVr/b9qkp6dLAwEOHDhQpymTbty4IQXR3t7eBgXRxbk/5nXlyhWp+bKutVPt2rWTrrtz587F0aNHpXPVz89PGrzK3d29yBruffv2QaVSFThn9suuXbuGUaNG5Tt2gdw5jBcuXCg9c9WpUydf/sW9vwDFO3aePHki9Yl+/fXXsX37dr3mG3/rrbfQvn17iKKIqVOnYtWqVRqt5jIyMnD58mX8+OOPBnXVM8bzw1tvvYWmTZtCFEVMnz5dIy4JDQ3Fxx9/LA2KPHPmzHzbX7lyRbr2qSscKrriHDMmJiZSnHj27Fn88MMPUo3y8+fP8dlnn+H+/fswMTHBl19+WWg5wsPDpWdvY9RGA0aokVbz8PDA9u3b8f777yMwMBDjx4/Hxo0bpRtQeHg4li5diqVLl8LExAS2trZIT0+XvjC5XI5ff/0134PGyJEjsXTpUmzfvh27d++Go6MjZDIZmjVrJr0x/u677zBmzBg8f/4c48aNg5mZGeRyOdLT02Fqaoqff/4Zy5YtK7B5mi55lFc//fST1onTAwMDNd6iu7i45Osf8ujRI/z666/49ddfIZfLYW1tjczMTI1Bfbp06YLFixdrzTs2NhZLlizBkiVLIAgC7O3tkZqaKvUNcXd3x+rVq/Xu+6pmZWWF9evXY/z48bh79y6GDx+eb0RdKysrLF682OiDJ6lUKpw4cULqf29lZQUzMzONz2dmZoZvvvkm39toILebwoEDB7Br1y4IggBra2uYmppqbC8IAsaMGVPg9EsnTpzA8uXLAeQOiCAIgvS55XI5Pv/88wLftvfu3RtTpkzB6tWrERYWhg8++AAymQwWFhYa54G7u3uB/99Ro0YhPDwcmzdvxpIlS7Bs2TLY2tpqfIbOnTtrfVESFxeH5cuXY/ny5dL5rlQqNS6eVlZW+P7777XOmxseHi69FTU1NYWNjQ0UCoXGS4mqVatiwYIFcHd3z7f9vXv3pHLJ5XLY2NggKytL47O7urpi+fLlBQ5sAuQGQf/++y8A4M0335TmTi9KcY5dURRx+vRpKViysrKCubk50tLSNG4wbdu2xdKlS/O9Of/zzz+lLhnp6elap3HL66+//kLNmjV1+lz6+s9//oPZs2dLn18URWlgSjs7OyxfvrxYtVD29vbo1q0bjh8/LjWbLqwGaPDgwTh48KA0X+ny5cthZ2eH1NRUqFQqdO3aFQ0bNixwOrLCtGvXDoMGDcKhQ4ewd+9e7N27F3Z2dkhLS4NKpcKYMWOQlpam9XoN5L6QyszMxM8//4xz587h3LlzMDMzg5WVlca9EiidPq/GMGXKFPj7++dbfuTIEY1BjApqGVSY4pzjoijCz89PGmTRwsIClpaWGueYTCbDhx9+qDE7gVp5OMeqV6+ONWvW4OOPP4afnx/69+8PW1tbKBQK6R7u6OiINWvWaK3xun79eoHlfjmg27ZtG9q0aaM1rY+Pj/Sd69piZ/HixdK1PDQ0VJpTXpsaNWpINcZ5Fef+mJf6RWnVqlX1anm3cOFCjBs3DsHBwZg5cybMzc0hk8mk65uLiwvWrFlT6MCUKpVKCpQ6d+6s0yBJQG4rqatXrwLIbUpsZWWFFy9eaDy7eXp6YuXKlflebBT3/gIU79hZs2aN1H0lNja2yJcHL/e3NTExwfLly/HFF1/g9OnTUlxhY2MDmUyG1NRUKfjVd+R4teI+P8hkMqxatQrjxo1DSEiI1GJE/Qyo3u/cuXO1tnapjIp7venfvz9CQ0OxYsUK7Ny5E7t27YKtra00b7epqSl++OGHIlt37tu3D6IoQi6X6/TiShdGC6SB3JqAbdu24f3338etW7cwbtw4bN68GdWrV8fq1atx5coV3LhxA9HR0YiPj4epqSnq1q2LNm3aYOzYsVoHqPjoo49gY2ODQ4cO4dGjR4iOjoYoihrNbxo3boy9e/dixYoVuHz5MtLS0mBtbY3OnTtjwoQJaNq0qTTJuza65FEZ/fTTTwgMDERQUBDi4+ORnJwMU1NT1KlTB02bNsVbb71VaLMZLy8vTJw4EVevXkVkZCSSk5Ph4OAADw8P9OvXD0OHDjX4QqZWv359HDlyBJs3b8bJkycRHh4OlUqF119/HR07dpRq/oytTZs2+PTTT3Hjxg2EhoYiKSkJaWlpsLGxQZ06ddC2bVuMGDGiwCYkkyZNQoMGDRAQEICwsDDEx8dL27u6uqJly5YYNmwYPD09CyzDBx98gJMnTyI4OFjqd/L666+jffv2GDVqVJEvDz755BN07doVu3btwvXr1/H8+XNkZ2ejatWq8PDwQJ8+fTBs2LBCg8NvvvkGXbp0wR9//IEbN24gKSkJtra28PLywpAhQzBgwACtXTbmz5+Pixcv4tq1a4iKikJCQgJycnLg6OiI+vXro0OHDhg6dGiBDw5r1qyBv78/AgMDER0djYSEBIiiiGrVqsHd3R2dO3fGkCFDtNbkV6tWDUuXLoW/vz9u3ryJmJgYJCYmwsTEBLVq1YKHhwd69OiBN998s8jakyNHjkgPRvoOAmPosdu1a1fMmzcPV65cwb179xAXFyfNi1y7dm00adIEAwYMKPDczPsGPSMjo8j+rdoGRVG/4a9Vq5bOD3faNG3aFPv27cOaNWvg5+eHhIQEVK9eHV26dMHUqVONMgXXoEGDcPz4cQC5DzCFTR0jl8uxadMmrFu3DocPH0ZkZCREUUTTpk0xePBgjBgxAitXrjS4LPPnz0fjxo2xf/9+hIWFQaVSwdvbG6NHj0a/fv2kWvOCvPvuu+jUqRP++OMPXLp0CREREUhNTYWNjQ1q166NFi1aoHv37oXOz/0qKO457u7ujq+//ho3btzA/fv3kZSUhNTUVFhYWKB+/fp444038M477xTYWsoY55gxvPHGGzhy5Ag2bdqEM2fOIDo6GoIgwN3dHd27d8f7779f4s1G1UGul5dXgWMPvCzv91fUiMnqKThfVtz7I5DbYkJ97Rg0aJDOL0qB3Jd4u3fvxo4dO3D48GE8fvwYoijC3d0dvXr1woQJE4p8SXjp0iVpHBNd7y9eXl747bff4O/vjzt37iAuLg5JSUkwMzNDnTp10KhRI/Tp0wd9+vTRGsQX9/5SXHn/92lpaQaND2RjY4M1a9bg7NmzOHjwIG7cuIG4uDiIoojq1avDzc0Nbdq0MajbkLGeH5ydnXHgwAHs2LEDPj4+ePz4MTIzM+Hi4oK2bdti3LhxWisBgP/df2UyWZED5b1Kpk+fjjfeeAM7duzAjRs3kJycjOrVq6NVq1YYP358gS3R1FQqlfQiu1u3bgZX8r1MEMvz0HFERFTqevXqhadPn2LevHl6v0CIiIiQanZOnTpV7gZSISIiKq9mzZqFv/76C2+99RYWLFhQ1sWhIhiljzQREVUOUVFRePr0KV577TWdRrAlIiIi47h8+TLkcrnW8WOo/GEgTUREEvXATp988kmh/fuIiIjIeCIjIxEREYFhw4YVOfo0lQ9s2k1EREbDpt1ERET0KmCNNBEREREREZEeWCNNREREREREpAfWSBMRERERERHpgYE0ERERERERkR5My7oARERkPEqlCgkJ6WVdDCLSwtnZtqyLQERERsIaaSIiIiIiIiI9MJAmIiIiIiIi0gMDaSIiIiIiIiI9MJAmIiIiIiIi0gMDaSIiIiIiIiI9MJAmIiIiIiIi0gMDaSIiIiIiIiI9MJAmIiIiIiIi0oNpWReAiIhIVyqViAfhSUhKz4KDtTncaztAJhPKulhERET0imEgTUREFcK1+zHY7RuCuORMaZmTvQVGdHdDS49qZVgyIiIietUIoiiKZV0IIiIyDqVShYSE9LIuhtFdux+DVQfuoJmbEwa0qwsXZ2tExqbjiN8T3AyJw5QhjRlMU7nn7Gxb1kUgIiIjYR9pIiIq11QqEbt9Q9DMzQnThjVBfRd7WJiZor6LPaYNa4Jmbk7Y7RsClYrvhYmIiKh0MJAmIqJy7UF4EuKSMzGgXV3IBM3+0DJBQP92dRGXnIkH4UllU0AiIiJ65TCQJiKici0pPQsA4OJsrXW9i5O1RjoiIiKiksZAmoiIyjUHa3MAQGSs9r7fkXHpGumIiIiIShoDaSIiKtfcazvAyd4CR/yeQPXS+JgqUYSP3xM42VvAvbZD2RSQiIiIXjkMpImIqFyTyQSM6O6GmyFxWLHvNkIik/EiKwchkclYse82bobEYUR3N84nTURERKWG018REVUilXX6K4DzSFPFx+mviIgqDwbSRESVSGUOpIHcqbAehCchKT0LDtbmcK/twJpoqjAYSBMRVR6mZV0AIiIiXclkAjzrVinrYhAREdErjn2kiYiIiIiIiPTAQJqIiIiIiIhID2zaTUREFUZOjgq+1yMQk/QC1Rws0d3bFaamfCdMREREpYuDjRERVSKVebCxPb4PcTwgQmMuaZkgoHcrV7zTvUEZloxINxxsjIio8mCNNBERlXt7fB/iX/9w2FmZYWjn19HUzQm3QuKw/1wY/vUPBwAG00RERFRq2B6OiIjKtZwcFY4HRMDOygy/TWmHalWscO9pIqpVscJvU9rBzsoMxwMikJOjKuuiEhER0SuCNdJERFSu+V7Pbc7t7eGE2euvIC45U1rnZG+BFu5OOHsjCr7XI9C7dZ0yLCkRERG9KhhIExFRuRaT9AIAcCYwCs3dnDD5LS+4OFsjMjYdR/ye4OyNKI10RERERCWNTbuJiKhcc7K3AAC4Oltj2rAmqO9iDwszU9R3sce0YU3g6mytkY6IiIiopDGQJiKics3V2QYAkJiapTFiNwCoRBGJqVka6YiIiIhKGpt2ExFRuZaWqQAApGfm4LNlF+BZtwrM5SbIUihx70ki0jNzNNIRERERlTQG0kREVK45WJsDAGo6WuFZfAau3o/VWK9erk5HREREVNIYSBMRUbnmXtsBFmYmeBafAVsrOTxrO8BMLkO2QoV74Ul4Fp8BCzMTuNd2KOuiEhER0SuCgTQREZVrKpWILIUSAPB6DVv0al0HLk7WiIxLR5ZCiVuPEpClUEKlEiGTCWVcWiIiInoVMJAmIqJyzfd6BEQR6NqiFu48SsAv269J65zsLdC1eS2c4TzSREREVIoYSBMRUbmmnh/6rQ6vY3QvDzwIT0JSehYcrM3hXtsByelZOHMjivNIExERUalhIE1EROVaNQdLAMCtkDh0bu4Cz7pVNNbfDI3XSEdERERU0jiPNBERlWvdvV0hEwTsPxeGHJVKY12OSoWD58IgEwR093YtoxISERHRq4aBNBERlWumpjL0buWKlIxsfLHiEs7ciERiaibO3IjEFysuISUjG71bucLUlLc0IiIiKh2CKIpiWReCiIiMQ6lUISEhvayLUSL2+D7E8YBwqPLctWQC0LtVbbzTvUHZFYxIR87OtmVdBCIiMhK+viciogqhvos9qtiaayyrYmuO+i72ZVQiIiIielWxRpqIqBKprDXS1+7HYNWBO2jm5oQB7erCxdkakbHpOOL3BDdD4jBlSGO09KhW1sUkKhRrpImIKg/WSBMRUbmmUonY7RuCZm5OmDKkMRQ5KtwIiYMiR4UpQxqjmZsTdvuGQKXie2EiIiIqHZz+ioiIyrUH4UmIS85El+a18N26y4hLzpTWOdlboEuzWrgREocH4Un5psYiIiIiKgkMpImownv06BECAgJw48YNxMTEICEhAVlZWXBwcEDVqlVRv359tGrVCt7e3rCysirr4pKektKzAAD7zj5CczcnTH7LS6Np975zjzTSEREREZU09pEmogopLi4O+/btw549exAVFSUt13ZJEwQBAGBiYoLu3btjxIgR6NChQ6mVtTRVxj7SwY8TsHDXDTRwtcfXo7wh+///JwCoRBH/3XEdDyOT8cXI5mj0WtUyLClR4dhHmoio8mCNNBFVKDExMVi6dCkOHToEpVIpBc7VqlVDo0aNUKVKFdjb28Pc3BzJyclITk5GREQE7t27B4VCgePHj+PEiROoU6cOZs6ciX79+pXxJ6IiFfW6Vx1X87UwERERlRIG0kRUYSxbtgybN2/GixcvYGpqii5dumDgwIFo1aoVqlevXui22dnZuHv3Lk6dOoXDhw/jyZMn+Oyzz7B161b8+OOP8PDwKKVPQfpKeZENAAiJSMaKfbfRv11duDhZIzIuHT5+TxASkayRjoiIiKiksWk3EVUYnp6eqFKlCsaPH4/hw4ejShXDB5YKCAjA2rVrceHCBUybNg3Tpk0zYknLTmVs2n3vSSJ+2xmIYV3q4eyNqHyDjXVuXgv7zz7CV++24GBjVK6xaTcRUeXBGmkiqjA+++wzjB492igDhrVq1QqtWrXCrVu3kJCQYITSUUlxr+0AJ3sLhEam4JcP2yIkIhlJ6VlwsDaHm6s9Vh24Ayd7C7jXdijrohIREdErgjXSRESVSGWskQaAa/djsOrAHTRzc8rXtPtmSBymDGmMlh7VyrqYRIVijTQRUeXBQJqIqBKprIE0kBtM7/YNyde0e0R3NwbRVCEwkCYiqjwYSBMRVSKVOZAGAJVKxIPwJKlpt3ttB8hkQtEbEpUDDKSJiCoP9pEmokrhxYsX2Lt3Ly5cuICoqChkZmbi5MmT0vrU1FScOXMGgiBg4MCBBucTGxuLixcv4s6dO7h9+zbu3r2LrKwstG7dGtu3by9wu+7duyMyMrLQfd+6dQvm5uYGl42IiIiISgcDaSKq8O7evYspU6YgOjpamldaEDRrKW1sbLB69WqEhYXB0dER7dq1MyivI0eO4NdffzW4rO7u7rCxsdG67uUykyY27SYiIqLygoE0EVVoiYmJ+PDDDxEbGwsvLy8MHDgQK1euRHq6ZvNmQRDw9ttv47fffoOvr6/BgbSNjQ3at2+PJk2aoEmTJggODsaqVat03n727Nlo06aNQXm/yvIONjb5LS+4OFsjMjYdR/yeYNWBOxxsjIiIiEqVrKwLQESVi6enJzp16qRz+u7du6NRo0YG57dlyxbExsaiXbt22Lt3L8aPHw8LCwutabt06QIAuHHjhsH5vf3229i8eTM+++wz9OrVC46Ojgbvi3SjUonY7RuCZm5OmDasCeq72MPCzBT1XewxbVgTNHNzwm7fEKhUHPKDiIiISgcDaSIyOn3HMCzOmIenT5+GIAj48ssvIZMVfkmrV68eTE1N8fTpU4Pzo9L3IDwJccmZGNCuLiAC954k4nJwNO49SQREoH+7uohLzsSD8KSyLioRERG9Iti0m4jKlEKhKDIALkx4eDjkcjkaNmxYZFpBEGBjY4O0tDSD8yuuXbt2YdOmTcjMzISTkxPeeOMNvPnmmwX2myYgKT0LABCTlIG1fwfl6yM9pNPrGumIiIiIShoDaSIqMykpKUhISICdnZ3B+xBFESYmJjoN1CWKIjIyMmBpaWlwfsXl4+Oj8ffhw4exdOlSLFq0CB06dCijUpVvDta5I5mv/+cummvpI73+8F2NdEREREQljYE0ERXLvXv3cO/ePY1lWVlZOHjwYIHbiKKIlJQUHDt2DCqVqlh9pKtXr46nT58iPj6+yP7Kt2/fRnZ2NurXr29wfoZq3bo12rZtiyZNmqBWrVpQKBS4du0ali1bhuDgYHz88cfYuXMnvLy8ip2XqWnl6rXjWbcKZIIAGys5ZrzTFKb/34LBo64Z6te2x8ylF5D2QgHPulUq3WcnIiKi8omBNBEVy8mTJ7Fy5UqNZWlpafj222+L3FYURQiCgHHjxhmcf+vWrfH06VPs27cPH374YaFpV6xYAUEQ0L59e4PzM9T8+fM1/ra0tES3bt3Qrl07vPfeewgKCsKCBQuwZcuWYuUjkwmoUsW6WPsob26HxEElikhNz8aag8EY3qMB6tSwxdPoVOw99RCp6dkQAUQnZ6GJm1NZF5eIiIheAQykiahYbG1tUbNmTenvqKgoyGQyVK9evcBtZDIZbGxs0KBBA4wYMQJvvPGGwfmPHTsW+/btw9q1a9G4cWOtQXJcXBx+/fVXnDt3DmZmZhg1apTB+RmbhYUFZs6ciUmTJuHKlStITk6Gvb29wftTqUSkpGQYsYRlL/xZMgBg8mAv/HU6FF8uPy+tc3awxIeDvLD2UBDCnyXD1bHsmu0TFaWyveQiInqVMZAmomJ5//338f7770t/e3p6okqVKvD19S2V/Bs0aIBPP/0UixYtwsSJE9GwYUOkpqYCAD7//HNERkYiKCgIOTk5AIBZs2ahVq1apVI2XXl7ewMAVCoVwsPDixVIA0BOjsoYxSo3bC3lAABHWwvMn9wOD8KTkJSeBQdrc7jXdsCjZylSusr22YmIiKh8YiBNREY1bdo0WFlZlWqekyZNgoODA3777TcEBwdLy318fKSptezs7PDdd99h8ODBpVo2Xcjlcul3pVJZhiUpn9xrO8DJ3gJH/J5g2rAm8KxbRVqnEkX4+D2Bk70F3Gs7lF0hiYiI6JXCQJqIjGratGllku/w4cPRv39/HD9+HNevX0dMTAyUSiWcnZ3h7e2Nvn37wtbWtkzKVpQHDx5Iv9eoUaMMS1I+yWQCRnR3w6oDd7Bi3230b1cXLk7WiIxLh4/fE9wMicOUIY0hkxU9cjsRERGRMTCQJqISk5CQgCtXriAqKgovXrwo8SDb2toaQ4YMwZAhQ0o0H2Nbv349AMDNza3QvuWvspYe1TBlSGPs9g3BL9uvScud7C0wZUhjtPSoVoalIyIiolcNA2kiMrqcnBwsXLgQf/75JxQKhbQ8byCdnJyMnj17IjMzE0ePHoWrq2tZFLVUbNy4EWZmZhg4cCCqVPlfs+TExEQsWbIEx44dAwDMmDGjrIpYIbT0qIYWDZzz9ZFmTTQRERGVNgbSRGR0n3zyiTTYmJubG8LCwvL1/bW3t8fAgQOxc+dOHD16FJMmTTIor7FjxwIAunXrhvHjxxeZfvr06UhJScHWrVsNyu/Zs2ca/ayzs7MBANevX0ebNm2k5R988IH0maKjo7Ft2zb8/PPPcHFxQdWqVZGZmYlHjx4hJycHMpkMn332Gfr06WNQmV4lMpmg0UeaiIiIqCwwkCYiozpy5AhOnToFJycnrFu3Do0aNULHjh0RHx+fL23fvn2xc+dOXLlyxeBA2t/fH4IgICAgACEhIfjxxx9halrwpS0wMFBrWXSlVCqRlJSUb3lOTo7G8szMTOn3AQMGAABu3bqFqKgo3Lt3DyYmJnB1dUXr1q3x3nvvoWHDhgaXiYiIiIhKFwNpIjKq/fv3QxAEfPnll2jUqFGhaZs2bQpBEBAaGlqsPE1MTCCKIvbv34+IiAgsX74cdnZ2xdpnQVxdXXH//n29tmnevDmaN29eIuUhIiIiotInK+sCEFHlop5+SpdmypaWlrC1tS1WDTEAODg4YPXq1bC0tIS/vz/eeecdPH36tFj7JCIiIiIqCANpIjKq1NRU2NrawsLCQqf0KpUKglD8waI6d+6MP//8E9WrV8fjx48xfPhwBAQEFHu/REREREQvYyBNREZlb2+P1NRUZGVlFZk2JiYGaWlpcHR0NErenp6e2LNnDxo1aoTk5GRMmDAB+/fvN8q+iYiIiIjUGEgTkVGp+0Vfvny5yLT79u0DALRo0cJo+VerVg1//vknunfvDoVCgVmzZmHRokVG2z8REREREQNpIjKqN998E6IoYunSpUhPTy8w3blz57Bq1SoIgqAxnZQxWFhYYOXKlRg3bhxEUcSGDRswY8YMjZG0iYiIiIgMxVG7icio3nzzTezZswdXr17FiBEjMHLkSCgUCgDAxYsXERkZCV9fX5w7dw4qlQrdunVDp06djF4OQRDwzTff4LXXXsO8efNw4sQJjBo1isE0ERERERWbIIqiWNaFIKLKJTk5GdOmTUNAQECBA4mJooj27dtj+fLlsLa2NjgvT09PODk54cKFCwWmuXDhAmbOnIn09HSIoghBEHD37l2D8yzPlEoVEhIKbglARGXH2dm2rItARERGwkCaiEqESqXC33//jX379uHmzZvIzs4GAJiamqJJkyYYMWIE3nrrLchkxeth0r17dzg6OmLv3r2Fpnv48CEmT56MqKgoBtKlQBRFZCtUZV0Mg5jJZUYZSZ7oZQykiYgqDwbSRFTiVCoVkpKSoFKp4ODgAFPTsulVkpiYiIcPHwIAWrduXSZlKGnlIZAWRRG/7riOkMjkMi2Hodxc7fHtKG8G02R0DKSJiCoP9pEmohInk8lQtWrVsi4GqlSpUmkD6HKHMSgRERFVYqyRJqJSlZOTgwcPHkAmk8HDw4O1fkZWHmqkgZJr2p2lUGLm8tz+8L9P7whzuYnR82DTbioprJEmIqo8WCNNREb16NEj+Pj4wNXVNd+0VleuXMHnn3+O+Ph4AEDNmjWxcOFCeHt767TvgIAAALnTWzVp0kRjmb5atWpl0HakG0EQYG5m/CA3L3O5SYnnQURERKQNA2kiMqpDhw5h3bp1mD59usby5ORkzJgxA8nJ/+s3GxUVhcmTJ8PHxwfOzs5F7nvMmDEQBAH16tXDkSNHNJbpQxAEBAcH67UNEREREZFa8YbLJSJ6yeXLlwEAffr00Vj+119/ITk5GbVq1cLmzZvx559/wt3dHWlpadi+fbvO+xdFESqVKt8yfX5e3p6IiIiISB+skSYio3r+/DkAoHbt2hrLT506BUEQ8Pnnn6Ndu3YAgB9++AHvvfceLly4gM8++6zIfd+7d0+nZUREREREJYk10kRkVAkJCbCzs4OZmZm0TKFQ4Pbt2zAxMUG3bt2k5d7e3jA1NcWTJ0/KoqhERERERAZhIE1ERiWTyZCRkaGx7O7du1AoFPD09ISVlZXGOhsbG2RnZ5dmEYmIiIiIioWBNBEZVfXq1ZGTk4PQ0FBp2ZkzZwAg3+jcoigiLS0NVapUKbHyJCQk4MSJEzh58iRSUlJKLB8iIiIienUwkCYio2rdujVEUcT8+fMRHx+Pu3fvYteuXRAEAV26dNFI++jRI+Tk5KBatWoG53f79m18++232LRpU751R44cQffu3TFjxgxMnz4dXbt2xYkTJwzOi4iIiIgIYCBNREY2fvx4mJmZ4cKFC+jYsSOGDh2KhIQEeHp6okOHDhppz58/DwBo2rSpwfkdPnwYBw8ehEymeTl7/vw5Zs2ahczMTGm07oyMDHz++ed4+vSpwfkRERERETGQJiKjqlevHlavXg1XV1eIoghBENChQwesWrUqX9r9+/cDANq0aWNwfgEBAQCA7t27ayzfs2cPMjMz4eHhgePHj+Ps2bNo1aoVFAoFtm3bZnB+RERERESc/oqIjK5Dhw44ceIEEhISYG1tDXNz83xpFAoFZs+eDQBo0qSJwXnFxsZCEATUqlVLY/mZM2cgCAJmzpyJOnXqAABmzZqFwYMH48qVKwbnR0RERETEQJqISkzVqlULXCeXy9G6deti55GUlARbW1uYmv7vcpaZmYl79+7BzMxMozm5p6cn5HI5IiIiip0vEREREb262LSbiCo0U1NTpKenayy7ffs2lEolGjdurDGfNQBYWVlBqVSWZhGJiIiIqJJhIE1EFZqLiwuUSiVu3bolLfP19YUgCPmm21IqlUhLS4Ojo2NpF5OIiIiIKhEG0kRUobVv3x6iKOI///kPbt68iZMnT2L37t0AgG7dummkffDgAZRKJapXr14WRSUiIiKiSoJ9pImoQps4cSIOHjyIoKAgjBw5EgAgiiLatm2br0ZaPQBZixYtyqKoRERERFRJsEaaiCq06tWrY9u2bWjTpg3Mzc3h5OSEd955B8uXL9dIJ4oi9u/fD1EUizXdFhERERERa6SJqMLz9PTEli1bCk2jUqmkNGzaTURERETFwUCaiF4JJiYmcHFxKetiEBEREVElwKbdRERERERERHpgIE1ERERERESkBzbtJiKDRUVFGW1ftWrVMtq+SDeiKCJboSrrYuglS6HU+ntFYiaXQRCEsi4GERERFYMgiqJY1oUgooqpYcOGRtmPIAgIDg42yr5edUqlCgkJ6UWmE0URv+64jpDI5FIoFeXl5mqPb0d5M5h+BTk725Z1EYiIyEhYI01EBjPWezi+zyt92QoVg+gyEhKRjGyFCuZmJmVdFCIiIjIQA2kiMtipU6e0Lr916xa+//57CIKAkSNHom3bttKUU8+fP8fly5exa9cuAMCPP/6IJk2alFqZKb/fp3eEuZxBXUnLUigxc/mFsi4GERERGQEDaSIymLbppJ4+fYq5c+eiRo0a2Lx5M5ycnDTW16tXD+3atcPYsWMxbtw4zJkzB/v37y+tIpMW5nIT1o4SERER6YGjdhORUa1evRppaWmYN29eviA6L0dHR8ybNw+pqalYvXp1KZaQiIiIiKh4WCNNREZ16dIlWFlZoVmzZkWmbdasGaysrHDp0qVSKBkVpKKOfl3R8HsmIiKqPBhIE5FRJSQkwNRU90uLKIpISEgowRJRUdhvl4iIiEg/DKSJyKgcHR3x/Plz+Pn5oV27doWm9fPzw4sXL1CjRg2d92+MKbc43RYRERERFQcDaSIyqs6dO2PPnj2YNWsWNm7ciNdff11rusePH2P27NkQBAGdO3fWef+cKsv4OGp36eCo3URERJUHA2kiMqqpU6fi2LFjePbsGQYNGoS+ffvmm/7qypUr+Pfff5GVlQV7e3tMnTpV5/3/+uuvBpXr+PHjOH36tEHbVnYctZuIiIhIPwykicioqlevjk2bNmHatGl49uwZ/vnnH/zzzz/50omiiBo1amDFihVSkK2LIUOG6FWeq1evYuHChbh58yYAQC6XY+TIkXrtg4iIiIgoLwbSRGR0Xl5eOHz4MP78808cPXoUDx48QE5ODgDAxMQEHh4e6NevH959913Y2NiUSBkePHiARYsW4dy5cxBFETKZDG+++SZmzJgBV1fXEsmTiIiIiF4NDKSJqERYW1tj0qRJmDRpEhQKBZKTkwEA9vb2kMvlJZZvVFQUli5disOHD0OlUkEURXTu3BmfffYZPD09SyxfIiIiInp1MJAmohInl8vh5ORUonkkJiZi9erV2LVrFxQKBURRRLNmzfDFF1+gVatWJZo3EREREb1aGEgTUYkSRRGJiYnIzMxErVq1jL7/Fy9eYPPmzdi0aRPS09MhiiLq1auHTz/9FL169TJ6fkREREREDKSJqEQEBQVh9erVuHTpEl68eJFv7ubk5GQsWrQIAPDdd9/BwsJCr/0rlUrs2rULq1evRnx8PERRRPXq1TFt2jQMGzYMMpnMqJ+HiIiIiEiNgTQRGd3Bgwcxe/ZsaYAxbezt7fH06VNcuXIFbdq0wYABA3Tev4+PD5YuXYqnT59CFEXY2dlh0qRJGDt2LMzNzY3xEYiIiIiICsQqGyIyqpCQEMyZMwc5OTkYM2YM9u3bhypVqmhNO3jwYIiiiHPnzum8/6FDh+Lzzz/HkydPYGZmhg8++ACnTp3CpEmTGEQTERERUalgjTQRGdXmzZuhUCgwatQozJo1C0DulFfatGvXDkBuM3BdBQcHQxAECIKAli1bIj4+Hr/88oteZRQEQe9tiIiIiIjUGEgTkVFduXIFgiBg0qRJRaatXr06LCws8OzZM73zEUURfn5+Bm3HQJqIiIiIioOBNBEZVUxMDCwtLVGjRg2d0ltYWCAtLU3n/XMqKyIiIiIqawykiciozMzMkJWVJdX8FiY7Oxupqamws7PTef/bt28vbhGJiIiIiIqFgTQRGVXt2rVx7949hIWFoV69eoWmPX/+PJRKJdzc3EqpdKRNlkJZ1kV4JfB7JiIiqjwYSBORUXXu3Bl3797F1q1b8eOPPxaYLi0tDYsWLYIgCOjRo0cplpAAQIQo/T5z+YUyLAkRERFRxcPpr4jIqN5//33Y2tpiz549+P3335GSkqKxPjMzE8ePH8fw4cPx6NEjODk54Z133imj0r66zOTaR1Knkufmag8zOW+/REREFZkgiqJYdDIiIt1dunQJU6ZMQVZWFkxMTCCKIlQqFRwdHZGUlASlUglRFGFlZYWNGzeiRYsWOu87ICDAKGWsrIOWKZUqJCSk65RWJYrIVighoPC+7OVJlkIp1aD/Pr0jzCvgCwEzuazI8QOocnJ2ti3rIhARkZGwaTcRGV379u2xe/du/PLLL7hy5Yq0PC4uTvq9devWmDNnDho0aKDXvseMGVPsIEQQBAQHBxdrH5WBTBBgYVZxbwPmchOYm1W8QJqIiIgqvor7BEVE5ZqHhwe2bt2KyMhIXL9+HTExMVAqlXB2doa3tzfq1q1brP2XVWOa2NhYXLx4EXfu3MHt27dx9+5dZGVloXXr1kWOKK5QKLB161b8/fffePr0KeRyOTw9PTFmzBj07t27lD4BERERERUXA2kiKlEuLi5wcXEx+n5dXV0xZMgQ9O3bFxYWFkbff0GOHDmCX3/9Ve/tsrKyMH78eFy7dg0mJiZwc3PDixcv4O/vD39/f0yaNAlffPFFCZSYiIiIiIyNgTQRVSjvvvsufHx8EBERgRUrVmDTpk3o378/hg0bhubNm5d4/jY2Nmjfvj2aNGmCJk2aIDg4GKtWrSpyuwULFuDatWtwdXXF+vXrpanBTp06hZkzZ2L9+vXw9vZG9+7dS/ojEBEREVExcbAxIioxp06dwoULFxAVFYXMzExs3bpVWpeRkYF79+5BEAS9BhsDgOzsbJw4cQL79u3D5cuXoVKpIAgCXnvtNQwbNgyDBg2Cs7OzsT+OVjt27MBPP/1UaNPuuLg4dO3aVWra3bZtW431S5cuxapVq+Dl5YX9+/cXqzz6DDZWEWVlK/Hx4rMAgNWfdWEfaapQONgYEVHlwRppIjK6Z8+eYdq0adKAXqIo5hsgTC6X4/PPP0d0dDR27dqFZs2a6bx/MzMzDBgwAAMGDMCzZ8+wf/9+HDhwAGFhYVi0aBF+//13dOzYEcOGDUO3bt1galq2lzpfX18oFAq89tpr+YJoABg5ciRWrVqFoKAgPH36FHXq1CmDUhIRERGRrjiRJREZVUZGBiZMmICgoCBUr14do0aNgqWlZb50crkcw4YNgyiKOHHihMH51axZE1OnTsXJkyexdetWvPnmm5DL5Thz5gxmzJiBTp064ddff8WTJ0+K87GK5caNGwCAli1bal1fvXp1uLq6aqQlIiIiovKLNdJEZFR//PEHwsLC0KhRI+zYsQNWVlb4999/kZmZmS9tz549sWLFCly/ft0oebdp0wZt2rRBWloajh49in379uHGjRvYtm0bTExM8NVXXxklH309fvwYAAqtaa5Tpw4iIiIQFhZW7PxMTSvvO1Kl6n+9kUxNZZX6sxIREVH5xUCaiIzq+PHjEAQB3377LaysrApN26BBA5iYmEiBprHY2NjA1dUVLi4uuHPnDpRKpVH3r6/k5GQAgL29fYFp1OtSUlKKlZdMJqBKFeti7aM8y8zKkX53cLCChTlvY0RERFT6+ARCREYVFhYGExMTeHt7F5nWxMQEtra2xQ4e1SIiInDgwAEcOHAAz549gyiKMDU1RdeuXdG3b1+j5GGIrKwsALnN2QtiZmYGAFpr7vWhUolIScko1j7Ks6zs/70USUrK4GBjVKFU5pdcRESvGgbSRGRU2dnZMDc3h4mJbgFOZmYmzM3NDc4vKysL//77L/bt24erV69CFEWIooj69etj6NChGDRoEJycnAzevzGoP59CoSgwTXZ2NgAYZU7snBxVsfdRXuX9bDk5KpjIhEJSExEREZUMBtJEZFROTk549uwZUlJSYGdnV2jahw8fIjMzE25ubnrnc+PGDezbtw9Hjx5Feno6RFGEjY2NNKe0PqOAlzT196Bu4q2Nel1R3xkRERERlT0G0kRkVN7e3jhy5Ah8fHwwcuTIQtNu2LABgiCgTZs2Ou9//fr10lRX6mm1WrdujWHDhqFPnz7Fqt0uKa+99hquX79e6MjhT58+ldISERERUfnG4U6JyKjee+89iKKIFStW4MGDB1rTZGdnY9GiRTh06BAEQcC7776r8/4XLVqEsLAwuLi4YNq0adK0V2+99Va5DKIBoHnz5gBQ4Ojkz58/R0REhEZaIiIiIiq/WCNNREbl7e2N0aNHY8eOHRgxYgQ6deqE9PR0AMDixYsRGRkJPz8/JCYmAgA+/vhjg5p2R0ZGYuXKlVi5cqXe2wqCgODgYL23M1SPHj3w008/4fHjx7h8+TLatm2rsX7Xrl0AgEaNGqFu3bqlVi4iIiIiMgxrpInI6GbNmoWPPvoI2dnZOH78OF68eAEgt1m2j48PEhISYGJigunTp2P69Ol67189oFhxfkqTk5MTRowYASD3u3n06JG0ztfXFxs2bAAATJ06tVTLRURERESGEcTSfqIkoldGZGQkDhw4gOvXryMmJgZKpRJOTk7w9vbG22+/jdq1a+u9zwMHDhilbEOGDDFou2fPnmHw4MHS39nZ2cjIyICpqSlsbGyk5R988AEmTZok/Z2ZmYlx48YhMDAQJiYmaNCgATIyMqS+0RMmTMDXX39t2IfJQ6lUISEhvdj7Ka+yspX4ePFZAMDqz7pw+iuqUJydbcu6CEREZCRs2k1EJUbdj9mYDA2AjUWpVCIpKSnf8pycHI3lL88HbWFhgW3btmHLli34559/8PjxY8jlcrRu3RqjR49Gnz59SrjkRERERGQsrJEmIqpEWCNNVH6xRpqIqPJgjTQRlar79+/Dz88PgiCgY8eOqF+/flkXiYiIiIhILwykicio/Pz8sHr1ajRv3hyfffaZxrrNmzdjwYIF0mBfMpkM33zzDcaMGWP0cty/fx/79+9HWFgYzMzM0KhRIwwfPhzOzs5Gz4uIiIiIXi0ctZuIjOrff/9FQEAAXFxcNJaHhYVh4cKFUKlUkMvlsLCwgFKpxK+//qrXVFTR0dH49ttvMXv2bGRnZ2tNs3fvXgwbNgzbtm3D+fPncerUKSxfvhwDBgwocC5nIiIiIiJdMZAmIqMKDAwEAHTu3Flj+d69e6FUKtGqVStcvnwZ/v7+6NOnD1QqFf7880+d9+/n54cDBw4gJiYGZmZm+dbfu3cPP/74I5RKJURRhLm5Oezs7CCKIlJSUjBjxgxpXmsiIiIiIkMwkCYio1LPEV2jRg2N5efPn4cgCJg6dSqsrKwgl8vx+eefAwACAgJ03n9AQAAEQUDfvn21rl+3bh1ycnJgYmKCX375BdevX8eVK1ewatUqWFpaIj4+HgcPHjT48xERERERMZAmIqNKSkqCtbU1BEGQlqWlpSEkJASWlpZo3bq1tLxOnTowNzfH8+fPdd7//fv3AQBt2rTJty47Oxu+vr4QBAGjRo3C0KFDIZPlXua6d++OiRMnQhRFnDlzxsBPR0RERETEQJqIjMzc3BxpaWnIO7NeYGAgRFFEs2bNpMBWzcLCQq/9x8fHw8zMLF8fbAAICgqS5m/WNt/00KFDAQAhISF65UlERERElBdH7SYio6pbty7u3r0Lf39/qdb4xIkTEAQBLVu21EibnZ2N1NRU1KxZU+f9x8fHw8rKSuu627dvAwCsra3h6emZb33NmjVhamqKxMREnfMjw4iiiGyFyuj7zVIotf5uTGZymUaLCiIiIqKXMZAmIqPq0qULgoODMWvWLHz22WeIiYnB/v37AQC9evXSSHv37l2oVCrUqlVL5/3L5XKkpqYiJycHpqaal7CgoCAA0BpEA4AgCLC2tuZgYyVMFEX8uuM6QiKTSzSfmcsvlMh+3Vzt8e0obwbTREREVCA27SYioxo/fjxq1qyJiIgIfP755/jvf/+LnJwc9OvXDx4eHhppT506BUEQ4O3trfP+a9SoAVEUtU6ZpR6IrEmTJlq3ValUSEtLg42NjX4fivTHGJSIiIgqMdZIE5FR2dnZYdeuXVi2bBlu3LgBW1tbdOvWDRMnTtRIl52djX379kEURbRt21bn/Xt7e+PRo0dYv349li9fLi2/dOkSoqKiIAgC2rdvr3Xbhw8fQqlUwtXV1bAPRzoRBAHfjvI2etPuwIex2Hc2FPEpWdIyRztzDOtSHy0aOBstHzbtJiIioqIIYt4RgYiIyrlbt27hnXfegSAI6Ny5M7p164bo6Gjs2LED6enpqFGjBk6ePAkTE5N8227ZsgXz58/HO++8g//85z9lUPqSp1SqkJBQ+ZquX7sfg5UH7sDMVIbsnP8F6Oq/pw5pjJYe1cqwhERFc3a2LesiEBGRkbBGmogqlKZNm2LMmDHYvn07zp07h3PnzgHI7ZcrCAK++uorrUE0APz9998QBAFvvPFGaRaZikmlErHtWO60Zw3rVsHA9q/BxdkakbHpOHzpMW6GxmP7sfto0cAZMhlrkomIiKjksY80EVU4s2bNwtdff41q1apBFEWIoghXV1f89ttv6Nevn9Ztrly5guDgYJiYmKBLly6lXGIqjntPE5GaoUADV3tMf7sp6rvYw8LMFPVdcv9u4GKPlAwF7j3laOxERERUOti0m4gqtISEBABA1apVC02nUCigUCggk8n0nru6IqmMTbv3nw3FYb8n+HJkczR8Lf//OehxAhbtuoGB7epiaJf6ZVBCIt2waTcRUeXBpt1EVKEVFUCryeVyyOXyEi4NlYiiWmurXwezVTcRERGVEjbtJiKics2zdhUAwMELYVC91IhKJYr4+2KYRjoiIiKiksYaaSKqVG7cuAGFQlHg+mbNmsHMzKwUS0TF5Vm3Cmyt5HgYkYxlf91Ek3pO0mjdtx/F4WFEMmyt5PCsy0CaiIiISgf7SBNRhbN161YcPXoUzZs3xzfffKOxrmPHjoiPjy9w25kzZ2Ly5MklXcQyUxn7SAP/m/6qIJz+iioC9pEmIqo82LSbiAyWlpaG9PTSDdrS0tKwfPly3L59G8OHD9eaRj2St7af9evXIzMzs1TLTMYjN5UV+jcRERFRaWDTbiIy2BtvvAFnZ2ecP39eWrZixQpYW1tj/PjxJZLn6dOnkZaWhp49e6J+fe0jNAuCgJMnT+Zb/tNPP+Hs2bM4duwYBg0aVCLlI+NTqUTs9g1BczcnTBnSGCERyUhKz4KDtTncXO2x6sAd7PYN4TzSREREVGr4Kp+IiuXl3iErVqzAxo0bSyy/8+fPQxCEIgNhFxeXfD/vvfceRFHExYsXS6x8ZHwPwpMQl5yJAe3qQiZoBsoyQUD/dnURl5yJB+FJZVNAIiIieuWwRpqIDCaXy/HixYtSzfPu3bsAgJYtW+q9rbe3NwAgODjYqGWikpWUngUAiEnKwNq/gxCX/L+m+U72FhjS6XWNdEREREQljYE0ERmsRo0aiIiIQGBgIFq0aFEqeT5//hxmZmYFzh9d2PiJNjY2sLGxQWxsbEkVj0qAg7U5AGD9P3fR3M0Jk9/ygouzNSJj03HE7wnWH76rkY6IiIiopDGQJiKDdenSBTt27MDYsWPh4eEBKysrAEBycjLGjh2r834EQcDWrVt1SpuRkQEbG5sC1+/cuRM5OTkFrjc1NUVaWprOZaOy5+ZiD5kgwMZSjilDG8NUltsrqb6LPaYMbYwvVlxC2gsF3Fzsy7ikRERE9KpgIE1EBps+fTquXr2Ke/fu4c6d/01NpFAo4O/vr/N+BEH3AaKsra0LDYTr1KlT6PapqamFBuJU/oREJkMlikjJyMaq/XfQv11duDhZIzIuHT5+T5CSkS2l41zSREREVBoYSBORwezt7bF//35cvHgRDx48wIsXL7BixQpYWVlhwoQJJZKnk5MTUlJSEBoaWuCo3QUJCQmBUqmEk5NTiZSNSoa67/OkNxvhwLlH+GX7NWmdk70FJg1shPWHg9lHmoiIiEoNA2kiKhaZTIZOnTqhU6dOACAF0tOmTSuR/Jo1a4ZHjx7h5MmTegfSJ06ckPZBFYe673M1B0vMn9wOD8KTpOmv3Gs74NGzFI10RERERCWN018RkVENHjwY/fr1K7H9d+/eHaIoYsuWLYiLi9N5u5iYGGzduhWCIKB79+4lVj4yPvfaDnCyt8ARvyeAAHjWrYK2jWrkNuMWAB+/J3Cyt4B7bYeyLioRERG9IgSxsCFuiYjKGVEU0a9fPzx58gSenp5YsWIFXFxcCt0mMjISU6dOxb1791CvXj34+PiUUmlLn1KpQkJCelkXw+iu3Y/BqgN30MzNKV8f6ZshcZgypDFaelQr62ISFcrZ2basi0BEREbCQJqISlRaWhqCg4MRHx8PAHB0dESjRo2KNeDXzZs3MXr0aOTk5MDS0hKDBw9Gt27d0LBhQ9jb547cnJycjLt378LX1xeHDh1CRkYGzMzMsGPHDjRt2tQon608qqyBNJAbTO/2Dck3j/SI7m4MoqlCYCBNRFR5MJAmohJx//59LFmyBOfPn4dKpdJYJ5PJ0KVLF3zyySfw8PAwaP8nT57El19+iRcvXhQ56rcoirC0tMSCBQvQs2dPg/KrKCpzIA0AKpWYr4+0TKb7qO9EZYmBNBFR5cFAmoiM7vjx4/jyyy+RnZ2Ngi4xgiDAzMwMCxcuRK9evQzK59GjR/j9999x4sSJQvPp1asXZs6ciXr16hmUT0VS2QNpooqMgTQRUeXBQJqIjCo8PBwDBgxAdnY2XFxc8MEHH6BDhw6oUaMGACA6OhoXL17Exo0bERERAXNzcxw+fBi1a9c2OM/Y2FhcuXIFISEhSEpKAgA4ODjAzc0Nbdq0gbOzszE+WoXAQJqo/GIgTURUeTCQJiKj+uGHH7Br1y40b94cGzduhLW1tdZ0GRkZmDBhAm7evIl3330Xc+fOLeWSVk4MpInKLwbSRESVB6e/IiKj8vPzgyAI+PHHHwsMogHAysoKP/74I0RRxMWLF0uxhERERERExcNAmoiMKjo6GtbW1joNIubh4QEbGxtER0eXQsmIiIiIiIzDtKwLQESVi6mpKXJycnRKK4oiFAoFTE15KSLdcNRuIiIiKg/49EpERlW3bl3cvXsX58+fR6dOnQpNe/78eWRlZaF+/fqlVDqqyDiPNBEREZUXbNpNREbVvXt3iKKIOXPmIDQ0tMB0ISEhmDt3LgRBQI8ePUqxhFQRXbsfg1UH7sDFyRqje7ljfH9PjO7lDhcna6w6cAfX7seUdRGJiIjoFcJRu4nIqNLS0jBgwAA8f/4ccrkcffv2Rbt27VC9enUAuX2o/fz8cOzYMSgUCtSoUQOHDx+GjY1NGZe8cqiMo3arVCK+WesHG0s5UjOyEZ+SJa1ztDOHrZUZ0l4oMH9yOzbzpnKNo3YTEVUeDKSJyOgePnyIjz76CJGRkRAE7YGNKIpwdXXF6tWr0aBBg1IuYeVVGQPpe08S8dvOQACAmakM2TkqaV3ev796twU861YpkzIS6YKBNBFR5cE+0kRkdA0aNMDff/+NP/74A//++y/u378PpVIJADAxMYGHhwf69++Pd999t9Apsl62bds2WFpaYvjw4SVVdCqHEtL+1ye64WtVMLDda3BxtkZkbDoO+z3GzZD4fOmIiIiIShJrpImoxCkUCiQnJwMA7O3tIZfLDdqPp6cnnJ2dcf78eWlZjx494OjoiD179hilrBVdZayR/vfKE+w5HYraztb4fkJryPK0clCJIn7Y5I+I2HS8060++rapW4YlJSoca6SJiCoP1kgTUYmTy+VwcnIyyr5efvcXGRmJrKysAlJTZZD+Inc6NTO5idb1ZnKZRjoiIiKiksZRu4mowrC2tkZSUpLUTJxeDeoK6NCoFKzYdxshkcl4kZWDkMhkrNh3G4+iUjXSEREREZU01kgTUYXRoEED3Lx5E7/99huGDx8OKysrAIBKpcKzZ8/y1VYXplatWiVVTDIyz7pVcNjvCWpUtUJ4TCp+2X5NWudoZ44aVa0QnZDBgcaIiIio1LCPNBFVGPv27cOsWbM0RgIXRbHAkcELIggCgoODjV28cqEy9pFWqUR8uuICUjMUaPJ6FVSvao1spRJmJiZ4npCO22GJsLOSY/G0jpz+iso19pEmIqo8WCNNRBXGsGHDkJycjE2bNiEuLk5aru/7QL4/rFhkMgFj+3hg5YE7uB2WiNthifnSjOnjwSCaiIiISg0DaSKqUCZMmIAJEyYgISEBL168QI8ePVC1alXs3bu3rItGpaCweaSJiIiISgsDaSKqkKpWrSr9LpPJ4OLiUoaloZKkUonY7RuC5m5O+GiQF84ERiIm6QWqOViiawsXrDkUhN2+IWjRwJm10kRERFQqGEgTUYW2bds2g+elporhQXgS4pIz0aV5LczecAVxyZnSupPXItClWS3cCInDg/AkDjhGREREpYKBNBFVaK1bty7rIlAJS0rPnSd839lHaO7mhMlvecHF2RqRsek44vcE+8490khHREREVNIYSBNRiRJFEYmJicjMzCzxKafi4uJw7Ngx3LlzB/Hx8QAAR0dHNG7cGH369IGTk1OJ5k8lw87KDADQwNUe04Y1gez/R2mv75L79393XMfDyGQpHREREVFJYyBNRCUiKCgIq1evxqVLl/DixYt8U04lJydj0aJFAIDvvvsOFhYWBuelVCqxdOlSbN68GTk5OQD+NzK3IAg4ePAg5s+fjwkTJmDGjBkwMTEpxiejUlfUIOvqbtEcjJ2IiIhKCQNpIjK6gwcPYvbs2VJQq429vT2ePn2KK1euoE2bNhgwYIDB+X311Vfw8fGBKIowMzND48aNUaNGDQBAdHQ07ty5g+zsbKxbtw5RUVFYsGCBwXlR6Ut5kQ0ACIlIxvK/bqFxPUfI5QIUChF3HsUjJCJZIx0RERFRSWMgTURGFRISgjlz5iAnJwdjxozB4MGD8cEHHyApKSlf2sGDB+Py5cs4d+6cwYH0yZMnceTIEQDA+PHj8fHHH8POzk4jTWpqKlavXo1Nmzbh8OHD6Nu3L3r06GFQflT6HKzNAQBtGlWH/90Y3AyNl9bJBAFtGlXH5eDnUjoiIiKiksZAmoiMavPmzVAoFBg1ahRmzZoFAAU2pW7Xrh2A3Gbghvrrr78gCAImT56MmTNnak1ja2uLr776CmZmZlizZg327t3LQLoCca/tAFsrOS4HP0fTelXRtL4T5HIZFAoVboXG4XLwc9hZyeFe26Gsi0pERESvCFlZF4CIKpcrV65AEARMmjSpyLTVq1eHhYUFnj17ZnB+t2/fhkwmw8SJE4tMO3HiRMhkMty+fdvg/KhsCTIBdWrY4g2PaqhTwxbC/88bze7RREREVJpYI01ERhUTEwNLS0upj3JRLCwskJaWZnB+ycnJsLGxga2tbZFpbW1tYWtri+TkZIPzK47ly5djxYoVhab54Ycf8O6775ZSiSqGB+FJSM1QYFiXejh7Iwq/bL8mrXOyt8DQLvWw/+wjziNNREREpYaBNBEZlZmZGbKysiCKIgRBKDRtdnY2UlNT8/Vp1oe9vT2SkpKQlpYGGxubQtOmpqYiNTUVVaqUbbDl6OiIunXral3n7OxcyqUp/9TzQ/do6Yp+beriQXgSktKz4GBtDvfaDshSKLH/7CPOI01ERESlhoE0ERlV7dq1ce/ePYSFhaFevXqFpj1//jyUSiXc3NwMzq9JkyY4e/YstmzZgmnTphWadsuWLVCpVGjcuLHB+RlD586dMX/+/DItQ0WiHkQsMjYd9V3s89U6R8ala6QjIiIiKmnsI01ERtW5c2eIooitW7cWmi4tLQ2LFi2CIAjFGvhr6NChEEURq1atwu+//4709HSteS1ZsgSrVq2CIAh4++23Dc6PSp97bQc42VvgiN8TqETN3tAqUYSP3xM42VtwsDEiIiIqNayRJiKjev/99/Hnn39iz549qFKlCiZMmKCxPjMzE+fOncOSJUsQFhYGZ2dnvPPOOwbn17t3b/Tr1w9Hjx7F2rVrsWXLFjRp0gTVqlUDADx//hx37tyRmpv3798fvXr1KtZnpNIlkwkY0d0Nqw7cwYp9t9G/XV24OFkjMi4dPn5PcDMkDlOGNIZMVnhXAiIiIiJjEURR5GCnRGRUly5dwpQpU5CVlQUTExOIogiVSgVHR0ckJSVBqVRCFEVYWVlh48aNaNGiRbHyUygUWLx4MbZv346cnBwAkPpnqy9xpqamGDNmDD777DPI5fLifUADqQcba9iwIerXr4/Y2FhYW1vDw8MDAwYMQIMGDYqdh1KpQkJC/lr5yuDa/Rjs9g1BXHKmtMzJ3gIjuruhpUe1MiwZkW6cnYseFJGIiCoGBtJEVCLu37+PX375BVeuXNG6vnXr1pgzZ45Rgke158+f4/jx47hz5w7i4+MB5A7s1bhxY/Tu3RvVq1c3Wl6GKGzUbkEQMHbsWHz99dcFzruti8ocSAOASiXmG2yMNdFUUTCQJiKqPBhIE1GJioyMxPXr1xETEwOlUglnZ2d4e3sXOGp1ZbZ7927ExcWhU6dOcHV1hY2NDcLCwvDnn39i165dAHLnuv7qq68MzkOpVCEl5YWxikxERlSlinVZF4GIiIyEgTQRUTmwfv16LFy4EKampjh27BhcXV0N2o8u044RERERUfEwkCYiKgeUSiW6du2KmJgYzJo1C2PHjjVwP6yRJiqvWCNNRFR5cNRuIjKqhg0bAgC6dOmCxYsXw8rKqtD0HTt2REJCAoKDg0ujeOWWiYkJmjVrhhMnTuDJkyfF2ldOjspIpSIiIiIibTiPNBEZlSiKEEURZ8+exXvvvYfnz5/rtA1BGk1cPfI4EREREZVPDKSJyOgsLS3h6OiIe/fu4e2338adO3fKukgVwsOHDwEANWrUKOOSEBEREVFhGEgTkdFZW1tj7969aNCgAWJjYzFmzBgcP368rItVrp05c0YKpDt06FDGpSEiIiKiwjCQJqISUbNmTezcuRMdOnTAixcvMHPmTKxbt66si1VmHj58iLlz5+LevXsay1UqFQ4fPozPP/8cANCtWzc0bdq0LIpIRERERDriYGNEVGJsbGywbt06/Oc//8Hu3buxZMkShIWF4aeffoKp6at1+cnJycHu3buxe/duODg4oFatWjAxMcHTp0+RnJwMAHjjjTfw22+/lXFJiYiIiKgor9aTLBGVOhMTE/z444947bXXsGDBAhw8eBARERFYsWIF7O3ty6RM2dnZuHTpEk6dOgVPT0+MGjWqxPN0cXHBzJkzcePGDYSGhuLJkyfIzs6Gvb09OnfujIEDB2LgwIEwMTEp8bIQERERUfEwkCaiUjF+/HjUrl0bX375Ja5evYp33nkHa9asKbX8k5OTcfr0afj6+uLChQt48SJ3ruWpU6eWSv52dnb4+OOPSyUvIiIiIipZDKSJqNT07NkTO3bswEcffYSnT59i5MiRyMzMLLH8IiIicOrUKZw8eRKBgYFQKpUQRRGCIKBZs2bo0aMHBg4cWGL5ExEREVHlxECaiEqVl5cX9u7di8mTJ+P+/fsAAEEQjLb/oKAgnDx5EqdOnZJGwRZFEebm5ujYsSN69OiBbt26wcnJyWh5EhEREdGrhYE0ERnV4MGDYWtrW2iaGjVqYOfOnZg5cybOnTtXrPxycnJw5coVnDp1Cr6+vnj+/DmA3ODZ3t4eXbt2RY8ePdCpUydYWloWKy8iIiIiIgAQRFEUy7oQRET6SEtLw7lz53Dq1CmcO3cOaWlpUF/KXFxc0KNHD/Ts2RMtW7Z85QbvUipVSEhIL+tiEJEWzs6Fv2QkIqKKgzXSRFShTJw4Ef7+/sjJyZGCZy8vL/To0QM9evSAh4dHGZeQiIiIiCo7BtJEVKFcvHgRpqamaN++Pbp3746ePXuievXqZV0sIiIiInqFsGk3ERlsxYoVAIAqVapIczGrl+lr2rRpOqXz8fFB586dYWNjY1A+lR2bdhOVX2zaTURUeTCQJiKDeXp6QhAEvP766/Dx8dFYpq+7d+8au3gAgISEBABA1apVS2T/5Q0DaaLyi4E0EVHlwabdRGSwVq1aAQBq1aqVb1lZio6OxqJFi3D69Gmkp+cGldbW1ujWrRs+/fRTjfISEREREemLNdJEVKk8efIE7777LhISEiAIAqpUqYLMzExkZGQAyG2GvnPnTrz22mtlW9ASwhppovKLNdJERJWHrKwLQERkTIsWLUJCQgI++ugj+Pv749KlS7h+/Tr++ecftG7dGomJiVi8eHFZF5OIiIiIKjDWSBNRhZKeng5ra+sC17dq1QrNmzfH+vXr861LSkpC+/btYW1tjYCAgJIsZplhjTRR+cUaaSKiyoM10kRUKrKzs3Hq1Cls2LAB27Ztw9WrVw3az4ABA3Dq1KkC1ysUCtjZ2WldZ21tDVNTU+Tk5BiUNxERERERwMHGiKiY0tLScPLkSQBA//79YWZmli/N7du3MWPGDERHR2ssb9asGZYvXw5nZ2ed83vx4gWmTZuGnj17Yvbs2fnmkG7YsCF8fX1x9epVvPHGG9JylUqFZcuWITs7Gy1atNDnIxIRERERaWDTbiIqlpMnT2LatGlo2LAhDhw4kG99fHw8Bg4ciKSkJLx8uREEAV5eXvjrr790zi8xMRHz58/HoUOHYGNjg08//VSawxoALl68iA8//BAqlQpeXl6oU6cOMjMzERwcjOfPn0MQBKxduxadOnUy/EOXY2zaTVR+sWk3EVHlwabdRFQs6ibaAwcO1Lp+/fr1SExMBAAMGTIEO3fuxKFDhzBu3DiIooigoCD8+++/OudXpUoV/Pe//8XmzZvh6OiIefPmYcSIEbh//z4AoEOHDli2bBlq1KiBO3fuwMfHB76+voiOjka1atXw+++/V9ogmoiIiIhKB2ukiahY3nvvPQQGBuLQoUNwd3fPt75Dhw5ISEhAt27dsGrVKo113377LQ4cOID+/fsbNJJ2dnY2Vq5ciY0bNwIAxo0bh+nTp8Pc3BwqlQp37txBREQEAMDV1RVeXl4wMTEx4FNWHKyRJiq/WCNNRFR5sEaaiIolNjYWJiYmcHNzy7fu4cOHiI+PBwCMGTMm3/qxY8cCAIKDgw3K28zMDJ9++ikOHDiAJk2aYMOGDRg4cCAuXLgAmUyGpk2bon///ujfvz+aNm1a6YNoIiIiIiodDKSJqFji4uJgY2MDmSz/5eTWrVsAALlcjpYtW+Zb36BBAwiCgJiYmGKVoUGDBti5cyd++OEHJCcnY9KkSfjiiy+QkJBQrP0SEREREWnDQJqIikWlUiEtLU3ruqCgIABA/fr1tY7mbWpqCjs7O2RlZRmlLCNHjoSPjw/69u2Lw4cPo1+/fnoNZEZEREREpAsG0kRULI6OjlAqlXj69Gm+dTdu3IAgCGjSpEmB22dkZMDS0lLvfAMCArBkyRLMnTsXS5Ysgb+/PwDAyckJS5Yswdq1a2FtbY05c+ZgzJgxCAsL0zsPIiIiIiJtGEgTUbE0atQIALB7926N5Y8fP8bdu3cBAK1atdK6bWRkJBQKBWrUqKFzfkqlEjNnzsTYsWOxbt067NmzB2vXrsX777+PGTNmQKlUAgC6dOkCHx8fjBs3DoGBgRg0aBBWrFgBhUJhyMckIiIiIpIwkCaiYhkwYABEUcTWrVuxYcMGPHr0CH5+fvjkk08giiIsLS3RrVs3rdsGBAQAyO3jrKt169bh33//haWlJd5//318//33GDduHKysrHDixAmsXbtWSmthYYGvv/4ae/fuhbu7O1asWIFBgwZJ+RIRERERGYLTXxFRsY0ZMwYBAQEQBCHfuilTpmD69Olat5s8eTLOnTuHWbNmYfTo0Trl1adPHzx9+hRbt25F69atpeUBAQEYM2YM6tati2PHjuXbThRFbN++Hb///jsyMzMNHim8vOP0V0TlF6e/IiKqPFgjTUTFtmrVKnTt2hWiKEo/ADB8+HBMnTpV6zaPHz/G+fPnAeQ2w9ZVZGQkLC0tNYJoILf5uKWlJaKiorRuJwgCxo4dCx8fnwJryImIiIiIdMEaaSIymidPnkj9ops0aQIXF5cC00ZERCAkJASmpqbo2LGjznl07NgR8fHxOHPmDKpXry4tj46ORteuXeHk5IQLFy4Y/iEqONZIE5VfrJEmIqo8TMu6AERUedStWxd169bVKa2rqytcXV31zqNz587Yv38/Pv74Y8ycORO1a9fG06dPsXTpUgiCgE6dOum9TyIiIiIifbBGmogqlLi4OIwcORIREREafbJFUUStWrWwe/duODs7l2EJyxZrpKkiEVUqKKPvQ8xIhmBlD5MaHhBklbfXGWukiYgqDwbSRFThpKSkYPPmzfDz80NiYiIcHBzQvn17jBs3Dvb29mVdvDLFQJoqCkXYVWRd3gUxNU5aJtg6wbztSMhff6MMS1ZyGEgTEVUeDKSJiCoRBtJUESjCriLzxEqY1GkG8xYDIavqClVCBLICD0P59CYsek2tlME0A2kiosqj8rafIiIionJHVKmQdXkXTOo0g2WfGTCp7gZBbgGT6m65f9dphqzLuyGqVGVdVCIiogIxkCYiIqJSo4y+DzE1DuYtBkIQNB9DBEEG8xYDIabGQhl9v4xKSEREVDQG0kRUYWzYsAGZmZlG3eft27dx9uxZo+6TiAomZiQDAGRVtY/aL6viopGOiIioPGIgTUQVxsKFC9GzZ09s2bIFKSkpxdrX1atXMXnyZLzzzju4ffu2kUpIREURrHIHBFQlRGhdr0qM1EhHRERUHjGQJqIKY/LkyUhLS8N///tfdOzYETNmzMCxY8cQHx9f5LYKhQK3bt3C77//jp49e2LMmDE4e/YsmjRpgp49e5ZC6YkIQO4UV7ZOyAo8DFHU7ActiipkBR6GYOsMkxoeZVRCIiKionHUbiKqUJ4/f47Fixfj8OHDUCqV0lzSNWvWhIeHB6pWrQp7e3uYmZkhJSUFycnJCA8Px71796BQKADkzjldp04dfPLJJxgwYEBZfhyj46jdVBHkG7W7igtUiZEctZuIiCoMBtJEVCE9f/4ce/bswb59+xAdHS0tVwfWeakvc6ampujSpQtGjBiBTp06aU1b0VX2QFqlEvEgPAlJ6VlwsDaHe20HyGSV7//4KtA+j7QzzNuOqJRBNMBAmoioMmEgTUQV3oMHDxAQEIBbt24hJiYGCQkJyMrKgoODA6pWrQo3Nze88cYb8Pb2ho2NTVkXt0RV5kD62v0Y7PYNQVzy/wacc7K3wIjubmjpUa0MS0aGElWq3FG8M5IhWNnnNvuWVd5eZwykiYgqDwbSRESVSGUNpK/dj8GqA3fQtL4jmtRzhFwug0Khwu1H8bgVGo8pQxozmKZyj4E0EVHlYVrWBSAiIiqMSiVit28I6tawRURsGm6G/m9wOUc7c9StYYvdviFo0cCZzbyJiIioVDCQJiKicu1BeBLikjMRl5yJ5m5O+GhQY7g4WyMyNh1H/J7gRkiclM6zbpUyLi0RERG9CipvRyQiIqoUElOzAABN6jli2rAmqO9iDwszU9R3sce0YU3QpF5VjXRUcYgqFXKi7kIRchk5UXchqlRFb0RERFQOsEaaiIjKtdSMbABAS3cnyF4aaV0mCGjh7ozbjxL+r727j4qyzv8//pqB4d5ERA1Ng8QGEVGhpV0P5R0mur+tNNfvmke3b7bauqXmHisOWYmlubW6oHzZzZss18p+m2bWrvndll11XUXNRBPzFhU00hCVe2Hm94e/mRiZUUZlBun5OIdzYK7PdV3vGThHX9fnzt4Otwbnq3aHy//Hv2i1q3YDAFoPeqQBAC1aSLBJkrTr4BlZrlgf02K1avfBMw7t0PLZ9pE2trtDQQ+9oJD//qOCHnpBxnZ3qPp/s3Xp2E5vlwgAwFURpAEALVpYSIAkae/RUi3+cK8OF59XVU2dDhef1+IP92rv0VKHdmjZrBaLara9L59ufRQ4bKp8OkXLYAqQT6foyz9366OabasZ5g0AaNEY2g0AaNHu7hqq8LYBCgk0qehMueau3GU/Ft42QJG3t1F51SXd3TXUe0Wiyeq/+VrWi2flP/hJGQyOz/MNBqP8+/0fVa57RfXffC3fzj29VCUAAFdHkAYAtGhGo0H/NTjavo/0sKRu8vM1qrbOon0N9pFm66tbg7XyvCTJGHaH0+PGdl0c2gEA0BIRpAEALV6iuaOmjIzT6n8cdthHOrxtgKaMjFOiuaMXq4M7DEFtJUmW0iL5dIpudNxyrtihHQAALRFBGgBwS0g0d1S/Hh108GSZyipqFBrsr7u7htITfYvxud0sQ5tw1ez+RAFDn5Kl5JCsledlCGorY6ceqtn9iQxtOsjndrO3SwUAwCWD1XrFEqgAgFtWfb1FpaUV3i4DuKrLq3Yvlnz8pPoG25b9/58Dhj7VKrfA6tChjbdLAADcJKzaDQAAAACAGxjaDQAAPOb77a/6yn/IFNUV5Mp64VsZbuso356DVPP5/6hm22r53pkgg5Hn/QCAlokgDQAAPMa2/ZVPzEBV/t80qfz7xeNq922UX8+Bqj/xJdtfAQBaNII0AADwGNu2VrU7/nJ5TnRDVRdVu+NDh3YAALREBGkAAOA5AbfZv/Xp0lO+XXtLPv5SfY3qTu5V/Yk9jdoBANDSEKQBAIDHWFV/+Rtff9WfOf59cJakwFDJ11+qq/m+HQAALRCreAAAAI+xnD54+Zu6GqmqzPFgVdnl1xu2AwCgBSJIAwAAz7FaG/xguOKgwUU7AABaFoI0AADwGKspoOFPVx510Q4AgJaFIA0AADzGUlp0U9sBAOANLDYGAB62bds2vfXWW9qzZ48qKyvVuXNnpaamatKkSQoKCvJ2eUCzslZX3tR2AAB4Az3SAOBBK1eu1GOPPaZ//vOf8vf3V/fu3VVcXKycnByNHj1aZWVl3i4RaFbWkqYtItbUdgAAeAM90gDgIfv27dPcuXMlSRkZGRozZowMBoNKSkr061//Wl999ZVmzZqlRYsWeblS3IqsVossF79rnmvLKtVdujkXq6tucru60uKbc09fkwyNFja7OYxt2stgoF8CAH5oDFYry2ICgCdMmTJFn3/+uR5++GHNnz/f4VhhYaGGDx8ui8WidevWKSYm5rruUV9vUWlpxc0oF7cQq9Wi8iWPe7uMH6yQXy1vUpju0KGNB6oBAHgCj1ABwAMqKiq0efNmSdKYMWMaHY+MjNSPf/xjSdKGDRs8WhsAAADcw9BuAPCAgoIC1dbWys/PT/Hx8U7bJCYmauvWrdqzZ4+Hq8OtzmAwKuRXy5tlaLdVVlX9b7b03fGbfm2PaX+nAof+plmGdzO0GwB+mAjSAOABx44dkyR17txZJpPJaZtu3bo5tL1evr78p/6HySiFdWqWK/uPyZDqam/KtcqW/KrJbUN/teSm3FO+fjIYmmeONADgh4kgDQAecP78eUlS27ZtXbaxHbO1vR5Go0Ht2gVf9/lAcytzo21Yx7DmKgMAgBtCkAYAD6ipqZEkl73RkuTn5+fQ9npYLFZduMD+u2jBOsZI3x5oUrtz51rXwnk85AKA1oMgDQAe4O/vL0m6dMn1FkK1tbUOba9XXZ3lhs4HmlNg6nRVvfNkk9rxtwwAaKmYSAcAHtCUYdtNGf4N3Op8AwJk7BB11TbGDlHyDQjwUEUAALiPIA0AHhAZGSlJOnXqlMte6RMnTji0BVqr4JEvuQzTxg5RCh75kocrAgDAPQztBgAP6Nmzp0wmk2pra5Wfn6/ExMRGbXbt2iVJ6tu3r4erAzwveORLqquuVs0//yTrxTMytOkg/4GT6YkGANwSCNIA4AEhISFKTk5Wbm6uPvjgg0ZBurCwUNu2bZMkpaameqNEwON8AwLkmzrN22UAAOA2hnYDgIdMmTJFBoNB69at0+rVq2W1WiVJ3377rWbMmCGLxaKUlBTFxMR4uVIAAABcjcFq+58cAKDZrVixQq+99pqsVqsiIiLUrl07HT58WLW1tYqKitK7776rsLDr3zu3vt6i0tLWtWUQ0Fp06NDG2yUAAG4SgjQAeNh//vMfLV++XPn5+aqsrFTnzp2VmpqqSZMmKTj4xvaZJUgDLRdBGgBaD4I0ALQiBGmg5SJIA0DrwRxpAAAAAADcQJAGAAAAAMANBGkAAAAAANzAHGkAAAAAANxAjzQAAAAAAG4gSAMAAAAA4AaCNAAAAAAAbiBIAwAAAADgBoI0AAAAAABuIEgDAAAAAOAGgjQAAAAAAG4gSAMAAAAA4AaCNAAAAAAAbiBIAwAAAADgBoI0AAAAAABuIEgDAAAAAOAGgjQAAAAAAG4gSAMAJElr1qyR2WzW888/7/B6UVGRzGazBg8e7KXKfti2b9/O5w8AQAvj6+0CAABXN378eOXl5V21TUxMjNatW+ehippm0aJFWrx4sSSpX79+ev/99522q6ioUEJCgiTp888/1x133HHD9y4oKNDf//53denSRaNGjbrh67VGZrP5us67Wb8jb1uzZo2Ki4uVkpKinj17erscAMAthiANALeIiIgIRUREOD0WGRnp2WLctHv3buXm5mrQoEEeuV9BQYEWL16spKQkgrQLtocXDdXW1mrfvn2SpLi4OPn5+TVq4+/v3+y1ecLatWuVl5enLl26EKQBAG4jSAPALeKRRx7R008/7e0y3Obj46P6+nr94Q9/0MCBA2UwGLxdEiS99957jV4rKirSkCFDJEmZmZmtoucZAIDmwBxpAECz6t+/v8LDw3XgwAH99a9/9XY5AAAAN4wgDQCtSFMWBnv++edlNpu1Zs0aj9QUFBSkyZMnS5KysrJUV1fn9jUOHjyotLQ0DR48WL1799Y999yj8ePHa/369Y3aDh48WGlpaZKkvLw8mc1m+5ftc3njjTdkNpu1cOHCRuenp6fLbDYrJiZGZWVlDsfKysoUExOj+Ph41dbWOhyrra3VihUrNHr0aCUkJCg+Pl6pqamaP3++SktLnb6v8ePH238XRUVFSktL0/3336/Y2Fi9+uqrTfpsfve738lsNis5OVlff/11k865HhcuXNBf/vIXPfXUUxo2bJj69u2rfv366cEHH1RmZqYuXrzo9LxFixbZF7GrqqrSwoULlZqaqvj4eD300EMObT/66CONHj1affv2VVJSkiZOnKidO3de8+/aYrHo448/1uOPP657771XcXFxuv/++/Xcc8/pyJEjDm1ti7fZ1h1IS0tz+BtZtGjRTfi0AACtHUO7AQDN7he/+IXeeustFRYWau3atfr5z3/e5HM/+OADzZ49W3V1dQoODtZdd92l0tJS5eXlKS8vT1u3btW8efPs7ePi4mQymVRYWKiQkBDdfffd9mMdOnSQJCUlJWnJkiVOF3Hbvn27JMlqtWrHjh0aOnSo/diOHTtktVrVp08fh/nDFy5c0MSJE5Wfny9JioqKUmBgoA4dOqTly5dr/fr1WrZsmcsFvo4dO6Z58+apqqpKPXr0UJs2bWQ0Xv1Zd11dnWbNmqU1a9bozjvv1LJly9S1a9drfZzXLTc3V+np6TKZTAoPD1f37t1VXl6uo0eP6uuvv9bf/vY3vf/++woNDXV6fnV1tcaNG6evvvpKUVFRio6Olslksh+fO3eu3n77bUlSx44d1bFjR+Xn52vChAl69tlnXdZVVVWlqVOnatOmTfZzIyIidPz4cX300UfasGGDsrKyNGDAAElSmzZtlJCQoIMHD6q8vFyRkZEKCwuzX8/VOgQAADREkAYANDs/Pz/95je/UXp6urKzs/XQQw85XcjqStu2bdNLL72kgIAAzZ49W6NGjbIHzLy8PM2YMUNr1qxRQkKCPZxnZWVpzZo1SktLU2xsrFauXNnouomJifL19dXevXtVWVmpoKAgSdKpU6d08uRJderUSSUlJdq+fbtDkLaF7HvvvdfhehkZGcrPz1dERISys7PVq1cvSdKZM2f0zDPPaMeOHZo6darWr1/v9H0vW7ZM9913n+bNm2cPddXV1S4/l+rqak2fPl25ubmKjY3V0qVL1b59+2t+njfCbDYrJydH/fv3V0BAgP31c+fOaeHChVq9erUWLFigjIwMp+dv3LhRXbp00fr16+0PN2zvMTc3V2+//bZ8fHyUkZGhRx55RAaDQbW1tXr99df1xhtvuKwrIyNDmzZtUnx8vObMmaOYmBhJ0qVLl5STk6Ps7GzNnDlTGzZsUFhYmGJjY/Xee+/ZV8OfPHkyC9IBANzG0G4AuEUsXrzYYQhqw6+ioiJvl3dNI0eOVGRkpE6fPq133323Sef8/ve/l8Vi0QsvvKDRo0c79NImJSVp9uzZkqSlS5e6VUtwcLDi4uJ06dIlffHFF/bXbUF5woQJCgoKsv9sY+vBbhikT548qU8//VSS9Morr9hDtHS5BzwzM1OBgYEqLCx0OUe8Xbt2WrBggUPPaMOw2tDFixc1ceJE5ebmKikpSStXrmz2EC1d3mJt8ODBjepq166dMjIydPvtt2v9+vWqr693en59fb0WLFjgMELAdi3b72/8+PEaPXq0fUE6Pz8/paenKy4uzuk1Dx8+rLVr1yosLEx//OMf7SFakkwmk6ZOnaqhQ4fq/Pnz+uCDD67/zQMAcAWCNADcIiIiIpSQkOD061bYksjHx0fTpk2TJL355puqqKi4avtvvvlG+fn5CgwM1M9+9jOnbQYMGGAfxl1SUuJWPUlJSZLkMLzb9n1ycrISEhJ06NAh+/zmc+fO6eDBgwoICFCfPn3s52zZskUWi0XR0dFKTk5udJ/27dvrwQcflCRt3rzZaS3Dhg1TcHDwNWs+c+aMxo0bp507dyolJUVLly5VSEhIE9/xjaurq9PGjRv18ssv64knntC4ceM0duxYjR07VuXl5aqsrFRhYaHTc3v06KHevXs3er2iosL+MGP06NFOz3U1FeCzzz6T1WrVkCFDXD5MSElJkaRr7sUOAIA7GNoNALeIW3X7q4aGDx+uN998UwUFBXr77bc1ZcoUl20PHDhg//6Xv/zlNa9dUlKiTp06NbmWpKQkvfnmmw69ztu3b1doaKjMZrPuvfdebdmyRdu3b9fw4cNdzo8+duyYJCk6OtrlvWy9sEePHnV6vHv37test7y8XGPHjtXJkyf1yCOPaM6cOfLx8WnSe70ZSkpKNGnSJIffizNXLtBmc9dddzl9/cSJE7JYLDKZTC4/B1f7PNsWV9u6davGjh3rtI1tEbRvvvnmamUDAOAWgjQAwGMMBoOmT5+uyZMna/ny5Ro3bpx8fZ3/U3ThwgVJlxeTajj82pWqqiq3aklMTJTJZNK+fftUUVGh0tJSFRcX64EHHpDBYLD3WNuCtLNh3ZLsPevh4eEu72U75qoXPjAw8Jr1VldXq7KyUtLlYOnJEC1dXt36wIEDio2N1dNPP61evXqpXbt29ocKtp5yV6uy2+ahX8n2mQQGBrpcYM1Vb70tJBcXF6u4uPiq9V9tzjkAAO4iSANAK2KbW2q1Wl22sYUxbxk4cKASEhL0xRdfaOnSpXryySedtrMFr+joaPsc5JspKChIcXFx2r17t3bt2qUzZ85I+j4ox8XFOcyTdrXQmC3knT171uW9bMeaMnzblfDwcKWnp2vatGl65ZVXZDQaNW7cuOu+nju+/fZb/fvf/1ZAQICWLVvmMJfbxlVP9LXYPpOqqipZLBanYdrVAwjb38jMmTP1xBNPXNf9AQC4HsyRBoBWxNaz+d1337ls42oOqyc988wzkqSVK1e6rNU2HPrkyZNu9ybaHihcS8Ne5yuDsq+vrxITE+3bOx06dEgBAQGKj493uEZUVJQk6dChQy7vc/DgQUmuhzc31ZAhQ5SZmSmTyaSMjAytWrXqhq7XVLbe3u7duzsN0efPn7/uv6tu3brJaDTq0qVLLoe+uxpO3qNHD0lX/+wBAGgOBGkAaEXCwsLUtm1b1dTUaP/+/Y2O79q1yz6v1JuSkpKUnJysqqoq5eTkOG3TrVs3xcbGqqamxu3AaFt87VoB3Baat2/frry8PLVv394ezhoez87OltVqVb9+/RptX5WcnCyj0agjR45oy5Ytje5RWlqqjz/+WJJ03333ufU+nBkyZIiysrI8GqZtD2jOnj3rdLTDihUrXA7pvpbg4GAlJCRIkj788EOnbVy9npqaKunyomOnTp1y6762FcMZ8g0AuB4EaQBoZQYMGCBJevXVV3Xu3Dn76/v379dzzz0nk8nkrdIcTJ8+XZK0bt06l22effZZ+fj4aMGCBVq6dGmjedAXLlzQunXrNH/+fIfXu3XrJuny9ki2VbedSUhIsM+TPn36tL2H2sYWpDdu3ChJjY5LUteuXfXTn/5UkjRr1iwVFBTYj509e1bTp09XVVWVIiMjNWLECJe1uGPw4MFatGiRTCaT5syZ0+xhunv37goNDVVJSYkyMzPtW1xZLBatWrVKf/rTn25o5XjbsOx33nlHa9eutb9eW1ur1157TXv27HF6XkxMjEaNGqWqqio99thjTlfmPnLkiLKysvSPf/zD4XXb38jOnTuvOhUCAABnmCMNAK3M1KlT9a9//Us7d+7UgAEDFBUVperqahUWFqp///7q06ePPvnkE2+Xqd69e+uBBx6wh1RnfvKTn2ju3Ll68cUX9frrryszM1N33XWX/Pz87IuDWa3WRgE3NjZWUVFROnbsmFJSUhQdHS1/f3+Fh4dr4cKF9naBgYH2edJS4/nPvXr1UkhIiMrLyyU5D9KS9OKLL+r48ePKz8/Xww8/rO7du8vf31+HDh3SpUuXFB4erqysrEa92Tdi0KBBWrRokZ5++mnNmTNHBoNBjz766E27fkMmk0m//e1vNWvWLOXk5Gj16tXq3LmzTp06pdLSUo0aNUpFRUXXvcXUoEGDNGHCBL3zzjt6/vnntXDhQnXs2FEnTpxQeXm5Zs6cqddee83p/OmXX35ZFRUV+uyzzzR+/HiFh4erc+fOqqur06lTp+xzt+fNm+dw3ogRI7Rq1Sp9+umn+vLLLxURESGj0aiRI0dq1KhR1/U+AAA/HPRIA0Ar07VrV7333nsaNmyYAgMDdfToURmNRs2YMUNLlixpMT3SkjRt2jSXKzXbPPzww/r00081YcIEdevWTSdOnND+/ftVU1Oj/v37Kz09Xa+//rrDOUajUUuWLNGIESMUFBSkffv2KS8vz2nPZsPwfGVQ9vHx0T333CPpcui+cn60zW233aZVq1YpLS1NcXFxOn36tA4fPqwuXbro8ccf18cffyyz2dykz8QdgwYN0uLFi+Xr66uMjAy9++67N/0eNmPGjFFWVpbi4+NVUVGhY8eOqUuXLpo9e7bmzp17w9dPT0/X/PnzFRcXp/Pnz+v48eOKi4vTihUr7PtzO9sz29/fX1lZWcrJydHQoUNlNBpVUFCgo0ePKjQ0VCNHjlR2drZ91IBNYmKiFixYoH79+qmsrEy7du1SXl7eNVf/BgBAkgxWxjMBAIAWbMOGDZo2bZpSUlKUnZ3t7XIAAKBHGgAAtGy2xcZsowMAAPA2gjQAAPC6P//5z/ryyy8dXisvL9fcuXO1adMmhYSE6MEHH/ROcQAAXIHFxgAAgNdt3rxZc+bMUWhoqLp27WrfV7q2tlYmk0mvvvqq2rdv7+0yAQCQRJAGAAAtwKOPPqrAwEDt3btXR44csa92/qMf/UgTJ05UTEyMt0sEAMCOxcYAAAAAAHADc6QBAAAAAHADQRoAAAAAADcQpAEAAAAAcANBGgAAAAAANxCkAQAAAABwA0EaAAAAAAA3EKQBAAAAAHADQRoAAAAAADf8PyqKwM0ytDMBAAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_compare(data_size_FN, data_size_T, 'percent', [\"Full Network\", \"Target\"], \n", + " y_label=\"Size of seed set\\n(%\\ of GSMN size)\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## iCN718 analyses" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Total number of solutions for iCN718 in Full Network: 1\n", + "size: 141\t count: 1\n" + ] + } + ], + "source": [ + "get_iCN718_sol(flux_precursor_FN_submin, \"Full Network\")" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Total number of solutions for iCN718 in Target: 246\n", + "size: 1\t count: 245\n", + "size: 2\t count: 1\n" + ] + } + ], + "source": [ + "get_iCN718_sol(flux_precursor_T_submin, \"Target\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "test", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.14" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/notebook/analyses/06_supp_iCN718.ipynb b/notebook/analyses/06_supp_iCN718.ipynb new file mode 100644 index 0000000..83005a7 --- /dev/null +++ b/notebook/analyses/06_supp_iCN718.ipynb @@ -0,0 +1,715 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Get iCN718 datas and compare\n", + "This notebook presents iCN718 data in Reasoning / Filter / Guess & Check / Guess & Check Diversity.\n", + "\n", + "\n", + "To run correctly this notebook and have the same results as the paper, you must first download the raw results: [https://doi.org/10.57745/OS1JND](https://doi.org/10.57745/OS1JND)\n", + "\n", + "This notebook is written with the hierarchy of downloaded files, if you want to try it with the test form the run notebooks, it is needed to first restructure your data to match the hierarchy of downloaded files.\n", + "\n", + "We suppose here that the downloaded files are in a directory named \"analyses\", this directory path can be changed to your directory path where the data are saved." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Requirements\n", + "Module *seaborn* and *scipy* are needed" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!pip install seaborn" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!pip install scipy" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Variable to change (if wanted)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "analyse_dir = \"../../analyses\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Initialisation and functions" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import pandas as pd\n", + "import seaborn as sns\n", + "import matplotlib.pyplot as plt\n", + "from scipy.stats import kruskal" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "metabolite_dir=os.path.join(analyse_dir, \"results\", \"metabolites_iCN718\")\n", + "metabolites_file = os.path.join(metabolite_dir, \"metabolites_occurences.tsv\")\n", + "file_exch = os.path.join(metabolite_dir, \"iCN718_exchanges.txt\")\n", + "iCN718_scope_dir = os.path.join(analyse_dir, \"scopes_iCN718\")\n", + "iCN_results_dir=os.path.join(analyse_dir,\"results\", \"iCN718_2000\")\n", + "supp_data_file=os.path.join(analyse_dir, \"results\", \"supp_data\", \"iCN718_2000_supp_data.tsv\")" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [], + "source": [ + "def get_metabolites(file):\n", + " metabolites=pd.read_csv(file,sep='\\t')\n", + " metabolites.rename( columns={'Unnamed: 0':'metabolites'}, inplace=True )\n", + " for index, met in metabolites.iterrows():\n", + " name=met[\"metabolites\"].removeprefix(\"M_\").strip().removesuffix(\"_e\").removesuffix(\"_c\")\n", + " metabolites.loc[index, 'metabolites']=name\n", + " metabolites=metabolites.groupby(['metabolites']).sum()\n", + "\n", + " return metabolites.reset_index()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "def plot_metabolites(metabolites:pd.DataFrame, mode, nb:int=0):\n", + "\n", + " with open(file_exch, 'r') as file:\n", + " exch_list = file.read().split()\n", + "\n", + " match mode:\n", + " case \"reasoning\":\n", + " col=\"nb_reasoning\"\n", + " case \"filter\":\n", + " col=\"nb_filter\"\n", + " case \"gc\":\n", + " col=\"nb_gc\"\n", + " case \"gcd\":\n", + " col=\"nb_gcd\"\n", + " \n", + " metabolites[\"colors\"]=\"internal\"\n", + " for index, line in metabolites.iterrows():\n", + " if line[col] == 0:\n", + " metabolites=metabolites.drop(index)\n", + " elif line[\"metabolites\"] in exch_list:\n", + " metabolites.loc[index, 'colors']=\"exchange\"\n", + " \n", + "\n", + " metabolites_mode=pd.DataFrame()\n", + " metabolites_mode=metabolites[[\"metabolites\",col,\"colors\"]]\n", + " metabolites_mode=metabolites_mode.sort_values(by=[col], ascending=False)\n", + " if nb!=0:\n", + " metabolites_mode=metabolites_mode.head(nb)\n", + " labels=metabolites_mode[\"metabolites\"].to_list()\n", + "\n", + " size=len(labels)\n", + " count=metabolites_mode['colors'].value_counts()\n", + "\n", + " print (\"exchange: \", count[\"exchange\"], \"/ total: \" , size)\n", + "\n", + " if nb==0:\n", + " x_limit = size*0.7\n", + " y_limit = 20\n", + " size=\"full\"\n", + " else:\n", + " x_limit = size*0.4\n", + " y_limit = 2\n", + " fig_size = (x_limit,y_limit)\n", + "\n", + " plt.figure(figsize=(fig_size))\n", + " fig=sns.barplot(data=metabolites_mode, x='metabolites', y=col, hue=\"colors\",palette=[\"#003380ff\",\"#dd8452ff\"],hue_order=[\"internal\",\"exchange\"])\n", + " fig.set_ylim(0, 2000)\n", + " if nb == 0:\n", + " plt.ylabel('Occurences',fontsize=30)\n", + " plt.xlabel('Metabolites',fontsize=30)\n", + " plt.xticks(labels,rotation=45, horizontalalignment='right', fontsize=30 )\n", + " plt.yticks(fontsize=30)\n", + " else:\n", + " plt.ylabel('Occurences')\n", + " plt.xlabel('Metabolites')\n", + " plt.xticks(labels,rotation=45, horizontalalignment='right' ) \n", + " sns.despine(bottom=True)\n", + " plt.tick_params(bottom=False, left=True)\n", + " plt.legend(frameon=False, bbox_to_anchor=(1, 1), loc='upper right', borderaxespad=-10)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "def get_total_nb_meta(supp_data_file):\n", + " table_all = pd.read_csv(supp_data_file, sep='\\t', lineterminator='\\n')\n", + " union_all = table_all.loc[table_all[\"type_data\"] == \"Union\"]\n", + " num_metabolite = union_all.groupby(['network'])['number_metabolites'].first()\n", + " return pd.DataFrame(num_metabolite)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "def plot_compare_multi(s2lp_reasoning, s2lp_filter, s2lp_gc, s2lp_gcd, col, do_kruskal:bool=True, y_label:str=\"\"):\n", + " \n", + " plt.style.use(\"seaborn-v0_8-colorblind\")\n", + " labels = [\"Reasoning\", \"Filter\", \"GC\", \"GCD\"]\n", + "\n", + " s2lp_reasoning=s2lp_reasoning.assign(Modes=labels[0])\n", + " s2lp_filter=s2lp_filter.assign(Modes=labels[1])\n", + " s2lp_gc=s2lp_gc.assign(Modes=labels[2])\n", + " s2lp_gcd=s2lp_gcd.assign(Modes=labels[3])\n", + " \n", + " concat_table = pd.concat([s2lp_reasoning, s2lp_filter, s2lp_gc, s2lp_gcd])\n", + "\n", + " reasoning=s2lp_reasoning[col].mean()\n", + " filter=s2lp_filter[col].mean()\n", + " gc=s2lp_gc[col].mean()\n", + " gcd=s2lp_gcd[col].mean()\n", + " print(\"Reasoning: \",reasoning, \"\\t Filter: \", filter, \"\\t GC: \", gc, \"\\t GCD: \", gcd)\n", + "\n", + " plt.figure(figsize=(3,4))\n", + " sns.set_theme(font_scale = 1.5)\n", + "\n", + " # KRUSKAL WALLIS TESTS\n", + " # Get the p-value from Kruskall Wallis test\n", + " if do_kruskal:\n", + " kstat, p_value = kruskal(concat_table[concat_table[\"Modes\"]==labels[0]][col], concat_table[concat_table[\"Modes\"]==labels[1]][col],\n", + " concat_table[concat_table[\"Modes\"]==labels[2]][col], concat_table[concat_table[\"Modes\"]==labels[3]][col])\n", + " else:\n", + " kstat = p_value = \"no differences\"\n", + "\n", + " sns.boxplot(data=concat_table, x=\"Modes\", y=col, hue=\"Modes\", fill=False, linewidth = 1.5)\n", + " plt.xticks(rotation=90) \n", + " plt.xlabel('')\n", + " plt.ylabel(y_label)\n", + " plt.title(f\"kstat = {kstat}, p-value = {p_value}, n={2000}\")\n", + " sns.despine(bottom=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [], + "source": [ + "def get_scopes(directory:str, mode:str, optim:str=None):\n", + " scope_all=pd.DataFrame(columns=['species','run','mode','optim', 'accu','model',\n", + " 'is_equal_union_species', 'missing', 'percentage_missing',\n", + " 'is_biomass_included', 'missing_biomass', 'percentage_missing_biomass',\n", + " 'is_exchange_included', 'missing_exchange', 'percentage_missing_exchange',\n", + " 'is_seed_included_to_exchange', 'missing_seed_into_exchange', 'percentage_missing_seed_into_exchange',\n", + " 'is_exchange_included_to_seed', 'missing_exchange_into_seed', 'percentage_missing_exchange_into_seeds'])\n", + " if mode == \"netseed\":\n", + " prefix=\"netseed\"\n", + " else:\n", + " prefix=\"scope\"\n", + " for species in os.listdir(directory):\n", + " file_path = os.path.join(directory, species, f\"{species}_{prefix}_compare.tsv\")\n", + " current_df = pd.read_csv(file_path, sep='\\t', lineterminator='\\n')\n", + " current_df['is_equal_union_species'] = current_df['is_equal_union_species'].map({True: 'True', False: 'False'})\n", + " current_df['is_biomass_included'] = current_df['is_biomass_included'].map({True: 'True', False: 'False'})\n", + " current_df['is_exchange_included'] = current_df['is_exchange_included'].map({True: 'True', False: 'False'})\n", + " current_df['is_seed_included_to_exchange'] = current_df['is_seed_included_to_exchange'].map({True: 'True', False: 'False'})\n", + " current_df['is_exchange_included_to_seed'] = current_df['is_exchange_included_to_seed'].map({True: 'True', False: 'False'})\n", + " current_df[\"percentage_similar\"]=100-current_df[\"percentage_missing\"]\n", + " current_df[\"percentage_similar_biomass\"]=100-current_df[\"percentage_missing_biomass\"]\n", + " current_df[\"percentage_similar_exchange\"]=100-current_df[\"percentage_missing_exchange\"]\n", + " current_df[\"percentage_similar_seed_into_exchange\"]=100-current_df[\"percentage_missing_seed_into_exchange\"]\n", + " current_df[\"percentage_similar_exchange_into_seeds\"]=100-current_df[\"percentage_missing_exchange_into_seeds\"]\n", + " scope_all=pd.concat([scope_all, current_df], ignore_index=True)\n", + " # in this notebook we use only the non accumulation mode for Seed2LP\n", + " if mode == \"full\":\n", + " scope_all = scope_all[scope_all[\"accu\"]==False]\n", + " scope = scope_all[scope_all[\"run\"]==\"full\"]\n", + " elif mode == \"target\":\n", + " scope_all = scope_all[scope_all[\"accu\"]==False]\n", + " scope = scope_all[scope_all[\"run\"]==\"target\"]\n", + " else:\n", + " scope = scope_all\n", + "\n", + " if optim==\"submin\":\n", + " return scope[scope[\"optim\"]==\"subset_minimal\"]\n", + " elif optim==\"min\":\n", + " return scope[scope[\"optim\"]==\"minimize\"]\n", + " else:\n", + " return scope" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [], + "source": [ + "def get_separate_scope(scope_table):\n", + " # REASONING\n", + " scope_reasoning = scope_table[scope_table[\"mode\"]==\"reasoning\"]\n", + "\n", + " # FILTER\n", + " scope_fil = scope_table[scope_table[\"mode\"]==\"reasoning_filter\"]\n", + "\n", + " # GC\n", + " scope_gc = scope_table[scope_table[\"mode\"]==\"reasoning_guess_check\"]\n", + "\n", + " # GCD\n", + " scope_gcd = scope_table[scope_table[\"mode\"]==\"reasoning_guess_check_diversity\"]\n", + "\n", + " return scope_reasoning, scope_fil, scope_gc, scope_gcd" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "def get_fluxes(directory:str, mode:str, optim:str=None):\n", + " flux_all=pd.DataFrame(columns=['species', 'biomass_reaction', 'solver_type', 'search_mode',\n", + " 'search_type', 'accumulation', 'model', 'size', 'lp_flux', 'cobra_flux_init',\n", + " 'cobra_flux_no_import', 'cobra_flux_seeds', 'cobra_flux_demands',\n", + " 'has_flux', 'has_flux_seeds', 'has_flux_demands', 'timer'])\n", + " flux_all['accumulation'] = flux_all['accumulation'].astype('bool')\n", + " flux_all['has_flux'] = flux_all['has_flux'].astype('bool')\n", + " flux_all['has_flux_seeds'] = flux_all['has_flux_seeds'].astype('bool')\n", + " flux_all['has_flux_demands'] = flux_all['has_flux_demands'].astype('bool')\n", + "\n", + " for dirpath, _, filenames in os.walk(directory):\n", + " for filename in [f for f in filenames if (f.endswith(\"_fluxes.tsv\") or f.endswith(\"_fluxes_from_result.tsv\"))]:\n", + " # By default in this notebook we want the no accumulation mode for seed2lp results\n", + " if \"_no_accu_\" in filename \\\n", + " and ((mode == \"full\" and \"_fn_\" in filename) \\\n", + " or (mode == \"target\" and \"_tgt_\" in filename))\\\n", + " or mode == \"netseed\":\n", + " file_path=os.path.join(dirpath, filename)\n", + " current_df = pd.read_csv(file_path, sep='\\t', lineterminator='\\n')\n", + " current_df['accumulation'] = current_df['accumulation'].astype('bool')\n", + " current_df['has_flux'] = current_df['has_flux'].astype('bool')\n", + " current_df['has_flux_seeds'] = current_df['has_flux_seeds'].astype('bool')\n", + " current_df['has_flux_demands'] = current_df['has_flux_demands'].astype('bool')\n", + " flux_all=pd.concat([flux_all if not flux_all.empty else None, current_df], ignore_index=True)\n", + " \n", + " flux_all = flux_all[flux_all[\"search_type\"]!=\"Optimum\"]\n", + " flux_all = flux_all[flux_all[\"model\"]!=\"model_one_solution\"]\n", + " if optim==\"submin\":\n", + " return flux_all[flux_all[\"search_mode\"]==\"Subset Minimal\"]\n", + " elif optim==\"min\":\n", + " return flux_all[flux_all[\"search_mode\"]==\"Minimize\"]\n", + " else:\n", + " return flux_all" + ] + }, + { + "cell_type": "code", + "execution_count": 77, + "metadata": {}, + "outputs": [], + "source": [ + "def prepare_data_size(table, nb_total_meta_df):\n", + "\n", + " # CLASSIC\n", + " table_reasoning = table[table[\"solver_type\"]==\"REASONING\"]\n", + " list_reasoning_size = table_reasoning[['model', \"size\"]]\n", + " list_reasoning_size[\"percent\"]=list_reasoning_size[\"size\"] / nb_total_meta_df.loc[\"iCN718\"]['number_metabolites'] *100\n", + "\n", + " # FILTER\n", + " table_filter = table[table[\"solver_type\"]==\"REASONING FILTER\"]\n", + " list_filter_size = table_filter[['model', \"size\"]]\n", + " list_filter_size[\"percent\"]=list_filter_size[\"size\"] / nb_total_meta_df.loc[\"iCN718\"]['number_metabolites'] *100\n", + "\n", + " # GUESS_CHECK\n", + " table_gc = table[table[\"solver_type\"]==\"REASONING GUESS-CHECK\"]\n", + " list_gc_size = table_gc[['model', \"size\"]]\n", + " list_gc_size[\"percent\"]=list_gc_size[\"size\"] / nb_total_meta_df.loc[\"iCN718\"]['number_metabolites'] *100\n", + "\n", + " # GUESS_CHECK_DIV\n", + " table_gcd = table[table[\"solver_type\"]==\"REASONING GUESS-CHECK DIVERSITY\"]\n", + " list_gcd_size = table_gcd[['model', \"size\"]]\n", + " list_gcd_size[\"percent\"]=list_gcd_size[\"size\"] / nb_total_meta_df.loc[\"iCN718\"]['number_metabolites'] *100\n", + "\n", + "\n", + " return list_reasoning_size, list_filter_size, list_gc_size, list_gcd_size" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Get data" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "metadata": {}, + "outputs": [], + "source": [ + "# Get the number of all metabolites per network\n", + "nb_total_meta_df = get_total_nb_meta(supp_data_file)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "metabolites = get_metabolites(metabolites_file)" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_103060/955389531.py:21: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", + " scope_all=pd.concat([scope_all, current_df], ignore_index=True)\n" + ] + } + ], + "source": [ + "scope_iCN718 = get_scopes(iCN718_scope_dir, \"target\", \"submin\")" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [], + "source": [ + "scope_reasoning, scope_fil, scope_gc, scope_gcd = get_separate_scope(scope_iCN718)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "flux_iCN718 = get_fluxes(iCN_results_dir, \"target\", \"submin\")" + ] + }, + { + "cell_type": "code", + "execution_count": 78, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_103060/3342964518.py:6: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " list_reasoning_size[\"percent\"]=list_reasoning_size[\"size\"] / nb_total_meta_df.loc[\"iCN718\"]['number_metabolites'] *100\n", + "/tmp/ipykernel_103060/3342964518.py:11: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " list_filter_size[\"percent\"]=list_filter_size[\"size\"] / nb_total_meta_df.loc[\"iCN718\"]['number_metabolites'] *100\n", + "/tmp/ipykernel_103060/3342964518.py:16: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " list_gc_size[\"percent\"]=list_gc_size[\"size\"] / nb_total_meta_df.loc[\"iCN718\"]['number_metabolites'] *100\n", + "/tmp/ipykernel_103060/3342964518.py:21: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " list_gcd_size[\"percent\"]=list_gcd_size[\"size\"] / nb_total_meta_df.loc[\"iCN718\"]['number_metabolites'] *100\n" + ] + } + ], + "source": [ + "reasoning_size, filter_size, gc_size, gcd_size = prepare_data_size(flux_iCN718, nb_total_meta_df)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# PLOT" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Set of seeds solution and scope analyses" + ] + }, + { + "cell_type": "code", + "execution_count": 79, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Reasoning: 2.3779166666666662 \t Filter: 5.995416666666666 \t GC: 4.5458333333333325 \t GCD: 3.929583333333333\n" + ] + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_compare_multi(scope_reasoning, scope_fil, scope_gc, scope_gcd, 'percentage_similar_exchange_into_seeds', \n", + " y_label=\"%\\ of all exchanged metabolites\\npresent in the seed set\")" + ] + }, + { + "cell_type": "code", + "execution_count": 80, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Reasoning: 1.6251689189189187 \t Filter: 1.8078265765765764 \t GC: 1.6422297297297295 \t GCD: 1.7537725225225227\n" + ] + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_compare_multi(reasoning_size, filter_size, gc_size, gcd_size , 'percent',\n", + " y_label=\"Size of seed set\\n(%\\ of GSMN size)\")" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Reasoning: 73.57714932126697 \t Filter: 73.302092760181 \t GC: 73.06515837104071 \t GCD: 73.37556561085972\n" + ] + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_compare_multi(scope_reasoning, scope_fil, scope_gc, scope_gcd, 'percentage_similar', \n", + " y_label=\"%\\ of GSMN metabolites\\nreachable from seeds\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Metabolite occurences in solutions" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "exchange: 50 / total: 188\n" + ] + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_metabolites(metabolites,\"reasoning\")" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "exchange: 36 / total: 111\n" + ] + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_metabolites(metabolites,\"filter\")" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "exchange: 49 / total: 130\n" + ] + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_metabolites(metabolites,\"gc\")" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "exchange: 38 / total: 141\n" + ] + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_metabolites(metabolites,\"gcd\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "test", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.14" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/notebook/run/01_get_sbml_BiGG.ipynb b/notebook/run/01_get_sbml_BiGG.ipynb new file mode 100644 index 0000000..43ec847 --- /dev/null +++ b/notebook/run/01_get_sbml_BiGG.ipynb @@ -0,0 +1,167 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# BiGG SBML" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This notebook explains how to retrieve BiGG SBML file\n", + "\n", + "> Note:\n", + ">\n", + "> All SBML Files used in the paper are available: [https://doi.org/10.57745/OS1JND](https://doi.org/10.57745/OS1JND)\n", + ">\n", + "> After downloadind and unzipping the package, go to analyses/data/bigg/sbml\n", + ">\n", + "> iAT PLT 636.xml is removed from source files because of a lack of biomass reaction\n", + "\n", + "> Warning:\n", + ">\n", + "> To run the notbook it is needed to install Jupyter notebook (on a conda env is better):\n", + ">\n", + "> `pip install jupyter`\n", + "\n", + "> Note:\n", + ">\n", + "> Do not forget your conda environment (e.g. select python from conda env if using Visual Studio code or activate conda env then execute notebook by command line)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Requirements\n", + "Module *requests* is needed" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!pip install requests" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## **Slurm-based cluster**: Reproducing paper data\n", + "Slurm-based scripts for cluster are available:\n", + "- Change **_source_** variable by the path of your conda environement with seed2lp installed in files: \n", + " - [01_job_retrieve_bigg_sbml.sh](../../scripts/plafrim_cluster/01_job_retrieve_bigg_sbml.sh)\n", + "- launch:\n", + " - [01_job_retrieve_bigg_sbml.sh](../../scripts/plafrim_cluster/01_job_retrieve_bigg_sbml.sh): `sbatch 01_job_retrieve_bigg_sbml.sh` " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## **LAUNCH**\n", + "\n", + "The python script is available on directory **_scripts_**: [01_retrieve_bigg_sbml.py](../../scripts/01_retrieve_bigg_sbml.py)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Variable to change (if wanted)" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "analyse_dir = \"../../analyses\"\n", + "data_dir = f\"{analyse_dir}/data/\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Execute" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "from os import path, makedirs, remove" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + ">>> BIGG data import...\tDone.\n" + ] + } + ], + "source": [ + "file = \"../../scripts/01_retrieve_bigg_sbml.py\"\n", + "\n", + "if not path.isdir(data_dir):\n", + " makedirs(data_dir)\n", + " \n", + "!python {file} {data_dir}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Delete iAT PLT 636.xml file (no biomass referred)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "filepath=path.join(data_dir,\"bigg/sbml/iAT_PLT_636.xml\")\n", + "remove(filepath)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "s2lp", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.9" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/notebook/run/02_get_objectives.ipynb b/notebook/run/02_get_objectives.ipynb new file mode 100644 index 0000000..bee6407 --- /dev/null +++ b/notebook/run/02_get_objectives.ipynb @@ -0,0 +1,5572 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Get OBJECTIVE from source sbml file" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This notebook explains how to retrieve the objective functions from BiGG SBML files and write it into multiple files and must be run **AFTER** retrieving BiGG SBML files (see notebook [01_get_sbml_BiGG.ipynb](./01_get_sbml_BiGG.ipynb))\n", + "\n", + "> Note:\n", + ">\n", + "> All objective files used are available: [https://doi.org/10.57745/OS1JND](https://doi.org/10.57745/OS1JND)\n", + ">\n", + "> After downloading and unzipping the package, go to analyses/data/objective " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Requirements\n", + "Module *pandas* is needed" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Requirement already satisfied: pandas in /home/cghassem/miniconda3/envs/test/lib/python3.10/site-packages (2.2.2)\n", + "Requirement already satisfied: numpy>=1.22.4 in /home/cghassem/miniconda3/envs/test/lib/python3.10/site-packages (from pandas) (2.1.1)\n", + "Requirement already satisfied: python-dateutil>=2.8.2 in /home/cghassem/miniconda3/envs/test/lib/python3.10/site-packages (from pandas) (2.9.0.post0)\n", + "Requirement already satisfied: pytz>=2020.1 in /home/cghassem/miniconda3/envs/test/lib/python3.10/site-packages (from pandas) (2024.1)\n", + "Requirement already satisfied: tzdata>=2022.7 in /home/cghassem/miniconda3/envs/test/lib/python3.10/site-packages (from pandas) (2024.1)\n", + "Requirement already satisfied: six>=1.5 in /home/cghassem/miniconda3/envs/test/lib/python3.10/site-packages (from python-dateutil>=2.8.2->pandas) (1.16.0)\n" + ] + } + ], + "source": [ + "!pip install pandas" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## **Slurm-based cluster**: Reproducing paper data\n", + "Slurm-based scripts for cluster are available:\n", + "- Launch if needed \n", + " - [01_job_retrieve_bigg_sbml.sh](../../scripts/plafrim_cluster/01_job_retrieve_bigg_sbml.sh): `sbatch 01_job_retrieve_bigg_sbml.sh`\n", + " - or copy your local files into you cluster\n", + "- Change **_source_** variable by the path of your conda environement with seed2lp installed in files: \n", + " - [02_01_job_get_objective.sh](../../scripts/plafrim_cluster/02_01_job_get_objective.sh)\n", + "- launch:\n", + " - [02_01_job_get_objective.sh](../../scripts/plafrim_cluster/02_01_job_get_objective.sh): `sbatch 02_01_job_get_objective.sh` " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## **LAUNCH**\n", + "\n", + "The python script is available on directory **_scripts_**: [02_get_objective.py](../../scripts/02_get_objective.py)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Variable to change (if wanted)" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "analyse_dir = \"../../analyses\"\n", + "data_dir = f\"{analyse_dir}/data/\"\n", + "sbml_dir = f\"{data_dir}/bigg/sbml\"\n", + "objective_dir = f\"{data_dir}/objective\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Execute" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "from os import path, makedirs" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "iCN900\n", + "iEC1364_W\n", + "STM_v1_0\n", + "iECDH10B_1368\n", + "iEcolC_1368\n", + "iEC1349_Crooks\n", + "iIS312_Epimastigote\n", + "iIS312_Amastigote\n", + "iEcE24377_1341\n", + "iEcDH1_1363\n", + "e_coli_core\n", + "iECED1_1282\n", + "iECIAI1_1343\n", + "iAM_Pb448\n", + "iEC1372_W3110\n", + "iLJ478\n", + "iECs_1301\n", + "iSbBS512_1146\n", + "iECO26_1355\n", + "iNF517\n", + "iECH74115_1262\n", + "iS_1188\n", + "iEcHS_1320\n", + "iJN746\n", + "iAPECO1_1312\n", + "iAM_Pk459\n", + "Recon3D\n", + "iAM_Pf480\n", + "iIS312_Trypomastigote\n", + "iECSF_1327\n", + "iRC1080\n", + "iZ_1308\n", + "iUTI89_1310\n", + "iML1515\n", + "iAF692\n", + "iSFV_1184\n", + "ic_1306\n", + "iND750\n", + "iUMN146_1321\n", + "iSBO_1134\n", + "iBWG_1329\n", + "iIT341\n", + "iJB785\n", + "iY75_1357\n", + "iCHOv1_DG44\n", + "iSDY_1059\n", + "iECSP_1301\n", + "iYO844\n", + "iETEC_1333\n", + "iAB_RBC_283\n", + "iLF82_1304\n", + "iECOK1_1307\n", + "iJR904\n", + "iAF1260\n", + "iECIAI39_1322\n", + "iNRG857_1313\n", + "iPC815\n", + "iEC1344_C\n", + "iAF1260b\n", + "iAM_Pv461\n", + "iSSON_1240\n", + "iEK1008\n", + "iECP_1309\n", + "iB21_1397\n", + "iCN718\n", + "iIS312\n", + "RECON1\n", + "iECABU_c1320\n", + "iEC1368_DH5a\n", + "iYS1720\n", + "iJN678\n", + "iECW_1372\n", + "iECBD_1354\n", + "iYS854\n", + "iECS88_1305\n", + "iMM1415\n", + "iYL1228\n", + "iECD_1391\n", + "iEC55989_1330\n", + "iAM_Pc455\n", + "iJN1463\n", + "iSB619\n", + "iG2583_1286\n", + "iAF987\n", + "iECO111_1330\n", + "iECUMN_1333\n", + "iEcSMS35_1347\n", + "iCHOv1\n", + "iUMNK88_1353\n", + "iEKO11_1354\n", + "iNJ661\n", + "iSFxv_1172\n", + "iJO1366\n", + "iMM904\n", + "iSF_1195\n", + "iECB_1328\n", + "iECO103_1326\n", + "iEC1356_Bl21DE3\n", + "iWFL_1372\n", + "iECSE_1348\n", + "iHN637\n", + "iSynCJ816\n", + "iECDH1ME8569_1439\n", + "iE2348C_1286\n", + "iLB1027_lipid\n", + "iEC042_1314\n", + "iECNA114_1301\n" + ] + } + ], + "source": [ + "file = \"../../scripts/02_get_objective.py\"\n", + "\n", + "if not path.isdir(objective_dir):\n", + " makedirs(objective_dir)\n", + " \n", + "!python {file} {sbml_dir} {objective_dir}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## For launching precursor\n", + "Precursor has a target mode and needs a target file including targetted metabolites. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## **Slurm-based cluster**: Reproducing paper data\n", + "Slurm-based scripts for cluster are available:\n", + "- Launch if needed \n", + " - [01_job_retrieve_bigg_sbml.sh](../../scripts/plafrim_cluster/01_job_retrieve_bigg_sbml.sh): `sbatch 01_job_retrieve_bigg_sbml.sh`\n", + " - or copy your local files into you cluster\n", + "- Change **_source_** variable by the path of your conda environement with seed2lp installed in files: \n", + " - [02_02_job_get_targets.sh](../../scripts/plafrim_cluster/02_02_job_get_targets.sh)\n", + "- launch:\n", + " - [02_02_job_get_targets.sh](../../scripts/plafrim_cluster/02_02_job_get_targets.sh): `sbatch 02_02_job_get_targets.sh` " + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "from os import listdir" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This run might take more than 10 min" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: iCN900\n", + "\n", + "____________________________________________\n", + "\n", + " TARGETS \n", + " FOR TARGET MODE AND FBA \n", + "____________________________________________\n", + "\n", + "Targets set:\n", + " Reactant of objective reaction\n", + " from target file\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " OBJECTVE \n", + " FOR HYBRID \n", + "____________________________________________\n", + "\n", + "Objective set:\n", + " Objective reaction from target file\n", + "\n", + "\n", + "Objective : R_BIOMASS__5\n", + "\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " NETWORK \n", + "____________________________________________\n", + "\n", + "Import reaction: Kept\n", + "Product of import reaction set as seed: No\n", + "Targets can be seeds: No\n", + "Accumulation: Forbidden\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + "List of targets: ['M_CELLWALL_c', 'M_DNA_c', 'M_LIPID_c', 'M_PROTEIN_c', 'M_RNA_c', 'M_SOLPOOL_c', 'M_atp_c', 'M_h2o_c']\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: iEC1364_W\n", + "\n", + "____________________________________________\n", + "\n", + " TARGETS \n", + " FOR TARGET MODE AND FBA \n", + "____________________________________________\n", + "\n", + "Targets set:\n", + " Reactant of objective reaction\n", + " from target file\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " OBJECTVE \n", + " FOR HYBRID \n", + "____________________________________________\n", + "\n", + "Objective set:\n", + " Objective reaction from target file\n", + "\n", + "\n", + "Objective : R_BIOMASS_Ec_iJO1366_core_53p95M\n", + "\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " NETWORK \n", + "____________________________________________\n", + "\n", + "Import reaction: Kept\n", + "Product of import reaction set as seed: No\n", + "Targets can be seeds: No\n", + "Accumulation: Forbidden\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + "List of targets: ['M_10fthf_c', 'M_2fe2s_c', 'M_2ohph_c', 'M_4fe4s_c', 'M_ala__L_c', 'M_amet_c', 'M_arg__L_c', 'M_asn__L_c', 'M_asp__L_c', 'M_atp_c', 'M_bmocogdp_c', 'M_btn_c', 'M_ca2_c', 'M_cl_c', 'M_coa_c', 'M_cobalt2_c', 'M_ctp_c', 'M_cu2_c', 'M_cys__L_c', 'M_datp_c', 'M_dctp_c', 'M_dgtp_c', 'M_dttp_c', 'M_fad_c', 'M_fe2_c', 'M_fe3_c', 'M_gln__L_c', 'M_glu__L_c', 'M_gly_c', 'M_gtp_c', 'M_h2o_c', 'M_his__L_c', 'M_ile__L_c', 'M_k_c', 'M_kdo2lipid4_e', 'M_leu__L_c', 'M_lys__L_c', 'M_met__L_c', 'M_mg2_c', 'M_mlthf_c', 'M_mn2_c', 'M_mobd_c', 'M_murein5px4p_p', 'M_nad_c', 'M_nadp_c', 'M_nh4_c', 'M_ni2_c', 'M_pe160_c', 'M_pe160_p', 'M_pe161_c', 'M_pe161_p', 'M_phe__L_c', 'M_pheme_c', 'M_pro__L_c', 'M_pydx5p_c', 'M_ribflv_c', 'M_ser__L_c', 'M_sheme_c', 'M_so4_c', 'M_thf_c', 'M_thmpp_c', 'M_thr__L_c', 'M_trp__L_c', 'M_tyr__L_c', 'M_udcpdp_c', 'M_utp_c', 'M_val__L_c', 'M_zn2_c']\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: STM_v1_0\n", + "\n", + "____________________________________________\n", + "\n", + " TARGETS \n", + " FOR TARGET MODE AND FBA \n", + "____________________________________________\n", + "\n", + "Targets set:\n", + " Reactant of objective reaction\n", + " from target file\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " OBJECTVE \n", + " FOR HYBRID \n", + "____________________________________________\n", + "\n", + "Objective set:\n", + " Objective reaction from target file\n", + "\n", + "\n", + "Objective : R_BIOMASS_iRR1083_metals\n", + "\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " NETWORK \n", + "____________________________________________\n", + "\n", + "Import reaction: Kept\n", + "Product of import reaction set as seed: No\n", + "Targets can be seeds: No\n", + "Accumulation: Forbidden\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + "List of targets: ['M_12dgr2_ST_p', 'M_5mthf_c', 'M_accoa_c', 'M_ala__L_c', 'M_amp_c', 'M_arg__L_c', 'M_asn__L_c', 'M_asp__L_c', 'M_atp_c', 'M_ca2_c', 'M_cl_c', 'M_clpn2_ST_p', 'M_coa_c', 'M_cobalt2_c', 'M_colipaOA_e', 'M_ctp_c', 'M_cu2_c', 'M_cys__L_c', 'M_datp_c', 'M_dctp_c', 'M_dgtp_c', 'M_dttp_c', 'M_fad_c', 'M_fe2_c', 'M_gln__L_c', 'M_glu__L_c', 'M_gly_c', 'M_glycogen_c', 'M_gtp_c', 'M_h2o_c', 'M_his__L_c', 'M_ile__L_c', 'M_k_c', 'M_leu__L_c', 'M_lys__L_c', 'M_met__L_c', 'M_mg2_c', 'M_mn2_c', 'M_mobd_c', 'M_nad_c', 'M_nadh_c', 'M_nadp_c', 'M_nadph_c', 'M_pa2_ST_p', 'M_pe2_ST_p', 'M_peptido_ST_p', 'M_pg2_ST_p', 'M_phe__L_c', 'M_pro__L_c', 'M_ps2_ST_p', 'M_ptrc_c', 'M_ser__L_c', 'M_sheme_c', 'M_spmd_c', 'M_succoa_c', 'M_thr__L_c', 'M_trp__L_c', 'M_tyr__L_c', 'M_udpg_c', 'M_utp_c', 'M_val__L_c', 'M_zn2_c']\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: iECDH10B_1368\n", + "\n", + "____________________________________________\n", + "\n", + " TARGETS \n", + " FOR TARGET MODE AND FBA \n", + "____________________________________________\n", + "\n", + "Targets set:\n", + " Reactant of objective reaction\n", + " from target file\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " OBJECTVE \n", + " FOR HYBRID \n", + "____________________________________________\n", + "\n", + "Objective set:\n", + " Objective reaction from target file\n", + "\n", + "\n", + "Objective : R_BIOMASS_Ec_iJO1366_core_53p95M\n", + "\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " NETWORK \n", + "____________________________________________\n", + "\n", + "Import reaction: Kept\n", + "Product of import reaction set as seed: No\n", + "Targets can be seeds: No\n", + "Accumulation: Forbidden\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + "List of targets: ['M_10fthf_c', 'M_2fe2s_c', 'M_2ohph_c', 'M_4fe4s_c', 'M_ala__L_c', 'M_amet_c', 'M_arg__L_c', 'M_asn__L_c', 'M_asp__L_c', 'M_atp_c', 'M_bmocogdp_c', 'M_btn_c', 'M_ca2_c', 'M_cl_c', 'M_coa_c', 'M_cobalt2_c', 'M_ctp_c', 'M_cu2_c', 'M_cys__L_c', 'M_datp_c', 'M_dctp_c', 'M_dgtp_c', 'M_dttp_c', 'M_fad_c', 'M_fe2_c', 'M_fe3_c', 'M_gln__L_c', 'M_glu__L_c', 'M_gly_c', 'M_gtp_c', 'M_h2o_c', 'M_his__L_c', 'M_ile__L_c', 'M_k_c', 'M_kdo2lipid4_e', 'M_leu__L_c', 'M_lys__L_c', 'M_met__L_c', 'M_mg2_c', 'M_mlthf_c', 'M_mn2_c', 'M_mobd_c', 'M_murein5px4p_p', 'M_nad_c', 'M_nadp_c', 'M_nh4_c', 'M_ni2_c', 'M_pe160_c', 'M_pe160_p', 'M_pe161_c', 'M_pe161_p', 'M_phe__L_c', 'M_pheme_c', 'M_pro__L_c', 'M_pydx5p_c', 'M_ribflv_c', 'M_ser__L_c', 'M_sheme_c', 'M_so4_c', 'M_thf_c', 'M_thmpp_c', 'M_thr__L_c', 'M_trp__L_c', 'M_tyr__L_c', 'M_udcpdp_c', 'M_utp_c', 'M_val__L_c', 'M_zn2_c']\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: iEcolC_1368\n", + "\n", + "____________________________________________\n", + "\n", + " TARGETS \n", + " FOR TARGET MODE AND FBA \n", + "____________________________________________\n", + "\n", + "Targets set:\n", + " Reactant of objective reaction\n", + " from target file\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " OBJECTVE \n", + " FOR HYBRID \n", + "____________________________________________\n", + "\n", + "Objective set:\n", + " Objective reaction from target file\n", + "\n", + "\n", + "Objective : R_BIOMASS_Ec_iJO1366_core_53p95M\n", + "\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " NETWORK \n", + "____________________________________________\n", + "\n", + "Import reaction: Kept\n", + "Product of import reaction set as seed: No\n", + "Targets can be seeds: No\n", + "Accumulation: Forbidden\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + "List of targets: ['M_10fthf_c', 'M_2fe2s_c', 'M_2ohph_c', 'M_4fe4s_c', 'M_ala__L_c', 'M_amet_c', 'M_arg__L_c', 'M_asn__L_c', 'M_asp__L_c', 'M_atp_c', 'M_bmocogdp_c', 'M_btn_c', 'M_ca2_c', 'M_cl_c', 'M_coa_c', 'M_cobalt2_c', 'M_ctp_c', 'M_cu2_c', 'M_cys__L_c', 'M_datp_c', 'M_dctp_c', 'M_dgtp_c', 'M_dttp_c', 'M_fad_c', 'M_fe2_c', 'M_fe3_c', 'M_gln__L_c', 'M_glu__L_c', 'M_gly_c', 'M_gtp_c', 'M_h2o_c', 'M_his__L_c', 'M_ile__L_c', 'M_k_c', 'M_kdo2lipid4_e', 'M_leu__L_c', 'M_lys__L_c', 'M_met__L_c', 'M_mg2_c', 'M_mlthf_c', 'M_mn2_c', 'M_mobd_c', 'M_murein5px4p_p', 'M_nad_c', 'M_nadp_c', 'M_nh4_c', 'M_ni2_c', 'M_pe160_c', 'M_pe160_p', 'M_pe161_c', 'M_pe161_p', 'M_phe__L_c', 'M_pheme_c', 'M_pro__L_c', 'M_pydx5p_c', 'M_ribflv_c', 'M_ser__L_c', 'M_sheme_c', 'M_so4_c', 'M_thf_c', 'M_thmpp_c', 'M_thr__L_c', 'M_trp__L_c', 'M_tyr__L_c', 'M_udcpdp_c', 'M_utp_c', 'M_val__L_c', 'M_zn2_c']\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: iEC1349_Crooks\n", + "\n", + "____________________________________________\n", + "\n", + " TARGETS \n", + " FOR TARGET MODE AND FBA \n", + "____________________________________________\n", + "\n", + "Targets set:\n", + " Reactant of objective reaction\n", + " from target file\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " OBJECTVE \n", + " FOR HYBRID \n", + "____________________________________________\n", + "\n", + "Objective set:\n", + " Objective reaction from target file\n", + "\n", + "\n", + "Objective : R_BIOMASS_Ec_iJO1366_core_53p95M\n", + "\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " NETWORK \n", + "____________________________________________\n", + "\n", + "Import reaction: Kept\n", + "Product of import reaction set as seed: No\n", + "Targets can be seeds: No\n", + "Accumulation: Forbidden\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + "List of targets: ['M_10fthf_c', 'M_2fe2s_c', 'M_2ohph_c', 'M_4fe4s_c', 'M_ala__L_c', 'M_amet_c', 'M_arg__L_c', 'M_asn__L_c', 'M_asp__L_c', 'M_atp_c', 'M_bmocogdp_c', 'M_btn_c', 'M_ca2_c', 'M_cl_c', 'M_coa_c', 'M_cobalt2_c', 'M_ctp_c', 'M_cu2_c', 'M_cys__L_c', 'M_datp_c', 'M_dctp_c', 'M_dgtp_c', 'M_dttp_c', 'M_fad_c', 'M_fe2_c', 'M_fe3_c', 'M_gln__L_c', 'M_glu__L_c', 'M_gly_c', 'M_gtp_c', 'M_h2o_c', 'M_his__L_c', 'M_ile__L_c', 'M_k_c', 'M_kdo2lipid4_e', 'M_leu__L_c', 'M_lys__L_c', 'M_met__L_c', 'M_mg2_c', 'M_mlthf_c', 'M_mn2_c', 'M_mobd_c', 'M_murein5px4p_p', 'M_nad_c', 'M_nadp_c', 'M_nh4_c', 'M_ni2_c', 'M_pe160_c', 'M_pe160_p', 'M_pe161_c', 'M_pe161_p', 'M_phe__L_c', 'M_pheme_c', 'M_pro__L_c', 'M_pydx5p_c', 'M_ribflv_c', 'M_ser__L_c', 'M_sheme_c', 'M_so4_c', 'M_thf_c', 'M_thmpp_c', 'M_thr__L_c', 'M_trp__L_c', 'M_tyr__L_c', 'M_udcpdp_c', 'M_utp_c', 'M_val__L_c', 'M_zn2_c']\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: iIS312_Epimastigote\n", + "\n", + "____________________________________________\n", + "\n", + " TARGETS \n", + " FOR TARGET MODE AND FBA \n", + "____________________________________________\n", + "\n", + "Targets set:\n", + " Reactant of objective reaction\n", + " from target file\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " OBJECTVE \n", + " FOR HYBRID \n", + "____________________________________________\n", + "\n", + "Objective set:\n", + " Objective reaction from target file\n", + "\n", + "\n", + "Objective : R_BIOMASS_t_cruzi\n", + "\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " NETWORK \n", + "____________________________________________\n", + "\n", + "Import reaction: Kept\n", + "Product of import reaction set as seed: No\n", + "Targets can be seeds: No\n", + "Accumulation: Forbidden\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + "List of targets: ['M_3pg_c', 'M_accoa_c', 'M_akg_c', 'M_ala__L_c', 'M_arg__L_c', 'M_asn__L_c', 'M_asp__L_c', 'M_atp_c', 'M_cys__L_c', 'M_e4p_c', 'M_ergst_c', 'M_g3p_c', 'M_g6p_B_c', 'M_gln__L_c', 'M_glu__L_c', 'M_gly_c', 'M_gmp_c', 'M_h2o_c', 'M_his__L_c', 'M_ile__L_c', 'M_leu__L_c', 'M_lys__L_c', 'M_met__L_c', 'M_nad_c', 'M_nadph_c', 'M_nh4_c', 'M_oaa_c', 'M_pep_c', 'M_phe__L_c', 'M_pro__L_c', 'M_pyr_c', 'M_r5p_c', 'M_ser__L_c', 'M_thr__L_c', 'M_ump_c', 'M_val__L_c']\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: iIS312_Amastigote\n", + "\n", + "____________________________________________\n", + "\n", + " TARGETS \n", + " FOR TARGET MODE AND FBA \n", + "____________________________________________\n", + "\n", + "Targets set:\n", + " Reactant of objective reaction\n", + " from target file\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " OBJECTVE \n", + " FOR HYBRID \n", + "____________________________________________\n", + "\n", + "Objective set:\n", + " Objective reaction from target file\n", + "\n", + "\n", + "Objective : R_BIOMASS_t_cruzi\n", + "\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " NETWORK \n", + "____________________________________________\n", + "\n", + "Import reaction: Kept\n", + "Product of import reaction set as seed: No\n", + "Targets can be seeds: No\n", + "Accumulation: Forbidden\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + "List of targets: ['M_3pg_c', 'M_accoa_c', 'M_akg_c', 'M_ala__L_c', 'M_arg__L_c', 'M_asn__L_c', 'M_asp__L_c', 'M_atp_c', 'M_cys__L_c', 'M_e4p_c', 'M_ergst_c', 'M_g3p_c', 'M_g6p_B_c', 'M_gln__L_c', 'M_glu__L_c', 'M_gly_c', 'M_gmp_c', 'M_h2o_c', 'M_his__L_c', 'M_ile__L_c', 'M_leu__L_c', 'M_lys__L_c', 'M_met__L_c', 'M_nad_c', 'M_nadph_c', 'M_nh4_c', 'M_oaa_c', 'M_pep_c', 'M_phe__L_c', 'M_pro__L_c', 'M_pyr_c', 'M_r5p_c', 'M_ser__L_c', 'M_thr__L_c', 'M_ump_c', 'M_val__L_c']\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: iEcE24377_1341\n", + "\n", + "____________________________________________\n", + "\n", + " TARGETS \n", + " FOR TARGET MODE AND FBA \n", + "____________________________________________\n", + "\n", + "Targets set:\n", + " Reactant of objective reaction\n", + " from target file\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " OBJECTVE \n", + " FOR HYBRID \n", + "____________________________________________\n", + "\n", + "Objective set:\n", + " Objective reaction from target file\n", + "\n", + "\n", + "Objective : R_BIOMASS_Ec_iJO1366_core_53p95M\n", + "\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " NETWORK \n", + "____________________________________________\n", + "\n", + "Import reaction: Kept\n", + "Product of import reaction set as seed: No\n", + "Targets can be seeds: No\n", + "Accumulation: Forbidden\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + "List of targets: ['M_10fthf_c', 'M_2fe2s_c', 'M_2ohph_c', 'M_4fe4s_c', 'M_ala__L_c', 'M_amet_c', 'M_arg__L_c', 'M_asn__L_c', 'M_asp__L_c', 'M_atp_c', 'M_bmocogdp_c', 'M_btn_c', 'M_ca2_c', 'M_cl_c', 'M_coa_c', 'M_cobalt2_c', 'M_ctp_c', 'M_cu2_c', 'M_cys__L_c', 'M_datp_c', 'M_dctp_c', 'M_dgtp_c', 'M_dttp_c', 'M_fad_c', 'M_fe2_c', 'M_fe3_c', 'M_gln__L_c', 'M_glu__L_c', 'M_gly_c', 'M_gtp_c', 'M_h2o_c', 'M_his__L_c', 'M_ile__L_c', 'M_k_c', 'M_kdo2lipid4_e', 'M_leu__L_c', 'M_lys__L_c', 'M_met__L_c', 'M_mg2_c', 'M_mlthf_c', 'M_mn2_c', 'M_mobd_c', 'M_murein5px4p_p', 'M_nad_c', 'M_nadp_c', 'M_nh4_c', 'M_ni2_c', 'M_pe160_c', 'M_pe160_p', 'M_pe161_c', 'M_pe161_p', 'M_phe__L_c', 'M_pheme_c', 'M_pro__L_c', 'M_pydx5p_c', 'M_ribflv_c', 'M_ser__L_c', 'M_sheme_c', 'M_so4_c', 'M_thf_c', 'M_thmpp_c', 'M_thr__L_c', 'M_trp__L_c', 'M_tyr__L_c', 'M_udcpdp_c', 'M_utp_c', 'M_val__L_c', 'M_zn2_c']\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: iEcDH1_1363\n", + "\n", + "____________________________________________\n", + "\n", + " TARGETS \n", + " FOR TARGET MODE AND FBA \n", + "____________________________________________\n", + "\n", + "Targets set:\n", + " Reactant of objective reaction\n", + " from target file\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " OBJECTVE \n", + " FOR HYBRID \n", + "____________________________________________\n", + "\n", + "Objective set:\n", + " Objective reaction from target file\n", + "\n", + "\n", + "Objective : R_BIOMASS_Ec_iJO1366_core_53p95M\n", + "\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " NETWORK \n", + "____________________________________________\n", + "\n", + "Import reaction: Kept\n", + "Product of import reaction set as seed: No\n", + "Targets can be seeds: No\n", + "Accumulation: Forbidden\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + "List of targets: ['M_10fthf_c', 'M_2fe2s_c', 'M_2ohph_c', 'M_4fe4s_c', 'M_ala__L_c', 'M_amet_c', 'M_arg__L_c', 'M_asn__L_c', 'M_asp__L_c', 'M_atp_c', 'M_bmocogdp_c', 'M_btn_c', 'M_ca2_c', 'M_cl_c', 'M_coa_c', 'M_cobalt2_c', 'M_ctp_c', 'M_cu2_c', 'M_cys__L_c', 'M_datp_c', 'M_dctp_c', 'M_dgtp_c', 'M_dttp_c', 'M_fad_c', 'M_fe2_c', 'M_fe3_c', 'M_gln__L_c', 'M_glu__L_c', 'M_gly_c', 'M_gtp_c', 'M_h2o_c', 'M_his__L_c', 'M_ile__L_c', 'M_k_c', 'M_kdo2lipid4_e', 'M_leu__L_c', 'M_lys__L_c', 'M_met__L_c', 'M_mg2_c', 'M_mlthf_c', 'M_mn2_c', 'M_mobd_c', 'M_murein5px4p_p', 'M_nad_c', 'M_nadp_c', 'M_nh4_c', 'M_ni2_c', 'M_pe160_c', 'M_pe160_p', 'M_pe161_c', 'M_pe161_p', 'M_phe__L_c', 'M_pheme_c', 'M_pro__L_c', 'M_pydx5p_c', 'M_ribflv_c', 'M_ser__L_c', 'M_sheme_c', 'M_so4_c', 'M_thf_c', 'M_thmpp_c', 'M_thr__L_c', 'M_trp__L_c', 'M_tyr__L_c', 'M_udcpdp_c', 'M_utp_c', 'M_val__L_c', 'M_zn2_c']\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: e_coli_core\n", + "\n", + "____________________________________________\n", + "\n", + " TARGETS \n", + " FOR TARGET MODE AND FBA \n", + "____________________________________________\n", + "\n", + "Targets set:\n", + " Reactant of objective reaction\n", + " from target file\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " OBJECTVE \n", + " FOR HYBRID \n", + "____________________________________________\n", + "\n", + "Objective set:\n", + " Objective reaction from target file\n", + "\n", + "\n", + "Objective : R_BIOMASS_Ecoli_core_w_GAM\n", + "\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " NETWORK \n", + "____________________________________________\n", + "\n", + "Import reaction: Kept\n", + "Product of import reaction set as seed: No\n", + "Targets can be seeds: No\n", + "Accumulation: Forbidden\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + "List of targets: ['M_3pg_c', 'M_accoa_c', 'M_atp_c', 'M_e4p_c', 'M_f6p_c', 'M_g3p_c', 'M_g6p_c', 'M_gln__L_c', 'M_glu__L_c', 'M_h2o_c', 'M_nad_c', 'M_nadph_c', 'M_oaa_c', 'M_pep_c', 'M_pyr_c', 'M_r5p_c']\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: iECED1_1282\n", + "\n", + "____________________________________________\n", + "\n", + " TARGETS \n", + " FOR TARGET MODE AND FBA \n", + "____________________________________________\n", + "\n", + "Targets set:\n", + " Reactant of objective reaction\n", + " from target file\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " OBJECTVE \n", + " FOR HYBRID \n", + "____________________________________________\n", + "\n", + "Objective set:\n", + " Objective reaction from target file\n", + "\n", + "\n", + "Objective : R_BIOMASS_Ec_iJO1366_core_53p95M\n", + "\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " NETWORK \n", + "____________________________________________\n", + "\n", + "Import reaction: Kept\n", + "Product of import reaction set as seed: No\n", + "Targets can be seeds: No\n", + "Accumulation: Forbidden\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + "List of targets: ['M_10fthf_c', 'M_2fe2s_c', 'M_2ohph_c', 'M_4fe4s_c', 'M_ala__L_c', 'M_amet_c', 'M_arg__L_c', 'M_asn__L_c', 'M_asp__L_c', 'M_atp_c', 'M_bmocogdp_c', 'M_btn_c', 'M_ca2_c', 'M_cl_c', 'M_coa_c', 'M_cobalt2_c', 'M_ctp_c', 'M_cu2_c', 'M_cys__L_c', 'M_datp_c', 'M_dctp_c', 'M_dgtp_c', 'M_dttp_c', 'M_fad_c', 'M_fe2_c', 'M_fe3_c', 'M_gln__L_c', 'M_glu__L_c', 'M_gly_c', 'M_gtp_c', 'M_h2o_c', 'M_his__L_c', 'M_ile__L_c', 'M_k_c', 'M_kdo2lipid4_e', 'M_leu__L_c', 'M_lys__L_c', 'M_met__L_c', 'M_mg2_c', 'M_mlthf_c', 'M_mn2_c', 'M_mobd_c', 'M_murein5px4p_p', 'M_nad_c', 'M_nadp_c', 'M_nh4_c', 'M_ni2_c', 'M_pe160_c', 'M_pe160_p', 'M_pe161_c', 'M_pe161_p', 'M_phe__L_c', 'M_pheme_c', 'M_pro__L_c', 'M_pydx5p_c', 'M_ribflv_c', 'M_ser__L_c', 'M_sheme_c', 'M_so4_c', 'M_thf_c', 'M_thmpp_c', 'M_thr__L_c', 'M_trp__L_c', 'M_tyr__L_c', 'M_udcpdp_c', 'M_utp_c', 'M_val__L_c', 'M_zn2_c']\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: iECIAI1_1343\n", + "\n", + "____________________________________________\n", + "\n", + " TARGETS \n", + " FOR TARGET MODE AND FBA \n", + "____________________________________________\n", + "\n", + "Targets set:\n", + " Reactant of objective reaction\n", + " from target file\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " OBJECTVE \n", + " FOR HYBRID \n", + "____________________________________________\n", + "\n", + "Objective set:\n", + " Objective reaction from target file\n", + "\n", + "\n", + "Objective : R_BIOMASS_Ec_iJO1366_core_53p95M\n", + "\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " NETWORK \n", + "____________________________________________\n", + "\n", + "Import reaction: Kept\n", + "Product of import reaction set as seed: No\n", + "Targets can be seeds: No\n", + "Accumulation: Forbidden\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + "List of targets: ['M_10fthf_c', 'M_2fe2s_c', 'M_2ohph_c', 'M_4fe4s_c', 'M_ala__L_c', 'M_amet_c', 'M_arg__L_c', 'M_asn__L_c', 'M_asp__L_c', 'M_atp_c', 'M_bmocogdp_c', 'M_btn_c', 'M_ca2_c', 'M_cl_c', 'M_coa_c', 'M_cobalt2_c', 'M_ctp_c', 'M_cu2_c', 'M_cys__L_c', 'M_datp_c', 'M_dctp_c', 'M_dgtp_c', 'M_dttp_c', 'M_fad_c', 'M_fe2_c', 'M_fe3_c', 'M_gln__L_c', 'M_glu__L_c', 'M_gly_c', 'M_gtp_c', 'M_h2o_c', 'M_his__L_c', 'M_ile__L_c', 'M_k_c', 'M_kdo2lipid4_e', 'M_leu__L_c', 'M_lys__L_c', 'M_met__L_c', 'M_mg2_c', 'M_mlthf_c', 'M_mn2_c', 'M_mobd_c', 'M_murein5px4p_p', 'M_nad_c', 'M_nadp_c', 'M_nh4_c', 'M_ni2_c', 'M_pe160_c', 'M_pe160_p', 'M_pe161_c', 'M_pe161_p', 'M_phe__L_c', 'M_pheme_c', 'M_pro__L_c', 'M_pydx5p_c', 'M_ribflv_c', 'M_ser__L_c', 'M_sheme_c', 'M_so4_c', 'M_thf_c', 'M_thmpp_c', 'M_thr__L_c', 'M_trp__L_c', 'M_tyr__L_c', 'M_udcpdp_c', 'M_utp_c', 'M_val__L_c', 'M_zn2_c']\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: iAM_Pb448\n", + "\n", + "____________________________________________\n", + "\n", + " TARGETS \n", + " FOR TARGET MODE AND FBA \n", + "____________________________________________\n", + "\n", + "Targets set:\n", + " Reactant of objective reaction\n", + " from target file\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " OBJECTVE \n", + " FOR HYBRID \n", + "____________________________________________\n", + "\n", + "Objective set:\n", + " Objective reaction from target file\n", + "\n", + "\n", + "Objective : R_BIOMASS__4\n", + "\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " NETWORK \n", + "____________________________________________\n", + "\n", + "Import reaction: Kept\n", + "Product of import reaction set as seed: No\n", + "Targets can be seeds: No\n", + "Accumulation: Forbidden\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + "List of targets: ['M_5thf_c', 'M_ala__L_c', 'M_arg__L_c', 'M_asn__L_c', 'M_asp__L_c', 'M_atp_c', 'M_clpn_pf_16_0_18_1_16_0_18_1_m', 'M_coa_c', 'M_ctp_c', 'M_cys__L_c', 'M_datp_c', 'M_dctp_c', 'M_dgtp_c', 'M_dttp_c', 'M_fad_c', 'M_gdpfuc_c', 'M_gdpmann_c', 'M_gln__L_c', 'M_glu__L_c', 'M_gly_c', 'M_gthrd_c', 'M_gtp_c', 'M_h2o_c', 'M_his__L_c', 'M_ile__L_c', 'M_leu__L_c', 'M_lipopb_m', 'M_lys__L_c', 'M_mem3gacpail_pf_18_0_18_1_16_0_r', 'M_met__L_c', 'M_nad_c', 'M_nadp_c', 'M_pail_pf_16_0_16_0_c', 'M_pail_pf_16_0_18_0_c', 'M_pail_pf_16_0_18_1_c', 'M_pail_pf_18_0_18_0_c', 'M_pail_pf_18_0_18_1_c', 'M_pail_pf_18_1_18_1_c', 'M_pchol_pf_16_0_16_0_c', 'M_pchol_pf_16_0_18_0_c', 'M_pchol_pf_16_0_18_1_c', 'M_pchol_pf_16_0_18_2_c', 'M_pchol_pf_16_0_20_4_c', 'M_pchol_pf_18_1_18_1_c', 'M_pchol_pf_18_1_18_2_c', 'M_pchol_pf_18_2_18_2_c', 'M_pe_pf_16_0_16_0_c', 'M_pe_pf_16_0_18_1_c', 'M_pe_pf_16_0_18_2_c', 'M_pe_pf_18_1_18_1_c', 'M_pe_pf_18_1_18_2_c', 'M_pe_pf_18_2_18_2_c', 'M_pe_pf_20_4_20_4_c', 'M_phe__L_c', 'M_pheme_c', 'M_pro__L_c', 'M_ps_pf_18_0_18_1_c', 'M_ps_pf_18_0_20_4_c', 'M_ps_pf_18_1_18_1_c', 'M_ps_pf_18_1_20_4_c', 'M_ps_pf_20_4_20_4_c', 'M_ptrc_c', 'M_pydx5p_c', 'M_q8h2_m', 'M_ser__L_c', 'M_sphymyln_pf_18_1_16_0_g', 'M_sphymyln_pf_18_1_18_0_g', 'M_sphymyln_pf_18_1_24_0_g', 'M_spmd_c', 'M_tag_pf_16_0_18_0_18_0_c', 'M_tag_pf_18_0_18_0_18_1_c', 'M_tag_pf_18_0_18_1_16_0_c', 'M_tag_pf_18_1_18_2_18_2_c', 'M_thmpp_c', 'M_thr__L_c', 'M_trp__L_c', 'M_tyr__L_c', 'M_utp_c', 'M_val__L_c']\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: iEC1372_W3110\n", + "\n", + "____________________________________________\n", + "\n", + " TARGETS \n", + " FOR TARGET MODE AND FBA \n", + "____________________________________________\n", + "\n", + "Targets set:\n", + " Reactant of objective reaction\n", + " from target file\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " OBJECTVE \n", + " FOR HYBRID \n", + "____________________________________________\n", + "\n", + "Objective set:\n", + " Objective reaction from target file\n", + "\n", + "\n", + "Objective : R_BIOMASS_Ec_iJO1366_core_53p95M\n", + "\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " NETWORK \n", + "____________________________________________\n", + "\n", + "Import reaction: Kept\n", + "Product of import reaction set as seed: No\n", + "Targets can be seeds: No\n", + "Accumulation: Forbidden\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + "List of targets: ['M_10fthf_c', 'M_2fe2s_c', 'M_2ohph_c', 'M_4fe4s_c', 'M_ala__L_c', 'M_amet_c', 'M_arg__L_c', 'M_asn__L_c', 'M_asp__L_c', 'M_atp_c', 'M_bmocogdp_c', 'M_btn_c', 'M_ca2_c', 'M_cl_c', 'M_coa_c', 'M_cobalt2_c', 'M_ctp_c', 'M_cu2_c', 'M_cys__L_c', 'M_datp_c', 'M_dctp_c', 'M_dgtp_c', 'M_dttp_c', 'M_fad_c', 'M_fe2_c', 'M_fe3_c', 'M_gln__L_c', 'M_glu__L_c', 'M_gly_c', 'M_gtp_c', 'M_h2o_c', 'M_his__L_c', 'M_ile__L_c', 'M_k_c', 'M_kdo2lipid4_e', 'M_leu__L_c', 'M_lys__L_c', 'M_met__L_c', 'M_mg2_c', 'M_mlthf_c', 'M_mn2_c', 'M_mobd_c', 'M_murein5px4p_p', 'M_nad_c', 'M_nadp_c', 'M_nh4_c', 'M_ni2_c', 'M_pe160_c', 'M_pe160_p', 'M_pe161_c', 'M_pe161_p', 'M_phe__L_c', 'M_pheme_c', 'M_pro__L_c', 'M_pydx5p_c', 'M_ribflv_c', 'M_ser__L_c', 'M_sheme_c', 'M_so4_c', 'M_thf_c', 'M_thmpp_c', 'M_thr__L_c', 'M_trp__L_c', 'M_tyr__L_c', 'M_udcpdp_c', 'M_utp_c', 'M_val__L_c', 'M_zn2_c']\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: iLJ478\n", + "\n", + "____________________________________________\n", + "\n", + " TARGETS \n", + " FOR TARGET MODE AND FBA \n", + "____________________________________________\n", + "\n", + "Targets set:\n", + " Reactant of objective reaction\n", + " from target file\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " OBJECTVE \n", + " FOR HYBRID \n", + "____________________________________________\n", + "\n", + "Objective set:\n", + " Objective reaction from target file\n", + "\n", + "\n", + "Objective : R_BIOMASS_Ecoli_TM\n", + "\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " NETWORK \n", + "____________________________________________\n", + "\n", + "Import reaction: Kept\n", + "Product of import reaction set as seed: No\n", + "Targets can be seeds: No\n", + "Accumulation: Forbidden\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + "List of targets: ['M_5mthf_c', 'M_TM_GL1_c', 'M_TM_GL2_c', 'M_accoa_c', 'M_ala__L_c', 'M_amp_c', 'M_arg__L_c', 'M_asp__L_c', 'M_atp_c', 'M_coa_c', 'M_ctp_c', 'M_cys__L_c', 'M_datp_c', 'M_dctp_c', 'M_dgtp_c', 'M_dmosACP_c', 'M_dmtaACP_c', 'M_dttp_c', 'M_fad_c', 'M_gln__L_c', 'M_glu__L_c', 'M_gly_c', 'M_glycogen_c', 'M_gtp_c', 'M_h2o_c', 'M_his__L_c', 'M_ile__L_c', 'M_leu__L_c', 'M_lys__L_c', 'M_met__L_c', 'M_nad_c', 'M_nadh_c', 'M_nadp_c', 'M_nadph_c', 'M_pa160_c', 'M_peptido_TM_c', 'M_pgp160_c', 'M_phe__L_c', 'M_pro__L_c', 'M_ptrc_c', 'M_ser__L_c', 'M_spmd_c', 'M_succoa_c', 'M_thr__L_c', 'M_trp__L_c', 'M_tyr__L_c', 'M_udcpdp_c', 'M_utp_c', 'M_val__L_c']\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: iECs_1301\n", + "\n", + "____________________________________________\n", + "\n", + " TARGETS \n", + " FOR TARGET MODE AND FBA \n", + "____________________________________________\n", + "\n", + "Targets set:\n", + " Reactant of objective reaction\n", + " from target file\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " OBJECTVE \n", + " FOR HYBRID \n", + "____________________________________________\n", + "\n", + "Objective set:\n", + " Objective reaction from target file\n", + "\n", + "\n", + "Objective : R_BIOMASS_Ec_iJO1366_core_53p95M\n", + "\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " NETWORK \n", + "____________________________________________\n", + "\n", + "Import reaction: Kept\n", + "Product of import reaction set as seed: No\n", + "Targets can be seeds: No\n", + "Accumulation: Forbidden\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + "List of targets: ['M_10fthf_c', 'M_2fe2s_c', 'M_2ohph_c', 'M_4fe4s_c', 'M_ala__L_c', 'M_amet_c', 'M_arg__L_c', 'M_asn__L_c', 'M_asp__L_c', 'M_atp_c', 'M_bmocogdp_c', 'M_btn_c', 'M_ca2_c', 'M_cl_c', 'M_coa_c', 'M_cobalt2_c', 'M_ctp_c', 'M_cu2_c', 'M_cys__L_c', 'M_datp_c', 'M_dctp_c', 'M_dgtp_c', 'M_dttp_c', 'M_fad_c', 'M_fe2_c', 'M_fe3_c', 'M_gln__L_c', 'M_glu__L_c', 'M_gly_c', 'M_gtp_c', 'M_h2o_c', 'M_his__L_c', 'M_ile__L_c', 'M_k_c', 'M_kdo2lipid4_e', 'M_leu__L_c', 'M_lys__L_c', 'M_met__L_c', 'M_mg2_c', 'M_mlthf_c', 'M_mn2_c', 'M_mobd_c', 'M_murein5px4p_p', 'M_nad_c', 'M_nadp_c', 'M_nh4_c', 'M_ni2_c', 'M_pe160_c', 'M_pe160_p', 'M_pe161_c', 'M_pe161_p', 'M_phe__L_c', 'M_pheme_c', 'M_pro__L_c', 'M_pydx5p_c', 'M_ribflv_c', 'M_ser__L_c', 'M_sheme_c', 'M_so4_c', 'M_thf_c', 'M_thmpp_c', 'M_thr__L_c', 'M_trp__L_c', 'M_tyr__L_c', 'M_udcpdp_c', 'M_utp_c', 'M_val__L_c', 'M_zn2_c']\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: iSbBS512_1146\n", + "\n", + "____________________________________________\n", + "\n", + " TARGETS \n", + " FOR TARGET MODE AND FBA \n", + "____________________________________________\n", + "\n", + "Targets set:\n", + " Reactant of objective reaction\n", + " from target file\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " OBJECTVE \n", + " FOR HYBRID \n", + "____________________________________________\n", + "\n", + "Objective set:\n", + " Objective reaction from target file\n", + "\n", + "\n", + "Objective : R_BIOMASS_Ec_iJO1366_core_53p95M\n", + "\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " NETWORK \n", + "____________________________________________\n", + "\n", + "Import reaction: Kept\n", + "Product of import reaction set as seed: No\n", + "Targets can be seeds: No\n", + "Accumulation: Forbidden\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + "List of targets: ['M_10fthf_c', 'M_2fe2s_c', 'M_2ohph_c', 'M_4fe4s_c', 'M_ala__L_c', 'M_amet_c', 'M_arg__L_c', 'M_asn__L_c', 'M_asp__L_c', 'M_atp_c', 'M_bmocogdp_c', 'M_btn_c', 'M_ca2_c', 'M_cl_c', 'M_coa_c', 'M_cobalt2_c', 'M_ctp_c', 'M_cu2_c', 'M_cys__L_c', 'M_datp_c', 'M_dctp_c', 'M_dgtp_c', 'M_dttp_c', 'M_fad_c', 'M_fe2_c', 'M_fe3_c', 'M_gln__L_c', 'M_glu__L_c', 'M_gly_c', 'M_gtp_c', 'M_h2o_c', 'M_his__L_c', 'M_ile__L_c', 'M_k_c', 'M_kdo2lipid4_e', 'M_leu__L_c', 'M_lys__L_c', 'M_met__L_c', 'M_mg2_c', 'M_mlthf_c', 'M_mn2_c', 'M_mobd_c', 'M_murein5px4p_p', 'M_nad_c', 'M_nadp_c', 'M_nh4_c', 'M_ni2_c', 'M_pe160_c', 'M_pe160_p', 'M_pe161_c', 'M_pe161_p', 'M_phe__L_c', 'M_pheme_c', 'M_pro__L_c', 'M_pydx5p_c', 'M_ribflv_c', 'M_ser__L_c', 'M_sheme_c', 'M_so4_c', 'M_thf_c', 'M_thmpp_c', 'M_thr__L_c', 'M_trp__L_c', 'M_tyr__L_c', 'M_udcpdp_c', 'M_utp_c', 'M_val__L_c', 'M_zn2_c']\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: iECO26_1355\n", + "\n", + "____________________________________________\n", + "\n", + " TARGETS \n", + " FOR TARGET MODE AND FBA \n", + "____________________________________________\n", + "\n", + "Targets set:\n", + " Reactant of objective reaction\n", + " from target file\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " OBJECTVE \n", + " FOR HYBRID \n", + "____________________________________________\n", + "\n", + "Objective set:\n", + " Objective reaction from target file\n", + "\n", + "\n", + "Objective : R_BIOMASS_Ec_iJO1366_core_53p95M\n", + "\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " NETWORK \n", + "____________________________________________\n", + "\n", + "Import reaction: Kept\n", + "Product of import reaction set as seed: No\n", + "Targets can be seeds: No\n", + "Accumulation: Forbidden\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + "List of targets: ['M_10fthf_c', 'M_2fe2s_c', 'M_2ohph_c', 'M_4fe4s_c', 'M_ala__L_c', 'M_amet_c', 'M_arg__L_c', 'M_asn__L_c', 'M_asp__L_c', 'M_atp_c', 'M_bmocogdp_c', 'M_btn_c', 'M_ca2_c', 'M_cl_c', 'M_coa_c', 'M_cobalt2_c', 'M_ctp_c', 'M_cu2_c', 'M_cys__L_c', 'M_datp_c', 'M_dctp_c', 'M_dgtp_c', 'M_dttp_c', 'M_fad_c', 'M_fe2_c', 'M_fe3_c', 'M_gln__L_c', 'M_glu__L_c', 'M_gly_c', 'M_gtp_c', 'M_h2o_c', 'M_his__L_c', 'M_ile__L_c', 'M_k_c', 'M_kdo2lipid4_e', 'M_leu__L_c', 'M_lys__L_c', 'M_met__L_c', 'M_mg2_c', 'M_mlthf_c', 'M_mn2_c', 'M_mobd_c', 'M_murein5px4p_p', 'M_nad_c', 'M_nadp_c', 'M_nh4_c', 'M_ni2_c', 'M_pe160_c', 'M_pe160_p', 'M_pe161_c', 'M_pe161_p', 'M_phe__L_c', 'M_pheme_c', 'M_pro__L_c', 'M_pydx5p_c', 'M_ribflv_c', 'M_ser__L_c', 'M_sheme_c', 'M_so4_c', 'M_thf_c', 'M_thmpp_c', 'M_thr__L_c', 'M_trp__L_c', 'M_tyr__L_c', 'M_udcpdp_c', 'M_utp_c', 'M_val__L_c', 'M_zn2_c']\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: iNF517\n", + "\n", + "____________________________________________\n", + "\n", + " TARGETS \n", + " FOR TARGET MODE AND FBA \n", + "____________________________________________\n", + "\n", + "Targets set:\n", + " Reactant of objective reaction\n", + " from target file\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " OBJECTVE \n", + " FOR HYBRID \n", + "____________________________________________\n", + "\n", + "Objective set:\n", + " Objective reaction from target file\n", + "\n", + "\n", + "Objective : R_BIOMASS_LLA\n", + "\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " NETWORK \n", + "____________________________________________\n", + "\n", + "Import reaction: Kept\n", + "Product of import reaction set as seed: No\n", + "Targets can be seeds: No\n", + "Accumulation: Forbidden\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + "List of targets: ['M_CPS_LLA_c', 'M_DNA_LLA_c', 'M_LTAAlaGal_LLA_c', 'M_PG_c', 'M_PROT_LLA_v3_c', 'M_RNA_LLA_c', 'M_atp_c', 'M_clpn_LLA_c', 'M_coa_c', 'M_d12dg_LLA_c', 'M_h2o_c', 'M_lyspg_LLA_c', 'M_m12dg_LLA_c', 'M_nad_c', 'M_pg_LLA_c', 'M_thf_c', 'M_thmpp_c', 'M_udcpdp_c']\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: iECH74115_1262\n", + "\n", + "____________________________________________\n", + "\n", + " TARGETS \n", + " FOR TARGET MODE AND FBA \n", + "____________________________________________\n", + "\n", + "Targets set:\n", + " Reactant of objective reaction\n", + " from target file\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " OBJECTVE \n", + " FOR HYBRID \n", + "____________________________________________\n", + "\n", + "Objective set:\n", + " Objective reaction from target file\n", + "\n", + "\n", + "Objective : R_BIOMASS_Ec_iJO1366_core_53p95M\n", + "\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " NETWORK \n", + "____________________________________________\n", + "\n", + "Import reaction: Kept\n", + "Product of import reaction set as seed: No\n", + "Targets can be seeds: No\n", + "Accumulation: Forbidden\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + "List of targets: ['M_10fthf_c', 'M_2fe2s_c', 'M_2ohph_c', 'M_4fe4s_c', 'M_ala__L_c', 'M_amet_c', 'M_arg__L_c', 'M_asn__L_c', 'M_asp__L_c', 'M_atp_c', 'M_bmocogdp_c', 'M_btn_c', 'M_ca2_c', 'M_cl_c', 'M_coa_c', 'M_cobalt2_c', 'M_ctp_c', 'M_cu2_c', 'M_cys__L_c', 'M_datp_c', 'M_dctp_c', 'M_dgtp_c', 'M_dttp_c', 'M_fad_c', 'M_fe2_c', 'M_fe3_c', 'M_gln__L_c', 'M_glu__L_c', 'M_gly_c', 'M_gtp_c', 'M_h2o_c', 'M_his__L_c', 'M_ile__L_c', 'M_k_c', 'M_kdo2lipid4_e', 'M_leu__L_c', 'M_lys__L_c', 'M_met__L_c', 'M_mg2_c', 'M_mlthf_c', 'M_mn2_c', 'M_mobd_c', 'M_murein5px4p_p', 'M_nad_c', 'M_nadp_c', 'M_nh4_c', 'M_ni2_c', 'M_pe160_c', 'M_pe160_p', 'M_pe161_c', 'M_pe161_p', 'M_phe__L_c', 'M_pheme_c', 'M_pro__L_c', 'M_pydx5p_c', 'M_ribflv_c', 'M_ser__L_c', 'M_sheme_c', 'M_so4_c', 'M_thf_c', 'M_thmpp_c', 'M_thr__L_c', 'M_trp__L_c', 'M_tyr__L_c', 'M_udcpdp_c', 'M_utp_c', 'M_val__L_c', 'M_zn2_c']\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: iS_1188\n", + "\n", + "____________________________________________\n", + "\n", + " TARGETS \n", + " FOR TARGET MODE AND FBA \n", + "____________________________________________\n", + "\n", + "Targets set:\n", + " Reactant of objective reaction\n", + " from target file\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " OBJECTVE \n", + " FOR HYBRID \n", + "____________________________________________\n", + "\n", + "Objective set:\n", + " Objective reaction from target file\n", + "\n", + "\n", + "Objective : R_BIOMASS_Ec_iJO1366_core_53p95M\n", + "\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " NETWORK \n", + "____________________________________________\n", + "\n", + "Import reaction: Kept\n", + "Product of import reaction set as seed: No\n", + "Targets can be seeds: No\n", + "Accumulation: Forbidden\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + "List of targets: ['M_10fthf_c', 'M_2fe2s_c', 'M_2ohph_c', 'M_4fe4s_c', 'M_ala__L_c', 'M_amet_c', 'M_arg__L_c', 'M_asn__L_c', 'M_asp__L_c', 'M_atp_c', 'M_bmocogdp_c', 'M_btn_c', 'M_ca2_c', 'M_cl_c', 'M_coa_c', 'M_cobalt2_c', 'M_ctp_c', 'M_cu2_c', 'M_cys__L_c', 'M_datp_c', 'M_dctp_c', 'M_dgtp_c', 'M_dttp_c', 'M_fad_c', 'M_fe2_c', 'M_fe3_c', 'M_gln__L_c', 'M_glu__L_c', 'M_gly_c', 'M_gtp_c', 'M_h2o_c', 'M_his__L_c', 'M_ile__L_c', 'M_k_c', 'M_kdo2lipid4_e', 'M_leu__L_c', 'M_lys__L_c', 'M_met__L_c', 'M_mg2_c', 'M_mlthf_c', 'M_mn2_c', 'M_mobd_c', 'M_murein5px4p_p', 'M_nad_c', 'M_nadp_c', 'M_nh4_c', 'M_ni2_c', 'M_pe160_c', 'M_pe160_p', 'M_pe161_c', 'M_pe161_p', 'M_phe__L_c', 'M_pheme_c', 'M_pro__L_c', 'M_pydx5p_c', 'M_ribflv_c', 'M_ser__L_c', 'M_sheme_c', 'M_so4_c', 'M_thf_c', 'M_thmpp_c', 'M_thr__L_c', 'M_trp__L_c', 'M_tyr__L_c', 'M_udcpdp_c', 'M_utp_c', 'M_val__L_c', 'M_zn2_c']\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: iEcHS_1320\n", + "\n", + "____________________________________________\n", + "\n", + " TARGETS \n", + " FOR TARGET MODE AND FBA \n", + "____________________________________________\n", + "\n", + "Targets set:\n", + " Reactant of objective reaction\n", + " from target file\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " OBJECTVE \n", + " FOR HYBRID \n", + "____________________________________________\n", + "\n", + "Objective set:\n", + " Objective reaction from target file\n", + "\n", + "\n", + "Objective : R_BIOMASS_Ec_iJO1366_core_53p95M\n", + "\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " NETWORK \n", + "____________________________________________\n", + "\n", + "Import reaction: Kept\n", + "Product of import reaction set as seed: No\n", + "Targets can be seeds: No\n", + "Accumulation: Forbidden\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + "List of targets: ['M_10fthf_c', 'M_2fe2s_c', 'M_2ohph_c', 'M_4fe4s_c', 'M_ala__L_c', 'M_amet_c', 'M_arg__L_c', 'M_asn__L_c', 'M_asp__L_c', 'M_atp_c', 'M_bmocogdp_c', 'M_btn_c', 'M_ca2_c', 'M_cl_c', 'M_coa_c', 'M_cobalt2_c', 'M_ctp_c', 'M_cu2_c', 'M_cys__L_c', 'M_datp_c', 'M_dctp_c', 'M_dgtp_c', 'M_dttp_c', 'M_fad_c', 'M_fe2_c', 'M_fe3_c', 'M_gln__L_c', 'M_glu__L_c', 'M_gly_c', 'M_gtp_c', 'M_h2o_c', 'M_his__L_c', 'M_ile__L_c', 'M_k_c', 'M_kdo2lipid4_e', 'M_leu__L_c', 'M_lys__L_c', 'M_met__L_c', 'M_mg2_c', 'M_mlthf_c', 'M_mn2_c', 'M_mobd_c', 'M_murein5px4p_p', 'M_nad_c', 'M_nadp_c', 'M_nh4_c', 'M_ni2_c', 'M_pe160_c', 'M_pe160_p', 'M_pe161_c', 'M_pe161_p', 'M_phe__L_c', 'M_pheme_c', 'M_pro__L_c', 'M_pydx5p_c', 'M_ribflv_c', 'M_ser__L_c', 'M_sheme_c', 'M_so4_c', 'M_thf_c', 'M_thmpp_c', 'M_thr__L_c', 'M_trp__L_c', 'M_tyr__L_c', 'M_udcpdp_c', 'M_utp_c', 'M_val__L_c', 'M_zn2_c']\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: iJN746\n", + "\n", + "____________________________________________\n", + "\n", + " TARGETS \n", + " FOR TARGET MODE AND FBA \n", + "____________________________________________\n", + "\n", + "Targets set:\n", + " Reactant of objective reaction\n", + " from target file\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " OBJECTVE \n", + " FOR HYBRID \n", + "____________________________________________\n", + "\n", + "Objective set:\n", + " Objective reaction from target file\n", + "\n", + "\n", + "Objective : R_BIOMASS_KT_TEMP\n", + "\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " NETWORK \n", + "____________________________________________\n", + "\n", + "Import reaction: Kept\n", + "Product of import reaction set as seed: No\n", + "Targets can be seeds: No\n", + "Accumulation: Forbidden\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + "List of targets: ['M_5mthf_c', 'M_accoa_c', 'M_ala__L_c', 'M_amp_c', 'M_arg__L_c', 'M_asn__L_c', 'M_asp__L_c', 'M_atp_c', 'M_coa_c', 'M_cpe160_c', 'M_cpe180_c', 'M_cpg160_c', 'M_cpg180_c', 'M_ctp_c', 'M_cys__L_c', 'M_datp_c', 'M_dctp_c', 'M_dgtp_c', 'M_dttp_c', 'M_fad_c', 'M_gln__L_c', 'M_glu__L_c', 'M_gly_c', 'M_gtp_c', 'M_h2o_c', 'M_hemeO_c', 'M_his__L_c', 'M_ile__L_c', 'M_leu__L_c', 'M_lys__L_c', 'M_met__L_c', 'M_nad_c', 'M_nadh_c', 'M_nadp_c', 'M_nadph_c', 'M_pe120_c', 'M_pe160_c', 'M_pe161_c', 'M_pe180_c', 'M_pe181_c', 'M_pg120_c', 'M_pg160_c', 'M_pg180_c', 'M_phe__L_c', 'M_pro__L_c', 'M_ptrc_c', 'M_ser__L_c', 'M_sheme_c', 'M_succoa_c', 'M_thr__L_c', 'M_trp__L_c', 'M_tyr__L_c', 'M_udpg_c', 'M_utp_c', 'M_val__L_c']\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: iAPECO1_1312\n", + "\n", + "____________________________________________\n", + "\n", + " TARGETS \n", + " FOR TARGET MODE AND FBA \n", + "____________________________________________\n", + "\n", + "Targets set:\n", + " Reactant of objective reaction\n", + " from target file\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " OBJECTVE \n", + " FOR HYBRID \n", + "____________________________________________\n", + "\n", + "Objective set:\n", + " Objective reaction from target file\n", + "\n", + "\n", + "Objective : R_BIOMASS_Ec_iJO1366_core_53p95M\n", + "\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " NETWORK \n", + "____________________________________________\n", + "\n", + "Import reaction: Kept\n", + "Product of import reaction set as seed: No\n", + "Targets can be seeds: No\n", + "Accumulation: Forbidden\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + "List of targets: ['M_10fthf_c', 'M_2fe2s_c', 'M_2ohph_c', 'M_4fe4s_c', 'M_ala__L_c', 'M_amet_c', 'M_arg__L_c', 'M_asn__L_c', 'M_asp__L_c', 'M_atp_c', 'M_bmocogdp_c', 'M_btn_c', 'M_ca2_c', 'M_cl_c', 'M_coa_c', 'M_cobalt2_c', 'M_ctp_c', 'M_cu2_c', 'M_cys__L_c', 'M_datp_c', 'M_dctp_c', 'M_dgtp_c', 'M_dttp_c', 'M_fad_c', 'M_fe2_c', 'M_fe3_c', 'M_gln__L_c', 'M_glu__L_c', 'M_gly_c', 'M_gtp_c', 'M_h2o_c', 'M_his__L_c', 'M_ile__L_c', 'M_k_c', 'M_kdo2lipid4_e', 'M_leu__L_c', 'M_lys__L_c', 'M_met__L_c', 'M_mg2_c', 'M_mlthf_c', 'M_mn2_c', 'M_mobd_c', 'M_murein5px4p_p', 'M_nad_c', 'M_nadp_c', 'M_nh4_c', 'M_ni2_c', 'M_pe160_c', 'M_pe160_p', 'M_pe161_c', 'M_pe161_p', 'M_phe__L_c', 'M_pheme_c', 'M_pro__L_c', 'M_pydx5p_c', 'M_ribflv_c', 'M_ser__L_c', 'M_sheme_c', 'M_so4_c', 'M_thf_c', 'M_thmpp_c', 'M_thr__L_c', 'M_trp__L_c', 'M_tyr__L_c', 'M_udcpdp_c', 'M_utp_c', 'M_val__L_c', 'M_zn2_c']\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: iAM_Pk459\n", + "\n", + "____________________________________________\n", + "\n", + " TARGETS \n", + " FOR TARGET MODE AND FBA \n", + "____________________________________________\n", + "\n", + "Targets set:\n", + " Reactant of objective reaction\n", + " from target file\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " OBJECTVE \n", + " FOR HYBRID \n", + "____________________________________________\n", + "\n", + "Objective set:\n", + " Objective reaction from target file\n", + "\n", + "\n", + "Objective : R_BIOMASS__4\n", + "\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " NETWORK \n", + "____________________________________________\n", + "\n", + "Import reaction: Kept\n", + "Product of import reaction set as seed: No\n", + "Targets can be seeds: No\n", + "Accumulation: Forbidden\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + "List of targets: ['M_5thf_c', 'M_ala__L_c', 'M_arg__L_c', 'M_asn__L_c', 'M_asp__L_c', 'M_atp_c', 'M_clpn_pf_16_0_18_1_16_0_18_1_m', 'M_coa_c', 'M_ctp_c', 'M_cys__L_c', 'M_datp_c', 'M_dctp_c', 'M_dgtp_c', 'M_dttp_c', 'M_fad_c', 'M_gdpfuc_c', 'M_gdpmann_c', 'M_gln__L_c', 'M_glu__L_c', 'M_gly_c', 'M_gthrd_c', 'M_gtp_c', 'M_h2o_c', 'M_his__L_c', 'M_ile__L_c', 'M_leu__L_c', 'M_lipopb_m', 'M_lys__L_c', 'M_mem3gacpail_pf_18_0_18_1_16_0_r', 'M_met__L_c', 'M_nad_c', 'M_nadp_c', 'M_pail_pf_16_0_16_0_c', 'M_pail_pf_16_0_18_0_c', 'M_pail_pf_16_0_18_1_c', 'M_pail_pf_18_0_18_0_c', 'M_pail_pf_18_0_18_1_c', 'M_pail_pf_18_1_18_1_c', 'M_pchol_pf_16_0_16_0_c', 'M_pchol_pf_16_0_18_0_c', 'M_pchol_pf_16_0_18_1_c', 'M_pchol_pf_16_0_18_2_c', 'M_pchol_pf_16_0_20_4_c', 'M_pchol_pf_18_1_18_1_c', 'M_pchol_pf_18_1_18_2_c', 'M_pchol_pf_18_2_18_2_c', 'M_pe_pf_16_0_16_0_c', 'M_pe_pf_16_0_18_1_c', 'M_pe_pf_16_0_18_2_c', 'M_pe_pf_18_1_18_1_c', 'M_pe_pf_18_1_18_2_c', 'M_pe_pf_18_2_18_2_c', 'M_pe_pf_20_4_20_4_c', 'M_phe__L_c', 'M_pheme_c', 'M_pro__L_c', 'M_ps_pf_18_0_18_1_c', 'M_ps_pf_18_0_20_4_c', 'M_ps_pf_18_1_18_1_c', 'M_ps_pf_18_1_20_4_c', 'M_ps_pf_20_4_20_4_c', 'M_ptrc_c', 'M_pydx5p_c', 'M_q8h2_m', 'M_ser__L_c', 'M_sphymyln_pf_18_1_16_0_g', 'M_sphymyln_pf_18_1_18_0_g', 'M_sphymyln_pf_18_1_24_0_g', 'M_spmd_c', 'M_tag_pf_16_0_18_0_18_0_c', 'M_tag_pf_18_0_18_0_18_1_c', 'M_tag_pf_18_0_18_1_16_0_c', 'M_tag_pf_18_1_18_2_18_2_c', 'M_thmpp_c', 'M_thr__L_c', 'M_trp__L_c', 'M_tyr__L_c', 'M_utp_c', 'M_val__L_c']\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: Recon3D\n", + "\n", + "____________________________________________\n", + "\n", + " TARGETS \n", + " FOR TARGET MODE AND FBA \n", + "____________________________________________\n", + "\n", + "Targets set:\n", + " Reactant of objective reaction\n", + " from target file\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " OBJECTVE \n", + " FOR HYBRID \n", + "____________________________________________\n", + "\n", + "Objective set:\n", + " Objective reaction from target file\n", + "\n", + "\n", + "Objective : R_BIOMASS_maintenance\n", + "\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " NETWORK \n", + "____________________________________________\n", + "\n", + "Import reaction: Kept\n", + "Product of import reaction set as seed: No\n", + "Targets can be seeds: No\n", + "Accumulation: Forbidden\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + "List of targets: ['M_ala__L_c', 'M_arg__L_c', 'M_asn__L_c', 'M_asp__L_c', 'M_atp_c', 'M_chsterol_c', 'M_clpn_hs_c', 'M_ctp_c', 'M_cys__L_c', 'M_g6p_c', 'M_gln__L_c', 'M_glu__L_c', 'M_gly_c', 'M_gtp_c', 'M_h2o_c', 'M_his__L_c', 'M_ile__L_c', 'M_leu__L_c', 'M_lys__L_c', 'M_met__L_c', 'M_pail_hs_c', 'M_pchol_hs_c', 'M_pe_hs_c', 'M_pglyc_hs_c', 'M_phe__L_c', 'M_pro__L_c', 'M_ps_hs_c', 'M_ser__L_c', 'M_sphmyln_hs_c', 'M_thr__L_c', 'M_trp__L_c', 'M_tyr__L_c', 'M_utp_c', 'M_val__L_c']\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: iAM_Pf480\n", + "\n", + "____________________________________________\n", + "\n", + " TARGETS \n", + " FOR TARGET MODE AND FBA \n", + "____________________________________________\n", + "\n", + "Targets set:\n", + " Reactant of objective reaction\n", + " from target file\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " OBJECTVE \n", + " FOR HYBRID \n", + "____________________________________________\n", + "\n", + "Objective set:\n", + " Objective reaction from target file\n", + "\n", + "\n", + "Objective : R_BIOMASS__4\n", + "\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " NETWORK \n", + "____________________________________________\n", + "\n", + "Import reaction: Kept\n", + "Product of import reaction set as seed: No\n", + "Targets can be seeds: No\n", + "Accumulation: Forbidden\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + "List of targets: ['M_5thf_c', 'M_ala__L_c', 'M_arg__L_c', 'M_asn__L_c', 'M_asp__L_c', 'M_atp_c', 'M_clpn_pf_16_0_18_1_16_0_18_1_m', 'M_coa_c', 'M_ctp_c', 'M_cys__L_c', 'M_datp_c', 'M_dctp_c', 'M_dgtp_c', 'M_dttp_c', 'M_fad_c', 'M_gdpfuc_c', 'M_gdpmann_c', 'M_gln__L_c', 'M_glu__L_c', 'M_gly_c', 'M_gthrd_c', 'M_gtp_c', 'M_h2o_c', 'M_his__L_c', 'M_ile__L_c', 'M_leu__L_c', 'M_lipopb_m', 'M_lys__L_c', 'M_mem3gacpail_pf_18_0_18_1_16_0_r', 'M_met__L_c', 'M_nad_c', 'M_nadp_c', 'M_pail_pf_16_0_16_0_c', 'M_pail_pf_16_0_18_0_c', 'M_pail_pf_16_0_18_1_c', 'M_pail_pf_18_0_18_0_c', 'M_pail_pf_18_0_18_1_c', 'M_pail_pf_18_1_18_1_c', 'M_pchol_pf_16_0_16_0_c', 'M_pchol_pf_16_0_18_0_c', 'M_pchol_pf_16_0_18_1_c', 'M_pchol_pf_16_0_18_2_c', 'M_pchol_pf_16_0_20_4_c', 'M_pchol_pf_18_1_18_1_c', 'M_pchol_pf_18_1_18_2_c', 'M_pchol_pf_18_2_18_2_c', 'M_pe_pf_16_0_16_0_c', 'M_pe_pf_16_0_18_1_c', 'M_pe_pf_16_0_18_2_c', 'M_pe_pf_18_1_18_1_c', 'M_pe_pf_18_1_18_2_c', 'M_pe_pf_18_2_18_2_c', 'M_pe_pf_20_4_20_4_c', 'M_phe__L_c', 'M_pheme_c', 'M_pro__L_c', 'M_ps_pf_18_0_18_1_c', 'M_ps_pf_18_0_20_4_c', 'M_ps_pf_18_1_18_1_c', 'M_ps_pf_18_1_20_4_c', 'M_ps_pf_20_4_20_4_c', 'M_ptrc_c', 'M_pydx5p_c', 'M_q8h2_m', 'M_ser__L_c', 'M_sphymyln_pf_18_1_16_0_g', 'M_sphymyln_pf_18_1_18_0_g', 'M_sphymyln_pf_18_1_24_0_g', 'M_spmd_c', 'M_tag_pf_16_0_18_0_18_0_c', 'M_tag_pf_18_0_18_0_18_1_c', 'M_tag_pf_18_0_18_1_16_0_c', 'M_tag_pf_18_1_18_2_18_2_c', 'M_thmpp_c', 'M_thr__L_c', 'M_trp__L_c', 'M_tyr__L_c', 'M_utp_c', 'M_val__L_c']\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: iIS312_Trypomastigote\n", + "\n", + "____________________________________________\n", + "\n", + " TARGETS \n", + " FOR TARGET MODE AND FBA \n", + "____________________________________________\n", + "\n", + "Targets set:\n", + " Reactant of objective reaction\n", + " from target file\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " OBJECTVE \n", + " FOR HYBRID \n", + "____________________________________________\n", + "\n", + "Objective set:\n", + " Objective reaction from target file\n", + "\n", + "\n", + "Objective : R_BIOMASS_trypo\n", + "\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " NETWORK \n", + "____________________________________________\n", + "\n", + "Import reaction: Kept\n", + "Product of import reaction set as seed: No\n", + "Targets can be seeds: No\n", + "Accumulation: Forbidden\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + "List of targets: ['M_3pg_c', 'M_accoa_c', 'M_akg_c', 'M_ala__L_c', 'M_arg__L_c', 'M_asn__L_c', 'M_asp__L_c', 'M_atp_c', 'M_cys__L_c', 'M_ergst_c', 'M_g6p_B_c', 'M_gln__L_c', 'M_glu__L_c', 'M_gly_c', 'M_h2o_c', 'M_his__L_c', 'M_ile__L_c', 'M_leu__L_c', 'M_lys__L_c', 'M_met__L_c', 'M_nad_c', 'M_nadph_c', 'M_nh4_c', 'M_oaa_c', 'M_pep_c', 'M_phe__L_c', 'M_pro__L_c', 'M_pyr_c', 'M_ser__L_c', 'M_thr__L_c', 'M_val__L_c']\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: iECSF_1327\n", + "\n", + "____________________________________________\n", + "\n", + " TARGETS \n", + " FOR TARGET MODE AND FBA \n", + "____________________________________________\n", + "\n", + "Targets set:\n", + " Reactant of objective reaction\n", + " from target file\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " OBJECTVE \n", + " FOR HYBRID \n", + "____________________________________________\n", + "\n", + "Objective set:\n", + " Objective reaction from target file\n", + "\n", + "\n", + "Objective : R_BIOMASS_Ec_iJO1366_core_53p95M\n", + "\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " NETWORK \n", + "____________________________________________\n", + "\n", + "Import reaction: Kept\n", + "Product of import reaction set as seed: No\n", + "Targets can be seeds: No\n", + "Accumulation: Forbidden\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + "List of targets: ['M_10fthf_c', 'M_2fe2s_c', 'M_2ohph_c', 'M_4fe4s_c', 'M_ala__L_c', 'M_amet_c', 'M_arg__L_c', 'M_asn__L_c', 'M_asp__L_c', 'M_atp_c', 'M_bmocogdp_c', 'M_btn_c', 'M_ca2_c', 'M_cl_c', 'M_coa_c', 'M_cobalt2_c', 'M_ctp_c', 'M_cu2_c', 'M_cys__L_c', 'M_datp_c', 'M_dctp_c', 'M_dgtp_c', 'M_dttp_c', 'M_fad_c', 'M_fe2_c', 'M_fe3_c', 'M_gln__L_c', 'M_glu__L_c', 'M_gly_c', 'M_gtp_c', 'M_h2o_c', 'M_his__L_c', 'M_ile__L_c', 'M_k_c', 'M_kdo2lipid4_e', 'M_leu__L_c', 'M_lys__L_c', 'M_met__L_c', 'M_mg2_c', 'M_mlthf_c', 'M_mn2_c', 'M_mobd_c', 'M_murein5px4p_p', 'M_nad_c', 'M_nadp_c', 'M_nh4_c', 'M_ni2_c', 'M_pe160_c', 'M_pe160_p', 'M_pe161_c', 'M_pe161_p', 'M_phe__L_c', 'M_pheme_c', 'M_pro__L_c', 'M_pydx5p_c', 'M_ribflv_c', 'M_ser__L_c', 'M_sheme_c', 'M_so4_c', 'M_thf_c', 'M_thmpp_c', 'M_thr__L_c', 'M_trp__L_c', 'M_tyr__L_c', 'M_udcpdp_c', 'M_utp_c', 'M_val__L_c', 'M_zn2_c']\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: iRC1080\n", + "\n", + "____________________________________________\n", + "\n", + " TARGETS \n", + " FOR TARGET MODE AND FBA \n", + "____________________________________________\n", + "\n", + "Targets set:\n", + " Reactant of objective reaction\n", + " from target file\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " OBJECTVE \n", + " FOR HYBRID \n", + "____________________________________________\n", + "\n", + "Objective set:\n", + " Objective reaction from target file\n", + "\n", + "\n", + "Objective : R_BIOMASS_Chlamy_auto\n", + "\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " NETWORK \n", + "____________________________________________\n", + "\n", + "Import reaction: Kept\n", + "Product of import reaction set as seed: No\n", + "Targets can be seeds: No\n", + "Accumulation: Forbidden\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + "List of targets: ['M_ac_c', 'M_acaro_h', 'M_ala__L_c', 'M_anxan_u', 'M_arab__L_c', 'M_arg__L_c', 'M_asn__L_c', 'M_asp__L_c', 'M_asqdca18111Z160_c', 'M_asqdca1819Z160_c', 'M_asqdca1829Z12Z160_c', 'M_asqdca1839Z12Z15Z160_c', 'M_asqdpa18111Z160_c', 'M_asqdpa1819Z160_c', 'M_asqdpa1829Z12Z160_c', 'M_asqdpa1839Z12Z15Z160_c', 'M_atp_c', 'M_but_c', 'M_caro_u', 'M_chlb_u', 'M_cholphya_u', 'M_ctp_c', 'M_cys__L_c', 'M_datp_c', 'M_dctp_c', 'M_dgdg1819Z160_h', 'M_dgdg1819Z1617Z_h', 'M_dgdg1819Z1619Z_h', 'M_dgdg1819Z1627Z10Z_h', 'M_dgdg1819Z1634Z7Z10Z_h', 'M_dgdg1819Z1637Z10Z13Z_h', 'M_dgdg1829Z12Z160_h', 'M_dgdg1829Z12Z1617Z_h', 'M_dgdg1829Z12Z1619Z_h', 'M_dgdg1829Z12Z1627Z10Z_h', 'M_dgdg1829Z12Z1634Z7Z10Z_h', 'M_dgdg1829Z12Z1637Z10Z13Z_h', 'M_dgdg1839Z12Z15Z160_h', 'M_dgdg1839Z12Z15Z1627Z10Z_h', 'M_dgdg1839Z12Z15Z1634Z7Z10Z_h', 'M_dgdg1839Z12Z15Z1637Z10Z13Z_h', 'M_dgdg1839Z12Z15Z1644Z7Z10Z13Z_h', 'M_dgtp_c', 'M_dgts16018111Z_c', 'M_dgts1601819Z_c', 'M_dgts1601829Z12Z_c', 'M_dgts1601835Z9Z12Z_c', 'M_dgts1601845Z9Z12Z15Z_c', 'M_dgts18111Z18111Z_c', 'M_dgts18111Z1819Z_c', 'M_dgts18111Z1829Z12Z_c', 'M_dgts18111Z1835Z9Z12Z_c', 'M_dgts18111Z1845Z9Z12Z15Z_c', 'M_dgts1819Z18111Z_c', 'M_dgts1819Z1819Z_c', 'M_dgts1819Z1829Z12Z_c', 'M_dgts1819Z1835Z9Z12Z_c', 'M_dgts1819Z1845Z9Z12Z15Z_c', 'M_dgts1829Z12Z18111Z_c', 'M_dgts1829Z12Z1819Z_c', 'M_dgts1829Z12Z1829Z12Z_c', 'M_dgts1829Z12Z1835Z9Z12Z_c', 'M_dgts1829Z12Z1845Z9Z12Z15Z_c', 'M_dgts1839Z12Z15Z18111Z_c', 'M_dgts1839Z12Z15Z1819Z_c', 'M_dgts1839Z12Z15Z1835Z9Z12Z_c', 'M_dgts1839Z12Z15Z1845Z9Z12Z15Z_c', 'M_dttp_c', 'M_gal_c', 'M_gln__L_c', 'M_glu__L_c', 'M_gly_c', 'M_glyc_c', 'M_gtp_c', 'M_h2o_c', 'M_his__L_c', 'M_ile__L_c', 'M_leu__L_c', 'M_loroxan_u', 'M_lut_u', 'M_lys__L_c', 'M_man_c', 'M_met__L_c', 'M_mgdg1829Z12Z160_h', 'M_mgdg1829Z12Z1617Z_h', 'M_mgdg1829Z12Z1619Z_h', 'M_mgdg1829Z12Z1627Z10Z_h', 'M_mgdg1829Z12Z1634Z7Z10Z_h', 'M_mgdg1829Z12Z1637Z10Z13Z_h', 'M_mgdg1829Z12Z1644Z7Z10Z13Z_h', 'M_mgdg1839Z12Z15Z160_h', 'M_mgdg1839Z12Z15Z1627Z10Z_h', 'M_mgdg1839Z12Z15Z1634Z7Z10Z_h', 'M_mgdg1839Z12Z15Z1637Z10Z13Z_h', 'M_mgdg1839Z12Z15Z1644Z7Z10Z13Z_h', 'M_neoxan_u', 'M_pail18111Z160_c', 'M_pail1819Z160_c', 'M_pe1801835Z9Z12Z_c', 'M_pe1801845Z9Z12Z15Z_c', 'M_pe18111Z1835Z9Z12Z_c', 'M_pe18111Z1845Z9Z12Z15Z_c', 'M_pe1819Z1835Z9Z12Z_c', 'M_pe1819Z1845Z9Z12Z15Z_c', 'M_pe1829Z12Z1835Z9Z12Z_c', 'M_pg18111Z160_h', 'M_pg18111Z1613E_h', 'M_pg1819Z160_h', 'M_pg1819Z1613E_h', 'M_pg1829Z12Z160_h', 'M_pg1829Z12Z1613E_h', 'M_pg1839Z12Z15Z160_h', 'M_pg1839Z12Z15Z1613E_h', 'M_phe__L_c', 'M_ppa_c', 'M_pro__L_c', 'M_rhodopsin_s', 'M_ser__L_c', 'M_sqdg160_h', 'M_sqdg18111Z160_h', 'M_sqdg1819Z160_h', 'M_sqdg1829Z12Z160_h', 'M_sqdg1839Z12Z15Z160_h', 'M_starch300_h', 'M_tag16018111Z160_c', 'M_tag16018111Z180_c', 'M_tag16018111Z18111Z_c', 'M_tag16018111Z1819Z_c', 'M_tag16018111Z1835Z9Z12Z_c', 'M_tag16018111Z1845Z9Z12Z15Z_c', 'M_tag1601819Z160_c', 'M_tag1601819Z180_c', 'M_tag1601819Z18111Z_c', 'M_tag1601819Z1819Z_c', 'M_tag1601819Z1835Z9Z12Z_c', 'M_tag1601819Z1845Z9Z12Z15Z_c', 'M_tag1801819Z160_c', 'M_tag1801819Z180_c', 'M_tag1801819Z18111Z_c', 'M_tag1801819Z1819Z_c', 'M_tag1801819Z1835Z9Z12Z_c', 'M_tag1801819Z1845Z9Z12Z15Z_c', 'M_tag18111Z18111Z160_c', 'M_tag18111Z18111Z180_c', 'M_tag18111Z18111Z18111Z_c', 'M_tag18111Z18111Z1819Z_c', 'M_tag18111Z18111Z1835Z9Z12Z_c', 'M_tag18111Z18111Z1845Z9Z12Z15Z_c', 'M_tag18111Z1819Z160_c', 'M_tag18111Z1819Z180_c', 'M_tag18111Z1819Z18111Z_c', 'M_tag18111Z1819Z1819Z_c', 'M_tag18111Z1819Z1835Z9Z12Z_c', 'M_tag18111Z1819Z1845Z9Z12Z15Z_c', 'M_tag1819Z18111Z160_c', 'M_tag1819Z18111Z180_c', 'M_tag1819Z18111Z18111Z_c', 'M_tag1819Z18111Z1819Z_c', 'M_tag1819Z18111Z1835Z9Z12Z_c', 'M_tag1819Z18111Z1845Z9Z12Z15Z_c', 'M_tag1819Z1819Z160_c', 'M_tag1819Z1819Z180_c', 'M_tag1819Z1819Z18111Z_c', 'M_tag1819Z1819Z1819Z_c', 'M_tag1819Z1819Z1835Z9Z12Z_c', 'M_tag1819Z1819Z1845Z9Z12Z15Z_c', 'M_thr__L_c', 'M_trp__L_c', 'M_tyr__L_c', 'M_utp_c', 'M_val__L_c', 'M_vioxan_u', 'M_zeax_u']\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: iZ_1308\n", + "\n", + "____________________________________________\n", + "\n", + " TARGETS \n", + " FOR TARGET MODE AND FBA \n", + "____________________________________________\n", + "\n", + "Targets set:\n", + " Reactant of objective reaction\n", + " from target file\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " OBJECTVE \n", + " FOR HYBRID \n", + "____________________________________________\n", + "\n", + "Objective set:\n", + " Objective reaction from target file\n", + "\n", + "\n", + "Objective : R_BIOMASS_Ec_iJO1366_core_53p95M\n", + "\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " NETWORK \n", + "____________________________________________\n", + "\n", + "Import reaction: Kept\n", + "Product of import reaction set as seed: No\n", + "Targets can be seeds: No\n", + "Accumulation: Forbidden\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + "List of targets: ['M_10fthf_c', 'M_2fe2s_c', 'M_2ohph_c', 'M_4fe4s_c', 'M_ala__L_c', 'M_amet_c', 'M_arg__L_c', 'M_asn__L_c', 'M_asp__L_c', 'M_atp_c', 'M_bmocogdp_c', 'M_btn_c', 'M_ca2_c', 'M_cl_c', 'M_coa_c', 'M_cobalt2_c', 'M_ctp_c', 'M_cu2_c', 'M_cys__L_c', 'M_datp_c', 'M_dctp_c', 'M_dgtp_c', 'M_dttp_c', 'M_fad_c', 'M_fe2_c', 'M_fe3_c', 'M_gln__L_c', 'M_glu__L_c', 'M_gly_c', 'M_gtp_c', 'M_h2o_c', 'M_his__L_c', 'M_ile__L_c', 'M_k_c', 'M_kdo2lipid4_e', 'M_leu__L_c', 'M_lys__L_c', 'M_met__L_c', 'M_mg2_c', 'M_mlthf_c', 'M_mn2_c', 'M_mobd_c', 'M_murein5px4p_p', 'M_nad_c', 'M_nadp_c', 'M_nh4_c', 'M_ni2_c', 'M_pe160_c', 'M_pe160_p', 'M_pe161_c', 'M_pe161_p', 'M_phe__L_c', 'M_pheme_c', 'M_pro__L_c', 'M_pydx5p_c', 'M_ribflv_c', 'M_ser__L_c', 'M_sheme_c', 'M_so4_c', 'M_thf_c', 'M_thmpp_c', 'M_thr__L_c', 'M_trp__L_c', 'M_tyr__L_c', 'M_udcpdp_c', 'M_utp_c', 'M_val__L_c', 'M_zn2_c']\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: iUTI89_1310\n", + "\n", + "____________________________________________\n", + "\n", + " TARGETS \n", + " FOR TARGET MODE AND FBA \n", + "____________________________________________\n", + "\n", + "Targets set:\n", + " Reactant of objective reaction\n", + " from target file\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " OBJECTVE \n", + " FOR HYBRID \n", + "____________________________________________\n", + "\n", + "Objective set:\n", + " Objective reaction from target file\n", + "\n", + "\n", + "Objective : R_BIOMASS_Ec_iJO1366_core_53p95M\n", + "\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " NETWORK \n", + "____________________________________________\n", + "\n", + "Import reaction: Kept\n", + "Product of import reaction set as seed: No\n", + "Targets can be seeds: No\n", + "Accumulation: Forbidden\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + "List of targets: ['M_10fthf_c', 'M_2fe2s_c', 'M_2ohph_c', 'M_4fe4s_c', 'M_ala__L_c', 'M_amet_c', 'M_arg__L_c', 'M_asn__L_c', 'M_asp__L_c', 'M_atp_c', 'M_bmocogdp_c', 'M_btn_c', 'M_ca2_c', 'M_cl_c', 'M_coa_c', 'M_cobalt2_c', 'M_ctp_c', 'M_cu2_c', 'M_cys__L_c', 'M_datp_c', 'M_dctp_c', 'M_dgtp_c', 'M_dttp_c', 'M_fad_c', 'M_fe2_c', 'M_fe3_c', 'M_gln__L_c', 'M_glu__L_c', 'M_gly_c', 'M_gtp_c', 'M_h2o_c', 'M_his__L_c', 'M_ile__L_c', 'M_k_c', 'M_kdo2lipid4_e', 'M_leu__L_c', 'M_lys__L_c', 'M_met__L_c', 'M_mg2_c', 'M_mlthf_c', 'M_mn2_c', 'M_mobd_c', 'M_murein5px4p_p', 'M_nad_c', 'M_nadp_c', 'M_nh4_c', 'M_ni2_c', 'M_pe160_c', 'M_pe160_p', 'M_pe161_c', 'M_pe161_p', 'M_phe__L_c', 'M_pheme_c', 'M_pro__L_c', 'M_pydx5p_c', 'M_ribflv_c', 'M_ser__L_c', 'M_sheme_c', 'M_so4_c', 'M_thf_c', 'M_thmpp_c', 'M_thr__L_c', 'M_trp__L_c', 'M_tyr__L_c', 'M_udcpdp_c', 'M_utp_c', 'M_val__L_c', 'M_zn2_c']\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: iML1515\n", + "\n", + "____________________________________________\n", + "\n", + " TARGETS \n", + " FOR TARGET MODE AND FBA \n", + "____________________________________________\n", + "\n", + "Targets set:\n", + " Reactant of objective reaction\n", + " from target file\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " OBJECTVE \n", + " FOR HYBRID \n", + "____________________________________________\n", + "\n", + "Objective set:\n", + " Objective reaction from target file\n", + "\n", + "\n", + "Objective : R_BIOMASS_Ec_iML1515_core_75p37M\n", + "\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " NETWORK \n", + "____________________________________________\n", + "\n", + "Import reaction: Kept\n", + "Product of import reaction set as seed: No\n", + "Targets can be seeds: No\n", + "Accumulation: Forbidden\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + "List of targets: ['M_10fthf_c', 'M_2fe2s_c', 'M_2ohph_c', 'M_4fe4s_c', 'M_ala__L_c', 'M_amet_c', 'M_arg__L_c', 'M_asn__L_c', 'M_asp__L_c', 'M_atp_c', 'M_btn_c', 'M_ca2_c', 'M_cl_c', 'M_coa_c', 'M_cobalt2_c', 'M_ctp_c', 'M_cu2_c', 'M_cys__L_c', 'M_datp_c', 'M_dctp_c', 'M_dgtp_c', 'M_dttp_c', 'M_fad_c', 'M_fe2_c', 'M_fe3_c', 'M_gln__L_c', 'M_glu__L_c', 'M_gly_c', 'M_gtp_c', 'M_h2o_c', 'M_his__L_c', 'M_ile__L_c', 'M_k_c', 'M_kdo2lipid4_e', 'M_leu__L_c', 'M_lys__L_c', 'M_met__L_c', 'M_mg2_c', 'M_mlthf_c', 'M_mn2_c', 'M_mobd_c', 'M_murein5px4p_p', 'M_nad_c', 'M_nadp_c', 'M_nh4_c', 'M_ni2_c', 'M_pe160_p', 'M_pe161_p', 'M_phe__L_c', 'M_pheme_c', 'M_pro__L_c', 'M_pydx5p_c', 'M_ribflv_c', 'M_ser__L_c', 'M_sheme_c', 'M_so4_c', 'M_succoa_c', 'M_thf_c', 'M_thmpp_c', 'M_thr__L_c', 'M_trp__L_c', 'M_tyr__L_c', 'M_udcpdp_c', 'M_utp_c', 'M_val__L_c', 'M_zn2_c']\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: iAF692\n", + "\n", + "____________________________________________\n", + "\n", + " TARGETS \n", + " FOR TARGET MODE AND FBA \n", + "____________________________________________\n", + "\n", + "Targets set:\n", + " Reactant of objective reaction\n", + " from target file\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " OBJECTVE \n", + " FOR HYBRID \n", + "____________________________________________\n", + "\n", + "Objective set:\n", + " Objective reaction from target file\n", + "\n", + "\n", + "Objective : R_BIOMASS_Mb_30\n", + "\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " NETWORK \n", + "____________________________________________\n", + "\n", + "Import reaction: Kept\n", + "Product of import reaction set as seed: No\n", + "Targets can be seeds: No\n", + "Accumulation: Forbidden\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + "List of targets: ['M_3hdpgpe_c', 'M_3hdpgpg_c', 'M_3hdpgpi_c', 'M_3hdpgps_c', 'M_accoa_c', 'M_adocblhbi_c', 'M_ala__L_c', 'M_amp_c', 'M_arg__L_c', 'M_asn__L_c', 'M_asp__L_c', 'M_atp_c', 'M_coa_c', 'M_cob_c', 'M_com_c', 'M_ctp_c', 'M_cys__L_c', 'M_datp_c', 'M_dctp_c', 'M_dgtp_c', 'M_dpgpe_c', 'M_dpgpg_c', 'M_dpgpi_c', 'M_dpgps_c', 'M_dttp_c', 'M_f390a_c', 'M_f390g_c', 'M_f420_2_c', 'M_f420_3_c', 'M_f420_4_c', 'M_f420_5_c', 'M_f420_6_c', 'M_f420_7_c', 'M_f430_c', 'M_gdpgpi_c', 'M_gln__L_c', 'M_glu__L_c', 'M_gly_c', 'M_glycogen_c', 'M_gtp_c', 'M_h2o_c', 'M_h4spt_c', 'M_his__L_c', 'M_hspmd_c', 'M_ile__L_c', 'M_leu__L_c', 'M_lys__L_c', 'M_met__L_c', 'M_nad_c', 'M_nadh_c', 'M_nadp_c', 'M_nadph_c', 'M_phe__L_c', 'M_pro__L_c', 'M_ptrc_c', 'M_ser__L_c', 'M_succoa_c', 'M_thf_c', 'M_thr__L_c', 'M_trp__L_c', 'M_tyr__L_c', 'M_utp_c', 'M_val__L_c']\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: iSFV_1184\n", + "\n", + "____________________________________________\n", + "\n", + " TARGETS \n", + " FOR TARGET MODE AND FBA \n", + "____________________________________________\n", + "\n", + "Targets set:\n", + " Reactant of objective reaction\n", + " from target file\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " OBJECTVE \n", + " FOR HYBRID \n", + "____________________________________________\n", + "\n", + "Objective set:\n", + " Objective reaction from target file\n", + "\n", + "\n", + "Objective : R_BIOMASS_Ec_iJO1366_core_53p95M\n", + "\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " NETWORK \n", + "____________________________________________\n", + "\n", + "Import reaction: Kept\n", + "Product of import reaction set as seed: No\n", + "Targets can be seeds: No\n", + "Accumulation: Forbidden\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + "List of targets: ['M_10fthf_c', 'M_2fe2s_c', 'M_2ohph_c', 'M_4fe4s_c', 'M_ala__L_c', 'M_amet_c', 'M_arg__L_c', 'M_asn__L_c', 'M_asp__L_c', 'M_atp_c', 'M_bmocogdp_c', 'M_btn_c', 'M_ca2_c', 'M_cl_c', 'M_coa_c', 'M_cobalt2_c', 'M_ctp_c', 'M_cu2_c', 'M_cys__L_c', 'M_datp_c', 'M_dctp_c', 'M_dgtp_c', 'M_dttp_c', 'M_fad_c', 'M_fe2_c', 'M_fe3_c', 'M_gln__L_c', 'M_glu__L_c', 'M_gly_c', 'M_gtp_c', 'M_h2o_c', 'M_his__L_c', 'M_ile__L_c', 'M_k_c', 'M_kdo2lipid4_e', 'M_leu__L_c', 'M_lys__L_c', 'M_met__L_c', 'M_mg2_c', 'M_mlthf_c', 'M_mn2_c', 'M_mobd_c', 'M_murein5px4p_p', 'M_nad_c', 'M_nadp_c', 'M_nh4_c', 'M_ni2_c', 'M_pe160_c', 'M_pe160_p', 'M_pe161_c', 'M_pe161_p', 'M_phe__L_c', 'M_pheme_c', 'M_pro__L_c', 'M_pydx5p_c', 'M_ribflv_c', 'M_ser__L_c', 'M_sheme_c', 'M_so4_c', 'M_thf_c', 'M_thmpp_c', 'M_thr__L_c', 'M_trp__L_c', 'M_tyr__L_c', 'M_udcpdp_c', 'M_utp_c', 'M_val__L_c', 'M_zn2_c']\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: ic_1306\n", + "\n", + "____________________________________________\n", + "\n", + " TARGETS \n", + " FOR TARGET MODE AND FBA \n", + "____________________________________________\n", + "\n", + "Targets set:\n", + " Reactant of objective reaction\n", + " from target file\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " OBJECTVE \n", + " FOR HYBRID \n", + "____________________________________________\n", + "\n", + "Objective set:\n", + " Objective reaction from target file\n", + "\n", + "\n", + "Objective : R_BIOMASS_Ec_iJO1366_core_53p95M\n", + "\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " NETWORK \n", + "____________________________________________\n", + "\n", + "Import reaction: Kept\n", + "Product of import reaction set as seed: No\n", + "Targets can be seeds: No\n", + "Accumulation: Forbidden\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + "List of targets: ['M_10fthf_c', 'M_2fe2s_c', 'M_2ohph_c', 'M_4fe4s_c', 'M_ala__L_c', 'M_amet_c', 'M_arg__L_c', 'M_asn__L_c', 'M_asp__L_c', 'M_atp_c', 'M_bmocogdp_c', 'M_btn_c', 'M_ca2_c', 'M_cl_c', 'M_coa_c', 'M_cobalt2_c', 'M_ctp_c', 'M_cu2_c', 'M_cys__L_c', 'M_datp_c', 'M_dctp_c', 'M_dgtp_c', 'M_dttp_c', 'M_fad_c', 'M_fe2_c', 'M_fe3_c', 'M_gln__L_c', 'M_glu__L_c', 'M_gly_c', 'M_gtp_c', 'M_h2o_c', 'M_his__L_c', 'M_ile__L_c', 'M_k_c', 'M_kdo2lipid4_e', 'M_leu__L_c', 'M_lys__L_c', 'M_met__L_c', 'M_mg2_c', 'M_mlthf_c', 'M_mn2_c', 'M_mobd_c', 'M_murein5px4p_p', 'M_nad_c', 'M_nadp_c', 'M_nh4_c', 'M_ni2_c', 'M_pe160_c', 'M_pe160_p', 'M_pe161_c', 'M_pe161_p', 'M_phe__L_c', 'M_pheme_c', 'M_pro__L_c', 'M_pydx5p_c', 'M_ribflv_c', 'M_ser__L_c', 'M_sheme_c', 'M_so4_c', 'M_thf_c', 'M_thmpp_c', 'M_thr__L_c', 'M_trp__L_c', 'M_tyr__L_c', 'M_udcpdp_c', 'M_utp_c', 'M_val__L_c', 'M_zn2_c']\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: iND750\n", + "\n", + "____________________________________________\n", + "\n", + " TARGETS \n", + " FOR TARGET MODE AND FBA \n", + "____________________________________________\n", + "\n", + "Targets set:\n", + " Reactant of objective reaction\n", + " from target file\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " OBJECTVE \n", + " FOR HYBRID \n", + "____________________________________________\n", + "\n", + "Objective set:\n", + " Objective reaction from target file\n", + "\n", + "\n", + "Objective : R_BIOMASS_SC4_bal\n", + "\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " NETWORK \n", + "____________________________________________\n", + "\n", + "Import reaction: Kept\n", + "Product of import reaction set as seed: No\n", + "Targets can be seeds: No\n", + "Accumulation: Forbidden\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + "List of targets: ['M_13BDglcn_c', 'M_ala__L_c', 'M_amp_c', 'M_arg__L_c', 'M_asn__L_c', 'M_asp__L_c', 'M_atp_c', 'M_cmp_c', 'M_cys__L_c', 'M_damp_c', 'M_dcmp_c', 'M_dgmp_c', 'M_dtmp_c', 'M_ergst_c', 'M_gln__L_c', 'M_glu__L_c', 'M_gly_c', 'M_glycogen_c', 'M_gmp_c', 'M_h2o_c', 'M_his__L_c', 'M_ile__L_c', 'M_leu__L_c', 'M_lys__L_c', 'M_mannan_c', 'M_met__L_c', 'M_pa_SC_c', 'M_pc_SC_c', 'M_pe_SC_c', 'M_phe__L_c', 'M_pro__L_c', 'M_ps_SC_c', 'M_ptd1ino_SC_c', 'M_ser__L_c', 'M_so4_c', 'M_thr__L_c', 'M_tre_c', 'M_triglyc_SC_c', 'M_trp__L_c', 'M_tyr__L_c', 'M_ump_c', 'M_val__L_c', 'M_zymst_c']\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: iUMN146_1321\n", + "\n", + "____________________________________________\n", + "\n", + " TARGETS \n", + " FOR TARGET MODE AND FBA \n", + "____________________________________________\n", + "\n", + "Targets set:\n", + " Reactant of objective reaction\n", + " from target file\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " OBJECTVE \n", + " FOR HYBRID \n", + "____________________________________________\n", + "\n", + "Objective set:\n", + " Objective reaction from target file\n", + "\n", + "\n", + "Objective : R_BIOMASS_Ec_iJO1366_core_53p95M\n", + "\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " NETWORK \n", + "____________________________________________\n", + "\n", + "Import reaction: Kept\n", + "Product of import reaction set as seed: No\n", + "Targets can be seeds: No\n", + "Accumulation: Forbidden\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + "List of targets: ['M_10fthf_c', 'M_2fe2s_c', 'M_2ohph_c', 'M_4fe4s_c', 'M_ala__L_c', 'M_amet_c', 'M_arg__L_c', 'M_asn__L_c', 'M_asp__L_c', 'M_atp_c', 'M_bmocogdp_c', 'M_btn_c', 'M_ca2_c', 'M_cl_c', 'M_coa_c', 'M_cobalt2_c', 'M_ctp_c', 'M_cu2_c', 'M_cys__L_c', 'M_datp_c', 'M_dctp_c', 'M_dgtp_c', 'M_dttp_c', 'M_fad_c', 'M_fe2_c', 'M_fe3_c', 'M_gln__L_c', 'M_glu__L_c', 'M_gly_c', 'M_gtp_c', 'M_h2o_c', 'M_his__L_c', 'M_ile__L_c', 'M_k_c', 'M_kdo2lipid4_e', 'M_leu__L_c', 'M_lys__L_c', 'M_met__L_c', 'M_mg2_c', 'M_mlthf_c', 'M_mn2_c', 'M_mobd_c', 'M_murein5px4p_p', 'M_nad_c', 'M_nadp_c', 'M_nh4_c', 'M_ni2_c', 'M_pe160_c', 'M_pe160_p', 'M_pe161_c', 'M_pe161_p', 'M_phe__L_c', 'M_pheme_c', 'M_pro__L_c', 'M_pydx5p_c', 'M_ribflv_c', 'M_ser__L_c', 'M_sheme_c', 'M_so4_c', 'M_thf_c', 'M_thmpp_c', 'M_thr__L_c', 'M_trp__L_c', 'M_tyr__L_c', 'M_udcpdp_c', 'M_utp_c', 'M_val__L_c', 'M_zn2_c']\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: iSBO_1134\n", + "\n", + "____________________________________________\n", + "\n", + " TARGETS \n", + " FOR TARGET MODE AND FBA \n", + "____________________________________________\n", + "\n", + "Targets set:\n", + " Reactant of objective reaction\n", + " from target file\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " OBJECTVE \n", + " FOR HYBRID \n", + "____________________________________________\n", + "\n", + "Objective set:\n", + " Objective reaction from target file\n", + "\n", + "\n", + "Objective : R_BIOMASS_Ec_iJO1366_core_53p95M\n", + "\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " NETWORK \n", + "____________________________________________\n", + "\n", + "Import reaction: Kept\n", + "Product of import reaction set as seed: No\n", + "Targets can be seeds: No\n", + "Accumulation: Forbidden\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + "List of targets: ['M_10fthf_c', 'M_2fe2s_c', 'M_2ohph_c', 'M_4fe4s_c', 'M_ala__L_c', 'M_amet_c', 'M_arg__L_c', 'M_asn__L_c', 'M_asp__L_c', 'M_atp_c', 'M_bmocogdp_c', 'M_btn_c', 'M_ca2_c', 'M_cl_c', 'M_coa_c', 'M_cobalt2_c', 'M_ctp_c', 'M_cu2_c', 'M_cys__L_c', 'M_datp_c', 'M_dctp_c', 'M_dgtp_c', 'M_dttp_c', 'M_fad_c', 'M_fe2_c', 'M_fe3_c', 'M_gln__L_c', 'M_glu__L_c', 'M_gly_c', 'M_gtp_c', 'M_h2o_c', 'M_his__L_c', 'M_ile__L_c', 'M_k_c', 'M_kdo2lipid4_e', 'M_leu__L_c', 'M_lys__L_c', 'M_met__L_c', 'M_mg2_c', 'M_mlthf_c', 'M_mn2_c', 'M_mobd_c', 'M_murein5px4p_p', 'M_nad_c', 'M_nadp_c', 'M_nh4_c', 'M_ni2_c', 'M_pe160_c', 'M_pe160_p', 'M_pe161_c', 'M_pe161_p', 'M_phe__L_c', 'M_pheme_c', 'M_pro__L_c', 'M_pydx5p_c', 'M_ribflv_c', 'M_ser__L_c', 'M_sheme_c', 'M_so4_c', 'M_thf_c', 'M_thmpp_c', 'M_thr__L_c', 'M_trp__L_c', 'M_tyr__L_c', 'M_udcpdp_c', 'M_utp_c', 'M_val__L_c', 'M_zn2_c']\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: iBWG_1329\n", + "\n", + "____________________________________________\n", + "\n", + " TARGETS \n", + " FOR TARGET MODE AND FBA \n", + "____________________________________________\n", + "\n", + "Targets set:\n", + " Reactant of objective reaction\n", + " from target file\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " OBJECTVE \n", + " FOR HYBRID \n", + "____________________________________________\n", + "\n", + "Objective set:\n", + " Objective reaction from target file\n", + "\n", + "\n", + "Objective : R_BIOMASS_Ec_iJO1366_core_53p95M\n", + "\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " NETWORK \n", + "____________________________________________\n", + "\n", + "Import reaction: Kept\n", + "Product of import reaction set as seed: No\n", + "Targets can be seeds: No\n", + "Accumulation: Forbidden\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + "List of targets: ['M_10fthf_c', 'M_2fe2s_c', 'M_2ohph_c', 'M_4fe4s_c', 'M_ala__L_c', 'M_amet_c', 'M_arg__L_c', 'M_asn__L_c', 'M_asp__L_c', 'M_atp_c', 'M_bmocogdp_c', 'M_btn_c', 'M_ca2_c', 'M_cl_c', 'M_coa_c', 'M_cobalt2_c', 'M_ctp_c', 'M_cu2_c', 'M_cys__L_c', 'M_datp_c', 'M_dctp_c', 'M_dgtp_c', 'M_dttp_c', 'M_fad_c', 'M_fe2_c', 'M_fe3_c', 'M_gln__L_c', 'M_glu__L_c', 'M_gly_c', 'M_gtp_c', 'M_h2o_c', 'M_his__L_c', 'M_ile__L_c', 'M_k_c', 'M_kdo2lipid4_e', 'M_leu__L_c', 'M_lys__L_c', 'M_met__L_c', 'M_mg2_c', 'M_mlthf_c', 'M_mn2_c', 'M_mobd_c', 'M_murein5px4p_p', 'M_nad_c', 'M_nadp_c', 'M_nh4_c', 'M_ni2_c', 'M_pe160_c', 'M_pe160_p', 'M_pe161_c', 'M_pe161_p', 'M_phe__L_c', 'M_pheme_c', 'M_pro__L_c', 'M_pydx5p_c', 'M_ribflv_c', 'M_ser__L_c', 'M_sheme_c', 'M_so4_c', 'M_thf_c', 'M_thmpp_c', 'M_thr__L_c', 'M_trp__L_c', 'M_tyr__L_c', 'M_udcpdp_c', 'M_utp_c', 'M_val__L_c', 'M_zn2_c']\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: iIT341\n", + "\n", + "____________________________________________\n", + "\n", + " TARGETS \n", + " FOR TARGET MODE AND FBA \n", + "____________________________________________\n", + "\n", + "Targets set:\n", + " Reactant of objective reaction\n", + " from target file\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " OBJECTVE \n", + " FOR HYBRID \n", + "____________________________________________\n", + "\n", + "Objective set:\n", + " Objective reaction from target file\n", + "\n", + "\n", + "Objective : R_BIOMASS_HP_published\n", + "\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " NETWORK \n", + "____________________________________________\n", + "\n", + "Import reaction: Kept\n", + "Product of import reaction set as seed: No\n", + "Targets can be seeds: No\n", + "Accumulation: Forbidden\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + "List of targets: ['M_5mthf_c', 'M_accoa_c', 'M_ala__L_c', 'M_amp_c', 'M_arg__L_c', 'M_asn__L_c', 'M_asp__L_c', 'M_atp_c', 'M_btn_c', 'M_clpn_HP_c', 'M_coa_c', 'M_ctp_c', 'M_cys__L_c', 'M_datp_c', 'M_dctp_c', 'M_dgtp_c', 'M_dttp_c', 'M_fad_c', 'M_gln__L_c', 'M_glu__L_c', 'M_gly_c', 'M_gtp_c', 'M_h2o_c', 'M_his__L_c', 'M_ile__L_c', 'M_leu__L_c', 'M_lps_HP_c', 'M_lys__L_c', 'M_met__L_c', 'M_mqn6_c', 'M_nad_c', 'M_nadh_c', 'M_nadp_c', 'M_nadph_c', 'M_pe_HP_c', 'M_peptido_EC_c', 'M_pg_HP_c', 'M_phe__L_c', 'M_pheme_c', 'M_pro__L_c', 'M_ps_HP_c', 'M_ptrc_c', 'M_ser__L_c', 'M_spmd_c', 'M_succoa_c', 'M_thm_c', 'M_thr__L_c', 'M_trp__L_c', 'M_tyr__L_c', 'M_udpg_c', 'M_utp_c', 'M_val__L_c']\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: iJB785\n", + "\n", + "____________________________________________\n", + "\n", + " TARGETS \n", + " FOR TARGET MODE AND FBA \n", + "____________________________________________\n", + "\n", + "Targets set:\n", + " Reactant of objective reaction\n", + " from target file\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " OBJECTVE \n", + " FOR HYBRID \n", + "____________________________________________\n", + "\n", + "Objective set:\n", + " Objective reaction from target file\n", + "\n", + "\n", + "Objective : R_BIOMASS__1\n", + "\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " NETWORK \n", + "____________________________________________\n", + "\n", + "Import reaction: Kept\n", + "Product of import reaction set as seed: No\n", + "Targets can be seeds: No\n", + "Accumulation: Forbidden\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + "List of targets: ['M_atp_c', 'M_bm_carbs_c', 'M_bm_cofactors_c', 'M_bm_cw_c', 'M_bm_dna_c', 'M_bm_memlip_c', 'M_bm_pigm_c', 'M_bm_pro_c', 'M_bm_rna_c', 'M_h2o_c']\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: iY75_1357\n", + "\n", + "____________________________________________\n", + "\n", + " TARGETS \n", + " FOR TARGET MODE AND FBA \n", + "____________________________________________\n", + "\n", + "Targets set:\n", + " Reactant of objective reaction\n", + " from target file\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " OBJECTVE \n", + " FOR HYBRID \n", + "____________________________________________\n", + "\n", + "Objective set:\n", + " Objective reaction from target file\n", + "\n", + "\n", + "Objective : R_BIOMASS_Ec_iJO1366_core_53p95M\n", + "\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " NETWORK \n", + "____________________________________________\n", + "\n", + "Import reaction: Kept\n", + "Product of import reaction set as seed: No\n", + "Targets can be seeds: No\n", + "Accumulation: Forbidden\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + "List of targets: ['M_10fthf_c', 'M_2fe2s_c', 'M_2ohph_c', 'M_4fe4s_c', 'M_ala__L_c', 'M_amet_c', 'M_arg__L_c', 'M_asn__L_c', 'M_asp__L_c', 'M_atp_c', 'M_bmocogdp_c', 'M_btn_c', 'M_ca2_c', 'M_cl_c', 'M_coa_c', 'M_cobalt2_c', 'M_ctp_c', 'M_cu2_c', 'M_cys__L_c', 'M_datp_c', 'M_dctp_c', 'M_dgtp_c', 'M_dttp_c', 'M_fad_c', 'M_fe2_c', 'M_fe3_c', 'M_gln__L_c', 'M_glu__L_c', 'M_gly_c', 'M_gtp_c', 'M_h2o_c', 'M_his__L_c', 'M_ile__L_c', 'M_k_c', 'M_kdo2lipid4_e', 'M_leu__L_c', 'M_lys__L_c', 'M_met__L_c', 'M_mg2_c', 'M_mlthf_c', 'M_mn2_c', 'M_mobd_c', 'M_murein5px4p_p', 'M_nad_c', 'M_nadp_c', 'M_nh4_c', 'M_ni2_c', 'M_pe160_c', 'M_pe160_p', 'M_pe161_c', 'M_pe161_p', 'M_phe__L_c', 'M_pheme_c', 'M_pro__L_c', 'M_pydx5p_c', 'M_ribflv_c', 'M_ser__L_c', 'M_sheme_c', 'M_so4_c', 'M_thf_c', 'M_thmpp_c', 'M_thr__L_c', 'M_trp__L_c', 'M_tyr__L_c', 'M_udcpdp_c', 'M_utp_c', 'M_val__L_c', 'M_zn2_c']\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: iCHOv1_DG44\n", + "\n", + "____________________________________________\n", + "\n", + " TARGETS \n", + " FOR TARGET MODE AND FBA \n", + "____________________________________________\n", + "\n", + "Targets set:\n", + " Reactant of objective reaction\n", + " from target file\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " OBJECTVE \n", + " FOR HYBRID \n", + "____________________________________________\n", + "\n", + "Objective set:\n", + " Objective reaction from target file\n", + "\n", + "\n", + "Objective : R_BIOMASS_cho_producing_1\n", + "\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " NETWORK \n", + "____________________________________________\n", + "\n", + "Import reaction: Kept\n", + "Product of import reaction set as seed: No\n", + "Targets can be seeds: No\n", + "Accumulation: Forbidden\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + "List of targets: ['M_ala__L_c', 'M_arg__L_c', 'M_asn__L_c', 'M_asp__L_c', 'M_atp_c', 'M_chsterol_c', 'M_clpn_cho_c', 'M_ctp_c', 'M_cys__L_c', 'M_datp_c', 'M_dctp_c', 'M_dgtp_c', 'M_dttp_c', 'M_gln__L_c', 'M_glu__L_c', 'M_gly_c', 'M_glygn2_c', 'M_gtp_c', 'M_h2o_c', 'M_his__L_c', 'M_ile__L_c', 'M_leu__L_c', 'M_lys__L_c', 'M_met__L_c', 'M_pa_cho_c', 'M_pail_cho_c', 'M_pchol_cho_c', 'M_pe_cho_c', 'M_pglyc_cho_c', 'M_phe__L_c', 'M_pro__L_c', 'M_ps_cho_c', 'M_ser__L_c', 'M_sphmyln_cho_c', 'M_tag_cho_c', 'M_thr__L_c', 'M_trp__L_c', 'M_tyr__L_c', 'M_utp_c', 'M_val__L_c', 'M_xolest_cho_c']\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: iSDY_1059\n", + "\n", + "____________________________________________\n", + "\n", + " TARGETS \n", + " FOR TARGET MODE AND FBA \n", + "____________________________________________\n", + "\n", + "Targets set:\n", + " Reactant of objective reaction\n", + " from target file\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " OBJECTVE \n", + " FOR HYBRID \n", + "____________________________________________\n", + "\n", + "Objective set:\n", + " Objective reaction from target file\n", + "\n", + "\n", + "Objective : R_BIOMASS_Ec_iJO1366_core_53p95M\n", + "\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " NETWORK \n", + "____________________________________________\n", + "\n", + "Import reaction: Kept\n", + "Product of import reaction set as seed: No\n", + "Targets can be seeds: No\n", + "Accumulation: Forbidden\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + "List of targets: ['M_10fthf_c', 'M_2fe2s_c', 'M_2ohph_c', 'M_4fe4s_c', 'M_ala__L_c', 'M_amet_c', 'M_arg__L_c', 'M_asn__L_c', 'M_asp__L_c', 'M_atp_c', 'M_bmocogdp_c', 'M_btn_c', 'M_ca2_c', 'M_cl_c', 'M_coa_c', 'M_cobalt2_c', 'M_ctp_c', 'M_cu2_c', 'M_cys__L_c', 'M_datp_c', 'M_dctp_c', 'M_dgtp_c', 'M_dttp_c', 'M_fad_c', 'M_fe2_c', 'M_fe3_c', 'M_gln__L_c', 'M_glu__L_c', 'M_gly_c', 'M_gtp_c', 'M_h2o_c', 'M_his__L_c', 'M_ile__L_c', 'M_k_c', 'M_kdo2lipid4_e', 'M_leu__L_c', 'M_lys__L_c', 'M_met__L_c', 'M_mg2_c', 'M_mlthf_c', 'M_mn2_c', 'M_mobd_c', 'M_murein5px4p_p', 'M_nad_c', 'M_nadp_c', 'M_nh4_c', 'M_ni2_c', 'M_pe160_c', 'M_pe160_p', 'M_pe161_c', 'M_pe161_p', 'M_phe__L_c', 'M_pheme_c', 'M_pro__L_c', 'M_pydx5p_c', 'M_ribflv_c', 'M_ser__L_c', 'M_sheme_c', 'M_so4_c', 'M_thf_c', 'M_thmpp_c', 'M_thr__L_c', 'M_trp__L_c', 'M_tyr__L_c', 'M_udcpdp_c', 'M_utp_c', 'M_val__L_c', 'M_zn2_c']\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: iECSP_1301\n", + "\n", + "____________________________________________\n", + "\n", + " TARGETS \n", + " FOR TARGET MODE AND FBA \n", + "____________________________________________\n", + "\n", + "Targets set:\n", + " Reactant of objective reaction\n", + " from target file\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " OBJECTVE \n", + " FOR HYBRID \n", + "____________________________________________\n", + "\n", + "Objective set:\n", + " Objective reaction from target file\n", + "\n", + "\n", + "Objective : R_BIOMASS_Ec_iJO1366_core_53p95M\n", + "\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " NETWORK \n", + "____________________________________________\n", + "\n", + "Import reaction: Kept\n", + "Product of import reaction set as seed: No\n", + "Targets can be seeds: No\n", + "Accumulation: Forbidden\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + "List of targets: ['M_10fthf_c', 'M_2fe2s_c', 'M_2ohph_c', 'M_4fe4s_c', 'M_ala__L_c', 'M_amet_c', 'M_arg__L_c', 'M_asn__L_c', 'M_asp__L_c', 'M_atp_c', 'M_bmocogdp_c', 'M_btn_c', 'M_ca2_c', 'M_cl_c', 'M_coa_c', 'M_cobalt2_c', 'M_ctp_c', 'M_cu2_c', 'M_cys__L_c', 'M_datp_c', 'M_dctp_c', 'M_dgtp_c', 'M_dttp_c', 'M_fad_c', 'M_fe2_c', 'M_fe3_c', 'M_gln__L_c', 'M_glu__L_c', 'M_gly_c', 'M_gtp_c', 'M_h2o_c', 'M_his__L_c', 'M_ile__L_c', 'M_k_c', 'M_kdo2lipid4_e', 'M_leu__L_c', 'M_lys__L_c', 'M_met__L_c', 'M_mg2_c', 'M_mlthf_c', 'M_mn2_c', 'M_mobd_c', 'M_murein5px4p_p', 'M_nad_c', 'M_nadp_c', 'M_nh4_c', 'M_ni2_c', 'M_pe160_c', 'M_pe160_p', 'M_pe161_c', 'M_pe161_p', 'M_phe__L_c', 'M_pheme_c', 'M_pro__L_c', 'M_pydx5p_c', 'M_ribflv_c', 'M_ser__L_c', 'M_sheme_c', 'M_so4_c', 'M_thf_c', 'M_thmpp_c', 'M_thr__L_c', 'M_trp__L_c', 'M_tyr__L_c', 'M_udcpdp_c', 'M_utp_c', 'M_val__L_c', 'M_zn2_c']\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: iYO844\n", + "\n", + "____________________________________________\n", + "\n", + " TARGETS \n", + " FOR TARGET MODE AND FBA \n", + "____________________________________________\n", + "\n", + "Targets set:\n", + " Reactant of objective reaction\n", + " from target file\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " OBJECTVE \n", + " FOR HYBRID \n", + "____________________________________________\n", + "\n", + "Objective set:\n", + " Objective reaction from target file\n", + "\n", + "\n", + "Objective : R_BIOMASS_BS_10\n", + "\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " NETWORK \n", + "____________________________________________\n", + "\n", + "Import reaction: Kept\n", + "Product of import reaction set as seed: No\n", + "Targets can be seeds: No\n", + "Accumulation: Forbidden\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + "List of targets: ['M_10fthf_c', 'M_ala__L_c', 'M_amp_c', 'M_arg__L_c', 'M_asn__L_c', 'M_asp__L_c', 'M_atp_c', 'M_ca2_c', 'M_cdlp_BS_c', 'M_cdp_c', 'M_cmp_c', 'M_ctp_c', 'M_cys__L_c', 'M_d12dg_BS_c', 'M_datp_c', 'M_dctp_c', 'M_dgtp_c', 'M_dttp_c', 'M_fe3_c', 'M_gdp_c', 'M_gln__L_c', 'M_glu__L_c', 'M_gly_c', 'M_gmp_c', 'M_gtca1_45_BS_c', 'M_gtca2_45_BS_c', 'M_gtca3_45_BS_c', 'M_gtp_c', 'M_h2o_c', 'M_his__L_c', 'M_ile__L_c', 'M_k_c', 'M_leu__L_c', 'M_lipo1_24_BS_c', 'M_lipo2_24_BS_c', 'M_lipo3_24_BS_c', 'M_lipo4_24_BS_c', 'M_lys__L_c', 'M_lysylpgly_BS_c', 'M_m12dg_BS_c', 'M_met__L_c', 'M_mg2_c', 'M_mql7_c', 'M_nad_c', 'M_nadp_c', 'M_nadph_c', 'M_peptido_BS_c', 'M_pgly_BS_c', 'M_phe__L_c', 'M_ppi_c', 'M_pro__L_c', 'M_psetha_BS_c', 'M_ser__L_c', 'M_t12dg_BS_c', 'M_tcam_BS_c', 'M_thr__L_c', 'M_trp__L_c', 'M_tyr__L_c', 'M_utp_c', 'M_val__L_c']\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: iETEC_1333\n", + "\n", + "____________________________________________\n", + "\n", + " TARGETS \n", + " FOR TARGET MODE AND FBA \n", + "____________________________________________\n", + "\n", + "Targets set:\n", + " Reactant of objective reaction\n", + " from target file\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " OBJECTVE \n", + " FOR HYBRID \n", + "____________________________________________\n", + "\n", + "Objective set:\n", + " Objective reaction from target file\n", + "\n", + "\n", + "Objective : R_BIOMASS_Ec_iJO1366_core_53p95M\n", + "\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " NETWORK \n", + "____________________________________________\n", + "\n", + "Import reaction: Kept\n", + "Product of import reaction set as seed: No\n", + "Targets can be seeds: No\n", + "Accumulation: Forbidden\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + "List of targets: ['M_10fthf_c', 'M_2fe2s_c', 'M_2ohph_c', 'M_4fe4s_c', 'M_ala__L_c', 'M_amet_c', 'M_arg__L_c', 'M_asn__L_c', 'M_asp__L_c', 'M_atp_c', 'M_bmocogdp_c', 'M_btn_c', 'M_ca2_c', 'M_cl_c', 'M_coa_c', 'M_cobalt2_c', 'M_ctp_c', 'M_cu2_c', 'M_cys__L_c', 'M_datp_c', 'M_dctp_c', 'M_dgtp_c', 'M_dttp_c', 'M_fad_c', 'M_fe2_c', 'M_fe3_c', 'M_gln__L_c', 'M_glu__L_c', 'M_gly_c', 'M_gtp_c', 'M_h2o_c', 'M_his__L_c', 'M_ile__L_c', 'M_k_c', 'M_kdo2lipid4_e', 'M_leu__L_c', 'M_lys__L_c', 'M_met__L_c', 'M_mg2_c', 'M_mlthf_c', 'M_mn2_c', 'M_mobd_c', 'M_murein5px4p_p', 'M_nad_c', 'M_nadp_c', 'M_nh4_c', 'M_ni2_c', 'M_pe160_c', 'M_pe160_p', 'M_pe161_c', 'M_pe161_p', 'M_phe__L_c', 'M_pheme_c', 'M_pro__L_c', 'M_pydx5p_c', 'M_ribflv_c', 'M_ser__L_c', 'M_sheme_c', 'M_so4_c', 'M_thf_c', 'M_thmpp_c', 'M_thr__L_c', 'M_trp__L_c', 'M_tyr__L_c', 'M_udcpdp_c', 'M_utp_c', 'M_val__L_c', 'M_zn2_c']\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: iAB_RBC_283\n", + "\n", + "____________________________________________\n", + "\n", + " TARGETS \n", + " FOR TARGET MODE AND FBA \n", + "____________________________________________\n", + "\n", + "Targets set:\n", + " Reactant of objective reaction\n", + " from target file\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " OBJECTVE \n", + " FOR HYBRID \n", + "____________________________________________\n", + "\n", + "Objective set:\n", + " Objective reaction from target file\n", + "\n", + "\n", + "Objective : R_NaKt\n", + "\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " NETWORK \n", + "____________________________________________\n", + "\n", + "Import reaction: Kept\n", + "Product of import reaction set as seed: No\n", + "Targets can be seeds: No\n", + "Accumulation: Forbidden\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + "List of targets: ['M_atp_c', 'M_h2o_c', 'M_k_e', 'M_na1_c']\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: iLF82_1304\n", + "\n", + "____________________________________________\n", + "\n", + " TARGETS \n", + " FOR TARGET MODE AND FBA \n", + "____________________________________________\n", + "\n", + "Targets set:\n", + " Reactant of objective reaction\n", + " from target file\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " OBJECTVE \n", + " FOR HYBRID \n", + "____________________________________________\n", + "\n", + "Objective set:\n", + " Objective reaction from target file\n", + "\n", + "\n", + "Objective : R_BIOMASS_Ec_iJO1366_core_53p95M\n", + "\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " NETWORK \n", + "____________________________________________\n", + "\n", + "Import reaction: Kept\n", + "Product of import reaction set as seed: No\n", + "Targets can be seeds: No\n", + "Accumulation: Forbidden\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + "List of targets: ['M_10fthf_c', 'M_2fe2s_c', 'M_2ohph_c', 'M_4fe4s_c', 'M_ala__L_c', 'M_amet_c', 'M_arg__L_c', 'M_asn__L_c', 'M_asp__L_c', 'M_atp_c', 'M_bmocogdp_c', 'M_btn_c', 'M_ca2_c', 'M_cl_c', 'M_coa_c', 'M_cobalt2_c', 'M_ctp_c', 'M_cu2_c', 'M_cys__L_c', 'M_datp_c', 'M_dctp_c', 'M_dgtp_c', 'M_dttp_c', 'M_fad_c', 'M_fe2_c', 'M_fe3_c', 'M_gln__L_c', 'M_glu__L_c', 'M_gly_c', 'M_gtp_c', 'M_h2o_c', 'M_his__L_c', 'M_ile__L_c', 'M_k_c', 'M_kdo2lipid4_e', 'M_leu__L_c', 'M_lys__L_c', 'M_met__L_c', 'M_mg2_c', 'M_mlthf_c', 'M_mn2_c', 'M_mobd_c', 'M_murein5px4p_p', 'M_nad_c', 'M_nadp_c', 'M_nh4_c', 'M_ni2_c', 'M_pe160_c', 'M_pe160_p', 'M_pe161_c', 'M_pe161_p', 'M_phe__L_c', 'M_pheme_c', 'M_pro__L_c', 'M_pydx5p_c', 'M_ribflv_c', 'M_ser__L_c', 'M_sheme_c', 'M_so4_c', 'M_thf_c', 'M_thmpp_c', 'M_thr__L_c', 'M_trp__L_c', 'M_tyr__L_c', 'M_udcpdp_c', 'M_utp_c', 'M_val__L_c', 'M_zn2_c']\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: iECOK1_1307\n", + "\n", + "____________________________________________\n", + "\n", + " TARGETS \n", + " FOR TARGET MODE AND FBA \n", + "____________________________________________\n", + "\n", + "Targets set:\n", + " Reactant of objective reaction\n", + " from target file\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " OBJECTVE \n", + " FOR HYBRID \n", + "____________________________________________\n", + "\n", + "Objective set:\n", + " Objective reaction from target file\n", + "\n", + "\n", + "Objective : R_BIOMASS_Ec_iJO1366_core_53p95M\n", + "\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " NETWORK \n", + "____________________________________________\n", + "\n", + "Import reaction: Kept\n", + "Product of import reaction set as seed: No\n", + "Targets can be seeds: No\n", + "Accumulation: Forbidden\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + "List of targets: ['M_10fthf_c', 'M_2fe2s_c', 'M_2ohph_c', 'M_4fe4s_c', 'M_ala__L_c', 'M_amet_c', 'M_arg__L_c', 'M_asn__L_c', 'M_asp__L_c', 'M_atp_c', 'M_bmocogdp_c', 'M_btn_c', 'M_ca2_c', 'M_cl_c', 'M_coa_c', 'M_cobalt2_c', 'M_ctp_c', 'M_cu2_c', 'M_cys__L_c', 'M_datp_c', 'M_dctp_c', 'M_dgtp_c', 'M_dttp_c', 'M_fad_c', 'M_fe2_c', 'M_fe3_c', 'M_gln__L_c', 'M_glu__L_c', 'M_gly_c', 'M_gtp_c', 'M_h2o_c', 'M_his__L_c', 'M_ile__L_c', 'M_k_c', 'M_kdo2lipid4_e', 'M_leu__L_c', 'M_lys__L_c', 'M_met__L_c', 'M_mg2_c', 'M_mlthf_c', 'M_mn2_c', 'M_mobd_c', 'M_murein5px4p_p', 'M_nad_c', 'M_nadp_c', 'M_nh4_c', 'M_ni2_c', 'M_pe160_c', 'M_pe160_p', 'M_pe161_c', 'M_pe161_p', 'M_phe__L_c', 'M_pheme_c', 'M_pro__L_c', 'M_pydx5p_c', 'M_ribflv_c', 'M_ser__L_c', 'M_sheme_c', 'M_so4_c', 'M_thf_c', 'M_thmpp_c', 'M_thr__L_c', 'M_trp__L_c', 'M_tyr__L_c', 'M_udcpdp_c', 'M_utp_c', 'M_val__L_c', 'M_zn2_c']\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: iJR904\n", + "\n", + "____________________________________________\n", + "\n", + " TARGETS \n", + " FOR TARGET MODE AND FBA \n", + "____________________________________________\n", + "\n", + "Targets set:\n", + " Reactant of objective reaction\n", + " from target file\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " OBJECTVE \n", + " FOR HYBRID \n", + "____________________________________________\n", + "\n", + "Objective set:\n", + " Objective reaction from target file\n", + "\n", + "\n", + "Objective : R_BIOMASS_Ecoli\n", + "\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " NETWORK \n", + "____________________________________________\n", + "\n", + "Import reaction: Kept\n", + "Product of import reaction set as seed: No\n", + "Targets can be seeds: No\n", + "Accumulation: Forbidden\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + "List of targets: ['M_5mthf_c', 'M_accoa_c', 'M_ala__L_c', 'M_amp_c', 'M_arg__L_c', 'M_asn__L_c', 'M_asp__L_c', 'M_atp_c', 'M_clpn_EC_c', 'M_coa_c', 'M_ctp_c', 'M_cys__L_c', 'M_datp_c', 'M_dctp_c', 'M_dgtp_c', 'M_dttp_c', 'M_fad_c', 'M_gln__L_c', 'M_glu__L_c', 'M_gly_c', 'M_glycogen_c', 'M_gtp_c', 'M_h2o_c', 'M_his__L_c', 'M_ile__L_c', 'M_leu__L_c', 'M_lps_EC_c', 'M_lys__L_c', 'M_met__L_c', 'M_nad_c', 'M_nadh_c', 'M_nadp_c', 'M_nadph_c', 'M_pe_EC_c', 'M_peptido_EC_c', 'M_pg_EC_c', 'M_phe__L_c', 'M_pro__L_c', 'M_ps_EC_c', 'M_ptrc_c', 'M_ser__L_c', 'M_spmd_c', 'M_succoa_c', 'M_thr__L_c', 'M_trp__L_c', 'M_tyr__L_c', 'M_udpg_c', 'M_utp_c', 'M_val__L_c']\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: iAF1260\n", + "\n", + "____________________________________________\n", + "\n", + " TARGETS \n", + " FOR TARGET MODE AND FBA \n", + "____________________________________________\n", + "\n", + "Targets set:\n", + " Reactant of objective reaction\n", + " from target file\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " OBJECTVE \n", + " FOR HYBRID \n", + "____________________________________________\n", + "\n", + "Objective set:\n", + " Objective reaction from target file\n", + "\n", + "\n", + "Objective : R_BIOMASS_Ec_iAF1260_core_59p81M\n", + "\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " NETWORK \n", + "____________________________________________\n", + "\n", + "Import reaction: Kept\n", + "Product of import reaction set as seed: No\n", + "Targets can be seeds: No\n", + "Accumulation: Forbidden\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + "List of targets: ['M_10fthf_c', 'M_2ohph_c', 'M_ala__L_c', 'M_amet_c', 'M_arg__L_c', 'M_asn__L_c', 'M_asp__L_c', 'M_atp_c', 'M_ca2_c', 'M_cl_c', 'M_coa_c', 'M_cobalt2_c', 'M_ctp_c', 'M_cu2_c', 'M_cys__L_c', 'M_datp_c', 'M_dctp_c', 'M_dgtp_c', 'M_dttp_c', 'M_fad_c', 'M_fe2_c', 'M_fe3_c', 'M_gln__L_c', 'M_glu__L_c', 'M_gly_c', 'M_gtp_c', 'M_h2o_c', 'M_his__L_c', 'M_ile__L_c', 'M_k_c', 'M_kdo2lipid4_e', 'M_leu__L_c', 'M_lys__L_c', 'M_met__L_c', 'M_mg2_c', 'M_mlthf_c', 'M_mn2_c', 'M_mobd_c', 'M_murein5px4p_p', 'M_nad_c', 'M_nadp_c', 'M_nh4_c', 'M_pe160_c', 'M_pe160_p', 'M_pe161_c', 'M_pe161_p', 'M_phe__L_c', 'M_pheme_c', 'M_pro__L_c', 'M_pydx5p_c', 'M_ribflv_c', 'M_ser__L_c', 'M_sheme_c', 'M_so4_c', 'M_thf_c', 'M_thmpp_c', 'M_thr__L_c', 'M_trp__L_c', 'M_tyr__L_c', 'M_udcpdp_c', 'M_utp_c', 'M_val__L_c', 'M_zn2_c']\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: iECIAI39_1322\n", + "\n", + "____________________________________________\n", + "\n", + " TARGETS \n", + " FOR TARGET MODE AND FBA \n", + "____________________________________________\n", + "\n", + "Targets set:\n", + " Reactant of objective reaction\n", + " from target file\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " OBJECTVE \n", + " FOR HYBRID \n", + "____________________________________________\n", + "\n", + "Objective set:\n", + " Objective reaction from target file\n", + "\n", + "\n", + "Objective : R_BIOMASS_Ec_iJO1366_core_53p95M\n", + "\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " NETWORK \n", + "____________________________________________\n", + "\n", + "Import reaction: Kept\n", + "Product of import reaction set as seed: No\n", + "Targets can be seeds: No\n", + "Accumulation: Forbidden\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + "List of targets: ['M_10fthf_c', 'M_2fe2s_c', 'M_2ohph_c', 'M_4fe4s_c', 'M_ala__L_c', 'M_amet_c', 'M_arg__L_c', 'M_asn__L_c', 'M_asp__L_c', 'M_atp_c', 'M_bmocogdp_c', 'M_btn_c', 'M_ca2_c', 'M_cl_c', 'M_coa_c', 'M_cobalt2_c', 'M_ctp_c', 'M_cu2_c', 'M_cys__L_c', 'M_datp_c', 'M_dctp_c', 'M_dgtp_c', 'M_dttp_c', 'M_fad_c', 'M_fe2_c', 'M_fe3_c', 'M_gln__L_c', 'M_glu__L_c', 'M_gly_c', 'M_gtp_c', 'M_h2o_c', 'M_his__L_c', 'M_ile__L_c', 'M_k_c', 'M_kdo2lipid4_e', 'M_leu__L_c', 'M_lys__L_c', 'M_met__L_c', 'M_mg2_c', 'M_mlthf_c', 'M_mn2_c', 'M_mobd_c', 'M_murein5px4p_p', 'M_nad_c', 'M_nadp_c', 'M_nh4_c', 'M_ni2_c', 'M_pe160_c', 'M_pe160_p', 'M_pe161_c', 'M_pe161_p', 'M_phe__L_c', 'M_pheme_c', 'M_pro__L_c', 'M_pydx5p_c', 'M_ribflv_c', 'M_ser__L_c', 'M_sheme_c', 'M_so4_c', 'M_thf_c', 'M_thmpp_c', 'M_thr__L_c', 'M_trp__L_c', 'M_tyr__L_c', 'M_udcpdp_c', 'M_utp_c', 'M_val__L_c', 'M_zn2_c']\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: iNRG857_1313\n", + "\n", + "____________________________________________\n", + "\n", + " TARGETS \n", + " FOR TARGET MODE AND FBA \n", + "____________________________________________\n", + "\n", + "Targets set:\n", + " Reactant of objective reaction\n", + " from target file\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " OBJECTVE \n", + " FOR HYBRID \n", + "____________________________________________\n", + "\n", + "Objective set:\n", + " Objective reaction from target file\n", + "\n", + "\n", + "Objective : R_BIOMASS_Ec_iJO1366_core_53p95M\n", + "\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " NETWORK \n", + "____________________________________________\n", + "\n", + "Import reaction: Kept\n", + "Product of import reaction set as seed: No\n", + "Targets can be seeds: No\n", + "Accumulation: Forbidden\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + "List of targets: ['M_10fthf_c', 'M_2fe2s_c', 'M_2ohph_c', 'M_4fe4s_c', 'M_ala__L_c', 'M_amet_c', 'M_arg__L_c', 'M_asn__L_c', 'M_asp__L_c', 'M_atp_c', 'M_bmocogdp_c', 'M_btn_c', 'M_ca2_c', 'M_cl_c', 'M_coa_c', 'M_cobalt2_c', 'M_ctp_c', 'M_cu2_c', 'M_cys__L_c', 'M_datp_c', 'M_dctp_c', 'M_dgtp_c', 'M_dttp_c', 'M_fad_c', 'M_fe2_c', 'M_fe3_c', 'M_gln__L_c', 'M_glu__L_c', 'M_gly_c', 'M_gtp_c', 'M_h2o_c', 'M_his__L_c', 'M_ile__L_c', 'M_k_c', 'M_kdo2lipid4_e', 'M_leu__L_c', 'M_lys__L_c', 'M_met__L_c', 'M_mg2_c', 'M_mlthf_c', 'M_mn2_c', 'M_mobd_c', 'M_murein5px4p_p', 'M_nad_c', 'M_nadp_c', 'M_nh4_c', 'M_ni2_c', 'M_pe160_c', 'M_pe160_p', 'M_pe161_c', 'M_pe161_p', 'M_phe__L_c', 'M_pheme_c', 'M_pro__L_c', 'M_pydx5p_c', 'M_ribflv_c', 'M_ser__L_c', 'M_sheme_c', 'M_so4_c', 'M_thf_c', 'M_thmpp_c', 'M_thr__L_c', 'M_trp__L_c', 'M_tyr__L_c', 'M_udcpdp_c', 'M_utp_c', 'M_val__L_c', 'M_zn2_c']\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: iPC815\n", + "\n", + "____________________________________________\n", + "\n", + " TARGETS \n", + " FOR TARGET MODE AND FBA \n", + "____________________________________________\n", + "\n", + "Targets set:\n", + " Reactant of objective reaction\n", + " from target file\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " OBJECTVE \n", + " FOR HYBRID \n", + "____________________________________________\n", + "\n", + "Objective set:\n", + " Objective reaction from target file\n", + "\n", + "\n", + "Objective : R_BIOMASS_37C\n", + "\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " NETWORK \n", + "____________________________________________\n", + "\n", + "Import reaction: Kept\n", + "Product of import reaction set as seed: No\n", + "Targets can be seeds: No\n", + "Accumulation: Forbidden\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + "List of targets: ['M_5mthf_c', 'M_accoa_c', 'M_ala__L_c', 'M_amp_c', 'M_arg__L_c', 'M_asn__L_c', 'M_asp__L_c', 'M_atp_c', 'M_clpn140_p', 'M_clpn160_p', 'M_clpn161_p', 'M_clpn181_p', 'M_coa_c', 'M_ctp_c', 'M_cys__L_c', 'M_datp_c', 'M_dctp_c', 'M_dgtp_c', 'M_dttp_c', 'M_fad_c', 'M_gln__L_c', 'M_glu__L_c', 'M_gly_c', 'M_glycogen_c', 'M_gtp_c', 'M_h2o_c', 'M_his__L_c', 'M_ile__L_c', 'M_leu__L_c', 'M_lps_tetra_c', 'M_lys__L_c', 'M_met__L_c', 'M_nad_c', 'M_nadh_c', 'M_nadp_c', 'M_nadph_c', 'M_pe140_p', 'M_pe160_p', 'M_pe161_p', 'M_pe181_p', 'M_pg140_p', 'M_pg160_p', 'M_pg161_p', 'M_pg181_p', 'M_phe__L_c', 'M_pro__L_c', 'M_ptrc_c', 'M_ser__L_c', 'M_spmd_c', 'M_succoa_c', 'M_thr__L_c', 'M_trp__L_c', 'M_tyr__L_c', 'M_udpg_c', 'M_utp_c', 'M_val__L_c']\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: iEC1344_C\n", + "\n", + "____________________________________________\n", + "\n", + " TARGETS \n", + " FOR TARGET MODE AND FBA \n", + "____________________________________________\n", + "\n", + "Targets set:\n", + " Reactant of objective reaction\n", + " from target file\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " OBJECTVE \n", + " FOR HYBRID \n", + "____________________________________________\n", + "\n", + "Objective set:\n", + " Objective reaction from target file\n", + "\n", + "\n", + "Objective : R_BIOMASS_Ec_iJO1366_core_53p95M\n", + "\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " NETWORK \n", + "____________________________________________\n", + "\n", + "Import reaction: Kept\n", + "Product of import reaction set as seed: No\n", + "Targets can be seeds: No\n", + "Accumulation: Forbidden\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + "List of targets: ['M_10fthf_c', 'M_2fe2s_c', 'M_2ohph_c', 'M_4fe4s_c', 'M_ala__L_c', 'M_amet_c', 'M_arg__L_c', 'M_asn__L_c', 'M_asp__L_c', 'M_atp_c', 'M_bmocogdp_c', 'M_btn_c', 'M_ca2_c', 'M_cl_c', 'M_coa_c', 'M_cobalt2_c', 'M_ctp_c', 'M_cu2_c', 'M_cys__L_c', 'M_datp_c', 'M_dctp_c', 'M_dgtp_c', 'M_dttp_c', 'M_fad_c', 'M_fe2_c', 'M_fe3_c', 'M_gln__L_c', 'M_glu__L_c', 'M_gly_c', 'M_gtp_c', 'M_h2o_c', 'M_his__L_c', 'M_ile__L_c', 'M_k_c', 'M_kdo2lipid4_e', 'M_leu__L_c', 'M_lys__L_c', 'M_met__L_c', 'M_mg2_c', 'M_mlthf_c', 'M_mn2_c', 'M_mobd_c', 'M_murein5px4p_p', 'M_nad_c', 'M_nadp_c', 'M_nh4_c', 'M_ni2_c', 'M_pe160_c', 'M_pe160_p', 'M_pe161_c', 'M_pe161_p', 'M_phe__L_c', 'M_pheme_c', 'M_pro__L_c', 'M_pydx5p_c', 'M_ribflv_c', 'M_ser__L_c', 'M_sheme_c', 'M_so4_c', 'M_thf_c', 'M_thmpp_c', 'M_thr__L_c', 'M_trp__L_c', 'M_tyr__L_c', 'M_udcpdp_c', 'M_utp_c', 'M_val__L_c', 'M_zn2_c']\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: iAF1260b\n", + "\n", + "____________________________________________\n", + "\n", + " TARGETS \n", + " FOR TARGET MODE AND FBA \n", + "____________________________________________\n", + "\n", + "Targets set:\n", + " Reactant of objective reaction\n", + " from target file\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " OBJECTVE \n", + " FOR HYBRID \n", + "____________________________________________\n", + "\n", + "Objective set:\n", + " Objective reaction from target file\n", + "\n", + "\n", + "Objective : R_BIOMASS_Ec_iAF1260_core_59p81M\n", + "\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " NETWORK \n", + "____________________________________________\n", + "\n", + "Import reaction: Kept\n", + "Product of import reaction set as seed: No\n", + "Targets can be seeds: No\n", + "Accumulation: Forbidden\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + "List of targets: ['M_10fthf_c', 'M_2ohph_c', 'M_ala__L_c', 'M_amet_c', 'M_arg__L_c', 'M_asn__L_c', 'M_asp__L_c', 'M_atp_c', 'M_ca2_c', 'M_cl_c', 'M_coa_c', 'M_cobalt2_c', 'M_ctp_c', 'M_cu2_c', 'M_cys__L_c', 'M_datp_c', 'M_dctp_c', 'M_dgtp_c', 'M_dttp_c', 'M_fad_c', 'M_fe2_c', 'M_fe3_c', 'M_gln__L_c', 'M_glu__L_c', 'M_gly_c', 'M_gtp_c', 'M_h2o_c', 'M_his__L_c', 'M_ile__L_c', 'M_k_c', 'M_kdo2lipid4_e', 'M_leu__L_c', 'M_lys__L_c', 'M_met__L_c', 'M_mg2_c', 'M_mlthf_c', 'M_mn2_c', 'M_mobd_c', 'M_murein5px4p_p', 'M_nad_c', 'M_nadp_c', 'M_nh4_c', 'M_pe160_c', 'M_pe160_p', 'M_pe161_c', 'M_pe161_p', 'M_phe__L_c', 'M_pheme_c', 'M_pro__L_c', 'M_pydx5p_c', 'M_ribflv_c', 'M_ser__L_c', 'M_sheme_c', 'M_so4_c', 'M_thf_c', 'M_thmpp_c', 'M_thr__L_c', 'M_trp__L_c', 'M_tyr__L_c', 'M_udcpdp_c', 'M_utp_c', 'M_val__L_c', 'M_zn2_c']\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: iAM_Pv461\n", + "\n", + "____________________________________________\n", + "\n", + " TARGETS \n", + " FOR TARGET MODE AND FBA \n", + "____________________________________________\n", + "\n", + "Targets set:\n", + " Reactant of objective reaction\n", + " from target file\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " OBJECTVE \n", + " FOR HYBRID \n", + "____________________________________________\n", + "\n", + "Objective set:\n", + " Objective reaction from target file\n", + "\n", + "\n", + "Objective : R_BIOMASS__4\n", + "\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " NETWORK \n", + "____________________________________________\n", + "\n", + "Import reaction: Kept\n", + "Product of import reaction set as seed: No\n", + "Targets can be seeds: No\n", + "Accumulation: Forbidden\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + "List of targets: ['M_5thf_c', 'M_ala__L_c', 'M_arg__L_c', 'M_asn__L_c', 'M_asp__L_c', 'M_atp_c', 'M_clpn_pf_16_0_18_1_16_0_18_1_m', 'M_coa_c', 'M_ctp_c', 'M_cys__L_c', 'M_datp_c', 'M_dctp_c', 'M_dgtp_c', 'M_dttp_c', 'M_fad_c', 'M_gdpfuc_c', 'M_gdpmann_c', 'M_gln__L_c', 'M_glu__L_c', 'M_gly_c', 'M_gthrd_c', 'M_gtp_c', 'M_h2o_c', 'M_his__L_c', 'M_ile__L_c', 'M_leu__L_c', 'M_lipopb_m', 'M_lys__L_c', 'M_mem3gacpail_pf_18_0_18_1_16_0_r', 'M_met__L_c', 'M_nad_c', 'M_nadp_c', 'M_pail_pf_16_0_16_0_c', 'M_pail_pf_16_0_18_0_c', 'M_pail_pf_16_0_18_1_c', 'M_pail_pf_18_0_18_0_c', 'M_pail_pf_18_0_18_1_c', 'M_pail_pf_18_1_18_1_c', 'M_pchol_pf_16_0_16_0_c', 'M_pchol_pf_16_0_18_0_c', 'M_pchol_pf_16_0_18_1_c', 'M_pchol_pf_16_0_18_2_c', 'M_pchol_pf_16_0_20_4_c', 'M_pchol_pf_18_1_18_1_c', 'M_pchol_pf_18_1_18_2_c', 'M_pchol_pf_18_2_18_2_c', 'M_pe_pf_16_0_16_0_c', 'M_pe_pf_16_0_18_1_c', 'M_pe_pf_16_0_18_2_c', 'M_pe_pf_18_1_18_1_c', 'M_pe_pf_18_1_18_2_c', 'M_pe_pf_18_2_18_2_c', 'M_pe_pf_20_4_20_4_c', 'M_phe__L_c', 'M_pheme_c', 'M_pro__L_c', 'M_ps_pf_18_0_18_1_c', 'M_ps_pf_18_0_20_4_c', 'M_ps_pf_18_1_18_1_c', 'M_ps_pf_18_1_20_4_c', 'M_ps_pf_20_4_20_4_c', 'M_ptrc_c', 'M_pydx5p_c', 'M_q8h2_m', 'M_ser__L_c', 'M_sphymyln_pf_18_1_16_0_g', 'M_sphymyln_pf_18_1_18_0_g', 'M_sphymyln_pf_18_1_24_0_g', 'M_spmd_c', 'M_tag_pf_16_0_18_0_18_0_c', 'M_tag_pf_18_0_18_0_18_1_c', 'M_tag_pf_18_0_18_1_16_0_c', 'M_tag_pf_18_1_18_2_18_2_c', 'M_thmpp_c', 'M_thr__L_c', 'M_trp__L_c', 'M_tyr__L_c', 'M_utp_c', 'M_val__L_c']\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: iSSON_1240\n", + "\n", + "____________________________________________\n", + "\n", + " TARGETS \n", + " FOR TARGET MODE AND FBA \n", + "____________________________________________\n", + "\n", + "Targets set:\n", + " Reactant of objective reaction\n", + " from target file\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " OBJECTVE \n", + " FOR HYBRID \n", + "____________________________________________\n", + "\n", + "Objective set:\n", + " Objective reaction from target file\n", + "\n", + "\n", + "Objective : R_BIOMASS_Ec_iJO1366_core_53p95M\n", + "\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " NETWORK \n", + "____________________________________________\n", + "\n", + "Import reaction: Kept\n", + "Product of import reaction set as seed: No\n", + "Targets can be seeds: No\n", + "Accumulation: Forbidden\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + "List of targets: ['M_10fthf_c', 'M_2fe2s_c', 'M_2ohph_c', 'M_4fe4s_c', 'M_ala__L_c', 'M_amet_c', 'M_arg__L_c', 'M_asn__L_c', 'M_asp__L_c', 'M_atp_c', 'M_bmocogdp_c', 'M_btn_c', 'M_ca2_c', 'M_cl_c', 'M_coa_c', 'M_cobalt2_c', 'M_ctp_c', 'M_cu2_c', 'M_cys__L_c', 'M_datp_c', 'M_dctp_c', 'M_dgtp_c', 'M_dttp_c', 'M_fad_c', 'M_fe2_c', 'M_fe3_c', 'M_gln__L_c', 'M_glu__L_c', 'M_gly_c', 'M_gtp_c', 'M_h2o_c', 'M_his__L_c', 'M_ile__L_c', 'M_k_c', 'M_kdo2lipid4_e', 'M_leu__L_c', 'M_lys__L_c', 'M_met__L_c', 'M_mg2_c', 'M_mlthf_c', 'M_mn2_c', 'M_mobd_c', 'M_murein5px4p_p', 'M_nad_c', 'M_nadp_c', 'M_nh4_c', 'M_ni2_c', 'M_pe160_c', 'M_pe160_p', 'M_pe161_c', 'M_pe161_p', 'M_phe__L_c', 'M_pheme_c', 'M_pro__L_c', 'M_pydx5p_c', 'M_ribflv_c', 'M_ser__L_c', 'M_sheme_c', 'M_so4_c', 'M_thf_c', 'M_thmpp_c', 'M_thr__L_c', 'M_trp__L_c', 'M_tyr__L_c', 'M_udcpdp_c', 'M_utp_c', 'M_val__L_c', 'M_zn2_c']\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: iEK1008\n", + "\n", + "____________________________________________\n", + "\n", + " TARGETS \n", + " FOR TARGET MODE AND FBA \n", + "____________________________________________\n", + "\n", + "Targets set:\n", + " Reactant of objective reaction\n", + " from target file\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " OBJECTVE \n", + " FOR HYBRID \n", + "____________________________________________\n", + "\n", + "Objective set:\n", + " Objective reaction from target file\n", + "\n", + "\n", + "Objective : R_BIOMASS__2\n", + "\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " NETWORK \n", + "____________________________________________\n", + "\n", + "Import reaction: Kept\n", + "Product of import reaction set as seed: No\n", + "Targets can be seeds: No\n", + "Accumulation: Forbidden\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + "List of targets: ['M_14glucan_c', 'M_Ac1PIM1_c', 'M_Ac1PIM2_c', 'M_Ac1PIM3_c', 'M_Ac1PIM4_c', 'M_Ac2PIM2_c', 'M_PIM3_c', 'M_PIM4_c', 'M_PIM5_c', 'M_PIM6_c', 'M_acgam1p_c', 'M_ala__L_c', 'M_amet_c', 'M_arabinanagalfragund_c', 'M_arach_c', 'M_arg__L_c', 'M_asn__L_c', 'M_asp__L_c', 'M_atp_c', 'M_c78mycolatepp_c', 'M_clpn160190_c', 'M_cmp_c', 'M_coa_c', 'M_ctp_c', 'M_cys__L_c', 'M_damp_c', 'M_datp_c', 'M_dctp_c', 'M_dgtp_c', 'M_dttp_c', 'M_fad_c', 'M_fdxox_c', 'M_fdxrd_c', 'M_fe2_c', 'M_fe3_c', 'M_fmn_c', 'M_gdpmann_c', 'M_glc__D_c', 'M_gln__L_c', 'M_glu__L_c', 'M_gly_c', 'M_glyc_c', 'M_glycogen_c', 'M_gmp_c', 'M_gtp_c', 'M_h2o_c', 'M_hdca_c', 'M_hdcea_c', 'M_hemeA_c', 'M_hemeO_c', 'M_hexc_c', 'M_his__L_c', 'M_hphthiocnylcoa_c', 'M_ile__L_c', 'M_leu__L_c', 'M_lys__L_c', 'M_man_c', 'M_mbhn_c', 'M_met__L_c', 'M_mocdca_c', 'M_mql8_c', 'M_msh_c', 'M_nad_c', 'M_nadp_c', 'M_ocdca_c', 'M_ocdcea_c', 'M_pa160190_c', 'M_pa160_c', 'M_pa190190_c', 'M_pat_c', 'M_pe160_c', 'M_peptido_TB1_c', 'M_peptido_TB2_c', 'M_pg160190_c', 'M_pg160_c', 'M_pg190_c', 'M_phdca_c', 'M_phe__L_c', 'M_pro__L_c', 'M_rib__D_c', 'M_ser__L_c', 'M_sheme_c', 'M_tat_c', 'M_thf_c', 'M_thmpp_c', 'M_thr__L_c', 'M_tmha1_c', 'M_tmha2_c', 'M_tmha3_c', 'M_tmha4_c', 'M_tre6p_c', 'M_tre_c', 'M_tres_c', 'M_trp__L_c', 'M_ttdca_c', 'M_tyr__L_c', 'M_uaaAgla_c', 'M_uaaGgla_c', 'M_uaagmda_c', 'M_uamr_c', 'M_ugagmda_c', 'M_utp_c', 'M_val__L_c']\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: iECP_1309\n", + "\n", + "____________________________________________\n", + "\n", + " TARGETS \n", + " FOR TARGET MODE AND FBA \n", + "____________________________________________\n", + "\n", + "Targets set:\n", + " Reactant of objective reaction\n", + " from target file\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " OBJECTVE \n", + " FOR HYBRID \n", + "____________________________________________\n", + "\n", + "Objective set:\n", + " Objective reaction from target file\n", + "\n", + "\n", + "Objective : R_BIOMASS_Ec_iJO1366_core_53p95M\n", + "\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " NETWORK \n", + "____________________________________________\n", + "\n", + "Import reaction: Kept\n", + "Product of import reaction set as seed: No\n", + "Targets can be seeds: No\n", + "Accumulation: Forbidden\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + "List of targets: ['M_10fthf_c', 'M_2fe2s_c', 'M_2ohph_c', 'M_4fe4s_c', 'M_ala__L_c', 'M_amet_c', 'M_arg__L_c', 'M_asn__L_c', 'M_asp__L_c', 'M_atp_c', 'M_bmocogdp_c', 'M_btn_c', 'M_ca2_c', 'M_cl_c', 'M_coa_c', 'M_cobalt2_c', 'M_ctp_c', 'M_cu2_c', 'M_cys__L_c', 'M_datp_c', 'M_dctp_c', 'M_dgtp_c', 'M_dttp_c', 'M_fad_c', 'M_fe2_c', 'M_fe3_c', 'M_gln__L_c', 'M_glu__L_c', 'M_gly_c', 'M_gtp_c', 'M_h2o_c', 'M_his__L_c', 'M_ile__L_c', 'M_k_c', 'M_kdo2lipid4_e', 'M_leu__L_c', 'M_lys__L_c', 'M_met__L_c', 'M_mg2_c', 'M_mlthf_c', 'M_mn2_c', 'M_mobd_c', 'M_murein5px4p_p', 'M_nad_c', 'M_nadp_c', 'M_nh4_c', 'M_ni2_c', 'M_pe160_c', 'M_pe160_p', 'M_pe161_c', 'M_pe161_p', 'M_phe__L_c', 'M_pheme_c', 'M_pro__L_c', 'M_pydx5p_c', 'M_ribflv_c', 'M_ser__L_c', 'M_sheme_c', 'M_so4_c', 'M_thf_c', 'M_thmpp_c', 'M_thr__L_c', 'M_trp__L_c', 'M_tyr__L_c', 'M_udcpdp_c', 'M_utp_c', 'M_val__L_c', 'M_zn2_c']\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: iB21_1397\n", + "\n", + "____________________________________________\n", + "\n", + " TARGETS \n", + " FOR TARGET MODE AND FBA \n", + "____________________________________________\n", + "\n", + "Targets set:\n", + " Reactant of objective reaction\n", + " from target file\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " OBJECTVE \n", + " FOR HYBRID \n", + "____________________________________________\n", + "\n", + "Objective set:\n", + " Objective reaction from target file\n", + "\n", + "\n", + "Objective : R_BIOMASS_Ec_iJO1366_core_53p95M\n", + "\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " NETWORK \n", + "____________________________________________\n", + "\n", + "Import reaction: Kept\n", + "Product of import reaction set as seed: No\n", + "Targets can be seeds: No\n", + "Accumulation: Forbidden\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + "List of targets: ['M_10fthf_c', 'M_2fe2s_c', 'M_2ohph_c', 'M_4fe4s_c', 'M_ala__L_c', 'M_amet_c', 'M_arg__L_c', 'M_asn__L_c', 'M_asp__L_c', 'M_atp_c', 'M_bmocogdp_c', 'M_btn_c', 'M_ca2_c', 'M_cl_c', 'M_coa_c', 'M_cobalt2_c', 'M_ctp_c', 'M_cu2_c', 'M_cys__L_c', 'M_datp_c', 'M_dctp_c', 'M_dgtp_c', 'M_dttp_c', 'M_fad_c', 'M_fe2_c', 'M_fe3_c', 'M_gln__L_c', 'M_glu__L_c', 'M_gly_c', 'M_gtp_c', 'M_h2o_c', 'M_his__L_c', 'M_ile__L_c', 'M_k_c', 'M_kdo2lipid4_e', 'M_leu__L_c', 'M_lys__L_c', 'M_met__L_c', 'M_mg2_c', 'M_mlthf_c', 'M_mn2_c', 'M_mobd_c', 'M_murein5px4p_p', 'M_nad_c', 'M_nadp_c', 'M_nh4_c', 'M_ni2_c', 'M_pe160_c', 'M_pe160_p', 'M_pe161_c', 'M_pe161_p', 'M_phe__L_c', 'M_pheme_c', 'M_pro__L_c', 'M_pydx5p_c', 'M_ribflv_c', 'M_ser__L_c', 'M_sheme_c', 'M_so4_c', 'M_thf_c', 'M_thmpp_c', 'M_thr__L_c', 'M_trp__L_c', 'M_tyr__L_c', 'M_udcpdp_c', 'M_utp_c', 'M_val__L_c', 'M_zn2_c']\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: iCN718\n", + "\n", + "____________________________________________\n", + "\n", + " TARGETS \n", + " FOR TARGET MODE AND FBA \n", + "____________________________________________\n", + "\n", + "Targets set:\n", + " Reactant of objective reaction\n", + " from target file\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " OBJECTVE \n", + " FOR HYBRID \n", + "____________________________________________\n", + "\n", + "Objective set:\n", + " Objective reaction from target file\n", + "\n", + "\n", + "Objective : R_BIOMASS__3\n", + "\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " NETWORK \n", + "____________________________________________\n", + "\n", + "Import reaction: Kept\n", + "Product of import reaction set as seed: No\n", + "Targets can be seeds: No\n", + "Accumulation: Forbidden\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + "List of targets: ['M_atp_c', 'M_cav_c', 'M_lipids_c', 'M_lps_c', 'M_peptido_c', 'M_phospholipid_c', 'M_protein_c', 'M_rna_c']\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: iIS312\n", + "\n", + "____________________________________________\n", + "\n", + " TARGETS \n", + " FOR TARGET MODE AND FBA \n", + "____________________________________________\n", + "\n", + "Targets set:\n", + " Reactant of objective reaction\n", + " from target file\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " OBJECTVE \n", + " FOR HYBRID \n", + "____________________________________________\n", + "\n", + "Objective set:\n", + " Objective reaction from target file\n", + "\n", + "\n", + "Objective : R_BIOMASS_t_cruzi\n", + "\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " NETWORK \n", + "____________________________________________\n", + "\n", + "Import reaction: Kept\n", + "Product of import reaction set as seed: No\n", + "Targets can be seeds: No\n", + "Accumulation: Forbidden\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + "List of targets: ['M_3pg_c', 'M_accoa_c', 'M_akg_c', 'M_ala__L_c', 'M_arg__L_c', 'M_asn__L_c', 'M_asp__L_c', 'M_atp_c', 'M_cys__L_c', 'M_e4p_c', 'M_ergst_c', 'M_g3p_c', 'M_g6p_B_c', 'M_gln__L_c', 'M_glu__L_c', 'M_gly_c', 'M_gmp_c', 'M_h2o_c', 'M_his__L_c', 'M_ile__L_c', 'M_leu__L_c', 'M_lys__L_c', 'M_met__L_c', 'M_nad_c', 'M_nadph_c', 'M_nh4_c', 'M_oaa_c', 'M_pep_c', 'M_phe__L_c', 'M_pro__L_c', 'M_pyr_c', 'M_r5p_c', 'M_ser__L_c', 'M_thr__L_c', 'M_ump_c', 'M_val__L_c']\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: RECON1\n", + "\n", + "____________________________________________\n", + "\n", + " TARGETS \n", + " FOR TARGET MODE AND FBA \n", + "____________________________________________\n", + "\n", + "Targets set:\n", + " Reactant of objective reaction\n", + " from target file\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " OBJECTVE \n", + " FOR HYBRID \n", + "____________________________________________\n", + "\n", + "Objective set:\n", + " Objective reaction from target file\n", + "\n", + "\n", + "Objective : R_S6T14g\n", + "\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " NETWORK \n", + "____________________________________________\n", + "\n", + "Import reaction: Kept\n", + "Product of import reaction set as seed: No\n", + "Targets can be seeds: No\n", + "Accumulation: Forbidden\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + "List of targets: ['M_ksi_pre34_g', 'M_paps_g']\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: iECABU_c1320\n", + "\n", + "____________________________________________\n", + "\n", + " TARGETS \n", + " FOR TARGET MODE AND FBA \n", + "____________________________________________\n", + "\n", + "Targets set:\n", + " Reactant of objective reaction\n", + " from target file\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " OBJECTVE \n", + " FOR HYBRID \n", + "____________________________________________\n", + "\n", + "Objective set:\n", + " Objective reaction from target file\n", + "\n", + "\n", + "Objective : R_BIOMASS_Ec_iJO1366_core_53p95M\n", + "\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " NETWORK \n", + "____________________________________________\n", + "\n", + "Import reaction: Kept\n", + "Product of import reaction set as seed: No\n", + "Targets can be seeds: No\n", + "Accumulation: Forbidden\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + "List of targets: ['M_10fthf_c', 'M_2fe2s_c', 'M_2ohph_c', 'M_4fe4s_c', 'M_ala__L_c', 'M_amet_c', 'M_arg__L_c', 'M_asn__L_c', 'M_asp__L_c', 'M_atp_c', 'M_bmocogdp_c', 'M_btn_c', 'M_ca2_c', 'M_cl_c', 'M_coa_c', 'M_cobalt2_c', 'M_ctp_c', 'M_cu2_c', 'M_cys__L_c', 'M_datp_c', 'M_dctp_c', 'M_dgtp_c', 'M_dttp_c', 'M_fad_c', 'M_fe2_c', 'M_fe3_c', 'M_gln__L_c', 'M_glu__L_c', 'M_gly_c', 'M_gtp_c', 'M_h2o_c', 'M_his__L_c', 'M_ile__L_c', 'M_k_c', 'M_kdo2lipid4_e', 'M_leu__L_c', 'M_lys__L_c', 'M_met__L_c', 'M_mg2_c', 'M_mlthf_c', 'M_mn2_c', 'M_mobd_c', 'M_murein5px4p_p', 'M_nad_c', 'M_nadp_c', 'M_nh4_c', 'M_ni2_c', 'M_pe160_c', 'M_pe160_p', 'M_pe161_c', 'M_pe161_p', 'M_phe__L_c', 'M_pheme_c', 'M_pro__L_c', 'M_pydx5p_c', 'M_ribflv_c', 'M_ser__L_c', 'M_sheme_c', 'M_so4_c', 'M_thf_c', 'M_thmpp_c', 'M_thr__L_c', 'M_trp__L_c', 'M_tyr__L_c', 'M_udcpdp_c', 'M_utp_c', 'M_val__L_c', 'M_zn2_c']\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: iEC1368_DH5a\n", + "\n", + "____________________________________________\n", + "\n", + " TARGETS \n", + " FOR TARGET MODE AND FBA \n", + "____________________________________________\n", + "\n", + "Targets set:\n", + " Reactant of objective reaction\n", + " from target file\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " OBJECTVE \n", + " FOR HYBRID \n", + "____________________________________________\n", + "\n", + "Objective set:\n", + " Objective reaction from target file\n", + "\n", + "\n", + "Objective : R_BIOMASS_Ec_iJO1366_core_53p95M\n", + "\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " NETWORK \n", + "____________________________________________\n", + "\n", + "Import reaction: Kept\n", + "Product of import reaction set as seed: No\n", + "Targets can be seeds: No\n", + "Accumulation: Forbidden\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + "List of targets: ['M_10fthf_c', 'M_2fe2s_c', 'M_2ohph_c', 'M_4fe4s_c', 'M_ala__L_c', 'M_amet_c', 'M_arg__L_c', 'M_asn__L_c', 'M_asp__L_c', 'M_atp_c', 'M_bmocogdp_c', 'M_btn_c', 'M_ca2_c', 'M_cl_c', 'M_coa_c', 'M_cobalt2_c', 'M_ctp_c', 'M_cu2_c', 'M_cys__L_c', 'M_datp_c', 'M_dctp_c', 'M_dgtp_c', 'M_dttp_c', 'M_fad_c', 'M_fe2_c', 'M_fe3_c', 'M_gln__L_c', 'M_glu__L_c', 'M_gly_c', 'M_gtp_c', 'M_h2o_c', 'M_his__L_c', 'M_ile__L_c', 'M_k_c', 'M_kdo2lipid4_e', 'M_leu__L_c', 'M_lys__L_c', 'M_met__L_c', 'M_mg2_c', 'M_mlthf_c', 'M_mn2_c', 'M_mobd_c', 'M_murein5px4p_p', 'M_nad_c', 'M_nadp_c', 'M_nh4_c', 'M_ni2_c', 'M_pe160_c', 'M_pe160_p', 'M_pe161_c', 'M_pe161_p', 'M_phe__L_c', 'M_pheme_c', 'M_pro__L_c', 'M_pydx5p_c', 'M_ribflv_c', 'M_ser__L_c', 'M_sheme_c', 'M_so4_c', 'M_thf_c', 'M_thmpp_c', 'M_thr__L_c', 'M_trp__L_c', 'M_tyr__L_c', 'M_udcpdp_c', 'M_utp_c', 'M_val__L_c', 'M_zn2_c']\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: iYS1720\n", + "\n", + "____________________________________________\n", + "\n", + " TARGETS \n", + " FOR TARGET MODE AND FBA \n", + "____________________________________________\n", + "\n", + "Targets set:\n", + " Reactant of objective reaction\n", + " from target file\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " OBJECTVE \n", + " FOR HYBRID \n", + "____________________________________________\n", + "\n", + "Objective set:\n", + " Objective reaction from target file\n", + "\n", + "\n", + "Objective : R_BIOMASS_iRR1083_1\n", + "\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " NETWORK \n", + "____________________________________________\n", + "\n", + "Import reaction: Kept\n", + "Product of import reaction set as seed: No\n", + "Targets can be seeds: No\n", + "Accumulation: Forbidden\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + "List of targets: ['M_12dgr2_ST_p', 'M_5mthf_c', 'M_accoa_c', 'M_ala__L_c', 'M_amp_c', 'M_arg__L_c', 'M_asn__L_c', 'M_asp__L_c', 'M_atp_c', 'M_clpn2_ST_p', 'M_coa_c', 'M_colipa_e', 'M_ctp_c', 'M_cys__L_c', 'M_datp_c', 'M_dctp_c', 'M_dgtp_c', 'M_dttp_c', 'M_fad_c', 'M_gln__L_c', 'M_glu__L_c', 'M_gly_c', 'M_glycogen_c', 'M_gtp_c', 'M_h2o_c', 'M_his__L_c', 'M_ile__L_c', 'M_leu__L_c', 'M_lys__L_c', 'M_met__L_c', 'M_nad_c', 'M_nadh_c', 'M_nadp_c', 'M_nadph_c', 'M_pa2_ST_p', 'M_pe2_ST_p', 'M_peptido_ST_p', 'M_pg2_ST_p', 'M_phe__L_c', 'M_pro__L_c', 'M_ps2_ST_p', 'M_ptrc_c', 'M_ser__L_c', 'M_spmd_c', 'M_succoa_c', 'M_thr__L_c', 'M_trp__L_c', 'M_tyr__L_c', 'M_udpg_c', 'M_utp_c', 'M_val__L_c']\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: iJN678\n", + "\n", + "____________________________________________\n", + "\n", + " TARGETS \n", + " FOR TARGET MODE AND FBA \n", + "____________________________________________\n", + "\n", + "Targets set:\n", + " Reactant of objective reaction\n", + " from target file\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " OBJECTVE \n", + " FOR HYBRID \n", + "____________________________________________\n", + "\n", + "Objective set:\n", + " Objective reaction from target file\n", + "\n", + "\n", + "Objective : R_BIOMASS_Ec_SynHetero\n", + "\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " NETWORK \n", + "____________________________________________\n", + "\n", + "Import reaction: Kept\n", + "Product of import reaction set as seed: No\n", + "Targets can be seeds: No\n", + "Accumulation: Forbidden\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + "List of targets: ['M_10fthf_c', 'M_5mthf_c', 'M_accoa_c', 'M_adocbl_c', 'M_ala__L_c', 'M_amet_c', 'M_arg__L_c', 'M_asn__L_c', 'M_asp__L_c', 'M_atp_c', 'M_avite1_c', 'M_bvite_c', 'M_ca2_c', 'M_caro_c', 'M_cholphya_c', 'M_chor_c', 'M_coa_c', 'M_cobalt2_c', 'M_ctp_c', 'M_cu2_c', 'M_cys__L_c', 'M_datp_c', 'M_dctp_c', 'M_dgdg160_c', 'M_dgdg161_c', 'M_dgdg180_c', 'M_dgdg181_9_c', 'M_dgdg181_c', 'M_dgdg182_9_12_c', 'M_dgdg183_6_9_12_c', 'M_dgdg183_9_12_15_c', 'M_dgdg184_6_9_12_15_c', 'M_dgtp_c', 'M_dtocophe_c', 'M_dttp_c', 'M_echin_c', 'M_fad_c', 'M_fe2_c', 'M_fe3_c', 'M_gcarote_c', 'M_gln__L_c', 'M_glu__L_c', 'M_gly_c', 'M_gthrd_c', 'M_gtocophe_c', 'M_gtp_c', 'M_h2o_c', 'M_hemeA_c', 'M_hemeO_c', 'M_his__L_c', 'M_ile__L_c', 'M_k_c', 'M_leu__L_c', 'M_lipidAds_c', 'M_lys__L_c', 'M_malcoa_c', 'M_met__L_c', 'M_mg2_c', 'M_mgdg160_c', 'M_mgdg161_c', 'M_mgdg180_c', 'M_mgdg181_9_c', 'M_mgdg181_c', 'M_mgdg182_9_12_c', 'M_mgdg183_6_9_12_c', 'M_mgdg183_9_12_15_c', 'M_mgdg184_6_9_12_15_c', 'M_mlthf_c', 'M_mn2_c', 'M_mobd_c', 'M_na1_c', 'M_nad_c', 'M_nadh_c', 'M_nadp_c', 'M_nadph_c', 'M_nh4_c', 'M_peptido_syn_p', 'M_pg160_c', 'M_pg161_c', 'M_pg180_c', 'M_pg181_9_c', 'M_pg181_c', 'M_pg182_9_12_c', 'M_pg183_6_9_12_c', 'M_pg183_9_12_15_c', 'M_pg184_6_9_12_15_c', 'M_phe__L_c', 'M_pheme_c', 'M_phllqne_c', 'M_pro__L_c', 'M_ptrc_c', 'M_ribflv_c', 'M_ser__L_c', 'M_so4_c', 'M_spmd_c', 'M_sqdg160_c', 'M_sqdg161_c', 'M_sqdg180_c', 'M_sqdg181_9_c', 'M_sqdg181_c', 'M_sqdg182_9_12_c', 'M_sqdg183_6_9_12_c', 'M_sqdg183_9_12_15_c', 'M_sqdg184_6_9_12_15_c', 'M_succoa_c', 'M_thf_c', 'M_thr__L_c', 'M_trp__L_c', 'M_tyr__L_c', 'M_udcpdp_c', 'M_utp_c', 'M_val__L_c', 'M_zeax_c', 'M_zn2_c']\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: iECW_1372\n", + "\n", + "____________________________________________\n", + "\n", + " TARGETS \n", + " FOR TARGET MODE AND FBA \n", + "____________________________________________\n", + "\n", + "Targets set:\n", + " Reactant of objective reaction\n", + " from target file\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " OBJECTVE \n", + " FOR HYBRID \n", + "____________________________________________\n", + "\n", + "Objective set:\n", + " Objective reaction from target file\n", + "\n", + "\n", + "Objective : R_BIOMASS_Ec_iJO1366_core_53p95M\n", + "\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " NETWORK \n", + "____________________________________________\n", + "\n", + "Import reaction: Kept\n", + "Product of import reaction set as seed: No\n", + "Targets can be seeds: No\n", + "Accumulation: Forbidden\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + "List of targets: ['M_10fthf_c', 'M_2fe2s_c', 'M_2ohph_c', 'M_4fe4s_c', 'M_ala__L_c', 'M_amet_c', 'M_arg__L_c', 'M_asn__L_c', 'M_asp__L_c', 'M_atp_c', 'M_bmocogdp_c', 'M_btn_c', 'M_ca2_c', 'M_cl_c', 'M_coa_c', 'M_cobalt2_c', 'M_ctp_c', 'M_cu2_c', 'M_cys__L_c', 'M_datp_c', 'M_dctp_c', 'M_dgtp_c', 'M_dttp_c', 'M_fad_c', 'M_fe2_c', 'M_fe3_c', 'M_gln__L_c', 'M_glu__L_c', 'M_gly_c', 'M_gtp_c', 'M_h2o_c', 'M_his__L_c', 'M_ile__L_c', 'M_k_c', 'M_kdo2lipid4_e', 'M_leu__L_c', 'M_lys__L_c', 'M_met__L_c', 'M_mg2_c', 'M_mlthf_c', 'M_mn2_c', 'M_mobd_c', 'M_murein5px4p_p', 'M_nad_c', 'M_nadp_c', 'M_nh4_c', 'M_ni2_c', 'M_pe160_c', 'M_pe160_p', 'M_pe161_c', 'M_pe161_p', 'M_phe__L_c', 'M_pheme_c', 'M_pro__L_c', 'M_pydx5p_c', 'M_ribflv_c', 'M_ser__L_c', 'M_sheme_c', 'M_so4_c', 'M_thf_c', 'M_thmpp_c', 'M_thr__L_c', 'M_trp__L_c', 'M_tyr__L_c', 'M_udcpdp_c', 'M_utp_c', 'M_val__L_c', 'M_zn2_c']\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: iECBD_1354\n", + "\n", + "____________________________________________\n", + "\n", + " TARGETS \n", + " FOR TARGET MODE AND FBA \n", + "____________________________________________\n", + "\n", + "Targets set:\n", + " Reactant of objective reaction\n", + " from target file\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " OBJECTVE \n", + " FOR HYBRID \n", + "____________________________________________\n", + "\n", + "Objective set:\n", + " Objective reaction from target file\n", + "\n", + "\n", + "Objective : R_BIOMASS_Ec_iJO1366_core_53p95M\n", + "\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " NETWORK \n", + "____________________________________________\n", + "\n", + "Import reaction: Kept\n", + "Product of import reaction set as seed: No\n", + "Targets can be seeds: No\n", + "Accumulation: Forbidden\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + "List of targets: ['M_10fthf_c', 'M_2fe2s_c', 'M_2ohph_c', 'M_4fe4s_c', 'M_ala__L_c', 'M_amet_c', 'M_arg__L_c', 'M_asn__L_c', 'M_asp__L_c', 'M_atp_c', 'M_bmocogdp_c', 'M_btn_c', 'M_ca2_c', 'M_cl_c', 'M_coa_c', 'M_cobalt2_c', 'M_ctp_c', 'M_cu2_c', 'M_cys__L_c', 'M_datp_c', 'M_dctp_c', 'M_dgtp_c', 'M_dttp_c', 'M_fad_c', 'M_fe2_c', 'M_fe3_c', 'M_gln__L_c', 'M_glu__L_c', 'M_gly_c', 'M_gtp_c', 'M_h2o_c', 'M_his__L_c', 'M_ile__L_c', 'M_k_c', 'M_kdo2lipid4_e', 'M_leu__L_c', 'M_lys__L_c', 'M_met__L_c', 'M_mg2_c', 'M_mlthf_c', 'M_mn2_c', 'M_mobd_c', 'M_murein5px4p_p', 'M_nad_c', 'M_nadp_c', 'M_nh4_c', 'M_ni2_c', 'M_pe160_c', 'M_pe160_p', 'M_pe161_c', 'M_pe161_p', 'M_phe__L_c', 'M_pheme_c', 'M_pro__L_c', 'M_pydx5p_c', 'M_ribflv_c', 'M_ser__L_c', 'M_sheme_c', 'M_so4_c', 'M_thf_c', 'M_thmpp_c', 'M_thr__L_c', 'M_trp__L_c', 'M_tyr__L_c', 'M_udcpdp_c', 'M_utp_c', 'M_val__L_c', 'M_zn2_c']\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: iYS854\n", + "\n", + "____________________________________________\n", + "\n", + " TARGETS \n", + " FOR TARGET MODE AND FBA \n", + "____________________________________________\n", + "\n", + "Targets set:\n", + " Reactant of objective reaction\n", + " from target file\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " OBJECTVE \n", + " FOR HYBRID \n", + "____________________________________________\n", + "\n", + "Objective set:\n", + " Objective reaction from target file\n", + "\n", + "\n", + "Objective : R_BIOMASS_iYS_wild_type\n", + "\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " NETWORK \n", + "____________________________________________\n", + "\n", + "Import reaction: Kept\n", + "Product of import reaction set as seed: No\n", + "Targets can be seeds: No\n", + "Accumulation: Forbidden\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + "List of targets: ['M_1gCELLWALL2_c', 'M_1gDNA2_c', 'M_1gLIPIDS2_c', 'M_1gMeasuredSolutes2_c', 'M_1gOtherSolutes2_c', 'M_1gPROTEIN2_c', 'M_1gRNA2_c']\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: iECS88_1305\n", + "\n", + "____________________________________________\n", + "\n", + " TARGETS \n", + " FOR TARGET MODE AND FBA \n", + "____________________________________________\n", + "\n", + "Targets set:\n", + " Reactant of objective reaction\n", + " from target file\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " OBJECTVE \n", + " FOR HYBRID \n", + "____________________________________________\n", + "\n", + "Objective set:\n", + " Objective reaction from target file\n", + "\n", + "\n", + "Objective : R_BIOMASS_Ec_iJO1366_core_53p95M\n", + "\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " NETWORK \n", + "____________________________________________\n", + "\n", + "Import reaction: Kept\n", + "Product of import reaction set as seed: No\n", + "Targets can be seeds: No\n", + "Accumulation: Forbidden\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + "List of targets: ['M_10fthf_c', 'M_2fe2s_c', 'M_2ohph_c', 'M_4fe4s_c', 'M_ala__L_c', 'M_amet_c', 'M_arg__L_c', 'M_asn__L_c', 'M_asp__L_c', 'M_atp_c', 'M_bmocogdp_c', 'M_btn_c', 'M_ca2_c', 'M_cl_c', 'M_coa_c', 'M_cobalt2_c', 'M_ctp_c', 'M_cu2_c', 'M_cys__L_c', 'M_datp_c', 'M_dctp_c', 'M_dgtp_c', 'M_dttp_c', 'M_fad_c', 'M_fe2_c', 'M_fe3_c', 'M_gln__L_c', 'M_glu__L_c', 'M_gly_c', 'M_gtp_c', 'M_h2o_c', 'M_his__L_c', 'M_ile__L_c', 'M_k_c', 'M_kdo2lipid4_e', 'M_leu__L_c', 'M_lys__L_c', 'M_met__L_c', 'M_mg2_c', 'M_mlthf_c', 'M_mn2_c', 'M_mobd_c', 'M_murein5px4p_p', 'M_nad_c', 'M_nadp_c', 'M_nh4_c', 'M_ni2_c', 'M_pe160_c', 'M_pe160_p', 'M_pe161_c', 'M_pe161_p', 'M_phe__L_c', 'M_pheme_c', 'M_pro__L_c', 'M_pydx5p_c', 'M_ribflv_c', 'M_ser__L_c', 'M_sheme_c', 'M_so4_c', 'M_thf_c', 'M_thmpp_c', 'M_thr__L_c', 'M_trp__L_c', 'M_tyr__L_c', 'M_udcpdp_c', 'M_utp_c', 'M_val__L_c', 'M_zn2_c']\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: iMM1415\n", + "\n", + "____________________________________________\n", + "\n", + " TARGETS \n", + " FOR TARGET MODE AND FBA \n", + "____________________________________________\n", + "\n", + "Targets set:\n", + " Reactant of objective reaction\n", + " from target file\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " OBJECTVE \n", + " FOR HYBRID \n", + "____________________________________________\n", + "\n", + "Objective set:\n", + " Objective reaction from target file\n", + "\n", + "\n", + "Objective : R_BIOMASS_mm_1_no_glygln\n", + "\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " NETWORK \n", + "____________________________________________\n", + "\n", + "Import reaction: Kept\n", + "Product of import reaction set as seed: No\n", + "Targets can be seeds: No\n", + "Accumulation: Forbidden\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + "List of targets: ['M_ala__L_c', 'M_amp_c', 'M_arg__L_c', 'M_asn__L_c', 'M_asp__L_c', 'M_atp_c', 'M_chsterol_c', 'M_clpn_hs_c', 'M_cmp_c', 'M_cys__L_c', 'M_damp_c', 'M_dcmp_c', 'M_dgmp_c', 'M_dtmp_c', 'M_gln__L_c', 'M_glu__L_c', 'M_gly_c', 'M_gmp_c', 'M_h2o_c', 'M_his__L_c', 'M_ile__L_c', 'M_leu__L_c', 'M_lys__L_c', 'M_met__L_c', 'M_pail_hs_c', 'M_pchol_hs_c', 'M_pe_hs_c', 'M_pglyc_hs_c', 'M_phe__L_c', 'M_pro__L_c', 'M_ps_hs_c', 'M_ser__L_c', 'M_sphmyln_hs_c', 'M_thr__L_c', 'M_trp__L_c', 'M_tyr__L_c', 'M_ump_c', 'M_val__L_c']\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: iYL1228\n", + "\n", + "____________________________________________\n", + "\n", + " TARGETS \n", + " FOR TARGET MODE AND FBA \n", + "____________________________________________\n", + "\n", + "Targets set:\n", + " Reactant of objective reaction\n", + " from target file\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " OBJECTVE \n", + " FOR HYBRID \n", + "____________________________________________\n", + "\n", + "Objective set:\n", + " Objective reaction from target file\n", + "\n", + "\n", + "Objective : R_BIOMASS_\n", + "\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " NETWORK \n", + "____________________________________________\n", + "\n", + "Import reaction: Kept\n", + "Product of import reaction set as seed: No\n", + "Targets can be seeds: No\n", + "Accumulation: Forbidden\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + "List of targets: ['M_adphep_LD_c', 'M_ala__L_c', 'M_arg__L_c', 'M_asn__L_c', 'M_asp__L_c', 'M_atp_c', 'M_clpn160_p', 'M_clpn161_p', 'M_clpn181_p', 'M_ctp_c', 'M_cys__L_c', 'M_datp_c', 'M_dctp_c', 'M_dgtp_c', 'M_dtdprmn_c', 'M_dttp_c', 'M_gam6p_c', 'M_gln__L_c', 'M_glu__L_c', 'M_gly_c', 'M_gtp_c', 'M_h2o_c', 'M_his__L_c', 'M_ile__L_c', 'M_kdo_c', 'M_leu__L_c', 'M_lipidA_c', 'M_lys__L_c', 'M_man1p_c', 'M_met__L_c', 'M_murein5px4p_p', 'M_pe160_c', 'M_pe160_p', 'M_pe161_c', 'M_pe161_p', 'M_pe181_c', 'M_pe181_p', 'M_pg160_c', 'M_pg160_p', 'M_pg161_c', 'M_pg161_p', 'M_pg181_c', 'M_pg181_p', 'M_phe__L_c', 'M_pro__L_c', 'M_ser__L_c', 'M_thr__L_c', 'M_trp__L_c', 'M_ttdca_c', 'M_tyr__L_c', 'M_udpg_c', 'M_udpgal_c', 'M_udpgalur_c', 'M_utp_c', 'M_val__L_c']\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: iECD_1391\n", + "\n", + "____________________________________________\n", + "\n", + " TARGETS \n", + " FOR TARGET MODE AND FBA \n", + "____________________________________________\n", + "\n", + "Targets set:\n", + " Reactant of objective reaction\n", + " from target file\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " OBJECTVE \n", + " FOR HYBRID \n", + "____________________________________________\n", + "\n", + "Objective set:\n", + " Objective reaction from target file\n", + "\n", + "\n", + "Objective : R_BIOMASS_Ec_iJO1366_core_53p95M\n", + "\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " NETWORK \n", + "____________________________________________\n", + "\n", + "Import reaction: Kept\n", + "Product of import reaction set as seed: No\n", + "Targets can be seeds: No\n", + "Accumulation: Forbidden\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + "List of targets: ['M_10fthf_c', 'M_2fe2s_c', 'M_2ohph_c', 'M_4fe4s_c', 'M_ala__L_c', 'M_amet_c', 'M_arg__L_c', 'M_asn__L_c', 'M_asp__L_c', 'M_atp_c', 'M_bmocogdp_c', 'M_btn_c', 'M_ca2_c', 'M_cl_c', 'M_coa_c', 'M_cobalt2_c', 'M_ctp_c', 'M_cu2_c', 'M_cys__L_c', 'M_datp_c', 'M_dctp_c', 'M_dgtp_c', 'M_dttp_c', 'M_fad_c', 'M_fe2_c', 'M_fe3_c', 'M_gln__L_c', 'M_glu__L_c', 'M_gly_c', 'M_gtp_c', 'M_h2o_c', 'M_his__L_c', 'M_ile__L_c', 'M_k_c', 'M_kdo2lipid4_e', 'M_leu__L_c', 'M_lys__L_c', 'M_met__L_c', 'M_mg2_c', 'M_mlthf_c', 'M_mn2_c', 'M_mobd_c', 'M_murein5px4p_p', 'M_nad_c', 'M_nadp_c', 'M_nh4_c', 'M_ni2_c', 'M_pe160_c', 'M_pe160_p', 'M_pe161_c', 'M_pe161_p', 'M_phe__L_c', 'M_pheme_c', 'M_pro__L_c', 'M_pydx5p_c', 'M_ribflv_c', 'M_ser__L_c', 'M_sheme_c', 'M_so4_c', 'M_thf_c', 'M_thmpp_c', 'M_thr__L_c', 'M_trp__L_c', 'M_tyr__L_c', 'M_udcpdp_c', 'M_utp_c', 'M_val__L_c', 'M_zn2_c']\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: iEC55989_1330\n", + "\n", + "____________________________________________\n", + "\n", + " TARGETS \n", + " FOR TARGET MODE AND FBA \n", + "____________________________________________\n", + "\n", + "Targets set:\n", + " Reactant of objective reaction\n", + " from target file\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " OBJECTVE \n", + " FOR HYBRID \n", + "____________________________________________\n", + "\n", + "Objective set:\n", + " Objective reaction from target file\n", + "\n", + "\n", + "Objective : R_BIOMASS_Ec_iJO1366_core_53p95M\n", + "\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " NETWORK \n", + "____________________________________________\n", + "\n", + "Import reaction: Kept\n", + "Product of import reaction set as seed: No\n", + "Targets can be seeds: No\n", + "Accumulation: Forbidden\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + "List of targets: ['M_10fthf_c', 'M_2fe2s_c', 'M_2ohph_c', 'M_4fe4s_c', 'M_ala__L_c', 'M_amet_c', 'M_arg__L_c', 'M_asn__L_c', 'M_asp__L_c', 'M_atp_c', 'M_bmocogdp_c', 'M_btn_c', 'M_ca2_c', 'M_cl_c', 'M_coa_c', 'M_cobalt2_c', 'M_ctp_c', 'M_cu2_c', 'M_cys__L_c', 'M_datp_c', 'M_dctp_c', 'M_dgtp_c', 'M_dttp_c', 'M_fad_c', 'M_fe2_c', 'M_fe3_c', 'M_gln__L_c', 'M_glu__L_c', 'M_gly_c', 'M_gtp_c', 'M_h2o_c', 'M_his__L_c', 'M_ile__L_c', 'M_k_c', 'M_kdo2lipid4_e', 'M_leu__L_c', 'M_lys__L_c', 'M_met__L_c', 'M_mg2_c', 'M_mlthf_c', 'M_mn2_c', 'M_mobd_c', 'M_murein5px4p_p', 'M_nad_c', 'M_nadp_c', 'M_nh4_c', 'M_ni2_c', 'M_pe160_c', 'M_pe160_p', 'M_pe161_c', 'M_pe161_p', 'M_phe__L_c', 'M_pheme_c', 'M_pro__L_c', 'M_pydx5p_c', 'M_ribflv_c', 'M_ser__L_c', 'M_sheme_c', 'M_so4_c', 'M_thf_c', 'M_thmpp_c', 'M_thr__L_c', 'M_trp__L_c', 'M_tyr__L_c', 'M_udcpdp_c', 'M_utp_c', 'M_val__L_c', 'M_zn2_c']\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: iAM_Pc455\n", + "\n", + "____________________________________________\n", + "\n", + " TARGETS \n", + " FOR TARGET MODE AND FBA \n", + "____________________________________________\n", + "\n", + "Targets set:\n", + " Reactant of objective reaction\n", + " from target file\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " OBJECTVE \n", + " FOR HYBRID \n", + "____________________________________________\n", + "\n", + "Objective set:\n", + " Objective reaction from target file\n", + "\n", + "\n", + "Objective : R_BIOMASS__4\n", + "\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " NETWORK \n", + "____________________________________________\n", + "\n", + "Import reaction: Kept\n", + "Product of import reaction set as seed: No\n", + "Targets can be seeds: No\n", + "Accumulation: Forbidden\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + "List of targets: ['M_5thf_c', 'M_ala__L_c', 'M_arg__L_c', 'M_asn__L_c', 'M_asp__L_c', 'M_atp_c', 'M_clpn_pf_16_0_18_1_16_0_18_1_m', 'M_coa_c', 'M_ctp_c', 'M_cys__L_c', 'M_datp_c', 'M_dctp_c', 'M_dgtp_c', 'M_dttp_c', 'M_fad_c', 'M_gdpfuc_c', 'M_gdpmann_c', 'M_gln__L_c', 'M_glu__L_c', 'M_gly_c', 'M_gthrd_c', 'M_gtp_c', 'M_h2o_c', 'M_his__L_c', 'M_ile__L_c', 'M_leu__L_c', 'M_lipopb_m', 'M_lys__L_c', 'M_mem3gacpail_pf_18_0_18_1_16_0_r', 'M_met__L_c', 'M_nad_c', 'M_nadp_c', 'M_pail_pf_16_0_16_0_c', 'M_pail_pf_16_0_18_0_c', 'M_pail_pf_16_0_18_1_c', 'M_pail_pf_18_0_18_0_c', 'M_pail_pf_18_0_18_1_c', 'M_pail_pf_18_1_18_1_c', 'M_pchol_pf_16_0_16_0_c', 'M_pchol_pf_16_0_18_0_c', 'M_pchol_pf_16_0_18_1_c', 'M_pchol_pf_16_0_18_2_c', 'M_pchol_pf_16_0_20_4_c', 'M_pchol_pf_18_1_18_1_c', 'M_pchol_pf_18_1_18_2_c', 'M_pchol_pf_18_2_18_2_c', 'M_pe_pf_16_0_16_0_c', 'M_pe_pf_16_0_18_1_c', 'M_pe_pf_16_0_18_2_c', 'M_pe_pf_18_1_18_1_c', 'M_pe_pf_18_1_18_2_c', 'M_pe_pf_18_2_18_2_c', 'M_pe_pf_20_4_20_4_c', 'M_phe__L_c', 'M_pheme_c', 'M_pro__L_c', 'M_ps_pf_18_0_18_1_c', 'M_ps_pf_18_0_20_4_c', 'M_ps_pf_18_1_18_1_c', 'M_ps_pf_18_1_20_4_c', 'M_ps_pf_20_4_20_4_c', 'M_ptrc_c', 'M_pydx5p_c', 'M_q8h2_m', 'M_ser__L_c', 'M_sphymyln_pf_18_1_16_0_g', 'M_sphymyln_pf_18_1_18_0_g', 'M_sphymyln_pf_18_1_24_0_g', 'M_spmd_c', 'M_tag_pf_16_0_18_0_18_0_c', 'M_tag_pf_18_0_18_0_18_1_c', 'M_tag_pf_18_0_18_1_16_0_c', 'M_tag_pf_18_1_18_2_18_2_c', 'M_thmpp_c', 'M_thr__L_c', 'M_trp__L_c', 'M_tyr__L_c', 'M_utp_c', 'M_val__L_c']\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: iJN1463\n", + "\n", + "____________________________________________\n", + "\n", + " TARGETS \n", + " FOR TARGET MODE AND FBA \n", + "____________________________________________\n", + "\n", + "Targets set:\n", + " Reactant of objective reaction\n", + " from target file\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " OBJECTVE \n", + " FOR HYBRID \n", + "____________________________________________\n", + "\n", + "Objective set:\n", + " Objective reaction from target file\n", + "\n", + "\n", + "Objective : R_BIOMASS_KT2440_WT3\n", + "\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " NETWORK \n", + "____________________________________________\n", + "\n", + "Import reaction: Kept\n", + "Product of import reaction set as seed: No\n", + "Targets can be seeds: No\n", + "Accumulation: Forbidden\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + "List of targets: ['M_10fthf_c', 'M_2fe2s_c', 'M_4fe4s_c', 'M_5mthf_c', 'M_accoa_c', 'M_adocbl_c', 'M_ala__L_c', 'M_amet_c', 'M_arg__L_c', 'M_asn__L_c', 'M_asp__L_c', 'M_atp_c', 'M_bmocogdp_c', 'M_btamp_c', 'M_ca2_c', 'M_chor_c', 'M_cl_c', 'M_clpn140_p', 'M_clpn160_p', 'M_clpn161_p', 'M_clpn180_p', 'M_clpn181_p', 'M_coa_c', 'M_cobalt2_c', 'M_cpe160_c', 'M_cpg160_c', 'M_ctp_c', 'M_cu2_c', 'M_cys__L_c', 'M_datp_c', 'M_dctp_c', 'M_dgtp_c', 'M_dttp_c', 'M_fad_c', 'M_fe2_c', 'M_fe3_c', 'M_gln__L_c', 'M_glu__L_c', 'M_gly_c', 'M_gthrd_c', 'M_gtp_c', 'M_h2o_c', 'M_hemeO_c', 'M_his__L_c', 'M_ile__L_c', 'M_k_c', 'M_leu__L_c', 'M_lipopb_c', 'M_lpspput_e', 'M_lys__L_c', 'M_malcoa_c', 'M_met__L_c', 'M_mg2_c', 'M_mlthf_c', 'M_mn2_c', 'M_mobd_c', 'M_mocogdp_c', 'M_murein3p3p_p', 'M_murein3px4p_p', 'M_murein4p4p_p', 'M_murein4px4p_p', 'M_murein4px4px4p_p', 'M_na1_c', 'M_nad_c', 'M_nadh_c', 'M_nadp_c', 'M_nadph_c', 'M_nh4_c', 'M_ni2_c', 'M_pe140_c', 'M_pe160_c', 'M_pe161_c', 'M_pe180_c', 'M_pe181_c', 'M_pg140_c', 'M_pg160_c', 'M_pg161_c', 'M_pg180_c', 'M_pg181_c', 'M_phe__L_c', 'M_pheme_c', 'M_pqqh2_c', 'M_pro__L_c', 'M_ptrc_c', 'M_pydx5p_c', 'M_pyovd_kt_e', 'M_q8h2_c', 'M_ribflv_c', 'M_ser__L_c', 'M_sheme_c', 'M_so4_c', 'M_succoa_c', 'M_thf_c', 'M_thmnp_c', 'M_thmpp_c', 'M_thr__L_c', 'M_trp__L_c', 'M_tyr__L_c', 'M_udcpdp_c', 'M_utp_c', 'M_val__L_c', 'M_zn2_c']\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: iSB619\n", + "\n", + "____________________________________________\n", + "\n", + " TARGETS \n", + " FOR TARGET MODE AND FBA \n", + "____________________________________________\n", + "\n", + "Targets set:\n", + " Reactant of objective reaction\n", + " from target file\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " OBJECTVE \n", + " FOR HYBRID \n", + "____________________________________________\n", + "\n", + "Objective set:\n", + " Objective reaction from target file\n", + "\n", + "\n", + "Objective : R_BIOMASS_SA_8a\n", + "\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " NETWORK \n", + "____________________________________________\n", + "\n", + "Import reaction: Kept\n", + "Product of import reaction set as seed: No\n", + "Targets can be seeds: No\n", + "Accumulation: Forbidden\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + "List of targets: ['M_10fthf_c', 'M_12dgr_SA_c', 'M_26dap_LL_c', 'M_SA_FREE_FA_c', 'M_acgam1p_c', 'M_acgam_c', 'M_ala__L_c', 'M_amp_c', 'M_arg__L_c', 'M_asn__L_c', 'M_asp__L_c', 'M_atp_c', 'M_clpn_SA_c', 'M_cmp_c', 'M_cys__L_c', 'M_damp_c', 'M_dcmp_c', 'M_dgdcg_SA2_c', 'M_dgmp_c', 'M_dtmp_c', 'M_fad_c', 'M_gam1p_c', 'M_glcp_SA_c', 'M_gln__L_c', 'M_glu__L_c', 'M_gly_c', 'M_gmp_c', 'M_h2o_c', 'M_hemeO_c', 'M_his__L_c', 'M_ile__L_c', 'M_leu__L_c', 'M_lys__L_c', 'M_met__L_c', 'M_mlthf_c', 'M_nad_c', 'M_nadp_c', 'M_pala_SA2_c', 'M_pe_SA_c', 'M_pg_SA_c', 'M_pgly_SA2_c', 'M_phe__L_c', 'M_pleu_SA2_c', 'M_plys_SA2_c', 'M_pro__L_c', 'M_ptrc_c', 'M_ser__L_c', 'M_sheme_c', 'M_spmd_c', 'M_tcam_c', 'M_thf_c', 'M_thm_c', 'M_thr__L_c', 'M_trp__L_c', 'M_tyr__L_c', 'M_uamr_c', 'M_ump_c', 'M_val__L_c']\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: iG2583_1286\n", + "\n", + "____________________________________________\n", + "\n", + " TARGETS \n", + " FOR TARGET MODE AND FBA \n", + "____________________________________________\n", + "\n", + "Targets set:\n", + " Reactant of objective reaction\n", + " from target file\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " OBJECTVE \n", + " FOR HYBRID \n", + "____________________________________________\n", + "\n", + "Objective set:\n", + " Objective reaction from target file\n", + "\n", + "\n", + "Objective : R_BIOMASS_Ec_iJO1366_core_53p95M\n", + "\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " NETWORK \n", + "____________________________________________\n", + "\n", + "Import reaction: Kept\n", + "Product of import reaction set as seed: No\n", + "Targets can be seeds: No\n", + "Accumulation: Forbidden\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + "List of targets: ['M_10fthf_c', 'M_2fe2s_c', 'M_2ohph_c', 'M_4fe4s_c', 'M_ala__L_c', 'M_amet_c', 'M_arg__L_c', 'M_asn__L_c', 'M_asp__L_c', 'M_atp_c', 'M_bmocogdp_c', 'M_btn_c', 'M_ca2_c', 'M_cl_c', 'M_coa_c', 'M_cobalt2_c', 'M_ctp_c', 'M_cu2_c', 'M_cys__L_c', 'M_datp_c', 'M_dctp_c', 'M_dgtp_c', 'M_dttp_c', 'M_fad_c', 'M_fe2_c', 'M_fe3_c', 'M_gln__L_c', 'M_glu__L_c', 'M_gly_c', 'M_gtp_c', 'M_h2o_c', 'M_his__L_c', 'M_ile__L_c', 'M_k_c', 'M_kdo2lipid4_e', 'M_leu__L_c', 'M_lys__L_c', 'M_met__L_c', 'M_mg2_c', 'M_mlthf_c', 'M_mn2_c', 'M_mobd_c', 'M_murein5px4p_p', 'M_nad_c', 'M_nadp_c', 'M_nh4_c', 'M_ni2_c', 'M_pe160_c', 'M_pe160_p', 'M_pe161_c', 'M_pe161_p', 'M_phe__L_c', 'M_pheme_c', 'M_pro__L_c', 'M_pydx5p_c', 'M_ribflv_c', 'M_ser__L_c', 'M_sheme_c', 'M_so4_c', 'M_thf_c', 'M_thmpp_c', 'M_thr__L_c', 'M_trp__L_c', 'M_tyr__L_c', 'M_udcpdp_c', 'M_utp_c', 'M_val__L_c', 'M_zn2_c']\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: iAF987\n", + "\n", + "____________________________________________\n", + "\n", + " TARGETS \n", + " FOR TARGET MODE AND FBA \n", + "____________________________________________\n", + "\n", + "Targets set:\n", + " Reactant of objective reaction\n", + " from target file\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " OBJECTVE \n", + " FOR HYBRID \n", + "____________________________________________\n", + "\n", + "Objective set:\n", + " Objective reaction from target file\n", + "\n", + "\n", + "Objective : R_BIOMASS_Gm_GS15_WT_79p20M\n", + "\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " NETWORK \n", + "____________________________________________\n", + "\n", + "Import reaction: Kept\n", + "Product of import reaction set as seed: No\n", + "Targets can be seeds: No\n", + "Accumulation: Forbidden\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + "List of targets: ['M_10fthf_c', 'M_2dmmql8_c', 'M_2fe2s_c', 'M_4fe4s_c', 'M_5mthf_c', 'M_accoa_c', 'M_adocbl_c', 'M_ala__L_c', 'M_amet_c', 'M_arg__L_c', 'M_asn__L_c', 'M_asp__L_c', 'M_atp_c', 'M_bmocogdp_c', 'M_btn_c', 'M_ca2_c', 'M_chor_c', 'M_cl_c', 'M_clpn140_p', 'M_clpn160_p', 'M_clpn161_p', 'M_clpn181_p', 'M_coa_c', 'M_cobalt2_c', 'M_ctp_c', 'M_cu2_c', 'M_cys__L_c', 'M_datp_c', 'M_dctp_c', 'M_dgtp_c', 'M_dttp_c', 'M_fad_c', 'M_fe2_c', 'M_gln__L_c', 'M_glu__L_c', 'M_gly_c', 'M_glycogen_c', 'M_gm1lipa_e', 'M_gtp_c', 'M_h2o_c', 'M_hemeO_c', 'M_his__L_c', 'M_ile__L_c', 'M_k_c', 'M_leu__L_c', 'M_lys__L_c', 'M_malcoa_c', 'M_met__L_c', 'M_mg2_c', 'M_mlthf_c', 'M_mn2_c', 'M_mobd_c', 'M_mocogdp_c', 'M_mql8_c', 'M_murein3p3p_p', 'M_murein3px4p_p', 'M_murein4p4p_p', 'M_murein4px4p_p', 'M_murein4px4px4p_p', 'M_nad_c', 'M_nadh_c', 'M_nadp_c', 'M_nadph_c', 'M_nh4_c', 'M_ni2_c', 'M_pe140_c', 'M_pe140_p', 'M_pe160_c', 'M_pe160_p', 'M_pe161_c', 'M_pe161_p', 'M_pe181_c', 'M_pe181_p', 'M_pg140_c', 'M_pg140_p', 'M_pg160_c', 'M_pg160_p', 'M_pg161_c', 'M_pg161_p', 'M_pg181_c', 'M_pg181_p', 'M_phe__L_c', 'M_pheme_c', 'M_pro__L_c', 'M_ptrc_c', 'M_pydx5p_c', 'M_ribflv_c', 'M_ser__L_c', 'M_sheme_c', 'M_so4_c', 'M_spmd_c', 'M_succoa_c', 'M_thf_c', 'M_thmpp_c', 'M_thr__L_c', 'M_trp__L_c', 'M_tyr__L_c', 'M_udcpdp_c', 'M_utp_c', 'M_val__L_c', 'M_zn2_c']\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: iECO111_1330\n", + "\n", + "____________________________________________\n", + "\n", + " TARGETS \n", + " FOR TARGET MODE AND FBA \n", + "____________________________________________\n", + "\n", + "Targets set:\n", + " Reactant of objective reaction\n", + " from target file\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " OBJECTVE \n", + " FOR HYBRID \n", + "____________________________________________\n", + "\n", + "Objective set:\n", + " Objective reaction from target file\n", + "\n", + "\n", + "Objective : R_BIOMASS_Ec_iJO1366_core_53p95M\n", + "\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " NETWORK \n", + "____________________________________________\n", + "\n", + "Import reaction: Kept\n", + "Product of import reaction set as seed: No\n", + "Targets can be seeds: No\n", + "Accumulation: Forbidden\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + "List of targets: ['M_10fthf_c', 'M_2fe2s_c', 'M_2ohph_c', 'M_4fe4s_c', 'M_ala__L_c', 'M_amet_c', 'M_arg__L_c', 'M_asn__L_c', 'M_asp__L_c', 'M_atp_c', 'M_bmocogdp_c', 'M_btn_c', 'M_ca2_c', 'M_cl_c', 'M_coa_c', 'M_cobalt2_c', 'M_ctp_c', 'M_cu2_c', 'M_cys__L_c', 'M_datp_c', 'M_dctp_c', 'M_dgtp_c', 'M_dttp_c', 'M_fad_c', 'M_fe2_c', 'M_fe3_c', 'M_gln__L_c', 'M_glu__L_c', 'M_gly_c', 'M_gtp_c', 'M_h2o_c', 'M_his__L_c', 'M_ile__L_c', 'M_k_c', 'M_kdo2lipid4_e', 'M_leu__L_c', 'M_lys__L_c', 'M_met__L_c', 'M_mg2_c', 'M_mlthf_c', 'M_mn2_c', 'M_mobd_c', 'M_murein5px4p_p', 'M_nad_c', 'M_nadp_c', 'M_nh4_c', 'M_ni2_c', 'M_pe160_c', 'M_pe160_p', 'M_pe161_c', 'M_pe161_p', 'M_phe__L_c', 'M_pheme_c', 'M_pro__L_c', 'M_pydx5p_c', 'M_ribflv_c', 'M_ser__L_c', 'M_sheme_c', 'M_so4_c', 'M_thf_c', 'M_thmpp_c', 'M_thr__L_c', 'M_trp__L_c', 'M_tyr__L_c', 'M_udcpdp_c', 'M_utp_c', 'M_val__L_c', 'M_zn2_c']\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: iECUMN_1333\n", + "\n", + "____________________________________________\n", + "\n", + " TARGETS \n", + " FOR TARGET MODE AND FBA \n", + "____________________________________________\n", + "\n", + "Targets set:\n", + " Reactant of objective reaction\n", + " from target file\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " OBJECTVE \n", + " FOR HYBRID \n", + "____________________________________________\n", + "\n", + "Objective set:\n", + " Objective reaction from target file\n", + "\n", + "\n", + "Objective : R_BIOMASS_Ec_iJO1366_core_53p95M\n", + "\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " NETWORK \n", + "____________________________________________\n", + "\n", + "Import reaction: Kept\n", + "Product of import reaction set as seed: No\n", + "Targets can be seeds: No\n", + "Accumulation: Forbidden\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + "List of targets: ['M_10fthf_c', 'M_2fe2s_c', 'M_2ohph_c', 'M_4fe4s_c', 'M_ala__L_c', 'M_amet_c', 'M_arg__L_c', 'M_asn__L_c', 'M_asp__L_c', 'M_atp_c', 'M_bmocogdp_c', 'M_btn_c', 'M_ca2_c', 'M_cl_c', 'M_coa_c', 'M_cobalt2_c', 'M_ctp_c', 'M_cu2_c', 'M_cys__L_c', 'M_datp_c', 'M_dctp_c', 'M_dgtp_c', 'M_dttp_c', 'M_fad_c', 'M_fe2_c', 'M_fe3_c', 'M_gln__L_c', 'M_glu__L_c', 'M_gly_c', 'M_gtp_c', 'M_h2o_c', 'M_his__L_c', 'M_ile__L_c', 'M_k_c', 'M_kdo2lipid4_e', 'M_leu__L_c', 'M_lys__L_c', 'M_met__L_c', 'M_mg2_c', 'M_mlthf_c', 'M_mn2_c', 'M_mobd_c', 'M_murein5px4p_p', 'M_nad_c', 'M_nadp_c', 'M_nh4_c', 'M_ni2_c', 'M_pe160_c', 'M_pe160_p', 'M_pe161_c', 'M_pe161_p', 'M_phe__L_c', 'M_pheme_c', 'M_pro__L_c', 'M_pydx5p_c', 'M_ribflv_c', 'M_ser__L_c', 'M_sheme_c', 'M_so4_c', 'M_thf_c', 'M_thmpp_c', 'M_thr__L_c', 'M_trp__L_c', 'M_tyr__L_c', 'M_udcpdp_c', 'M_utp_c', 'M_val__L_c', 'M_zn2_c']\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: iEcSMS35_1347\n", + "\n", + "____________________________________________\n", + "\n", + " TARGETS \n", + " FOR TARGET MODE AND FBA \n", + "____________________________________________\n", + "\n", + "Targets set:\n", + " Reactant of objective reaction\n", + " from target file\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " OBJECTVE \n", + " FOR HYBRID \n", + "____________________________________________\n", + "\n", + "Objective set:\n", + " Objective reaction from target file\n", + "\n", + "\n", + "Objective : R_BIOMASS_Ec_iJO1366_core_53p95M\n", + "\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " NETWORK \n", + "____________________________________________\n", + "\n", + "Import reaction: Kept\n", + "Product of import reaction set as seed: No\n", + "Targets can be seeds: No\n", + "Accumulation: Forbidden\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + "List of targets: ['M_10fthf_c', 'M_2fe2s_c', 'M_2ohph_c', 'M_4fe4s_c', 'M_ala__L_c', 'M_amet_c', 'M_arg__L_c', 'M_asn__L_c', 'M_asp__L_c', 'M_atp_c', 'M_bmocogdp_c', 'M_btn_c', 'M_ca2_c', 'M_cl_c', 'M_coa_c', 'M_cobalt2_c', 'M_ctp_c', 'M_cu2_c', 'M_cys__L_c', 'M_datp_c', 'M_dctp_c', 'M_dgtp_c', 'M_dttp_c', 'M_fad_c', 'M_fe2_c', 'M_fe3_c', 'M_gln__L_c', 'M_glu__L_c', 'M_gly_c', 'M_gtp_c', 'M_h2o_c', 'M_his__L_c', 'M_ile__L_c', 'M_k_c', 'M_kdo2lipid4_e', 'M_leu__L_c', 'M_lys__L_c', 'M_met__L_c', 'M_mg2_c', 'M_mlthf_c', 'M_mn2_c', 'M_mobd_c', 'M_murein5px4p_p', 'M_nad_c', 'M_nadp_c', 'M_nh4_c', 'M_ni2_c', 'M_pe160_c', 'M_pe160_p', 'M_pe161_c', 'M_pe161_p', 'M_phe__L_c', 'M_pheme_c', 'M_pro__L_c', 'M_pydx5p_c', 'M_ribflv_c', 'M_ser__L_c', 'M_sheme_c', 'M_so4_c', 'M_thf_c', 'M_thmpp_c', 'M_thr__L_c', 'M_trp__L_c', 'M_tyr__L_c', 'M_udcpdp_c', 'M_utp_c', 'M_val__L_c', 'M_zn2_c']\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: iCHOv1\n", + "\n", + "____________________________________________\n", + "\n", + " TARGETS \n", + " FOR TARGET MODE AND FBA \n", + "____________________________________________\n", + "\n", + "Targets set:\n", + " Reactant of objective reaction\n", + " from target file\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " OBJECTVE \n", + " FOR HYBRID \n", + "____________________________________________\n", + "\n", + "Objective set:\n", + " Objective reaction from target file\n", + "\n", + "\n", + "Objective : R_BIOMASS_cho\n", + "\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " NETWORK \n", + "____________________________________________\n", + "\n", + "Import reaction: Kept\n", + "Product of import reaction set as seed: No\n", + "Targets can be seeds: No\n", + "Accumulation: Forbidden\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + "List of targets: ['M_ala__L_c', 'M_arg__L_c', 'M_asn__L_c', 'M_asp__L_c', 'M_atp_c', 'M_chsterol_c', 'M_clpn_cho_c', 'M_ctp_c', 'M_cys__L_c', 'M_datp_c', 'M_dctp_c', 'M_dgtp_c', 'M_dttp_c', 'M_gln__L_c', 'M_glu__L_c', 'M_gly_c', 'M_glygn2_c', 'M_gtp_c', 'M_h2o_c', 'M_his__L_c', 'M_ile__L_c', 'M_leu__L_c', 'M_lys__L_c', 'M_met__L_c', 'M_pa_cho_c', 'M_pail_cho_c', 'M_pchol_cho_c', 'M_pe_cho_c', 'M_pglyc_cho_c', 'M_phe__L_c', 'M_pro__L_c', 'M_ps_cho_c', 'M_ser__L_c', 'M_sphmyln_cho_c', 'M_tag_cho_c', 'M_thr__L_c', 'M_trp__L_c', 'M_tyr__L_c', 'M_utp_c', 'M_val__L_c', 'M_xolest_cho_c']\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: iUMNK88_1353\n", + "\n", + "____________________________________________\n", + "\n", + " TARGETS \n", + " FOR TARGET MODE AND FBA \n", + "____________________________________________\n", + "\n", + "Targets set:\n", + " Reactant of objective reaction\n", + " from target file\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " OBJECTVE \n", + " FOR HYBRID \n", + "____________________________________________\n", + "\n", + "Objective set:\n", + " Objective reaction from target file\n", + "\n", + "\n", + "Objective : R_BIOMASS_Ec_iJO1366_core_53p95M\n", + "\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " NETWORK \n", + "____________________________________________\n", + "\n", + "Import reaction: Kept\n", + "Product of import reaction set as seed: No\n", + "Targets can be seeds: No\n", + "Accumulation: Forbidden\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + "List of targets: ['M_10fthf_c', 'M_2fe2s_c', 'M_2ohph_c', 'M_4fe4s_c', 'M_ala__L_c', 'M_amet_c', 'M_arg__L_c', 'M_asn__L_c', 'M_asp__L_c', 'M_atp_c', 'M_bmocogdp_c', 'M_btn_c', 'M_ca2_c', 'M_cl_c', 'M_coa_c', 'M_cobalt2_c', 'M_ctp_c', 'M_cu2_c', 'M_cys__L_c', 'M_datp_c', 'M_dctp_c', 'M_dgtp_c', 'M_dttp_c', 'M_fad_c', 'M_fe2_c', 'M_fe3_c', 'M_gln__L_c', 'M_glu__L_c', 'M_gly_c', 'M_gtp_c', 'M_h2o_c', 'M_his__L_c', 'M_ile__L_c', 'M_k_c', 'M_kdo2lipid4_e', 'M_leu__L_c', 'M_lys__L_c', 'M_met__L_c', 'M_mg2_c', 'M_mlthf_c', 'M_mn2_c', 'M_mobd_c', 'M_murein5px4p_p', 'M_nad_c', 'M_nadp_c', 'M_nh4_c', 'M_ni2_c', 'M_pe160_c', 'M_pe160_p', 'M_pe161_c', 'M_pe161_p', 'M_phe__L_c', 'M_pheme_c', 'M_pro__L_c', 'M_pydx5p_c', 'M_ribflv_c', 'M_ser__L_c', 'M_sheme_c', 'M_so4_c', 'M_thf_c', 'M_thmpp_c', 'M_thr__L_c', 'M_trp__L_c', 'M_tyr__L_c', 'M_udcpdp_c', 'M_utp_c', 'M_val__L_c', 'M_zn2_c']\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: iEKO11_1354\n", + "\n", + "____________________________________________\n", + "\n", + " TARGETS \n", + " FOR TARGET MODE AND FBA \n", + "____________________________________________\n", + "\n", + "Targets set:\n", + " Reactant of objective reaction\n", + " from target file\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " OBJECTVE \n", + " FOR HYBRID \n", + "____________________________________________\n", + "\n", + "Objective set:\n", + " Objective reaction from target file\n", + "\n", + "\n", + "Objective : R_BIOMASS_Ec_iJO1366_core_53p95M\n", + "\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " NETWORK \n", + "____________________________________________\n", + "\n", + "Import reaction: Kept\n", + "Product of import reaction set as seed: No\n", + "Targets can be seeds: No\n", + "Accumulation: Forbidden\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + "List of targets: ['M_10fthf_c', 'M_2fe2s_c', 'M_2ohph_c', 'M_4fe4s_c', 'M_ala__L_c', 'M_amet_c', 'M_arg__L_c', 'M_asn__L_c', 'M_asp__L_c', 'M_atp_c', 'M_bmocogdp_c', 'M_btn_c', 'M_ca2_c', 'M_cl_c', 'M_coa_c', 'M_cobalt2_c', 'M_ctp_c', 'M_cu2_c', 'M_cys__L_c', 'M_datp_c', 'M_dctp_c', 'M_dgtp_c', 'M_dttp_c', 'M_fad_c', 'M_fe2_c', 'M_fe3_c', 'M_gln__L_c', 'M_glu__L_c', 'M_gly_c', 'M_gtp_c', 'M_h2o_c', 'M_his__L_c', 'M_ile__L_c', 'M_k_c', 'M_kdo2lipid4_e', 'M_leu__L_c', 'M_lys__L_c', 'M_met__L_c', 'M_mg2_c', 'M_mlthf_c', 'M_mn2_c', 'M_mobd_c', 'M_murein5px4p_p', 'M_nad_c', 'M_nadp_c', 'M_nh4_c', 'M_ni2_c', 'M_pe160_c', 'M_pe160_p', 'M_pe161_c', 'M_pe161_p', 'M_phe__L_c', 'M_pheme_c', 'M_pro__L_c', 'M_pydx5p_c', 'M_ribflv_c', 'M_ser__L_c', 'M_sheme_c', 'M_so4_c', 'M_thf_c', 'M_thmpp_c', 'M_thr__L_c', 'M_trp__L_c', 'M_tyr__L_c', 'M_udcpdp_c', 'M_utp_c', 'M_val__L_c', 'M_zn2_c']\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: iNJ661\n", + "\n", + "____________________________________________\n", + "\n", + " TARGETS \n", + " FOR TARGET MODE AND FBA \n", + "____________________________________________\n", + "\n", + "Targets set:\n", + " Reactant of objective reaction\n", + " from target file\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " OBJECTVE \n", + " FOR HYBRID \n", + "____________________________________________\n", + "\n", + "Objective set:\n", + " Objective reaction from target file\n", + "\n", + "\n", + "Objective : R_BIOMASS_Mtb_9_60atp\n", + "\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " NETWORK \n", + "____________________________________________\n", + "\n", + "Import reaction: Kept\n", + "Product of import reaction set as seed: No\n", + "Targets can be seeds: No\n", + "Accumulation: Forbidden\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + "List of targets: ['M_Ac1PIM1_c', 'M_Ac1PIM2_c', 'M_Ac1PIM3_c', 'M_Ac1PIM4_c', 'M_Ac2PIM2_c', 'M_PIM1_c', 'M_PIM2_c', 'M_PIM3_c', 'M_PIM4_c', 'M_PIM5_c', 'M_PIM6_c', 'M_acgam1p_c', 'M_ala__L_c', 'M_amp_c', 'M_arabinanagalfragund_c', 'M_arach_c', 'M_arg__L_c', 'M_asn__L_c', 'M_asp__L_c', 'M_atp_c', 'M_clpn160190_c', 'M_cmp_c', 'M_cys__L_c', 'M_damp_c', 'M_dcmp_c', 'M_dgmp_c', 'M_dtmp_c', 'M_fcmcbtt_c', 'M_gal_c', 'M_glc__D_c', 'M_gln__L_c', 'M_glu__L_c', 'M_gly_c', 'M_glyc_c', 'M_gmp_c', 'M_h2o_c', 'M_hdca_c', 'M_hdcea_c', 'M_hexc_c', 'M_his__L_c', 'M_ile__L_c', 'M_kmycolate_c', 'M_leu__L_c', 'M_lys__L_c', 'M_man_c', 'M_mbhn_c', 'M_mcbts_c', 'M_met__L_c', 'M_mfrrppdima_c', 'M_mkmycolate_c', 'M_mmmycolate_c', 'M_mocdca_c', 'M_mycolate_c', 'M_ocdca_c', 'M_ocdcea_c', 'M_pa160190_c', 'M_pa160_c', 'M_pa190190_c', 'M_pat_c', 'M_pdima_c', 'M_pe160_c', 'M_peptido_TB1_c', 'M_peptido_TB2_c', 'M_pg160190_c', 'M_pg160_c', 'M_pg190_c', 'M_phdca_c', 'M_phe__L_c', 'M_ppdima_c', 'M_pro__L_c', 'M_rib__D_c', 'M_ser__L_c', 'M_sl1_c', 'M_tat_c', 'M_thr__L_c', 'M_tmha1_c', 'M_tmha2_c', 'M_tmha3_c', 'M_tmha4_c', 'M_tre6p_c', 'M_tre_c', 'M_tres_c', 'M_trp__L_c', 'M_ttdca_c', 'M_tyr__L_c', 'M_uaaAgla_c', 'M_uaaGgla_c', 'M_uaagmda_c', 'M_uamr_c', 'M_ugagmda_c', 'M_ump_c', 'M_val__L_c']\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: iSFxv_1172\n", + "\n", + "____________________________________________\n", + "\n", + " TARGETS \n", + " FOR TARGET MODE AND FBA \n", + "____________________________________________\n", + "\n", + "Targets set:\n", + " Reactant of objective reaction\n", + " from target file\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " OBJECTVE \n", + " FOR HYBRID \n", + "____________________________________________\n", + "\n", + "Objective set:\n", + " Objective reaction from target file\n", + "\n", + "\n", + "Objective : R_BIOMASS_Ec_iJO1366_core_53p95M\n", + "\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " NETWORK \n", + "____________________________________________\n", + "\n", + "Import reaction: Kept\n", + "Product of import reaction set as seed: No\n", + "Targets can be seeds: No\n", + "Accumulation: Forbidden\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + "List of targets: ['M_10fthf_c', 'M_2fe2s_c', 'M_2ohph_c', 'M_4fe4s_c', 'M_ala__L_c', 'M_amet_c', 'M_arg__L_c', 'M_asn__L_c', 'M_asp__L_c', 'M_atp_c', 'M_bmocogdp_c', 'M_btn_c', 'M_ca2_c', 'M_cl_c', 'M_coa_c', 'M_cobalt2_c', 'M_ctp_c', 'M_cu2_c', 'M_cys__L_c', 'M_datp_c', 'M_dctp_c', 'M_dgtp_c', 'M_dttp_c', 'M_fad_c', 'M_fe2_c', 'M_fe3_c', 'M_gln__L_c', 'M_glu__L_c', 'M_gly_c', 'M_gtp_c', 'M_h2o_c', 'M_his__L_c', 'M_ile__L_c', 'M_k_c', 'M_kdo2lipid4_e', 'M_leu__L_c', 'M_lys__L_c', 'M_met__L_c', 'M_mg2_c', 'M_mlthf_c', 'M_mn2_c', 'M_mobd_c', 'M_murein5px4p_p', 'M_nad_c', 'M_nadp_c', 'M_nh4_c', 'M_ni2_c', 'M_pe160_c', 'M_pe160_p', 'M_pe161_c', 'M_pe161_p', 'M_phe__L_c', 'M_pheme_c', 'M_pro__L_c', 'M_pydx5p_c', 'M_ribflv_c', 'M_ser__L_c', 'M_sheme_c', 'M_so4_c', 'M_thf_c', 'M_thmpp_c', 'M_thr__L_c', 'M_trp__L_c', 'M_tyr__L_c', 'M_udcpdp_c', 'M_utp_c', 'M_val__L_c', 'M_zn2_c']\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: iJO1366\n", + "\n", + "____________________________________________\n", + "\n", + " TARGETS \n", + " FOR TARGET MODE AND FBA \n", + "____________________________________________\n", + "\n", + "Targets set:\n", + " Reactant of objective reaction\n", + " from target file\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " OBJECTVE \n", + " FOR HYBRID \n", + "____________________________________________\n", + "\n", + "Objective set:\n", + " Objective reaction from target file\n", + "\n", + "\n", + "Objective : R_BIOMASS_Ec_iJO1366_core_53p95M\n", + "\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " NETWORK \n", + "____________________________________________\n", + "\n", + "Import reaction: Kept\n", + "Product of import reaction set as seed: No\n", + "Targets can be seeds: No\n", + "Accumulation: Forbidden\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + "List of targets: ['M_10fthf_c', 'M_2fe2s_c', 'M_2ohph_c', 'M_4fe4s_c', 'M_ala__L_c', 'M_amet_c', 'M_arg__L_c', 'M_asn__L_c', 'M_asp__L_c', 'M_atp_c', 'M_bmocogdp_c', 'M_btn_c', 'M_ca2_c', 'M_cl_c', 'M_coa_c', 'M_cobalt2_c', 'M_ctp_c', 'M_cu2_c', 'M_cys__L_c', 'M_datp_c', 'M_dctp_c', 'M_dgtp_c', 'M_dttp_c', 'M_fad_c', 'M_fe2_c', 'M_fe3_c', 'M_gln__L_c', 'M_glu__L_c', 'M_gly_c', 'M_gtp_c', 'M_h2o_c', 'M_his__L_c', 'M_ile__L_c', 'M_k_c', 'M_kdo2lipid4_e', 'M_leu__L_c', 'M_lys__L_c', 'M_met__L_c', 'M_mg2_c', 'M_mlthf_c', 'M_mn2_c', 'M_mobd_c', 'M_murein5px4p_p', 'M_nad_c', 'M_nadp_c', 'M_nh4_c', 'M_ni2_c', 'M_pe160_c', 'M_pe160_p', 'M_pe161_c', 'M_pe161_p', 'M_phe__L_c', 'M_pheme_c', 'M_pro__L_c', 'M_pydx5p_c', 'M_ribflv_c', 'M_ser__L_c', 'M_sheme_c', 'M_so4_c', 'M_thf_c', 'M_thmpp_c', 'M_thr__L_c', 'M_trp__L_c', 'M_tyr__L_c', 'M_udcpdp_c', 'M_utp_c', 'M_val__L_c', 'M_zn2_c']\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: iMM904\n", + "\n", + "____________________________________________\n", + "\n", + " TARGETS \n", + " FOR TARGET MODE AND FBA \n", + "____________________________________________\n", + "\n", + "Targets set:\n", + " Reactant of objective reaction\n", + " from target file\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " OBJECTVE \n", + " FOR HYBRID \n", + "____________________________________________\n", + "\n", + "Objective set:\n", + " Objective reaction from target file\n", + "\n", + "\n", + "Objective : R_BIOMASS_SC5_notrace\n", + "\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " NETWORK \n", + "____________________________________________\n", + "\n", + "Import reaction: Kept\n", + "Product of import reaction set as seed: No\n", + "Targets can be seeds: No\n", + "Accumulation: Forbidden\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + "List of targets: ['M_13BDglcn_c', 'M_ala__L_c', 'M_amp_c', 'M_arg__L_c', 'M_asn__L_c', 'M_asp__L_c', 'M_atp_c', 'M_cmp_c', 'M_cys__L_c', 'M_damp_c', 'M_dcmp_c', 'M_dgmp_c', 'M_dtmp_c', 'M_ergst_c', 'M_gln__L_c', 'M_glu__L_c', 'M_gly_c', 'M_glycogen_c', 'M_gmp_c', 'M_h2o_c', 'M_his__L_c', 'M_ile__L_c', 'M_leu__L_c', 'M_lys__L_c', 'M_mannan_c', 'M_met__L_c', 'M_pa_SC_c', 'M_pc_SC_c', 'M_pe_SC_c', 'M_phe__L_c', 'M_pro__L_c', 'M_ps_SC_c', 'M_ptd1ino_SC_c', 'M_ribflv_c', 'M_ser__L_c', 'M_so4_c', 'M_thr__L_c', 'M_tre_c', 'M_triglyc_SC_c', 'M_trp__L_c', 'M_tyr__L_c', 'M_ump_c', 'M_val__L_c', 'M_zymst_c']\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: iSF_1195\n", + "\n", + "____________________________________________\n", + "\n", + " TARGETS \n", + " FOR TARGET MODE AND FBA \n", + "____________________________________________\n", + "\n", + "Targets set:\n", + " Reactant of objective reaction\n", + " from target file\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " OBJECTVE \n", + " FOR HYBRID \n", + "____________________________________________\n", + "\n", + "Objective set:\n", + " Objective reaction from target file\n", + "\n", + "\n", + "Objective : R_BIOMASS_Ec_iJO1366_core_53p95M\n", + "\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " NETWORK \n", + "____________________________________________\n", + "\n", + "Import reaction: Kept\n", + "Product of import reaction set as seed: No\n", + "Targets can be seeds: No\n", + "Accumulation: Forbidden\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + "List of targets: ['M_10fthf_c', 'M_2fe2s_c', 'M_2ohph_c', 'M_4fe4s_c', 'M_ala__L_c', 'M_amet_c', 'M_arg__L_c', 'M_asn__L_c', 'M_asp__L_c', 'M_atp_c', 'M_bmocogdp_c', 'M_btn_c', 'M_ca2_c', 'M_cl_c', 'M_coa_c', 'M_cobalt2_c', 'M_ctp_c', 'M_cu2_c', 'M_cys__L_c', 'M_datp_c', 'M_dctp_c', 'M_dgtp_c', 'M_dttp_c', 'M_fad_c', 'M_fe2_c', 'M_fe3_c', 'M_gln__L_c', 'M_glu__L_c', 'M_gly_c', 'M_gtp_c', 'M_h2o_c', 'M_his__L_c', 'M_ile__L_c', 'M_k_c', 'M_kdo2lipid4_e', 'M_leu__L_c', 'M_lys__L_c', 'M_met__L_c', 'M_mg2_c', 'M_mlthf_c', 'M_mn2_c', 'M_mobd_c', 'M_murein5px4p_p', 'M_nad_c', 'M_nadp_c', 'M_nh4_c', 'M_ni2_c', 'M_pe160_c', 'M_pe160_p', 'M_pe161_c', 'M_pe161_p', 'M_phe__L_c', 'M_pheme_c', 'M_pro__L_c', 'M_pydx5p_c', 'M_ribflv_c', 'M_ser__L_c', 'M_sheme_c', 'M_so4_c', 'M_thf_c', 'M_thmpp_c', 'M_thr__L_c', 'M_trp__L_c', 'M_tyr__L_c', 'M_udcpdp_c', 'M_utp_c', 'M_val__L_c', 'M_zn2_c']\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: iECB_1328\n", + "\n", + "____________________________________________\n", + "\n", + " TARGETS \n", + " FOR TARGET MODE AND FBA \n", + "____________________________________________\n", + "\n", + "Targets set:\n", + " Reactant of objective reaction\n", + " from target file\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " OBJECTVE \n", + " FOR HYBRID \n", + "____________________________________________\n", + "\n", + "Objective set:\n", + " Objective reaction from target file\n", + "\n", + "\n", + "Objective : R_BIOMASS_Ec_iJO1366_core_53p95M\n", + "\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " NETWORK \n", + "____________________________________________\n", + "\n", + "Import reaction: Kept\n", + "Product of import reaction set as seed: No\n", + "Targets can be seeds: No\n", + "Accumulation: Forbidden\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + "List of targets: ['M_10fthf_c', 'M_2fe2s_c', 'M_2ohph_c', 'M_4fe4s_c', 'M_ala__L_c', 'M_amet_c', 'M_arg__L_c', 'M_asn__L_c', 'M_asp__L_c', 'M_atp_c', 'M_bmocogdp_c', 'M_btn_c', 'M_ca2_c', 'M_cl_c', 'M_coa_c', 'M_cobalt2_c', 'M_ctp_c', 'M_cu2_c', 'M_cys__L_c', 'M_datp_c', 'M_dctp_c', 'M_dgtp_c', 'M_dttp_c', 'M_fad_c', 'M_fe2_c', 'M_fe3_c', 'M_gln__L_c', 'M_glu__L_c', 'M_gly_c', 'M_gtp_c', 'M_h2o_c', 'M_his__L_c', 'M_ile__L_c', 'M_k_c', 'M_kdo2lipid4_e', 'M_leu__L_c', 'M_lys__L_c', 'M_met__L_c', 'M_mg2_c', 'M_mlthf_c', 'M_mn2_c', 'M_mobd_c', 'M_murein5px4p_p', 'M_nad_c', 'M_nadp_c', 'M_nh4_c', 'M_ni2_c', 'M_pe160_c', 'M_pe160_p', 'M_pe161_c', 'M_pe161_p', 'M_phe__L_c', 'M_pheme_c', 'M_pro__L_c', 'M_pydx5p_c', 'M_ribflv_c', 'M_ser__L_c', 'M_sheme_c', 'M_so4_c', 'M_thf_c', 'M_thmpp_c', 'M_thr__L_c', 'M_trp__L_c', 'M_tyr__L_c', 'M_udcpdp_c', 'M_utp_c', 'M_val__L_c', 'M_zn2_c']\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: iECO103_1326\n", + "\n", + "____________________________________________\n", + "\n", + " TARGETS \n", + " FOR TARGET MODE AND FBA \n", + "____________________________________________\n", + "\n", + "Targets set:\n", + " Reactant of objective reaction\n", + " from target file\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " OBJECTVE \n", + " FOR HYBRID \n", + "____________________________________________\n", + "\n", + "Objective set:\n", + " Objective reaction from target file\n", + "\n", + "\n", + "Objective : R_BIOMASS_Ec_iJO1366_core_53p95M\n", + "\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " NETWORK \n", + "____________________________________________\n", + "\n", + "Import reaction: Kept\n", + "Product of import reaction set as seed: No\n", + "Targets can be seeds: No\n", + "Accumulation: Forbidden\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + "List of targets: ['M_10fthf_c', 'M_2fe2s_c', 'M_2ohph_c', 'M_4fe4s_c', 'M_ala__L_c', 'M_amet_c', 'M_arg__L_c', 'M_asn__L_c', 'M_asp__L_c', 'M_atp_c', 'M_bmocogdp_c', 'M_btn_c', 'M_ca2_c', 'M_cl_c', 'M_coa_c', 'M_cobalt2_c', 'M_ctp_c', 'M_cu2_c', 'M_cys__L_c', 'M_datp_c', 'M_dctp_c', 'M_dgtp_c', 'M_dttp_c', 'M_fad_c', 'M_fe2_c', 'M_fe3_c', 'M_gln__L_c', 'M_glu__L_c', 'M_gly_c', 'M_gtp_c', 'M_h2o_c', 'M_his__L_c', 'M_ile__L_c', 'M_k_c', 'M_kdo2lipid4_e', 'M_leu__L_c', 'M_lys__L_c', 'M_met__L_c', 'M_mg2_c', 'M_mlthf_c', 'M_mn2_c', 'M_mobd_c', 'M_murein5px4p_p', 'M_nad_c', 'M_nadp_c', 'M_nh4_c', 'M_ni2_c', 'M_pe160_c', 'M_pe160_p', 'M_pe161_c', 'M_pe161_p', 'M_phe__L_c', 'M_pheme_c', 'M_pro__L_c', 'M_pydx5p_c', 'M_ribflv_c', 'M_ser__L_c', 'M_sheme_c', 'M_so4_c', 'M_thf_c', 'M_thmpp_c', 'M_thr__L_c', 'M_trp__L_c', 'M_tyr__L_c', 'M_udcpdp_c', 'M_utp_c', 'M_val__L_c', 'M_zn2_c']\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: iEC1356_Bl21DE3\n", + "\n", + "____________________________________________\n", + "\n", + " TARGETS \n", + " FOR TARGET MODE AND FBA \n", + "____________________________________________\n", + "\n", + "Targets set:\n", + " Reactant of objective reaction\n", + " from target file\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " OBJECTVE \n", + " FOR HYBRID \n", + "____________________________________________\n", + "\n", + "Objective set:\n", + " Objective reaction from target file\n", + "\n", + "\n", + "Objective : R_BIOMASS_Ec_iJO1366_core_53p95M\n", + "\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " NETWORK \n", + "____________________________________________\n", + "\n", + "Import reaction: Kept\n", + "Product of import reaction set as seed: No\n", + "Targets can be seeds: No\n", + "Accumulation: Forbidden\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + "List of targets: ['M_10fthf_c', 'M_2fe2s_c', 'M_2ohph_c', 'M_4fe4s_c', 'M_ala__L_c', 'M_amet_c', 'M_arg__L_c', 'M_asn__L_c', 'M_asp__L_c', 'M_atp_c', 'M_bmocogdp_c', 'M_btn_c', 'M_ca2_c', 'M_cl_c', 'M_coa_c', 'M_cobalt2_c', 'M_ctp_c', 'M_cu2_c', 'M_cys__L_c', 'M_datp_c', 'M_dctp_c', 'M_dgtp_c', 'M_dttp_c', 'M_fad_c', 'M_fe2_c', 'M_fe3_c', 'M_gln__L_c', 'M_glu__L_c', 'M_gly_c', 'M_gtp_c', 'M_h2o_c', 'M_his__L_c', 'M_ile__L_c', 'M_k_c', 'M_kdo2lipid4_e', 'M_leu__L_c', 'M_lys__L_c', 'M_met__L_c', 'M_mg2_c', 'M_mlthf_c', 'M_mn2_c', 'M_mobd_c', 'M_murein5px4p_p', 'M_nad_c', 'M_nadp_c', 'M_nh4_c', 'M_ni2_c', 'M_pe160_c', 'M_pe160_p', 'M_pe161_c', 'M_pe161_p', 'M_phe__L_c', 'M_pheme_c', 'M_pro__L_c', 'M_pydx5p_c', 'M_ribflv_c', 'M_ser__L_c', 'M_sheme_c', 'M_so4_c', 'M_thf_c', 'M_thmpp_c', 'M_thr__L_c', 'M_trp__L_c', 'M_tyr__L_c', 'M_udcpdp_c', 'M_utp_c', 'M_val__L_c', 'M_zn2_c']\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: iWFL_1372\n", + "\n", + "____________________________________________\n", + "\n", + " TARGETS \n", + " FOR TARGET MODE AND FBA \n", + "____________________________________________\n", + "\n", + "Targets set:\n", + " Reactant of objective reaction\n", + " from target file\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " OBJECTVE \n", + " FOR HYBRID \n", + "____________________________________________\n", + "\n", + "Objective set:\n", + " Objective reaction from target file\n", + "\n", + "\n", + "Objective : R_BIOMASS_Ec_iJO1366_core_53p95M\n", + "\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " NETWORK \n", + "____________________________________________\n", + "\n", + "Import reaction: Kept\n", + "Product of import reaction set as seed: No\n", + "Targets can be seeds: No\n", + "Accumulation: Forbidden\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + "List of targets: ['M_10fthf_c', 'M_2fe2s_c', 'M_2ohph_c', 'M_4fe4s_c', 'M_ala__L_c', 'M_amet_c', 'M_arg__L_c', 'M_asn__L_c', 'M_asp__L_c', 'M_atp_c', 'M_bmocogdp_c', 'M_btn_c', 'M_ca2_c', 'M_cl_c', 'M_coa_c', 'M_cobalt2_c', 'M_ctp_c', 'M_cu2_c', 'M_cys__L_c', 'M_datp_c', 'M_dctp_c', 'M_dgtp_c', 'M_dttp_c', 'M_fad_c', 'M_fe2_c', 'M_fe3_c', 'M_gln__L_c', 'M_glu__L_c', 'M_gly_c', 'M_gtp_c', 'M_h2o_c', 'M_his__L_c', 'M_ile__L_c', 'M_k_c', 'M_kdo2lipid4_e', 'M_leu__L_c', 'M_lys__L_c', 'M_met__L_c', 'M_mg2_c', 'M_mlthf_c', 'M_mn2_c', 'M_mobd_c', 'M_murein5px4p_p', 'M_nad_c', 'M_nadp_c', 'M_nh4_c', 'M_ni2_c', 'M_pe160_c', 'M_pe160_p', 'M_pe161_c', 'M_pe161_p', 'M_phe__L_c', 'M_pheme_c', 'M_pro__L_c', 'M_pydx5p_c', 'M_ribflv_c', 'M_ser__L_c', 'M_sheme_c', 'M_so4_c', 'M_thf_c', 'M_thmpp_c', 'M_thr__L_c', 'M_trp__L_c', 'M_tyr__L_c', 'M_udcpdp_c', 'M_utp_c', 'M_val__L_c', 'M_zn2_c']\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: iECSE_1348\n", + "\n", + "____________________________________________\n", + "\n", + " TARGETS \n", + " FOR TARGET MODE AND FBA \n", + "____________________________________________\n", + "\n", + "Targets set:\n", + " Reactant of objective reaction\n", + " from target file\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " OBJECTVE \n", + " FOR HYBRID \n", + "____________________________________________\n", + "\n", + "Objective set:\n", + " Objective reaction from target file\n", + "\n", + "\n", + "Objective : R_BIOMASS_Ec_iJO1366_core_53p95M\n", + "\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " NETWORK \n", + "____________________________________________\n", + "\n", + "Import reaction: Kept\n", + "Product of import reaction set as seed: No\n", + "Targets can be seeds: No\n", + "Accumulation: Forbidden\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + "List of targets: ['M_10fthf_c', 'M_2fe2s_c', 'M_2ohph_c', 'M_4fe4s_c', 'M_ala__L_c', 'M_amet_c', 'M_arg__L_c', 'M_asn__L_c', 'M_asp__L_c', 'M_atp_c', 'M_bmocogdp_c', 'M_btn_c', 'M_ca2_c', 'M_cl_c', 'M_coa_c', 'M_cobalt2_c', 'M_ctp_c', 'M_cu2_c', 'M_cys__L_c', 'M_datp_c', 'M_dctp_c', 'M_dgtp_c', 'M_dttp_c', 'M_fad_c', 'M_fe2_c', 'M_fe3_c', 'M_gln__L_c', 'M_glu__L_c', 'M_gly_c', 'M_gtp_c', 'M_h2o_c', 'M_his__L_c', 'M_ile__L_c', 'M_k_c', 'M_kdo2lipid4_e', 'M_leu__L_c', 'M_lys__L_c', 'M_met__L_c', 'M_mg2_c', 'M_mlthf_c', 'M_mn2_c', 'M_mobd_c', 'M_murein5px4p_p', 'M_nad_c', 'M_nadp_c', 'M_nh4_c', 'M_ni2_c', 'M_pe160_c', 'M_pe160_p', 'M_pe161_c', 'M_pe161_p', 'M_phe__L_c', 'M_pheme_c', 'M_pro__L_c', 'M_pydx5p_c', 'M_ribflv_c', 'M_ser__L_c', 'M_sheme_c', 'M_so4_c', 'M_thf_c', 'M_thmpp_c', 'M_thr__L_c', 'M_trp__L_c', 'M_tyr__L_c', 'M_udcpdp_c', 'M_utp_c', 'M_val__L_c', 'M_zn2_c']\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: iHN637\n", + "\n", + "____________________________________________\n", + "\n", + " TARGETS \n", + " FOR TARGET MODE AND FBA \n", + "____________________________________________\n", + "\n", + "Targets set:\n", + " Reactant of objective reaction\n", + " from target file\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " OBJECTVE \n", + " FOR HYBRID \n", + "____________________________________________\n", + "\n", + "Objective set:\n", + " Objective reaction from target file\n", + "\n", + "\n", + "Objective : R_BIOMASS_Cl_DSM_WT_46p666M1\n", + "\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " NETWORK \n", + "____________________________________________\n", + "\n", + "Import reaction: Kept\n", + "Product of import reaction set as seed: No\n", + "Targets can be seeds: No\n", + "Accumulation: Forbidden\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + "List of targets: ['M_10fthf_c', 'M_5mthf_c', 'M_accoa_c', 'M_adocbl_c', 'M_ala__L_c', 'M_amet_c', 'M_arg__L_c', 'M_asn__L_c', 'M_asp__L_c', 'M_atp_c', 'M_btn_c', 'M_ca2_c', 'M_chor_c', 'M_cl_c', 'M_clpn140_c', 'M_clpn160_c', 'M_clpn161_c', 'M_clpn181_c', 'M_coa_c', 'M_cobalt2_c', 'M_ctp_c', 'M_cu2_c', 'M_cys__L_c', 'M_datp_c', 'M_dctp_c', 'M_dgtp_c', 'M_dttp_c', 'M_fad_c', 'M_fe2_c', 'M_gln__L_c', 'M_glu__L_c', 'M_gly_c', 'M_gtp_c', 'M_h2o_c', 'M_his__L_c', 'M_ile__L_c', 'M_k_c', 'M_leu__L_c', 'M_lys__L_c', 'M_malcoa_c', 'M_met__L_c', 'M_mg2_c', 'M_mlthf_c', 'M_mn2_c', 'M_mobd_c', 'M_murein4p4p_e', 'M_murein4px4p_e', 'M_murein4px4px4p_e', 'M_nad_c', 'M_nadh_c', 'M_nadp_c', 'M_nadph_c', 'M_nh4_c', 'M_ni2_c', 'M_pe140_c', 'M_pe160_c', 'M_pe161_c', 'M_pe181_c', 'M_pg140_c', 'M_pg160_c', 'M_pg161_c', 'M_pg181_c', 'M_phe__L_c', 'M_pro__L_c', 'M_pydx5p_c', 'M_ribflv_c', 'M_ser__L_c', 'M_so4_c', 'M_teca_CL_c', 'M_thf_c', 'M_thmpp_c', 'M_thr__L_c', 'M_trp__L_c', 'M_tyr__L_c', 'M_udcpdp_c', 'M_utp_c', 'M_val__L_c', 'M_zn2_c']\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: iSynCJ816\n", + "\n", + "____________________________________________\n", + "\n", + " TARGETS \n", + " FOR TARGET MODE AND FBA \n", + "____________________________________________\n", + "\n", + "Targets set:\n", + " Reactant of objective reaction\n", + " from target file\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " OBJECTVE \n", + " FOR HYBRID \n", + "____________________________________________\n", + "\n", + "Objective set:\n", + " Objective reaction from target file\n", + "\n", + "\n", + "Objective : R_BIOMASS_Ec_SynAuto_1\n", + "\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " NETWORK \n", + "____________________________________________\n", + "\n", + "Import reaction: Kept\n", + "Product of import reaction set as seed: No\n", + "Targets can be seeds: No\n", + "Accumulation: Forbidden\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + "List of targets: ['M_10fthf_c', 'M_5mthf_c', 'M_accoa_c', 'M_adocbl_c', 'M_ala__L_c', 'M_amet_c', 'M_arg__L_c', 'M_asn__L_c', 'M_asp__L_c', 'M_atp_c', 'M_avite1_c', 'M_bvite_c', 'M_ca2_c', 'M_caro_c', 'M_cholphya_c', 'M_chor_c', 'M_coa_c', 'M_cobalt2_c', 'M_ctp_c', 'M_cu2_c', 'M_cys__L_c', 'M_datp_c', 'M_dctp_c', 'M_dgdg160_c', 'M_dgdg161_c', 'M_dgdg180_c', 'M_dgdg181_9_c', 'M_dgdg181_c', 'M_dgdg182_9_12_c', 'M_dgdg183_6_9_12_c', 'M_dgdg183_9_12_15_c', 'M_dgdg184_6_9_12_15_c', 'M_dgtp_c', 'M_dtocophe_c', 'M_dttp_c', 'M_e11_p', 'M_echin_c', 'M_fad_c', 'M_fe2_c', 'M_fe3_c', 'M_gcarote_c', 'M_gln__L_c', 'M_glu__L_c', 'M_gly_c', 'M_glycogen_c', 'M_gthrd_c', 'M_gtocophe_c', 'M_gtp_c', 'M_h2o_c', 'M_hemeA_c', 'M_hemeO_c', 'M_his__L_c', 'M_ile__L_c', 'M_k_c', 'M_leu__L_c', 'M_lipidAds_c', 'M_lys__L_c', 'M_malcoa_c', 'M_met__L_c', 'M_mg2_c', 'M_mgdg160_c', 'M_mgdg161_c', 'M_mgdg180_c', 'M_mgdg181_9_c', 'M_mgdg181_c', 'M_mgdg182_9_12_c', 'M_mgdg183_6_9_12_c', 'M_mgdg183_9_12_15_c', 'M_mgdg184_6_9_12_15_c', 'M_mlthf_c', 'M_mn2_c', 'M_mobd_c', 'M_na1_c', 'M_nad_c', 'M_nadh_c', 'M_nadp_c', 'M_nadph_c', 'M_nh4_c', 'M_pg160_c', 'M_pg161_c', 'M_pg180_c', 'M_pg181_9_c', 'M_pg181_c', 'M_pg182_9_12_c', 'M_pg183_6_9_12_c', 'M_pg183_9_12_15_c', 'M_pg184_6_9_12_15_c', 'M_phe__L_c', 'M_pheme_c', 'M_phllqne_c', 'M_pro__L_c', 'M_ptrc_c', 'M_ribflv_c', 'M_ser__L_c', 'M_so4_c', 'M_spmd_c', 'M_sqdg160_c', 'M_sqdg161_c', 'M_sqdg180_c', 'M_sqdg181_9_c', 'M_sqdg181_c', 'M_sqdg182_9_12_c', 'M_sqdg183_6_9_12_c', 'M_sqdg183_9_12_15_c', 'M_sqdg184_6_9_12_15_c', 'M_succoa_c', 'M_thf_c', 'M_thr__L_c', 'M_trp__L_c', 'M_tyr__L_c', 'M_udcpdp_c', 'M_utp_c', 'M_val__L_c', 'M_zeax_c', 'M_zn2_c']\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: iECDH1ME8569_1439\n", + "\n", + "____________________________________________\n", + "\n", + " TARGETS \n", + " FOR TARGET MODE AND FBA \n", + "____________________________________________\n", + "\n", + "Targets set:\n", + " Reactant of objective reaction\n", + " from target file\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " OBJECTVE \n", + " FOR HYBRID \n", + "____________________________________________\n", + "\n", + "Objective set:\n", + " Objective reaction from target file\n", + "\n", + "\n", + "Objective : R_BIOMASS_Ec_iJO1366_core_53p95M\n", + "\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " NETWORK \n", + "____________________________________________\n", + "\n", + "Import reaction: Kept\n", + "Product of import reaction set as seed: No\n", + "Targets can be seeds: No\n", + "Accumulation: Forbidden\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + "List of targets: ['M_10fthf_c', 'M_2fe2s_c', 'M_2ohph_c', 'M_4fe4s_c', 'M_ala__L_c', 'M_amet_c', 'M_arg__L_c', 'M_asn__L_c', 'M_asp__L_c', 'M_atp_c', 'M_bmocogdp_c', 'M_btn_c', 'M_ca2_c', 'M_cl_c', 'M_coa_c', 'M_cobalt2_c', 'M_ctp_c', 'M_cu2_c', 'M_cys__L_c', 'M_datp_c', 'M_dctp_c', 'M_dgtp_c', 'M_dttp_c', 'M_fad_c', 'M_fe2_c', 'M_fe3_c', 'M_gln__L_c', 'M_glu__L_c', 'M_gly_c', 'M_gtp_c', 'M_h2o_c', 'M_his__L_c', 'M_ile__L_c', 'M_k_c', 'M_kdo2lipid4_e', 'M_leu__L_c', 'M_lys__L_c', 'M_met__L_c', 'M_mg2_c', 'M_mlthf_c', 'M_mn2_c', 'M_mobd_c', 'M_murein5px4p_p', 'M_nad_c', 'M_nadp_c', 'M_nh4_c', 'M_ni2_c', 'M_pe160_c', 'M_pe160_p', 'M_pe161_c', 'M_pe161_p', 'M_phe__L_c', 'M_pheme_c', 'M_pro__L_c', 'M_pydx5p_c', 'M_ribflv_c', 'M_ser__L_c', 'M_sheme_c', 'M_so4_c', 'M_thf_c', 'M_thmpp_c', 'M_thr__L_c', 'M_trp__L_c', 'M_tyr__L_c', 'M_udcpdp_c', 'M_utp_c', 'M_val__L_c', 'M_zn2_c']\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: iE2348C_1286\n", + "\n", + "____________________________________________\n", + "\n", + " TARGETS \n", + " FOR TARGET MODE AND FBA \n", + "____________________________________________\n", + "\n", + "Targets set:\n", + " Reactant of objective reaction\n", + " from target file\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " OBJECTVE \n", + " FOR HYBRID \n", + "____________________________________________\n", + "\n", + "Objective set:\n", + " Objective reaction from target file\n", + "\n", + "\n", + "Objective : R_BIOMASS_Ec_iJO1366_core_53p95M\n", + "\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " NETWORK \n", + "____________________________________________\n", + "\n", + "Import reaction: Kept\n", + "Product of import reaction set as seed: No\n", + "Targets can be seeds: No\n", + "Accumulation: Forbidden\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + "List of targets: ['M_10fthf_c', 'M_2fe2s_c', 'M_2ohph_c', 'M_4fe4s_c', 'M_ala__L_c', 'M_amet_c', 'M_arg__L_c', 'M_asn__L_c', 'M_asp__L_c', 'M_atp_c', 'M_bmocogdp_c', 'M_btn_c', 'M_ca2_c', 'M_cl_c', 'M_coa_c', 'M_cobalt2_c', 'M_ctp_c', 'M_cu2_c', 'M_cys__L_c', 'M_datp_c', 'M_dctp_c', 'M_dgtp_c', 'M_dttp_c', 'M_fad_c', 'M_fe2_c', 'M_fe3_c', 'M_gln__L_c', 'M_glu__L_c', 'M_gly_c', 'M_gtp_c', 'M_h2o_c', 'M_his__L_c', 'M_ile__L_c', 'M_k_c', 'M_kdo2lipid4_e', 'M_leu__L_c', 'M_lys__L_c', 'M_met__L_c', 'M_mg2_c', 'M_mlthf_c', 'M_mn2_c', 'M_mobd_c', 'M_murein5px4p_p', 'M_nad_c', 'M_nadp_c', 'M_nh4_c', 'M_ni2_c', 'M_pe160_c', 'M_pe160_p', 'M_pe161_c', 'M_pe161_p', 'M_phe__L_c', 'M_pheme_c', 'M_pro__L_c', 'M_pydx5p_c', 'M_ribflv_c', 'M_ser__L_c', 'M_sheme_c', 'M_so4_c', 'M_thf_c', 'M_thmpp_c', 'M_thr__L_c', 'M_trp__L_c', 'M_tyr__L_c', 'M_udcpdp_c', 'M_utp_c', 'M_val__L_c', 'M_zn2_c']\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: iLB1027_lipid\n", + "\n", + "____________________________________________\n", + "\n", + " TARGETS \n", + " FOR TARGET MODE AND FBA \n", + "____________________________________________\n", + "\n", + "Targets set:\n", + " Reactant of objective reaction\n", + " from target file\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " OBJECTVE \n", + " FOR HYBRID \n", + "____________________________________________\n", + "\n", + "Objective set:\n", + " Objective reaction from target file\n", + "\n", + "\n", + "Objective : R_DM_biomass_c\n", + "\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " NETWORK \n", + "____________________________________________\n", + "\n", + "Import reaction: Kept\n", + "Product of import reaction set as seed: No\n", + "Targets can be seeds: No\n", + "Accumulation: Forbidden\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + "List of targets: ['M_biomass_c']\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: iEC042_1314\n", + "\n", + "____________________________________________\n", + "\n", + " TARGETS \n", + " FOR TARGET MODE AND FBA \n", + "____________________________________________\n", + "\n", + "Targets set:\n", + " Reactant of objective reaction\n", + " from target file\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " OBJECTVE \n", + " FOR HYBRID \n", + "____________________________________________\n", + "\n", + "Objective set:\n", + " Objective reaction from target file\n", + "\n", + "\n", + "Objective : R_BIOMASS_Ec_iJO1366_core_53p95M\n", + "\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " NETWORK \n", + "____________________________________________\n", + "\n", + "Import reaction: Kept\n", + "Product of import reaction set as seed: No\n", + "Targets can be seeds: No\n", + "Accumulation: Forbidden\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + "List of targets: ['M_10fthf_c', 'M_2fe2s_c', 'M_2ohph_c', 'M_4fe4s_c', 'M_ala__L_c', 'M_amet_c', 'M_arg__L_c', 'M_asn__L_c', 'M_asp__L_c', 'M_atp_c', 'M_bmocogdp_c', 'M_btn_c', 'M_ca2_c', 'M_cl_c', 'M_coa_c', 'M_cobalt2_c', 'M_ctp_c', 'M_cu2_c', 'M_cys__L_c', 'M_datp_c', 'M_dctp_c', 'M_dgtp_c', 'M_dttp_c', 'M_fad_c', 'M_fe2_c', 'M_fe3_c', 'M_gln__L_c', 'M_glu__L_c', 'M_gly_c', 'M_gtp_c', 'M_h2o_c', 'M_his__L_c', 'M_ile__L_c', 'M_k_c', 'M_kdo2lipid4_e', 'M_leu__L_c', 'M_lys__L_c', 'M_met__L_c', 'M_mg2_c', 'M_mlthf_c', 'M_mn2_c', 'M_mobd_c', 'M_murein5px4p_p', 'M_nad_c', 'M_nadp_c', 'M_nh4_c', 'M_ni2_c', 'M_pe160_c', 'M_pe160_p', 'M_pe161_c', 'M_pe161_p', 'M_phe__L_c', 'M_pheme_c', 'M_pro__L_c', 'M_pydx5p_c', 'M_ribflv_c', 'M_ser__L_c', 'M_sheme_c', 'M_so4_c', 'M_thf_c', 'M_thmpp_c', 'M_thr__L_c', 'M_trp__L_c', 'M_tyr__L_c', 'M_udcpdp_c', 'M_utp_c', 'M_val__L_c', 'M_zn2_c']\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: iECNA114_1301\n", + "\n", + "____________________________________________\n", + "\n", + " TARGETS \n", + " FOR TARGET MODE AND FBA \n", + "____________________________________________\n", + "\n", + "Targets set:\n", + " Reactant of objective reaction\n", + " from target file\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " OBJECTVE \n", + " FOR HYBRID \n", + "____________________________________________\n", + "\n", + "Objective set:\n", + " Objective reaction from target file\n", + "\n", + "\n", + "Objective : R_BIOMASS_Ec_iJO1366_core_53p95M\n", + "\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " NETWORK \n", + "____________________________________________\n", + "\n", + "Import reaction: Kept\n", + "Product of import reaction set as seed: No\n", + "Targets can be seeds: No\n", + "Accumulation: Forbidden\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + "List of targets: ['M_10fthf_c', 'M_2fe2s_c', 'M_2ohph_c', 'M_4fe4s_c', 'M_ala__L_c', 'M_amet_c', 'M_arg__L_c', 'M_asn__L_c', 'M_asp__L_c', 'M_atp_c', 'M_bmocogdp_c', 'M_btn_c', 'M_ca2_c', 'M_cl_c', 'M_coa_c', 'M_cobalt2_c', 'M_ctp_c', 'M_cu2_c', 'M_cys__L_c', 'M_datp_c', 'M_dctp_c', 'M_dgtp_c', 'M_dttp_c', 'M_fad_c', 'M_fe2_c', 'M_fe3_c', 'M_gln__L_c', 'M_glu__L_c', 'M_gly_c', 'M_gtp_c', 'M_h2o_c', 'M_his__L_c', 'M_ile__L_c', 'M_k_c', 'M_kdo2lipid4_e', 'M_leu__L_c', 'M_lys__L_c', 'M_met__L_c', 'M_mg2_c', 'M_mlthf_c', 'M_mn2_c', 'M_mobd_c', 'M_murein5px4p_p', 'M_nad_c', 'M_nadp_c', 'M_nh4_c', 'M_ni2_c', 'M_pe160_c', 'M_pe160_p', 'M_pe161_c', 'M_pe161_p', 'M_phe__L_c', 'M_pheme_c', 'M_pro__L_c', 'M_pydx5p_c', 'M_ribflv_c', 'M_ser__L_c', 'M_sheme_c', 'M_so4_c', 'M_thf_c', 'M_thmpp_c', 'M_thr__L_c', 'M_trp__L_c', 'M_tyr__L_c', 'M_udcpdp_c', 'M_utp_c', 'M_val__L_c', 'M_zn2_c']\n" + ] + } + ], + "source": [ + "target_dir=path.join(data_dir,\"target\")\n", + "if not path.isdir(target_dir):\n", + " makedirs(target_dir)\n", + "\n", + "for filename in listdir(sbml_dir):\n", + " species = f'{path.splitext(path.basename(filename))[0]}'\n", + " sbml_path = path.join(sbml_dir,filename)\n", + "\n", + " objective_path = path.join(objective_dir,f\"{species}_target.txt\")\n", + " with open(objective_path) as f:\n", + " objective = f.readline()\n", + " target_command=f\"objective_targets {sbml_path} {target_dir} -o {objective}\"\n", + " !seed2lp {target_command}" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "s2lp", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.14" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/notebook/run/03_run_seed2lp.ipynb b/notebook/run/03_run_seed2lp.ipynb new file mode 100644 index 0000000..5bf29c2 --- /dev/null +++ b/notebook/run/03_run_seed2lp.ipynb @@ -0,0 +1,2443 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Seed2LP: Run" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This notebook explains how to run seed2lp, and must be run **AFTER** retrieving BiGG SBML file (see notebook [01_get_sbml_BiGG.ipynb](./01_get_sbml_BiGG.ipynb)) ant getting objective (see notebook [02_get_objectives.ipynb](./02_get_objectives.ipynb))\n", + "\n", + "> Note:\n", + ">\n", + "> The Seed2lp (seed searching and flux) result files are available: [https://doi.org/10.57745/OS1JND](https://doi.org/10.57745/OS1JND)\n", + ">\n", + "> After downloadind and unzipping the package, go to \n", + "> - Reasoning results and flux: analyses/results/s2lp_reasoning \n", + "> - Filter, Guess&Check and Guess&Check with diversity (hybrid cobra): analyses/results/s2lp_hyb_cobra " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## **WARNING**\n", + "This notebook will run Seed2LP for e_coli_core from BiGG with the target and full network seed searching mode, using all methods (*Reasoning*, *Hybrid-Filter*, *Hybrid-GC*, *Hybrid-GCDiv*, *Hybrid-lpx*), no accumulation allowed, NE seed inference, using subset minimal and minimize otptimisations. Seed2LP is set to find 30 solutions and will stop if exceed 10 min.\n", + "\n", + "In the paper, the time limit is set to 45 min and number of solutions limit to 1000, it was run for all 107 networks, with and without accumulation, using subset minimal and minimize otptimization, separating the methods (*Reasoning*, *Hybrid-Filter*, *Hybrid-GC*, *Hybrid-GCDiv*, *Hybrid-lpx* and *FBA*) by not using \"all\" as a value of argument. Pratical reasons explain this choice: benchmarks were run on a computing cluster, launching tasks in parallel, and using computing nodes in \"exclusive\" mode for *Hybrid-lpx* and *FBA* to get enough memory to calculate fluxes.\n", + "\n", + "To avoid a long time running within the notebook, the notebook will copy e_coli_core in a sbml directory on a path that you can change." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Requirements\n", + "Module *seed2lp* needed\n", + "\n", + "> Advice:\n", + "> \n", + "> Use a conda env called s2lp with python 3.10 for plafrim cluster scripts" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!pip install seed2lp" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## **Slurm-based cluster**: Reproducing paper data\n", + "Slurm-based scripts for cluster are available with 45 min and 1000 for all networks:\n", + "- Launch if needed \n", + " - [01_job_retrieve_bigg_sbml.sh](../../scripts/plafrim_cluster/01_job_retrieve_bigg_sbml.sh): `sbatch 01_job_retrieve_bigg_sbml.sh`\n", + " - [02_job_get_objective.sh](../../scripts/plafrim_cluster/02_job_get_objective.sh): `sbatch 02_job_get_objective.sh`\n", + " - or copy your local files into you cluster\n", + "- Change **_source_** variable by the path of your conda environement with seed2lp installed in files: \n", + " - [03_01_execute_workflow_search.sh](../../scripts/plafrim_cluster/03_01_execute_workflow_search.sh)\n", + " - [03_02_execute_workflow_search_exclusive.sh](../../scripts/plafrim_cluster/03_02_execute_workflow_search_exclusive.sh)\n", + " - [03_01_job_run_s2lp.sh](../../scripts/plafrim_cluster/03_01_job_run_s2lp.sh) \n", + " - [03_02_job_run_s2lp_exclusive.sh](../../scripts/plafrim_cluster/03_02_job_run_s2lp_exclusive.sh) \n", + "- launch:\n", + " - [03_01_execute_workflow_search.sh](../../scripts/plafrim_cluster/03_01_execute_workflow_search.sh): `sbatch 03_01_execute_workflow_search.sh`\n", + " - [03_02_execute_workflow_search_exclusive.sh](../../scripts/plafrim_cluster/03_02_execute_workflow_search_exclusive.sh): `sbatch 03_02_execute_workflow_search_exclusive.sh`" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## **LAUNCH**" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Variable to change (if wanted)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "analyse_dir = \"../../analyses\"\n", + "data_dir = f\"{analyse_dir}/data/\"\n", + "result_dir=f\"{analyse_dir}/results\"\n", + "temp_dir = \"../../tmp/\"\n", + "\n", + "\n", + "time_limit = 10 # time limit\n", + "number_solution = 30 # number solutions" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Execute" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "from os import path, makedirs, listdir\n", + "from shutil import copyfile" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "sbml_dir = f\"{data_dir}/bigg/sbml\"\n", + "e_coli_dir = f\"{data_dir}/bigg/sbml_e_coli_core\"\n", + "result_dir = f\"{result_dir}/s2lp\"\n", + "objective_dir = f\"{data_dir}/objective\"" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'../../data/bigg/sbml_e_coli_core/e_coli_core.xml'" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "if not path.isdir(e_coli_dir):\n", + " makedirs(e_coli_dir)\n", + " copyfile(path.join(sbml_dir, \"e_coli_core.xml\"), path.join(e_coli_dir, \"e_coli_core.xml\"))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This function will execute seed2lp for e_coli_core:\n", + "- Target and Full Network\n", + "- subset minimal and minimize\n", + "- *Reasoning*, *Hybrid-Filter*, *Hybrid-GC*, *Hybrid-GCDiv* and *Hybrid-lpx*\n", + "- no accumulation\n", + "- maximisation (of flux in Objective reaction)\n", + "- Limitations: 30 solutions and 10 min\n", + "\n", + "Also, it will check the flux for each solution and write it into files." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "def run_s2lp(in_dir:str):\n", + " for filename in listdir(in_dir):\n", + " species = f'{path.splitext(path.basename(filename))[0]}'\n", + " sbml_path = path.join(in_dir,filename)\n", + " objective_path = path.join(objective_dir,f\"{species}_target.txt\")\n", + " with open(objective_path) as f:\n", + " objective = f.readline()\n", + " result_path = path.join(result_dir,species)\n", + "\n", + " run={\"target\":f\"-tf {objective_path}\",\n", + " \"full\":f\"-o {objective}\"}\n", + "\n", + " for key,value in run.items():\n", + " command = f\"{key} {sbml_path} {result_path} --temp {temp_dir} -tl {time_limit} -nbs {number_solution} -cf -max {value}\"\n", + " !seed2lp {command}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The execution might take more than 30min due to finding minimal solutions and due to *Hybrid-lpx* mode (requires lot of time to calculate fluxes)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: e_coli_core\n", + "\n", + "____________________________________________\n", + "\n", + " TARGETS \n", + " FOR TARGET MODE AND FBA \n", + "____________________________________________\n", + "\n", + "Targets set:\n", + " Reactant of objective reaction\n", + " from target file\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " OBJECTVE \n", + " FOR HYBRID \n", + "____________________________________________\n", + "\n", + "Objective set:\n", + " Objective reaction from target file\n", + "\n", + "\n", + "Objective : R_BIOMASS_Ecoli_core_w_GAM\n", + "\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " NETWORK \n", + "____________________________________________\n", + "\n", + "Import reaction: Removed\n", + "Targets can be seeds: No\n", + "Accumulation: Forbidden\n", + "\n", + "\n", + "\u001b[0;93mWARNING : - R_FORt: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + "\u001b[0m\n", + "\u001b[0;96m\n", + "############################################\n", + "############################################\n", + " \u001b[1mREASONING\u001b[0;96m\n", + "############################################\n", + "############################################\n", + "\u001b[0m\n", + "Mode : TARGET\n", + "Option: TARGETS ARE FORBIDDEN SEEDS\n", + "ACCUMULATION: Forbidden\n", + "Time limit: 10.0 minutes\n", + "Solution number limit: 30\n", + "\u001b[0;94m\n", + "____________________________________________\u001b[0m\n", + "\u001b[0;94m____________________________________________\n", + "\u001b[0m\n", + " Sub Mode: \u001b[1mSUBSET MINIMAL\u001b[0m \n", + "\u001b[0;94m____________________________________________\u001b[0m\n", + "\u001b[0;94m____________________________________________\n", + "\u001b[0m\n", + "\n", + "················ \u001b[1mClassic mode\u001b[0m ···············\n", + "\n", + "~~~~~~~~~~~~~~~~ \u001b[1mEnumeration\u001b[0m ~~~~~~~~~~~~~~~\n", + "SOLVING...\n", + "\n", + "Answer: 1 (7 seeds) \n", + "M_adp_c, M_coa_c, M_glu__L_e, M_h2o_e, M_nadh_c, M_nadp_c, M_pyr_e\n", + "\n", + "Answer: 2 (7 seeds) \n", + "M_acald_e, M_adp_c, M_coa_c, M_glu__L_e, M_h2o_e, M_nadh_c, M_nadp_c\n", + "\n", + "Answer: 3 (7 seeds) \n", + "M_actp_c, M_adp_c, M_coa_c, M_glu__L_e, M_h2o_e, M_nadh_c, M_nadp_c\n", + "\n", + "Answer: 4 (7 seeds) \n", + "M_adp_c, M_cit_c, M_coa_c, M_glu__L_e, M_nadh_c, M_nadp_c, M_pyr_e\n", + "\n", + "Answer: 5 (7 seeds) \n", + "M_actp_c, M_adp_c, M_cit_c, M_coa_c, M_glu__L_e, M_nadh_c, M_nadp_c\n", + "\n", + "Answer: 6 (7 seeds) \n", + "M_acald_e, M_adp_c, M_cit_c, M_coa_c, M_glu__L_e, M_nadh_c, M_nadp_c\n", + "\n", + "Answer: 7 (7 seeds) \n", + "M_adp_c, M_coa_c, M_fum_c, M_glu__L_e, M_h2o_e, M_nadh_c, M_nadp_c\n", + "\n", + "Answer: 8 (7 seeds) \n", + "M_adp_c, M_cit_c, M_coa_c, M_fum_c, M_glu__L_e, M_nadh_c, M_nadp_c\n", + "\n", + "Answer: 9 (6 seeds) \n", + "M_adp_c, M_coa_c, M_glu__L_e, M_mal__L_c, M_nadh_c, M_nadp_c\n", + "\n", + "Answer: 10 (7 seeds) \n", + "M_adp_c, M_coa_c, M_etoh_c, M_glu__L_e, M_h2o_e, M_nadh_c, M_nadp_c\n", + "\n", + "Answer: 11 (7 seeds) \n", + "M_adp_c, M_cit_c, M_coa_c, M_etoh_c, M_glu__L_e, M_nadh_c, M_nadp_c\n", + "\n", + "Answer: 12 (7 seeds) \n", + "M_adp_c, M_coa_c, M_glu__L_e, M_h2o_e, M_lac__D_e, M_nadh_c, M_nadp_c\n", + "\n", + "Answer: 13 (7 seeds) \n", + "M_adp_c, M_cit_c, M_coa_c, M_glu__L_e, M_lac__D_e, M_nadh_c, M_nadp_c\n", + "\n", + "Answer: 14 (7 seeds) \n", + "M_adp_c, M_coa_c, M_etoh_e, M_glu__L_e, M_h2o_e, M_nadh_c, M_nadp_c\n", + "\n", + "Answer: 15 (7 seeds) \n", + "M_adp_c, M_cit_c, M_coa_c, M_etoh_e, M_glu__L_e, M_nadh_c, M_nadp_c\n", + "\n", + "Answer: 16 (8 seeds) \n", + "M_adp_c, M_akg_c, M_coa_c, M_fdp_c, M_glu__L_e, M_nadh_c, M_nadp_c, M_s7p_c\n", + "\n", + "Answer: 17 (8 seeds) \n", + "M_adp_c, M_coa_c, M_fdp_c, M_glu__L_e, M_nadh_c, M_nadp_c, M_pi_c, M_s7p_c\n", + "\n", + "Answer: 18 (8 seeds) \n", + "M_actp_c, M_adp_c, M_coa_c, M_fdp_c, M_glu__L_e, M_nadh_c, M_nadp_c, M_s7p_c\n", + "\n", + "Answer: 19 (9 seeds) \n", + "M_acald_e, M_adp_c, M_coa_c, M_fdp_c, M_glu__L_e, M_nadh_c, M_nadp_c, M_pi_e, M_s7p_c\n", + "\n", + "Answer: 20 (7 seeds) \n", + "M_adp_c, M_coa_c, M_fdp_c, M_glu__L_e, M_h2o_e, M_nadh_c, M_nadp_c\n", + "\n", + "Answer: 21 (9 seeds) \n", + "M_adp_c, M_coa_c, M_etoh_c, M_fdp_c, M_glu__L_e, M_nadh_c, M_nadp_c, M_pi_e, M_s7p_c\n", + "\n", + "Answer: 22 (8 seeds) \n", + "M_acald_e, M_adp_c, M_coa_c, M_glu__L_e, M_h_c, M_nadh_c, M_nadp_c, M_pi_e\n", + "\n", + "Answer: 23 (8 seeds) \n", + "M_adp_c, M_akg_c, M_coa_c, M_glu__L_e, M_h_c, M_nadh_c, M_nadp_c, M_pyr_e\n", + "\n", + "Answer: 24 (8 seeds) \n", + "M_adp_c, M_akg_c, M_coa_c, M_dhap_c, M_glu__L_e, M_h_c, M_nadh_c, M_nadp_c\n", + "\n", + "Answer: 25 (8 seeds) \n", + "M_acald_e, M_adp_c, M_akg_c, M_coa_c, M_glu__L_e, M_h_c, M_nadh_c, M_nadp_c\n", + "\n", + "Answer: 26 (8 seeds) \n", + "M_adp_c, M_coa_c, M_glu__L_e, M_h_c, M_nadh_c, M_nadp_c, M_pi_c, M_pyr_e\n", + "\n", + "Answer: 27 (8 seeds) \n", + "M_adp_c, M_coa_c, M_dhap_c, M_glu__L_e, M_h_c, M_nadh_c, M_nadp_c, M_pi_c\n", + "\n", + "Answer: 28 (8 seeds) \n", + "M_acald_e, M_adp_c, M_coa_c, M_glu__L_e, M_h_c, M_nadh_c, M_nadp_c, M_pi_c\n", + "\n", + "Answer: 29 (8 seeds) \n", + "M_adp_c, M_coa_c, M_fum_c, M_glu__L_e, M_h_c, M_nadh_c, M_nadp_c, M_pi_c\n", + "\n", + "Answer: 30 (8 seeds) \n", + "M_adp_c, M_akg_c, M_coa_c, M_fum_c, M_glu__L_e, M_h_c, M_nadh_c, M_nadp_c\n", + "\n", + "\n", + "················ \u001b[1mFilter mode\u001b[0m ···············\n", + "\n", + "~~~~~~~~~~~~~~~~ \u001b[1mEnumeration\u001b[0m ~~~~~~~~~~~~~~~\n", + "SOLVING...\n", + "\n", + "Answer: 1 (7 seeds)\n", + "M_actp_c, M_adp_c, M_icit_c, M_nadh_c, M_nadp_c, M_nh4_e, M_succoa_c\n", + "\n", + "Answer: 2 (8 seeds)\n", + "M_actp_c, M_adp_c, M_akg_c, M_h_e, M_nadh_c, M_nadp_c, M_nh4_e, M_succoa_c\n", + "\n", + "Answer: 3 (7 seeds)\n", + "M_actp_c, M_adp_c, M_coa_c, M_icit_c, M_nadh_c, M_nadp_c, M_nh4_e\n", + "\n", + "Answer: 4 (8 seeds)\n", + "M_actp_c, M_adp_c, M_akg_c, M_coa_c, M_h_e, M_nadh_c, M_nadp_c, M_nh4_e\n", + "\n", + "Answer: 5 (8 seeds)\n", + "M_actp_c, M_adp_c, M_akg_c, M_h_c, M_nadh_c, M_nadp_c, M_nh4_e, M_succoa_c\n", + "\n", + "Answer: 6 (8 seeds)\n", + "M_actp_c, M_adp_c, M_akg_c, M_coa_c, M_h_c, M_nadh_c, M_nadp_c, M_nh4_e\n", + "\n", + "Answer: 7 (8 seeds)\n", + "M_acon_C_c, M_actp_c, M_adp_c, M_h2o_e, M_nadh_c, M_nadp_c, M_nh4_e, M_succoa_c\n", + "\n", + "Answer: 8 (8 seeds)\n", + "M_actp_c, M_adp_c, M_glx_c, M_h2o_e, M_nadh_c, M_nadp_c, M_nh4_e, M_succoa_c\n", + "\n", + "Answer: 9 (8 seeds)\n", + "M_actp_c, M_adp_c, M_akg_c, M_h2o_e, M_nadh_c, M_nadp_c, M_nh4_e, M_succoa_c\n", + "\n", + "Answer: 10 (7 seeds)\n", + "M_actp_c, M_adp_c, M_glu__L_e, M_h2o_e, M_nadh_c, M_nadp_c, M_succoa_c\n", + "\n", + "Answer: 11 (8 seeds)\n", + "M_actp_c, M_adp_c, M_akg_e, M_h2o_e, M_nadh_c, M_nadp_c, M_nh4_e, M_succoa_c\n", + "\n", + "Answer: 12 (8 seeds)\n", + "M_acon_C_c, M_actp_c, M_adp_c, M_coa_c, M_h2o_e, M_nadh_c, M_nadp_c, M_nh4_e\n", + "\n", + "Answer: 13 (8 seeds)\n", + "M_actp_c, M_adp_c, M_coa_c, M_glx_c, M_h2o_e, M_nadh_c, M_nadp_c, M_nh4_e\n", + "\n", + "Answer: 14 (8 seeds)\n", + "M_actp_c, M_adp_c, M_akg_c, M_coa_c, M_h2o_e, M_nadh_c, M_nadp_c, M_nh4_e\n", + "\n", + "Answer: 15 (7 seeds)\n", + "M_actp_c, M_adp_c, M_coa_c, M_glu__L_e, M_h2o_e, M_nadh_c, M_nadp_c\n", + "\n", + "Answer: 16 (8 seeds)\n", + "M_actp_c, M_adp_c, M_akg_e, M_coa_c, M_h2o_e, M_nadh_c, M_nadp_c, M_nh4_e\n", + "\n", + "Answer: 17 (7 seeds)\n", + "M_actp_c, M_adp_c, M_cit_c, M_nadh_c, M_nadp_c, M_nh4_e, M_succoa_c\n", + "\n", + "Answer: 18 (7 seeds)\n", + "M_actp_c, M_adp_c, M_cit_c, M_coa_c, M_nadh_c, M_nadp_c, M_nh4_e\n", + "\n", + "Answer: 19 (8 seeds)\n", + "M_adp_c, M_h_e, M_lac__D_e, M_nadh_c, M_nadp_c, M_nh4_e, M_pi_c, M_succoa_c\n", + "\n", + "Answer: 20 (8 seeds)\n", + "M_adp_c, M_h_e, M_lac__D_e, M_nadh_c, M_nadp_c, M_nh4_e, M_pi_e, M_succoa_c\n", + "\n", + "Answer: 21 (8 seeds)\n", + "M_adp_c, M_h_c, M_lac__D_e, M_nadh_c, M_nadp_c, M_nh4_e, M_pi_c, M_succoa_c\n", + "\n", + "Answer: 22 (9 seeds)\n", + "M_adp_c, M_etoh_c, M_h_c, M_lac__D_e, M_nadh_c, M_nadp_c, M_nh4_e, M_pi_e, M_succoa_c\n", + "\n", + "Answer: 23 (9 seeds)\n", + "M_acald_e, M_adp_c, M_h_c, M_lac__D_e, M_nadh_c, M_nadp_c, M_nh4_e, M_pi_e, M_succoa_c\n", + "\n", + "Answer: 24 (9 seeds)\n", + "M_adp_c, M_coa_c, M_etoh_c, M_h_c, M_lac__D_e, M_nadh_c, M_nadp_c, M_nh4_e, M_pi_e\n", + "\n", + "Answer: 25 (9 seeds)\n", + "M_acald_e, M_adp_c, M_coa_c, M_h_c, M_lac__D_e, M_nadh_c, M_nadp_c, M_nh4_e, M_pi_e\n", + "\n", + "Answer: 26 (8 seeds)\n", + "M_adp_c, M_coa_c, M_h_c, M_lac__D_e, M_nadh_c, M_nadp_c, M_nh4_e, M_pi_c\n", + "\n", + "Answer: 27 (8 seeds)\n", + "M_adp_c, M_coa_c, M_h_e, M_lac__D_e, M_nadh_c, M_nadp_c, M_nh4_e, M_pi_e\n", + "\n", + "Answer: 28 (8 seeds)\n", + "M_adp_c, M_coa_c, M_h_e, M_lac__D_e, M_nadh_c, M_nadp_c, M_nh4_e, M_pi_c\n", + "\n", + "Answer: 29 (8 seeds)\n", + "M_adp_c, M_coa_c, M_glu__L_e, M_h_c, M_lac__D_e, M_nadh_c, M_nadp_c, M_pi_c\n", + "\n", + "Answer: 30 (8 seeds)\n", + "M_adp_c, M_glu__L_e, M_h_c, M_lac__D_e, M_nadh_c, M_nadp_c, M_pi_c, M_succoa_c\n", + "\n", + "Rejected solution during process: 197 \n", + "\n", + "\n", + "·············· \u001b[1mGuess-Check mode\u001b[0m ············\n", + "\n", + "~~~~~~~~~~~~~~~~ \u001b[1mEnumeration\u001b[0m ~~~~~~~~~~~~~~~\n", + "SOLVING...\n", + "\n", + "Answer: 1 (8 seeds)\n", + "M_adp_c, M_coa_c, M_gln__L_e, M_h_c, M_mal__L_e, M_nadh_c, M_nadp_c, M_pi_c\n", + "\n", + "Answer: 2 (8 seeds)\n", + "M_adp_c, M_gln__L_e, M_h_c, M_mal__L_e, M_nadh_c, M_nadp_c, M_pi_c, M_succoa_c\n", + "\n", + "Answer: 3 (8 seeds)\n", + "M_adp_c, M_glu__L_e, M_h_c, M_mal__L_e, M_nadh_c, M_nadp_c, M_pi_c, M_succoa_c\n", + "\n", + "Answer: 4 (8 seeds)\n", + "M_adp_c, M_coa_c, M_glu__L_e, M_h_c, M_mal__L_e, M_nadh_c, M_nadp_c, M_pi_c\n", + "\n", + "Answer: 5 (8 seeds)\n", + "M_adp_c, M_coa_c, M_h_c, M_mal__L_e, M_nadh_c, M_nadp_c, M_nh4_e, M_pi_c\n", + "\n", + "Answer: 6 (8 seeds)\n", + "M_adp_c, M_h_c, M_mal__L_e, M_nadh_c, M_nadp_c, M_nh4_e, M_pi_c, M_succoa_c\n", + "\n", + "Answer: 7 (8 seeds)\n", + "M_ac_c, M_adp_c, M_gln__L_e, M_h_e, M_nadh_c, M_nadp_c, M_pi_e, M_succoa_c\n", + "\n", + "Answer: 8 (8 seeds)\n", + "M_ac_c, M_adp_c, M_gln__L_e, M_h_e, M_nadh_c, M_nadp_c, M_pi_c, M_succoa_c\n", + "\n", + "Answer: 9 (8 seeds)\n", + "M_ac_c, M_adp_c, M_gln__L_e, M_h_c, M_nadh_c, M_nadp_c, M_pi_e, M_succoa_c\n", + "\n", + "Answer: 10 (8 seeds)\n", + "M_actp_c, M_adp_c, M_gln__L_e, M_h_c, M_mal__L_e, M_nadh_c, M_nadp_c, M_succoa_c\n", + "\n", + "Answer: 11 (8 seeds)\n", + "M_ac_e, M_adp_c, M_gln__L_e, M_h_c, M_nadh_c, M_nadp_c, M_pi_c, M_succoa_c\n", + "\n", + "Answer: 12 (8 seeds)\n", + "M_ac_c, M_adp_c, M_gln__L_e, M_h_c, M_nadh_c, M_nadp_c, M_pi_c, M_succoa_c\n", + "\n", + "Answer: 13 (8 seeds)\n", + "M_ac_e, M_adp_c, M_gln__L_e, M_h_e, M_nadh_c, M_nadp_c, M_pi_c, M_succoa_c\n", + "\n", + "Answer: 14 (9 seeds)\n", + "M_ac_e, M_adp_c, M_akg_c, M_gln__L_e, M_h_c, M_nadh_c, M_nadp_c, M_pi_e, M_succoa_c\n", + "\n", + "Answer: 15 (7 seeds)\n", + "M_actp_c, M_adp_c, M_gln__L_e, M_h2o_e, M_nadh_c, M_nadp_c, M_succoa_c\n", + "\n", + "Answer: 16 (8 seeds)\n", + "M_ac_e, M_adp_c, M_coa_c, M_gln__L_e, M_h_c, M_nadh_c, M_nadp_c, M_pi_c\n", + "\n", + "Answer: 17 (7 seeds)\n", + "M_actp_c, M_adp_c, M_coa_c, M_gln__L_e, M_h2o_e, M_nadh_c, M_nadp_c\n", + "\n", + "Answer: 18 (9 seeds)\n", + "M_ac_c, M_adp_c, M_glx_c, M_h_c, M_nadh_c, M_nadp_c, M_nh4_e, M_pi_e, M_succoa_c\n", + "\n", + "Answer: 19 (9 seeds)\n", + "M_ac_c, M_acon_C_c, M_adp_c, M_h_c, M_nadh_c, M_nadp_c, M_nh4_e, M_pi_e, M_succoa_c\n", + "\n", + "Answer: 20 (9 seeds)\n", + "M_ac_c, M_adp_c, M_fum_e, M_h_c, M_nadh_c, M_nadp_c, M_nh4_e, M_pi_e, M_succoa_c\n", + "\n", + "Answer: 21 (9 seeds)\n", + "M_ac_c, M_adp_c, M_fum_c, M_h_c, M_nadh_c, M_nadp_c, M_nh4_e, M_pi_e, M_succoa_c\n", + "\n", + "Answer: 22 (8 seeds)\n", + "M_actp_c, M_adp_c, M_h_c, M_mal__L_e, M_nadh_c, M_nadp_c, M_nh4_e, M_succoa_c\n", + "\n", + "Answer: 23 (8 seeds)\n", + "M_actp_c, M_adp_c, M_akg_c, M_h_c, M_nadh_c, M_nadp_c, M_nh4_e, M_succoa_c\n", + "\n", + "Answer: 24 (8 seeds)\n", + "M_actp_c, M_adp_c, M_akg_c, M_h_e, M_nadh_c, M_nadp_c, M_nh4_e, M_succoa_c\n", + "\n", + "Answer: 25 (9 seeds)\n", + "M_ac_e, M_acon_C_c, M_adp_c, M_h_e, M_nadh_c, M_nadp_c, M_nh4_e, M_pi_e, M_succoa_c\n", + "\n", + "Answer: 26 (9 seeds)\n", + "M_ac_c, M_adp_c, M_glx_c, M_h_e, M_nadh_c, M_nadp_c, M_nh4_e, M_pi_e, M_succoa_c\n", + "\n", + "Answer: 27 (9 seeds)\n", + "M_ac_c, M_acon_C_c, M_adp_c, M_h_e, M_nadh_c, M_nadp_c, M_nh4_e, M_pi_e, M_succoa_c\n", + "\n", + "Answer: 28 (9 seeds)\n", + "M_ac_e, M_adp_c, M_glx_c, M_h_c, M_nadh_c, M_nadp_c, M_nh4_e, M_pi_c, M_succoa_c\n", + "\n", + "Answer: 29 (9 seeds)\n", + "M_ac_e, M_adp_c, M_coa_c, M_glx_c, M_h_c, M_nadh_c, M_nadp_c, M_nh4_e, M_pi_c\n", + "\n", + "Answer: 30 (9 seeds)\n", + "M_ac_e, M_acon_C_c, M_adp_c, M_coa_c, M_h_c, M_nadh_c, M_nadp_c, M_nh4_e, M_pi_c\n", + "\n", + "Rejected solution during process: 376 \n", + "\n", + "\n", + "····· \u001b[1mGuess-Check with diversity mode\u001b[0m ······\n", + "\n", + "~~~~~~~~~~~~~~~~ \u001b[1mEnumeration\u001b[0m ~~~~~~~~~~~~~~~\n", + "SOLVING...\n", + "\n", + "Answer: 1 (6 seeds)\n", + "M_2pg_c, M_adp_c, M_coa_c, M_nadh_c, M_nadp_c, M_nh4_e\n", + "\n", + "Answer: 2 (7 seeds)\n", + "M_actp_c, M_adp_c, M_coa_c, M_gln__L_e, M_h2o_e, M_nadh_c, M_nadp_c\n", + "\n", + "Answer: 3 (8 seeds)\n", + "M_adp_c, M_gln__L_e, M_h_c, M_mal__L_e, M_nadh_c, M_nadp_c, M_pi_c, M_succoa_c\n", + "\n", + "Answer: 4 (6 seeds)\n", + "M_2pg_c, M_adp_c, M_nadh_c, M_nadp_c, M_nh4_e, M_succoa_c\n", + "\n", + "Answer: 5 (8 seeds)\n", + "M_adp_c, M_glu__L_e, M_h_c, M_mal__L_e, M_nadh_c, M_nadp_c, M_pi_c, M_succoa_c\n", + "\n", + "Answer: 6 (8 seeds)\n", + "M_adp_c, M_coa_c, M_gln__L_e, M_h_c, M_mal__L_e, M_nadh_c, M_nadp_c, M_pi_c\n", + "\n", + "Answer: 7 (8 seeds)\n", + "M_adp_c, M_h_c, M_mal__L_e, M_nadh_c, M_nadp_c, M_nh4_e, M_pi_c, M_succoa_c\n", + "\n", + "Answer: 8 (8 seeds)\n", + "M_adp_c, M_coa_c, M_glu__L_e, M_h_c, M_mal__L_e, M_nadh_c, M_nadp_c, M_pi_c\n", + "\n", + "Answer: 9 (8 seeds)\n", + "M_actp_c, M_adp_c, M_glx_c, M_h2o_e, M_nadh_c, M_nadp_c, M_nh4_e, M_succoa_c\n", + "\n", + "Answer: 10 (8 seeds)\n", + "M_actp_c, M_adp_c, M_gln__L_e, M_h_c, M_mal__L_e, M_nadh_c, M_nadp_c, M_succoa_c\n", + "\n", + "Answer: 11 (8 seeds)\n", + "M_actp_c, M_adp_c, M_glu__L_e, M_h_c, M_mal__L_e, M_nadh_c, M_nadp_c, M_succoa_c\n", + "\n", + "Answer: 12 (8 seeds)\n", + "M_ac_c, M_adp_c, M_gln__L_e, M_h_c, M_nadh_c, M_nadp_c, M_pi_e, M_succoa_c\n", + "\n", + "Answer: 13 (8 seeds)\n", + "M_ac_c, M_adp_c, M_gln__L_e, M_h_e, M_nadh_c, M_nadp_c, M_pi_e, M_succoa_c\n", + "\n", + "Answer: 14 (8 seeds)\n", + "M_ac_c, M_adp_c, M_glu__L_e, M_h_e, M_nadh_c, M_nadp_c, M_pi_e, M_succoa_c\n", + "\n", + "Answer: 15 (8 seeds)\n", + "M_adp_c, M_coa_c, M_h_c, M_mal__L_e, M_nadh_c, M_nadp_c, M_nh4_e, M_pi_c\n", + "\n", + "Answer: 16 (8 seeds)\n", + "M_ac_e, M_adp_c, M_gln__L_e, M_h_c, M_nadh_c, M_nadp_c, M_pi_c, M_succoa_c\n", + "\n", + "Answer: 17 (9 seeds)\n", + "M_ac_e, M_adp_c, M_glx_c, M_h_c, M_nadh_c, M_nadp_c, M_nh4_e, M_pi_c, M_succoa_c\n", + "\n", + "Answer: 18 (8 seeds)\n", + "M_ac_c, M_adp_c, M_gln__L_e, M_h_c, M_nadh_c, M_nadp_c, M_pi_c, M_succoa_c\n", + "\n", + "Answer: 19 (9 seeds)\n", + "M_ac_e, M_adp_c, M_akg_c, M_gln__L_e, M_h_c, M_nadh_c, M_nadp_c, M_pi_e, M_succoa_c\n", + "\n", + "Answer: 20 (8 seeds)\n", + "M_ac_e, M_adp_c, M_gln__L_e, M_h_e, M_nadh_c, M_nadp_c, M_pi_c, M_succoa_c\n", + "\n", + "Answer: 21 (8 seeds)\n", + "M_ac_c, M_adp_c, M_gln__L_e, M_h_e, M_nadh_c, M_nadp_c, M_pi_c, M_succoa_c\n", + "\n", + "Answer: 22 (7 seeds)\n", + "M_actp_c, M_adp_c, M_glu__L_e, M_h2o_e, M_nadh_c, M_nadp_c, M_succoa_c\n", + "\n", + "Answer: 23 (7 seeds)\n", + "M_actp_c, M_adp_c, M_gln__L_e, M_h2o_e, M_nadh_c, M_nadp_c, M_succoa_c\n", + "\n", + "Answer: 24 (8 seeds)\n", + "M_ac_e, M_adp_c, M_glu__L_e, M_h_c, M_nadh_c, M_nadp_c, M_pi_c, M_succoa_c\n", + "\n", + "Answer: 25 (8 seeds)\n", + "M_ac_e, M_adp_c, M_glu__L_e, M_h_e, M_nadh_c, M_nadp_c, M_pi_c, M_succoa_c\n", + "\n", + "Answer: 26 (8 seeds)\n", + "M_actp_c, M_adp_c, M_h_c, M_mal__L_e, M_nadh_c, M_nadp_c, M_nh4_e, M_succoa_c\n", + "\n", + "Answer: 27 (9 seeds)\n", + "M_ac_e, M_acon_C_c, M_adp_c, M_h_c, M_nadh_c, M_nadp_c, M_nh4_e, M_pi_c, M_succoa_c\n", + "\n", + "Answer: 28 (9 seeds)\n", + "M_ac_e, M_adp_c, M_glx_c, M_h_e, M_nadh_c, M_nadp_c, M_nh4_e, M_pi_c, M_succoa_c\n", + "\n", + "Answer: 29 (9 seeds)\n", + "M_ac_e, M_acon_C_c, M_adp_c, M_h_e, M_nadh_c, M_nadp_c, M_nh4_e, M_pi_e, M_succoa_c\n", + "\n", + "Answer: 30 (9 seeds)\n", + "M_ac_c, M_acon_C_c, M_adp_c, M_h_e, M_nadh_c, M_nadp_c, M_nh4_e, M_pi_e, M_succoa_c\n", + "\n", + "Rejected solution during process: 389 \n", + "\n", + "\u001b[0;94m\n", + "____________________________________________\u001b[0m\n", + "\u001b[0;94m____________________________________________\n", + "\u001b[0m\n", + " Sub Mode: \u001b[1mMINIMIZE\u001b[0m \n", + "\u001b[0;94m____________________________________________\u001b[0m\n", + "\u001b[0;94m____________________________________________\n", + "\u001b[0m\n", + "\n", + "················ \u001b[1mClassic mode\u001b[0m ···············\n", + "Finding optimum...\n", + "SOLVING...\n", + "\n", + "Optimum found.\n", + "Minimal size of seed set is 6\n", + "\n", + "\n", + "~~~~~~~~~~~~~~~~ \u001b[1mEnumeration\u001b[0m ~~~~~~~~~~~~~~~\n", + "SOLVING...\n", + "\n", + "Answer: 1 (6 seeds) \n", + "M_2pg_c, M_adp_c, M_glu__L_e, M_nadh_c, M_nadp_c, M_succoa_c\n", + "\n", + "Answer: 2 (6 seeds) \n", + "M_2pg_c, M_adp_c, M_nadh_c, M_nadp_c, M_nh4_e, M_succoa_c\n", + "\n", + "Answer: 3 (6 seeds) \n", + "M_adp_c, M_mal__L_c, M_nadh_c, M_nadp_c, M_nh4_e, M_succoa_c\n", + "\n", + "Answer: 4 (6 seeds) \n", + "M_13dpg_c, M_adp_c, M_nadh_c, M_nadp_c, M_nh4_e, M_succoa_c\n", + "\n", + "Answer: 5 (6 seeds) \n", + "M_2pg_c, M_adp_c, M_gln__L_e, M_nadh_c, M_nadp_c, M_succoa_c\n", + "\n", + "Answer: 6 (6 seeds) \n", + "M_adp_c, M_gln__L_e, M_mal__L_c, M_nadh_c, M_nadp_c, M_succoa_c\n", + "\n", + "Answer: 7 (6 seeds) \n", + "M_13dpg_c, M_adp_c, M_gln__L_e, M_nadh_c, M_nadp_c, M_succoa_c\n", + "\n", + "Answer: 8 (6 seeds) \n", + "M_13dpg_c, M_adp_c, M_glu__L_e, M_nadh_c, M_nadp_c, M_succoa_c\n", + "\n", + "Answer: 9 (6 seeds) \n", + "M_adp_c, M_glu__L_e, M_mal__L_c, M_nadh_c, M_nadp_c, M_succoa_c\n", + "\n", + "Answer: 10 (6 seeds) \n", + "M_13dpg_c, M_adp_c, M_coa_c, M_glu__L_e, M_nadh_c, M_nadp_c\n", + "\n", + "Answer: 11 (6 seeds) \n", + "M_adp_c, M_coa_c, M_glu__L_e, M_mal__L_c, M_nadh_c, M_nadp_c\n", + "\n", + "Answer: 12 (6 seeds) \n", + "M_2pg_c, M_adp_c, M_coa_c, M_glu__L_e, M_nadh_c, M_nadp_c\n", + "\n", + "Answer: 13 (6 seeds) \n", + "M_13dpg_c, M_adp_c, M_coa_c, M_nadh_c, M_nadp_c, M_nh4_e\n", + "\n", + "Answer: 14 (6 seeds) \n", + "M_13dpg_c, M_adp_c, M_coa_c, M_gln__L_e, M_nadh_c, M_nadp_c\n", + "\n", + "Answer: 15 (6 seeds) \n", + "M_adp_c, M_coa_c, M_mal__L_c, M_nadh_c, M_nadp_c, M_nh4_e\n", + "\n", + "Answer: 16 (6 seeds) \n", + "M_2pg_c, M_adp_c, M_coa_c, M_nadh_c, M_nadp_c, M_nh4_e\n", + "\n", + "Answer: 17 (6 seeds) \n", + "M_2pg_c, M_adp_c, M_coa_c, M_gln__L_e, M_nadh_c, M_nadp_c\n", + "\n", + "Answer: 18 (6 seeds) \n", + "M_adp_c, M_coa_c, M_gln__L_e, M_mal__L_c, M_nadh_c, M_nadp_c\n", + "\n", + "\n", + "················ \u001b[1mFilter mode\u001b[0m ···············\n", + "Finding optimum...\n", + "SOLVING...\n", + "\n", + "Optimum found.\n", + "Minimal size of seed set is 6\n", + "\n", + "\n", + "~~~~~~~~~~~~~~~~ \u001b[1mEnumeration\u001b[0m ~~~~~~~~~~~~~~~\n", + "SOLVING...\n", + "\n", + "Answer: 1 (6 seeds)\n", + "M_2pg_c, M_adp_c, M_coa_c, M_nadh_c, M_nadp_c, M_nh4_e\n", + "\n", + "Answer: 2 (6 seeds)\n", + "M_2pg_c, M_adp_c, M_nadh_c, M_nadp_c, M_nh4_e, M_succoa_c\n", + "\n", + "Rejected solution during process: 16 \n", + "\n", + "\n", + "·············· \u001b[1mGuess-Check mode\u001b[0m ············\n", + "Finding optimum...\n", + "SOLVING...\n", + "\n", + "Optimum found.\n", + "Minimal size of seed set is 6\n", + "\n", + "Rejected solution during process: 11 \n", + "\n", + "\n", + "~~~~~~~~~~~~~~~~ \u001b[1mEnumeration\u001b[0m ~~~~~~~~~~~~~~~\n", + "SOLVING...\n", + "\n", + "Answer: 1 (6 seeds)\n", + "M_2pg_c, M_adp_c, M_coa_c, M_nadh_c, M_nadp_c, M_nh4_e\n", + "\n", + "Answer: 2 (6 seeds)\n", + "M_2pg_c, M_adp_c, M_nadh_c, M_nadp_c, M_nh4_e, M_succoa_c\n", + "\n", + "Rejected solution during process: 16 \n", + "\n", + "\n", + "····· \u001b[1mGuess-Check with diversity mode\u001b[0m ······\n", + "Finding optimum...\n", + "SOLVING...\n", + "\n", + "Optimum found.\n", + "Minimal size of seed set is 6\n", + "\n", + "Rejected solution during process: 8 \n", + "\n", + "\n", + "~~~~~~~~~~~~~~~~ \u001b[1mEnumeration\u001b[0m ~~~~~~~~~~~~~~~\n", + "SOLVING...\n", + "\n", + "Answer: 1 (6 seeds)\n", + "M_2pg_c, M_adp_c, M_nadh_c, M_nadp_c, M_nh4_e, M_succoa_c\n", + "\n", + "Answer: 2 (6 seeds)\n", + "M_2pg_c, M_adp_c, M_coa_c, M_nadh_c, M_nadp_c, M_nh4_e\n", + "\n", + "Rejected solution during process: 16 \n", + "\n", + "\u001b[0;96m############################################\n", + "\n", + "\u001b[0m\n", + "\u001b[0;96m\n", + "############################################\n", + "############################################\n", + " \u001b[1mHYBRID\u001b[0;96m\n", + "############################################\n", + "############################################\n", + "\u001b[0m\n", + "Mode : TARGET\n", + "Option: TARGETS ARE FORBIDDEN SEEDS\n", + "ACCUMULATION: Forbidden\n", + "Flux: MAXIMIZATION\n", + "Time limit: 10.0 minutes\n", + "Solution number limit: 30\n", + "\u001b[0;94m\n", + "____________________________________________\u001b[0m\n", + "\u001b[0;94m____________________________________________\n", + "\u001b[0m\n", + " Sub Mode: \u001b[1mSUBSET MINIMAL\u001b[0m \n", + "\u001b[0;94m____________________________________________\u001b[0m\n", + "\u001b[0;94m____________________________________________\n", + "\u001b[0m\n", + "\n", + "~~~~~~~~~~~~~~~~ \u001b[1mEnumeration\u001b[0m ~~~~~~~~~~~~~~~\n", + "SOLVING...\n", + "\n", + "\u001b[0;91mERROR : Timeout: 10.0 min expired\u001b[0m\n", + "\u001b[0;93mWARNING : Output not totally recovered. Json has been repaired but might miss results\u001b[0m\n", + "Unsatisfiable problem\n", + "\u001b[0;91mERROR : Unsatisfiable problem\u001b[0m\n", + "\u001b[0;94m\n", + "____________________________________________\u001b[0m\n", + "\u001b[0;94m____________________________________________\n", + "\u001b[0m\n", + " Sub Mode: \u001b[1mMINIMIZE\u001b[0m \n", + "\u001b[0;94m____________________________________________\u001b[0m\n", + "\u001b[0;94m____________________________________________\n", + "\u001b[0m\n", + "Finding optimum...\n", + "SOLVING...\n", + "\n", + "Optimum Found\n", + "Minimal size of seed set is 6\n", + "\n", + "\n", + "~~~~~~~~~~~~~~~~ \u001b[1mEnumeration\u001b[0m ~~~~~~~~~~~~~~~\n", + "SOLVING...\n", + "\n", + "Answer: 1 (6 seeds) \n", + "M_2pg_c, M_adp_c, M_nadh_c, M_nadp_c, M_nh4_e, M_succoa_c\n", + "Assignment:\n", + "\"R_BIOMASS_Ecoli_core_w_GAM\" = 0.0001\n", + "\n", + "Answer: 2 (6 seeds) \n", + "M_2pg_c, M_adp_c, M_coa_c, M_nadh_c, M_nadp_c, M_nh4_e\n", + "Assignment:\n", + "\"R_BIOMASS_Ecoli_core_w_GAM\" = 0.0001002104\n", + "\n", + "\u001b[0;96m############################################\n", + "\n", + "\u001b[0m\n", + "\n", + "TIME DATA EXTRACTION : 0.079s\n", + "TIME TOTAL SEED SEARCH: 810.122s\n", + "TIME TOTAL : 810.201s\n", + "\n", + "\n", + "\u001b[0;96m\n", + "############################################\n", + "############################################\n", + " \u001b[1mCHECK FLUX\u001b[0;96m\n", + "############################################\n", + "############################################\n", + "\u001b[0m\n", + "---------------- FLUX INIT -----------------\n", + "\n", + "{'BIOMASS_Ecoli_core_w_GAM': np.float64(0.8739215069684295)}\n", + "\n", + "\n", + "--------------- MEDIUM INIT ----------------\n", + "\n", + "EX_co2_e -1000.0 1000.0\n", + "EX_glc__D_e -10.0 1000.0\n", + "EX_h_e -1000.0 1000.0\n", + "EX_h2o_e -1000.0 1000.0\n", + "EX_nh4_e -1000.0 1000.0\n", + "EX_o2_e -1000.0 1000.0\n", + "EX_pi_e -1000.0 1000.0\n", + "\n", + "\n", + "---------- STOP IMPORT FLUX -------------\n", + "\n", + "{'BIOMASS_Ecoli_core_w_GAM': 0.0}\n", + "\n", + "\n", + "\u001b[0;94m\n", + "____________________________________________\n", + "____________________________________________\n", + "\u001b[0m\n", + " RESULTS \n", + "\u001b[0;94m____________________________________________\n", + "____________________________________________\n", + "\u001b[0m\n", + " \u001b[1m \"Cobra (seeds)\" \u001b[0m indicates the maximum flux \n", + "obtained in FBA from the seeds after shutting \n", + "off all other exchange reactions. If the maximum \n", + "flux is null, a test is performed opening demand \n", + "reactions for the objective reaction's products, \n", + "in order to test the effect of their accumulation \n", + "(\u001b[1m\"cobra (demands)\"\u001b[0m ). If this test is not performed, \n", + "\"NA\" value is indicated.\n", + "\u001b[0;93m\n", + "____________________________________________\n", + "____________________________________________\n", + "\u001b[0m\n", + " Subset Minimal \n", + "\u001b[0;93m--------------------------------------------\u001b[0m\n", + " REASONING \n", + "\u001b[0;93m . . . . . . . . . . \u001b[0m\n", + "name | cobra (seeds) | cobra (demands)\n", + "-----|---------------|-----------------\n", + "model_1 | \u001b[0;91m-7.86166742663191e-17\u001b[0m | \u001b[0;91m4.232985273912001e-15\u001b[0m\n", + "model_2 | \u001b[0;91m-6.359795915135753e-15\u001b[0m | \u001b[0;91m-3.132829936350669e-16\u001b[0m\n", + "model_3 | \u001b[0;96m12.18876131827227\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_4 | \u001b[0;91m6.113009953421643e-15\u001b[0m | \u001b[0;91m2.0094165626068292e-16\u001b[0m\n", + "model_5 | \u001b[0;91m-7.671877442968764e-16\u001b[0m | \u001b[0;91m2.1925482217146207e-16\u001b[0m\n", + "model_6 | \u001b[0;91m8.010758262783273e-15\u001b[0m | \u001b[0;91m3.542992415462609e-16\u001b[0m\n", + "model_7 | \u001b[0;91m2.336066763024368e-15\u001b[0m | \u001b[0;91m2.6582641914797665e-16\u001b[0m\n", + "model_8 | Infeasible | Infeasible\n", + "model_9 | \u001b[0;91m9.895839948359787e-17\u001b[0m | \u001b[0;91m3.1981673001529087e-16\u001b[0m\n", + "model_10 | \u001b[0;91m0.0\u001b[0m | \u001b[0;91m0.0\u001b[0m\n", + "model_11 | \u001b[0;91m-8.059888334404866e-17\u001b[0m | \u001b[0;91m0.0\u001b[0m\n", + "model_12 | \u001b[0;91m-1.0671296118196804e-15\u001b[0m | \u001b[0;91m-2.04482478772909e-15\u001b[0m\n", + "model_13 | \u001b[0;91m1.764675099286478e-14\u001b[0m | \u001b[0;91m1.401199030435258e-15\u001b[0m\n", + "model_14 | Infeasible | Infeasible\n", + "model_15 | Infeasible | Infeasible\n", + "model_16 | \u001b[0;96m18.991034830032238\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_17 | \u001b[0;96m19.049540798163616\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_18 | \u001b[0;96m16.31693176867618\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_19 | \u001b[0;96m16.316931768676206\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_20 | \u001b[0;96m31.320746297127958\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_21 | \u001b[0;96m17.72710498102588\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_22 | Infeasible | Infeasible\n", + "model_23 | \u001b[0;91m3.0367318785771547e-16\u001b[0m | \u001b[0;91m-1.1740341664808016e-16\u001b[0m\n", + "model_24 | \u001b[0;91m6.424784538198656e-16\u001b[0m | \u001b[0;91m3.756138359665475e-17\u001b[0m\n", + "model_25 | \u001b[0;91m5.556451161671328e-18\u001b[0m | \u001b[0;91m-4.02391907128601e-16\u001b[0m\n", + "model_26 | \u001b[0;91m-6.1365336365489405e-16\u001b[0m | \u001b[0;91m1.3865196427072677e-16\u001b[0m\n", + "model_27 | \u001b[0;91m5.666584191074749e-16\u001b[0m | \u001b[0;91m-9.18801314400082e-17\u001b[0m\n", + "model_28 | \u001b[0;91m-9.443691818151632e-17\u001b[0m | \u001b[0;91m-3.6775698751518996e-17\u001b[0m\n", + "model_29 | \u001b[0;91m-1.8250154686436248e-16\u001b[0m | \u001b[0;91m0.0\u001b[0m\n", + "model_30 | \u001b[0;91m5.4122018009124276e-17\u001b[0m | \u001b[0;91m-4.02391907128601e-16\u001b[0m\n", + "\u001b[0;93m--------------------------------------------\u001b[0m\n", + " REASONING FILTER \n", + "\u001b[0;93m . . . . . . . . . . \u001b[0m\n", + "name | cobra (seeds) | cobra (demands)\n", + "-----|---------------|-----------------\n", + "model_1 | \u001b[0;96m16.834862348370564\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_2 | \u001b[0;96m1.9991088778356052\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_3 | \u001b[0;96m16.834862348370596\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_4 | \u001b[0;96m1.999108877835598\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_5 | \u001b[0;96m2.075985128450594\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_6 | \u001b[0;96m2.0759851284505304\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_7 | \u001b[0;96m17.006099936628743\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_8 | \u001b[0;96m13.935327681972675\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_9 | \u001b[0;96m14.642986340274444\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_10 | \u001b[0;96m12.188761318272235\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_11 | \u001b[0;96m12.18876131827224\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_12 | \u001b[0;96m17.006099936627766\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_13 | \u001b[0;96m13.935327681972707\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_14 | \u001b[0;96m14.64298634027443\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_15 | \u001b[0;96m12.18876131827227\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_16 | \u001b[0;96m12.188761318272231\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_17 | \u001b[0;96m16.749549580740705\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_18 | \u001b[0;96m16.749549580740783\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_19 | \u001b[0;96m5.152488440937438\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_20 | \u001b[0;96m2.532277073726985\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_21 | \u001b[0;96m2.858013136143328\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_22 | \u001b[0;96m2.7977445276309836\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_23 | \u001b[0;96m2.8580131361434518\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_24 | \u001b[0;96m2.797744527630954\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_25 | \u001b[0;96m2.858013136143486\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_26 | \u001b[0;96m2.8580131361433225\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_27 | \u001b[0;96m2.532277073726926\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_28 | \u001b[0;96m5.152488440937373\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_29 | \u001b[0;96m2.8661695958375812\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_30 | \u001b[0;96m2.866169595837584\u001b[0m | \u001b[0mNA\u001b[0m\n", + "\u001b[0;93m--------------------------------------------\u001b[0m\n", + " REASONING GUESS-CHECK \n", + "\u001b[0;93m . . . . . . . . . . \u001b[0m\n", + "name | cobra (seeds) | cobra (demands)\n", + "-----|---------------|-----------------\n", + "model_1 | \u001b[0;96m7.61320930892982\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_2 | \u001b[0;96m7.613209308929621\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_3 | \u001b[0;96m7.718756894709091\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_4 | \u001b[0;96m7.718756894709114\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_5 | \u001b[0;96m7.59197315149503\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_6 | \u001b[0;96m7.591973151495022\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_7 | \u001b[0;96m1.4935652934354573\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_8 | \u001b[0;96m3.0389952150398627\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_9 | \u001b[0;96m1.4751626112228777\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_10 | \u001b[0;96m9.069599000351898\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_11 | \u001b[0;96m1.4751626112228184\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_12 | \u001b[0;96m3.0015508104103894\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_13 | \u001b[0;96m1.4935652934354127\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_14 | \u001b[0;96m2.0723410952847257\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_15 | \u001b[0;96m9.866945125890751\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_16 | \u001b[0;96m1.4751626112228235\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_17 | \u001b[0;96m9.866945125890755\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_18 | \u001b[0;96m2.6397083767393887\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_19 | \u001b[0;96m4.3544455429244975\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_20 | \u001b[0;96m2.594634531830012\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_21 | \u001b[0;96m2.82597561790438\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_22 | \u001b[0;96m9.93390213175503\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_23 | \u001b[0;96m2.075985128450594\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_24 | \u001b[0;96m1.9991088778356052\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_25 | \u001b[0;96m4.34491641862442\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_26 | \u001b[0;96m2.7117327673274443\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_27 | \u001b[0;96m6.554450611696359\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_28 | \u001b[0;96m2.6397083767394043\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_29 | \u001b[0;96m2.639708376739409\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_30 | \u001b[0;96m4.354445542924756\u001b[0m | \u001b[0mNA\u001b[0m\n", + "\u001b[0;93m--------------------------------------------\u001b[0m\n", + " REASONING GUESS-CHECK DIVERSITY \n", + "\u001b[0;93m . . . . . . . . . . \u001b[0m\n", + "name | cobra (seeds) | cobra (demands)\n", + "-----|---------------|-----------------\n", + "model_1 | \u001b[0;96m17.92499217379125\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_2 | \u001b[0;96m9.866945125890755\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_3 | \u001b[0;96m7.613209308929621\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_4 | \u001b[0;96m17.924992173791278\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_5 | \u001b[0;96m7.718756894709091\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_6 | \u001b[0;96m7.61320930892982\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_7 | \u001b[0;96m7.591973151495022\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_8 | \u001b[0;96m7.718756894709114\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_9 | \u001b[0;96m13.935327681972675\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_10 | \u001b[0;96m9.069599000351898\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_11 | \u001b[0;96m9.515765567084044\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_12 | \u001b[0;96m1.475162611222834\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_13 | \u001b[0;96m1.4935652934354149\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_14 | \u001b[0;96m2.1790253143379994\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_15 | \u001b[0;96m7.59197315149503\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_16 | \u001b[0;96m1.4751626112228184\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_17 | \u001b[0;96m2.6397083767394043\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_18 | \u001b[0;96m3.001550810410376\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_19 | \u001b[0;96m2.0723410952847257\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_20 | \u001b[0;96m1.4935652934354127\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_21 | \u001b[0;96m3.0389952150398627\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_22 | \u001b[0;96m12.188761318272235\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_23 | \u001b[0;96m9.866945125890751\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_24 | \u001b[0;96m2.1582719353259834\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_25 | \u001b[0;96m2.179025314338004\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_26 | \u001b[0;96m9.93390213175503\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_27 | \u001b[0;96m4.354445542924701\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_28 | \u001b[0;96m2.7117327673274425\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_29 | \u001b[0;96m4.34491641862442\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_30 | \u001b[0;96m6.554450611696359\u001b[0m | \u001b[0mNA\u001b[0m\n", + "\u001b[0;93m\n", + "____________________________________________\n", + "____________________________________________\n", + "\u001b[0m\n", + " Minimize \n", + "\u001b[0;93m--------------------------------------------\u001b[0m\n", + " REASONING \n", + "\u001b[0;93m . . . . . . . . . . \u001b[0m\n", + "name | cobra (seeds) | cobra (demands)\n", + "-----|---------------|-----------------\n", + "model_1 | \u001b[0;91m-9.196485181120621e-16\u001b[0m | \u001b[0;91m-9.208326484467427e-16\u001b[0m\n", + "model_2 | \u001b[0;96m17.924992173791278\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_3 | \u001b[0;91m9.25209265848178e-16\u001b[0m | \u001b[0;91m-9.163605184929316e-17\u001b[0m\n", + "model_4 | Infeasible | Infeasible\n", + "model_5 | \u001b[0;91m-5.33709546052286e-16\u001b[0m | \u001b[0;91m2.3638609559709758e-17\u001b[0m\n", + "model_6 | \u001b[0;91m3.0217866279511706e-16\u001b[0m | \u001b[0;91m0.0\u001b[0m\n", + "model_7 | Infeasible | Infeasible\n", + "model_8 | Infeasible | Infeasible\n", + "model_9 | \u001b[0;91m5.236162733319999e-16\u001b[0m | \u001b[0;91m1.190016087135862e-15\u001b[0m\n", + "model_10 | Infeasible | Infeasible\n", + "model_11 | \u001b[0;91m9.895839948359787e-17\u001b[0m | \u001b[0;91m3.1981673001529087e-16\u001b[0m\n", + "model_12 | \u001b[0;91m-2.3353677926587578e-15\u001b[0m | \u001b[0;91m2.4005332370708894e-15\u001b[0m\n", + "model_13 | Infeasible | Infeasible\n", + "model_14 | Infeasible | Infeasible\n", + "model_15 | \u001b[0;91m2.844701110419275e-16\u001b[0m | \u001b[0;91m-5.055241681445326e-16\u001b[0m\n", + "model_16 | \u001b[0;96m17.92499217379125\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_17 | \u001b[0;91m-1.3553091859961835e-15\u001b[0m | \u001b[0;91m2.005455751743571e-16\u001b[0m\n", + "model_18 | \u001b[0;91m4.1887802369222987e-16\u001b[0m | \u001b[0;91m0.0\u001b[0m\n", + "\u001b[0;93m--------------------------------------------\u001b[0m\n", + " REASONING FILTER \n", + "\u001b[0;93m . . . . . . . . . . \u001b[0m\n", + "name | cobra (seeds) | cobra (demands)\n", + "-----|---------------|-----------------\n", + "model_1 | \u001b[0;96m17.92499217379125\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_2 | \u001b[0;96m17.924992173791278\u001b[0m | \u001b[0mNA\u001b[0m\n", + "\u001b[0;93m--------------------------------------------\u001b[0m\n", + " REASONING GUESS-CHECK \n", + "\u001b[0;93m . . . . . . . . . . \u001b[0m\n", + "name | cobra (seeds) | cobra (demands)\n", + "-----|---------------|-----------------\n", + "model_1 | \u001b[0;96m17.92499217379125\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_2 | \u001b[0;96m17.924992173791278\u001b[0m | \u001b[0mNA\u001b[0m\n", + "\u001b[0;93m--------------------------------------------\u001b[0m\n", + " REASONING GUESS-CHECK DIVERSITY \n", + "\u001b[0;93m . . . . . . . . . . \u001b[0m\n", + "name | cobra (seeds) | cobra (demands)\n", + "-----|---------------|-----------------\n", + "model_1 | \u001b[0;96m17.924992173791278\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_2 | \u001b[0;96m17.92499217379125\u001b[0m | \u001b[0mNA\u001b[0m\n", + "\u001b[0;93m--------------------------------------------\u001b[0m\n", + " HYBRID \n", + "\u001b[0;93m . . . . . . . . . . \u001b[0m\n", + "name | cobra (seeds) | cobra (demands) | LP\n", + "-----|---------------|-----------------|----\n", + "model_1 | \u001b[0;96m17.924992173791278\u001b[0m | \u001b[0mNA\u001b[0m | \u001b[0;96m0.0001\u001b[0m\n", + "model_2 | \u001b[0;96m17.92499217379125\u001b[0m | \u001b[0mNA\u001b[0m | \u001b[0;96m0.0001\u001b[0m\n", + "\u001b[0;93m\n", + "____________________________________________\n", + "\u001b[0m\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: e_coli_core\n", + "\n", + "____________________________________________\n", + "\n", + " TARGETS \n", + " FOR TARGET MODE AND FBA \n", + "____________________________________________\n", + "\n", + "Targets set:\n", + "All metabolites as target\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " OBJECTVE \n", + " FOR HYBRID \n", + "____________________________________________\n", + "\n", + "Objective set:\n", + " Objective reaction from command line\n", + "\n", + "\n", + "Objective : R_BIOMASS_Ecoli_core_w_GAM\n", + "\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " NETWORK \n", + "____________________________________________\n", + "\n", + "Import reaction: Removed\n", + "Accumulation: Forbidden\n", + "\n", + "\n", + "\u001b[0;93mWARNING : - R_FORt: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + "\u001b[0m\n", + "\u001b[0;96m\n", + "############################################\n", + "############################################\n", + " \u001b[1mREASONING\u001b[0;96m\n", + "############################################\n", + "############################################\n", + "\u001b[0m\n", + "Mode : FULL NETWORK\n", + "ACCUMULATION: Forbidden\n", + "Time limit: 10.0 minutes\n", + "Solution number limit: 30\n", + "\u001b[0;94m\n", + "____________________________________________\u001b[0m\n", + "\u001b[0;94m____________________________________________\n", + "\u001b[0m\n", + " Sub Mode: \u001b[1mSUBSET MINIMAL\u001b[0m \n", + "\u001b[0;94m____________________________________________\u001b[0m\n", + "\u001b[0;94m____________________________________________\n", + "\u001b[0m\n", + "\n", + "················ \u001b[1mClassic mode\u001b[0m ···············\n", + "\n", + "~~~~~~~~~~~~~~~~ \u001b[1mEnumeration\u001b[0m ~~~~~~~~~~~~~~~\n", + "SOLVING...\n", + "\n", + "Answer: 1 (27 seeds) \n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_actp_c, M_adp_c, M_akg_e, M_co2_e, M_coa_c, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nadh_c, M_nadph_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8h2_c, M_r5p_c\n", + "\n", + "Answer: 2 (27 seeds) \n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_accoa_c, M_actp_c, M_adp_c, M_akg_e, M_co2_e, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nadh_c, M_nadph_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8h2_c, M_r5p_c\n", + "\n", + "Answer: 3 (27 seeds) \n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_actp_c, M_adp_c, M_akg_e, M_co2_e, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nadh_c, M_nadph_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8h2_c, M_r5p_c, M_succoa_c\n", + "\n", + "Answer: 4 (27 seeds) \n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_actp_c, M_adp_c, M_akg_e, M_co2_e, M_coa_c, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nadh_c, M_nadph_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8_c, M_r5p_c\n", + "\n", + "Answer: 5 (27 seeds) \n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_accoa_c, M_actp_c, M_adp_c, M_akg_e, M_co2_e, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nadh_c, M_nadph_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8_c, M_r5p_c\n", + "\n", + "Answer: 6 (27 seeds) \n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_actp_c, M_adp_c, M_akg_e, M_co2_e, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nadh_c, M_nadph_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8_c, M_r5p_c, M_succoa_c\n", + "\n", + "Answer: 7 (27 seeds) \n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_actp_c, M_adp_c, M_akg_e, M_co2_e, M_coa_c, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nadh_c, M_nadp_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8_c, M_r5p_c\n", + "\n", + "Answer: 8 (27 seeds) \n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_actp_c, M_adp_c, M_akg_e, M_co2_e, M_coa_c, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nadh_c, M_nadp_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8h2_c, M_r5p_c\n", + "\n", + "Answer: 9 (27 seeds) \n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_accoa_c, M_actp_c, M_adp_c, M_akg_e, M_co2_e, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nadh_c, M_nadp_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8_c, M_r5p_c\n", + "\n", + "Answer: 10 (27 seeds) \n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_actp_c, M_adp_c, M_akg_e, M_co2_e, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nadh_c, M_nadp_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8_c, M_r5p_c, M_succoa_c\n", + "\n", + "Answer: 11 (27 seeds) \n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_accoa_c, M_actp_c, M_adp_c, M_akg_e, M_co2_e, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nadh_c, M_nadp_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8h2_c, M_r5p_c\n", + "\n", + "Answer: 12 (27 seeds) \n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_actp_c, M_adp_c, M_akg_e, M_co2_e, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nadh_c, M_nadp_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8h2_c, M_r5p_c, M_succoa_c\n", + "\n", + "Answer: 13 (27 seeds) \n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_actp_c, M_adp_c, M_akg_e, M_co2_e, M_coa_c, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nad_c, M_nadp_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8h2_c, M_r5p_c\n", + "\n", + "Answer: 14 (27 seeds) \n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_actp_c, M_adp_c, M_akg_e, M_co2_e, M_coa_c, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nad_c, M_nadph_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8h2_c, M_r5p_c\n", + "\n", + "Answer: 15 (27 seeds) \n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_actp_c, M_adp_c, M_akg_e, M_co2_e, M_coa_c, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nad_c, M_nadp_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8_c, M_r5p_c\n", + "\n", + "Answer: 16 (27 seeds) \n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_actp_c, M_adp_c, M_akg_e, M_co2_e, M_coa_c, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nad_c, M_nadph_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8_c, M_r5p_c\n", + "\n", + "Answer: 17 (27 seeds) \n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_accoa_c, M_actp_c, M_adp_c, M_akg_e, M_co2_e, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nad_c, M_nadp_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8_c, M_r5p_c\n", + "\n", + "Answer: 18 (27 seeds) \n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_actp_c, M_adp_c, M_akg_e, M_co2_e, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nad_c, M_nadp_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8_c, M_r5p_c, M_succoa_c\n", + "\n", + "Answer: 19 (27 seeds) \n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_accoa_c, M_actp_c, M_adp_c, M_akg_e, M_co2_e, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nad_c, M_nadph_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8_c, M_r5p_c\n", + "\n", + "Answer: 20 (27 seeds) \n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_actp_c, M_adp_c, M_akg_e, M_co2_e, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nad_c, M_nadph_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8_c, M_r5p_c, M_succoa_c\n", + "\n", + "Answer: 21 (27 seeds) \n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_accoa_c, M_actp_c, M_adp_c, M_akg_e, M_co2_e, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nad_c, M_nadp_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8h2_c, M_r5p_c\n", + "\n", + "Answer: 22 (27 seeds) \n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_accoa_c, M_actp_c, M_adp_c, M_akg_e, M_co2_e, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nad_c, M_nadph_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8h2_c, M_r5p_c\n", + "\n", + "Answer: 23 (27 seeds) \n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_actp_c, M_adp_c, M_akg_e, M_co2_e, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nad_c, M_nadp_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8h2_c, M_r5p_c, M_succoa_c\n", + "\n", + "Answer: 24 (27 seeds) \n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_actp_c, M_adp_c, M_akg_e, M_co2_e, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nad_c, M_nadph_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8h2_c, M_r5p_c, M_succoa_c\n", + "\n", + "Answer: 25 (27 seeds) \n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_actp_c, M_akg_e, M_atp_c, M_co2_e, M_coa_c, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nad_c, M_nadp_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8h2_c, M_r5p_c\n", + "\n", + "Answer: 26 (27 seeds) \n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_actp_c, M_akg_e, M_atp_c, M_co2_e, M_coa_c, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nadh_c, M_nadp_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8h2_c, M_r5p_c\n", + "\n", + "Answer: 27 (27 seeds) \n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_actp_c, M_akg_e, M_atp_c, M_co2_e, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nad_c, M_nadp_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8h2_c, M_r5p_c, M_succoa_c\n", + "\n", + "Answer: 28 (27 seeds) \n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_actp_c, M_akg_e, M_atp_c, M_co2_e, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nadh_c, M_nadp_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8h2_c, M_r5p_c, M_succoa_c\n", + "\n", + "Answer: 29 (27 seeds) \n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_actp_c, M_akg_e, M_atp_c, M_co2_e, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nad_c, M_nadp_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8_c, M_r5p_c, M_succoa_c\n", + "\n", + "Answer: 30 (27 seeds) \n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_actp_c, M_akg_e, M_atp_c, M_co2_e, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nadh_c, M_nadp_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8_c, M_r5p_c, M_succoa_c\n", + "\n", + "\n", + "················ \u001b[1mFilter mode\u001b[0m ···············\n", + "\n", + "~~~~~~~~~~~~~~~~ \u001b[1mEnumeration\u001b[0m ~~~~~~~~~~~~~~~\n", + "SOLVING...\n", + "\n", + "Answer: 1 (27 seeds)\n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_accoa_c, M_actp_c, M_adp_c, M_akg_e, M_co2_e, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nadh_c, M_nadp_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8_c, M_r5p_c\n", + "\n", + "Answer: 2 (27 seeds)\n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_accoa_c, M_actp_c, M_adp_c, M_akg_e, M_co2_e, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nadh_c, M_nadp_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8h2_c, M_r5p_c\n", + "\n", + "Answer: 3 (27 seeds)\n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_accoa_c, M_actp_c, M_adp_c, M_akg_e, M_co2_e, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nad_c, M_nadp_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8_c, M_r5p_c\n", + "\n", + "Answer: 4 (27 seeds)\n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_accoa_c, M_actp_c, M_adp_c, M_akg_e, M_co2_e, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nad_c, M_nadp_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8h2_c, M_r5p_c\n", + "\n", + "Answer: 5 (27 seeds)\n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_actp_c, M_adp_c, M_akg_e, M_co2_e, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nad_c, M_nadp_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8_c, M_r5p_c, M_succoa_c\n", + "\n", + "Answer: 6 (27 seeds)\n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_actp_c, M_adp_c, M_akg_e, M_co2_e, M_coa_c, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nad_c, M_nadp_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8_c, M_r5p_c\n", + "\n", + "Answer: 7 (27 seeds)\n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_actp_c, M_adp_c, M_akg_e, M_co2_e, M_coa_c, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nadh_c, M_nadp_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8_c, M_r5p_c\n", + "\n", + "Answer: 8 (27 seeds)\n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_actp_c, M_adp_c, M_akg_e, M_co2_e, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nadh_c, M_nadp_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8_c, M_r5p_c, M_succoa_c\n", + "\n", + "Answer: 9 (27 seeds)\n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_actp_c, M_adp_c, M_akg_e, M_co2_e, M_coa_c, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nadh_c, M_nadp_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8h2_c, M_r5p_c\n", + "\n", + "Answer: 10 (27 seeds)\n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_actp_c, M_adp_c, M_akg_e, M_co2_e, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nadh_c, M_nadp_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8h2_c, M_r5p_c, M_succoa_c\n", + "\n", + "Answer: 11 (27 seeds)\n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_actp_c, M_adp_c, M_akg_e, M_co2_e, M_coa_c, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nad_c, M_nadp_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8h2_c, M_r5p_c\n", + "\n", + "Answer: 12 (27 seeds)\n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_actp_c, M_adp_c, M_akg_e, M_co2_e, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nad_c, M_nadp_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8h2_c, M_r5p_c, M_succoa_c\n", + "\n", + "Answer: 13 (27 seeds)\n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_accoa_c, M_actp_c, M_adp_c, M_akg_e, M_co2_e, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nad_c, M_nadph_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8h2_c, M_r5p_c\n", + "\n", + "Answer: 14 (27 seeds)\n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_actp_c, M_adp_c, M_akg_e, M_co2_e, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nad_c, M_nadph_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8h2_c, M_r5p_c, M_succoa_c\n", + "\n", + "Answer: 15 (27 seeds)\n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_actp_c, M_adp_c, M_akg_e, M_co2_e, M_coa_c, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nad_c, M_nadph_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8h2_c, M_r5p_c\n", + "\n", + "Answer: 16 (27 seeds)\n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_accoa_c, M_actp_c, M_adp_c, M_akg_e, M_co2_e, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nad_c, M_nadph_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8_c, M_r5p_c\n", + "\n", + "Answer: 17 (27 seeds)\n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_actp_c, M_adp_c, M_akg_e, M_co2_e, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nad_c, M_nadph_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8_c, M_r5p_c, M_succoa_c\n", + "\n", + "Answer: 18 (27 seeds)\n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_actp_c, M_adp_c, M_akg_e, M_co2_e, M_coa_c, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nad_c, M_nadph_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8_c, M_r5p_c\n", + "\n", + "Answer: 19 (27 seeds)\n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_accoa_c, M_actp_c, M_adp_c, M_akg_e, M_co2_e, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nadh_c, M_nadph_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8h2_c, M_r5p_c\n", + "\n", + "Answer: 20 (27 seeds)\n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_accoa_c, M_actp_c, M_adp_c, M_akg_e, M_co2_e, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nadh_c, M_nadph_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8_c, M_r5p_c\n", + "\n", + "Answer: 21 (27 seeds)\n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_actp_c, M_adp_c, M_akg_e, M_co2_e, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nadh_c, M_nadph_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8h2_c, M_r5p_c, M_succoa_c\n", + "\n", + "Answer: 22 (27 seeds)\n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_actp_c, M_adp_c, M_akg_e, M_co2_e, M_coa_c, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nadh_c, M_nadph_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8h2_c, M_r5p_c\n", + "\n", + "Answer: 23 (27 seeds)\n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_actp_c, M_adp_c, M_akg_e, M_co2_e, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nadh_c, M_nadph_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8_c, M_r5p_c, M_succoa_c\n", + "\n", + "Answer: 24 (27 seeds)\n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_actp_c, M_adp_c, M_akg_e, M_co2_e, M_coa_c, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nadh_c, M_nadph_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8_c, M_r5p_c\n", + "\n", + "Answer: 25 (27 seeds)\n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_accoa_c, M_actp_c, M_akg_e, M_atp_c, M_co2_e, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nadh_c, M_nadph_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8_c, M_r5p_c\n", + "\n", + "Answer: 26 (27 seeds)\n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_accoa_c, M_actp_c, M_akg_e, M_atp_c, M_co2_e, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nadh_c, M_nadp_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8_c, M_r5p_c\n", + "\n", + "Answer: 27 (27 seeds)\n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_accoa_c, M_actp_c, M_akg_e, M_atp_c, M_co2_e, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nad_c, M_nadph_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8_c, M_r5p_c\n", + "\n", + "Answer: 28 (27 seeds)\n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_accoa_c, M_actp_c, M_akg_e, M_atp_c, M_co2_e, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nad_c, M_nadp_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8_c, M_r5p_c\n", + "\n", + "Answer: 29 (27 seeds)\n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_actp_c, M_akg_e, M_atp_c, M_co2_e, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nad_c, M_nadph_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8_c, M_r5p_c, M_succoa_c\n", + "\n", + "Answer: 30 (27 seeds)\n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_actp_c, M_akg_e, M_atp_c, M_co2_e, M_coa_c, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nad_c, M_nadph_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8_c, M_r5p_c\n", + "\n", + "\n", + "·············· \u001b[1mGuess-Check mode\u001b[0m ············\n", + "\n", + "~~~~~~~~~~~~~~~~ \u001b[1mEnumeration\u001b[0m ~~~~~~~~~~~~~~~\n", + "SOLVING...\n", + "\n", + "Answer: 1 (27 seeds)\n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_accoa_c, M_actp_c, M_adp_c, M_akg_e, M_co2_e, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nadh_c, M_nadp_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8_c, M_r5p_c\n", + "\n", + "Answer: 2 (27 seeds)\n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_actp_c, M_adp_c, M_akg_e, M_co2_e, M_coa_c, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nad_c, M_nadph_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8_c, M_r5p_c\n", + "\n", + "Answer: 3 (27 seeds)\n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_actp_c, M_adp_c, M_akg_e, M_co2_e, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nad_c, M_nadph_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8_c, M_r5p_c, M_succoa_c\n", + "\n", + "Answer: 4 (27 seeds)\n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_accoa_c, M_actp_c, M_adp_c, M_akg_e, M_co2_e, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nad_c, M_nadph_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8_c, M_r5p_c\n", + "\n", + "Answer: 5 (27 seeds)\n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_accoa_c, M_actp_c, M_adp_c, M_akg_e, M_co2_e, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nad_c, M_nadp_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8_c, M_r5p_c\n", + "\n", + "Answer: 6 (27 seeds)\n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_actp_c, M_akg_e, M_atp_c, M_co2_e, M_coa_c, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nadh_c, M_nadp_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8h2_c, M_r5p_c\n", + "\n", + "Answer: 7 (27 seeds)\n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_actp_c, M_akg_e, M_atp_c, M_co2_e, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nadh_c, M_nadp_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8h2_c, M_r5p_c, M_succoa_c\n", + "\n", + "Answer: 8 (27 seeds)\n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_accoa_c, M_actp_c, M_akg_e, M_atp_c, M_co2_e, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nadh_c, M_nadp_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8h2_c, M_r5p_c\n", + "\n", + "Answer: 9 (27 seeds)\n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_actp_c, M_akg_e, M_atp_c, M_co2_e, M_coa_c, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nadh_c, M_nadph_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8h2_c, M_r5p_c\n", + "\n", + "Answer: 10 (27 seeds)\n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_actp_c, M_adp_c, M_akg_e, M_co2_e, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nad_c, M_nadp_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8_c, M_r5p_c, M_succoa_c\n", + "\n", + "Answer: 11 (27 seeds)\n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_actp_c, M_akg_e, M_atp_c, M_co2_e, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nad_c, M_nadph_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8_c, M_r5p_c, M_succoa_c\n", + "\n", + "Answer: 12 (27 seeds)\n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_accoa_c, M_actp_c, M_akg_e, M_atp_c, M_co2_e, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nadh_c, M_nadph_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8h2_c, M_r5p_c\n", + "\n", + "Answer: 13 (27 seeds)\n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_actp_c, M_adp_c, M_akg_e, M_co2_e, M_coa_c, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nadh_c, M_nadph_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8h2_c, M_r5p_c\n", + "\n", + "Answer: 14 (27 seeds)\n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_actp_c, M_adp_c, M_akg_e, M_co2_e, M_coa_c, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nad_c, M_nadp_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8_c, M_r5p_c\n", + "\n", + "Answer: 15 (27 seeds)\n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_actp_c, M_akg_e, M_atp_c, M_co2_e, M_coa_c, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nad_c, M_nadp_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8_c, M_r5p_c\n", + "\n", + "Answer: 16 (27 seeds)\n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_actp_c, M_adp_c, M_akg_e, M_co2_e, M_coa_c, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nadh_c, M_nadp_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8_c, M_r5p_c\n", + "\n", + "Answer: 17 (27 seeds)\n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_accoa_c, M_actp_c, M_adp_c, M_akg_e, M_co2_e, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nadh_c, M_nadph_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8h2_c, M_r5p_c\n", + "\n", + "Answer: 18 (27 seeds)\n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_accoa_c, M_actp_c, M_adp_c, M_akg_e, M_co2_e, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nadh_c, M_nadp_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8h2_c, M_r5p_c\n", + "\n", + "Answer: 19 (27 seeds)\n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_accoa_c, M_actp_c, M_adp_c, M_akg_e, M_co2_e, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nad_c, M_nadph_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8h2_c, M_r5p_c\n", + "\n", + "Answer: 20 (27 seeds)\n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_actp_c, M_akg_e, M_atp_c, M_co2_e, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nadh_c, M_nadph_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8_c, M_r5p_c, M_succoa_c\n", + "\n", + "Answer: 21 (27 seeds)\n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_actp_c, M_akg_e, M_atp_c, M_co2_e, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nad_c, M_nadph_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8h2_c, M_r5p_c, M_succoa_c\n", + "\n", + "Answer: 22 (27 seeds)\n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_actp_c, M_adp_c, M_akg_e, M_co2_e, M_coa_c, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nad_c, M_nadp_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8h2_c, M_r5p_c\n", + "\n", + "Answer: 23 (27 seeds)\n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_accoa_c, M_actp_c, M_adp_c, M_akg_e, M_co2_e, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nad_c, M_nadp_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8h2_c, M_r5p_c\n", + "\n", + "Answer: 24 (27 seeds)\n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_actp_c, M_akg_e, M_atp_c, M_co2_e, M_coa_c, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nad_c, M_nadp_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8h2_c, M_r5p_c\n", + "\n", + "Answer: 25 (27 seeds)\n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_actp_c, M_akg_e, M_atp_c, M_co2_e, M_coa_c, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nadh_c, M_nadph_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8_c, M_r5p_c\n", + "\n", + "Answer: 26 (27 seeds)\n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_accoa_c, M_actp_c, M_akg_e, M_atp_c, M_co2_e, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nadh_c, M_nadph_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8_c, M_r5p_c\n", + "\n", + "Answer: 27 (27 seeds)\n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_actp_c, M_adp_c, M_akg_e, M_co2_e, M_coa_c, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nadh_c, M_nadph_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8_c, M_r5p_c\n", + "\n", + "Answer: 28 (27 seeds)\n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_actp_c, M_akg_e, M_atp_c, M_co2_e, M_coa_c, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nadh_c, M_nadp_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8_c, M_r5p_c\n", + "\n", + "Answer: 29 (27 seeds)\n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_accoa_c, M_actp_c, M_akg_e, M_atp_c, M_co2_e, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nad_c, M_nadp_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8h2_c, M_r5p_c\n", + "\n", + "Answer: 30 (27 seeds)\n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_accoa_c, M_actp_c, M_akg_e, M_atp_c, M_co2_e, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nad_c, M_nadph_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8h2_c, M_r5p_c\n", + "\n", + "\n", + "····· \u001b[1mGuess-Check with diversity mode\u001b[0m ······\n", + "\n", + "~~~~~~~~~~~~~~~~ \u001b[1mEnumeration\u001b[0m ~~~~~~~~~~~~~~~\n", + "SOLVING...\n", + "\n", + "Answer: 1 (27 seeds)\n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_actp_c, M_adp_c, M_akg_e, M_co2_e, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nad_c, M_nadph_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8h2_c, M_r5p_c, M_succoa_c\n", + "\n", + "Answer: 2 (27 seeds)\n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_actp_c, M_akg_e, M_atp_c, M_co2_e, M_coa_c, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nadh_c, M_nadp_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8h2_c, M_r5p_c\n", + "\n", + "Answer: 3 (27 seeds)\n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_actp_c, M_adp_c, M_akg_e, M_co2_e, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nad_c, M_nadp_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8h2_c, M_r5p_c, M_succoa_c\n", + "\n", + "Answer: 4 (27 seeds)\n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_accoa_c, M_actp_c, M_adp_c, M_akg_e, M_co2_e, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nad_c, M_nadph_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8h2_c, M_r5p_c\n", + "\n", + "Answer: 5 (27 seeds)\n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_actp_c, M_adp_c, M_akg_e, M_co2_e, M_coa_c, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nadh_c, M_nadph_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8_c, M_r5p_c\n", + "\n", + "Answer: 6 (27 seeds)\n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_accoa_c, M_actp_c, M_adp_c, M_akg_e, M_co2_e, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nad_c, M_nadp_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8h2_c, M_r5p_c\n", + "\n", + "Answer: 7 (27 seeds)\n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_actp_c, M_akg_e, M_atp_c, M_co2_e, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nadh_c, M_nadph_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8_c, M_r5p_c, M_succoa_c\n", + "\n", + "Answer: 8 (27 seeds)\n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_accoa_c, M_actp_c, M_adp_c, M_akg_e, M_co2_e, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nadh_c, M_nadph_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8_c, M_r5p_c\n", + "\n", + "Answer: 9 (27 seeds)\n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_actp_c, M_akg_e, M_atp_c, M_co2_e, M_coa_c, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nad_c, M_nadph_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8h2_c, M_r5p_c\n", + "\n", + "Answer: 10 (27 seeds)\n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_actp_c, M_akg_e, M_atp_c, M_co2_e, M_coa_c, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nadh_c, M_nadph_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8_c, M_r5p_c\n", + "\n", + "Answer: 11 (27 seeds)\n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_actp_c, M_adp_c, M_akg_e, M_co2_e, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nad_c, M_nadp_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8_c, M_r5p_c, M_succoa_c\n", + "\n", + "Answer: 12 (27 seeds)\n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_accoa_c, M_actp_c, M_adp_c, M_akg_e, M_co2_e, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nadh_c, M_nadp_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8h2_c, M_r5p_c\n", + "\n", + "Answer: 13 (27 seeds)\n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_actp_c, M_adp_c, M_akg_e, M_co2_e, M_coa_c, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nad_c, M_nadp_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8_c, M_r5p_c\n", + "\n", + "Answer: 14 (27 seeds)\n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_actp_c, M_adp_c, M_akg_e, M_co2_e, M_coa_c, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nad_c, M_nadph_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8h2_c, M_r5p_c\n", + "\n", + "Answer: 15 (27 seeds)\n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_accoa_c, M_actp_c, M_adp_c, M_akg_e, M_co2_e, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nad_c, M_nadp_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8_c, M_r5p_c\n", + "\n", + "Answer: 16 (27 seeds)\n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_actp_c, M_akg_e, M_atp_c, M_co2_e, M_coa_c, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nadh_c, M_nadp_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8_c, M_r5p_c\n", + "\n", + "Answer: 17 (27 seeds)\n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_accoa_c, M_actp_c, M_akg_e, M_atp_c, M_co2_e, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nadh_c, M_nadp_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8h2_c, M_r5p_c\n", + "\n", + "Answer: 18 (27 seeds)\n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_actp_c, M_adp_c, M_akg_e, M_co2_e, M_coa_c, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nad_c, M_nadph_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8_c, M_r5p_c\n", + "\n", + "Answer: 19 (27 seeds)\n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_actp_c, M_akg_e, M_atp_c, M_co2_e, M_coa_c, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nad_c, M_nadph_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8_c, M_r5p_c\n", + "\n", + "Answer: 20 (27 seeds)\n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_accoa_c, M_actp_c, M_akg_e, M_atp_c, M_co2_e, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nad_c, M_nadph_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8h2_c, M_r5p_c\n", + "\n", + "Answer: 21 (27 seeds)\n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_accoa_c, M_actp_c, M_akg_e, M_atp_c, M_co2_e, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nadh_c, M_nadp_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8_c, M_r5p_c\n", + "\n", + "Answer: 22 (27 seeds)\n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_actp_c, M_akg_e, M_atp_c, M_co2_e, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nad_c, M_nadph_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8h2_c, M_r5p_c, M_succoa_c\n", + "\n", + "Answer: 23 (27 seeds)\n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_accoa_c, M_actp_c, M_akg_e, M_atp_c, M_co2_e, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nadh_c, M_nadph_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8_c, M_r5p_c\n", + "\n", + "Answer: 24 (27 seeds)\n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_actp_c, M_akg_e, M_atp_c, M_co2_e, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nadh_c, M_nadp_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8h2_c, M_r5p_c, M_succoa_c\n", + "\n", + "Answer: 25 (27 seeds)\n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_accoa_c, M_actp_c, M_adp_c, M_akg_e, M_co2_e, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nadh_c, M_nadp_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8_c, M_r5p_c\n", + "\n", + "Answer: 26 (27 seeds)\n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_accoa_c, M_actp_c, M_akg_e, M_atp_c, M_co2_e, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nad_c, M_nadp_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8_c, M_r5p_c\n", + "\n", + "Answer: 27 (27 seeds)\n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_actp_c, M_akg_e, M_atp_c, M_co2_e, M_coa_c, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nadh_c, M_nadph_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8h2_c, M_r5p_c\n", + "\n", + "Answer: 28 (27 seeds)\n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_actp_c, M_adp_c, M_akg_e, M_co2_e, M_coa_c, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nadh_c, M_nadph_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8h2_c, M_r5p_c\n", + "\n", + "Answer: 29 (27 seeds)\n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_actp_c, M_akg_e, M_atp_c, M_co2_e, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nad_c, M_nadph_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8_c, M_r5p_c, M_succoa_c\n", + "\n", + "Answer: 30 (27 seeds)\n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_actp_c, M_adp_c, M_akg_e, M_co2_e, M_coa_c, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nad_c, M_nadp_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8h2_c, M_r5p_c\n", + "\n", + "\u001b[0;94m\n", + "____________________________________________\u001b[0m\n", + "\u001b[0;94m____________________________________________\n", + "\u001b[0m\n", + " Sub Mode: \u001b[1mMINIMIZE\u001b[0m \n", + "\u001b[0;94m____________________________________________\u001b[0m\n", + "\u001b[0;94m____________________________________________\n", + "\u001b[0m\n", + "\n", + "················ \u001b[1mClassic mode\u001b[0m ···············\n", + "Finding optimum...\n", + "SOLVING...\n", + "\n", + "Optimum found.\n", + "Minimal size of seed set is 27\n", + "\n", + "\n", + "~~~~~~~~~~~~~~~~ \u001b[1mEnumeration\u001b[0m ~~~~~~~~~~~~~~~\n", + "SOLVING...\n", + "\n", + "Answer: 1 (27 seeds) \n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_actp_c, M_adp_c, M_akg_e, M_co2_e, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nad_c, M_nadp_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8h2_c, M_r5p_c, M_succoa_c\n", + "\n", + "Answer: 2 (27 seeds) \n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_actp_c, M_adp_c, M_akg_e, M_co2_e, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nad_c, M_nadph_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8h2_c, M_r5p_c, M_succoa_c\n", + "\n", + "Answer: 3 (27 seeds) \n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_actp_c, M_adp_c, M_akg_e, M_co2_e, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nadh_c, M_nadph_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8h2_c, M_r5p_c, M_succoa_c\n", + "\n", + "Answer: 4 (27 seeds) \n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_actp_c, M_adp_c, M_akg_e, M_co2_e, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nadh_c, M_nadp_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8h2_c, M_r5p_c, M_succoa_c\n", + "\n", + "Answer: 5 (27 seeds) \n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_actp_c, M_adp_c, M_akg_e, M_co2_e, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nad_c, M_nadph_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8_c, M_r5p_c, M_succoa_c\n", + "\n", + "Answer: 6 (27 seeds) \n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_actp_c, M_adp_c, M_akg_e, M_co2_e, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nadh_c, M_nadph_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8_c, M_r5p_c, M_succoa_c\n", + "\n", + "Answer: 7 (27 seeds) \n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_actp_c, M_adp_c, M_akg_e, M_co2_e, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nad_c, M_nadp_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8_c, M_r5p_c, M_succoa_c\n", + "\n", + "Answer: 8 (27 seeds) \n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_actp_c, M_adp_c, M_akg_e, M_co2_e, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nadh_c, M_nadp_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8_c, M_r5p_c, M_succoa_c\n", + "\n", + "Answer: 9 (27 seeds) \n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_accoa_c, M_actp_c, M_adp_c, M_akg_e, M_co2_e, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nad_c, M_nadph_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8_c, M_r5p_c\n", + "\n", + "Answer: 10 (27 seeds) \n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_accoa_c, M_actp_c, M_adp_c, M_akg_e, M_co2_e, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nadh_c, M_nadph_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8_c, M_r5p_c\n", + "\n", + "Answer: 11 (27 seeds) \n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_accoa_c, M_actp_c, M_adp_c, M_akg_e, M_co2_e, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nad_c, M_nadph_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8h2_c, M_r5p_c\n", + "\n", + "Answer: 12 (27 seeds) \n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_accoa_c, M_actp_c, M_adp_c, M_akg_e, M_co2_e, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nadh_c, M_nadph_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8h2_c, M_r5p_c\n", + "\n", + "Answer: 13 (27 seeds) \n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_accoa_c, M_actp_c, M_adp_c, M_akg_e, M_co2_e, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nad_c, M_nadp_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8_c, M_r5p_c\n", + "\n", + "Answer: 14 (27 seeds) \n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_accoa_c, M_actp_c, M_adp_c, M_akg_e, M_co2_e, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nadh_c, M_nadp_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8_c, M_r5p_c\n", + "\n", + "Answer: 15 (27 seeds) \n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_accoa_c, M_actp_c, M_adp_c, M_akg_e, M_co2_e, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nad_c, M_nadp_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8h2_c, M_r5p_c\n", + "\n", + "Answer: 16 (27 seeds) \n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_accoa_c, M_actp_c, M_adp_c, M_akg_e, M_co2_e, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nadh_c, M_nadp_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8h2_c, M_r5p_c\n", + "\n", + "Answer: 17 (27 seeds) \n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_actp_c, M_adp_c, M_akg_e, M_co2_e, M_coa_c, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nad_c, M_nadph_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8_c, M_r5p_c\n", + "\n", + "Answer: 18 (27 seeds) \n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_actp_c, M_adp_c, M_akg_e, M_co2_e, M_coa_c, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nadh_c, M_nadph_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8_c, M_r5p_c\n", + "\n", + "Answer: 19 (27 seeds) \n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_actp_c, M_adp_c, M_akg_e, M_co2_e, M_coa_c, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nad_c, M_nadph_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8h2_c, M_r5p_c\n", + "\n", + "Answer: 20 (27 seeds) \n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_actp_c, M_adp_c, M_akg_e, M_co2_e, M_coa_c, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nadh_c, M_nadph_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8h2_c, M_r5p_c\n", + "\n", + "Answer: 21 (27 seeds) \n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_actp_c, M_adp_c, M_akg_e, M_co2_e, M_coa_c, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nad_c, M_nadp_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8_c, M_r5p_c\n", + "\n", + "Answer: 22 (27 seeds) \n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_actp_c, M_adp_c, M_akg_e, M_co2_e, M_coa_c, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nadh_c, M_nadp_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8_c, M_r5p_c\n", + "\n", + "Answer: 23 (27 seeds) \n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_actp_c, M_adp_c, M_akg_e, M_co2_e, M_coa_c, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nad_c, M_nadp_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8h2_c, M_r5p_c\n", + "\n", + "Answer: 24 (27 seeds) \n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_actp_c, M_adp_c, M_akg_e, M_co2_e, M_coa_c, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nadh_c, M_nadp_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8h2_c, M_r5p_c\n", + "\n", + "Answer: 25 (27 seeds) \n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_accoa_c, M_actp_c, M_akg_e, M_atp_c, M_co2_e, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nad_c, M_nadph_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8_c, M_r5p_c\n", + "\n", + "Answer: 26 (27 seeds) \n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_actp_c, M_akg_e, M_atp_c, M_co2_e, M_coa_c, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nad_c, M_nadph_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8_c, M_r5p_c\n", + "\n", + "Answer: 27 (27 seeds) \n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_accoa_c, M_actp_c, M_akg_e, M_atp_c, M_co2_e, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nadh_c, M_nadph_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8_c, M_r5p_c\n", + "\n", + "Answer: 28 (27 seeds) \n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_actp_c, M_akg_e, M_atp_c, M_co2_e, M_coa_c, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nadh_c, M_nadph_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8_c, M_r5p_c\n", + "\n", + "Answer: 29 (27 seeds) \n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_actp_c, M_akg_e, M_atp_c, M_co2_e, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nad_c, M_nadph_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8_c, M_r5p_c, M_succoa_c\n", + "\n", + "Answer: 30 (27 seeds) \n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_actp_c, M_akg_e, M_atp_c, M_co2_e, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nadh_c, M_nadph_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8_c, M_r5p_c, M_succoa_c\n", + "\n", + "\n", + "················ \u001b[1mFilter mode\u001b[0m ···············\n", + "Finding optimum...\n", + "SOLVING...\n", + "\n", + "Optimum found.\n", + "Minimal size of seed set is 27\n", + "\n", + "\n", + "~~~~~~~~~~~~~~~~ \u001b[1mEnumeration\u001b[0m ~~~~~~~~~~~~~~~\n", + "SOLVING...\n", + "\n", + "Answer: 1 (27 seeds)\n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_actp_c, M_adp_c, M_akg_e, M_co2_e, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nadh_c, M_nadph_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8h2_c, M_r5p_c, M_succoa_c\n", + "\n", + "Answer: 2 (27 seeds)\n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_actp_c, M_adp_c, M_akg_e, M_co2_e, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nad_c, M_nadph_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8h2_c, M_r5p_c, M_succoa_c\n", + "\n", + "Answer: 3 (27 seeds)\n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_actp_c, M_adp_c, M_akg_e, M_co2_e, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nad_c, M_nadp_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8h2_c, M_r5p_c, M_succoa_c\n", + "\n", + "Answer: 4 (27 seeds)\n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_actp_c, M_adp_c, M_akg_e, M_co2_e, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nadh_c, M_nadp_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8h2_c, M_r5p_c, M_succoa_c\n", + "\n", + "Answer: 5 (27 seeds)\n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_actp_c, M_adp_c, M_akg_e, M_co2_e, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nad_c, M_nadph_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8_c, M_r5p_c, M_succoa_c\n", + "\n", + "Answer: 6 (27 seeds)\n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_actp_c, M_adp_c, M_akg_e, M_co2_e, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nad_c, M_nadp_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8_c, M_r5p_c, M_succoa_c\n", + "\n", + "Answer: 7 (27 seeds)\n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_actp_c, M_adp_c, M_akg_e, M_co2_e, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nadh_c, M_nadph_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8_c, M_r5p_c, M_succoa_c\n", + "\n", + "Answer: 8 (27 seeds)\n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_actp_c, M_adp_c, M_akg_e, M_co2_e, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nadh_c, M_nadp_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8_c, M_r5p_c, M_succoa_c\n", + "\n", + "Answer: 9 (27 seeds)\n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_accoa_c, M_actp_c, M_adp_c, M_akg_e, M_co2_e, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nadh_c, M_nadp_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8_c, M_r5p_c\n", + "\n", + "Answer: 10 (27 seeds)\n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_accoa_c, M_actp_c, M_adp_c, M_akg_e, M_co2_e, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nadh_c, M_nadp_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8h2_c, M_r5p_c\n", + "\n", + "Answer: 11 (27 seeds)\n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_accoa_c, M_actp_c, M_adp_c, M_akg_e, M_co2_e, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nadh_c, M_nadph_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8_c, M_r5p_c\n", + "\n", + "Answer: 12 (27 seeds)\n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_accoa_c, M_actp_c, M_adp_c, M_akg_e, M_co2_e, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nadh_c, M_nadph_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8h2_c, M_r5p_c\n", + "\n", + "Answer: 13 (27 seeds)\n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_accoa_c, M_actp_c, M_adp_c, M_akg_e, M_co2_e, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nad_c, M_nadp_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8_c, M_r5p_c\n", + "\n", + "Answer: 14 (27 seeds)\n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_accoa_c, M_actp_c, M_adp_c, M_akg_e, M_co2_e, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nad_c, M_nadp_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8h2_c, M_r5p_c\n", + "\n", + "Answer: 15 (27 seeds)\n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_accoa_c, M_actp_c, M_adp_c, M_akg_e, M_co2_e, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nad_c, M_nadph_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8_c, M_r5p_c\n", + "\n", + "Answer: 16 (27 seeds)\n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_accoa_c, M_actp_c, M_adp_c, M_akg_e, M_co2_e, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nad_c, M_nadph_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8h2_c, M_r5p_c\n", + "\n", + "Answer: 17 (27 seeds)\n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_actp_c, M_adp_c, M_akg_e, M_co2_e, M_coa_c, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nadh_c, M_nadp_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8h2_c, M_r5p_c\n", + "\n", + "Answer: 18 (27 seeds)\n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_actp_c, M_adp_c, M_akg_e, M_co2_e, M_coa_c, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nad_c, M_nadp_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8h2_c, M_r5p_c\n", + "\n", + "Answer: 19 (27 seeds)\n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_actp_c, M_adp_c, M_akg_e, M_co2_e, M_coa_c, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nadh_c, M_nadph_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8h2_c, M_r5p_c\n", + "\n", + "Answer: 20 (27 seeds)\n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_actp_c, M_adp_c, M_akg_e, M_co2_e, M_coa_c, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nad_c, M_nadph_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8h2_c, M_r5p_c\n", + "\n", + "Answer: 21 (27 seeds)\n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_actp_c, M_adp_c, M_akg_e, M_co2_e, M_coa_c, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nadh_c, M_nadp_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8_c, M_r5p_c\n", + "\n", + "Answer: 22 (27 seeds)\n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_actp_c, M_adp_c, M_akg_e, M_co2_e, M_coa_c, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nad_c, M_nadp_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8_c, M_r5p_c\n", + "\n", + "Answer: 23 (27 seeds)\n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_actp_c, M_adp_c, M_akg_e, M_co2_e, M_coa_c, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nadh_c, M_nadph_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8_c, M_r5p_c\n", + "\n", + "Answer: 24 (27 seeds)\n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_actp_c, M_adp_c, M_akg_e, M_co2_e, M_coa_c, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nad_c, M_nadph_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8_c, M_r5p_c\n", + "\n", + "Answer: 25 (27 seeds)\n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_actp_c, M_akg_e, M_atp_c, M_co2_e, M_coa_c, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nadh_c, M_nadp_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8h2_c, M_r5p_c\n", + "\n", + "Answer: 26 (27 seeds)\n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_actp_c, M_akg_e, M_atp_c, M_co2_e, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nadh_c, M_nadp_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8h2_c, M_r5p_c, M_succoa_c\n", + "\n", + "Answer: 27 (27 seeds)\n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_actp_c, M_akg_e, M_atp_c, M_co2_e, M_coa_c, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nad_c, M_nadp_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8h2_c, M_r5p_c\n", + "\n", + "Answer: 28 (27 seeds)\n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_actp_c, M_akg_e, M_atp_c, M_co2_e, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nad_c, M_nadp_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8h2_c, M_r5p_c, M_succoa_c\n", + "\n", + "Answer: 29 (27 seeds)\n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_actp_c, M_akg_e, M_atp_c, M_co2_e, M_coa_c, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nadh_c, M_nadph_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8h2_c, M_r5p_c\n", + "\n", + "Answer: 30 (27 seeds)\n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_actp_c, M_akg_e, M_atp_c, M_co2_e, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nadh_c, M_nadph_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8h2_c, M_r5p_c, M_succoa_c\n", + "\n", + "\n", + "·············· \u001b[1mGuess-Check mode\u001b[0m ············\n", + "Finding optimum...\n", + "SOLVING...\n", + "\n", + "Optimum found.\n", + "Minimal size of seed set is 27\n", + "\n", + "\n", + "~~~~~~~~~~~~~~~~ \u001b[1mEnumeration\u001b[0m ~~~~~~~~~~~~~~~\n", + "SOLVING...\n", + "\n", + "Answer: 1 (27 seeds)\n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_actp_c, M_adp_c, M_akg_e, M_co2_e, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nadh_c, M_nadph_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8h2_c, M_r5p_c, M_succoa_c\n", + "\n", + "Answer: 2 (27 seeds)\n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_actp_c, M_akg_e, M_atp_c, M_co2_e, M_coa_c, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nad_c, M_nadph_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8_c, M_r5p_c\n", + "\n", + "Answer: 3 (27 seeds)\n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_actp_c, M_adp_c, M_akg_e, M_co2_e, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nad_c, M_nadph_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8_c, M_r5p_c, M_succoa_c\n", + "\n", + "Answer: 4 (27 seeds)\n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_actp_c, M_adp_c, M_akg_e, M_co2_e, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nad_c, M_nadp_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8_c, M_r5p_c, M_succoa_c\n", + "\n", + "Answer: 5 (27 seeds)\n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_actp_c, M_adp_c, M_akg_e, M_co2_e, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nad_c, M_nadph_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8h2_c, M_r5p_c, M_succoa_c\n", + "\n", + "Answer: 6 (27 seeds)\n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_actp_c, M_adp_c, M_akg_e, M_co2_e, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nadh_c, M_nadph_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8_c, M_r5p_c, M_succoa_c\n", + "\n", + "Answer: 7 (27 seeds)\n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_accoa_c, M_actp_c, M_adp_c, M_akg_e, M_co2_e, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nad_c, M_nadph_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8_c, M_r5p_c\n", + "\n", + "Answer: 8 (27 seeds)\n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_accoa_c, M_actp_c, M_adp_c, M_akg_e, M_co2_e, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nad_c, M_nadp_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8_c, M_r5p_c\n", + "\n", + "Answer: 9 (27 seeds)\n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_accoa_c, M_actp_c, M_adp_c, M_akg_e, M_co2_e, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nad_c, M_nadph_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8h2_c, M_r5p_c\n", + "\n", + "Answer: 10 (27 seeds)\n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_actp_c, M_akg_e, M_atp_c, M_co2_e, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nadh_c, M_nadph_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8h2_c, M_r5p_c, M_succoa_c\n", + "\n", + "Answer: 11 (27 seeds)\n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_actp_c, M_akg_e, M_atp_c, M_co2_e, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nadh_c, M_nadph_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8_c, M_r5p_c, M_succoa_c\n", + "\n", + "Answer: 12 (27 seeds)\n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_actp_c, M_akg_e, M_atp_c, M_co2_e, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nad_c, M_nadph_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8h2_c, M_r5p_c, M_succoa_c\n", + "\n", + "Answer: 13 (27 seeds)\n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_actp_c, M_adp_c, M_akg_e, M_co2_e, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nad_c, M_nadp_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8h2_c, M_r5p_c, M_succoa_c\n", + "\n", + "Answer: 14 (27 seeds)\n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_accoa_c, M_actp_c, M_adp_c, M_akg_e, M_co2_e, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nad_c, M_nadp_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8h2_c, M_r5p_c\n", + "\n", + "Answer: 15 (27 seeds)\n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_actp_c, M_adp_c, M_akg_e, M_co2_e, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nadh_c, M_nadp_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8h2_c, M_r5p_c, M_succoa_c\n", + "\n", + "Answer: 16 (27 seeds)\n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_actp_c, M_akg_e, M_atp_c, M_co2_e, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nad_c, M_nadph_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8_c, M_r5p_c, M_succoa_c\n", + "\n", + "Answer: 17 (27 seeds)\n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_accoa_c, M_actp_c, M_akg_e, M_atp_c, M_co2_e, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nadh_c, M_nadph_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8_c, M_r5p_c\n", + "\n", + "Answer: 18 (27 seeds)\n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_accoa_c, M_actp_c, M_adp_c, M_akg_e, M_co2_e, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nadh_c, M_nadp_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8h2_c, M_r5p_c\n", + "\n", + "Answer: 19 (27 seeds)\n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_actp_c, M_adp_c, M_akg_e, M_co2_e, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nadh_c, M_nadp_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8_c, M_r5p_c, M_succoa_c\n", + "\n", + "Answer: 20 (27 seeds)\n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_accoa_c, M_actp_c, M_akg_e, M_atp_c, M_co2_e, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nad_c, M_nadph_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8_c, M_r5p_c\n", + "\n", + "Answer: 21 (27 seeds)\n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_actp_c, M_akg_e, M_atp_c, M_co2_e, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nad_c, M_nadp_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8_c, M_r5p_c, M_succoa_c\n", + "\n", + "Answer: 22 (27 seeds)\n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_actp_c, M_akg_e, M_atp_c, M_co2_e, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nadh_c, M_nadp_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8h2_c, M_r5p_c, M_succoa_c\n", + "\n", + "Answer: 23 (27 seeds)\n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_accoa_c, M_actp_c, M_akg_e, M_atp_c, M_co2_e, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nad_c, M_nadph_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8h2_c, M_r5p_c\n", + "\n", + "Answer: 24 (27 seeds)\n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_actp_c, M_akg_e, M_atp_c, M_co2_e, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nad_c, M_nadp_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8h2_c, M_r5p_c, M_succoa_c\n", + "\n", + "Answer: 25 (27 seeds)\n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_accoa_c, M_actp_c, M_akg_e, M_atp_c, M_co2_e, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nad_c, M_nadp_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8_c, M_r5p_c\n", + "\n", + "Answer: 26 (27 seeds)\n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_actp_c, M_akg_e, M_atp_c, M_co2_e, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nadh_c, M_nadp_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8_c, M_r5p_c, M_succoa_c\n", + "\n", + "Answer: 27 (27 seeds)\n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_accoa_c, M_actp_c, M_akg_e, M_atp_c, M_co2_e, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nadh_c, M_nadph_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8h2_c, M_r5p_c\n", + "\n", + "Answer: 28 (27 seeds)\n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_accoa_c, M_actp_c, M_akg_e, M_atp_c, M_co2_e, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nadh_c, M_nadp_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8h2_c, M_r5p_c\n", + "\n", + "Answer: 29 (27 seeds)\n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_accoa_c, M_actp_c, M_adp_c, M_akg_e, M_co2_e, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nadh_c, M_nadph_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8_c, M_r5p_c\n", + "\n", + "Answer: 30 (27 seeds)\n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_accoa_c, M_actp_c, M_adp_c, M_akg_e, M_co2_e, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nadh_c, M_nadp_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8_c, M_r5p_c\n", + "\n", + "\n", + "····· \u001b[1mGuess-Check with diversity mode\u001b[0m ······\n", + "Finding optimum...\n", + "SOLVING...\n", + "\n", + "Optimum found.\n", + "Minimal size of seed set is 27\n", + "\n", + "\n", + "~~~~~~~~~~~~~~~~ \u001b[1mEnumeration\u001b[0m ~~~~~~~~~~~~~~~\n", + "SOLVING...\n", + "\n", + "Answer: 1 (27 seeds)\n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_actp_c, M_adp_c, M_akg_e, M_co2_e, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nadh_c, M_nadph_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8h2_c, M_r5p_c, M_succoa_c\n", + "\n", + "Answer: 2 (27 seeds)\n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_actp_c, M_adp_c, M_akg_e, M_co2_e, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nadh_c, M_nadph_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8_c, M_r5p_c, M_succoa_c\n", + "\n", + "Answer: 3 (27 seeds)\n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_actp_c, M_adp_c, M_akg_e, M_co2_e, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nad_c, M_nadph_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8_c, M_r5p_c, M_succoa_c\n", + "\n", + "Answer: 4 (27 seeds)\n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_actp_c, M_adp_c, M_akg_e, M_co2_e, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nad_c, M_nadph_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8h2_c, M_r5p_c, M_succoa_c\n", + "\n", + "Answer: 5 (27 seeds)\n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_actp_c, M_adp_c, M_akg_e, M_co2_e, M_coa_c, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nad_c, M_nadph_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8h2_c, M_r5p_c\n", + "\n", + "Answer: 6 (27 seeds)\n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_accoa_c, M_actp_c, M_adp_c, M_akg_e, M_co2_e, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nad_c, M_nadph_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8h2_c, M_r5p_c\n", + "\n", + "Answer: 7 (27 seeds)\n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_accoa_c, M_actp_c, M_adp_c, M_akg_e, M_co2_e, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nadh_c, M_nadph_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8h2_c, M_r5p_c\n", + "\n", + "Answer: 8 (27 seeds)\n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_actp_c, M_adp_c, M_akg_e, M_co2_e, M_coa_c, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nadh_c, M_nadph_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8h2_c, M_r5p_c\n", + "\n", + "Answer: 9 (27 seeds)\n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_actp_c, M_adp_c, M_akg_e, M_co2_e, M_coa_c, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nadh_c, M_nadph_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8_c, M_r5p_c\n", + "\n", + "Answer: 10 (27 seeds)\n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_actp_c, M_adp_c, M_akg_e, M_co2_e, M_coa_c, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nad_c, M_nadph_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8_c, M_r5p_c\n", + "\n", + "Answer: 11 (27 seeds)\n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_accoa_c, M_actp_c, M_adp_c, M_akg_e, M_co2_e, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nad_c, M_nadph_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8_c, M_r5p_c\n", + "\n", + "Answer: 12 (27 seeds)\n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_accoa_c, M_actp_c, M_adp_c, M_akg_e, M_co2_e, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nadh_c, M_nadph_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8_c, M_r5p_c\n", + "\n", + "Answer: 13 (27 seeds)\n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_accoa_c, M_actp_c, M_adp_c, M_akg_e, M_co2_e, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nadh_c, M_nadp_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8_c, M_r5p_c\n", + "\n", + "Answer: 14 (27 seeds)\n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_accoa_c, M_actp_c, M_adp_c, M_akg_e, M_co2_e, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nadh_c, M_nadp_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8h2_c, M_r5p_c\n", + "\n", + "Answer: 15 (27 seeds)\n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_actp_c, M_adp_c, M_akg_e, M_co2_e, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nadh_c, M_nadp_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8h2_c, M_r5p_c, M_succoa_c\n", + "\n", + "Answer: 16 (27 seeds)\n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_actp_c, M_adp_c, M_akg_e, M_co2_e, M_coa_c, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nadh_c, M_nadp_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8h2_c, M_r5p_c\n", + "\n", + "Answer: 17 (27 seeds)\n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_actp_c, M_adp_c, M_akg_e, M_co2_e, M_coa_c, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nad_c, M_nadp_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8h2_c, M_r5p_c\n", + "\n", + "Answer: 18 (27 seeds)\n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_actp_c, M_adp_c, M_akg_e, M_co2_e, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nad_c, M_nadp_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8h2_c, M_r5p_c, M_succoa_c\n", + "\n", + "Answer: 19 (27 seeds)\n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_actp_c, M_adp_c, M_akg_e, M_co2_e, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nad_c, M_nadp_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8_c, M_r5p_c, M_succoa_c\n", + "\n", + "Answer: 20 (27 seeds)\n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_actp_c, M_adp_c, M_akg_e, M_co2_e, M_coa_c, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nad_c, M_nadp_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8_c, M_r5p_c\n", + "\n", + "Answer: 21 (27 seeds)\n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_actp_c, M_adp_c, M_akg_e, M_co2_e, M_coa_c, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nadh_c, M_nadp_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8_c, M_r5p_c\n", + "\n", + "Answer: 22 (27 seeds)\n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_actp_c, M_adp_c, M_akg_e, M_co2_e, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nadh_c, M_nadp_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8_c, M_r5p_c, M_succoa_c\n", + "\n", + "Answer: 23 (27 seeds)\n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_accoa_c, M_actp_c, M_adp_c, M_akg_e, M_co2_e, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nad_c, M_nadp_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8_c, M_r5p_c\n", + "\n", + "Answer: 24 (27 seeds)\n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_accoa_c, M_actp_c, M_adp_c, M_akg_e, M_co2_e, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nad_c, M_nadp_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8h2_c, M_r5p_c\n", + "\n", + "Answer: 25 (27 seeds)\n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_accoa_c, M_actp_c, M_akg_e, M_atp_c, M_co2_e, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nad_c, M_nadp_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8h2_c, M_r5p_c\n", + "\n", + "Answer: 26 (27 seeds)\n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_accoa_c, M_actp_c, M_akg_e, M_atp_c, M_co2_e, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nad_c, M_nadph_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8h2_c, M_r5p_c\n", + "\n", + "Answer: 27 (27 seeds)\n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_accoa_c, M_actp_c, M_akg_e, M_atp_c, M_co2_e, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nadh_c, M_nadph_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8h2_c, M_r5p_c\n", + "\n", + "Answer: 28 (27 seeds)\n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_accoa_c, M_actp_c, M_akg_e, M_atp_c, M_co2_e, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nadh_c, M_nadp_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8h2_c, M_r5p_c\n", + "\n", + "Answer: 29 (27 seeds)\n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_accoa_c, M_actp_c, M_akg_e, M_atp_c, M_co2_e, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nadh_c, M_nadp_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8_c, M_r5p_c\n", + "\n", + "Answer: 30 (27 seeds)\n", + "M_13dpg_c, M_2pg_c, M_6pgl_c, M_ac_e, M_acald_e, M_accoa_c, M_actp_c, M_akg_e, M_atp_c, M_co2_e, M_etoh_e, M_fru_e, M_fum_e, M_glc__D_e, M_gln__L_e, M_glu__L_e, M_h2o_e, M_lac__D_e, M_mal__L_e, M_nadh_c, M_nadph_c, M_nh4_e, M_o2_e, M_pi_e, M_pyr_e, M_q8_c, M_r5p_c\n", + "\n", + "\u001b[0;96m############################################\n", + "\n", + "\u001b[0m\n", + "\u001b[0;96m\n", + "############################################\n", + "############################################\n", + " \u001b[1mHYBRID\u001b[0;96m\n", + "############################################\n", + "############################################\n", + "\u001b[0m\n", + "Mode : FULL NETWORK\n", + "ACCUMULATION: Forbidden\n", + "Flux: MAXIMIZATION\n", + "Time limit: 10.0 minutes\n", + "Solution number limit: 30\n", + "\u001b[0;94m\n", + "____________________________________________\u001b[0m\n", + "\u001b[0;94m____________________________________________\n", + "\u001b[0m\n", + " Sub Mode: \u001b[1mSUBSET MINIMAL\u001b[0m \n", + "\u001b[0;94m____________________________________________\u001b[0m\n", + "\u001b[0;94m____________________________________________\n", + "\u001b[0m\n", + "\n", + "~~~~~~~~~~~~~~~~ \u001b[1mEnumeration\u001b[0m ~~~~~~~~~~~~~~~\n", + "SOLVING...\n", + "\n", + "\u001b[0;91mERROR : Timeout: 10.0 min expired\u001b[0m\n", + "\u001b[0;93mWARNING : Output not totally recovered. Json has been repaired but might miss results\u001b[0m\n", + "Unsatisfiable problem\n", + "\u001b[0;91mERROR : Unsatisfiable problem\u001b[0m\n", + "\u001b[0;94m\n", + "____________________________________________\u001b[0m\n", + "\u001b[0;94m____________________________________________\n", + "\u001b[0m\n", + " Sub Mode: \u001b[1mMINIMIZE\u001b[0m \n", + "\u001b[0;94m____________________________________________\u001b[0m\n", + "\u001b[0;94m____________________________________________\n", + "\u001b[0m\n", + "Finding optimum...\n", + "SOLVING...\n", + "\n", + "\u001b[0;91mERROR : Timeout: 10.0 min expired\u001b[0m\n", + "\u001b[0;93mWARNING : Output not totally recovered. Json has been repaired but might miss results\u001b[0m\n", + "Unsatisfiable problem\n", + "\u001b[0;91mERROR : Unsatisfiable problem\u001b[0m\n", + "\u001b[0;96m############################################\n", + "\n", + "\u001b[0m\n", + "\n", + "TIME DATA EXTRACTION : 0.076s\n", + "TIME TOTAL SEED SEARCH: 1343.968s\n", + "TIME TOTAL : 1344.044s\n", + "\n", + "\n", + "\u001b[0;96m\n", + "############################################\n", + "############################################\n", + " \u001b[1mCHECK FLUX\u001b[0;96m\n", + "############################################\n", + "############################################\n", + "\u001b[0m\n", + "---------------- FLUX INIT -----------------\n", + "\n", + "{'BIOMASS_Ecoli_core_w_GAM': np.float64(0.8739215069684295)}\n", + "\n", + "\n", + "--------------- MEDIUM INIT ----------------\n", + "\n", + "EX_co2_e -1000.0 1000.0\n", + "EX_glc__D_e -10.0 1000.0\n", + "EX_h_e -1000.0 1000.0\n", + "EX_h2o_e -1000.0 1000.0\n", + "EX_nh4_e -1000.0 1000.0\n", + "EX_o2_e -1000.0 1000.0\n", + "EX_pi_e -1000.0 1000.0\n", + "\n", + "\n", + "---------- STOP IMPORT FLUX -------------\n", + "\n", + "{'BIOMASS_Ecoli_core_w_GAM': 0.0}\n", + "\n", + "\n", + "\u001b[0;94m\n", + "____________________________________________\n", + "____________________________________________\n", + "\u001b[0m\n", + " RESULTS \n", + "\u001b[0;94m____________________________________________\n", + "____________________________________________\n", + "\u001b[0m\n", + " \u001b[1m \"Cobra (seeds)\" \u001b[0m indicates the maximum flux \n", + "obtained in FBA from the seeds after shutting \n", + "off all other exchange reactions. If the maximum \n", + "flux is null, a test is performed opening demand \n", + "reactions for the objective reaction's products, \n", + "in order to test the effect of their accumulation \n", + "(\u001b[1m\"cobra (demands)\"\u001b[0m ). If this test is not performed, \n", + "\"NA\" value is indicated.\n", + "\u001b[0;93m\n", + "____________________________________________\n", + "____________________________________________\n", + "\u001b[0m\n", + " Subset Minimal \n", + "\u001b[0;93m--------------------------------------------\u001b[0m\n", + " REASONING \n", + "\u001b[0;93m . . . . . . . . . . \u001b[0m\n", + "name | cobra (seeds) | cobra (demands)\n", + "-----|---------------|-----------------\n", + "model_1 | \u001b[0;96m56.969530026817715\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_2 | \u001b[0;96m56.969530026817225\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_3 | \u001b[0;96m56.96953002681805\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_4 | \u001b[0;96m56.96953002681699\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_5 | \u001b[0;96m56.969530026817225\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_6 | \u001b[0;96m56.96953002681805\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_7 | \u001b[0;96m56.96953002681699\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_8 | \u001b[0;96m56.96953002681699\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_9 | \u001b[0;96m56.969530026817225\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_10 | \u001b[0;96m56.96953002681805\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_11 | \u001b[0;96m56.969530026817225\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_12 | \u001b[0;96m56.96953002681805\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_13 | \u001b[0;96m56.96953002681699\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_14 | \u001b[0;96m56.96953002681699\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_15 | \u001b[0;96m56.96953002681699\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_16 | \u001b[0;96m56.96953002681699\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_17 | \u001b[0;96m56.969530026817225\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_18 | \u001b[0;96m56.96953002681805\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_19 | \u001b[0;96m56.969530026817225\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_20 | \u001b[0;96m56.96953002681805\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_21 | \u001b[0;96m56.969530026817225\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_22 | \u001b[0;96m56.969530026817225\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_23 | \u001b[0;96m56.96953002681805\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_24 | \u001b[0;96m56.96953002681805\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_25 | \u001b[0;96m56.96953002681699\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_26 | \u001b[0;96m56.96953002681699\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_27 | \u001b[0;96m56.96953002682003\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_28 | \u001b[0;96m56.96953002682003\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_29 | \u001b[0;96m56.96953002682003\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_30 | \u001b[0;96m56.96953002682003\u001b[0m | \u001b[0mNA\u001b[0m\n", + "\u001b[0;93m--------------------------------------------\u001b[0m\n", + " REASONING FILTER \n", + "\u001b[0;93m . . . . . . . . . . \u001b[0m\n", + "name | cobra (seeds) | cobra (demands)\n", + "-----|---------------|-----------------\n", + "model_1 | \u001b[0;96m56.969530026817225\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_2 | \u001b[0;96m56.969530026817225\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_3 | \u001b[0;96m56.969530026817225\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_4 | \u001b[0;96m56.969530026817225\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_5 | \u001b[0;96m56.96953002681805\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_6 | \u001b[0;96m56.96953002681699\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_7 | \u001b[0;96m56.96953002681699\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_8 | \u001b[0;96m56.96953002682003\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_9 | \u001b[0;96m56.96953002681699\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_10 | \u001b[0;96m56.96953002682003\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_11 | \u001b[0;96m56.96953002681699\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_12 | \u001b[0;96m56.96953002682003\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_13 | \u001b[0;96m56.969530026817225\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_14 | \u001b[0;96m56.96953002681805\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_15 | \u001b[0;96m56.96953002681699\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_16 | \u001b[0;96m56.969530026817225\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_17 | \u001b[0;96m56.96953002681805\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_18 | \u001b[0;96m56.96953002681699\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_19 | \u001b[0;96m56.969530026817225\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_20 | \u001b[0;96m56.969530026817225\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_21 | \u001b[0;96m56.96953002681805\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_22 | \u001b[0;96m56.96953002681699\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_23 | \u001b[0;96m56.96953002682003\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_24 | \u001b[0;96m56.96953002681699\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_25 | \u001b[0;96m56.969530026817225\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_26 | \u001b[0;96m56.969530026817225\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_27 | \u001b[0;96m56.969530026817225\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_28 | \u001b[0;96m56.969530026817225\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_29 | \u001b[0;96m56.96953002681805\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_30 | \u001b[0;96m56.96953002681699\u001b[0m | \u001b[0mNA\u001b[0m\n", + "\u001b[0;93m--------------------------------------------\u001b[0m\n", + " REASONING GUESS-CHECK \n", + "\u001b[0;93m . . . . . . . . . . \u001b[0m\n", + "name | cobra (seeds) | cobra (demands)\n", + "-----|---------------|-----------------\n", + "model_1 | \u001b[0;96m56.969530026817225\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_2 | \u001b[0;96m56.96953002681699\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_3 | \u001b[0;96m56.96953002682003\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_4 | \u001b[0;96m56.969530026817225\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_5 | \u001b[0;96m56.969530026817225\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_6 | \u001b[0;96m56.96953002681699\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_7 | \u001b[0;96m56.96953002682003\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_8 | \u001b[0;96m56.969530026817225\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_9 | \u001b[0;96m56.96953002681699\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_10 | \u001b[0;96m56.96953002682003\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_11 | \u001b[0;96m56.96953002682003\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_12 | \u001b[0;96m56.969530026817225\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_13 | \u001b[0;96m56.96953002681699\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_14 | \u001b[0;96m56.96953002681699\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_15 | \u001b[0;96m56.96953002681699\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_16 | \u001b[0;96m56.96953002681699\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_17 | \u001b[0;96m56.969530026817225\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_18 | \u001b[0;96m56.969530026817225\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_19 | \u001b[0;96m56.969530026817225\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_20 | \u001b[0;96m56.96953002681805\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_21 | \u001b[0;96m56.96953002681805\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_22 | \u001b[0;96m56.96953002681699\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_23 | \u001b[0;96m56.969530026817225\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_24 | \u001b[0;96m56.96953002681699\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_25 | \u001b[0;96m56.96953002681699\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_26 | \u001b[0;96m56.969530026817225\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_27 | \u001b[0;96m56.96953002681699\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_28 | \u001b[0;96m56.96953002681699\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_29 | \u001b[0;96m56.969530026817225\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_30 | \u001b[0;96m56.969530026817225\u001b[0m | \u001b[0mNA\u001b[0m\n", + "\u001b[0;93m--------------------------------------------\u001b[0m\n", + " REASONING GUESS-CHECK DIVERSITY \n", + "\u001b[0;93m . . . . . . . . . . \u001b[0m\n", + "name | cobra (seeds) | cobra (demands)\n", + "-----|---------------|-----------------\n", + "model_1 | \u001b[0;96m56.96953002681805\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_2 | \u001b[0;96m56.96953002681699\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_3 | \u001b[0;96m56.96953002682003\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_4 | \u001b[0;96m56.969530026817225\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_5 | \u001b[0;96m56.96953002681699\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_6 | \u001b[0;96m56.969530026817225\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_7 | \u001b[0;96m56.96953002681805\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_8 | \u001b[0;96m56.969530026817225\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_9 | \u001b[0;96m56.96953002681699\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_10 | \u001b[0;96m56.96953002681699\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_11 | \u001b[0;96m56.96953002682003\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_12 | \u001b[0;96m56.969530026817225\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_13 | \u001b[0;96m56.96953002681699\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_14 | \u001b[0;96m56.96953002681699\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_15 | \u001b[0;96m56.969530026817225\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_16 | \u001b[0;96m56.96953002681699\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_17 | \u001b[0;96m56.969530026817225\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_18 | \u001b[0;96m56.96953002681699\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_19 | \u001b[0;96m56.96953002681699\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_20 | \u001b[0;96m56.969530026817225\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_21 | \u001b[0;96m56.969530026817225\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_22 | \u001b[0;96m56.96953002681805\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_23 | \u001b[0;96m56.969530026817225\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_24 | \u001b[0;96m56.96953002681805\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_25 | \u001b[0;96m56.969530026817225\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_26 | \u001b[0;96m56.969530026817225\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_27 | \u001b[0;96m56.96953002681699\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_28 | \u001b[0;96m56.96953002681699\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_29 | \u001b[0;96m56.96953002682003\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_30 | \u001b[0;96m56.96953002681699\u001b[0m | \u001b[0mNA\u001b[0m\n", + "\u001b[0;93m\n", + "____________________________________________\n", + "____________________________________________\n", + "\u001b[0m\n", + " Minimize \n", + "\u001b[0;93m--------------------------------------------\u001b[0m\n", + " REASONING \n", + "\u001b[0;93m . . . . . . . . . . \u001b[0m\n", + "name | cobra (seeds) | cobra (demands)\n", + "-----|---------------|-----------------\n", + "model_1 | \u001b[0;96m56.96953002681805\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_2 | \u001b[0;96m56.96953002681805\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_3 | \u001b[0;96m56.96953002681805\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_4 | \u001b[0;96m56.96953002681805\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_5 | \u001b[0;96m56.96953002681805\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_6 | \u001b[0;96m56.96953002681805\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_7 | \u001b[0;96m56.96953002681805\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_8 | \u001b[0;96m56.96953002681805\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_9 | \u001b[0;96m56.969530026817225\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_10 | \u001b[0;96m56.969530026817225\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_11 | \u001b[0;96m56.969530026817225\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_12 | \u001b[0;96m56.969530026817225\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_13 | \u001b[0;96m56.969530026817225\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_14 | \u001b[0;96m56.969530026817225\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_15 | \u001b[0;96m56.969530026817225\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_16 | \u001b[0;96m56.969530026817225\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_17 | \u001b[0;96m56.96953002681699\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_18 | \u001b[0;96m56.96953002681699\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_19 | \u001b[0;96m56.96953002681699\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_20 | \u001b[0;96m56.96953002681699\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_21 | \u001b[0;96m56.96953002681699\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_22 | \u001b[0;96m56.96953002681699\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_23 | \u001b[0;96m56.96953002681699\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_24 | \u001b[0;96m56.96953002681699\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_25 | \u001b[0;96m56.969530026817225\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_26 | \u001b[0;96m56.96953002681699\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_27 | \u001b[0;96m56.969530026817225\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_28 | \u001b[0;96m56.96953002681699\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_29 | \u001b[0;96m56.96953002682003\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_30 | \u001b[0;96m56.96953002682003\u001b[0m | \u001b[0mNA\u001b[0m\n", + "\u001b[0;93m--------------------------------------------\u001b[0m\n", + " REASONING FILTER \n", + "\u001b[0;93m . . . . . . . . . . \u001b[0m\n", + "name | cobra (seeds) | cobra (demands)\n", + "-----|---------------|-----------------\n", + "model_1 | \u001b[0;96m56.96953002682003\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_2 | \u001b[0;96m56.96953002682003\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_3 | \u001b[0;96m56.96953002682003\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_4 | \u001b[0;96m56.96953002682003\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_5 | \u001b[0;96m56.96953002682003\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_6 | \u001b[0;96m56.96953002682003\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_7 | \u001b[0;96m56.96953002682003\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_8 | \u001b[0;96m56.96953002682003\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_9 | \u001b[0;96m56.969530026817225\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_10 | \u001b[0;96m56.969530026817225\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_11 | \u001b[0;96m56.969530026817225\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_12 | \u001b[0;96m56.969530026817225\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_13 | \u001b[0;96m56.969530026817225\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_14 | \u001b[0;96m56.969530026817225\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_15 | \u001b[0;96m56.969530026817225\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_16 | \u001b[0;96m56.969530026817225\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_17 | \u001b[0;96m56.96953002681699\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_18 | \u001b[0;96m56.96953002681699\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_19 | \u001b[0;96m56.96953002681699\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_20 | \u001b[0;96m56.96953002681699\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_21 | \u001b[0;96m56.96953002681699\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_22 | \u001b[0;96m56.96953002681699\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_23 | \u001b[0;96m56.96953002681699\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_24 | \u001b[0;96m56.96953002681699\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_25 | \u001b[0;96m56.96953002681699\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_26 | \u001b[0;96m56.96953002682003\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_27 | \u001b[0;96m56.96953002681699\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_28 | \u001b[0;96m56.96953002682003\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_29 | \u001b[0;96m56.96953002681699\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_30 | \u001b[0;96m56.96953002682003\u001b[0m | \u001b[0mNA\u001b[0m\n", + "\u001b[0;93m--------------------------------------------\u001b[0m\n", + " REASONING GUESS-CHECK \n", + "\u001b[0;93m . . . . . . . . . . \u001b[0m\n", + "name | cobra (seeds) | cobra (demands)\n", + "-----|---------------|-----------------\n", + "model_1 | \u001b[0;96m56.96953002682003\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_2 | \u001b[0;96m56.96953002681699\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_3 | \u001b[0;96m56.96953002682003\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_4 | \u001b[0;96m56.96953002682003\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_5 | \u001b[0;96m56.96953002682003\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_6 | \u001b[0;96m56.96953002682003\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_7 | \u001b[0;96m56.969530026817225\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_8 | \u001b[0;96m56.969530026817225\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_9 | \u001b[0;96m56.969530026817225\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_10 | \u001b[0;96m56.96953002681805\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_11 | \u001b[0;96m56.96953002681805\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_12 | \u001b[0;96m56.96953002681805\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_13 | \u001b[0;96m56.96953002681805\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_14 | \u001b[0;96m56.969530026817225\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_15 | \u001b[0;96m56.96953002681805\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_16 | \u001b[0;96m56.96953002681805\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_17 | \u001b[0;96m56.969530026817225\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_18 | \u001b[0;96m56.969530026817225\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_19 | \u001b[0;96m56.96953002681805\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_20 | \u001b[0;96m56.969530026817225\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_21 | \u001b[0;96m56.96953002681805\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_22 | \u001b[0;96m56.96953002681805\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_23 | \u001b[0;96m56.969530026817225\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_24 | \u001b[0;96m56.96953002681805\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_25 | \u001b[0;96m56.969530026817225\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_26 | \u001b[0;96m56.96953002681805\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_27 | \u001b[0;96m56.969530026817225\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_28 | \u001b[0;96m56.969530026817225\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_29 | \u001b[0;96m56.969530026817225\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_30 | \u001b[0;96m56.969530026817225\u001b[0m | \u001b[0mNA\u001b[0m\n", + "\u001b[0;93m--------------------------------------------\u001b[0m\n", + " REASONING GUESS-CHECK DIVERSITY \n", + "\u001b[0;93m . . . . . . . . . . \u001b[0m\n", + "name | cobra (seeds) | cobra (demands)\n", + "-----|---------------|-----------------\n", + "model_1 | \u001b[0;96m56.96953002681805\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_2 | \u001b[0;96m56.96953002681805\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_3 | \u001b[0;96m56.96953002681805\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_4 | \u001b[0;96m56.96953002681805\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_5 | \u001b[0;96m56.96953002681699\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_6 | \u001b[0;96m56.969530026817225\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_7 | \u001b[0;96m56.969530026817225\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_8 | \u001b[0;96m56.96953002681699\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_9 | \u001b[0;96m56.96953002681699\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_10 | \u001b[0;96m56.96953002681699\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_11 | \u001b[0;96m56.969530026817225\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_12 | \u001b[0;96m56.969530026817225\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_13 | \u001b[0;96m56.969530026817225\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_14 | \u001b[0;96m56.969530026817225\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_15 | \u001b[0;96m56.96953002681805\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_16 | \u001b[0;96m56.96953002681699\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_17 | \u001b[0;96m56.96953002681699\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_18 | \u001b[0;96m56.96953002682003\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_19 | \u001b[0;96m56.96953002682003\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_20 | \u001b[0;96m56.96953002681699\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_21 | \u001b[0;96m56.96953002681699\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_22 | \u001b[0;96m56.96953002682003\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_23 | \u001b[0;96m56.969530026817225\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_24 | \u001b[0;96m56.969530026817225\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_25 | \u001b[0;96m56.969530026817225\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_26 | \u001b[0;96m56.969530026817225\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_27 | \u001b[0;96m56.969530026817225\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_28 | \u001b[0;96m56.969530026817225\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_29 | \u001b[0;96m56.969530026817225\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_30 | \u001b[0;96m56.969530026817225\u001b[0m | \u001b[0mNA\u001b[0m\n", + "\u001b[0;93m\n", + "____________________________________________\n", + "\u001b[0m\n" + ] + } + ], + "source": [ + "# \"e_coli_dir\" can be cahnged by \"sbml_dir\" for all files run\n", + "# But not recommanded within notebook\n", + "\n", + "run_s2lp(e_coli_dir)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### **List of output files**\n", + "\n", + "In the result directory (initially \"../../results/s2lp\") you will find 4 files.\n", + "\n", + "Seed2lp results files:\n", + "- e_coli_core_rm_rxn_tgt_taf_all_max_no_accu_results.json -> Target\n", + "- e_coli_core_rm_rxn_fn_all_max_no_accu_results.json -> Full Network\n", + "\n", + "Fluxes files:\n", + "- e_coli_core_rm_rxn_tgt_taf_all_max_no_accu_fluxes.tsv -> Target\n", + "- e_coli_core_rm_rxn_fn_all_max_no_accu_fluxes.tsv -> Full Network" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "> Note:\n", + ">\n", + "> You will find log files in [result_directory]/e_coli_core/logs\n", + ">\n", + "> Example: ../../results/s2lp/e_coli_core/logs" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "s2lp", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.14" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/notebook/run/04_normalise_network.ipynb b/notebook/run/04_normalise_network.ipynb new file mode 100644 index 0000000..551c5b5 --- /dev/null +++ b/notebook/run/04_normalise_network.ipynb @@ -0,0 +1,41173 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Network normalisation: Rewrite SBML after correction by Seed2LP" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This notebook explains how to obtain normalised networks and export them into sbml format. It is launched with the default option \"remove import reaction\" which is the option used in the paper: it removes the possibility for the model to import exchanged metabolites (which are hardcoded existing seeds).\n", + "\n", + "> Note:\n", + ">\n", + "> All normalised network sbml files are available on [https://doi.org/10.57745/OS1JND](https://doi.org/10.57745/OS1JND)\n", + ">\n", + "> After downloading and unzipping the package, go to analyses/data/sbml_corrected\n", + "\n", + "> Info:\n", + ">\n", + "> This step is needed to be done before getting results data to make sure to get truly used metabolite for Full Network analyse, and not metabolites from deleted reaction and never used in any other reaction (deleted reaction can be for example reaction with boundaries [0,0])\n", + ">\n", + "> It is also needed to be done before launching Netseed or Precursor." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## **Slurm-based cluster**: Reproducing paper data\n", + "Slurm-based scripts for cluster are available:\n", + "- Launch if needed \n", + " - [01_job_retrieve_bigg_sbml.sh](../../scripts/plafrim_cluster/01_job_retrieve_bigg_sbml.sh): `sbatch 01_job_retrieve_bigg_sbml.sh`\n", + " - [02_job_get_objective.sh](../../scripts/plafrim_cluster/02_job_get_objective.sh): `sbatch 02_job_get_objective.sh`\n", + " - or copy your local files into you cluster\n", + "- Change **_source_** variable by the path of your conda environement with seed2lp installed in files: [04_job_run_sbml_normalisation.sh](../../scripts/plafrim_cluster/04_job_run_sbml_normalisation.sh)\n", + "- launch [04_job_run_sbml_normalisation.sh](../../scripts/plafrim_cluster/04_job_run_sbml_normalisation.sh): `sbatch 04_job_run_sbml_normalisation.sh`" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Requirements\n", + "Module *seed2lp* and *pandas* needed\n", + "\n", + "> Advice:\n", + "> \n", + "> Use a conda env called s2lp" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## **LAUNCH**" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Variable to change (if wanted)" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "analyse_dir = \"../../analyses\"\n", + "data_dir = f\"{analyse_dir}/data/\"\n", + "result_dir=f\"{analyse_dir}/results\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Execute" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "from os import path, listdir" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "sbml_dir=f\"{data_dir}/bigg/sbml\"\n", + "norm_sbml_dir=f\"{data_dir}/sbml_corrected\"" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "def run_normalisation(in_dir:str, out_dir:str):\n", + " for filename in listdir(in_dir):\n", + " sbml_in_path = path.join(in_dir,filename)\n", + " !seed2lp network {sbml_in_path} {out_dir} -wf" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This run takes over 10 min to perform all the 107 networks" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: iCN900\n", + "\u001b[0;93mWARNING : - R_ASPTA: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_GLUR: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + "\n", + " - R_SPTc: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + " - R_PGAMT: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_AGPR: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_PMANM: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_LDH_L: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_PFK_adp: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_FDH: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_TAGURr: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_IMPC: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_CBMKr: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_NAabcO: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_LDH_D: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_SUCOAACTr: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_BTNTe: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_COBALTt5: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_ADtr: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_4ABZt: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_IPPMIb: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_CAt6: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_HMR_3951: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_HBCHLR: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_ACOTA: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_HBCO_nadp: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_BUTCT: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_BUTKr: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_MGt5: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_Kt3r: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_NARK: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_r2465_1: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + "\n", + " - R_G6PI_1_2: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + " - R_GLYAT: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_HSDy: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_ASAD: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + "\n", + " - R_PGI1c: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + " - R_GAPD: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + "\n", + " - R_FldAct: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_CMH2: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + " - R_FNRR: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + "\n", + " - R_FNRR2: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + " - R_FDH6r: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_RPI: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_ORPT: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_DURAD: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_DHORTS: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + "\n", + " - R_CPS_1: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + " - R_DURAD2: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_IPPMIa: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_VPAMTr: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_ALATA_D2: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_ALCD19: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_ALCD19y: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + "\u001b[0m\n", + "\n", + "\n", + "\u001b[0;36m############################################\n", + "############################################\n", + " WRITING SBML FILE \n", + "############################################\n", + "############################################\n", + "\u001b[0m\n", + "\u001b[0;93mWARNING : IMPORT REACTION REMOVED BY DEFAULT\u001b[0m\n", + "\u001b[0;93mWARNING : If you want to keep import reaction\n", + "use option -kir / --keep-import-reactions\u001b[0m\n", + "\u001b[0;93mWARNING : Reaction with tag reversible modified:\n", + "\t- R_ASPTA\n", + "\t- R_GLUR\n", + "\t- R_PGAMT\n", + "\t- R_AGPR\n", + "\t- R_PMANM\n", + "\t- R_LDH_L\n", + "\t- R_PFK_adp\n", + "\t- R_FDH\n", + "\t- R_TAGURr\n", + "\t- R_IMPC\n", + "\t- R_CBMKr\n", + "\t- R_NAabcO\n", + "\t- R_LDH_D\n", + "\t- R_SUCOAACTr\n", + "\t- R_BTNTe\n", + "\t- R_COBALTt5\n", + "\t- R_ADtr\n", + "\t- R_4ABZt\n", + "\t- R_IPPMIb\n", + "\t- R_CAt6\n", + "\t- R_HMR_3951\n", + "\t- R_HBCHLR\n", + "\t- R_ACOTA\n", + "\t- R_HBCO_nadp\n", + "\t- R_BUTCT\n", + "\t- R_BUTKr\n", + "\t- R_MGt5\n", + "\t- R_Kt3r\n", + "\t- R_NARK\n", + "\t- R_r2465_1\n", + "\t- R_GLYAT\n", + "\t- R_HSDy\n", + "\t- R_ASAD\n", + "\t- R_GAPD\n", + "\t- R_FNRR\n", + "\t- R_FDH6r\n", + "\t- R_RPI\n", + "\t- R_ORPT\n", + "\t- R_DURAD\n", + "\t- R_DHORTS\n", + "\t- R_DURAD2\n", + "\t- R_IPPMIa\n", + "\t- R_VPAMTr\n", + "\t- R_ALATA_D2\n", + "\t- R_ALCD19\n", + "\t- R_ALCD19y\u001b[0m\n", + "\u001b[0;93mWARNING : Reaction with Reactants and Products exchanged:\n", + "\t- R_ASPTA\n", + "\t- R_GLUR\n", + "\t- R_PGAMT\n", + "\t- R_AGPR\n", + "\t- R_PMANM\n", + "\t- R_LDH_L\n", + "\t- R_PFK_adp\n", + "\t- R_FDH\n", + "\t- R_TAGURr\n", + "\t- R_IMPC\n", + "\t- R_CBMKr\n", + "\t- R_NAabcO\n", + "\t- R_LDH_D\n", + "\t- R_SUCOAACTr\n", + "\t- R_BTNTe\n", + "\t- R_COBALTt5\n", + "\t- R_ADtr\n", + "\t- R_4ABZt\n", + "\t- R_IPPMIb\n", + "\t- R_CAt6\n", + "\t- R_HMR_3951\n", + "\t- R_HBCHLR\n", + "\t- R_ACOTA\n", + "\t- R_HBCO_nadp\n", + "\t- R_BUTCT\n", + "\t- R_BUTKr\n", + "\t- R_MGt5\n", + "\t- R_Kt3r\n", + "\t- R_NARK\n", + "\t- R_r2465_1\n", + "\t- R_GLYAT\n", + "\t- R_HSDy\n", + "\t- R_ASAD\n", + "\t- R_GAPD\n", + "\t- R_FNRR\n", + "\t- R_FDH6r\n", + "\t- R_RPI\n", + "\t- R_ORPT\n", + "\t- R_DURAD\n", + "\t- R_DHORTS\n", + "\t- R_DURAD2\n", + "\t- R_IPPMIa\n", + "\t- R_VPAMTr\n", + "\t- R_ALATA_D2\n", + "\t- R_ALCD19\n", + "\t- R_ALCD19y\u001b[0m\n", + "\u001b[0;93mWARNING : Import reaction removed:\n", + "\t- R_DM_biomass_c\n", + "\t- R_EX_tre_e\n", + "\t- R_EX_co1dam_e\n", + "\t- R_EX_phe__L_e\n", + "\t- R_EX_thm_e\n", + "\t- R_EX_leu__L_e\n", + "\t- R_EX_dachi_e\n", + "\t- R_EX_k_e\n", + "\t- R_EX_pydxn_e\n", + "\t- R_EX_ac_e\n", + "\t- R_EX_cdpg_e\n", + "\t- R_EX_etoh_e\n", + "\t- R_EX_malt_e\n", + "\t- R_EX_galt_e\n", + "\t- R_EX_Glc_aD_e\n", + "\t- R_EX_glyc_e\n", + "\t- R_EX_mnl_e\n", + "\t- R_EX_d23hb_e\n", + "\t- R_EX_gly_e\n", + "\t- R_EX_gln__L_e\n", + "\t- R_EX_isobuta_e\n", + "\t- R_EX_fe2_e\n", + "\t- R_EX_m4po_e\n", + "\t- R_EX_tmam_e\n", + "\t- R_EX_lys__L_e\n", + "\t- R_EX_ad_e\n", + "\t- R_EX_ala__L_e\n", + "\t- R_EX_ca2_e\n", + "\t- R_EX_acetone_e\n", + "\t- R_EX_m2but_e\n", + "\t- R_EX_uri_e\n", + "\t- R_EX_xan_e\n", + "\t- R_EX_2ddglcn_e\n", + "\t- R_EX_dad_2_e\n", + "\t- R_EX_escut_e\n", + "\t- R_EX_ins_e\n", + "\t- R_EX_ribflv_e\n", + "\t- R_EX_escul_e\n", + "\t- R_EX_fru_e\n", + "\t- R_EX_nac_e\n", + "\t- R_EX_glc__D_e\n", + "\t- R_EX_arg__L_e\n", + "\t- R_EX_gthrd_e\n", + "\t- R_EX_ile__L_e\n", + "\t- R_EX_mg2_e\n", + "\t- R_EX_dcyt_e\n", + "\t- R_EX_mevR_e\n", + "\t- R_EX_csn_e\n", + "\t- R_EX_fuc_e\n", + "\t- R_EX_pro__L_e\n", + "\t- R_EX_xyl__D_e\n", + "\t- R_EX_pi_e\n", + "\t- R_EX_tyr__L_e\n", + "\t- R_EX_dhptd_e\n", + "\t- R_EX_acnam_e\n", + "\t- R_EX_trp__L_e\n", + "\t- R_EX_sbt__D_e\n", + "\t- R_EX_raffin_e\n", + "\t- R_EX_so3_e\n", + "\t- R_EX_arab__L_e\n", + "\t- R_EX_cellb_e\n", + "\t- R_EX_ncam_e\n", + "\t- R_EX_chor_e\n", + "\t- R_EX_ptrc_e\n", + "\t- R_EX_met__L_e\n", + "\t- R_EX_abg4_e\n", + "\t- R_EX_asp__L_e\n", + "\t- R_EX_ibtol_e\n", + "\t- R_EX_cys__L_e\n", + "\t- R_EX_gal_bD_e\n", + "\t- R_EX_thr__L_e\n", + "\t- R_EX_ppat_e\n", + "\t- R_EX_succ_e\n", + "\t- R_EX_isocap_e\n", + "\t- R_EX_for_e\n", + "\t- R_EX_dtbt_e\n", + "\t- R_EX_glcn_e\n", + "\t- R_EX_no2_e\n", + "\t- R_EX_23dappa_e\n", + "\t- R_EX_man_e\n", + "\t- R_EX_his__L_e\n", + "\t- R_EX_btn_e\n", + "\t- R_EX_a5pn_e\n", + "\t- R_EX_thymd_e\n", + "\t- R_EX_ser__L_e\n", + "\t- R_EX_g3p_e\n", + "\t- R_EX_h2_e\n", + "\t- R_EX_hxan_e\n", + "\t- R_EX_AEP_e\n", + "\t- R_EX_man6pglyc_e\n", + "\t- R_EX_ival_e\n", + "\t- R_EX_acgam_e\n", + "\t- R_EX_pnto__R_e\n", + "\t- R_EX_na1_e\n", + "\t- R_EX_mal__L_e\n", + "\t- R_EX_tgt_e\n", + "\t- R_EX_zn2_e\n", + "\t- R_EX_ura_e\n", + "\t- R_EX_adn_e\n", + "\t- R_EX_nh4_e\n", + "\t- R_EX_h_e\n", + "\t- R_EX_rib__D_e\n", + "\t- R_EX_asn__L_e\n", + "\t- R_EX_sucr_e\n", + "\t- R_EX_co2_e\n", + "\t- R_EX_uaccg_e\n", + "\t- R_EX_glyb_e\n", + "\t- R_EX_spmd_e\n", + "\t- R_EX_ham_e\n", + "\t- R_EX_val__L_e\n", + "\t- R_EX_met__D_e\n", + "\t- R_EX_h2o_e\n", + "\t- R_EX_glu__L_e\n", + "\t- R_EX_hco3_e\n", + "\t- R_EX_4abz_e\n", + "\t- R_EX_etha_e\n", + "\t- R_EX_no3_e\n", + "\t- R_EX_akg_e\n", + "\t- R_EX_sel_e\n", + "\t- R_EX_slnt_e\n", + "\t- R_EX_gthox_e\n", + "\t- R_EX_lac__L_e\n", + "\t- R_EX_fum_e\n", + "\t- R_EX_acgal_e\n", + "\t- R_EX_salcn_e\n", + "\t- R_EX_2hxmp_e\n", + "\t- R_EX_arbt_e\n", + "\t- R_EX_hqn_e\n", + "\t- R_EX_gam_e\n", + "\t- R_EX_chol_e\n", + "\t- R_EX_tagur_e\n", + "\t- R_EX_cobalt2_e\u001b[0m\n", + "File saved at: ../../data/sbml_corrected/iCN900.xml\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: iEC1364_W\n", + "\u001b[0;93mWARNING : \n", + " - R_CAT: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_DHPTDNR: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_DHPTDNRN: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_FHL: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SPODM: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SPODMpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCASPtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCFUMtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCMALtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCTARTtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + " - R_EDTXSCOF is multiple (more than 1) metabolites import/export reaction. \n", + "\tDeleted as it is an import reaction.\n", + " - R_HPA3MOFAD: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + "\u001b[0m\n", + "\n", + "\n", + "\u001b[0;36m############################################\n", + "############################################\n", + " WRITING SBML FILE \n", + "############################################\n", + "############################################\n", + "\u001b[0m\n", + "\u001b[0;93mWARNING : IMPORT REACTION REMOVED BY DEFAULT\u001b[0m\n", + "\u001b[0;93mWARNING : If you want to keep import reaction\n", + "use option -kir / --keep-import-reactions\u001b[0m\n", + "\u001b[0;93mWARNING : Reaction with tag reversible modified:\n", + "\t- R_HPA3MOFAD\u001b[0m\n", + "\u001b[0;93mWARNING : Reaction with Reactants and Products exchanged:\n", + "\t- R_HPA3MOFAD\u001b[0m\n", + "\u001b[0;93mWARNING : Import reaction removed:\n", + "\t- R_EX_co2_e\n", + "\t- R_EX_cobalt2_e\n", + "\t- R_EX_colipa_e\n", + "\t- R_EX_colipap_e\n", + "\t- R_EX_cpgn_e\n", + "\t- R_EX_cpgn_un_e\n", + "\t- R_EX_fe3dcit_e\n", + "\t- R_EX_crn_e\n", + "\t- R_EX_crn__D_e\n", + "\t- R_EX_fe3dhbzs_e\n", + "\t- R_EX_fe3hox_e\n", + "\t- R_EX_csn_e\n", + "\t- R_EX_fe3hox_un_e\n", + "\t- R_EX_cu_e\n", + "\t- R_EX_fecrm_e\n", + "\t- R_EX_fecrm_un_e\n", + "\t- R_EX_cu2_e\n", + "\t- R_EX_feenter_e\n", + "\t- R_EX_cyan_e\n", + "\t- R_EX_feoxam_e\n", + "\t- R_EX_feoxam_un_e\n", + "\t- R_EX_cynt_e\n", + "\t- R_EX_but_e\n", + "\t- R_EX_for_e\n", + "\t- R_EX_cys__D_e\n", + "\t- R_EX_fru_e\n", + "\t- R_EX_frulys_e\n", + "\t- R_EX_cys__L_e\n", + "\t- R_EX_fruur_e\n", + "\t- R_EX_fuc__L_e\n", + "\t- R_EX_fum_e\n", + "\t- R_EX_all__D_e\n", + "\t- R_EX_alltn_e\n", + "\t- R_EX_amp_e\n", + "\t- R_EX_anhgm_e\n", + "\t- R_EX_arab__L_e\n", + "\t- R_EX_arbt_e\n", + "\t- R_EX_arbtn_e\n", + "\t- R_EX_arbtn_fe3_e\n", + "\t- R_EX_arg__L_e\n", + "\t- R_EX_ascb__L_e\n", + "\t- R_EX_fusa_e\n", + "\t- R_EX_g1p_e\n", + "\t- R_EX_g3pc_e\n", + "\t- R_EX_g3pe_e\n", + "\t- R_EX_g3pg_e\n", + "\t- R_EX_g3pi_e\n", + "\t- R_EX_g3ps_e\n", + "\t- R_EX_cytd_e\n", + "\t- R_EX_dad_2_e\n", + "\t- R_EX_g6p_e\n", + "\t- R_EX_gal_e\n", + "\t- R_EX_gal_bD_e\n", + "\t- R_EX_asn__L_e\n", + "\t- R_EX_aso3_e\n", + "\t- R_EX_gal1p_e\n", + "\t- R_EX_asp__L_e\n", + "\t- R_EX_damp_e\n", + "\t- R_EX_btn_e\n", + "\t- R_EX_galct__D_e\n", + "\t- R_EX_galctn__D_e\n", + "\t- R_EX_butso3_e\n", + "\t- R_EX_dca_e\n", + "\t- R_EX_ca2_e\n", + "\t- R_EX_cbi_e\n", + "\t- R_EX_galctn__L_e\n", + "\t- R_EX_galt_e\n", + "\t- R_EX_galur_e\n", + "\t- R_EX_cbl1_e\n", + "\t- R_EX_cd2_e\n", + "\t- R_EX_dcmp_e\n", + "\t- R_EX_cgly_e\n", + "\t- R_EX_chol_e\n", + "\t- R_EX_chtbs_e\n", + "\t- R_EX_cit_e\n", + "\t- R_EX_gam_e\n", + "\t- R_EX_gam6p_e\n", + "\t- R_EX_gbbtn_e\n", + "\t- R_EX_gdp_e\n", + "\t- R_EX_glc__D_e\n", + "\t- R_EX_glcn_e\n", + "\t- R_EX_cl_e\n", + "\t- R_EX_dcyt_e\n", + "\t- R_EX_cm_e\n", + "\t- R_EX_cmp_e\n", + "\t- R_EX_glcr_e\n", + "\t- R_EX_glcur_e\n", + "\t- R_EX_glcur1p_e\n", + "\t- R_EX_ddca_e\n", + "\t- R_EX_gln__L_e\n", + "\t- R_EX_glu__L_e\n", + "\t- R_EX_dgmp_e\n", + "\t- R_EX_gly_e\n", + "\t- R_EX_glyald_e\n", + "\t- R_DM_4crsol_c\n", + "\t- R_EX_dgsn_e\n", + "\t- R_EX_glyb_e\n", + "\t- R_EX_glyc_e\n", + "\t- R_EX_glyc__R_e\n", + "\t- R_EX_glyc2p_e\n", + "\t- R_EX_dha_e\n", + "\t- R_EX_glyc3p_e\n", + "\t- R_DM_5drib_c\n", + "\t- R_EX_glyclt_e\n", + "\t- R_EX_dimp_e\n", + "\t- R_EX_gmp_e\n", + "\t- R_DM_aacald_c\n", + "\t- R_EX_gsn_e\n", + "\t- R_EX_gthox_e\n", + "\t- R_EX_gthrd_e\n", + "\t- R_EX_gtp_e\n", + "\t- R_DM_amob_c\n", + "\t- R_DM_mththf_c\n", + "\t- R_DM_oxam_c\n", + "\t- R_EX_gua_e\n", + "\t- R_EX_h_e\n", + "\t- R_EX_h2_e\n", + "\t- R_EX_din_e\n", + "\t- R_EX_h2o_e\n", + "\t- R_EX_h2o2_e\n", + "\t- R_EX_12ppd__R_e\n", + "\t- R_EX_12ppd__S_e\n", + "\t- R_EX_14glucan_e\n", + "\t- R_EX_h2s_e\n", + "\t- R_EX_dms_e\n", + "\t- R_EX_hacolipa_e\n", + "\t- R_EX_15dap_e\n", + "\t- R_EX_23camp_e\n", + "\t- R_EX_halipa_e\n", + "\t- R_EX_dmso_e\n", + "\t- R_EX_23ccmp_e\n", + "\t- R_EX_dopa_e\n", + "\t- R_EX_23cgmp_e\n", + "\t- R_EX_23cump_e\n", + "\t- R_EX_hdca_e\n", + "\t- R_EX_hdcea_e\n", + "\t- R_EX_23dappa_e\n", + "\t- R_EX_doxrbcn_e\n", + "\t- R_EX_26dap__M_e\n", + "\t- R_EX_hg2_e\n", + "\t- R_EX_his__L_e\n", + "\t- R_EX_hom__L_e\n", + "\t- R_EX_hxa_e\n", + "\t- R_EX_hxan_e\n", + "\t- R_EX_2ddglcn_e\n", + "\t- R_EX_34dhpac_e\n", + "\t- R_EX_3amp_e\n", + "\t- R_EX_3cmp_e\n", + "\t- R_EX_dtmp_e\n", + "\t- R_EX_3gmp_e\n", + "\t- R_EX_3hcinnm_e\n", + "\t- R_EX_3hpp_e\n", + "\t- R_EX_dump_e\n", + "\t- R_EX_3hpppn_e\n", + "\t- R_EX_duri_e\n", + "\t- R_EX_3ump_e\n", + "\t- R_EX_4abut_e\n", + "\t- R_EX_idon__L_e\n", + "\t- R_EX_4hoxpacd_e\n", + "\t- R_EX_eca4colipa_e\n", + "\t- R_EX_5dglcn_e\n", + "\t- R_EX_ile__L_e\n", + "\t- R_EX_imp_e\n", + "\t- R_EX_indole_e\n", + "\t- R_EX_5mtr_e\n", + "\t- R_EX_enlipa_e\n", + "\t- R_EX_LalaDglu_e\n", + "\t- R_EX_LalaDgluMdap_e\n", + "\t- R_EX_inost_e\n", + "\t- R_EX_ins_e\n", + "\t- R_EX_LalaDgluMdapDala_e\n", + "\t- R_EX_isetac_e\n", + "\t- R_EX_enter_e\n", + "\t- R_EX_k_e\n", + "\t- R_EX_LalaLglu_e\n", + "\t- R_EX_ac_e\n", + "\t- R_EX_etha_e\n", + "\t- R_EX_acac_e\n", + "\t- R_EX_kdo2lipid4_e\n", + "\t- R_EX_lac__D_e\n", + "\t- R_EX_lac__L_e\n", + "\t- R_EX_lcts_e\n", + "\t- R_EX_acald_e\n", + "\t- R_EX_acgal_e\n", + "\t- R_EX_acgal1p_e\n", + "\t- R_EX_acgam_e\n", + "\t- R_EX_leu__L_e\n", + "\t- R_EX_lipa_e\n", + "\t- R_EX_lipa_cold_e\n", + "\t- R_EX_acgam1p_e\n", + "\t- R_EX_acmana_e\n", + "\t- R_EX_ethso3_e\n", + "\t- R_EX_acmum_e\n", + "\t- R_EX_acnam_e\n", + "\t- R_EX_lipoate_e\n", + "\t- R_EX_lys__L_e\n", + "\t- R_EX_lyx__L_e\n", + "\t- R_EX_mal__D_e\n", + "\t- R_EX_etoh_e\n", + "\t- R_EX_mal__L_e\n", + "\t- R_EX_acolipa_e\n", + "\t- R_EX_acser_e\n", + "\t- R_EX_ade_e\n", + "\t- R_EX_f6p_e\n", + "\t- R_EX_adn_e\n", + "\t- R_EX_malt_e\n", + "\t- R_EX_malthx_e\n", + "\t- R_EX_adocbl_e\n", + "\t- R_EX_fald_e\n", + "\t- R_EX_ag_e\n", + "\t- R_EX_maltpt_e\n", + "\t- R_EX_agm_e\n", + "\t- R_EX_malttr_e\n", + "\t- R_EX_fe2_e\n", + "\t- R_EX_maltttr_e\n", + "\t- R_EX_akg_e\n", + "\t- R_EX_ala_B_e\n", + "\t- R_EX_ala__D_e\n", + "\t- R_EX_ala__L_e\n", + "\t- R_EX_alaala_e\n", + "\t- R_EX_man_e\n", + "\t- R_EX_man6p_e\n", + "\t- R_EX_manglyc_e\n", + "\t- R_EX_melib_e\n", + "\t- R_EX_fe3_e\n", + "\t- R_EX_meoh_e\n", + "\t- R_EX_met__D_e\n", + "\t- R_EX_met__L_e\n", + "\t- R_EX_metsox_R__L_e\n", + "\t- R_EX_metsox_S__L_e\n", + "\t- R_EX_mg2_e\n", + "\t- R_EX_mincyc_e\n", + "\t- R_EX_minohp_e\n", + "\t- R_EX_mmet_e\n", + "\t- R_EX_slnt_e\n", + "\t- R_EX_so2_e\n", + "\t- R_EX_so3_e\n", + "\t- R_EX_so4_e\n", + "\t- R_EX_spmd_e\n", + "\t- R_EX_succ_e\n", + "\t- R_EX_sucr_e\n", + "\t- R_EX_mn2_e\n", + "\t- R_EX_mnl_e\n", + "\t- R_EX_mobd_e\n", + "\t- R_EX_sulfac_e\n", + "\t- R_EX_tartr__D_e\n", + "\t- R_EX_tartr__L_e\n", + "\t- R_EX_mso3_e\n", + "\t- R_EX_n2o_e\n", + "\t- R_EX_na1_e\n", + "\t- R_EX_nac_e\n", + "\t- R_EX_nh4_e\n", + "\t- R_EX_ni2_e\n", + "\t- R_EX_nmn_e\n", + "\t- R_EX_taur_e\n", + "\t- R_EX_tcynt_e\n", + "\t- R_EX_thm_e\n", + "\t- R_EX_thr__L_e\n", + "\t- R_EX_thrp_e\n", + "\t- R_EX_no_e\n", + "\t- R_EX_no2_e\n", + "\t- R_EX_no3_e\n", + "\t- R_EX_thym_e\n", + "\t- R_EX_thymd_e\n", + "\t- R_EX_novbcn_e\n", + "\t- R_EX_o16a4colipa_e\n", + "\t- R_EX_tma_e\n", + "\t- R_EX_tmao_e\n", + "\t- R_EX_tre_e\n", + "\t- R_EX_o2_e\n", + "\t- R_EX_o2s_e\n", + "\t- R_EX_trp__L_e\n", + "\t- R_EX_ocdca_e\n", + "\t- R_EX_ocdcea_e\n", + "\t- R_EX_octa_e\n", + "\t- R_EX_tsul_e\n", + "\t- R_EX_ttdca_e\n", + "\t- R_EX_ttdcea_e\n", + "\t- R_EX_orn_e\n", + "\t- R_EX_orot_e\n", + "\t- R_EX_ttrcyc_e\n", + "\t- R_EX_pacald_e\n", + "\t- R_EX_peamn_e\n", + "\t- R_EX_tungs_e\n", + "\t- R_EX_tym_e\n", + "\t- R_EX_tyr__L_e\n", + "\t- R_EX_tyrp_e\n", + "\t- R_EX_phe__L_e\n", + "\t- R_EX_pheme_e\n", + "\t- R_EX_pi_e\n", + "\t- R_EX_pnto__R_e\n", + "\t- R_EX_ppa_e\n", + "\t- R_EX_uacgam_e\n", + "\t- R_EX_udpacgal_e\n", + "\t- R_EX_udpg_e\n", + "\t- R_EX_udpgal_e\n", + "\t- R_EX_udpglcur_e\n", + "\t- R_EX_ppal_e\n", + "\t- R_EX_pppn_e\n", + "\t- R_EX_ppt_e\n", + "\t- R_EX_pro__L_e\n", + "\t- R_EX_ump_e\n", + "\t- R_EX_progly_e\n", + "\t- R_EX_ura_e\n", + "\t- R_EX_urea_e\n", + "\t- R_EX_psclys_e\n", + "\t- R_EX_pser__L_e\n", + "\t- R_EX_uri_e\n", + "\t- R_EX_val__L_e\n", + "\t- R_EX_ptrc_e\n", + "\t- R_EX_pydam_e\n", + "\t- R_EX_xan_e\n", + "\t- R_EX_pydx_e\n", + "\t- R_EX_xmp_e\n", + "\t- R_EX_pydxn_e\n", + "\t- R_EX_pyr_e\n", + "\t- R_EX_quin_e\n", + "\t- R_EX_xtsn_e\n", + "\t- R_EX_xyl__D_e\n", + "\t- R_EX_xylu__L_e\n", + "\t- R_EX_zn2_e\n", + "\t- R_EX_r5p_e\n", + "\t- R_EX_rfamp_e\n", + "\t- R_EX_rib__D_e\n", + "\t- R_EX_rmn_e\n", + "\t- R_EX_sbt__D_e\n", + "\t- R_EX_sel_e\n", + "\t- R_EX_ser__D_e\n", + "\t- R_EX_ser__L_e\n", + "\t- R_EX_skm_e\n", + "\t- R_DM_4hba_c\n", + "\t- R_DM_hmfurn_c\n", + "\t- R_EX_3hoxpac_e\n", + "\t- R_EX_3ntym_e\n", + "\t- R_EX_4hoxpac_e\n", + "\t- R_EX_6apa_e\n", + "\t- R_EX_cellb_e\n", + "\t- R_EX_galam_e\n", + "\t- R_EX_lipidA_core_e\n", + "\t- R_EX_o6a4colipa_e\n", + "\t- R_EX_peng_e\n", + "\t- R_EX_raffin_e\n", + "\t- R_EX_salcn_e\n", + "\t- R_EDTXSCOF\n", + "\t- R_EX_arab__D_e\u001b[0m\n", + "File saved at: ../../data/sbml_corrected/iEC1364_W.xml\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: STM_v1_0\n", + "\u001b[0;93mWARNING : \n", + " - R_EX_chitob_e: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_EX_pydxn_e: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_EX_pydx_e: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_FHL: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SPODM: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SPODMpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_CAT: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_OAO4t3pp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_OAO4t3ex: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_HISDr: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_AB6PGH: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SARCOX: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_URCN: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_CHITPH: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_CHITOBpts: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_OA4L_ST: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_OA4VL_ST: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\u001b[0m\n", + "\n", + "\n", + "\u001b[0;36m############################################\n", + "############################################\n", + " WRITING SBML FILE \n", + "############################################\n", + "############################################\n", + "\u001b[0m\n", + "\u001b[0;93mWARNING : IMPORT REACTION REMOVED BY DEFAULT\u001b[0m\n", + "\u001b[0;93mWARNING : If you want to keep import reaction\n", + "use option -kir / --keep-import-reactions\u001b[0m\n", + "\u001b[0;93mWARNING : Import reaction removed:\n", + "\t- R_DM_4hba_c\n", + "\t- R_DM_5drib_c\n", + "\t- R_DM_hmfurn_c\n", + "\t- R_DM_oxam_c\n", + "\t- R_EX_alaala_e\n", + "\t- R_EX_all__D_e\n", + "\t- R_EX_alltn_e\n", + "\t- R_EX_amp_e\n", + "\t- R_EX_anhgm_e\n", + "\t- R_EX_arab__L_e\n", + "\t- R_EX_arbtn_e\n", + "\t- R_EX_arbtn_fe3_e\n", + "\t- R_EX_arg__L_e\n", + "\t- R_EX_ascb__L_e\n", + "\t- R_EX_asn__L_e\n", + "\t- R_EX_12ppd__R_e\n", + "\t- R_EX_aso3_e\n", + "\t- R_EX_asp__L_e\n", + "\t- R_EX_12ppd__S_e\n", + "\t- R_EX_but_e\n", + "\t- R_EX_14glucan_e\n", + "\t- R_EX_15dap_e\n", + "\t- R_EX_butso3_e\n", + "\t- R_EX_ca2_e\n", + "\t- R_EX_cbi_e\n", + "\t- R_EX_cbl1_e\n", + "\t- R_EX_23camp_e\n", + "\t- R_EX_23ccmp_e\n", + "\t- R_EX_23cgmp_e\n", + "\t- R_EX_cd2_e\n", + "\t- R_EX_23cump_e\n", + "\t- R_EX_23dappa_e\n", + "\t- R_EX_26dap__M_e\n", + "\t- R_EX_cgly_e\n", + "\t- R_EX_chol_e\n", + "\t- R_EX_cit_e\n", + "\t- R_EX_2ddglcn_e\n", + "\t- R_EX_34dhpac_e\n", + "\t- R_EX_3amp_e\n", + "\t- R_EX_3cmp_e\n", + "\t- R_EX_3gmp_e\n", + "\t- R_EX_icit_e\n", + "\t- R_EX_acon_C_e\n", + "\t- R_EX_cl_e\n", + "\t- R_EX_cmp_e\n", + "\t- R_EX_co2_e\n", + "\t- R_EX_cobalt2_e\n", + "\t- R_EX_colipa_e\n", + "\t- R_EX_3hcinnm_e\n", + "\t- R_EX_3hpppn_e\n", + "\t- R_EX_cpgn_e\n", + "\t- R_EX_cpgn_un_e\n", + "\t- R_EX_crn_e\n", + "\t- R_EX_3ump_e\n", + "\t- R_EX_csn_e\n", + "\t- R_EX_cu2_e\n", + "\t- R_EX_cu_e\n", + "\t- R_EX_4abut_e\n", + "\t- R_EX_4hoxpacd_e\n", + "\t- R_EX_5dglcn_e\n", + "\t- R_EX_cyan_e\n", + "\t- R_EX_LalaDgluMdapDala_e\n", + "\t- R_EX_LalaDgluMdap_e\n", + "\t- R_EX_ac_e\n", + "\t- R_EX_cynt_e\n", + "\t- R_EX_cys__D_e\n", + "\t- R_EX_cys__L_e\n", + "\t- R_EX_acac_e\n", + "\t- R_EX_acald_e\n", + "\t- R_EX_acgal1p_e\n", + "\t- R_EX_acgal_e\n", + "\t- R_EX_cysi__L_e\n", + "\t- R_EX_cytd_e\n", + "\t- R_EX_dad_2_e\n", + "\t- R_EX_damp_e\n", + "\t- R_EX_dca_e\n", + "\t- R_EX_dcmp_e\n", + "\t- R_EX_acgam1p_e\n", + "\t- R_EX_acgam_e\n", + "\t- R_EX_acmana_e\n", + "\t- R_EX_acmum_e\n", + "\t- R_EX_acnam_e\n", + "\t- R_EX_acolipa_e\n", + "\t- R_EX_dcyt_e\n", + "\t- R_EX_ddca_e\n", + "\t- R_EX_dgmp_e\n", + "\t- R_EX_dgsn_e\n", + "\t- R_EX_acser_e\n", + "\t- R_EX_ade_e\n", + "\t- R_EX_adn_e\n", + "\t- R_EX_adocbl_e\n", + "\t- R_EX_dha_e\n", + "\t- R_EX_dimp_e\n", + "\t- R_EX_din_e\n", + "\t- R_EX_ag_e\n", + "\t- R_EX_agm_e\n", + "\t- R_EX_dms_e\n", + "\t- R_EX_dmso_e\n", + "\t- R_EX_akg_e\n", + "\t- R_EX_ala_B_e\n", + "\t- R_EX_ala__D_e\n", + "\t- R_EX_dopa_e\n", + "\t- R_EX_g1p_e\n", + "\t- R_EX_dtmp_e\n", + "\t- R_EX_dump_e\n", + "\t- R_EX_duri_e\n", + "\t- R_EX_eca4colipa_e\n", + "\t- R_EX_enlipa_e\n", + "\t- R_EX_enter_e\n", + "\t- R_EX_etha_e\n", + "\t- R_EX_ala__L_e\n", + "\t- R_EX_melib_e\n", + "\t- R_EX_met__D_e\n", + "\t- R_EX_met__L_e\n", + "\t- R_EX_metsox_R__L_e\n", + "\t- R_EX_metsox_S__L_e\n", + "\t- R_EX_mg2_e\n", + "\t- R_EX_ethso3_e\n", + "\t- R_EX_etoh_e\n", + "\t- R_EX_minohp_e\n", + "\t- R_EX_f6p_e\n", + "\t- R_EX_g3pc_e\n", + "\t- R_EX_fald_e\n", + "\t- R_EX_fe2_e\n", + "\t- R_EX_fe3_e\n", + "\t- R_EX_fe3dcit_e\n", + "\t- R_EX_mmet_e\n", + "\t- R_EX_mn2_e\n", + "\t- R_EX_mnl_e\n", + "\t- R_EX_fe3dhbzs_e\n", + "\t- R_EX_mobd_e\n", + "\t- R_EX_g3pe_e\n", + "\t- R_EX_mso3_e\n", + "\t- R_EX_fe3hox_e\n", + "\t- R_EX_fe3hox_un_e\n", + "\t- R_EX_fecrm_e\n", + "\t- R_EX_n2o_e\n", + "\t- R_EX_g3pg_e\n", + "\t- R_EX_na1_e\n", + "\t- R_EX_fecrm_un_e\n", + "\t- R_EX_feenter_e\n", + "\t- R_EX_nac_e\n", + "\t- R_EX_feoxam_e\n", + "\t- R_EX_g3pi_e\n", + "\t- R_EX_feoxam_un_e\n", + "\t- R_EX_nh4_e\n", + "\t- R_EX_ni2_e\n", + "\t- R_EX_for_e\n", + "\t- R_EX_fru_e\n", + "\t- R_EX_frulys_e\n", + "\t- R_EX_fruur_e\n", + "\t- R_EX_nmn_e\n", + "\t- R_EX_no2_e\n", + "\t- R_EX_no3_e\n", + "\t- R_EX_no_e\n", + "\t- R_EX_g3ps_e\n", + "\t- R_EX_o2_e\n", + "\t- R_EX_o2s_e\n", + "\t- R_EX_fuc__L_e\n", + "\t- R_EX_fum_e\n", + "\t- R_EX_ocdca_e\n", + "\t- R_EX_g6p_e\n", + "\t- R_EX_ocdcea_e\n", + "\t- R_EX_octa_e\n", + "\t- R_EX_gal1p_e\n", + "\t- R_EX_orn_e\n", + "\t- R_EX_orot_e\n", + "\t- R_EX_udpgal_e\n", + "\t- R_EX_udpglcur_e\n", + "\t- R_EX_gal_bD_e\n", + "\t- R_EX_ump_e\n", + "\t- R_EX_gal_e\n", + "\t- R_EX_ura_e\n", + "\t- R_EX_urea_e\n", + "\t- R_EX_oaa_e\n", + "\t- R_EX_pacald_e\n", + "\t- R_EX_peamn_e\n", + "\t- R_EX_galct__D_e\n", + "\t- R_EX_phe__L_e\n", + "\t- R_EX_pheme_e\n", + "\t- R_EX_uri_e\n", + "\t- R_EX_pi_e\n", + "\t- R_EX_galctn__D_e\n", + "\t- R_EX_val__L_e\n", + "\t- R_EX_xan_e\n", + "\t- R_EX_pnto__R_e\n", + "\t- R_EX_galctn__L_e\n", + "\t- R_EX_ppa_e\n", + "\t- R_EX_xmp_e\n", + "\t- R_EX_xtsn_e\n", + "\t- R_EX_xyl__D_e\n", + "\t- R_EX_xylu__L_e\n", + "\t- R_EX_zn2_e\n", + "\t- R_EX_ppal_e\n", + "\t- R_EX_pppn_e\n", + "\t- R_EX_ppt_e\n", + "\t- R_EX_pro__L_e\n", + "\t- R_EX_progly_e\n", + "\t- R_EX_galt_e\n", + "\t- R_EX_psclys_e\n", + "\t- R_EX_pser__L_e\n", + "\t- R_EX_ptrc_e\n", + "\t- R_EX_galur_e\n", + "\t- R_EX_pyr_e\n", + "\t- R_EX_r5p_e\n", + "\t- R_EX_gam6p_e\n", + "\t- R_EX_rib__D_e\n", + "\t- R_EX_rmn_e\n", + "\t- R_EX_sbt__D_e\n", + "\t- R_EX_gam_e\n", + "\t- R_EX_ser__D_e\n", + "\t- R_EX_ser__L_e\n", + "\t- R_EX_gbbtn_e\n", + "\t- R_EX_skm_e\n", + "\t- R_EX_so2_e\n", + "\t- R_EX_so3_e\n", + "\t- R_EX_so4_e\n", + "\t- R_EX_spmd_e\n", + "\t- R_EX_succ_e\n", + "\t- R_EX_sucr_e\n", + "\t- R_EX_gdp_e\n", + "\t- R_EX_sulfac_e\n", + "\t- R_EX_tartr__L_e\n", + "\t- R_EX_taur_e\n", + "\t- R_EX_tcynt_e\n", + "\t- R_EX_glc__D_e\n", + "\t- R_EX_thm_e\n", + "\t- R_EX_thr__L_e\n", + "\t- R_EX_glcn_e\n", + "\t- R_EX_thrp_e\n", + "\t- R_EX_thym_e\n", + "\t- R_EX_glcr_e\n", + "\t- R_EX_thymd_e\n", + "\t- R_EX_tma_e\n", + "\t- R_EX_tmao_e\n", + "\t- R_EX_tre_e\n", + "\t- R_EX_trp__L_e\n", + "\t- R_EX_tsul_e\n", + "\t- R_EX_ttdca_e\n", + "\t- R_EX_ttdcea_e\n", + "\t- R_EX_glcur1p_e\n", + "\t- R_EX_tungs_e\n", + "\t- R_EX_tym_e\n", + "\t- R_EX_tyr__L_e\n", + "\t- R_EX_glcur_e\n", + "\t- R_EX_tyrp_e\n", + "\t- R_EX_uacgam_e\n", + "\t- R_EX_udpacgal_e\n", + "\t- R_EX_gln__L_e\n", + "\t- R_EX_udpg_e\n", + "\t- R_EX_glu__L_e\n", + "\t- R_EX_gly_e\n", + "\t- R_EX_glyald_e\n", + "\t- R_EX_glyb_e\n", + "\t- R_EX_glyc2p_e\n", + "\t- R_EX_glyc3p_e\n", + "\t- R_EX_glyc__R_e\n", + "\t- R_EX_glyc_e\n", + "\t- R_EX_glyclt_e\n", + "\t- R_EX_gmp_e\n", + "\t- R_EX_gsn_e\n", + "\t- R_EX_gthox_e\n", + "\t- R_EX_gthrd_e\n", + "\t- R_EX_gtp_e\n", + "\t- R_EX_gua_e\n", + "\t- R_EX_h2_e\n", + "\t- R_EX_h2o2_e\n", + "\t- R_EX_h2o_e\n", + "\t- R_EX_h2s_e\n", + "\t- R_EX_h_e\n", + "\t- R_EX_hacolipa_e\n", + "\t- R_EX_halipa_e\n", + "\t- R_EX_hdca_e\n", + "\t- R_EX_hdcea_e\n", + "\t- R_EX_hg2_e\n", + "\t- R_EX_his__L_e\n", + "\t- R_EX_hom__L_e\n", + "\t- R_EX_hxa_e\n", + "\t- R_EX_hxan_e\n", + "\t- R_EX_idon__L_e\n", + "\t- R_EX_ile__L_e\n", + "\t- R_EX_imp_e\n", + "\t- R_EX_indole_e\n", + "\t- R_EX_inost_e\n", + "\t- R_EX_ins_e\n", + "\t- R_EX_isetac_e\n", + "\t- R_EX_k_e\n", + "\t- R_EX_kdo2lipid4_e\n", + "\t- R_EX_lac__D_e\n", + "\t- R_EX_lac__L_e\n", + "\t- R_EX_lcts_e\n", + "\t- R_EX_leu__L_e\n", + "\t- R_EX_lipa_cold_e\n", + "\t- R_EX_lipa_e\n", + "\t- R_EX_lys__L_e\n", + "\t- R_EX_lyx__L_e\n", + "\t- R_EX_mal__D_e\n", + "\t- R_EX_mal__L_e\n", + "\t- R_EX_malt_e\n", + "\t- R_EX_malthx_e\n", + "\t- R_EX_maltpt_e\n", + "\t- R_EX_malttr_e\n", + "\t- R_EX_maltttr_e\n", + "\t- R_EX_man6p_e\n", + "\t- R_EX_man_e\n", + "\t- R_EX_manglyc_e\n", + "\t- R_EX_airs_e\n", + "\t- R_EX_AEP_e\n", + "\t- R_EX_4hoxpac_e\n", + "\t- R_EX_remnant1_e\n", + "\t- R_EX_colipa20Oag_e\n", + "\t- R_EX_salchs4_e\n", + "\t- R_EX_salchs4fe_e\n", + "\t- R_EX_salchsx_e\n", + "\t- R_EX_pep_e\n", + "\t- R_EX_2pg_e\n", + "\t- R_EX_3pg_e\n", + "\t- R_EX_tet_e\n", + "\t- R_EX_frmd_e\n", + "\t- R_EX_tartr__D_e\n", + "\t- R_EX_tag__D_e\n", + "\t- R_EX_4abz_e\n", + "\t- R_EX_feroxB_e\n", + "\t- R_EX_feroxBfe_e\n", + "\t- R_EX_feroxE_e\n", + "\t- R_EX_feroxEfe_e\n", + "\t- R_EX_feroxG_e\n", + "\t- R_EX_feroxGfe_e\n", + "\t- R_EX_rnam_e\n", + "\t- R_EX_udcpo5_e\n", + "\t- R_EX_tcb_e\n", + "\t- R_EX_tton_e\n", + "\t- R_EX_colipaOA_e\n", + "\t- R_EX_udcpo4_e\n", + "\t- R_EX_4hthr_e\n", + "\t- R_EX_23dhbzs3_e\n", + "\t- R_EX_fe3dhbzs3_e\n", + "\t- R_EX_drib_e\n", + "\t- R_EX_salchs2_e\n", + "\t- R_EX_salchs2fe_e\n", + "\t- R_EX_guln__L_e\n", + "\t- R_EX_dxyl_e\u001b[0m\n", + "File saved at: ../../data/sbml_corrected/STM_v1_0.xml\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: iECDH10B_1368\n", + "\u001b[0;93mWARNING : \n", + " - R_CAT: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_DHPTDNR: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_DHPTDNRN: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_FHL: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SPODM: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SPODMpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCASPtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCFUMtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCMALtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCTARTtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_CHITPH: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_URCN: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_EX_chitob_e: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_CHITOBpts: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\u001b[0m\n", + "\n", + "\n", + "\u001b[0;36m############################################\n", + "############################################\n", + " WRITING SBML FILE \n", + "############################################\n", + "############################################\n", + "\u001b[0m\n", + "\u001b[0;93mWARNING : IMPORT REACTION REMOVED BY DEFAULT\u001b[0m\n", + "\u001b[0;93mWARNING : If you want to keep import reaction\n", + "use option -kir / --keep-import-reactions\u001b[0m\n", + "\u001b[0;93mWARNING : Import reaction removed:\n", + "\t- R_EX_arg__L_e\n", + "\t- R_EX_arbtn_fe3_e\n", + "\t- R_EX_arbtn_e\n", + "\t- R_EX_ascb__L_e\n", + "\t- R_EX_aso3_e\n", + "\t- R_EX_asn__L_e\n", + "\t- R_EX_asp__L_e\n", + "\t- R_EX_anhgm_e\n", + "\t- R_EX_btn_e\n", + "\t- R_EX_arab__L_e\n", + "\t- R_EX_arbt_e\n", + "\t- R_DM_4crsol_c\n", + "\t- R_DM_5drib_c\n", + "\t- R_DM_aacald_c\n", + "\t- R_EX_but_e\n", + "\t- R_DM_amob_c\n", + "\t- R_DM_mththf_c\n", + "\t- R_EX_gal_e\n", + "\t- R_EX_butso3_e\n", + "\t- R_EX_gal_bD_e\n", + "\t- R_DM_oxam_c\n", + "\t- R_EX_gal1p_e\n", + "\t- R_EX_ca2_e\n", + "\t- R_EX_12ppd__R_e\n", + "\t- R_EX_galct__D_e\n", + "\t- R_EX_galctn__D_e\n", + "\t- R_EX_galctn__L_e\n", + "\t- R_EX_12ppd__S_e\n", + "\t- R_EX_galt_e\n", + "\t- R_EX_cbi_e\n", + "\t- R_EX_galur_e\n", + "\t- R_EX_gam_e\n", + "\t- R_EX_gam6p_e\n", + "\t- R_EX_gbbtn_e\n", + "\t- R_EX_gdp_e\n", + "\t- R_EX_glc__D_e\n", + "\t- R_EX_glcn_e\n", + "\t- R_EX_glcr_e\n", + "\t- R_EX_glcur_e\n", + "\t- R_EX_14glucan_e\n", + "\t- R_EX_15dap_e\n", + "\t- R_EX_23camp_e\n", + "\t- R_EX_23ccmp_e\n", + "\t- R_EX_23cgmp_e\n", + "\t- R_EX_23cump_e\n", + "\t- R_EX_23dappa_e\n", + "\t- R_EX_26dap__M_e\n", + "\t- R_EX_2ddglcn_e\n", + "\t- R_EX_34dhpac_e\n", + "\t- R_EX_3amp_e\n", + "\t- R_EX_3cmp_e\n", + "\t- R_EX_3gmp_e\n", + "\t- R_EX_glcur1p_e\n", + "\t- R_EX_gln__L_e\n", + "\t- R_EX_glu__L_e\n", + "\t- R_EX_gly_e\n", + "\t- R_EX_3hcinnm_e\n", + "\t- R_EX_3hpp_e\n", + "\t- R_EX_cbl1_e\n", + "\t- R_EX_3hpppn_e\n", + "\t- R_EX_glyald_e\n", + "\t- R_EX_glyb_e\n", + "\t- R_EX_glyc_e\n", + "\t- R_EX_glyc__R_e\n", + "\t- R_EX_3ump_e\n", + "\t- R_EX_4abut_e\n", + "\t- R_EX_cd2_e\n", + "\t- R_EX_4hoxpacd_e\n", + "\t- R_EX_glyc2p_e\n", + "\t- R_EX_glyc3p_e\n", + "\t- R_EX_glyclt_e\n", + "\t- R_EX_gmp_e\n", + "\t- R_EX_5dglcn_e\n", + "\t- R_EX_5mtr_e\n", + "\t- R_EX_LalaDglu_e\n", + "\t- R_EX_LalaDgluMdap_e\n", + "\t- R_EX_LalaDgluMdapDala_e\n", + "\t- R_EX_gsn_e\n", + "\t- R_EX_gthox_e\n", + "\t- R_EX_cgly_e\n", + "\t- R_EX_gthrd_e\n", + "\t- R_EX_gtp_e\n", + "\t- R_EX_LalaLglu_e\n", + "\t- R_EX_ac_e\n", + "\t- R_EX_acac_e\n", + "\t- R_EX_gua_e\n", + "\t- R_EX_h_e\n", + "\t- R_EX_acald_e\n", + "\t- R_EX_chol_e\n", + "\t- R_EX_acgal_e\n", + "\t- R_EX_h2_e\n", + "\t- R_EX_h2o_e\n", + "\t- R_EX_h2o2_e\n", + "\t- R_EX_acgal1p_e\n", + "\t- R_EX_chtbs_e\n", + "\t- R_EX_acgam_e\n", + "\t- R_EX_h2s_e\n", + "\t- R_EX_hacolipa_e\n", + "\t- R_EX_acgam1p_e\n", + "\t- R_EX_halipa_e\n", + "\t- R_EX_acmana_e\n", + "\t- R_EX_cit_e\n", + "\t- R_EX_acmum_e\n", + "\t- R_EX_hdca_e\n", + "\t- R_EX_hdcea_e\n", + "\t- R_EX_acnam_e\n", + "\t- R_EX_acolipa_e\n", + "\t- R_EX_acser_e\n", + "\t- R_EX_hg2_e\n", + "\t- R_EX_his__L_e\n", + "\t- R_EX_hom__L_e\n", + "\t- R_EX_hxa_e\n", + "\t- R_EX_ade_e\n", + "\t- R_EX_adn_e\n", + "\t- R_EX_hxan_e\n", + "\t- R_EX_cl_e\n", + "\t- R_EX_idon__L_e\n", + "\t- R_EX_adocbl_e\n", + "\t- R_EX_ag_e\n", + "\t- R_EX_agm_e\n", + "\t- R_EX_ile__L_e\n", + "\t- R_EX_cm_e\n", + "\t- R_EX_imp_e\n", + "\t- R_EX_indole_e\n", + "\t- R_EX_akg_e\n", + "\t- R_EX_ala_B_e\n", + "\t- R_EX_inost_e\n", + "\t- R_EX_ala__D_e\n", + "\t- R_EX_cmp_e\n", + "\t- R_EX_ala__L_e\n", + "\t- R_EX_ins_e\n", + "\t- R_EX_isetac_e\n", + "\t- R_EX_alaala_e\n", + "\t- R_EX_co2_e\n", + "\t- R_EX_all__D_e\n", + "\t- R_EX_alltn_e\n", + "\t- R_EX_k_e\n", + "\t- R_EX_kdo2lipid4_e\n", + "\t- R_EX_cobalt2_e\n", + "\t- R_EX_lac__D_e\n", + "\t- R_EX_amp_e\n", + "\t- R_EX_colipa_e\n", + "\t- R_EX_lac__L_e\n", + "\t- R_EX_colipap_e\n", + "\t- R_EX_lcts_e\n", + "\t- R_EX_leu__L_e\n", + "\t- R_EX_lipa_e\n", + "\t- R_EX_lipa_cold_e\n", + "\t- R_EX_lipoate_e\n", + "\t- R_EX_mg2_e\n", + "\t- R_EX_mincyc_e\n", + "\t- R_EX_minohp_e\n", + "\t- R_EX_mmet_e\n", + "\t- R_EX_lys__L_e\n", + "\t- R_EX_cpgn_e\n", + "\t- R_EX_lyx__L_e\n", + "\t- R_EX_mn2_e\n", + "\t- R_EX_mnl_e\n", + "\t- R_EX_mobd_e\n", + "\t- R_EX_mal__D_e\n", + "\t- R_EX_mso3_e\n", + "\t- R_EX_cpgn_un_e\n", + "\t- R_EX_n2o_e\n", + "\t- R_EX_mal__L_e\n", + "\t- R_EX_malt_e\n", + "\t- R_EX_na1_e\n", + "\t- R_EX_crn_e\n", + "\t- R_EX_nac_e\n", + "\t- R_EX_malthx_e\n", + "\t- R_EX_maltpt_e\n", + "\t- R_EX_nh4_e\n", + "\t- R_EX_crn__D_e\n", + "\t- R_EX_ni2_e\n", + "\t- R_EX_malttr_e\n", + "\t- R_EX_maltttr_e\n", + "\t- R_EX_nmn_e\n", + "\t- R_EX_man_e\n", + "\t- R_EX_csn_e\n", + "\t- R_EX_man6p_e\n", + "\t- R_EX_manglyc_e\n", + "\t- R_EX_no_e\n", + "\t- R_EX_no2_e\n", + "\t- R_EX_no3_e\n", + "\t- R_EX_novbcn_e\n", + "\t- R_EX_o16a4colipa_e\n", + "\t- R_EX_o2_e\n", + "\t- R_EX_melib_e\n", + "\t- R_EX_meoh_e\n", + "\t- R_EX_met__D_e\n", + "\t- R_EX_met__L_e\n", + "\t- R_EX_o2s_e\n", + "\t- R_EX_cu_e\n", + "\t- R_EX_ocdca_e\n", + "\t- R_EX_ocdcea_e\n", + "\t- R_EX_octa_e\n", + "\t- R_EX_orn_e\n", + "\t- R_EX_metsox_R__L_e\n", + "\t- R_EX_metsox_S__L_e\n", + "\t- R_EX_cu2_e\n", + "\t- R_EX_tym_e\n", + "\t- R_EX_orot_e\n", + "\t- R_EX_pacald_e\n", + "\t- R_EX_tyr__L_e\n", + "\t- R_EX_cyan_e\n", + "\t- R_EX_tyrp_e\n", + "\t- R_EX_uacgam_e\n", + "\t- R_EX_peamn_e\n", + "\t- R_EX_phe__L_e\n", + "\t- R_EX_pheme_e\n", + "\t- R_EX_pi_e\n", + "\t- R_EX_udpacgal_e\n", + "\t- R_EX_udpg_e\n", + "\t- R_EX_pnto__R_e\n", + "\t- R_EX_cynt_e\n", + "\t- R_EX_ppa_e\n", + "\t- R_EX_udpgal_e\n", + "\t- R_EX_udpglcur_e\n", + "\t- R_EX_ump_e\n", + "\t- R_EX_ppal_e\n", + "\t- R_EX_ura_e\n", + "\t- R_EX_pppn_e\n", + "\t- R_EX_ppt_e\n", + "\t- R_EX_pro__L_e\n", + "\t- R_EX_progly_e\n", + "\t- R_EX_urea_e\n", + "\t- R_EX_uri_e\n", + "\t- R_EX_val__L_e\n", + "\t- R_EX_psclys_e\n", + "\t- R_EX_cys__D_e\n", + "\t- R_EX_pser__L_e\n", + "\t- R_EX_ptrc_e\n", + "\t- R_EX_pydam_e\n", + "\t- R_EX_xan_e\n", + "\t- R_EX_xmp_e\n", + "\t- R_EX_xtsn_e\n", + "\t- R_EX_xyl__D_e\n", + "\t- R_EX_pydx_e\n", + "\t- R_EX_cys__L_e\n", + "\t- R_EX_pydxn_e\n", + "\t- R_EX_pyr_e\n", + "\t- R_EX_xylu__L_e\n", + "\t- R_EX_zn2_e\n", + "\t- R_EX_quin_e\n", + "\t- R_EX_cytd_e\n", + "\t- R_EX_r5p_e\n", + "\t- R_EX_rfamp_e\n", + "\t- R_EX_rib__D_e\n", + "\t- R_EX_rmn_e\n", + "\t- R_EX_dad_2_e\n", + "\t- R_EX_sbt__D_e\n", + "\t- R_EX_sel_e\n", + "\t- R_EX_ser__D_e\n", + "\t- R_EX_ser__L_e\n", + "\t- R_EX_skm_e\n", + "\t- R_EX_slnt_e\n", + "\t- R_EX_so2_e\n", + "\t- R_EX_so3_e\n", + "\t- R_EX_so4_e\n", + "\t- R_EX_spmd_e\n", + "\t- R_EX_damp_e\n", + "\t- R_EX_succ_e\n", + "\t- R_EX_sucr_e\n", + "\t- R_EX_sulfac_e\n", + "\t- R_EX_tartr__D_e\n", + "\t- R_EX_dca_e\n", + "\t- R_EX_tartr__L_e\n", + "\t- R_EX_taur_e\n", + "\t- R_EX_tcynt_e\n", + "\t- R_EX_dcmp_e\n", + "\t- R_EX_thm_e\n", + "\t- R_EX_thr__L_e\n", + "\t- R_EX_thrp_e\n", + "\t- R_EX_thym_e\n", + "\t- R_EX_thymd_e\n", + "\t- R_EX_tma_e\n", + "\t- R_EX_tmao_e\n", + "\t- R_EX_dcyt_e\n", + "\t- R_EX_tre_e\n", + "\t- R_EX_trp__L_e\n", + "\t- R_EX_ddca_e\n", + "\t- R_EX_tsul_e\n", + "\t- R_EX_ttdca_e\n", + "\t- R_EX_ttdcea_e\n", + "\t- R_EX_dgmp_e\n", + "\t- R_EX_ttrcyc_e\n", + "\t- R_EX_tungs_e\n", + "\t- R_EX_dgsn_e\n", + "\t- R_EX_dha_e\n", + "\t- R_EX_dimp_e\n", + "\t- R_EX_din_e\n", + "\t- R_EX_dms_e\n", + "\t- R_EX_dmso_e\n", + "\t- R_EX_dopa_e\n", + "\t- R_EX_doxrbcn_e\n", + "\t- R_EX_dtmp_e\n", + "\t- R_EX_dump_e\n", + "\t- R_EX_duri_e\n", + "\t- R_EX_eca4colipa_e\n", + "\t- R_EX_enlipa_e\n", + "\t- R_EX_enter_e\n", + "\t- R_EX_etha_e\n", + "\t- R_EX_ethso3_e\n", + "\t- R_EX_etoh_e\n", + "\t- R_EX_f6p_e\n", + "\t- R_EX_fald_e\n", + "\t- R_EX_fe2_e\n", + "\t- R_EX_fe3_e\n", + "\t- R_EX_fe3dcit_e\n", + "\t- R_EX_fe3dhbzs_e\n", + "\t- R_EX_fe3hox_e\n", + "\t- R_EX_fe3hox_un_e\n", + "\t- R_EX_fecrm_e\n", + "\t- R_EX_fecrm_un_e\n", + "\t- R_EX_feenter_e\n", + "\t- R_EX_feoxam_e\n", + "\t- R_EX_feoxam_un_e\n", + "\t- R_EX_for_e\n", + "\t- R_EX_fru_e\n", + "\t- R_EX_frulys_e\n", + "\t- R_EX_fruur_e\n", + "\t- R_EX_fuc__L_e\n", + "\t- R_EX_fum_e\n", + "\t- R_EX_fusa_e\n", + "\t- R_EX_g1p_e\n", + "\t- R_EX_g3pc_e\n", + "\t- R_EX_g3pe_e\n", + "\t- R_EX_g3pg_e\n", + "\t- R_EX_g3pi_e\n", + "\t- R_EX_g3ps_e\n", + "\t- R_EX_g6p_e\n", + "\t- R_EX_3pg_e\n", + "\t- R_EX_salchs4_e\n", + "\t- R_EX_tag__D_e\n", + "\t- R_EX_oaa_e\n", + "\t- R_EX_salchs2fe_e\n", + "\t- R_EX_cysi__L_e\n", + "\t- R_EX_fe3dhbzs3_e\n", + "\t- R_EX_4hphac_e\n", + "\t- R_EX_4abz_e\n", + "\t- R_EX_drib_e\n", + "\t- R_EX_acon_C_e\n", + "\t- R_EX_feroxBfe_e\n", + "\t- R_EX_salchs4fe_e\n", + "\t- R_EX_2pg_e\n", + "\t- R_EX_feroxB_e\n", + "\t- R_EX_tcb_e\n", + "\t- R_EX_tton_e\n", + "\t- R_EX_frmd_e\n", + "\t- R_EX_colipaOA_e\n", + "\t- R_EX_salchsx_e\n", + "\t- R_EX_4hthr_e\n", + "\t- R_EX_feroxEfe_e\n", + "\t- R_EX_AEP_e\n", + "\t- R_EX_icit_e\n", + "\t- R_EX_feroxE_e\n", + "\t- R_EX_pep_e\n", + "\t- R_EX_colipa20Oag_e\n", + "\t- R_EX_remnant1_e\n", + "\t- R_EX_23dhbzs3_e\n", + "\t- R_EX_tet_e\n", + "\t- R_EX_salchs2_e\n", + "\t- R_EX_rnam_e\n", + "\t- R_EX_udcpo4_e\n", + "\t- R_EX_airs_e\n", + "\t- R_EX_dxyl_e\n", + "\t- R_EX_feroxGfe_e\n", + "\t- R_EX_udcpo5_e\n", + "\t- R_EX_guln__L_e\n", + "\t- R_EX_feroxG_e\n", + "\t- R_EX_3ntym_e\n", + "\t- R_EX_6apa_e\n", + "\t- R_EX_peng_e\n", + "\t- R_EX_pac_e\n", + "\t- R_EX_salcn_e\n", + "\t- R_EX_galam_e\n", + "\t- R_EX_diact_e\n", + "\t- R_EX_raffin_e\n", + "\t- R_EX_cellb_e\n", + "\t- R_EX_btd_RR_e\n", + "\t- R_EX_rbt_e\n", + "\t- R_EX_arab__D_e\n", + "\t- R_EX_bz_e\n", + "\t- R_EX_13ppd_e\n", + "\t- R_EX_abt__D_e\n", + "\t- R_EX_fe_e\n", + "\t- R_EX_kokdolipidA_e\n", + "\t- R_EX_3hoxpac_e\n", + "\t- R_EX_o6a4colipa_e\n", + "\t- R_EX_4hoxpac_e\n", + "\t- R_DM_lipidA_core_e_p\u001b[0m\n", + "File saved at: ../../data/sbml_corrected/iECDH10B_1368.xml\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: iEcolC_1368\n", + "\u001b[0;93mWARNING : \n", + " - R_CAT: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_FHL: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_DHPTDNR: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_DHPTDNRN: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCASPtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCFUMtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCMALtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCTARTtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SPODM: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SPODMpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_EX_chitob_e: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_CHITOBpts: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_CHITPH: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_URCN: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\u001b[0m\n", + "\n", + "\n", + "\u001b[0;36m############################################\n", + "############################################\n", + " WRITING SBML FILE \n", + "############################################\n", + "############################################\n", + "\u001b[0m\n", + "\u001b[0;93mWARNING : IMPORT REACTION REMOVED BY DEFAULT\u001b[0m\n", + "\u001b[0;93mWARNING : If you want to keep import reaction\n", + "use option -kir / --keep-import-reactions\u001b[0m\n", + "\u001b[0;93mWARNING : Import reaction removed:\n", + "\t- R_EX_LalaDgluMdapDala_e\n", + "\t- R_EX_LalaLglu_e\n", + "\t- R_EX_LalaDgluMdap_e\n", + "\t- R_EX_LalaDglu_e\n", + "\t- R_EX_3amp_e\n", + "\t- R_EX_ac_e\n", + "\t- R_EX_acac_e\n", + "\t- R_EX_acald_e\n", + "\t- R_EX_acgal_e\n", + "\t- R_DM_4crsol_c\n", + "\t- R_DM_5drib_c\n", + "\t- R_DM_aacald_c\n", + "\t- R_DM_amob_c\n", + "\t- R_EX_acgal1p_e\n", + "\t- R_DM_mththf_c\n", + "\t- R_EX_acgam_e\n", + "\t- R_EX_enter_e\n", + "\t- R_EX_etha_e\n", + "\t- R_EX_acgam1p_e\n", + "\t- R_EX_ethso3_e\n", + "\t- R_EX_acmana_e\n", + "\t- R_DM_oxam_c\n", + "\t- R_EX_acmum_e\n", + "\t- R_EX_acnam_e\n", + "\t- R_EX_etoh_e\n", + "\t- R_EX_f6p_e\n", + "\t- R_EX_fald_e\n", + "\t- R_EX_acolipa_e\n", + "\t- R_EX_acser_e\n", + "\t- R_EX_ade_e\n", + "\t- R_EX_fe2_e\n", + "\t- R_EX_fe3_e\n", + "\t- R_EX_fe3dcit_e\n", + "\t- R_EX_fe3dhbzs_e\n", + "\t- R_EX_fe3hox_e\n", + "\t- R_EX_fe3hox_un_e\n", + "\t- R_EX_fecrm_e\n", + "\t- R_EX_fecrm_un_e\n", + "\t- R_EX_feenter_e\n", + "\t- R_EX_feoxam_e\n", + "\t- R_EX_feoxam_un_e\n", + "\t- R_EX_adn_e\n", + "\t- R_EX_adocbl_e\n", + "\t- R_EX_ag_e\n", + "\t- R_EX_agm_e\n", + "\t- R_EX_akg_e\n", + "\t- R_EX_ala_B_e\n", + "\t- R_EX_ala__D_e\n", + "\t- R_EX_ala__L_e\n", + "\t- R_EX_alaala_e\n", + "\t- R_EX_all__D_e\n", + "\t- R_EX_alltn_e\n", + "\t- R_EX_amp_e\n", + "\t- R_EX_anhgm_e\n", + "\t- R_EX_for_e\n", + "\t- R_EX_fru_e\n", + "\t- R_EX_frulys_e\n", + "\t- R_EX_fruur_e\n", + "\t- R_EX_fuc__L_e\n", + "\t- R_EX_fum_e\n", + "\t- R_EX_arab__L_e\n", + "\t- R_EX_arbt_e\n", + "\t- R_EX_fusa_e\n", + "\t- R_EX_arbtn_e\n", + "\t- R_EX_arbtn_fe3_e\n", + "\t- R_EX_g1p_e\n", + "\t- R_EX_g3pc_e\n", + "\t- R_EX_g3pe_e\n", + "\t- R_EX_arg__L_e\n", + "\t- R_EX_ascb__L_e\n", + "\t- R_EX_asn__L_e\n", + "\t- R_EX_aso3_e\n", + "\t- R_EX_asp__L_e\n", + "\t- R_EX_g3pg_e\n", + "\t- R_EX_g3pi_e\n", + "\t- R_EX_g3ps_e\n", + "\t- R_EX_g6p_e\n", + "\t- R_EX_gal_e\n", + "\t- R_EX_btn_e\n", + "\t- R_EX_but_e\n", + "\t- R_EX_butso3_e\n", + "\t- R_EX_gal_bD_e\n", + "\t- R_EX_gal1p_e\n", + "\t- R_EX_ca2_e\n", + "\t- R_EX_12ppd__R_e\n", + "\t- R_EX_cbi_e\n", + "\t- R_EX_cbl1_e\n", + "\t- R_EX_cd2_e\n", + "\t- R_EX_cgly_e\n", + "\t- R_EX_galct__D_e\n", + "\t- R_EX_galctn__D_e\n", + "\t- R_EX_galctn__L_e\n", + "\t- R_EX_galt_e\n", + "\t- R_EX_galur_e\n", + "\t- R_EX_chol_e\n", + "\t- R_EX_chtbs_e\n", + "\t- R_EX_cit_e\n", + "\t- R_EX_gam_e\n", + "\t- R_EX_12ppd__S_e\n", + "\t- R_EX_gam6p_e\n", + "\t- R_EX_gbbtn_e\n", + "\t- R_EX_cl_e\n", + "\t- R_EX_cm_e\n", + "\t- R_EX_cmp_e\n", + "\t- R_EX_co2_e\n", + "\t- R_EX_gdp_e\n", + "\t- R_EX_glc__D_e\n", + "\t- R_EX_glcn_e\n", + "\t- R_EX_glcr_e\n", + "\t- R_EX_glcur_e\n", + "\t- R_EX_glcur1p_e\n", + "\t- R_EX_cobalt2_e\n", + "\t- R_EX_colipa_e\n", + "\t- R_EX_colipap_e\n", + "\t- R_EX_cpgn_e\n", + "\t- R_EX_gln__L_e\n", + "\t- R_EX_14glucan_e\n", + "\t- R_EX_glu__L_e\n", + "\t- R_EX_gly_e\n", + "\t- R_EX_cpgn_un_e\n", + "\t- R_EX_crn_e\n", + "\t- R_EX_glyald_e\n", + "\t- R_EX_crn__D_e\n", + "\t- R_EX_15dap_e\n", + "\t- R_EX_csn_e\n", + "\t- R_EX_cu_e\n", + "\t- R_EX_cu2_e\n", + "\t- R_EX_glyb_e\n", + "\t- R_EX_glyc_e\n", + "\t- R_EX_glyc__R_e\n", + "\t- R_EX_glyc2p_e\n", + "\t- R_EX_glyc3p_e\n", + "\t- R_EX_cyan_e\n", + "\t- R_EX_cynt_e\n", + "\t- R_EX_glyclt_e\n", + "\t- R_EX_cys__D_e\n", + "\t- R_EX_gmp_e\n", + "\t- R_EX_cys__L_e\n", + "\t- R_EX_gsn_e\n", + "\t- R_EX_23camp_e\n", + "\t- R_EX_gthox_e\n", + "\t- R_EX_cytd_e\n", + "\t- R_EX_gthrd_e\n", + "\t- R_EX_dad_2_e\n", + "\t- R_EX_23ccmp_e\n", + "\t- R_EX_damp_e\n", + "\t- R_EX_dca_e\n", + "\t- R_EX_dcmp_e\n", + "\t- R_EX_dcyt_e\n", + "\t- R_EX_ddca_e\n", + "\t- R_EX_gtp_e\n", + "\t- R_EX_gua_e\n", + "\t- R_EX_h_e\n", + "\t- R_EX_h2_e\n", + "\t- R_EX_h2o_e\n", + "\t- R_EX_h2o2_e\n", + "\t- R_EX_h2s_e\n", + "\t- R_EX_hacolipa_e\n", + "\t- R_EX_halipa_e\n", + "\t- R_EX_dgmp_e\n", + "\t- R_EX_dgsn_e\n", + "\t- R_EX_dha_e\n", + "\t- R_EX_dimp_e\n", + "\t- R_EX_din_e\n", + "\t- R_EX_hdca_e\n", + "\t- R_EX_hdcea_e\n", + "\t- R_EX_23cgmp_e\n", + "\t- R_EX_hg2_e\n", + "\t- R_EX_his__L_e\n", + "\t- R_EX_dms_e\n", + "\t- R_EX_dmso_e\n", + "\t- R_EX_hom__L_e\n", + "\t- R_EX_dopa_e\n", + "\t- R_EX_hxa_e\n", + "\t- R_EX_doxrbcn_e\n", + "\t- R_EX_23cump_e\n", + "\t- R_EX_dtmp_e\n", + "\t- R_EX_dump_e\n", + "\t- R_EX_hxan_e\n", + "\t- R_EX_idon__L_e\n", + "\t- R_EX_ile__L_e\n", + "\t- R_EX_duri_e\n", + "\t- R_EX_23dappa_e\n", + "\t- R_EX_eca4colipa_e\n", + "\t- R_EX_enlipa_e\n", + "\t- R_EX_imp_e\n", + "\t- R_EX_indole_e\n", + "\t- R_EX_inost_e\n", + "\t- R_EX_sbt__D_e\n", + "\t- R_EX_26dap__M_e\n", + "\t- R_EX_sel_e\n", + "\t- R_EX_ins_e\n", + "\t- R_EX_isetac_e\n", + "\t- R_EX_k_e\n", + "\t- R_EX_ser__D_e\n", + "\t- R_EX_ser__L_e\n", + "\t- R_EX_kdo2lipid4_e\n", + "\t- R_EX_2ddglcn_e\n", + "\t- R_EX_lac__D_e\n", + "\t- R_EX_lac__L_e\n", + "\t- R_EX_skm_e\n", + "\t- R_EX_slnt_e\n", + "\t- R_EX_34dhpac_e\n", + "\t- R_EX_so2_e\n", + "\t- R_EX_so3_e\n", + "\t- R_EX_so4_e\n", + "\t- R_EX_lcts_e\n", + "\t- R_EX_leu__L_e\n", + "\t- R_EX_lipa_e\n", + "\t- R_EX_lipa_cold_e\n", + "\t- R_EX_lipoate_e\n", + "\t- R_EX_spmd_e\n", + "\t- R_EX_succ_e\n", + "\t- R_EX_3cmp_e\n", + "\t- R_EX_sucr_e\n", + "\t- R_EX_sulfac_e\n", + "\t- R_EX_lys__L_e\n", + "\t- R_EX_lyx__L_e\n", + "\t- R_EX_mal__D_e\n", + "\t- R_EX_tartr__D_e\n", + "\t- R_EX_tartr__L_e\n", + "\t- R_EX_taur_e\n", + "\t- R_EX_tcynt_e\n", + "\t- R_EX_mal__L_e\n", + "\t- R_EX_malt_e\n", + "\t- R_EX_malthx_e\n", + "\t- R_EX_maltpt_e\n", + "\t- R_EX_malttr_e\n", + "\t- R_EX_maltttr_e\n", + "\t- R_EX_thm_e\n", + "\t- R_EX_thr__L_e\n", + "\t- R_EX_man_e\n", + "\t- R_EX_thrp_e\n", + "\t- R_EX_3gmp_e\n", + "\t- R_EX_thym_e\n", + "\t- R_EX_thymd_e\n", + "\t- R_EX_tma_e\n", + "\t- R_EX_tmao_e\n", + "\t- R_EX_man6p_e\n", + "\t- R_EX_manglyc_e\n", + "\t- R_EX_melib_e\n", + "\t- R_EX_meoh_e\n", + "\t- R_EX_met__D_e\n", + "\t- R_EX_tre_e\n", + "\t- R_EX_trp__L_e\n", + "\t- R_EX_met__L_e\n", + "\t- R_EX_tsul_e\n", + "\t- R_EX_metsox_R__L_e\n", + "\t- R_EX_ttdca_e\n", + "\t- R_EX_3hcinnm_e\n", + "\t- R_EX_ttdcea_e\n", + "\t- R_EX_ttrcyc_e\n", + "\t- R_EX_metsox_S__L_e\n", + "\t- R_EX_mg2_e\n", + "\t- R_EX_mincyc_e\n", + "\t- R_EX_minohp_e\n", + "\t- R_EX_tungs_e\n", + "\t- R_EX_3hpp_e\n", + "\t- R_EX_tym_e\n", + "\t- R_EX_tyr__L_e\n", + "\t- R_EX_tyrp_e\n", + "\t- R_EX_uacgam_e\n", + "\t- R_EX_udpacgal_e\n", + "\t- R_EX_udpg_e\n", + "\t- R_EX_udpgal_e\n", + "\t- R_EX_udpglcur_e\n", + "\t- R_EX_mmet_e\n", + "\t- R_EX_mn2_e\n", + "\t- R_EX_mnl_e\n", + "\t- R_EX_mobd_e\n", + "\t- R_EX_mso3_e\n", + "\t- R_EX_n2o_e\n", + "\t- R_EX_na1_e\n", + "\t- R_EX_nac_e\n", + "\t- R_EX_nh4_e\n", + "\t- R_EX_ni2_e\n", + "\t- R_EX_nmn_e\n", + "\t- R_EX_no_e\n", + "\t- R_EX_ump_e\n", + "\t- R_EX_ura_e\n", + "\t- R_EX_urea_e\n", + "\t- R_EX_uri_e\n", + "\t- R_EX_val__L_e\n", + "\t- R_EX_xan_e\n", + "\t- R_EX_no2_e\n", + "\t- R_EX_no3_e\n", + "\t- R_EX_xmp_e\n", + "\t- R_EX_3hpppn_e\n", + "\t- R_EX_xtsn_e\n", + "\t- R_EX_xyl__D_e\n", + "\t- R_EX_novbcn_e\n", + "\t- R_EX_o16a4colipa_e\n", + "\t- R_EX_o2_e\n", + "\t- R_EX_o2s_e\n", + "\t- R_EX_ocdca_e\n", + "\t- R_EX_xylu__L_e\n", + "\t- R_EX_zn2_e\n", + "\t- R_EX_ocdcea_e\n", + "\t- R_EX_octa_e\n", + "\t- R_EX_orn_e\n", + "\t- R_EX_orot_e\n", + "\t- R_EX_pacald_e\n", + "\t- R_EX_peamn_e\n", + "\t- R_EX_phe__L_e\n", + "\t- R_EX_pheme_e\n", + "\t- R_EX_3ump_e\n", + "\t- R_EX_pi_e\n", + "\t- R_EX_pnto__R_e\n", + "\t- R_EX_ppa_e\n", + "\t- R_EX_ppal_e\n", + "\t- R_EX_pppn_e\n", + "\t- R_EX_ppt_e\n", + "\t- R_EX_4abut_e\n", + "\t- R_EX_pro__L_e\n", + "\t- R_EX_progly_e\n", + "\t- R_EX_psclys_e\n", + "\t- R_EX_pser__L_e\n", + "\t- R_EX_ptrc_e\n", + "\t- R_EX_pydam_e\n", + "\t- R_EX_pydx_e\n", + "\t- R_EX_4hoxpacd_e\n", + "\t- R_EX_pydxn_e\n", + "\t- R_EX_pyr_e\n", + "\t- R_EX_quin_e\n", + "\t- R_EX_r5p_e\n", + "\t- R_EX_rfamp_e\n", + "\t- R_EX_5dglcn_e\n", + "\t- R_EX_rib__D_e\n", + "\t- R_EX_rmn_e\n", + "\t- R_EX_5mtr_e\n", + "\t- R_EX_rbt_e\n", + "\t- R_EX_acon_C_e\n", + "\t- R_EX_icit_e\n", + "\t- R_EX_feroxBfe_e\n", + "\t- R_EX_pep_e\n", + "\t- R_EX_4hphac_e\n", + "\t- R_EX_feroxB_e\n", + "\t- R_EX_tcb_e\n", + "\t- R_EX_tton_e\n", + "\t- R_EX_4hthr_e\n", + "\t- R_EX_4abz_e\n", + "\t- R_EX_feroxEfe_e\n", + "\t- R_EX_colipa20Oag_e\n", + "\t- R_EX_feroxE_e\n", + "\t- R_EX_23dhbzs3_e\n", + "\t- R_EX_tet_e\n", + "\t- R_EX_salchs4_e\n", + "\t- R_EX_remnant1_e\n", + "\t- R_EX_udcpo5_e\n", + "\t- R_EX_oaa_e\n", + "\t- R_EX_salchs2fe_e\n", + "\t- R_EX_bz_e\n", + "\t- R_EX_13ppd_e\n", + "\t- R_EX_abt__D_e\n", + "\t- R_EX_cysi__L_e\n", + "\t- R_EX_fe3dhbzs3_e\n", + "\t- R_EX_drib_e\n", + "\t- R_EX_salchs4fe_e\n", + "\t- R_EX_2pg_e\n", + "\t- R_EX_fe_e\n", + "\t- R_EX_kokdolipidA_e\n", + "\t- R_EX_salchs2_e\n", + "\t- R_EX_frmd_e\n", + "\t- R_EX_colipaOA_e\n", + "\t- R_EX_salchsx_e\n", + "\t- R_EX_3hoxpac_e\n", + "\t- R_EX_AEP_e\n", + "\t- R_EX_o6a4colipa_e\n", + "\t- R_EX_arab__D_e\n", + "\t- R_EX_rnam_e\n", + "\t- R_EX_3ntym_e\n", + "\t- R_EX_6apa_e\n", + "\t- R_EX_4hoxpac_e\n", + "\t- R_EX_peng_e\n", + "\t- R_EX_airs_e\n", + "\t- R_EX_pac_e\n", + "\t- R_EX_dxyl_e\n", + "\t- R_EX_salcn_e\n", + "\t- R_EX_3pg_e\n", + "\t- R_EX_feroxGfe_e\n", + "\t- R_DM_lipidA_core_e_p\n", + "\t- R_EX_galam_e\n", + "\t- R_EX_raffin_e\n", + "\t- R_EX_udcpo4_e\n", + "\t- R_EX_tag__D_e\n", + "\t- R_EX_guln__L_e\n", + "\t- R_EX_feroxG_e\n", + "\t- R_EX_diact_e\n", + "\t- R_EX_cellb_e\n", + "\t- R_EX_btd_RR_e\u001b[0m\n", + "File saved at: ../../data/sbml_corrected/iEcolC_1368.xml\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: iEC1349_Crooks\n", + "\u001b[0;93mWARNING : \n", + " - R_CAT: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_DHPTDNR: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_DHPTDNRN: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_FHL: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCASPtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCTARTtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCMALtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCFUMtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SPODM: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SPODMpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + " - R_EDTXSCOF is multiple (more than 1) metabolites import/export reaction. \n", + "\tDeleted as it is an import reaction.\n", + " - R_DURAD: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_GAL6PI: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_PPND2: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_INDMT: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + "\n", + " - R_DMSOR3: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + " - R_DURAD2: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_FDH6r: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + "\u001b[0m\n", + "\n", + "\n", + "\u001b[0;36m############################################\n", + "############################################\n", + " WRITING SBML FILE \n", + "############################################\n", + "############################################\n", + "\u001b[0m\n", + "\u001b[0;93mWARNING : IMPORT REACTION REMOVED BY DEFAULT\u001b[0m\n", + "\u001b[0;93mWARNING : If you want to keep import reaction\n", + "use option -kir / --keep-import-reactions\u001b[0m\n", + "\u001b[0;93mWARNING : Reaction with tag reversible modified:\n", + "\t- R_DURAD\n", + "\t- R_GAL6PI\n", + "\t- R_PPND2\n", + "\t- R_INDMT\n", + "\t- R_DURAD2\n", + "\t- R_FDH6r\u001b[0m\n", + "\u001b[0;93mWARNING : Reaction with Reactants and Products exchanged:\n", + "\t- R_DURAD\n", + "\t- R_GAL6PI\n", + "\t- R_PPND2\n", + "\t- R_INDMT\n", + "\t- R_DURAD2\n", + "\t- R_FDH6r\u001b[0m\n", + "\u001b[0;93mWARNING : Import reaction removed:\n", + "\t- R_EX_mmet_e\n", + "\t- R_EX_mn2_e\n", + "\t- R_EX_mnl_e\n", + "\t- R_EX_mobd_e\n", + "\t- R_EX_mso3_e\n", + "\t- R_EX_n2o_e\n", + "\t- R_EX_na1_e\n", + "\t- R_EX_nac_e\n", + "\t- R_EX_nh4_e\n", + "\t- R_EX_ni2_e\n", + "\t- R_EX_nmn_e\n", + "\t- R_EX_no_e\n", + "\t- R_EX_no2_e\n", + "\t- R_EX_no3_e\n", + "\t- R_EX_doxrbcn_e\n", + "\t- R_EX_ac_e\n", + "\t- R_EX_acac_e\n", + "\t- R_EX_acald_e\n", + "\t- R_EX_acgal_e\n", + "\t- R_EX_dtmp_e\n", + "\t- R_EX_dump_e\n", + "\t- R_EX_acgal1p_e\n", + "\t- R_DM_4crsol_c\n", + "\t- R_EX_duri_e\n", + "\t- R_EX_acgam_e\n", + "\t- R_EX_acgam1p_e\n", + "\t- R_EX_eca4colipa_e\n", + "\t- R_EX_acmana_e\n", + "\t- R_EX_acmum_e\n", + "\t- R_EX_enlipa_e\n", + "\t- R_EX_acnam_e\n", + "\t- R_EX_acolipa_e\n", + "\t- R_EX_enter_e\n", + "\t- R_EX_acser_e\n", + "\t- R_EX_etha_e\n", + "\t- R_EX_ade_e\n", + "\t- R_EX_adn_e\n", + "\t- R_EX_adocbl_e\n", + "\t- R_EX_ag_e\n", + "\t- R_EX_agm_e\n", + "\t- R_EX_akg_e\n", + "\t- R_EX_ala_B_e\n", + "\t- R_EX_ala__D_e\n", + "\t- R_EX_ala__L_e\n", + "\t- R_EX_alaala_e\n", + "\t- R_DM_5drib_c\n", + "\t- R_DM_aacald_c\n", + "\t- R_DM_amob_c\n", + "\t- R_DM_mththf_c\n", + "\t- R_DM_oxam_c\n", + "\t- R_EX_12ppd__R_e\n", + "\t- R_EX_all__D_e\n", + "\t- R_EX_ethso3_e\n", + "\t- R_EX_alltn_e\n", + "\t- R_EX_12ppd__S_e\n", + "\t- R_EX_14glucan_e\n", + "\t- R_EX_amp_e\n", + "\t- R_EX_15dap_e\n", + "\t- R_EX_etoh_e\n", + "\t- R_EX_23camp_e\n", + "\t- R_EX_anhgm_e\n", + "\t- R_EX_arab__L_e\n", + "\t- R_EX_f6p_e\n", + "\t- R_EX_arbt_e\n", + "\t- R_EX_arbtn_e\n", + "\t- R_EX_23ccmp_e\n", + "\t- R_EX_23cgmp_e\n", + "\t- R_EX_23cump_e\n", + "\t- R_EX_23dappa_e\n", + "\t- R_EX_26dap__M_e\n", + "\t- R_EX_2ddglcn_e\n", + "\t- R_EX_34dhpac_e\n", + "\t- R_EX_3amp_e\n", + "\t- R_EX_arbtn_fe3_e\n", + "\t- R_EX_arg__L_e\n", + "\t- R_EX_ascb__L_e\n", + "\t- R_EX_asn__L_e\n", + "\t- R_EX_aso3_e\n", + "\t- R_EX_asp__L_e\n", + "\t- R_EX_btn_e\n", + "\t- R_EX_3cmp_e\n", + "\t- R_EX_3gmp_e\n", + "\t- R_EX_but_e\n", + "\t- R_EX_3hcinnm_e\n", + "\t- R_EX_butso3_e\n", + "\t- R_EX_fald_e\n", + "\t- R_EX_ca2_e\n", + "\t- R_EX_cbi_e\n", + "\t- R_EX_3hpp_e\n", + "\t- R_EX_3hpppn_e\n", + "\t- R_EX_3ump_e\n", + "\t- R_EX_cbl1_e\n", + "\t- R_EX_cd2_e\n", + "\t- R_EX_4abut_e\n", + "\t- R_EX_cgly_e\n", + "\t- R_EX_fe2_e\n", + "\t- R_EX_chol_e\n", + "\t- R_EX_4hoxpacd_e\n", + "\t- R_EX_5dglcn_e\n", + "\t- R_EX_chtbs_e\n", + "\t- R_EX_fe3_e\n", + "\t- R_EX_cit_e\n", + "\t- R_EX_cl_e\n", + "\t- R_EX_5mtr_e\n", + "\t- R_EX_LalaDglu_e\n", + "\t- R_EX_LalaDgluMdap_e\n", + "\t- R_EX_LalaDgluMdapDala_e\n", + "\t- R_EX_LalaLglu_e\n", + "\t- R_EX_cm_e\n", + "\t- R_EX_cmp_e\n", + "\t- R_EX_co2_e\n", + "\t- R_EX_cobalt2_e\n", + "\t- R_EX_fe3dcit_e\n", + "\t- R_EX_colipa_e\n", + "\t- R_EX_colipap_e\n", + "\t- R_EX_fe3dhbzs_e\n", + "\t- R_EX_cpgn_e\n", + "\t- R_EX_cpgn_un_e\n", + "\t- R_EX_crn_e\n", + "\t- R_EX_hg2_e\n", + "\t- R_EX_his__L_e\n", + "\t- R_EX_crn__D_e\n", + "\t- R_EX_fe3hox_e\n", + "\t- R_EX_csn_e\n", + "\t- R_EX_cu_e\n", + "\t- R_EX_hom__L_e\n", + "\t- R_EX_hxa_e\n", + "\t- R_EX_hxan_e\n", + "\t- R_EX_cu2_e\n", + "\t- R_EX_idon__L_e\n", + "\t- R_EX_fe3hox_un_e\n", + "\t- R_EX_ile__L_e\n", + "\t- R_EX_cyan_e\n", + "\t- R_EX_cynt_e\n", + "\t- R_EX_imp_e\n", + "\t- R_EX_fecrm_e\n", + "\t- R_EX_indole_e\n", + "\t- R_EX_inost_e\n", + "\t- R_EX_ins_e\n", + "\t- R_EX_cys__D_e\n", + "\t- R_EX_cys__L_e\n", + "\t- R_EX_cytd_e\n", + "\t- R_EX_dad_2_e\n", + "\t- R_EX_damp_e\n", + "\t- R_EX_dca_e\n", + "\t- R_EX_dcmp_e\n", + "\t- R_EX_isetac_e\n", + "\t- R_EX_k_e\n", + "\t- R_EX_kdo2lipid4_e\n", + "\t- R_EX_lac__D_e\n", + "\t- R_EX_lac__L_e\n", + "\t- R_EX_dcyt_e\n", + "\t- R_EX_ddca_e\n", + "\t- R_EX_dgmp_e\n", + "\t- R_EX_lcts_e\n", + "\t- R_EX_dgsn_e\n", + "\t- R_EX_leu__L_e\n", + "\t- R_EX_fecrm_un_e\n", + "\t- R_EX_lipa_e\n", + "\t- R_EX_lipa_cold_e\n", + "\t- R_EX_dha_e\n", + "\t- R_EX_dimp_e\n", + "\t- R_EX_din_e\n", + "\t- R_EX_lipoate_e\n", + "\t- R_EX_feenter_e\n", + "\t- R_EX_lys__L_e\n", + "\t- R_EX_dms_e\n", + "\t- R_EX_feoxam_e\n", + "\t- R_EX_dmso_e\n", + "\t- R_EX_feoxam_un_e\n", + "\t- R_EX_dopa_e\n", + "\t- R_EX_for_e\n", + "\t- R_EX_lyx__L_e\n", + "\t- R_EX_fru_e\n", + "\t- R_EX_mal__D_e\n", + "\t- R_EX_frulys_e\n", + "\t- R_EX_mal__L_e\n", + "\t- R_EX_fruur_e\n", + "\t- R_EX_malt_e\n", + "\t- R_EX_malthx_e\n", + "\t- R_EX_maltpt_e\n", + "\t- R_EX_malttr_e\n", + "\t- R_EX_maltttr_e\n", + "\t- R_EX_fuc__L_e\n", + "\t- R_EX_man_e\n", + "\t- R_EX_man6p_e\n", + "\t- R_EX_manglyc_e\n", + "\t- R_EX_melib_e\n", + "\t- R_EX_novbcn_e\n", + "\t- R_EX_o16a4colipa_e\n", + "\t- R_EX_o2_e\n", + "\t- R_EX_o2s_e\n", + "\t- R_EX_ocdca_e\n", + "\t- R_EX_meoh_e\n", + "\t- R_EX_met__D_e\n", + "\t- R_EX_ocdcea_e\n", + "\t- R_EX_fum_e\n", + "\t- R_EX_octa_e\n", + "\t- R_EX_orn_e\n", + "\t- R_EX_met__L_e\n", + "\t- R_EX_metsox_R__L_e\n", + "\t- R_EX_metsox_S__L_e\n", + "\t- R_EX_orot_e\n", + "\t- R_EX_fusa_e\n", + "\t- R_EX_pacald_e\n", + "\t- R_EX_peamn_e\n", + "\t- R_EX_phe__L_e\n", + "\t- R_EX_mg2_e\n", + "\t- R_EX_mincyc_e\n", + "\t- R_EX_minohp_e\n", + "\t- R_EX_pheme_e\n", + "\t- R_EX_g1p_e\n", + "\t- R_EX_pi_e\n", + "\t- R_EX_pnto__R_e\n", + "\t- R_EX_g3pc_e\n", + "\t- R_EX_ppa_e\n", + "\t- R_EX_ppal_e\n", + "\t- R_EX_pppn_e\n", + "\t- R_EX_ppt_e\n", + "\t- R_EX_pro__L_e\n", + "\t- R_EX_progly_e\n", + "\t- R_EX_xylu__L_e\n", + "\t- R_EX_zn2_e\n", + "\t- R_EX_psclys_e\n", + "\t- R_EX_pser__L_e\n", + "\t- R_EX_g3pe_e\n", + "\t- R_EX_ptrc_e\n", + "\t- R_EX_pydam_e\n", + "\t- R_EX_pydx_e\n", + "\t- R_EX_pydxn_e\n", + "\t- R_EX_g3pg_e\n", + "\t- R_EX_pyr_e\n", + "\t- R_EX_quin_e\n", + "\t- R_EX_r5p_e\n", + "\t- R_EX_g3pi_e\n", + "\t- R_EX_rfamp_e\n", + "\t- R_EX_rib__D_e\n", + "\t- R_EX_rmn_e\n", + "\t- R_EX_sbt__D_e\n", + "\t- R_EX_sel_e\n", + "\t- R_EX_ser__D_e\n", + "\t- R_EX_ser__L_e\n", + "\t- R_EX_skm_e\n", + "\t- R_EX_g3ps_e\n", + "\t- R_EX_slnt_e\n", + "\t- R_EX_so2_e\n", + "\t- R_EX_so3_e\n", + "\t- R_EX_g6p_e\n", + "\t- R_EX_so4_e\n", + "\t- R_EX_spmd_e\n", + "\t- R_EX_gal_e\n", + "\t- R_EX_succ_e\n", + "\t- R_EX_sucr_e\n", + "\t- R_EX_gal_bD_e\n", + "\t- R_EX_sulfac_e\n", + "\t- R_EX_tartr__D_e\n", + "\t- R_EX_tartr__L_e\n", + "\t- R_EX_taur_e\n", + "\t- R_EX_tcynt_e\n", + "\t- R_EX_thm_e\n", + "\t- R_EX_thr__L_e\n", + "\t- R_EX_thrp_e\n", + "\t- R_EX_thym_e\n", + "\t- R_EX_gal1p_e\n", + "\t- R_EX_thymd_e\n", + "\t- R_EX_tma_e\n", + "\t- R_EX_tmao_e\n", + "\t- R_EX_galct__D_e\n", + "\t- R_EX_tre_e\n", + "\t- R_EX_trp__L_e\n", + "\t- R_EX_tsul_e\n", + "\t- R_EX_ttdca_e\n", + "\t- R_EX_galctn__D_e\n", + "\t- R_EX_ttdcea_e\n", + "\t- R_EX_ttrcyc_e\n", + "\t- R_EX_galctn__L_e\n", + "\t- R_EX_tungs_e\n", + "\t- R_EX_tym_e\n", + "\t- R_EX_tyr__L_e\n", + "\t- R_EX_tyrp_e\n", + "\t- R_EX_uacgam_e\n", + "\t- R_EX_udpacgal_e\n", + "\t- R_EX_udpg_e\n", + "\t- R_EX_galt_e\n", + "\t- R_EX_udpgal_e\n", + "\t- R_EX_udpglcur_e\n", + "\t- R_EX_ump_e\n", + "\t- R_EX_ura_e\n", + "\t- R_EX_urea_e\n", + "\t- R_EX_uri_e\n", + "\t- R_EX_galur_e\n", + "\t- R_EX_val__L_e\n", + "\t- R_EX_xan_e\n", + "\t- R_EX_gam_e\n", + "\t- R_EX_xmp_e\n", + "\t- R_EX_xtsn_e\n", + "\t- R_EX_gam6p_e\n", + "\t- R_EX_xyl__D_e\n", + "\t- R_EX_gbbtn_e\n", + "\t- R_EX_gdp_e\n", + "\t- R_EX_glc__D_e\n", + "\t- R_EX_glcn_e\n", + "\t- R_EX_glcr_e\n", + "\t- R_EX_glcur_e\n", + "\t- R_EX_glcur1p_e\n", + "\t- R_EX_gln__L_e\n", + "\t- R_EX_glu__L_e\n", + "\t- R_EX_gly_e\n", + "\t- R_EX_glyald_e\n", + "\t- R_EX_glyb_e\n", + "\t- R_EX_glyc_e\n", + "\t- R_EX_glyc__R_e\n", + "\t- R_EX_glyc2p_e\n", + "\t- R_EX_glyc3p_e\n", + "\t- R_EX_glyclt_e\n", + "\t- R_EX_gmp_e\n", + "\t- R_EX_gsn_e\n", + "\t- R_EX_gthox_e\n", + "\t- R_EX_gthrd_e\n", + "\t- R_EX_gtp_e\n", + "\t- R_EX_gua_e\n", + "\t- R_EX_h_e\n", + "\t- R_EX_h2_e\n", + "\t- R_EX_h2o_e\n", + "\t- R_EX_h2o2_e\n", + "\t- R_EX_h2s_e\n", + "\t- R_EX_hacolipa_e\n", + "\t- R_EX_halipa_e\n", + "\t- R_EX_hdca_e\n", + "\t- R_EX_hdcea_e\n", + "\t- R_EX_3hoxpac_e\n", + "\t- R_EX_3ntym_e\n", + "\t- R_EX_4hoxpac_e\n", + "\t- R_EX_6apa_e\n", + "\t- R_EDTXSCOF\n", + "\t- R_EX_cellb_e\n", + "\t- R_EX_galam_e\n", + "\t- R_EX_lipidA_core_e\n", + "\t- R_EX_arab__D_e\n", + "\t- R_EX_o6a4colipa_e\n", + "\t- R_EX_peng_e\n", + "\t- R_EX_raffin_e\n", + "\t- R_EX_salcn_e\n", + "\t- R_DM_4hba_c\n", + "\t- R_DM_hmfurn_c\u001b[0m\n", + "File saved at: ../../data/sbml_corrected/iEC1349_Crooks.xml\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: iIS312_Epimastigote\n", + "\u001b[0;93mWARNING : - R_SUCCtp: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_ALATA_L: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_EX_pro__L_e: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_EX_glu__L_e: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_EX_thr__L_e: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_EX_b_D_glucose_e: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_EX_a_D_glucose_e: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_EX_asp__L_e: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_EX_pi_e: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + "\n", + " - R_ASNNg: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + " - R_CYSTA: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + "\n", + " - R_ARGTRS: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + " - R_MCPST: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + "\n", + " - R_OPAH: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_PI4P5K_LM: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_G3PD2m: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + " - R_r0202: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + "\n", + " - R_RPEg: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_NAPRT: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + " - R_NITR: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_ALATA_Lm: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + "\u001b[0m\n", + "\n", + "\n", + "\u001b[0;36m############################################\n", + "############################################\n", + " WRITING SBML FILE \n", + "############################################\n", + "############################################\n", + "\u001b[0m\n", + "\u001b[0;93mWARNING : IMPORT REACTION REMOVED BY DEFAULT\u001b[0m\n", + "\u001b[0;93mWARNING : If you want to keep import reaction\n", + "use option -kir / --keep-import-reactions\u001b[0m\n", + "\u001b[0;93mWARNING : Reaction with tag reversible modified:\n", + "\t- R_SUCCtp\n", + "\t- R_ALATA_L\n", + "\t- R_EX_pro__L_e\n", + "\t- R_EX_glu__L_e\n", + "\t- R_EX_thr__L_e\n", + "\t- R_EX_b_D_glucose_e\n", + "\t- R_EX_a_D_glucose_e\n", + "\t- R_EX_asp__L_e\n", + "\t- R_EX_pi_e\n", + "\t- R_CYSTA\n", + "\t- R_MCPST\n", + "\t- R_r0202\n", + "\t- R_NITR\n", + "\t- R_ALATA_Lm\u001b[0m\n", + "\u001b[0;93mWARNING : Reaction with Reactants and Products exchanged:\n", + "\t- R_SUCCtp\n", + "\t- R_ALATA_L\n", + "\t- R_EX_pro__L_e\n", + "\t- R_EX_glu__L_e\n", + "\t- R_EX_thr__L_e\n", + "\t- R_EX_b_D_glucose_e\n", + "\t- R_EX_a_D_glucose_e\n", + "\t- R_EX_asp__L_e\n", + "\t- R_EX_pi_e\n", + "\t- R_CYSTA\n", + "\t- R_MCPST\n", + "\t- R_r0202\n", + "\t- R_NITR\n", + "\t- R_ALATA_Lm\u001b[0m\n", + "\u001b[0;93mWARNING : Import reaction removed:\n", + "\t- R_EX_h2o_e\n", + "\t- R_EX_pro__L_e\n", + "\t- R_EX_glu__L_e\n", + "\t- R_EX_co2_e\n", + "\t- R_EX_nh4_e\n", + "\t- R_EX_glyc_e\n", + "\t- R_EX_gly_e\n", + "\t- R_EX_o2_e\n", + "\t- R_EX_h_e\n", + "\t- R_EX_thr__L_e\n", + "\t- R_EX_ala__L_e\n", + "\t- R_EX_succ_e\n", + "\t- R_EX_b_D_glucose_e\n", + "\t- R_EX_a_D_glucose_e\n", + "\t- R_EX_ac_e\n", + "\t- R_EX_asp__L_e\n", + "\t- R_EX_pi_e\n", + "\t- R_EX_gln__L_e\n", + "\t- R_EX_arg__L_e\n", + "\t- R_EX_urea_e\n", + "\t- R_EX_phe__L_e\n", + "\t- R_EX_ile__L_e\n", + "\t- R_EX_his__L_e\n", + "\t- R_EX_val__L_e\n", + "\t- R_EX_leu__L_e\n", + "\t- R_EX_lys__L_e\n", + "\t- R_EX_ala__D_e\n", + "\t- R_EX_ergst_e\n", + "\t- R_EX_ser__L_e\n", + "\t- R_EX_ura_e\n", + "\t- R_EX_met__L_e\n", + "\t- R_EX_cys__L_e\n", + "\t- R_EX_asn__L_e\n", + "\t- R_EX_chol_e\n", + "\t- R_EX_cit_e\n", + "\t- R_EX_gthrd_e\n", + "\t- R_EX_h2s_e\n", + "\t- R_EX_nac_e\n", + "\t- R_EX_ncam_e\n", + "\t- R_EX_inost_e\n", + "\t- R_EX_nh3_e\n", + "\t- R_EX_gua_e\n", + "\t- R_EX_drib_e\n", + "\t- R_EX_thym_e\n", + "\t- R_EX_hxan_e\u001b[0m\n", + "File saved at: ../../data/sbml_corrected/iIS312_Epimastigote.xml\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: iIS312_Amastigote\n", + "\u001b[0;93mWARNING : \n", + " - R_FUMm: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_CYOR_u6m_1: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_PDHam1m: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_ICDHym: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_GCC2cm: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + " - R_SUCCtp: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + "\n", + " - R_PYK: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + " - R_ALATA_L: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + "\n", + " - R_PPM: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + " - R_EX_pro__L_e: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_EX_glu__L_e: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_EX_thr__L_e: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_EX_b_D_glucose_e: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_EX_a_D_glucose_e: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_EX_asp__L_e: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_EX_pi_e: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + "\n", + " - R_ASNTRS: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + " - R_CYSTA: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + "\n", + " - R_UAGDP: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_GF6PTA: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCOAS1m: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + " - R_MCPST: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + "\n", + " - R_DRBK: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_ADSS: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_HXPRTg: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_ADSL1r: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_ADSL2r: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_G5SD: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_LEUTRS: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + " - R_ALATA_Lm: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + "\n", + " - R_AKGDam: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_ATPS3m: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_AKGDbm: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_PPDKgr: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + " - R_r0202: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + "\n", + " - R_DRBKg: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_RBKrg: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_NADH2_u6am: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_ASCTmr: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_GALUi: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_GLNTRS: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_ALCD19: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_GLYTRS: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_MDH: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_NITR: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_TRYP: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_TRYPg: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_TRYPm: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_TMDS: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_DHFR: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_DURIK1m: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_TMDK1m: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_C14STRr: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_DPMVDx: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_PMEVKx: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\u001b[0m\n", + "\n", + "\n", + "\u001b[0;36m############################################\n", + "############################################\n", + " WRITING SBML FILE \n", + "############################################\n", + "############################################\n", + "\u001b[0m\n", + "\u001b[0;93mWARNING : IMPORT REACTION REMOVED BY DEFAULT\u001b[0m\n", + "\u001b[0;93mWARNING : If you want to keep import reaction\n", + "use option -kir / --keep-import-reactions\u001b[0m\n", + "\u001b[0;93mWARNING : Reaction with tag reversible modified:\n", + "\t- R_SUCCtp\n", + "\t- R_ALATA_L\n", + "\t- R_EX_pro__L_e\n", + "\t- R_EX_glu__L_e\n", + "\t- R_EX_thr__L_e\n", + "\t- R_EX_b_D_glucose_e\n", + "\t- R_EX_a_D_glucose_e\n", + "\t- R_EX_asp__L_e\n", + "\t- R_EX_pi_e\n", + "\t- R_CYSTA\n", + "\t- R_MCPST\n", + "\t- R_ALATA_Lm\n", + "\t- R_r0202\u001b[0m\n", + "\u001b[0;93mWARNING : Reaction with Reactants and Products exchanged:\n", + "\t- R_SUCCtp\n", + "\t- R_ALATA_L\n", + "\t- R_EX_pro__L_e\n", + "\t- R_EX_glu__L_e\n", + "\t- R_EX_thr__L_e\n", + "\t- R_EX_b_D_glucose_e\n", + "\t- R_EX_a_D_glucose_e\n", + "\t- R_EX_asp__L_e\n", + "\t- R_EX_pi_e\n", + "\t- R_CYSTA\n", + "\t- R_MCPST\n", + "\t- R_ALATA_Lm\n", + "\t- R_r0202\u001b[0m\n", + "\u001b[0;93mWARNING : Import reaction removed:\n", + "\t- R_EX_h2o_e\n", + "\t- R_EX_pro__L_e\n", + "\t- R_EX_glu__L_e\n", + "\t- R_EX_co2_e\n", + "\t- R_EX_nh4_e\n", + "\t- R_EX_glyc_e\n", + "\t- R_EX_gly_e\n", + "\t- R_EX_o2_e\n", + "\t- R_EX_h_e\n", + "\t- R_EX_thr__L_e\n", + "\t- R_EX_ala__L_e\n", + "\t- R_EX_succ_e\n", + "\t- R_EX_b_D_glucose_e\n", + "\t- R_EX_a_D_glucose_e\n", + "\t- R_EX_ac_e\n", + "\t- R_EX_asp__L_e\n", + "\t- R_EX_pi_e\n", + "\t- R_EX_gln__L_e\n", + "\t- R_EX_arg__L_e\n", + "\t- R_EX_urea_e\n", + "\t- R_EX_phe__L_e\n", + "\t- R_EX_ile__L_e\n", + "\t- R_EX_his__L_e\n", + "\t- R_EX_val__L_e\n", + "\t- R_EX_leu__L_e\n", + "\t- R_EX_lys__L_e\n", + "\t- R_EX_ala__D_e\n", + "\t- R_EX_ergst_e\n", + "\t- R_EX_ser__L_e\n", + "\t- R_EX_ura_e\n", + "\t- R_EX_met__L_e\n", + "\t- R_EX_cys__L_e\n", + "\t- R_EX_asn__L_e\n", + "\t- R_EX_chol_e\n", + "\t- R_EX_cit_e\n", + "\t- R_EX_gthrd_e\n", + "\t- R_EX_h2s_e\n", + "\t- R_EX_nac_e\n", + "\t- R_EX_ncam_e\n", + "\t- R_EX_inost_e\n", + "\t- R_EX_nh3_e\n", + "\t- R_EX_gua_e\n", + "\t- R_EX_drib_e\n", + "\t- R_EX_thym_e\n", + "\t- R_EX_hxan_e\u001b[0m\n", + "File saved at: ../../data/sbml_corrected/iIS312_Amastigote.xml\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: iEcE24377_1341\n", + "\u001b[0;93mWARNING : \n", + " - R_CAT: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_DHPTDNR: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_DHPTDNRN: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_FHL: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SPODM: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SPODMpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCASPtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCFUMtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCMALtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_CHITPH: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_URCN: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_EX_chitob_e: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_CHITOBpts: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\u001b[0m\n", + "\n", + "\n", + "\u001b[0;36m############################################\n", + "############################################\n", + " WRITING SBML FILE \n", + "############################################\n", + "############################################\n", + "\u001b[0m\n", + "\u001b[0;93mWARNING : IMPORT REACTION REMOVED BY DEFAULT\u001b[0m\n", + "\u001b[0;93mWARNING : If you want to keep import reaction\n", + "use option -kir / --keep-import-reactions\u001b[0m\n", + "\u001b[0;93mWARNING : Import reaction removed:\n", + "\t- R_EX_5dglcn_e\n", + "\t- R_EX_LalaDgluMdap_e\n", + "\t- R_EX_LalaDgluMdapDala_e\n", + "\t- R_EX_5mtr_e\n", + "\t- R_EX_LalaDglu_e\n", + "\t- R_EX_3ump_e\n", + "\t- R_EX_ac_e\n", + "\t- R_EX_LalaLglu_e\n", + "\t- R_EX_4abut_e\n", + "\t- R_EX_acac_e\n", + "\t- R_EX_4hoxpacd_e\n", + "\t- R_DM_4crsol_c\n", + "\t- R_EX_acald_e\n", + "\t- R_DM_5drib_c\n", + "\t- R_DM_aacald_c\n", + "\t- R_DM_amob_c\n", + "\t- R_EX_acgal_e\n", + "\t- R_DM_mththf_c\n", + "\t- R_DM_oxam_c\n", + "\t- R_EX_acgal1p_e\n", + "\t- R_EX_acgam_e\n", + "\t- R_EX_12ppd__R_e\n", + "\t- R_EX_12ppd__S_e\n", + "\t- R_EX_14glucan_e\n", + "\t- R_EX_duri_e\n", + "\t- R_EX_acgam1p_e\n", + "\t- R_EX_15dap_e\n", + "\t- R_EX_23camp_e\n", + "\t- R_EX_eca4colipa_e\n", + "\t- R_EX_23ccmp_e\n", + "\t- R_EX_acmana_e\n", + "\t- R_EX_23cgmp_e\n", + "\t- R_EX_23cump_e\n", + "\t- R_EX_23dappa_e\n", + "\t- R_EX_26dap__M_e\n", + "\t- R_EX_2ddglcn_e\n", + "\t- R_EX_34dhpac_e\n", + "\t- R_EX_3amp_e\n", + "\t- R_EX_3cmp_e\n", + "\t- R_EX_3gmp_e\n", + "\t- R_EX_3hcinnm_e\n", + "\t- R_EX_enlipa_e\n", + "\t- R_EX_enter_e\n", + "\t- R_EX_etha_e\n", + "\t- R_EX_ethso3_e\n", + "\t- R_EX_etoh_e\n", + "\t- R_EX_f6p_e\n", + "\t- R_EX_fald_e\n", + "\t- R_EX_fe2_e\n", + "\t- R_EX_fe3_e\n", + "\t- R_EX_fe3dcit_e\n", + "\t- R_EX_fe3dhbzs_e\n", + "\t- R_EX_fe3hox_e\n", + "\t- R_EX_fe3hox_un_e\n", + "\t- R_EX_fecrm_e\n", + "\t- R_EX_3hpp_e\n", + "\t- R_EX_3hpppn_e\n", + "\t- R_EX_fecrm_un_e\n", + "\t- R_EX_acmum_e\n", + "\t- R_EX_feenter_e\n", + "\t- R_EX_feoxam_e\n", + "\t- R_EX_acnam_e\n", + "\t- R_EX_feoxam_un_e\n", + "\t- R_EX_acolipa_e\n", + "\t- R_EX_for_e\n", + "\t- R_EX_fru_e\n", + "\t- R_EX_acser_e\n", + "\t- R_EX_frulys_e\n", + "\t- R_EX_fruur_e\n", + "\t- R_EX_fuc__L_e\n", + "\t- R_EX_fum_e\n", + "\t- R_EX_idon__L_e\n", + "\t- R_EX_ile__L_e\n", + "\t- R_EX_imp_e\n", + "\t- R_EX_indole_e\n", + "\t- R_EX_fusa_e\n", + "\t- R_EX_g1p_e\n", + "\t- R_EX_inost_e\n", + "\t- R_EX_ade_e\n", + "\t- R_EX_ins_e\n", + "\t- R_EX_isetac_e\n", + "\t- R_EX_g3pc_e\n", + "\t- R_EX_g3pe_e\n", + "\t- R_EX_g3pg_e\n", + "\t- R_EX_g3pi_e\n", + "\t- R_EX_k_e\n", + "\t- R_EX_kdo2lipid4_e\n", + "\t- R_EX_adn_e\n", + "\t- R_EX_lac__D_e\n", + "\t- R_EX_g3ps_e\n", + "\t- R_EX_adocbl_e\n", + "\t- R_EX_g6p_e\n", + "\t- R_EX_gal_e\n", + "\t- R_EX_lac__L_e\n", + "\t- R_EX_lcts_e\n", + "\t- R_EX_leu__L_e\n", + "\t- R_EX_gal_bD_e\n", + "\t- R_EX_ag_e\n", + "\t- R_EX_gal1p_e\n", + "\t- R_EX_galct__D_e\n", + "\t- R_EX_lipa_e\n", + "\t- R_EX_lipa_cold_e\n", + "\t- R_EX_lipoate_e\n", + "\t- R_EX_galctn__D_e\n", + "\t- R_EX_lys__L_e\n", + "\t- R_EX_lyx__L_e\n", + "\t- R_EX_mal__D_e\n", + "\t- R_EX_mal__L_e\n", + "\t- R_EX_galctn__L_e\n", + "\t- R_EX_galt_e\n", + "\t- R_EX_malt_e\n", + "\t- R_EX_malthx_e\n", + "\t- R_EX_galur_e\n", + "\t- R_EX_gam_e\n", + "\t- R_EX_maltpt_e\n", + "\t- R_EX_agm_e\n", + "\t- R_EX_malttr_e\n", + "\t- R_EX_maltttr_e\n", + "\t- R_EX_gam6p_e\n", + "\t- R_EX_gbbtn_e\n", + "\t- R_EX_gdp_e\n", + "\t- R_EX_man_e\n", + "\t- R_EX_akg_e\n", + "\t- R_EX_man6p_e\n", + "\t- R_EX_manglyc_e\n", + "\t- R_EX_glc__D_e\n", + "\t- R_EX_glcn_e\n", + "\t- R_EX_melib_e\n", + "\t- R_EX_ala_B_e\n", + "\t- R_EX_meoh_e\n", + "\t- R_EX_met__D_e\n", + "\t- R_EX_glcr_e\n", + "\t- R_EX_glcur_e\n", + "\t- R_EX_met__L_e\n", + "\t- R_EX_ala__D_e\n", + "\t- R_EX_metsox_R__L_e\n", + "\t- R_EX_metsox_S__L_e\n", + "\t- R_EX_mg2_e\n", + "\t- R_EX_glcur1p_e\n", + "\t- R_EX_gln__L_e\n", + "\t- R_EX_glu__L_e\n", + "\t- R_EX_mincyc_e\n", + "\t- R_EX_ala__L_e\n", + "\t- R_EX_minohp_e\n", + "\t- R_EX_mmet_e\n", + "\t- R_EX_mn2_e\n", + "\t- R_EX_mnl_e\n", + "\t- R_EX_gly_e\n", + "\t- R_EX_glyald_e\n", + "\t- R_EX_glyb_e\n", + "\t- R_EX_glyc_e\n", + "\t- R_EX_glyc__R_e\n", + "\t- R_EX_glyc2p_e\n", + "\t- R_EX_mobd_e\n", + "\t- R_EX_mso3_e\n", + "\t- R_EX_glyc3p_e\n", + "\t- R_EX_n2o_e\n", + "\t- R_EX_alaala_e\n", + "\t- R_EX_na1_e\n", + "\t- R_EX_nac_e\n", + "\t- R_EX_glyclt_e\n", + "\t- R_EX_gmp_e\n", + "\t- R_EX_gsn_e\n", + "\t- R_EX_gthox_e\n", + "\t- R_EX_nh4_e\n", + "\t- R_EX_ni2_e\n", + "\t- R_EX_gthrd_e\n", + "\t- R_EX_all__D_e\n", + "\t- R_EX_gtp_e\n", + "\t- R_EX_gua_e\n", + "\t- R_EX_nmn_e\n", + "\t- R_EX_no_e\n", + "\t- R_EX_no2_e\n", + "\t- R_EX_h_e\n", + "\t- R_EX_alltn_e\n", + "\t- R_EX_h2_e\n", + "\t- R_EX_no3_e\n", + "\t- R_EX_novbcn_e\n", + "\t- R_EX_h2o_e\n", + "\t- R_EX_amp_e\n", + "\t- R_EX_h2o2_e\n", + "\t- R_EX_h2s_e\n", + "\t- R_EX_hacolipa_e\n", + "\t- R_EX_halipa_e\n", + "\t- R_EX_o16a4colipa_e\n", + "\t- R_EX_o2_e\n", + "\t- R_EX_o2s_e\n", + "\t- R_EX_ocdca_e\n", + "\t- R_EX_ocdcea_e\n", + "\t- R_EX_octa_e\n", + "\t- R_EX_hdca_e\n", + "\t- R_EX_hdcea_e\n", + "\t- R_EX_orn_e\n", + "\t- R_EX_hg2_e\n", + "\t- R_EX_anhgm_e\n", + "\t- R_EX_his__L_e\n", + "\t- R_EX_hom__L_e\n", + "\t- R_EX_orot_e\n", + "\t- R_EX_pacald_e\n", + "\t- R_EX_peamn_e\n", + "\t- R_EX_phe__L_e\n", + "\t- R_EX_hxa_e\n", + "\t- R_EX_hxan_e\n", + "\t- R_EX_arab__L_e\n", + "\t- R_EX_pheme_e\n", + "\t- R_EX_pi_e\n", + "\t- R_EX_pnto__R_e\n", + "\t- R_EX_ppa_e\n", + "\t- R_EX_rfamp_e\n", + "\t- R_EX_ppal_e\n", + "\t- R_EX_arbt_e\n", + "\t- R_EX_pppn_e\n", + "\t- R_EX_ppt_e\n", + "\t- R_EX_rib__D_e\n", + "\t- R_EX_rmn_e\n", + "\t- R_EX_sbt__D_e\n", + "\t- R_EX_pro__L_e\n", + "\t- R_EX_progly_e\n", + "\t- R_EX_sel_e\n", + "\t- R_EX_ser__D_e\n", + "\t- R_EX_ser__L_e\n", + "\t- R_EX_psclys_e\n", + "\t- R_EX_pser__L_e\n", + "\t- R_EX_ptrc_e\n", + "\t- R_EX_pydam_e\n", + "\t- R_EX_skm_e\n", + "\t- R_EX_arbtn_e\n", + "\t- R_EX_slnt_e\n", + "\t- R_EX_so2_e\n", + "\t- R_EX_so3_e\n", + "\t- R_EX_pydx_e\n", + "\t- R_EX_pydxn_e\n", + "\t- R_EX_pyr_e\n", + "\t- R_EX_quin_e\n", + "\t- R_EX_r5p_e\n", + "\t- R_EX_so4_e\n", + "\t- R_EX_spmd_e\n", + "\t- R_EX_succ_e\n", + "\t- R_EX_arbtn_fe3_e\n", + "\t- R_EX_sucr_e\n", + "\t- R_EX_sulfac_e\n", + "\t- R_EX_tartr__D_e\n", + "\t- R_EX_arg__L_e\n", + "\t- R_EX_tartr__L_e\n", + "\t- R_EX_taur_e\n", + "\t- R_EX_ascb__L_e\n", + "\t- R_EX_tcynt_e\n", + "\t- R_EX_thm_e\n", + "\t- R_EX_asn__L_e\n", + "\t- R_EX_thr__L_e\n", + "\t- R_EX_thrp_e\n", + "\t- R_EX_thym_e\n", + "\t- R_EX_thymd_e\n", + "\t- R_EX_tma_e\n", + "\t- R_EX_tmao_e\n", + "\t- R_EX_tre_e\n", + "\t- R_EX_trp__L_e\n", + "\t- R_EX_aso3_e\n", + "\t- R_EX_tsul_e\n", + "\t- R_EX_ttdca_e\n", + "\t- R_EX_ttdcea_e\n", + "\t- R_EX_ttrcyc_e\n", + "\t- R_EX_asp__L_e\n", + "\t- R_EX_tungs_e\n", + "\t- R_EX_tym_e\n", + "\t- R_EX_btn_e\n", + "\t- R_EX_tyr__L_e\n", + "\t- R_EX_tyrp_e\n", + "\t- R_EX_uacgam_e\n", + "\t- R_EX_udpacgal_e\n", + "\t- R_EX_udpg_e\n", + "\t- R_EX_udpgal_e\n", + "\t- R_EX_udpglcur_e\n", + "\t- R_EX_ump_e\n", + "\t- R_EX_but_e\n", + "\t- R_EX_ura_e\n", + "\t- R_EX_urea_e\n", + "\t- R_EX_uri_e\n", + "\t- R_EX_val__L_e\n", + "\t- R_EX_butso3_e\n", + "\t- R_EX_xan_e\n", + "\t- R_EX_xmp_e\n", + "\t- R_EX_xtsn_e\n", + "\t- R_EX_ca2_e\n", + "\t- R_EX_xyl__D_e\n", + "\t- R_EX_xylu__L_e\n", + "\t- R_EX_zn2_e\n", + "\t- R_EX_cbi_e\n", + "\t- R_EX_cbl1_e\n", + "\t- R_EX_cd2_e\n", + "\t- R_EX_cgly_e\n", + "\t- R_EX_chol_e\n", + "\t- R_EX_chtbs_e\n", + "\t- R_EX_cit_e\n", + "\t- R_EX_cl_e\n", + "\t- R_EX_cm_e\n", + "\t- R_EX_cmp_e\n", + "\t- R_EX_co2_e\n", + "\t- R_EX_cobalt2_e\n", + "\t- R_EX_colipa_e\n", + "\t- R_EX_colipap_e\n", + "\t- R_EX_cpgn_e\n", + "\t- R_EX_cpgn_un_e\n", + "\t- R_EX_crn_e\n", + "\t- R_EX_crn__D_e\n", + "\t- R_EX_csn_e\n", + "\t- R_EX_cu_e\n", + "\t- R_EX_cu2_e\n", + "\t- R_EX_cyan_e\n", + "\t- R_EX_cynt_e\n", + "\t- R_EX_cys__D_e\n", + "\t- R_EX_cys__L_e\n", + "\t- R_EX_cytd_e\n", + "\t- R_EX_dad_2_e\n", + "\t- R_EX_damp_e\n", + "\t- R_EX_dca_e\n", + "\t- R_EX_dcmp_e\n", + "\t- R_EX_dcyt_e\n", + "\t- R_EX_ddca_e\n", + "\t- R_EX_dgmp_e\n", + "\t- R_EX_dgsn_e\n", + "\t- R_EX_dha_e\n", + "\t- R_EX_dimp_e\n", + "\t- R_EX_din_e\n", + "\t- R_EX_dms_e\n", + "\t- R_EX_dmso_e\n", + "\t- R_EX_dopa_e\n", + "\t- R_EX_doxrbcn_e\n", + "\t- R_EX_dtmp_e\n", + "\t- R_EX_dump_e\n", + "\t- R_EX_feroxE_e\n", + "\t- R_EX_pep_e\n", + "\t- R_EX_23dhbzs3_e\n", + "\t- R_EX_tet_e\n", + "\t- R_EX_salchs4_e\n", + "\t- R_EX_tag__D_e\n", + "\t- R_EX_oaa_e\n", + "\t- R_EX_salchs2fe_e\n", + "\t- R_EX_4hphac_e\n", + "\t- R_EX_4abz_e\n", + "\t- R_EX_cysi__L_e\n", + "\t- R_EX_acon_C_e\n", + "\t- R_EX_fe3dhbzs3_e\n", + "\t- R_EX_drib_e\n", + "\t- R_EX_feroxBfe_e\n", + "\t- R_EX_salchs4fe_e\n", + "\t- R_EX_2pg_e\n", + "\t- R_EX_tcb_e\n", + "\t- R_EX_feroxB_e\n", + "\t- R_EX_tton_e\n", + "\t- R_EX_4hthr_e\n", + "\t- R_EX_feroxEfe_e\n", + "\t- R_EX_frmd_e\n", + "\t- R_EX_salchs2_e\n", + "\t- R_EX_colipaOA_e\n", + "\t- R_EX_salchsx_e\n", + "\t- R_EX_AEP_e\n", + "\t- R_EX_rnam_e\n", + "\t- R_EX_airs_e\n", + "\t- R_EX_dxyl_e\n", + "\t- R_EX_icit_e\n", + "\t- R_EX_colipa20Oag_e\n", + "\t- R_EX_3pg_e\n", + "\t- R_EX_udcpo4_e\n", + "\t- R_EX_feroxGfe_e\n", + "\t- R_EX_udcpo5_e\n", + "\t- R_EX_guln__L_e\n", + "\t- R_EX_remnant1_e\n", + "\t- R_EX_feroxG_e\n", + "\t- R_EX_fe_e\n", + "\t- R_EX_kokdolipidA_e\n", + "\t- R_EX_3hoxpac_e\n", + "\t- R_EX_diact_e\n", + "\t- R_DM_lipidA_core_e_p\n", + "\t- R_EX_btd_RR_e\n", + "\t- R_EX_rbt_e\n", + "\t- R_EX_cellb_e\n", + "\t- R_EX_bz_e\n", + "\t- R_EX_13ppd_e\n", + "\t- R_EX_abt__D_e\n", + "\t- R_EX_3ntym_e\n", + "\t- R_EX_6apa_e\n", + "\t- R_EX_4hoxpac_e\n", + "\t- R_EX_peng_e\n", + "\t- R_EX_pac_e\n", + "\t- R_EX_salcn_e\n", + "\t- R_EX_galam_e\n", + "\t- R_EX_raffin_e\n", + "\t- R_EX_o6a4colipa_e\n", + "\t- R_EX_arab__D_e\u001b[0m\n", + "File saved at: ../../data/sbml_corrected/iEcE24377_1341.xml\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: iEcDH1_1363\n", + "\u001b[0;93mWARNING : \n", + " - R_DHPTDNR: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_DHPTDNRN: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_CAT: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_FHL: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SPODM: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SPODMpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCASPtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCFUMtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCMALtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCTARTtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_CHITPH: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_URCN: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_EX_chitob_e: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_CHITOBpts: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\u001b[0m\n", + "\n", + "\n", + "\u001b[0;36m############################################\n", + "############################################\n", + " WRITING SBML FILE \n", + "############################################\n", + "############################################\n", + "\u001b[0m\n", + "\u001b[0;93mWARNING : IMPORT REACTION REMOVED BY DEFAULT\u001b[0m\n", + "\u001b[0;93mWARNING : If you want to keep import reaction\n", + "use option -kir / --keep-import-reactions\u001b[0m\n", + "\u001b[0;93mWARNING : Import reaction removed:\n", + "\t- R_EX_3gmp_e\n", + "\t- R_EX_3hpppn_e\n", + "\t- R_EX_3ump_e\n", + "\t- R_EX_3hcinnm_e\n", + "\t- R_EX_3hpp_e\n", + "\t- R_EX_dimp_e\n", + "\t- R_EX_4abut_e\n", + "\t- R_DM_4crsol_c\n", + "\t- R_DM_5drib_c\n", + "\t- R_DM_aacald_c\n", + "\t- R_EX_4hoxpacd_e\n", + "\t- R_EX_5dglcn_e\n", + "\t- R_EX_5mtr_e\n", + "\t- R_EX_LalaDglu_e\n", + "\t- R_DM_amob_c\n", + "\t- R_DM_mththf_c\n", + "\t- R_EX_din_e\n", + "\t- R_DM_oxam_c\n", + "\t- R_EX_LalaDgluMdap_e\n", + "\t- R_EX_LalaDgluMdapDala_e\n", + "\t- R_EX_LalaLglu_e\n", + "\t- R_EX_12ppd__R_e\n", + "\t- R_EX_ac_e\n", + "\t- R_EX_dms_e\n", + "\t- R_EX_acac_e\n", + "\t- R_EX_acald_e\n", + "\t- R_EX_12ppd__S_e\n", + "\t- R_EX_14glucan_e\n", + "\t- R_EX_15dap_e\n", + "\t- R_EX_acgal_e\n", + "\t- R_EX_dmso_e\n", + "\t- R_EX_acgal1p_e\n", + "\t- R_EX_acgam_e\n", + "\t- R_EX_acgam1p_e\n", + "\t- R_EX_acmana_e\n", + "\t- R_EX_acmum_e\n", + "\t- R_EX_acnam_e\n", + "\t- R_EX_acolipa_e\n", + "\t- R_EX_acser_e\n", + "\t- R_EX_23camp_e\n", + "\t- R_EX_23ccmp_e\n", + "\t- R_EX_23cgmp_e\n", + "\t- R_EX_23cump_e\n", + "\t- R_EX_23dappa_e\n", + "\t- R_EX_26dap__M_e\n", + "\t- R_EX_2ddglcn_e\n", + "\t- R_EX_34dhpac_e\n", + "\t- R_EX_3amp_e\n", + "\t- R_EX_3cmp_e\n", + "\t- R_EX_ade_e\n", + "\t- R_EX_adn_e\n", + "\t- R_EX_adocbl_e\n", + "\t- R_EX_ag_e\n", + "\t- R_EX_dopa_e\n", + "\t- R_EX_agm_e\n", + "\t- R_EX_akg_e\n", + "\t- R_EX_ala_B_e\n", + "\t- R_EX_halipa_e\n", + "\t- R_EX_hdca_e\n", + "\t- R_EX_hdcea_e\n", + "\t- R_EX_ala__D_e\n", + "\t- R_EX_hg2_e\n", + "\t- R_EX_doxrbcn_e\n", + "\t- R_EX_his__L_e\n", + "\t- R_EX_ala__L_e\n", + "\t- R_EX_alaala_e\n", + "\t- R_EX_dtmp_e\n", + "\t- R_EX_all__D_e\n", + "\t- R_EX_alltn_e\n", + "\t- R_EX_amp_e\n", + "\t- R_EX_anhgm_e\n", + "\t- R_EX_arab__L_e\n", + "\t- R_EX_arbt_e\n", + "\t- R_EX_hom__L_e\n", + "\t- R_EX_hxa_e\n", + "\t- R_EX_hxan_e\n", + "\t- R_EX_idon__L_e\n", + "\t- R_EX_ile__L_e\n", + "\t- R_EX_imp_e\n", + "\t- R_EX_indole_e\n", + "\t- R_EX_arbtn_e\n", + "\t- R_EX_arbtn_fe3_e\n", + "\t- R_EX_inost_e\n", + "\t- R_EX_arg__L_e\n", + "\t- R_EX_dump_e\n", + "\t- R_EX_ascb__L_e\n", + "\t- R_EX_asn__L_e\n", + "\t- R_EX_ins_e\n", + "\t- R_EX_isetac_e\n", + "\t- R_EX_k_e\n", + "\t- R_EX_kdo2lipid4_e\n", + "\t- R_EX_aso3_e\n", + "\t- R_EX_asp__L_e\n", + "\t- R_EX_lac__D_e\n", + "\t- R_EX_duri_e\n", + "\t- R_EX_lac__L_e\n", + "\t- R_EX_btn_e\n", + "\t- R_EX_but_e\n", + "\t- R_EX_lcts_e\n", + "\t- R_EX_eca4colipa_e\n", + "\t- R_EX_leu__L_e\n", + "\t- R_EX_lipa_e\n", + "\t- R_EX_butso3_e\n", + "\t- R_EX_ca2_e\n", + "\t- R_EX_cbi_e\n", + "\t- R_EX_lipa_cold_e\n", + "\t- R_EX_lipoate_e\n", + "\t- R_EX_lys__L_e\n", + "\t- R_EX_cbl1_e\n", + "\t- R_EX_cd2_e\n", + "\t- R_EX_cgly_e\n", + "\t- R_EX_chol_e\n", + "\t- R_EX_lyx__L_e\n", + "\t- R_EX_mal__D_e\n", + "\t- R_EX_chtbs_e\n", + "\t- R_EX_mal__L_e\n", + "\t- R_EX_enlipa_e\n", + "\t- R_EX_malt_e\n", + "\t- R_EX_malthx_e\n", + "\t- R_EX_cit_e\n", + "\t- R_EX_cl_e\n", + "\t- R_EX_cm_e\n", + "\t- R_EX_maltpt_e\n", + "\t- R_EX_enter_e\n", + "\t- R_EX_malttr_e\n", + "\t- R_EX_cmp_e\n", + "\t- R_EX_co2_e\n", + "\t- R_EX_maltttr_e\n", + "\t- R_EX_etha_e\n", + "\t- R_EX_man_e\n", + "\t- R_EX_cobalt2_e\n", + "\t- R_EX_colipa_e\n", + "\t- R_EX_ethso3_e\n", + "\t- R_EX_colipap_e\n", + "\t- R_EX_man6p_e\n", + "\t- R_EX_manglyc_e\n", + "\t- R_EX_cpgn_e\n", + "\t- R_EX_etoh_e\n", + "\t- R_EX_cpgn_un_e\n", + "\t- R_EX_crn_e\n", + "\t- R_EX_melib_e\n", + "\t- R_EX_meoh_e\n", + "\t- R_EX_crn__D_e\n", + "\t- R_EX_met__D_e\n", + "\t- R_EX_f6p_e\n", + "\t- R_EX_met__L_e\n", + "\t- R_EX_metsox_R__L_e\n", + "\t- R_EX_metsox_S__L_e\n", + "\t- R_EX_mg2_e\n", + "\t- R_EX_csn_e\n", + "\t- R_EX_cu_e\n", + "\t- R_EX_cu2_e\n", + "\t- R_EX_cyan_e\n", + "\t- R_EX_cynt_e\n", + "\t- R_EX_mincyc_e\n", + "\t- R_EX_minohp_e\n", + "\t- R_EX_cys__D_e\n", + "\t- R_EX_fald_e\n", + "\t- R_EX_cys__L_e\n", + "\t- R_EX_cytd_e\n", + "\t- R_EX_mmet_e\n", + "\t- R_EX_mn2_e\n", + "\t- R_EX_dad_2_e\n", + "\t- R_EX_fe2_e\n", + "\t- R_EX_damp_e\n", + "\t- R_EX_dca_e\n", + "\t- R_EX_mnl_e\n", + "\t- R_EX_mobd_e\n", + "\t- R_EX_mso3_e\n", + "\t- R_EX_fe3_e\n", + "\t- R_EX_n2o_e\n", + "\t- R_EX_na1_e\n", + "\t- R_EX_dcmp_e\n", + "\t- R_EX_dcyt_e\n", + "\t- R_EX_ddca_e\n", + "\t- R_EX_dgmp_e\n", + "\t- R_EX_nac_e\n", + "\t- R_EX_nh4_e\n", + "\t- R_EX_dgsn_e\n", + "\t- R_EX_ni2_e\n", + "\t- R_EX_fe3dcit_e\n", + "\t- R_EX_nmn_e\n", + "\t- R_EX_dha_e\n", + "\t- R_EX_no_e\n", + "\t- R_EX_no2_e\n", + "\t- R_EX_no3_e\n", + "\t- R_EX_novbcn_e\n", + "\t- R_EX_pser__L_e\n", + "\t- R_EX_ptrc_e\n", + "\t- R_EX_o16a4colipa_e\n", + "\t- R_EX_fe3dhbzs_e\n", + "\t- R_EX_o2_e\n", + "\t- R_EX_pydam_e\n", + "\t- R_EX_pydx_e\n", + "\t- R_EX_o2s_e\n", + "\t- R_EX_pydxn_e\n", + "\t- R_EX_ocdca_e\n", + "\t- R_EX_fe3hox_e\n", + "\t- R_EX_ocdcea_e\n", + "\t- R_EX_octa_e\n", + "\t- R_EX_orn_e\n", + "\t- R_EX_pyr_e\n", + "\t- R_EX_quin_e\n", + "\t- R_EX_r5p_e\n", + "\t- R_EX_orot_e\n", + "\t- R_EX_rfamp_e\n", + "\t- R_EX_fe3hox_un_e\n", + "\t- R_EX_rib__D_e\n", + "\t- R_EX_pacald_e\n", + "\t- R_EX_peamn_e\n", + "\t- R_EX_rmn_e\n", + "\t- R_EX_phe__L_e\n", + "\t- R_EX_sbt__D_e\n", + "\t- R_EX_fecrm_e\n", + "\t- R_EX_sel_e\n", + "\t- R_EX_pheme_e\n", + "\t- R_EX_ser__D_e\n", + "\t- R_EX_pi_e\n", + "\t- R_EX_ser__L_e\n", + "\t- R_EX_fecrm_un_e\n", + "\t- R_EX_skm_e\n", + "\t- R_EX_slnt_e\n", + "\t- R_EX_so2_e\n", + "\t- R_EX_so3_e\n", + "\t- R_EX_pnto__R_e\n", + "\t- R_EX_ppa_e\n", + "\t- R_EX_ppal_e\n", + "\t- R_EX_pppn_e\n", + "\t- R_EX_ppt_e\n", + "\t- R_EX_pro__L_e\n", + "\t- R_EX_so4_e\n", + "\t- R_EX_progly_e\n", + "\t- R_EX_feenter_e\n", + "\t- R_EX_psclys_e\n", + "\t- R_EX_spmd_e\n", + "\t- R_EX_succ_e\n", + "\t- R_EX_sucr_e\n", + "\t- R_EX_feoxam_e\n", + "\t- R_EX_sulfac_e\n", + "\t- R_EX_tartr__D_e\n", + "\t- R_EX_feoxam_un_e\n", + "\t- R_EX_tartr__L_e\n", + "\t- R_EX_taur_e\n", + "\t- R_EX_for_e\n", + "\t- R_EX_tcynt_e\n", + "\t- R_EX_thm_e\n", + "\t- R_EX_thr__L_e\n", + "\t- R_EX_thrp_e\n", + "\t- R_EX_fru_e\n", + "\t- R_EX_thym_e\n", + "\t- R_EX_thymd_e\n", + "\t- R_EX_tma_e\n", + "\t- R_EX_tmao_e\n", + "\t- R_EX_tre_e\n", + "\t- R_EX_trp__L_e\n", + "\t- R_EX_frulys_e\n", + "\t- R_EX_tsul_e\n", + "\t- R_EX_ttdca_e\n", + "\t- R_EX_ttdcea_e\n", + "\t- R_EX_ttrcyc_e\n", + "\t- R_EX_fruur_e\n", + "\t- R_EX_tungs_e\n", + "\t- R_EX_tym_e\n", + "\t- R_EX_fuc__L_e\n", + "\t- R_EX_tyr__L_e\n", + "\t- R_EX_fum_e\n", + "\t- R_EX_tyrp_e\n", + "\t- R_EX_uacgam_e\n", + "\t- R_EX_udpacgal_e\n", + "\t- R_EX_udpg_e\n", + "\t- R_EX_udpgal_e\n", + "\t- R_EX_udpglcur_e\n", + "\t- R_EX_ump_e\n", + "\t- R_EX_ura_e\n", + "\t- R_EX_fusa_e\n", + "\t- R_EX_urea_e\n", + "\t- R_EX_uri_e\n", + "\t- R_EX_val__L_e\n", + "\t- R_EX_xan_e\n", + "\t- R_EX_g1p_e\n", + "\t- R_EX_xmp_e\n", + "\t- R_EX_xtsn_e\n", + "\t- R_EX_g3pc_e\n", + "\t- R_EX_xyl__D_e\n", + "\t- R_EX_xylu__L_e\n", + "\t- R_EX_g3pe_e\n", + "\t- R_EX_zn2_e\n", + "\t- R_EX_g3pg_e\n", + "\t- R_EX_g3pi_e\n", + "\t- R_EX_g3ps_e\n", + "\t- R_EX_g6p_e\n", + "\t- R_EX_gal_e\n", + "\t- R_EX_gal_bD_e\n", + "\t- R_EX_gal1p_e\n", + "\t- R_EX_galct__D_e\n", + "\t- R_EX_galctn__D_e\n", + "\t- R_EX_galctn__L_e\n", + "\t- R_EX_galt_e\n", + "\t- R_EX_galur_e\n", + "\t- R_EX_gam_e\n", + "\t- R_EX_gam6p_e\n", + "\t- R_EX_gbbtn_e\n", + "\t- R_EX_gdp_e\n", + "\t- R_EX_glc__D_e\n", + "\t- R_EX_glcn_e\n", + "\t- R_EX_glcr_e\n", + "\t- R_EX_glcur_e\n", + "\t- R_EX_glcur1p_e\n", + "\t- R_EX_gln__L_e\n", + "\t- R_EX_glu__L_e\n", + "\t- R_EX_gly_e\n", + "\t- R_EX_glyald_e\n", + "\t- R_EX_glyb_e\n", + "\t- R_EX_glyc_e\n", + "\t- R_EX_glyc__R_e\n", + "\t- R_EX_glyc2p_e\n", + "\t- R_EX_glyc3p_e\n", + "\t- R_EX_glyclt_e\n", + "\t- R_EX_gmp_e\n", + "\t- R_EX_gsn_e\n", + "\t- R_EX_gthox_e\n", + "\t- R_EX_gthrd_e\n", + "\t- R_EX_gtp_e\n", + "\t- R_EX_gua_e\n", + "\t- R_EX_h_e\n", + "\t- R_EX_h2_e\n", + "\t- R_EX_h2o_e\n", + "\t- R_EX_h2o2_e\n", + "\t- R_EX_h2s_e\n", + "\t- R_EX_hacolipa_e\n", + "\t- R_EX_feroxGfe_e\n", + "\t- R_EX_23dhbzs3_e\n", + "\t- R_EX_tet_e\n", + "\t- R_EX_salchs4_e\n", + "\t- R_EX_oaa_e\n", + "\t- R_EX_salchs2fe_e\n", + "\t- R_EX_4hphac_e\n", + "\t- R_EX_4abz_e\n", + "\t- R_EX_cysi__L_e\n", + "\t- R_EX_acon_C_e\n", + "\t- R_EX_fe3dhbzs3_e\n", + "\t- R_EX_feroxB_e\n", + "\t- R_EX_tcb_e\n", + "\t- R_EX_tton_e\n", + "\t- R_EX_4hthr_e\n", + "\t- R_EX_feroxEfe_e\n", + "\t- R_EX_drib_e\n", + "\t- R_EX_feroxBfe_e\n", + "\t- R_EX_salchs4fe_e\n", + "\t- R_EX_2pg_e\n", + "\t- R_EX_feroxE_e\n", + "\t- R_EX_pep_e\n", + "\t- R_EX_frmd_e\n", + "\t- R_EX_colipaOA_e\n", + "\t- R_EX_3ntym_e\n", + "\t- R_EX_salchsx_e\n", + "\t- R_EX_AEP_e\n", + "\t- R_EX_icit_e\n", + "\t- R_EX_6apa_e\n", + "\t- R_EX_colipa20Oag_e\n", + "\t- R_EX_remnant1_e\n", + "\t- R_EX_peng_e\n", + "\t- R_EX_pac_e\n", + "\t- R_EX_udcpo4_e\n", + "\t- R_EX_salcn_e\n", + "\t- R_EX_galam_e\n", + "\t- R_EX_salchs2_e\n", + "\t- R_EX_raffin_e\n", + "\t- R_EX_arab__D_e\n", + "\t- R_EX_rnam_e\n", + "\t- R_EX_airs_e\n", + "\t- R_EX_dxyl_e\n", + "\t- R_EX_3pg_e\n", + "\t- R_EX_tag__D_e\n", + "\t- R_EX_udcpo5_e\n", + "\t- R_EX_guln__L_e\n", + "\t- R_EX_feroxG_e\n", + "\t- R_EX_diact_e\n", + "\t- R_EX_cellb_e\n", + "\t- R_EX_btd_RR_e\n", + "\t- R_EX_rbt_e\n", + "\t- R_EX_bz_e\n", + "\t- R_EX_13ppd_e\n", + "\t- R_EX_abt__D_e\n", + "\t- R_EX_fe_e\n", + "\t- R_EX_kokdolipidA_e\n", + "\t- R_EX_3hoxpac_e\n", + "\t- R_EX_o6a4colipa_e\n", + "\t- R_EX_4hoxpac_e\n", + "\t- R_DM_lipidA_core_e_p\u001b[0m\n", + "File saved at: ../../data/sbml_corrected/iEcDH1_1363.xml\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: e_coli_core\n", + "\u001b[0;93mWARNING : - R_FORt: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + "\u001b[0m\n", + "\n", + "\n", + "\u001b[0;36m############################################\n", + "############################################\n", + " WRITING SBML FILE \n", + "############################################\n", + "############################################\n", + "\u001b[0m\n", + "\u001b[0;93mWARNING : IMPORT REACTION REMOVED BY DEFAULT\u001b[0m\n", + "\u001b[0;93mWARNING : If you want to keep import reaction\n", + "use option -kir / --keep-import-reactions\u001b[0m\n", + "\u001b[0;93mWARNING : Reaction with tag reversible modified:\n", + "\t- R_FORt\u001b[0m\n", + "\u001b[0;93mWARNING : Reaction with Reactants and Products exchanged:\n", + "\t- R_FORt\u001b[0m\n", + "\u001b[0;93mWARNING : Import reaction removed:\n", + "\t- R_EX_ac_e\n", + "\t- R_EX_acald_e\n", + "\t- R_EX_akg_e\n", + "\t- R_EX_co2_e\n", + "\t- R_EX_etoh_e\n", + "\t- R_EX_for_e\n", + "\t- R_EX_fru_e\n", + "\t- R_EX_fum_e\n", + "\t- R_EX_glc__D_e\n", + "\t- R_EX_gln__L_e\n", + "\t- R_EX_glu__L_e\n", + "\t- R_EX_h_e\n", + "\t- R_EX_h2o_e\n", + "\t- R_EX_lac__D_e\n", + "\t- R_EX_mal__L_e\n", + "\t- R_EX_nh4_e\n", + "\t- R_EX_o2_e\n", + "\t- R_EX_pi_e\n", + "\t- R_EX_pyr_e\n", + "\t- R_EX_succ_e\u001b[0m\n", + "File saved at: ../../data/sbml_corrected/e_coli_core.xml\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: iECED1_1282\n", + "\u001b[0;93mWARNING : \n", + " - R_CAT: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_FHL: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_DHPTDNR: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_DHPTDNRN: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCASPtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCFUMtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCMALtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCTARTtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SPODM: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SPODMpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_CHITOBpts: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_URCN: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_CHITPH: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_EX_chitob_e: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\u001b[0m\n", + "\n", + "\n", + "\u001b[0;36m############################################\n", + "############################################\n", + " WRITING SBML FILE \n", + "############################################\n", + "############################################\n", + "\u001b[0m\n", + "\u001b[0;93mWARNING : IMPORT REACTION REMOVED BY DEFAULT\u001b[0m\n", + "\u001b[0;93mWARNING : If you want to keep import reaction\n", + "use option -kir / --keep-import-reactions\u001b[0m\n", + "\u001b[0;93mWARNING : Import reaction removed:\n", + "\t- R_EX_acac_e\n", + "\t- R_DM_4crsol_c\n", + "\t- R_DM_5drib_c\n", + "\t- R_DM_aacald_c\n", + "\t- R_EX_ac_e\n", + "\t- R_DM_amob_c\n", + "\t- R_DM_mththf_c\n", + "\t- R_EX_acgal_e\n", + "\t- R_DM_oxam_c\n", + "\t- R_EX_acald_e\n", + "\t- R_EX_12ppd__R_e\n", + "\t- R_EX_12ppd__S_e\n", + "\t- R_EX_14glucan_e\n", + "\t- R_EX_15dap_e\n", + "\t- R_EX_acgal1p_e\n", + "\t- R_EX_23camp_e\n", + "\t- R_EX_acgam_e\n", + "\t- R_EX_enlipa_e\n", + "\t- R_EX_acgam1p_e\n", + "\t- R_EX_enter_e\n", + "\t- R_EX_23ccmp_e\n", + "\t- R_EX_etha_e\n", + "\t- R_EX_acmana_e\n", + "\t- R_EX_acmum_e\n", + "\t- R_EX_acnam_e\n", + "\t- R_EX_ethso3_e\n", + "\t- R_EX_23cgmp_e\n", + "\t- R_EX_etoh_e\n", + "\t- R_EX_acolipa_e\n", + "\t- R_EX_acser_e\n", + "\t- R_EX_f6p_e\n", + "\t- R_EX_fald_e\n", + "\t- R_EX_fe2_e\n", + "\t- R_EX_fe3_e\n", + "\t- R_EX_fe3dcit_e\n", + "\t- R_EX_fe3dhbzs_e\n", + "\t- R_EX_fe3hox_e\n", + "\t- R_EX_fe3hox_un_e\n", + "\t- R_EX_ade_e\n", + "\t- R_EX_adn_e\n", + "\t- R_EX_adocbl_e\n", + "\t- R_EX_ag_e\n", + "\t- R_EX_agm_e\n", + "\t- R_EX_akg_e\n", + "\t- R_EX_ala_B_e\n", + "\t- R_EX_ala__D_e\n", + "\t- R_EX_ala__L_e\n", + "\t- R_EX_alaala_e\n", + "\t- R_EX_all__D_e\n", + "\t- R_EX_alltn_e\n", + "\t- R_EX_fecrm_e\n", + "\t- R_EX_fecrm_un_e\n", + "\t- R_EX_feenter_e\n", + "\t- R_EX_feoxam_e\n", + "\t- R_EX_feoxam_un_e\n", + "\t- R_EX_amp_e\n", + "\t- R_EX_anhgm_e\n", + "\t- R_EX_23cump_e\n", + "\t- R_EX_arab__L_e\n", + "\t- R_EX_arbt_e\n", + "\t- R_EX_for_e\n", + "\t- R_EX_fru_e\n", + "\t- R_EX_frulys_e\n", + "\t- R_EX_arbtn_e\n", + "\t- R_EX_23dappa_e\n", + "\t- R_EX_arbtn_fe3_e\n", + "\t- R_EX_arg__L_e\n", + "\t- R_EX_fruur_e\n", + "\t- R_EX_fuc__L_e\n", + "\t- R_EX_fum_e\n", + "\t- R_EX_fusa_e\n", + "\t- R_EX_g1p_e\n", + "\t- R_EX_g3pc_e\n", + "\t- R_EX_ascb__L_e\n", + "\t- R_EX_asn__L_e\n", + "\t- R_EX_aso3_e\n", + "\t- R_EX_asp__L_e\n", + "\t- R_EX_g3pe_e\n", + "\t- R_EX_g3pg_e\n", + "\t- R_EX_btn_e\n", + "\t- R_EX_26dap__M_e\n", + "\t- R_EX_but_e\n", + "\t- R_EX_butso3_e\n", + "\t- R_EX_ca2_e\n", + "\t- R_EX_g3pi_e\n", + "\t- R_EX_g3ps_e\n", + "\t- R_EX_g6p_e\n", + "\t- R_EX_gal_e\n", + "\t- R_EX_cbi_e\n", + "\t- R_EX_gal_bD_e\n", + "\t- R_EX_cbl1_e\n", + "\t- R_EX_gal1p_e\n", + "\t- R_EX_2ddglcn_e\n", + "\t- R_EX_galct__D_e\n", + "\t- R_EX_galctn__D_e\n", + "\t- R_EX_cd2_e\n", + "\t- R_EX_cgly_e\n", + "\t- R_EX_chol_e\n", + "\t- R_EX_34dhpac_e\n", + "\t- R_EX_chtbs_e\n", + "\t- R_EX_cit_e\n", + "\t- R_EX_galctn__L_e\n", + "\t- R_EX_galt_e\n", + "\t- R_EX_galur_e\n", + "\t- R_EX_cl_e\n", + "\t- R_EX_cm_e\n", + "\t- R_EX_gam_e\n", + "\t- R_EX_gam6p_e\n", + "\t- R_EX_gbbtn_e\n", + "\t- R_EX_cmp_e\n", + "\t- R_EX_co2_e\n", + "\t- R_EX_cobalt2_e\n", + "\t- R_EX_colipa_e\n", + "\t- R_EX_colipap_e\n", + "\t- R_EX_gdp_e\n", + "\t- R_EX_3amp_e\n", + "\t- R_EX_glc__D_e\n", + "\t- R_EX_cpgn_e\n", + "\t- R_EX_cpgn_un_e\n", + "\t- R_EX_glcn_e\n", + "\t- R_EX_glcr_e\n", + "\t- R_EX_crn_e\n", + "\t- R_EX_3cmp_e\n", + "\t- R_EX_crn__D_e\n", + "\t- R_EX_csn_e\n", + "\t- R_EX_glcur_e\n", + "\t- R_EX_glcur1p_e\n", + "\t- R_EX_gln__L_e\n", + "\t- R_EX_cu_e\n", + "\t- R_EX_3gmp_e\n", + "\t- R_EX_glu__L_e\n", + "\t- R_EX_cu2_e\n", + "\t- R_EX_gly_e\n", + "\t- R_EX_cyan_e\n", + "\t- R_EX_3hcinnm_e\n", + "\t- R_EX_cynt_e\n", + "\t- R_EX_glyald_e\n", + "\t- R_EX_glyb_e\n", + "\t- R_EX_cys__D_e\n", + "\t- R_EX_glyc_e\n", + "\t- R_EX_3hpp_e\n", + "\t- R_EX_glyc__R_e\n", + "\t- R_EX_glyc2p_e\n", + "\t- R_EX_cys__L_e\n", + "\t- R_EX_cytd_e\n", + "\t- R_EX_dad_2_e\n", + "\t- R_EX_damp_e\n", + "\t- R_EX_dca_e\n", + "\t- R_EX_dcmp_e\n", + "\t- R_EX_glyc3p_e\n", + "\t- R_EX_glyclt_e\n", + "\t- R_EX_gmp_e\n", + "\t- R_EX_gsn_e\n", + "\t- R_EX_gthox_e\n", + "\t- R_EX_dcyt_e\n", + "\t- R_EX_ddca_e\n", + "\t- R_EX_3hpppn_e\n", + "\t- R_EX_dgmp_e\n", + "\t- R_EX_dgsn_e\n", + "\t- R_EX_gthrd_e\n", + "\t- R_EX_gtp_e\n", + "\t- R_EX_gua_e\n", + "\t- R_EX_dha_e\n", + "\t- R_EX_dimp_e\n", + "\t- R_EX_h_e\n", + "\t- R_EX_h2_e\n", + "\t- R_EX_din_e\n", + "\t- R_EX_3ump_e\n", + "\t- R_EX_dms_e\n", + "\t- R_EX_dmso_e\n", + "\t- R_EX_h2o_e\n", + "\t- R_EX_h2o2_e\n", + "\t- R_EX_dopa_e\n", + "\t- R_EX_h2s_e\n", + "\t- R_EX_doxrbcn_e\n", + "\t- R_EX_4abut_e\n", + "\t- R_EX_dtmp_e\n", + "\t- R_EX_dump_e\n", + "\t- R_EX_hacolipa_e\n", + "\t- R_EX_halipa_e\n", + "\t- R_EX_hdca_e\n", + "\t- R_EX_duri_e\n", + "\t- R_EX_4hoxpacd_e\n", + "\t- R_EX_eca4colipa_e\n", + "\t- R_EX_imp_e\n", + "\t- R_EX_hdcea_e\n", + "\t- R_EX_hg2_e\n", + "\t- R_EX_his__L_e\n", + "\t- R_EX_hom__L_e\n", + "\t- R_EX_hxa_e\n", + "\t- R_EX_hxan_e\n", + "\t- R_EX_indole_e\n", + "\t- R_EX_5dglcn_e\n", + "\t- R_EX_inost_e\n", + "\t- R_EX_ins_e\n", + "\t- R_EX_idon__L_e\n", + "\t- R_EX_ile__L_e\n", + "\t- R_EX_isetac_e\n", + "\t- R_EX_5mtr_e\n", + "\t- R_EX_k_e\n", + "\t- R_EX_kdo2lipid4_e\n", + "\t- R_EX_LalaDglu_e\n", + "\t- R_EX_lac__D_e\n", + "\t- R_EX_rmn_e\n", + "\t- R_EX_sbt__D_e\n", + "\t- R_EX_sel_e\n", + "\t- R_EX_lac__L_e\n", + "\t- R_EX_ser__D_e\n", + "\t- R_EX_lcts_e\n", + "\t- R_EX_LalaDgluMdap_e\n", + "\t- R_EX_leu__L_e\n", + "\t- R_EX_lipa_e\n", + "\t- R_EX_ser__L_e\n", + "\t- R_EX_skm_e\n", + "\t- R_EX_LalaDgluMdapDala_e\n", + "\t- R_EX_slnt_e\n", + "\t- R_EX_lipa_cold_e\n", + "\t- R_EX_lipoate_e\n", + "\t- R_EX_so2_e\n", + "\t- R_EX_lys__L_e\n", + "\t- R_EX_lyx__L_e\n", + "\t- R_EX_mal__D_e\n", + "\t- R_EX_so3_e\n", + "\t- R_EX_so4_e\n", + "\t- R_EX_spmd_e\n", + "\t- R_EX_mal__L_e\n", + "\t- R_EX_LalaLglu_e\n", + "\t- R_EX_malt_e\n", + "\t- R_EX_malthx_e\n", + "\t- R_EX_maltpt_e\n", + "\t- R_EX_succ_e\n", + "\t- R_EX_sucr_e\n", + "\t- R_EX_sulfac_e\n", + "\t- R_EX_tartr__D_e\n", + "\t- R_EX_tartr__L_e\n", + "\t- R_EX_taur_e\n", + "\t- R_EX_tcynt_e\n", + "\t- R_EX_thm_e\n", + "\t- R_EX_malttr_e\n", + "\t- R_EX_maltttr_e\n", + "\t- R_EX_man_e\n", + "\t- R_EX_man6p_e\n", + "\t- R_EX_manglyc_e\n", + "\t- R_EX_melib_e\n", + "\t- R_EX_meoh_e\n", + "\t- R_EX_thr__L_e\n", + "\t- R_EX_thrp_e\n", + "\t- R_EX_thym_e\n", + "\t- R_EX_met__D_e\n", + "\t- R_EX_met__L_e\n", + "\t- R_EX_metsox_R__L_e\n", + "\t- R_EX_thymd_e\n", + "\t- R_EX_tma_e\n", + "\t- R_EX_metsox_S__L_e\n", + "\t- R_EX_mg2_e\n", + "\t- R_EX_mincyc_e\n", + "\t- R_EX_minohp_e\n", + "\t- R_EX_mmet_e\n", + "\t- R_EX_mn2_e\n", + "\t- R_EX_tmao_e\n", + "\t- R_EX_tre_e\n", + "\t- R_EX_trp__L_e\n", + "\t- R_EX_tsul_e\n", + "\t- R_EX_ttdca_e\n", + "\t- R_EX_ttdcea_e\n", + "\t- R_EX_mnl_e\n", + "\t- R_EX_mobd_e\n", + "\t- R_EX_mso3_e\n", + "\t- R_EX_ttrcyc_e\n", + "\t- R_EX_tungs_e\n", + "\t- R_EX_n2o_e\n", + "\t- R_EX_tym_e\n", + "\t- R_EX_tyr__L_e\n", + "\t- R_EX_tyrp_e\n", + "\t- R_EX_na1_e\n", + "\t- R_EX_nac_e\n", + "\t- R_EX_nh4_e\n", + "\t- R_EX_ni2_e\n", + "\t- R_EX_uacgam_e\n", + "\t- R_EX_nmn_e\n", + "\t- R_EX_udpacgal_e\n", + "\t- R_EX_udpg_e\n", + "\t- R_EX_udpgal_e\n", + "\t- R_EX_no_e\n", + "\t- R_EX_no2_e\n", + "\t- R_EX_udpglcur_e\n", + "\t- R_EX_ump_e\n", + "\t- R_EX_ura_e\n", + "\t- R_EX_urea_e\n", + "\t- R_EX_uri_e\n", + "\t- R_EX_no3_e\n", + "\t- R_EX_novbcn_e\n", + "\t- R_EX_o16a4colipa_e\n", + "\t- R_EX_o2_e\n", + "\t- R_EX_o2s_e\n", + "\t- R_EX_ocdca_e\n", + "\t- R_EX_ocdcea_e\n", + "\t- R_EX_val__L_e\n", + "\t- R_EX_octa_e\n", + "\t- R_EX_xan_e\n", + "\t- R_EX_xmp_e\n", + "\t- R_EX_orn_e\n", + "\t- R_EX_orot_e\n", + "\t- R_EX_pacald_e\n", + "\t- R_EX_peamn_e\n", + "\t- R_EX_xtsn_e\n", + "\t- R_EX_xyl__D_e\n", + "\t- R_EX_phe__L_e\n", + "\t- R_EX_pheme_e\n", + "\t- R_EX_pi_e\n", + "\t- R_EX_pnto__R_e\n", + "\t- R_EX_xylu__L_e\n", + "\t- R_EX_zn2_e\n", + "\t- R_EX_ppa_e\n", + "\t- R_EX_ppal_e\n", + "\t- R_EX_pppn_e\n", + "\t- R_EX_ppt_e\n", + "\t- R_EX_pro__L_e\n", + "\t- R_EX_progly_e\n", + "\t- R_EX_psclys_e\n", + "\t- R_EX_pser__L_e\n", + "\t- R_EX_ptrc_e\n", + "\t- R_EX_pydam_e\n", + "\t- R_EX_pydx_e\n", + "\t- R_EX_pydxn_e\n", + "\t- R_EX_pyr_e\n", + "\t- R_EX_quin_e\n", + "\t- R_EX_r5p_e\n", + "\t- R_EX_rfamp_e\n", + "\t- R_EX_rib__D_e\n", + "\t- R_EX_salchs2_e\n", + "\t- R_EX_fe_e\n", + "\t- R_EX_feroxE_e\n", + "\t- R_EX_pep_e\n", + "\t- R_EX_23dhbzs3_e\n", + "\t- R_EX_tet_e\n", + "\t- R_EX_salchs4_e\n", + "\t- R_EX_tag__D_e\n", + "\t- R_EX_rnam_e\n", + "\t- R_EX_oaa_e\n", + "\t- R_EX_salchs2fe_e\n", + "\t- R_EX_feroxEfe_e\n", + "\t- R_EX_cysi__L_e\n", + "\t- R_EX_airs_e\n", + "\t- R_EX_fe3dhbzs3_e\n", + "\t- R_EX_dxyl_e\n", + "\t- R_EX_3pg_e\n", + "\t- R_EX_drib_e\n", + "\t- R_EX_feroxBfe_e\n", + "\t- R_EX_salchs4fe_e\n", + "\t- R_EX_2pg_e\n", + "\t- R_EX_udcpo4_e\n", + "\t- R_EX_feroxGfe_e\n", + "\t- R_EX_frmd_e\n", + "\t- R_EX_colipaOA_e\n", + "\t- R_EX_salchsx_e\n", + "\t- R_EX_AEP_e\n", + "\t- R_EX_icit_e\n", + "\t- R_EX_udcpo5_e\n", + "\t- R_EX_guln__L_e\n", + "\t- R_EX_feroxG_e\n", + "\t- R_EX_colipa20Oag_e\n", + "\t- R_EX_remnant1_e\n", + "\t- R_EX_kokdolipidA_e\n", + "\t- R_EX_diact_e\n", + "\t- R_EX_3hoxpac_e\n", + "\t- R_EX_4hoxpac_e\n", + "\t- R_EX_btd_RR_e\n", + "\t- R_EX_13ppd_e\n", + "\t- R_EX_rbt_e\n", + "\t- R_EX_cellb_e\n", + "\t- R_EX_bz_e\n", + "\t- R_EX_3ntym_e\n", + "\t- R_EX_6apa_e\n", + "\t- R_EX_abt__D_e\n", + "\t- R_EX_peng_e\n", + "\t- R_EX_pac_e\n", + "\t- R_EX_salcn_e\n", + "\t- R_EX_galam_e\n", + "\t- R_DM_lipidA_core_e_p\n", + "\t- R_EX_raffin_e\n", + "\t- R_EX_o6a4colipa_e\n", + "\t- R_EX_4hphac_e\n", + "\t- R_EX_arab__D_e\n", + "\t- R_EX_4abz_e\n", + "\t- R_EX_acon_C_e\n", + "\t- R_EX_tcb_e\n", + "\t- R_EX_feroxB_e\n", + "\t- R_EX_tton_e\n", + "\t- R_EX_4hthr_e\u001b[0m\n", + "File saved at: ../../data/sbml_corrected/iECED1_1282.xml\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: iECIAI1_1343\n", + "\u001b[0;93mWARNING : \n", + " - R_CAT: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SPODM: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_DHPTDNR: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_DHPTDNRN: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_FHL: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SPODMpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCASPtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCFUMtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCMALtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCTARTtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_CHITOBpts: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_CHITPH: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_URCN: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_EX_chitob_e: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\u001b[0m\n", + "\n", + "\n", + "\u001b[0;36m############################################\n", + "############################################\n", + " WRITING SBML FILE \n", + "############################################\n", + "############################################\n", + "\u001b[0m\n", + "\u001b[0;93mWARNING : IMPORT REACTION REMOVED BY DEFAULT\u001b[0m\n", + "\u001b[0;93mWARNING : If you want to keep import reaction\n", + "use option -kir / --keep-import-reactions\u001b[0m\n", + "\u001b[0;93mWARNING : Import reaction removed:\n", + "\t- R_EX_lcts_e\n", + "\t- R_EX_lys__L_e\n", + "\t- R_EX_mal__L_e\n", + "\t- R_EX_malt_e\n", + "\t- R_EX_man_e\n", + "\t- R_EX_met__L_e\n", + "\t- R_EX_mg2_e\n", + "\t- R_EX_mn2_e\n", + "\t- R_EX_mnl_e\n", + "\t- R_EX_mobd_e\n", + "\t- R_EX_na1_e\n", + "\t- R_EX_nac_e\n", + "\t- R_EX_nh4_e\n", + "\t- R_EX_ni2_e\n", + "\t- R_EX_no2_e\n", + "\t- R_EX_no3_e\n", + "\t- R_EX_o2_e\n", + "\t- R_EX_orn_e\n", + "\t- R_EX_phe__L_e\n", + "\t- R_EX_pheme_e\n", + "\t- R_EX_pi_e\n", + "\t- R_EX_pro__L_e\n", + "\t- R_EX_ptrc_e\n", + "\t- R_EX_rib__D_e\n", + "\t- R_EX_salcn_e\n", + "\t- R_EX_ser__L_e\n", + "\t- R_EX_so4_e\n", + "\t- R_EX_spmd_e\n", + "\t- R_EX_sucr_e\n", + "\t- R_EX_thm_e\n", + "\t- R_EX_thr__L_e\n", + "\t- R_EX_thymd_e\n", + "\t- R_EX_tre_e\n", + "\t- R_EX_trp__L_e\n", + "\t- R_EX_tyr__L_e\n", + "\t- R_EX_ura_e\n", + "\t- R_EX_urea_e\n", + "\t- R_EX_uri_e\n", + "\t- R_EX_val__L_e\n", + "\t- R_EX_xan_e\n", + "\t- R_EX_zn2_e\n", + "\t- R_EX_acgam_e\n", + "\t- R_EX_acnam_e\n", + "\t- R_EX_adn_e\n", + "\t- R_EX_akg_e\n", + "\t- R_EX_ala__D_e\n", + "\t- R_EX_ala__L_e\n", + "\t- R_EX_arg__L_e\n", + "\t- R_EX_asp__L_e\n", + "\t- R_EX_btn_e\n", + "\t- R_EX_chol_e\n", + "\t- R_EX_co2_e\n", + "\t- R_EX_cobalt2_e\n", + "\t- R_EX_crn_e\n", + "\t- R_EX_csn_e\n", + "\t- R_EX_cu2_e\n", + "\t- R_EX_cys__L_e\n", + "\t- R_EX_cytd_e\n", + "\t- R_EX_etoh_e\n", + "\t- R_EX_fe2_e\n", + "\t- R_EX_for_e\n", + "\t- R_EX_fru_e\n", + "\t- R_EX_galt_e\n", + "\t- R_EX_glc__D_e\n", + "\t- R_EX_glcn_e\n", + "\t- R_EX_glu__L_e\n", + "\t- R_EX_gly_e\n", + "\t- R_EX_glyald_e\n", + "\t- R_EX_glyb_e\n", + "\t- R_EX_glyc3p_e\n", + "\t- R_EX_glyc_e\n", + "\t- R_EX_gua_e\n", + "\t- R_EX_h2o_e\n", + "\t- R_EX_h_e\n", + "\t- R_EX_hdca_e\n", + "\t- R_EX_his__L_e\n", + "\t- R_EX_ile__L_e\n", + "\t- R_EX_k_e\n", + "\t- R_EX_lac__D_e\n", + "\t- R_EX_lac__L_e\n", + "\t- R_EX_leu__L_e\n", + "\t- R_EX_4abut_e\n", + "\t- R_EX_4abz_e\n", + "\t- R_EX_ac_e\n", + "\t- R_EX_acald_e\n", + "\t- R_EX_ade_e\n", + "\t- R_EX_alltn_e\n", + "\t- R_EX_arab__D_e\n", + "\t- R_EX_arab__L_e\n", + "\t- R_EX_asn__L_e\n", + "\t- R_EX_btd_RR_e\n", + "\t- R_EX_cit_e\n", + "\t- R_EX_dca_e\n", + "\t- R_EX_dcyt_e\n", + "\t- R_EX_ddca_e\n", + "\t- R_EX_dgsn_e\n", + "\t- R_EX_din_e\n", + "\t- R_EX_duri_e\n", + "\t- R_EX_etha_e\n", + "\t- R_EX_fum_e\n", + "\t- R_EX_g3pc_e\n", + "\t- R_EX_g3pi_e\n", + "\t- R_EX_gal_e\n", + "\t- R_EX_galur_e\n", + "\t- R_EX_gam6p_e\n", + "\t- R_EX_gln__L_e\n", + "\t- R_EX_gsn_e\n", + "\t- R_EX_gthox_e\n", + "\t- R_EX_gthrd_e\n", + "\t- R_EX_hdcea_e\n", + "\t- R_EX_hxan_e\n", + "\t- R_EX_inost_e\n", + "\t- R_EX_ins_e\n", + "\t- R_EX_melib_e\n", + "\t- R_EX_mmet_e\n", + "\t- R_EX_nmn_e\n", + "\t- R_EX_oaa_e\n", + "\t- R_EX_ocdca_e\n", + "\t- R_EX_ocdcea_e\n", + "\t- R_EX_pacald_e\n", + "\t- R_EX_pnto__R_e\n", + "\t- R_EX_pyr_e\n", + "\t- R_EX_sbt__D_e\n", + "\t- R_EX_so3_e\n", + "\t- R_EX_succ_e\n", + "\t- R_EX_taur_e\n", + "\t- R_EX_thym_e\n", + "\t- R_EX_ttdca_e\n", + "\t- R_EX_xtsn_e\n", + "\t- R_EX_xyl__D_e\n", + "\t- R_EX_12ppd__R_e\n", + "\t- R_EX_12ppd__S_e\n", + "\t- R_EX_14glucan_e\n", + "\t- R_EX_15dap_e\n", + "\t- R_EX_23camp_e\n", + "\t- R_EX_23ccmp_e\n", + "\t- R_EX_23cgmp_e\n", + "\t- R_EX_23cump_e\n", + "\t- R_DM_4crsol_c\n", + "\t- R_EX_23dappa_e\n", + "\t- R_EX_26dap__M_e\n", + "\t- R_DM_5drib_c\n", + "\t- R_EX_2ddglcn_e\n", + "\t- R_DM_aacald_c\n", + "\t- R_DM_amob_c\n", + "\t- R_DM_mththf_c\n", + "\t- R_EX_34dhpac_e\n", + "\t- R_EX_3amp_e\n", + "\t- R_DM_oxam_c\n", + "\t- R_EX_3cmp_e\n", + "\t- R_EX_3gmp_e\n", + "\t- R_EX_3hcinnm_e\n", + "\t- R_EX_3hpp_e\n", + "\t- R_EX_3hpppn_e\n", + "\t- R_EX_3ump_e\n", + "\t- R_EX_4hoxpacd_e\n", + "\t- R_EX_enter_e\n", + "\t- R_EX_ethso3_e\n", + "\t- R_EX_f6p_e\n", + "\t- R_EX_fald_e\n", + "\t- R_EX_5dglcn_e\n", + "\t- R_EX_5mtr_e\n", + "\t- R_EX_LalaDglu_e\n", + "\t- R_EX_fe3_e\n", + "\t- R_EX_fe3dcit_e\n", + "\t- R_EX_fe3dhbzs_e\n", + "\t- R_EX_fe3hox_e\n", + "\t- R_EX_LalaDgluMdap_e\n", + "\t- R_EX_LalaDgluMdapDala_e\n", + "\t- R_EX_LalaLglu_e\n", + "\t- R_EX_fe3hox_un_e\n", + "\t- R_EX_fecrm_e\n", + "\t- R_EX_acac_e\n", + "\t- R_EX_acgal_e\n", + "\t- R_EX_fecrm_un_e\n", + "\t- R_EX_feenter_e\n", + "\t- R_EX_feoxam_e\n", + "\t- R_EX_feoxam_un_e\n", + "\t- R_EX_acgal1p_e\n", + "\t- R_EX_acgam1p_e\n", + "\t- R_EX_acmana_e\n", + "\t- R_EX_acmum_e\n", + "\t- R_EX_acolipa_e\n", + "\t- R_EX_acser_e\n", + "\t- R_EX_adocbl_e\n", + "\t- R_EX_ag_e\n", + "\t- R_EX_frulys_e\n", + "\t- R_EX_fruur_e\n", + "\t- R_EX_fuc__L_e\n", + "\t- R_EX_fusa_e\n", + "\t- R_EX_g1p_e\n", + "\t- R_EX_g3pe_e\n", + "\t- R_EX_agm_e\n", + "\t- R_EX_ala_B_e\n", + "\t- R_EX_g3pg_e\n", + "\t- R_EX_alaala_e\n", + "\t- R_EX_all__D_e\n", + "\t- R_EX_amp_e\n", + "\t- R_EX_anhgm_e\n", + "\t- R_EX_g3ps_e\n", + "\t- R_EX_g6p_e\n", + "\t- R_EX_gal_bD_e\n", + "\t- R_EX_gal1p_e\n", + "\t- R_EX_arbt_e\n", + "\t- R_EX_arbtn_e\n", + "\t- R_EX_arbtn_fe3_e\n", + "\t- R_EX_galct__D_e\n", + "\t- R_EX_galctn__D_e\n", + "\t- R_EX_galctn__L_e\n", + "\t- R_EX_ascb__L_e\n", + "\t- R_EX_aso3_e\n", + "\t- R_EX_but_e\n", + "\t- R_EX_gam_e\n", + "\t- R_EX_gbbtn_e\n", + "\t- R_EX_gdp_e\n", + "\t- R_EX_butso3_e\n", + "\t- R_EX_ca2_e\n", + "\t- R_EX_cbi_e\n", + "\t- R_EX_cbl1_e\n", + "\t- R_EX_cd2_e\n", + "\t- R_EX_cgly_e\n", + "\t- R_EX_chtbs_e\n", + "\t- R_EX_glcr_e\n", + "\t- R_EX_glcur_e\n", + "\t- R_EX_glcur1p_e\n", + "\t- R_EX_glyc__R_e\n", + "\t- R_EX_glyc2p_e\n", + "\t- R_EX_glyclt_e\n", + "\t- R_EX_gmp_e\n", + "\t- R_EX_gtp_e\n", + "\t- R_EX_h2_e\n", + "\t- R_EX_cl_e\n", + "\t- R_EX_cm_e\n", + "\t- R_EX_h2o2_e\n", + "\t- R_EX_cmp_e\n", + "\t- R_EX_colipa_e\n", + "\t- R_EX_colipap_e\n", + "\t- R_EX_cpgn_e\n", + "\t- R_EX_h2s_e\n", + "\t- R_EX_hacolipa_e\n", + "\t- R_EX_halipa_e\n", + "\t- R_EX_hg2_e\n", + "\t- R_EX_cpgn_un_e\n", + "\t- R_EX_crn__D_e\n", + "\t- R_EX_hom__L_e\n", + "\t- R_EX_cu_e\n", + "\t- R_EX_cyan_e\n", + "\t- R_EX_cynt_e\n", + "\t- R_EX_hxa_e\n", + "\t- R_EX_idon__L_e\n", + "\t- R_EX_cys__D_e\n", + "\t- R_EX_imp_e\n", + "\t- R_EX_dad_2_e\n", + "\t- R_EX_damp_e\n", + "\t- R_EX_dcmp_e\n", + "\t- R_EX_indole_e\n", + "\t- R_EX_isetac_e\n", + "\t- R_EX_kdo2lipid4_e\n", + "\t- R_EX_lipa_e\n", + "\t- R_EX_lipa_cold_e\n", + "\t- R_EX_lipoate_e\n", + "\t- R_EX_dgmp_e\n", + "\t- R_EX_dha_e\n", + "\t- R_EX_dimp_e\n", + "\t- R_EX_dms_e\n", + "\t- R_EX_dmso_e\n", + "\t- R_EX_dopa_e\n", + "\t- R_EX_doxrbcn_e\n", + "\t- R_EX_lyx__L_e\n", + "\t- R_EX_mal__D_e\n", + "\t- R_EX_dtmp_e\n", + "\t- R_EX_malthx_e\n", + "\t- R_EX_maltpt_e\n", + "\t- R_EX_dump_e\n", + "\t- R_EX_eca4colipa_e\n", + "\t- R_EX_malttr_e\n", + "\t- R_EX_maltttr_e\n", + "\t- R_EX_man6p_e\n", + "\t- R_EX_enlipa_e\n", + "\t- R_EX_ppt_e\n", + "\t- R_EX_manglyc_e\n", + "\t- R_EX_meoh_e\n", + "\t- R_EX_met__D_e\n", + "\t- R_EX_metsox_R__L_e\n", + "\t- R_EX_progly_e\n", + "\t- R_EX_psclys_e\n", + "\t- R_EX_pser__L_e\n", + "\t- R_EX_metsox_S__L_e\n", + "\t- R_EX_mincyc_e\n", + "\t- R_EX_minohp_e\n", + "\t- R_EX_mso3_e\n", + "\t- R_EX_n2o_e\n", + "\t- R_EX_no_e\n", + "\t- R_EX_novbcn_e\n", + "\t- R_EX_o16a4colipa_e\n", + "\t- R_EX_pydam_e\n", + "\t- R_EX_pydx_e\n", + "\t- R_EX_pydxn_e\n", + "\t- R_EX_quin_e\n", + "\t- R_EX_r5p_e\n", + "\t- R_EX_rfamp_e\n", + "\t- R_EX_rmn_e\n", + "\t- R_EX_o2s_e\n", + "\t- R_EX_octa_e\n", + "\t- R_EX_orot_e\n", + "\t- R_EX_sel_e\n", + "\t- R_EX_ser__D_e\n", + "\t- R_EX_skm_e\n", + "\t- R_EX_slnt_e\n", + "\t- R_EX_so2_e\n", + "\t- R_EX_peamn_e\n", + "\t- R_EX_ppa_e\n", + "\t- R_EX_ppal_e\n", + "\t- R_EX_pppn_e\n", + "\t- R_EX_sulfac_e\n", + "\t- R_EX_tartr__D_e\n", + "\t- R_EX_tartr__L_e\n", + "\t- R_EX_tcynt_e\n", + "\t- R_EX_thrp_e\n", + "\t- R_EX_tma_e\n", + "\t- R_EX_tmao_e\n", + "\t- R_EX_tsul_e\n", + "\t- R_EX_ttdcea_e\n", + "\t- R_EX_ttrcyc_e\n", + "\t- R_EX_tungs_e\n", + "\t- R_EX_tym_e\n", + "\t- R_EX_tyrp_e\n", + "\t- R_EX_uacgam_e\n", + "\t- R_EX_udpacgal_e\n", + "\t- R_EX_udpg_e\n", + "\t- R_EX_udpgal_e\n", + "\t- R_EX_udpglcur_e\n", + "\t- R_EX_ump_e\n", + "\t- R_EX_xmp_e\n", + "\t- R_EX_xylu__L_e\n", + "\t- R_EX_feroxB_e\n", + "\t- R_EX_tcb_e\n", + "\t- R_EX_tton_e\n", + "\t- R_EX_4hthr_e\n", + "\t- R_EX_feroxEfe_e\n", + "\t- R_EX_feroxE_e\n", + "\t- R_EX_pep_e\n", + "\t- R_EX_23dhbzs3_e\n", + "\t- R_EX_tet_e\n", + "\t- R_EX_salchs4_e\n", + "\t- R_EX_tag__D_e\n", + "\t- R_EX_salchs2fe_e\n", + "\t- R_EX_cysi__L_e\n", + "\t- R_EX_fe3dhbzs3_e\n", + "\t- R_EX_drib_e\n", + "\t- R_EX_4hphac_e\n", + "\t- R_EX_feroxBfe_e\n", + "\t- R_EX_salchs4fe_e\n", + "\t- R_EX_2pg_e\n", + "\t- R_EX_acon_C_e\n", + "\t- R_EX_frmd_e\n", + "\t- R_EX_colipaOA_e\n", + "\t- R_EX_salchsx_e\n", + "\t- R_EX_AEP_e\n", + "\t- R_EX_icit_e\n", + "\t- R_EX_colipa20Oag_e\n", + "\t- R_EX_rnam_e\n", + "\t- R_EX_airs_e\n", + "\t- R_EX_dxyl_e\n", + "\t- R_EX_3pg_e\n", + "\t- R_EX_remnant1_e\n", + "\t- R_EX_udcpo4_e\n", + "\t- R_EX_salchs2_e\n", + "\t- R_EX_feroxGfe_e\n", + "\t- R_EX_udcpo5_e\n", + "\t- R_EX_3ntym_e\n", + "\t- R_EX_guln__L_e\n", + "\t- R_EX_6apa_e\n", + "\t- R_EX_feroxG_e\n", + "\t- R_EX_4hoxpac_e\n", + "\t- R_EX_peng_e\n", + "\t- R_EX_pac_e\n", + "\t- R_EX_diact_e\n", + "\t- R_EX_galam_e\n", + "\t- R_EX_raffin_e\n", + "\t- R_DM_lipidA_core_e_p\n", + "\t- R_EX_13ppd_e\n", + "\t- R_EX_rbt_e\n", + "\t- R_EX_cellb_e\n", + "\t- R_EX_bz_e\n", + "\t- R_EX_abt__D_e\n", + "\t- R_EX_fe_e\n", + "\t- R_EX_kokdolipidA_e\n", + "\t- R_EX_3hoxpac_e\n", + "\t- R_EX_o6a4colipa_e\u001b[0m\n", + "File saved at: ../../data/sbml_corrected/iECIAI1_1343.xml\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: iAM_Pb448\n", + "\u001b[0;93mWARNING : - R_ARGLYSex: Reactants and products exchanged.\n", + " Boundaries was: [-1000000.0 ; 0.0]\n", + " - R_RE1342C: Reactants and products exchanged.\n", + " Boundaries was: [-1000000.0 ; 0.0]\n", + "\u001b[0m\n", + "\n", + "\n", + "\u001b[0;36m############################################\n", + "############################################\n", + " WRITING SBML FILE \n", + "############################################\n", + "############################################\n", + "\u001b[0m\n", + "\u001b[0;93mWARNING : IMPORT REACTION REMOVED BY DEFAULT\u001b[0m\n", + "\u001b[0;93mWARNING : If you want to keep import reaction\n", + "use option -kir / --keep-import-reactions\u001b[0m\n", + "\u001b[0;93mWARNING : Reaction with tag reversible modified:\n", + "\t- R_ARGLYSex\n", + "\t- R_RE1342C\u001b[0m\n", + "\u001b[0;93mWARNING : Reaction with Reactants and Products exchanged:\n", + "\t- R_ARGLYSex\n", + "\t- R_RE1342C\u001b[0m\n", + "\u001b[0;93mWARNING : Import reaction removed:\n", + "\t- R_EX_h2o_e\n", + "\t- R_DM_hmz_l\n", + "\t- R_SK_gcald_c\n", + "\t- R_DM_5mdr1p_c\n", + "\t- R_DM_hcys__L_c\n", + "\t- R_EX_lgn_e\n", + "\t- R_EX_4hba_e\n", + "\t- R_SK_dxyl5p_c\n", + "\t- R_SK_accoa_h\n", + "\t- R_EX_ptrc_e\n", + "\t- R_EX_spmd_e\n", + "\t- R_DM_oxptn_c\n", + "\t- R_EX_orn_e\n", + "\t- R_EX_chsterol_e\n", + "\t- R_EX_acgam_e\n", + "\t- R_EX_4abz_e\n", + "\t- R_EX_4ahmmp_e\n", + "\t- R_EX_5fthf_e\n", + "\t- R_EX_Hb_e\n", + "\t- R_EX_ac_e\n", + "\t- R_EX_ade_e\n", + "\t- R_EX_adn_e\n", + "\t- R_EX_adp_e\n", + "\t- R_EX_ala__L_e\n", + "\t- R_EX_amp_e\n", + "\t- R_EX_arachd_e\n", + "\t- R_EX_arg__L_e\n", + "\t- R_EX_asn__L_e\n", + "\t- R_EX_asp__L_e\n", + "\t- R_EX_atp_e\n", + "\t- R_EX_ca2_e\n", + "\t- R_EX_chol_e\n", + "\t- R_EX_cl_e\n", + "\t- R_EX_co2_e\n", + "\t- R_EX_cobalt2_e\n", + "\t- R_EX_csn_e\n", + "\t- R_EX_cu_e\n", + "\t- R_EX_cu2_e\n", + "\t- R_EX_cys__L_e\n", + "\t- R_EX_dha_e\n", + "\t- R_EX_dhpt_e\n", + "\t- R_EX_fe2_e\n", + "\t- R_EX_fol_e\n", + "\t- R_EX_for_e\n", + "\t- R_EX_fru_e\n", + "\t- R_EX_glc__D_e\n", + "\t- R_EX_gln__L_e\n", + "\t- R_EX_glu__L_e\n", + "\t- R_EX_gly_e\n", + "\t- R_EX_glyc_e\n", + "\t- R_EX_gsn_e\n", + "\t- R_EX_gthox_e\n", + "\t- R_EX_gthrd_e\n", + "\t- R_EX_gua_e\n", + "\t- R_EX_h_e\n", + "\t- R_DM_saccrp__L_c\n", + "\t- R_EX_Lpipecol_e\n", + "\t- R_EX_4pyrdx_e\n", + "\t- R_DM_pail345p_pf_16_0_18_1_c\n", + "\t- R_DM_mi13456p_c\n", + "\t- R_DM_pail345p_pf_18_0_18_1_c\n", + "\t- R_EX_hco3_e\n", + "\t- R_EX_hdca_e\n", + "\t- R_EX_his__L_e\n", + "\t- R_EX_hxan_e\n", + "\t- R_EX_ile__L_e\n", + "\t- R_EX_ins_e\n", + "\t- R_EX_k_e\n", + "\t- R_EX_lac__D_e\n", + "\t- R_EX_lac__L_e\n", + "\t- R_EX_leu__L_e\n", + "\t- R_EX_lipoate_e\n", + "\t- R_EX_lnlc_e\n", + "\t- R_EX_lys__L_e\n", + "\t- R_EX_man_e\n", + "\t- R_EX_met__L_e\n", + "\t- R_EX_mg2_e\n", + "\t- R_EX_mthgxl_e\n", + "\t- R_EX_na1_e\n", + "\t- R_EX_nac_e\n", + "\t- R_EX_ncam_e\n", + "\t- R_EX_nh4_e\n", + "\t- R_EX_ni2_e\n", + "\t- R_EX_o2_e\n", + "\t- R_EX_ocdca_e\n", + "\t- R_EX_ocdcea_e\n", + "\t- R_EX_phe__L_e\n", + "\t- R_EX_pi_e\n", + "\t- R_EX_pnto__R_e\n", + "\t- R_EX_pro__L_e\n", + "\t- R_EX_pyr_e\n", + "\t- R_EX_ribflv_e\n", + "\t- R_EX_ser__L_e\n", + "\t- R_EX_thm_e\n", + "\t- R_EX_thr__L_e\n", + "\t- R_EX_thym_e\n", + "\t- R_EX_thymd_e\n", + "\t- R_EX_trp__L_e\n", + "\t- R_EX_ttdca_e\n", + "\t- R_EX_tyr__L_e\n", + "\t- R_EX_ura_e\n", + "\t- R_EX_uri_e\n", + "\t- R_EX_val__L_e\u001b[0m\n", + "File saved at: ../../data/sbml_corrected/iAM_Pb448.xml\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: iEC1372_W3110\n", + "\u001b[0;93mWARNING : \n", + " - R_CAT: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_DHPTDNRN: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_DHPTDNR: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_FHL: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCTARTtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCFUMtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCMALtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCASPtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SPODM: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SPODMpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + " - R_EDTXSCOF is multiple (more than 1) metabolites import/export reaction. \n", + "\tDeleted as it is an import reaction.\n", + "\u001b[0m\n", + "\n", + "\n", + "\u001b[0;36m############################################\n", + "############################################\n", + " WRITING SBML FILE \n", + "############################################\n", + "############################################\n", + "\u001b[0m\n", + "\u001b[0;93mWARNING : IMPORT REACTION REMOVED BY DEFAULT\u001b[0m\n", + "\u001b[0;93mWARNING : If you want to keep import reaction\n", + "use option -kir / --keep-import-reactions\u001b[0m\n", + "\u001b[0;93mWARNING : Import reaction removed:\n", + "\t- R_EX_aso3_e\n", + "\t- R_EX_asp__L_e\n", + "\t- R_EX_btn_e\n", + "\t- R_EX_but_e\n", + "\t- R_EX_butso3_e\n", + "\t- R_EX_ca2_e\n", + "\t- R_EX_cbi_e\n", + "\t- R_EX_cbl1_e\n", + "\t- R_EX_cd2_e\n", + "\t- R_EX_cgly_e\n", + "\t- R_EX_chol_e\n", + "\t- R_EX_chtbs_e\n", + "\t- R_EX_cit_e\n", + "\t- R_EX_cl_e\n", + "\t- R_EX_cm_e\n", + "\t- R_EX_cmp_e\n", + "\t- R_EX_co2_e\n", + "\t- R_EX_asn__L_e\n", + "\t- R_DM_4crsol_c\n", + "\t- R_EX_cobalt2_e\n", + "\t- R_EX_g3ps_e\n", + "\t- R_EX_colipa_e\n", + "\t- R_EX_g6p_e\n", + "\t- R_EX_colipap_e\n", + "\t- R_EX_gal_e\n", + "\t- R_EX_gal_bD_e\n", + "\t- R_EX_cpgn_e\n", + "\t- R_EX_gal1p_e\n", + "\t- R_EX_galct__D_e\n", + "\t- R_DM_5drib_c\n", + "\t- R_EX_cpgn_un_e\n", + "\t- R_DM_aacald_c\n", + "\t- R_DM_amob_c\n", + "\t- R_DM_mththf_c\n", + "\t- R_DM_oxam_c\n", + "\t- R_EX_12ppd__R_e\n", + "\t- R_EX_12ppd__S_e\n", + "\t- R_EX_14glucan_e\n", + "\t- R_EX_15dap_e\n", + "\t- R_EX_galctn__D_e\n", + "\t- R_EX_galctn__L_e\n", + "\t- R_EX_galt_e\n", + "\t- R_EX_galur_e\n", + "\t- R_EX_gam_e\n", + "\t- R_EX_gam6p_e\n", + "\t- R_EX_gbbtn_e\n", + "\t- R_EX_gdp_e\n", + "\t- R_EX_glc__D_e\n", + "\t- R_EX_glcn_e\n", + "\t- R_EX_glcr_e\n", + "\t- R_EX_glcur_e\n", + "\t- R_EX_glcur1p_e\n", + "\t- R_EX_gln__L_e\n", + "\t- R_EX_23camp_e\n", + "\t- R_EX_23ccmp_e\n", + "\t- R_EX_23cgmp_e\n", + "\t- R_EX_23cump_e\n", + "\t- R_EX_23dappa_e\n", + "\t- R_EX_26dap__M_e\n", + "\t- R_EX_glu__L_e\n", + "\t- R_EX_gly_e\n", + "\t- R_EX_2ddglcn_e\n", + "\t- R_EX_crn_e\n", + "\t- R_EX_34dhpac_e\n", + "\t- R_EX_3amp_e\n", + "\t- R_EX_3cmp_e\n", + "\t- R_EX_3gmp_e\n", + "\t- R_EX_3hcinnm_e\n", + "\t- R_EX_glyald_e\n", + "\t- R_EX_glyb_e\n", + "\t- R_EX_glyc_e\n", + "\t- R_EX_glyc__R_e\n", + "\t- R_EX_glyc2p_e\n", + "\t- R_EX_glyc3p_e\n", + "\t- R_EX_glyclt_e\n", + "\t- R_EX_3hpp_e\n", + "\t- R_EX_3hpppn_e\n", + "\t- R_EX_3ump_e\n", + "\t- R_EX_gmp_e\n", + "\t- R_EX_crn__D_e\n", + "\t- R_EX_gsn_e\n", + "\t- R_EX_gthox_e\n", + "\t- R_EX_gthrd_e\n", + "\t- R_EX_gtp_e\n", + "\t- R_EX_4abut_e\n", + "\t- R_EX_4hoxpacd_e\n", + "\t- R_EX_5dglcn_e\n", + "\t- R_EX_5mtr_e\n", + "\t- R_EX_gua_e\n", + "\t- R_EX_csn_e\n", + "\t- R_EX_h_e\n", + "\t- R_EX_h2_e\n", + "\t- R_EX_LalaDglu_e\n", + "\t- R_EX_LalaDgluMdap_e\n", + "\t- R_EX_h2o_e\n", + "\t- R_EX_cu_e\n", + "\t- R_EX_h2o2_e\n", + "\t- R_EX_LalaDgluMdapDala_e\n", + "\t- R_EX_LalaLglu_e\n", + "\t- R_EX_ac_e\n", + "\t- R_EX_h2s_e\n", + "\t- R_EX_acac_e\n", + "\t- R_EX_cu2_e\n", + "\t- R_EX_hacolipa_e\n", + "\t- R_EX_halipa_e\n", + "\t- R_EX_hdca_e\n", + "\t- R_EX_hdcea_e\n", + "\t- R_EX_acald_e\n", + "\t- R_EX_acgal_e\n", + "\t- R_EX_acgal1p_e\n", + "\t- R_EX_hg2_e\n", + "\t- R_EX_acgam_e\n", + "\t- R_EX_cyan_e\n", + "\t- R_EX_acgam1p_e\n", + "\t- R_EX_acmana_e\n", + "\t- R_EX_his__L_e\n", + "\t- R_EX_hom__L_e\n", + "\t- R_EX_hxa_e\n", + "\t- R_EX_hxan_e\n", + "\t- R_EX_acmum_e\n", + "\t- R_EX_cynt_e\n", + "\t- R_EX_acnam_e\n", + "\t- R_EX_idon__L_e\n", + "\t- R_EX_acolipa_e\n", + "\t- R_EX_cys__D_e\n", + "\t- R_EX_acser_e\n", + "\t- R_EX_ade_e\n", + "\t- R_EX_adn_e\n", + "\t- R_EX_ile__L_e\n", + "\t- R_EX_imp_e\n", + "\t- R_EX_indole_e\n", + "\t- R_EX_adocbl_e\n", + "\t- R_EX_inost_e\n", + "\t- R_EX_ag_e\n", + "\t- R_EX_cys__L_e\n", + "\t- R_EX_agm_e\n", + "\t- R_EX_akg_e\n", + "\t- R_EX_ins_e\n", + "\t- R_EX_isetac_e\n", + "\t- R_EX_k_e\n", + "\t- R_EX_ala_B_e\n", + "\t- R_EX_ala__D_e\n", + "\t- R_EX_ala__L_e\n", + "\t- R_EX_alaala_e\n", + "\t- R_EX_all__D_e\n", + "\t- R_EX_kdo2lipid4_e\n", + "\t- R_EX_lac__D_e\n", + "\t- R_EX_lac__L_e\n", + "\t- R_EX_lcts_e\n", + "\t- R_EX_leu__L_e\n", + "\t- R_EX_lipa_e\n", + "\t- R_EX_alltn_e\n", + "\t- R_EX_amp_e\n", + "\t- R_EX_anhgm_e\n", + "\t- R_EX_lipa_cold_e\n", + "\t- R_EX_arab__L_e\n", + "\t- R_EX_cytd_e\n", + "\t- R_EX_arbt_e\n", + "\t- R_EX_arbtn_e\n", + "\t- R_EX_lipoate_e\n", + "\t- R_EX_lys__L_e\n", + "\t- R_EX_lyx__L_e\n", + "\t- R_EX_mal__D_e\n", + "\t- R_EX_arbtn_fe3_e\n", + "\t- R_EX_arg__L_e\n", + "\t- R_EX_mal__L_e\n", + "\t- R_EX_ascb__L_e\n", + "\t- R_EX_dad_2_e\n", + "\t- R_EX_malt_e\n", + "\t- R_EX_malthx_e\n", + "\t- R_EX_maltpt_e\n", + "\t- R_EX_damp_e\n", + "\t- R_EX_malttr_e\n", + "\t- R_EX_metsox_R__L_e\n", + "\t- R_EX_metsox_S__L_e\n", + "\t- R_EX_maltttr_e\n", + "\t- R_EX_dca_e\n", + "\t- R_EX_man_e\n", + "\t- R_EX_man6p_e\n", + "\t- R_EX_manglyc_e\n", + "\t- R_EX_melib_e\n", + "\t- R_EX_meoh_e\n", + "\t- R_EX_mg2_e\n", + "\t- R_EX_mincyc_e\n", + "\t- R_EX_minohp_e\n", + "\t- R_EX_mmet_e\n", + "\t- R_EX_mn2_e\n", + "\t- R_EX_mnl_e\n", + "\t- R_EX_mobd_e\n", + "\t- R_EX_met__D_e\n", + "\t- R_EX_met__L_e\n", + "\t- R_EX_mso3_e\n", + "\t- R_EX_n2o_e\n", + "\t- R_EX_dcmp_e\n", + "\t- R_EX_na1_e\n", + "\t- R_EX_nac_e\n", + "\t- R_EX_ttrcyc_e\n", + "\t- R_EX_tungs_e\n", + "\t- R_EX_nh4_e\n", + "\t- R_EX_tym_e\n", + "\t- R_EX_dcyt_e\n", + "\t- R_EX_tyr__L_e\n", + "\t- R_EX_tyrp_e\n", + "\t- R_EX_ni2_e\n", + "\t- R_EX_nmn_e\n", + "\t- R_EX_no_e\n", + "\t- R_EX_uacgam_e\n", + "\t- R_EX_ddca_e\n", + "\t- R_EX_udpacgal_e\n", + "\t- R_EX_udpg_e\n", + "\t- R_EX_no2_e\n", + "\t- R_EX_no3_e\n", + "\t- R_EX_udpgal_e\n", + "\t- R_EX_novbcn_e\n", + "\t- R_EX_udpglcur_e\n", + "\t- R_EX_dgmp_e\n", + "\t- R_EX_ump_e\n", + "\t- R_EX_ura_e\n", + "\t- R_EX_urea_e\n", + "\t- R_EX_uri_e\n", + "\t- R_EX_val__L_e\n", + "\t- R_EX_xan_e\n", + "\t- R_EX_o16a4colipa_e\n", + "\t- R_EX_o2_e\n", + "\t- R_EX_o2s_e\n", + "\t- R_EX_ocdca_e\n", + "\t- R_EX_ocdcea_e\n", + "\t- R_EX_octa_e\n", + "\t- R_EX_orn_e\n", + "\t- R_EX_orot_e\n", + "\t- R_EX_xmp_e\n", + "\t- R_EX_xtsn_e\n", + "\t- R_EX_dgsn_e\n", + "\t- R_EX_xyl__D_e\n", + "\t- R_EX_xylu__L_e\n", + "\t- R_EX_zn2_e\n", + "\t- R_EX_pacald_e\n", + "\t- R_EX_peamn_e\n", + "\t- R_EX_phe__L_e\n", + "\t- R_EX_pheme_e\n", + "\t- R_EX_pi_e\n", + "\t- R_EX_pnto__R_e\n", + "\t- R_EX_ppa_e\n", + "\t- R_EX_dha_e\n", + "\t- R_EX_ppal_e\n", + "\t- R_EX_dimp_e\n", + "\t- R_EX_pppn_e\n", + "\t- R_EX_ppt_e\n", + "\t- R_EX_pro__L_e\n", + "\t- R_EX_progly_e\n", + "\t- R_EX_psclys_e\n", + "\t- R_EX_pser__L_e\n", + "\t- R_EX_ptrc_e\n", + "\t- R_EX_pydam_e\n", + "\t- R_EX_pydx_e\n", + "\t- R_EX_pydxn_e\n", + "\t- R_EX_pyr_e\n", + "\t- R_EX_quin_e\n", + "\t- R_EX_din_e\n", + "\t- R_EX_r5p_e\n", + "\t- R_EX_rfamp_e\n", + "\t- R_EX_rib__D_e\n", + "\t- R_EX_dms_e\n", + "\t- R_EX_rmn_e\n", + "\t- R_EX_sbt__D_e\n", + "\t- R_EX_sel_e\n", + "\t- R_EX_dmso_e\n", + "\t- R_EX_ser__D_e\n", + "\t- R_EX_ser__L_e\n", + "\t- R_EX_skm_e\n", + "\t- R_EX_slnt_e\n", + "\t- R_EX_so2_e\n", + "\t- R_EX_so3_e\n", + "\t- R_EX_dopa_e\n", + "\t- R_EX_so4_e\n", + "\t- R_EX_spmd_e\n", + "\t- R_EX_succ_e\n", + "\t- R_EX_doxrbcn_e\n", + "\t- R_EX_sucr_e\n", + "\t- R_EX_sulfac_e\n", + "\t- R_EX_tartr__D_e\n", + "\t- R_EX_dtmp_e\n", + "\t- R_EX_tartr__L_e\n", + "\t- R_EX_taur_e\n", + "\t- R_EX_tcynt_e\n", + "\t- R_EX_dump_e\n", + "\t- R_EX_thm_e\n", + "\t- R_EX_thr__L_e\n", + "\t- R_EX_thrp_e\n", + "\t- R_EX_thym_e\n", + "\t- R_EX_thymd_e\n", + "\t- R_EX_tma_e\n", + "\t- R_EX_tmao_e\n", + "\t- R_EX_tre_e\n", + "\t- R_EX_trp__L_e\n", + "\t- R_EX_duri_e\n", + "\t- R_EX_tsul_e\n", + "\t- R_EX_ttdca_e\n", + "\t- R_EX_ttdcea_e\n", + "\t- R_EX_eca4colipa_e\n", + "\t- R_EX_enlipa_e\n", + "\t- R_EX_enter_e\n", + "\t- R_EX_etha_e\n", + "\t- R_EX_ethso3_e\n", + "\t- R_EX_etoh_e\n", + "\t- R_EX_f6p_e\n", + "\t- R_EX_fald_e\n", + "\t- R_EX_fe2_e\n", + "\t- R_EX_fe3_e\n", + "\t- R_EX_fe3dcit_e\n", + "\t- R_EX_fe3dhbzs_e\n", + "\t- R_EX_fe3hox_e\n", + "\t- R_EX_fe3hox_un_e\n", + "\t- R_EX_fecrm_e\n", + "\t- R_EX_fecrm_un_e\n", + "\t- R_EX_feenter_e\n", + "\t- R_EX_feoxam_e\n", + "\t- R_EX_feoxam_un_e\n", + "\t- R_EX_for_e\n", + "\t- R_EX_fru_e\n", + "\t- R_EX_frulys_e\n", + "\t- R_EX_fruur_e\n", + "\t- R_EX_fuc__L_e\n", + "\t- R_EX_fum_e\n", + "\t- R_EX_fusa_e\n", + "\t- R_EX_g1p_e\n", + "\t- R_EX_g3pc_e\n", + "\t- R_EX_g3pe_e\n", + "\t- R_EX_g3pg_e\n", + "\t- R_EX_g3pi_e\n", + "\t- R_EX_3hoxpac_e\n", + "\t- R_EX_3ntym_e\n", + "\t- R_EX_4hoxpac_e\n", + "\t- R_EX_6apa_e\n", + "\t- R_EX_cellb_e\n", + "\t- R_EX_galam_e\n", + "\t- R_EX_lipidA_core_e\n", + "\t- R_EX_o6a4colipa_e\n", + "\t- R_EX_peng_e\n", + "\t- R_EX_raffin_e\n", + "\t- R_EX_salcn_e\n", + "\t- R_DM_4hba_c\n", + "\t- R_DM_hmfurn_c\n", + "\t- R_EDTXSCOF\n", + "\t- R_EX_arab__D_e\u001b[0m\n", + "File saved at: ../../data/sbml_corrected/iEC1372_W3110.xml\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: iLJ478\n", + "\u001b[0;93mWARNING : - R_GLYCt: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_CYSTA: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_PGAMT: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + "\u001b[0m\n", + "\n", + "\n", + "\u001b[0;36m############################################\n", + "############################################\n", + " WRITING SBML FILE \n", + "############################################\n", + "############################################\n", + "\u001b[0m\n", + "\u001b[0;93mWARNING : IMPORT REACTION REMOVED BY DEFAULT\u001b[0m\n", + "\u001b[0;93mWARNING : If you want to keep import reaction\n", + "use option -kir / --keep-import-reactions\u001b[0m\n", + "\u001b[0;93mWARNING : Reaction with tag reversible modified:\n", + "\t- R_GLYCt\n", + "\t- R_CYSTA\n", + "\t- R_PGAMT\u001b[0m\n", + "\u001b[0;93mWARNING : Reaction with Reactants and Products exchanged:\n", + "\t- R_GLYCt\n", + "\t- R_CYSTA\n", + "\t- R_PGAMT\u001b[0m\n", + "\u001b[0;93mWARNING : Import reaction removed:\n", + "\t- R_SK_5mta_c\n", + "\t- R_SK_apoACP_c\n", + "\t- R_SK_gcald_c\n", + "\t- R_SK_pap_c\n", + "\t- R_DM_dip_d_d_c\n", + "\t- R_DM_pydx5p_c\n", + "\t- R_DM_sprm_c\n", + "\t- R_DM_thmpp_c\n", + "\t- R_EX_ac_e\n", + "\t- R_EX_ala__L_e\n", + "\t- R_EX_amylose300_e\n", + "\t- R_EX_arab__L_e\n", + "\t- R_EX_cell4_e\n", + "\t- R_EX_cell500_e\n", + "\t- R_EX_cell6_e\n", + "\t- R_EX_cellb_e\n", + "\t- R_EX_co2_e\n", + "\t- R_EX_csn_e\n", + "\t- R_EX_fe2_e\n", + "\t- R_EX_fe3_e\n", + "\t- R_EX_fru_e\n", + "\t- R_EX_gal_e\n", + "\t- R_EX_galman4_e\n", + "\t- R_EX_galman6_e\n", + "\t- R_EX_galman600_e\n", + "\t- R_EX_glc__D_e\n", + "\t- R_EX_glcman4_e\n", + "\t- R_EX_glcman6_e\n", + "\t- R_EX_glcman600_e\n", + "\t- R_EX_glucan1500_e\n", + "\t- R_EX_glucan4_e\n", + "\t- R_EX_glucan6_e\n", + "\t- R_EX_glyc_e\n", + "\t- R_EX_glyc3p_e\n", + "\t- R_EX_glycogen1500_e\n", + "\t- R_EX_h_e\n", + "\t- R_EX_h2_e\n", + "\t- R_EX_h2o_e\n", + "\t- R_EX_h2s_e\n", + "\t- R_EX_hco3_e\n", + "\t- R_EX_ile__L_e\n", + "\t- R_EX_inost_e\n", + "\t- R_EX_k_e\n", + "\t- R_EX_lac__L_e\n", + "\t- R_EX_lcts_e\n", + "\t- R_EX_leu__L_e\n", + "\t- R_EX_lmn30_e\n", + "\t- R_EX_lmn2_e\n", + "\t- R_EX_malt_e\n", + "\t- R_EX_malttr_e\n", + "\t- R_EX_maltttr_e\n", + "\t- R_EX_man_e\n", + "\t- R_EX_manb_e\n", + "\t- R_EX_mantr_e\n", + "\t- R_EX_manttr_e\n", + "\t- R_EX_melib_e\n", + "\t- R_EX_mg2_e\n", + "\t- R_EX_na1_e\n", + "\t- R_EX_nh4_e\n", + "\t- R_EX_oxa_e\n", + "\t- R_EX_pi_e\n", + "\t- R_EX_ptrc_e\n", + "\t- R_EX_pullulan1200_e\n", + "\t- R_EX_raffin_e\n", + "\t- R_EX_rib__D_e\n", + "\t- R_EX_rmn_e\n", + "\t- R_EX_s_e\n", + "\t- R_EX_spmd_e\n", + "\t- R_EX_starch1200_e\n", + "\t- R_EX_sucr_e\n", + "\t- R_EX_thr__L_e\n", + "\t- R_EX_tre_e\n", + "\t- R_EX_ura_e\n", + "\t- R_EX_val__L_e\n", + "\t- R_EX_xan_e\n", + "\t- R_EX_xtsn_e\n", + "\t- R_EX_xyl__D_e\n", + "\t- R_EX_xyl3_e\n", + "\t- R_EX_xylan12_e\n", + "\t- R_EX_xylan4_e\n", + "\t- R_EX_xylan8_e\n", + "\t- R_EX_xylb_e\n", + "\t- R_EX_zn2_e\n", + "\t- R_DM_4hba_c\u001b[0m\n", + "File saved at: ../../data/sbml_corrected/iLJ478.xml\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: iECs_1301\n", + "\u001b[0;93mWARNING : \n", + " - R_CAT: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_DHPTDNR: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_DHPTDNRN: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_FHL: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SPODM: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SPODMpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCASPtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCFUMtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCMALtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCTARTtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_CHITOBpts: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_EX_chitob_e: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_CHITPH: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_URCN: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\u001b[0m\n", + "\n", + "\n", + "\u001b[0;36m############################################\n", + "############################################\n", + " WRITING SBML FILE \n", + "############################################\n", + "############################################\n", + "\u001b[0m\n", + "\u001b[0;93mWARNING : IMPORT REACTION REMOVED BY DEFAULT\u001b[0m\n", + "\u001b[0;93mWARNING : If you want to keep import reaction\n", + "use option -kir / --keep-import-reactions\u001b[0m\n", + "\u001b[0;93mWARNING : Import reaction removed:\n", + "\t- R_EX_acnam_e\n", + "\t- R_EX_acser_e\n", + "\t- R_EX_acmana_e\n", + "\t- R_EX_acolipa_e\n", + "\t- R_EX_ade_e\n", + "\t- R_DM_4crsol_c\n", + "\t- R_DM_5drib_c\n", + "\t- R_EX_acmum_e\n", + "\t- R_DM_aacald_c\n", + "\t- R_EX_adn_e\n", + "\t- R_DM_amob_c\n", + "\t- R_DM_mththf_c\n", + "\t- R_EX_adocbl_e\n", + "\t- R_DM_oxam_c\n", + "\t- R_EX_ag_e\n", + "\t- R_EX_agm_e\n", + "\t- R_EX_12ppd__R_e\n", + "\t- R_EX_akg_e\n", + "\t- R_EX_12ppd__S_e\n", + "\t- R_EX_14glucan_e\n", + "\t- R_EX_ala_B_e\n", + "\t- R_EX_15dap_e\n", + "\t- R_EX_23camp_e\n", + "\t- R_EX_23ccmp_e\n", + "\t- R_EX_fe3_e\n", + "\t- R_EX_ala__D_e\n", + "\t- R_EX_fe3dcit_e\n", + "\t- R_EX_23cgmp_e\n", + "\t- R_EX_23cump_e\n", + "\t- R_EX_fe3dhbzs_e\n", + "\t- R_EX_23dappa_e\n", + "\t- R_EX_ala__L_e\n", + "\t- R_EX_26dap__M_e\n", + "\t- R_EX_2ddglcn_e\n", + "\t- R_EX_34dhpac_e\n", + "\t- R_EX_3amp_e\n", + "\t- R_EX_3cmp_e\n", + "\t- R_EX_3gmp_e\n", + "\t- R_EX_3hcinnm_e\n", + "\t- R_EX_3hpp_e\n", + "\t- R_EX_3hpppn_e\n", + "\t- R_EX_fe3hox_e\n", + "\t- R_EX_fe3hox_un_e\n", + "\t- R_EX_fecrm_e\n", + "\t- R_EX_fecrm_un_e\n", + "\t- R_EX_feenter_e\n", + "\t- R_EX_feoxam_e\n", + "\t- R_EX_feoxam_un_e\n", + "\t- R_EX_for_e\n", + "\t- R_EX_fru_e\n", + "\t- R_EX_frulys_e\n", + "\t- R_EX_3ump_e\n", + "\t- R_EX_4abut_e\n", + "\t- R_EX_4hoxpacd_e\n", + "\t- R_EX_fruur_e\n", + "\t- R_EX_5dglcn_e\n", + "\t- R_EX_fuc__L_e\n", + "\t- R_EX_alaala_e\n", + "\t- R_EX_fum_e\n", + "\t- R_EX_fusa_e\n", + "\t- R_EX_g1p_e\n", + "\t- R_EX_5mtr_e\n", + "\t- R_EX_LalaDglu_e\n", + "\t- R_EX_LalaDgluMdap_e\n", + "\t- R_EX_LalaDgluMdapDala_e\n", + "\t- R_EX_LalaLglu_e\n", + "\t- R_EX_g3pc_e\n", + "\t- R_EX_g3pe_e\n", + "\t- R_EX_g3pg_e\n", + "\t- R_EX_g3pi_e\n", + "\t- R_EX_g3ps_e\n", + "\t- R_EX_g6p_e\n", + "\t- R_EX_ac_e\n", + "\t- R_EX_acac_e\n", + "\t- R_EX_acald_e\n", + "\t- R_EX_acgal_e\n", + "\t- R_EX_acgal1p_e\n", + "\t- R_EX_acgam_e\n", + "\t- R_EX_acgam1p_e\n", + "\t- R_EX_gal_e\n", + "\t- R_EX_gal_bD_e\n", + "\t- R_EX_all__D_e\n", + "\t- R_EX_gal1p_e\n", + "\t- R_EX_galct__D_e\n", + "\t- R_EX_galctn__D_e\n", + "\t- R_EX_galctn__L_e\n", + "\t- R_EX_alltn_e\n", + "\t- R_EX_galt_e\n", + "\t- R_EX_galur_e\n", + "\t- R_EX_gam_e\n", + "\t- R_EX_amp_e\n", + "\t- R_EX_gam6p_e\n", + "\t- R_EX_gbbtn_e\n", + "\t- R_EX_anhgm_e\n", + "\t- R_EX_gdp_e\n", + "\t- R_EX_lac__L_e\n", + "\t- R_EX_lcts_e\n", + "\t- R_EX_glc__D_e\n", + "\t- R_EX_leu__L_e\n", + "\t- R_EX_arab__L_e\n", + "\t- R_EX_lipa_e\n", + "\t- R_EX_glcn_e\n", + "\t- R_EX_glcr_e\n", + "\t- R_EX_glcur_e\n", + "\t- R_EX_lipa_cold_e\n", + "\t- R_EX_lipoate_e\n", + "\t- R_EX_lys__L_e\n", + "\t- R_EX_lyx__L_e\n", + "\t- R_EX_mal__D_e\n", + "\t- R_EX_glcur1p_e\n", + "\t- R_EX_gln__L_e\n", + "\t- R_EX_glu__L_e\n", + "\t- R_EX_gly_e\n", + "\t- R_EX_glyald_e\n", + "\t- R_EX_mal__L_e\n", + "\t- R_EX_arbt_e\n", + "\t- R_EX_malt_e\n", + "\t- R_EX_malthx_e\n", + "\t- R_EX_glyb_e\n", + "\t- R_EX_glyc_e\n", + "\t- R_EX_glyc__R_e\n", + "\t- R_EX_maltpt_e\n", + "\t- R_EX_glyc2p_e\n", + "\t- R_EX_arbtn_e\n", + "\t- R_EX_glyc3p_e\n", + "\t- R_EX_malttr_e\n", + "\t- R_EX_maltttr_e\n", + "\t- R_EX_glyclt_e\n", + "\t- R_EX_man_e\n", + "\t- R_EX_arbtn_fe3_e\n", + "\t- R_EX_man6p_e\n", + "\t- R_EX_manglyc_e\n", + "\t- R_EX_gmp_e\n", + "\t- R_EX_arg__L_e\n", + "\t- R_EX_gsn_e\n", + "\t- R_EX_gthox_e\n", + "\t- R_EX_melib_e\n", + "\t- R_EX_meoh_e\n", + "\t- R_EX_met__D_e\n", + "\t- R_EX_gthrd_e\n", + "\t- R_EX_ascb__L_e\n", + "\t- R_EX_gtp_e\n", + "\t- R_EX_met__L_e\n", + "\t- R_EX_metsox_R__L_e\n", + "\t- R_EX_metsox_S__L_e\n", + "\t- R_EX_mg2_e\n", + "\t- R_EX_mincyc_e\n", + "\t- R_EX_minohp_e\n", + "\t- R_EX_gua_e\n", + "\t- R_EX_h_e\n", + "\t- R_EX_h2_e\n", + "\t- R_EX_h2o_e\n", + "\t- R_EX_mmet_e\n", + "\t- R_EX_asn__L_e\n", + "\t- R_EX_mn2_e\n", + "\t- R_EX_mnl_e\n", + "\t- R_EX_mobd_e\n", + "\t- R_EX_h2o2_e\n", + "\t- R_EX_h2s_e\n", + "\t- R_EX_hacolipa_e\n", + "\t- R_EX_mso3_e\n", + "\t- R_EX_aso3_e\n", + "\t- R_EX_n2o_e\n", + "\t- R_EX_halipa_e\n", + "\t- R_EX_asp__L_e\n", + "\t- R_EX_hdca_e\n", + "\t- R_EX_na1_e\n", + "\t- R_EX_nac_e\n", + "\t- R_EX_nh4_e\n", + "\t- R_EX_hdcea_e\n", + "\t- R_EX_btn_e\n", + "\t- R_EX_hg2_e\n", + "\t- R_EX_ni2_e\n", + "\t- R_EX_nmn_e\n", + "\t- R_EX_his__L_e\n", + "\t- R_EX_but_e\n", + "\t- R_EX_hom__L_e\n", + "\t- R_EX_no_e\n", + "\t- R_EX_no2_e\n", + "\t- R_EX_butso3_e\n", + "\t- R_EX_no3_e\n", + "\t- R_EX_novbcn_e\n", + "\t- R_EX_o16a4colipa_e\n", + "\t- R_EX_o2_e\n", + "\t- R_EX_o2s_e\n", + "\t- R_EX_hxa_e\n", + "\t- R_EX_hxan_e\n", + "\t- R_EX_idon__L_e\n", + "\t- R_EX_ile__L_e\n", + "\t- R_EX_imp_e\n", + "\t- R_EX_indole_e\n", + "\t- R_EX_inost_e\n", + "\t- R_EX_ocdca_e\n", + "\t- R_EX_ocdcea_e\n", + "\t- R_EX_ins_e\n", + "\t- R_EX_ca2_e\n", + "\t- R_EX_isetac_e\n", + "\t- R_EX_k_e\n", + "\t- R_EX_octa_e\n", + "\t- R_EX_orn_e\n", + "\t- R_EX_orot_e\n", + "\t- R_EX_cbi_e\n", + "\t- R_EX_pacald_e\n", + "\t- R_EX_kdo2lipid4_e\n", + "\t- R_EX_lac__D_e\n", + "\t- R_EX_peamn_e\n", + "\t- R_EX_cbl1_e\n", + "\t- R_EX_phe__L_e\n", + "\t- R_EX_pheme_e\n", + "\t- R_EX_pi_e\n", + "\t- R_EX_cd2_e\n", + "\t- R_EX_pnto__R_e\n", + "\t- R_EX_so3_e\n", + "\t- R_EX_cgly_e\n", + "\t- R_EX_so4_e\n", + "\t- R_EX_spmd_e\n", + "\t- R_EX_ppa_e\n", + "\t- R_EX_ppal_e\n", + "\t- R_EX_pppn_e\n", + "\t- R_EX_succ_e\n", + "\t- R_EX_sucr_e\n", + "\t- R_EX_sulfac_e\n", + "\t- R_EX_ppt_e\n", + "\t- R_EX_pro__L_e\n", + "\t- R_EX_progly_e\n", + "\t- R_EX_psclys_e\n", + "\t- R_EX_tartr__D_e\n", + "\t- R_EX_tartr__L_e\n", + "\t- R_EX_pser__L_e\n", + "\t- R_EX_chol_e\n", + "\t- R_EX_ptrc_e\n", + "\t- R_EX_pydam_e\n", + "\t- R_EX_taur_e\n", + "\t- R_EX_tcynt_e\n", + "\t- R_EX_thm_e\n", + "\t- R_EX_pydx_e\n", + "\t- R_EX_thr__L_e\n", + "\t- R_EX_pydxn_e\n", + "\t- R_EX_chtbs_e\n", + "\t- R_EX_pyr_e\n", + "\t- R_EX_thrp_e\n", + "\t- R_EX_thym_e\n", + "\t- R_EX_cit_e\n", + "\t- R_EX_thymd_e\n", + "\t- R_EX_tma_e\n", + "\t- R_EX_quin_e\n", + "\t- R_EX_r5p_e\n", + "\t- R_EX_rfamp_e\n", + "\t- R_EX_tmao_e\n", + "\t- R_EX_cl_e\n", + "\t- R_EX_tre_e\n", + "\t- R_EX_trp__L_e\n", + "\t- R_EX_rib__D_e\n", + "\t- R_EX_rmn_e\n", + "\t- R_EX_cm_e\n", + "\t- R_EX_sbt__D_e\n", + "\t- R_EX_sel_e\n", + "\t- R_EX_ser__D_e\n", + "\t- R_EX_ser__L_e\n", + "\t- R_EX_skm_e\n", + "\t- R_EX_slnt_e\n", + "\t- R_EX_so2_e\n", + "\t- R_EX_tsul_e\n", + "\t- R_EX_ttdca_e\n", + "\t- R_EX_ttdcea_e\n", + "\t- R_EX_ttrcyc_e\n", + "\t- R_EX_tungs_e\n", + "\t- R_EX_cmp_e\n", + "\t- R_EX_tym_e\n", + "\t- R_EX_tyr__L_e\n", + "\t- R_EX_tyrp_e\n", + "\t- R_EX_co2_e\n", + "\t- R_EX_uacgam_e\n", + "\t- R_EX_udpacgal_e\n", + "\t- R_EX_udpg_e\n", + "\t- R_EX_udpgal_e\n", + "\t- R_EX_udpglcur_e\n", + "\t- R_EX_cobalt2_e\n", + "\t- R_EX_ump_e\n", + "\t- R_EX_ura_e\n", + "\t- R_EX_colipa_e\n", + "\t- R_EX_urea_e\n", + "\t- R_EX_uri_e\n", + "\t- R_EX_val__L_e\n", + "\t- R_EX_xan_e\n", + "\t- R_EX_xmp_e\n", + "\t- R_EX_xtsn_e\n", + "\t- R_EX_xyl__D_e\n", + "\t- R_EX_xylu__L_e\n", + "\t- R_EX_colipap_e\n", + "\t- R_EX_zn2_e\n", + "\t- R_EX_cpgn_e\n", + "\t- R_EX_cpgn_un_e\n", + "\t- R_EX_crn_e\n", + "\t- R_EX_crn__D_e\n", + "\t- R_EX_csn_e\n", + "\t- R_EX_cu_e\n", + "\t- R_EX_cu2_e\n", + "\t- R_EX_cyan_e\n", + "\t- R_EX_cynt_e\n", + "\t- R_EX_cys__D_e\n", + "\t- R_EX_cys__L_e\n", + "\t- R_EX_cytd_e\n", + "\t- R_EX_dad_2_e\n", + "\t- R_EX_damp_e\n", + "\t- R_EX_dca_e\n", + "\t- R_EX_dcmp_e\n", + "\t- R_EX_dcyt_e\n", + "\t- R_EX_ddca_e\n", + "\t- R_EX_dgmp_e\n", + "\t- R_EX_dgsn_e\n", + "\t- R_EX_dha_e\n", + "\t- R_EX_dimp_e\n", + "\t- R_EX_din_e\n", + "\t- R_EX_dms_e\n", + "\t- R_EX_dmso_e\n", + "\t- R_EX_dopa_e\n", + "\t- R_EX_doxrbcn_e\n", + "\t- R_EX_dtmp_e\n", + "\t- R_EX_dump_e\n", + "\t- R_EX_duri_e\n", + "\t- R_EX_eca4colipa_e\n", + "\t- R_EX_enlipa_e\n", + "\t- R_EX_enter_e\n", + "\t- R_EX_etha_e\n", + "\t- R_EX_ethso3_e\n", + "\t- R_EX_etoh_e\n", + "\t- R_EX_f6p_e\n", + "\t- R_EX_fald_e\n", + "\t- R_EX_fe2_e\n", + "\t- R_EX_tton_e\n", + "\t- R_EX_4hthr_e\n", + "\t- R_EX_feroxEfe_e\n", + "\t- R_EX_feroxE_e\n", + "\t- R_EX_pep_e\n", + "\t- R_EX_4hphac_e\n", + "\t- R_EX_4abz_e\n", + "\t- R_EX_23dhbzs3_e\n", + "\t- R_EX_acon_C_e\n", + "\t- R_EX_tet_e\n", + "\t- R_EX_tcb_e\n", + "\t- R_EX_salchs4_e\n", + "\t- R_EX_feroxB_e\n", + "\t- R_EX_feroxGfe_e\n", + "\t- R_EX_tag__D_e\n", + "\t- R_EX_oaa_e\n", + "\t- R_EX_salchs2fe_e\n", + "\t- R_EX_fe_e\n", + "\t- R_EX_kokdolipidA_e\n", + "\t- R_EX_cysi__L_e\n", + "\t- R_EX_3hoxpac_e\n", + "\t- R_EX_fe3dhbzs3_e\n", + "\t- R_EX_salchs2_e\n", + "\t- R_EX_4hoxpac_e\n", + "\t- R_EX_drib_e\n", + "\t- R_EX_feroxBfe_e\n", + "\t- R_EX_salchs4fe_e\n", + "\t- R_EX_3ntym_e\n", + "\t- R_EX_2pg_e\n", + "\t- R_EX_6apa_e\n", + "\t- R_EX_rnam_e\n", + "\t- R_EX_airs_e\n", + "\t- R_EX_frmd_e\n", + "\t- R_EX_dxyl_e\n", + "\t- R_EX_colipaOA_e\n", + "\t- R_EX_peng_e\n", + "\t- R_EX_pac_e\n", + "\t- R_EX_salchsx_e\n", + "\t- R_EX_AEP_e\n", + "\t- R_EX_3pg_e\n", + "\t- R_EX_icit_e\n", + "\t- R_EX_salcn_e\n", + "\t- R_EX_galam_e\n", + "\t- R_DM_lipidA_core_e_p\n", + "\t- R_EX_raffin_e\n", + "\t- R_EX_colipa20Oag_e\n", + "\t- R_EX_remnant1_e\n", + "\t- R_EX_o6a4colipa_e\n", + "\t- R_EX_arab__D_e\n", + "\t- R_EX_udcpo4_e\n", + "\t- R_EX_udcpo5_e\n", + "\t- R_EX_guln__L_e\n", + "\t- R_EX_feroxG_e\n", + "\t- R_EX_diact_e\n", + "\t- R_EX_cellb_e\n", + "\t- R_EX_btd_RR_e\n", + "\t- R_EX_13ppd_e\n", + "\t- R_EX_rbt_e\n", + "\t- R_EX_bz_e\n", + "\t- R_EX_abt__D_e\u001b[0m\n", + "File saved at: ../../data/sbml_corrected/iECs_1301.xml\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: iSbBS512_1146\n", + "\u001b[0;93mWARNING : \n", + " - R_CAT: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_DHPTDNR: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_DHPTDNRN: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCFUMtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCMALtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SPODM: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SPODMpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCASPtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_CHITOBpts: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_URCN: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_EX_chitob_e: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\u001b[0m\n", + "\n", + "\n", + "\u001b[0;36m############################################\n", + "############################################\n", + " WRITING SBML FILE \n", + "############################################\n", + "############################################\n", + "\u001b[0m\n", + "\u001b[0;93mWARNING : IMPORT REACTION REMOVED BY DEFAULT\u001b[0m\n", + "\u001b[0;93mWARNING : If you want to keep import reaction\n", + "use option -kir / --keep-import-reactions\u001b[0m\n", + "\u001b[0;93mWARNING : Import reaction removed:\n", + "\t- R_EX_doxrbcn_e\n", + "\t- R_EX_dtmp_e\n", + "\t- R_EX_dump_e\n", + "\t- R_EX_duri_e\n", + "\t- R_EX_eca4colipa_e\n", + "\t- R_EX_enlipa_e\n", + "\t- R_EX_enter_e\n", + "\t- R_EX_etha_e\n", + "\t- R_EX_ethso3_e\n", + "\t- R_EX_etoh_e\n", + "\t- R_EX_f6p_e\n", + "\t- R_EX_fald_e\n", + "\t- R_EX_fe2_e\n", + "\t- R_EX_LalaDgluMdapDala_e\n", + "\t- R_EX_fe3_e\n", + "\t- R_EX_fe3dcit_e\n", + "\t- R_EX_LalaLglu_e\n", + "\t- R_EX_fe3dhbzs_e\n", + "\t- R_EX_ac_e\n", + "\t- R_EX_acac_e\n", + "\t- R_DM_4crsol_c\n", + "\t- R_DM_5drib_c\n", + "\t- R_EX_acald_e\n", + "\t- R_DM_aacald_c\n", + "\t- R_EX_fe3hox_e\n", + "\t- R_EX_acgal_e\n", + "\t- R_DM_amob_c\n", + "\t- R_EX_fe3hox_un_e\n", + "\t- R_DM_mththf_c\n", + "\t- R_DM_oxam_c\n", + "\t- R_EX_acgal1p_e\n", + "\t- R_EX_acgam_e\n", + "\t- R_EX_fecrm_e\n", + "\t- R_EX_acgam1p_e\n", + "\t- R_EX_acmana_e\n", + "\t- R_EX_acmum_e\n", + "\t- R_EX_acnam_e\n", + "\t- R_EX_acolipa_e\n", + "\t- R_EX_acser_e\n", + "\t- R_EX_ade_e\n", + "\t- R_EX_adn_e\n", + "\t- R_EX_adocbl_e\n", + "\t- R_EX_12ppd__R_e\n", + "\t- R_EX_12ppd__S_e\n", + "\t- R_EX_14glucan_e\n", + "\t- R_EX_15dap_e\n", + "\t- R_EX_23camp_e\n", + "\t- R_EX_23ccmp_e\n", + "\t- R_EX_23cgmp_e\n", + "\t- R_EX_23cump_e\n", + "\t- R_EX_23dappa_e\n", + "\t- R_EX_26dap__M_e\n", + "\t- R_EX_2ddglcn_e\n", + "\t- R_EX_34dhpac_e\n", + "\t- R_EX_ag_e\n", + "\t- R_EX_agm_e\n", + "\t- R_EX_akg_e\n", + "\t- R_EX_ala_B_e\n", + "\t- R_EX_ala__D_e\n", + "\t- R_EX_3amp_e\n", + "\t- R_EX_3cmp_e\n", + "\t- R_EX_ala__L_e\n", + "\t- R_EX_fecrm_un_e\n", + "\t- R_EX_alaala_e\n", + "\t- R_EX_all__D_e\n", + "\t- R_EX_alltn_e\n", + "\t- R_EX_3gmp_e\n", + "\t- R_EX_3hcinnm_e\n", + "\t- R_EX_3hpp_e\n", + "\t- R_EX_3hpppn_e\n", + "\t- R_EX_3ump_e\n", + "\t- R_EX_4abut_e\n", + "\t- R_EX_4hoxpacd_e\n", + "\t- R_EX_5dglcn_e\n", + "\t- R_EX_5mtr_e\n", + "\t- R_EX_LalaDglu_e\n", + "\t- R_EX_LalaDgluMdap_e\n", + "\t- R_EX_amp_e\n", + "\t- R_EX_anhgm_e\n", + "\t- R_EX_arab__L_e\n", + "\t- R_EX_arbt_e\n", + "\t- R_EX_arbtn_e\n", + "\t- R_EX_arbtn_fe3_e\n", + "\t- R_EX_arg__L_e\n", + "\t- R_EX_ascb__L_e\n", + "\t- R_EX_asn__L_e\n", + "\t- R_EX_aso3_e\n", + "\t- R_EX_asp__L_e\n", + "\t- R_EX_btn_e\n", + "\t- R_EX_but_e\n", + "\t- R_EX_butso3_e\n", + "\t- R_EX_hom__L_e\n", + "\t- R_EX_hxa_e\n", + "\t- R_EX_hxan_e\n", + "\t- R_EX_ca2_e\n", + "\t- R_EX_feenter_e\n", + "\t- R_EX_cbi_e\n", + "\t- R_EX_cbl1_e\n", + "\t- R_EX_cd2_e\n", + "\t- R_EX_cgly_e\n", + "\t- R_EX_idon__L_e\n", + "\t- R_EX_ile__L_e\n", + "\t- R_EX_imp_e\n", + "\t- R_EX_indole_e\n", + "\t- R_EX_inost_e\n", + "\t- R_EX_chol_e\n", + "\t- R_EX_chtbs_e\n", + "\t- R_EX_cit_e\n", + "\t- R_EX_cl_e\n", + "\t- R_EX_cm_e\n", + "\t- R_EX_ins_e\n", + "\t- R_EX_isetac_e\n", + "\t- R_EX_k_e\n", + "\t- R_EX_kdo2lipid4_e\n", + "\t- R_EX_lac__D_e\n", + "\t- R_EX_lac__L_e\n", + "\t- R_EX_cmp_e\n", + "\t- R_EX_co2_e\n", + "\t- R_EX_cobalt2_e\n", + "\t- R_EX_lcts_e\n", + "\t- R_EX_leu__L_e\n", + "\t- R_EX_colipa_e\n", + "\t- R_EX_feoxam_e\n", + "\t- R_EX_colipap_e\n", + "\t- R_EX_cpgn_e\n", + "\t- R_EX_cpgn_un_e\n", + "\t- R_EX_lipa_e\n", + "\t- R_EX_lipa_cold_e\n", + "\t- R_EX_lipoate_e\n", + "\t- R_EX_lys__L_e\n", + "\t- R_EX_crn_e\n", + "\t- R_EX_crn__D_e\n", + "\t- R_EX_feoxam_un_e\n", + "\t- R_EX_csn_e\n", + "\t- R_EX_lyx__L_e\n", + "\t- R_EX_mal__D_e\n", + "\t- R_EX_cu_e\n", + "\t- R_EX_mal__L_e\n", + "\t- R_EX_for_e\n", + "\t- R_EX_malt_e\n", + "\t- R_EX_malthx_e\n", + "\t- R_EX_cu2_e\n", + "\t- R_EX_cyan_e\n", + "\t- R_EX_cynt_e\n", + "\t- R_EX_cys__D_e\n", + "\t- R_EX_cys__L_e\n", + "\t- R_EX_cytd_e\n", + "\t- R_EX_dad_2_e\n", + "\t- R_EX_maltpt_e\n", + "\t- R_EX_malttr_e\n", + "\t- R_EX_maltttr_e\n", + "\t- R_EX_man_e\n", + "\t- R_EX_man6p_e\n", + "\t- R_EX_manglyc_e\n", + "\t- R_EX_melib_e\n", + "\t- R_EX_meoh_e\n", + "\t- R_EX_damp_e\n", + "\t- R_EX_dca_e\n", + "\t- R_EX_met__D_e\n", + "\t- R_EX_fru_e\n", + "\t- R_EX_met__L_e\n", + "\t- R_EX_dcmp_e\n", + "\t- R_EX_dcyt_e\n", + "\t- R_EX_ddca_e\n", + "\t- R_EX_dgmp_e\n", + "\t- R_EX_metsox_R__L_e\n", + "\t- R_EX_metsox_S__L_e\n", + "\t- R_EX_mg2_e\n", + "\t- R_EX_dgsn_e\n", + "\t- R_EX_frulys_e\n", + "\t- R_EX_dha_e\n", + "\t- R_EX_mincyc_e\n", + "\t- R_EX_dimp_e\n", + "\t- R_EX_fruur_e\n", + "\t- R_EX_din_e\n", + "\t- R_EX_dms_e\n", + "\t- R_EX_minohp_e\n", + "\t- R_EX_mmet_e\n", + "\t- R_EX_mn2_e\n", + "\t- R_EX_mnl_e\n", + "\t- R_EX_dmso_e\n", + "\t- R_EX_dopa_e\n", + "\t- R_EX_mobd_e\n", + "\t- R_EX_mso3_e\n", + "\t- R_EX_n2o_e\n", + "\t- R_EX_na1_e\n", + "\t- R_EX_pyr_e\n", + "\t- R_EX_fuc__L_e\n", + "\t- R_EX_quin_e\n", + "\t- R_EX_r5p_e\n", + "\t- R_EX_rfamp_e\n", + "\t- R_EX_nac_e\n", + "\t- R_EX_nh4_e\n", + "\t- R_EX_ni2_e\n", + "\t- R_EX_nmn_e\n", + "\t- R_EX_rib__D_e\n", + "\t- R_EX_fum_e\n", + "\t- R_EX_rmn_e\n", + "\t- R_EX_sbt__D_e\n", + "\t- R_EX_no_e\n", + "\t- R_EX_no2_e\n", + "\t- R_EX_sel_e\n", + "\t- R_EX_fusa_e\n", + "\t- R_EX_ser__D_e\n", + "\t- R_EX_no3_e\n", + "\t- R_EX_novbcn_e\n", + "\t- R_EX_ser__L_e\n", + "\t- R_EX_skm_e\n", + "\t- R_EX_g1p_e\n", + "\t- R_EX_slnt_e\n", + "\t- R_EX_so2_e\n", + "\t- R_EX_o16a4colipa_e\n", + "\t- R_EX_o2_e\n", + "\t- R_EX_o2s_e\n", + "\t- R_EX_ocdca_e\n", + "\t- R_EX_ocdcea_e\n", + "\t- R_EX_so3_e\n", + "\t- R_EX_so4_e\n", + "\t- R_EX_g3pc_e\n", + "\t- R_EX_spmd_e\n", + "\t- R_EX_succ_e\n", + "\t- R_EX_sucr_e\n", + "\t- R_EX_sulfac_e\n", + "\t- R_EX_tartr__D_e\n", + "\t- R_EX_tartr__L_e\n", + "\t- R_EX_octa_e\n", + "\t- R_EX_orn_e\n", + "\t- R_EX_orot_e\n", + "\t- R_EX_pacald_e\n", + "\t- R_EX_peamn_e\n", + "\t- R_EX_phe__L_e\n", + "\t- R_EX_pheme_e\n", + "\t- R_EX_pi_e\n", + "\t- R_EX_taur_e\n", + "\t- R_EX_tcynt_e\n", + "\t- R_EX_g3pe_e\n", + "\t- R_EX_thm_e\n", + "\t- R_EX_thr__L_e\n", + "\t- R_EX_thrp_e\n", + "\t- R_EX_thym_e\n", + "\t- R_EX_pnto__R_e\n", + "\t- R_EX_ppa_e\n", + "\t- R_EX_ppal_e\n", + "\t- R_EX_pppn_e\n", + "\t- R_EX_ppt_e\n", + "\t- R_EX_pro__L_e\n", + "\t- R_EX_thymd_e\n", + "\t- R_EX_g3pg_e\n", + "\t- R_EX_tma_e\n", + "\t- R_EX_tmao_e\n", + "\t- R_EX_progly_e\n", + "\t- R_EX_psclys_e\n", + "\t- R_EX_tre_e\n", + "\t- R_EX_pser__L_e\n", + "\t- R_EX_trp__L_e\n", + "\t- R_EX_ptrc_e\n", + "\t- R_EX_g3pi_e\n", + "\t- R_EX_pydam_e\n", + "\t- R_EX_pydx_e\n", + "\t- R_EX_pydxn_e\n", + "\t- R_EX_tsul_e\n", + "\t- R_EX_ttdca_e\n", + "\t- R_EX_ttdcea_e\n", + "\t- R_EX_ttrcyc_e\n", + "\t- R_EX_tungs_e\n", + "\t- R_EX_tym_e\n", + "\t- R_EX_tyr__L_e\n", + "\t- R_EX_tyrp_e\n", + "\t- R_EX_g3ps_e\n", + "\t- R_EX_uacgam_e\n", + "\t- R_EX_udpacgal_e\n", + "\t- R_EX_udpg_e\n", + "\t- R_EX_udpgal_e\n", + "\t- R_EX_g6p_e\n", + "\t- R_EX_udpglcur_e\n", + "\t- R_EX_ump_e\n", + "\t- R_EX_ura_e\n", + "\t- R_EX_gal_e\n", + "\t- R_EX_urea_e\n", + "\t- R_EX_gal_bD_e\n", + "\t- R_EX_uri_e\n", + "\t- R_EX_val__L_e\n", + "\t- R_EX_xan_e\n", + "\t- R_EX_xmp_e\n", + "\t- R_EX_xtsn_e\n", + "\t- R_EX_xyl__D_e\n", + "\t- R_EX_xylu__L_e\n", + "\t- R_EX_zn2_e\n", + "\t- R_EX_gal1p_e\n", + "\t- R_EX_galct__D_e\n", + "\t- R_EX_galctn__D_e\n", + "\t- R_EX_galctn__L_e\n", + "\t- R_EX_galt_e\n", + "\t- R_EX_galur_e\n", + "\t- R_EX_gam_e\n", + "\t- R_EX_gam6p_e\n", + "\t- R_EX_gbbtn_e\n", + "\t- R_EX_gdp_e\n", + "\t- R_EX_glc__D_e\n", + "\t- R_EX_glcn_e\n", + "\t- R_EX_glcr_e\n", + "\t- R_EX_glcur_e\n", + "\t- R_EX_glcur1p_e\n", + "\t- R_EX_gln__L_e\n", + "\t- R_EX_glu__L_e\n", + "\t- R_EX_gly_e\n", + "\t- R_EX_glyald_e\n", + "\t- R_EX_glyb_e\n", + "\t- R_EX_glyc_e\n", + "\t- R_EX_glyc__R_e\n", + "\t- R_EX_glyc2p_e\n", + "\t- R_EX_glyc3p_e\n", + "\t- R_EX_glyclt_e\n", + "\t- R_EX_gmp_e\n", + "\t- R_EX_gsn_e\n", + "\t- R_EX_gthox_e\n", + "\t- R_EX_gthrd_e\n", + "\t- R_EX_gtp_e\n", + "\t- R_EX_gua_e\n", + "\t- R_EX_h_e\n", + "\t- R_EX_h2_e\n", + "\t- R_EX_h2o_e\n", + "\t- R_EX_h2o2_e\n", + "\t- R_EX_h2s_e\n", + "\t- R_EX_hacolipa_e\n", + "\t- R_EX_halipa_e\n", + "\t- R_EX_hdca_e\n", + "\t- R_EX_hdcea_e\n", + "\t- R_EX_hg2_e\n", + "\t- R_EX_his__L_e\n", + "\t- R_EX_tcb_e\n", + "\t- R_EX_feroxB_e\n", + "\t- R_EX_tton_e\n", + "\t- R_EX_4hthr_e\n", + "\t- R_EX_feroxEfe_e\n", + "\t- R_EX_feroxBfe_e\n", + "\t- R_EX_feroxE_e\n", + "\t- R_EX_cysi__L_e\n", + "\t- R_EX_23dhbzs3_e\n", + "\t- R_EX_tet_e\n", + "\t- R_EX_salchs4_e\n", + "\t- R_EX_4hphac_e\n", + "\t- R_EX_oaa_e\n", + "\t- R_EX_salchs2fe_e\n", + "\t- R_EX_4abz_e\n", + "\t- R_EX_acon_C_e\n", + "\t- R_EX_fe3dhbzs3_e\n", + "\t- R_EX_drib_e\n", + "\t- R_EX_salchs4fe_e\n", + "\t- R_EX_remnant1_e\n", + "\t- R_EX_2pg_e\n", + "\t- R_EX_udcpo5_e\n", + "\t- R_EX_colipa20Oag_e\n", + "\t- R_EX_frmd_e\n", + "\t- R_EX_colipaOA_e\n", + "\t- R_EX_salchsx_e\n", + "\t- R_EX_AEP_e\n", + "\t- R_EX_salchs2_e\n", + "\t- R_EX_rnam_e\n", + "\t- R_EX_icit_e\n", + "\t- R_EX_airs_e\n", + "\t- R_EX_pep_e\n", + "\t- R_EX_3pg_e\n", + "\t- R_EX_feroxGfe_e\n", + "\t- R_EX_dxyl_e\n", + "\t- R_EX_fe_e\n", + "\t- R_EX_udcpo4_e\n", + "\t- R_EX_kokdolipidA_e\n", + "\t- R_EX_tag__D_e\n", + "\t- R_EX_guln__L_e\n", + "\t- R_EX_3hoxpac_e\n", + "\t- R_EX_feroxG_e\n", + "\t- R_DM_lipidA_core_e_p\n", + "\t- R_EX_diact_e\n", + "\t- R_EX_3ntym_e\n", + "\t- R_EX_6apa_e\n", + "\t- R_EX_4hoxpac_e\n", + "\t- R_EX_peng_e\n", + "\t- R_EX_pac_e\n", + "\t- R_EX_cellb_e\n", + "\t- R_EX_btd_RR_e\n", + "\t- R_EX_salcn_e\n", + "\t- R_EX_rbt_e\n", + "\t- R_EX_bz_e\n", + "\t- R_EX_13ppd_e\n", + "\t- R_EX_abt__D_e\n", + "\t- R_EX_galam_e\n", + "\t- R_EX_raffin_e\n", + "\t- R_EX_o6a4colipa_e\n", + "\t- R_EX_arab__D_e\u001b[0m\n", + "File saved at: ../../data/sbml_corrected/iSbBS512_1146.xml\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: iECO26_1355\n", + "\u001b[0;93mWARNING : \n", + " - R_CAT: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_DHPTDNR: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_DHPTDNRN: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_FHL: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_EX_chitob_e: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCTARTtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SPODM: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SPODMpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCASPtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCFUMtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCMALtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_CHITOBpts: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_CHITPH: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_URCN: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\u001b[0m\n", + "\n", + "\n", + "\u001b[0;36m############################################\n", + "############################################\n", + " WRITING SBML FILE \n", + "############################################\n", + "############################################\n", + "\u001b[0m\n", + "\u001b[0;93mWARNING : IMPORT REACTION REMOVED BY DEFAULT\u001b[0m\n", + "\u001b[0;93mWARNING : If you want to keep import reaction\n", + "use option -kir / --keep-import-reactions\u001b[0m\n", + "\u001b[0;93mWARNING : Import reaction removed:\n", + "\t- R_EX_LalaDglu_e\n", + "\t- R_EX_LalaDgluMdap_e\n", + "\t- R_EX_5dglcn_e\n", + "\t- R_EX_LalaDgluMdapDala_e\n", + "\t- R_EX_5mtr_e\n", + "\t- R_EX_LalaLglu_e\n", + "\t- R_EX_ac_e\n", + "\t- R_EX_acac_e\n", + "\t- R_DM_4crsol_c\n", + "\t- R_DM_5drib_c\n", + "\t- R_DM_aacald_c\n", + "\t- R_EX_acald_e\n", + "\t- R_DM_amob_c\n", + "\t- R_DM_mththf_c\n", + "\t- R_DM_oxam_c\n", + "\t- R_EX_acgal_e\n", + "\t- R_EX_acgal1p_e\n", + "\t- R_EX_12ppd__R_e\n", + "\t- R_EX_12ppd__S_e\n", + "\t- R_EX_14glucan_e\n", + "\t- R_EX_15dap_e\n", + "\t- R_EX_23camp_e\n", + "\t- R_EX_23ccmp_e\n", + "\t- R_EX_23cgmp_e\n", + "\t- R_EX_23cump_e\n", + "\t- R_EX_23dappa_e\n", + "\t- R_EX_26dap__M_e\n", + "\t- R_EX_2ddglcn_e\n", + "\t- R_EX_34dhpac_e\n", + "\t- R_EX_doxrbcn_e\n", + "\t- R_EX_dtmp_e\n", + "\t- R_EX_dump_e\n", + "\t- R_EX_duri_e\n", + "\t- R_EX_eca4colipa_e\n", + "\t- R_EX_enlipa_e\n", + "\t- R_EX_enter_e\n", + "\t- R_EX_etha_e\n", + "\t- R_EX_ethso3_e\n", + "\t- R_EX_etoh_e\n", + "\t- R_EX_f6p_e\n", + "\t- R_EX_fald_e\n", + "\t- R_EX_3amp_e\n", + "\t- R_EX_3cmp_e\n", + "\t- R_EX_3gmp_e\n", + "\t- R_EX_3hcinnm_e\n", + "\t- R_EX_3hpp_e\n", + "\t- R_EX_3hpppn_e\n", + "\t- R_EX_3ump_e\n", + "\t- R_EX_4abut_e\n", + "\t- R_EX_4hoxpacd_e\n", + "\t- R_EX_fe2_e\n", + "\t- R_EX_fe3_e\n", + "\t- R_EX_fe3dcit_e\n", + "\t- R_EX_fe3dhbzs_e\n", + "\t- R_EX_fe3hox_e\n", + "\t- R_EX_acgam_e\n", + "\t- R_EX_fe3hox_un_e\n", + "\t- R_EX_fecrm_e\n", + "\t- R_EX_fecrm_un_e\n", + "\t- R_EX_hom__L_e\n", + "\t- R_EX_hxa_e\n", + "\t- R_EX_hxan_e\n", + "\t- R_EX_feenter_e\n", + "\t- R_EX_acgam1p_e\n", + "\t- R_EX_feoxam_e\n", + "\t- R_EX_feoxam_un_e\n", + "\t- R_EX_idon__L_e\n", + "\t- R_EX_ile__L_e\n", + "\t- R_EX_imp_e\n", + "\t- R_EX_indole_e\n", + "\t- R_EX_inost_e\n", + "\t- R_EX_ins_e\n", + "\t- R_EX_for_e\n", + "\t- R_EX_fru_e\n", + "\t- R_EX_frulys_e\n", + "\t- R_EX_fruur_e\n", + "\t- R_EX_fuc__L_e\n", + "\t- R_EX_fum_e\n", + "\t- R_EX_fusa_e\n", + "\t- R_EX_isetac_e\n", + "\t- R_EX_k_e\n", + "\t- R_EX_kdo2lipid4_e\n", + "\t- R_EX_lac__D_e\n", + "\t- R_EX_g1p_e\n", + "\t- R_EX_g3pc_e\n", + "\t- R_EX_acmana_e\n", + "\t- R_EX_g3pe_e\n", + "\t- R_EX_g3pg_e\n", + "\t- R_EX_lac__L_e\n", + "\t- R_EX_lcts_e\n", + "\t- R_EX_leu__L_e\n", + "\t- R_EX_g3pi_e\n", + "\t- R_EX_acmum_e\n", + "\t- R_EX_g3ps_e\n", + "\t- R_EX_g6p_e\n", + "\t- R_EX_lipa_e\n", + "\t- R_EX_lipa_cold_e\n", + "\t- R_EX_lipoate_e\n", + "\t- R_EX_lys__L_e\n", + "\t- R_EX_gal_e\n", + "\t- R_EX_gal_bD_e\n", + "\t- R_EX_acnam_e\n", + "\t- R_EX_gal1p_e\n", + "\t- R_EX_galct__D_e\n", + "\t- R_EX_lyx__L_e\n", + "\t- R_EX_mal__D_e\n", + "\t- R_EX_mal__L_e\n", + "\t- R_EX_malt_e\n", + "\t- R_EX_galctn__D_e\n", + "\t- R_EX_galctn__L_e\n", + "\t- R_EX_galt_e\n", + "\t- R_EX_galur_e\n", + "\t- R_EX_malthx_e\n", + "\t- R_EX_maltpt_e\n", + "\t- R_EX_acolipa_e\n", + "\t- R_EX_malttr_e\n", + "\t- R_EX_maltttr_e\n", + "\t- R_EX_man_e\n", + "\t- R_EX_gam_e\n", + "\t- R_EX_gam6p_e\n", + "\t- R_EX_gbbtn_e\n", + "\t- R_EX_man6p_e\n", + "\t- R_EX_gdp_e\n", + "\t- R_EX_manglyc_e\n", + "\t- R_EX_acser_e\n", + "\t- R_EX_melib_e\n", + "\t- R_EX_glc__D_e\n", + "\t- R_EX_glcn_e\n", + "\t- R_EX_meoh_e\n", + "\t- R_EX_ade_e\n", + "\t- R_EX_met__D_e\n", + "\t- R_EX_met__L_e\n", + "\t- R_EX_glcr_e\n", + "\t- R_EX_glcur_e\n", + "\t- R_EX_metsox_R__L_e\n", + "\t- R_EX_glcur1p_e\n", + "\t- R_EX_adn_e\n", + "\t- R_EX_gln__L_e\n", + "\t- R_EX_metsox_S__L_e\n", + "\t- R_EX_mg2_e\n", + "\t- R_EX_adocbl_e\n", + "\t- R_EX_mincyc_e\n", + "\t- R_EX_minohp_e\n", + "\t- R_EX_mmet_e\n", + "\t- R_EX_mn2_e\n", + "\t- R_EX_mnl_e\n", + "\t- R_EX_glu__L_e\n", + "\t- R_EX_gly_e\n", + "\t- R_EX_glyald_e\n", + "\t- R_EX_glyb_e\n", + "\t- R_EX_glyc_e\n", + "\t- R_EX_glyc__R_e\n", + "\t- R_EX_glyc2p_e\n", + "\t- R_EX_glyc3p_e\n", + "\t- R_EX_mobd_e\n", + "\t- R_EX_mso3_e\n", + "\t- R_EX_ag_e\n", + "\t- R_EX_n2o_e\n", + "\t- R_EX_na1_e\n", + "\t- R_EX_nac_e\n", + "\t- R_EX_glyclt_e\n", + "\t- R_EX_gmp_e\n", + "\t- R_EX_gsn_e\n", + "\t- R_EX_nh4_e\n", + "\t- R_EX_gthox_e\n", + "\t- R_EX_ni2_e\n", + "\t- R_EX_agm_e\n", + "\t- R_EX_gthrd_e\n", + "\t- R_EX_gtp_e\n", + "\t- R_EX_nmn_e\n", + "\t- R_EX_akg_e\n", + "\t- R_EX_no_e\n", + "\t- R_EX_no2_e\n", + "\t- R_EX_gua_e\n", + "\t- R_EX_h_e\n", + "\t- R_EX_h2_e\n", + "\t- R_EX_no3_e\n", + "\t- R_EX_novbcn_e\n", + "\t- R_EX_ala_B_e\n", + "\t- R_EX_o16a4colipa_e\n", + "\t- R_EX_o2_e\n", + "\t- R_EX_o2s_e\n", + "\t- R_EX_ocdca_e\n", + "\t- R_EX_ocdcea_e\n", + "\t- R_EX_h2o_e\n", + "\t- R_EX_h2o2_e\n", + "\t- R_EX_h2s_e\n", + "\t- R_EX_hacolipa_e\n", + "\t- R_EX_halipa_e\n", + "\t- R_EX_hdca_e\n", + "\t- R_EX_hdcea_e\n", + "\t- R_EX_hg2_e\n", + "\t- R_EX_octa_e\n", + "\t- R_EX_orn_e\n", + "\t- R_EX_orot_e\n", + "\t- R_EX_his__L_e\n", + "\t- R_EX_ala__D_e\n", + "\t- R_EX_pacald_e\n", + "\t- R_EX_peamn_e\n", + "\t- R_EX_phe__L_e\n", + "\t- R_EX_ala__L_e\n", + "\t- R_EX_pheme_e\n", + "\t- R_EX_pi_e\n", + "\t- R_EX_pnto__R_e\n", + "\t- R_EX_pyr_e\n", + "\t- R_EX_quin_e\n", + "\t- R_EX_r5p_e\n", + "\t- R_EX_rfamp_e\n", + "\t- R_EX_ppa_e\n", + "\t- R_EX_alaala_e\n", + "\t- R_EX_ppal_e\n", + "\t- R_EX_pppn_e\n", + "\t- R_EX_ppt_e\n", + "\t- R_EX_rib__D_e\n", + "\t- R_EX_rmn_e\n", + "\t- R_EX_sbt__D_e\n", + "\t- R_EX_pro__L_e\n", + "\t- R_EX_all__D_e\n", + "\t- R_EX_progly_e\n", + "\t- R_EX_psclys_e\n", + "\t- R_EX_pser__L_e\n", + "\t- R_EX_ptrc_e\n", + "\t- R_EX_sel_e\n", + "\t- R_EX_ser__D_e\n", + "\t- R_EX_ser__L_e\n", + "\t- R_EX_skm_e\n", + "\t- R_EX_slnt_e\n", + "\t- R_EX_so2_e\n", + "\t- R_EX_so3_e\n", + "\t- R_EX_pydam_e\n", + "\t- R_EX_pydx_e\n", + "\t- R_EX_pydxn_e\n", + "\t- R_EX_so4_e\n", + "\t- R_EX_alltn_e\n", + "\t- R_EX_spmd_e\n", + "\t- R_EX_succ_e\n", + "\t- R_EX_amp_e\n", + "\t- R_EX_sucr_e\n", + "\t- R_EX_sulfac_e\n", + "\t- R_EX_anhgm_e\n", + "\t- R_EX_tartr__D_e\n", + "\t- R_EX_tartr__L_e\n", + "\t- R_EX_taur_e\n", + "\t- R_EX_tcynt_e\n", + "\t- R_EX_arab__L_e\n", + "\t- R_EX_thm_e\n", + "\t- R_EX_thr__L_e\n", + "\t- R_EX_thrp_e\n", + "\t- R_EX_thym_e\n", + "\t- R_EX_thymd_e\n", + "\t- R_EX_tma_e\n", + "\t- R_EX_tmao_e\n", + "\t- R_EX_tre_e\n", + "\t- R_EX_arbt_e\n", + "\t- R_EX_trp__L_e\n", + "\t- R_EX_tsul_e\n", + "\t- R_EX_ttdca_e\n", + "\t- R_EX_arbtn_e\n", + "\t- R_EX_ttdcea_e\n", + "\t- R_EX_ttrcyc_e\n", + "\t- R_EX_tungs_e\n", + "\t- R_EX_arbtn_fe3_e\n", + "\t- R_EX_tym_e\n", + "\t- R_EX_tyr__L_e\n", + "\t- R_EX_arg__L_e\n", + "\t- R_EX_tyrp_e\n", + "\t- R_EX_uacgam_e\n", + "\t- R_EX_udpacgal_e\n", + "\t- R_EX_udpg_e\n", + "\t- R_EX_udpgal_e\n", + "\t- R_EX_udpglcur_e\n", + "\t- R_EX_ump_e\n", + "\t- R_EX_ura_e\n", + "\t- R_EX_ascb__L_e\n", + "\t- R_EX_urea_e\n", + "\t- R_EX_uri_e\n", + "\t- R_EX_val__L_e\n", + "\t- R_EX_xan_e\n", + "\t- R_EX_xmp_e\n", + "\t- R_EX_asn__L_e\n", + "\t- R_EX_xtsn_e\n", + "\t- R_EX_xyl__D_e\n", + "\t- R_EX_xylu__L_e\n", + "\t- R_EX_zn2_e\n", + "\t- R_EX_aso3_e\n", + "\t- R_EX_asp__L_e\n", + "\t- R_EX_btn_e\n", + "\t- R_EX_but_e\n", + "\t- R_EX_butso3_e\n", + "\t- R_EX_ca2_e\n", + "\t- R_EX_cbi_e\n", + "\t- R_EX_cbl1_e\n", + "\t- R_EX_cd2_e\n", + "\t- R_EX_cgly_e\n", + "\t- R_EX_chol_e\n", + "\t- R_EX_chtbs_e\n", + "\t- R_EX_cit_e\n", + "\t- R_EX_cl_e\n", + "\t- R_EX_cm_e\n", + "\t- R_EX_cmp_e\n", + "\t- R_EX_co2_e\n", + "\t- R_EX_cobalt2_e\n", + "\t- R_EX_colipa_e\n", + "\t- R_EX_colipap_e\n", + "\t- R_EX_cpgn_e\n", + "\t- R_EX_cpgn_un_e\n", + "\t- R_EX_crn_e\n", + "\t- R_EX_crn__D_e\n", + "\t- R_EX_csn_e\n", + "\t- R_EX_cu_e\n", + "\t- R_EX_cu2_e\n", + "\t- R_EX_cyan_e\n", + "\t- R_EX_cynt_e\n", + "\t- R_EX_cys__D_e\n", + "\t- R_EX_cys__L_e\n", + "\t- R_EX_cytd_e\n", + "\t- R_EX_dad_2_e\n", + "\t- R_EX_damp_e\n", + "\t- R_EX_dca_e\n", + "\t- R_EX_dcmp_e\n", + "\t- R_EX_dcyt_e\n", + "\t- R_EX_ddca_e\n", + "\t- R_EX_dgmp_e\n", + "\t- R_EX_dgsn_e\n", + "\t- R_EX_dha_e\n", + "\t- R_EX_dimp_e\n", + "\t- R_EX_din_e\n", + "\t- R_EX_dms_e\n", + "\t- R_EX_dmso_e\n", + "\t- R_EX_dopa_e\n", + "\t- R_EX_feroxB_e\n", + "\t- R_EX_tcb_e\n", + "\t- R_EX_tton_e\n", + "\t- R_EX_4hthr_e\n", + "\t- R_EX_feroxEfe_e\n", + "\t- R_EX_feroxE_e\n", + "\t- R_EX_pep_e\n", + "\t- R_EX_23dhbzs3_e\n", + "\t- R_EX_tet_e\n", + "\t- R_EX_feroxBfe_e\n", + "\t- R_EX_salchs4_e\n", + "\t- R_EX_tag__D_e\n", + "\t- R_EX_oaa_e\n", + "\t- R_EX_salchs2fe_e\n", + "\t- R_EX_4hphac_e\n", + "\t- R_EX_cysi__L_e\n", + "\t- R_EX_4abz_e\n", + "\t- R_EX_fe3dhbzs3_e\n", + "\t- R_EX_drib_e\n", + "\t- R_EX_acon_C_e\n", + "\t- R_EX_salchs4fe_e\n", + "\t- R_EX_2pg_e\n", + "\t- R_EX_icit_e\n", + "\t- R_EX_frmd_e\n", + "\t- R_EX_colipaOA_e\n", + "\t- R_EX_salchsx_e\n", + "\t- R_EX_AEP_e\n", + "\t- R_EX_colipa20Oag_e\n", + "\t- R_EX_diact_e\n", + "\t- R_EX_remnant1_e\n", + "\t- R_EX_feroxGfe_e\n", + "\t- R_EX_cellb_e\n", + "\t- R_EX_salcn_e\n", + "\t- R_EX_btd_RR_e\n", + "\t- R_EX_rbt_e\n", + "\t- R_EX_salchs2_e\n", + "\t- R_EX_rnam_e\n", + "\t- R_EX_airs_e\n", + "\t- R_EX_dxyl_e\n", + "\t- R_EX_galam_e\n", + "\t- R_EX_3pg_e\n", + "\t- R_EX_bz_e\n", + "\t- R_EX_13ppd_e\n", + "\t- R_EX_abt__D_e\n", + "\t- R_EX_udcpo4_e\n", + "\t- R_EX_udcpo5_e\n", + "\t- R_EX_guln__L_e\n", + "\t- R_EX_feroxG_e\n", + "\t- R_EX_fe_e\n", + "\t- R_EX_kokdolipidA_e\n", + "\t- R_EX_3hoxpac_e\n", + "\t- R_EX_raffin_e\n", + "\t- R_EX_o6a4colipa_e\n", + "\t- R_EX_4hoxpac_e\n", + "\t- R_EX_arab__D_e\n", + "\t- R_EX_3ntym_e\n", + "\t- R_EX_6apa_e\n", + "\t- R_DM_lipidA_core_e_p\n", + "\t- R_EX_peng_e\n", + "\t- R_EX_pac_e\u001b[0m\n", + "File saved at: ../../data/sbml_corrected/iECO26_1355.xml\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: iNF517\n", + "\u001b[0;93mWARNING : \n", + " - R_BIOMASS_LLA_noATPnoH: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_ACONT: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_CITt2r: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_CITt3: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + " - R_EX_ala__L_e: Reactants and products exchanged.\n", + " Boundaries was: [-0.04 ; 0.0]\n", + " - R_EX_arg__L_e: Reactants and products exchanged.\n", + " Boundaries was: [-0.02 ; -0.01]\n", + "\n", + " - R_CYTB_B2: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + " - R_EX_cys__L_e: Reactants and products exchanged.\n", + " Boundaries was: [-0.02 ; 0.0]\n", + " - R_EX_glc__D_e: Reactants and products exchanged.\n", + " Boundaries was: [-2.12 ; -0.92]\n", + " - R_EX_glu__L_e: Reactants and products exchanged.\n", + " Boundaries was: [-0.05 ; -0.01]\n", + "\n", + " - R_G3PD4: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_ILEt2r_copy1: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + " - R_EX_gly_e: Reactants and products exchanged.\n", + " Boundaries was: [-0.04 ; 0.0]\n", + "\n", + " - R_GTHPi: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_GTHOr: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + " - R_EX_his__L_e: Reactants and products exchanged.\n", + " Boundaries was: [-0.01 ; 0.0]\n", + " - R_EX_ile__L_e: Reactants and products exchanged.\n", + " Boundaries was: [-0.05 ; -0.01]\n", + " - R_GLYCt: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_EX_leu__L_e: Reactants and products exchanged.\n", + " Boundaries was: [-0.06 ; -0.01]\n", + " - R_EX_lys__L_e: Reactants and products exchanged.\n", + " Boundaries was: [-0.04 ; 0.0]\n", + "\n", + " - R_LEUt2r_copy1: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_ICDHyr: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + " - R_EX_met__L_e: Reactants and products exchanged.\n", + " Boundaries was: [-0.01 ; 0.0]\n", + "\n", + " - R_O2t: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_PROTS_LLA: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_PROTS_LLA_v2: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_NADH4: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_NOX2: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + " - R_EX_ser__L_e: Reactants and products exchanged.\n", + " Boundaries was: [-0.11 ; -0.04]\n", + "\n", + " - R_NADHPO: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + " - R_EX_thr__L_e: Reactants and products exchanged.\n", + " Boundaries was: [-0.06 ; -0.02]\n", + "\n", + " - R_EX_trp__L_e: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + " - R_EX_val__L_e: Reactants and products exchanged.\n", + " Boundaries was: [-0.05 ; -0.01]\n", + "\n", + " - R_VALt2r_copy1: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_OIVD1r: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_OIVD2: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_OIVD3: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_PDH: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_DHORD6: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\u001b[0m\n", + "\n", + "\n", + "\u001b[0;36m############################################\n", + "############################################\n", + " WRITING SBML FILE \n", + "############################################\n", + "############################################\n", + "\u001b[0m\n", + "\u001b[0;93mWARNING : IMPORT REACTION REMOVED BY DEFAULT\u001b[0m\n", + "\u001b[0;93mWARNING : If you want to keep import reaction\n", + "use option -kir / --keep-import-reactions\u001b[0m\n", + "\u001b[0;93mWARNING : Reaction with tag reversible modified:\n", + "\t- R_EX_ala__L_e\n", + "\t- R_EX_arg__L_e\n", + "\t- R_EX_cys__L_e\n", + "\t- R_EX_glc__D_e\n", + "\t- R_EX_glu__L_e\n", + "\t- R_EX_gly_e\n", + "\t- R_EX_his__L_e\n", + "\t- R_EX_ile__L_e\n", + "\t- R_GLYCt\n", + "\t- R_EX_leu__L_e\n", + "\t- R_EX_lys__L_e\n", + "\t- R_EX_met__L_e\n", + "\t- R_EX_ser__L_e\n", + "\t- R_EX_thr__L_e\n", + "\t- R_EX_val__L_e\u001b[0m\n", + "\u001b[0;93mWARNING : Reaction with Reactants and Products exchanged:\n", + "\t- R_EX_ala__L_e\n", + "\t- R_EX_arg__L_e\n", + "\t- R_EX_cys__L_e\n", + "\t- R_EX_glc__D_e\n", + "\t- R_EX_glu__L_e\n", + "\t- R_EX_gly_e\n", + "\t- R_EX_his__L_e\n", + "\t- R_EX_ile__L_e\n", + "\t- R_GLYCt\n", + "\t- R_EX_leu__L_e\n", + "\t- R_EX_lys__L_e\n", + "\t- R_EX_met__L_e\n", + "\t- R_EX_ser__L_e\n", + "\t- R_EX_thr__L_e\n", + "\t- R_EX_val__L_e\u001b[0m\n", + "\u001b[0;93mWARNING : Import reaction removed:\n", + "\t- R_EX_2aeppn_e\n", + "\t- R_EX_2h3mb_e\n", + "\t- R_EX_2h3mp_e\n", + "\t- R_EX_2hxic__L_e\n", + "\t- R_EX_2mba_e\n", + "\t- R_EX_2mbald_e\n", + "\t- R_EX_2mpa_e\n", + "\t- R_EX_3mba_e\n", + "\t- R_EX_3mbal_e\n", + "\t- R_EX_4abut_e\n", + "\t- R_EX_4abz_e\n", + "\t- R_EX_ac_e\n", + "\t- R_EX_acald_e\n", + "\t- R_EX_acgala_e\n", + "\t- R_EX_acgam_e\n", + "\t- R_EX_acmana_e\n", + "\t- R_EX_actn__R_e\n", + "\t- R_EX_ade_e\n", + "\t- R_EX_akg_e\n", + "\t- R_EX_ala__D_e\n", + "\t- R_EX_ala__L_e\n", + "\t- R_EX_arg__L_e\n", + "\t- R_EX_asn__L_e\n", + "\t- R_EX_asp__L_e\n", + "\t- R_EX_btd_RR_e\n", + "\t- R_EX_bzal_e\n", + "\t- R_EX_cellb_e\n", + "\t- R_EX_ch4s_e\n", + "\t- R_EX_chol_e\n", + "\t- R_EX_cit_e\n", + "\t- R_EX_co2_e\n", + "\t- R_EX_cys__L_e\n", + "\t- R_EX_dha_e\n", + "\t- R_EX_diact_e\n", + "\t- R_EX_drib_e\n", + "\t- R_EX_etoh_e\n", + "\t- R_EX_fe2_e\n", + "\t- R_EX_fe3_e\n", + "\t- R_EX_fol_e\n", + "\t- R_EX_for_e\n", + "\t- R_EX_fru_e\n", + "\t- R_EX_gal_e\n", + "\t- R_EX_galt_e\n", + "\t- R_EX_gcald_e\n", + "\t- R_EX_glc__D_e\n", + "\t- R_EX_glcn__D_e\n", + "\t- R_EX_gln__L_e\n", + "\t- R_EX_glu__L_e\n", + "\t- R_EX_gly_e\n", + "\t- R_EX_glyb_e\n", + "\t- R_EX_glyc3p_e\n", + "\t- R_EX_glyc_e\n", + "\t- R_EX_gua_e\n", + "\t- R_EX_h2o_e\n", + "\t- R_EX_h2s_e\n", + "\t- R_EX_h_e\n", + "\t- R_EX_his__L_e\n", + "\t- R_EX_hxan_e\n", + "\t- R_EX_ile__L_e\n", + "\t- R_EX_ins_e\n", + "\t- R_EX_lac__L_e\n", + "\t- R_EX_lcts_e\n", + "\t- R_EX_leu__L_e\n", + "\t- R_EX_lys__L_e\n", + "\t- R_EX_mal__L_e\n", + "\t- R_EX_malt_e\n", + "\t- R_EX_man_e\n", + "\t- R_EX_met__D_e\n", + "\t- R_EX_met__L_e\n", + "\t- R_EX_mn2_e\n", + "\t- R_EX_mnl_e\n", + "\t- R_EX_nac_e\n", + "\t- R_EX_nh4_e\n", + "\t- R_EX_nmn_e\n", + "\t- R_EX_o2_e\n", + "\t- R_EX_orn__L_e\n", + "\t- R_EX_orot_e\n", + "\t- R_EX_pacald_e\n", + "\t- R_EX_pea_e\n", + "\t- R_EX_phe__L_e\n", + "\t- R_EX_pi_e\n", + "\t- R_EX_pnto__R_e\n", + "\t- R_EX_ppi_e\n", + "\t- R_EX_pro__L_e\n", + "\t- R_EX_ptrc_e\n", + "\t- R_EX_pyr_e\n", + "\t- R_EX_rib__D_e\n", + "\t- R_EX_ribflv_e\n", + "\t- R_EX_sbt__D_e\n", + "\t- R_EX_ser__D_e\n", + "\t- R_EX_ser__L_e\n", + "\t- R_EX_so4_e\n", + "\t- R_EX_spmd_e\n", + "\t- R_EX_succ_e\n", + "\t- R_EX_sucr_e\n", + "\t- R_EX_thm_e\n", + "\t- R_EX_thr__L_e\n", + "\t- R_EX_thymd_e\n", + "\t- R_EX_tre_e\n", + "\t- R_EX_tyr__L_e\n", + "\t- R_EX_ura_e\n", + "\t- R_EX_val__L_e\n", + "\t- R_EX_xan_e\n", + "\t- R_EX_zn2_e\u001b[0m\n", + "File saved at: ../../data/sbml_corrected/iNF517.xml\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: iECH74115_1262\n", + "\u001b[0;93mWARNING : \n", + " - R_CAT: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_DHPTDNR: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_DHPTDNRN: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SPODM: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SPODMpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCASPtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCFUMtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCMALtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCTARTtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_CHITPH: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_URCN: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_EX_chitob_e: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_CHITOBpts: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\u001b[0m\n", + "\n", + "\n", + "\u001b[0;36m############################################\n", + "############################################\n", + " WRITING SBML FILE \n", + "############################################\n", + "############################################\n", + "\u001b[0m\n", + "\u001b[0;93mWARNING : IMPORT REACTION REMOVED BY DEFAULT\u001b[0m\n", + "\u001b[0;93mWARNING : If you want to keep import reaction\n", + "use option -kir / --keep-import-reactions\u001b[0m\n", + "\u001b[0;93mWARNING : Import reaction removed:\n", + "\t- R_DM_amob_c\n", + "\t- R_EX_colipap_e\n", + "\t- R_EX_cpgn_e\n", + "\t- R_EX_cpgn_un_e\n", + "\t- R_DM_aacald_c\n", + "\t- R_DM_oxam_c\n", + "\t- R_DM_mththf_c\n", + "\t- R_EX_crn_e\n", + "\t- R_DM_4crsol_c\n", + "\t- R_DM_5drib_c\n", + "\t- R_EX_crn__D_e\n", + "\t- R_EX_12ppd__R_e\n", + "\t- R_EX_csn_e\n", + "\t- R_EX_12ppd__S_e\n", + "\t- R_EX_14glucan_e\n", + "\t- R_EX_glyald_e\n", + "\t- R_EX_glyb_e\n", + "\t- R_EX_15dap_e\n", + "\t- R_EX_cu_e\n", + "\t- R_EX_23camp_e\n", + "\t- R_EX_glyc_e\n", + "\t- R_EX_glyc__R_e\n", + "\t- R_EX_glyc2p_e\n", + "\t- R_EX_23ccmp_e\n", + "\t- R_EX_cu2_e\n", + "\t- R_EX_23cgmp_e\n", + "\t- R_EX_glyc3p_e\n", + "\t- R_EX_glyclt_e\n", + "\t- R_EX_gmp_e\n", + "\t- R_EX_23cump_e\n", + "\t- R_EX_cyan_e\n", + "\t- R_EX_23dappa_e\n", + "\t- R_EX_26dap__M_e\n", + "\t- R_EX_2ddglcn_e\n", + "\t- R_EX_34dhpac_e\n", + "\t- R_EX_3amp_e\n", + "\t- R_EX_3cmp_e\n", + "\t- R_EX_3gmp_e\n", + "\t- R_EX_3hcinnm_e\n", + "\t- R_EX_3hpp_e\n", + "\t- R_EX_3hpppn_e\n", + "\t- R_EX_3ump_e\n", + "\t- R_EX_gsn_e\n", + "\t- R_EX_gthox_e\n", + "\t- R_EX_gthrd_e\n", + "\t- R_EX_gtp_e\n", + "\t- R_EX_gua_e\n", + "\t- R_EX_h_e\n", + "\t- R_EX_h2_e\n", + "\t- R_EX_h2o_e\n", + "\t- R_EX_h2o2_e\n", + "\t- R_EX_h2s_e\n", + "\t- R_EX_hacolipa_e\n", + "\t- R_EX_halipa_e\n", + "\t- R_EX_hdca_e\n", + "\t- R_EX_hdcea_e\n", + "\t- R_EX_hg2_e\n", + "\t- R_EX_4abut_e\n", + "\t- R_EX_4hoxpacd_e\n", + "\t- R_EX_5dglcn_e\n", + "\t- R_EX_5mtr_e\n", + "\t- R_EX_LalaDglu_e\n", + "\t- R_EX_LalaDgluMdap_e\n", + "\t- R_EX_his__L_e\n", + "\t- R_EX_hom__L_e\n", + "\t- R_EX_LalaDgluMdapDala_e\n", + "\t- R_EX_cynt_e\n", + "\t- R_EX_LalaLglu_e\n", + "\t- R_EX_ac_e\n", + "\t- R_EX_acac_e\n", + "\t- R_EX_acald_e\n", + "\t- R_EX_acgal_e\n", + "\t- R_EX_hxa_e\n", + "\t- R_EX_hxan_e\n", + "\t- R_EX_acgal1p_e\n", + "\t- R_EX_cys__D_e\n", + "\t- R_EX_acgam_e\n", + "\t- R_EX_acgam1p_e\n", + "\t- R_EX_cys__L_e\n", + "\t- R_EX_acmana_e\n", + "\t- R_EX_acmum_e\n", + "\t- R_EX_acnam_e\n", + "\t- R_EX_acolipa_e\n", + "\t- R_EX_idon__L_e\n", + "\t- R_EX_ile__L_e\n", + "\t- R_EX_acser_e\n", + "\t- R_EX_cytd_e\n", + "\t- R_EX_ade_e\n", + "\t- R_EX_adn_e\n", + "\t- R_EX_adocbl_e\n", + "\t- R_EX_imp_e\n", + "\t- R_EX_indole_e\n", + "\t- R_EX_inost_e\n", + "\t- R_EX_ag_e\n", + "\t- R_EX_agm_e\n", + "\t- R_EX_ins_e\n", + "\t- R_EX_akg_e\n", + "\t- R_EX_dad_2_e\n", + "\t- R_EX_ala_B_e\n", + "\t- R_EX_isetac_e\n", + "\t- R_EX_k_e\n", + "\t- R_EX_kdo2lipid4_e\n", + "\t- R_EX_ala__D_e\n", + "\t- R_EX_damp_e\n", + "\t- R_EX_ala__L_e\n", + "\t- R_EX_lac__D_e\n", + "\t- R_EX_dca_e\n", + "\t- R_EX_lac__L_e\n", + "\t- R_EX_lcts_e\n", + "\t- R_EX_leu__L_e\n", + "\t- R_EX_lipa_e\n", + "\t- R_EX_alaala_e\n", + "\t- R_EX_all__D_e\n", + "\t- R_EX_alltn_e\n", + "\t- R_EX_amp_e\n", + "\t- R_EX_anhgm_e\n", + "\t- R_EX_arab__L_e\n", + "\t- R_EX_arbt_e\n", + "\t- R_EX_arbtn_e\n", + "\t- R_EX_lipa_cold_e\n", + "\t- R_EX_lipoate_e\n", + "\t- R_EX_lys__L_e\n", + "\t- R_EX_arbtn_fe3_e\n", + "\t- R_EX_dcmp_e\n", + "\t- R_EX_arg__L_e\n", + "\t- R_EX_lyx__L_e\n", + "\t- R_EX_mal__D_e\n", + "\t- R_EX_mal__L_e\n", + "\t- R_EX_malt_e\n", + "\t- R_EX_ascb__L_e\n", + "\t- R_EX_asn__L_e\n", + "\t- R_EX_malthx_e\n", + "\t- R_EX_aso3_e\n", + "\t- R_EX_dcyt_e\n", + "\t- R_EX_asp__L_e\n", + "\t- R_EX_btn_e\n", + "\t- R_EX_maltpt_e\n", + "\t- R_EX_malttr_e\n", + "\t- R_EX_but_e\n", + "\t- R_EX_ddca_e\n", + "\t- R_EX_butso3_e\n", + "\t- R_EX_maltttr_e\n", + "\t- R_EX_man_e\n", + "\t- R_EX_ca2_e\n", + "\t- R_EX_dgmp_e\n", + "\t- R_EX_cbi_e\n", + "\t- R_EX_cbl1_e\n", + "\t- R_EX_cd2_e\n", + "\t- R_EX_cgly_e\n", + "\t- R_EX_man6p_e\n", + "\t- R_EX_manglyc_e\n", + "\t- R_EX_melib_e\n", + "\t- R_EX_meoh_e\n", + "\t- R_EX_met__D_e\n", + "\t- R_EX_met__L_e\n", + "\t- R_EX_metsox_R__L_e\n", + "\t- R_EX_metsox_S__L_e\n", + "\t- R_EX_chol_e\n", + "\t- R_EX_chtbs_e\n", + "\t- R_EX_cit_e\n", + "\t- R_EX_mg2_e\n", + "\t- R_EX_cl_e\n", + "\t- R_EX_dgsn_e\n", + "\t- R_EX_cm_e\n", + "\t- R_EX_cmp_e\n", + "\t- R_EX_co2_e\n", + "\t- R_EX_mincyc_e\n", + "\t- R_EX_minohp_e\n", + "\t- R_EX_mmet_e\n", + "\t- R_EX_mn2_e\n", + "\t- R_EX_mnl_e\n", + "\t- R_EX_cobalt2_e\n", + "\t- R_EX_colipa_e\n", + "\t- R_EX_mobd_e\n", + "\t- R_EX_dha_e\n", + "\t- R_EX_mso3_e\n", + "\t- R_EX_dimp_e\n", + "\t- R_EX_n2o_e\n", + "\t- R_EX_na1_e\n", + "\t- R_EX_nac_e\n", + "\t- R_EX_o2s_e\n", + "\t- R_EX_ocdca_e\n", + "\t- R_EX_ocdcea_e\n", + "\t- R_EX_octa_e\n", + "\t- R_EX_orn_e\n", + "\t- R_EX_orot_e\n", + "\t- R_EX_pacald_e\n", + "\t- R_EX_nh4_e\n", + "\t- R_EX_ni2_e\n", + "\t- R_EX_nmn_e\n", + "\t- R_EX_no_e\n", + "\t- R_EX_no2_e\n", + "\t- R_EX_peamn_e\n", + "\t- R_EX_phe__L_e\n", + "\t- R_EX_no3_e\n", + "\t- R_EX_din_e\n", + "\t- R_EX_novbcn_e\n", + "\t- R_EX_o16a4colipa_e\n", + "\t- R_EX_o2_e\n", + "\t- R_EX_pheme_e\n", + "\t- R_EX_pi_e\n", + "\t- R_EX_pnto__R_e\n", + "\t- R_EX_ppa_e\n", + "\t- R_EX_dms_e\n", + "\t- R_EX_ppal_e\n", + "\t- R_EX_pppn_e\n", + "\t- R_EX_dmso_e\n", + "\t- R_EX_ppt_e\n", + "\t- R_EX_pro__L_e\n", + "\t- R_EX_progly_e\n", + "\t- R_EX_dopa_e\n", + "\t- R_EX_psclys_e\n", + "\t- R_EX_pser__L_e\n", + "\t- R_EX_ptrc_e\n", + "\t- R_EX_pydam_e\n", + "\t- R_EX_pydx_e\n", + "\t- R_EX_pydxn_e\n", + "\t- R_EX_pyr_e\n", + "\t- R_EX_quin_e\n", + "\t- R_EX_r5p_e\n", + "\t- R_EX_doxrbcn_e\n", + "\t- R_EX_rfamp_e\n", + "\t- R_EX_rib__D_e\n", + "\t- R_EX_rmn_e\n", + "\t- R_EX_dtmp_e\n", + "\t- R_EX_sbt__D_e\n", + "\t- R_EX_sel_e\n", + "\t- R_EX_dump_e\n", + "\t- R_EX_ser__D_e\n", + "\t- R_EX_ser__L_e\n", + "\t- R_EX_duri_e\n", + "\t- R_EX_skm_e\n", + "\t- R_EX_slnt_e\n", + "\t- R_EX_so2_e\n", + "\t- R_EX_so3_e\n", + "\t- R_EX_so4_e\n", + "\t- R_EX_spmd_e\n", + "\t- R_EX_eca4colipa_e\n", + "\t- R_EX_succ_e\n", + "\t- R_EX_sucr_e\n", + "\t- R_EX_enlipa_e\n", + "\t- R_EX_sulfac_e\n", + "\t- R_EX_tartr__D_e\n", + "\t- R_EX_tartr__L_e\n", + "\t- R_EX_taur_e\n", + "\t- R_EX_enter_e\n", + "\t- R_EX_tcynt_e\n", + "\t- R_EX_thm_e\n", + "\t- R_EX_thr__L_e\n", + "\t- R_EX_etha_e\n", + "\t- R_EX_thrp_e\n", + "\t- R_EX_thym_e\n", + "\t- R_EX_thymd_e\n", + "\t- R_EX_tma_e\n", + "\t- R_EX_tmao_e\n", + "\t- R_EX_tre_e\n", + "\t- R_EX_ethso3_e\n", + "\t- R_EX_trp__L_e\n", + "\t- R_EX_tsul_e\n", + "\t- R_EX_ttdca_e\n", + "\t- R_EX_ttdcea_e\n", + "\t- R_EX_ttrcyc_e\n", + "\t- R_EX_tungs_e\n", + "\t- R_EX_etoh_e\n", + "\t- R_EX_tym_e\n", + "\t- R_EX_tyr__L_e\n", + "\t- R_EX_f6p_e\n", + "\t- R_EX_tyrp_e\n", + "\t- R_EX_uacgam_e\n", + "\t- R_EX_udpacgal_e\n", + "\t- R_EX_udpg_e\n", + "\t- R_EX_fald_e\n", + "\t- R_EX_udpgal_e\n", + "\t- R_EX_udpglcur_e\n", + "\t- R_EX_ump_e\n", + "\t- R_EX_ura_e\n", + "\t- R_EX_urea_e\n", + "\t- R_EX_uri_e\n", + "\t- R_EX_val__L_e\n", + "\t- R_EX_xan_e\n", + "\t- R_EX_xmp_e\n", + "\t- R_EX_fe2_e\n", + "\t- R_EX_xtsn_e\n", + "\t- R_EX_xyl__D_e\n", + "\t- R_EX_xylu__L_e\n", + "\t- R_EX_fe3_e\n", + "\t- R_EX_zn2_e\n", + "\t- R_EX_fe3dcit_e\n", + "\t- R_EX_fe3dhbzs_e\n", + "\t- R_EX_fe3hox_e\n", + "\t- R_EX_fe3hox_un_e\n", + "\t- R_EX_fecrm_e\n", + "\t- R_EX_fecrm_un_e\n", + "\t- R_EX_feenter_e\n", + "\t- R_EX_feoxam_e\n", + "\t- R_EX_feoxam_un_e\n", + "\t- R_EX_for_e\n", + "\t- R_EX_fru_e\n", + "\t- R_EX_frulys_e\n", + "\t- R_EX_fruur_e\n", + "\t- R_EX_fuc__L_e\n", + "\t- R_EX_fum_e\n", + "\t- R_EX_fusa_e\n", + "\t- R_EX_g1p_e\n", + "\t- R_EX_g3pc_e\n", + "\t- R_EX_g3pe_e\n", + "\t- R_EX_g3pg_e\n", + "\t- R_EX_g3pi_e\n", + "\t- R_EX_g3ps_e\n", + "\t- R_EX_g6p_e\n", + "\t- R_EX_gal_e\n", + "\t- R_EX_gal_bD_e\n", + "\t- R_EX_gal1p_e\n", + "\t- R_EX_galct__D_e\n", + "\t- R_EX_galctn__D_e\n", + "\t- R_EX_galctn__L_e\n", + "\t- R_EX_galt_e\n", + "\t- R_EX_galur_e\n", + "\t- R_EX_gam_e\n", + "\t- R_EX_gam6p_e\n", + "\t- R_EX_gbbtn_e\n", + "\t- R_EX_gdp_e\n", + "\t- R_EX_glc__D_e\n", + "\t- R_EX_glcn_e\n", + "\t- R_EX_glcr_e\n", + "\t- R_EX_glcur_e\n", + "\t- R_EX_glcur1p_e\n", + "\t- R_EX_gln__L_e\n", + "\t- R_EX_glu__L_e\n", + "\t- R_EX_gly_e\n", + "\t- R_EX_23dhbzs3_e\n", + "\t- R_EX_tet_e\n", + "\t- R_EX_salchs4_e\n", + "\t- R_EX_4hphac_e\n", + "\t- R_EX_tag__D_e\n", + "\t- R_EX_4abz_e\n", + "\t- R_EX_oaa_e\n", + "\t- R_EX_salchs2fe_e\n", + "\t- R_EX_acon_C_e\n", + "\t- R_EX_cysi__L_e\n", + "\t- R_EX_fe3dhbzs3_e\n", + "\t- R_EX_drib_e\n", + "\t- R_EX_feroxB_e\n", + "\t- R_EX_tcb_e\n", + "\t- R_EX_tton_e\n", + "\t- R_EX_4hthr_e\n", + "\t- R_EX_feroxBfe_e\n", + "\t- R_EX_salchs4fe_e\n", + "\t- R_EX_2pg_e\n", + "\t- R_EX_feroxEfe_e\n", + "\t- R_EX_feroxE_e\n", + "\t- R_EX_frmd_e\n", + "\t- R_EX_pep_e\n", + "\t- R_EX_colipaOA_e\n", + "\t- R_EX_salchsx_e\n", + "\t- R_EX_AEP_e\n", + "\t- R_EX_rnam_e\n", + "\t- R_EX_airs_e\n", + "\t- R_EX_dxyl_e\n", + "\t- R_EX_3pg_e\n", + "\t- R_EX_icit_e\n", + "\t- R_EX_colipa20Oag_e\n", + "\t- R_EX_remnant1_e\n", + "\t- R_EX_udcpo4_e\n", + "\t- R_EX_salchs2_e\n", + "\t- R_EX_feroxGfe_e\n", + "\t- R_EX_udcpo5_e\n", + "\t- R_EX_guln__L_e\n", + "\t- R_EX_3hoxpac_e\n", + "\t- R_EX_feroxG_e\n", + "\t- R_EX_4hoxpac_e\n", + "\t- R_EX_diact_e\n", + "\t- R_EX_cellb_e\n", + "\t- R_EX_btd_RR_e\n", + "\t- R_EX_13ppd_e\n", + "\t- R_EX_rbt_e\n", + "\t- R_EX_bz_e\n", + "\t- R_EX_abt__D_e\n", + "\t- R_EX_fe_e\n", + "\t- R_EX_kokdolipidA_e\n", + "\t- R_EX_3ntym_e\n", + "\t- R_EX_6apa_e\n", + "\t- R_EX_peng_e\n", + "\t- R_EX_pac_e\n", + "\t- R_EX_salcn_e\n", + "\t- R_EX_galam_e\n", + "\t- R_DM_lipidA_core_e_p\n", + "\t- R_EX_raffin_e\n", + "\t- R_EX_o6a4colipa_e\n", + "\t- R_EX_arab__D_e\u001b[0m\n", + "File saved at: ../../data/sbml_corrected/iECH74115_1262.xml\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: iS_1188\n", + "\u001b[0;93mWARNING : \n", + " - R_CAT: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_DHPTDNR: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_DHPTDNRN: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCTARTtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SPODM: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SPODMpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCASPtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCFUMtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCMALtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_EX_chitob_e: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_CHITOBpts: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_CHITPH: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_URCN: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\u001b[0m\n", + "\n", + "\n", + "\u001b[0;36m############################################\n", + "############################################\n", + " WRITING SBML FILE \n", + "############################################\n", + "############################################\n", + "\u001b[0m\n", + "\u001b[0;93mWARNING : IMPORT REACTION REMOVED BY DEFAULT\u001b[0m\n", + "\u001b[0;93mWARNING : If you want to keep import reaction\n", + "use option -kir / --keep-import-reactions\u001b[0m\n", + "\u001b[0;93mWARNING : Import reaction removed:\n", + "\t- R_EX_3amp_e\n", + "\t- R_EX_3hcinnm_e\n", + "\t- R_EX_34dhpac_e\n", + "\t- R_EX_3cmp_e\n", + "\t- R_EX_3hpp_e\n", + "\t- R_EX_3gmp_e\n", + "\t- R_EX_14glucan_e\n", + "\t- R_EX_3hpppn_e\n", + "\t- R_DM_4crsol_c\n", + "\t- R_EX_3ump_e\n", + "\t- R_DM_5drib_c\n", + "\t- R_EX_4abut_e\n", + "\t- R_DM_aacald_c\n", + "\t- R_DM_amob_c\n", + "\t- R_EX_4hoxpacd_e\n", + "\t- R_DM_mththf_c\n", + "\t- R_EX_5dglcn_e\n", + "\t- R_DM_oxam_c\n", + "\t- R_EX_5mtr_e\n", + "\t- R_EX_LalaDglu_e\n", + "\t- R_EX_12ppd__R_e\n", + "\t- R_EX_ddca_e\n", + "\t- R_EX_LalaDgluMdap_e\n", + "\t- R_EX_LalaDgluMdapDala_e\n", + "\t- R_EX_dgmp_e\n", + "\t- R_EX_dgsn_e\n", + "\t- R_EX_12ppd__S_e\n", + "\t- R_EX_15dap_e\n", + "\t- R_EX_23camp_e\n", + "\t- R_EX_dha_e\n", + "\t- R_EX_LalaLglu_e\n", + "\t- R_EX_dimp_e\n", + "\t- R_EX_din_e\n", + "\t- R_EX_dms_e\n", + "\t- R_EX_dmso_e\n", + "\t- R_EX_dopa_e\n", + "\t- R_EX_doxrbcn_e\n", + "\t- R_EX_dtmp_e\n", + "\t- R_EX_dump_e\n", + "\t- R_EX_duri_e\n", + "\t- R_EX_eca4colipa_e\n", + "\t- R_EX_enlipa_e\n", + "\t- R_EX_enter_e\n", + "\t- R_EX_23ccmp_e\n", + "\t- R_EX_23cgmp_e\n", + "\t- R_EX_23cump_e\n", + "\t- R_EX_23dappa_e\n", + "\t- R_EX_26dap__M_e\n", + "\t- R_EX_2ddglcn_e\n", + "\t- R_EX_h2o_e\n", + "\t- R_EX_h2o2_e\n", + "\t- R_EX_h2s_e\n", + "\t- R_EX_hacolipa_e\n", + "\t- R_EX_halipa_e\n", + "\t- R_EX_hdca_e\n", + "\t- R_EX_etha_e\n", + "\t- R_EX_ethso3_e\n", + "\t- R_EX_etoh_e\n", + "\t- R_EX_f6p_e\n", + "\t- R_EX_hdcea_e\n", + "\t- R_EX_ac_e\n", + "\t- R_EX_hg2_e\n", + "\t- R_EX_his__L_e\n", + "\t- R_EX_fald_e\n", + "\t- R_EX_fe2_e\n", + "\t- R_EX_fe3_e\n", + "\t- R_EX_fe3dcit_e\n", + "\t- R_EX_hom__L_e\n", + "\t- R_EX_hxa_e\n", + "\t- R_EX_hxan_e\n", + "\t- R_EX_fe3dhbzs_e\n", + "\t- R_EX_fe3hox_e\n", + "\t- R_EX_fe3hox_un_e\n", + "\t- R_EX_fecrm_e\n", + "\t- R_EX_fecrm_un_e\n", + "\t- R_EX_acac_e\n", + "\t- R_EX_feenter_e\n", + "\t- R_EX_feoxam_e\n", + "\t- R_EX_acald_e\n", + "\t- R_EX_feoxam_un_e\n", + "\t- R_EX_for_e\n", + "\t- R_EX_fru_e\n", + "\t- R_EX_frulys_e\n", + "\t- R_EX_idon__L_e\n", + "\t- R_EX_acgal_e\n", + "\t- R_EX_ile__L_e\n", + "\t- R_EX_imp_e\n", + "\t- R_EX_fruur_e\n", + "\t- R_EX_fuc__L_e\n", + "\t- R_EX_acgal1p_e\n", + "\t- R_EX_fum_e\n", + "\t- R_EX_fusa_e\n", + "\t- R_EX_g1p_e\n", + "\t- R_EX_indole_e\n", + "\t- R_EX_inost_e\n", + "\t- R_EX_ins_e\n", + "\t- R_EX_isetac_e\n", + "\t- R_EX_g3pc_e\n", + "\t- R_EX_acgam_e\n", + "\t- R_EX_g3pe_e\n", + "\t- R_EX_g3pg_e\n", + "\t- R_EX_k_e\n", + "\t- R_EX_kdo2lipid4_e\n", + "\t- R_EX_g3pi_e\n", + "\t- R_EX_lac__D_e\n", + "\t- R_EX_lac__L_e\n", + "\t- R_EX_g3ps_e\n", + "\t- R_EX_g6p_e\n", + "\t- R_EX_gal_e\n", + "\t- R_EX_gal_bD_e\n", + "\t- R_EX_lcts_e\n", + "\t- R_EX_leu__L_e\n", + "\t- R_EX_acgam1p_e\n", + "\t- R_EX_lipa_e\n", + "\t- R_EX_lipa_cold_e\n", + "\t- R_EX_gal1p_e\n", + "\t- R_EX_galct__D_e\n", + "\t- R_EX_galctn__D_e\n", + "\t- R_EX_galctn__L_e\n", + "\t- R_EX_lipoate_e\n", + "\t- R_EX_lys__L_e\n", + "\t- R_EX_acmana_e\n", + "\t- R_EX_galt_e\n", + "\t- R_EX_galur_e\n", + "\t- R_EX_lyx__L_e\n", + "\t- R_EX_mal__D_e\n", + "\t- R_EX_gam_e\n", + "\t- R_EX_acmum_e\n", + "\t- R_EX_gam6p_e\n", + "\t- R_EX_gbbtn_e\n", + "\t- R_EX_mal__L_e\n", + "\t- R_EX_malt_e\n", + "\t- R_EX_acnam_e\n", + "\t- R_EX_malthx_e\n", + "\t- R_EX_maltpt_e\n", + "\t- R_EX_gdp_e\n", + "\t- R_EX_glc__D_e\n", + "\t- R_EX_glcn_e\n", + "\t- R_EX_glcr_e\n", + "\t- R_EX_malttr_e\n", + "\t- R_EX_acolipa_e\n", + "\t- R_EX_maltttr_e\n", + "\t- R_EX_man_e\n", + "\t- R_EX_man6p_e\n", + "\t- R_EX_manglyc_e\n", + "\t- R_EX_glcur_e\n", + "\t- R_EX_glcur1p_e\n", + "\t- R_EX_gln__L_e\n", + "\t- R_EX_glu__L_e\n", + "\t- R_EX_gly_e\n", + "\t- R_EX_glyald_e\n", + "\t- R_EX_melib_e\n", + "\t- R_EX_meoh_e\n", + "\t- R_EX_glyb_e\n", + "\t- R_EX_met__D_e\n", + "\t- R_EX_acser_e\n", + "\t- R_EX_met__L_e\n", + "\t- R_EX_metsox_R__L_e\n", + "\t- R_EX_glyc_e\n", + "\t- R_EX_glyc__R_e\n", + "\t- R_EX_glyc2p_e\n", + "\t- R_EX_metsox_S__L_e\n", + "\t- R_EX_mg2_e\n", + "\t- R_EX_glyc3p_e\n", + "\t- R_EX_mincyc_e\n", + "\t- R_EX_glyclt_e\n", + "\t- R_EX_ade_e\n", + "\t- R_EX_gmp_e\n", + "\t- R_EX_gsn_e\n", + "\t- R_EX_gthox_e\n", + "\t- R_EX_minohp_e\n", + "\t- R_EX_mmet_e\n", + "\t- R_EX_mn2_e\n", + "\t- R_EX_gthrd_e\n", + "\t- R_EX_gtp_e\n", + "\t- R_EX_mnl_e\n", + "\t- R_EX_adn_e\n", + "\t- R_EX_mobd_e\n", + "\t- R_EX_mso3_e\n", + "\t- R_EX_gua_e\n", + "\t- R_EX_h_e\n", + "\t- R_EX_h2_e\n", + "\t- R_EX_n2o_e\n", + "\t- R_EX_na1_e\n", + "\t- R_EX_nac_e\n", + "\t- R_EX_nh4_e\n", + "\t- R_EX_ppt_e\n", + "\t- R_EX_adocbl_e\n", + "\t- R_EX_pro__L_e\n", + "\t- R_EX_progly_e\n", + "\t- R_EX_psclys_e\n", + "\t- R_EX_ni2_e\n", + "\t- R_EX_nmn_e\n", + "\t- R_EX_no_e\n", + "\t- R_EX_ag_e\n", + "\t- R_EX_no2_e\n", + "\t- R_EX_no3_e\n", + "\t- R_EX_pser__L_e\n", + "\t- R_EX_ptrc_e\n", + "\t- R_EX_pydam_e\n", + "\t- R_EX_novbcn_e\n", + "\t- R_EX_agm_e\n", + "\t- R_EX_o16a4colipa_e\n", + "\t- R_EX_pydx_e\n", + "\t- R_EX_akg_e\n", + "\t- R_EX_pydxn_e\n", + "\t- R_EX_pyr_e\n", + "\t- R_EX_quin_e\n", + "\t- R_EX_o2_e\n", + "\t- R_EX_o2s_e\n", + "\t- R_EX_ocdca_e\n", + "\t- R_EX_ala_B_e\n", + "\t- R_EX_ocdcea_e\n", + "\t- R_EX_octa_e\n", + "\t- R_EX_r5p_e\n", + "\t- R_EX_rfamp_e\n", + "\t- R_EX_rib__D_e\n", + "\t- R_EX_orn_e\n", + "\t- R_EX_rmn_e\n", + "\t- R_EX_sbt__D_e\n", + "\t- R_EX_sel_e\n", + "\t- R_EX_ser__D_e\n", + "\t- R_EX_orot_e\n", + "\t- R_EX_pacald_e\n", + "\t- R_EX_peamn_e\n", + "\t- R_EX_ala__D_e\n", + "\t- R_EX_phe__L_e\n", + "\t- R_EX_pheme_e\n", + "\t- R_EX_ser__L_e\n", + "\t- R_EX_skm_e\n", + "\t- R_EX_slnt_e\n", + "\t- R_EX_so2_e\n", + "\t- R_EX_pi_e\n", + "\t- R_EX_so3_e\n", + "\t- R_EX_ala__L_e\n", + "\t- R_EX_so4_e\n", + "\t- R_EX_spmd_e\n", + "\t- R_EX_succ_e\n", + "\t- R_EX_pnto__R_e\n", + "\t- R_EX_ppa_e\n", + "\t- R_EX_ppal_e\n", + "\t- R_EX_pppn_e\n", + "\t- R_EX_alaala_e\n", + "\t- R_EX_sucr_e\n", + "\t- R_EX_sulfac_e\n", + "\t- R_EX_all__D_e\n", + "\t- R_EX_tartr__D_e\n", + "\t- R_EX_tartr__L_e\n", + "\t- R_EX_alltn_e\n", + "\t- R_EX_taur_e\n", + "\t- R_EX_tcynt_e\n", + "\t- R_EX_amp_e\n", + "\t- R_EX_thm_e\n", + "\t- R_EX_thr__L_e\n", + "\t- R_EX_thrp_e\n", + "\t- R_EX_thym_e\n", + "\t- R_EX_thymd_e\n", + "\t- R_EX_tma_e\n", + "\t- R_EX_tmao_e\n", + "\t- R_EX_anhgm_e\n", + "\t- R_EX_tre_e\n", + "\t- R_EX_trp__L_e\n", + "\t- R_EX_tsul_e\n", + "\t- R_EX_ttdca_e\n", + "\t- R_EX_arab__L_e\n", + "\t- R_EX_ttdcea_e\n", + "\t- R_EX_ttrcyc_e\n", + "\t- R_EX_tungs_e\n", + "\t- R_EX_tym_e\n", + "\t- R_EX_arbt_e\n", + "\t- R_EX_tyr__L_e\n", + "\t- R_EX_tyrp_e\n", + "\t- R_EX_uacgam_e\n", + "\t- R_EX_udpacgal_e\n", + "\t- R_EX_udpg_e\n", + "\t- R_EX_udpgal_e\n", + "\t- R_EX_arbtn_e\n", + "\t- R_EX_udpglcur_e\n", + "\t- R_EX_ump_e\n", + "\t- R_EX_arbtn_fe3_e\n", + "\t- R_EX_ura_e\n", + "\t- R_EX_urea_e\n", + "\t- R_EX_uri_e\n", + "\t- R_EX_val__L_e\n", + "\t- R_EX_xan_e\n", + "\t- R_EX_xmp_e\n", + "\t- R_EX_arg__L_e\n", + "\t- R_EX_xtsn_e\n", + "\t- R_EX_xyl__D_e\n", + "\t- R_EX_xylu__L_e\n", + "\t- R_EX_zn2_e\n", + "\t- R_EX_ascb__L_e\n", + "\t- R_EX_asn__L_e\n", + "\t- R_EX_aso3_e\n", + "\t- R_EX_asp__L_e\n", + "\t- R_EX_btn_e\n", + "\t- R_EX_but_e\n", + "\t- R_EX_butso3_e\n", + "\t- R_EX_ca2_e\n", + "\t- R_EX_cbi_e\n", + "\t- R_EX_cbl1_e\n", + "\t- R_EX_cd2_e\n", + "\t- R_EX_cgly_e\n", + "\t- R_EX_chol_e\n", + "\t- R_EX_chtbs_e\n", + "\t- R_EX_cit_e\n", + "\t- R_EX_cl_e\n", + "\t- R_EX_cm_e\n", + "\t- R_EX_cmp_e\n", + "\t- R_EX_co2_e\n", + "\t- R_EX_cobalt2_e\n", + "\t- R_EX_colipa_e\n", + "\t- R_EX_colipap_e\n", + "\t- R_EX_cpgn_e\n", + "\t- R_EX_cpgn_un_e\n", + "\t- R_EX_crn_e\n", + "\t- R_EX_crn__D_e\n", + "\t- R_EX_csn_e\n", + "\t- R_EX_cu_e\n", + "\t- R_EX_cu2_e\n", + "\t- R_EX_cyan_e\n", + "\t- R_EX_cynt_e\n", + "\t- R_EX_cys__D_e\n", + "\t- R_EX_cys__L_e\n", + "\t- R_EX_cytd_e\n", + "\t- R_EX_dad_2_e\n", + "\t- R_EX_damp_e\n", + "\t- R_EX_dca_e\n", + "\t- R_EX_dcmp_e\n", + "\t- R_EX_dcyt_e\n", + "\t- R_EX_feroxBfe_e\n", + "\t- R_EX_4hphac_e\n", + "\t- R_EX_4abz_e\n", + "\t- R_EX_salchsx_e\n", + "\t- R_EX_AEP_e\n", + "\t- R_EX_icit_e\n", + "\t- R_EX_pep_e\n", + "\t- R_EX_dxyl_e\n", + "\t- R_EX_colipa20Oag_e\n", + "\t- R_EX_remnant1_e\n", + "\t- R_EX_udcpo5_e\n", + "\t- R_EX_salchs2_e\n", + "\t- R_EX_acon_C_e\n", + "\t- R_EX_rnam_e\n", + "\t- R_EX_13ppd_e\n", + "\t- R_EX_rbt_e\n", + "\t- R_EX_bz_e\n", + "\t- R_EX_airs_e\n", + "\t- R_EX_abt__D_e\n", + "\t- R_EX_3pg_e\n", + "\t- R_EX_feroxGfe_e\n", + "\t- R_EX_feroxB_e\n", + "\t- R_EX_fe_e\n", + "\t- R_EX_udcpo4_e\n", + "\t- R_EX_tag__D_e\n", + "\t- R_EX_kokdolipidA_e\n", + "\t- R_EX_tcb_e\n", + "\t- R_EX_tton_e\n", + "\t- R_EX_4hthr_e\n", + "\t- R_EX_guln__L_e\n", + "\t- R_EX_feroxG_e\n", + "\t- R_EX_3hoxpac_e\n", + "\t- R_EX_4hoxpac_e\n", + "\t- R_EX_diact_e\n", + "\t- R_DM_lipidA_core_e_p\n", + "\t- R_EX_feroxEfe_e\n", + "\t- R_EX_cellb_e\n", + "\t- R_EX_feroxE_e\n", + "\t- R_EX_btd_RR_e\n", + "\t- R_EX_3ntym_e\n", + "\t- R_EX_6apa_e\n", + "\t- R_EX_peng_e\n", + "\t- R_EX_pac_e\n", + "\t- R_EX_salcn_e\n", + "\t- R_EX_galam_e\n", + "\t- R_EX_cysi__L_e\n", + "\t- R_EX_raffin_e\n", + "\t- R_EX_23dhbzs3_e\n", + "\t- R_EX_o6a4colipa_e\n", + "\t- R_EX_tet_e\n", + "\t- R_EX_arab__D_e\n", + "\t- R_EX_salchs4_e\n", + "\t- R_SK_thf_c\n", + "\t- R_EX_oaa_e\n", + "\t- R_EX_salchs2fe_e\n", + "\t- R_EX_fe3dhbzs3_e\n", + "\t- R_EX_drib_e\n", + "\t- R_EX_salchs4fe_e\n", + "\t- R_EX_2pg_e\n", + "\t- R_EX_frmd_e\n", + "\t- R_EX_colipaOA_e\u001b[0m\n", + "File saved at: ../../data/sbml_corrected/iS_1188.xml\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: iEcHS_1320\n", + "\u001b[0;93mWARNING : \n", + " - R_CAT: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_DHPTDNR: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_DHPTDNRN: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_FHL: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SPODM: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SPODMpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCASPtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCFUMtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCMALtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCTARTtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_CHITPH: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_URCN: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_EX_chitob_e: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\u001b[0m\n", + "\n", + "\n", + "\u001b[0;36m############################################\n", + "############################################\n", + " WRITING SBML FILE \n", + "############################################\n", + "############################################\n", + "\u001b[0m\n", + "\u001b[0;93mWARNING : IMPORT REACTION REMOVED BY DEFAULT\u001b[0m\n", + "\u001b[0;93mWARNING : If you want to keep import reaction\n", + "use option -kir / --keep-import-reactions\u001b[0m\n", + "\u001b[0;93mWARNING : Import reaction removed:\n", + "\t- R_EX_3hpppn_e\n", + "\t- R_EX_4hoxpacd_e\n", + "\t- R_EX_5mtr_e\n", + "\t- R_EX_3ump_e\n", + "\t- R_EX_5dglcn_e\n", + "\t- R_EX_LalaDgluMdap_e\n", + "\t- R_EX_LalaDglu_e\n", + "\t- R_EX_4abut_e\n", + "\t- R_EX_3cmp_e\n", + "\t- R_EX_LalaDgluMdapDala_e\n", + "\t- R_EX_3gmp_e\n", + "\t- R_EX_3hpp_e\n", + "\t- R_EX_3hcinnm_e\n", + "\t- R_EX_LalaLglu_e\n", + "\t- R_DM_4crsol_c\n", + "\t- R_DM_5drib_c\n", + "\t- R_EX_dtmp_e\n", + "\t- R_DM_aacald_c\n", + "\t- R_DM_amob_c\n", + "\t- R_DM_mththf_c\n", + "\t- R_DM_oxam_c\n", + "\t- R_EX_ac_e\n", + "\t- R_EX_acac_e\n", + "\t- R_EX_acald_e\n", + "\t- R_EX_acgal_e\n", + "\t- R_EX_acgal1p_e\n", + "\t- R_EX_acgam_e\n", + "\t- R_EX_12ppd__R_e\n", + "\t- R_EX_dump_e\n", + "\t- R_EX_12ppd__S_e\n", + "\t- R_EX_14glucan_e\n", + "\t- R_EX_15dap_e\n", + "\t- R_EX_23camp_e\n", + "\t- R_EX_23ccmp_e\n", + "\t- R_EX_23cgmp_e\n", + "\t- R_EX_23cump_e\n", + "\t- R_EX_23dappa_e\n", + "\t- R_EX_26dap__M_e\n", + "\t- R_EX_acgam1p_e\n", + "\t- R_EX_acmana_e\n", + "\t- R_EX_acmum_e\n", + "\t- R_EX_acnam_e\n", + "\t- R_EX_acolipa_e\n", + "\t- R_EX_acser_e\n", + "\t- R_EX_ade_e\n", + "\t- R_EX_adn_e\n", + "\t- R_EX_adocbl_e\n", + "\t- R_EX_ag_e\n", + "\t- R_EX_agm_e\n", + "\t- R_EX_akg_e\n", + "\t- R_EX_2ddglcn_e\n", + "\t- R_EX_34dhpac_e\n", + "\t- R_EX_3amp_e\n", + "\t- R_EX_ala_B_e\n", + "\t- R_EX_duri_e\n", + "\t- R_EX_ala__D_e\n", + "\t- R_EX_ala__L_e\n", + "\t- R_EX_hxa_e\n", + "\t- R_EX_hxan_e\n", + "\t- R_EX_eca4colipa_e\n", + "\t- R_EX_idon__L_e\n", + "\t- R_EX_alaala_e\n", + "\t- R_EX_all__D_e\n", + "\t- R_EX_enlipa_e\n", + "\t- R_EX_alltn_e\n", + "\t- R_EX_amp_e\n", + "\t- R_EX_ile__L_e\n", + "\t- R_EX_imp_e\n", + "\t- R_EX_indole_e\n", + "\t- R_EX_inost_e\n", + "\t- R_EX_ins_e\n", + "\t- R_EX_anhgm_e\n", + "\t- R_EX_arab__L_e\n", + "\t- R_EX_arbt_e\n", + "\t- R_EX_arbtn_e\n", + "\t- R_EX_arbtn_fe3_e\n", + "\t- R_EX_isetac_e\n", + "\t- R_EX_k_e\n", + "\t- R_EX_kdo2lipid4_e\n", + "\t- R_EX_arg__L_e\n", + "\t- R_EX_lac__D_e\n", + "\t- R_EX_enter_e\n", + "\t- R_EX_lac__L_e\n", + "\t- R_EX_ascb__L_e\n", + "\t- R_EX_asn__L_e\n", + "\t- R_EX_aso3_e\n", + "\t- R_EX_lcts_e\n", + "\t- R_EX_etha_e\n", + "\t- R_EX_leu__L_e\n", + "\t- R_EX_lipa_e\n", + "\t- R_EX_asp__L_e\n", + "\t- R_EX_btn_e\n", + "\t- R_EX_lipa_cold_e\n", + "\t- R_EX_ethso3_e\n", + "\t- R_EX_lipoate_e\n", + "\t- R_EX_but_e\n", + "\t- R_EX_etoh_e\n", + "\t- R_EX_butso3_e\n", + "\t- R_EX_lys__L_e\n", + "\t- R_EX_ca2_e\n", + "\t- R_EX_lyx__L_e\n", + "\t- R_EX_f6p_e\n", + "\t- R_EX_mal__D_e\n", + "\t- R_EX_mal__L_e\n", + "\t- R_EX_cbi_e\n", + "\t- R_EX_cbl1_e\n", + "\t- R_EX_cd2_e\n", + "\t- R_EX_cgly_e\n", + "\t- R_EX_chol_e\n", + "\t- R_EX_chtbs_e\n", + "\t- R_EX_malt_e\n", + "\t- R_EX_malthx_e\n", + "\t- R_EX_maltpt_e\n", + "\t- R_EX_malttr_e\n", + "\t- R_EX_maltttr_e\n", + "\t- R_EX_cit_e\n", + "\t- R_EX_cl_e\n", + "\t- R_EX_cm_e\n", + "\t- R_EX_man_e\n", + "\t- R_EX_man6p_e\n", + "\t- R_EX_cmp_e\n", + "\t- R_EX_fald_e\n", + "\t- R_EX_co2_e\n", + "\t- R_EX_cobalt2_e\n", + "\t- R_EX_manglyc_e\n", + "\t- R_EX_melib_e\n", + "\t- R_EX_meoh_e\n", + "\t- R_EX_colipa_e\n", + "\t- R_EX_colipap_e\n", + "\t- R_EX_met__D_e\n", + "\t- R_EX_fe2_e\n", + "\t- R_EX_met__L_e\n", + "\t- R_EX_metsox_R__L_e\n", + "\t- R_EX_cpgn_e\n", + "\t- R_EX_cpgn_un_e\n", + "\t- R_EX_crn_e\n", + "\t- R_EX_metsox_S__L_e\n", + "\t- R_EX_crn__D_e\n", + "\t- R_EX_fe3_e\n", + "\t- R_EX_csn_e\n", + "\t- R_EX_cu_e\n", + "\t- R_EX_mg2_e\n", + "\t- R_EX_mincyc_e\n", + "\t- R_EX_minohp_e\n", + "\t- R_EX_mmet_e\n", + "\t- R_EX_mn2_e\n", + "\t- R_EX_cu2_e\n", + "\t- R_EX_cyan_e\n", + "\t- R_EX_cynt_e\n", + "\t- R_EX_cys__D_e\n", + "\t- R_EX_mnl_e\n", + "\t- R_EX_mobd_e\n", + "\t- R_EX_fe3dcit_e\n", + "\t- R_EX_mso3_e\n", + "\t- R_EX_n2o_e\n", + "\t- R_EX_na1_e\n", + "\t- R_EX_cys__L_e\n", + "\t- R_EX_cytd_e\n", + "\t- R_EX_dad_2_e\n", + "\t- R_EX_nac_e\n", + "\t- R_EX_damp_e\n", + "\t- R_EX_nh4_e\n", + "\t- R_EX_dca_e\n", + "\t- R_EX_fe3dhbzs_e\n", + "\t- R_EX_dcmp_e\n", + "\t- R_EX_dcyt_e\n", + "\t- R_EX_ni2_e\n", + "\t- R_EX_nmn_e\n", + "\t- R_EX_no_e\n", + "\t- R_EX_ddca_e\n", + "\t- R_EX_fe3hox_e\n", + "\t- R_EX_dgmp_e\n", + "\t- R_EX_dgsn_e\n", + "\t- R_EX_no2_e\n", + "\t- R_EX_no3_e\n", + "\t- R_EX_novbcn_e\n", + "\t- R_EX_dha_e\n", + "\t- R_EX_o16a4colipa_e\n", + "\t- R_EX_fe3hox_un_e\n", + "\t- R_EX_o2_e\n", + "\t- R_EX_o2s_e\n", + "\t- R_EX_dimp_e\n", + "\t- R_EX_din_e\n", + "\t- R_EX_dms_e\n", + "\t- R_EX_dmso_e\n", + "\t- R_EX_ocdca_e\n", + "\t- R_EX_ocdcea_e\n", + "\t- R_EX_octa_e\n", + "\t- R_EX_orn_e\n", + "\t- R_EX_dopa_e\n", + "\t- R_EX_doxrbcn_e\n", + "\t- R_EX_orot_e\n", + "\t- R_EX_pacald_e\n", + "\t- R_EX_fecrm_e\n", + "\t- R_EX_peamn_e\n", + "\t- R_EX_phe__L_e\n", + "\t- R_EX_quin_e\n", + "\t- R_EX_r5p_e\n", + "\t- R_EX_pheme_e\n", + "\t- R_EX_rfamp_e\n", + "\t- R_EX_fecrm_un_e\n", + "\t- R_EX_rib__D_e\n", + "\t- R_EX_pi_e\n", + "\t- R_EX_pnto__R_e\n", + "\t- R_EX_feenter_e\n", + "\t- R_EX_ppa_e\n", + "\t- R_EX_ppal_e\n", + "\t- R_EX_rmn_e\n", + "\t- R_EX_sbt__D_e\n", + "\t- R_EX_sel_e\n", + "\t- R_EX_ser__D_e\n", + "\t- R_EX_pppn_e\n", + "\t- R_EX_ppt_e\n", + "\t- R_EX_feoxam_e\n", + "\t- R_EX_pro__L_e\n", + "\t- R_EX_ser__L_e\n", + "\t- R_EX_skm_e\n", + "\t- R_EX_slnt_e\n", + "\t- R_EX_progly_e\n", + "\t- R_EX_psclys_e\n", + "\t- R_EX_feoxam_un_e\n", + "\t- R_EX_pser__L_e\n", + "\t- R_EX_ptrc_e\n", + "\t- R_EX_pydam_e\n", + "\t- R_EX_pydx_e\n", + "\t- R_EX_pydxn_e\n", + "\t- R_EX_so2_e\n", + "\t- R_EX_so3_e\n", + "\t- R_EX_so4_e\n", + "\t- R_EX_spmd_e\n", + "\t- R_EX_succ_e\n", + "\t- R_EX_sucr_e\n", + "\t- R_EX_sulfac_e\n", + "\t- R_EX_pyr_e\n", + "\t- R_EX_for_e\n", + "\t- R_EX_tartr__D_e\n", + "\t- R_EX_tartr__L_e\n", + "\t- R_EX_taur_e\n", + "\t- R_EX_tcynt_e\n", + "\t- R_EX_thm_e\n", + "\t- R_EX_fru_e\n", + "\t- R_EX_thr__L_e\n", + "\t- R_EX_frulys_e\n", + "\t- R_EX_thrp_e\n", + "\t- R_EX_thym_e\n", + "\t- R_EX_thymd_e\n", + "\t- R_EX_fruur_e\n", + "\t- R_EX_tma_e\n", + "\t- R_EX_tmao_e\n", + "\t- R_EX_tre_e\n", + "\t- R_EX_trp__L_e\n", + "\t- R_EX_tsul_e\n", + "\t- R_EX_ttdca_e\n", + "\t- R_EX_ttdcea_e\n", + "\t- R_EX_fuc__L_e\n", + "\t- R_EX_ttrcyc_e\n", + "\t- R_EX_tungs_e\n", + "\t- R_EX_tym_e\n", + "\t- R_EX_fum_e\n", + "\t- R_EX_tyr__L_e\n", + "\t- R_EX_tyrp_e\n", + "\t- R_EX_uacgam_e\n", + "\t- R_EX_udpacgal_e\n", + "\t- R_EX_udpg_e\n", + "\t- R_EX_fusa_e\n", + "\t- R_EX_udpgal_e\n", + "\t- R_EX_udpglcur_e\n", + "\t- R_EX_ump_e\n", + "\t- R_EX_ura_e\n", + "\t- R_EX_urea_e\n", + "\t- R_EX_uri_e\n", + "\t- R_EX_g1p_e\n", + "\t- R_EX_val__L_e\n", + "\t- R_EX_xan_e\n", + "\t- R_EX_xmp_e\n", + "\t- R_EX_g3pc_e\n", + "\t- R_EX_xtsn_e\n", + "\t- R_EX_xyl__D_e\n", + "\t- R_EX_g3pe_e\n", + "\t- R_EX_xylu__L_e\n", + "\t- R_EX_zn2_e\n", + "\t- R_EX_g3pg_e\n", + "\t- R_EX_g3pi_e\n", + "\t- R_EX_g3ps_e\n", + "\t- R_EX_g6p_e\n", + "\t- R_EX_gal_e\n", + "\t- R_EX_gal_bD_e\n", + "\t- R_EX_gal1p_e\n", + "\t- R_EX_galct__D_e\n", + "\t- R_EX_galctn__D_e\n", + "\t- R_EX_galctn__L_e\n", + "\t- R_EX_galt_e\n", + "\t- R_EX_galur_e\n", + "\t- R_EX_gam_e\n", + "\t- R_EX_gam6p_e\n", + "\t- R_EX_gbbtn_e\n", + "\t- R_EX_gdp_e\n", + "\t- R_EX_glc__D_e\n", + "\t- R_EX_glcn_e\n", + "\t- R_EX_glcr_e\n", + "\t- R_EX_glcur_e\n", + "\t- R_EX_glcur1p_e\n", + "\t- R_EX_gln__L_e\n", + "\t- R_EX_glu__L_e\n", + "\t- R_EX_gly_e\n", + "\t- R_EX_glyald_e\n", + "\t- R_EX_glyb_e\n", + "\t- R_EX_glyc_e\n", + "\t- R_EX_glyc__R_e\n", + "\t- R_EX_glyc2p_e\n", + "\t- R_EX_glyc3p_e\n", + "\t- R_EX_glyclt_e\n", + "\t- R_EX_gmp_e\n", + "\t- R_EX_gsn_e\n", + "\t- R_EX_gthox_e\n", + "\t- R_EX_gthrd_e\n", + "\t- R_EX_gtp_e\n", + "\t- R_EX_gua_e\n", + "\t- R_EX_h_e\n", + "\t- R_EX_h2_e\n", + "\t- R_EX_h2o_e\n", + "\t- R_EX_h2o2_e\n", + "\t- R_EX_h2s_e\n", + "\t- R_EX_hacolipa_e\n", + "\t- R_EX_halipa_e\n", + "\t- R_EX_hdca_e\n", + "\t- R_EX_hdcea_e\n", + "\t- R_EX_hg2_e\n", + "\t- R_EX_his__L_e\n", + "\t- R_EX_hom__L_e\n", + "\t- R_EX_feroxBfe_e\n", + "\t- R_EX_4hphac_e\n", + "\t- R_EX_4abz_e\n", + "\t- R_EX_acon_C_e\n", + "\t- R_EX_oaa_e\n", + "\t- R_EX_salchs2fe_e\n", + "\t- R_EX_cysi__L_e\n", + "\t- R_EX_feroxB_e\n", + "\t- R_EX_tcb_e\n", + "\t- R_EX_tton_e\n", + "\t- R_EX_4hthr_e\n", + "\t- R_EX_fe3dhbzs3_e\n", + "\t- R_EX_feroxEfe_e\n", + "\t- R_EX_feroxE_e\n", + "\t- R_EX_drib_e\n", + "\t- R_EX_23dhbzs3_e\n", + "\t- R_EX_tet_e\n", + "\t- R_EX_salchs4fe_e\n", + "\t- R_EX_2pg_e\n", + "\t- R_EX_salchs4_e\n", + "\t- R_EX_frmd_e\n", + "\t- R_EX_colipaOA_e\n", + "\t- R_EX_salchsx_e\n", + "\t- R_EX_AEP_e\n", + "\t- R_EX_icit_e\n", + "\t- R_EX_pep_e\n", + "\t- R_EX_colipa20Oag_e\n", + "\t- R_EX_remnant1_e\n", + "\t- R_EX_udcpo5_e\n", + "\t- R_EX_3ntym_e\n", + "\t- R_EX_6apa_e\n", + "\t- R_EX_salchs2_e\n", + "\t- R_EX_udcpo4_e\n", + "\t- R_EX_4hoxpac_e\n", + "\t- R_EX_rnam_e\n", + "\t- R_EX_peng_e\n", + "\t- R_EX_pac_e\n", + "\t- R_EX_airs_e\n", + "\t- R_EX_dxyl_e\n", + "\t- R_EX_3pg_e\n", + "\t- R_EX_feroxGfe_e\n", + "\t- R_EX_salcn_e\n", + "\t- R_EX_tag__D_e\n", + "\t- R_DM_lipidA_core_e_p\n", + "\t- R_EX_galam_e\n", + "\t- R_EX_raffin_e\n", + "\t- R_EX_o6a4colipa_e\n", + "\t- R_EX_guln__L_e\n", + "\t- R_EX_arab__D_e\n", + "\t- R_EX_feroxG_e\n", + "\t- R_EX_diact_e\n", + "\t- R_EX_cellb_e\n", + "\t- R_EX_btd_RR_e\n", + "\t- R_EX_rbt_e\n", + "\t- R_EX_bz_e\n", + "\t- R_EX_13ppd_e\n", + "\t- R_EX_abt__D_e\n", + "\t- R_EX_fe_e\n", + "\t- R_EX_kokdolipidA_e\n", + "\t- R_EX_3hoxpac_e\u001b[0m\n", + "File saved at: ../../data/sbml_corrected/iEcHS_1320.xml\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: iJN746\n", + "\u001b[0;93mWARNING : - R_ASAD: Reactants and products exchanged.\n", + " Boundaries was: [-999999.0 ; 0.0]\n", + "\u001b[0m\n", + "\n", + "\n", + "\u001b[0;36m############################################\n", + "############################################\n", + " WRITING SBML FILE \n", + "############################################\n", + "############################################\n", + "\u001b[0m\n", + "\u001b[0;93mWARNING : IMPORT REACTION REMOVED BY DEFAULT\u001b[0m\n", + "\u001b[0;93mWARNING : If you want to keep import reaction\n", + "use option -kir / --keep-import-reactions\u001b[0m\n", + "\u001b[0;93mWARNING : Reaction with tag reversible modified:\n", + "\t- R_ASAD\u001b[0m\n", + "\u001b[0;93mWARNING : Reaction with Reactants and Products exchanged:\n", + "\t- R_ASAD\u001b[0m\n", + "\u001b[0;93mWARNING : Import reaction removed:\n", + "\t- R_EX_gly_e\n", + "\t- R_EX_glyald_e\n", + "\t- R_EX_cit_e\n", + "\t- R_EX_co2_e\n", + "\t- R_EX_chols_e\n", + "\t- R_EX_glyc_e\n", + "\t- R_EX_glyb_e\n", + "\t- R_DM_aacald_c\n", + "\t- R_DM_C100mclPHA_c\n", + "\t- R_DM_C120mclPHA_c\n", + "\t- R_DM_C121mclPHA_c\n", + "\t- R_EX_glyclt_e\n", + "\t- R_DM_C140mclPHA_c\n", + "\t- R_DM_C141mclPHA_c\n", + "\t- R_DM_C60mclPHA_c\n", + "\t- R_DM_C80mclPHA_c\n", + "\t- R_DM_dad_5_c\n", + "\t- R_DM_fald_c\n", + "\t- R_DM_mclPHA_c\n", + "\t- R_EX_h2_e\n", + "\t- R_DM_ptal_c\n", + "\t- R_EX_h2o_e\n", + "\t- R_EX_h_e\n", + "\t- R_EX_hco3_e\n", + "\t- R_EX_hdca_e\n", + "\t- R_EX_2ddglcn_e\n", + "\t- R_EX_his__L_e\n", + "\t- R_EX_2dhglcn_e\n", + "\t- R_EX_34dhbz_e\n", + "\t- R_EX_hxa_e\n", + "\t- R_EX_34dhcinm_e\n", + "\t- R_EX_3oxoadp_e\n", + "\t- R_EX_icit_e\n", + "\t- R_EX_4abut_e\n", + "\t- R_EX_4hbz_e\n", + "\t- R_EX_T4hcinnm_e\n", + "\t- R_EX_ac_e\n", + "\t- R_EX_acon_C_e\n", + "\t- R_EX_ile__L_e\n", + "\t- R_EX_akg_e\n", + "\t- R_EX_ala__L_e\n", + "\t- R_EX_arg__L_e\n", + "\t- R_EX_isetac_e\n", + "\t- R_EX_asp__L_e\n", + "\t- R_EX_bz_e\n", + "\t- R_EX_catechol_e\n", + "\t- R_EX_chol_e\n", + "\t- R_EX_lac__D_e\n", + "\t- R_EX_cobalt2_e\n", + "\t- R_EX_confrl_e\n", + "\t- R_EX_crn_e\n", + "\t- R_EX_lac__L_e\n", + "\t- R_EX_cys__L_e\n", + "\t- R_EX_dca_e\n", + "\t- R_EX_ddca_e\n", + "\t- R_EX_ethso3_e\n", + "\t- R_EX_fe2_e\n", + "\t- R_EX_fer_e\n", + "\t- R_EX_fru_e\n", + "\t- R_EX_fum_e\n", + "\t- R_EX_leu__L_e\n", + "\t- R_EX_ga_e\n", + "\t- R_EX_glc__D_e\n", + "\t- R_EX_glcn_e\n", + "\t- R_EX_glu__L_e\n", + "\t- R_EX_lys__L_e\n", + "\t- R_EX_m_xyl_e\n", + "\t- R_EX_mal__L_e\n", + "\t- R_EX_met__L_e\n", + "\t- R_EX_mso3_e\n", + "\t- R_EX_na1_e\n", + "\t- R_EX_nac_e\n", + "\t- R_EX_nh4_e\n", + "\t- R_EX_ni2_e\n", + "\t- R_EX_no2_e\n", + "\t- R_EX_o2_e\n", + "\t- R_EX_octa_e\n", + "\t- R_EX_orn_e\n", + "\t- R_EX_p_xyl_e\n", + "\t- R_EX_pac_e\n", + "\t- R_EX_pentso3_e\n", + "\t- R_EX_phe__L_e\n", + "\t- R_EX_pi_e\n", + "\t- R_EX_pro__L_e\n", + "\t- R_EX_ptrc_e\n", + "\t- R_EX_quin_e\n", + "\t- R_EX_rib__D_e\n", + "\t- R_EX_ser__L_e\n", + "\t- R_EX_so4_e\n", + "\t- R_EX_spmd_e\n", + "\t- R_EX_succ_e\n", + "\t- R_EX_taur_e\n", + "\t- R_EX_thr__L_e\n", + "\t- R_EX_tol_e\n", + "\t- R_EX_tsul_e\n", + "\t- R_EX_ttdca_e\n", + "\t- R_EX_tyr__L_e\n", + "\t- R_EX_val__L_e\n", + "\t- R_EX_vanln_e\n", + "\t- R_EX_vanlt_e\n", + "\t- R_SK_5mthglu_c\n", + "\t- R_SK_dna5mtc_c\n", + "\t- R_SK_dna_c\n", + "\t- R_SK_mclPHAg_c\n", + "\t- R_SK_pqq_c\n", + "\t- R_SK_pqqh2_c\n", + "\t- R_SK_thglu_c\u001b[0m\n", + "File saved at: ../../data/sbml_corrected/iJN746.xml\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: iAPECO1_1312\n", + "\u001b[0;93mWARNING : \n", + " - R_CAT: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_DHPTDNR: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_DHPTDNRN: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_FHL: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCTARTtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCASPtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCMALtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCFUMtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SPODM: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SPODMpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_EX_chitob_e: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_CHITPH: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_URCN: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_CHITOBpts: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\u001b[0m\n", + "\n", + "\n", + "\u001b[0;36m############################################\n", + "############################################\n", + " WRITING SBML FILE \n", + "############################################\n", + "############################################\n", + "\u001b[0m\n", + "\u001b[0;93mWARNING : IMPORT REACTION REMOVED BY DEFAULT\u001b[0m\n", + "\u001b[0;93mWARNING : If you want to keep import reaction\n", + "use option -kir / --keep-import-reactions\u001b[0m\n", + "\u001b[0;93mWARNING : Import reaction removed:\n", + "\t- R_EX_3hpppn_e\n", + "\t- R_EX_3ump_e\n", + "\t- R_EX_4abut_e\n", + "\t- R_EX_4hoxpacd_e\n", + "\t- R_EX_5dglcn_e\n", + "\t- R_EX_5mtr_e\n", + "\t- R_EX_LalaDglu_e\n", + "\t- R_EX_2ddglcn_e\n", + "\t- R_EX_acgal_e\n", + "\t- R_EX_acgal1p_e\n", + "\t- R_EX_LalaDgluMdap_e\n", + "\t- R_EX_acgam_e\n", + "\t- R_EX_34dhpac_e\n", + "\t- R_EX_3amp_e\n", + "\t- R_EX_3cmp_e\n", + "\t- R_EX_acgam1p_e\n", + "\t- R_EX_acmana_e\n", + "\t- R_EX_3gmp_e\n", + "\t- R_EX_LalaDgluMdapDala_e\n", + "\t- R_EX_3hcinnm_e\n", + "\t- R_EX_acmum_e\n", + "\t- R_EX_LalaLglu_e\n", + "\t- R_EX_3hpp_e\n", + "\t- R_EX_acnam_e\n", + "\t- R_EX_ac_e\n", + "\t- R_EX_acolipa_e\n", + "\t- R_EX_acac_e\n", + "\t- R_EX_acser_e\n", + "\t- R_EX_acald_e\n", + "\t- R_EX_ade_e\n", + "\t- R_EX_adn_e\n", + "\t- R_EX_adocbl_e\n", + "\t- R_EX_ala_B_e\n", + "\t- R_EX_ag_e\n", + "\t- R_EX_agm_e\n", + "\t- R_EX_akg_e\n", + "\t- R_DM_4crsol_c\n", + "\t- R_DM_5drib_c\n", + "\t- R_DM_aacald_c\n", + "\t- R_DM_amob_c\n", + "\t- R_DM_mththf_c\n", + "\t- R_DM_oxam_c\n", + "\t- R_EX_12ppd__R_e\n", + "\t- R_EX_12ppd__S_e\n", + "\t- R_EX_14glucan_e\n", + "\t- R_EX_15dap_e\n", + "\t- R_EX_23camp_e\n", + "\t- R_EX_ala__D_e\n", + "\t- R_EX_23ccmp_e\n", + "\t- R_EX_23cgmp_e\n", + "\t- R_EX_feenter_e\n", + "\t- R_EX_feoxam_e\n", + "\t- R_EX_23cump_e\n", + "\t- R_EX_feoxam_un_e\n", + "\t- R_EX_ala__L_e\n", + "\t- R_EX_for_e\n", + "\t- R_EX_fru_e\n", + "\t- R_EX_23dappa_e\n", + "\t- R_EX_26dap__M_e\n", + "\t- R_EX_frulys_e\n", + "\t- R_EX_alaala_e\n", + "\t- R_EX_fruur_e\n", + "\t- R_EX_fuc__L_e\n", + "\t- R_EX_all__D_e\n", + "\t- R_EX_fum_e\n", + "\t- R_EX_fusa_e\n", + "\t- R_EX_alltn_e\n", + "\t- R_EX_g1p_e\n", + "\t- R_EX_g3pc_e\n", + "\t- R_EX_g3pe_e\n", + "\t- R_EX_g3pg_e\n", + "\t- R_EX_g3pi_e\n", + "\t- R_EX_g3ps_e\n", + "\t- R_EX_g6p_e\n", + "\t- R_EX_lyx__L_e\n", + "\t- R_EX_mal__D_e\n", + "\t- R_EX_gal_e\n", + "\t- R_EX_mal__L_e\n", + "\t- R_EX_amp_e\n", + "\t- R_EX_malt_e\n", + "\t- R_EX_malthx_e\n", + "\t- R_EX_gal_bD_e\n", + "\t- R_EX_gal1p_e\n", + "\t- R_EX_galct__D_e\n", + "\t- R_EX_galctn__D_e\n", + "\t- R_EX_maltpt_e\n", + "\t- R_EX_malttr_e\n", + "\t- R_EX_galctn__L_e\n", + "\t- R_EX_anhgm_e\n", + "\t- R_EX_galt_e\n", + "\t- R_EX_galur_e\n", + "\t- R_EX_gam_e\n", + "\t- R_EX_maltttr_e\n", + "\t- R_EX_arab__L_e\n", + "\t- R_EX_gam6p_e\n", + "\t- R_EX_gbbtn_e\n", + "\t- R_EX_man_e\n", + "\t- R_EX_arbt_e\n", + "\t- R_EX_man6p_e\n", + "\t- R_EX_gdp_e\n", + "\t- R_EX_glc__D_e\n", + "\t- R_EX_manglyc_e\n", + "\t- R_EX_arbtn_e\n", + "\t- R_EX_melib_e\n", + "\t- R_EX_glcn_e\n", + "\t- R_EX_glcr_e\n", + "\t- R_EX_arbtn_fe3_e\n", + "\t- R_EX_glcur_e\n", + "\t- R_EX_glcur1p_e\n", + "\t- R_EX_gln__L_e\n", + "\t- R_EX_glu__L_e\n", + "\t- R_EX_gly_e\n", + "\t- R_EX_meoh_e\n", + "\t- R_EX_met__D_e\n", + "\t- R_EX_met__L_e\n", + "\t- R_EX_metsox_R__L_e\n", + "\t- R_EX_metsox_S__L_e\n", + "\t- R_EX_mg2_e\n", + "\t- R_EX_mincyc_e\n", + "\t- R_EX_minohp_e\n", + "\t- R_EX_glyald_e\n", + "\t- R_EX_glyb_e\n", + "\t- R_EX_glyc_e\n", + "\t- R_EX_mmet_e\n", + "\t- R_EX_arg__L_e\n", + "\t- R_EX_mn2_e\n", + "\t- R_EX_mnl_e\n", + "\t- R_EX_mobd_e\n", + "\t- R_EX_glyc__R_e\n", + "\t- R_EX_glyc2p_e\n", + "\t- R_EX_glyc3p_e\n", + "\t- R_EX_mso3_e\n", + "\t- R_EX_glyclt_e\n", + "\t- R_EX_ascb__L_e\n", + "\t- R_EX_gmp_e\n", + "\t- R_EX_gsn_e\n", + "\t- R_EX_n2o_e\n", + "\t- R_EX_na1_e\n", + "\t- R_EX_gthox_e\n", + "\t- R_EX_asn__L_e\n", + "\t- R_EX_gthrd_e\n", + "\t- R_EX_nac_e\n", + "\t- R_EX_aso3_e\n", + "\t- R_EX_nh4_e\n", + "\t- R_EX_ni2_e\n", + "\t- R_EX_nmn_e\n", + "\t- R_EX_no_e\n", + "\t- R_EX_no2_e\n", + "\t- R_EX_no3_e\n", + "\t- R_EX_novbcn_e\n", + "\t- R_EX_o16a4colipa_e\n", + "\t- R_EX_o2_e\n", + "\t- R_EX_o2s_e\n", + "\t- R_EX_ocdca_e\n", + "\t- R_EX_gtp_e\n", + "\t- R_EX_gua_e\n", + "\t- R_EX_h_e\n", + "\t- R_EX_h2_e\n", + "\t- R_EX_h2o_e\n", + "\t- R_EX_h2o2_e\n", + "\t- R_EX_h2s_e\n", + "\t- R_EX_hacolipa_e\n", + "\t- R_EX_halipa_e\n", + "\t- R_EX_hdca_e\n", + "\t- R_EX_hdcea_e\n", + "\t- R_EX_hg2_e\n", + "\t- R_EX_ocdcea_e\n", + "\t- R_EX_octa_e\n", + "\t- R_EX_orn_e\n", + "\t- R_EX_orot_e\n", + "\t- R_EX_pacald_e\n", + "\t- R_EX_his__L_e\n", + "\t- R_EX_hom__L_e\n", + "\t- R_EX_asp__L_e\n", + "\t- R_EX_hxa_e\n", + "\t- R_EX_hxan_e\n", + "\t- R_EX_peamn_e\n", + "\t- R_EX_phe__L_e\n", + "\t- R_EX_pheme_e\n", + "\t- R_EX_idon__L_e\n", + "\t- R_EX_pi_e\n", + "\t- R_EX_ile__L_e\n", + "\t- R_EX_btn_e\n", + "\t- R_EX_imp_e\n", + "\t- R_EX_indole_e\n", + "\t- R_EX_inost_e\n", + "\t- R_EX_ins_e\n", + "\t- R_EX_isetac_e\n", + "\t- R_EX_k_e\n", + "\t- R_EX_pnto__R_e\n", + "\t- R_EX_ppa_e\n", + "\t- R_EX_ppal_e\n", + "\t- R_EX_pppn_e\n", + "\t- R_EX_ppt_e\n", + "\t- R_EX_pro__L_e\n", + "\t- R_EX_progly_e\n", + "\t- R_EX_kdo2lipid4_e\n", + "\t- R_EX_lac__D_e\n", + "\t- R_EX_psclys_e\n", + "\t- R_EX_but_e\n", + "\t- R_EX_pser__L_e\n", + "\t- R_EX_ptrc_e\n", + "\t- R_EX_pydam_e\n", + "\t- R_EX_lac__L_e\n", + "\t- R_EX_lcts_e\n", + "\t- R_EX_leu__L_e\n", + "\t- R_EX_pydx_e\n", + "\t- R_EX_lipa_e\n", + "\t- R_EX_pydxn_e\n", + "\t- R_EX_butso3_e\n", + "\t- R_EX_pyr_e\n", + "\t- R_EX_quin_e\n", + "\t- R_EX_lipa_cold_e\n", + "\t- R_EX_lipoate_e\n", + "\t- R_EX_ca2_e\n", + "\t- R_EX_lys__L_e\n", + "\t- R_EX_r5p_e\n", + "\t- R_EX_cbi_e\n", + "\t- R_EX_rfamp_e\n", + "\t- R_EX_rib__D_e\n", + "\t- R_EX_tartr__L_e\n", + "\t- R_EX_taur_e\n", + "\t- R_EX_tcynt_e\n", + "\t- R_EX_thm_e\n", + "\t- R_EX_rmn_e\n", + "\t- R_EX_sbt__D_e\n", + "\t- R_EX_sel_e\n", + "\t- R_EX_ser__D_e\n", + "\t- R_EX_thr__L_e\n", + "\t- R_EX_thrp_e\n", + "\t- R_EX_ser__L_e\n", + "\t- R_EX_cbl1_e\n", + "\t- R_EX_skm_e\n", + "\t- R_EX_slnt_e\n", + "\t- R_EX_thym_e\n", + "\t- R_EX_thymd_e\n", + "\t- R_EX_tma_e\n", + "\t- R_EX_tmao_e\n", + "\t- R_EX_so2_e\n", + "\t- R_EX_cd2_e\n", + "\t- R_EX_so3_e\n", + "\t- R_EX_tre_e\n", + "\t- R_EX_trp__L_e\n", + "\t- R_EX_so4_e\n", + "\t- R_EX_cgly_e\n", + "\t- R_EX_spmd_e\n", + "\t- R_EX_succ_e\n", + "\t- R_EX_tsul_e\n", + "\t- R_EX_chol_e\n", + "\t- R_EX_ttdca_e\n", + "\t- R_EX_ttdcea_e\n", + "\t- R_EX_sucr_e\n", + "\t- R_EX_sulfac_e\n", + "\t- R_EX_tartr__D_e\n", + "\t- R_EX_ttrcyc_e\n", + "\t- R_EX_chtbs_e\n", + "\t- R_EX_tungs_e\n", + "\t- R_EX_tym_e\n", + "\t- R_EX_cit_e\n", + "\t- R_EX_tyr__L_e\n", + "\t- R_EX_cl_e\n", + "\t- R_EX_tyrp_e\n", + "\t- R_EX_uacgam_e\n", + "\t- R_EX_udpacgal_e\n", + "\t- R_EX_udpg_e\n", + "\t- R_EX_cm_e\n", + "\t- R_EX_udpgal_e\n", + "\t- R_EX_udpglcur_e\n", + "\t- R_EX_cmp_e\n", + "\t- R_EX_ump_e\n", + "\t- R_EX_ura_e\n", + "\t- R_EX_co2_e\n", + "\t- R_EX_urea_e\n", + "\t- R_EX_uri_e\n", + "\t- R_EX_cobalt2_e\n", + "\t- R_EX_val__L_e\n", + "\t- R_EX_xan_e\n", + "\t- R_EX_colipa_e\n", + "\t- R_EX_xmp_e\n", + "\t- R_EX_colipap_e\n", + "\t- R_EX_xtsn_e\n", + "\t- R_EX_xyl__D_e\n", + "\t- R_EX_xylu__L_e\n", + "\t- R_EX_cpgn_e\n", + "\t- R_EX_zn2_e\n", + "\t- R_EX_cpgn_un_e\n", + "\t- R_EX_crn_e\n", + "\t- R_EX_crn__D_e\n", + "\t- R_EX_csn_e\n", + "\t- R_EX_cu_e\n", + "\t- R_EX_cu2_e\n", + "\t- R_EX_cyan_e\n", + "\t- R_EX_cynt_e\n", + "\t- R_EX_cys__D_e\n", + "\t- R_EX_cys__L_e\n", + "\t- R_EX_cytd_e\n", + "\t- R_EX_dad_2_e\n", + "\t- R_EX_damp_e\n", + "\t- R_EX_dca_e\n", + "\t- R_EX_dcmp_e\n", + "\t- R_EX_dcyt_e\n", + "\t- R_EX_ddca_e\n", + "\t- R_EX_dgmp_e\n", + "\t- R_EX_dgsn_e\n", + "\t- R_EX_dha_e\n", + "\t- R_EX_dimp_e\n", + "\t- R_EX_oaa_e\n", + "\t- R_EX_din_e\n", + "\t- R_EX_dms_e\n", + "\t- R_EX_dmso_e\n", + "\t- R_EX_dopa_e\n", + "\t- R_EX_doxrbcn_e\n", + "\t- R_EX_dtmp_e\n", + "\t- R_EX_dump_e\n", + "\t- R_EX_duri_e\n", + "\t- R_EX_eca4colipa_e\n", + "\t- R_EX_enlipa_e\n", + "\t- R_EX_enter_e\n", + "\t- R_EX_etha_e\n", + "\t- R_EX_ethso3_e\n", + "\t- R_EX_etoh_e\n", + "\t- R_EX_f6p_e\n", + "\t- R_EX_fald_e\n", + "\t- R_EX_fe2_e\n", + "\t- R_EX_fe3_e\n", + "\t- R_EX_fe3dcit_e\n", + "\t- R_EX_fe3dhbzs_e\n", + "\t- R_EX_fe3hox_e\n", + "\t- R_EX_fe3hox_un_e\n", + "\t- R_EX_fecrm_e\n", + "\t- R_EX_fecrm_un_e\n", + "\t- R_EX_4hphac_e\n", + "\t- R_EX_4abz_e\n", + "\t- R_EX_acon_C_e\n", + "\t- R_EX_feroxB_e\n", + "\t- R_EX_tcb_e\n", + "\t- R_EX_tton_e\n", + "\t- R_EX_4hthr_e\n", + "\t- R_EX_feroxEfe_e\n", + "\t- R_EX_feroxE_e\n", + "\t- R_EX_pep_e\n", + "\t- R_EX_23dhbzs3_e\n", + "\t- R_EX_tet_e\n", + "\t- R_EX_salchs4_e\n", + "\t- R_EX_tag__D_e\n", + "\t- R_EX_salchs2fe_e\n", + "\t- R_EX_cysi__L_e\n", + "\t- R_EX_fe3dhbzs3_e\n", + "\t- R_EX_drib_e\n", + "\t- R_EX_feroxBfe_e\n", + "\t- R_EX_salchs4fe_e\n", + "\t- R_EX_2pg_e\n", + "\t- R_EX_frmd_e\n", + "\t- R_EX_colipaOA_e\n", + "\t- R_EX_salchsx_e\n", + "\t- R_EX_AEP_e\n", + "\t- R_EX_icit_e\n", + "\t- R_EX_colipa20Oag_e\n", + "\t- R_EX_remnant1_e\n", + "\t- R_EX_feroxGfe_e\n", + "\t- R_EX_udcpo5_e\n", + "\t- R_EX_guln__L_e\n", + "\t- R_EX_feroxG_e\n", + "\t- R_EX_salchs2_e\n", + "\t- R_EX_diact_e\n", + "\t- R_EX_rnam_e\n", + "\t- R_EX_airs_e\n", + "\t- R_EX_dxyl_e\n", + "\t- R_EX_btd_RR_e\n", + "\t- R_EX_13ppd_e\n", + "\t- R_EX_rbt_e\n", + "\t- R_EX_3pg_e\n", + "\t- R_EX_udcpo4_e\n", + "\t- R_EX_cellb_e\n", + "\t- R_EX_bz_e\n", + "\t- R_EX_abt__D_e\n", + "\t- R_EX_fe_e\n", + "\t- R_EX_kokdolipidA_e\n", + "\t- R_EX_3hoxpac_e\n", + "\t- R_EX_o6a4colipa_e\n", + "\t- R_EX_3ntym_e\n", + "\t- R_EX_6apa_e\n", + "\t- R_EX_4hoxpac_e\n", + "\t- R_EX_peng_e\n", + "\t- R_EX_pac_e\n", + "\t- R_EX_salcn_e\n", + "\t- R_EX_galam_e\n", + "\t- R_EX_raffin_e\n", + "\t- R_DM_lipidA_core_e_p\n", + "\t- R_EX_arab__D_e\u001b[0m\n", + "File saved at: ../../data/sbml_corrected/iAPECO1_1312.xml\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: iAM_Pk459\n", + "\u001b[0;93mWARNING : - R_ARGLYSex: Reactants and products exchanged.\n", + " Boundaries was: [-1000000.0 ; 0.0]\n", + " - R_RE1342C: Reactants and products exchanged.\n", + " Boundaries was: [-1000000.0 ; 0.0]\n", + "\u001b[0m\n", + "\n", + "\n", + "\u001b[0;36m############################################\n", + "############################################\n", + " WRITING SBML FILE \n", + "############################################\n", + "############################################\n", + "\u001b[0m\n", + "\u001b[0;93mWARNING : IMPORT REACTION REMOVED BY DEFAULT\u001b[0m\n", + "\u001b[0;93mWARNING : If you want to keep import reaction\n", + "use option -kir / --keep-import-reactions\u001b[0m\n", + "\u001b[0;93mWARNING : Reaction with tag reversible modified:\n", + "\t- R_ARGLYSex\n", + "\t- R_RE1342C\u001b[0m\n", + "\u001b[0;93mWARNING : Reaction with Reactants and Products exchanged:\n", + "\t- R_ARGLYSex\n", + "\t- R_RE1342C\u001b[0m\n", + "\u001b[0;93mWARNING : Import reaction removed:\n", + "\t- R_EX_pi_e\n", + "\t- R_DM_hmz_l\n", + "\t- R_SK_gcald_c\n", + "\t- R_DM_5mdr1p_c\n", + "\t- R_DM_hcys__L_c\n", + "\t- R_EX_Lpipecol_e\n", + "\t- R_EX_4pyrdx_e\n", + "\t- R_DM_pail345p_pf_16_0_18_1_c\n", + "\t- R_DM_mi13456p_c\n", + "\t- R_DM_pail345p_pf_18_0_18_1_c\n", + "\t- R_EX_4hba_e\n", + "\t- R_SK_thbpt4acam_c\n", + "\t- R_SK_dhbpt_c\n", + "\t- R_SK_thbpt_c\n", + "\t- R_SK_no_c\n", + "\t- R_SK_citr__L_c\n", + "\t- R_SK_dxyl5p_c\n", + "\t- R_SK_accoa_h\n", + "\t- R_EX_ptrc_e\n", + "\t- R_EX_spmd_e\n", + "\t- R_DM_oxptn_c\n", + "\t- R_EX_orn_e\n", + "\t- R_EX_chsterol_e\n", + "\t- R_EX_acgam_e\n", + "\t- R_EX_4abz_e\n", + "\t- R_EX_4ahmmp_e\n", + "\t- R_EX_5fthf_e\n", + "\t- R_EX_Hb_e\n", + "\t- R_EX_ac_e\n", + "\t- R_EX_ade_e\n", + "\t- R_EX_adn_e\n", + "\t- R_EX_adp_e\n", + "\t- R_EX_ala__L_e\n", + "\t- R_EX_amp_e\n", + "\t- R_EX_arachd_e\n", + "\t- R_EX_arg__L_e\n", + "\t- R_EX_asn__L_e\n", + "\t- R_EX_asp__L_e\n", + "\t- R_EX_atp_e\n", + "\t- R_EX_ca2_e\n", + "\t- R_EX_chol_e\n", + "\t- R_EX_cl_e\n", + "\t- R_EX_co2_e\n", + "\t- R_EX_cobalt2_e\n", + "\t- R_EX_csn_e\n", + "\t- R_EX_cu_e\n", + "\t- R_EX_cu2_e\n", + "\t- R_EX_cys__L_e\n", + "\t- R_EX_dha_e\n", + "\t- R_EX_dhpt_e\n", + "\t- R_EX_fe2_e\n", + "\t- R_EX_fol_e\n", + "\t- R_EX_for_e\n", + "\t- R_EX_fru_e\n", + "\t- R_EX_glc__D_e\n", + "\t- R_EX_gln__L_e\n", + "\t- R_EX_glu__L_e\n", + "\t- R_EX_gly_e\n", + "\t- R_EX_glyc_e\n", + "\t- R_EX_gsn_e\n", + "\t- R_EX_gthox_e\n", + "\t- R_EX_gthrd_e\n", + "\t- R_EX_gua_e\n", + "\t- R_EX_h_e\n", + "\t- R_EX_h2o_e\n", + "\t- R_EX_hco3_e\n", + "\t- R_EX_hdca_e\n", + "\t- R_EX_his__L_e\n", + "\t- R_EX_hxan_e\n", + "\t- R_EX_ile__L_e\n", + "\t- R_EX_ins_e\n", + "\t- R_EX_k_e\n", + "\t- R_EX_lac__D_e\n", + "\t- R_EX_lac__L_e\n", + "\t- R_EX_leu__L_e\n", + "\t- R_EX_lipoate_e\n", + "\t- R_EX_lnlc_e\n", + "\t- R_EX_lys__L_e\n", + "\t- R_EX_man_e\n", + "\t- R_EX_met__L_e\n", + "\t- R_EX_mg2_e\n", + "\t- R_EX_mthgxl_e\n", + "\t- R_EX_na1_e\n", + "\t- R_EX_nac_e\n", + "\t- R_EX_ncam_e\n", + "\t- R_EX_nh4_e\n", + "\t- R_EX_ni2_e\n", + "\t- R_EX_o2_e\n", + "\t- R_EX_ocdca_e\n", + "\t- R_EX_ocdcea_e\n", + "\t- R_EX_phe__L_e\n", + "\t- R_EX_lgn_e\n", + "\t- R_DM_saccrp__L_c\n", + "\t- R_EX_pnto__R_e\n", + "\t- R_EX_pro__L_e\n", + "\t- R_EX_pyr_e\n", + "\t- R_EX_ribflv_e\n", + "\t- R_EX_ser__L_e\n", + "\t- R_EX_thm_e\n", + "\t- R_EX_thr__L_e\n", + "\t- R_EX_thym_e\n", + "\t- R_EX_thymd_e\n", + "\t- R_EX_trp__L_e\n", + "\t- R_EX_ttdca_e\n", + "\t- R_EX_tyr__L_e\n", + "\t- R_EX_ura_e\n", + "\t- R_EX_uri_e\n", + "\t- R_EX_val__L_e\u001b[0m\n", + "File saved at: ../../data/sbml_corrected/iAM_Pk459.xml\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: Recon3D\n", + "\u001b[0;93mWARNING : - R_ABTt: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_HMGCOASm: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_RNTR1: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_SPHPL: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_ACACT7m: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_ECOAH5p: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_HACD6m: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_HACD5p: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_ECOAH4p: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_r1392: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_ALAtmi: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_PHEt2m: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_GTHRDt2: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_PHEMEt: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_Kt1: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_GLYCLm: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_G3PD1irm: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_LANOSTt: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_ACChex: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_FA160tp: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_SO4t_e: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_ACOAH: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_LCARS: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_FE3t: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_PNTOte: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_SQLter: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_G3PD2: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_HMR_3996: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_HMGCOAS: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_ALCD19y: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_ACACT6m: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_HACD6p: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_HACD4p: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_r1391: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_HISt2m: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + "\u001b[0m\n", + "\n", + "\n", + "\u001b[0;36m############################################\n", + "############################################\n", + " WRITING SBML FILE \n", + "############################################\n", + "############################################\n", + "\u001b[0m\n", + "\u001b[0;93mWARNING : IMPORT REACTION REMOVED BY DEFAULT\u001b[0m\n", + "\u001b[0;93mWARNING : If you want to keep import reaction\n", + "use option -kir / --keep-import-reactions\u001b[0m\n", + "\u001b[0;93mWARNING : Reaction with tag reversible modified:\n", + "\t- R_ABTt\n", + "\t- R_HMGCOASm\n", + "\t- R_RNTR1\n", + "\t- R_SPHPL\n", + "\t- R_ACACT7m\n", + "\t- R_ECOAH5p\n", + "\t- R_HACD6m\n", + "\t- R_HACD5p\n", + "\t- R_ECOAH4p\n", + "\t- R_r1392\n", + "\t- R_ALAtmi\n", + "\t- R_PHEt2m\n", + "\t- R_GTHRDt2\n", + "\t- R_PHEMEt\n", + "\t- R_Kt1\n", + "\t- R_GLYCLm\n", + "\t- R_G3PD1irm\n", + "\t- R_LANOSTt\n", + "\t- R_ACChex\n", + "\t- R_FA160tp\n", + "\t- R_SO4t_e\n", + "\t- R_ACOAH\n", + "\t- R_LCARS\n", + "\t- R_FE3t\n", + "\t- R_PNTOte\n", + "\t- R_SQLter\n", + "\t- R_G3PD2\n", + "\t- R_HMR_3996\n", + "\t- R_HMGCOAS\n", + "\t- R_ALCD19y\n", + "\t- R_ACACT6m\n", + "\t- R_HACD6p\n", + "\t- R_HACD4p\n", + "\t- R_r1391\n", + "\t- R_HISt2m\u001b[0m\n", + "\u001b[0;93mWARNING : Reaction with Reactants and Products exchanged:\n", + "\t- R_ABTt\n", + "\t- R_HMGCOASm\n", + "\t- R_RNTR1\n", + "\t- R_SPHPL\n", + "\t- R_ACACT7m\n", + "\t- R_ECOAH5p\n", + "\t- R_HACD6m\n", + "\t- R_HACD5p\n", + "\t- R_ECOAH4p\n", + "\t- R_r1392\n", + "\t- R_ALAtmi\n", + "\t- R_PHEt2m\n", + "\t- R_GTHRDt2\n", + "\t- R_PHEMEt\n", + "\t- R_Kt1\n", + "\t- R_GLYCLm\n", + "\t- R_G3PD1irm\n", + "\t- R_LANOSTt\n", + "\t- R_ACChex\n", + "\t- R_FA160tp\n", + "\t- R_SO4t_e\n", + "\t- R_ACOAH\n", + "\t- R_LCARS\n", + "\t- R_FE3t\n", + "\t- R_PNTOte\n", + "\t- R_SQLter\n", + "\t- R_G3PD2\n", + "\t- R_HMR_3996\n", + "\t- R_HMGCOAS\n", + "\t- R_ALCD19y\n", + "\t- R_ACACT6m\n", + "\t- R_HACD6p\n", + "\t- R_HACD4p\n", + "\t- R_r1391\n", + "\t- R_HISt2m\u001b[0m\n", + "\u001b[0;93mWARNING : Import reaction removed:\n", + "\t- R_EX_5adtststerone_e\n", + "\t- R_EX_5adtststerones_e\n", + "\t- R_EX_5fthf_e\n", + "\t- R_EX_5htrp_e\n", + "\t- R_EX_5mthf_e\n", + "\t- R_EX_5thf_e\n", + "\t- R_EX_6dhf_e\n", + "\t- R_EX_6htststerone_e\n", + "\t- R_DM_Asn_X_Ser_Thr_l\n", + "\t- R_DM_core5_g\n", + "\t- R_DM_dem2emgacpail_prot_hs_r\n", + "\t- R_DM_dgpi_prot_hs_r\n", + "\t- R_DM_dsT_antigen_g\n", + "\t- R_DM_kdn_c\n", + "\t- R_DM_melanin_c\n", + "\t- R_DM_n5m2masn_g\n", + "\t- R_DM_oretn_n\n", + "\t- R_DM_Ser_Thr_l\n", + "\t- R_DM_Ser_Gly_Ala_X_Gly_l\n", + "\t- R_DM_sprm_c\n", + "\t- R_EX_10fthf5glu_e\n", + "\t- R_EX_10fthf6glu_e\n", + "\t- R_EX_10fthf7glu_e\n", + "\t- R_EX_11_cis_retfa_e\n", + "\t- R_EX_13_cis_retnglc_e\n", + "\t- R_EX_24nph_e\n", + "\t- R_EX_25hvitd3_e\n", + "\t- R_EX_2hb_e\n", + "\t- R_EX_2mcit_e\n", + "\t- R_EX_34dhphe_e\n", + "\t- R_EX_35cgmp_e\n", + "\t- R_EX_4hphac_e\n", + "\t- R_EX_4mptnl_e\n", + "\t- R_EX_7dhf_e\n", + "\t- R_EX_7thf_e\n", + "\t- R_EX_9_cis_retfa_e\n", + "\t- R_EX_acetone_e\n", + "\t- R_EX_acgalfucgalacgalfuc12gal14acglcgalgluside_hs_e\n", + "\t- R_EX_acnacngal14acglcgalgluside_hs_e\n", + "\t- R_EX_adp_e\n", + "\t- R_EX_ahandrostanglc_e\n", + "\t- R_EX_ala_B_e\n", + "\t- R_EX_ala__D_e\n", + "\t- R_EX_aldstrn_e\n", + "\t- R_EX_andrstrn_e\n", + "\t- R_EX_andrstrnglc_e\n", + "\t- R_EX_antipyrene_e\n", + "\t- R_EX_appnn_e\n", + "\t- R_EX_arach_e\n", + "\t- R_EX_avite1_e\n", + "\t- R_EX_avite2_e\n", + "\t- R_EX_bhb_e\n", + "\t- R_EX_bildglcur_e\n", + "\t- R_EX_biocyt_e\n", + "\t- R_EX_bvite_e\n", + "\t- R_EX_caro_e\n", + "\t- R_EX_carveol_e\n", + "\t- R_EX_chol_e\n", + "\t- R_EX_cholate_e\n", + "\t- R_EX_chtn_e\n", + "\t- R_EX_clpnd_e\n", + "\t- R_EX_coumarin_e\n", + "\t- R_EX_creat_e\n", + "\t- R_EX_crmp_hs_e\n", + "\t- R_EX_crn_e\n", + "\t- R_EX_crtstrn_e\n", + "\t- R_EX_crvnc_e\n", + "\t- R_EX_cspg_c_e\n", + "\t- R_EX_cspg_e_e\n", + "\t- R_EX_cyan_e\n", + "\t- R_EX_dag_hs_e\n", + "\t- R_EX_dcsptn1_e\n", + "\t- R_EX_dhdascb_e\n", + "\t- R_EX_dheas_e\n", + "\t- R_EX_dhf_e\n", + "\t- R_EX_digalsgalside_hs_e\n", + "\t- R_EX_dlnlcg_e\n", + "\t- R_EX_dmantipyrine_e\n", + "\t- R_EX_dmhptcrn_e\n", + "\t- R_EX_dopa_e\n", + "\t- R_EX_eaflatoxin_e\n", + "\t- R_EX_ebastine_e\n", + "\t- R_EX_eicostet_e\n", + "\t- R_EX_estradiolglc_e\n", + "\t- R_EX_fucacngalacglcgalgluside_hs_e\n", + "\t- R_EX_fucfuc12gal14acglcgalgluside_hs_e\n", + "\t- R_EX_fucfucfucgalacglc13galacglcgal14acglcgalgluside_hs_e\n", + "\t- R_EX_fucfucgalacglcgalgluside_hs_e\n", + "\t- R_EX_fucgal14acglcgalgluside_hs_e\n", + "\t- R_EX_fucgalgbside_hs_e\n", + "\t- R_EX_fuc__L_e\n", + "\t- R_EX_galacglcgalgbside_hs_e\n", + "\t- R_EX_galfucgalacglcgal14acglcgalgluside_hs_e\n", + "\t- R_EX_gd1c_hs_e\n", + "\t- R_EX_gdp_e\n", + "\t- R_EX_gluala_e\n", + "\t- R_EX_glyc__S_e\n", + "\t- R_EX_gp1calpha_hs_e\n", + "\t- R_EX_gq1balpha_hs_e\n", + "\t- R_EX_gsn_e\n", + "\t- R_EX_gt1a_hs_e\n", + "\t- R_EX_gthox_e\n", + "\t- R_EX_ha_e\n", + "\t- R_EX_ha_pre1_e\n", + "\t- R_EX_hcoumarin_e\n", + "\t- R_EX_hdcea_e\n", + "\t- R_EX_hexc_e\n", + "\t- R_EX_hom__L_e\n", + "\t- R_EX_hspg_e\n", + "\t- R_EX_htaxol_e\n", + "\t- R_EX_i_e\n", + "\t- R_EX_imp_e\n", + "\t- R_EX_ksi_e\n", + "\t- R_EX_ksii_core4_e\n", + "\t- R_EX_lac__D_e\n", + "\t- R_EX_cysi__L_e\n", + "\t- R_EX_leuktrB4_e\n", + "\t- R_EX_leuktrC4_e\n", + "\t- R_EX_leuktrD4_e\n", + "\t- R_EX_leuktrE4_e\n", + "\t- R_EX_limnen_e\n", + "\t- R_EX_lipoate_e\n", + "\t- R_EX_lnlc_e\n", + "\t- R_EX_lnlnca_e\n", + "\t- R_EX_lpchol_hs_e\n", + "\t- R_EX_meoh_e\n", + "\t- R_EX_mercplaccys_e\n", + "\t- R_EX_mthgxl_e\n", + "\t- R_EX_nad_e\n", + "\t- R_EX_nadp_e\n", + "\t- R_EX_nifedipine_e\n", + "\t- R_EX_npthl_e\n", + "\t- R_EX_nrpphr_e\n", + "\t- R_EX_oagd3_hs_e\n", + "\t- R_EX_oagt3_hs_e\n", + "\t- R_EX_ocdca_e\n", + "\t- R_EX_ocdcea_e\n", + "\t- R_EX_octa_e\n", + "\t- R_EX_onpthl_e\n", + "\t- R_EX_oxa_e\n", + "\t- R_EX_paf_hs_e\n", + "\t- R_EX_pchol_hs_e\n", + "\t- R_EX_perillyl_e\n", + "\t- R_EX_pheacgln_e\n", + "\t- R_EX_phllqne_e\n", + "\t- R_EX_phyt_e\n", + "\t- R_EX_prostgd2_e\n", + "\t- R_EX_prostge2_e\n", + "\t- R_EX_prostgf2_e\n", + "\t- R_EX_ps_hs_e\n", + "\t- R_EX_retfa_e\n", + "\t- R_EX_retinol_e\n", + "\t- R_EX_Rtotal2_e\n", + "\t- R_EX_Rtotal3_e\n", + "\t- R_EX_s2l2n2m2masn_e\n", + "\t- R_EX_sarcs_e\n", + "\t- R_EX_spc_hs_e\n", + "\t- R_EX_sph1p_e\n", + "\t- R_EX_strdnc_e\n", + "\t- R_EX_tag_hs_e\n", + "\t- R_EX_taxol_e\n", + "\t- R_EX_tchola_e\n", + "\t- R_EX_tcynt_e\n", + "\t- R_EX_tdchola_e\n", + "\t- R_EX_tetpent3_e\n", + "\t- R_EX_tetpent6_e\n", + "\t- R_EX_thmmp_e\n", + "\t- R_EX_thym_e\n", + "\t- R_EX_triodthy_e\n", + "\t- R_EX_triodthysuf_e\n", + "\t- R_EX_tststeroneglc_e\n", + "\t- R_EX_tststerones_e\n", + "\t- R_EX_ttdca_e\n", + "\t- R_EX_tymsf_e\n", + "\t- R_EX_Tyr_ggn_e\n", + "\t- R_EX_udp_e\n", + "\t- R_EX_vitd3_e\n", + "\t- R_EX_whddca_e\n", + "\t- R_EX_whhdca_e\n", + "\t- R_EX_whttdca_e\n", + "\t- R_EX_xoltri25_e\n", + "\t- R_EX_xoltri27_e\n", + "\t- R_EX_4abutn_e\n", + "\t- R_EX_acmana_e\n", + "\t- R_EX_ahdt_e\n", + "\t- R_EX_dgmp_e\n", + "\t- R_EX_dgtp_e\n", + "\t- R_EX_dha_e\n", + "\t- R_EX_dhap_e\n", + "\t- R_EX_dttp_e\n", + "\t- R_EX_isomal_e\n", + "\t- R_EX_HC00250_e\n", + "\t- R_EX_HC01361_e\n", + "\t- R_EX_HC01440_e\n", + "\t- R_EX_HC01446_e\n", + "\t- R_EX_HC01577_e\n", + "\t- R_EX_HC02160_e\n", + "\t- R_EX_orot_e\n", + "\t- R_EX_udpg_e\n", + "\t- R_EX_so3_e\n", + "\t- R_EX_CE2250_e\n", + "\t- R_EX_CE1935_e\n", + "\t- R_EX_CE1943_e\n", + "\t- R_EX_CE1939_e\n", + "\t- R_EX_CE2915_e\n", + "\t- R_EX_CE4722_e\n", + "\t- R_EX_CE4723_e\n", + "\t- R_EX_prostgh2_e\n", + "\t- R_EX_citr__L_e\n", + "\t- R_EX_HC00822_e\n", + "\t- R_EX_C02528_e\n", + "\t- R_EX_HC02192_e\n", + "\t- R_EX_HC02195_e\n", + "\t- R_EX_HC02191_e\n", + "\t- R_EX_HC02198_e\n", + "\t- R_EX_HC02203_e\n", + "\t- R_EX_HC02204_e\n", + "\t- R_EX_HC02205_e\n", + "\t- R_EX_HC02206_e\n", + "\t- R_EX_HC02207_e\n", + "\t- R_EX_HC02213_e\n", + "\t- R_EX_HC02217_e\n", + "\t- R_EX_arachcoa_e\n", + "\t- R_EX_coa_e\n", + "\t- R_EX_CE4724_e\n", + "\t- R_EX_malthp_e\n", + "\t- R_EX_CE2838_e\n", + "\t- R_EX_cynt_e\n", + "\t- R_EX_3ump_e\n", + "\t- R_EX_CE5786_e\n", + "\t- R_EX_CE5797_e\n", + "\t- R_EX_CE5787_e\n", + "\t- R_EX_CE5868_e\n", + "\t- R_EX_CE4881_e\n", + "\t- R_EX_CE5854_e\n", + "\t- R_EX_cdpea_e\n", + "\t- R_EX_CE5853_e\n", + "\t- R_EX_CE1925_e\n", + "\t- R_SK_dd2coa_c\n", + "\t- R_SK_decdicoa_c\n", + "\t- R_EX_3hdececrn_e\n", + "\t- R_EX_3ivcrn_e\n", + "\t- R_EX_3thexddcoacrn_e\n", + "\t- R_EX_c101crn_e\n", + "\t- R_EX_c10crn_e\n", + "\t- R_EX_c10dc_e\n", + "\t- R_EX_c12dc_e\n", + "\t- R_EX_c3dc_e\n", + "\t- R_EX_c4dc_e\n", + "\t- R_EX_c5dc_e\n", + "\t- R_EX_c6crn_e\n", + "\t- R_EX_c8crn_e\n", + "\t- R_EX_decdicrn_e\n", + "\t- R_EX_docosac_e\n", + "\t- R_EX_docosdiac_e\n", + "\t- R_EX_dca_e\n", + "\t- R_EX_dchac_e\n", + "\t- R_SK_tetdec2coa_c\n", + "\t- R_SK_tetdece1coa_c\n", + "\t- R_SK_octdececoa_c\n", + "\t- R_EX_gltdechol_e\n", + "\t- R_EX_glysar_e\n", + "\t- R_EX_gumtchol_e\n", + "\t- R_EX_leugly_e\n", + "\t- R_EX_leuleu_e\n", + "\t- R_EX_pect_e\n", + "\t- R_EX_pectingchol_e\n", + "\t- R_EX_pectintchol_e\n", + "\t- R_EX_psyl_e\n", + "\t- R_EX_psylchol_e\n", + "\t- R_EX_psyltchol_e\n", + "\t- R_EX_slfcys_e\n", + "\t- R_EX_pan4p_e\n", + "\t- R_EX_ptth_e\n", + "\t- R_EX_q10h2_e\n", + "\t- R_DM_taur_c\n", + "\t- R_DM_pmtcoa_r\n", + "\t- R_EX_4hpro_LT_e\n", + "\t- R_EX_bglc_e\n", + "\t- R_EX_carn_e\n", + "\t- R_EX_glgchlo_e\n", + "\t- R_SK_5HPET_c\n", + "\t- R_EX_crtn_e\n", + "\t- R_EX_cyst__L_e\n", + "\t- R_EX_dcmp_e\n", + "\t- R_DM_4hpro_LT_m\n", + "\t- R_DM_Lcystin_c\n", + "\t- R_DM_ncam_c\n", + "\t- R_EX_34hpp_e\n", + "\t- R_EX_glyald_e\n", + "\t- R_EX_3mob_e\n", + "\t- R_EX_3mop_e\n", + "\t- R_EX_ahcys_e\n", + "\t- R_EX_aicar_e\n", + "\t- R_EX_anth_e\n", + "\t- R_EX_icit_e\n", + "\t- R_EX_idour_e\n", + "\t- R_EX_Lkynr_e\n", + "\t- R_EX_quln_e\n", + "\t- R_EX_xmp_e\n", + "\t- R_EX_3pg_e\n", + "\t- R_EX_3hanthrn_e\n", + "\t- R_EX_glyc3p_e\n", + "\t- R_EX_2pg_e\n", + "\t- R_EX_acrn_e\n", + "\t- R_EX_pcrn_e\n", + "\t- R_EX_lneldccrn_e\n", + "\t- R_EX_odecrn_e\n", + "\t- R_EX_pmtcrn_e\n", + "\t- R_DM_ascb__L_c\n", + "\t- R_EX_3mtp_e\n", + "\t- R_EX_C14769_e\n", + "\t- R_EX_56dthm_e\n", + "\t- R_EX_56dura_e\n", + "\t- R_EX_5HPET_e\n", + "\t- R_EX_acthr__L_e\n", + "\t- R_EX_adpoh_e\n", + "\t- R_EX_amet_e\n", + "\t- R_EX_15HPET_e\n", + "\t- R_EX_15kprostgf2_e\n", + "\t- R_EX_21hprgnlone_e\n", + "\t- R_EX_3hmp_e\n", + "\t- R_EX_3mhis_e\n", + "\t- R_EX_and19one_e\n", + "\t- R_EX_aracheth_e\n", + "\t- R_EX_C02356_e\n", + "\t- R_EX_C02712_e\n", + "\t- R_EX_C04717_e\n", + "\t- R_EX_C05957_e\n", + "\t- R_EX_C06314_e\n", + "\t- R_EX_pcholet_hs_e\n", + "\t- R_EX_C14771_e\n", + "\t- R_EX_CE1273_e\n", + "\t- R_EX_CE2176_e\n", + "\t- R_EX_pcholn201_hs_e\n", + "\t- R_EX_pcholn205_hs_e\n", + "\t- R_EX_pcholn226_hs_e\n", + "\t- R_EX_pcholn24_hs_e\n", + "\t- R_EX_eidi1114ac_e\n", + "\t- R_EX_CE6031_e\n", + "\t- R_EX_CE7082_e\n", + "\t- R_EX_pcholn261_hs_e\n", + "\t- R_EX_pcholpalm_hs_e\n", + "\t- R_EX_pe14_hs_e\n", + "\t- R_EX_pe15_hs_e\n", + "\t- R_EX_pe161_hs_e\n", + "\t- R_EX_pe203_hs_e\n", + "\t- R_EX_pe226_hs_e\n", + "\t- R_EX_CE7083_e\n", + "\t- R_EX_docohxeth_e\n", + "\t- R_EX_elaidcrn_e\n", + "\t- R_EX_HC00900_e\n", + "\t- R_EX_hexdiac_e\n", + "\t- R_EX_hgentis_e\n", + "\t- R_EX_leuktrB4wcooh_e\n", + "\t- R_EX_pe2linl_hs_e\n", + "\t- R_EX_pear_hs_e\n", + "\t- R_EX_pendecaeth_e\n", + "\t- R_EX_lineth_e\n", + "\t- R_EX_lnlccrn_e\n", + "\t- R_EX_Lpipecol_e\n", + "\t- R_EX_pepalm_hs_e\n", + "\t- R_EX_peste_hs_e\n", + "\t- R_EX_pmeth_e\n", + "\t- R_EX_lthstrl_e\n", + "\t- R_EX_maglinl_hs_e\n", + "\t- R_EX_sebacid_e\n", + "\t- R_EX_sphmyln180241_hs_e\n", + "\t- R_EX_magste_hs_e\n", + "\t- R_EX_mev__R_e\n", + "\t- R_EX_sphmyln18114_hs_e\n", + "\t- R_EX_sphmyln18115_hs_e\n", + "\t- R_EX_pailpalm_hs_e\n", + "\t- R_EX_pailste_hs_e\n", + "\t- R_EX_sphmyln18116_hs_e\n", + "\t- R_EX_pchol2linl_hs_e\n", + "\t- R_EX_pchol2ole_hs_e\n", + "\t- R_EX_pchol2palm_hs_e\n", + "\t- R_EX_pcholar_hs_e\n", + "\t- R_EX_sphmyln181181_hs_e\n", + "\t- R_EX_sphmyln18120_hs_e\n", + "\t- R_EX_sphmyln18121_hs_e\n", + "\t- R_EX_sphmyln18122_hs_e\n", + "\t- R_EX_sphmyln181221_hs_e\n", + "\t- R_EX_subeac_e\n", + "\t- R_EX_tetdeca511ac_e\n", + "\t- R_EX_thrnt_e\n", + "\t- R_EX_ttdcrn_e\n", + "\t- R_EX_txb2_e\n", + "\t- R_EX_wharachd_e\n", + "\t- R_EX_xolest181_hs_e\n", + "\t- R_EX_xolest182_hs_e\n", + "\t- R_EX_xolest204_hs_e\n", + "\t- R_EX_achom__L_e\n", + "\t- R_EX_phacgly_e\n", + "\t- R_EX_3moxtyr_e\n", + "\t- R_EX_alltn_e\n", + "\t- R_EX_glyc2p_e\n", + "\t- R_EX_glyclt_e\n", + "\t- R_EX_oaa_e\n", + "\t- R_EX_phlac_e\n", + "\t- R_EX_ttdcea_e\n", + "\t- R_EX_alaargcys_e\n", + "\t- R_EX_acleu__L_e\n", + "\t- R_DM_k_c\n", + "\t- R_EX_alaarggly_e\n", + "\t- R_EX_pheacgly_e\n", + "\t- R_EX_bz_e\n", + "\t- R_EX_alaglylys_e\n", + "\t- R_EX_pcresol_e\n", + "\t- R_EX_pcs_e\n", + "\t- R_EX_hisargser_e\n", + "\t- R_EX_hiscyscys_e\n", + "\t- R_EX_alahisala_e\n", + "\t- R_EX_hisglu_e\n", + "\t- R_EX_hisglugln_e\n", + "\t- R_EX_hislysala_e\n", + "\t- R_EX_hislysglu_e\n", + "\t- R_EX_C05300_e\n", + "\t- R_EX_argalaala_e\n", + "\t- R_EX_retinal_e\n", + "\t- R_EX_1mncam_e\n", + "\t- R_EX_progly_e\n", + "\t- R_EX_dhbpt_e\n", + "\t- R_EX_hislysthr_e\n", + "\t- R_EX_hislysval_e\n", + "\t- R_EX_hismetgln_e\n", + "\t- R_EX_hisphearg_e\n", + "\t- R_EX_hisprolys_e\n", + "\t- R_EX_histrphis_e\n", + "\t- R_EX_ileargile_e\n", + "\t- R_EX_argalaphe_e\n", + "\t- R_EX_ileasp_e\n", + "\t- R_EX_ileprolys_e\n", + "\t- R_EX_prolyspro_e\n", + "\t- R_EX_prophe_e\n", + "\t- R_EX_argalathr_e\n", + "\t- R_EX_proproarg_e\n", + "\t- R_EX_propropro_e\n", + "\t- R_EX_leuasnasp_e\n", + "\t- R_EX_leuasplys_e\n", + "\t- R_EX_protrplys_e\n", + "\t- R_EX_provalgln_e\n", + "\t- R_EX_leuleutrp_e\n", + "\t- R_EX_argarg_e\n", + "\t- R_EX_leuproarg_e\n", + "\t- R_EX_leusertrp_e\n", + "\t- R_EX_serlyshis_e\n", + "\t- R_EX_thrglntyr_e\n", + "\t- R_EX_thrphearg_e\n", + "\t- R_EX_thrserarg_e\n", + "\t- R_EX_leutrp_e\n", + "\t- R_EX_thrthrarg_e\n", + "\t- R_EX_leuval_e\n", + "\t- R_EX_thrtyrmet_e\n", + "\t- R_EX_lysargleu_e\n", + "\t- R_EX_argarglys_e\n", + "\t- R_EX_lyscyshis_e\n", + "\t- R_EX_lysglnphe_e\n", + "\t- R_EX_lystrparg_e\n", + "\t- R_EX_lysvalphe_e\n", + "\t- R_EX_trpargala_e\n", + "\t- R_EX_trpglngln_e\n", + "\t- R_EX_trpglugly_e\n", + "\t- R_EX_trpglupro_e\n", + "\t- R_EX_trpglyleu_e\n", + "\t- R_EX_trpglyphe_e\n", + "\t- R_EX_trpilelys_e\n", + "\t- R_EX_trpiletrp_e\n", + "\t- R_EX_trpleuval_e\n", + "\t- R_EX_metglntyr_e\n", + "\t- R_EX_metphearg_e\n", + "\t- R_EX_pheasnmet_e\n", + "\t- R_EX_pheasp_e\n", + "\t- R_EX_pheleuasp_e\n", + "\t- R_EX_pheleuhis_e\n", + "\t- R_EX_trplys_e\n", + "\t- R_EX_trpmetval_e\n", + "\t- R_EX_trptyrgln_e\n", + "\t- R_EX_phelyspro_e\n", + "\t- R_EX_phephethr_e\n", + "\t- R_EX_pheproarg_e\n", + "\t- R_EX_trptyrtyr_e\n", + "\t- R_EX_phesertrp_e\n", + "\t- R_EX_tyrargser_e\n", + "\t- R_EX_argcysgly_e\n", + "\t- R_EX_tyrcysthr_e\n", + "\t- R_EX_tyrleuarg_e\n", + "\t- R_EX_tyrphetyr_e\n", + "\t- R_EX_tyrthr_e\n", + "\t- R_EX_tyrtrpphe_e\n", + "\t- R_EX_tyrtyr_e\n", + "\t- R_EX_tyrvalmet_e\n", + "\t- R_EX_valarggly_e\n", + "\t- R_EX_valhisasn_e\n", + "\t- R_EX_valphearg_e\n", + "\t- R_EX_valprotrp_e\n", + "\t- R_EX_valserarg_e\n", + "\t- R_EX_phetyrgln_e\n", + "\t- R_EX_phetyrlys_e\n", + "\t- R_EX_proargcys_e\n", + "\t- R_EX_proasncys_e\n", + "\t- R_EX_proglnpro_e\n", + "\t- R_EX_prohis_e\n", + "\t- R_EX_prohistyr_e\n", + "\t- R_EX_argglupro_e\n", + "\t- R_EX_argglygly_e\n", + "\t- R_EX_argphearg_e\n", + "\t- R_EX_argpromet_e\n", + "\t- R_EX_argprothr_e\n", + "\t- R_EX_argtyrval_e\n", + "\t- R_EX_argvalcys_e\n", + "\t- R_EX_argvaltrp_e\n", + "\t- R_EX_asnasnarg_e\n", + "\t- R_EX_asncyscys_e\n", + "\t- R_EX_asnpheasp_e\n", + "\t- R_EX_asnphecys_e\n", + "\t- R_EX_asntyrthr_e\n", + "\t- R_EX_aspglu_e\n", + "\t- R_EX_aspglupro_e\n", + "\t- R_EX_asphiscys_e\n", + "\t- R_EX_aspvalasn_e\n", + "\t- R_EX_cyssermet_e\n", + "\t- R_EX_cystyrasn_e\n", + "\t- R_EX_glnasngln_e\n", + "\t- R_EX_hxa_e\n", + "\t- R_EX_glnhislys_e\n", + "\t- R_EX_glnproglu_e\n", + "\t- R_EX_gluargleu_e\n", + "\t- R_EX_gluasnleu_e\n", + "\t- R_EX_gluthrlys_e\n", + "\t- R_EX_glutrpala_e\n", + "\t- R_EX_glyhisasn_e\n", + "\t- R_EX_glylyscys_e\n", + "\t- R_SK_adprbp_c\n", + "\t- R_EX_glylysphe_e\n", + "\t- R_SK_bandmt_c\n", + "\t- R_EX_core4_e\n", + "\t- R_EX_core5_e\n", + "\t- R_SK_for_c\n", + "\t- R_EX_glyvalhis_e\n", + "\t- R_SK_mi1345p_c\n", + "\t- R_EX_dsT_antigen_e\n", + "\t- R_EX_f1a_e\n", + "\t- R_SK_mi134p_c\n", + "\t- R_SK_mi145p_c\n", + "\t- R_EX_galam_e\n", + "\t- R_DM_pchol_hs_c\n", + "\t- R_EX_gncore1_e\n", + "\t- R_EX_mqn7_e\n", + "\t- R_SK_crvnc_c\n", + "\t- R_EX_acgal_e\n", + "\t- R_EX_acnam_e\n", + "\t- R_EX_pcreat_e\n", + "\t- R_EX_HC00342_e\n", + "\t- R_EX_CE2934_e\n", + "\t- R_DM_mqn10_c\n", + "\t- R_DM_mqn7_c\n", + "\t- R_DM_mqn8_c\n", + "\t- R_DM_mqn9_c\n", + "\t- R_SK_doco13ecoa_c\n", + "\t- R_SK_fad_c\n", + "\t- R_SK_25hvitd2_c\n", + "\t- R_SK_9_cis_retfa_c\n", + "\t- R_SK_glygn2_c\n", + "\t- R_SK_HC02193_c\n", + "\t- R_SK_lnlc_c\n", + "\t- R_SK_HC02195_c\n", + "\t- R_SK_HC02197_c\n", + "\t- R_SK_HC02198_c\n", + "\t- R_SK_Tyr_ggn_c\n", + "\t- R_DM_avite1_c\n", + "\t- R_SK_btn_c\n", + "\t- R_SK_c226coa_c\n", + "\t- R_SK_lnlccoa_c\n", + "\t- R_SK_lnlncgcoa_c\n", + "\t- R_SK_nad_c\n", + "\t- R_SK_pmtcoa_c\n", + "\t- R_SK_pydx_c\n", + "\t- R_SK_pydxn_c\n", + "\t- R_SK_retinol_c\n", + "\t- R_SK_tag_hs_c\n", + "\t- R_SK_tchola_c\n", + "\t- R_SK_tdechola_c\n", + "\t- R_EX_acac_e\n", + "\t- R_EX_adn_e\n", + "\t- R_SK_thf_c\n", + "\t- R_EX_asp__L_e\n", + "\t- R_EX_but_e\n", + "\t- R_EX_din_e\n", + "\t- R_EX_fe2_e\n", + "\t- R_EX_fe3_e\n", + "\t- R_EX_gal_e\n", + "\t- R_EX_glu__L_e\n", + "\t- R_EX_glyb_e\n", + "\t- R_EX_glyleu_e\n", + "\t- R_EX_h2o_e\n", + "\t- R_EX_ins_e\n", + "\t- R_EX_k_e\n", + "\t- R_EX_lac__L_e\n", + "\t- R_EX_leu__L_e\n", + "\t- R_SK_tmndnc_c\n", + "\t- R_EX_lys__L_e\n", + "\t- R_EX_mal__L_e\n", + "\t- R_EX_no2_e\n", + "\t- R_EX_o2_e\n", + "\t- R_EX_orn_e\n", + "\t- R_EX_pi_e\n", + "\t- R_SK_tmndnccoa_c\n", + "\t- R_EX_ribflv_e\n", + "\t- R_SK_vitd3_c\n", + "\t- R_EX_succ_e\n", + "\t- R_EX_thymd_e\n", + "\t- R_EX_urea_e\n", + "\t- R_EX_uri_e\n", + "\t- R_EX_dxtrn_e\n", + "\t- R_EX_dhcholestanate_e\n", + "\t- R_SK_dhcholestanate_c\n", + "\t- R_EX_pnto__R_e\n", + "\t- R_EX_gly_e\n", + "\t- R_EX_cys__L_e\n", + "\t- R_EX_thr__L_e\n", + "\t- R_EX_gln__L_e\n", + "\t- R_EX_thcholstoic_e\n", + "\t- R_EX_phe__L_e\n", + "\t- R_EX_tyr__L_e\n", + "\t- R_EX_arg__L_e\n", + "\t- R_EX_for_e\n", + "\t- R_EX_nac_e\n", + "\t- R_EX_ind3ac_e\n", + "\t- R_EX_nh4_e\n", + "\t- R_SK_thcholstoic_c\n", + "\t- R_EX_ac_e\n", + "\t- R_EX_mqn8_e\n", + "\t- R_EX_xol7aone_e\n", + "\t- R_SK_xol7aone_c\n", + "\t- R_SK_xoldiolone_c\n", + "\t- R_SK_dchac_c\n", + "\t- R_SK_CE1273_c\n", + "\t- R_EX_drib_e\n", + "\t- R_EX_fru_e\n", + "\t- R_EX_glyc_e\n", + "\t- R_EX_malttr_e\n", + "\t- R_EX_man_e\n", + "\t- R_EX_ptrc_e\n", + "\t- R_EX_rib__D_e\n", + "\t- R_EX_spmd_e\n", + "\t- R_EX_pydxn_e\n", + "\t- R_EX_pydx_e\n", + "\t- R_EX_pydam_e\n", + "\t- R_EX_arab__L_e\n", + "\t- R_EX_gam_e\n", + "\t- R_EX_pyr_e\n", + "\t- R_EX_tre_e\n", + "\t- R_EX_4abut_e\n", + "\t- R_EX_btn_e\n", + "\t- R_EX_ade_e\n", + "\t- R_EX_etoh_e\n", + "\t- R_EX_acald_e\n", + "\t- R_EX_taur_e\n", + "\t- R_EX_tym_e\n", + "\t- R_EX_3hadpac_e\n", + "\t- R_EX_ethmalac_e\n", + "\t- R_EX_lanost_e\n", + "\t- R_EX_hexgly_e\n", + "\t- R_EX_subgly_e\n", + "\t- R_EX_4ohbut_e\n", + "\t- R_EX_3mox4hoxm_e\n", + "\t- R_EX_CE2026_e\n", + "\t- R_EX_thexdd_e\n", + "\t- R_EX_hexdtr_e\n", + "\t- R_EX_eic21114tr_e\n", + "\t- R_EX_CE4968_e\n", + "\t- R_EX_dhea_e\n", + "\t- R_EX_ahandrostan_e\n", + "\t- R_EX_andrstandn_e\n", + "\t- R_EX_CE2209_e\n", + "\t- R_EX_estrone_e\n", + "\t- R_EX_C05299_e\n", + "\t- R_EX_C05302_e\n", + "\t- R_EX_11docrtsl_e\n", + "\t- R_EX_agm_e\n", + "\t- R_EX_prgnlone_e\n", + "\t- R_EX_17ahprgnlone_e\n", + "\t- R_EX_C03681_e\n", + "\t- R_EX_prgnlones_e\n", + "\t- R_EX_CE1352_e\n", + "\t- R_EX_xol24oh_e\n", + "\t- R_EX_xol25oh_e\n", + "\t- R_EX_chsterols_e\n", + "\t- R_EX_3ityr__L_e\n", + "\t- R_EX_T4hcinnm_e\n", + "\t- R_EX_35diotyr_e\n", + "\t- R_EX_13_cis_retn_e\n", + "\t- R_DM_myelin_hs_c\n", + "\t- R_EX_18harachd_e\n", + "\t- R_EX_HC00460_e\n", + "\t- R_EX_vanillac_e\n", + "\t- R_EX_CE7090_e\n", + "\t- R_EX_CE7096_e\n", + "\t- R_EX_CE4877_e\n", + "\t- R_EX_CE1447_e\n", + "\t- R_EX_trypta_e\n", + "\t- R_EX_CE2006_e\n", + "\t- R_EX_C10164_e\n", + "\t- R_EX_n8aspmd_e\n", + "\t- R_EX_CE6205_e\n", + "\t- R_EX_C09642_e\n", + "\t- R_EX_C05769_e\n", + "\t- R_EX_C05770_e\n", + "\t- R_EX_ppbng_e\n", + "\t- R_EX_12ppd__R_e\n", + "\t- R_EX_dopa4sf_e\n", + "\t- R_EX_CE0737_e\n", + "\t- R_EX_2hiv_e\n", + "\t- R_EX_4glu56dihdind_e\n", + "\t- R_EX_hdd2crn_e\n", + "\t- R_EX_im4ac_e\n", + "\t- R_EX_CE1918_e\n", + "\t- R_EX_mlthf_e\n", + "\t- R_EX_CE5025_e\n", + "\t- R_EX_CE2172_e\n", + "\t- R_EX_ppp9_e\n", + "\t- R_EX_aact_e\n", + "\t- R_EX_CE5629_e\n", + "\t- R_EX_CE2705_e\n", + "\t- R_EX_prist_e\n", + "\t- R_EX_CE2049_e\n", + "\t- R_EX_gd3_hs_e\n", + "\t- R_EX_CE2047_e\n", + "\t- R_EX_gm3_hs_e\n", + "\t- R_EX_2m3hbu_e\n", + "\t- R_EX_5a2opntn_e\n", + "\t- R_EX_arg__D_e\n", + "\t- R_EX_idl_hs_e\n", + "\t- R_EX_ldl_hs_e\n", + "\t- R_EX_hdl_hs_e\n", + "\t- R_EX_HC00005_e\n", + "\t- R_DM_gd3_hs_g\n", + "\t- R_DM_pail35p_hs_n\n", + "\t- R_DM_gd3_hs_m\n", + "\t- R_DM_pcreat_c\n", + "\t- R_DM_na1_x\n", + "\t- R_DM_na1_g\n", + "\t- R_EX_2m3hvac_e\n", + "\t- R_DM_thm_m\n", + "\t- R_DM_15HPET_n\n", + "\t- R_EX_pail_hs_e\n", + "\t- R_EX_CE1243_e\n", + "\t- R_EX_galgluside_hs_e\n", + "\t- R_EX_ga1_hs_e\n", + "\t- R_EX_gt1b_hs_e\n", + "\t- R_EX_3mglutac_e\n", + "\t- R_EX_cmpacna_e\n", + "\t- R_EX_gd2_hs_e\n", + "\t- R_SK_34dhpac_c\n", + "\t- R_DM_ts3_c\n", + "\t- R_DM_sph1p_n\n", + "\t- R_DM_sphs1p_n\n", + "\t- R_DM_gda1_hs_n\n", + "\t- R_DM_phsph1p_c\n", + "\t- R_DM_gd3_hs_l\n", + "\t- R_EX_3mglutr_e\n", + "\t- R_DM_icdchol_c\n", + "\t- R_DM_lca24g_c\n", + "\t- R_DM_3dhdchol_c\n", + "\t- R_DM_7dhcdchol_c\n", + "\t- R_DM_7dhchol_c\n", + "\t- R_DM_lca3g_c\n", + "\t- R_DM_tcdca3s_c\n", + "\t- R_DM_thyochol_c\n", + "\t- R_DM_ca24g_c\n", + "\t- R_DM_ca3s_c\n", + "\t- R_DM_dca24g_c\n", + "\t- R_DM_dca3s_c\n", + "\t- R_DM_uchol_c\n", + "\t- R_DM_gca3s_c\n", + "\t- R_DM_udca3s_c\n", + "\t- R_EX_12dhchol_e\n", + "\t- R_EX_3dhchol_e\n", + "\t- R_DM_gcdca3s_c\n", + "\t- R_DM_gdca3s_c\n", + "\t- R_DM_gudca3s_c\n", + "\t- R_DM_hca24g_c\n", + "\t- R_EX_3dhlchol_e\n", + "\t- R_EX_7dhcdchol_e\n", + "\t- R_EX_ca24g_e\n", + "\t- R_EX_ca3s_e\n", + "\t- R_EX_cdca24g_e\n", + "\t- R_EX_coprost_e\n", + "\t- R_EX_dca3s_e\n", + "\t- R_EX_hdca24g_e\n", + "\t- R_EX_hdca6g_e\n", + "\t- R_DM_hca6g_c\n", + "\t- R_DM_hdca24g_c\n", + "\t- R_EX_icdchol_e\n", + "\t- R_EX_lca3g_e\n", + "\t- R_EX_tca3s_e\n", + "\t- R_EX_tdca3s_e\n", + "\t- R_EX_thyochol_e\n", + "\t- R_EX_tudca3s_e\n", + "\t- R_EX_mem2emgacpail_prot_hs_e\n", + "\t- R_EX_nadh_e\n", + "\t- R_EX_glc__D_e\n", + "\t- R_EX_3hlvstacid_e\n", + "\t- R_EX_3hpvs_e\n", + "\t- R_EX_13dmt_e\n", + "\t- R_EX_1513tacr_e\n", + "\t- R_EX_1hibupglu__S_e\n", + "\t- R_EX_2hatvacidgluc_e\n", + "\t- R_EX_2hibupglu__S_e\n", + "\t- R_EX_31dmt_e\n", + "\t- R_EX_35dsmv_e\n", + "\t- R_EX_3hibup__R_e\n", + "\t- R_EX_3ohacmp_e\n", + "\t- R_EX_4hatvacid_e\n", + "\t- R_EX_4hmdgluc_e\n", + "\t- R_EX_56dhpvs_e\n", + "\t- R_EX_5ohfvs_e\n", + "\t- R_EX_6epvs_e\n", + "\t- R_EX_lvst_e\n", + "\t- R_EX_meracmp_e\n", + "\t- R_EX_nfdac_e\n", + "\t- R_EX_oxyp_e\n", + "\t- R_EX_ptvstm3_e\n", + "\t- R_EX_6hmsmvacid_e\n", + "\t- R_EX_6ohfvs_e\n", + "\t- R_EX_7ahglz_e\n", + "\t- R_EX_7bhglz_e\n", + "\t- R_EX_7hpvs_e\n", + "\t- R_EX_acmpglut_e\n", + "\t- R_EX_am19cs_e\n", + "\t- R_EX_pvs_e\n", + "\t- R_EX_pvsgluc_e\n", + "\t- R_EX_smv_e\n", + "\t- R_EX_smvacid_e\n", + "\t- R_EX_sulpacmp_e\n", + "\t- R_EX_tacr_e\n", + "\t- R_EX_thrfvs_e\n", + "\t- R_EX_tmd_e\n", + "\t- R_EX_tmdm1_e\n", + "\t- R_EX_tmdm3_e\n", + "\t- R_EX_am1alcs_e\n", + "\t- R_EX_am1c4n9cs_e\n", + "\t- R_EX_am1cglc_e\n", + "\t- R_EX_am1csa_e\n", + "\t- R_EX_tripvs_e\n", + "\t- R_EX_am4ncs_e\n", + "\t- R_EX_am9csa_e\n", + "\t- R_EX_atvacid_e\n", + "\t- R_EX_caribup__R_e\n", + "\t- R_EX_crvs_e\n", + "\t- R_EX_crvsm24_e\n", + "\t- R_EX_csa_e\n", + "\t- R_EX_dhglz_e\n", + "\t- R_EX_dspvs_e\n", + "\t- R_EX_epoxtac_e\n", + "\t- R_EX_fvs_e\n", + "\t- R_EX_fvstetglu_e\n", + "\t- R_EX_glc3meacp_e\n", + "\t- R_EX_glz_e\n", + "\t- R_EX_gtacmp_e\n", + "\t- R_EX_ibup__R_e\n", + "\t- R_EX_ibupgluc_e\n", + "\t- R_EX_isolvstacid_e\n", + "\t- R_EX_lst4exp_e\n", + "\t- R_EX_lstn_e\n", + "\t- R_EX_lstnm1_e\n", + "\t- R_EX_caproic_e\n", + "\t- R_EX_1a25dhvitd2_e\n", + "\t- R_EX_M00234_e\n", + "\t- R_EX_M01807_e\n", + "\t- R_EX_M00503_e\n", + "\t- R_EX_M02837_e\n", + "\t- R_EX_M01820_e\n", + "\t- R_EX_M00510_e\n", + "\t- R_EX_M00008_e\n", + "\t- R_EX_gpi_sig_e\n", + "\t- R_EX_kdn_e\n", + "\t- R_EX_m3gacpail_prot_hs_e\n", + "\t- R_EX_dolichol__L_e\n", + "\t- R_SK_his__L_c\n", + "\t- R_SK_lys__L_c\n", + "\t- R_EX_M00019_e\n", + "\t- R_EX_M00115_e\n", + "\t- R_EX_M00117_e\n", + "\t- R_EX_M00315_e\n", + "\t- R_EX_M00341_e\n", + "\t- R_EX_M01197_e\n", + "\t- R_EX_M01235_e\n", + "\t- R_SK_thr__L_c\n", + "\t- R_SK_val__L_c\n", + "\t- R_SK_ala__L_c\n", + "\t- R_EX_M01238_e\n", + "\t- R_SK_arg__L_c\n", + "\t- R_SK_asn__L_c\n", + "\t- R_SK_pro__L_c\n", + "\t- R_EX_M01582_e\n", + "\t- R_EX_M02053_e\n", + "\t- R_EX_M02457_e\n", + "\t- R_EX_M02745_e\n", + "\t- R_SK_tyr__L_c\n", + "\t- R_DM_CE5026_c\n", + "\t- R_DM_CE1562_c\n", + "\t- R_DM_ind56qn_c\n", + "\t- R_DM_CE5025_c\n", + "\t- R_DM_CE4888_c\n", + "\t- R_DM_4abut_c\n", + "\t- R_DM_srtn_c\n", + "\t- R_DM_adrnl_c\n", + "\t- R_EX_M02560_e\n", + "\t- R_EX_C01601_e\n", + "\t- R_EX_M02108_e\n", + "\t- R_DM_nrpphr_c\n", + "\t- R_EX_M03134_e\n", + "\t- R_DM_tym_c\n", + "\t- R_DM_Lkynr_c\n", + "\t- R_EX_M01111_e\n", + "\t- R_EX_h2co3_e\n", + "\t- R_EX_M01870_e\n", + "\t- R_EX_ditp_e\n", + "\t- R_EX_M02446_e\n", + "\t- R_EX_M02447_e\n", + "\t- R_EX_itacon_e\n", + "\t- R_EX_4pyrdx_e\n", + "\t- R_EX_5adtststeroneglc_e\n", + "\t- R_EX_5dhf_e\n", + "\t- R_EX_5homeprazole_e\n", + "\t- R_DM_13_cis_oretn_n\n", + "\t- R_DM_13_cis_retn_n\n", + "\t- R_DM_avite2_c\n", + "\t- R_DM_bvite_c\n", + "\t- R_DM_core7_g\n", + "\t- R_DM_core8_g\n", + "\t- R_DM_datp_m\n", + "\t- R_DM_datp_n\n", + "\t- R_DM_dctp_m\n", + "\t- R_DM_dctp_n\n", + "\t- R_DM_dgtp_m\n", + "\t- R_DM_dgtp_n\n", + "\t- R_DM_dttp_m\n", + "\t- R_DM_dttp_n\n", + "\t- R_DM_ethamp_r\n", + "\t- R_DM_gncore2_g\n", + "\t- R_DM_gpi_sig_r\n", + "\t- R_DM_hretn_n\n", + "\t- R_DM_m3gacpail_prot_hs_r\n", + "\t- R_DM_mem2emgacpail_prot_hs_r\n", + "\t- R_DM_sTn_antigen_g\n", + "\t- R_SK_T_antigen_g\n", + "\t- R_DM_yvite_c\n", + "\t- R_EX_10fthf_e\n", + "\t- R_EX_1glyc_hs_e\n", + "\t- R_EX_2425dhvitd2_e\n", + "\t- R_EX_2425dhvitd3_e\n", + "\t- R_EX_25hvitd2_e\n", + "\t- R_EX_34dhoxpeg_e\n", + "\t- R_EX_3aib_e\n", + "\t- R_EX_3aib__D_e\n", + "\t- R_EX_3mlda_e\n", + "\t- R_EX_4hdebrisoquine_e\n", + "\t- R_EX_4mtolbutamide_e\n", + "\t- R_EX_4nph_e\n", + "\t- R_EX_4nphsf_e\n", + "\t- R_EX_6thf_e\n", + "\t- R_EX_abt_e\n", + "\t- R_EX_acgalfucgalacgalfucgalacglcgal14acglcgalgluside_hs_e\n", + "\t- R_EX_ach_e\n", + "\t- R_EX_acn13acngalgbside_hs_e\n", + "\t- R_EX_acn23acngalgbside_hs_e\n", + "\t- R_EX_acnacngalgbside_hs_e\n", + "\t- R_EX_acngalacglcgal14acglcgalgluside_hs_e\n", + "\t- R_EX_adprbp_e\n", + "\t- R_EX_adrn_e\n", + "\t- R_EX_adrnl_e\n", + "\t- R_EX_aflatoxin_e\n", + "\t- R_EX_ak2lgchol_hs_e\n", + "\t- R_EX_amp_e\n", + "\t- R_EX_apnnox_e\n", + "\t- R_EX_aprgstrn_e\n", + "\t- R_EX_aqcobal_e\n", + "\t- R_EX_arachd_e\n", + "\t- R_EX_ascb__L_e\n", + "\t- R_EX_asp__D_e\n", + "\t- R_EX_atp_e\n", + "\t- R_EX_bilglcur_e\n", + "\t- R_EX_bilirub_e\n", + "\t- R_EX_camp_e\n", + "\t- R_EX_chsterol_e\n", + "\t- R_EX_cmp_e\n", + "\t- R_EX_co_e\n", + "\t- R_EX_crtsl_e\n", + "\t- R_EX_cspg_a_e\n", + "\t- R_EX_cspg_b_e\n", + "\t- R_EX_cspg_d_e\n", + "\t- R_EX_debrisoquine_e\n", + "\t- R_EX_dgchol_e\n", + "\t- R_EX_dopasf_e\n", + "\t- R_EX_ebastineoh_e\n", + "\t- R_EX_elaid_e\n", + "\t- R_EX_estradiol_e\n", + "\t- R_EX_estriolglc_e\n", + "\t- R_EX_estroneglc_e\n", + "\t- R_EX_estrones_e\n", + "\t- R_EX_fuc13galacglcgal14acglcgalgluside_hs_e\n", + "\t- R_EX_fuc14galacglcgalgluside_hs_e\n", + "\t- R_EX_fucacgalfucgalacglcgalgluside_hs_e\n", + "\t- R_EX_fucacngal14acglcgalgluside_hs_e\n", + "\t- R_EX_fucfuc132galacglcgal14acglcgalgluside_hs_e\n", + "\t- R_EX_fucfucfucgalacglcgal14acglcgalgluside_hs_e\n", + "\t- R_EX_fucgalfucgalacglcgalgluside_hs_e\n", + "\t- R_EX_galfuc12gal14acglcgalgluside_hs_e\n", + "\t- R_EX_galgalfucfucgalacglcgalacglcgal14acglcgalgluside_hs_e\n", + "\t- R_EX_galgalgalthcrm_hs_e\n", + "\t- R_EX_gbside_hs_e\n", + "\t- R_EX_gchola_e\n", + "\t- R_EX_gd1b2_hs_e\n", + "\t- R_EX_glygn2_e\n", + "\t- R_EX_glygn4_e\n", + "\t- R_EX_glygn5_e\n", + "\t- R_EX_gmp_e\n", + "\t- R_EX_gp1c_hs_e\n", + "\t- R_EX_gq1b_hs_e\n", + "\t- R_EX_gthrd_e\n", + "\t- R_EX_gtp_e\n", + "\t- R_EX_h2o2_e\n", + "\t- R_EX_hco3_e\n", + "\t- R_EX_hdca_e\n", + "\t- R_EX_hestratriol_e\n", + "\t- R_EX_hista_e\n", + "\t- R_EX_hpdca_e\n", + "\t- R_EX_idp_e\n", + "\t- R_EX_inost_e\n", + "\t- R_EX_ksi_deg1_e\n", + "\t- R_EX_ksii_core2_e\n", + "\t- R_EX_leuktrA4_e\n", + "\t- R_EX_leuktrF4_e\n", + "\t- R_EX_lgnc_e\n", + "\t- R_EX_lneldc_e\n", + "\t- R_EX_lnlncg_e\n", + "\t- R_EX_mag_hs_e\n", + "\t- R_EX_n2m2nmasn_e\n", + "\t- R_EX_ncam_e\n", + "\t- R_EX_no_e\n", + "\t- R_EX_nrpphrsf_e\n", + "\t- R_EX_nrvnc_e\n", + "\t- R_EX_o2s_e\n", + "\t- R_EX_omeprazole_e\n", + "\t- R_EX_pe_hs_e\n", + "\t- R_EX_peplys_e\n", + "\t- R_EX_pglyc_hs_e\n", + "\t- R_EX_prgstrn_e\n", + "\t- R_EX_prostge1_e\n", + "\t- R_EX_ptdca_e\n", + "\t- R_EX_rbt_e\n", + "\t- R_EX_retinol_9_cis_e\n", + "\t- R_EX_retinol_cis_11_e\n", + "\t- R_EX_retn_e\n", + "\t- R_EX_retnglc_e\n", + "\t- R_EX_Rtotal_e\n", + "\t- R_EX_s2l2fn2m2masn_e\n", + "\t- R_EX_sl__L_e\n", + "\t- R_EX_sphs1p_e\n", + "\t- R_EX_srtn_e\n", + "\t- R_EX_strch1_e\n", + "\t- R_EX_strch2_e\n", + "\t- R_EX_tag__D_e\n", + "\t- R_EX_tethex3_e\n", + "\t- R_EX_tettet6_e\n", + "\t- R_EX_thf_e\n", + "\t- R_EX_thmtp_e\n", + "\t- R_EX_thyox__L_e\n", + "\t- R_EX_tmndnc_e\n", + "\t- R_EX_tolbutamide_e\n", + "\t- R_EX_tststerone_e\n", + "\t- R_EX_tsul_e\n", + "\t- R_EX_txa2_e\n", + "\t- R_EX_ump_e\n", + "\t- R_EX_urate_e\n", + "\t- R_EX_utp_e\n", + "\t- R_EX_vacc_e\n", + "\t- R_EX_whtststerone_e\n", + "\t- R_EX_xolest_hs_e\n", + "\t- R_EX_xolest2_hs_e\n", + "\t- R_EX_xoltri24_e\n", + "\t- R_EX_xylt_e\n", + "\t- R_EX_yvite_e\n", + "\t- R_EX_sprm_e\n", + "\t- R_EX_CE1940_e\n", + "\t- R_EX_CE1936_e\n", + "\t- R_EX_maltpt_e\n", + "\t- R_EX_CE2916_e\n", + "\t- R_EX_CE2917_e\n", + "\t- R_EX_CE2839_e\n", + "\t- R_EX_CE1950_e\n", + "\t- R_EX_23cump_e\n", + "\t- R_EX_CE5788_e\n", + "\t- R_EX_CE5789_e\n", + "\t- R_EX_CE5798_e\n", + "\t- R_EX_CE5791_e\n", + "\t- R_EX_CE5867_e\n", + "\t- R_EX_CE5869_e\n", + "\t- R_EX_CE4633_e\n", + "\t- R_EX_CE1926_e\n", + "\t- R_EX_udpgal_e\n", + "\t- R_EX_crm_hs_e\n", + "\t- R_EX_galside_hs_e\n", + "\t- R_EX_CE0074_e\n", + "\t- R_EX_12dgr120_e\n", + "\t- R_EX_C04849_e\n", + "\t- R_SK_c101coa_c\n", + "\t- R_EX_3bcrn_e\n", + "\t- R_EX_3ddcrn_e\n", + "\t- R_EX_3deccrn_e\n", + "\t- R_EX_3hexdcrn_e\n", + "\t- R_EX_3octdec2crn_e\n", + "\t- R_EX_3octdeccrn_e\n", + "\t- R_EX_3octdece1crn_e\n", + "\t- R_EX_3tdcrn_e\n", + "\t- R_EX_3tetd7ecoacrn_e\n", + "\t- R_EX_3ttetddcoacrn_e\n", + "\t- R_EX_c16dc_e\n", + "\t- R_EX_c4crn_e\n", + "\t- R_EX_c51crn_e\n", + "\t- R_EX_c6dc_e\n", + "\t- R_EX_c81crn_e\n", + "\t- R_EX_c8dc_e\n", + "\t- R_EX_ddece1crn_e\n", + "\t- R_EX_ddeccrn_e\n", + "\t- R_EX_doco13ac_e\n", + "\t- R_EX_ivcrn_e\n", + "\t- R_EX_tetdec2crn_e\n", + "\t- R_EX_tetdece1crn_e\n", + "\t- R_EX_prostgi2_e\n", + "\t- R_EX_cdp_e\n", + "\t- R_EX_dtdp_e\n", + "\t- R_EX_HC00955_e\n", + "\t- R_EX_HC00004_e\n", + "\t- R_EX_C02470_e\n", + "\t- R_EX_HC02193_e\n", + "\t- R_EX_HC02196_e\n", + "\t- R_EX_HC02220_e\n", + "\t- R_EX_HC02194_e\n", + "\t- R_EX_HC02197_e\n", + "\t- R_EX_HC02187_e\n", + "\t- R_EX_HC02180_e\n", + "\t- R_EX_HC02202_e\n", + "\t- R_EX_malcoa_e\n", + "\t- R_EX_gltcho_e\n", + "\t- R_SK_c81coa_c\n", + "\t- R_EX_glygly_e\n", + "\t- R_EX_gum_e\n", + "\t- R_EX_gumdchac_e\n", + "\t- R_EX_gumgchol_e\n", + "\t- R_EX_pectindchac_e\n", + "\t- R_EX_psyltdechol_e\n", + "\t- R_EX_tdechola_e\n", + "\t- R_EX_cysam_e\n", + "\t- R_EX_dpcoa_e\n", + "\t- R_EX_fmn_e\n", + "\t- R_EX_hyptaur_e\n", + "\t- R_EX_oh1_e\n", + "\t- R_EX_q10_e\n", + "\t- R_EX_argsuc_e\n", + "\t- R_EX_stcrn_e\n", + "\t- R_EX_hdcecrn_e\n", + "\t- R_DM_1a25dhvitd3_n\n", + "\t- R_DM_4abut_n\n", + "\t- R_DM_5HPET_r\n", + "\t- R_DM_pe_hs_r\n", + "\t- R_EX_7dhchsterol_e\n", + "\t- R_EX_acgly_e\n", + "\t- R_EX_aclys_e\n", + "\t- R_EX_acorn_e\n", + "\t- R_EX_alaala_e\n", + "\t- R_DM_anth_c\n", + "\t- R_DM_fol_c\n", + "\t- R_DM_pnto__R_c\n", + "\t- R_EX_4mop_e\n", + "\t- R_EX_5mta_e\n", + "\t- R_EX_adpac_e\n", + "\t- R_EX_biliverd_e\n", + "\t- R_EX_C04805_e\n", + "\t- R_EX_C06315_e\n", + "\t- R_EX_C11695_e\n", + "\t- R_EX_C14768_e\n", + "\t- R_EX_C14770_e\n", + "\t- R_EX_C14825_e\n", + "\t- R_EX_C14826_e\n", + "\t- R_EX_CE0955_e\n", + "\t- R_EX_5oxpro_e\n", + "\t- R_EX_cbasp_e\n", + "\t- R_EX_CE1556_e\n", + "\t- R_EX_CE2028_e\n", + "\t- R_EX_CE2445_e\n", + "\t- R_EX_CE2537_e\n", + "\t- R_EX_CE5304_e\n", + "\t- R_EX_CE6247_e\n", + "\t- R_EX_CE7172_e\n", + "\t- R_EX_cortsn_e\n", + "\t- R_EX_didecaeth_e\n", + "\t- R_EX_diholineth_e\n", + "\t- R_EX_docteteth_e\n", + "\t- R_EX_dodecanac_e\n", + "\t- R_EX_forglu_e\n", + "\t- R_EX_hepdeceth_e\n", + "\t- R_EX_hexdeceeth_e\n", + "\t- R_EX_hmcarn_e\n", + "\t- R_EX_hmcr_e\n", + "\t- R_EX_hxcoa_e\n", + "\t- R_EX_leuktrB4woh_e\n", + "\t- R_EX_magarachi_hs_e\n", + "\t- R_EX_magole_hs_e\n", + "\t- R_EX_magpalm_hs_e\n", + "\t- R_EX_mi1p__D_e\n", + "\t- R_EX_Nacasp_e\n", + "\t- R_EX_nwharg_e\n", + "\t- R_EX_oleth_e\n", + "\t- R_EX_pailar_hs_e\n", + "\t- R_EX_pchol2ste_hs_e\n", + "\t- R_EX_4tmeabutn_e\n", + "\t- R_EX_acile__L_e\n", + "\t- R_EX_hisasp_e\n", + "\t- R_EX_hisglnala_e\n", + "\t- R_EX_hisglylys_e\n", + "\t- R_EX_hishislys_e\n", + "\t- R_EX_hislysile_e\n", + "\t- R_EX_hismet_e\n", + "\t- R_EX_ileasnhis_e\n", + "\t- R_EX_ileglnglu_e\n", + "\t- R_EX_ileglyarg_e\n", + "\t- R_EX_ileserarg_e\n", + "\t- R_EX_iletrptyr_e\n", + "\t- R_EX_leualaarg_e\n", + "\t- R_EX_leupro_e\n", + "\t- R_EX_leutrparg_e\n", + "\t- R_EX_leutyrtyr_e\n", + "\t- R_EX_lysgluglu_e\n", + "\t- R_EX_lyslyslys_e\n", + "\t- R_EX_lyspheile_e\n", + "\t- R_EX_lystyrile_e\n", + "\t- R_EX_lysvaltrp_e\n", + "\t- R_EX_metargleu_e\n", + "\t- R_EX_12HPET_e\n", + "\t- R_EX_2oxoadp_e\n", + "\t- R_EX_34hpl_e\n", + "\t- R_EX_3hpp_e\n", + "\t- R_EX_3hpppnohgluc_e\n", + "\t- R_EX_metasntyr_e\n", + "\t- R_EX_metglyarg_e\n", + "\t- R_EX_methislys_e\n", + "\t- R_EX_metmetile_e\n", + "\t- R_EX_mettrpphe_e\n", + "\t- R_EX_pheglnphe_e\n", + "\t- R_EX_pheleu_e\n", + "\t- R_EX_phelysala_e\n", + "\t- R_EX_3uib_e\n", + "\t- R_EX_4aabutn_e\n", + "\t- R_EX_pcholdoc_hs_e\n", + "\t- R_EX_pcholeic_hs_e\n", + "\t- R_EX_pcholhep_hs_e\n", + "\t- R_EX_phephe_e\n", + "\t- R_EX_phepheasn_e\n", + "\t- R_EX_phethrlys_e\n", + "\t- R_EX_phetrpleu_e\n", + "\t- R_EX_pchollinl_hs_e\n", + "\t- R_EX_pcholmyr_hs_e\n", + "\t- R_EX_pcholn15_hs_e\n", + "\t- R_EX_pcholn183_hs_e\n", + "\t- R_EX_pcholn1836_hs_e\n", + "\t- R_EX_pcholn19_hs_e\n", + "\t- R_EX_pcholn203_hs_e\n", + "\t- R_EX_pcholn204_hs_e\n", + "\t- R_EX_pcholn224_hs_e\n", + "\t- R_EX_phetyr_e\n", + "\t- R_EX_proargasp_e\n", + "\t- R_EX_procys_e\n", + "\t- R_EX_proglulys_e\n", + "\t- R_EX_pcholn225_hs_e\n", + "\t- R_EX_pcholn2254_hs_e\n", + "\t- R_EX_pcholn28_hs_e\n", + "\t- R_EX_pcholn281_hs_e\n", + "\t- R_EX_pcholole_hs_e\n", + "\t- R_EX_pcholpalme_hs_e\n", + "\t- R_EX_pcholste_hs_e\n", + "\t- R_EX_pcollg5hlys_e\n", + "\t- R_EX_pe12_hs_e\n", + "\t- R_EX_pe13_hs_e\n", + "\t- R_EX_pe17_hs_e\n", + "\t- R_EX_pe224_hs_e\n", + "\t- R_EX_pedh203_hs_e\n", + "\t- R_EX_pelinl_hs_e\n", + "\t- R_EX_peole_hs_e\n", + "\t- R_EX_saccrp__L_e\n", + "\t- R_EX_sphmyln181161_hs_e\n", + "\t- R_EX_sphmyln18117_hs_e\n", + "\t- R_EX_sphmyln18118_hs_e\n", + "\t- R_EX_sphmyln181201_hs_e\n", + "\t- R_EX_sphmyln18123_hs_e\n", + "\t- R_EX_sphmyln1824_hs_e\n", + "\t- R_EX_sphmyln1825_hs_e\n", + "\t- R_EX_steeth_e\n", + "\t- R_EX_tetdecaeth_e\n", + "\t- R_EX_tmlys_e\n", + "\t- R_EX_trideceth_e\n", + "\t- R_EX_urcan_e\n", + "\t- R_EX_xolest183_hs_e\n", + "\t- R_EX_xolest205_hs_e\n", + "\t- R_EX_xolest226_hs_e\n", + "\t- R_EX_estriol_e\n", + "\t- R_EX_3hpppn_e\n", + "\t- R_EX_5aop_e\n", + "\t- R_EX_abt__D_e\n", + "\t- R_EX_acglu_e\n", + "\t- R_EX_CE2510_e\n", + "\t- R_EX_ddca_e\n", + "\t- R_EX_glyc__R_e\n", + "\t- R_EX_Lcyst_e\n", + "\t- R_EX_pac_e\n", + "\t- R_EX_pser__L_e\n", + "\t- R_EX_bgly_e\n", + "\t- R_EX_normete__L_e\n", + "\t- R_EX_mepi_e\n", + "\t- R_EX_maltttr_e\n", + "\t- R_EX_thbpt_e\n", + "\t- R_EX_adprib_e\n", + "\t- R_DM_itp_n\n", + "\t- R_EX_proleuarg_e\n", + "\t- R_EX_protrpthr_e\n", + "\t- R_EX_serargala_e\n", + "\t- R_EX_serargtrp_e\n", + "\t- R_EX_sercysarg_e\n", + "\t- R_EX_serglyglu_e\n", + "\t- R_EX_serphelys_e\n", + "\t- R_EX_sertrphis_e\n", + "\t- R_EX_thrargtyr_e\n", + "\t- R_EX_thrasntyr_e\n", + "\t- R_EX_thrglnglu_e\n", + "\t- R_EX_thrhishis_e\n", + "\t- R_EX_thrilearg_e\n", + "\t- R_EX_thrmetarg_e\n", + "\t- R_EX_trpalapro_e\n", + "\t- R_EX_trpaspasp_e\n", + "\t- R_EX_trpgluleu_e\n", + "\t- R_EX_trpglutyr_e\n", + "\t- R_EX_trpglyval_e\n", + "\t- R_EX_trphismet_e\n", + "\t- R_EX_trpmetarg_e\n", + "\t- R_EX_trpphe_e\n", + "\t- R_EX_trpprogly_e\n", + "\t- R_EX_trpproleu_e\n", + "\t- R_EX_trpproval_e\n", + "\t- R_EX_trpsertyr_e\n", + "\t- R_EX_trpthrglu_e\n", + "\t- R_EX_trpthrile_e\n", + "\t- R_EX_trpthrtyr_e\n", + "\t- R_EX_trpvalasp_e\n", + "\t- R_EX_sphmyln_hs_e\n", + "\t- R_EX_Lhcystin_e\n", + "\t- R_EX_tyrala_e\n", + "\t- R_EX_tyralaphe_e\n", + "\t- R_EX_tyrargglu_e\n", + "\t- R_EX_tyrasparg_e\n", + "\t- R_EX_tyrcysgly_e\n", + "\t- R_EX_tyrglu_e\n", + "\t- R_EX_valleuphe_e\n", + "\t- R_EX_vallystyr_e\n", + "\t- R_EX_valtrpphe_e\n", + "\t- R_EX_valtrpval_e\n", + "\t- R_EX_valval_e\n", + "\t- R_EX_trpglyasp_e\n", + "\t- R_DM_pe_hs_c\n", + "\t- R_SK_akg_c\n", + "\t- R_SK_mi14p_c\n", + "\t- R_SK_band_c\n", + "\t- R_EX_akg_e\n", + "\t- R_EX_asn__L_e\n", + "\t- R_EX_cgly_e\n", + "\t- R_EX_co2_e\n", + "\t- R_EX_cytd_e\n", + "\t- R_EX_dad_2_e\n", + "\t- R_EX_dcyt_e\n", + "\t- R_EX_dgsn_e\n", + "\t- R_EX_duri_e\n", + "\t- R_EX_fum_e\n", + "\t- R_EX_glyphe_e\n", + "\t- R_EX_glypro_e\n", + "\t- R_EX_h_e\n", + "\t- R_EX_ile__L_e\n", + "\t- R_EX_met__L_e\n", + "\t- R_EX_na1_e\n", + "\t- R_EX_ppi_e\n", + "\t- R_EX_pro__L_e\n", + "\t- R_EX_ser__L_e\n", + "\t- R_EX_so4_e\n", + "\t- R_EX_val__L_e\n", + "\t- R_EX_gua_e\n", + "\t- R_EX_4hbz_e\n", + "\t- R_EX_34dhpha_e\n", + "\t- R_EX_ppa_e\n", + "\t- R_EX_lcts_e\n", + "\t- R_EX_sucr_e\n", + "\t- R_EX_pheme_e\n", + "\t- R_EX_homoval_e\n", + "\t- R_EX_core7_e\n", + "\t- R_EX_core8_e\n", + "\t- R_EX_gncore2_e\n", + "\t- R_EX_mqn10_e\n", + "\t- R_EX_mqn11_e\n", + "\t- R_EX_mqn9_e\n", + "\t- R_EX_s2l2n2m2m_e\n", + "\t- R_EX_sTn_antigen_e\n", + "\t- R_EX_lpam_e\n", + "\t- R_EX_pa_hs_e\n", + "\t- R_DM_mqn11_c\n", + "\t- R_DM_galam_c\n", + "\t- R_DM_C02712_c\n", + "\t- R_SK_11_cis_retfa_c\n", + "\t- R_SK_C02528_c\n", + "\t- R_SK_HC02191_c\n", + "\t- R_SK_HC02192_c\n", + "\t- R_SK_HC02194_c\n", + "\t- R_SK_HC02196_c\n", + "\t- R_SK_HC02220_c\n", + "\t- R_SK_chol_c\n", + "\t- R_SK_cholate_c\n", + "\t- R_SK_coa_c\n", + "\t- R_EX_CE4969_e\n", + "\t- R_EX_CE1310_e\n", + "\t- R_EX_sucsal_e\n", + "\t- R_EX_CE7081_e\n", + "\t- R_EX_egme_e\n", + "\t- R_EX_12harachd_e\n", + "\t- R_EX_sql_e\n", + "\t- R_EX_orn__D_e\n", + "\t- R_EX_melatn_e\n", + "\t- R_EX_6hoxmelatn_e\n", + "\t- R_EX_CE4890_e\n", + "\t- R_EX_C05767_e\n", + "\t- R_EX_mhista_e\n", + "\t- R_EX_13dampp_e\n", + "\t- R_EX_mma_e\n", + "\t- R_EX_ametam_e\n", + "\t- R_EX_xylu__L_e\n", + "\t- R_EX_xylu__D_e\n", + "\t- R_EX_sphings_e\n", + "\t- R_EX_sphgn_e\n", + "\t- R_EX_N1aspmd_e\n", + "\t- R_EX_ala__L_e\n", + "\t- R_EX_his__L_e\n", + "\t- R_EX_1a25dhvitd3_e\n", + "\t- R_EX_fdp_e\n", + "\t- R_EX_coke_e\n", + "\t- R_EX_HC00006_e\n", + "\t- R_EX_HC00007_e\n", + "\t- R_EX_HC00008_e\n", + "\t- R_EX_HC00009_e\n", + "\t- R_DM_k_g\n", + "\t- R_DM_na1_r\n", + "\t- R_DM_na1_c\n", + "\t- R_DM_retn_n\n", + "\t- R_DM_hhxdcal_c\n", + "\t- R_DM_15HPET_x\n", + "\t- R_EX_acgam_e\n", + "\t- R_EX_cit_e\n", + "\t- R_EX_csn_e\n", + "\t- R_EX_etha_e\n", + "\t- R_EX_fol_e\n", + "\t- R_EX_galt_e\n", + "\t- R_DM_15HPET_r\n", + "\t- R_SK_Ser_Gly_Ala_X_Gly_r\n", + "\t- R_EX_CE5026_e\n", + "\t- R_EX_5cysgly34dhphe_e\n", + "\t- R_EX_CE1261_e\n", + "\t- R_EX_gm1_hs_e\n", + "\t- R_EX_gm2_hs_e\n", + "\t- R_EX_gm1b_hs_e\n", + "\t- R_EX_gd1b_hs_e\n", + "\t- R_EX_glcr_e\n", + "\t- R_EX_glcur_e\n", + "\t- R_EX_hxan_e\n", + "\t- R_EX_malt_e\n", + "\t- R_EX_malthx_e\n", + "\t- R_EX_thm_e\n", + "\t- R_EX_trp__L_e\n", + "\t- R_EX_ura_e\n", + "\t- R_EX_xan_e\n", + "\t- R_EX_xyl__D_e\n", + "\t- R_EX_mvlac_e\n", + "\t- R_EX_tiggly_e\n", + "\t- R_EX_3ohglutac_e\n", + "\t- R_EX_glutcon_e\n", + "\t- R_EX_3hivac_e\n", + "\t- R_EX_3ohsebac_e\n", + "\t- R_EX_3ohsubac_e\n", + "\t- R_EX_5ohhexa_e\n", + "\t- R_EX_7ohocata_e\n", + "\t- R_EX_methsucc_e\n", + "\t- R_EX_hpdece_e\n", + "\t- R_EX_5eipenc_e\n", + "\t- R_EX_5g2oxpt_e\n", + "\t- R_EX_andrstndn_e\n", + "\t- R_EX_eandrstrn_e\n", + "\t- R_EX_C05298_e\n", + "\t- R_EX_C05301_e\n", + "\t- R_EX_CE5072_e\n", + "\t- R_EX_11docrtstrn_e\n", + "\t- R_EX_CE2211_e\n", + "\t- R_EX_17ahprgstrn_e\n", + "\t- R_EX_HC02020_e\n", + "\t- R_EX_xol27oh_e\n", + "\t- R_EX_dsmsterol_e\n", + "\t- R_EX_CE1617_e\n", + "\t- R_EX_34dhoxmand_e\n", + "\t- R_EX_fna5moxam_e\n", + "\t- R_EX_CE5643_e\n", + "\t- R_EX_CE7085_e\n", + "\t- R_EX_CE1401_e\n", + "\t- R_EX_glucys_e\n", + "\t- R_EX_dopa4glcur_e\n", + "\t- R_EX_dopa3glcur_e\n", + "\t- R_EX_5cysdopa_e\n", + "\t- R_EX_gluside_hs_e\n", + "\t- R_EX_gd1a_hs_e\n", + "\t- R_EX_34dhpe_e\n", + "\t- R_DM_no2_c\n", + "\t- R_DM_gm1_hs_n\n", + "\t- R_DM_6hddopaqn_c\n", + "\t- R_DM_12dhchol_c\n", + "\t- R_DM_3dhcdchol_c\n", + "\t- R_DM_3dhchol_c\n", + "\t- R_DM_3dhlchol_c\n", + "\t- R_DM_cdca24g_c\n", + "\t- R_DM_cdca3g_c\n", + "\t- R_DM_coprost_c\n", + "\t- R_DM_dca3g_c\n", + "\t- R_EX_12htacr_e\n", + "\t- R_EX_1331tacr_e\n", + "\t- R_EX_14hmdz_e\n", + "\t- R_EX_1531tacr_e\n", + "\t- R_EX_15dmt_e\n", + "\t- R_EX_1hibup__S_e\n", + "\t- R_EX_1hmdgluc_e\n", + "\t- R_EX_1ohmdz_e\n", + "\t- R_EX_2hatvacid_e\n", + "\t- R_EX_2hatvlac_e\n", + "\t- R_EX_2hatvlacgluc_e\n", + "\t- R_EX_2hibup__R_e\n", + "\t- R_EX_2hibup__S_e\n", + "\t- R_EX_35dhpvs_e\n", + "\t- R_EX_3hibup__S_e\n", + "\t- R_EX_3hibupglu__S_e\n", + "\t- R_EX_lstnm4_e\n", + "\t- R_EX_lstnm5_e\n", + "\t- R_EX_lstnm7_e\n", + "\t- R_DM_hdca6g_c\n", + "\t- R_DM_hyochol_c\n", + "\t- R_DM_isochol_c\n", + "\t- R_DM_lca3s_c\n", + "\t- R_DM_tca3s_c\n", + "\t- R_DM_tdca3s_c\n", + "\t- R_DM_tudca3s_c\n", + "\t- R_EX_3dhcdchol_e\n", + "\t- R_EX_3dhdchol_e\n", + "\t- R_EX_7dhchol_e\n", + "\t- R_EX_cdca3g_e\n", + "\t- R_EX_dca24g_e\n", + "\t- R_EX_dca3g_e\n", + "\t- R_EX_gca3s_e\n", + "\t- R_EX_gcdca3s_e\n", + "\t- R_EX_gdca3s_e\n", + "\t- R_EX_gudca3s_e\n", + "\t- R_EX_hca24g_e\n", + "\t- R_EX_hca6g_e\n", + "\t- R_EX_hyochol_e\n", + "\t- R_EX_isochol_e\n", + "\t- R_EX_lca24g_e\n", + "\t- R_EX_lca3s_e\n", + "\t- R_EX_tcdca3s_e\n", + "\t- R_EX_uchol_e\n", + "\t- R_EX_udca3s_e\n", + "\t- R_EX_mdz_e\n", + "\t- R_EX_mdzglc_e\n", + "\t- R_EX_mhglz_e\n", + "\t- R_EX_ndersv_e\n", + "\t- R_EX_nfd_e\n", + "\t- R_EX_nfdlac_e\n", + "\t- R_EX_nfdnpy_e\n", + "\t- R_EX_nfdoh_e\n", + "\t- R_EX_oxy1rb_e\n", + "\t- R_EX_oxy7rb_e\n", + "\t- R_EX_profvs_e\n", + "\t- R_EX_ptvst_e\n", + "\t- R_EX_ptvstlac_e\n", + "\t- R_EX_rsv_e\n", + "\t- R_EX_rsvlac_e\n", + "\t- R_EX_s3meacmp_e\n", + "\t- R_EX_stacmp_e\n", + "\t- R_EX_tauribup__S_e\n", + "\t- R_EX_tlacfvs_e\n", + "\t- R_EX_tmdm5_e\n", + "\t- R_EX_tsacmgluc_e\n", + "\t- R_EX_tsacmsul_e\n", + "\t- R_EX_rbl__D_e\n", + "\t- R_EX_M01966_e\n", + "\t- R_EX_M02155_e\n", + "\t- R_EX_M01989_e\n", + "\t- R_EX_M01881_e\n", + "\t- R_EX_M03131_e\n", + "\t- R_EX_n5m2masn_e\n", + "\t- R_EX_hretn_e\n", + "\t- R_SK_ile__L_c\n", + "\t- R_SK_leu__L_c\n", + "\t- R_SK_met__L_c\n", + "\t- R_SK_phe__L_c\n", + "\t- R_SK_trp__L_c\n", + "\t- R_SK_asp__L_c\n", + "\t- R_SK_cys__L_c\n", + "\t- R_SK_gln__L_c\n", + "\t- R_SK_glu__L_c\n", + "\t- R_SK_ser__L_c\n", + "\t- R_SK_gly_c\n", + "\t- R_SK_4abut_l\n", + "\t- R_DM_CE1261_c\n", + "\t- R_DM_4glu56dihdind_c\n", + "\t- R_DM_5cysdopa_c\n", + "\t- R_DM_dopa_c\n", + "\t- R_DM_ach_c\n", + "\t- R_DM_hista_c\n", + "\t- R_DM_kynate_c\n", + "\t- R_DM_cbl2_m\n", + "\t- R_DM_1a2425thvitd2_m\n", + "\t- R_DM_btn_m\n", + "\t- R_DM_btn_n\n", + "\t- R_DM_protein_c\n", + "\t- R_EX_3hpvstet_e\n", + "\t- R_EX_3hsmvacid_e\n", + "\t- R_EX_3ispvs_e\n", + "\t- R_EX_4bhglz_e\n", + "\t- R_EX_4hatvlac_e\n", + "\t- R_EX_4ohmdz_e\n", + "\t- R_EX_56eppvs_e\n", + "\t- R_EX_5ohfvsglu_e\n", + "\t- R_EX_6ahglz_e\n", + "\t- R_EX_6bhglz_e\n", + "\t- R_EX_6bhglzglc_e\n", + "\t- R_EX_6csmvacid_e\n", + "\t- R_EX_6hlvst_e\n", + "\t- R_EX_6hsmvacid_e\n", + "\t- R_EX_6melvacid_e\n", + "\t- R_EX_6melvst_e\n", + "\t- R_EX_6ohfvsglu_e\n", + "\t- R_EX_7bhglzglc_e\n", + "\t- R_EX_acmp_e\n", + "\t- R_EX_acmpglu_e\n", + "\t- R_EX_allop_e\n", + "\t- R_EX_am1a4ncs_e\n", + "\t- R_EX_am1accs_e\n", + "\t- R_EX_am1acs_e\n", + "\t- R_EX_am1c9cs_e\n", + "\t- R_EX_am1ccs_e\n", + "\t- R_EX_am4n9cs_e\n", + "\t- R_EX_atvlac_e\n", + "\t- R_EX_caribup_s_e\n", + "\t- R_EX_caribupglu__S_e\n", + "\t- R_EX_crglz_e\n", + "\t- R_EX_crvsm1_e\n", + "\t- R_EX_crvsm23_e\n", + "\t- R_EX_csasulp_e\n", + "\t- R_EX_cysacmp_e\n", + "\t- R_EX_deoxfvs_e\n", + "\t- R_EX_desfvs_e\n", + "\t- R_EX_fvstet_e\n", + "\t- R_EX_ibup__S_e\n", + "\t- R_EX_lstn1gluc_e\n", + "\t- R_EX_lstnm2_e\n", + "\t- R_EX_C13856_e\n", + "\t- R_EX_M02956_e\n", + "\t- R_EX_M00241_e\n", + "\t- R_EX_M00003_e\n", + "\t- R_EX_M00010_e\n", + "\t- R_EX_M00017_e\n", + "\t- R_EX_M00021_e\n", + "\t- R_EX_M00260_e\n", + "\t- R_EX_M00265_e\n", + "\t- R_EX_M01207_e\n", + "\t- R_EX_M02613_e\n", + "\t- R_EX_M03045_e\n", + "\t- R_EX_M03051_e\n", + "\t- R_EX_M03153_e\n", + "\t- R_EX_M02561_e\n", + "\t- R_EX_M02909_e\n", + "\t- R_EX_M03117_e\n", + "\t- R_EX_M01872_e\n", + "\t- R_EX_hnifedipine_e\n", + "\t- R_EX_M02449_e\n", + "\t- R_EX_M02451_e\n", + "\t- R_EX_adpman_e\n", + "\t- R_SK_citr__L_c\n", + "\t- R_SK_pre_prot_r\n", + "\t- R_EX_ctp_e\n", + "\t- R_EX_dtmp_e\n", + "\t- R_EX_fad_e\n", + "\t- R_EX_fald_e\n", + "\t- R_EX_g1p_e\n", + "\t- R_EX_HC01104_e\n", + "\t- R_EX_HC01441_e\n", + "\t- R_EX_HC01444_e\n", + "\t- R_EX_HC01609_e\n", + "\t- R_EX_cpppg1_e\n", + "\t- R_EX_HC01700_e\n", + "\t- R_EX_HC02161_e\n", + "\t- R_EX_itp_e\n", + "\t- R_EX_prpp_e\n", + "\t- R_EX_pydx5p_e\n", + "\t- R_EX_5hoxindoa_e\n", + "\t- R_EX_cala_e\n", + "\t- R_EX_cholp_e\n", + "\t- R_EX_dmgly_e\n", + "\t- R_EX_ethamp_e\n", + "\t- R_EX_g3pc_e\n", + "\t- R_EX_gudac_e\n", + "\t- R_EX_hcys__L_e\n", + "\t- R_EX_kynate_e\n", + "\t- R_EX_L2aadp_e\n", + "\t- R_EX_pep_e\n", + "\t- R_EX_xtsn_e\n", + "\t- R_EX_udpglcur_e\n", + "\t- R_EX_hLkynr_e\n", + "\t- R_EX_nicrnt_e\n", + "\t- R_EX_orot5p_e\n", + "\t- R_EX_alaasnleu_e\n", + "\t- R_EX_alalysthr_e\n", + "\t- R_EX_argargmet_e\n", + "\t- R_EX_argcysser_e\n", + "\t- R_EX_arggluglu_e\n", + "\t- R_EX_arghisthr_e\n", + "\t- R_EX_argleuphe_e\n", + "\t- R_EX_arglysasp_e\n", + "\t- R_EX_argserser_e\n", + "\t- R_EX_asnmetpro_e\n", + "\t- R_EX_asntyrgly_e\n", + "\t- R_EX_asntyrphe_e\n", + "\t- R_EX_aspalaarg_e\n", + "\t- R_EX_aspasnglu_e\n", + "\t- R_EX_aspglutrp_e\n", + "\t- R_EX_asphispro_e\n", + "\t- R_EX_asplysglu_e\n", + "\t- R_EX_asplyshis_e\n", + "\t- R_EX_aspmetasp_e\n", + "\t- R_EX_aspprolys_e\n", + "\t- R_EX_cysasnmet_e\n", + "\t- R_EX_cysaspphe_e\n", + "\t- R_EX_cyscys_e\n", + "\t- R_EX_cysglnmet_e\n", + "\t- R_EX_cysgluhis_e\n", + "\t- R_EX_cysglutrp_e\n", + "\t- R_EX_cysleuthr_e\n", + "\t- R_EX_glnhishis_e\n", + "\t- R_EX_glnlyslys_e\n", + "\t- R_EX_glnlystrp_e\n", + "\t- R_EX_glntrpglu_e\n", + "\t- R_EX_glntyrleu_e\n", + "\t- R_EX_gluglu_e\n", + "\t- R_EX_gluilelys_e\n", + "\t- R_EX_gluleu_e\n", + "\t- R_EX_glumet_e\n", + "\t- R_EX_glumethis_e\n", + "\t- R_EX_gluthr_e\n", + "\t- R_EX_glyhislys_e\n", + "\t- R_EX_glytyrlys_e\n", + "\t- R_EX_hisargcys_e\n", + "\t- R_SK_dgchol_c\n", + "\t- R_SK_fe3_c\n", + "\t- R_SK_gchola_c\n", + "\t- R_SK_hdca_c\n", + "\t- R_SK_lnlncacoa_c\n", + "\t- R_SK_nadp_c\n", + "\t- R_SK_odecoa_c\n", + "\t- R_SK_phyQ_c\n", + "\t- R_SK_pydam_c\n", + "\t- R_SK_retfa_c\n", + "\t- R_SK_stcoa_c\n", + "\t- R_SK_tdchola_c\n", + "\t- R_DM_thmpp_c\n", + "\t- R_DM_thmtp_c\n", + "\t- R_EX_xol7ah3_e\n", + "\t- R_SK_xol7ah3_c\n", + "\t- R_EX_xoldiolone_e\n", + "\t- R_EX_7klitchol_e\n", + "\t- R_SK_7klitchol_c\n", + "\t- R_EX_2obut_e\n", + "\t- R_EX_glcn_e\n", + "\t- R_EX_phpyr_e\n", + "\t- R_EX_2hyoxplac_e\n", + "\t- R_EX_glx_e\n", + "\t- R_EX_CE4970_e\n", + "\t- R_EX_actyr_e\n", + "\t- R_EX_sucaceto_e\n", + "\t- R_EX_nacvanala_e\n", + "\t- R_EX_2h3mv_e\n", + "\t- R_EX_3h3mglt_e\n", + "\t- R_EX_ppiogly_e\n", + "\t- R_EX_sbt__D_e\n", + "\t- R_EX_lvstacid_e\n", + "\t- R_EX_ptvstm13_e\u001b[0m\n", + "File saved at: ../../data/sbml_corrected/Recon3D.xml\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: iAM_Pf480\n", + "\u001b[0;93mWARNING : - R_ARGLYSex: Reactants and products exchanged.\n", + " Boundaries was: [-1000000.0 ; 0.0]\n", + " - R_RE1342C: Reactants and products exchanged.\n", + " Boundaries was: [-1000000.0 ; 0.0]\n", + "\u001b[0m\n", + "\n", + "\n", + "\u001b[0;36m############################################\n", + "############################################\n", + " WRITING SBML FILE \n", + "############################################\n", + "############################################\n", + "\u001b[0m\n", + "\u001b[0;93mWARNING : IMPORT REACTION REMOVED BY DEFAULT\u001b[0m\n", + "\u001b[0;93mWARNING : If you want to keep import reaction\n", + "use option -kir / --keep-import-reactions\u001b[0m\n", + "\u001b[0;93mWARNING : Reaction with tag reversible modified:\n", + "\t- R_ARGLYSex\n", + "\t- R_RE1342C\u001b[0m\n", + "\u001b[0;93mWARNING : Reaction with Reactants and Products exchanged:\n", + "\t- R_ARGLYSex\n", + "\t- R_RE1342C\u001b[0m\n", + "\u001b[0;93mWARNING : Import reaction removed:\n", + "\t- R_EX_4ahmmp_e\n", + "\t- R_EX_5fthf_e\n", + "\t- R_EX_Hb_e\n", + "\t- R_EX_ac_e\n", + "\t- R_EX_4abz_e\n", + "\t- R_EX_ade_e\n", + "\t- R_EX_hdca_e\n", + "\t- R_EX_adn_e\n", + "\t- R_EX_his__L_e\n", + "\t- R_EX_adp_e\n", + "\t- R_EX_hxan_e\n", + "\t- R_EX_ala__L_e\n", + "\t- R_EX_amp_e\n", + "\t- R_EX_ile__L_e\n", + "\t- R_EX_ins_e\n", + "\t- R_EX_arachd_e\n", + "\t- R_EX_k_e\n", + "\t- R_EX_lac__D_e\n", + "\t- R_EX_lac__L_e\n", + "\t- R_EX_leu__L_e\n", + "\t- R_EX_lipoate_e\n", + "\t- R_EX_arg__L_e\n", + "\t- R_EX_asn__L_e\n", + "\t- R_EX_asp__L_e\n", + "\t- R_EX_atp_e\n", + "\t- R_EX_ca2_e\n", + "\t- R_EX_chol_e\n", + "\t- R_EX_cl_e\n", + "\t- R_EX_co2_e\n", + "\t- R_EX_lnlc_e\n", + "\t- R_EX_cobalt2_e\n", + "\t- R_EX_lys__L_e\n", + "\t- R_EX_man_e\n", + "\t- R_EX_met__L_e\n", + "\t- R_EX_mg2_e\n", + "\t- R_EX_mthgxl_e\n", + "\t- R_EX_na1_e\n", + "\t- R_EX_nac_e\n", + "\t- R_EX_csn_e\n", + "\t- R_EX_cu_e\n", + "\t- R_EX_cu2_e\n", + "\t- R_EX_cys__L_e\n", + "\t- R_EX_dha_e\n", + "\t- R_EX_ncam_e\n", + "\t- R_EX_dhpt_e\n", + "\t- R_EX_nh4_e\n", + "\t- R_EX_ni2_e\n", + "\t- R_EX_o2_e\n", + "\t- R_EX_fe2_e\n", + "\t- R_EX_ocdca_e\n", + "\t- R_EX_ocdcea_e\n", + "\t- R_EX_phe__L_e\n", + "\t- R_EX_pi_e\n", + "\t- R_EX_pnto__R_e\n", + "\t- R_EX_pro__L_e\n", + "\t- R_EX_pyr_e\n", + "\t- R_EX_fol_e\n", + "\t- R_EX_ribflv_e\n", + "\t- R_EX_ser__L_e\n", + "\t- R_EX_for_e\n", + "\t- R_EX_thm_e\n", + "\t- R_EX_fru_e\n", + "\t- R_EX_thr__L_e\n", + "\t- R_EX_thym_e\n", + "\t- R_EX_thymd_e\n", + "\t- R_EX_glc__D_e\n", + "\t- R_EX_trp__L_e\n", + "\t- R_EX_gln__L_e\n", + "\t- R_EX_ttdca_e\n", + "\t- R_EX_tyr__L_e\n", + "\t- R_EX_glu__L_e\n", + "\t- R_EX_ura_e\n", + "\t- R_EX_uri_e\n", + "\t- R_EX_val__L_e\n", + "\t- R_EX_gly_e\n", + "\t- R_EX_glyc_e\n", + "\t- R_EX_gsn_e\n", + "\t- R_EX_gthox_e\n", + "\t- R_EX_gthrd_e\n", + "\t- R_EX_gua_e\n", + "\t- R_EX_h_e\n", + "\t- R_EX_h2o_e\n", + "\t- R_EX_hco3_e\n", + "\t- R_DM_hmz_l\n", + "\t- R_SK_gcald_c\n", + "\t- R_DM_5mdr1p_c\n", + "\t- R_DM_hcys__L_c\n", + "\t- R_DM_pail345p_pf_18_0_18_1_c\n", + "\t- R_DM_saccrp__L_c\n", + "\t- R_EX_lgn_e\n", + "\t- R_EX_Lpipecol_e\n", + "\t- R_EX_4pyrdx_e\n", + "\t- R_EX_spmd_e\n", + "\t- R_DM_pail345p_pf_16_0_18_1_c\n", + "\t- R_DM_mi13456p_c\n", + "\t- R_DM_oxptn_c\n", + "\t- R_EX_orn_e\n", + "\t- R_EX_chsterol_e\n", + "\t- R_EX_acgam_e\n", + "\t- R_EX_4hba_e\n", + "\t- R_SK_thbpt4acam_c\n", + "\t- R_SK_dhbpt_c\n", + "\t- R_SK_thbpt_c\n", + "\t- R_SK_no_c\n", + "\t- R_SK_citr__L_c\n", + "\t- R_SK_dxyl5p_c\n", + "\t- R_SK_accoa_h\n", + "\t- R_EX_ptrc_e\u001b[0m\n", + "File saved at: ../../data/sbml_corrected/iAM_Pf480.xml\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: iIS312_Trypomastigote\n", + "\u001b[0;93mWARNING : - R_ALATA_Lm: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + "\n", + " - R_FBPg: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_ATPS3m: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_PRO1xm: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + " - R_SUCCtp: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_ALATA_L: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_EX_pro__L_e: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_EX_glu__L_e: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_EX_thr__L_e: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_EX_b_D_glucose_e: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_EX_a_D_glucose_e: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_EX_asp__L_e: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_EX_pi_e: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + "\n", + " - R_ASPTRS: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_ALATRS: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + " - R_CYSTA: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + "\n", + " - R_G6PDAr: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_ACCOACrm: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_FBP26: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_GLUCYS: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_GTHS: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_NADS2: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_DHAK: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_DAGAT_LM: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_MGLYCL_LM: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_GLYCK: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_ETHAMK: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_AGPATim_LM: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_AGPATi_LM: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_DHAPAx_LM: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_THRS: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_GMPS2: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_GUAD: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_DTMPK: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_CTPS1n: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_CTPS2n: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_3DSPHRr: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_C24STRer: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_HMGCOARx: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_IPDDIx: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_ODHmi: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_MCCCrm: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_3MBTm: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_CLPNSm_LM: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_P5CR: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_GDHm: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_A1Eg: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_ATPS: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_r0202: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_FBP: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_GLUDxi: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_GLYKg: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_NADH2_u6m: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_NADH2_u6cm: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_HACOADm: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + " - R_MCPST: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + "\n", + " - R_HBUHL1m: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_HDEHL4m: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_HACD1m_1: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_3OAS40_m: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_MCMAT2m: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_MCMAT3m: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_MCMAT4m: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_MCMAT5m: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_MCMAT6m: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_MCMAT7m: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_MCMAT8m: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_THRTRS: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_AHCi: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_ADMDC: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_NADK: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_ADNUC: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_GNNUC: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_INSH: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_XTSNH: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + " - R_NITR: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + "\n", + " - R_HCO3Em: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_GSPMDS: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_RBLKg: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_ADNK1: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_IMPDg: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_MEVK1x: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_MI1PSB: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\u001b[0m\n", + "\n", + "\n", + "\u001b[0;36m############################################\n", + "############################################\n", + " WRITING SBML FILE \n", + "############################################\n", + "############################################\n", + "\u001b[0m\n", + "\u001b[0;93mWARNING : IMPORT REACTION REMOVED BY DEFAULT\u001b[0m\n", + "\u001b[0;93mWARNING : If you want to keep import reaction\n", + "use option -kir / --keep-import-reactions\u001b[0m\n", + "\u001b[0;93mWARNING : Reaction with tag reversible modified:\n", + "\t- R_ALATA_Lm\n", + "\t- R_SUCCtp\n", + "\t- R_ALATA_L\n", + "\t- R_EX_pro__L_e\n", + "\t- R_EX_glu__L_e\n", + "\t- R_EX_thr__L_e\n", + "\t- R_EX_b_D_glucose_e\n", + "\t- R_EX_a_D_glucose_e\n", + "\t- R_EX_asp__L_e\n", + "\t- R_EX_pi_e\n", + "\t- R_CYSTA\n", + "\t- R_MCPST\n", + "\t- R_NITR\u001b[0m\n", + "\u001b[0;93mWARNING : Reaction with Reactants and Products exchanged:\n", + "\t- R_ALATA_Lm\n", + "\t- R_SUCCtp\n", + "\t- R_ALATA_L\n", + "\t- R_EX_pro__L_e\n", + "\t- R_EX_glu__L_e\n", + "\t- R_EX_thr__L_e\n", + "\t- R_EX_b_D_glucose_e\n", + "\t- R_EX_a_D_glucose_e\n", + "\t- R_EX_asp__L_e\n", + "\t- R_EX_pi_e\n", + "\t- R_CYSTA\n", + "\t- R_MCPST\n", + "\t- R_NITR\u001b[0m\n", + "\u001b[0;93mWARNING : Import reaction removed:\n", + "\t- R_EX_h2o_e\n", + "\t- R_EX_pro__L_e\n", + "\t- R_EX_glu__L_e\n", + "\t- R_EX_co2_e\n", + "\t- R_EX_nh4_e\n", + "\t- R_EX_glyc_e\n", + "\t- R_EX_gly_e\n", + "\t- R_EX_o2_e\n", + "\t- R_EX_h_e\n", + "\t- R_EX_thr__L_e\n", + "\t- R_EX_ala__L_e\n", + "\t- R_EX_succ_e\n", + "\t- R_EX_b_D_glucose_e\n", + "\t- R_EX_a_D_glucose_e\n", + "\t- R_EX_ac_e\n", + "\t- R_EX_asp__L_e\n", + "\t- R_EX_pi_e\n", + "\t- R_EX_gln__L_e\n", + "\t- R_EX_arg__L_e\n", + "\t- R_EX_urea_e\n", + "\t- R_EX_phe__L_e\n", + "\t- R_EX_ile__L_e\n", + "\t- R_EX_his__L_e\n", + "\t- R_EX_val__L_e\n", + "\t- R_EX_leu__L_e\n", + "\t- R_EX_lys__L_e\n", + "\t- R_EX_ala__D_e\n", + "\t- R_EX_ergst_e\n", + "\t- R_EX_ser__L_e\n", + "\t- R_EX_ura_e\n", + "\t- R_EX_met__L_e\n", + "\t- R_EX_cys__L_e\n", + "\t- R_EX_asn__L_e\n", + "\t- R_EX_chol_e\n", + "\t- R_EX_cit_e\n", + "\t- R_EX_gthrd_e\n", + "\t- R_EX_h2s_e\n", + "\t- R_EX_nac_e\n", + "\t- R_EX_ncam_e\n", + "\t- R_EX_inost_e\n", + "\t- R_EX_nh3_e\n", + "\t- R_EX_gua_e\n", + "\t- R_EX_drib_e\n", + "\t- R_EX_thym_e\n", + "\t- R_EX_hxan_e\u001b[0m\n", + "File saved at: ../../data/sbml_corrected/iIS312_Trypomastigote.xml\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: iECSF_1327\n", + "\u001b[0;93mWARNING : \n", + " - R_CAT: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_DHPTDNR: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_DHPTDNRN: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_FHL: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SPODM: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SPODMpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCASPtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCFUMtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCMALtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCTARTtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_EX_chitob_e: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_CHITOBpts: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_CHITPH: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_URCN: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\u001b[0m\n", + "\n", + "\n", + "\u001b[0;36m############################################\n", + "############################################\n", + " WRITING SBML FILE \n", + "############################################\n", + "############################################\n", + "\u001b[0m\n", + "\u001b[0;93mWARNING : IMPORT REACTION REMOVED BY DEFAULT\u001b[0m\n", + "\u001b[0;93mWARNING : If you want to keep import reaction\n", + "use option -kir / --keep-import-reactions\u001b[0m\n", + "\u001b[0;93mWARNING : Import reaction removed:\n", + "\t- R_EX_amp_e\n", + "\t- R_EX_arab__L_e\n", + "\t- R_EX_arbt_e\n", + "\t- R_EX_arbtn_e\n", + "\t- R_EX_g3pc_e\n", + "\t- R_EX_anhgm_e\n", + "\t- R_EX_arbtn_fe3_e\n", + "\t- R_EX_arg__L_e\n", + "\t- R_EX_ascb__L_e\n", + "\t- R_EX_asn__L_e\n", + "\t- R_EX_aso3_e\n", + "\t- R_EX_alltn_e\n", + "\t- R_DM_4crsol_c\n", + "\t- R_DM_5drib_c\n", + "\t- R_DM_aacald_c\n", + "\t- R_DM_amob_c\n", + "\t- R_DM_mththf_c\n", + "\t- R_DM_oxam_c\n", + "\t- R_EX_12ppd__R_e\n", + "\t- R_EX_asp__L_e\n", + "\t- R_EX_btn_e\n", + "\t- R_EX_but_e\n", + "\t- R_EX_butso3_e\n", + "\t- R_EX_ca2_e\n", + "\t- R_EX_12ppd__S_e\n", + "\t- R_EX_g3pe_e\n", + "\t- R_EX_14glucan_e\n", + "\t- R_EX_15dap_e\n", + "\t- R_EX_cbi_e\n", + "\t- R_EX_cbl1_e\n", + "\t- R_EX_cd2_e\n", + "\t- R_EX_cgly_e\n", + "\t- R_EX_chol_e\n", + "\t- R_EX_chtbs_e\n", + "\t- R_EX_cit_e\n", + "\t- R_EX_cl_e\n", + "\t- R_EX_cm_e\n", + "\t- R_EX_cmp_e\n", + "\t- R_EX_co2_e\n", + "\t- R_EX_cobalt2_e\n", + "\t- R_EX_23camp_e\n", + "\t- R_EX_23ccmp_e\n", + "\t- R_EX_23cgmp_e\n", + "\t- R_EX_23cump_e\n", + "\t- R_EX_23dappa_e\n", + "\t- R_EX_26dap__M_e\n", + "\t- R_EX_2ddglcn_e\n", + "\t- R_EX_34dhpac_e\n", + "\t- R_EX_3amp_e\n", + "\t- R_EX_3cmp_e\n", + "\t- R_EX_3gmp_e\n", + "\t- R_EX_3hcinnm_e\n", + "\t- R_EX_3hpp_e\n", + "\t- R_EX_3hpppn_e\n", + "\t- R_EX_3ump_e\n", + "\t- R_EX_colipa_e\n", + "\t- R_EX_colipap_e\n", + "\t- R_EX_cpgn_e\n", + "\t- R_EX_cpgn_un_e\n", + "\t- R_EX_crn_e\n", + "\t- R_EX_crn__D_e\n", + "\t- R_EX_4abut_e\n", + "\t- R_EX_4hoxpacd_e\n", + "\t- R_EX_csn_e\n", + "\t- R_EX_g3pg_e\n", + "\t- R_EX_cu_e\n", + "\t- R_EX_cu2_e\n", + "\t- R_EX_cyan_e\n", + "\t- R_EX_cynt_e\n", + "\t- R_EX_5dglcn_e\n", + "\t- R_EX_5mtr_e\n", + "\t- R_EX_LalaDglu_e\n", + "\t- R_EX_LalaDgluMdap_e\n", + "\t- R_EX_LalaDgluMdapDala_e\n", + "\t- R_EX_LalaLglu_e\n", + "\t- R_EX_ac_e\n", + "\t- R_EX_acac_e\n", + "\t- R_EX_acald_e\n", + "\t- R_EX_acgal_e\n", + "\t- R_EX_cys__D_e\n", + "\t- R_EX_cys__L_e\n", + "\t- R_EX_cytd_e\n", + "\t- R_EX_dad_2_e\n", + "\t- R_EX_damp_e\n", + "\t- R_EX_dca_e\n", + "\t- R_EX_dcmp_e\n", + "\t- R_EX_dcyt_e\n", + "\t- R_EX_ddca_e\n", + "\t- R_EX_acgal1p_e\n", + "\t- R_EX_acgam_e\n", + "\t- R_EX_acgam1p_e\n", + "\t- R_EX_acmana_e\n", + "\t- R_EX_dgmp_e\n", + "\t- R_EX_dgsn_e\n", + "\t- R_EX_acmum_e\n", + "\t- R_EX_g3pi_e\n", + "\t- R_EX_acnam_e\n", + "\t- R_EX_acolipa_e\n", + "\t- R_EX_acser_e\n", + "\t- R_EX_dha_e\n", + "\t- R_EX_dimp_e\n", + "\t- R_EX_din_e\n", + "\t- R_EX_dms_e\n", + "\t- R_EX_g3ps_e\n", + "\t- R_EX_dmso_e\n", + "\t- R_EX_ade_e\n", + "\t- R_EX_adn_e\n", + "\t- R_EX_adocbl_e\n", + "\t- R_EX_ag_e\n", + "\t- R_EX_agm_e\n", + "\t- R_EX_akg_e\n", + "\t- R_EX_dopa_e\n", + "\t- R_EX_doxrbcn_e\n", + "\t- R_EX_dtmp_e\n", + "\t- R_EX_dump_e\n", + "\t- R_EX_duri_e\n", + "\t- R_EX_eca4colipa_e\n", + "\t- R_EX_enlipa_e\n", + "\t- R_EX_ala_B_e\n", + "\t- R_EX_ala__D_e\n", + "\t- R_EX_ala__L_e\n", + "\t- R_EX_enter_e\n", + "\t- R_EX_alaala_e\n", + "\t- R_EX_etha_e\n", + "\t- R_EX_g6p_e\n", + "\t- R_EX_ethso3_e\n", + "\t- R_EX_etoh_e\n", + "\t- R_EX_f6p_e\n", + "\t- R_EX_all__D_e\n", + "\t- R_EX_fald_e\n", + "\t- R_EX_gal_e\n", + "\t- R_EX_fe2_e\n", + "\t- R_EX_fe3_e\n", + "\t- R_EX_gal_bD_e\n", + "\t- R_EX_gal1p_e\n", + "\t- R_EX_fe3dcit_e\n", + "\t- R_EX_fe3dhbzs_e\n", + "\t- R_EX_fe3hox_e\n", + "\t- R_EX_galct__D_e\n", + "\t- R_EX_fe3hox_un_e\n", + "\t- R_EX_melib_e\n", + "\t- R_EX_meoh_e\n", + "\t- R_EX_fecrm_e\n", + "\t- R_EX_galctn__D_e\n", + "\t- R_EX_fecrm_un_e\n", + "\t- R_EX_feenter_e\n", + "\t- R_EX_feoxam_e\n", + "\t- R_EX_feoxam_un_e\n", + "\t- R_EX_for_e\n", + "\t- R_EX_met__D_e\n", + "\t- R_EX_met__L_e\n", + "\t- R_EX_metsox_R__L_e\n", + "\t- R_EX_metsox_S__L_e\n", + "\t- R_EX_mg2_e\n", + "\t- R_EX_mincyc_e\n", + "\t- R_EX_minohp_e\n", + "\t- R_EX_mmet_e\n", + "\t- R_EX_mn2_e\n", + "\t- R_EX_fru_e\n", + "\t- R_EX_frulys_e\n", + "\t- R_EX_fruur_e\n", + "\t- R_EX_fuc__L_e\n", + "\t- R_EX_fum_e\n", + "\t- R_EX_mnl_e\n", + "\t- R_EX_mobd_e\n", + "\t- R_EX_fusa_e\n", + "\t- R_EX_mso3_e\n", + "\t- R_EX_galctn__L_e\n", + "\t- R_EX_n2o_e\n", + "\t- R_EX_na1_e\n", + "\t- R_EX_g1p_e\n", + "\t- R_EX_nac_e\n", + "\t- R_EX_galt_e\n", + "\t- R_EX_nh4_e\n", + "\t- R_EX_ni2_e\n", + "\t- R_EX_trp__L_e\n", + "\t- R_EX_nmn_e\n", + "\t- R_EX_galur_e\n", + "\t- R_EX_no_e\n", + "\t- R_EX_no2_e\n", + "\t- R_EX_no3_e\n", + "\t- R_EX_gam_e\n", + "\t- R_EX_novbcn_e\n", + "\t- R_EX_o16a4colipa_e\n", + "\t- R_EX_o2_e\n", + "\t- R_EX_o2s_e\n", + "\t- R_EX_tsul_e\n", + "\t- R_EX_ocdca_e\n", + "\t- R_EX_gam6p_e\n", + "\t- R_EX_ocdcea_e\n", + "\t- R_EX_octa_e\n", + "\t- R_EX_ttdca_e\n", + "\t- R_EX_ttdcea_e\n", + "\t- R_EX_ttrcyc_e\n", + "\t- R_EX_tungs_e\n", + "\t- R_EX_orn_e\n", + "\t- R_EX_orot_e\n", + "\t- R_EX_tym_e\n", + "\t- R_EX_gbbtn_e\n", + "\t- R_EX_tyr__L_e\n", + "\t- R_EX_pacald_e\n", + "\t- R_EX_peamn_e\n", + "\t- R_EX_tyrp_e\n", + "\t- R_EX_uacgam_e\n", + "\t- R_EX_phe__L_e\n", + "\t- R_EX_gdp_e\n", + "\t- R_EX_pheme_e\n", + "\t- R_EX_pi_e\n", + "\t- R_EX_pnto__R_e\n", + "\t- R_EX_udpacgal_e\n", + "\t- R_EX_udpg_e\n", + "\t- R_EX_udpgal_e\n", + "\t- R_EX_udpglcur_e\n", + "\t- R_EX_ppa_e\n", + "\t- R_EX_ppal_e\n", + "\t- R_EX_ump_e\n", + "\t- R_EX_glc__D_e\n", + "\t- R_EX_ura_e\n", + "\t- R_EX_urea_e\n", + "\t- R_EX_uri_e\n", + "\t- R_EX_val__L_e\n", + "\t- R_EX_xan_e\n", + "\t- R_EX_pppn_e\n", + "\t- R_EX_ppt_e\n", + "\t- R_EX_pro__L_e\n", + "\t- R_EX_progly_e\n", + "\t- R_EX_psclys_e\n", + "\t- R_EX_pser__L_e\n", + "\t- R_EX_ptrc_e\n", + "\t- R_EX_xmp_e\n", + "\t- R_EX_xtsn_e\n", + "\t- R_EX_xyl__D_e\n", + "\t- R_EX_xylu__L_e\n", + "\t- R_EX_pydam_e\n", + "\t- R_EX_pydx_e\n", + "\t- R_EX_pydxn_e\n", + "\t- R_EX_zn2_e\n", + "\t- R_EX_pyr_e\n", + "\t- R_EX_glcn_e\n", + "\t- R_EX_quin_e\n", + "\t- R_EX_r5p_e\n", + "\t- R_EX_rfamp_e\n", + "\t- R_EX_rib__D_e\n", + "\t- R_EX_rmn_e\n", + "\t- R_EX_glcr_e\n", + "\t- R_EX_sbt__D_e\n", + "\t- R_EX_sel_e\n", + "\t- R_EX_ser__D_e\n", + "\t- R_EX_ser__L_e\n", + "\t- R_EX_skm_e\n", + "\t- R_EX_slnt_e\n", + "\t- R_EX_so2_e\n", + "\t- R_EX_so3_e\n", + "\t- R_EX_so4_e\n", + "\t- R_EX_spmd_e\n", + "\t- R_EX_succ_e\n", + "\t- R_EX_sucr_e\n", + "\t- R_EX_sulfac_e\n", + "\t- R_EX_glcur_e\n", + "\t- R_EX_tartr__D_e\n", + "\t- R_EX_tartr__L_e\n", + "\t- R_EX_taur_e\n", + "\t- R_EX_tcynt_e\n", + "\t- R_EX_thm_e\n", + "\t- R_EX_thr__L_e\n", + "\t- R_EX_thrp_e\n", + "\t- R_EX_thym_e\n", + "\t- R_EX_thymd_e\n", + "\t- R_EX_tma_e\n", + "\t- R_EX_glcur1p_e\n", + "\t- R_EX_tmao_e\n", + "\t- R_EX_tre_e\n", + "\t- R_EX_gln__L_e\n", + "\t- R_EX_glu__L_e\n", + "\t- R_EX_gly_e\n", + "\t- R_EX_glyald_e\n", + "\t- R_EX_glyb_e\n", + "\t- R_EX_glyc_e\n", + "\t- R_EX_glyc__R_e\n", + "\t- R_EX_glyc2p_e\n", + "\t- R_EX_glyc3p_e\n", + "\t- R_EX_glyclt_e\n", + "\t- R_EX_gmp_e\n", + "\t- R_EX_gsn_e\n", + "\t- R_EX_gthox_e\n", + "\t- R_EX_gthrd_e\n", + "\t- R_EX_gtp_e\n", + "\t- R_EX_gua_e\n", + "\t- R_EX_h_e\n", + "\t- R_EX_h2_e\n", + "\t- R_EX_h2o_e\n", + "\t- R_EX_h2o2_e\n", + "\t- R_EX_h2s_e\n", + "\t- R_EX_hacolipa_e\n", + "\t- R_EX_halipa_e\n", + "\t- R_EX_hdca_e\n", + "\t- R_EX_hdcea_e\n", + "\t- R_EX_hg2_e\n", + "\t- R_EX_his__L_e\n", + "\t- R_EX_hom__L_e\n", + "\t- R_EX_hxa_e\n", + "\t- R_EX_hxan_e\n", + "\t- R_EX_idon__L_e\n", + "\t- R_EX_ile__L_e\n", + "\t- R_EX_imp_e\n", + "\t- R_EX_indole_e\n", + "\t- R_EX_inost_e\n", + "\t- R_EX_ins_e\n", + "\t- R_EX_isetac_e\n", + "\t- R_EX_k_e\n", + "\t- R_EX_kdo2lipid4_e\n", + "\t- R_EX_lac__D_e\n", + "\t- R_EX_lac__L_e\n", + "\t- R_EX_lcts_e\n", + "\t- R_EX_leu__L_e\n", + "\t- R_EX_lipa_e\n", + "\t- R_EX_lipa_cold_e\n", + "\t- R_EX_lipoate_e\n", + "\t- R_EX_lys__L_e\n", + "\t- R_EX_lyx__L_e\n", + "\t- R_EX_mal__D_e\n", + "\t- R_EX_mal__L_e\n", + "\t- R_EX_malt_e\n", + "\t- R_EX_malthx_e\n", + "\t- R_EX_maltpt_e\n", + "\t- R_EX_malttr_e\n", + "\t- R_EX_maltttr_e\n", + "\t- R_EX_man_e\n", + "\t- R_EX_man6p_e\n", + "\t- R_EX_manglyc_e\n", + "\t- R_EX_feroxBfe_e\n", + "\t- R_EX_salchs4fe_e\n", + "\t- R_EX_4hphac_e\n", + "\t- R_EX_4abz_e\n", + "\t- R_EX_acon_C_e\n", + "\t- R_EX_feroxB_e\n", + "\t- R_EX_tcb_e\n", + "\t- R_EX_tton_e\n", + "\t- R_EX_4hthr_e\n", + "\t- R_EX_feroxEfe_e\n", + "\t- R_EX_feroxE_e\n", + "\t- R_EX_pep_e\n", + "\t- R_EX_23dhbzs3_e\n", + "\t- R_EX_2pg_e\n", + "\t- R_EX_tet_e\n", + "\t- R_EX_salchs4_e\n", + "\t- R_EX_tag__D_e\n", + "\t- R_EX_oaa_e\n", + "\t- R_EX_frmd_e\n", + "\t- R_EX_colipaOA_e\n", + "\t- R_EX_salchsx_e\n", + "\t- R_EX_AEP_e\n", + "\t- R_EX_salchs2fe_e\n", + "\t- R_EX_icit_e\n", + "\t- R_EX_colipa20Oag_e\n", + "\t- R_EX_cysi__L_e\n", + "\t- R_EX_fe3dhbzs3_e\n", + "\t- R_EX_drib_e\n", + "\t- R_EX_remnant1_e\n", + "\t- R_EX_diact_e\n", + "\t- R_EX_salchs2_e\n", + "\t- R_EX_rnam_e\n", + "\t- R_EX_cellb_e\n", + "\t- R_EX_airs_e\n", + "\t- R_EX_dxyl_e\n", + "\t- R_EX_btd_RR_e\n", + "\t- R_EX_3pg_e\n", + "\t- R_EX_13ppd_e\n", + "\t- R_EX_rbt_e\n", + "\t- R_EX_udcpo4_e\n", + "\t- R_EX_feroxGfe_e\n", + "\t- R_EX_udcpo5_e\n", + "\t- R_EX_bz_e\n", + "\t- R_EX_guln__L_e\n", + "\t- R_EX_feroxG_e\n", + "\t- R_EX_abt__D_e\n", + "\t- R_EX_fe_e\n", + "\t- R_EX_kokdolipidA_e\n", + "\t- R_EX_3hoxpac_e\n", + "\t- R_EX_4hoxpac_e\n", + "\t- R_EX_3ntym_e\n", + "\t- R_EX_6apa_e\n", + "\t- R_EX_peng_e\n", + "\t- R_EX_pac_e\n", + "\t- R_EX_salcn_e\n", + "\t- R_EX_galam_e\n", + "\t- R_DM_lipidA_core_e_p\n", + "\t- R_EX_raffin_e\n", + "\t- R_EX_o6a4colipa_e\n", + "\t- R_EX_arab__D_e\u001b[0m\n", + "File saved at: ../../data/sbml_corrected/iECSF_1327.xml\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: iRC1080\n", + "\u001b[0;93mWARNING : \n", + " - R_EX_hxan_e: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_EX_alltn_e: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_EX_orn_e: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_EX_gln__L_e: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_EX_urate_e: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_EX_so3_e: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_EX_s_e: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + " - R_EX_no3_e: Reactants and products exchanged.\n", + " Boundaries was: [-10.0 ; 0.0]\n", + " - R_EX_so4_e: Reactants and products exchanged.\n", + " Boundaries was: [-10.0 ; 0.0]\n", + " - R_EX_fe2_e: Reactants and products exchanged.\n", + " Boundaries was: [-10.0 ; 0.0]\n", + " - R_EX_fe3_e: Reactants and products exchanged.\n", + " Boundaries was: [-10.0 ; 0.0]\n", + "\n", + " - R_EX_slnt_e: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_EX_leu__L_e: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_EX_arg__L_e: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_EX_lac__D_e: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_EX_6mpur_e: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + " - R_EX_pi_e: Reactants and products exchanged.\n", + " Boundaries was: [-10.0 ; 0.0]\n", + " - R_EX_nh4_e: Reactants and products exchanged.\n", + " Boundaries was: [-10.0 ; 0.0]\n", + " - R_EX_mg2_e: Reactants and products exchanged.\n", + " Boundaries was: [-10.0 ; 0.0]\n", + "\n", + " - R_EX_tgua_e: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_EX_tega_e: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_EX_5flura_e: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_EX_cital_e: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_EX_lido_e: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + " - R_EX_na1_e: Reactants and products exchanged.\n", + " Boundaries was: [-10.0 ; 0.0]\n", + " - R_EX_photonVis_e: Reactants and products exchanged.\n", + " Boundaries was: [-2000.0 ; 0.0]\n", + "\n", + " - R_EX_hco3_e: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + " - R_SK_starch300_h: Reactants and products exchanged.\n", + " Boundaries was: [-10.0 ; 0.0]\n", + "\n", + " - R_EX_rib__D_e: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_EX_no2_e: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_EX_his__L_e: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_EX_urea_e: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_EX_ad_e: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_EX_gua_e: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_EX_ade_e: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_EX_alltt_e: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + " - R_D_LACt2: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_ETOHt: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_FORt2: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_AGPR: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_BTCOARx: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_HACD1m: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_NADPPPS: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_GAPD: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_GLYCLm: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_NTP10: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_ALCD2y: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + "\u001b[0m\n", + "\n", + "\n", + "\u001b[0;36m############################################\n", + "############################################\n", + " WRITING SBML FILE \n", + "############################################\n", + "############################################\n", + "\u001b[0m\n", + "\u001b[0;93mWARNING : IMPORT REACTION REMOVED BY DEFAULT\u001b[0m\n", + "\u001b[0;93mWARNING : If you want to keep import reaction\n", + "use option -kir / --keep-import-reactions\u001b[0m\n", + "\u001b[0;93mWARNING : Reaction with tag reversible modified:\n", + "\t- R_EX_no3_e\n", + "\t- R_EX_so4_e\n", + "\t- R_EX_fe2_e\n", + "\t- R_EX_fe3_e\n", + "\t- R_EX_pi_e\n", + "\t- R_EX_nh4_e\n", + "\t- R_EX_mg2_e\n", + "\t- R_EX_na1_e\n", + "\t- R_EX_photonVis_e\n", + "\t- R_SK_starch300_h\n", + "\t- R_D_LACt2\n", + "\t- R_ETOHt\n", + "\t- R_FORt2\n", + "\t- R_AGPR\n", + "\t- R_BTCOARx\n", + "\t- R_HACD1m\n", + "\t- R_NADPPPS\n", + "\t- R_GAPD\n", + "\t- R_GLYCLm\n", + "\t- R_NTP10\n", + "\t- R_ALCD2y\u001b[0m\n", + "\u001b[0;93mWARNING : Reaction with Reactants and Products exchanged:\n", + "\t- R_EX_no3_e\n", + "\t- R_EX_so4_e\n", + "\t- R_EX_fe2_e\n", + "\t- R_EX_fe3_e\n", + "\t- R_EX_pi_e\n", + "\t- R_EX_nh4_e\n", + "\t- R_EX_mg2_e\n", + "\t- R_EX_na1_e\n", + "\t- R_EX_photonVis_e\n", + "\t- R_SK_starch300_h\n", + "\t- R_D_LACt2\n", + "\t- R_ETOHt\n", + "\t- R_FORt2\n", + "\t- R_AGPR\n", + "\t- R_BTCOARx\n", + "\t- R_HACD1m\n", + "\t- R_NADPPPS\n", + "\t- R_GAPD\n", + "\t- R_GLYCLm\n", + "\t- R_NTP10\n", + "\t- R_ALCD2y\u001b[0m\n", + "\u001b[0;93mWARNING : Import reaction removed:\n", + "\t- R_EX_no3_e\n", + "\t- R_EX_so4_e\n", + "\t- R_EX_fe2_e\n", + "\t- R_EX_fe3_e\n", + "\t- R_EX_etoh_e\n", + "\t- R_EX_for_e\n", + "\t- R_EX_h2_e\n", + "\t- R_EX_glyclt_e\n", + "\t- R_EX_succ_e\n", + "\t- R_EX_h_e\n", + "\t- R_EX_h2o_e\n", + "\t- R_EX_pi_e\n", + "\t- R_EX_nh4_e\n", + "\t- R_EX_mg2_e\n", + "\t- R_EX_na1_e\n", + "\t- R_EX_photonVis_e\n", + "\t- R_EX_o2_e\n", + "\t- R_EX_co2_e\n", + "\t- R_DM_apotfen_h\n", + "\t- R_DM_asnglcnacglcnacman_man_man_c\n", + "\t- R_DM_bhb_m\n", + "\t- R_DM_bhb_x\n", + "\t- R_DM_cat_c\n", + "\t- R_DM_ficytb5_c\n", + "\t- R_DM_glu5sa_m\n", + "\t- R_DM_hcarn_c\n", + "\t- R_DM_icit_h\n", + "\t- R_DM_leutrna_c\n", + "\t- R_SK_starch300_h\n", + "\t- R_EX_ac_e\n", + "\t- R_DM_na1_c\n", + "\t- R_DM_na1_h\n", + "\t- R_DM_na1_m\n", + "\t- R_DM_na1_x\n", + "\t- R_DM_no_h\n", + "\t- R_DM_o2D_u\n", + "\t- R_DM_photon298_c\n", + "\t- R_DM_photon437_u\n", + "\t- R_DM_photon438_u\n", + "\t- R_DM_photon450_h\n", + "\t- R_DM_photon490_s\n", + "\t- R_DM_photon646_h\n", + "\t- R_DM_photon673_u\n", + "\t- R_DM_photon680_u\n", + "\t- R_DM_udg_m\n", + "\t- R_DM_urdglyc_m\u001b[0m\n", + "File saved at: ../../data/sbml_corrected/iRC1080.xml\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: iZ_1308\n", + "\u001b[0;93mWARNING : \n", + " - R_CAT: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_DHPTDNR: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_DHPTDNRN: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_FHL: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SPODM: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SPODMpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCASPtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCFUMtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCMALtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCTARTtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_CHITOBpts: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_EX_chitob_e: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_CHITPH: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_URCN: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\u001b[0m\n", + "\n", + "\n", + "\u001b[0;36m############################################\n", + "############################################\n", + " WRITING SBML FILE \n", + "############################################\n", + "############################################\n", + "\u001b[0m\n", + "\u001b[0;93mWARNING : IMPORT REACTION REMOVED BY DEFAULT\u001b[0m\n", + "\u001b[0;93mWARNING : If you want to keep import reaction\n", + "use option -kir / --keep-import-reactions\u001b[0m\n", + "\u001b[0;93mWARNING : Import reaction removed:\n", + "\t- R_EX_ade_e\n", + "\t- R_EX_adn_e\n", + "\t- R_EX_adocbl_e\n", + "\t- R_EX_ag_e\n", + "\t- R_EX_agm_e\n", + "\t- R_EX_akg_e\n", + "\t- R_EX_ala_B_e\n", + "\t- R_EX_ala__D_e\n", + "\t- R_EX_ala__L_e\n", + "\t- R_EX_alaala_e\n", + "\t- R_EX_all__D_e\n", + "\t- R_EX_alltn_e\n", + "\t- R_EX_amp_e\n", + "\t- R_DM_4crsol_c\n", + "\t- R_EX_anhgm_e\n", + "\t- R_DM_5drib_c\n", + "\t- R_EX_arab__L_e\n", + "\t- R_DM_aacald_c\n", + "\t- R_DM_amob_c\n", + "\t- R_DM_mththf_c\n", + "\t- R_EX_arbt_e\n", + "\t- R_DM_oxam_c\n", + "\t- R_EX_fe3dcit_e\n", + "\t- R_EX_fe3dhbzs_e\n", + "\t- R_EX_arbtn_e\n", + "\t- R_EX_12ppd__R_e\n", + "\t- R_EX_12ppd__S_e\n", + "\t- R_EX_arbtn_fe3_e\n", + "\t- R_EX_14glucan_e\n", + "\t- R_EX_fe3hox_e\n", + "\t- R_EX_fe3hox_un_e\n", + "\t- R_EX_15dap_e\n", + "\t- R_EX_23camp_e\n", + "\t- R_EX_23ccmp_e\n", + "\t- R_EX_23cgmp_e\n", + "\t- R_EX_23cump_e\n", + "\t- R_EX_23dappa_e\n", + "\t- R_EX_26dap__M_e\n", + "\t- R_EX_2ddglcn_e\n", + "\t- R_EX_fecrm_e\n", + "\t- R_EX_fecrm_un_e\n", + "\t- R_EX_feenter_e\n", + "\t- R_EX_feoxam_e\n", + "\t- R_EX_feoxam_un_e\n", + "\t- R_EX_for_e\n", + "\t- R_EX_fru_e\n", + "\t- R_EX_frulys_e\n", + "\t- R_EX_fruur_e\n", + "\t- R_EX_34dhpac_e\n", + "\t- R_EX_3amp_e\n", + "\t- R_EX_3cmp_e\n", + "\t- R_EX_3gmp_e\n", + "\t- R_EX_fuc__L_e\n", + "\t- R_EX_3hcinnm_e\n", + "\t- R_EX_arg__L_e\n", + "\t- R_EX_3hpp_e\n", + "\t- R_EX_3hpppn_e\n", + "\t- R_EX_3ump_e\n", + "\t- R_EX_fum_e\n", + "\t- R_EX_fusa_e\n", + "\t- R_EX_g1p_e\n", + "\t- R_EX_g3pc_e\n", + "\t- R_EX_4abut_e\n", + "\t- R_EX_g3pe_e\n", + "\t- R_EX_4hoxpacd_e\n", + "\t- R_EX_ascb__L_e\n", + "\t- R_EX_5dglcn_e\n", + "\t- R_EX_g3pg_e\n", + "\t- R_EX_asn__L_e\n", + "\t- R_EX_g3pi_e\n", + "\t- R_EX_g3ps_e\n", + "\t- R_EX_g6p_e\n", + "\t- R_EX_gal_e\n", + "\t- R_EX_5mtr_e\n", + "\t- R_EX_LalaDglu_e\n", + "\t- R_EX_LalaDgluMdap_e\n", + "\t- R_EX_LalaDgluMdapDala_e\n", + "\t- R_EX_LalaLglu_e\n", + "\t- R_EX_ac_e\n", + "\t- R_EX_acac_e\n", + "\t- R_EX_gal_bD_e\n", + "\t- R_EX_gal1p_e\n", + "\t- R_EX_galct__D_e\n", + "\t- R_EX_galctn__D_e\n", + "\t- R_EX_acald_e\n", + "\t- R_EX_acgal_e\n", + "\t- R_EX_aso3_e\n", + "\t- R_EX_acgal1p_e\n", + "\t- R_EX_galctn__L_e\n", + "\t- R_EX_galt_e\n", + "\t- R_EX_asp__L_e\n", + "\t- R_EX_galur_e\n", + "\t- R_EX_gam_e\n", + "\t- R_EX_gam6p_e\n", + "\t- R_EX_acgam_e\n", + "\t- R_EX_acgam1p_e\n", + "\t- R_EX_acmana_e\n", + "\t- R_EX_acmum_e\n", + "\t- R_EX_gbbtn_e\n", + "\t- R_EX_gdp_e\n", + "\t- R_EX_acnam_e\n", + "\t- R_EX_acolipa_e\n", + "\t- R_EX_glc__D_e\n", + "\t- R_EX_btn_e\n", + "\t- R_EX_glcn_e\n", + "\t- R_EX_glcr_e\n", + "\t- R_EX_glcur_e\n", + "\t- R_EX_glcur1p_e\n", + "\t- R_EX_gln__L_e\n", + "\t- R_EX_acser_e\n", + "\t- R_EX_lcts_e\n", + "\t- R_EX_glu__L_e\n", + "\t- R_EX_but_e\n", + "\t- R_EX_gly_e\n", + "\t- R_EX_glyald_e\n", + "\t- R_EX_leu__L_e\n", + "\t- R_EX_lipa_e\n", + "\t- R_EX_lipa_cold_e\n", + "\t- R_EX_lipoate_e\n", + "\t- R_EX_glyb_e\n", + "\t- R_EX_glyc_e\n", + "\t- R_EX_butso3_e\n", + "\t- R_EX_glyc__R_e\n", + "\t- R_EX_glyc2p_e\n", + "\t- R_EX_lys__L_e\n", + "\t- R_EX_lyx__L_e\n", + "\t- R_EX_mal__D_e\n", + "\t- R_EX_glyc3p_e\n", + "\t- R_EX_ca2_e\n", + "\t- R_EX_glyclt_e\n", + "\t- R_EX_mal__L_e\n", + "\t- R_EX_malt_e\n", + "\t- R_EX_gmp_e\n", + "\t- R_EX_cbi_e\n", + "\t- R_EX_gsn_e\n", + "\t- R_EX_malthx_e\n", + "\t- R_EX_maltpt_e\n", + "\t- R_EX_cbl1_e\n", + "\t- R_EX_malttr_e\n", + "\t- R_EX_gthox_e\n", + "\t- R_EX_gthrd_e\n", + "\t- R_EX_gtp_e\n", + "\t- R_EX_maltttr_e\n", + "\t- R_EX_man_e\n", + "\t- R_EX_man6p_e\n", + "\t- R_EX_manglyc_e\n", + "\t- R_EX_melib_e\n", + "\t- R_EX_meoh_e\n", + "\t- R_EX_gua_e\n", + "\t- R_EX_h_e\n", + "\t- R_EX_h2_e\n", + "\t- R_EX_h2o_e\n", + "\t- R_EX_h2o2_e\n", + "\t- R_EX_met__D_e\n", + "\t- R_EX_cd2_e\n", + "\t- R_EX_h2s_e\n", + "\t- R_EX_hacolipa_e\n", + "\t- R_EX_halipa_e\n", + "\t- R_EX_met__L_e\n", + "\t- R_EX_metsox_R__L_e\n", + "\t- R_EX_hdca_e\n", + "\t- R_EX_cgly_e\n", + "\t- R_EX_hdcea_e\n", + "\t- R_EX_hg2_e\n", + "\t- R_EX_metsox_S__L_e\n", + "\t- R_EX_mg2_e\n", + "\t- R_EX_mincyc_e\n", + "\t- R_EX_his__L_e\n", + "\t- R_EX_chol_e\n", + "\t- R_EX_hom__L_e\n", + "\t- R_EX_hxa_e\n", + "\t- R_EX_minohp_e\n", + "\t- R_EX_mmet_e\n", + "\t- R_EX_mn2_e\n", + "\t- R_EX_hxan_e\n", + "\t- R_EX_mnl_e\n", + "\t- R_EX_chtbs_e\n", + "\t- R_EX_mobd_e\n", + "\t- R_EX_idon__L_e\n", + "\t- R_EX_ile__L_e\n", + "\t- R_EX_mso3_e\n", + "\t- R_EX_n2o_e\n", + "\t- R_EX_na1_e\n", + "\t- R_EX_nac_e\n", + "\t- R_EX_nh4_e\n", + "\t- R_EX_imp_e\n", + "\t- R_EX_indole_e\n", + "\t- R_EX_inost_e\n", + "\t- R_EX_ins_e\n", + "\t- R_EX_isetac_e\n", + "\t- R_EX_ni2_e\n", + "\t- R_EX_cit_e\n", + "\t- R_EX_nmn_e\n", + "\t- R_EX_no_e\n", + "\t- R_EX_no2_e\n", + "\t- R_EX_k_e\n", + "\t- R_EX_kdo2lipid4_e\n", + "\t- R_EX_lac__D_e\n", + "\t- R_EX_no3_e\n", + "\t- R_EX_lac__L_e\n", + "\t- R_EX_cl_e\n", + "\t- R_EX_novbcn_e\n", + "\t- R_EX_o16a4colipa_e\n", + "\t- R_EX_cm_e\n", + "\t- R_EX_o2_e\n", + "\t- R_EX_o2s_e\n", + "\t- R_EX_ocdca_e\n", + "\t- R_EX_cmp_e\n", + "\t- R_EX_ocdcea_e\n", + "\t- R_EX_octa_e\n", + "\t- R_EX_so4_e\n", + "\t- R_EX_spmd_e\n", + "\t- R_EX_succ_e\n", + "\t- R_EX_orn_e\n", + "\t- R_EX_co2_e\n", + "\t- R_EX_orot_e\n", + "\t- R_EX_sucr_e\n", + "\t- R_EX_pacald_e\n", + "\t- R_EX_cobalt2_e\n", + "\t- R_EX_peamn_e\n", + "\t- R_EX_phe__L_e\n", + "\t- R_EX_pheme_e\n", + "\t- R_EX_pi_e\n", + "\t- R_EX_pnto__R_e\n", + "\t- R_EX_sulfac_e\n", + "\t- R_EX_tartr__D_e\n", + "\t- R_EX_tartr__L_e\n", + "\t- R_EX_taur_e\n", + "\t- R_EX_tcynt_e\n", + "\t- R_EX_thm_e\n", + "\t- R_EX_thr__L_e\n", + "\t- R_EX_ppa_e\n", + "\t- R_EX_ppal_e\n", + "\t- R_EX_pppn_e\n", + "\t- R_EX_thrp_e\n", + "\t- R_EX_colipa_e\n", + "\t- R_EX_thym_e\n", + "\t- R_EX_thymd_e\n", + "\t- R_EX_ppt_e\n", + "\t- R_EX_pro__L_e\n", + "\t- R_EX_progly_e\n", + "\t- R_EX_tma_e\n", + "\t- R_EX_colipap_e\n", + "\t- R_EX_tmao_e\n", + "\t- R_EX_tre_e\n", + "\t- R_EX_psclys_e\n", + "\t- R_EX_pser__L_e\n", + "\t- R_EX_ptrc_e\n", + "\t- R_EX_trp__L_e\n", + "\t- R_EX_cpgn_e\n", + "\t- R_EX_tsul_e\n", + "\t- R_EX_pydam_e\n", + "\t- R_EX_pydx_e\n", + "\t- R_EX_cpgn_un_e\n", + "\t- R_EX_pydxn_e\n", + "\t- R_EX_ttdca_e\n", + "\t- R_EX_ttdcea_e\n", + "\t- R_EX_ttrcyc_e\n", + "\t- R_EX_tungs_e\n", + "\t- R_EX_tym_e\n", + "\t- R_EX_pyr_e\n", + "\t- R_EX_quin_e\n", + "\t- R_EX_r5p_e\n", + "\t- R_EX_rfamp_e\n", + "\t- R_EX_tyr__L_e\n", + "\t- R_EX_crn_e\n", + "\t- R_EX_tyrp_e\n", + "\t- R_EX_uacgam_e\n", + "\t- R_EX_udpacgal_e\n", + "\t- R_EX_rib__D_e\n", + "\t- R_EX_rmn_e\n", + "\t- R_EX_sbt__D_e\n", + "\t- R_EX_sel_e\n", + "\t- R_EX_ser__D_e\n", + "\t- R_EX_udpg_e\n", + "\t- R_EX_udpgal_e\n", + "\t- R_EX_crn__D_e\n", + "\t- R_EX_udpglcur_e\n", + "\t- R_EX_ump_e\n", + "\t- R_EX_ser__L_e\n", + "\t- R_EX_skm_e\n", + "\t- R_EX_slnt_e\n", + "\t- R_EX_ura_e\n", + "\t- R_EX_urea_e\n", + "\t- R_EX_so2_e\n", + "\t- R_EX_uri_e\n", + "\t- R_EX_so3_e\n", + "\t- R_EX_csn_e\n", + "\t- R_EX_val__L_e\n", + "\t- R_EX_xan_e\n", + "\t- R_EX_cu_e\n", + "\t- R_EX_xmp_e\n", + "\t- R_EX_cu2_e\n", + "\t- R_EX_xtsn_e\n", + "\t- R_EX_xyl__D_e\n", + "\t- R_EX_xylu__L_e\n", + "\t- R_EX_zn2_e\n", + "\t- R_EX_cyan_e\n", + "\t- R_EX_cynt_e\n", + "\t- R_EX_cys__D_e\n", + "\t- R_EX_cys__L_e\n", + "\t- R_EX_cytd_e\n", + "\t- R_EX_dad_2_e\n", + "\t- R_EX_damp_e\n", + "\t- R_EX_dca_e\n", + "\t- R_EX_dcmp_e\n", + "\t- R_EX_dcyt_e\n", + "\t- R_EX_ddca_e\n", + "\t- R_EX_dgmp_e\n", + "\t- R_EX_dgsn_e\n", + "\t- R_EX_dha_e\n", + "\t- R_EX_dimp_e\n", + "\t- R_EX_din_e\n", + "\t- R_EX_dms_e\n", + "\t- R_EX_dmso_e\n", + "\t- R_EX_dopa_e\n", + "\t- R_EX_doxrbcn_e\n", + "\t- R_EX_dtmp_e\n", + "\t- R_EX_dump_e\n", + "\t- R_EX_duri_e\n", + "\t- R_EX_eca4colipa_e\n", + "\t- R_EX_enlipa_e\n", + "\t- R_EX_enter_e\n", + "\t- R_EX_etha_e\n", + "\t- R_EX_ethso3_e\n", + "\t- R_EX_etoh_e\n", + "\t- R_EX_f6p_e\n", + "\t- R_EX_fald_e\n", + "\t- R_EX_fe2_e\n", + "\t- R_EX_fe3_e\n", + "\t- R_EX_tcb_e\n", + "\t- R_EX_feroxB_e\n", + "\t- R_EX_tton_e\n", + "\t- R_EX_4hthr_e\n", + "\t- R_EX_feroxEfe_e\n", + "\t- R_EX_feroxE_e\n", + "\t- R_EX_pep_e\n", + "\t- R_EX_23dhbzs3_e\n", + "\t- R_EX_tet_e\n", + "\t- R_EX_4hphac_e\n", + "\t- R_EX_4abz_e\n", + "\t- R_EX_acon_C_e\n", + "\t- R_EX_salchs4_e\n", + "\t- R_EX_tag__D_e\n", + "\t- R_EX_feroxGfe_e\n", + "\t- R_EX_oaa_e\n", + "\t- R_EX_salchs2fe_e\n", + "\t- R_EX_salchs2_e\n", + "\t- R_EX_cysi__L_e\n", + "\t- R_EX_rnam_e\n", + "\t- R_EX_airs_e\n", + "\t- R_EX_dxyl_e\n", + "\t- R_EX_fe3dhbzs3_e\n", + "\t- R_EX_3pg_e\n", + "\t- R_EX_drib_e\n", + "\t- R_EX_feroxBfe_e\n", + "\t- R_EX_salchs4fe_e\n", + "\t- R_EX_2pg_e\n", + "\t- R_EX_frmd_e\n", + "\t- R_EX_colipaOA_e\n", + "\t- R_EX_udcpo4_e\n", + "\t- R_EX_salchsx_e\n", + "\t- R_EX_AEP_e\n", + "\t- R_EX_icit_e\n", + "\t- R_EX_udcpo5_e\n", + "\t- R_EX_guln__L_e\n", + "\t- R_EX_colipa20Oag_e\n", + "\t- R_EX_feroxG_e\n", + "\t- R_EX_remnant1_e\n", + "\t- R_EX_fe_e\n", + "\t- R_EX_kokdolipidA_e\n", + "\t- R_EX_3hoxpac_e\n", + "\t- R_EX_diact_e\n", + "\t- R_EX_4hoxpac_e\n", + "\t- R_EX_cellb_e\n", + "\t- R_EX_btd_RR_e\n", + "\t- R_EX_13ppd_e\n", + "\t- R_EX_rbt_e\n", + "\t- R_EX_bz_e\n", + "\t- R_EX_abt__D_e\n", + "\t- R_EX_3ntym_e\n", + "\t- R_EX_6apa_e\n", + "\t- R_EX_peng_e\n", + "\t- R_EX_pac_e\n", + "\t- R_EX_salcn_e\n", + "\t- R_EX_galam_e\n", + "\t- R_DM_lipidA_core_e_p\n", + "\t- R_EX_raffin_e\n", + "\t- R_EX_o6a4colipa_e\n", + "\t- R_EX_arab__D_e\u001b[0m\n", + "File saved at: ../../data/sbml_corrected/iZ_1308.xml\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: iUTI89_1310\n", + "\u001b[0;93mWARNING : \n", + " - R_CAT: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_DHPTDNR: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_DHPTDNRN: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_FHL: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SPODM: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SPODMpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCASPtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCFUMtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCMALtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCTARTtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_URCN: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_EX_chitob_e: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_CHITOBpts: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_CHITPH: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\u001b[0m\n", + "\n", + "\n", + "\u001b[0;36m############################################\n", + "############################################\n", + " WRITING SBML FILE \n", + "############################################\n", + "############################################\n", + "\u001b[0m\n", + "\u001b[0;93mWARNING : IMPORT REACTION REMOVED BY DEFAULT\u001b[0m\n", + "\u001b[0;93mWARNING : If you want to keep import reaction\n", + "use option -kir / --keep-import-reactions\u001b[0m\n", + "\u001b[0;93mWARNING : Import reaction removed:\n", + "\t- R_EX_26dap__M_e\n", + "\t- R_EX_34dhpac_e\n", + "\t- R_EX_23camp_e\n", + "\t- R_EX_23dappa_e\n", + "\t- R_EX_3amp_e\n", + "\t- R_EX_3cmp_e\n", + "\t- R_EX_2ddglcn_e\n", + "\t- R_DM_4crsol_c\n", + "\t- R_EX_23cgmp_e\n", + "\t- R_EX_23ccmp_e\n", + "\t- R_EX_3gmp_e\n", + "\t- R_DM_5drib_c\n", + "\t- R_EX_3hcinnm_e\n", + "\t- R_EX_23cump_e\n", + "\t- R_DM_aacald_c\n", + "\t- R_EX_3hpp_e\n", + "\t- R_EX_3hpppn_e\n", + "\t- R_DM_amob_c\n", + "\t- R_EX_3ump_e\n", + "\t- R_EX_4abut_e\n", + "\t- R_EX_4hoxpacd_e\n", + "\t- R_DM_mththf_c\n", + "\t- R_EX_5dglcn_e\n", + "\t- R_DM_oxam_c\n", + "\t- R_EX_5mtr_e\n", + "\t- R_EX_LalaDglu_e\n", + "\t- R_EX_LalaDgluMdap_e\n", + "\t- R_EX_LalaDgluMdapDala_e\n", + "\t- R_EX_LalaLglu_e\n", + "\t- R_EX_dcmp_e\n", + "\t- R_EX_dcyt_e\n", + "\t- R_EX_ddca_e\n", + "\t- R_EX_dgmp_e\n", + "\t- R_EX_dgsn_e\n", + "\t- R_EX_dha_e\n", + "\t- R_EX_dimp_e\n", + "\t- R_EX_ac_e\n", + "\t- R_EX_acac_e\n", + "\t- R_EX_acald_e\n", + "\t- R_EX_acgal_e\n", + "\t- R_EX_acgal1p_e\n", + "\t- R_EX_acgam_e\n", + "\t- R_EX_acgam1p_e\n", + "\t- R_EX_din_e\n", + "\t- R_EX_dms_e\n", + "\t- R_EX_acmana_e\n", + "\t- R_EX_dmso_e\n", + "\t- R_EX_12ppd__R_e\n", + "\t- R_EX_dopa_e\n", + "\t- R_EX_doxrbcn_e\n", + "\t- R_EX_acmum_e\n", + "\t- R_EX_acnam_e\n", + "\t- R_EX_dtmp_e\n", + "\t- R_EX_acolipa_e\n", + "\t- R_EX_dump_e\n", + "\t- R_EX_12ppd__S_e\n", + "\t- R_EX_duri_e\n", + "\t- R_EX_acser_e\n", + "\t- R_EX_ade_e\n", + "\t- R_EX_eca4colipa_e\n", + "\t- R_EX_14glucan_e\n", + "\t- R_EX_enlipa_e\n", + "\t- R_EX_enter_e\n", + "\t- R_EX_etha_e\n", + "\t- R_EX_adn_e\n", + "\t- R_EX_adocbl_e\n", + "\t- R_EX_ag_e\n", + "\t- R_EX_agm_e\n", + "\t- R_EX_akg_e\n", + "\t- R_EX_ala_B_e\n", + "\t- R_EX_ethso3_e\n", + "\t- R_EX_etoh_e\n", + "\t- R_EX_f6p_e\n", + "\t- R_EX_fald_e\n", + "\t- R_EX_fe2_e\n", + "\t- R_EX_fe3_e\n", + "\t- R_EX_ala__D_e\n", + "\t- R_EX_ala__L_e\n", + "\t- R_EX_alaala_e\n", + "\t- R_EX_fe3dcit_e\n", + "\t- R_EX_all__D_e\n", + "\t- R_EX_fe3dhbzs_e\n", + "\t- R_EX_15dap_e\n", + "\t- R_EX_fe3hox_e\n", + "\t- R_EX_fe3hox_un_e\n", + "\t- R_EX_alltn_e\n", + "\t- R_EX_amp_e\n", + "\t- R_EX_anhgm_e\n", + "\t- R_EX_fecrm_e\n", + "\t- R_EX_arab__L_e\n", + "\t- R_EX_fecrm_un_e\n", + "\t- R_EX_arbt_e\n", + "\t- R_EX_h_e\n", + "\t- R_EX_arbtn_e\n", + "\t- R_EX_arbtn_fe3_e\n", + "\t- R_EX_arg__L_e\n", + "\t- R_EX_feenter_e\n", + "\t- R_EX_feoxam_e\n", + "\t- R_EX_feoxam_un_e\n", + "\t- R_EX_for_e\n", + "\t- R_EX_fru_e\n", + "\t- R_EX_ascb__L_e\n", + "\t- R_EX_asn__L_e\n", + "\t- R_EX_h2_e\n", + "\t- R_EX_aso3_e\n", + "\t- R_EX_frulys_e\n", + "\t- R_EX_fruur_e\n", + "\t- R_EX_fuc__L_e\n", + "\t- R_EX_fum_e\n", + "\t- R_EX_fusa_e\n", + "\t- R_EX_g1p_e\n", + "\t- R_EX_g3pc_e\n", + "\t- R_EX_g3pe_e\n", + "\t- R_EX_asp__L_e\n", + "\t- R_EX_btn_e\n", + "\t- R_EX_but_e\n", + "\t- R_EX_butso3_e\n", + "\t- R_EX_ca2_e\n", + "\t- R_EX_cbi_e\n", + "\t- R_EX_g3pg_e\n", + "\t- R_EX_g3pi_e\n", + "\t- R_EX_cbl1_e\n", + "\t- R_EX_g3ps_e\n", + "\t- R_EX_cd2_e\n", + "\t- R_EX_h2o_e\n", + "\t- R_EX_cgly_e\n", + "\t- R_EX_chol_e\n", + "\t- R_EX_g6p_e\n", + "\t- R_EX_gal_e\n", + "\t- R_EX_gal_bD_e\n", + "\t- R_EX_chtbs_e\n", + "\t- R_EX_cit_e\n", + "\t- R_EX_gal1p_e\n", + "\t- R_EX_h2o2_e\n", + "\t- R_EX_galct__D_e\n", + "\t- R_EX_galctn__D_e\n", + "\t- R_EX_galctn__L_e\n", + "\t- R_EX_cl_e\n", + "\t- R_EX_cm_e\n", + "\t- R_EX_cmp_e\n", + "\t- R_EX_galt_e\n", + "\t- R_EX_co2_e\n", + "\t- R_EX_galur_e\n", + "\t- R_EX_h2s_e\n", + "\t- R_EX_gam_e\n", + "\t- R_EX_gam6p_e\n", + "\t- R_EX_gbbtn_e\n", + "\t- R_EX_gdp_e\n", + "\t- R_EX_cobalt2_e\n", + "\t- R_EX_colipa_e\n", + "\t- R_EX_colipap_e\n", + "\t- R_EX_cpgn_e\n", + "\t- R_EX_glc__D_e\n", + "\t- R_EX_cpgn_un_e\n", + "\t- R_EX_glcn_e\n", + "\t- R_EX_hacolipa_e\n", + "\t- R_EX_glcr_e\n", + "\t- R_EX_crn_e\n", + "\t- R_EX_crn__D_e\n", + "\t- R_EX_csn_e\n", + "\t- R_EX_glcur_e\n", + "\t- R_EX_glcur1p_e\n", + "\t- R_EX_cu_e\n", + "\t- R_EX_halipa_e\n", + "\t- R_EX_cu2_e\n", + "\t- R_EX_gln__L_e\n", + "\t- R_EX_glu__L_e\n", + "\t- R_EX_cyan_e\n", + "\t- R_EX_hdca_e\n", + "\t- R_EX_cynt_e\n", + "\t- R_EX_cys__D_e\n", + "\t- R_EX_gly_e\n", + "\t- R_EX_glyald_e\n", + "\t- R_EX_cys__L_e\n", + "\t- R_EX_hdcea_e\n", + "\t- R_EX_cytd_e\n", + "\t- R_EX_dad_2_e\n", + "\t- R_EX_glyb_e\n", + "\t- R_EX_glyc_e\n", + "\t- R_EX_glyc__R_e\n", + "\t- R_EX_damp_e\n", + "\t- R_EX_dca_e\n", + "\t- R_EX_glyc2p_e\n", + "\t- R_EX_glyc3p_e\n", + "\t- R_EX_glyclt_e\n", + "\t- R_EX_hg2_e\n", + "\t- R_EX_gmp_e\n", + "\t- R_EX_gsn_e\n", + "\t- R_EX_gthox_e\n", + "\t- R_EX_ppal_e\n", + "\t- R_EX_pppn_e\n", + "\t- R_EX_ppt_e\n", + "\t- R_EX_pro__L_e\n", + "\t- R_EX_gthrd_e\n", + "\t- R_EX_gtp_e\n", + "\t- R_EX_progly_e\n", + "\t- R_EX_his__L_e\n", + "\t- R_EX_psclys_e\n", + "\t- R_EX_pser__L_e\n", + "\t- R_EX_gua_e\n", + "\t- R_EX_hom__L_e\n", + "\t- R_EX_ptrc_e\n", + "\t- R_EX_pydam_e\n", + "\t- R_EX_pydx_e\n", + "\t- R_EX_pydxn_e\n", + "\t- R_EX_hxa_e\n", + "\t- R_EX_pyr_e\n", + "\t- R_EX_quin_e\n", + "\t- R_EX_r5p_e\n", + "\t- R_EX_hxan_e\n", + "\t- R_EX_rfamp_e\n", + "\t- R_EX_rib__D_e\n", + "\t- R_EX_rmn_e\n", + "\t- R_EX_sbt__D_e\n", + "\t- R_EX_sel_e\n", + "\t- R_EX_ser__D_e\n", + "\t- R_EX_ser__L_e\n", + "\t- R_EX_idon__L_e\n", + "\t- R_EX_skm_e\n", + "\t- R_EX_slnt_e\n", + "\t- R_EX_so2_e\n", + "\t- R_EX_ile__L_e\n", + "\t- R_EX_so3_e\n", + "\t- R_EX_so4_e\n", + "\t- R_EX_spmd_e\n", + "\t- R_EX_succ_e\n", + "\t- R_EX_imp_e\n", + "\t- R_EX_sucr_e\n", + "\t- R_EX_sulfac_e\n", + "\t- R_EX_tartr__D_e\n", + "\t- R_EX_tartr__L_e\n", + "\t- R_EX_taur_e\n", + "\t- R_EX_tcynt_e\n", + "\t- R_EX_thm_e\n", + "\t- R_EX_thr__L_e\n", + "\t- R_EX_indole_e\n", + "\t- R_EX_thrp_e\n", + "\t- R_EX_thym_e\n", + "\t- R_EX_thymd_e\n", + "\t- R_EX_tma_e\n", + "\t- R_EX_inost_e\n", + "\t- R_EX_tmao_e\n", + "\t- R_EX_tre_e\n", + "\t- R_EX_trp__L_e\n", + "\t- R_EX_ins_e\n", + "\t- R_EX_tsul_e\n", + "\t- R_EX_isetac_e\n", + "\t- R_EX_ttdca_e\n", + "\t- R_EX_ttdcea_e\n", + "\t- R_EX_ttrcyc_e\n", + "\t- R_EX_tungs_e\n", + "\t- R_EX_tym_e\n", + "\t- R_EX_tyr__L_e\n", + "\t- R_EX_tyrp_e\n", + "\t- R_EX_uacgam_e\n", + "\t- R_EX_udpacgal_e\n", + "\t- R_EX_k_e\n", + "\t- R_EX_udpg_e\n", + "\t- R_EX_udpgal_e\n", + "\t- R_EX_udpglcur_e\n", + "\t- R_EX_kdo2lipid4_e\n", + "\t- R_EX_ump_e\n", + "\t- R_EX_ura_e\n", + "\t- R_EX_lac__D_e\n", + "\t- R_EX_urea_e\n", + "\t- R_EX_uri_e\n", + "\t- R_EX_val__L_e\n", + "\t- R_EX_lac__L_e\n", + "\t- R_EX_xan_e\n", + "\t- R_EX_xmp_e\n", + "\t- R_EX_xtsn_e\n", + "\t- R_EX_xyl__D_e\n", + "\t- R_EX_xylu__L_e\n", + "\t- R_EX_zn2_e\n", + "\t- R_EX_lcts_e\n", + "\t- R_EX_leu__L_e\n", + "\t- R_EX_lipa_e\n", + "\t- R_EX_lipa_cold_e\n", + "\t- R_EX_udcpo4_e\n", + "\t- R_EX_lipoate_e\n", + "\t- R_EX_lys__L_e\n", + "\t- R_EX_lyx__L_e\n", + "\t- R_EX_mal__D_e\n", + "\t- R_EX_mal__L_e\n", + "\t- R_EX_malt_e\n", + "\t- R_EX_malthx_e\n", + "\t- R_EX_maltpt_e\n", + "\t- R_EX_malttr_e\n", + "\t- R_EX_maltttr_e\n", + "\t- R_EX_man_e\n", + "\t- R_EX_man6p_e\n", + "\t- R_EX_manglyc_e\n", + "\t- R_EX_melib_e\n", + "\t- R_EX_meoh_e\n", + "\t- R_EX_met__D_e\n", + "\t- R_EX_met__L_e\n", + "\t- R_EX_metsox_R__L_e\n", + "\t- R_EX_metsox_S__L_e\n", + "\t- R_EX_mg2_e\n", + "\t- R_EX_mincyc_e\n", + "\t- R_EX_minohp_e\n", + "\t- R_EX_mmet_e\n", + "\t- R_EX_mn2_e\n", + "\t- R_EX_mnl_e\n", + "\t- R_EX_mobd_e\n", + "\t- R_EX_mso3_e\n", + "\t- R_EX_n2o_e\n", + "\t- R_EX_na1_e\n", + "\t- R_EX_nac_e\n", + "\t- R_EX_nh4_e\n", + "\t- R_EX_ni2_e\n", + "\t- R_EX_nmn_e\n", + "\t- R_EX_no_e\n", + "\t- R_EX_no2_e\n", + "\t- R_EX_no3_e\n", + "\t- R_EX_novbcn_e\n", + "\t- R_EX_o16a4colipa_e\n", + "\t- R_EX_o2_e\n", + "\t- R_EX_o2s_e\n", + "\t- R_EX_ocdca_e\n", + "\t- R_EX_ocdcea_e\n", + "\t- R_EX_octa_e\n", + "\t- R_EX_orn_e\n", + "\t- R_EX_orot_e\n", + "\t- R_EX_pacald_e\n", + "\t- R_EX_peamn_e\n", + "\t- R_EX_phe__L_e\n", + "\t- R_EX_pheme_e\n", + "\t- R_EX_pi_e\n", + "\t- R_EX_pnto__R_e\n", + "\t- R_EX_ppa_e\n", + "\t- R_EX_oaa_e\n", + "\t- R_EX_salchs2fe_e\n", + "\t- R_EX_cysi__L_e\n", + "\t- R_EX_fe3dhbzs3_e\n", + "\t- R_EX_drib_e\n", + "\t- R_EX_feroxBfe_e\n", + "\t- R_EX_salchs4fe_e\n", + "\t- R_EX_4hphac_e\n", + "\t- R_EX_2pg_e\n", + "\t- R_EX_4abz_e\n", + "\t- R_EX_frmd_e\n", + "\t- R_EX_colipaOA_e\n", + "\t- R_EX_acon_C_e\n", + "\t- R_EX_feroxB_e\n", + "\t- R_EX_tcb_e\n", + "\t- R_EX_tton_e\n", + "\t- R_EX_salchsx_e\n", + "\t- R_EX_AEP_e\n", + "\t- R_EX_4hthr_e\n", + "\t- R_EX_icit_e\n", + "\t- R_EX_feroxEfe_e\n", + "\t- R_EX_feroxE_e\n", + "\t- R_EX_colipa20Oag_e\n", + "\t- R_EX_remnant1_e\n", + "\t- R_EX_pep_e\n", + "\t- R_EX_23dhbzs3_e\n", + "\t- R_EX_tet_e\n", + "\t- R_EX_feroxGfe_e\n", + "\t- R_EX_salchs4_e\n", + "\t- R_EX_tag__D_e\n", + "\t- R_EX_salchs2_e\n", + "\t- R_EX_rnam_e\n", + "\t- R_EX_airs_e\n", + "\t- R_EX_dxyl_e\n", + "\t- R_EX_3pg_e\n", + "\t- R_EX_3ntym_e\n", + "\t- R_EX_6apa_e\n", + "\t- R_EX_udcpo5_e\n", + "\t- R_EX_4hoxpac_e\n", + "\t- R_EX_guln__L_e\n", + "\t- R_EX_peng_e\n", + "\t- R_EX_pac_e\n", + "\t- R_EX_salcn_e\n", + "\t- R_EX_galam_e\n", + "\t- R_EX_raffin_e\n", + "\t- R_EX_feroxG_e\n", + "\t- R_DM_lipidA_core_e_p\n", + "\t- R_EX_arab__D_e\n", + "\t- R_EX_diact_e\n", + "\t- R_EX_btd_RR_e\n", + "\t- R_EX_13ppd_e\n", + "\t- R_EX_rbt_e\n", + "\t- R_EX_cellb_e\n", + "\t- R_EX_bz_e\n", + "\t- R_EX_abt__D_e\n", + "\t- R_EX_fe_e\n", + "\t- R_EX_kokdolipidA_e\n", + "\t- R_EX_3hoxpac_e\n", + "\t- R_EX_o6a4colipa_e\u001b[0m\n", + "File saved at: ../../data/sbml_corrected/iUTI89_1310.xml\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: iML1515\n", + "\u001b[0;93mWARNING : \n", + " - R_FHL: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\u001b[0m\n", + "\n", + "\n", + "\u001b[0;36m############################################\n", + "############################################\n", + " WRITING SBML FILE \n", + "############################################\n", + "############################################\n", + "\u001b[0m\n", + "\u001b[0;93mWARNING : IMPORT REACTION REMOVED BY DEFAULT\u001b[0m\n", + "\u001b[0;93mWARNING : If you want to keep import reaction\n", + "use option -kir / --keep-import-reactions\u001b[0m\n", + "\u001b[0;93mWARNING : Import reaction removed:\n", + "\t- R_EX_pi_e\n", + "\t- R_EX_co2_e\n", + "\t- R_EX_met__L_e\n", + "\t- R_EX_metsox_S__L_e\n", + "\t- R_EX_acgam_e\n", + "\t- R_EX_cellb_e\n", + "\t- R_EX_crn_e\n", + "\t- R_EX_hxan_e\n", + "\t- R_EX_ile__L_e\n", + "\t- R_EX_chol_e\n", + "\t- R_EX_fe3_e\n", + "\t- R_EX_lac__L_e\n", + "\t- R_EX_leu__L_e\n", + "\t- R_EX_glcn_e\n", + "\t- R_EX_no3_e\n", + "\t- R_EX_h_e\n", + "\t- R_EX_orn_e\n", + "\t- R_EX_gln__L_e\n", + "\t- R_EX_pro__L_e\n", + "\t- R_EX_glyc_e\n", + "\t- R_EX_man_e\n", + "\t- R_EX_ade_e\n", + "\t- R_EX_mn2_e\n", + "\t- R_EX_4abut_e\n", + "\t- R_EX_ac_e\n", + "\t- R_EX_akg_e\n", + "\t- R_EX_ala__L_e\n", + "\t- R_EX_arg__L_e\n", + "\t- R_EX_asp__L_e\n", + "\t- R_EX_pyr_e\n", + "\t- R_EX_succ_e\n", + "\t- R_EX_thymd_e\n", + "\t- R_EX_rib__D_e\n", + "\t- R_EX_tyr__L_e\n", + "\t- R_EX_cytd_e\n", + "\t- R_EX_dcyt_e\n", + "\t- R_EX_fum_e\n", + "\t- R_EX_sbt__D_e\n", + "\t- R_EX_glu__L_e\n", + "\t- R_EX_gua_e\n", + "\t- R_EX_btn_e\n", + "\t- R_EX_ptrc_e\n", + "\t- R_EX_spmd_e\n", + "\t- R_EX_thym_e\n", + "\t- R_EX_xtsn_e\n", + "\t- R_EX_fe2_e\n", + "\t- R_EX_glc__D_e\n", + "\t- R_EX_alltn_e\n", + "\t- R_EX_ura_e\n", + "\t- R_EX_val__L_e\n", + "\t- R_EX_xan_e\n", + "\t- R_EX_dgsn_e\n", + "\t- R_EX_arab__L_e\n", + "\t- R_EX_fru_e\n", + "\t- R_EX_gal_e\n", + "\t- R_EX_xyl__D_e\n", + "\t- R_EX_duri_e\n", + "\t- R_EX_for_e\n", + "\t- R_EX_gly_e\n", + "\t- R_EX_h2_e\n", + "\t- R_EX_lys__L_e\n", + "\t- R_EX_ser__L_e\n", + "\t- R_EX_thm_e\n", + "\t- R_EX_trp__L_e\n", + "\t- R_EX_din_e\n", + "\t- R_EX_fmn_e\n", + "\t- R_EX_gthox_e\n", + "\t- R_EX_tmao_e\n", + "\t- R_EX_acald_e\n", + "\t- R_EX_melib_e\n", + "\t- R_EX_sucr_e\n", + "\t- R_EX_tre_e\n", + "\t- R_EX_zn2_e\n", + "\t- R_EX_hdcea_e\n", + "\t- R_EX_lac__D_e\n", + "\t- R_EX_ttdcea_e\n", + "\t- R_EX_h2s_e\n", + "\t- R_EX_gthrd_e\n", + "\t- R_EX_26dap__M_e\n", + "\t- R_EX_acnam_e\n", + "\t- R_EX_amp_e\n", + "\t- R_EX_dha_e\n", + "\t- R_EX_fuc__L_e\n", + "\t- R_EX_met__D_e\n", + "\t- R_EX_tartr__L_e\n", + "\t- R_EX_taur_e\n", + "\t- R_EX_maltpt_e\n", + "\t- R_EX_glyald_e\n", + "\t- R_EX_glcur_e\n", + "\t- R_EX_nmn_e\n", + "\t- R_EX_urea_e\n", + "\t- R_EX_f6p_e\n", + "\t- R_EX_rmn_e\n", + "\t- R_EX_ttdca_e\n", + "\t- R_EX_glyclt_e\n", + "\t- R_EX_g6p_e\n", + "\t- R_EX_2ddglcn_e\n", + "\t- R_EX_mmet_e\n", + "\t- R_EX_galct__D_e\n", + "\t- R_EX_idon__L_e\n", + "\t- R_EX_indole_e\n", + "\t- R_EX_3hcinnm_e\n", + "\t- R_EX_acac_e\n", + "\t- R_EX_acmana_e\n", + "\t- R_EX_galur_e\n", + "\t- R_DM_amob_c\n", + "\t- R_EX_adn_e\n", + "\t- R_EX_ins_e\n", + "\t- R_EX_mg2_e\n", + "\t- R_EX_glyc3p_e\n", + "\t- R_EX_but_e\n", + "\t- R_EX_lcts_e\n", + "\t- R_EX_ocdca_e\n", + "\t- R_EX_cbl1_e\n", + "\t- R_EX_nac_e\n", + "\t- R_EX_dad_2_e\n", + "\t- R_EX_ser__D_e\n", + "\t- R_EX_gam6p_e\n", + "\t- R_EX_15dap_e\n", + "\t- R_EX_ocdcea_e\n", + "\t- R_EX_cynt_e\n", + "\t- R_EX_galctn__D_e\n", + "\t- R_EX_glyb_e\n", + "\t- R_EX_ca2_e\n", + "\t- R_EX_so3_e\n", + "\t- R_EX_ascb__L_e\n", + "\t- R_EX_ala_B_e\n", + "\t- R_EX_agm_e\n", + "\t- R_EX_minohp_e\n", + "\t- R_EX_ppt_e\n", + "\t- R_EX_hxa_e\n", + "\t- R_EX_lyx__L_e\n", + "\t- R_EX_acmum_e\n", + "\t- R_EX_dca_e\n", + "\t- R_EX_all__D_e\n", + "\t- R_EX_octa_e\n", + "\t- R_EX_gbbtn_e\n", + "\t- R_EX_xylu__L_e\n", + "\t- R_EX_gdp_e\n", + "\t- R_EX_gtp_e\n", + "\t- R_EX_tma_e\n", + "\t- R_EX_fruur_e\n", + "\t- R_EX_malttr_e\n", + "\t- R_EX_cbi_e\n", + "\t- R_EX_arbtn_fe3_e\n", + "\t- R_EX_cpgn_e\n", + "\t- R_EX_ch4_e\n", + "\t- R_EX_adocbl_e\n", + "\t- R_EX_ppa_e\n", + "\t- R_EX_ni2_e\n", + "\t- R_EX_no_e\n", + "\t- R_EX_fad_e\n", + "\t- R_EX_meoh_e\n", + "\t- R_EX_fe3dcit_e\n", + "\t- R_EX_fe3hox_e\n", + "\t- R_EX_fecrm_e\n", + "\t- R_EX_feenter_e\n", + "\t- R_EX_feoxam_e\n", + "\t- R_EX_3amp_e\n", + "\t- R_EX_3gmp_e\n", + "\t- R_EX_3ump_e\n", + "\t- R_EX_cu2_e\n", + "\t- R_EX_glyc__R_e\n", + "\t- R_EX_eca4colipa_e\n", + "\t- R_EX_mal__D_e\n", + "\t- R_EX_sel_e\n", + "\t- R_EX_hg2_e\n", + "\t- R_EX_cobalt2_e\n", + "\t- R_EX_pheme_e\n", + "\t- R_EX_mso3_e\n", + "\t- R_EX_23camp_e\n", + "\t- R_EX_23cgmp_e\n", + "\t- R_EX_23cump_e\n", + "\t- R_EX_sulfac_e\n", + "\t- R_EX_butso3_e\n", + "\t- R_EX_ethso3_e\n", + "\t- R_EX_tym_e\n", + "\t- R_EX_imp_e\n", + "\t- R_EX_fe3hox_un_e\n", + "\t- R_EX_23dappa_e\n", + "\t- R_EX_cd2_e\n", + "\t- R_EX_enter_e\n", + "\t- R_EX_fecrm_un_e\n", + "\t- R_EX_cgly_e\n", + "\t- R_EX_chtbs_e\n", + "\t- R_EX_gmp_e\n", + "\t- R_EX_h2o2_e\n", + "\t- R_EX_ump_e\n", + "\t- R_EX_pydam_e\n", + "\t- R_EX_frulys_e\n", + "\t- R_EX_enlipa_e\n", + "\t- R_EX_hacolipa_e\n", + "\t- R_EX_etha_e\n", + "\t- R_EX_hdca_e\n", + "\t- R_EX_glcr_e\n", + "\t- R_EX_h2o_e\n", + "\t- R_EX_ala__D_e\n", + "\t- R_EX_mnl_e\n", + "\t- R_EX_malt_e\n", + "\t- R_EX_pnto__R_e\n", + "\t- R_EX_asn__L_e\n", + "\t- R_EX_cit_e\n", + "\t- R_EX_mal__L_e\n", + "\t- R_EX_no2_e\n", + "\t- R_EX_gam_e\n", + "\t- R_EX_inost_e\n", + "\t- R_EX_mobd_e\n", + "\t- R_EX_so4_e\n", + "\t- R_EX_phe__L_e\n", + "\t- R_EX_thr__L_e\n", + "\t- R_EX_uri_e\n", + "\t- R_EX_nh4_e\n", + "\t- R_EX_ddca_e\n", + "\t- R_EX_r5p_e\n", + "\t- R_EX_glyc2p_e\n", + "\t- R_EX_halipa_e\n", + "\t- R_EX_cmp_e\n", + "\t- R_EX_malthx_e\n", + "\t- R_EX_csn_e\n", + "\t- R_EX_etoh_e\n", + "\t- R_EX_g3pe_e\n", + "\t- R_EX_pacald_e\n", + "\t- R_EX_his__L_e\n", + "\t- R_EX_k_e\n", + "\t- R_EX_na1_e\n", + "\t- R_EX_dmso_e\n", + "\t- R_EX_12ppd__S_e\n", + "\t- R_EX_pppn_e\n", + "\t- R_EX_acgam1p_e\n", + "\t- R_EX_galt_e\n", + "\t- R_EX_glcur1p_e\n", + "\t- R_EX_thrp_e\n", + "\t- R_EX_fald_e\n", + "\t- R_EX_pydxn_e\n", + "\t- R_EX_acgal_e\n", + "\t- R_EX_so2_e\n", + "\t- R_EX_dms_e\n", + "\t- R_EX_acgal1p_e\n", + "\t- R_EX_cyan_e\n", + "\t- R_EX_tyrp_e\n", + "\t- R_EX_uacgam_e\n", + "\t- R_EX_udpacgal_e\n", + "\t- R_EX_udpg_e\n", + "\t- R_EX_udpgal_e\n", + "\t- R_EX_udpglcur_e\n", + "\t- R_EX_14glucan_e\n", + "\t- R_EX_pser__L_e\n", + "\t- R_EX_xmp_e\n", + "\t- R_EX_damp_e\n", + "\t- R_EX_dimp_e\n", + "\t- R_EX_isetac_e\n", + "\t- R_EX_aso3_e\n", + "\t- R_EX_tcynt_e\n", + "\t- R_EX_fe3dhbzs_e\n", + "\t- R_EX_feoxam_un_e\n", + "\t- R_EX_g3pc_e\n", + "\t- R_EX_g3pg_e\n", + "\t- R_EX_g3pi_e\n", + "\t- R_EX_g3ps_e\n", + "\t- R_EX_peamn_e\n", + "\t- R_EX_34dhpac_e\n", + "\t- R_EX_cys__L_e\n", + "\t- R_EX_gsn_e\n", + "\t- R_EX_orot_e\n", + "\t- R_EX_manglyc_e\n", + "\t- R_EX_metsox_R__L_e\n", + "\t- R_EX_cl_e\n", + "\t- R_EX_dgmp_e\n", + "\t- R_EX_4hoxpacd_e\n", + "\t- R_EX_3hpppn_e\n", + "\t- R_EX_man6p_e\n", + "\t- R_EX_tsul_e\n", + "\t- R_EX_n2o_e\n", + "\t- R_EX_maltttr_e\n", + "\t- R_EX_alaala_e\n", + "\t- R_EX_dtmp_e\n", + "\t- R_DM_5drib_c\n", + "\t- R_EX_lipoate_e\n", + "\t- R_EX_pydx_e\n", + "\t- R_EX_g1p_e\n", + "\t- R_DM_oxam_c\n", + "\t- R_EX_o2_e\n", + "\t- R_EX_progly_e\n", + "\t- R_EX_gal_bD_e\n", + "\t- R_EX_dump_e\n", + "\t- R_EX_12ppd__R_e\n", + "\t- R_EX_o2s_e\n", + "\t- R_EX_gal1p_e\n", + "\t- R_EX_dcmp_e\n", + "\t- R_EX_23ccmp_e\n", + "\t- R_EX_3cmp_e\n", + "\t- R_EX_urate_e\n", + "\t- R_EX_skm_e\n", + "\t- R_EX_cpgn_un_e\n", + "\t- R_EX_ag_e\n", + "\t- R_EX_cys__D_e\n", + "\t- R_EX_acser_e\n", + "\t- R_EX_cu_e\n", + "\t- R_EX_arbtn_e\n", + "\t- R_EX_dopa_e\n", + "\t- R_EX_tartr__D_e\n", + "\t- R_EX_galctn__L_e\n", + "\t- R_EX_hom__L_e\n", + "\t- R_EX_tungs_e\n", + "\t- R_EX_crn__D_e\n", + "\t- R_EX_LalaDgluMdapDala_e\n", + "\t- R_EX_5dglcn_e\n", + "\t- R_EX_LalaDgluMdap_e\n", + "\t- R_EX_psclys_e\n", + "\t- R_EX_anhgm_e\n", + "\t- R_DM_aacald_c\n", + "\t- R_EX_ppal_e\n", + "\t- R_EX_slnt_e\n", + "\t- R_EX_LalaDglu_e\n", + "\t- R_EX_5mtr_e\n", + "\t- R_EX_LalaLglu_e\n", + "\t- R_EX_ttrcyc_e\n", + "\t- R_EX_mincyc_e\n", + "\t- R_DM_4crsol_c\n", + "\t- R_EX_arbt_e\n", + "\t- R_EX_dxylnt_e\n", + "\t- R_EX_mththf_e\n", + "\t- R_EX_doxrbcn_e\n", + "\t- R_EX_dhps_e\n", + "\t- R_EX_cs1_e\n", + "\t- R_EX_fusa_e\n", + "\t- R_EX_mepn_e\n", + "\t- R_EX_cm_e\n", + "\t- R_EX_novbcn_e\n", + "\t- R_EX_2dglc_e\n", + "\t- R_EX_sq_e\n", + "\t- R_EX_4abzglu_e\n", + "\t- R_EX_rfamp_e\n", + "\t- R_EX_metglcur_e\n", + "\t- R_EX_quin_e\n", + "\t- R_EX_3hpp_e\n", + "\t- R_DM_mththf_c\u001b[0m\n", + "File saved at: ../../data/sbml_corrected/iML1515.xml\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: iAF692\n", + "\u001b[0;93mWARNING : - R_ASAD: Reactants and products exchanged.\n", + " Boundaries was: [-999999.0 ; 0.0]\n", + " - R_EX_h2s_e: Reactants and products exchanged.\n", + " Boundaries was: [-999999.0 ; 0.0]\n", + " - R_GLUDy: Reactants and products exchanged.\n", + " Boundaries was: [-999999.0 ; 0.0]\n", + " - R_HMGCOAR: Reactants and products exchanged.\n", + " Boundaries was: [-999999.0 ; 0.0]\n", + " - R_HMGCOAS: Reactants and products exchanged.\n", + " Boundaries was: [-999999.0 ; 0.0]\n", + "\u001b[0m\n", + "\n", + "\n", + "\u001b[0;36m############################################\n", + "############################################\n", + " WRITING SBML FILE \n", + "############################################\n", + "############################################\n", + "\u001b[0m\n", + "\u001b[0;93mWARNING : IMPORT REACTION REMOVED BY DEFAULT\u001b[0m\n", + "\u001b[0;93mWARNING : If you want to keep import reaction\n", + "use option -kir / --keep-import-reactions\u001b[0m\n", + "\u001b[0;93mWARNING : Reaction with tag reversible modified:\n", + "\t- R_ASAD\n", + "\t- R_EX_h2s_e\n", + "\t- R_GLUDy\n", + "\t- R_HMGCOAR\n", + "\t- R_HMGCOAS\u001b[0m\n", + "\u001b[0;93mWARNING : Reaction with Reactants and Products exchanged:\n", + "\t- R_ASAD\n", + "\t- R_EX_h2s_e\n", + "\t- R_GLUDy\n", + "\t- R_HMGCOAR\n", + "\t- R_HMGCOAS\u001b[0m\n", + "\u001b[0;93mWARNING : Import reaction removed:\n", + "\t- R_EX_so4_e\n", + "\t- R_EX_thm_e\n", + "\t- R_EX_tma_e\n", + "\t- R_EX_pi_e\n", + "\t- R_EX_pnto__R_e\n", + "\t- R_EX_pro__L_e\n", + "\t- R_EX_pyr_e\n", + "\t- R_EX_unknown_rbfdeg_e\n", + "\t- R_EX_s_e\n", + "\t- R_EX_ribflv_e\n", + "\t- R_EX_so3_e\n", + "\t- R_EX_urea_e\n", + "\t- R_EX_val__L_e\n", + "\t- R_EX_zn2_e\n", + "\t- R_EX_4abz_e\n", + "\t- R_EX_4hphac_e\n", + "\t- R_EX_ac_e\n", + "\t- R_EX_actn__R_e\n", + "\t- R_EX_ala__L_e\n", + "\t- R_EX_alac__S_e\n", + "\t- R_EX_btn_e\n", + "\t- R_EX_ca2_e\n", + "\t- R_EX_cbi_e\n", + "\t- R_EX_cbl1_e\n", + "\t- R_EX_cbl1hbi_e\n", + "\t- R_EX_cd2_e\n", + "\t- R_EX_ch4_e\n", + "\t- R_EX_ch4s_e\n", + "\t- R_EX_cit_e\n", + "\t- R_EX_cl_e\n", + "\t- R_EX_co2_e\n", + "\t- R_EX_co_e\n", + "\t- R_EX_cobalt2_e\n", + "\t- R_EX_cu2_e\n", + "\t- R_EX_cys__L_e\n", + "\t- R_EX_dma_e\n", + "\t- R_EX_dms_e\n", + "\t- R_EX_etha_e\n", + "\t- R_EX_fe2_e\n", + "\t- R_EX_fe3_e\n", + "\t- R_EX_fol_e\n", + "\t- R_EX_gcald_e\n", + "\t- R_EX_glcn_e\n", + "\t- R_EX_glu__L_e\n", + "\t- R_EX_gly_e\n", + "\t- R_EX_glyald_e\n", + "\t- R_EX_glyb_e\n", + "\t- R_EX_glyc_e\n", + "\t- R_EX_h2_e\n", + "\t- R_EX_h2o_e\n", + "\t- R_EX_h2s_e\n", + "\t- R_EX_h_e\n", + "\t- R_EX_ile__L_e\n", + "\t- R_EX_ind3ac_e\n", + "\t- R_EX_k_e\n", + "\t- R_EX_leu__L_e\n", + "\t- R_EX_lys__L_e\n", + "\t- R_EX_meoh_e\n", + "\t- R_EX_mg2_e\n", + "\t- R_EX_mma_e\n", + "\t- R_EX_mn2_e\n", + "\t- R_EX_mobd_e\n", + "\t- R_EX_n2_e\n", + "\t- R_EX_na1_e\n", + "\t- R_EX_nac_e\n", + "\t- R_EX_nh4_e\n", + "\t- R_EX_ni2_e\n", + "\t- R_EX_pac_e\n", + "\t- R_EX_tsul_e\n", + "\t- R_EX_unknown_cbl1deg_e\u001b[0m\n", + "File saved at: ../../data/sbml_corrected/iAF692.xml\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: iSFV_1184\n", + "\u001b[0;93mWARNING : \n", + " - R_DHPTDNR: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_DHPTDNRN: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_CAT: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SPODM: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SPODMpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCASPtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCFUMtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCMALtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCTARTtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_EX_chitob_e: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_CHITOBpts: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_CHITPH: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_URCN: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\u001b[0m\n", + "\n", + "\n", + "\u001b[0;36m############################################\n", + "############################################\n", + " WRITING SBML FILE \n", + "############################################\n", + "############################################\n", + "\u001b[0m\n", + "\u001b[0;93mWARNING : IMPORT REACTION REMOVED BY DEFAULT\u001b[0m\n", + "\u001b[0;93mWARNING : If you want to keep import reaction\n", + "use option -kir / --keep-import-reactions\u001b[0m\n", + "\u001b[0;93mWARNING : Import reaction removed:\n", + "\t- R_EX_LalaDglu_e\n", + "\t- R_EX_4hoxpacd_e\n", + "\t- R_EX_5dglcn_e\n", + "\t- R_EX_LalaDgluMdap_e\n", + "\t- R_EX_5mtr_e\n", + "\t- R_EX_LalaDgluMdapDala_e\n", + "\t- R_EX_LalaLglu_e\n", + "\t- R_EX_ac_e\n", + "\t- R_DM_4crsol_c\n", + "\t- R_EX_acac_e\n", + "\t- R_DM_5drib_c\n", + "\t- R_DM_aacald_c\n", + "\t- R_EX_acald_e\n", + "\t- R_DM_amob_c\n", + "\t- R_EX_acgal_e\n", + "\t- R_DM_mththf_c\n", + "\t- R_EX_dopa_e\n", + "\t- R_EX_acgal1p_e\n", + "\t- R_EX_doxrbcn_e\n", + "\t- R_DM_oxam_c\n", + "\t- R_EX_acgam_e\n", + "\t- R_EX_12ppd__R_e\n", + "\t- R_EX_dtmp_e\n", + "\t- R_EX_dump_e\n", + "\t- R_EX_duri_e\n", + "\t- R_EX_eca4colipa_e\n", + "\t- R_EX_12ppd__S_e\n", + "\t- R_EX_enlipa_e\n", + "\t- R_EX_acgam1p_e\n", + "\t- R_EX_enter_e\n", + "\t- R_EX_etha_e\n", + "\t- R_EX_ethso3_e\n", + "\t- R_EX_etoh_e\n", + "\t- R_EX_f6p_e\n", + "\t- R_EX_fald_e\n", + "\t- R_EX_fe2_e\n", + "\t- R_EX_fe3_e\n", + "\t- R_EX_14glucan_e\n", + "\t- R_EX_15dap_e\n", + "\t- R_EX_23camp_e\n", + "\t- R_EX_23ccmp_e\n", + "\t- R_EX_23cgmp_e\n", + "\t- R_EX_23cump_e\n", + "\t- R_EX_23dappa_e\n", + "\t- R_EX_26dap__M_e\n", + "\t- R_EX_2ddglcn_e\n", + "\t- R_EX_34dhpac_e\n", + "\t- R_EX_3amp_e\n", + "\t- R_EX_fe3dcit_e\n", + "\t- R_EX_fe3dhbzs_e\n", + "\t- R_EX_fe3hox_e\n", + "\t- R_EX_fe3hox_un_e\n", + "\t- R_EX_3cmp_e\n", + "\t- R_EX_fecrm_e\n", + "\t- R_EX_acmana_e\n", + "\t- R_EX_fecrm_un_e\n", + "\t- R_EX_feenter_e\n", + "\t- R_EX_3gmp_e\n", + "\t- R_EX_3hcinnm_e\n", + "\t- R_EX_3hpp_e\n", + "\t- R_EX_3hpppn_e\n", + "\t- R_EX_feoxam_e\n", + "\t- R_EX_acmum_e\n", + "\t- R_EX_feoxam_un_e\n", + "\t- R_EX_3ump_e\n", + "\t- R_EX_4abut_e\n", + "\t- R_EX_for_e\n", + "\t- R_EX_acnam_e\n", + "\t- R_EX_fru_e\n", + "\t- R_EX_frulys_e\n", + "\t- R_EX_fruur_e\n", + "\t- R_EX_his__L_e\n", + "\t- R_EX_hom__L_e\n", + "\t- R_EX_fuc__L_e\n", + "\t- R_EX_acolipa_e\n", + "\t- R_EX_fum_e\n", + "\t- R_EX_fusa_e\n", + "\t- R_EX_hxa_e\n", + "\t- R_EX_hxan_e\n", + "\t- R_EX_g1p_e\n", + "\t- R_EX_acser_e\n", + "\t- R_EX_g3pc_e\n", + "\t- R_EX_g3pe_e\n", + "\t- R_EX_ade_e\n", + "\t- R_EX_g3pg_e\n", + "\t- R_EX_adn_e\n", + "\t- R_EX_g3pi_e\n", + "\t- R_EX_adocbl_e\n", + "\t- R_EX_g3ps_e\n", + "\t- R_EX_g6p_e\n", + "\t- R_EX_ag_e\n", + "\t- R_EX_gal_e\n", + "\t- R_EX_agm_e\n", + "\t- R_EX_gal_bD_e\n", + "\t- R_EX_gal1p_e\n", + "\t- R_EX_galct__D_e\n", + "\t- R_EX_galctn__D_e\n", + "\t- R_EX_akg_e\n", + "\t- R_EX_galctn__L_e\n", + "\t- R_EX_galt_e\n", + "\t- R_EX_galur_e\n", + "\t- R_EX_idon__L_e\n", + "\t- R_EX_gam_e\n", + "\t- R_EX_ala_B_e\n", + "\t- R_EX_gam6p_e\n", + "\t- R_EX_gbbtn_e\n", + "\t- R_EX_gdp_e\n", + "\t- R_EX_glc__D_e\n", + "\t- R_EX_ile__L_e\n", + "\t- R_EX_imp_e\n", + "\t- R_EX_indole_e\n", + "\t- R_EX_inost_e\n", + "\t- R_EX_ins_e\n", + "\t- R_EX_isetac_e\n", + "\t- R_EX_k_e\n", + "\t- R_EX_glcn_e\n", + "\t- R_EX_glcr_e\n", + "\t- R_EX_glcur_e\n", + "\t- R_EX_kdo2lipid4_e\n", + "\t- R_EX_ala__D_e\n", + "\t- R_EX_lac__D_e\n", + "\t- R_EX_glcur1p_e\n", + "\t- R_EX_gln__L_e\n", + "\t- R_EX_glu__L_e\n", + "\t- R_EX_lac__L_e\n", + "\t- R_EX_gly_e\n", + "\t- R_EX_lcts_e\n", + "\t- R_EX_ala__L_e\n", + "\t- R_EX_leu__L_e\n", + "\t- R_EX_glyald_e\n", + "\t- R_EX_lipa_e\n", + "\t- R_EX_glyb_e\n", + "\t- R_EX_lipa_cold_e\n", + "\t- R_EX_glyc_e\n", + "\t- R_EX_alaala_e\n", + "\t- R_EX_glyc__R_e\n", + "\t- R_EX_lipoate_e\n", + "\t- R_EX_lys__L_e\n", + "\t- R_EX_all__D_e\n", + "\t- R_EX_lyx__L_e\n", + "\t- R_EX_glyc2p_e\n", + "\t- R_EX_glyc3p_e\n", + "\t- R_EX_alltn_e\n", + "\t- R_EX_glyclt_e\n", + "\t- R_EX_gmp_e\n", + "\t- R_EX_gsn_e\n", + "\t- R_EX_mal__D_e\n", + "\t- R_EX_mal__L_e\n", + "\t- R_EX_malt_e\n", + "\t- R_EX_malthx_e\n", + "\t- R_EX_maltpt_e\n", + "\t- R_EX_malttr_e\n", + "\t- R_EX_gthox_e\n", + "\t- R_EX_gthrd_e\n", + "\t- R_EX_amp_e\n", + "\t- R_EX_gtp_e\n", + "\t- R_EX_gua_e\n", + "\t- R_EX_maltttr_e\n", + "\t- R_EX_man_e\n", + "\t- R_EX_man6p_e\n", + "\t- R_EX_h_e\n", + "\t- R_EX_anhgm_e\n", + "\t- R_EX_h2_e\n", + "\t- R_EX_manglyc_e\n", + "\t- R_EX_melib_e\n", + "\t- R_EX_meoh_e\n", + "\t- R_EX_h2o_e\n", + "\t- R_EX_h2o2_e\n", + "\t- R_EX_arab__L_e\n", + "\t- R_EX_h2s_e\n", + "\t- R_EX_hacolipa_e\n", + "\t- R_EX_met__D_e\n", + "\t- R_EX_met__L_e\n", + "\t- R_EX_metsox_R__L_e\n", + "\t- R_EX_halipa_e\n", + "\t- R_EX_metsox_S__L_e\n", + "\t- R_EX_arbt_e\n", + "\t- R_EX_mg2_e\n", + "\t- R_EX_hdca_e\n", + "\t- R_EX_arbtn_e\n", + "\t- R_EX_hdcea_e\n", + "\t- R_EX_hg2_e\n", + "\t- R_EX_mincyc_e\n", + "\t- R_EX_minohp_e\n", + "\t- R_EX_pydxn_e\n", + "\t- R_EX_arbtn_fe3_e\n", + "\t- R_EX_pyr_e\n", + "\t- R_EX_quin_e\n", + "\t- R_EX_arg__L_e\n", + "\t- R_EX_r5p_e\n", + "\t- R_EX_rfamp_e\n", + "\t- R_EX_ascb__L_e\n", + "\t- R_EX_rib__D_e\n", + "\t- R_EX_mmet_e\n", + "\t- R_EX_rmn_e\n", + "\t- R_EX_asn__L_e\n", + "\t- R_EX_sbt__D_e\n", + "\t- R_EX_mn2_e\n", + "\t- R_EX_mnl_e\n", + "\t- R_EX_mobd_e\n", + "\t- R_EX_sel_e\n", + "\t- R_EX_mso3_e\n", + "\t- R_EX_aso3_e\n", + "\t- R_EX_n2o_e\n", + "\t- R_EX_na1_e\n", + "\t- R_EX_ser__D_e\n", + "\t- R_EX_ser__L_e\n", + "\t- R_EX_skm_e\n", + "\t- R_EX_slnt_e\n", + "\t- R_EX_nac_e\n", + "\t- R_EX_so2_e\n", + "\t- R_EX_asp__L_e\n", + "\t- R_EX_so3_e\n", + "\t- R_EX_nh4_e\n", + "\t- R_EX_ni2_e\n", + "\t- R_EX_btn_e\n", + "\t- R_EX_nmn_e\n", + "\t- R_EX_no_e\n", + "\t- R_EX_so4_e\n", + "\t- R_EX_spmd_e\n", + "\t- R_EX_succ_e\n", + "\t- R_EX_sucr_e\n", + "\t- R_EX_sulfac_e\n", + "\t- R_EX_no2_e\n", + "\t- R_EX_no3_e\n", + "\t- R_EX_tartr__D_e\n", + "\t- R_EX_tartr__L_e\n", + "\t- R_EX_taur_e\n", + "\t- R_EX_tcynt_e\n", + "\t- R_EX_thm_e\n", + "\t- R_EX_novbcn_e\n", + "\t- R_EX_thr__L_e\n", + "\t- R_EX_but_e\n", + "\t- R_EX_thrp_e\n", + "\t- R_EX_thym_e\n", + "\t- R_EX_thymd_e\n", + "\t- R_EX_tma_e\n", + "\t- R_EX_o16a4colipa_e\n", + "\t- R_EX_o2_e\n", + "\t- R_EX_o2s_e\n", + "\t- R_EX_ocdca_e\n", + "\t- R_EX_tmao_e\n", + "\t- R_EX_ocdcea_e\n", + "\t- R_EX_tre_e\n", + "\t- R_EX_octa_e\n", + "\t- R_EX_trp__L_e\n", + "\t- R_EX_butso3_e\n", + "\t- R_EX_orn_e\n", + "\t- R_EX_ca2_e\n", + "\t- R_EX_orot_e\n", + "\t- R_EX_tsul_e\n", + "\t- R_EX_ttdca_e\n", + "\t- R_EX_pacald_e\n", + "\t- R_EX_peamn_e\n", + "\t- R_EX_cbi_e\n", + "\t- R_EX_phe__L_e\n", + "\t- R_EX_pheme_e\n", + "\t- R_EX_pi_e\n", + "\t- R_EX_pnto__R_e\n", + "\t- R_EX_ppa_e\n", + "\t- R_EX_ppal_e\n", + "\t- R_EX_pppn_e\n", + "\t- R_EX_ttdcea_e\n", + "\t- R_EX_ttrcyc_e\n", + "\t- R_EX_tungs_e\n", + "\t- R_EX_tym_e\n", + "\t- R_EX_tyr__L_e\n", + "\t- R_EX_tyrp_e\n", + "\t- R_EX_uacgam_e\n", + "\t- R_EX_udpacgal_e\n", + "\t- R_EX_udpg_e\n", + "\t- R_EX_udpgal_e\n", + "\t- R_EX_ppt_e\n", + "\t- R_EX_pro__L_e\n", + "\t- R_EX_progly_e\n", + "\t- R_EX_psclys_e\n", + "\t- R_EX_udpglcur_e\n", + "\t- R_EX_cbl1_e\n", + "\t- R_EX_ump_e\n", + "\t- R_EX_ura_e\n", + "\t- R_EX_urea_e\n", + "\t- R_EX_pser__L_e\n", + "\t- R_EX_ptrc_e\n", + "\t- R_EX_pydam_e\n", + "\t- R_EX_pydx_e\n", + "\t- R_EX_uri_e\n", + "\t- R_EX_val__L_e\n", + "\t- R_EX_xan_e\n", + "\t- R_EX_cd2_e\n", + "\t- R_EX_xmp_e\n", + "\t- R_EX_xtsn_e\n", + "\t- R_EX_xyl__D_e\n", + "\t- R_EX_xylu__L_e\n", + "\t- R_EX_zn2_e\n", + "\t- R_EX_cgly_e\n", + "\t- R_EX_chol_e\n", + "\t- R_EX_chtbs_e\n", + "\t- R_EX_cit_e\n", + "\t- R_EX_cl_e\n", + "\t- R_EX_cm_e\n", + "\t- R_EX_cmp_e\n", + "\t- R_EX_co2_e\n", + "\t- R_EX_cobalt2_e\n", + "\t- R_EX_colipa_e\n", + "\t- R_EX_colipap_e\n", + "\t- R_EX_cpgn_e\n", + "\t- R_EX_cpgn_un_e\n", + "\t- R_EX_crn_e\n", + "\t- R_EX_crn__D_e\n", + "\t- R_EX_csn_e\n", + "\t- R_EX_cu_e\n", + "\t- R_EX_cu2_e\n", + "\t- R_EX_cyan_e\n", + "\t- R_EX_cynt_e\n", + "\t- R_EX_cys__D_e\n", + "\t- R_EX_cys__L_e\n", + "\t- R_EX_cytd_e\n", + "\t- R_EX_dad_2_e\n", + "\t- R_EX_damp_e\n", + "\t- R_EX_dca_e\n", + "\t- R_EX_dcmp_e\n", + "\t- R_EX_dcyt_e\n", + "\t- R_EX_ddca_e\n", + "\t- R_EX_dgmp_e\n", + "\t- R_EX_dgsn_e\n", + "\t- R_EX_dha_e\n", + "\t- R_EX_dimp_e\n", + "\t- R_EX_din_e\n", + "\t- R_EX_dms_e\n", + "\t- R_EX_dmso_e\n", + "\t- R_EX_4abz_e\n", + "\t- R_EX_acon_C_e\n", + "\t- R_EX_feroxB_e\n", + "\t- R_EX_tcb_e\n", + "\t- R_EX_tton_e\n", + "\t- R_EX_feroxBfe_e\n", + "\t- R_EX_4hphac_e\n", + "\t- R_EX_4hthr_e\n", + "\t- R_EX_feroxEfe_e\n", + "\t- R_EX_frmd_e\n", + "\t- R_EX_colipaOA_e\n", + "\t- R_EX_salchsx_e\n", + "\t- R_EX_feroxE_e\n", + "\t- R_EX_AEP_e\n", + "\t- R_EX_icit_e\n", + "\t- R_EX_cysi__L_e\n", + "\t- R_EX_23dhbzs3_e\n", + "\t- R_EX_pep_e\n", + "\t- R_EX_dxyl_e\n", + "\t- R_EX_colipa20Oag_e\n", + "\t- R_EX_tet_e\n", + "\t- R_EX_salchs4_e\n", + "\t- R_EX_remnant1_e\n", + "\t- R_EX_udcpo5_e\n", + "\t- R_EX_oaa_e\n", + "\t- R_EX_salchs2_e\n", + "\t- R_EX_salchs2fe_e\n", + "\t- R_EX_rnam_e\n", + "\t- R_EX_diact_e\n", + "\t- R_EX_airs_e\n", + "\t- R_EX_3pg_e\n", + "\t- R_EX_fe3dhbzs3_e\n", + "\t- R_EX_drib_e\n", + "\t- R_EX_salchs4fe_e\n", + "\t- R_EX_2pg_e\n", + "\t- R_EX_feroxGfe_e\n", + "\t- R_EX_udcpo4_e\n", + "\t- R_EX_tag__D_e\n", + "\t- R_EX_guln__L_e\n", + "\t- R_EX_cellb_e\n", + "\t- R_EX_feroxG_e\n", + "\t- R_EX_btd_RR_e\n", + "\t- R_EX_rbt_e\n", + "\t- R_EX_bz_e\n", + "\t- R_EX_13ppd_e\n", + "\t- R_EX_abt__D_e\n", + "\t- R_EX_fe_e\n", + "\t- R_EX_kokdolipidA_e\n", + "\t- R_EX_3hoxpac_e\n", + "\t- R_EX_4hoxpac_e\n", + "\t- R_DM_lipidA_core_e_p\n", + "\t- R_EX_3ntym_e\n", + "\t- R_EX_6apa_e\n", + "\t- R_EX_peng_e\n", + "\t- R_EX_pac_e\n", + "\t- R_EX_salcn_e\n", + "\t- R_EX_galam_e\n", + "\t- R_EX_raffin_e\n", + "\t- R_EX_o6a4colipa_e\n", + "\t- R_EX_arab__D_e\u001b[0m\n", + "File saved at: ../../data/sbml_corrected/iSFV_1184.xml\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: ic_1306\n", + "\u001b[0;93mWARNING : \n", + " - R_CAT: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_DHPTDNR: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_DHPTDNRN: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_FHL: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCTARTtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCFUMtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCASPtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCMALtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SPODM: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SPODMpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_EX_chitob_e: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_URCN: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_CHITPH: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_CHITOBpts: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\u001b[0m\n", + "\n", + "\n", + "\u001b[0;36m############################################\n", + "############################################\n", + " WRITING SBML FILE \n", + "############################################\n", + "############################################\n", + "\u001b[0m\n", + "\u001b[0;93mWARNING : IMPORT REACTION REMOVED BY DEFAULT\u001b[0m\n", + "\u001b[0;93mWARNING : If you want to keep import reaction\n", + "use option -kir / --keep-import-reactions\u001b[0m\n", + "\u001b[0;93mWARNING : Import reaction removed:\n", + "\t- R_EX_agm_e\n", + "\t- R_EX_akg_e\n", + "\t- R_EX_ala_B_e\n", + "\t- R_EX_ala__D_e\n", + "\t- R_EX_ala__L_e\n", + "\t- R_EX_alaala_e\n", + "\t- R_EX_all__D_e\n", + "\t- R_EX_acmum_e\n", + "\t- R_EX_arbtn_fe3_e\n", + "\t- R_EX_arg__L_e\n", + "\t- R_EX_acnam_e\n", + "\t- R_EX_alltn_e\n", + "\t- R_EX_acolipa_e\n", + "\t- R_EX_ascb__L_e\n", + "\t- R_EX_amp_e\n", + "\t- R_EX_asn__L_e\n", + "\t- R_EX_aso3_e\n", + "\t- R_EX_anhgm_e\n", + "\t- R_EX_asp__L_e\n", + "\t- R_EX_btn_e\n", + "\t- R_EX_arab__L_e\n", + "\t- R_EX_but_e\n", + "\t- R_EX_butso3_e\n", + "\t- R_EX_ca2_e\n", + "\t- R_EX_acser_e\n", + "\t- R_EX_ade_e\n", + "\t- R_EX_cbi_e\n", + "\t- R_EX_arbt_e\n", + "\t- R_EX_adn_e\n", + "\t- R_EX_arbtn_e\n", + "\t- R_EX_adocbl_e\n", + "\t- R_EX_ag_e\n", + "\t- R_EX_colipa_e\n", + "\t- R_EX_cbl1_e\n", + "\t- R_EX_cd2_e\n", + "\t- R_EX_cgly_e\n", + "\t- R_EX_chol_e\n", + "\t- R_EX_chtbs_e\n", + "\t- R_EX_cit_e\n", + "\t- R_EX_cl_e\n", + "\t- R_EX_cm_e\n", + "\t- R_EX_cmp_e\n", + "\t- R_EX_co2_e\n", + "\t- R_EX_cynt_e\n", + "\t- R_EX_cys__D_e\n", + "\t- R_EX_cobalt2_e\n", + "\t- R_EX_cys__L_e\n", + "\t- R_EX_cytd_e\n", + "\t- R_EX_dopa_e\n", + "\t- R_EX_colipap_e\n", + "\t- R_EX_dha_e\n", + "\t- R_EX_dimp_e\n", + "\t- R_EX_dad_2_e\n", + "\t- R_EX_damp_e\n", + "\t- R_EX_cpgn_e\n", + "\t- R_EX_dca_e\n", + "\t- R_EX_dcmp_e\n", + "\t- R_EX_din_e\n", + "\t- R_EX_dms_e\n", + "\t- R_EX_dmso_e\n", + "\t- R_EX_dcyt_e\n", + "\t- R_EX_cpgn_un_e\n", + "\t- R_EX_ddca_e\n", + "\t- R_EX_doxrbcn_e\n", + "\t- R_EX_dtmp_e\n", + "\t- R_EX_crn_e\n", + "\t- R_EX_dump_e\n", + "\t- R_EX_duri_e\n", + "\t- R_EX_eca4colipa_e\n", + "\t- R_EX_dgmp_e\n", + "\t- R_EX_dgsn_e\n", + "\t- R_EX_enlipa_e\n", + "\t- R_EX_crn__D_e\n", + "\t- R_EX_enter_e\n", + "\t- R_EX_etha_e\n", + "\t- R_EX_fe2_e\n", + "\t- R_EX_ethso3_e\n", + "\t- R_EX_csn_e\n", + "\t- R_EX_etoh_e\n", + "\t- R_EX_f6p_e\n", + "\t- R_EX_fald_e\n", + "\t- R_EX_fe3_e\n", + "\t- R_EX_feoxam_un_e\n", + "\t- R_EX_for_e\n", + "\t- R_EX_g1p_e\n", + "\t- R_EX_fru_e\n", + "\t- R_EX_fe3dcit_e\n", + "\t- R_EX_fe3dhbzs_e\n", + "\t- R_EX_cu_e\n", + "\t- R_EX_fe3hox_e\n", + "\t- R_EX_frulys_e\n", + "\t- R_EX_cu2_e\n", + "\t- R_EX_fruur_e\n", + "\t- R_EX_fuc__L_e\n", + "\t- R_EX_fe3hox_un_e\n", + "\t- R_EX_fecrm_e\n", + "\t- R_EX_fecrm_un_e\n", + "\t- R_EX_feenter_e\n", + "\t- R_EX_fum_e\n", + "\t- R_EX_fusa_e\n", + "\t- R_EX_feoxam_e\n", + "\t- R_EX_cyan_e\n", + "\t- R_EX_g3pc_e\n", + "\t- R_EX_g3pe_e\n", + "\t- R_EX_galct__D_e\n", + "\t- R_EX_g3pg_e\n", + "\t- R_EX_g3pi_e\n", + "\t- R_EX_g3ps_e\n", + "\t- R_EX_g6p_e\n", + "\t- R_EX_gal_e\n", + "\t- R_EX_gal_bD_e\n", + "\t- R_EX_gal1p_e\n", + "\t- R_EX_galctn__D_e\n", + "\t- R_EX_galctn__L_e\n", + "\t- R_EX_galt_e\n", + "\t- R_EX_galur_e\n", + "\t- R_EX_gam_e\n", + "\t- R_EX_glcn_e\n", + "\t- R_EX_gam6p_e\n", + "\t- R_EX_gbbtn_e\n", + "\t- R_EX_glcr_e\n", + "\t- R_EX_gdp_e\n", + "\t- R_EX_glcur_e\n", + "\t- R_EX_glcur1p_e\n", + "\t- R_EX_gln__L_e\n", + "\t- R_EX_glu__L_e\n", + "\t- R_EX_gly_e\n", + "\t- R_EX_glyald_e\n", + "\t- R_EX_glc__D_e\n", + "\t- R_EX_glyb_e\n", + "\t- R_EX_glyc_e\n", + "\t- R_EX_h2o2_e\n", + "\t- R_EX_h2s_e\n", + "\t- R_EX_glyc__R_e\n", + "\t- R_EX_glyc2p_e\n", + "\t- R_EX_glyc3p_e\n", + "\t- R_EX_glyclt_e\n", + "\t- R_EX_gmp_e\n", + "\t- R_EX_gsn_e\n", + "\t- R_DM_4crsol_c\n", + "\t- R_EX_gthox_e\n", + "\t- R_EX_gthrd_e\n", + "\t- R_EX_gtp_e\n", + "\t- R_EX_gua_e\n", + "\t- R_EX_h_e\n", + "\t- R_EX_h2_e\n", + "\t- R_EX_h2o_e\n", + "\t- R_EX_pser__L_e\n", + "\t- R_EX_ptrc_e\n", + "\t- R_EX_pydam_e\n", + "\t- R_EX_pydx_e\n", + "\t- R_EX_pydxn_e\n", + "\t- R_EX_pyr_e\n", + "\t- R_EX_hacolipa_e\n", + "\t- R_EX_halipa_e\n", + "\t- R_EX_hdca_e\n", + "\t- R_EX_hdcea_e\n", + "\t- R_EX_hg2_e\n", + "\t- R_EX_his__L_e\n", + "\t- R_EX_hom__L_e\n", + "\t- R_EX_hxa_e\n", + "\t- R_EX_hxan_e\n", + "\t- R_EX_idon__L_e\n", + "\t- R_EX_ile__L_e\n", + "\t- R_EX_imp_e\n", + "\t- R_EX_indole_e\n", + "\t- R_EX_quin_e\n", + "\t- R_EX_r5p_e\n", + "\t- R_EX_rfamp_e\n", + "\t- R_EX_rib__D_e\n", + "\t- R_EX_rmn_e\n", + "\t- R_EX_sbt__D_e\n", + "\t- R_EX_inost_e\n", + "\t- R_EX_ins_e\n", + "\t- R_DM_5drib_c\n", + "\t- R_EX_isetac_e\n", + "\t- R_EX_k_e\n", + "\t- R_EX_kdo2lipid4_e\n", + "\t- R_EX_sel_e\n", + "\t- R_EX_ser__D_e\n", + "\t- R_EX_ser__L_e\n", + "\t- R_EX_skm_e\n", + "\t- R_EX_lac__D_e\n", + "\t- R_DM_aacald_c\n", + "\t- R_EX_lac__L_e\n", + "\t- R_EX_lcts_e\n", + "\t- R_EX_leu__L_e\n", + "\t- R_EX_lipa_e\n", + "\t- R_EX_lipa_cold_e\n", + "\t- R_EX_lipoate_e\n", + "\t- R_EX_lys__L_e\n", + "\t- R_EX_slnt_e\n", + "\t- R_EX_so2_e\n", + "\t- R_EX_so3_e\n", + "\t- R_EX_so4_e\n", + "\t- R_EX_spmd_e\n", + "\t- R_EX_succ_e\n", + "\t- R_EX_sucr_e\n", + "\t- R_EX_sulfac_e\n", + "\t- R_EX_tartr__D_e\n", + "\t- R_EX_lyx__L_e\n", + "\t- R_EX_mal__D_e\n", + "\t- R_EX_mal__L_e\n", + "\t- R_EX_malt_e\n", + "\t- R_EX_tartr__L_e\n", + "\t- R_DM_amob_c\n", + "\t- R_EX_taur_e\n", + "\t- R_EX_tcynt_e\n", + "\t- R_EX_thm_e\n", + "\t- R_EX_thr__L_e\n", + "\t- R_EX_malthx_e\n", + "\t- R_EX_maltpt_e\n", + "\t- R_EX_malttr_e\n", + "\t- R_EX_maltttr_e\n", + "\t- R_EX_thrp_e\n", + "\t- R_EX_man_e\n", + "\t- R_DM_mththf_c\n", + "\t- R_EX_man6p_e\n", + "\t- R_EX_manglyc_e\n", + "\t- R_EX_thym_e\n", + "\t- R_EX_thymd_e\n", + "\t- R_EX_tma_e\n", + "\t- R_EX_tmao_e\n", + "\t- R_EX_melib_e\n", + "\t- R_EX_meoh_e\n", + "\t- R_EX_met__D_e\n", + "\t- R_EX_met__L_e\n", + "\t- R_EX_metsox_R__L_e\n", + "\t- R_EX_tre_e\n", + "\t- R_EX_trp__L_e\n", + "\t- R_EX_metsox_S__L_e\n", + "\t- R_DM_oxam_c\n", + "\t- R_EX_mg2_e\n", + "\t- R_EX_mincyc_e\n", + "\t- R_EX_minohp_e\n", + "\t- R_EX_tsul_e\n", + "\t- R_EX_ttdca_e\n", + "\t- R_EX_ttdcea_e\n", + "\t- R_EX_ttrcyc_e\n", + "\t- R_EX_mmet_e\n", + "\t- R_EX_mn2_e\n", + "\t- R_EX_mnl_e\n", + "\t- R_EX_mobd_e\n", + "\t- R_EX_tungs_e\n", + "\t- R_EX_tym_e\n", + "\t- R_EX_tyr__L_e\n", + "\t- R_EX_mso3_e\n", + "\t- R_EX_tyrp_e\n", + "\t- R_EX_n2o_e\n", + "\t- R_EX_na1_e\n", + "\t- R_EX_nac_e\n", + "\t- R_EX_uacgam_e\n", + "\t- R_EX_udpacgal_e\n", + "\t- R_EX_udpg_e\n", + "\t- R_EX_nh4_e\n", + "\t- R_EX_udpgal_e\n", + "\t- R_EX_ni2_e\n", + "\t- R_EX_udpglcur_e\n", + "\t- R_EX_12ppd__R_e\n", + "\t- R_EX_ump_e\n", + "\t- R_EX_ura_e\n", + "\t- R_EX_urea_e\n", + "\t- R_EX_nmn_e\n", + "\t- R_EX_no_e\n", + "\t- R_EX_no2_e\n", + "\t- R_EX_no3_e\n", + "\t- R_EX_novbcn_e\n", + "\t- R_EX_o16a4colipa_e\n", + "\t- R_EX_o2_e\n", + "\t- R_EX_uri_e\n", + "\t- R_EX_val__L_e\n", + "\t- R_EX_xan_e\n", + "\t- R_EX_o2s_e\n", + "\t- R_EX_ocdca_e\n", + "\t- R_EX_xmp_e\n", + "\t- R_EX_xtsn_e\n", + "\t- R_EX_ocdcea_e\n", + "\t- R_EX_12ppd__S_e\n", + "\t- R_EX_octa_e\n", + "\t- R_EX_orn_e\n", + "\t- R_EX_orot_e\n", + "\t- R_EX_xyl__D_e\n", + "\t- R_EX_xylu__L_e\n", + "\t- R_EX_zn2_e\n", + "\t- R_EX_pacald_e\n", + "\t- R_EX_peamn_e\n", + "\t- R_EX_phe__L_e\n", + "\t- R_EX_14glucan_e\n", + "\t- R_EX_pheme_e\n", + "\t- R_EX_pi_e\n", + "\t- R_EX_pnto__R_e\n", + "\t- R_EX_ppa_e\n", + "\t- R_EX_ppal_e\n", + "\t- R_EX_pppn_e\n", + "\t- R_EX_ppt_e\n", + "\t- R_EX_pro__L_e\n", + "\t- R_EX_progly_e\n", + "\t- R_EX_15dap_e\n", + "\t- R_EX_psclys_e\n", + "\t- R_EX_23camp_e\n", + "\t- R_EX_23ccmp_e\n", + "\t- R_EX_23cgmp_e\n", + "\t- R_EX_23cump_e\n", + "\t- R_EX_23dappa_e\n", + "\t- R_EX_26dap__M_e\n", + "\t- R_EX_2ddglcn_e\n", + "\t- R_EX_34dhpac_e\n", + "\t- R_EX_3amp_e\n", + "\t- R_EX_3cmp_e\n", + "\t- R_EX_3gmp_e\n", + "\t- R_EX_3hcinnm_e\n", + "\t- R_EX_3hpp_e\n", + "\t- R_EX_3hpppn_e\n", + "\t- R_EX_3ump_e\n", + "\t- R_EX_4abut_e\n", + "\t- R_EX_4hoxpacd_e\n", + "\t- R_EX_5dglcn_e\n", + "\t- R_EX_5mtr_e\n", + "\t- R_EX_LalaDglu_e\n", + "\t- R_EX_LalaDgluMdap_e\n", + "\t- R_EX_LalaDgluMdapDala_e\n", + "\t- R_EX_LalaLglu_e\n", + "\t- R_EX_ac_e\n", + "\t- R_EX_acac_e\n", + "\t- R_EX_acald_e\n", + "\t- R_EX_acgal_e\n", + "\t- R_EX_acgal1p_e\n", + "\t- R_EX_acgam_e\n", + "\t- R_EX_acgam1p_e\n", + "\t- R_EX_acmana_e\n", + "\t- R_EX_remnant1_e\n", + "\t- R_EX_4hphac_e\n", + "\t- R_EX_4abz_e\n", + "\t- R_EX_acon_C_e\n", + "\t- R_EX_feroxB_e\n", + "\t- R_EX_tcb_e\n", + "\t- R_EX_tton_e\n", + "\t- R_EX_4hthr_e\n", + "\t- R_EX_feroxE_e\n", + "\t- R_EX_pep_e\n", + "\t- R_EX_23dhbzs3_e\n", + "\t- R_EX_tet_e\n", + "\t- R_EX_salchs4_e\n", + "\t- R_EX_tag__D_e\n", + "\t- R_EX_oaa_e\n", + "\t- R_EX_salchs2fe_e\n", + "\t- R_EX_feroxEfe_e\n", + "\t- R_EX_cysi__L_e\n", + "\t- R_EX_fe3dhbzs3_e\n", + "\t- R_EX_drib_e\n", + "\t- R_EX_feroxBfe_e\n", + "\t- R_EX_salchs4fe_e\n", + "\t- R_EX_2pg_e\n", + "\t- R_EX_frmd_e\n", + "\t- R_EX_colipaOA_e\n", + "\t- R_EX_salchsx_e\n", + "\t- R_EX_AEP_e\n", + "\t- R_EX_icit_e\n", + "\t- R_EX_colipa20Oag_e\n", + "\t- R_EX_salchs2_e\n", + "\t- R_EX_rnam_e\n", + "\t- R_EX_airs_e\n", + "\t- R_EX_fe_e\n", + "\t- R_EX_dxyl_e\n", + "\t- R_EX_3pg_e\n", + "\t- R_EX_udcpo4_e\n", + "\t- R_EX_feroxGfe_e\n", + "\t- R_EX_udcpo5_e\n", + "\t- R_EX_guln__L_e\n", + "\t- R_EX_feroxG_e\n", + "\t- R_EX_diact_e\n", + "\t- R_EX_kokdolipidA_e\n", + "\t- R_EX_3hoxpac_e\n", + "\t- R_EX_btd_RR_e\n", + "\t- R_EX_13ppd_e\n", + "\t- R_EX_rbt_e\n", + "\t- R_EX_cellb_e\n", + "\t- R_EX_bz_e\n", + "\t- R_EX_o6a4colipa_e\n", + "\t- R_EX_abt__D_e\n", + "\t- R_EX_3ntym_e\n", + "\t- R_EX_6apa_e\n", + "\t- R_EX_4hoxpac_e\n", + "\t- R_EX_peng_e\n", + "\t- R_EX_pac_e\n", + "\t- R_EX_salcn_e\n", + "\t- R_EX_galam_e\n", + "\t- R_EX_raffin_e\n", + "\t- R_DM_lipidA_core_e_p\n", + "\t- R_EX_arab__D_e\u001b[0m\n", + "File saved at: ../../data/sbml_corrected/ic_1306.xml\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: iND750\n", + "\u001b[0;93mWARNING : - R_ASAD: Reactants and products exchanged.\n", + " Boundaries was: [-999999.0 ; 0.0]\n", + " - R_HSDy: Reactants and products exchanged.\n", + " Boundaries was: [-999999.0 ; 0.0]\n", + " - R_THRA: Reactants and products exchanged.\n", + " Boundaries was: [-999999.0 ; 0.0]\n", + "\u001b[0m\n", + "\n", + "\n", + "\u001b[0;36m############################################\n", + "############################################\n", + " WRITING SBML FILE \n", + "############################################\n", + "############################################\n", + "\u001b[0m\n", + "\u001b[0;93mWARNING : IMPORT REACTION REMOVED BY DEFAULT\u001b[0m\n", + "\u001b[0;93mWARNING : If you want to keep import reaction\n", + "use option -kir / --keep-import-reactions\u001b[0m\n", + "\u001b[0;93mWARNING : Reaction with tag reversible modified:\n", + "\t- R_ASAD\n", + "\t- R_HSDy\n", + "\t- R_THRA\u001b[0m\n", + "\u001b[0;93mWARNING : Reaction with Reactants and Products exchanged:\n", + "\t- R_ASAD\n", + "\t- R_HSDy\n", + "\t- R_THRA\u001b[0m\n", + "\u001b[0;93mWARNING : Import reaction removed:\n", + "\t- R_EX_met__L_e\n", + "\t- R_EX_mmet_e\n", + "\t- R_EX_na1_e\n", + "\t- R_EX_nh4_e\n", + "\t- R_EX_nmn_e\n", + "\t- R_EX_o2_e\n", + "\t- R_EX_ocdca_e\n", + "\t- R_EX_ocdcea_e\n", + "\t- R_EX_ocdcya_e\n", + "\t- R_EX_orn_e\n", + "\t- R_EX_pap_e\n", + "\t- R_EX_pepd_e\n", + "\t- R_EX_phe__L_e\n", + "\t- R_EX_pi_e\n", + "\t- R_EX_pnto__R_e\n", + "\t- R_EX_pro__L_e\n", + "\t- R_EX_ptrc_e\n", + "\t- R_EX_pyr_e\n", + "\t- R_EX_rib__D_e\n", + "\t- R_EX_ribflv_e\n", + "\t- R_EX_sbt__D_e\n", + "\t- R_EX_sbt__L_e\n", + "\t- R_EX_ser__L_e\n", + "\t- R_EX_so4_e\n", + "\t- R_EX_spmd_e\n", + "\t- R_EX_sprm_e\n", + "\t- R_EX_srb__L_e\n", + "\t- R_EX_succ_e\n", + "\t- R_EX_sucr_e\n", + "\t- R_EX_thm_e\n", + "\t- R_EX_thmmp_e\n", + "\t- R_EX_thmpp_e\n", + "\t- R_EX_thr__L_e\n", + "\t- R_EX_thym_e\n", + "\t- R_EX_thymd_e\n", + "\t- R_EX_tre_e\n", + "\t- R_EX_trp__L_e\n", + "\t- R_EX_ttdca_e\n", + "\t- R_EX_tyr__L_e\n", + "\t- R_EX_ura_e\n", + "\t- R_EX_urea_e\n", + "\t- R_EX_uri_e\n", + "\t- R_EX_val__L_e\n", + "\t- R_EX_xan_e\n", + "\t- R_EX_xtsn_e\n", + "\t- R_EX_xyl__D_e\n", + "\t- R_EX_xylt_e\n", + "\t- R_EX_zymst_e\n", + "\t- R_EX_glu__L_e\n", + "\t- R_EX_gly_e\n", + "\t- R_EX_glyc_e\n", + "\t- R_EX_gsn_e\n", + "\t- R_EX_ala__L_e\n", + "\t- R_EX_alltn_e\n", + "\t- R_EX_alltt_e\n", + "\t- R_EX_amet_e\n", + "\t- R_EX_arab__D_e\n", + "\t- R_EX_arab__L_e\n", + "\t- R_EX_arg__L_e\n", + "\t- R_EX_asn__L_e\n", + "\t- R_EX_asp__L_e\n", + "\t- R_EX_btn_e\n", + "\t- R_EX_chol_e\n", + "\t- R_EX_cit_e\n", + "\t- R_EX_co2_e\n", + "\t- R_EX_crn_e\n", + "\t- R_EX_csn_e\n", + "\t- R_EX_cys__L_e\n", + "\t- R_EX_cytd_e\n", + "\t- R_EX_dad_2_e\n", + "\t- R_EX_dann_e\n", + "\t- R_EX_dcyt_e\n", + "\t- R_EX_dgsn_e\n", + "\t- R_EX_din_e\n", + "\t- R_EX_dttp_e\n", + "\t- R_EX_duri_e\n", + "\t- R_EX_ergst_e\n", + "\t- R_EX_etoh_e\n", + "\t- R_EX_fmn_e\n", + "\t- R_EX_for_e\n", + "\t- R_EX_fru_e\n", + "\t- R_EX_fum_e\n", + "\t- R_EX_gal_e\n", + "\t- R_EX_gam6p_e\n", + "\t- R_EX_gcald_e\n", + "\t- R_EX_glc__D_e\n", + "\t- R_EX_gln__L_e\n", + "\t- R_EX_gthox_e\n", + "\t- R_EX_gua_e\n", + "\t- R_EX_h2o_e\n", + "\t- R_EX_h_e\n", + "\t- R_EX_hdca_e\n", + "\t- R_EX_hdcea_e\n", + "\t- R_EX_his__L_e\n", + "\t- R_EX_hxan_e\n", + "\t- R_EX_ile__L_e\n", + "\t- R_EX_inost_e\n", + "\t- R_EX_ins_e\n", + "\t- R_EX_k_e\n", + "\t- R_EX_lac__L_e\n", + "\t- R_EX_leu__L_e\n", + "\t- R_EX_lys__L_e\n", + "\t- R_EX_mal__L_e\n", + "\t- R_EX_malt_e\n", + "\t- R_EX_man_e\n", + "\t- R_EX_melib_e\n", + "\t- R_EX_ade_e\n", + "\t- R_EX_adn_e\n", + "\t- R_EX_akg_e\n", + "\t- R_EX_13BDglcn_e\n", + "\t- R_EX_4abut_e\n", + "\t- R_EX_5aop_e\n", + "\t- R_EX_8aonn_e\n", + "\t- R_EX_abt_e\n", + "\t- R_EX_ac_e\n", + "\t- R_EX_acald_e\u001b[0m\n", + "File saved at: ../../data/sbml_corrected/iND750.xml\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: iUMN146_1321\n", + "\u001b[0;93mWARNING : \n", + " - R_CAT: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_DHPTDNR: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_DHPTDNRN: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_FHL: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SPODM: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SPODMpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCASPtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCFUMtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCMALtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCTARTtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_CHITPH: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_EX_chitob_e: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_URCN: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_CHITOBpts: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\u001b[0m\n", + "\n", + "\n", + "\u001b[0;36m############################################\n", + "############################################\n", + " WRITING SBML FILE \n", + "############################################\n", + "############################################\n", + "\u001b[0m\n", + "\u001b[0;93mWARNING : IMPORT REACTION REMOVED BY DEFAULT\u001b[0m\n", + "\u001b[0;93mWARNING : If you want to keep import reaction\n", + "use option -kir / --keep-import-reactions\u001b[0m\n", + "\u001b[0;93mWARNING : Import reaction removed:\n", + "\t- R_EX_cpgn_e\n", + "\t- R_EX_crn_e\n", + "\t- R_EX_cpgn_un_e\n", + "\t- R_EX_colipap_e\n", + "\t- R_DM_4crsol_c\n", + "\t- R_DM_5drib_c\n", + "\t- R_DM_aacald_c\n", + "\t- R_DM_oxam_c\n", + "\t- R_EX_crn__D_e\n", + "\t- R_DM_amob_c\n", + "\t- R_EX_12ppd__R_e\n", + "\t- R_DM_mththf_c\n", + "\t- R_EX_csn_e\n", + "\t- R_EX_cu_e\n", + "\t- R_EX_12ppd__S_e\n", + "\t- R_EX_cu2_e\n", + "\t- R_EX_glyc_e\n", + "\t- R_EX_14glucan_e\n", + "\t- R_EX_glyc__R_e\n", + "\t- R_EX_cyan_e\n", + "\t- R_EX_cynt_e\n", + "\t- R_EX_glyc2p_e\n", + "\t- R_EX_cys__D_e\n", + "\t- R_EX_glyc3p_e\n", + "\t- R_EX_15dap_e\n", + "\t- R_EX_glyclt_e\n", + "\t- R_EX_cys__L_e\n", + "\t- R_EX_cytd_e\n", + "\t- R_EX_gmp_e\n", + "\t- R_EX_dad_2_e\n", + "\t- R_EX_23camp_e\n", + "\t- R_EX_damp_e\n", + "\t- R_EX_dca_e\n", + "\t- R_EX_dcmp_e\n", + "\t- R_EX_dcyt_e\n", + "\t- R_EX_ddca_e\n", + "\t- R_EX_dgmp_e\n", + "\t- R_EX_dgsn_e\n", + "\t- R_EX_dha_e\n", + "\t- R_EX_gsn_e\n", + "\t- R_EX_gthox_e\n", + "\t- R_EX_gthrd_e\n", + "\t- R_EX_gtp_e\n", + "\t- R_EX_gua_e\n", + "\t- R_EX_h_e\n", + "\t- R_EX_h2_e\n", + "\t- R_EX_h2o_e\n", + "\t- R_EX_h2o2_e\n", + "\t- R_EX_h2s_e\n", + "\t- R_EX_hacolipa_e\n", + "\t- R_EX_halipa_e\n", + "\t- R_EX_hdca_e\n", + "\t- R_EX_dimp_e\n", + "\t- R_EX_din_e\n", + "\t- R_EX_dms_e\n", + "\t- R_EX_dmso_e\n", + "\t- R_EX_hdcea_e\n", + "\t- R_EX_dopa_e\n", + "\t- R_EX_23ccmp_e\n", + "\t- R_EX_doxrbcn_e\n", + "\t- R_EX_dtmp_e\n", + "\t- R_EX_dump_e\n", + "\t- R_EX_duri_e\n", + "\t- R_EX_hg2_e\n", + "\t- R_EX_his__L_e\n", + "\t- R_EX_hom__L_e\n", + "\t- R_EX_eca4colipa_e\n", + "\t- R_EX_hxa_e\n", + "\t- R_EX_23cgmp_e\n", + "\t- R_EX_hxan_e\n", + "\t- R_EX_idon__L_e\n", + "\t- R_EX_ile__L_e\n", + "\t- R_EX_enlipa_e\n", + "\t- R_EX_enter_e\n", + "\t- R_EX_etha_e\n", + "\t- R_EX_ethso3_e\n", + "\t- R_EX_etoh_e\n", + "\t- R_EX_imp_e\n", + "\t- R_EX_f6p_e\n", + "\t- R_EX_23cump_e\n", + "\t- R_EX_fald_e\n", + "\t- R_EX_indole_e\n", + "\t- R_EX_inost_e\n", + "\t- R_EX_ins_e\n", + "\t- R_EX_fe2_e\n", + "\t- R_EX_fe3_e\n", + "\t- R_EX_isetac_e\n", + "\t- R_EX_fe3dcit_e\n", + "\t- R_EX_k_e\n", + "\t- R_EX_23dappa_e\n", + "\t- R_EX_kdo2lipid4_e\n", + "\t- R_EX_fe3dhbzs_e\n", + "\t- R_EX_26dap__M_e\n", + "\t- R_EX_fe3hox_e\n", + "\t- R_EX_lac__D_e\n", + "\t- R_EX_fe3hox_un_e\n", + "\t- R_EX_2ddglcn_e\n", + "\t- R_EX_fecrm_e\n", + "\t- R_EX_fecrm_un_e\n", + "\t- R_EX_lac__L_e\n", + "\t- R_EX_lcts_e\n", + "\t- R_EX_34dhpac_e\n", + "\t- R_EX_leu__L_e\n", + "\t- R_EX_feenter_e\n", + "\t- R_EX_lipa_e\n", + "\t- R_EX_3amp_e\n", + "\t- R_EX_lipa_cold_e\n", + "\t- R_EX_lipoate_e\n", + "\t- R_EX_lys__L_e\n", + "\t- R_EX_lyx__L_e\n", + "\t- R_EX_mal__D_e\n", + "\t- R_EX_feoxam_e\n", + "\t- R_EX_feoxam_un_e\n", + "\t- R_EX_for_e\n", + "\t- R_EX_fru_e\n", + "\t- R_EX_frulys_e\n", + "\t- R_EX_fruur_e\n", + "\t- R_EX_mal__L_e\n", + "\t- R_EX_fuc__L_e\n", + "\t- R_EX_malt_e\n", + "\t- R_EX_3cmp_e\n", + "\t- R_EX_malthx_e\n", + "\t- R_EX_maltpt_e\n", + "\t- R_EX_fum_e\n", + "\t- R_EX_fusa_e\n", + "\t- R_EX_g1p_e\n", + "\t- R_EX_malttr_e\n", + "\t- R_EX_g3pc_e\n", + "\t- R_EX_maltttr_e\n", + "\t- R_EX_3gmp_e\n", + "\t- R_EX_man_e\n", + "\t- R_EX_g3pe_e\n", + "\t- R_EX_g3pg_e\n", + "\t- R_EX_g3pi_e\n", + "\t- R_EX_man6p_e\n", + "\t- R_EX_3hcinnm_e\n", + "\t- R_EX_manglyc_e\n", + "\t- R_EX_g3ps_e\n", + "\t- R_EX_g6p_e\n", + "\t- R_EX_3hpp_e\n", + "\t- R_EX_gal_e\n", + "\t- R_EX_gal_bD_e\n", + "\t- R_EX_melib_e\n", + "\t- R_EX_meoh_e\n", + "\t- R_EX_met__D_e\n", + "\t- R_EX_met__L_e\n", + "\t- R_EX_metsox_R__L_e\n", + "\t- R_EX_gal1p_e\n", + "\t- R_EX_galct__D_e\n", + "\t- R_EX_galctn__D_e\n", + "\t- R_EX_galctn__L_e\n", + "\t- R_EX_galt_e\n", + "\t- R_EX_metsox_S__L_e\n", + "\t- R_EX_mg2_e\n", + "\t- R_EX_galur_e\n", + "\t- R_EX_mincyc_e\n", + "\t- R_EX_3hpppn_e\n", + "\t- R_EX_minohp_e\n", + "\t- R_EX_gam_e\n", + "\t- R_EX_gam6p_e\n", + "\t- R_EX_gbbtn_e\n", + "\t- R_EX_mmet_e\n", + "\t- R_EX_mn2_e\n", + "\t- R_EX_3ump_e\n", + "\t- R_EX_mnl_e\n", + "\t- R_EX_gdp_e\n", + "\t- R_EX_glc__D_e\n", + "\t- R_EX_glcn_e\n", + "\t- R_EX_mobd_e\n", + "\t- R_EX_4abut_e\n", + "\t- R_EX_mso3_e\n", + "\t- R_EX_glcr_e\n", + "\t- R_EX_glcur_e\n", + "\t- R_EX_n2o_e\n", + "\t- R_EX_glcur1p_e\n", + "\t- R_EX_4hoxpacd_e\n", + "\t- R_EX_gln__L_e\n", + "\t- R_EX_na1_e\n", + "\t- R_EX_nac_e\n", + "\t- R_EX_glu__L_e\n", + "\t- R_EX_nh4_e\n", + "\t- R_EX_5dglcn_e\n", + "\t- R_EX_ni2_e\n", + "\t- R_EX_nmn_e\n", + "\t- R_EX_no_e\n", + "\t- R_EX_no2_e\n", + "\t- R_EX_gly_e\n", + "\t- R_EX_glyald_e\n", + "\t- R_EX_glyb_e\n", + "\t- R_EX_no3_e\n", + "\t- R_EX_5mtr_e\n", + "\t- R_EX_novbcn_e\n", + "\t- R_EX_o16a4colipa_e\n", + "\t- R_EX_ocdcea_e\n", + "\t- R_EX_octa_e\n", + "\t- R_EX_orn_e\n", + "\t- R_EX_orot_e\n", + "\t- R_EX_o2_e\n", + "\t- R_EX_o2s_e\n", + "\t- R_EX_LalaDglu_e\n", + "\t- R_EX_ocdca_e\n", + "\t- R_EX_pacald_e\n", + "\t- R_EX_peamn_e\n", + "\t- R_EX_phe__L_e\n", + "\t- R_EX_pheme_e\n", + "\t- R_EX_LalaDgluMdap_e\n", + "\t- R_EX_pi_e\n", + "\t- R_EX_pnto__R_e\n", + "\t- R_EX_ppa_e\n", + "\t- R_EX_ppal_e\n", + "\t- R_EX_LalaDgluMdapDala_e\n", + "\t- R_EX_pppn_e\n", + "\t- R_EX_ppt_e\n", + "\t- R_EX_pro__L_e\n", + "\t- R_EX_progly_e\n", + "\t- R_EX_psclys_e\n", + "\t- R_EX_pser__L_e\n", + "\t- R_EX_ptrc_e\n", + "\t- R_EX_pydam_e\n", + "\t- R_EX_LalaLglu_e\n", + "\t- R_EX_pydx_e\n", + "\t- R_EX_pydxn_e\n", + "\t- R_EX_pyr_e\n", + "\t- R_EX_ac_e\n", + "\t- R_EX_quin_e\n", + "\t- R_EX_r5p_e\n", + "\t- R_EX_acac_e\n", + "\t- R_EX_rfamp_e\n", + "\t- R_EX_rib__D_e\n", + "\t- R_EX_rmn_e\n", + "\t- R_EX_sbt__D_e\n", + "\t- R_EX_sel_e\n", + "\t- R_EX_ser__D_e\n", + "\t- R_EX_ser__L_e\n", + "\t- R_EX_skm_e\n", + "\t- R_EX_slnt_e\n", + "\t- R_EX_so2_e\n", + "\t- R_EX_so3_e\n", + "\t- R_EX_acald_e\n", + "\t- R_EX_so4_e\n", + "\t- R_EX_spmd_e\n", + "\t- R_EX_succ_e\n", + "\t- R_EX_sucr_e\n", + "\t- R_EX_sulfac_e\n", + "\t- R_EX_tartr__D_e\n", + "\t- R_EX_tartr__L_e\n", + "\t- R_EX_taur_e\n", + "\t- R_EX_tcynt_e\n", + "\t- R_EX_acgal_e\n", + "\t- R_EX_thm_e\n", + "\t- R_EX_thr__L_e\n", + "\t- R_EX_thrp_e\n", + "\t- R_EX_thym_e\n", + "\t- R_EX_thymd_e\n", + "\t- R_EX_acgal1p_e\n", + "\t- R_EX_tma_e\n", + "\t- R_EX_tmao_e\n", + "\t- R_EX_tre_e\n", + "\t- R_EX_trp__L_e\n", + "\t- R_EX_acgam_e\n", + "\t- R_EX_tsul_e\n", + "\t- R_EX_ttdca_e\n", + "\t- R_EX_ttdcea_e\n", + "\t- R_EX_ttrcyc_e\n", + "\t- R_EX_tungs_e\n", + "\t- R_EX_tym_e\n", + "\t- R_EX_acgam1p_e\n", + "\t- R_EX_tyr__L_e\n", + "\t- R_EX_tyrp_e\n", + "\t- R_EX_uacgam_e\n", + "\t- R_EX_acmana_e\n", + "\t- R_EX_udpacgal_e\n", + "\t- R_EX_udpg_e\n", + "\t- R_EX_udpgal_e\n", + "\t- R_EX_acmum_e\n", + "\t- R_EX_udpglcur_e\n", + "\t- R_EX_ump_e\n", + "\t- R_EX_ura_e\n", + "\t- R_EX_urea_e\n", + "\t- R_EX_acnam_e\n", + "\t- R_EX_uri_e\n", + "\t- R_EX_val__L_e\n", + "\t- R_EX_xan_e\n", + "\t- R_EX_xmp_e\n", + "\t- R_EX_xtsn_e\n", + "\t- R_EX_acolipa_e\n", + "\t- R_EX_xyl__D_e\n", + "\t- R_EX_xylu__L_e\n", + "\t- R_EX_zn2_e\n", + "\t- R_EX_acser_e\n", + "\t- R_EX_ade_e\n", + "\t- R_EX_adn_e\n", + "\t- R_EX_adocbl_e\n", + "\t- R_EX_ag_e\n", + "\t- R_EX_agm_e\n", + "\t- R_EX_akg_e\n", + "\t- R_EX_ala_B_e\n", + "\t- R_EX_ala__D_e\n", + "\t- R_EX_ala__L_e\n", + "\t- R_EX_alaala_e\n", + "\t- R_EX_all__D_e\n", + "\t- R_EX_alltn_e\n", + "\t- R_EX_amp_e\n", + "\t- R_EX_anhgm_e\n", + "\t- R_EX_arab__L_e\n", + "\t- R_EX_arbt_e\n", + "\t- R_EX_arbtn_e\n", + "\t- R_EX_arbtn_fe3_e\n", + "\t- R_EX_arg__L_e\n", + "\t- R_EX_ascb__L_e\n", + "\t- R_EX_asn__L_e\n", + "\t- R_EX_aso3_e\n", + "\t- R_EX_asp__L_e\n", + "\t- R_EX_btn_e\n", + "\t- R_EX_but_e\n", + "\t- R_EX_butso3_e\n", + "\t- R_EX_ca2_e\n", + "\t- R_EX_cbi_e\n", + "\t- R_EX_cbl1_e\n", + "\t- R_EX_cd2_e\n", + "\t- R_EX_cgly_e\n", + "\t- R_EX_chol_e\n", + "\t- R_EX_chtbs_e\n", + "\t- R_EX_cit_e\n", + "\t- R_EX_cl_e\n", + "\t- R_EX_cm_e\n", + "\t- R_EX_cmp_e\n", + "\t- R_EX_co2_e\n", + "\t- R_EX_cobalt2_e\n", + "\t- R_EX_colipa_e\n", + "\t- R_EX_4hphac_e\n", + "\t- R_EX_4abz_e\n", + "\t- R_EX_acon_C_e\n", + "\t- R_EX_23dhbzs3_e\n", + "\t- R_EX_tet_e\n", + "\t- R_EX_salchs4_e\n", + "\t- R_EX_tag__D_e\n", + "\t- R_EX_oaa_e\n", + "\t- R_EX_salchs2fe_e\n", + "\t- R_EX_feroxB_e\n", + "\t- R_EX_tcb_e\n", + "\t- R_EX_cysi__L_e\n", + "\t- R_EX_tton_e\n", + "\t- R_EX_4hthr_e\n", + "\t- R_EX_feroxEfe_e\n", + "\t- R_EX_feroxE_e\n", + "\t- R_EX_fe3dhbzs3_e\n", + "\t- R_EX_pep_e\n", + "\t- R_EX_drib_e\n", + "\t- R_EX_rnam_e\n", + "\t- R_EX_airs_e\n", + "\t- R_EX_dxyl_e\n", + "\t- R_EX_3pg_e\n", + "\t- R_EX_feroxBfe_e\n", + "\t- R_EX_salchs4fe_e\n", + "\t- R_EX_2pg_e\n", + "\t- R_EX_frmd_e\n", + "\t- R_EX_colipaOA_e\n", + "\t- R_EX_3hoxpac_e\n", + "\t- R_EX_o6a4colipa_e\n", + "\t- R_EX_salchsx_e\n", + "\t- R_EX_AEP_e\n", + "\t- R_EX_udcpo4_e\n", + "\t- R_EX_icit_e\n", + "\t- R_EX_feroxGfe_e\n", + "\t- R_EX_colipa20Oag_e\n", + "\t- R_EX_remnant1_e\n", + "\t- R_EX_udcpo5_e\n", + "\t- R_EX_guln__L_e\n", + "\t- R_EX_feroxG_e\n", + "\t- R_EX_salchs2_e\n", + "\t- R_EX_3ntym_e\n", + "\t- R_EX_6apa_e\n", + "\t- R_EX_diact_e\n", + "\t- R_EX_cellb_e\n", + "\t- R_EX_btd_RR_e\n", + "\t- R_EX_13ppd_e\n", + "\t- R_EX_4hoxpac_e\n", + "\t- R_EX_rbt_e\n", + "\t- R_EX_bz_e\n", + "\t- R_EX_peng_e\n", + "\t- R_EX_pac_e\n", + "\t- R_EX_abt__D_e\n", + "\t- R_EX_salcn_e\n", + "\t- R_EX_fe_e\n", + "\t- R_EX_kokdolipidA_e\n", + "\t- R_EX_galam_e\n", + "\t- R_EX_raffin_e\n", + "\t- R_DM_lipidA_core_e_p\n", + "\t- R_EX_arab__D_e\u001b[0m\n", + "File saved at: ../../data/sbml_corrected/iUMN146_1321.xml\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: iSBO_1134\n", + "\u001b[0;93mWARNING : \n", + " - R_CAT: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_DHPTDNR: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_DHPTDNRN: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SPODM: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SPODMpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCASPtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCFUMtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCMALtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCTARTtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_EX_chitob_e: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_CHITOBpts: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_URCN: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\u001b[0m\n", + "\n", + "\n", + "\u001b[0;36m############################################\n", + "############################################\n", + " WRITING SBML FILE \n", + "############################################\n", + "############################################\n", + "\u001b[0m\n", + "\u001b[0;93mWARNING : IMPORT REACTION REMOVED BY DEFAULT\u001b[0m\n", + "\u001b[0;93mWARNING : If you want to keep import reaction\n", + "use option -kir / --keep-import-reactions\u001b[0m\n", + "\u001b[0;93mWARNING : Import reaction removed:\n", + "\t- R_DM_4crsol_c\n", + "\t- R_DM_5drib_c\n", + "\t- R_DM_aacald_c\n", + "\t- R_DM_amob_c\n", + "\t- R_DM_mththf_c\n", + "\t- R_DM_oxam_c\n", + "\t- R_EX_12ppd__R_e\n", + "\t- R_EX_12ppd__S_e\n", + "\t- R_EX_14glucan_e\n", + "\t- R_EX_15dap_e\n", + "\t- R_EX_23camp_e\n", + "\t- R_EX_23ccmp_e\n", + "\t- R_EX_23cgmp_e\n", + "\t- R_EX_23cump_e\n", + "\t- R_EX_amp_e\n", + "\t- R_EX_23dappa_e\n", + "\t- R_EX_anhgm_e\n", + "\t- R_EX_26dap__M_e\n", + "\t- R_EX_arab__L_e\n", + "\t- R_EX_2ddglcn_e\n", + "\t- R_EX_arbt_e\n", + "\t- R_EX_34dhpac_e\n", + "\t- R_EX_arbtn_e\n", + "\t- R_EX_3amp_e\n", + "\t- R_EX_arbtn_fe3_e\n", + "\t- R_EX_arg__L_e\n", + "\t- R_EX_3cmp_e\n", + "\t- R_EX_ascb__L_e\n", + "\t- R_EX_asn__L_e\n", + "\t- R_EX_3gmp_e\n", + "\t- R_EX_aso3_e\n", + "\t- R_EX_asp__L_e\n", + "\t- R_EX_btn_e\n", + "\t- R_EX_but_e\n", + "\t- R_EX_butso3_e\n", + "\t- R_EX_ca2_e\n", + "\t- R_EX_cbi_e\n", + "\t- R_EX_cbl1_e\n", + "\t- R_EX_cd2_e\n", + "\t- R_EX_cgly_e\n", + "\t- R_EX_3hcinnm_e\n", + "\t- R_EX_chol_e\n", + "\t- R_EX_fruur_e\n", + "\t- R_EX_chtbs_e\n", + "\t- R_EX_cit_e\n", + "\t- R_EX_fuc__L_e\n", + "\t- R_EX_cl_e\n", + "\t- R_EX_3hpp_e\n", + "\t- R_EX_cm_e\n", + "\t- R_EX_fum_e\n", + "\t- R_EX_fusa_e\n", + "\t- R_EX_3hpppn_e\n", + "\t- R_EX_g1p_e\n", + "\t- R_EX_g3pc_e\n", + "\t- R_EX_cmp_e\n", + "\t- R_EX_co2_e\n", + "\t- R_EX_cobalt2_e\n", + "\t- R_EX_3ump_e\n", + "\t- R_EX_colipa_e\n", + "\t- R_EX_colipap_e\n", + "\t- R_EX_g3pe_e\n", + "\t- R_EX_g3pg_e\n", + "\t- R_EX_g3pi_e\n", + "\t- R_EX_4abut_e\n", + "\t- R_EX_g3ps_e\n", + "\t- R_EX_g6p_e\n", + "\t- R_EX_cpgn_e\n", + "\t- R_EX_cpgn_un_e\n", + "\t- R_EX_4hoxpacd_e\n", + "\t- R_EX_crn_e\n", + "\t- R_EX_crn__D_e\n", + "\t- R_EX_csn_e\n", + "\t- R_EX_cu_e\n", + "\t- R_EX_cu2_e\n", + "\t- R_EX_gal_e\n", + "\t- R_EX_gal_bD_e\n", + "\t- R_EX_gal1p_e\n", + "\t- R_EX_galct__D_e\n", + "\t- R_EX_galctn__D_e\n", + "\t- R_EX_galctn__L_e\n", + "\t- R_EX_cyan_e\n", + "\t- R_EX_cynt_e\n", + "\t- R_EX_cys__D_e\n", + "\t- R_EX_galt_e\n", + "\t- R_EX_5dglcn_e\n", + "\t- R_EX_galur_e\n", + "\t- R_EX_gam_e\n", + "\t- R_EX_cys__L_e\n", + "\t- R_EX_cytd_e\n", + "\t- R_EX_dad_2_e\n", + "\t- R_EX_damp_e\n", + "\t- R_EX_gam6p_e\n", + "\t- R_EX_5mtr_e\n", + "\t- R_EX_gbbtn_e\n", + "\t- R_EX_gdp_e\n", + "\t- R_EX_dca_e\n", + "\t- R_EX_dcmp_e\n", + "\t- R_EX_dcyt_e\n", + "\t- R_EX_glc__D_e\n", + "\t- R_EX_ddca_e\n", + "\t- R_EX_LalaDglu_e\n", + "\t- R_EX_dgmp_e\n", + "\t- R_EX_dgsn_e\n", + "\t- R_EX_glcn_e\n", + "\t- R_EX_glcr_e\n", + "\t- R_EX_glcur_e\n", + "\t- R_EX_LalaDgluMdap_e\n", + "\t- R_EX_glcur1p_e\n", + "\t- R_EX_gln__L_e\n", + "\t- R_EX_glu__L_e\n", + "\t- R_EX_gly_e\n", + "\t- R_EX_dha_e\n", + "\t- R_EX_dimp_e\n", + "\t- R_EX_din_e\n", + "\t- R_EX_dms_e\n", + "\t- R_EX_glyald_e\n", + "\t- R_EX_glyb_e\n", + "\t- R_EX_dmso_e\n", + "\t- R_EX_glyc_e\n", + "\t- R_EX_dopa_e\n", + "\t- R_EX_LalaDgluMdapDala_e\n", + "\t- R_EX_doxrbcn_e\n", + "\t- R_EX_glyc__R_e\n", + "\t- R_EX_glyc2p_e\n", + "\t- R_EX_glyc3p_e\n", + "\t- R_EX_dtmp_e\n", + "\t- R_EX_glyclt_e\n", + "\t- R_EX_LalaLglu_e\n", + "\t- R_EX_gmp_e\n", + "\t- R_EX_dump_e\n", + "\t- R_EX_duri_e\n", + "\t- R_EX_eca4colipa_e\n", + "\t- R_EX_gsn_e\n", + "\t- R_EX_ac_e\n", + "\t- R_EX_gthox_e\n", + "\t- R_EX_enlipa_e\n", + "\t- R_EX_acac_e\n", + "\t- R_EX_enter_e\n", + "\t- R_EX_etha_e\n", + "\t- R_EX_gthrd_e\n", + "\t- R_EX_gtp_e\n", + "\t- R_EX_gua_e\n", + "\t- R_EX_ethso3_e\n", + "\t- R_EX_h_e\n", + "\t- R_EX_h2_e\n", + "\t- R_EX_h2o_e\n", + "\t- R_EX_h2o2_e\n", + "\t- R_EX_etoh_e\n", + "\t- R_EX_f6p_e\n", + "\t- R_EX_fald_e\n", + "\t- R_EX_h2s_e\n", + "\t- R_EX_fe2_e\n", + "\t- R_EX_acald_e\n", + "\t- R_EX_fe3_e\n", + "\t- R_EX_fe3dcit_e\n", + "\t- R_EX_fe3dhbzs_e\n", + "\t- R_EX_hacolipa_e\n", + "\t- R_EX_halipa_e\n", + "\t- R_EX_hdca_e\n", + "\t- R_EX_hdcea_e\n", + "\t- R_EX_fe3hox_e\n", + "\t- R_EX_hg2_e\n", + "\t- R_EX_acgal_e\n", + "\t- R_EX_his__L_e\n", + "\t- R_EX_fe3hox_un_e\n", + "\t- R_EX_fecrm_e\n", + "\t- R_EX_hom__L_e\n", + "\t- R_EX_acgal1p_e\n", + "\t- R_EX_hxa_e\n", + "\t- R_EX_hxan_e\n", + "\t- R_EX_fecrm_un_e\n", + "\t- R_EX_feenter_e\n", + "\t- R_EX_feoxam_e\n", + "\t- R_EX_acgam_e\n", + "\t- R_EX_feoxam_un_e\n", + "\t- R_EX_idon__L_e\n", + "\t- R_EX_for_e\n", + "\t- R_EX_ile__L_e\n", + "\t- R_EX_acgam1p_e\n", + "\t- R_EX_imp_e\n", + "\t- R_EX_indole_e\n", + "\t- R_EX_fru_e\n", + "\t- R_EX_frulys_e\n", + "\t- R_EX_inost_e\n", + "\t- R_EX_ins_e\n", + "\t- R_EX_isetac_e\n", + "\t- R_EX_malttr_e\n", + "\t- R_EX_k_e\n", + "\t- R_EX_maltttr_e\n", + "\t- R_EX_acmana_e\n", + "\t- R_EX_man_e\n", + "\t- R_EX_kdo2lipid4_e\n", + "\t- R_EX_lac__D_e\n", + "\t- R_EX_man6p_e\n", + "\t- R_EX_acmum_e\n", + "\t- R_EX_manglyc_e\n", + "\t- R_EX_melib_e\n", + "\t- R_EX_lac__L_e\n", + "\t- R_EX_lcts_e\n", + "\t- R_EX_meoh_e\n", + "\t- R_EX_acnam_e\n", + "\t- R_EX_met__D_e\n", + "\t- R_EX_met__L_e\n", + "\t- R_EX_leu__L_e\n", + "\t- R_EX_lipa_e\n", + "\t- R_EX_lipa_cold_e\n", + "\t- R_EX_acolipa_e\n", + "\t- R_EX_lipoate_e\n", + "\t- R_EX_lys__L_e\n", + "\t- R_EX_lyx__L_e\n", + "\t- R_EX_metsox_R__L_e\n", + "\t- R_EX_metsox_S__L_e\n", + "\t- R_EX_mg2_e\n", + "\t- R_EX_mincyc_e\n", + "\t- R_EX_minohp_e\n", + "\t- R_EX_mal__D_e\n", + "\t- R_EX_mal__L_e\n", + "\t- R_EX_acser_e\n", + "\t- R_EX_malt_e\n", + "\t- R_EX_malthx_e\n", + "\t- R_EX_maltpt_e\n", + "\t- R_EX_mmet_e\n", + "\t- R_EX_mn2_e\n", + "\t- R_EX_mnl_e\n", + "\t- R_EX_mobd_e\n", + "\t- R_EX_mso3_e\n", + "\t- R_EX_n2o_e\n", + "\t- R_EX_ade_e\n", + "\t- R_EX_na1_e\n", + "\t- R_EX_thym_e\n", + "\t- R_EX_thymd_e\n", + "\t- R_EX_tma_e\n", + "\t- R_EX_nac_e\n", + "\t- R_EX_tmao_e\n", + "\t- R_EX_adn_e\n", + "\t- R_EX_tre_e\n", + "\t- R_EX_trp__L_e\n", + "\t- R_EX_nh4_e\n", + "\t- R_EX_ni2_e\n", + "\t- R_EX_nmn_e\n", + "\t- R_EX_adocbl_e\n", + "\t- R_EX_no_e\n", + "\t- R_EX_no2_e\n", + "\t- R_EX_no3_e\n", + "\t- R_EX_tsul_e\n", + "\t- R_EX_ttdca_e\n", + "\t- R_EX_ttdcea_e\n", + "\t- R_EX_novbcn_e\n", + "\t- R_EX_ag_e\n", + "\t- R_EX_ttrcyc_e\n", + "\t- R_EX_tungs_e\n", + "\t- R_EX_o16a4colipa_e\n", + "\t- R_EX_o2_e\n", + "\t- R_EX_tym_e\n", + "\t- R_EX_agm_e\n", + "\t- R_EX_tyr__L_e\n", + "\t- R_EX_o2s_e\n", + "\t- R_EX_ocdca_e\n", + "\t- R_EX_ocdcea_e\n", + "\t- R_EX_octa_e\n", + "\t- R_EX_tyrp_e\n", + "\t- R_EX_uacgam_e\n", + "\t- R_EX_udpacgal_e\n", + "\t- R_EX_orn_e\n", + "\t- R_EX_udpg_e\n", + "\t- R_EX_akg_e\n", + "\t- R_EX_udpgal_e\n", + "\t- R_EX_udpglcur_e\n", + "\t- R_EX_orot_e\n", + "\t- R_EX_pacald_e\n", + "\t- R_EX_peamn_e\n", + "\t- R_EX_ala_B_e\n", + "\t- R_EX_phe__L_e\n", + "\t- R_EX_pheme_e\n", + "\t- R_EX_ump_e\n", + "\t- R_EX_ura_e\n", + "\t- R_EX_urea_e\n", + "\t- R_EX_uri_e\n", + "\t- R_EX_pi_e\n", + "\t- R_EX_pnto__R_e\n", + "\t- R_EX_val__L_e\n", + "\t- R_EX_ala__D_e\n", + "\t- R_EX_xan_e\n", + "\t- R_EX_ppa_e\n", + "\t- R_EX_ala__L_e\n", + "\t- R_EX_ppal_e\n", + "\t- R_EX_pppn_e\n", + "\t- R_EX_xmp_e\n", + "\t- R_EX_xtsn_e\n", + "\t- R_EX_xyl__D_e\n", + "\t- R_EX_ppt_e\n", + "\t- R_EX_alaala_e\n", + "\t- R_EX_xylu__L_e\n", + "\t- R_EX_zn2_e\n", + "\t- R_EX_pro__L_e\n", + "\t- R_EX_progly_e\n", + "\t- R_EX_psclys_e\n", + "\t- R_EX_pser__L_e\n", + "\t- R_EX_all__D_e\n", + "\t- R_EX_ptrc_e\n", + "\t- R_EX_pydam_e\n", + "\t- R_EX_pydx_e\n", + "\t- R_EX_pydxn_e\n", + "\t- R_EX_pyr_e\n", + "\t- R_EX_quin_e\n", + "\t- R_EX_alltn_e\n", + "\t- R_EX_r5p_e\n", + "\t- R_EX_rfamp_e\n", + "\t- R_EX_rib__D_e\n", + "\t- R_EX_rmn_e\n", + "\t- R_EX_sbt__D_e\n", + "\t- R_EX_sel_e\n", + "\t- R_EX_ser__D_e\n", + "\t- R_EX_ser__L_e\n", + "\t- R_EX_skm_e\n", + "\t- R_EX_slnt_e\n", + "\t- R_EX_so2_e\n", + "\t- R_EX_so3_e\n", + "\t- R_EX_so4_e\n", + "\t- R_EX_spmd_e\n", + "\t- R_EX_succ_e\n", + "\t- R_EX_sucr_e\n", + "\t- R_EX_sulfac_e\n", + "\t- R_EX_tartr__D_e\n", + "\t- R_EX_tartr__L_e\n", + "\t- R_EX_taur_e\n", + "\t- R_EX_tcynt_e\n", + "\t- R_EX_thm_e\n", + "\t- R_EX_thr__L_e\n", + "\t- R_EX_thrp_e\n", + "\t- R_EX_feroxBfe_e\n", + "\t- R_EX_4hphac_e\n", + "\t- R_EX_frmd_e\n", + "\t- R_EX_colipaOA_e\n", + "\t- R_EX_salchsx_e\n", + "\t- R_EX_4abz_e\n", + "\t- R_EX_AEP_e\n", + "\t- R_EX_icit_e\n", + "\t- R_EX_pep_e\n", + "\t- R_EX_cellb_e\n", + "\t- R_EX_feroxB_e\n", + "\t- R_EX_tcb_e\n", + "\t- R_EX_btd_RR_e\n", + "\t- R_EX_colipa20Oag_e\n", + "\t- R_EX_13ppd_e\n", + "\t- R_EX_rbt_e\n", + "\t- R_EX_bz_e\n", + "\t- R_EX_tton_e\n", + "\t- R_EX_4hthr_e\n", + "\t- R_EX_abt__D_e\n", + "\t- R_EX_feroxE_e\n", + "\t- R_EX_acon_C_e\n", + "\t- R_EX_23dhbzs3_e\n", + "\t- R_EX_tet_e\n", + "\t- R_EX_remnant1_e\n", + "\t- R_EX_salchs4_e\n", + "\t- R_EX_udcpo5_e\n", + "\t- R_EX_fe_e\n", + "\t- R_EX_oaa_e\n", + "\t- R_EX_salchs2fe_e\n", + "\t- R_EX_feroxEfe_e\n", + "\t- R_EX_cysi__L_e\n", + "\t- R_EX_kokdolipidA_e\n", + "\t- R_EX_fe3dhbzs3_e\n", + "\t- R_EX_drib_e\n", + "\t- R_EX_3hoxpac_e\n", + "\t- R_EX_salchs4fe_e\n", + "\t- R_DM_lipidA_core_e_p\n", + "\t- R_EX_2pg_e\n", + "\t- R_EX_salchs2_e\n", + "\t- R_EX_o6a4colipa_e\n", + "\t- R_EX_arab__D_e\n", + "\t- R_EX_3ntym_e\n", + "\t- R_EX_6apa_e\n", + "\t- R_EX_rnam_e\n", + "\t- R_EX_4hoxpac_e\n", + "\t- R_EX_peng_e\n", + "\t- R_EX_pac_e\n", + "\t- R_EX_airs_e\n", + "\t- R_EX_dxyl_e\n", + "\t- R_EX_salcn_e\n", + "\t- R_EX_3pg_e\n", + "\t- R_EX_galam_e\n", + "\t- R_EX_raffin_e\n", + "\t- R_SK_thf_c\n", + "\t- R_EX_feroxGfe_e\n", + "\t- R_EX_udcpo4_e\n", + "\t- R_EX_tag__D_e\n", + "\t- R_EX_guln__L_e\n", + "\t- R_EX_feroxG_e\n", + "\t- R_EX_diact_e\u001b[0m\n", + "File saved at: ../../data/sbml_corrected/iSBO_1134.xml\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: iBWG_1329\n", + "\u001b[0;93mWARNING : \n", + " - R_CAT: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_DHPTDNR: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_DHPTDNRN: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_FHL: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCTARTtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SPODM: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCASPtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCFUMtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCMALtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SPODMpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_EX_chitob_e: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_URCN: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_CHITPH: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_CHITOBpts: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\u001b[0m\n", + "\n", + "\n", + "\u001b[0;36m############################################\n", + "############################################\n", + " WRITING SBML FILE \n", + "############################################\n", + "############################################\n", + "\u001b[0m\n", + "\u001b[0;93mWARNING : IMPORT REACTION REMOVED BY DEFAULT\u001b[0m\n", + "\u001b[0;93mWARNING : If you want to keep import reaction\n", + "use option -kir / --keep-import-reactions\u001b[0m\n", + "\u001b[0;93mWARNING : Import reaction removed:\n", + "\t- R_EX_fecrm_e\n", + "\t- R_EX_fecrm_un_e\n", + "\t- R_EX_feenter_e\n", + "\t- R_EX_feoxam_e\n", + "\t- R_EX_feoxam_un_e\n", + "\t- R_EX_for_e\n", + "\t- R_EX_fru_e\n", + "\t- R_EX_frulys_e\n", + "\t- R_EX_fruur_e\n", + "\t- R_EX_agm_e\n", + "\t- R_EX_fuc__L_e\n", + "\t- R_EX_akg_e\n", + "\t- R_EX_ala_B_e\n", + "\t- R_EX_fum_e\n", + "\t- R_EX_ala__D_e\n", + "\t- R_EX_ala__L_e\n", + "\t- R_EX_alaala_e\n", + "\t- R_DM_4crsol_c\n", + "\t- R_DM_5drib_c\n", + "\t- R_EX_all__D_e\n", + "\t- R_EX_alltn_e\n", + "\t- R_DM_aacald_c\n", + "\t- R_EX_amp_e\n", + "\t- R_EX_fusa_e\n", + "\t- R_EX_anhgm_e\n", + "\t- R_DM_amob_c\n", + "\t- R_DM_mththf_c\n", + "\t- R_EX_arab__L_e\n", + "\t- R_DM_oxam_c\n", + "\t- R_EX_arbt_e\n", + "\t- R_EX_g1p_e\n", + "\t- R_EX_arbtn_e\n", + "\t- R_EX_arbtn_fe3_e\n", + "\t- R_EX_12ppd__R_e\n", + "\t- R_EX_12ppd__S_e\n", + "\t- R_EX_14glucan_e\n", + "\t- R_EX_15dap_e\n", + "\t- R_EX_23camp_e\n", + "\t- R_EX_23ccmp_e\n", + "\t- R_EX_23cgmp_e\n", + "\t- R_EX_23cump_e\n", + "\t- R_EX_arg__L_e\n", + "\t- R_EX_ascb__L_e\n", + "\t- R_EX_asn__L_e\n", + "\t- R_EX_aso3_e\n", + "\t- R_EX_asp__L_e\n", + "\t- R_EX_btn_e\n", + "\t- R_EX_but_e\n", + "\t- R_EX_butso3_e\n", + "\t- R_EX_ca2_e\n", + "\t- R_EX_cbi_e\n", + "\t- R_EX_23dappa_e\n", + "\t- R_EX_26dap__M_e\n", + "\t- R_EX_2ddglcn_e\n", + "\t- R_EX_cbl1_e\n", + "\t- R_EX_cd2_e\n", + "\t- R_EX_34dhpac_e\n", + "\t- R_EX_g3pc_e\n", + "\t- R_EX_3amp_e\n", + "\t- R_EX_3cmp_e\n", + "\t- R_EX_3gmp_e\n", + "\t- R_EX_3hcinnm_e\n", + "\t- R_EX_cgly_e\n", + "\t- R_EX_chol_e\n", + "\t- R_EX_chtbs_e\n", + "\t- R_EX_cit_e\n", + "\t- R_EX_cl_e\n", + "\t- R_EX_3hpp_e\n", + "\t- R_EX_g3pe_e\n", + "\t- R_EX_3hpppn_e\n", + "\t- R_EX_3ump_e\n", + "\t- R_EX_4abut_e\n", + "\t- R_EX_4hoxpacd_e\n", + "\t- R_EX_5dglcn_e\n", + "\t- R_EX_5mtr_e\n", + "\t- R_EX_LalaDglu_e\n", + "\t- R_EX_cm_e\n", + "\t- R_EX_cmp_e\n", + "\t- R_EX_co2_e\n", + "\t- R_EX_cobalt2_e\n", + "\t- R_EX_colipa_e\n", + "\t- R_EX_colipap_e\n", + "\t- R_EX_cpgn_e\n", + "\t- R_EX_cpgn_un_e\n", + "\t- R_EX_LalaDgluMdap_e\n", + "\t- R_EX_LalaDgluMdapDala_e\n", + "\t- R_EX_LalaLglu_e\n", + "\t- R_EX_crn_e\n", + "\t- R_EX_g3pg_e\n", + "\t- R_EX_crn__D_e\n", + "\t- R_EX_csn_e\n", + "\t- R_EX_cu_e\n", + "\t- R_EX_ac_e\n", + "\t- R_EX_acac_e\n", + "\t- R_EX_acald_e\n", + "\t- R_EX_acgal_e\n", + "\t- R_EX_acgal1p_e\n", + "\t- R_EX_cu2_e\n", + "\t- R_EX_cyan_e\n", + "\t- R_EX_g3pi_e\n", + "\t- R_EX_cynt_e\n", + "\t- R_EX_cys__D_e\n", + "\t- R_EX_acgam_e\n", + "\t- R_EX_acgam1p_e\n", + "\t- R_EX_acmana_e\n", + "\t- R_EX_acmum_e\n", + "\t- R_EX_acnam_e\n", + "\t- R_EX_acolipa_e\n", + "\t- R_EX_acser_e\n", + "\t- R_EX_cys__L_e\n", + "\t- R_EX_cytd_e\n", + "\t- R_EX_dad_2_e\n", + "\t- R_EX_damp_e\n", + "\t- R_EX_dca_e\n", + "\t- R_EX_ade_e\n", + "\t- R_EX_adn_e\n", + "\t- R_EX_adocbl_e\n", + "\t- R_EX_dcmp_e\n", + "\t- R_EX_dcyt_e\n", + "\t- R_EX_ag_e\n", + "\t- R_EX_g3ps_e\n", + "\t- R_EX_ddca_e\n", + "\t- R_EX_dgmp_e\n", + "\t- R_EX_g6p_e\n", + "\t- R_EX_dgsn_e\n", + "\t- R_EX_dha_e\n", + "\t- R_EX_dimp_e\n", + "\t- R_EX_lipoate_e\n", + "\t- R_EX_lys__L_e\n", + "\t- R_EX_lyx__L_e\n", + "\t- R_EX_din_e\n", + "\t- R_EX_mal__D_e\n", + "\t- R_EX_gal_e\n", + "\t- R_EX_mal__L_e\n", + "\t- R_EX_dms_e\n", + "\t- R_EX_dmso_e\n", + "\t- R_EX_dopa_e\n", + "\t- R_EX_malt_e\n", + "\t- R_EX_malthx_e\n", + "\t- R_EX_doxrbcn_e\n", + "\t- R_EX_maltpt_e\n", + "\t- R_EX_gal_bD_e\n", + "\t- R_EX_malttr_e\n", + "\t- R_EX_maltttr_e\n", + "\t- R_EX_dtmp_e\n", + "\t- R_EX_dump_e\n", + "\t- R_EX_duri_e\n", + "\t- R_EX_eca4colipa_e\n", + "\t- R_EX_enlipa_e\n", + "\t- R_EX_enter_e\n", + "\t- R_EX_etha_e\n", + "\t- R_EX_man_e\n", + "\t- R_EX_man6p_e\n", + "\t- R_EX_manglyc_e\n", + "\t- R_EX_melib_e\n", + "\t- R_EX_meoh_e\n", + "\t- R_EX_met__D_e\n", + "\t- R_EX_met__L_e\n", + "\t- R_EX_ethso3_e\n", + "\t- R_EX_etoh_e\n", + "\t- R_EX_f6p_e\n", + "\t- R_EX_fald_e\n", + "\t- R_EX_fe2_e\n", + "\t- R_EX_metsox_R__L_e\n", + "\t- R_EX_metsox_S__L_e\n", + "\t- R_EX_fe3_e\n", + "\t- R_EX_mg2_e\n", + "\t- R_EX_gal1p_e\n", + "\t- R_EX_mincyc_e\n", + "\t- R_EX_minohp_e\n", + "\t- R_EX_fe3dcit_e\n", + "\t- R_EX_fe3dhbzs_e\n", + "\t- R_EX_mmet_e\n", + "\t- R_EX_galct__D_e\n", + "\t- R_EX_mn2_e\n", + "\t- R_EX_mnl_e\n", + "\t- R_EX_mobd_e\n", + "\t- R_EX_fe3hox_e\n", + "\t- R_EX_fe3hox_un_e\n", + "\t- R_EX_mso3_e\n", + "\t- R_EX_galctn__D_e\n", + "\t- R_EX_n2o_e\n", + "\t- R_EX_na1_e\n", + "\t- R_EX_nac_e\n", + "\t- R_EX_nh4_e\n", + "\t- R_EX_ni2_e\n", + "\t- R_EX_nmn_e\n", + "\t- R_EX_no_e\n", + "\t- R_EX_sulfac_e\n", + "\t- R_EX_tartr__D_e\n", + "\t- R_EX_tartr__L_e\n", + "\t- R_EX_taur_e\n", + "\t- R_EX_tcynt_e\n", + "\t- R_EX_thm_e\n", + "\t- R_EX_thr__L_e\n", + "\t- R_EX_no2_e\n", + "\t- R_EX_no3_e\n", + "\t- R_EX_galctn__L_e\n", + "\t- R_EX_novbcn_e\n", + "\t- R_EX_o16a4colipa_e\n", + "\t- R_EX_o2_e\n", + "\t- R_EX_o2s_e\n", + "\t- R_EX_thrp_e\n", + "\t- R_EX_thym_e\n", + "\t- R_EX_thymd_e\n", + "\t- R_EX_tma_e\n", + "\t- R_EX_tmao_e\n", + "\t- R_EX_ocdca_e\n", + "\t- R_EX_ocdcea_e\n", + "\t- R_EX_galt_e\n", + "\t- R_EX_octa_e\n", + "\t- R_EX_orn_e\n", + "\t- R_EX_orot_e\n", + "\t- R_EX_tre_e\n", + "\t- R_EX_trp__L_e\n", + "\t- R_EX_tsul_e\n", + "\t- R_EX_ttdca_e\n", + "\t- R_EX_pacald_e\n", + "\t- R_EX_galur_e\n", + "\t- R_EX_peamn_e\n", + "\t- R_EX_phe__L_e\n", + "\t- R_EX_pheme_e\n", + "\t- R_EX_pi_e\n", + "\t- R_EX_pnto__R_e\n", + "\t- R_EX_ppa_e\n", + "\t- R_EX_ttdcea_e\n", + "\t- R_EX_ttrcyc_e\n", + "\t- R_EX_tungs_e\n", + "\t- R_EX_tym_e\n", + "\t- R_EX_tyr__L_e\n", + "\t- R_EX_tyrp_e\n", + "\t- R_EX_uacgam_e\n", + "\t- R_EX_udpacgal_e\n", + "\t- R_EX_ppal_e\n", + "\t- R_EX_pppn_e\n", + "\t- R_EX_ppt_e\n", + "\t- R_EX_pro__L_e\n", + "\t- R_EX_udpg_e\n", + "\t- R_EX_udpgal_e\n", + "\t- R_EX_gam_e\n", + "\t- R_EX_udpglcur_e\n", + "\t- R_EX_ump_e\n", + "\t- R_EX_ura_e\n", + "\t- R_EX_progly_e\n", + "\t- R_EX_psclys_e\n", + "\t- R_EX_pser__L_e\n", + "\t- R_EX_ptrc_e\n", + "\t- R_EX_pydam_e\n", + "\t- R_EX_urea_e\n", + "\t- R_EX_uri_e\n", + "\t- R_EX_val__L_e\n", + "\t- R_EX_pydx_e\n", + "\t- R_EX_gam6p_e\n", + "\t- R_EX_pydxn_e\n", + "\t- R_EX_pyr_e\n", + "\t- R_EX_quin_e\n", + "\t- R_EX_xan_e\n", + "\t- R_EX_xmp_e\n", + "\t- R_EX_xtsn_e\n", + "\t- R_EX_xyl__D_e\n", + "\t- R_EX_xylu__L_e\n", + "\t- R_EX_zn2_e\n", + "\t- R_EX_r5p_e\n", + "\t- R_EX_rfamp_e\n", + "\t- R_EX_rib__D_e\n", + "\t- R_EX_rmn_e\n", + "\t- R_EX_sbt__D_e\n", + "\t- R_EX_sel_e\n", + "\t- R_EX_ser__D_e\n", + "\t- R_EX_gbbtn_e\n", + "\t- R_EX_ser__L_e\n", + "\t- R_EX_skm_e\n", + "\t- R_EX_slnt_e\n", + "\t- R_EX_so2_e\n", + "\t- R_EX_so3_e\n", + "\t- R_EX_gdp_e\n", + "\t- R_EX_so4_e\n", + "\t- R_EX_spmd_e\n", + "\t- R_EX_succ_e\n", + "\t- R_EX_sucr_e\n", + "\t- R_EX_glc__D_e\n", + "\t- R_EX_glcn_e\n", + "\t- R_EX_glcr_e\n", + "\t- R_EX_glcur_e\n", + "\t- R_EX_glcur1p_e\n", + "\t- R_EX_gln__L_e\n", + "\t- R_EX_glu__L_e\n", + "\t- R_EX_gly_e\n", + "\t- R_EX_glyald_e\n", + "\t- R_EX_glyb_e\n", + "\t- R_EX_glyc_e\n", + "\t- R_EX_glyc__R_e\n", + "\t- R_EX_glyc2p_e\n", + "\t- R_EX_glyc3p_e\n", + "\t- R_EX_glyclt_e\n", + "\t- R_EX_gmp_e\n", + "\t- R_EX_gsn_e\n", + "\t- R_EX_gthox_e\n", + "\t- R_EX_gthrd_e\n", + "\t- R_EX_gtp_e\n", + "\t- R_EX_gua_e\n", + "\t- R_EX_h_e\n", + "\t- R_EX_h2_e\n", + "\t- R_EX_h2o_e\n", + "\t- R_EX_h2o2_e\n", + "\t- R_EX_h2s_e\n", + "\t- R_EX_hacolipa_e\n", + "\t- R_EX_halipa_e\n", + "\t- R_EX_hdca_e\n", + "\t- R_EX_hdcea_e\n", + "\t- R_EX_hg2_e\n", + "\t- R_EX_his__L_e\n", + "\t- R_EX_hom__L_e\n", + "\t- R_EX_hxa_e\n", + "\t- R_EX_hxan_e\n", + "\t- R_EX_idon__L_e\n", + "\t- R_EX_ile__L_e\n", + "\t- R_EX_imp_e\n", + "\t- R_EX_indole_e\n", + "\t- R_EX_inost_e\n", + "\t- R_EX_ins_e\n", + "\t- R_EX_isetac_e\n", + "\t- R_EX_k_e\n", + "\t- R_EX_kdo2lipid4_e\n", + "\t- R_EX_lac__D_e\n", + "\t- R_EX_lac__L_e\n", + "\t- R_EX_lcts_e\n", + "\t- R_EX_leu__L_e\n", + "\t- R_EX_lipa_e\n", + "\t- R_EX_lipa_cold_e\n", + "\t- R_EX_4hphac_e\n", + "\t- R_EX_4abz_e\n", + "\t- R_EX_acon_C_e\n", + "\t- R_EX_feroxB_e\n", + "\t- R_EX_tcb_e\n", + "\t- R_EX_tton_e\n", + "\t- R_EX_4hthr_e\n", + "\t- R_EX_feroxEfe_e\n", + "\t- R_EX_feroxE_e\n", + "\t- R_EX_pep_e\n", + "\t- R_EX_23dhbzs3_e\n", + "\t- R_EX_tet_e\n", + "\t- R_EX_salchs4_e\n", + "\t- R_EX_tag__D_e\n", + "\t- R_EX_oaa_e\n", + "\t- R_EX_salchs2fe_e\n", + "\t- R_EX_cysi__L_e\n", + "\t- R_EX_fe3dhbzs3_e\n", + "\t- R_EX_drib_e\n", + "\t- R_EX_feroxBfe_e\n", + "\t- R_EX_salchs4fe_e\n", + "\t- R_EX_2pg_e\n", + "\t- R_EX_frmd_e\n", + "\t- R_EX_colipaOA_e\n", + "\t- R_EX_salchsx_e\n", + "\t- R_EX_AEP_e\n", + "\t- R_EX_icit_e\n", + "\t- R_EX_colipa20Oag_e\n", + "\t- R_EX_remnant1_e\n", + "\t- R_EX_salchs2_e\n", + "\t- R_EX_rnam_e\n", + "\t- R_EX_airs_e\n", + "\t- R_EX_dxyl_e\n", + "\t- R_EX_3pg_e\n", + "\t- R_EX_udcpo4_e\n", + "\t- R_EX_feroxGfe_e\n", + "\t- R_EX_udcpo5_e\n", + "\t- R_EX_guln__L_e\n", + "\t- R_EX_feroxG_e\n", + "\t- R_EX_diact_e\n", + "\t- R_EX_cellb_e\n", + "\t- R_EX_btd_RR_e\n", + "\t- R_EX_rbt_e\n", + "\t- R_EX_bz_e\n", + "\t- R_EX_13ppd_e\n", + "\t- R_EX_abt__D_e\n", + "\t- R_EX_fe_e\n", + "\t- R_EX_kokdolipidA_e\n", + "\t- R_EX_3hoxpac_e\n", + "\t- R_EX_o6a4colipa_e\n", + "\t- R_EX_4hoxpac_e\n", + "\t- R_DM_lipidA_core_e_p\n", + "\t- R_EX_3ntym_e\n", + "\t- R_EX_6apa_e\n", + "\t- R_EX_peng_e\n", + "\t- R_EX_pac_e\n", + "\t- R_EX_salcn_e\n", + "\t- R_EX_galam_e\n", + "\t- R_EX_raffin_e\n", + "\t- R_EX_arab__D_e\u001b[0m\n", + "File saved at: ../../data/sbml_corrected/iBWG_1329.xml\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: iIT341\n", + "\u001b[0;93mWARNING : - R_EX_o2_e: Reactants and products exchanged.\n", + " Boundaries was: [-12.0 ; 0.0]\n", + " - R_FMNRx2: Reactants and products exchanged.\n", + " Boundaries was: [-999999.0 ; 0.0]\n", + " - R_KARA1: Reactants and products exchanged.\n", + " Boundaries was: [-999999.0 ; 0.0]\n", + "\u001b[0m\n", + "\n", + "\n", + "\u001b[0;36m############################################\n", + "############################################\n", + " WRITING SBML FILE \n", + "############################################\n", + "############################################\n", + "\u001b[0m\n", + "\u001b[0;93mWARNING : IMPORT REACTION REMOVED BY DEFAULT\u001b[0m\n", + "\u001b[0;93mWARNING : If you want to keep import reaction\n", + "use option -kir / --keep-import-reactions\u001b[0m\n", + "\u001b[0;93mWARNING : Reaction with tag reversible modified:\n", + "\t- R_EX_o2_e\n", + "\t- R_FMNRx2\n", + "\t- R_KARA1\u001b[0m\n", + "\u001b[0;93mWARNING : Reaction with Reactants and Products exchanged:\n", + "\t- R_EX_o2_e\n", + "\t- R_FMNRx2\n", + "\t- R_KARA1\u001b[0m\n", + "\u001b[0;93mWARNING : Import reaction removed:\n", + "\t- R_EX_pime_e\n", + "\t- R_EX_phe__L_e\n", + "\t- R_EX_pheme_e\n", + "\t- R_EX_pi_e\n", + "\t- R_EX_fe2_e\n", + "\t- R_EX_fe3_e\n", + "\t- R_EX_for_e\n", + "\t- R_EX_fum_e\n", + "\t- R_EX_gal_e\n", + "\t- R_EX_glc__D_e\n", + "\t- R_EX_gln__L_e\n", + "\t- R_EX_glu__L_e\n", + "\t- R_EX_gly_e\n", + "\t- R_EX_gsn_e\n", + "\t- R_EX_gua_e\n", + "\t- R_EX_h2_e\n", + "\t- R_EX_h2co3_e\n", + "\t- R_EX_h2o_e\n", + "\t- R_EX_h_e\n", + "\t- R_EX_his__L_e\n", + "\t- R_EX_hxan_e\n", + "\t- R_EX_ile__L_e\n", + "\t- R_EX_lac__L_e\n", + "\t- R_EX_leu__L_e\n", + "\t- R_EX_lys__L_e\n", + "\t- R_EX_mal__L_e\n", + "\t- R_EX_met__L_e\n", + "\t- R_EX_na1_e\n", + "\t- R_EX_nh4_e\n", + "\t- R_EX_ni2_e\n", + "\t- R_EX_nmn_e\n", + "\t- R_EX_no2_e\n", + "\t- R_EX_no3_e\n", + "\t- R_EX_no_e\n", + "\t- R_EX_o2_e\n", + "\t- R_EX_orn_e\n", + "\t- R_EX_orot_e\n", + "\t- R_EX_pro__L_e\n", + "\t- R_EX_pyr_e\n", + "\t- R_DM_hmfurn_c\n", + "\t- R_EX_ser__D_e\n", + "\t- R_EX_ser__L_e\n", + "\t- R_EX_so4_e\n", + "\t- R_EX_succ_e\n", + "\t- R_EX_thm_e\n", + "\t- R_EX_thr__L_e\n", + "\t- R_EX_thymd_e\n", + "\t- R_EX_trp__L_e\n", + "\t- R_EX_tyr__L_e\n", + "\t- R_EX_ura_e\n", + "\t- R_EX_urea_e\n", + "\t- R_EX_uri_e\n", + "\t- R_EX_val__L_e\n", + "\t- R_EX_xan_e\n", + "\t- R_EX_etoh_e\n", + "\t- R_EX_aa_e\n", + "\t- R_EX_ac_e\n", + "\t- R_EX_acac_e\n", + "\t- R_EX_acald_e\n", + "\t- R_EX_ad_e\n", + "\t- R_EX_ade_e\n", + "\t- R_EX_adn_e\n", + "\t- R_EX_akg_e\n", + "\t- R_EX_ala__D_e\n", + "\t- R_EX_ala__L_e\n", + "\t- R_EX_arg__L_e\n", + "\t- R_EX_asn__L_e\n", + "\t- R_EX_asp__L_e\n", + "\t- R_EX_cit_e\n", + "\t- R_EX_co2_e\n", + "\t- R_EX_cys__L_e\n", + "\t- R_EX_cytd_e\n", + "\t- R_EX_dad_2_e\n", + "\t- R_EX_dcyt_e\n", + "\t- R_EX_duri_e\n", + "\t- R_SK_ahcys_c\n", + "\t- R_DM_amob_c\u001b[0m\n", + "File saved at: ../../data/sbml_corrected/iIT341.xml\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: iJB785\n", + "\u001b[0;93mWARNING : - R_HSDy: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_ASAD: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + "\n", + " - R_DPOR: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_CPPPGO2: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + " - R_IPPMIb: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_LDAPAT: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_KARA1: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_SK_amylose_c: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + "\n", + " - R_SK_14glucan_c: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SK_glycogen_c: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + " - R_EX_leu__L_e: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_IPPMIa: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_GLYCLTDx: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + "\n", + " - R_NDH_1_4_um_copy2: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_NDH_1_1_um_copy2: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\u001b[0m\n", + "\n", + "\n", + "\u001b[0;36m############################################\n", + "############################################\n", + " WRITING SBML FILE \n", + "############################################\n", + "############################################\n", + "\u001b[0m\n", + "\u001b[0;93mWARNING : IMPORT REACTION REMOVED BY DEFAULT\u001b[0m\n", + "\u001b[0;93mWARNING : If you want to keep import reaction\n", + "use option -kir / --keep-import-reactions\u001b[0m\n", + "\u001b[0;93mWARNING : Reaction with tag reversible modified:\n", + "\t- R_HSDy\n", + "\t- R_ASAD\n", + "\t- R_IPPMIb\n", + "\t- R_LDAPAT\n", + "\t- R_KARA1\n", + "\t- R_SK_amylose_c\n", + "\t- R_EX_leu__L_e\n", + "\t- R_IPPMIa\n", + "\t- R_GLYCLTDx\u001b[0m\n", + "\u001b[0;93mWARNING : Reaction with Reactants and Products exchanged:\n", + "\t- R_HSDy\n", + "\t- R_ASAD\n", + "\t- R_IPPMIb\n", + "\t- R_LDAPAT\n", + "\t- R_KARA1\n", + "\t- R_SK_amylose_c\n", + "\t- R_EX_leu__L_e\n", + "\t- R_IPPMIa\n", + "\t- R_GLYCLTDx\u001b[0m\n", + "\u001b[0;93mWARNING : Import reaction removed:\n", + "\t- R_EX_gln__L_e\n", + "\t- R_EX_hco3_e\n", + "\t- R_EX_mn2_e\n", + "\t- R_EX_arg__L_e\n", + "\t- R_SK_for_c\n", + "\t- R_EX_mg2_e\n", + "\t- R_EX_ptrc_e\n", + "\t- R_DM_h2_c\n", + "\t- R_EX_ca2_e\n", + "\t- R_EX_nh4_e\n", + "\t- R_DM_dialurate_c\n", + "\t- R_DM_co_c\n", + "\t- R_EX_fe2_e\n", + "\t- R_EX_cu2_e\n", + "\t- R_DM_5drib_c\n", + "\t- R_SK_amylose_c\n", + "\t- R_EX_k_e\n", + "\t- R_EX_h2o_e\n", + "\t- R_EX_o2_e\n", + "\t- R_EX_co2_e\n", + "\t- R_EX_leu__L_e\n", + "\t- R_EX_cobalt2_e\n", + "\t- R_EX_no3_e\n", + "\t- R_EX_zn2_e\n", + "\t- R_EX_fe3_e\n", + "\t- R_EX_so4_e\n", + "\t- R_EX_spmd_e\n", + "\t- R_EX_mobd_e\n", + "\t- R_EX_ni2_e\n", + "\t- R_EX_na1_e\n", + "\t- R_EX_cynt_e\n", + "\t- R_EX_h_e\n", + "\t- R_EX_photon410_e\n", + "\t- R_EX_photon430_e\n", + "\t- R_EX_photon450_e\n", + "\t- R_EX_photon470_e\n", + "\t- R_EX_photon490_e\n", + "\t- R_EX_photon510_e\n", + "\t- R_DM_amob_c\n", + "\t- R_EX_photon530_e\n", + "\t- R_EX_photon550_e\n", + "\t- R_EX_photon570_e\n", + "\t- R_EX_photon590_e\n", + "\t- R_EX_photon610_e\n", + "\t- R_EX_photon630_e\n", + "\t- R_EX_photon650_e\n", + "\t- R_EX_photon670_e\n", + "\t- R_EX_photon690_e\n", + "\t- R_EX_pi_e\n", + "\t- R_DM_pho_loss_c\n", + "\t- R_EX_meoh_e\n", + "\t- R_EX_cl_e\n", + "\t- R_SK_fum_c\n", + "\t- R_DM_succ_c\n", + "\t- R_SK_akg_c\n", + "\t- R_DM_ac_c\n", + "\t- R_DM_lac__D_c\u001b[0m\n", + "File saved at: ../../data/sbml_corrected/iJB785.xml\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: iY75_1357\n", + "\u001b[0;93mWARNING : \n", + " - R_CAT: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_FHL: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCASPtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCFUMtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCMALtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCTARTtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SPODM: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SPODMpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_EX_chitob_e: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_URCN: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_CHITOBpts: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_CHITPH: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_DHPTDNR: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_DHPTDNRN: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\u001b[0m\n", + "\n", + "\n", + "\u001b[0;36m############################################\n", + "############################################\n", + " WRITING SBML FILE \n", + "############################################\n", + "############################################\n", + "\u001b[0m\n", + "\u001b[0;93mWARNING : IMPORT REACTION REMOVED BY DEFAULT\u001b[0m\n", + "\u001b[0;93mWARNING : If you want to keep import reaction\n", + "use option -kir / --keep-import-reactions\u001b[0m\n", + "\u001b[0;93mWARNING : Import reaction removed:\n", + "\t- R_EX_5dglcn_e\n", + "\t- R_EX_5mtr_e\n", + "\t- R_EX_LalaDglu_e\n", + "\t- R_EX_4abut_e\n", + "\t- R_EX_4hoxpacd_e\n", + "\t- R_EX_LalaDgluMdapDala_e\n", + "\t- R_EX_LalaDgluMdap_e\n", + "\t- R_EX_acac_e\n", + "\t- R_EX_LalaLglu_e\n", + "\t- R_EX_ac_e\n", + "\t- R_EX_acald_e\n", + "\t- R_DM_4crsol_c\n", + "\t- R_DM_5drib_c\n", + "\t- R_DM_aacald_c\n", + "\t- R_EX_acgal_e\n", + "\t- R_DM_amob_c\n", + "\t- R_DM_mththf_c\n", + "\t- R_DM_oxam_c\n", + "\t- R_EX_acgal1p_e\n", + "\t- R_EX_acgam_e\n", + "\t- R_EX_eca4colipa_e\n", + "\t- R_EX_acgam1p_e\n", + "\t- R_EX_enlipa_e\n", + "\t- R_EX_12ppd__R_e\n", + "\t- R_EX_12ppd__S_e\n", + "\t- R_EX_14glucan_e\n", + "\t- R_EX_enter_e\n", + "\t- R_EX_etha_e\n", + "\t- R_EX_15dap_e\n", + "\t- R_EX_ethso3_e\n", + "\t- R_EX_etoh_e\n", + "\t- R_EX_f6p_e\n", + "\t- R_EX_fald_e\n", + "\t- R_EX_fe2_e\n", + "\t- R_EX_fe3_e\n", + "\t- R_EX_23camp_e\n", + "\t- R_EX_23ccmp_e\n", + "\t- R_EX_23cgmp_e\n", + "\t- R_EX_23cump_e\n", + "\t- R_EX_23dappa_e\n", + "\t- R_EX_26dap__M_e\n", + "\t- R_EX_2ddglcn_e\n", + "\t- R_EX_34dhpac_e\n", + "\t- R_EX_fe3dcit_e\n", + "\t- R_EX_fe3dhbzs_e\n", + "\t- R_EX_fe3hox_e\n", + "\t- R_EX_3amp_e\n", + "\t- R_EX_3cmp_e\n", + "\t- R_EX_fe3hox_un_e\n", + "\t- R_EX_acmana_e\n", + "\t- R_EX_fecrm_e\n", + "\t- R_EX_fecrm_un_e\n", + "\t- R_EX_3gmp_e\n", + "\t- R_EX_3hcinnm_e\n", + "\t- R_EX_3hpp_e\n", + "\t- R_EX_3hpppn_e\n", + "\t- R_EX_3ump_e\n", + "\t- R_EX_feenter_e\n", + "\t- R_EX_feoxam_e\n", + "\t- R_EX_acmum_e\n", + "\t- R_EX_feoxam_un_e\n", + "\t- R_EX_acnam_e\n", + "\t- R_EX_for_e\n", + "\t- R_EX_acolipa_e\n", + "\t- R_EX_fru_e\n", + "\t- R_EX_ile__L_e\n", + "\t- R_EX_frulys_e\n", + "\t- R_EX_fruur_e\n", + "\t- R_EX_fuc__L_e\n", + "\t- R_EX_imp_e\n", + "\t- R_EX_indole_e\n", + "\t- R_EX_inost_e\n", + "\t- R_EX_ins_e\n", + "\t- R_EX_fum_e\n", + "\t- R_EX_fusa_e\n", + "\t- R_EX_isetac_e\n", + "\t- R_EX_acser_e\n", + "\t- R_EX_k_e\n", + "\t- R_EX_kdo2lipid4_e\n", + "\t- R_EX_g1p_e\n", + "\t- R_EX_g3pc_e\n", + "\t- R_EX_g3pe_e\n", + "\t- R_EX_lac__D_e\n", + "\t- R_EX_g3pg_e\n", + "\t- R_EX_lac__L_e\n", + "\t- R_EX_g3pi_e\n", + "\t- R_EX_ade_e\n", + "\t- R_EX_g3ps_e\n", + "\t- R_EX_g6p_e\n", + "\t- R_EX_lcts_e\n", + "\t- R_EX_leu__L_e\n", + "\t- R_EX_lipa_e\n", + "\t- R_EX_gal_e\n", + "\t- R_EX_lipa_cold_e\n", + "\t- R_EX_adn_e\n", + "\t- R_EX_lipoate_e\n", + "\t- R_EX_gal_bD_e\n", + "\t- R_EX_gal1p_e\n", + "\t- R_EX_lys__L_e\n", + "\t- R_EX_adocbl_e\n", + "\t- R_EX_lyx__L_e\n", + "\t- R_EX_mal__D_e\n", + "\t- R_EX_galct__D_e\n", + "\t- R_EX_galctn__D_e\n", + "\t- R_EX_galctn__L_e\n", + "\t- R_EX_galt_e\n", + "\t- R_EX_galur_e\n", + "\t- R_EX_gam_e\n", + "\t- R_EX_mal__L_e\n", + "\t- R_EX_malt_e\n", + "\t- R_EX_malthx_e\n", + "\t- R_EX_maltpt_e\n", + "\t- R_EX_gam6p_e\n", + "\t- R_EX_gbbtn_e\n", + "\t- R_EX_gdp_e\n", + "\t- R_EX_ag_e\n", + "\t- R_EX_glc__D_e\n", + "\t- R_EX_glcn_e\n", + "\t- R_EX_malttr_e\n", + "\t- R_EX_maltttr_e\n", + "\t- R_EX_glcr_e\n", + "\t- R_EX_agm_e\n", + "\t- R_EX_glcur_e\n", + "\t- R_EX_glcur1p_e\n", + "\t- R_EX_man_e\n", + "\t- R_EX_man6p_e\n", + "\t- R_EX_manglyc_e\n", + "\t- R_EX_melib_e\n", + "\t- R_EX_gln__L_e\n", + "\t- R_EX_glu__L_e\n", + "\t- R_EX_meoh_e\n", + "\t- R_EX_gly_e\n", + "\t- R_EX_akg_e\n", + "\t- R_EX_glyald_e\n", + "\t- R_EX_glyb_e\n", + "\t- R_EX_met__D_e\n", + "\t- R_EX_met__L_e\n", + "\t- R_EX_metsox_R__L_e\n", + "\t- R_EX_glyc_e\n", + "\t- R_EX_glyc__R_e\n", + "\t- R_EX_metsox_S__L_e\n", + "\t- R_EX_glyc2p_e\n", + "\t- R_EX_glyc3p_e\n", + "\t- R_EX_mg2_e\n", + "\t- R_EX_mincyc_e\n", + "\t- R_EX_minohp_e\n", + "\t- R_EX_glyclt_e\n", + "\t- R_EX_gmp_e\n", + "\t- R_EX_mmet_e\n", + "\t- R_EX_ala_B_e\n", + "\t- R_EX_mn2_e\n", + "\t- R_EX_mnl_e\n", + "\t- R_EX_mobd_e\n", + "\t- R_EX_gsn_e\n", + "\t- R_EX_gthox_e\n", + "\t- R_EX_gthrd_e\n", + "\t- R_EX_gtp_e\n", + "\t- R_EX_mso3_e\n", + "\t- R_EX_gua_e\n", + "\t- R_EX_ala__D_e\n", + "\t- R_EX_h_e\n", + "\t- R_EX_h2_e\n", + "\t- R_EX_n2o_e\n", + "\t- R_EX_na1_e\n", + "\t- R_EX_nac_e\n", + "\t- R_EX_ala__L_e\n", + "\t- R_EX_nh4_e\n", + "\t- R_EX_ni2_e\n", + "\t- R_EX_h2o_e\n", + "\t- R_EX_h2o2_e\n", + "\t- R_EX_h2s_e\n", + "\t- R_EX_nmn_e\n", + "\t- R_EX_no_e\n", + "\t- R_EX_alaala_e\n", + "\t- R_EX_no2_e\n", + "\t- R_EX_hacolipa_e\n", + "\t- R_EX_halipa_e\n", + "\t- R_EX_no3_e\n", + "\t- R_EX_all__D_e\n", + "\t- R_EX_novbcn_e\n", + "\t- R_EX_o16a4colipa_e\n", + "\t- R_EX_o2_e\n", + "\t- R_EX_o2s_e\n", + "\t- R_EX_ocdca_e\n", + "\t- R_EX_ocdcea_e\n", + "\t- R_EX_hdca_e\n", + "\t- R_EX_hdcea_e\n", + "\t- R_EX_hg2_e\n", + "\t- R_EX_his__L_e\n", + "\t- R_EX_hom__L_e\n", + "\t- R_EX_hxa_e\n", + "\t- R_EX_hxan_e\n", + "\t- R_EX_octa_e\n", + "\t- R_EX_orn_e\n", + "\t- R_EX_idon__L_e\n", + "\t- R_EX_alltn_e\n", + "\t- R_EX_orot_e\n", + "\t- R_EX_pacald_e\n", + "\t- R_EX_amp_e\n", + "\t- R_EX_peamn_e\n", + "\t- R_EX_rib__D_e\n", + "\t- R_EX_rmn_e\n", + "\t- R_EX_phe__L_e\n", + "\t- R_EX_anhgm_e\n", + "\t- R_EX_pheme_e\n", + "\t- R_EX_pi_e\n", + "\t- R_EX_sbt__D_e\n", + "\t- R_EX_sel_e\n", + "\t- R_EX_pnto__R_e\n", + "\t- R_EX_ser__D_e\n", + "\t- R_EX_arab__L_e\n", + "\t- R_EX_ser__L_e\n", + "\t- R_EX_ppa_e\n", + "\t- R_EX_ppal_e\n", + "\t- R_EX_arbt_e\n", + "\t- R_EX_pppn_e\n", + "\t- R_EX_ppt_e\n", + "\t- R_EX_skm_e\n", + "\t- R_EX_slnt_e\n", + "\t- R_EX_so2_e\n", + "\t- R_EX_so3_e\n", + "\t- R_EX_pro__L_e\n", + "\t- R_EX_progly_e\n", + "\t- R_EX_psclys_e\n", + "\t- R_EX_so4_e\n", + "\t- R_EX_arbtn_e\n", + "\t- R_EX_spmd_e\n", + "\t- R_EX_succ_e\n", + "\t- R_EX_pser__L_e\n", + "\t- R_EX_ptrc_e\n", + "\t- R_EX_pydam_e\n", + "\t- R_EX_pydx_e\n", + "\t- R_EX_sucr_e\n", + "\t- R_EX_arbtn_fe3_e\n", + "\t- R_EX_sulfac_e\n", + "\t- R_EX_pydxn_e\n", + "\t- R_EX_pyr_e\n", + "\t- R_EX_tartr__D_e\n", + "\t- R_EX_arg__L_e\n", + "\t- R_EX_tartr__L_e\n", + "\t- R_EX_taur_e\n", + "\t- R_EX_quin_e\n", + "\t- R_EX_r5p_e\n", + "\t- R_EX_rfamp_e\n", + "\t- R_EX_tcynt_e\n", + "\t- R_EX_ascb__L_e\n", + "\t- R_EX_thm_e\n", + "\t- R_EX_thr__L_e\n", + "\t- R_EX_asn__L_e\n", + "\t- R_EX_thrp_e\n", + "\t- R_EX_thym_e\n", + "\t- R_EX_aso3_e\n", + "\t- R_EX_thymd_e\n", + "\t- R_EX_tma_e\n", + "\t- R_EX_tmao_e\n", + "\t- R_EX_tre_e\n", + "\t- R_EX_trp__L_e\n", + "\t- R_EX_tsul_e\n", + "\t- R_EX_asp__L_e\n", + "\t- R_EX_ttdca_e\n", + "\t- R_EX_ttdcea_e\n", + "\t- R_EX_btn_e\n", + "\t- R_EX_ttrcyc_e\n", + "\t- R_EX_tungs_e\n", + "\t- R_EX_tym_e\n", + "\t- R_EX_tyr__L_e\n", + "\t- R_EX_tyrp_e\n", + "\t- R_EX_but_e\n", + "\t- R_EX_uacgam_e\n", + "\t- R_EX_udpacgal_e\n", + "\t- R_EX_butso3_e\n", + "\t- R_EX_udpg_e\n", + "\t- R_EX_udpgal_e\n", + "\t- R_EX_ca2_e\n", + "\t- R_EX_udpglcur_e\n", + "\t- R_EX_ump_e\n", + "\t- R_EX_ura_e\n", + "\t- R_EX_urea_e\n", + "\t- R_EX_uri_e\n", + "\t- R_EX_val__L_e\n", + "\t- R_EX_xan_e\n", + "\t- R_EX_cbi_e\n", + "\t- R_EX_xmp_e\n", + "\t- R_EX_xtsn_e\n", + "\t- R_EX_xyl__D_e\n", + "\t- R_EX_xylu__L_e\n", + "\t- R_EX_zn2_e\n", + "\t- R_EX_cbl1_e\n", + "\t- R_EX_cd2_e\n", + "\t- R_EX_cgly_e\n", + "\t- R_EX_chol_e\n", + "\t- R_EX_chtbs_e\n", + "\t- R_EX_cit_e\n", + "\t- R_EX_cl_e\n", + "\t- R_EX_cm_e\n", + "\t- R_EX_cmp_e\n", + "\t- R_EX_co2_e\n", + "\t- R_EX_cobalt2_e\n", + "\t- R_EX_colipa_e\n", + "\t- R_EX_colipap_e\n", + "\t- R_EX_cpgn_e\n", + "\t- R_EX_cpgn_un_e\n", + "\t- R_EX_crn_e\n", + "\t- R_EX_crn__D_e\n", + "\t- R_EX_csn_e\n", + "\t- R_EX_cu_e\n", + "\t- R_EX_cu2_e\n", + "\t- R_EX_cyan_e\n", + "\t- R_EX_cynt_e\n", + "\t- R_EX_cys__D_e\n", + "\t- R_EX_cys__L_e\n", + "\t- R_EX_cytd_e\n", + "\t- R_EX_dad_2_e\n", + "\t- R_EX_damp_e\n", + "\t- R_EX_dca_e\n", + "\t- R_EX_dcmp_e\n", + "\t- R_EX_dcyt_e\n", + "\t- R_EX_ddca_e\n", + "\t- R_EX_dgmp_e\n", + "\t- R_EX_dgsn_e\n", + "\t- R_EX_dha_e\n", + "\t- R_EX_dimp_e\n", + "\t- R_EX_din_e\n", + "\t- R_EX_dms_e\n", + "\t- R_EX_dmso_e\n", + "\t- R_EX_dopa_e\n", + "\t- R_EX_doxrbcn_e\n", + "\t- R_EX_dtmp_e\n", + "\t- R_EX_dump_e\n", + "\t- R_EX_duri_e\n", + "\t- R_EX_diact_e\n", + "\t- R_EX_4hphac_e\n", + "\t- R_EX_4abz_e\n", + "\t- R_EX_acon_C_e\n", + "\t- R_EX_feroxB_e\n", + "\t- R_EX_tcb_e\n", + "\t- R_EX_tton_e\n", + "\t- R_EX_4hthr_e\n", + "\t- R_EX_feroxEfe_e\n", + "\t- R_EX_feroxE_e\n", + "\t- R_EX_pep_e\n", + "\t- R_EX_23dhbzs3_e\n", + "\t- R_EX_tet_e\n", + "\t- R_EX_salchs4_e\n", + "\t- R_EX_tag__D_e\n", + "\t- R_EX_oaa_e\n", + "\t- R_EX_salchs2fe_e\n", + "\t- R_EX_cysi__L_e\n", + "\t- R_EX_fe3dhbzs3_e\n", + "\t- R_EX_drib_e\n", + "\t- R_EX_feroxBfe_e\n", + "\t- R_EX_salchs4fe_e\n", + "\t- R_EX_2pg_e\n", + "\t- R_EX_frmd_e\n", + "\t- R_EX_cellb_e\n", + "\t- R_EX_colipaOA_e\n", + "\t- R_EX_salchsx_e\n", + "\t- R_EX_AEP_e\n", + "\t- R_EX_icit_e\n", + "\t- R_EX_colipa20Oag_e\n", + "\t- R_EX_remnant1_e\n", + "\t- R_EX_btd_RR_e\n", + "\t- R_EX_salchs2_e\n", + "\t- R_EX_rnam_e\n", + "\t- R_EX_airs_e\n", + "\t- R_EX_dxyl_e\n", + "\t- R_EX_3pg_e\n", + "\t- R_EX_udcpo4_e\n", + "\t- R_EX_feroxGfe_e\n", + "\t- R_EX_rbt_e\n", + "\t- R_EX_udcpo5_e\n", + "\t- R_EX_guln__L_e\n", + "\t- R_EX_feroxG_e\n", + "\t- R_EX_bz_e\n", + "\t- R_EX_13ppd_e\n", + "\t- R_EX_abt__D_e\n", + "\t- R_EX_fe_e\n", + "\t- R_EX_kokdolipidA_e\n", + "\t- R_EX_3hoxpac_e\n", + "\t- R_EX_o6a4colipa_e\n", + "\t- R_EX_4hoxpac_e\n", + "\t- R_DM_lipidA_core_e_p\n", + "\t- R_EX_3ntym_e\n", + "\t- R_EX_6apa_e\n", + "\t- R_EX_peng_e\n", + "\t- R_EX_pac_e\n", + "\t- R_EX_salcn_e\n", + "\t- R_EX_galam_e\n", + "\t- R_EX_raffin_e\n", + "\t- R_EX_arab__D_e\u001b[0m\n", + "File saved at: ../../data/sbml_corrected/iY75_1357.xml\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: iCHOv1_DG44\n", + "\u001b[0;93mWARNING : - R_LCARS: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_HMGCOAS: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + "\n", + " - R_ORNTArm: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + " - R_IMPC: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_ECOAH4p: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_HACD4p: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_ABTt: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_EX_arg__L_e: Reactants and products exchanged.\n", + " Boundaries was: [-0.02013 ; -0.02013]\n", + " - R_EX_asn__L_e: Reactants and products exchanged.\n", + " Boundaries was: [-0.04058 ; -0.04058]\n", + " - R_EX_asp__L_e: Reactants and products exchanged.\n", + " Boundaries was: [-0.00976 ; -0.00976]\n", + " - R_EX_cys__L_e: Reactants and products exchanged.\n", + " Boundaries was: [-0.00539 ; -0.00539]\n", + " - R_EX_glc__D_e: Reactants and products exchanged.\n", + " Boundaries was: [-0.1979 ; -0.1979]\n", + " - R_EX_gln__L_e: Reactants and products exchanged.\n", + " Boundaries was: [-0.0674 ; -0.0674]\n", + " - R_EX_his__L_e: Reactants and products exchanged.\n", + " Boundaries was: [-0.00407 ; -0.00407]\n", + " - R_EX_ile__L_e: Reactants and products exchanged.\n", + " Boundaries was: [-0.01055 ; -0.01055]\n", + " - R_EX_phe__L_e: Reactants and products exchanged.\n", + " Boundaries was: [-0.00592 ; -0.00592]\n", + " - R_EX_pro__L_e: Reactants and products exchanged.\n", + " Boundaries was: [-0.00847 ; -0.00847]\n", + " - R_EX_leu__L_e: Reactants and products exchanged.\n", + " Boundaries was: [-0.01567 ; -0.01567]\n", + " - R_EX_lys__L_e: Reactants and products exchanged.\n", + " Boundaries was: [-0.01403 ; -0.01403]\n", + " - R_EX_met__L_e: Reactants and products exchanged.\n", + " Boundaries was: [-0.00632 ; -0.00632]\n", + "\n", + " - R_EX_sbt__D_e: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + " - R_EX_ser__L_e: Reactants and products exchanged.\n", + " Boundaries was: [-0.04813 ; -0.04813]\n", + " - R_EX_trp__L_e: Reactants and products exchanged.\n", + " Boundaries was: [-0.0032 ; -0.0032]\n", + " - R_EX_o2_e: Reactants and products exchanged.\n", + " Boundaries was: [-1.125 ; -1.125]\n", + " - R_EX_thr__L_e: Reactants and products exchanged.\n", + " Boundaries was: [-0.01058 ; -0.01058]\n", + " - R_EX_tyr__L_e: Reactants and products exchanged.\n", + " Boundaries was: [-0.00982 ; -0.00982]\n", + " - R_EX_val__L_e: Reactants and products exchanged.\n", + " Boundaries was: [-0.01246 ; -0.01246]\n", + " - R_OAADCm: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_ACACT7m: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_SPHPL: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_Kt1: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_GTHRDt2: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_HISt2m: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_ALAtmi: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_PHEt2m: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + "\n", + " - R_DM_epo_g: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\u001b[0m\n", + "\n", + "\n", + "\u001b[0;36m############################################\n", + "############################################\n", + " WRITING SBML FILE \n", + "############################################\n", + "############################################\n", + "\u001b[0m\n", + "\u001b[0;93mWARNING : IMPORT REACTION REMOVED BY DEFAULT\u001b[0m\n", + "\u001b[0;93mWARNING : If you want to keep import reaction\n", + "use option -kir / --keep-import-reactions\u001b[0m\n", + "\u001b[0;93mWARNING : Reaction with tag reversible modified:\n", + "\t- R_LCARS\n", + "\t- R_HMGCOAS\n", + "\t- R_IMPC\n", + "\t- R_ECOAH4p\n", + "\t- R_HACD4p\n", + "\t- R_ABTt\n", + "\t- R_EX_arg__L_e\n", + "\t- R_EX_asn__L_e\n", + "\t- R_EX_asp__L_e\n", + "\t- R_EX_cys__L_e\n", + "\t- R_EX_glc__D_e\n", + "\t- R_EX_gln__L_e\n", + "\t- R_EX_his__L_e\n", + "\t- R_EX_ile__L_e\n", + "\t- R_EX_phe__L_e\n", + "\t- R_EX_pro__L_e\n", + "\t- R_EX_leu__L_e\n", + "\t- R_EX_lys__L_e\n", + "\t- R_EX_met__L_e\n", + "\t- R_EX_ser__L_e\n", + "\t- R_EX_trp__L_e\n", + "\t- R_EX_o2_e\n", + "\t- R_EX_thr__L_e\n", + "\t- R_EX_tyr__L_e\n", + "\t- R_EX_val__L_e\n", + "\t- R_OAADCm\n", + "\t- R_ACACT7m\n", + "\t- R_SPHPL\n", + "\t- R_Kt1\n", + "\t- R_GTHRDt2\n", + "\t- R_HISt2m\n", + "\t- R_ALAtmi\n", + "\t- R_PHEt2m\u001b[0m\n", + "\u001b[0;93mWARNING : Reaction with Reactants and Products exchanged:\n", + "\t- R_LCARS\n", + "\t- R_HMGCOAS\n", + "\t- R_IMPC\n", + "\t- R_ECOAH4p\n", + "\t- R_HACD4p\n", + "\t- R_ABTt\n", + "\t- R_EX_arg__L_e\n", + "\t- R_EX_asn__L_e\n", + "\t- R_EX_asp__L_e\n", + "\t- R_EX_cys__L_e\n", + "\t- R_EX_glc__D_e\n", + "\t- R_EX_gln__L_e\n", + "\t- R_EX_his__L_e\n", + "\t- R_EX_ile__L_e\n", + "\t- R_EX_phe__L_e\n", + "\t- R_EX_pro__L_e\n", + "\t- R_EX_leu__L_e\n", + "\t- R_EX_lys__L_e\n", + "\t- R_EX_met__L_e\n", + "\t- R_EX_ser__L_e\n", + "\t- R_EX_trp__L_e\n", + "\t- R_EX_o2_e\n", + "\t- R_EX_thr__L_e\n", + "\t- R_EX_tyr__L_e\n", + "\t- R_EX_val__L_e\n", + "\t- R_OAADCm\n", + "\t- R_ACACT7m\n", + "\t- R_SPHPL\n", + "\t- R_Kt1\n", + "\t- R_GTHRDt2\n", + "\t- R_HISt2m\n", + "\t- R_ALAtmi\n", + "\t- R_PHEt2m\u001b[0m\n", + "\u001b[0;93mWARNING : Import reaction removed:\n", + "\t- R_EX_12HPET_e\n", + "\t- R_EX_ddca_e\n", + "\t- R_EX_cpppg1_e\n", + "\t- R_EX_ttdcrn_e\n", + "\t- R_DM_13_cis_oretn_n\n", + "\t- R_DM_13_cis_retn_n\n", + "\t- R_DM_Asn_X_Ser_Thr_l\n", + "\t- R_DM_datp_m\n", + "\t- R_DM_datp_n\n", + "\t- R_DM_dctp_m\n", + "\t- R_DM_dctp_n\n", + "\t- R_DM_dgtp_m\n", + "\t- R_DM_dgtp_n\n", + "\t- R_DM_dttp_n\n", + "\t- R_DM_gpi_sig_r\n", + "\t- R_DM_hretn_n\n", + "\t- R_DM_oretn_n\n", + "\t- R_DM_Ser_Gly_Ala_X_Gly_l\n", + "\t- R_DM_Ser_Thr_l\n", + "\t- R_EX_10fthf_e\n", + "\t- R_EX_10fthf5glu_e\n", + "\t- R_EX_10fthf6glu_e\n", + "\t- R_EX_10fthf7glu_e\n", + "\t- R_EX_11_cis_retfa_e\n", + "\t- R_EX_13_cis_retnglc_e\n", + "\t- R_EX_1glyc_cho_e\n", + "\t- R_EX_1mncam_e\n", + "\t- R_EX_2mcit_e\n", + "\t- R_EX_34dhoxpeg_e\n", + "\t- R_EX_34dhphe_e\n", + "\t- R_EX_23cump_e\n", + "\t- R_EX_2425dhvitd2_e\n", + "\t- R_EX_34hpp_e\n", + "\t- R_EX_35cgmp_e\n", + "\t- R_EX_2425dhvitd3_e\n", + "\t- R_EX_24nph_e\n", + "\t- R_EX_25hvitd2_e\n", + "\t- R_EX_25hvitd3_e\n", + "\t- R_EX_3aib__D_e\n", + "\t- R_EX_3aib_e\n", + "\t- R_EX_3bcrn_e\n", + "\t- R_EX_3ddcrn_e\n", + "\t- R_EX_2hb_e\n", + "\t- R_EX_3hexdcrn_e\n", + "\t- R_EX_3mlda_e\n", + "\t- R_EX_3mob_e\n", + "\t- R_EX_apnnox_e\n", + "\t- R_EX_appnn_e\n", + "\t- R_EX_aprgstrn_e\n", + "\t- R_EX_3mop_e\n", + "\t- R_EX_3tdcrn_e\n", + "\t- R_EX_arab__L_e\n", + "\t- R_EX_arach_e\n", + "\t- R_EX_arachcoa_e\n", + "\t- R_EX_3ump_e\n", + "\t- R_EX_4abut_e\n", + "\t- R_EX_arachd_e\n", + "\t- R_EX_4abutn_e\n", + "\t- R_EX_4hdebrisoquine_e\n", + "\t- R_EX_arg__L_e\n", + "\t- R_EX_ascb__L_e\n", + "\t- R_EX_asn__L_e\n", + "\t- R_EX_asp__D_e\n", + "\t- R_EX_4hphac_e\n", + "\t- R_EX_4hpro_LT_e\n", + "\t- R_EX_4mop_e\n", + "\t- R_EX_asp__L_e\n", + "\t- R_EX_atp_e\n", + "\t- R_EX_avite1_e\n", + "\t- R_EX_avite2_e\n", + "\t- R_EX_bglc_e\n", + "\t- R_EX_4mptnl_e\n", + "\t- R_EX_4mtolbutamide_e\n", + "\t- R_EX_4nph_e\n", + "\t- R_EX_4nphsf_e\n", + "\t- R_EX_bhb_e\n", + "\t- R_EX_4pyrdx_e\n", + "\t- R_EX_bildglcur_e\n", + "\t- R_EX_5adtststerone_e\n", + "\t- R_EX_5adtststeroneglc_e\n", + "\t- R_EX_5adtststerones_e\n", + "\t- R_EX_5dhf_e\n", + "\t- R_EX_bilirub_e\n", + "\t- R_EX_biocyt_e\n", + "\t- R_EX_btn_e\n", + "\t- R_EX_but_e\n", + "\t- R_EX_5fthf_e\n", + "\t- R_EX_5homeprazole_e\n", + "\t- R_EX_bvite_e\n", + "\t- R_EX_C02470_e\n", + "\t- R_EX_C02528_e\n", + "\t- R_EX_5htrp_e\n", + "\t- R_EX_5mta_e\n", + "\t- R_EX_C04849_e\n", + "\t- R_EX_5mthf_e\n", + "\t- R_EX_5oxpro_e\n", + "\t- R_EX_5thf_e\n", + "\t- R_EX_c10dc_e\n", + "\t- R_EX_c4crn_e\n", + "\t- R_EX_c51crn_e\n", + "\t- R_EX_c8crn_e\n", + "\t- R_EX_camp_e\n", + "\t- R_EX_carn_e\n", + "\t- R_EX_caro_e\n", + "\t- R_EX_6dhf_e\n", + "\t- R_EX_6htststerone_e\n", + "\t- R_EX_6thf_e\n", + "\t- R_EX_7dhf_e\n", + "\t- R_EX_7thf_e\n", + "\t- R_EX_9_cis_retfa_e\n", + "\t- R_EX_carveol_e\n", + "\t- R_EX_cbasp_e\n", + "\t- R_EX_abt_e\n", + "\t- R_EX_cdp_e\n", + "\t- R_EX_ac_e\n", + "\t- R_EX_acac_e\n", + "\t- R_EX_acald_e\n", + "\t- R_EX_acetone_e\n", + "\t- R_EX_CE0074_e\n", + "\t- R_EX_CE1925_e\n", + "\t- R_EX_CE1926_e\n", + "\t- R_EX_CE1935_e\n", + "\t- R_EX_CE1936_e\n", + "\t- R_EX_acgalfucgalacgalfuc12gal14acglcgalgluside_cho_e\n", + "\t- R_EX_acgalfucgalacgalfucgalacglcgal14acglcgalgluside_cho_e\n", + "\t- R_EX_acgam_e\n", + "\t- R_EX_ach_e\n", + "\t- R_EX_CE1939_e\n", + "\t- R_EX_CE1940_e\n", + "\t- R_EX_CE1943_e\n", + "\t- R_EX_CE2011_e\n", + "\t- R_EX_CE2250_e\n", + "\t- R_EX_acmana_e\n", + "\t- R_EX_acn13acngalgbside_cho_e\n", + "\t- R_EX_CE2838_e\n", + "\t- R_EX_CE2839_e\n", + "\t- R_EX_CE2915_e\n", + "\t- R_EX_CE2916_e\n", + "\t- R_EX_acn23acngalgbside_cho_e\n", + "\t- R_EX_acnacngal14acglcgalgluside_cho_e\n", + "\t- R_EX_acnacngalgbside_cho_e\n", + "\t- R_EX_acngalacglcgal14acglcgalgluside_cho_e\n", + "\t- R_EX_ade_e\n", + "\t- R_EX_adn_e\n", + "\t- R_EX_CE2917_e\n", + "\t- R_EX_adp_e\n", + "\t- R_EX_adprbp_e\n", + "\t- R_EX_adprib_e\n", + "\t- R_EX_CE4633_e\n", + "\t- R_EX_CE4722_e\n", + "\t- R_EX_adrn_e\n", + "\t- R_EX_adrnl_e\n", + "\t- R_EX_aflatoxin_e\n", + "\t- R_EX_ahcys_e\n", + "\t- R_EX_CE4723_e\n", + "\t- R_EX_CE4724_e\n", + "\t- R_EX_ahdt_e\n", + "\t- R_EX_aicar_e\n", + "\t- R_EX_CE4881_e\n", + "\t- R_EX_ak2lgchol_cho_e\n", + "\t- R_EX_akg_e\n", + "\t- R_EX_ala_B_e\n", + "\t- R_EX_CE5786_e\n", + "\t- R_EX_CE5788_e\n", + "\t- R_EX_CE5789_e\n", + "\t- R_EX_CE5797_e\n", + "\t- R_EX_ala__D_e\n", + "\t- R_EX_ala__L_e\n", + "\t- R_EX_CE5798_e\n", + "\t- R_EX_CE5853_e\n", + "\t- R_EX_CE5854_e\n", + "\t- R_EX_CE5867_e\n", + "\t- R_EX_CE5868_e\n", + "\t- R_EX_CE5869_e\n", + "\t- R_EX_alaala_e\n", + "\t- R_EX_aldstrn_e\n", + "\t- R_EX_amp_e\n", + "\t- R_EX_andrstrn_e\n", + "\t- R_EX_andrstrnglc_e\n", + "\t- R_EX_anth_e\n", + "\t- R_EX_antipyrene_e\n", + "\t- R_EX_cgly_e\n", + "\t- R_EX_chol_e\n", + "\t- R_EX_cholate_e\n", + "\t- R_EX_chsterol_e\n", + "\t- R_EX_crm_cho_e\n", + "\t- R_EX_chtn_e\n", + "\t- R_EX_cit_e\n", + "\t- R_EX_citr__L_e\n", + "\t- R_EX_crmp_cho_e\n", + "\t- R_EX_crn_e\n", + "\t- R_EX_cl_e\n", + "\t- R_EX_clpnd_e\n", + "\t- R_EX_crtsl_e\n", + "\t- R_EX_crtstrn_e\n", + "\t- R_EX_cmp_e\n", + "\t- R_EX_co_e\n", + "\t- R_EX_crvnc_e\n", + "\t- R_EX_csn_e\n", + "\t- R_EX_cspg_a_e\n", + "\t- R_EX_co2_e\n", + "\t- R_EX_coa_e\n", + "\t- R_EX_coumarin_e\n", + "\t- R_EX_creat_e\n", + "\t- R_EX_cspg_b_e\n", + "\t- R_EX_cspg_c_e\n", + "\t- R_EX_cspg_d_e\n", + "\t- R_EX_cspg_e_e\n", + "\t- R_EX_ctp_e\n", + "\t- R_EX_cyan_e\n", + "\t- R_EX_galfucgalacglcgal14acglcgalgluside_cho_e\n", + "\t- R_EX_galgalfucfucgalacglcgalacglcgal14acglcgalgluside_cho_e\n", + "\t- R_EX_galside_cho_e\n", + "\t- R_EX_cys__L_e\n", + "\t- R_EX_cysam_e\n", + "\t- R_EX_cytd_e\n", + "\t- R_EX_gam_e\n", + "\t- R_EX_gbside_cho_e\n", + "\t- R_EX_dad_2_e\n", + "\t- R_EX_gchola_e\n", + "\t- R_EX_gd1b2_cho_e\n", + "\t- R_EX_gd1c_cho_e\n", + "\t- R_EX_dag_cho_e\n", + "\t- R_EX_dca_e\n", + "\t- R_EX_dchac_e\n", + "\t- R_EX_gdp_e\n", + "\t- R_EX_glc__D_e\n", + "\t- R_EX_dcsptn1_e\n", + "\t- R_EX_dcyt_e\n", + "\t- R_EX_glcur_e\n", + "\t- R_EX_glgchlo_e\n", + "\t- R_EX_debrisoquine_e\n", + "\t- R_EX_gln__L_e\n", + "\t- R_EX_dgchol_e\n", + "\t- R_EX_gltcho_e\n", + "\t- R_EX_gltdechol_e\n", + "\t- R_EX_dgmp_e\n", + "\t- R_EX_dgsn_e\n", + "\t- R_EX_dgtp_e\n", + "\t- R_EX_dha_e\n", + "\t- R_EX_dhap_e\n", + "\t- R_EX_glu__L_e\n", + "\t- R_EX_gluala_e\n", + "\t- R_EX_gly_e\n", + "\t- R_EX_dhdascb_e\n", + "\t- R_EX_glyb_e\n", + "\t- R_EX_dheas_e\n", + "\t- R_EX_glyc_e\n", + "\t- R_EX_glyc__S_e\n", + "\t- R_EX_glygly_e\n", + "\t- R_EX_glygn2_e\n", + "\t- R_EX_dhf_e\n", + "\t- R_EX_digalsgalside_cho_e\n", + "\t- R_EX_din_e\n", + "\t- R_EX_dlnlcg_e\n", + "\t- R_EX_dmantipyrine_e\n", + "\t- R_EX_glygn4_e\n", + "\t- R_EX_glygn5_e\n", + "\t- R_EX_dopa_e\n", + "\t- R_EX_glyleu_e\n", + "\t- R_EX_glyphe_e\n", + "\t- R_EX_dopasf_e\n", + "\t- R_EX_dpcoa_e\n", + "\t- R_EX_glypro_e\n", + "\t- R_EX_glysar_e\n", + "\t- R_EX_drib_e\n", + "\t- R_EX_dtdp_e\n", + "\t- R_EX_gmp_e\n", + "\t- R_EX_gp1c_cho_e\n", + "\t- R_EX_gp1calpha_cho_e\n", + "\t- R_EX_gq1b_cho_e\n", + "\t- R_EX_gq1balpha_cho_e\n", + "\t- R_EX_gsn_e\n", + "\t- R_EX_gt1a_cho_e\n", + "\t- R_EX_gthox_e\n", + "\t- R_EX_gthrd_e\n", + "\t- R_EX_dtmp_e\n", + "\t- R_EX_dttp_e\n", + "\t- R_EX_duri_e\n", + "\t- R_EX_eaflatoxin_e\n", + "\t- R_EX_ebastine_e\n", + "\t- R_EX_ebastineoh_e\n", + "\t- R_EX_eicostet_e\n", + "\t- R_EX_elaid_e\n", + "\t- R_EX_estradiol_e\n", + "\t- R_EX_gtp_e\n", + "\t- R_EX_gua_e\n", + "\t- R_EX_gum_e\n", + "\t- R_EX_gumdchac_e\n", + "\t- R_EX_estradiolglc_e\n", + "\t- R_EX_estroneglc_e\n", + "\t- R_EX_gumgchol_e\n", + "\t- R_EX_gumtchol_e\n", + "\t- R_EX_estrones_e\n", + "\t- R_EX_etoh_e\n", + "\t- R_EX_fad_e\n", + "\t- R_EX_h_e\n", + "\t- R_EX_ha_e\n", + "\t- R_EX_fald_e\n", + "\t- R_EX_fe2_e\n", + "\t- R_EX_ha_pre1_e\n", + "\t- R_EX_HC00229_e\n", + "\t- R_EX_fe3_e\n", + "\t- R_EX_fmn_e\n", + "\t- R_EX_HC00250_e\n", + "\t- R_EX_HC00822_e\n", + "\t- R_EX_HC00955_e\n", + "\t- R_EX_HC01104_e\n", + "\t- R_EX_HC01361_e\n", + "\t- R_EX_HC01440_e\n", + "\t- R_EX_HC01441_e\n", + "\t- R_EX_fol_e\n", + "\t- R_EX_for_e\n", + "\t- R_EX_fru_e\n", + "\t- R_EX_fuc__L_e\n", + "\t- R_EX_fuc13galacglcgal14acglcgalgluside_cho_e\n", + "\t- R_EX_HC01444_e\n", + "\t- R_EX_HC01446_e\n", + "\t- R_EX_HC01577_e\n", + "\t- R_EX_HC01609_e\n", + "\t- R_EX_HC01700_e\n", + "\t- R_EX_HC02160_e\n", + "\t- R_EX_fuc14galacglcgalgluside_cho_e\n", + "\t- R_EX_fucacgalfucgalacglcgalgluside_cho_e\n", + "\t- R_EX_fucacngal14acglcgalgluside_cho_e\n", + "\t- R_EX_fucacngalacglcgalgluside_cho_e\n", + "\t- R_EX_fucfuc12gal14acglcgalgluside_cho_e\n", + "\t- R_EX_HC02161_e\n", + "\t- R_EX_HC02202_e\n", + "\t- R_EX_HC02203_e\n", + "\t- R_EX_HC02204_e\n", + "\t- R_EX_fucfuc132galacglcgal14acglcgalgluside_cho_e\n", + "\t- R_EX_fucfucfucgalacglc13galacglcgal14acglcgalgluside_cho_e\n", + "\t- R_EX_fucfucfucgalacglcgal14acglcgalgluside_cho_e\n", + "\t- R_EX_HC02205_e\n", + "\t- R_EX_HC02206_e\n", + "\t- R_EX_HC02207_e\n", + "\t- R_EX_fucfucgalacglcgalgluside_cho_e\n", + "\t- R_EX_fucgal14acglcgalgluside_cho_e\n", + "\t- R_EX_fucgalfucgalacglcgalgluside_cho_e\n", + "\t- R_EX_fucgalgbside_cho_e\n", + "\t- R_EX_g1p_e\n", + "\t- R_EX_gal_e\n", + "\t- R_EX_hco3_e\n", + "\t- R_EX_hcoumarin_e\n", + "\t- R_EX_hdca_e\n", + "\t- R_EX_hdcea_e\n", + "\t- R_EX_hestratriol_e\n", + "\t- R_EX_galacglcgalgbside_cho_e\n", + "\t- R_EX_galfuc12gal14acglcgalgluside_cho_e\n", + "\t- R_EX_hexc_e\n", + "\t- R_EX_his__L_e\n", + "\t- R_EX_hista_e\n", + "\t- R_EX_hom__L_e\n", + "\t- R_EX_hpdca_e\n", + "\t- R_EX_hyptaur_e\n", + "\t- R_EX_i_e\n", + "\t- R_EX_hspg_e\n", + "\t- R_EX_htaxol_e\n", + "\t- R_EX_hxan_e\n", + "\t- R_EX_idour_e\n", + "\t- R_EX_idp_e\n", + "\t- R_EX_ile__L_e\n", + "\t- R_EX_imp_e\n", + "\t- R_EX_pglyc_cho_e\n", + "\t- R_EX_inost_e\n", + "\t- R_EX_ins_e\n", + "\t- R_EX_ivcrn_e\n", + "\t- R_EX_ksi_deg1_e\n", + "\t- R_EX_phe__L_e\n", + "\t- R_EX_pheacgln_e\n", + "\t- R_EX_pheme_e\n", + "\t- R_EX_pi_e\n", + "\t- R_EX_pnto__R_e\n", + "\t- R_EX_ppa_e\n", + "\t- R_EX_ppi_e\n", + "\t- R_EX_ksi_e\n", + "\t- R_EX_ksii_core2_e\n", + "\t- R_EX_ksii_core4_e\n", + "\t- R_EX_lac__D_e\n", + "\t- R_EX_lac__L_e\n", + "\t- R_EX_lcts_e\n", + "\t- R_EX_prgstrn_e\n", + "\t- R_EX_pro__L_e\n", + "\t- R_EX_thym_e\n", + "\t- R_EX_progly_e\n", + "\t- R_EX_prostgd2_e\n", + "\t- R_EX_prostge1_e\n", + "\t- R_EX_prostge2_e\n", + "\t- R_EX_prostgf2_e\n", + "\t- R_EX_prostgh2_e\n", + "\t- R_EX_prostgi2_e\n", + "\t- R_EX_prpp_e\n", + "\t- R_EX_cysi__L_e\n", + "\t- R_EX_leu__L_e\n", + "\t- R_EX_leugly_e\n", + "\t- R_EX_leuktrA4_e\n", + "\t- R_EX_leuktrB4_e\n", + "\t- R_EX_leuktrC4_e\n", + "\t- R_EX_leuktrD4_e\n", + "\t- R_EX_leuktrE4_e\n", + "\t- R_EX_leuktrF4_e\n", + "\t- R_EX_leuleu_e\n", + "\t- R_EX_lgnc_e\n", + "\t- R_EX_limnen_e\n", + "\t- R_EX_ps_cho_e\n", + "\t- R_EX_psyl_e\n", + "\t- R_EX_psylchol_e\n", + "\t- R_EX_psyltchol_e\n", + "\t- R_EX_psyltdechol_e\n", + "\t- R_EX_ptdca_e\n", + "\t- R_EX_ptrc_e\n", + "\t- R_EX_ptth_e\n", + "\t- R_EX_pydam_e\n", + "\t- R_EX_lneldc_e\n", + "\t- R_EX_lnlc_e\n", + "\t- R_EX_lnlnca_e\n", + "\t- R_EX_lnlncg_e\n", + "\t- R_EX_lpchol_cho_e\n", + "\t- R_EX_lys__L_e\n", + "\t- R_EX_mag_cho_e\n", + "\t- R_EX_pydx_e\n", + "\t- R_EX_pydx5p_e\n", + "\t- R_EX_pydxn_e\n", + "\t- R_EX_pyr_e\n", + "\t- R_EX_mal__L_e\n", + "\t- R_EX_q10_e\n", + "\t- R_EX_thymd_e\n", + "\t- R_EX_q10h2_e\n", + "\t- R_EX_rbt_e\n", + "\t- R_EX_malcoa_e\n", + "\t- R_EX_malt_e\n", + "\t- R_EX_malthp_e\n", + "\t- R_EX_malthx_e\n", + "\t- R_EX_retfa_e\n", + "\t- R_EX_thyox__L_e\n", + "\t- R_EX_retinol_9_cis_e\n", + "\t- R_EX_retinol_cis_11_e\n", + "\t- R_EX_retinol_e\n", + "\t- R_EX_maltpt_e\n", + "\t- R_EX_malttr_e\n", + "\t- R_EX_maltttr_e\n", + "\t- R_EX_retn_e\n", + "\t- R_EX_tmndnc_e\n", + "\t- R_EX_retnglc_e\n", + "\t- R_EX_rib__D_e\n", + "\t- R_EX_man_e\n", + "\t- R_EX_meoh_e\n", + "\t- R_EX_ribflv_e\n", + "\t- R_EX_mepi_e\n", + "\t- R_EX_tolbutamide_e\n", + "\t- R_EX_mercplaccys_e\n", + "\t- R_EX_met__L_e\n", + "\t- R_EX_mthgxl_e\n", + "\t- R_EX_n2m2nmasn_e\n", + "\t- R_EX_Rtotal_e\n", + "\t- R_EX_Rtotal2_e\n", + "\t- R_EX_Rtotal3_e\n", + "\t- R_EX_s2l2fn2m2masn_e\n", + "\t- R_EX_s2l2n2m2masn_e\n", + "\t- R_EX_sarcs_e\n", + "\t- R_EX_nac_e\n", + "\t- R_EX_nad_e\n", + "\t- R_EX_tre_e\n", + "\t- R_EX_nadp_e\n", + "\t- R_EX_ncam_e\n", + "\t- R_EX_ser__D_e\n", + "\t- R_EX_ser__L_e\n", + "\t- R_EX_nh4_e\n", + "\t- R_EX_slfcys_e\n", + "\t- R_EX_no_e\n", + "\t- R_EX_sl__L_e\n", + "\t- R_EX_triodthy_e\n", + "\t- R_EX_so4_e\n", + "\t- R_EX_no2_e\n", + "\t- R_EX_npthl_e\n", + "\t- R_EX_triodthysuf_e\n", + "\t- R_EX_nrpphr_e\n", + "\t- R_EX_nrpphrsf_e\n", + "\t- R_EX_spc_cho_e\n", + "\t- R_EX_sph1p_e\n", + "\t- R_EX_nrvnc_e\n", + "\t- R_EX_sphs1p_e\n", + "\t- R_EX_trp__L_e\n", + "\t- R_EX_spmd_e\n", + "\t- R_EX_sprm_e\n", + "\t- R_EX_o2_e\n", + "\t- R_EX_oagt3_cho_e\n", + "\t- R_EX_ocdca_e\n", + "\t- R_EX_ocdcea_e\n", + "\t- R_EX_octa_e\n", + "\t- R_EX_omeprazole_e\n", + "\t- R_EX_onpthl_e\n", + "\t- R_EX_orn_e\n", + "\t- R_EX_srtn_e\n", + "\t- R_EX_strch1_e\n", + "\t- R_EX_strch2_e\n", + "\t- R_EX_strdnc_e\n", + "\t- R_EX_succ_e\n", + "\t- R_EX_sucr_e\n", + "\t- R_EX_tag_cho_e\n", + "\t- R_EX_orot_e\n", + "\t- R_EX_oxa_e\n", + "\t- R_EX_taur_e\n", + "\t- R_EX_tststerone_e\n", + "\t- R_EX_taxol_e\n", + "\t- R_EX_tchola_e\n", + "\t- R_EX_tcynt_e\n", + "\t- R_EX_tdchola_e\n", + "\t- R_EX_paf_cho_e\n", + "\t- R_EX_pan4p_e\n", + "\t- R_EX_pchol_cho_e\n", + "\t- R_EX_pe_cho_e\n", + "\t- R_EX_tdechola_e\n", + "\t- R_EX_tethex3_e\n", + "\t- R_EX_pect_e\n", + "\t- R_EX_tetpent3_e\n", + "\t- R_EX_pectindchac_e\n", + "\t- R_EX_tststeroneglc_e\n", + "\t- R_EX_pectingchol_e\n", + "\t- R_EX_pectintchol_e\n", + "\t- R_EX_tetpent6_e\n", + "\t- R_EX_tettet6_e\n", + "\t- R_EX_thf_e\n", + "\t- R_EX_peplys_e\n", + "\t- R_EX_perillyl_e\n", + "\t- R_EX_thm_e\n", + "\t- R_EX_thmmp_e\n", + "\t- R_EX_thmtp_e\n", + "\t- R_EX_thr__L_e\n", + "\t- R_EX_tststerones_e\n", + "\t- R_EX_tsul_e\n", + "\t- R_EX_ttdca_e\n", + "\t- R_EX_txa2_e\n", + "\t- R_EX_tymsf_e\n", + "\t- R_EX_Tyr_ggn_e\n", + "\t- R_EX_tyr__L_e\n", + "\t- R_EX_udp_e\n", + "\t- R_EX_udpg_e\n", + "\t- R_EX_udpgal_e\n", + "\t- R_EX_ump_e\n", + "\t- R_EX_ura_e\n", + "\t- R_EX_urate_e\n", + "\t- R_EX_urea_e\n", + "\t- R_EX_uri_e\n", + "\t- R_EX_utp_e\n", + "\t- R_EX_vacc_e\n", + "\t- R_EX_val__L_e\n", + "\t- R_EX_vitd3_e\n", + "\t- R_EX_whddca_e\n", + "\t- R_EX_whhdca_e\n", + "\t- R_EX_whttdca_e\n", + "\t- R_EX_xolest_cho_e\n", + "\t- R_EX_xolest2_cho_e\n", + "\t- R_EX_xoltri24_e\n", + "\t- R_EX_xoltri25_e\n", + "\t- R_EX_xoltri27_e\n", + "\t- R_EX_xyl__D_e\n", + "\t- R_EX_xylt_e\n", + "\t- R_EX_yvite_e\n", + "\t- R_SK_Asn_X_Ser_Thr_r\n", + "\t- R_SK_Ser_Thr_g\n", + "\t- R_SK_Tyr_ggn_c\n", + "\t- R_DM_igg_g\n", + "\t- R_SK_T_antigen_g\n", + "\t- R_DM_avite1_c\n", + "\t- R_DM_avite2_c\n", + "\t- R_DM_bvite_c\n", + "\t- R_DM_core5_g\n", + "\t- R_DM_core7_g\n", + "\t- R_DM_core8_g\n", + "\t- R_DM_dem2emgacpail_prot_cho_r\n", + "\t- R_DM_dgpi_prot_cho_r\n", + "\t- R_DM_dsT_antigen_g\n", + "\t- R_DM_dttp_m\n", + "\t- R_DM_ethamp_r\n", + "\t- R_DM_gncore2_g\n", + "\t- R_DM_kdn_c\n", + "\t- R_DM_n5m2masn_g\n", + "\t- R_DM_sTn_antigen_g\n", + "\t- R_DM_sprm_c\n", + "\t- R_DM_yvite_c\n", + "\t- R_DM_56iqcrbxlt_c\n", + "\t- R_SK_pre_prot_r\n", + "\t- R_SK_trp__L_c\u001b[0m\n", + "File saved at: ../../data/sbml_corrected/iCHOv1_DG44.xml\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: iSDY_1059\n", + "\u001b[0;93mWARNING : \n", + " - R_DHPTDNR: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_DHPTDNRN: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SPODM: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SPODMpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCASPtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCFUMtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCMALtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCTARTtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_EX_chitob_e: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_URCN: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\u001b[0m\n", + "\n", + "\n", + "\u001b[0;36m############################################\n", + "############################################\n", + " WRITING SBML FILE \n", + "############################################\n", + "############################################\n", + "\u001b[0m\n", + "\u001b[0;93mWARNING : IMPORT REACTION REMOVED BY DEFAULT\u001b[0m\n", + "\u001b[0;93mWARNING : If you want to keep import reaction\n", + "use option -kir / --keep-import-reactions\u001b[0m\n", + "\u001b[0;93mWARNING : Import reaction removed:\n", + "\t- R_EX_amp_e\n", + "\t- R_EX_arab__L_e\n", + "\t- R_EX_arbt_e\n", + "\t- R_DM_4crsol_c\n", + "\t- R_EX_anhgm_e\n", + "\t- R_EX_alltn_e\n", + "\t- R_EX_all__D_e\n", + "\t- R_EX_arbtn_e\n", + "\t- R_DM_5drib_c\n", + "\t- R_EX_arbtn_fe3_e\n", + "\t- R_DM_aacald_c\n", + "\t- R_EX_arg__L_e\n", + "\t- R_EX_g1p_e\n", + "\t- R_DM_amob_c\n", + "\t- R_EX_g3pc_e\n", + "\t- R_EX_g3pe_e\n", + "\t- R_EX_ascb__L_e\n", + "\t- R_EX_asn__L_e\n", + "\t- R_EX_aso3_e\n", + "\t- R_EX_g3pg_e\n", + "\t- R_EX_g3pi_e\n", + "\t- R_EX_asp__L_e\n", + "\t- R_DM_mththf_c\n", + "\t- R_EX_btn_e\n", + "\t- R_EX_but_e\n", + "\t- R_EX_g3ps_e\n", + "\t- R_EX_g6p_e\n", + "\t- R_DM_oxam_c\n", + "\t- R_EX_gal_e\n", + "\t- R_EX_gal_bD_e\n", + "\t- R_EX_butso3_e\n", + "\t- R_EX_ca2_e\n", + "\t- R_EX_gal1p_e\n", + "\t- R_EX_cbi_e\n", + "\t- R_EX_cbl1_e\n", + "\t- R_EX_cd2_e\n", + "\t- R_EX_cgly_e\n", + "\t- R_EX_chol_e\n", + "\t- R_EX_chtbs_e\n", + "\t- R_EX_cit_e\n", + "\t- R_EX_cl_e\n", + "\t- R_EX_cm_e\n", + "\t- R_EX_galct__D_e\n", + "\t- R_EX_galctn__D_e\n", + "\t- R_EX_galctn__L_e\n", + "\t- R_EX_galt_e\n", + "\t- R_EX_galur_e\n", + "\t- R_EX_gam_e\n", + "\t- R_EX_gam6p_e\n", + "\t- R_EX_gbbtn_e\n", + "\t- R_EX_gdp_e\n", + "\t- R_EX_glc__D_e\n", + "\t- R_EX_glcn_e\n", + "\t- R_EX_cmp_e\n", + "\t- R_EX_co2_e\n", + "\t- R_EX_cobalt2_e\n", + "\t- R_EX_colipa_e\n", + "\t- R_EX_glcr_e\n", + "\t- R_EX_glcur_e\n", + "\t- R_EX_colipap_e\n", + "\t- R_EX_cpgn_e\n", + "\t- R_EX_cpgn_un_e\n", + "\t- R_EX_glcur1p_e\n", + "\t- R_EX_gln__L_e\n", + "\t- R_EX_crn_e\n", + "\t- R_EX_crn__D_e\n", + "\t- R_EX_glu__L_e\n", + "\t- R_EX_gly_e\n", + "\t- R_EX_12ppd__R_e\n", + "\t- R_EX_glyald_e\n", + "\t- R_EX_glyb_e\n", + "\t- R_EX_glyc_e\n", + "\t- R_EX_glyc__R_e\n", + "\t- R_EX_glyc2p_e\n", + "\t- R_EX_csn_e\n", + "\t- R_EX_cu_e\n", + "\t- R_EX_cu2_e\n", + "\t- R_EX_cyan_e\n", + "\t- R_EX_cynt_e\n", + "\t- R_EX_cys__D_e\n", + "\t- R_EX_cys__L_e\n", + "\t- R_EX_cytd_e\n", + "\t- R_EX_dad_2_e\n", + "\t- R_EX_glyc3p_e\n", + "\t- R_EX_glyclt_e\n", + "\t- R_EX_gmp_e\n", + "\t- R_EX_gsn_e\n", + "\t- R_EX_damp_e\n", + "\t- R_EX_dca_e\n", + "\t- R_EX_gthox_e\n", + "\t- R_EX_gthrd_e\n", + "\t- R_EX_12ppd__S_e\n", + "\t- R_EX_gtp_e\n", + "\t- R_EX_gua_e\n", + "\t- R_EX_dcmp_e\n", + "\t- R_EX_dcyt_e\n", + "\t- R_EX_ddca_e\n", + "\t- R_EX_h_e\n", + "\t- R_EX_dgmp_e\n", + "\t- R_EX_14glucan_e\n", + "\t- R_EX_dgsn_e\n", + "\t- R_EX_h2_e\n", + "\t- R_EX_h2o_e\n", + "\t- R_EX_dha_e\n", + "\t- R_EX_15dap_e\n", + "\t- R_EX_dimp_e\n", + "\t- R_EX_din_e\n", + "\t- R_EX_h2o2_e\n", + "\t- R_EX_h2s_e\n", + "\t- R_EX_hacolipa_e\n", + "\t- R_EX_halipa_e\n", + "\t- R_EX_hdca_e\n", + "\t- R_EX_hdcea_e\n", + "\t- R_EX_dms_e\n", + "\t- R_EX_dmso_e\n", + "\t- R_EX_dopa_e\n", + "\t- R_EX_doxrbcn_e\n", + "\t- R_EX_dtmp_e\n", + "\t- R_EX_dump_e\n", + "\t- R_EX_hg2_e\n", + "\t- R_EX_his__L_e\n", + "\t- R_EX_duri_e\n", + "\t- R_EX_eca4colipa_e\n", + "\t- R_EX_hom__L_e\n", + "\t- R_EX_23camp_e\n", + "\t- R_EX_hxa_e\n", + "\t- R_EX_enlipa_e\n", + "\t- R_EX_23ccmp_e\n", + "\t- R_EX_enter_e\n", + "\t- R_EX_etha_e\n", + "\t- R_EX_hxan_e\n", + "\t- R_EX_23cgmp_e\n", + "\t- R_EX_idon__L_e\n", + "\t- R_EX_ile__L_e\n", + "\t- R_EX_ethso3_e\n", + "\t- R_EX_etoh_e\n", + "\t- R_EX_imp_e\n", + "\t- R_EX_f6p_e\n", + "\t- R_EX_23cump_e\n", + "\t- R_EX_fald_e\n", + "\t- R_EX_indole_e\n", + "\t- R_EX_inost_e\n", + "\t- R_EX_fe2_e\n", + "\t- R_EX_23dappa_e\n", + "\t- R_EX_fe3_e\n", + "\t- R_EX_fe3dcit_e\n", + "\t- R_EX_fe3dhbzs_e\n", + "\t- R_EX_fe3hox_e\n", + "\t- R_EX_fe3hox_un_e\n", + "\t- R_EX_ins_e\n", + "\t- R_EX_isetac_e\n", + "\t- R_EX_k_e\n", + "\t- R_EX_kdo2lipid4_e\n", + "\t- R_EX_lac__D_e\n", + "\t- R_EX_lac__L_e\n", + "\t- R_EX_fecrm_e\n", + "\t- R_EX_fecrm_un_e\n", + "\t- R_EX_feenter_e\n", + "\t- R_EX_lcts_e\n", + "\t- R_EX_feoxam_e\n", + "\t- R_EX_leu__L_e\n", + "\t- R_EX_26dap__M_e\n", + "\t- R_EX_lipa_e\n", + "\t- R_EX_lipa_cold_e\n", + "\t- R_EX_feoxam_un_e\n", + "\t- R_EX_for_e\n", + "\t- R_EX_2ddglcn_e\n", + "\t- R_EX_fru_e\n", + "\t- R_EX_frulys_e\n", + "\t- R_EX_lipoate_e\n", + "\t- R_EX_lys__L_e\n", + "\t- R_EX_lyx__L_e\n", + "\t- R_EX_fruur_e\n", + "\t- R_EX_fuc__L_e\n", + "\t- R_EX_34dhpac_e\n", + "\t- R_EX_fum_e\n", + "\t- R_EX_fusa_e\n", + "\t- R_EX_mal__D_e\n", + "\t- R_EX_mal__L_e\n", + "\t- R_EX_malt_e\n", + "\t- R_EX_malthx_e\n", + "\t- R_EX_3amp_e\n", + "\t- R_EX_maltpt_e\n", + "\t- R_EX_malttr_e\n", + "\t- R_EX_maltttr_e\n", + "\t- R_EX_man_e\n", + "\t- R_EX_man6p_e\n", + "\t- R_EX_manglyc_e\n", + "\t- R_EX_melib_e\n", + "\t- R_EX_meoh_e\n", + "\t- R_EX_met__D_e\n", + "\t- R_EX_met__L_e\n", + "\t- R_EX_metsox_R__L_e\n", + "\t- R_EX_metsox_S__L_e\n", + "\t- R_EX_mg2_e\n", + "\t- R_EX_mincyc_e\n", + "\t- R_EX_minohp_e\n", + "\t- R_EX_mmet_e\n", + "\t- R_EX_mn2_e\n", + "\t- R_EX_mnl_e\n", + "\t- R_EX_tre_e\n", + "\t- R_EX_trp__L_e\n", + "\t- R_EX_tsul_e\n", + "\t- R_EX_ttdca_e\n", + "\t- R_EX_ttdcea_e\n", + "\t- R_EX_ttrcyc_e\n", + "\t- R_EX_mobd_e\n", + "\t- R_EX_tungs_e\n", + "\t- R_EX_3cmp_e\n", + "\t- R_EX_tym_e\n", + "\t- R_EX_tyr__L_e\n", + "\t- R_EX_tyrp_e\n", + "\t- R_EX_mso3_e\n", + "\t- R_EX_n2o_e\n", + "\t- R_EX_na1_e\n", + "\t- R_EX_nac_e\n", + "\t- R_EX_uacgam_e\n", + "\t- R_EX_3gmp_e\n", + "\t- R_EX_udpacgal_e\n", + "\t- R_EX_udpg_e\n", + "\t- R_EX_udpgal_e\n", + "\t- R_EX_udpglcur_e\n", + "\t- R_EX_ump_e\n", + "\t- R_EX_ura_e\n", + "\t- R_EX_nh4_e\n", + "\t- R_EX_ni2_e\n", + "\t- R_EX_nmn_e\n", + "\t- R_EX_no_e\n", + "\t- R_EX_no2_e\n", + "\t- R_EX_no3_e\n", + "\t- R_EX_novbcn_e\n", + "\t- R_EX_o16a4colipa_e\n", + "\t- R_EX_urea_e\n", + "\t- R_EX_uri_e\n", + "\t- R_EX_val__L_e\n", + "\t- R_EX_xan_e\n", + "\t- R_EX_xmp_e\n", + "\t- R_EX_o2_e\n", + "\t- R_EX_3hcinnm_e\n", + "\t- R_EX_o2s_e\n", + "\t- R_EX_ocdca_e\n", + "\t- R_EX_ocdcea_e\n", + "\t- R_EX_octa_e\n", + "\t- R_EX_xtsn_e\n", + "\t- R_EX_xyl__D_e\n", + "\t- R_EX_xylu__L_e\n", + "\t- R_EX_zn2_e\n", + "\t- R_EX_orn_e\n", + "\t- R_EX_orot_e\n", + "\t- R_EX_3hpp_e\n", + "\t- R_EX_pacald_e\n", + "\t- R_EX_peamn_e\n", + "\t- R_EX_phe__L_e\n", + "\t- R_EX_pheme_e\n", + "\t- R_EX_pi_e\n", + "\t- R_EX_pnto__R_e\n", + "\t- R_EX_ppa_e\n", + "\t- R_EX_ppal_e\n", + "\t- R_EX_3hpppn_e\n", + "\t- R_EX_pppn_e\n", + "\t- R_EX_ppt_e\n", + "\t- R_EX_pro__L_e\n", + "\t- R_EX_3ump_e\n", + "\t- R_EX_progly_e\n", + "\t- R_EX_psclys_e\n", + "\t- R_EX_4abut_e\n", + "\t- R_EX_pser__L_e\n", + "\t- R_EX_ptrc_e\n", + "\t- R_EX_pydam_e\n", + "\t- R_EX_pydx_e\n", + "\t- R_EX_pydxn_e\n", + "\t- R_EX_pyr_e\n", + "\t- R_EX_quin_e\n", + "\t- R_EX_r5p_e\n", + "\t- R_EX_rfamp_e\n", + "\t- R_EX_rib__D_e\n", + "\t- R_EX_4hoxpacd_e\n", + "\t- R_EX_rmn_e\n", + "\t- R_EX_sbt__D_e\n", + "\t- R_EX_sel_e\n", + "\t- R_EX_5dglcn_e\n", + "\t- R_EX_ser__D_e\n", + "\t- R_EX_5mtr_e\n", + "\t- R_EX_ser__L_e\n", + "\t- R_EX_skm_e\n", + "\t- R_EX_slnt_e\n", + "\t- R_EX_LalaDglu_e\n", + "\t- R_EX_so2_e\n", + "\t- R_EX_so3_e\n", + "\t- R_EX_so4_e\n", + "\t- R_EX_spmd_e\n", + "\t- R_EX_succ_e\n", + "\t- R_EX_sucr_e\n", + "\t- R_EX_sulfac_e\n", + "\t- R_EX_tartr__D_e\n", + "\t- R_EX_tartr__L_e\n", + "\t- R_EX_LalaDgluMdap_e\n", + "\t- R_EX_taur_e\n", + "\t- R_EX_tcynt_e\n", + "\t- R_EX_thm_e\n", + "\t- R_EX_thr__L_e\n", + "\t- R_EX_thrp_e\n", + "\t- R_EX_LalaDgluMdapDala_e\n", + "\t- R_EX_thym_e\n", + "\t- R_EX_thymd_e\n", + "\t- R_EX_tma_e\n", + "\t- R_EX_tmao_e\n", + "\t- R_EX_LalaLglu_e\n", + "\t- R_EX_ac_e\n", + "\t- R_EX_acac_e\n", + "\t- R_EX_acald_e\n", + "\t- R_EX_acgal_e\n", + "\t- R_EX_acgal1p_e\n", + "\t- R_EX_acgam_e\n", + "\t- R_EX_acgam1p_e\n", + "\t- R_EX_acmana_e\n", + "\t- R_EX_acmum_e\n", + "\t- R_EX_acnam_e\n", + "\t- R_EX_acolipa_e\n", + "\t- R_EX_acser_e\n", + "\t- R_EX_ade_e\n", + "\t- R_EX_adn_e\n", + "\t- R_EX_adocbl_e\n", + "\t- R_EX_ag_e\n", + "\t- R_EX_agm_e\n", + "\t- R_EX_akg_e\n", + "\t- R_EX_ala_B_e\n", + "\t- R_EX_ala__D_e\n", + "\t- R_EX_ala__L_e\n", + "\t- R_EX_alaala_e\n", + "\t- R_EX_4hphac_e\n", + "\t- R_EX_colipa20Oag_e\n", + "\t- R_EX_23dhbzs3_e\n", + "\t- R_EX_tet_e\n", + "\t- R_EX_4abz_e\n", + "\t- R_EX_salchs4_e\n", + "\t- R_EX_oaa_e\n", + "\t- R_EX_salchs2fe_e\n", + "\t- R_EX_feroxB_e\n", + "\t- R_EX_cysi__L_e\n", + "\t- R_EX_tcb_e\n", + "\t- R_EX_tton_e\n", + "\t- R_EX_4hthr_e\n", + "\t- R_EX_feroxEfe_e\n", + "\t- R_EX_fe3dhbzs3_e\n", + "\t- R_EX_feroxE_e\n", + "\t- R_EX_pep_e\n", + "\t- R_EX_drib_e\n", + "\t- R_EX_feroxBfe_e\n", + "\t- R_EX_salchs4fe_e\n", + "\t- R_EX_2pg_e\n", + "\t- R_EX_frmd_e\n", + "\t- R_EX_colipaOA_e\n", + "\t- R_EX_salchsx_e\n", + "\t- R_EX_AEP_e\n", + "\t- R_EX_udcpo4_e\n", + "\t- R_EX_icit_e\n", + "\t- R_EX_tag__D_e\n", + "\t- R_EX_acon_C_e\n", + "\t- R_EX_remnant1_e\n", + "\t- R_EX_udcpo5_e\n", + "\t- R_EX_guln__L_e\n", + "\t- R_EX_3ntym_e\n", + "\t- R_EX_6apa_e\n", + "\t- R_EX_feroxG_e\n", + "\t- R_EX_salchs2_e\n", + "\t- R_EX_rnam_e\n", + "\t- R_EX_airs_e\n", + "\t- R_EX_dxyl_e\n", + "\t- R_EX_3pg_e\n", + "\t- R_EX_feroxGfe_e\n", + "\t- R_EX_diact_e\n", + "\t- R_EX_peng_e\n", + "\t- R_EX_pac_e\n", + "\t- R_EX_cellb_e\n", + "\t- R_EX_salcn_e\n", + "\t- R_EX_btd_RR_e\n", + "\t- R_EX_rbt_e\n", + "\t- R_EX_bz_e\n", + "\t- R_EX_13ppd_e\n", + "\t- R_EX_galam_e\n", + "\t- R_EX_abt__D_e\n", + "\t- R_EX_raffin_e\n", + "\t- R_DM_lipidA_core_e_p\n", + "\t- R_EX_o6a4colipa_e\n", + "\t- R_EX_fe_e\n", + "\t- R_EX_arab__D_e\n", + "\t- R_EX_kokdolipidA_e\n", + "\t- R_EX_3hoxpac_e\n", + "\t- R_EX_4hoxpac_e\n", + "\t- R_SK_thf_c\u001b[0m\n", + "File saved at: ../../data/sbml_corrected/iSDY_1059.xml\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: iECSP_1301\n", + "\u001b[0;93mWARNING : \n", + " - R_CAT: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_DHPTDNR: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_DHPTDNRN: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_FHL: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SPODM: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SPODMpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCASPtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCFUMtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCMALtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCTARTtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_EX_chitob_e: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_CHITOBpts: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_CHITPH: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_URCN: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\u001b[0m\n", + "\n", + "\n", + "\u001b[0;36m############################################\n", + "############################################\n", + " WRITING SBML FILE \n", + "############################################\n", + "############################################\n", + "\u001b[0m\n", + "\u001b[0;93mWARNING : IMPORT REACTION REMOVED BY DEFAULT\u001b[0m\n", + "\u001b[0;93mWARNING : If you want to keep import reaction\n", + "use option -kir / --keep-import-reactions\u001b[0m\n", + "\u001b[0;93mWARNING : Import reaction removed:\n", + "\t- R_EX_ag_e\n", + "\t- R_EX_agm_e\n", + "\t- R_EX_akg_e\n", + "\t- R_EX_acgam_e\n", + "\t- R_EX_acgal1p_e\n", + "\t- R_DM_4crsol_c\n", + "\t- R_EX_ala_B_e\n", + "\t- R_EX_ala__D_e\n", + "\t- R_EX_ala__L_e\n", + "\t- R_EX_acgam1p_e\n", + "\t- R_EX_acmana_e\n", + "\t- R_EX_acmum_e\n", + "\t- R_EX_acnam_e\n", + "\t- R_EX_acolipa_e\n", + "\t- R_EX_acser_e\n", + "\t- R_EX_ade_e\n", + "\t- R_EX_adn_e\n", + "\t- R_EX_adocbl_e\n", + "\t- R_EX_alaala_e\n", + "\t- R_EX_all__D_e\n", + "\t- R_DM_5drib_c\n", + "\t- R_EX_alltn_e\n", + "\t- R_DM_aacald_c\n", + "\t- R_EX_amp_e\n", + "\t- R_EX_anhgm_e\n", + "\t- R_EX_arab__L_e\n", + "\t- R_DM_amob_c\n", + "\t- R_EX_arbt_e\n", + "\t- R_EX_arbtn_e\n", + "\t- R_EX_feoxam_e\n", + "\t- R_EX_feoxam_un_e\n", + "\t- R_EX_arbtn_fe3_e\n", + "\t- R_DM_mththf_c\n", + "\t- R_EX_arg__L_e\n", + "\t- R_EX_ascb__L_e\n", + "\t- R_EX_asn__L_e\n", + "\t- R_EX_aso3_e\n", + "\t- R_EX_asp__L_e\n", + "\t- R_EX_btn_e\n", + "\t- R_EX_but_e\n", + "\t- R_EX_butso3_e\n", + "\t- R_EX_ca2_e\n", + "\t- R_EX_cbi_e\n", + "\t- R_EX_cbl1_e\n", + "\t- R_EX_for_e\n", + "\t- R_EX_fru_e\n", + "\t- R_EX_frulys_e\n", + "\t- R_EX_fruur_e\n", + "\t- R_EX_fuc__L_e\n", + "\t- R_EX_fum_e\n", + "\t- R_EX_fusa_e\n", + "\t- R_EX_g1p_e\n", + "\t- R_EX_g3pc_e\n", + "\t- R_EX_g3pe_e\n", + "\t- R_EX_g3pg_e\n", + "\t- R_EX_g3pi_e\n", + "\t- R_EX_g3ps_e\n", + "\t- R_EX_g6p_e\n", + "\t- R_EX_gal_e\n", + "\t- R_EX_gal_bD_e\n", + "\t- R_EX_cd2_e\n", + "\t- R_EX_cgly_e\n", + "\t- R_EX_chol_e\n", + "\t- R_EX_chtbs_e\n", + "\t- R_EX_cit_e\n", + "\t- R_EX_cl_e\n", + "\t- R_EX_cm_e\n", + "\t- R_EX_cmp_e\n", + "\t- R_EX_gal1p_e\n", + "\t- R_EX_galct__D_e\n", + "\t- R_EX_galctn__D_e\n", + "\t- R_DM_oxam_c\n", + "\t- R_EX_galctn__L_e\n", + "\t- R_EX_galt_e\n", + "\t- R_EX_galur_e\n", + "\t- R_EX_gam_e\n", + "\t- R_EX_gam6p_e\n", + "\t- R_EX_gbbtn_e\n", + "\t- R_EX_gdp_e\n", + "\t- R_EX_glc__D_e\n", + "\t- R_EX_glcn_e\n", + "\t- R_EX_glcr_e\n", + "\t- R_EX_glcur_e\n", + "\t- R_EX_glcur1p_e\n", + "\t- R_EX_gln__L_e\n", + "\t- R_EX_glu__L_e\n", + "\t- R_EX_gly_e\n", + "\t- R_EX_glyald_e\n", + "\t- R_EX_co2_e\n", + "\t- R_EX_cobalt2_e\n", + "\t- R_EX_colipa_e\n", + "\t- R_EX_colipap_e\n", + "\t- R_EX_cpgn_e\n", + "\t- R_EX_cpgn_un_e\n", + "\t- R_EX_crn_e\n", + "\t- R_EX_crn__D_e\n", + "\t- R_EX_csn_e\n", + "\t- R_EX_cu_e\n", + "\t- R_EX_cu2_e\n", + "\t- R_EX_cyan_e\n", + "\t- R_EX_cynt_e\n", + "\t- R_EX_cys__D_e\n", + "\t- R_EX_cys__L_e\n", + "\t- R_EX_cytd_e\n", + "\t- R_EX_dad_2_e\n", + "\t- R_EX_glyb_e\n", + "\t- R_EX_glyc_e\n", + "\t- R_EX_glyc__R_e\n", + "\t- R_EX_glyc2p_e\n", + "\t- R_EX_glyc3p_e\n", + "\t- R_EX_glyclt_e\n", + "\t- R_EX_gmp_e\n", + "\t- R_EX_gsn_e\n", + "\t- R_EX_gthox_e\n", + "\t- R_EX_gthrd_e\n", + "\t- R_EX_damp_e\n", + "\t- R_EX_dca_e\n", + "\t- R_EX_dcmp_e\n", + "\t- R_EX_dcyt_e\n", + "\t- R_EX_ddca_e\n", + "\t- R_EX_dgmp_e\n", + "\t- R_EX_dgsn_e\n", + "\t- R_EX_gtp_e\n", + "\t- R_EX_gua_e\n", + "\t- R_EX_h_e\n", + "\t- R_EX_dha_e\n", + "\t- R_EX_dimp_e\n", + "\t- R_EX_din_e\n", + "\t- R_EX_dms_e\n", + "\t- R_EX_dmso_e\n", + "\t- R_EX_dopa_e\n", + "\t- R_EX_h2_e\n", + "\t- R_EX_h2o_e\n", + "\t- R_EX_h2o2_e\n", + "\t- R_EX_h2s_e\n", + "\t- R_EX_doxrbcn_e\n", + "\t- R_EX_dtmp_e\n", + "\t- R_EX_hacolipa_e\n", + "\t- R_EX_dump_e\n", + "\t- R_EX_duri_e\n", + "\t- R_EX_halipa_e\n", + "\t- R_EX_hdca_e\n", + "\t- R_EX_hdcea_e\n", + "\t- R_EX_eca4colipa_e\n", + "\t- R_EX_enlipa_e\n", + "\t- R_EX_enter_e\n", + "\t- R_EX_etha_e\n", + "\t- R_EX_ethso3_e\n", + "\t- R_EX_hg2_e\n", + "\t- R_EX_his__L_e\n", + "\t- R_EX_hom__L_e\n", + "\t- R_EX_hxa_e\n", + "\t- R_EX_hxan_e\n", + "\t- R_EX_etoh_e\n", + "\t- R_EX_f6p_e\n", + "\t- R_EX_12ppd__R_e\n", + "\t- R_EX_fald_e\n", + "\t- R_EX_fe2_e\n", + "\t- R_EX_idon__L_e\n", + "\t- R_EX_12ppd__S_e\n", + "\t- R_EX_ile__L_e\n", + "\t- R_EX_imp_e\n", + "\t- R_EX_fe3_e\n", + "\t- R_EX_fe3dcit_e\n", + "\t- R_EX_fe3dhbzs_e\n", + "\t- R_EX_fe3hox_e\n", + "\t- R_EX_indole_e\n", + "\t- R_EX_inost_e\n", + "\t- R_EX_14glucan_e\n", + "\t- R_EX_ins_e\n", + "\t- R_EX_isetac_e\n", + "\t- R_EX_k_e\n", + "\t- R_EX_fe3hox_un_e\n", + "\t- R_EX_fecrm_e\n", + "\t- R_EX_fecrm_un_e\n", + "\t- R_EX_feenter_e\n", + "\t- R_EX_kdo2lipid4_e\n", + "\t- R_EX_15dap_e\n", + "\t- R_EX_lac__D_e\n", + "\t- R_EX_23camp_e\n", + "\t- R_EX_lac__L_e\n", + "\t- R_EX_mal__D_e\n", + "\t- R_EX_mal__L_e\n", + "\t- R_EX_malt_e\n", + "\t- R_EX_malthx_e\n", + "\t- R_EX_lcts_e\n", + "\t- R_EX_leu__L_e\n", + "\t- R_EX_lipa_e\n", + "\t- R_EX_lipa_cold_e\n", + "\t- R_EX_lipoate_e\n", + "\t- R_EX_maltpt_e\n", + "\t- R_EX_23ccmp_e\n", + "\t- R_EX_malttr_e\n", + "\t- R_EX_maltttr_e\n", + "\t- R_EX_lys__L_e\n", + "\t- R_EX_lyx__L_e\n", + "\t- R_EX_man_e\n", + "\t- R_EX_man6p_e\n", + "\t- R_EX_23cgmp_e\n", + "\t- R_EX_manglyc_e\n", + "\t- R_EX_melib_e\n", + "\t- R_EX_taur_e\n", + "\t- R_EX_23cump_e\n", + "\t- R_EX_tcynt_e\n", + "\t- R_EX_meoh_e\n", + "\t- R_EX_met__D_e\n", + "\t- R_EX_met__L_e\n", + "\t- R_EX_23dappa_e\n", + "\t- R_EX_metsox_R__L_e\n", + "\t- R_EX_thm_e\n", + "\t- R_EX_thr__L_e\n", + "\t- R_EX_metsox_S__L_e\n", + "\t- R_EX_26dap__M_e\n", + "\t- R_EX_thrp_e\n", + "\t- R_EX_thym_e\n", + "\t- R_EX_mg2_e\n", + "\t- R_EX_mincyc_e\n", + "\t- R_EX_thymd_e\n", + "\t- R_EX_2ddglcn_e\n", + "\t- R_EX_tma_e\n", + "\t- R_EX_tmao_e\n", + "\t- R_EX_tre_e\n", + "\t- R_EX_trp__L_e\n", + "\t- R_EX_tsul_e\n", + "\t- R_EX_minohp_e\n", + "\t- R_EX_mmet_e\n", + "\t- R_EX_mn2_e\n", + "\t- R_EX_mnl_e\n", + "\t- R_EX_mobd_e\n", + "\t- R_EX_mso3_e\n", + "\t- R_EX_n2o_e\n", + "\t- R_EX_na1_e\n", + "\t- R_EX_ttdca_e\n", + "\t- R_EX_ttdcea_e\n", + "\t- R_EX_ttrcyc_e\n", + "\t- R_EX_nac_e\n", + "\t- R_EX_tungs_e\n", + "\t- R_EX_tym_e\n", + "\t- R_EX_34dhpac_e\n", + "\t- R_EX_tyr__L_e\n", + "\t- R_EX_tyrp_e\n", + "\t- R_EX_nh4_e\n", + "\t- R_EX_ni2_e\n", + "\t- R_EX_nmn_e\n", + "\t- R_EX_no_e\n", + "\t- R_EX_no2_e\n", + "\t- R_EX_no3_e\n", + "\t- R_EX_uacgam_e\n", + "\t- R_EX_udpacgal_e\n", + "\t- R_EX_udpg_e\n", + "\t- R_EX_novbcn_e\n", + "\t- R_EX_o16a4colipa_e\n", + "\t- R_EX_udpgal_e\n", + "\t- R_EX_3amp_e\n", + "\t- R_EX_udpglcur_e\n", + "\t- R_EX_ump_e\n", + "\t- R_EX_ura_e\n", + "\t- R_EX_urea_e\n", + "\t- R_EX_uri_e\n", + "\t- R_EX_val__L_e\n", + "\t- R_EX_xan_e\n", + "\t- R_EX_o2_e\n", + "\t- R_EX_o2s_e\n", + "\t- R_EX_ocdca_e\n", + "\t- R_EX_ocdcea_e\n", + "\t- R_EX_octa_e\n", + "\t- R_EX_orn_e\n", + "\t- R_EX_xmp_e\n", + "\t- R_EX_xtsn_e\n", + "\t- R_EX_xyl__D_e\n", + "\t- R_EX_xylu__L_e\n", + "\t- R_EX_orot_e\n", + "\t- R_EX_3cmp_e\n", + "\t- R_EX_pacald_e\n", + "\t- R_EX_peamn_e\n", + "\t- R_EX_phe__L_e\n", + "\t- R_EX_zn2_e\n", + "\t- R_EX_pheme_e\n", + "\t- R_EX_3gmp_e\n", + "\t- R_EX_pi_e\n", + "\t- R_EX_pnto__R_e\n", + "\t- R_EX_3hcinnm_e\n", + "\t- R_EX_ppa_e\n", + "\t- R_EX_ppal_e\n", + "\t- R_EX_pppn_e\n", + "\t- R_EX_ppt_e\n", + "\t- R_EX_pro__L_e\n", + "\t- R_EX_progly_e\n", + "\t- R_EX_psclys_e\n", + "\t- R_EX_pser__L_e\n", + "\t- R_EX_ptrc_e\n", + "\t- R_EX_pydam_e\n", + "\t- R_EX_pydx_e\n", + "\t- R_EX_3hpp_e\n", + "\t- R_EX_pydxn_e\n", + "\t- R_EX_pyr_e\n", + "\t- R_EX_quin_e\n", + "\t- R_EX_r5p_e\n", + "\t- R_EX_rfamp_e\n", + "\t- R_EX_3hpppn_e\n", + "\t- R_EX_rib__D_e\n", + "\t- R_EX_rmn_e\n", + "\t- R_EX_sbt__D_e\n", + "\t- R_EX_sel_e\n", + "\t- R_EX_ser__D_e\n", + "\t- R_EX_ser__L_e\n", + "\t- R_EX_skm_e\n", + "\t- R_EX_slnt_e\n", + "\t- R_EX_3ump_e\n", + "\t- R_EX_so2_e\n", + "\t- R_EX_so3_e\n", + "\t- R_EX_so4_e\n", + "\t- R_EX_spmd_e\n", + "\t- R_EX_succ_e\n", + "\t- R_EX_sucr_e\n", + "\t- R_EX_4abut_e\n", + "\t- R_EX_sulfac_e\n", + "\t- R_EX_tartr__D_e\n", + "\t- R_EX_tartr__L_e\n", + "\t- R_EX_4hoxpacd_e\n", + "\t- R_EX_5dglcn_e\n", + "\t- R_EX_5mtr_e\n", + "\t- R_EX_LalaDglu_e\n", + "\t- R_EX_LalaDgluMdap_e\n", + "\t- R_EX_LalaDgluMdapDala_e\n", + "\t- R_EX_LalaLglu_e\n", + "\t- R_EX_ac_e\n", + "\t- R_EX_acac_e\n", + "\t- R_EX_acald_e\n", + "\t- R_EX_acgal_e\n", + "\t- R_EX_diact_e\n", + "\t- R_EX_4hphac_e\n", + "\t- R_EX_4abz_e\n", + "\t- R_EX_acon_C_e\n", + "\t- R_EX_frmd_e\n", + "\t- R_EX_colipaOA_e\n", + "\t- R_EX_salchsx_e\n", + "\t- R_EX_AEP_e\n", + "\t- R_EX_icit_e\n", + "\t- R_EX_feroxB_e\n", + "\t- R_EX_tcb_e\n", + "\t- R_EX_tton_e\n", + "\t- R_EX_4hthr_e\n", + "\t- R_EX_feroxEfe_e\n", + "\t- R_EX_colipa20Oag_e\n", + "\t- R_EX_feroxE_e\n", + "\t- R_EX_pep_e\n", + "\t- R_EX_23dhbzs3_e\n", + "\t- R_EX_tet_e\n", + "\t- R_EX_salchs4_e\n", + "\t- R_EX_remnant1_e\n", + "\t- R_EX_salchs2_e\n", + "\t- R_EX_tag__D_e\n", + "\t- R_EX_oaa_e\n", + "\t- R_EX_salchs2fe_e\n", + "\t- R_EX_rnam_e\n", + "\t- R_EX_airs_e\n", + "\t- R_EX_dxyl_e\n", + "\t- R_EX_3pg_e\n", + "\t- R_EX_cysi__L_e\n", + "\t- R_EX_fe3dhbzs3_e\n", + "\t- R_EX_drib_e\n", + "\t- R_EX_feroxBfe_e\n", + "\t- R_EX_salchs4fe_e\n", + "\t- R_EX_2pg_e\n", + "\t- R_EX_cellb_e\n", + "\t- R_EX_udcpo4_e\n", + "\t- R_EX_btd_RR_e\n", + "\t- R_EX_13ppd_e\n", + "\t- R_EX_rbt_e\n", + "\t- R_EX_bz_e\n", + "\t- R_EX_feroxGfe_e\n", + "\t- R_EX_abt__D_e\n", + "\t- R_EX_udcpo5_e\n", + "\t- R_EX_guln__L_e\n", + "\t- R_EX_feroxG_e\n", + "\t- R_EX_fe_e\n", + "\t- R_EX_kokdolipidA_e\n", + "\t- R_EX_3hoxpac_e\n", + "\t- R_EX_4hoxpac_e\n", + "\t- R_EX_3ntym_e\n", + "\t- R_EX_6apa_e\n", + "\t- R_EX_peng_e\n", + "\t- R_EX_pac_e\n", + "\t- R_EX_salcn_e\n", + "\t- R_EX_galam_e\n", + "\t- R_DM_lipidA_core_e_p\n", + "\t- R_EX_raffin_e\n", + "\t- R_EX_o6a4colipa_e\n", + "\t- R_EX_arab__D_e\u001b[0m\n", + "File saved at: ../../data/sbml_corrected/iECSP_1301.xml\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: iYO844\n", + "\u001b[0;93mWARNING : - R_EX_o2_e: Reactants and products exchanged.\n", + " Boundaries was: [-999999.0 ; 0.0]\n", + " - R_IMPC: Reactants and products exchanged.\n", + " Boundaries was: [-999999.0 ; 0.0]\n", + " - R_HMGCOAS: Reactants and products exchanged.\n", + " Boundaries was: [-999999.0 ; 0.0]\n", + " - R_SPMDt3i: Reactants and products exchanged.\n", + " Boundaries was: [-999999.0 ; 0.0]\n", + "\u001b[0m\n", + "\n", + "\n", + "\u001b[0;36m############################################\n", + "############################################\n", + " WRITING SBML FILE \n", + "############################################\n", + "############################################\n", + "\u001b[0m\n", + "\u001b[0;93mWARNING : IMPORT REACTION REMOVED BY DEFAULT\u001b[0m\n", + "\u001b[0;93mWARNING : If you want to keep import reaction\n", + "use option -kir / --keep-import-reactions\u001b[0m\n", + "\u001b[0;93mWARNING : Reaction with tag reversible modified:\n", + "\t- R_EX_o2_e\n", + "\t- R_IMPC\n", + "\t- R_HMGCOAS\n", + "\t- R_SPMDt3i\u001b[0m\n", + "\u001b[0;93mWARNING : Reaction with Reactants and Products exchanged:\n", + "\t- R_EX_o2_e\n", + "\t- R_IMPC\n", + "\t- R_HMGCOAS\n", + "\t- R_SPMDt3i\u001b[0m\n", + "\u001b[0;93mWARNING : Import reaction removed:\n", + "\t- R_EX_etha_e\n", + "\t- R_EX_drib_e\n", + "\t- R_EX_csn_e\n", + "\t- R_EX_5mtr_e\n", + "\t- R_EX_dtmp_e\n", + "\t- R_EX_6pgc_e\n", + "\t- R_EX_ctbt_e\n", + "\t- R_EX_cu2_e\n", + "\t- R_EX_Larab_e\n", + "\t- R_EX_ectoine_e\n", + "\t- R_EX_cys__D_e\n", + "\t- R_EX_cys__L_e\n", + "\t- R_EX_eths_e\n", + "\t- R_EX_cyst__L_e\n", + "\t- R_EX_Lcyst_e\n", + "\t- R_EX_cytd_e\n", + "\t- R_EX_abt__L_e\n", + "\t- R_EX_etoh_e\n", + "\t- R_EX_dad_2_e\n", + "\t- R_EX_dcyt_e\n", + "\t- R_EX_fe2_e\n", + "\t- R_EX_dextrin_e\n", + "\t- R_EX_dha_e\n", + "\t- R_EX_ac_e\n", + "\t- R_EX_acac_e\n", + "\t- R_EX_fe3_e\n", + "\t- R_EX_ferrich_e\n", + "\t- R_EX_diact_e\n", + "\t- R_EX_djenk_e\n", + "\t- R_EX_ferxa_e\n", + "\t- R_EX_fol_e\n", + "\t- R_EX_acgam_e\n", + "\t- R_EX_acmana_e\n", + "\t- R_EX_acnam_e\n", + "\t- R_EX_for_e\n", + "\t- R_EX_fru_e\n", + "\t- R_EX_actn__R_e\n", + "\t- R_EX_ade_e\n", + "\t- R_EX_adn_e\n", + "\t- R_EX_akg_e\n", + "\t- R_EX_ala_B_e\n", + "\t- R_EX_ala__D_e\n", + "\t- R_EX_ala_L_Thr__L_e\n", + "\t- R_EX_ala_L_asp__L_e\n", + "\t- R_EX_ala__L_e\n", + "\t- R_EX_ala_L_gln__L_e\n", + "\t- R_EX_ala_L_glu__L_e\n", + "\t- R_EX_L_alagly_e\n", + "\t- R_EX_ala_L_his__L_e\n", + "\t- R_EX_ala_L_leu__L_e\n", + "\t- R_EX_g6p_e\n", + "\t- R_EX_gal_e\n", + "\t- R_EX_galctr__D_e\n", + "\t- R_EX_galt_e\n", + "\t- R_EX_galur_e\n", + "\t- R_EX_gam6p_e\n", + "\t- R_EX_gam_e\n", + "\t- R_EX_gbbtn_e\n", + "\t- R_EX_glc__D_e\n", + "\t- R_EX_glcn__D_e\n", + "\t- R_EX_glcr_e\n", + "\t- R_EX_glcur_e\n", + "\t- R_EX_gln__L_e\n", + "\t- R_EX_glu__D_e\n", + "\t- R_EX_alaala_e\n", + "\t- R_EX_alltn_e\n", + "\t- R_EX_amp_e\n", + "\t- R_EX_amylase_e\n", + "\t- R_EX_fum_e\n", + "\t- R_EX_antim_e\n", + "\t- R_EX_g1p_e\n", + "\t- R_EX_glu__L_e\n", + "\t- R_EX_arab__D_e\n", + "\t- R_EX_arab__L_e\n", + "\t- R_EX_arbt_e\n", + "\t- R_EX_arg__L_e\n", + "\t- R_EX_argp_e\n", + "\t- R_EX_arsenb_e\n", + "\t- R_EX_arsna_e\n", + "\t- R_EX_arsni2_e\n", + "\t- R_EX_asn__L_e\n", + "\t- R_EX_asp__L_e\n", + "\t- R_EX_bilea_e\n", + "\t- R_EX_btd_RR_e\n", + "\t- R_EX_glx_e\n", + "\t- R_EX_buts_e\n", + "\t- R_EX_ca2_e\n", + "\t- R_EX_cbl2_e\n", + "\t- R_EX_pur_e\n", + "\t- R_EX_pyr_e\n", + "\t- R_EX_cd2_e\n", + "\t- R_EX_gly_asn__L_e\n", + "\t- R_EX_cellb_e\n", + "\t- R_EX_cgly_e\n", + "\t- R_EX_raffin_e\n", + "\t- R_EX_rib__D_e\n", + "\t- R_EX_gly_asp__L_e\n", + "\t- R_EX_ribflv_e\n", + "\t- R_EX_chitob_e\n", + "\t- R_EX_gly_e\n", + "\t- R_EX_chol_e\n", + "\t- R_EX_rmn_e\n", + "\t- R_EX_gly_gln__L_e\n", + "\t- R_EX_chols_e\n", + "\t- R_EX_chor_e\n", + "\t- R_EX_cit_e\n", + "\t- R_EX_salcn_e\n", + "\t- R_EX_gly_glu__L_e\n", + "\t- R_EX_sbt__D_e\n", + "\t- R_EX_ser__D_e\n", + "\t- R_EX_citr__L_e\n", + "\t- R_EX_cmp_e\n", + "\t- R_EX_ser__L_e\n", + "\t- R_EX_gly_met__L_e\n", + "\t- R_EX_so4_e\n", + "\t- R_EX_spmd_e\n", + "\t- R_EX_co2_e\n", + "\t- R_EX_cobalt2_e\n", + "\t- R_EX_crn_e\n", + "\t- R_EX_cro4_e\n", + "\t- R_EX_srb__L_e\n", + "\t- R_EX_starch_e\n", + "\t- R_EX_subtilisin_e\n", + "\t- R_EX_succ_e\n", + "\t- R_EX_sucr_e\n", + "\t- R_EX_gly_pro__L_e\n", + "\t- R_EX_sula_e\n", + "\t- R_EX_taur_e\n", + "\t- R_EX_glyb_e\n", + "\t- R_EX_thiog_e\n", + "\t- R_EX_glyc3p_e\n", + "\t- R_EX_glyc_e\n", + "\t- R_EX_glyclt_e\n", + "\t- R_EX_glycogen_e\n", + "\t- R_EX_gmp_e\n", + "\t- R_EX_gsn_e\n", + "\t- R_EX_thr__L_e\n", + "\t- R_EX_gthox_e\n", + "\t- R_EX_thym_e\n", + "\t- R_EX_gthrd_e\n", + "\t- R_EX_thymd_e\n", + "\t- R_EX_gua_e\n", + "\t- R_EX_tmp_e\n", + "\t- R_EX_tre_e\n", + "\t- R_EX_trp__L_e\n", + "\t- R_EX_h2o2_e\n", + "\t- R_EX_tyr__L_e\n", + "\t- R_EX_h2o_e\n", + "\t- R_EX_ump_e\n", + "\t- R_EX_ura_e\n", + "\t- R_EX_h_e\n", + "\t- R_EX_urate_e\n", + "\t- R_EX_hexs_e\n", + "\t- R_EX_urea_e\n", + "\t- R_EX_uri_e\n", + "\t- R_EX_val__L_e\n", + "\t- R_EX_xan_e\n", + "\t- R_EX_xtsn_e\n", + "\t- R_EX_xyl__D_e\n", + "\t- R_EX_zn2_e\n", + "\t- R_EX_hg2_e\n", + "\t- R_EX_his__L_e\n", + "\t- R_EX_hqn_e\n", + "\t- R_EX_hxan_e\n", + "\t- R_EX_icit_e\n", + "\t- R_EX_ile__L_e\n", + "\t- R_EX_inost_e\n", + "\t- R_EX_ins_e\n", + "\t- R_EX_istnt_e\n", + "\t- R_EX_k_e\n", + "\t- R_EX_lac__L_e\n", + "\t- R_EX_lanth_e\n", + "\t- R_EX_lcts_e\n", + "\t- R_EX_leu__L_e\n", + "\t- R_EX_lipt_e\n", + "\t- R_EX_lys__L_e\n", + "\t- R_EX_madg_e\n", + "\t- R_EX_mal__D_e\n", + "\t- R_EX_mal__L_e\n", + "\t- R_EX_malt_e\n", + "\t- R_EX_malttr_e\n", + "\t- R_EX_man1p_e\n", + "\t- R_EX_man6p_e\n", + "\t- R_EX_man_e\n", + "\t- R_EX_3ump_e\n", + "\t- R_EX_mbdg_e\n", + "\t- R_EX_melib_e\n", + "\t- R_EX_meoh_e\n", + "\t- R_EX_met__D_e\n", + "\t- R_EX_met_L_ala__L_e\n", + "\t- R_EX_met__L_e\n", + "\t- R_EX_metox__R_e\n", + "\t- R_EX_metox_e\n", + "\t- R_EX_mg2_e\n", + "\t- R_EX_mn2_e\n", + "\t- R_EX_mnl_e\n", + "\t- R_EX_mobd_e\n", + "\t- R_EX_mops_e\n", + "\t- R_EX_mso3_e\n", + "\t- R_EX_na1_e\n", + "\t- R_EX_nac_e\n", + "\t- R_EX_nh4_e\n", + "\t- R_EX_ni2_e\n", + "\t- R_EX_no2_e\n", + "\t- R_EX_no3_e\n", + "\t- R_EX_o2_e\n", + "\t- R_EX_orn__L_e\n", + "\t- R_EX_pala_e\n", + "\t- R_EX_4abut_e\n", + "\t- R_EX_pep_e\n", + "\t- R_EX_phe__L_e\n", + "\t- R_EX_pi_e\n", + "\t- R_EX_pnto__R_e\n", + "\t- R_EX_ppa_e\n", + "\t- R_EX_ppi_e\n", + "\t- R_EX_pro__L_e\n", + "\t- R_EX_prolb_e\n", + "\t- R_EX_pser__D_e\n", + "\t- R_EX_pser__L_e\n", + "\t- R_EX_2ddglcn_e\n", + "\t- R_EX_2hxmp_e\n", + "\t- R_EX_2pg_e\n", + "\t- R_EX_2pglyc_e\n", + "\t- R_EX_3amba_e\n", + "\t- R_EX_3amp_e\n", + "\t- R_EX_3cmp_e\n", + "\t- R_EX_3gmp_e\n", + "\t- R_EX_3pg_e\n", + "\t- R_EX_f6p_e\u001b[0m\n", + "File saved at: ../../data/sbml_corrected/iYO844.xml\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: iETEC_1333\n", + "\u001b[0;93mWARNING : \n", + " - R_DHPTDNR: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_DHPTDNRN: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_CAT: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_FHL: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SPODM: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SPODMpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCASPtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCFUMtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCMALtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCTARTtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_EX_chitob_e: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_CHITOBpts: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_CHITPH: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_URCN: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\u001b[0m\n", + "\n", + "\n", + "\u001b[0;36m############################################\n", + "############################################\n", + " WRITING SBML FILE \n", + "############################################\n", + "############################################\n", + "\u001b[0m\n", + "\u001b[0;93mWARNING : IMPORT REACTION REMOVED BY DEFAULT\u001b[0m\n", + "\u001b[0;93mWARNING : If you want to keep import reaction\n", + "use option -kir / --keep-import-reactions\u001b[0m\n", + "\u001b[0;93mWARNING : Import reaction removed:\n", + "\t- R_DM_4crsol_c\n", + "\t- R_EX_glc__D_e\n", + "\t- R_EX_glcn_e\n", + "\t- R_EX_glcr_e\n", + "\t- R_EX_glcur_e\n", + "\t- R_EX_glcur1p_e\n", + "\t- R_EX_gln__L_e\n", + "\t- R_EX_glu__L_e\n", + "\t- R_EX_gly_e\n", + "\t- R_EX_glyald_e\n", + "\t- R_EX_glyb_e\n", + "\t- R_EX_glyc_e\n", + "\t- R_EX_glyc__R_e\n", + "\t- R_EX_glyc2p_e\n", + "\t- R_DM_5drib_c\n", + "\t- R_DM_aacald_c\n", + "\t- R_DM_amob_c\n", + "\t- R_DM_mththf_c\n", + "\t- R_EX_glyc3p_e\n", + "\t- R_DM_oxam_c\n", + "\t- R_EX_cm_e\n", + "\t- R_EX_cmp_e\n", + "\t- R_EX_glyclt_e\n", + "\t- R_EX_12ppd__R_e\n", + "\t- R_EX_co2_e\n", + "\t- R_EX_gmp_e\n", + "\t- R_EX_cobalt2_e\n", + "\t- R_EX_colipa_e\n", + "\t- R_EX_12ppd__S_e\n", + "\t- R_EX_14glucan_e\n", + "\t- R_EX_15dap_e\n", + "\t- R_EX_colipap_e\n", + "\t- R_EX_cpgn_e\n", + "\t- R_EX_cpgn_un_e\n", + "\t- R_EX_crn_e\n", + "\t- R_EX_crn__D_e\n", + "\t- R_EX_csn_e\n", + "\t- R_EX_cu_e\n", + "\t- R_EX_cu2_e\n", + "\t- R_EX_23camp_e\n", + "\t- R_EX_23ccmp_e\n", + "\t- R_EX_23cgmp_e\n", + "\t- R_EX_23cump_e\n", + "\t- R_EX_23dappa_e\n", + "\t- R_EX_26dap__M_e\n", + "\t- R_EX_2ddglcn_e\n", + "\t- R_EX_34dhpac_e\n", + "\t- R_EX_3amp_e\n", + "\t- R_EX_3cmp_e\n", + "\t- R_EX_3gmp_e\n", + "\t- R_EX_cyan_e\n", + "\t- R_EX_cynt_e\n", + "\t- R_EX_cys__D_e\n", + "\t- R_EX_cys__L_e\n", + "\t- R_EX_3hcinnm_e\n", + "\t- R_EX_3hpp_e\n", + "\t- R_EX_cytd_e\n", + "\t- R_EX_3hpppn_e\n", + "\t- R_EX_gsn_e\n", + "\t- R_EX_3ump_e\n", + "\t- R_EX_4abut_e\n", + "\t- R_EX_dad_2_e\n", + "\t- R_EX_damp_e\n", + "\t- R_EX_dca_e\n", + "\t- R_EX_4hoxpacd_e\n", + "\t- R_EX_gthox_e\n", + "\t- R_EX_5dglcn_e\n", + "\t- R_EX_dcmp_e\n", + "\t- R_EX_5mtr_e\n", + "\t- R_EX_LalaDglu_e\n", + "\t- R_EX_LalaDgluMdap_e\n", + "\t- R_EX_LalaDgluMdapDala_e\n", + "\t- R_EX_dcyt_e\n", + "\t- R_EX_ddca_e\n", + "\t- R_EX_dgmp_e\n", + "\t- R_EX_dgsn_e\n", + "\t- R_EX_dha_e\n", + "\t- R_EX_LalaLglu_e\n", + "\t- R_EX_ac_e\n", + "\t- R_EX_dimp_e\n", + "\t- R_EX_gthrd_e\n", + "\t- R_EX_din_e\n", + "\t- R_EX_dms_e\n", + "\t- R_EX_dmso_e\n", + "\t- R_EX_acac_e\n", + "\t- R_EX_acald_e\n", + "\t- R_EX_acgal_e\n", + "\t- R_EX_acgal1p_e\n", + "\t- R_EX_dopa_e\n", + "\t- R_EX_gtp_e\n", + "\t- R_EX_doxrbcn_e\n", + "\t- R_EX_dtmp_e\n", + "\t- R_EX_acgam_e\n", + "\t- R_EX_acgam1p_e\n", + "\t- R_EX_acmana_e\n", + "\t- R_EX_dump_e\n", + "\t- R_EX_duri_e\n", + "\t- R_EX_acmum_e\n", + "\t- R_EX_gua_e\n", + "\t- R_EX_acnam_e\n", + "\t- R_EX_acolipa_e\n", + "\t- R_EX_eca4colipa_e\n", + "\t- R_EX_enlipa_e\n", + "\t- R_EX_enter_e\n", + "\t- R_EX_acser_e\n", + "\t- R_EX_h_e\n", + "\t- R_EX_ade_e\n", + "\t- R_EX_adn_e\n", + "\t- R_EX_adocbl_e\n", + "\t- R_EX_ag_e\n", + "\t- R_EX_agm_e\n", + "\t- R_EX_etha_e\n", + "\t- R_EX_ethso3_e\n", + "\t- R_EX_etoh_e\n", + "\t- R_EX_f6p_e\n", + "\t- R_EX_fald_e\n", + "\t- R_EX_fe2_e\n", + "\t- R_EX_fe3_e\n", + "\t- R_EX_fe3dcit_e\n", + "\t- R_EX_fe3dhbzs_e\n", + "\t- R_EX_akg_e\n", + "\t- R_EX_ala_B_e\n", + "\t- R_EX_ala__D_e\n", + "\t- R_EX_ala__L_e\n", + "\t- R_EX_fe3hox_e\n", + "\t- R_EX_alaala_e\n", + "\t- R_EX_fe3hox_un_e\n", + "\t- R_EX_h2_e\n", + "\t- R_EX_fecrm_e\n", + "\t- R_EX_fecrm_un_e\n", + "\t- R_EX_feenter_e\n", + "\t- R_EX_all__D_e\n", + "\t- R_EX_alltn_e\n", + "\t- R_EX_amp_e\n", + "\t- R_EX_anhgm_e\n", + "\t- R_EX_arab__L_e\n", + "\t- R_EX_feoxam_e\n", + "\t- R_EX_feoxam_un_e\n", + "\t- R_EX_arbt_e\n", + "\t- R_EX_arbtn_e\n", + "\t- R_EX_h2o_e\n", + "\t- R_EX_arbtn_fe3_e\n", + "\t- R_EX_for_e\n", + "\t- R_EX_fru_e\n", + "\t- R_EX_frulys_e\n", + "\t- R_EX_fruur_e\n", + "\t- R_EX_fuc__L_e\n", + "\t- R_EX_fum_e\n", + "\t- R_EX_arg__L_e\n", + "\t- R_EX_ascb__L_e\n", + "\t- R_EX_asn__L_e\n", + "\t- R_EX_aso3_e\n", + "\t- R_EX_asp__L_e\n", + "\t- R_EX_btn_e\n", + "\t- R_EX_fusa_e\n", + "\t- R_EX_g1p_e\n", + "\t- R_EX_but_e\n", + "\t- R_EX_g3pc_e\n", + "\t- R_EX_h2o2_e\n", + "\t- R_EX_g3pe_e\n", + "\t- R_EX_g3pg_e\n", + "\t- R_EX_g3pi_e\n", + "\t- R_EX_butso3_e\n", + "\t- R_EX_ca2_e\n", + "\t- R_EX_cbi_e\n", + "\t- R_EX_cbl1_e\n", + "\t- R_EX_g3ps_e\n", + "\t- R_EX_g6p_e\n", + "\t- R_EX_cd2_e\n", + "\t- R_EX_h2s_e\n", + "\t- R_EX_cgly_e\n", + "\t- R_EX_gal_e\n", + "\t- R_EX_gal_bD_e\n", + "\t- R_EX_chol_e\n", + "\t- R_EX_chtbs_e\n", + "\t- R_EX_gal1p_e\n", + "\t- R_EX_cit_e\n", + "\t- R_EX_galct__D_e\n", + "\t- R_EX_hacolipa_e\n", + "\t- R_EX_galctn__D_e\n", + "\t- R_EX_cl_e\n", + "\t- R_EX_galctn__L_e\n", + "\t- R_EX_galt_e\n", + "\t- R_EX_galur_e\n", + "\t- R_EX_gam_e\n", + "\t- R_EX_halipa_e\n", + "\t- R_EX_gam6p_e\n", + "\t- R_EX_gbbtn_e\n", + "\t- R_EX_gdp_e\n", + "\t- R_EX_hdca_e\n", + "\t- R_EX_hdcea_e\n", + "\t- R_EX_ni2_e\n", + "\t- R_EX_hg2_e\n", + "\t- R_EX_nmn_e\n", + "\t- R_EX_his__L_e\n", + "\t- R_EX_no_e\n", + "\t- R_EX_hom__L_e\n", + "\t- R_EX_no2_e\n", + "\t- R_EX_no3_e\n", + "\t- R_EX_novbcn_e\n", + "\t- R_EX_hxa_e\n", + "\t- R_EX_o16a4colipa_e\n", + "\t- R_EX_o2_e\n", + "\t- R_EX_hxan_e\n", + "\t- R_EX_o2s_e\n", + "\t- R_EX_val__L_e\n", + "\t- R_EX_ocdca_e\n", + "\t- R_EX_xan_e\n", + "\t- R_EX_idon__L_e\n", + "\t- R_EX_xmp_e\n", + "\t- R_EX_ocdcea_e\n", + "\t- R_EX_octa_e\n", + "\t- R_EX_xtsn_e\n", + "\t- R_EX_orn_e\n", + "\t- R_EX_xyl__D_e\n", + "\t- R_EX_orot_e\n", + "\t- R_EX_ile__L_e\n", + "\t- R_EX_pacald_e\n", + "\t- R_EX_xylu__L_e\n", + "\t- R_EX_zn2_e\n", + "\t- R_EX_peamn_e\n", + "\t- R_EX_phe__L_e\n", + "\t- R_EX_pheme_e\n", + "\t- R_EX_pi_e\n", + "\t- R_EX_pnto__R_e\n", + "\t- R_EX_ppa_e\n", + "\t- R_EX_imp_e\n", + "\t- R_EX_ppal_e\n", + "\t- R_EX_pppn_e\n", + "\t- R_EX_ppt_e\n", + "\t- R_EX_pro__L_e\n", + "\t- R_EX_indole_e\n", + "\t- R_EX_progly_e\n", + "\t- R_EX_psclys_e\n", + "\t- R_EX_pser__L_e\n", + "\t- R_EX_ptrc_e\n", + "\t- R_EX_inost_e\n", + "\t- R_EX_pydam_e\n", + "\t- R_EX_pydx_e\n", + "\t- R_EX_pydxn_e\n", + "\t- R_EX_pyr_e\n", + "\t- R_EX_quin_e\n", + "\t- R_EX_r5p_e\n", + "\t- R_EX_rfamp_e\n", + "\t- R_EX_rib__D_e\n", + "\t- R_EX_rmn_e\n", + "\t- R_EX_ins_e\n", + "\t- R_EX_sbt__D_e\n", + "\t- R_EX_sel_e\n", + "\t- R_EX_ser__D_e\n", + "\t- R_EX_ser__L_e\n", + "\t- R_EX_skm_e\n", + "\t- R_EX_slnt_e\n", + "\t- R_EX_isetac_e\n", + "\t- R_EX_so2_e\n", + "\t- R_EX_so3_e\n", + "\t- R_EX_so4_e\n", + "\t- R_EX_spmd_e\n", + "\t- R_EX_succ_e\n", + "\t- R_EX_sucr_e\n", + "\t- R_EX_sulfac_e\n", + "\t- R_EX_tartr__D_e\n", + "\t- R_EX_tartr__L_e\n", + "\t- R_EX_taur_e\n", + "\t- R_EX_k_e\n", + "\t- R_EX_tcynt_e\n", + "\t- R_EX_thm_e\n", + "\t- R_EX_kdo2lipid4_e\n", + "\t- R_EX_thr__L_e\n", + "\t- R_EX_lac__D_e\n", + "\t- R_EX_thrp_e\n", + "\t- R_EX_lac__L_e\n", + "\t- R_EX_thym_e\n", + "\t- R_EX_thymd_e\n", + "\t- R_EX_tma_e\n", + "\t- R_EX_tmao_e\n", + "\t- R_EX_lcts_e\n", + "\t- R_EX_tre_e\n", + "\t- R_EX_trp__L_e\n", + "\t- R_EX_tsul_e\n", + "\t- R_EX_ttdca_e\n", + "\t- R_EX_ttdcea_e\n", + "\t- R_EX_ttrcyc_e\n", + "\t- R_EX_tungs_e\n", + "\t- R_EX_tym_e\n", + "\t- R_EX_tyr__L_e\n", + "\t- R_EX_leu__L_e\n", + "\t- R_EX_tyrp_e\n", + "\t- R_EX_uacgam_e\n", + "\t- R_EX_udpacgal_e\n", + "\t- R_EX_udpg_e\n", + "\t- R_EX_udpgal_e\n", + "\t- R_EX_lipa_e\n", + "\t- R_EX_udpglcur_e\n", + "\t- R_EX_ump_e\n", + "\t- R_EX_ura_e\n", + "\t- R_EX_urea_e\n", + "\t- R_EX_uri_e\n", + "\t- R_EX_lipa_cold_e\n", + "\t- R_EX_lipoate_e\n", + "\t- R_EX_lys__L_e\n", + "\t- R_EX_lyx__L_e\n", + "\t- R_EX_mal__D_e\n", + "\t- R_EX_mal__L_e\n", + "\t- R_EX_malt_e\n", + "\t- R_EX_malthx_e\n", + "\t- R_EX_maltpt_e\n", + "\t- R_EX_malttr_e\n", + "\t- R_EX_maltttr_e\n", + "\t- R_EX_man_e\n", + "\t- R_EX_man6p_e\n", + "\t- R_EX_manglyc_e\n", + "\t- R_EX_melib_e\n", + "\t- R_EX_meoh_e\n", + "\t- R_EX_met__D_e\n", + "\t- R_EX_met__L_e\n", + "\t- R_EX_metsox_R__L_e\n", + "\t- R_EX_metsox_S__L_e\n", + "\t- R_EX_mg2_e\n", + "\t- R_EX_mincyc_e\n", + "\t- R_EX_minohp_e\n", + "\t- R_EX_mmet_e\n", + "\t- R_EX_mn2_e\n", + "\t- R_EX_mnl_e\n", + "\t- R_EX_mobd_e\n", + "\t- R_EX_mso3_e\n", + "\t- R_EX_n2o_e\n", + "\t- R_EX_na1_e\n", + "\t- R_EX_nac_e\n", + "\t- R_EX_nh4_e\n", + "\t- R_EX_salchsx_e\n", + "\t- R_EX_AEP_e\n", + "\t- R_EX_colipaOA_e\n", + "\t- R_EX_4hphac_e\n", + "\t- R_EX_4abz_e\n", + "\t- R_EX_acon_C_e\n", + "\t- R_EX_feroxB_e\n", + "\t- R_EX_icit_e\n", + "\t- R_EX_colipa20Oag_e\n", + "\t- R_EX_remnant1_e\n", + "\t- R_EX_tcb_e\n", + "\t- R_EX_tton_e\n", + "\t- R_EX_4hthr_e\n", + "\t- R_EX_feroxEfe_e\n", + "\t- R_EX_feroxE_e\n", + "\t- R_EX_pep_e\n", + "\t- R_EX_23dhbzs3_e\n", + "\t- R_EX_tet_e\n", + "\t- R_EX_rbt_e\n", + "\t- R_EX_salchs2_e\n", + "\t- R_EX_salchs4_e\n", + "\t- R_EX_tag__D_e\n", + "\t- R_EX_oaa_e\n", + "\t- R_EX_salchs2fe_e\n", + "\t- R_EX_rnam_e\n", + "\t- R_EX_cysi__L_e\n", + "\t- R_EX_fe3dhbzs3_e\n", + "\t- R_EX_airs_e\n", + "\t- R_EX_dxyl_e\n", + "\t- R_EX_3pg_e\n", + "\t- R_EX_drib_e\n", + "\t- R_EX_diact_e\n", + "\t- R_EX_feroxBfe_e\n", + "\t- R_EX_salchs4fe_e\n", + "\t- R_EX_2pg_e\n", + "\t- R_EX_udcpo4_e\n", + "\t- R_EX_frmd_e\n", + "\t- R_EX_feroxGfe_e\n", + "\t- R_EX_btd_RR_e\n", + "\t- R_EX_udcpo5_e\n", + "\t- R_EX_guln__L_e\n", + "\t- R_EX_feroxG_e\n", + "\t- R_EX_o6a4colipa_e\n", + "\t- R_EX_arab__D_e\n", + "\t- R_EX_bz_e\n", + "\t- R_EX_cellb_e\n", + "\t- R_EX_13ppd_e\n", + "\t- R_EX_abt__D_e\n", + "\t- R_EX_fe_e\n", + "\t- R_EX_kokdolipidA_e\n", + "\t- R_EX_3hoxpac_e\n", + "\t- R_DM_lipidA_core_e_p\n", + "\t- R_EX_3ntym_e\n", + "\t- R_EX_6apa_e\n", + "\t- R_EX_4hoxpac_e\n", + "\t- R_EX_peng_e\n", + "\t- R_EX_pac_e\n", + "\t- R_EX_salcn_e\n", + "\t- R_EX_galam_e\n", + "\t- R_EX_raffin_e\u001b[0m\n", + "File saved at: ../../data/sbml_corrected/iETEC_1333.xml\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: iAB_RBC_283\n", + "\u001b[0;93mWARNING : - R_EX_adrnl_e: Reactants and products exchanged.\n", + " Boundaries was: [-0.0378 ; 0.0]\n", + " - R_EX_arg__L_e: Reactants and products exchanged.\n", + " Boundaries was: [-0.1152 ; 0.0]\n", + " - R_EX_chol_e: Reactants and products exchanged.\n", + " Boundaries was: [-0.012 ; 0.0]\n", + " - R_EX_cys__L_e: Reactants and products exchanged.\n", + " Boundaries was: [-0.08 ; 0.0]\n", + " - R_EX_dhdascb_e: Reactants and products exchanged.\n", + " Boundaries was: [-0.1111 ; 0.0]\n", + " - R_EX_dopa_e: Reactants and products exchanged.\n", + " Boundaries was: [-0.0324 ; 0.0]\n", + " - R_EX_5aop_e: Reactants and products exchanged.\n", + " Boundaries was: [-0.02 ; 0.0]\n", + " - R_EX_etha_e: Reactants and products exchanged.\n", + " Boundaries was: [-0.012 ; 0.0]\n", + " - R_EX_gal_e: Reactants and products exchanged.\n", + " Boundaries was: [-0.3169 ; 0.0]\n", + " - R_EX_gam_e: Reactants and products exchanged.\n", + " Boundaries was: [-1e-05 ; 0.0]\n", + " - R_EX_acnam_e: Reactants and products exchanged.\n", + " Boundaries was: [-3.74e-05 ; 0.0]\n", + " - R_EX_glc__D_e: Reactants and products exchanged.\n", + " Boundaries was: [-1.12 ; 0.0]\n", + " - R_EX_gluala_e: Reactants and products exchanged.\n", + " Boundaries was: [-0.08 ; 0.0]\n", + " - R_EX_gly_e: Reactants and products exchanged.\n", + " Boundaries was: [-0.08 ; 0.0]\n", + " - R_EX_glyc_e: Reactants and products exchanged.\n", + " Boundaries was: [-0.012 ; 0.0]\n", + " - R_EX_hdca_e: Reactants and products exchanged.\n", + " Boundaries was: [-0.012 ; 0.0]\n", + " - R_EX_leuktrA4_e: Reactants and products exchanged.\n", + " Boundaries was: [-2.4e-06 ; 0.0]\n", + " - R_EX_lnlc_e: Reactants and products exchanged.\n", + " Boundaries was: [-0.006 ; 0.0]\n", + " - R_EX_man_e: Reactants and products exchanged.\n", + " Boundaries was: [-0.01 ; 0.0]\n", + " - R_EX_met__L_e: Reactants and products exchanged.\n", + " Boundaries was: [-0.01 ; 0.0]\n", + " - R_EX_nac_e: Reactants and products exchanged.\n", + " Boundaries was: [-0.001 ; 0.0]\n", + " - R_EX_nrpphr_e: Reactants and products exchanged.\n", + " Boundaries was: [-0.0288 ; 0.0]\n", + " - R_EX_ocdcea_e: Reactants and products exchanged.\n", + " Boundaries was: [-0.006 ; 0.0]\n", + " - R_EX_orot_e: Reactants and products exchanged.\n", + " Boundaries was: [-0.01 ; 0.0]\n", + " - R_EX_pi_e: Reactants and products exchanged.\n", + " Boundaries was: [-1.3047006519 ; 0.0]\n", + " - R_EX_pydam_e: Reactants and products exchanged.\n", + " Boundaries was: [-0.0001 ; 0.0]\n", + " - R_EX_thm_e: Reactants and products exchanged.\n", + " Boundaries was: [-6e-05 ; 0.0]\n", + "\u001b[0m\n", + "\n", + "\n", + "\u001b[0;36m############################################\n", + "############################################\n", + " WRITING SBML FILE \n", + "############################################\n", + "############################################\n", + "\u001b[0m\n", + "\u001b[0;93mWARNING : IMPORT REACTION REMOVED BY DEFAULT\u001b[0m\n", + "\u001b[0;93mWARNING : If you want to keep import reaction\n", + "use option -kir / --keep-import-reactions\u001b[0m\n", + "\u001b[0;93mWARNING : Reaction with tag reversible modified:\n", + "\t- R_EX_adrnl_e\n", + "\t- R_EX_arg__L_e\n", + "\t- R_EX_chol_e\n", + "\t- R_EX_cys__L_e\n", + "\t- R_EX_dhdascb_e\n", + "\t- R_EX_dopa_e\n", + "\t- R_EX_5aop_e\n", + "\t- R_EX_etha_e\n", + "\t- R_EX_gal_e\n", + "\t- R_EX_gam_e\n", + "\t- R_EX_acnam_e\n", + "\t- R_EX_glc__D_e\n", + "\t- R_EX_gluala_e\n", + "\t- R_EX_gly_e\n", + "\t- R_EX_glyc_e\n", + "\t- R_EX_hdca_e\n", + "\t- R_EX_leuktrA4_e\n", + "\t- R_EX_lnlc_e\n", + "\t- R_EX_man_e\n", + "\t- R_EX_met__L_e\n", + "\t- R_EX_nac_e\n", + "\t- R_EX_nrpphr_e\n", + "\t- R_EX_ocdcea_e\n", + "\t- R_EX_orot_e\n", + "\t- R_EX_pi_e\n", + "\t- R_EX_pydam_e\n", + "\t- R_EX_thm_e\u001b[0m\n", + "\u001b[0;93mWARNING : Reaction with Reactants and Products exchanged:\n", + "\t- R_EX_adrnl_e\n", + "\t- R_EX_arg__L_e\n", + "\t- R_EX_chol_e\n", + "\t- R_EX_cys__L_e\n", + "\t- R_EX_dhdascb_e\n", + "\t- R_EX_dopa_e\n", + "\t- R_EX_5aop_e\n", + "\t- R_EX_etha_e\n", + "\t- R_EX_gal_e\n", + "\t- R_EX_gam_e\n", + "\t- R_EX_acnam_e\n", + "\t- R_EX_glc__D_e\n", + "\t- R_EX_gluala_e\n", + "\t- R_EX_gly_e\n", + "\t- R_EX_glyc_e\n", + "\t- R_EX_hdca_e\n", + "\t- R_EX_leuktrA4_e\n", + "\t- R_EX_lnlc_e\n", + "\t- R_EX_man_e\n", + "\t- R_EX_met__L_e\n", + "\t- R_EX_nac_e\n", + "\t- R_EX_nrpphr_e\n", + "\t- R_EX_ocdcea_e\n", + "\t- R_EX_orot_e\n", + "\t- R_EX_pi_e\n", + "\t- R_EX_pydam_e\n", + "\t- R_EX_thm_e\u001b[0m\n", + "\u001b[0;93mWARNING : Import reaction removed:\n", + "\t- R_SK_pchol_hs_18_1_18_1_c\n", + "\t- R_SK_pchol_hs_18_1_18_2_c\n", + "\t- R_SK_pchol_hs_18_2_16_0_c\n", + "\t- R_SK_pchol_hs_18_2_18_1_c\n", + "\t- R_SK_pe_hs_16_0_16_0_c\n", + "\t- R_SK_pe_hs_16_0_18_1_c\n", + "\t- R_SK_pe_hs_16_0_18_2_c\n", + "\t- R_SK_pe_hs_18_1_18_1_c\n", + "\t- R_SK_pe_hs_18_1_18_2_c\n", + "\t- R_SK_pe_hs_18_2_16_0_c\n", + "\t- R_SK_pe_hs_18_2_18_1_c\n", + "\t- R_EX_ade_e\n", + "\t- R_EX_adn_e\n", + "\t- R_EX_35cgmp_e\n", + "\t- R_EX_adrnl_e\n", + "\t- R_EX_ala__L_e\n", + "\t- R_EX_arg__L_e\n", + "\t- R_EX_ascb__L_e\n", + "\t- R_EX_3moxtyr_e\n", + "\t- R_EX_4pyrdx_e\n", + "\t- R_EX_bilglcur_e\n", + "\t- R_EX_ca2_e\n", + "\t- R_EX_camp_e\n", + "\t- R_EX_chol_e\n", + "\t- R_EX_cl_e\n", + "\t- R_EX_co_e\n", + "\t- R_EX_co2_e\n", + "\t- R_EX_cys__L_e\n", + "\t- R_EX_dhdascb_e\n", + "\t- R_EX_dopa_e\n", + "\t- R_EX_5aop_e\n", + "\t- R_EX_etha_e\n", + "\t- R_EX_fe2_e\n", + "\t- R_EX_fru_e\n", + "\t- R_EX_fum_e\n", + "\t- R_EX_gal_e\n", + "\t- R_EX_ac_e\n", + "\t- R_EX_gam_e\n", + "\t- R_EX_acald_e\n", + "\t- R_EX_acnam_e\n", + "\t- R_EX_glc__D_e\n", + "\t- R_EX_gln__L_e\n", + "\t- R_EX_gluala_e\n", + "\t- R_EX_gly_e\n", + "\t- R_EX_glyc_e\n", + "\t- R_EX_gthox_e\n", + "\t- R_EX_h_e\n", + "\t- R_EX_h2o_e\n", + "\t- R_EX_h2o2_e\n", + "\t- R_EX_hco3_e\n", + "\t- R_EX_hcys__L_e\n", + "\t- R_EX_hdca_e\n", + "\t- R_EX_hxan_e\n", + "\t- R_EX_ins_e\n", + "\t- R_EX_k_e\n", + "\t- R_EX_lac__D_e\n", + "\t- R_EX_lac__L_e\n", + "\t- R_EX_leuktrA4_e\n", + "\t- R_EX_leuktrB4_e\n", + "\t- R_EX_lnlc_e\n", + "\t- R_EX_mal__L_e\n", + "\t- R_EX_man_e\n", + "\t- R_EX_mepi_e\n", + "\t- R_EX_met__L_e\n", + "\t- R_EX_na1_e\n", + "\t- R_EX_nac_e\n", + "\t- R_EX_ncam_e\n", + "\t- R_EX_nh4_e\n", + "\t- R_EX_normete__L_e\n", + "\t- R_EX_nrpphr_e\n", + "\t- R_EX_o2_e\n", + "\t- R_EX_ocdcea_e\n", + "\t- R_EX_orot_e\n", + "\t- R_EX_phe__L_e\n", + "\t- R_EX_pi_e\n", + "\t- R_EX_ptrc_e\n", + "\t- R_EX_pydam_e\n", + "\t- R_EX_pydx_e\n", + "\t- R_EX_pydxn_e\n", + "\t- R_EX_pyr_e\n", + "\t- R_EX_ribflv_e\n", + "\t- R_EX_spmd_e\n", + "\t- R_EX_sprm_e\n", + "\t- R_EX_thm_e\n", + "\t- R_EX_thmmp_e\n", + "\t- R_EX_urea_e\n", + "\t- R_EX_uri_e\n", + "\t- R_SK_adprbp_c\n", + "\t- R_SK_akg_c\n", + "\t- R_SK_band_c\n", + "\t- R_SK_bandmt_c\n", + "\t- R_SK_for_c\n", + "\t- R_SK_mi1345p_c\n", + "\t- R_SK_mi134p_c\n", + "\t- R_SK_mi145p_c\n", + "\t- R_SK_mi14p_c\n", + "\t- R_SK_pchol_hs_16_0_16_0_c\n", + "\t- R_SK_pchol_hs_16_0_18_1_c\n", + "\t- R_SK_pchol_hs_16_0_18_2_c\u001b[0m\n", + "File saved at: ../../data/sbml_corrected/iAB_RBC_283.xml\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: iLF82_1304\n", + "\u001b[0;93mWARNING : \n", + " - R_CAT: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_DHPTDNR: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_DHPTDNRN: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_FHL: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SPODM: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SPODMpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCASPtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCFUMtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCMALtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCTARTtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_EX_chitob_e: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_CHITOBpts: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_CHITPH: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_URCN: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\u001b[0m\n", + "\n", + "\n", + "\u001b[0;36m############################################\n", + "############################################\n", + " WRITING SBML FILE \n", + "############################################\n", + "############################################\n", + "\u001b[0m\n", + "\u001b[0;93mWARNING : IMPORT REACTION REMOVED BY DEFAULT\u001b[0m\n", + "\u001b[0;93mWARNING : If you want to keep import reaction\n", + "use option -kir / --keep-import-reactions\u001b[0m\n", + "\u001b[0;93mWARNING : Import reaction removed:\n", + "\t- R_EX_cynt_e\n", + "\t- R_EX_cys__D_e\n", + "\t- R_EX_cys__L_e\n", + "\t- R_EX_cytd_e\n", + "\t- R_EX_dad_2_e\n", + "\t- R_EX_damp_e\n", + "\t- R_EX_dca_e\n", + "\t- R_EX_dcmp_e\n", + "\t- R_EX_dcyt_e\n", + "\t- R_EX_ddca_e\n", + "\t- R_EX_dgmp_e\n", + "\t- R_EX_dgsn_e\n", + "\t- R_EX_dha_e\n", + "\t- R_EX_dimp_e\n", + "\t- R_EX_din_e\n", + "\t- R_EX_dms_e\n", + "\t- R_DM_oxam_c\n", + "\t- R_EX_dmso_e\n", + "\t- R_DM_4crsol_c\n", + "\t- R_DM_5drib_c\n", + "\t- R_EX_23cump_e\n", + "\t- R_DM_aacald_c\n", + "\t- R_DM_amob_c\n", + "\t- R_EX_23dappa_e\n", + "\t- R_EX_dopa_e\n", + "\t- R_EX_26dap__M_e\n", + "\t- R_EX_2ddglcn_e\n", + "\t- R_DM_mththf_c\n", + "\t- R_EX_12ppd__R_e\n", + "\t- R_EX_34dhpac_e\n", + "\t- R_EX_3amp_e\n", + "\t- R_EX_doxrbcn_e\n", + "\t- R_EX_3cmp_e\n", + "\t- R_EX_3gmp_e\n", + "\t- R_EX_3hcinnm_e\n", + "\t- R_EX_3hpp_e\n", + "\t- R_EX_3hpppn_e\n", + "\t- R_EX_3ump_e\n", + "\t- R_EX_12ppd__S_e\n", + "\t- R_EX_14glucan_e\n", + "\t- R_EX_15dap_e\n", + "\t- R_EX_23camp_e\n", + "\t- R_EX_23ccmp_e\n", + "\t- R_EX_23cgmp_e\n", + "\t- R_EX_dtmp_e\n", + "\t- R_EX_glyclt_e\n", + "\t- R_EX_dump_e\n", + "\t- R_EX_gmp_e\n", + "\t- R_EX_gsn_e\n", + "\t- R_EX_duri_e\n", + "\t- R_EX_gthox_e\n", + "\t- R_EX_eca4colipa_e\n", + "\t- R_EX_gthrd_e\n", + "\t- R_EX_gtp_e\n", + "\t- R_EX_gua_e\n", + "\t- R_EX_4abut_e\n", + "\t- R_EX_4hoxpacd_e\n", + "\t- R_EX_enlipa_e\n", + "\t- R_EX_5dglcn_e\n", + "\t- R_EX_5mtr_e\n", + "\t- R_EX_h_e\n", + "\t- R_EX_h2_e\n", + "\t- R_EX_LalaDglu_e\n", + "\t- R_EX_h2o_e\n", + "\t- R_EX_LalaDgluMdap_e\n", + "\t- R_EX_enter_e\n", + "\t- R_EX_LalaDgluMdapDala_e\n", + "\t- R_EX_LalaLglu_e\n", + "\t- R_EX_h2o2_e\n", + "\t- R_EX_h2s_e\n", + "\t- R_EX_hacolipa_e\n", + "\t- R_EX_halipa_e\n", + "\t- R_EX_hdca_e\n", + "\t- R_EX_ac_e\n", + "\t- R_EX_acac_e\n", + "\t- R_EX_acald_e\n", + "\t- R_EX_acgal_e\n", + "\t- R_EX_hdcea_e\n", + "\t- R_EX_hg2_e\n", + "\t- R_EX_etha_e\n", + "\t- R_EX_his__L_e\n", + "\t- R_EX_hom__L_e\n", + "\t- R_EX_hxa_e\n", + "\t- R_EX_acgal1p_e\n", + "\t- R_EX_acgam_e\n", + "\t- R_EX_acgam1p_e\n", + "\t- R_EX_hxan_e\n", + "\t- R_EX_ethso3_e\n", + "\t- R_EX_acmana_e\n", + "\t- R_EX_acmum_e\n", + "\t- R_EX_acnam_e\n", + "\t- R_EX_etoh_e\n", + "\t- R_EX_acolipa_e\n", + "\t- R_EX_acser_e\n", + "\t- R_EX_f6p_e\n", + "\t- R_EX_ade_e\n", + "\t- R_EX_idon__L_e\n", + "\t- R_EX_adn_e\n", + "\t- R_EX_fald_e\n", + "\t- R_EX_adocbl_e\n", + "\t- R_EX_ag_e\n", + "\t- R_EX_ile__L_e\n", + "\t- R_EX_imp_e\n", + "\t- R_EX_indole_e\n", + "\t- R_EX_fe2_e\n", + "\t- R_EX_inost_e\n", + "\t- R_EX_ins_e\n", + "\t- R_EX_isetac_e\n", + "\t- R_EX_k_e\n", + "\t- R_EX_kdo2lipid4_e\n", + "\t- R_EX_agm_e\n", + "\t- R_EX_akg_e\n", + "\t- R_EX_ala_B_e\n", + "\t- R_EX_ala__D_e\n", + "\t- R_EX_ala__L_e\n", + "\t- R_EX_alaala_e\n", + "\t- R_EX_all__D_e\n", + "\t- R_EX_lac__D_e\n", + "\t- R_EX_lac__L_e\n", + "\t- R_EX_alltn_e\n", + "\t- R_EX_fe3_e\n", + "\t- R_EX_amp_e\n", + "\t- R_EX_lcts_e\n", + "\t- R_EX_leu__L_e\n", + "\t- R_EX_lipa_e\n", + "\t- R_EX_anhgm_e\n", + "\t- R_EX_arab__L_e\n", + "\t- R_EX_lipa_cold_e\n", + "\t- R_EX_lipoate_e\n", + "\t- R_EX_arbt_e\n", + "\t- R_EX_fe3dcit_e\n", + "\t- R_EX_arbtn_e\n", + "\t- R_EX_arbtn_fe3_e\n", + "\t- R_EX_lys__L_e\n", + "\t- R_EX_lyx__L_e\n", + "\t- R_EX_mal__D_e\n", + "\t- R_EX_mal__L_e\n", + "\t- R_EX_malt_e\n", + "\t- R_EX_malthx_e\n", + "\t- R_EX_arg__L_e\n", + "\t- R_EX_ascb__L_e\n", + "\t- R_EX_asn__L_e\n", + "\t- R_EX_maltpt_e\n", + "\t- R_EX_aso3_e\n", + "\t- R_EX_asp__L_e\n", + "\t- R_EX_btn_e\n", + "\t- R_EX_but_e\n", + "\t- R_EX_malttr_e\n", + "\t- R_EX_maltttr_e\n", + "\t- R_EX_man_e\n", + "\t- R_EX_man6p_e\n", + "\t- R_EX_manglyc_e\n", + "\t- R_EX_melib_e\n", + "\t- R_EX_meoh_e\n", + "\t- R_EX_butso3_e\n", + "\t- R_EX_ca2_e\n", + "\t- R_EX_fe3dhbzs_e\n", + "\t- R_EX_cbi_e\n", + "\t- R_EX_cbl1_e\n", + "\t- R_EX_cd2_e\n", + "\t- R_EX_met__D_e\n", + "\t- R_EX_met__L_e\n", + "\t- R_EX_metsox_R__L_e\n", + "\t- R_EX_cgly_e\n", + "\t- R_EX_metsox_S__L_e\n", + "\t- R_EX_chol_e\n", + "\t- R_EX_fe3hox_e\n", + "\t- R_EX_chtbs_e\n", + "\t- R_EX_mg2_e\n", + "\t- R_EX_mincyc_e\n", + "\t- R_EX_minohp_e\n", + "\t- R_EX_cit_e\n", + "\t- R_EX_cl_e\n", + "\t- R_EX_mmet_e\n", + "\t- R_EX_fe3hox_un_e\n", + "\t- R_EX_mn2_e\n", + "\t- R_EX_mnl_e\n", + "\t- R_EX_cm_e\n", + "\t- R_EX_cmp_e\n", + "\t- R_EX_co2_e\n", + "\t- R_EX_mobd_e\n", + "\t- R_EX_mso3_e\n", + "\t- R_EX_n2o_e\n", + "\t- R_EX_na1_e\n", + "\t- R_EX_nac_e\n", + "\t- R_EX_nh4_e\n", + "\t- R_EX_cobalt2_e\n", + "\t- R_EX_colipa_e\n", + "\t- R_EX_colipap_e\n", + "\t- R_EX_cpgn_e\n", + "\t- R_EX_ni2_e\n", + "\t- R_EX_cpgn_un_e\n", + "\t- R_EX_nmn_e\n", + "\t- R_EX_crn_e\n", + "\t- R_EX_fecrm_e\n", + "\t- R_EX_crn__D_e\n", + "\t- R_EX_csn_e\n", + "\t- R_EX_no_e\n", + "\t- R_EX_no2_e\n", + "\t- R_EX_no3_e\n", + "\t- R_EX_novbcn_e\n", + "\t- R_EX_cu_e\n", + "\t- R_EX_cu2_e\n", + "\t- R_EX_o16a4colipa_e\n", + "\t- R_EX_cyan_e\n", + "\t- R_EX_fecrm_un_e\n", + "\t- R_EX_o2_e\n", + "\t- R_EX_o2s_e\n", + "\t- R_EX_feenter_e\n", + "\t- R_EX_ocdca_e\n", + "\t- R_EX_ocdcea_e\n", + "\t- R_EX_pacald_e\n", + "\t- R_EX_feoxam_e\n", + "\t- R_EX_peamn_e\n", + "\t- R_EX_phe__L_e\n", + "\t- R_EX_octa_e\n", + "\t- R_EX_orn_e\n", + "\t- R_EX_orot_e\n", + "\t- R_EX_feoxam_un_e\n", + "\t- R_EX_pheme_e\n", + "\t- R_EX_pi_e\n", + "\t- R_EX_pnto__R_e\n", + "\t- R_EX_ppa_e\n", + "\t- R_EX_ppal_e\n", + "\t- R_EX_pppn_e\n", + "\t- R_EX_ppt_e\n", + "\t- R_EX_pro__L_e\n", + "\t- R_EX_for_e\n", + "\t- R_EX_progly_e\n", + "\t- R_EX_psclys_e\n", + "\t- R_EX_pser__L_e\n", + "\t- R_EX_ptrc_e\n", + "\t- R_EX_fru_e\n", + "\t- R_EX_pydam_e\n", + "\t- R_EX_pydx_e\n", + "\t- R_EX_pydxn_e\n", + "\t- R_EX_pyr_e\n", + "\t- R_EX_frulys_e\n", + "\t- R_EX_quin_e\n", + "\t- R_EX_r5p_e\n", + "\t- R_EX_rfamp_e\n", + "\t- R_EX_rib__D_e\n", + "\t- R_EX_rmn_e\n", + "\t- R_EX_sbt__D_e\n", + "\t- R_EX_sel_e\n", + "\t- R_EX_ser__D_e\n", + "\t- R_EX_fruur_e\n", + "\t- R_EX_ser__L_e\n", + "\t- R_EX_skm_e\n", + "\t- R_EX_slnt_e\n", + "\t- R_EX_so2_e\n", + "\t- R_EX_fuc__L_e\n", + "\t- R_EX_so3_e\n", + "\t- R_EX_so4_e\n", + "\t- R_EX_spmd_e\n", + "\t- R_EX_fum_e\n", + "\t- R_EX_succ_e\n", + "\t- R_EX_sucr_e\n", + "\t- R_EX_sulfac_e\n", + "\t- R_EX_tartr__D_e\n", + "\t- R_EX_tartr__L_e\n", + "\t- R_EX_taur_e\n", + "\t- R_EX_tcynt_e\n", + "\t- R_EX_thm_e\n", + "\t- R_EX_fusa_e\n", + "\t- R_EX_thr__L_e\n", + "\t- R_EX_thrp_e\n", + "\t- R_EX_thym_e\n", + "\t- R_EX_thymd_e\n", + "\t- R_EX_tma_e\n", + "\t- R_EX_g1p_e\n", + "\t- R_EX_tmao_e\n", + "\t- R_EX_tre_e\n", + "\t- R_EX_trp__L_e\n", + "\t- R_EX_g3pc_e\n", + "\t- R_EX_tsul_e\n", + "\t- R_EX_ttdca_e\n", + "\t- R_EX_ttdcea_e\n", + "\t- R_EX_ttrcyc_e\n", + "\t- R_EX_tungs_e\n", + "\t- R_EX_tym_e\n", + "\t- R_EX_tyr__L_e\n", + "\t- R_EX_tyrp_e\n", + "\t- R_EX_g3pe_e\n", + "\t- R_EX_uacgam_e\n", + "\t- R_EX_udpacgal_e\n", + "\t- R_EX_udpg_e\n", + "\t- R_EX_g3pg_e\n", + "\t- R_EX_udpgal_e\n", + "\t- R_EX_udpglcur_e\n", + "\t- R_EX_ump_e\n", + "\t- R_EX_ura_e\n", + "\t- R_EX_urea_e\n", + "\t- R_EX_uri_e\n", + "\t- R_EX_g3pi_e\n", + "\t- R_EX_val__L_e\n", + "\t- R_EX_xan_e\n", + "\t- R_EX_xmp_e\n", + "\t- R_EX_xtsn_e\n", + "\t- R_EX_xyl__D_e\n", + "\t- R_EX_xylu__L_e\n", + "\t- R_EX_zn2_e\n", + "\t- R_EX_g3ps_e\n", + "\t- R_EX_g6p_e\n", + "\t- R_EX_gal_e\n", + "\t- R_EX_gal_bD_e\n", + "\t- R_EX_gal1p_e\n", + "\t- R_EX_galct__D_e\n", + "\t- R_EX_galctn__D_e\n", + "\t- R_EX_galctn__L_e\n", + "\t- R_EX_galt_e\n", + "\t- R_EX_galur_e\n", + "\t- R_EX_gam_e\n", + "\t- R_EX_gam6p_e\n", + "\t- R_EX_gbbtn_e\n", + "\t- R_EX_gdp_e\n", + "\t- R_EX_glc__D_e\n", + "\t- R_EX_glcn_e\n", + "\t- R_EX_glcr_e\n", + "\t- R_EX_glcur_e\n", + "\t- R_EX_glcur1p_e\n", + "\t- R_EX_gln__L_e\n", + "\t- R_EX_glu__L_e\n", + "\t- R_EX_gly_e\n", + "\t- R_EX_glyald_e\n", + "\t- R_EX_glyb_e\n", + "\t- R_EX_glyc_e\n", + "\t- R_EX_glyc__R_e\n", + "\t- R_EX_glyc2p_e\n", + "\t- R_EX_glyc3p_e\n", + "\t- R_EX_salchsx_e\n", + "\t- R_EX_AEP_e\n", + "\t- R_EX_cellb_e\n", + "\t- R_EX_bz_e\n", + "\t- R_EX_13ppd_e\n", + "\t- R_EX_colipaOA_e\n", + "\t- R_EX_4hphac_e\n", + "\t- R_EX_4abz_e\n", + "\t- R_EX_acon_C_e\n", + "\t- R_EX_feroxB_e\n", + "\t- R_EX_tcb_e\n", + "\t- R_EX_tton_e\n", + "\t- R_EX_4hthr_e\n", + "\t- R_EX_feroxEfe_e\n", + "\t- R_EX_feroxE_e\n", + "\t- R_EX_pep_e\n", + "\t- R_EX_23dhbzs3_e\n", + "\t- R_EX_tet_e\n", + "\t- R_EX_salchs4_e\n", + "\t- R_EX_tag__D_e\n", + "\t- R_EX_icit_e\n", + "\t- R_EX_colipa20Oag_e\n", + "\t- R_EX_oaa_e\n", + "\t- R_EX_salchs2fe_e\n", + "\t- R_EX_cysi__L_e\n", + "\t- R_EX_salchs2_e\n", + "\t- R_EX_fe3dhbzs3_e\n", + "\t- R_EX_drib_e\n", + "\t- R_EX_feroxBfe_e\n", + "\t- R_EX_salchs4fe_e\n", + "\t- R_EX_2pg_e\n", + "\t- R_EX_rnam_e\n", + "\t- R_EX_airs_e\n", + "\t- R_EX_dxyl_e\n", + "\t- R_EX_3pg_e\n", + "\t- R_EX_abt__D_e\n", + "\t- R_EX_frmd_e\n", + "\t- R_EX_arab__D_e\n", + "\t- R_EX_udcpo4_e\n", + "\t- R_EX_feroxGfe_e\n", + "\t- R_EX_udcpo5_e\n", + "\t- R_EX_guln__L_e\n", + "\t- R_EX_remnant1_e\n", + "\t- R_EX_feroxG_e\n", + "\t- R_EX_diact_e\n", + "\t- R_EX_fe_e\n", + "\t- R_EX_kokdolipidA_e\n", + "\t- R_EX_btd_RR_e\n", + "\t- R_EX_rbt_e\n", + "\t- R_EX_3hoxpac_e\n", + "\t- R_EX_o6a4colipa_e\n", + "\t- R_EX_3ntym_e\n", + "\t- R_EX_6apa_e\n", + "\t- R_EX_4hoxpac_e\n", + "\t- R_EX_peng_e\n", + "\t- R_EX_pac_e\n", + "\t- R_EX_salcn_e\n", + "\t- R_EX_galam_e\n", + "\t- R_EX_raffin_e\n", + "\t- R_DM_lipidA_core_e_p\u001b[0m\n", + "File saved at: ../../data/sbml_corrected/iLF82_1304.xml\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: iECOK1_1307\n", + "\u001b[0;93mWARNING : \n", + " - R_CAT: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_DHPTDNR: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_DHPTDNRN: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_FHL: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SPODM: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SPODMpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCASPtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCFUMtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCMALtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCTARTtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_EX_chitob_e: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_CHITOBpts: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_CHITPH: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_URCN: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\u001b[0m\n", + "\n", + "\n", + "\u001b[0;36m############################################\n", + "############################################\n", + " WRITING SBML FILE \n", + "############################################\n", + "############################################\n", + "\u001b[0m\n", + "\u001b[0;93mWARNING : IMPORT REACTION REMOVED BY DEFAULT\u001b[0m\n", + "\u001b[0;93mWARNING : If you want to keep import reaction\n", + "use option -kir / --keep-import-reactions\u001b[0m\n", + "\u001b[0;93mWARNING : Import reaction removed:\n", + "\t- R_DM_4crsol_c\n", + "\t- R_DM_5drib_c\n", + "\t- R_DM_aacald_c\n", + "\t- R_DM_amob_c\n", + "\t- R_DM_mththf_c\n", + "\t- R_DM_oxam_c\n", + "\t- R_EX_12ppd__R_e\n", + "\t- R_EX_12ppd__S_e\n", + "\t- R_EX_14glucan_e\n", + "\t- R_EX_15dap_e\n", + "\t- R_EX_ac_e\n", + "\t- R_EX_acac_e\n", + "\t- R_EX_23camp_e\n", + "\t- R_EX_acald_e\n", + "\t- R_EX_acgal_e\n", + "\t- R_EX_23ccmp_e\n", + "\t- R_EX_23cgmp_e\n", + "\t- R_EX_acgal1p_e\n", + "\t- R_EX_acgam_e\n", + "\t- R_EX_LalaLglu_e\n", + "\t- R_EX_acgam1p_e\n", + "\t- R_EX_acmana_e\n", + "\t- R_EX_23cump_e\n", + "\t- R_EX_acmum_e\n", + "\t- R_EX_23dappa_e\n", + "\t- R_EX_acnam_e\n", + "\t- R_EX_acolipa_e\n", + "\t- R_EX_26dap__M_e\n", + "\t- R_EX_acser_e\n", + "\t- R_EX_2ddglcn_e\n", + "\t- R_EX_ade_e\n", + "\t- R_EX_adn_e\n", + "\t- R_EX_adocbl_e\n", + "\t- R_EX_ag_e\n", + "\t- R_EX_agm_e\n", + "\t- R_EX_akg_e\n", + "\t- R_EX_ala_B_e\n", + "\t- R_EX_ala__D_e\n", + "\t- R_EX_ala__L_e\n", + "\t- R_EX_alaala_e\n", + "\t- R_EX_dump_e\n", + "\t- R_EX_duri_e\n", + "\t- R_EX_eca4colipa_e\n", + "\t- R_EX_enlipa_e\n", + "\t- R_EX_enter_e\n", + "\t- R_EX_etha_e\n", + "\t- R_EX_all__D_e\n", + "\t- R_EX_alltn_e\n", + "\t- R_EX_34dhpac_e\n", + "\t- R_EX_amp_e\n", + "\t- R_EX_anhgm_e\n", + "\t- R_EX_arab__L_e\n", + "\t- R_EX_ethso3_e\n", + "\t- R_EX_etoh_e\n", + "\t- R_EX_f6p_e\n", + "\t- R_EX_fald_e\n", + "\t- R_EX_fe2_e\n", + "\t- R_EX_arbt_e\n", + "\t- R_EX_arbtn_e\n", + "\t- R_EX_3amp_e\n", + "\t- R_EX_arbtn_fe3_e\n", + "\t- R_EX_3cmp_e\n", + "\t- R_EX_arg__L_e\n", + "\t- R_EX_3gmp_e\n", + "\t- R_EX_3hcinnm_e\n", + "\t- R_EX_3hpp_e\n", + "\t- R_EX_3hpppn_e\n", + "\t- R_EX_3ump_e\n", + "\t- R_EX_fe3_e\n", + "\t- R_EX_fe3dcit_e\n", + "\t- R_EX_fe3dhbzs_e\n", + "\t- R_EX_4abut_e\n", + "\t- R_EX_fe3hox_e\n", + "\t- R_EX_fe3hox_un_e\n", + "\t- R_EX_fecrm_e\n", + "\t- R_EX_ascb__L_e\n", + "\t- R_EX_asn__L_e\n", + "\t- R_EX_fecrm_un_e\n", + "\t- R_EX_4hoxpacd_e\n", + "\t- R_EX_feenter_e\n", + "\t- R_EX_feoxam_e\n", + "\t- R_EX_aso3_e\n", + "\t- R_EX_asp__L_e\n", + "\t- R_EX_feoxam_un_e\n", + "\t- R_EX_5dglcn_e\n", + "\t- R_EX_for_e\n", + "\t- R_EX_btn_e\n", + "\t- R_EX_but_e\n", + "\t- R_EX_fru_e\n", + "\t- R_EX_butso3_e\n", + "\t- R_EX_5mtr_e\n", + "\t- R_EX_ca2_e\n", + "\t- R_EX_frulys_e\n", + "\t- R_EX_cbi_e\n", + "\t- R_EX_fruur_e\n", + "\t- R_EX_LalaDglu_e\n", + "\t- R_EX_fuc__L_e\n", + "\t- R_EX_fum_e\n", + "\t- R_EX_fusa_e\n", + "\t- R_EX_g1p_e\n", + "\t- R_EX_cbl1_e\n", + "\t- R_EX_cd2_e\n", + "\t- R_EX_cgly_e\n", + "\t- R_EX_chol_e\n", + "\t- R_EX_g3pc_e\n", + "\t- R_EX_LalaDgluMdap_e\n", + "\t- R_EX_g3pe_e\n", + "\t- R_EX_g3pg_e\n", + "\t- R_EX_g3pi_e\n", + "\t- R_EX_g3ps_e\n", + "\t- R_EX_chtbs_e\n", + "\t- R_EX_cit_e\n", + "\t- R_EX_cl_e\n", + "\t- R_EX_cm_e\n", + "\t- R_EX_cmp_e\n", + "\t- R_EX_co2_e\n", + "\t- R_EX_g6p_e\n", + "\t- R_EX_gal_e\n", + "\t- R_EX_gal_bD_e\n", + "\t- R_EX_cobalt2_e\n", + "\t- R_EX_LalaDgluMdapDala_e\n", + "\t- R_EX_colipa_e\n", + "\t- R_EX_colipap_e\n", + "\t- R_EX_gal1p_e\n", + "\t- R_EX_galct__D_e\n", + "\t- R_EX_galctn__D_e\n", + "\t- R_EX_galctn__L_e\n", + "\t- R_EX_cpgn_e\n", + "\t- R_EX_cpgn_un_e\n", + "\t- R_EX_galt_e\n", + "\t- R_EX_hxan_e\n", + "\t- R_EX_galur_e\n", + "\t- R_EX_gam_e\n", + "\t- R_EX_gam6p_e\n", + "\t- R_EX_gbbtn_e\n", + "\t- R_EX_gdp_e\n", + "\t- R_EX_glc__D_e\n", + "\t- R_EX_crn_e\n", + "\t- R_EX_crn__D_e\n", + "\t- R_EX_csn_e\n", + "\t- R_EX_cu_e\n", + "\t- R_EX_cu2_e\n", + "\t- R_EX_cyan_e\n", + "\t- R_EX_cynt_e\n", + "\t- R_EX_cys__D_e\n", + "\t- R_EX_cys__L_e\n", + "\t- R_EX_cytd_e\n", + "\t- R_EX_dad_2_e\n", + "\t- R_EX_damp_e\n", + "\t- R_EX_dca_e\n", + "\t- R_EX_glcn_e\n", + "\t- R_EX_glcr_e\n", + "\t- R_EX_glcur_e\n", + "\t- R_EX_glcur1p_e\n", + "\t- R_EX_gln__L_e\n", + "\t- R_EX_glu__L_e\n", + "\t- R_EX_gly_e\n", + "\t- R_EX_dcmp_e\n", + "\t- R_EX_dcyt_e\n", + "\t- R_EX_ddca_e\n", + "\t- R_EX_glyald_e\n", + "\t- R_EX_dgmp_e\n", + "\t- R_EX_glyb_e\n", + "\t- R_EX_idon__L_e\n", + "\t- R_EX_glyc_e\n", + "\t- R_EX_glyc__R_e\n", + "\t- R_EX_glyc2p_e\n", + "\t- R_EX_glyc3p_e\n", + "\t- R_EX_glyclt_e\n", + "\t- R_EX_dgsn_e\n", + "\t- R_EX_dha_e\n", + "\t- R_EX_dimp_e\n", + "\t- R_EX_din_e\n", + "\t- R_EX_dms_e\n", + "\t- R_EX_dmso_e\n", + "\t- R_EX_gmp_e\n", + "\t- R_EX_gsn_e\n", + "\t- R_EX_gthox_e\n", + "\t- R_EX_dopa_e\n", + "\t- R_EX_doxrbcn_e\n", + "\t- R_EX_gthrd_e\n", + "\t- R_EX_ile__L_e\n", + "\t- R_EX_gtp_e\n", + "\t- R_EX_gua_e\n", + "\t- R_EX_h_e\n", + "\t- R_EX_h2_e\n", + "\t- R_EX_h2o_e\n", + "\t- R_EX_h2o2_e\n", + "\t- R_EX_dtmp_e\n", + "\t- R_EX_r5p_e\n", + "\t- R_EX_rfamp_e\n", + "\t- R_EX_rib__D_e\n", + "\t- R_EX_rmn_e\n", + "\t- R_EX_sbt__D_e\n", + "\t- R_EX_h2s_e\n", + "\t- R_EX_hacolipa_e\n", + "\t- R_EX_sel_e\n", + "\t- R_EX_halipa_e\n", + "\t- R_EX_imp_e\n", + "\t- R_EX_hdca_e\n", + "\t- R_EX_hdcea_e\n", + "\t- R_EX_ser__D_e\n", + "\t- R_EX_ser__L_e\n", + "\t- R_EX_skm_e\n", + "\t- R_EX_hg2_e\n", + "\t- R_EX_indole_e\n", + "\t- R_EX_his__L_e\n", + "\t- R_EX_hom__L_e\n", + "\t- R_EX_hxa_e\n", + "\t- R_EX_slnt_e\n", + "\t- R_EX_so2_e\n", + "\t- R_EX_so3_e\n", + "\t- R_EX_so4_e\n", + "\t- R_EX_inost_e\n", + "\t- R_EX_spmd_e\n", + "\t- R_EX_succ_e\n", + "\t- R_EX_ins_e\n", + "\t- R_EX_sucr_e\n", + "\t- R_EX_isetac_e\n", + "\t- R_EX_sulfac_e\n", + "\t- R_EX_tartr__D_e\n", + "\t- R_EX_tartr__L_e\n", + "\t- R_EX_taur_e\n", + "\t- R_EX_tcynt_e\n", + "\t- R_EX_thm_e\n", + "\t- R_EX_thr__L_e\n", + "\t- R_EX_thrp_e\n", + "\t- R_EX_thym_e\n", + "\t- R_EX_k_e\n", + "\t- R_EX_thymd_e\n", + "\t- R_EX_tma_e\n", + "\t- R_EX_tmao_e\n", + "\t- R_EX_tre_e\n", + "\t- R_EX_kdo2lipid4_e\n", + "\t- R_EX_trp__L_e\n", + "\t- R_EX_tsul_e\n", + "\t- R_EX_lac__D_e\n", + "\t- R_EX_ttdca_e\n", + "\t- R_EX_ttdcea_e\n", + "\t- R_EX_ttrcyc_e\n", + "\t- R_EX_tungs_e\n", + "\t- R_EX_tym_e\n", + "\t- R_EX_lac__L_e\n", + "\t- R_EX_tyr__L_e\n", + "\t- R_EX_lcts_e\n", + "\t- R_EX_tyrp_e\n", + "\t- R_EX_uacgam_e\n", + "\t- R_EX_udpacgal_e\n", + "\t- R_EX_leu__L_e\n", + "\t- R_EX_udpg_e\n", + "\t- R_EX_udpgal_e\n", + "\t- R_EX_udpglcur_e\n", + "\t- R_EX_ump_e\n", + "\t- R_EX_ura_e\n", + "\t- R_EX_urea_e\n", + "\t- R_EX_lipa_e\n", + "\t- R_EX_uri_e\n", + "\t- R_EX_val__L_e\n", + "\t- R_EX_xan_e\n", + "\t- R_EX_lipa_cold_e\n", + "\t- R_EX_xmp_e\n", + "\t- R_EX_xtsn_e\n", + "\t- R_EX_xyl__D_e\n", + "\t- R_EX_xylu__L_e\n", + "\t- R_EX_zn2_e\n", + "\t- R_EX_lipoate_e\n", + "\t- R_EX_lys__L_e\n", + "\t- R_EX_lyx__L_e\n", + "\t- R_EX_mal__D_e\n", + "\t- R_EX_mal__L_e\n", + "\t- R_EX_malt_e\n", + "\t- R_EX_malthx_e\n", + "\t- R_EX_maltpt_e\n", + "\t- R_EX_malttr_e\n", + "\t- R_EX_maltttr_e\n", + "\t- R_EX_man_e\n", + "\t- R_EX_man6p_e\n", + "\t- R_EX_manglyc_e\n", + "\t- R_EX_melib_e\n", + "\t- R_EX_meoh_e\n", + "\t- R_EX_met__D_e\n", + "\t- R_EX_met__L_e\n", + "\t- R_DM_lipidA_core_e_p\n", + "\t- R_EX_metsox_R__L_e\n", + "\t- R_EX_metsox_S__L_e\n", + "\t- R_EX_mg2_e\n", + "\t- R_EX_mincyc_e\n", + "\t- R_EX_minohp_e\n", + "\t- R_EX_mmet_e\n", + "\t- R_EX_mn2_e\n", + "\t- R_EX_mnl_e\n", + "\t- R_EX_mobd_e\n", + "\t- R_EX_mso3_e\n", + "\t- R_EX_n2o_e\n", + "\t- R_EX_na1_e\n", + "\t- R_EX_nac_e\n", + "\t- R_EX_nh4_e\n", + "\t- R_EX_ni2_e\n", + "\t- R_EX_nmn_e\n", + "\t- R_EX_no_e\n", + "\t- R_EX_no2_e\n", + "\t- R_EX_no3_e\n", + "\t- R_EX_novbcn_e\n", + "\t- R_EX_o16a4colipa_e\n", + "\t- R_EX_o2_e\n", + "\t- R_EX_o2s_e\n", + "\t- R_EX_ocdca_e\n", + "\t- R_EX_ocdcea_e\n", + "\t- R_EX_octa_e\n", + "\t- R_EX_orn_e\n", + "\t- R_EX_orot_e\n", + "\t- R_EX_pacald_e\n", + "\t- R_EX_peamn_e\n", + "\t- R_EX_phe__L_e\n", + "\t- R_EX_pheme_e\n", + "\t- R_EX_pi_e\n", + "\t- R_EX_pnto__R_e\n", + "\t- R_EX_ppa_e\n", + "\t- R_EX_ppal_e\n", + "\t- R_EX_pppn_e\n", + "\t- R_EX_ppt_e\n", + "\t- R_EX_pro__L_e\n", + "\t- R_EX_progly_e\n", + "\t- R_EX_psclys_e\n", + "\t- R_EX_pser__L_e\n", + "\t- R_EX_ptrc_e\n", + "\t- R_EX_pydam_e\n", + "\t- R_EX_pydx_e\n", + "\t- R_EX_pydxn_e\n", + "\t- R_EX_pyr_e\n", + "\t- R_EX_quin_e\n", + "\t- R_EX_4hphac_e\n", + "\t- R_EX_4abz_e\n", + "\t- R_EX_acon_C_e\n", + "\t- R_EX_frmd_e\n", + "\t- R_EX_colipaOA_e\n", + "\t- R_EX_feroxB_e\n", + "\t- R_EX_salchsx_e\n", + "\t- R_EX_AEP_e\n", + "\t- R_EX_tcb_e\n", + "\t- R_EX_tton_e\n", + "\t- R_EX_icit_e\n", + "\t- R_EX_4hthr_e\n", + "\t- R_EX_feroxEfe_e\n", + "\t- R_EX_colipa20Oag_e\n", + "\t- R_EX_feroxE_e\n", + "\t- R_EX_pep_e\n", + "\t- R_EX_23dhbzs3_e\n", + "\t- R_EX_tet_e\n", + "\t- R_EX_remnant1_e\n", + "\t- R_EX_salchs4_e\n", + "\t- R_EX_tag__D_e\n", + "\t- R_EX_salchs2_e\n", + "\t- R_EX_oaa_e\n", + "\t- R_EX_salchs2fe_e\n", + "\t- R_EX_rnam_e\n", + "\t- R_EX_airs_e\n", + "\t- R_EX_dxyl_e\n", + "\t- R_EX_cysi__L_e\n", + "\t- R_EX_3pg_e\n", + "\t- R_EX_fe3dhbzs3_e\n", + "\t- R_EX_drib_e\n", + "\t- R_EX_udcpo4_e\n", + "\t- R_EX_feroxBfe_e\n", + "\t- R_EX_salchs4fe_e\n", + "\t- R_EX_2pg_e\n", + "\t- R_EX_feroxGfe_e\n", + "\t- R_EX_udcpo5_e\n", + "\t- R_EX_guln__L_e\n", + "\t- R_EX_feroxG_e\n", + "\t- R_EX_diact_e\n", + "\t- R_EX_btd_RR_e\n", + "\t- R_EX_13ppd_e\n", + "\t- R_EX_rbt_e\n", + "\t- R_EX_cellb_e\n", + "\t- R_EX_bz_e\n", + "\t- R_EX_abt__D_e\n", + "\t- R_EX_arab__D_e\n", + "\t- R_EX_fe_e\n", + "\t- R_EX_kokdolipidA_e\n", + "\t- R_EX_3hoxpac_e\n", + "\t- R_EX_o6a4colipa_e\n", + "\t- R_EX_3ntym_e\n", + "\t- R_EX_6apa_e\n", + "\t- R_EX_4hoxpac_e\n", + "\t- R_EX_peng_e\n", + "\t- R_EX_pac_e\n", + "\t- R_EX_salcn_e\n", + "\t- R_EX_galam_e\n", + "\t- R_EX_raffin_e\u001b[0m\n", + "File saved at: ../../data/sbml_corrected/iECOK1_1307.xml\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: iJR904\n", + "\u001b[0;93mWARNING : - R_EX_glc__D_e: Reactants and products exchanged.\n", + " Boundaries was: [-10.0 ; 0.0]\n", + " - R_GLYAT: Reactants and products exchanged.\n", + " Boundaries was: [-999999.0 ; 0.0]\n", + " - R_EX_o2_e: Reactants and products exchanged.\n", + " Boundaries was: [-20.0 ; 0.0]\n", + " - R_KARA1: Reactants and products exchanged.\n", + " Boundaries was: [-999999.0 ; 0.0]\n", + " - R_SUCCt2r: Reactants and products exchanged.\n", + " Boundaries was: [-999999.0 ; 0.0]\n", + " - R_XYLI2: Reactants and products exchanged.\n", + " Boundaries was: [-999999.0 ; 0.0]\n", + "\u001b[0m\n", + "\n", + "\n", + "\u001b[0;36m############################################\n", + "############################################\n", + " WRITING SBML FILE \n", + "############################################\n", + "############################################\n", + "\u001b[0m\n", + "\u001b[0;93mWARNING : IMPORT REACTION REMOVED BY DEFAULT\u001b[0m\n", + "\u001b[0;93mWARNING : If you want to keep import reaction\n", + "use option -kir / --keep-import-reactions\u001b[0m\n", + "\u001b[0;93mWARNING : Reaction with tag reversible modified:\n", + "\t- R_EX_glc__D_e\n", + "\t- R_GLYAT\n", + "\t- R_EX_o2_e\n", + "\t- R_KARA1\n", + "\t- R_SUCCt2r\n", + "\t- R_XYLI2\u001b[0m\n", + "\u001b[0;93mWARNING : Reaction with Reactants and Products exchanged:\n", + "\t- R_EX_glc__D_e\n", + "\t- R_GLYAT\n", + "\t- R_EX_o2_e\n", + "\t- R_KARA1\n", + "\t- R_SUCCt2r\n", + "\t- R_XYLI2\u001b[0m\n", + "\u001b[0;93mWARNING : Import reaction removed:\n", + "\t- R_EX_h2o_e\n", + "\t- R_EX_12ppd__S_e\n", + "\t- R_EX_15dap_e\n", + "\t- R_EX_26dap__M_e\n", + "\t- R_EX_2ddglcn_e\n", + "\t- R_EX_3hcinnm_e\n", + "\t- R_EX_3hpppn_e\n", + "\t- R_EX_4abut_e\n", + "\t- R_EX_ac_e\n", + "\t- R_EX_acac_e\n", + "\t- R_EX_h_e\n", + "\t- R_EX_acald_e\n", + "\t- R_EX_acgam_e\n", + "\t- R_EX_acmana_e\n", + "\t- R_EX_hdca_e\n", + "\t- R_EX_acnam_e\n", + "\t- R_EX_ade_e\n", + "\t- R_EX_his__L_e\n", + "\t- R_EX_adn_e\n", + "\t- R_EX_akg_e\n", + "\t- R_EX_hxan_e\n", + "\t- R_EX_ala__D_e\n", + "\t- R_EX_idon__L_e\n", + "\t- R_EX_ala__L_e\n", + "\t- R_EX_alltn_e\n", + "\t- R_EX_amp_e\n", + "\t- R_EX_arab__L_e\n", + "\t- R_EX_arg__L_e\n", + "\t- R_EX_asn__L_e\n", + "\t- R_EX_asp__L_e\n", + "\t- R_EX_ile__L_e\n", + "\t- R_EX_but_e\n", + "\t- R_EX_cbl1_e\n", + "\t- R_EX_chol_e\n", + "\t- R_EX_indole_e\n", + "\t- R_EX_cit_e\n", + "\t- R_EX_co2_e\n", + "\t- R_EX_crn_e\n", + "\t- R_EX_csn_e\n", + "\t- R_EX_ins_e\n", + "\t- R_EX_cynt_e\n", + "\t- R_EX_k_e\n", + "\t- R_EX_cys__L_e\n", + "\t- R_EX_cytd_e\n", + "\t- R_EX_lac__D_e\n", + "\t- R_EX_dad_2_e\n", + "\t- R_EX_dcyt_e\n", + "\t- R_EX_dgsn_e\n", + "\t- R_EX_dha_e\n", + "\t- R_EX_din_e\n", + "\t- R_EX_dms_e\n", + "\t- R_EX_lac__L_e\n", + "\t- R_EX_dmso_e\n", + "\t- R_EX_duri_e\n", + "\t- R_EX_etoh_e\n", + "\t- R_EX_lcts_e\n", + "\t- R_EX_fe2_e\n", + "\t- R_EX_for_e\n", + "\t- R_EX_leu__L_e\n", + "\t- R_EX_fru_e\n", + "\t- R_EX_lys__L_e\n", + "\t- R_EX_fuc1p__L_e\n", + "\t- R_EX_fuc__L_e\n", + "\t- R_EX_fum_e\n", + "\t- R_EX_mal__L_e\n", + "\t- R_EX_g6p_e\n", + "\t- R_EX_gal_e\n", + "\t- R_EX_galct__D_e\n", + "\t- R_EX_galctn__D_e\n", + "\t- R_EX_galt_e\n", + "\t- R_EX_malt_e\n", + "\t- R_EX_galur_e\n", + "\t- R_EX_gam_e\n", + "\t- R_EX_gbbtn_e\n", + "\t- R_EX_glc__D_e\n", + "\t- R_EX_glcn_e\n", + "\t- R_EX_malthx_e\n", + "\t- R_EX_glcr_e\n", + "\t- R_EX_glcur_e\n", + "\t- R_EX_maltpt_e\n", + "\t- R_EX_gln__L_e\n", + "\t- R_EX_glu__L_e\n", + "\t- R_EX_gly_e\n", + "\t- R_EX_glyald_e\n", + "\t- R_EX_glyb_e\n", + "\t- R_EX_malttr_e\n", + "\t- R_EX_glyc3p_e\n", + "\t- R_EX_glyc_e\n", + "\t- R_EX_glyclt_e\n", + "\t- R_EX_gsn_e\n", + "\t- R_EX_gua_e\n", + "\t- R_EX_maltttr_e\n", + "\t- R_EX_man6p_e\n", + "\t- R_EX_man_e\n", + "\t- R_EX_melib_e\n", + "\t- R_EX_met__D_e\n", + "\t- R_EX_met__L_e\n", + "\t- R_EX_mnl_e\n", + "\t- R_EX_na1_e\n", + "\t- R_EX_nac_e\n", + "\t- R_EX_nad_e\n", + "\t- R_EX_nh4_e\n", + "\t- R_EX_nmn_e\n", + "\t- R_EX_no2_e\n", + "\t- R_EX_no3_e\n", + "\t- R_EX_o2_e\n", + "\t- R_EX_ocdca_e\n", + "\t- R_EX_orn_e\n", + "\t- R_EX_phe__L_e\n", + "\t- R_EX_pi_e\n", + "\t- R_EX_pnto__R_e\n", + "\t- R_EX_pppn_e\n", + "\t- R_EX_pro__L_e\n", + "\t- R_EX_ptrc_e\n", + "\t- R_EX_pyr_e\n", + "\t- R_EX_rib__D_e\n", + "\t- R_EX_rmn_e\n", + "\t- R_EX_sbt__D_e\n", + "\t- R_EX_ser__D_e\n", + "\t- R_EX_ser__L_e\n", + "\t- R_EX_so4_e\n", + "\t- R_EX_spmd_e\n", + "\t- R_EX_succ_e\n", + "\t- R_EX_sucr_e\n", + "\t- R_EX_tartr__L_e\n", + "\t- R_EX_taur_e\n", + "\t- R_EX_thm_e\n", + "\t- R_EX_thr__L_e\n", + "\t- R_EX_thymd_e\n", + "\t- R_EX_tma_e\n", + "\t- R_EX_tmao_e\n", + "\t- R_EX_tre_e\n", + "\t- R_EX_trp__L_e\n", + "\t- R_EX_tsul_e\n", + "\t- R_EX_ttdca_e\n", + "\t- R_EX_tyr__L_e\n", + "\t- R_EX_ura_e\n", + "\t- R_EX_urea_e\n", + "\t- R_EX_uri_e\n", + "\t- R_EX_val__L_e\n", + "\t- R_EX_xan_e\n", + "\t- R_EX_xtsn_e\n", + "\t- R_EX_xyl__D_e\u001b[0m\n", + "File saved at: ../../data/sbml_corrected/iJR904.xml\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: iAF1260\n", + "\u001b[0;93mWARNING : \n", + " - R_CAT: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_FHL: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SPODM: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SPODMpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\u001b[0m\n", + "\n", + "\n", + "\u001b[0;36m############################################\n", + "############################################\n", + " WRITING SBML FILE \n", + "############################################\n", + "############################################\n", + "\u001b[0m\n", + "\u001b[0;93mWARNING : IMPORT REACTION REMOVED BY DEFAULT\u001b[0m\n", + "\u001b[0;93mWARNING : If you want to keep import reaction\n", + "use option -kir / --keep-import-reactions\u001b[0m\n", + "\u001b[0;93mWARNING : Import reaction removed:\n", + "\t- R_EX_acser_e\n", + "\t- R_EX_ade_e\n", + "\t- R_DM_4hba_c\n", + "\t- R_DM_5drib_c\n", + "\t- R_DM_aacald_c\n", + "\t- R_DM_hmfurn_c\n", + "\t- R_DM_oxam_c\n", + "\t- R_EX_feoxam_un_e\n", + "\t- R_EX_adn_e\n", + "\t- R_EX_adocbl_e\n", + "\t- R_EX_ag_e\n", + "\t- R_EX_agm_e\n", + "\t- R_EX_for_e\n", + "\t- R_EX_akg_e\n", + "\t- R_EX_ala_B_e\n", + "\t- R_EX_ala__D_e\n", + "\t- R_EX_ala__L_e\n", + "\t- R_EX_alaala_e\n", + "\t- R_EX_all__D_e\n", + "\t- R_EX_alltn_e\n", + "\t- R_EX_12ppd__R_e\n", + "\t- R_EX_amp_e\n", + "\t- R_EX_fru_e\n", + "\t- R_EX_anhgm_e\n", + "\t- R_EX_arab__L_e\n", + "\t- R_EX_arbtn_e\n", + "\t- R_EX_12ppd__S_e\n", + "\t- R_EX_14glucan_e\n", + "\t- R_EX_15dap_e\n", + "\t- R_EX_23camp_e\n", + "\t- R_EX_arbtn_fe3_e\n", + "\t- R_EX_23ccmp_e\n", + "\t- R_EX_arg__L_e\n", + "\t- R_EX_frulys_e\n", + "\t- R_EX_ascb__L_e\n", + "\t- R_EX_asn__L_e\n", + "\t- R_EX_23cgmp_e\n", + "\t- R_EX_23cump_e\n", + "\t- R_EX_aso3_e\n", + "\t- R_EX_fruur_e\n", + "\t- R_EX_asp__L_e\n", + "\t- R_EX_23dappa_e\n", + "\t- R_EX_26dap__M_e\n", + "\t- R_EX_but_e\n", + "\t- R_EX_butso3_e\n", + "\t- R_EX_2ddglcn_e\n", + "\t- R_EX_fuc__L_e\n", + "\t- R_EX_34dhpac_e\n", + "\t- R_EX_3amp_e\n", + "\t- R_EX_3cmp_e\n", + "\t- R_EX_ca2_e\n", + "\t- R_EX_cbi_e\n", + "\t- R_EX_cbl1_e\n", + "\t- R_EX_cd2_e\n", + "\t- R_EX_cgly_e\n", + "\t- R_EX_3gmp_e\n", + "\t- R_EX_3hcinnm_e\n", + "\t- R_EX_3hpppn_e\n", + "\t- R_EX_3ump_e\n", + "\t- R_EX_chol_e\n", + "\t- R_EX_cit_e\n", + "\t- R_EX_cl_e\n", + "\t- R_EX_4abut_e\n", + "\t- R_EX_4hoxpacd_e\n", + "\t- R_EX_fum_e\n", + "\t- R_EX_5dglcn_e\n", + "\t- R_EX_LalaDgluMdapDala_e\n", + "\t- R_EX_cmp_e\n", + "\t- R_EX_co2_e\n", + "\t- R_EX_cobalt2_e\n", + "\t- R_EX_LalaDgluMdap_e\n", + "\t- R_EX_g1p_e\n", + "\t- R_EX_ac_e\n", + "\t- R_EX_acac_e\n", + "\t- R_EX_acald_e\n", + "\t- R_EX_colipa_e\n", + "\t- R_EX_cpgn_e\n", + "\t- R_EX_cpgn_un_e\n", + "\t- R_EX_crn_e\n", + "\t- R_EX_acgal1p_e\n", + "\t- R_EX_acgal_e\n", + "\t- R_EX_csn_e\n", + "\t- R_EX_acgam1p_e\n", + "\t- R_EX_g3pc_e\n", + "\t- R_EX_acgam_e\n", + "\t- R_EX_acmana_e\n", + "\t- R_EX_cu2_e\n", + "\t- R_EX_cu_e\n", + "\t- R_EX_cyan_e\n", + "\t- R_EX_acmum_e\n", + "\t- R_EX_acnam_e\n", + "\t- R_EX_acolipa_e\n", + "\t- R_EX_cynt_e\n", + "\t- R_EX_cys__D_e\n", + "\t- R_EX_cys__L_e\n", + "\t- R_EX_g3pe_e\n", + "\t- R_EX_cytd_e\n", + "\t- R_EX_dad_2_e\n", + "\t- R_EX_damp_e\n", + "\t- R_EX_malthx_e\n", + "\t- R_EX_g3pg_e\n", + "\t- R_EX_maltpt_e\n", + "\t- R_EX_malttr_e\n", + "\t- R_EX_dca_e\n", + "\t- R_EX_dcmp_e\n", + "\t- R_EX_dcyt_e\n", + "\t- R_EX_maltttr_e\n", + "\t- R_EX_ddca_e\n", + "\t- R_EX_man6p_e\n", + "\t- R_EX_g3pi_e\n", + "\t- R_EX_man_e\n", + "\t- R_EX_manglyc_e\n", + "\t- R_EX_dgmp_e\n", + "\t- R_EX_dgsn_e\n", + "\t- R_EX_melib_e\n", + "\t- R_EX_g3ps_e\n", + "\t- R_EX_met__D_e\n", + "\t- R_EX_dha_e\n", + "\t- R_EX_dimp_e\n", + "\t- R_EX_met__L_e\n", + "\t- R_EX_din_e\n", + "\t- R_EX_metsox_R__L_e\n", + "\t- R_EX_g6p_e\n", + "\t- R_EX_metsox_S__L_e\n", + "\t- R_EX_mg2_e\n", + "\t- R_EX_dms_e\n", + "\t- R_EX_dmso_e\n", + "\t- R_EX_dopa_e\n", + "\t- R_EX_dtmp_e\n", + "\t- R_EX_dump_e\n", + "\t- R_EX_duri_e\n", + "\t- R_EX_eca4colipa_e\n", + "\t- R_EX_enlipa_e\n", + "\t- R_EX_minohp_e\n", + "\t- R_EX_mmet_e\n", + "\t- R_EX_mn2_e\n", + "\t- R_EX_mnl_e\n", + "\t- R_EX_mobd_e\n", + "\t- R_EX_mso3_e\n", + "\t- R_EX_enter_e\n", + "\t- R_EX_etha_e\n", + "\t- R_EX_n2o_e\n", + "\t- R_EX_na1_e\n", + "\t- R_EX_ethso3_e\n", + "\t- R_EX_gal1p_e\n", + "\t- R_EX_etoh_e\n", + "\t- R_EX_f6p_e\n", + "\t- R_EX_fald_e\n", + "\t- R_EX_nac_e\n", + "\t- R_EX_nh4_e\n", + "\t- R_EX_ni2_e\n", + "\t- R_EX_nmn_e\n", + "\t- R_EX_fe2_e\n", + "\t- R_EX_no2_e\n", + "\t- R_EX_fe3_e\n", + "\t- R_EX_no3_e\n", + "\t- R_EX_gal_e\n", + "\t- R_EX_no_e\n", + "\t- R_EX_o16a4colipa_e\n", + "\t- R_EX_fe3dcit_e\n", + "\t- R_EX_fe3dhbzs_e\n", + "\t- R_EX_fe3hox_e\n", + "\t- R_EX_fe3hox_un_e\n", + "\t- R_EX_o2_e\n", + "\t- R_EX_fecrm_e\n", + "\t- R_EX_o2s_e\n", + "\t- R_EX_ocdca_e\n", + "\t- R_EX_ocdcea_e\n", + "\t- R_EX_octa_e\n", + "\t- R_EX_fecrm_un_e\n", + "\t- R_EX_feenter_e\n", + "\t- R_EX_feoxam_e\n", + "\t- R_EX_orn_e\n", + "\t- R_EX_gal_bD_e\n", + "\t- R_EX_orot_e\n", + "\t- R_EX_pacald_e\n", + "\t- R_EX_peamn_e\n", + "\t- R_EX_phe__L_e\n", + "\t- R_EX_tungs_e\n", + "\t- R_EX_galct__D_e\n", + "\t- R_EX_tym_e\n", + "\t- R_EX_pheme_e\n", + "\t- R_EX_pi_e\n", + "\t- R_EX_tyr__L_e\n", + "\t- R_EX_galctn__D_e\n", + "\t- R_EX_tyrp_e\n", + "\t- R_EX_pnto__R_e\n", + "\t- R_EX_ppa_e\n", + "\t- R_EX_ppal_e\n", + "\t- R_EX_uacgam_e\n", + "\t- R_EX_udpacgal_e\n", + "\t- R_EX_pppn_e\n", + "\t- R_EX_galctn__L_e\n", + "\t- R_EX_ppt_e\n", + "\t- R_EX_pro__L_e\n", + "\t- R_EX_udpg_e\n", + "\t- R_EX_udpgal_e\n", + "\t- R_EX_udpglcur_e\n", + "\t- R_EX_progly_e\n", + "\t- R_EX_galt_e\n", + "\t- R_EX_psclys_e\n", + "\t- R_EX_pser__L_e\n", + "\t- R_EX_ump_e\n", + "\t- R_EX_ura_e\n", + "\t- R_EX_urea_e\n", + "\t- R_EX_uri_e\n", + "\t- R_EX_val__L_e\n", + "\t- R_EX_xan_e\n", + "\t- R_EX_ptrc_e\n", + "\t- R_EX_pyr_e\n", + "\t- R_EX_r5p_e\n", + "\t- R_EX_rib__D_e\n", + "\t- R_EX_rmn_e\n", + "\t- R_EX_sbt__D_e\n", + "\t- R_EX_xmp_e\n", + "\t- R_EX_xtsn_e\n", + "\t- R_EX_galur_e\n", + "\t- R_EX_xyl__D_e\n", + "\t- R_EX_xylu__L_e\n", + "\t- R_EX_zn2_e\n", + "\t- R_EX_ser__D_e\n", + "\t- R_EX_ser__L_e\n", + "\t- R_EX_skm_e\n", + "\t- R_EX_so2_e\n", + "\t- R_EX_so3_e\n", + "\t- R_EX_gam6p_e\n", + "\t- R_EX_so4_e\n", + "\t- R_EX_spmd_e\n", + "\t- R_EX_succ_e\n", + "\t- R_EX_sucr_e\n", + "\t- R_EX_sulfac_e\n", + "\t- R_EX_gam_e\n", + "\t- R_EX_tartr__L_e\n", + "\t- R_EX_taur_e\n", + "\t- R_EX_tcynt_e\n", + "\t- R_EX_thm_e\n", + "\t- R_EX_thr__L_e\n", + "\t- R_EX_thrp_e\n", + "\t- R_EX_thym_e\n", + "\t- R_EX_thymd_e\n", + "\t- R_EX_tma_e\n", + "\t- R_EX_gbbtn_e\n", + "\t- R_EX_tmao_e\n", + "\t- R_EX_tre_e\n", + "\t- R_EX_trp__L_e\n", + "\t- R_EX_gdp_e\n", + "\t- R_EX_tsul_e\n", + "\t- R_EX_ttdca_e\n", + "\t- R_EX_ttdcea_e\n", + "\t- R_EX_glc__D_e\n", + "\t- R_EX_glcn_e\n", + "\t- R_EX_glcr_e\n", + "\t- R_EX_glcur1p_e\n", + "\t- R_EX_glcur_e\n", + "\t- R_EX_gln__L_e\n", + "\t- R_EX_glu__L_e\n", + "\t- R_EX_gly_e\n", + "\t- R_EX_glyald_e\n", + "\t- R_EX_glyb_e\n", + "\t- R_EX_glyc2p_e\n", + "\t- R_EX_glyc3p_e\n", + "\t- R_EX_glyc_e\n", + "\t- R_EX_glyc__R_e\n", + "\t- R_EX_glyclt_e\n", + "\t- R_EX_gmp_e\n", + "\t- R_EX_gsn_e\n", + "\t- R_EX_gthox_e\n", + "\t- R_EX_gthrd_e\n", + "\t- R_EX_gtp_e\n", + "\t- R_EX_gua_e\n", + "\t- R_EX_h2_e\n", + "\t- R_EX_h2o2_e\n", + "\t- R_EX_h2o_e\n", + "\t- R_EX_h2s_e\n", + "\t- R_EX_h_e\n", + "\t- R_EX_hacolipa_e\n", + "\t- R_EX_halipa_e\n", + "\t- R_EX_hdca_e\n", + "\t- R_EX_hdcea_e\n", + "\t- R_EX_hg2_e\n", + "\t- R_EX_his__L_e\n", + "\t- R_EX_hom__L_e\n", + "\t- R_EX_hxa_e\n", + "\t- R_EX_hxan_e\n", + "\t- R_EX_idon__L_e\n", + "\t- R_EX_ile__L_e\n", + "\t- R_EX_imp_e\n", + "\t- R_EX_indole_e\n", + "\t- R_EX_inost_e\n", + "\t- R_EX_ins_e\n", + "\t- R_EX_isetac_e\n", + "\t- R_EX_k_e\n", + "\t- R_EX_kdo2lipid4_e\n", + "\t- R_EX_lac__D_e\n", + "\t- R_EX_lac__L_e\n", + "\t- R_EX_lcts_e\n", + "\t- R_EX_leu__L_e\n", + "\t- R_EX_lipa_e\n", + "\t- R_EX_lipa_cold_e\n", + "\t- R_EX_lys__L_e\n", + "\t- R_EX_lyx__L_e\n", + "\t- R_EX_mal__D_e\n", + "\t- R_EX_mal__L_e\n", + "\t- R_EX_malt_e\u001b[0m\n", + "File saved at: ../../data/sbml_corrected/iAF1260.xml\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: iECIAI39_1322\n", + "\u001b[0;93mWARNING : \n", + " - R_CAT: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_DHPTDNR: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_DHPTDNRN: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_FHL: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCASPtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCFUMtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCMALtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCTARTtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SPODM: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SPODMpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_EX_chitob_e: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_CHITOBpts: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_CHITPH: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_URCN_copy1: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\u001b[0m\n", + "\n", + "\n", + "\u001b[0;36m############################################\n", + "############################################\n", + " WRITING SBML FILE \n", + "############################################\n", + "############################################\n", + "\u001b[0m\n", + "\u001b[0;93mWARNING : IMPORT REACTION REMOVED BY DEFAULT\u001b[0m\n", + "\u001b[0;93mWARNING : If you want to keep import reaction\n", + "use option -kir / --keep-import-reactions\u001b[0m\n", + "\u001b[0;93mWARNING : Import reaction removed:\n", + "\t- R_DM_4crsol_c\n", + "\t- R_DM_5drib_c\n", + "\t- R_DM_aacald_c\n", + "\t- R_DM_amob_c\n", + "\t- R_DM_mththf_c\n", + "\t- R_DM_oxam_c\n", + "\t- R_EX_12ppd__R_e\n", + "\t- R_EX_12ppd__S_e\n", + "\t- R_EX_14glucan_e\n", + "\t- R_EX_15dap_e\n", + "\t- R_EX_23camp_e\n", + "\t- R_EX_ac_e\n", + "\t- R_EX_23ccmp_e\n", + "\t- R_EX_acac_e\n", + "\t- R_EX_acald_e\n", + "\t- R_EX_23cgmp_e\n", + "\t- R_EX_acgal_e\n", + "\t- R_EX_23cump_e\n", + "\t- R_EX_23dappa_e\n", + "\t- R_EX_26dap__M_e\n", + "\t- R_EX_2ddglcn_e\n", + "\t- R_EX_acgal1p_e\n", + "\t- R_EX_dump_e\n", + "\t- R_EX_acgam_e\n", + "\t- R_EX_acgam1p_e\n", + "\t- R_EX_duri_e\n", + "\t- R_EX_acmana_e\n", + "\t- R_EX_34dhpac_e\n", + "\t- R_EX_acmum_e\n", + "\t- R_EX_acnam_e\n", + "\t- R_EX_eca4colipa_e\n", + "\t- R_EX_enlipa_e\n", + "\t- R_EX_enter_e\n", + "\t- R_EX_etha_e\n", + "\t- R_EX_ethso3_e\n", + "\t- R_EX_etoh_e\n", + "\t- R_EX_f6p_e\n", + "\t- R_EX_fald_e\n", + "\t- R_EX_fe2_e\n", + "\t- R_EX_fe3_e\n", + "\t- R_EX_acolipa_e\n", + "\t- R_EX_acser_e\n", + "\t- R_EX_ade_e\n", + "\t- R_EX_adn_e\n", + "\t- R_EX_adocbl_e\n", + "\t- R_EX_ag_e\n", + "\t- R_EX_agm_e\n", + "\t- R_EX_akg_e\n", + "\t- R_EX_ala_B_e\n", + "\t- R_EX_ala__D_e\n", + "\t- R_EX_ala__L_e\n", + "\t- R_EX_alaala_e\n", + "\t- R_EX_fe3dcit_e\n", + "\t- R_EX_fe3dhbzs_e\n", + "\t- R_EX_fe3hox_e\n", + "\t- R_EX_fe3hox_un_e\n", + "\t- R_EX_fecrm_e\n", + "\t- R_EX_fecrm_un_e\n", + "\t- R_EX_all__D_e\n", + "\t- R_EX_alltn_e\n", + "\t- R_EX_feenter_e\n", + "\t- R_EX_3amp_e\n", + "\t- R_EX_feoxam_e\n", + "\t- R_EX_feoxam_un_e\n", + "\t- R_EX_amp_e\n", + "\t- R_EX_anhgm_e\n", + "\t- R_EX_for_e\n", + "\t- R_EX_arab__L_e\n", + "\t- R_EX_arbt_e\n", + "\t- R_EX_arbtn_e\n", + "\t- R_EX_arbtn_fe3_e\n", + "\t- R_EX_fru_e\n", + "\t- R_EX_frulys_e\n", + "\t- R_EX_fruur_e\n", + "\t- R_EX_fuc__L_e\n", + "\t- R_EX_fum_e\n", + "\t- R_EX_arg__L_e\n", + "\t- R_EX_fusa_e\n", + "\t- R_EX_3cmp_e\n", + "\t- R_EX_g1p_e\n", + "\t- R_EX_g3pc_e\n", + "\t- R_EX_ascb__L_e\n", + "\t- R_EX_asn__L_e\n", + "\t- R_EX_aso3_e\n", + "\t- R_EX_g3pe_e\n", + "\t- R_EX_asp__L_e\n", + "\t- R_EX_3gmp_e\n", + "\t- R_EX_btn_e\n", + "\t- R_EX_but_e\n", + "\t- R_EX_g3pg_e\n", + "\t- R_EX_g3pi_e\n", + "\t- R_EX_g3ps_e\n", + "\t- R_EX_g6p_e\n", + "\t- R_EX_butso3_e\n", + "\t- R_EX_ca2_e\n", + "\t- R_EX_gal_e\n", + "\t- R_EX_3hcinnm_e\n", + "\t- R_EX_gal_bD_e\n", + "\t- R_EX_gal1p_e\n", + "\t- R_EX_cbi_e\n", + "\t- R_EX_cbl1_e\n", + "\t- R_EX_cd2_e\n", + "\t- R_EX_cgly_e\n", + "\t- R_EX_galct__D_e\n", + "\t- R_EX_galctn__D_e\n", + "\t- R_EX_3hpp_e\n", + "\t- R_EX_galctn__L_e\n", + "\t- R_EX_galt_e\n", + "\t- R_EX_galur_e\n", + "\t- R_EX_gam_e\n", + "\t- R_EX_gam6p_e\n", + "\t- R_EX_gbbtn_e\n", + "\t- R_EX_chol_e\n", + "\t- R_EX_chtbs_e\n", + "\t- R_EX_cit_e\n", + "\t- R_EX_cl_e\n", + "\t- R_EX_cm_e\n", + "\t- R_EX_cmp_e\n", + "\t- R_EX_co2_e\n", + "\t- R_EX_cobalt2_e\n", + "\t- R_EX_gdp_e\n", + "\t- R_EX_glc__D_e\n", + "\t- R_EX_glcn_e\n", + "\t- R_EX_colipa_e\n", + "\t- R_EX_colipap_e\n", + "\t- R_EX_glcr_e\n", + "\t- R_EX_3hpppn_e\n", + "\t- R_EX_glcur_e\n", + "\t- R_EX_glcur1p_e\n", + "\t- R_EX_gln__L_e\n", + "\t- R_EX_cpgn_e\n", + "\t- R_EX_cpgn_un_e\n", + "\t- R_EX_crn_e\n", + "\t- R_EX_glu__L_e\n", + "\t- R_EX_gly_e\n", + "\t- R_EX_crn__D_e\n", + "\t- R_EX_csn_e\n", + "\t- R_EX_3ump_e\n", + "\t- R_EX_cu_e\n", + "\t- R_EX_cu2_e\n", + "\t- R_EX_cyan_e\n", + "\t- R_EX_glyald_e\n", + "\t- R_EX_glyb_e\n", + "\t- R_EX_glyc_e\n", + "\t- R_EX_cynt_e\n", + "\t- R_EX_cys__D_e\n", + "\t- R_EX_cys__L_e\n", + "\t- R_EX_cytd_e\n", + "\t- R_EX_glyc__R_e\n", + "\t- R_EX_glyc2p_e\n", + "\t- R_EX_glyc3p_e\n", + "\t- R_EX_glyclt_e\n", + "\t- R_EX_gmp_e\n", + "\t- R_EX_dad_2_e\n", + "\t- R_EX_damp_e\n", + "\t- R_EX_4abut_e\n", + "\t- R_EX_dca_e\n", + "\t- R_EX_dcmp_e\n", + "\t- R_EX_gsn_e\n", + "\t- R_EX_gthox_e\n", + "\t- R_EX_gthrd_e\n", + "\t- R_EX_gtp_e\n", + "\t- R_EX_dcyt_e\n", + "\t- R_EX_ddca_e\n", + "\t- R_EX_gua_e\n", + "\t- R_EX_4hoxpacd_e\n", + "\t- R_EX_h_e\n", + "\t- R_EX_dgmp_e\n", + "\t- R_EX_dgsn_e\n", + "\t- R_EX_h2_e\n", + "\t- R_EX_5dglcn_e\n", + "\t- R_EX_h2o_e\n", + "\t- R_EX_h2o2_e\n", + "\t- R_EX_dha_e\n", + "\t- R_EX_dimp_e\n", + "\t- R_EX_din_e\n", + "\t- R_EX_dms_e\n", + "\t- R_EX_h2s_e\n", + "\t- R_EX_5mtr_e\n", + "\t- R_EX_hacolipa_e\n", + "\t- R_EX_halipa_e\n", + "\t- R_EX_hdca_e\n", + "\t- R_EX_dmso_e\n", + "\t- R_EX_dopa_e\n", + "\t- R_EX_doxrbcn_e\n", + "\t- R_EX_dtmp_e\n", + "\t- R_EX_hdcea_e\n", + "\t- R_EX_hg2_e\n", + "\t- R_EX_his__L_e\n", + "\t- R_EX_hom__L_e\n", + "\t- R_EX_hxa_e\n", + "\t- R_EX_hxan_e\n", + "\t- R_EX_LalaDglu_e\n", + "\t- R_EX_idon__L_e\n", + "\t- R_EX_ile__L_e\n", + "\t- R_EX_r5p_e\n", + "\t- R_EX_rfamp_e\n", + "\t- R_EX_imp_e\n", + "\t- R_EX_indole_e\n", + "\t- R_EX_LalaDgluMdap_e\n", + "\t- R_EX_inost_e\n", + "\t- R_EX_ins_e\n", + "\t- R_EX_rib__D_e\n", + "\t- R_EX_rmn_e\n", + "\t- R_EX_sbt__D_e\n", + "\t- R_EX_isetac_e\n", + "\t- R_EX_k_e\n", + "\t- R_EX_LalaDgluMdapDala_e\n", + "\t- R_EX_kdo2lipid4_e\n", + "\t- R_EX_lac__D_e\n", + "\t- R_EX_sel_e\n", + "\t- R_EX_ser__D_e\n", + "\t- R_EX_ser__L_e\n", + "\t- R_EX_LalaLglu_e\n", + "\t- R_EX_skm_e\n", + "\t- R_EX_lac__L_e\n", + "\t- R_EX_lcts_e\n", + "\t- R_EX_leu__L_e\n", + "\t- R_EX_slnt_e\n", + "\t- R_EX_lipa_e\n", + "\t- R_EX_lipa_cold_e\n", + "\t- R_EX_lipoate_e\n", + "\t- R_EX_lys__L_e\n", + "\t- R_EX_so2_e\n", + "\t- R_EX_so3_e\n", + "\t- R_EX_so4_e\n", + "\t- R_EX_spmd_e\n", + "\t- R_EX_succ_e\n", + "\t- R_EX_lyx__L_e\n", + "\t- R_EX_mal__D_e\n", + "\t- R_EX_mal__L_e\n", + "\t- R_EX_malt_e\n", + "\t- R_EX_malthx_e\n", + "\t- R_EX_maltpt_e\n", + "\t- R_EX_malttr_e\n", + "\t- R_EX_sucr_e\n", + "\t- R_EX_sulfac_e\n", + "\t- R_EX_tartr__D_e\n", + "\t- R_EX_tartr__L_e\n", + "\t- R_EX_taur_e\n", + "\t- R_EX_tcynt_e\n", + "\t- R_EX_maltttr_e\n", + "\t- R_EX_man_e\n", + "\t- R_EX_man6p_e\n", + "\t- R_EX_thm_e\n", + "\t- R_EX_manglyc_e\n", + "\t- R_EX_thr__L_e\n", + "\t- R_EX_thrp_e\n", + "\t- R_EX_thym_e\n", + "\t- R_EX_thymd_e\n", + "\t- R_EX_tma_e\n", + "\t- R_EX_melib_e\n", + "\t- R_EX_meoh_e\n", + "\t- R_EX_met__D_e\n", + "\t- R_EX_tmao_e\n", + "\t- R_EX_met__L_e\n", + "\t- R_EX_metsox_R__L_e\n", + "\t- R_EX_metsox_S__L_e\n", + "\t- R_EX_mg2_e\n", + "\t- R_EX_mincyc_e\n", + "\t- R_EX_tre_e\n", + "\t- R_EX_trp__L_e\n", + "\t- R_EX_tsul_e\n", + "\t- R_EX_ttdca_e\n", + "\t- R_EX_ttdcea_e\n", + "\t- R_EX_minohp_e\n", + "\t- R_EX_mmet_e\n", + "\t- R_EX_mn2_e\n", + "\t- R_EX_ttrcyc_e\n", + "\t- R_EX_mnl_e\n", + "\t- R_EX_mobd_e\n", + "\t- R_EX_mso3_e\n", + "\t- R_EX_n2o_e\n", + "\t- R_EX_tungs_e\n", + "\t- R_EX_tym_e\n", + "\t- R_EX_tyr__L_e\n", + "\t- R_EX_na1_e\n", + "\t- R_EX_tyrp_e\n", + "\t- R_EX_uacgam_e\n", + "\t- R_EX_udpacgal_e\n", + "\t- R_EX_udpg_e\n", + "\t- R_EX_udpgal_e\n", + "\t- R_EX_nac_e\n", + "\t- R_EX_nh4_e\n", + "\t- R_EX_ni2_e\n", + "\t- R_EX_nmn_e\n", + "\t- R_EX_no_e\n", + "\t- R_EX_udpglcur_e\n", + "\t- R_EX_ump_e\n", + "\t- R_EX_no2_e\n", + "\t- R_EX_ura_e\n", + "\t- R_EX_urea_e\n", + "\t- R_EX_uri_e\n", + "\t- R_EX_no3_e\n", + "\t- R_EX_novbcn_e\n", + "\t- R_EX_o16a4colipa_e\n", + "\t- R_EX_o2_e\n", + "\t- R_EX_o2s_e\n", + "\t- R_EX_ocdca_e\n", + "\t- R_EX_val__L_e\n", + "\t- R_EX_xan_e\n", + "\t- R_EX_xmp_e\n", + "\t- R_EX_xtsn_e\n", + "\t- R_EX_ocdcea_e\n", + "\t- R_EX_octa_e\n", + "\t- R_EX_orn_e\n", + "\t- R_EX_xyl__D_e\n", + "\t- R_EX_orot_e\n", + "\t- R_EX_pacald_e\n", + "\t- R_EX_peamn_e\n", + "\t- R_EX_xylu__L_e\n", + "\t- R_EX_zn2_e\n", + "\t- R_EX_phe__L_e\n", + "\t- R_EX_pheme_e\n", + "\t- R_EX_pi_e\n", + "\t- R_EX_pnto__R_e\n", + "\t- R_EX_ppa_e\n", + "\t- R_EX_ppal_e\n", + "\t- R_EX_pppn_e\n", + "\t- R_EX_ppt_e\n", + "\t- R_EX_pro__L_e\n", + "\t- R_EX_progly_e\n", + "\t- R_EX_psclys_e\n", + "\t- R_EX_pser__L_e\n", + "\t- R_EX_ptrc_e\n", + "\t- R_EX_pydam_e\n", + "\t- R_EX_pydx_e\n", + "\t- R_EX_pydxn_e\n", + "\t- R_EX_pyr_e\n", + "\t- R_EX_quin_e\n", + "\t- R_EX_frmd_e\n", + "\t- R_EX_colipaOA_e\n", + "\t- R_EX_salchsx_e\n", + "\t- R_EX_AEP_e\n", + "\t- R_EX_rbt_e\n", + "\t- R_EX_bz_e\n", + "\t- R_EX_4hphac_e\n", + "\t- R_EX_4abz_e\n", + "\t- R_EX_acon_C_e\n", + "\t- R_EX_icit_e\n", + "\t- R_EX_feroxB_e\n", + "\t- R_EX_tcb_e\n", + "\t- R_EX_tton_e\n", + "\t- R_EX_4hthr_e\n", + "\t- R_EX_feroxEfe_e\n", + "\t- R_EX_colipa20Oag_e\n", + "\t- R_EX_remnant1_e\n", + "\t- R_EX_feroxE_e\n", + "\t- R_EX_pep_e\n", + "\t- R_EX_salchs2_e\n", + "\t- R_EX_23dhbzs3_e\n", + "\t- R_EX_tet_e\n", + "\t- R_EX_rnam_e\n", + "\t- R_EX_airs_e\n", + "\t- R_EX_dxyl_e\n", + "\t- R_EX_salchs4_e\n", + "\t- R_EX_tag__D_e\n", + "\t- R_EX_oaa_e\n", + "\t- R_EX_3pg_e\n", + "\t- R_EX_salchs2fe_e\n", + "\t- R_EX_cysi__L_e\n", + "\t- R_EX_fe3dhbzs3_e\n", + "\t- R_EX_udcpo4_e\n", + "\t- R_EX_drib_e\n", + "\t- R_EX_feroxBfe_e\n", + "\t- R_EX_salchs4fe_e\n", + "\t- R_EX_2pg_e\n", + "\t- R_EX_feroxGfe_e\n", + "\t- R_EX_udcpo5_e\n", + "\t- R_EX_guln__L_e\n", + "\t- R_EX_feroxG_e\n", + "\t- R_EX_abt__D_e\n", + "\t- R_EX_fe_e\n", + "\t- R_EX_kokdolipidA_e\n", + "\t- R_EX_diact_e\n", + "\t- R_EX_3hoxpac_e\n", + "\t- R_EX_cellb_e\n", + "\t- R_EX_o6a4colipa_e\n", + "\t- R_EX_btd_RR_e\n", + "\t- R_EX_13ppd_e\n", + "\t- R_EX_arab__D_e\n", + "\t- R_EX_3ntym_e\n", + "\t- R_EX_6apa_e\n", + "\t- R_EX_4hoxpac_e\n", + "\t- R_EX_peng_e\n", + "\t- R_EX_pac_e\n", + "\t- R_EX_salcn_e\n", + "\t- R_EX_galam_e\n", + "\t- R_EX_raffin_e\n", + "\t- R_DM_lipidA_core_e_p\n", + "\t- R_SK_thf_c\u001b[0m\n", + "File saved at: ../../data/sbml_corrected/iECIAI39_1322.xml\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: iNRG857_1313\n", + "\u001b[0;93mWARNING : \n", + " - R_CAT: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_DHPTDNR: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_DHPTDNRN: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_FHL: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCFUMtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCMALtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCTARTtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SPODM: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SPODMpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCASPtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_EX_chitob_e: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_CHITOBpts: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_CHITPH: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_URCN: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\u001b[0m\n", + "\n", + "\n", + "\u001b[0;36m############################################\n", + "############################################\n", + " WRITING SBML FILE \n", + "############################################\n", + "############################################\n", + "\u001b[0m\n", + "\u001b[0;93mWARNING : IMPORT REACTION REMOVED BY DEFAULT\u001b[0m\n", + "\u001b[0;93mWARNING : If you want to keep import reaction\n", + "use option -kir / --keep-import-reactions\u001b[0m\n", + "\u001b[0;93mWARNING : Import reaction removed:\n", + "\t- R_EX_acolipa_e\n", + "\t- R_EX_fe3_e\n", + "\t- R_EX_fe3dcit_e\n", + "\t- R_EX_fe3dhbzs_e\n", + "\t- R_EX_fe3hox_e\n", + "\t- R_EX_fe3hox_un_e\n", + "\t- R_EX_fecrm_e\n", + "\t- R_EX_fecrm_un_e\n", + "\t- R_EX_feenter_e\n", + "\t- R_EX_feoxam_e\n", + "\t- R_EX_feoxam_un_e\n", + "\t- R_EX_for_e\n", + "\t- R_EX_fru_e\n", + "\t- R_EX_frulys_e\n", + "\t- R_EX_LalaLglu_e\n", + "\t- R_EX_fruur_e\n", + "\t- R_DM_4crsol_c\n", + "\t- R_EX_fuc__L_e\n", + "\t- R_DM_5drib_c\n", + "\t- R_DM_aacald_c\n", + "\t- R_DM_amob_c\n", + "\t- R_EX_fum_e\n", + "\t- R_DM_mththf_c\n", + "\t- R_EX_fusa_e\n", + "\t- R_DM_oxam_c\n", + "\t- R_EX_g1p_e\n", + "\t- R_EX_12ppd__R_e\n", + "\t- R_EX_g3pc_e\n", + "\t- R_EX_12ppd__S_e\n", + "\t- R_EX_14glucan_e\n", + "\t- R_EX_acser_e\n", + "\t- R_EX_ade_e\n", + "\t- R_EX_adn_e\n", + "\t- R_EX_adocbl_e\n", + "\t- R_EX_ag_e\n", + "\t- R_EX_agm_e\n", + "\t- R_EX_akg_e\n", + "\t- R_EX_ala_B_e\n", + "\t- R_EX_ala__D_e\n", + "\t- R_EX_ala__L_e\n", + "\t- R_EX_15dap_e\n", + "\t- R_EX_23camp_e\n", + "\t- R_EX_23ccmp_e\n", + "\t- R_EX_23cgmp_e\n", + "\t- R_EX_23cump_e\n", + "\t- R_EX_23dappa_e\n", + "\t- R_EX_26dap__M_e\n", + "\t- R_EX_2ddglcn_e\n", + "\t- R_EX_34dhpac_e\n", + "\t- R_EX_alaala_e\n", + "\t- R_EX_g3pe_e\n", + "\t- R_EX_3amp_e\n", + "\t- R_EX_3cmp_e\n", + "\t- R_EX_all__D_e\n", + "\t- R_EX_g3pg_e\n", + "\t- R_EX_alltn_e\n", + "\t- R_EX_3gmp_e\n", + "\t- R_EX_3hcinnm_e\n", + "\t- R_EX_3hpp_e\n", + "\t- R_EX_g3pi_e\n", + "\t- R_EX_3hpppn_e\n", + "\t- R_EX_amp_e\n", + "\t- R_EX_anhgm_e\n", + "\t- R_EX_3ump_e\n", + "\t- R_EX_g3ps_e\n", + "\t- R_EX_4abut_e\n", + "\t- R_EX_4hoxpacd_e\n", + "\t- R_EX_arab__L_e\n", + "\t- R_EX_arbt_e\n", + "\t- R_EX_arbtn_e\n", + "\t- R_EX_arbtn_fe3_e\n", + "\t- R_EX_arg__L_e\n", + "\t- R_EX_ascb__L_e\n", + "\t- R_EX_5dglcn_e\n", + "\t- R_EX_5mtr_e\n", + "\t- R_EX_LalaDglu_e\n", + "\t- R_EX_LalaDgluMdap_e\n", + "\t- R_EX_asn__L_e\n", + "\t- R_EX_aso3_e\n", + "\t- R_EX_g6p_e\n", + "\t- R_EX_asp__L_e\n", + "\t- R_EX_LalaDgluMdapDala_e\n", + "\t- R_EX_ac_e\n", + "\t- R_EX_acac_e\n", + "\t- R_EX_acald_e\n", + "\t- R_EX_btn_e\n", + "\t- R_EX_but_e\n", + "\t- R_EX_gal_e\n", + "\t- R_EX_butso3_e\n", + "\t- R_EX_acgal_e\n", + "\t- R_EX_ca2_e\n", + "\t- R_EX_acgal1p_e\n", + "\t- R_EX_gal_bD_e\n", + "\t- R_EX_acgam_e\n", + "\t- R_EX_cbi_e\n", + "\t- R_EX_cbl1_e\n", + "\t- R_EX_acgam1p_e\n", + "\t- R_EX_cd2_e\n", + "\t- R_EX_acmana_e\n", + "\t- R_EX_gal1p_e\n", + "\t- R_EX_acmum_e\n", + "\t- R_EX_cgly_e\n", + "\t- R_EX_galct__D_e\n", + "\t- R_EX_chol_e\n", + "\t- R_EX_chtbs_e\n", + "\t- R_EX_acnam_e\n", + "\t- R_EX_cit_e\n", + "\t- R_EX_cl_e\n", + "\t- R_EX_cm_e\n", + "\t- R_EX_cmp_e\n", + "\t- R_EX_lac__L_e\n", + "\t- R_EX_lcts_e\n", + "\t- R_EX_co2_e\n", + "\t- R_EX_leu__L_e\n", + "\t- R_EX_galctn__D_e\n", + "\t- R_EX_lipa_e\n", + "\t- R_EX_lipa_cold_e\n", + "\t- R_EX_cobalt2_e\n", + "\t- R_EX_colipa_e\n", + "\t- R_EX_colipap_e\n", + "\t- R_EX_lipoate_e\n", + "\t- R_EX_lys__L_e\n", + "\t- R_EX_galctn__L_e\n", + "\t- R_EX_lyx__L_e\n", + "\t- R_EX_mal__D_e\n", + "\t- R_EX_mal__L_e\n", + "\t- R_EX_malt_e\n", + "\t- R_EX_malthx_e\n", + "\t- R_EX_maltpt_e\n", + "\t- R_EX_cpgn_e\n", + "\t- R_EX_cpgn_un_e\n", + "\t- R_EX_crn_e\n", + "\t- R_EX_crn__D_e\n", + "\t- R_EX_csn_e\n", + "\t- R_EX_cu_e\n", + "\t- R_EX_cu2_e\n", + "\t- R_EX_cyan_e\n", + "\t- R_EX_cynt_e\n", + "\t- R_EX_malttr_e\n", + "\t- R_EX_maltttr_e\n", + "\t- R_EX_man_e\n", + "\t- R_EX_cys__D_e\n", + "\t- R_EX_galt_e\n", + "\t- R_EX_cys__L_e\n", + "\t- R_EX_cytd_e\n", + "\t- R_EX_dad_2_e\n", + "\t- R_EX_damp_e\n", + "\t- R_EX_dca_e\n", + "\t- R_EX_man6p_e\n", + "\t- R_EX_manglyc_e\n", + "\t- R_EX_melib_e\n", + "\t- R_EX_meoh_e\n", + "\t- R_EX_met__D_e\n", + "\t- R_EX_met__L_e\n", + "\t- R_EX_metsox_R__L_e\n", + "\t- R_EX_dcmp_e\n", + "\t- R_EX_dcyt_e\n", + "\t- R_EX_ddca_e\n", + "\t- R_EX_dgmp_e\n", + "\t- R_EX_metsox_S__L_e\n", + "\t- R_EX_mg2_e\n", + "\t- R_EX_dgsn_e\n", + "\t- R_EX_galur_e\n", + "\t- R_EX_dha_e\n", + "\t- R_EX_mincyc_e\n", + "\t- R_EX_minohp_e\n", + "\t- R_EX_dimp_e\n", + "\t- R_EX_gam_e\n", + "\t- R_EX_din_e\n", + "\t- R_EX_dms_e\n", + "\t- R_EX_mmet_e\n", + "\t- R_EX_mn2_e\n", + "\t- R_EX_mnl_e\n", + "\t- R_EX_mobd_e\n", + "\t- R_EX_dmso_e\n", + "\t- R_EX_dopa_e\n", + "\t- R_EX_mso3_e\n", + "\t- R_EX_gam6p_e\n", + "\t- R_EX_n2o_e\n", + "\t- R_EX_doxrbcn_e\n", + "\t- R_EX_dtmp_e\n", + "\t- R_EX_dump_e\n", + "\t- R_EX_duri_e\n", + "\t- R_EX_eca4colipa_e\n", + "\t- R_EX_na1_e\n", + "\t- R_EX_nac_e\n", + "\t- R_EX_nh4_e\n", + "\t- R_EX_ni2_e\n", + "\t- R_EX_nmn_e\n", + "\t- R_EX_no_e\n", + "\t- R_EX_no2_e\n", + "\t- R_EX_enlipa_e\n", + "\t- R_EX_enter_e\n", + "\t- R_EX_gbbtn_e\n", + "\t- R_EX_etha_e\n", + "\t- R_EX_ethso3_e\n", + "\t- R_EX_no3_e\n", + "\t- R_EX_novbcn_e\n", + "\t- R_EX_etoh_e\n", + "\t- R_EX_gdp_e\n", + "\t- R_EX_f6p_e\n", + "\t- R_EX_fald_e\n", + "\t- R_EX_o16a4colipa_e\n", + "\t- R_EX_o2_e\n", + "\t- R_EX_o2s_e\n", + "\t- R_EX_fe2_e\n", + "\t- R_EX_ocdca_e\n", + "\t- R_EX_glc__D_e\n", + "\t- R_EX_ocdcea_e\n", + "\t- R_EX_glcn_e\n", + "\t- R_EX_octa_e\n", + "\t- R_EX_so3_e\n", + "\t- R_EX_orn_e\n", + "\t- R_EX_glcr_e\n", + "\t- R_EX_orot_e\n", + "\t- R_EX_pacald_e\n", + "\t- R_EX_so4_e\n", + "\t- R_EX_spmd_e\n", + "\t- R_EX_peamn_e\n", + "\t- R_EX_phe__L_e\n", + "\t- R_EX_pheme_e\n", + "\t- R_EX_succ_e\n", + "\t- R_EX_sucr_e\n", + "\t- R_EX_pi_e\n", + "\t- R_EX_pnto__R_e\n", + "\t- R_EX_sulfac_e\n", + "\t- R_EX_glcur_e\n", + "\t- R_EX_tartr__D_e\n", + "\t- R_EX_ppa_e\n", + "\t- R_EX_ppal_e\n", + "\t- R_EX_pppn_e\n", + "\t- R_EX_ppt_e\n", + "\t- R_EX_tartr__L_e\n", + "\t- R_EX_taur_e\n", + "\t- R_EX_glcur1p_e\n", + "\t- R_EX_tcynt_e\n", + "\t- R_EX_pro__L_e\n", + "\t- R_EX_progly_e\n", + "\t- R_EX_gln__L_e\n", + "\t- R_EX_psclys_e\n", + "\t- R_EX_pser__L_e\n", + "\t- R_EX_ptrc_e\n", + "\t- R_EX_thm_e\n", + "\t- R_EX_thr__L_e\n", + "\t- R_EX_thrp_e\n", + "\t- R_EX_pydam_e\n", + "\t- R_EX_thym_e\n", + "\t- R_EX_pydx_e\n", + "\t- R_EX_glu__L_e\n", + "\t- R_EX_pydxn_e\n", + "\t- R_EX_pyr_e\n", + "\t- R_EX_thymd_e\n", + "\t- R_EX_tma_e\n", + "\t- R_EX_tmao_e\n", + "\t- R_EX_quin_e\n", + "\t- R_EX_gly_e\n", + "\t- R_EX_r5p_e\n", + "\t- R_EX_rfamp_e\n", + "\t- R_EX_rib__D_e\n", + "\t- R_EX_rmn_e\n", + "\t- R_EX_tre_e\n", + "\t- R_EX_trp__L_e\n", + "\t- R_EX_tsul_e\n", + "\t- R_EX_sbt__D_e\n", + "\t- R_EX_ttdca_e\n", + "\t- R_EX_glyald_e\n", + "\t- R_EX_ttdcea_e\n", + "\t- R_EX_ttrcyc_e\n", + "\t- R_EX_tungs_e\n", + "\t- R_EX_sel_e\n", + "\t- R_EX_ser__D_e\n", + "\t- R_EX_ser__L_e\n", + "\t- R_EX_skm_e\n", + "\t- R_EX_slnt_e\n", + "\t- R_EX_tym_e\n", + "\t- R_EX_glyb_e\n", + "\t- R_EX_tyr__L_e\n", + "\t- R_EX_so2_e\n", + "\t- R_EX_tyrp_e\n", + "\t- R_EX_glyc_e\n", + "\t- R_EX_uacgam_e\n", + "\t- R_EX_udpacgal_e\n", + "\t- R_EX_glyc__R_e\n", + "\t- R_EX_udpg_e\n", + "\t- R_EX_udpgal_e\n", + "\t- R_EX_udpglcur_e\n", + "\t- R_EX_ump_e\n", + "\t- R_EX_glyc2p_e\n", + "\t- R_EX_ura_e\n", + "\t- R_EX_urea_e\n", + "\t- R_EX_uri_e\n", + "\t- R_EX_val__L_e\n", + "\t- R_EX_xan_e\n", + "\t- R_EX_xmp_e\n", + "\t- R_EX_glyc3p_e\n", + "\t- R_EX_xtsn_e\n", + "\t- R_EX_xyl__D_e\n", + "\t- R_EX_glyclt_e\n", + "\t- R_EX_xylu__L_e\n", + "\t- R_EX_zn2_e\n", + "\t- R_EX_gmp_e\n", + "\t- R_EX_gsn_e\n", + "\t- R_EX_gthox_e\n", + "\t- R_EX_gthrd_e\n", + "\t- R_EX_gtp_e\n", + "\t- R_EX_gua_e\n", + "\t- R_EX_h_e\n", + "\t- R_EX_h2_e\n", + "\t- R_EX_h2o_e\n", + "\t- R_EX_h2o2_e\n", + "\t- R_EX_h2s_e\n", + "\t- R_EX_hacolipa_e\n", + "\t- R_EX_halipa_e\n", + "\t- R_EX_hdca_e\n", + "\t- R_EX_hdcea_e\n", + "\t- R_EX_hg2_e\n", + "\t- R_EX_his__L_e\n", + "\t- R_EX_hom__L_e\n", + "\t- R_EX_hxa_e\n", + "\t- R_EX_hxan_e\n", + "\t- R_EX_idon__L_e\n", + "\t- R_EX_ile__L_e\n", + "\t- R_EX_imp_e\n", + "\t- R_EX_indole_e\n", + "\t- R_EX_inost_e\n", + "\t- R_EX_ins_e\n", + "\t- R_EX_isetac_e\n", + "\t- R_EX_k_e\n", + "\t- R_EX_kdo2lipid4_e\n", + "\t- R_EX_lac__D_e\n", + "\t- R_EX_4hphac_e\n", + "\t- R_EX_frmd_e\n", + "\t- R_EX_colipaOA_e\n", + "\t- R_EX_salchsx_e\n", + "\t- R_EX_AEP_e\n", + "\t- R_EX_4abz_e\n", + "\t- R_EX_icit_e\n", + "\t- R_EX_colipa20Oag_e\n", + "\t- R_EX_acon_C_e\n", + "\t- R_EX_diact_e\n", + "\t- R_EX_salchs2_e\n", + "\t- R_EX_feroxB_e\n", + "\t- R_EX_tcb_e\n", + "\t- R_EX_tton_e\n", + "\t- R_EX_4hthr_e\n", + "\t- R_EX_feroxEfe_e\n", + "\t- R_EX_btd_RR_e\n", + "\t- R_EX_rnam_e\n", + "\t- R_EX_airs_e\n", + "\t- R_EX_rbt_e\n", + "\t- R_EX_dxyl_e\n", + "\t- R_EX_cellb_e\n", + "\t- R_EX_feroxE_e\n", + "\t- R_EX_bz_e\n", + "\t- R_EX_13ppd_e\n", + "\t- R_EX_3pg_e\n", + "\t- R_EX_abt__D_e\n", + "\t- R_EX_pep_e\n", + "\t- R_EX_udcpo4_e\n", + "\t- R_EX_feroxGfe_e\n", + "\t- R_EX_udcpo5_e\n", + "\t- R_EX_guln__L_e\n", + "\t- R_EX_fe_e\n", + "\t- R_EX_kokdolipidA_e\n", + "\t- R_EX_remnant1_e\n", + "\t- R_EX_feroxG_e\n", + "\t- R_EX_23dhbzs3_e\n", + "\t- R_EX_3hoxpac_e\n", + "\t- R_EX_o6a4colipa_e\n", + "\t- R_EX_tet_e\n", + "\t- R_EX_galam_e\n", + "\t- R_EX_raffin_e\n", + "\t- R_DM_lipidA_core_e_p\n", + "\t- R_EX_salchs4_e\n", + "\t- R_EX_tag__D_e\n", + "\t- R_EX_3ntym_e\n", + "\t- R_EX_6apa_e\n", + "\t- R_EX_4hoxpac_e\n", + "\t- R_EX_arab__D_e\n", + "\t- R_EX_peng_e\n", + "\t- R_EX_pac_e\n", + "\t- R_EX_salcn_e\n", + "\t- R_EX_oaa_e\n", + "\t- R_EX_salchs2fe_e\n", + "\t- R_EX_cysi__L_e\n", + "\t- R_EX_fe3dhbzs3_e\n", + "\t- R_EX_drib_e\n", + "\t- R_EX_feroxBfe_e\n", + "\t- R_EX_salchs4fe_e\n", + "\t- R_EX_2pg_e\u001b[0m\n", + "File saved at: ../../data/sbml_corrected/iNRG857_1313.xml\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: iPC815\n", + "\u001b[0;93mWARNING : - R_G3PD2: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + "\n", + " - R_MELIBtex: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + " - R_SDPTA: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + "\u001b[0m\n", + "\n", + "\n", + "\u001b[0;36m############################################\n", + "############################################\n", + " WRITING SBML FILE \n", + "############################################\n", + "############################################\n", + "\u001b[0m\n", + "\u001b[0;93mWARNING : IMPORT REACTION REMOVED BY DEFAULT\u001b[0m\n", + "\u001b[0;93mWARNING : If you want to keep import reaction\n", + "use option -kir / --keep-import-reactions\u001b[0m\n", + "\u001b[0;93mWARNING : Reaction with tag reversible modified:\n", + "\t- R_G3PD2\n", + "\t- R_SDPTA\u001b[0m\n", + "\u001b[0;93mWARNING : Reaction with Reactants and Products exchanged:\n", + "\t- R_G3PD2\n", + "\t- R_SDPTA\u001b[0m\n", + "\u001b[0;93mWARNING : Import reaction removed:\n", + "\t- R_EX_cys__L_e\n", + "\t- R_EX_cytd_e\n", + "\t- R_EX_dad_2_e\n", + "\t- R_EX_ala__D_e\n", + "\t- R_EX_damp_e\n", + "\t- R_EX_15dap_e\n", + "\t- R_EX_dca_e\n", + "\t- R_EX_dcmp_e\n", + "\t- R_EX_dcyt_e\n", + "\t- R_EX_ddca_e\n", + "\t- R_EX_2ddglcn_e\n", + "\t- R_EX_dgmp_e\n", + "\t- R_EX_dgsn_e\n", + "\t- R_EX_dha_e\n", + "\t- R_EX_dimp_e\n", + "\t- R_EX_din_e\n", + "\t- R_EX_lac__D_e\n", + "\t- R_EX_cys__D_e\n", + "\t- R_EX_dmso_e\n", + "\t- R_EX_dms_e\n", + "\t- R_EX_dopa_e\n", + "\t- R_EX_ser__D_e\n", + "\t- R_EX_dtmp_e\n", + "\t- R_EX_cu_e\n", + "\t- R_EX_cyan_e\n", + "\t- R_EX_12ppd__R_e\n", + "\t- R_EX_12ppd__S_e\n", + "\t- R_EX_14glucan_e\n", + "\t- R_EX_23camp_e\n", + "\t- R_EX_23ccmp_e\n", + "\t- R_EX_23cgmp_e\n", + "\t- R_EX_23cump_e\n", + "\t- R_EX_23dappa_e\n", + "\t- R_EX_26dap__M_e\n", + "\t- R_EX_34dhpac_e\n", + "\t- R_EX_3amp_e\n", + "\t- R_EX_3cmp_e\n", + "\t- R_EX_3gmp_e\n", + "\t- R_EX_LalaDgluMdap_e\n", + "\t- R_EX_3ump_e\n", + "\t- R_EX_4hoxpacd_e\n", + "\t- R_EX_LalaDgluMdapDala_e\n", + "\t- R_EX_5dglcn_e\n", + "\t- R_EX_4abut_e\n", + "\t- R_EX_acac_e\n", + "\t- R_EX_acald_e\n", + "\t- R_EX_acgal1p_e\n", + "\t- R_EX_acgal_e\n", + "\t- R_EX_acgam1p_e\n", + "\t- R_EX_acgam_e\n", + "\t- R_EX_acmana_e\n", + "\t- R_EX_acmum_e\n", + "\t- R_EX_acnam_e\n", + "\t- R_EX_acolipa_e\n", + "\t- R_EX_acser_e\n", + "\t- R_EX_ac_e\n", + "\t- R_EX_ade_e\n", + "\t- R_EX_cynt_e\n", + "\t- R_EX_dump_e\n", + "\t- R_EX_duri_e\n", + "\t- R_EX_adn_e\n", + "\t- R_EX_adocbl_e\n", + "\t- R_EX_agm_e\n", + "\t- R_EX_akg_e\n", + "\t- R_EX_alaala_e\n", + "\t- R_EX_ala__L_e\n", + "\t- R_EX_all__D_e\n", + "\t- R_EX_etha_e\n", + "\t- R_EX_ethso3_e\n", + "\t- R_EX_etoh_e\n", + "\t- R_EX_alltn_e\n", + "\t- R_EX_amp_e\n", + "\t- R_EX_anhgm_e\n", + "\t- R_EX_arab__L_e\n", + "\t- R_EX_f6p_e\n", + "\t- R_EX_fald_e\n", + "\t- R_EX_fe2_e\n", + "\t- R_EX_arg__L_e\n", + "\t- R_EX_ascb__L_e\n", + "\t- R_EX_asn__L_e\n", + "\t- R_EX_fe3dcit_e\n", + "\t- R_EX_fe3dhbzs_e\n", + "\t- R_EX_fe3hox_e\n", + "\t- R_EX_aso3_e\n", + "\t- R_EX_asp__L_e\n", + "\t- R_EX_fe3_e\n", + "\t- R_EX_fecrm_e\n", + "\t- R_EX_ala_B_e\n", + "\t- R_EX_butso3_e\n", + "\t- R_EX_feenter_e\n", + "\t- R_EX_but_e\n", + "\t- R_EX_ca2_e\n", + "\t- R_EX_feoxam_e\n", + "\t- R_EX_for_e\n", + "\t- R_EX_frulys_e\n", + "\t- R_EX_fru_e\n", + "\t- R_EX_fruur_e\n", + "\t- R_EX_cbi_e\n", + "\t- R_EX_cbl1_e\n", + "\t- R_EX_cd2_e\n", + "\t- R_EX_cgly_e\n", + "\t- R_EX_chol_e\n", + "\t- R_EX_cit_e\n", + "\t- R_EX_cl_e\n", + "\t- R_EX_cmp_e\n", + "\t- R_EX_co2_e\n", + "\t- R_EX_cobalt2_e\n", + "\t- R_EX_fuc__L_e\n", + "\t- R_EX_fum_e\n", + "\t- R_EX_colipa_e\n", + "\t- R_EX_cpgn_e\n", + "\t- R_EX_crn_e\n", + "\t- R_EX_csn_e\n", + "\t- R_EX_g1p_e\n", + "\t- R_EX_g3pc_e\n", + "\t- R_EX_cu2_e\n", + "\t- R_EX_tre_e\n", + "\t- R_EX_g3pe_e\n", + "\t- R_EX_g3pg_e\n", + "\t- R_EX_g3pi_e\n", + "\t- R_EX_g3ps_e\n", + "\t- R_EX_g6p_e\n", + "\t- R_EX_gal1p_e\n", + "\t- R_EX_lcts_e\n", + "\t- R_EX_leu__L_e\n", + "\t- R_EX_trp__L_e\n", + "\t- R_EX_lac__L_e\n", + "\t- R_EX_gal_bD_e\n", + "\t- R_EX_tsul_e\n", + "\t- R_EX_galctn__L_e\n", + "\t- R_EX_galctn__D_e\n", + "\t- R_EX_lys__L_e\n", + "\t- R_EX_lyx__L_e\n", + "\t- R_EX_mal__D_e\n", + "\t- R_EX_mal__L_e\n", + "\t- R_EX_malthx_e\n", + "\t- R_EX_maltpt_e\n", + "\t- R_EX_malt_e\n", + "\t- R_EX_galct__D_e\n", + "\t- R_EX_gal_e\n", + "\t- R_EX_galt_e\n", + "\t- R_EX_galur_e\n", + "\t- R_EX_gam6p_e\n", + "\t- R_EX_gam_e\n", + "\t- R_EX_malttr_e\n", + "\t- R_EX_maltttr_e\n", + "\t- R_EX_ttdca_e\n", + "\t- R_EX_man6p_e\n", + "\t- R_EX_manglyc_e\n", + "\t- R_EX_gbbtn_e\n", + "\t- R_EX_gdp_e\n", + "\t- R_EX_glcn_e\n", + "\t- R_EX_glcr_e\n", + "\t- R_EX_man_e\n", + "\t- R_EX_melib_e\n", + "\t- R_EX_glc__D_e\n", + "\t- R_EX_ttdcea_e\n", + "\t- R_EX_glcur1p_e\n", + "\t- R_EX_met__D_e\n", + "\t- R_EX_metsox_S__L_e\n", + "\t- R_EX_glcur_e\n", + "\t- R_EX_tungs_e\n", + "\t- R_EX_gln__L_e\n", + "\t- R_EX_metsox_R__L_e\n", + "\t- R_EX_met__L_e\n", + "\t- R_EX_glu__L_e\n", + "\t- R_EX_mg2_e\n", + "\t- R_EX_glyald_e\n", + "\t- R_EX_tym_e\n", + "\t- R_EX_glyb_e\n", + "\t- R_EX_minohp_e\n", + "\t- R_EX_mmet_e\n", + "\t- R_EX_glyc2p_e\n", + "\t- R_EX_tyrp_e\n", + "\t- R_EX_glyc3p_e\n", + "\t- R_EX_glyc__R_e\n", + "\t- R_EX_glyclt_e\n", + "\t- R_EX_glyc_e\n", + "\t- R_EX_gly_e\n", + "\t- R_EX_mnl_e\n", + "\t- R_EX_mn2_e\n", + "\t- R_EX_mobd_e\n", + "\t- R_EX_mso3_e\n", + "\t- R_EX_n2o_e\n", + "\t- R_EX_nac_e\n", + "\t- R_EX_na1_e\n", + "\t- R_EX_gmp_e\n", + "\t- R_EX_gsn_e\n", + "\t- R_EX_gthox_e\n", + "\t- R_EX_nh4_e\n", + "\t- R_EX_ni2_e\n", + "\t- R_EX_gthrd_e\n", + "\t- R_EX_nmn_e\n", + "\t- R_EX_gtp_e\n", + "\t- R_EX_no2_e\n", + "\t- R_EX_tyr__L_e\n", + "\t- R_EX_no3_e\n", + "\t- R_EX_no_e\n", + "\t- R_EX_h2o2_e\n", + "\t- R_EX_h2o_e\n", + "\t- R_EX_h2s_e\n", + "\t- R_EX_h2_e\n", + "\t- R_EX_o2s_e\n", + "\t- R_EX_uacgam_e\n", + "\t- R_EX_o2_e\n", + "\t- R_EX_ocdca_e\n", + "\t- R_EX_ocdcea_e\n", + "\t- R_EX_3hcinnm_e\n", + "\t- R_EX_udpacgal_e\n", + "\t- R_EX_hdca_e\n", + "\t- R_EX_hdcea_e\n", + "\t- R_EX_octa_e\n", + "\t- R_EX_orn_e\n", + "\t- R_EX_orot_e\n", + "\t- R_EX_pacald_e\n", + "\t- R_EX_peamn_e\n", + "\t- R_EX_pheme_e\n", + "\t- R_EX_phe__L_e\n", + "\t- R_EX_pi_e\n", + "\t- R_EX_hg2_e\n", + "\t- R_EX_his__L_e\n", + "\t- R_EX_hom__L_e\n", + "\t- R_EX_3hpppn_e\n", + "\t- R_EX_h_e\n", + "\t- R_EX_hxa_e\n", + "\t- R_EX_hxan_e\n", + "\t- R_EX_pnto__R_e\n", + "\t- R_EX_ppal_e\n", + "\t- R_EX_ppa_e\n", + "\t- R_EX_idon__L_e\n", + "\t- R_EX_udpgal_e\n", + "\t- R_EX_ile__L_e\n", + "\t- R_EX_imp_e\n", + "\t- R_EX_indole_e\n", + "\t- R_EX_inost_e\n", + "\t- R_EX_pppn_e\n", + "\t- R_EX_ppt_e\n", + "\t- R_EX_progly_e\n", + "\t- R_EX_pro__L_e\n", + "\t- R_EX_isetac_e\n", + "\t- R_EX_k_e\n", + "\t- R_EX_udpglcur_e\n", + "\t- R_EX_psclys_e\n", + "\t- R_EX_pser__L_e\n", + "\t- R_EX_udpg_e\n", + "\t- R_EX_ptrc_e\n", + "\t- R_EX_pyr_e\n", + "\t- R_EX_ump_e\n", + "\t- R_EX_r5p_e\n", + "\t- R_EX_ura_e\n", + "\t- R_EX_rib__D_e\n", + "\t- R_EX_rmn_e\n", + "\t- R_EX_uri_e\n", + "\t- R_EX_sbt__D_e\n", + "\t- R_EX_ser__L_e\n", + "\t- R_EX_skm_e\n", + "\t- R_EX_so2_e\n", + "\t- R_EX_so3_e\n", + "\t- R_EX_so4_e\n", + "\t- R_EX_spmd_e\n", + "\t- R_EX_val__L_e\n", + "\t- R_EX_succ_e\n", + "\t- R_EX_sulfac_e\n", + "\t- R_EX_tartr__L_e\n", + "\t- R_EX_xan_e\n", + "\t- R_EX_taur_e\n", + "\t- R_EX_tcynt_e\n", + "\t- R_EX_xmp_e\n", + "\t- R_EX_thymd_e\n", + "\t- R_EX_thm_e\n", + "\t- R_EX_thrp_e\n", + "\t- R_EX_xtsn_e\n", + "\t- R_EX_thr__L_e\n", + "\t- R_EX_thym_e\n", + "\t- R_EX_tmao_e\n", + "\t- R_EX_xyl__D_e\n", + "\t- R_EX_tma_e\n", + "\t- R_EX_xylu__L_e\n", + "\t- R_EX_zn2_e\n", + "\t- R_EX_urea_e\n", + "\t- R_EX_fe_e\u001b[0m\n", + "File saved at: ../../data/sbml_corrected/iPC815.xml\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: iEC1344_C\n", + "\u001b[0;93mWARNING : \n", + " - R_CAT: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_DHPTDNR: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_DHPTDNRN: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_FHL: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SPODM: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SPODMpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCASPtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCFUMtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCMALtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCTARTtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + " - R_EDTXSCOF is multiple (more than 1) metabolites import/export reaction. \n", + "\tDeleted as it is an import reaction.\n", + " - R_DURAD: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_34HPPOR: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_CHOLSH: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_CHDLDH: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_DURAD2: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_HPA3MOFAD: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + "\u001b[0m\n", + "\n", + "\n", + "\u001b[0;36m############################################\n", + "############################################\n", + " WRITING SBML FILE \n", + "############################################\n", + "############################################\n", + "\u001b[0m\n", + "\u001b[0;93mWARNING : IMPORT REACTION REMOVED BY DEFAULT\u001b[0m\n", + "\u001b[0;93mWARNING : If you want to keep import reaction\n", + "use option -kir / --keep-import-reactions\u001b[0m\n", + "\u001b[0;93mWARNING : Reaction with tag reversible modified:\n", + "\t- R_DURAD\n", + "\t- R_34HPPOR\n", + "\t- R_CHOLSH\n", + "\t- R_CHDLDH\n", + "\t- R_DURAD2\n", + "\t- R_HPA3MOFAD\u001b[0m\n", + "\u001b[0;93mWARNING : Reaction with Reactants and Products exchanged:\n", + "\t- R_DURAD\n", + "\t- R_34HPPOR\n", + "\t- R_CHOLSH\n", + "\t- R_CHDLDH\n", + "\t- R_DURAD2\n", + "\t- R_HPA3MOFAD\u001b[0m\n", + "\u001b[0;93mWARNING : Import reaction removed:\n", + "\t- R_EX_cm_e\n", + "\t- R_EX_cmp_e\n", + "\t- R_EX_co2_e\n", + "\t- R_EX_cobalt2_e\n", + "\t- R_EX_colipa_e\n", + "\t- R_EX_colipap_e\n", + "\t- R_EX_cpgn_e\n", + "\t- R_EX_cpgn_un_e\n", + "\t- R_EX_crn_e\n", + "\t- R_EX_crn__D_e\n", + "\t- R_EX_csn_e\n", + "\t- R_EX_glc__D_e\n", + "\t- R_EX_glcn_e\n", + "\t- R_EX_cu_e\n", + "\t- R_EX_glcr_e\n", + "\t- R_DM_4crsol_c\n", + "\t- R_EX_glcur_e\n", + "\t- R_EX_glcur1p_e\n", + "\t- R_EX_cu2_e\n", + "\t- R_EX_gln__L_e\n", + "\t- R_EX_glu__L_e\n", + "\t- R_DM_5drib_c\n", + "\t- R_EX_cyan_e\n", + "\t- R_DM_aacald_c\n", + "\t- R_DM_amob_c\n", + "\t- R_DM_mththf_c\n", + "\t- R_DM_oxam_c\n", + "\t- R_EX_gly_e\n", + "\t- R_EX_glyald_e\n", + "\t- R_EX_glyb_e\n", + "\t- R_EX_glyc_e\n", + "\t- R_EX_glyc__R_e\n", + "\t- R_EX_glyc2p_e\n", + "\t- R_EX_glyc3p_e\n", + "\t- R_EX_glyclt_e\n", + "\t- R_EX_gmp_e\n", + "\t- R_EX_gsn_e\n", + "\t- R_EX_gthox_e\n", + "\t- R_EX_gthrd_e\n", + "\t- R_EX_gtp_e\n", + "\t- R_EX_gua_e\n", + "\t- R_EX_h_e\n", + "\t- R_EX_h2_e\n", + "\t- R_EX_h2o_e\n", + "\t- R_EX_12ppd__R_e\n", + "\t- R_EX_12ppd__S_e\n", + "\t- R_EX_14glucan_e\n", + "\t- R_EX_15dap_e\n", + "\t- R_EX_23camp_e\n", + "\t- R_EX_23ccmp_e\n", + "\t- R_EX_23cgmp_e\n", + "\t- R_EX_23cump_e\n", + "\t- R_EX_23dappa_e\n", + "\t- R_EX_26dap__M_e\n", + "\t- R_EX_2ddglcn_e\n", + "\t- R_EX_h2o2_e\n", + "\t- R_EX_h2s_e\n", + "\t- R_EX_hacolipa_e\n", + "\t- R_EX_halipa_e\n", + "\t- R_EX_hdca_e\n", + "\t- R_EX_34dhpac_e\n", + "\t- R_EX_3amp_e\n", + "\t- R_EX_hdcea_e\n", + "\t- R_EX_3cmp_e\n", + "\t- R_EX_cynt_e\n", + "\t- R_EX_3gmp_e\n", + "\t- R_EX_3hcinnm_e\n", + "\t- R_EX_3hpp_e\n", + "\t- R_EX_3hpppn_e\n", + "\t- R_EX_3ump_e\n", + "\t- R_EX_4abut_e\n", + "\t- R_EX_hg2_e\n", + "\t- R_EX_his__L_e\n", + "\t- R_EX_hom__L_e\n", + "\t- R_EX_hxa_e\n", + "\t- R_EX_hxan_e\n", + "\t- R_EX_4hoxpacd_e\n", + "\t- R_EX_5dglcn_e\n", + "\t- R_EX_5mtr_e\n", + "\t- R_EX_LalaDglu_e\n", + "\t- R_EX_cys__D_e\n", + "\t- R_EX_LalaDgluMdap_e\n", + "\t- R_EX_cys__L_e\n", + "\t- R_EX_LalaDgluMdapDala_e\n", + "\t- R_EX_cytd_e\n", + "\t- R_EX_LalaLglu_e\n", + "\t- R_EX_ac_e\n", + "\t- R_EX_idon__L_e\n", + "\t- R_EX_ile__L_e\n", + "\t- R_EX_dad_2_e\n", + "\t- R_EX_imp_e\n", + "\t- R_EX_indole_e\n", + "\t- R_EX_acac_e\n", + "\t- R_EX_acald_e\n", + "\t- R_EX_acgal_e\n", + "\t- R_EX_inost_e\n", + "\t- R_EX_damp_e\n", + "\t- R_EX_ins_e\n", + "\t- R_EX_isetac_e\n", + "\t- R_EX_acgal1p_e\n", + "\t- R_EX_acgam_e\n", + "\t- R_EX_k_e\n", + "\t- R_EX_acgam1p_e\n", + "\t- R_EX_acmana_e\n", + "\t- R_EX_acmum_e\n", + "\t- R_EX_acnam_e\n", + "\t- R_EX_kdo2lipid4_e\n", + "\t- R_EX_lac__D_e\n", + "\t- R_EX_lac__L_e\n", + "\t- R_EX_acolipa_e\n", + "\t- R_EX_lcts_e\n", + "\t- R_EX_acser_e\n", + "\t- R_EX_dca_e\n", + "\t- R_EX_ade_e\n", + "\t- R_EX_adn_e\n", + "\t- R_EX_adocbl_e\n", + "\t- R_EX_leu__L_e\n", + "\t- R_EX_lipa_e\n", + "\t- R_EX_lipa_cold_e\n", + "\t- R_EX_ag_e\n", + "\t- R_EX_dcmp_e\n", + "\t- R_EX_agm_e\n", + "\t- R_EX_lipoate_e\n", + "\t- R_EX_dcyt_e\n", + "\t- R_EX_lys__L_e\n", + "\t- R_EX_lyx__L_e\n", + "\t- R_EX_akg_e\n", + "\t- R_EX_ala_B_e\n", + "\t- R_EX_ala__D_e\n", + "\t- R_EX_mal__D_e\n", + "\t- R_EX_ala__L_e\n", + "\t- R_EX_mal__L_e\n", + "\t- R_EX_ddca_e\n", + "\t- R_EX_malt_e\n", + "\t- R_EX_malthx_e\n", + "\t- R_EX_alaala_e\n", + "\t- R_EX_all__D_e\n", + "\t- R_EX_alltn_e\n", + "\t- R_EX_maltpt_e\n", + "\t- R_EX_dgmp_e\n", + "\t- R_EX_malttr_e\n", + "\t- R_EX_maltttr_e\n", + "\t- R_EX_man_e\n", + "\t- R_EX_man6p_e\n", + "\t- R_EX_manglyc_e\n", + "\t- R_EX_amp_e\n", + "\t- R_EX_anhgm_e\n", + "\t- R_EX_arab__L_e\n", + "\t- R_EX_arbt_e\n", + "\t- R_EX_arbtn_e\n", + "\t- R_EX_melib_e\n", + "\t- R_EX_meoh_e\n", + "\t- R_EX_arbtn_fe3_e\n", + "\t- R_EX_met__D_e\n", + "\t- R_EX_arg__L_e\n", + "\t- R_EX_dgsn_e\n", + "\t- R_EX_ascb__L_e\n", + "\t- R_EX_asn__L_e\n", + "\t- R_EX_aso3_e\n", + "\t- R_EX_met__L_e\n", + "\t- R_EX_metsox_R__L_e\n", + "\t- R_EX_metsox_S__L_e\n", + "\t- R_EX_mg2_e\n", + "\t- R_EX_mincyc_e\n", + "\t- R_EX_asp__L_e\n", + "\t- R_EX_btn_e\n", + "\t- R_EX_dha_e\n", + "\t- R_EX_but_e\n", + "\t- R_EX_minohp_e\n", + "\t- R_EX_mmet_e\n", + "\t- R_EX_mn2_e\n", + "\t- R_EX_butso3_e\n", + "\t- R_EX_dimp_e\n", + "\t- R_EX_ca2_e\n", + "\t- R_EX_cbi_e\n", + "\t- R_EX_mnl_e\n", + "\t- R_EX_mobd_e\n", + "\t- R_EX_mso3_e\n", + "\t- R_EX_din_e\n", + "\t- R_EX_n2o_e\n", + "\t- R_EX_na1_e\n", + "\t- R_EX_nac_e\n", + "\t- R_EX_nh4_e\n", + "\t- R_EX_cbl1_e\n", + "\t- R_EX_cd2_e\n", + "\t- R_EX_cgly_e\n", + "\t- R_EX_chol_e\n", + "\t- R_EX_chtbs_e\n", + "\t- R_EX_cit_e\n", + "\t- R_EX_cl_e\n", + "\t- R_EX_ni2_e\n", + "\t- R_EX_nmn_e\n", + "\t- R_EX_dms_e\n", + "\t- R_EX_no_e\n", + "\t- R_EX_no2_e\n", + "\t- R_EX_dmso_e\n", + "\t- R_EX_no3_e\n", + "\t- R_EX_novbcn_e\n", + "\t- R_EX_dopa_e\n", + "\t- R_EX_o16a4colipa_e\n", + "\t- R_EX_doxrbcn_e\n", + "\t- R_EX_o2_e\n", + "\t- R_EX_o2s_e\n", + "\t- R_EX_val__L_e\n", + "\t- R_EX_dtmp_e\n", + "\t- R_EX_xan_e\n", + "\t- R_EX_ocdca_e\n", + "\t- R_EX_ocdcea_e\n", + "\t- R_EX_octa_e\n", + "\t- R_EX_xmp_e\n", + "\t- R_EX_xtsn_e\n", + "\t- R_EX_orn_e\n", + "\t- R_EX_dump_e\n", + "\t- R_EX_orot_e\n", + "\t- R_EX_pacald_e\n", + "\t- R_EX_xyl__D_e\n", + "\t- R_EX_xylu__L_e\n", + "\t- R_EX_zn2_e\n", + "\t- R_EX_peamn_e\n", + "\t- R_EX_phe__L_e\n", + "\t- R_EX_pheme_e\n", + "\t- R_EX_pi_e\n", + "\t- R_EX_pnto__R_e\n", + "\t- R_EX_ppa_e\n", + "\t- R_EX_ppal_e\n", + "\t- R_EX_pppn_e\n", + "\t- R_EX_duri_e\n", + "\t- R_EX_ppt_e\n", + "\t- R_EX_pro__L_e\n", + "\t- R_EX_progly_e\n", + "\t- R_EX_psclys_e\n", + "\t- R_EX_eca4colipa_e\n", + "\t- R_EX_pser__L_e\n", + "\t- R_EX_ptrc_e\n", + "\t- R_EX_pydam_e\n", + "\t- R_EX_pydx_e\n", + "\t- R_EX_pydxn_e\n", + "\t- R_EX_pyr_e\n", + "\t- R_EX_quin_e\n", + "\t- R_EX_r5p_e\n", + "\t- R_EX_rfamp_e\n", + "\t- R_EX_rib__D_e\n", + "\t- R_EX_rmn_e\n", + "\t- R_EX_enlipa_e\n", + "\t- R_EX_sbt__D_e\n", + "\t- R_EX_sel_e\n", + "\t- R_EX_ser__D_e\n", + "\t- R_EX_ser__L_e\n", + "\t- R_EX_enter_e\n", + "\t- R_EX_skm_e\n", + "\t- R_EX_slnt_e\n", + "\t- R_EX_etha_e\n", + "\t- R_EX_so2_e\n", + "\t- R_EX_so3_e\n", + "\t- R_EX_ethso3_e\n", + "\t- R_EX_so4_e\n", + "\t- R_EX_spmd_e\n", + "\t- R_EX_succ_e\n", + "\t- R_EX_sucr_e\n", + "\t- R_EX_sulfac_e\n", + "\t- R_EX_etoh_e\n", + "\t- R_EX_tartr__D_e\n", + "\t- R_EX_tartr__L_e\n", + "\t- R_EX_f6p_e\n", + "\t- R_EX_taur_e\n", + "\t- R_EX_tcynt_e\n", + "\t- R_EX_thm_e\n", + "\t- R_EX_thr__L_e\n", + "\t- R_EX_fald_e\n", + "\t- R_EX_thrp_e\n", + "\t- R_EX_thym_e\n", + "\t- R_EX_thymd_e\n", + "\t- R_EX_fe2_e\n", + "\t- R_EX_tma_e\n", + "\t- R_EX_tmao_e\n", + "\t- R_EX_fe3_e\n", + "\t- R_EX_tre_e\n", + "\t- R_EX_trp__L_e\n", + "\t- R_EX_tsul_e\n", + "\t- R_EX_ttdca_e\n", + "\t- R_EX_ttdcea_e\n", + "\t- R_EX_ttrcyc_e\n", + "\t- R_EX_tungs_e\n", + "\t- R_EX_tym_e\n", + "\t- R_EX_fe3dcit_e\n", + "\t- R_EX_tyr__L_e\n", + "\t- R_EX_tyrp_e\n", + "\t- R_EX_uacgam_e\n", + "\t- R_EX_udpacgal_e\n", + "\t- R_EX_udpg_e\n", + "\t- R_EX_udpgal_e\n", + "\t- R_EX_fe3dhbzs_e\n", + "\t- R_EX_udpglcur_e\n", + "\t- R_EX_ump_e\n", + "\t- R_EX_ura_e\n", + "\t- R_EX_fe3hox_e\n", + "\t- R_EX_urea_e\n", + "\t- R_EX_uri_e\n", + "\t- R_EX_fe3hox_un_e\n", + "\t- R_EX_fecrm_e\n", + "\t- R_EX_fecrm_un_e\n", + "\t- R_EX_feenter_e\n", + "\t- R_EX_feoxam_e\n", + "\t- R_EX_feoxam_un_e\n", + "\t- R_EX_for_e\n", + "\t- R_EX_fru_e\n", + "\t- R_EX_frulys_e\n", + "\t- R_EX_fruur_e\n", + "\t- R_EX_fuc__L_e\n", + "\t- R_EX_fum_e\n", + "\t- R_EX_fusa_e\n", + "\t- R_EX_g1p_e\n", + "\t- R_EX_g3pc_e\n", + "\t- R_EX_g3pe_e\n", + "\t- R_EX_g3pg_e\n", + "\t- R_EX_g3pi_e\n", + "\t- R_EX_g3ps_e\n", + "\t- R_EX_g6p_e\n", + "\t- R_EX_gal_e\n", + "\t- R_EX_gal_bD_e\n", + "\t- R_EX_gal1p_e\n", + "\t- R_EX_galct__D_e\n", + "\t- R_EX_galctn__D_e\n", + "\t- R_EX_galctn__L_e\n", + "\t- R_EX_galt_e\n", + "\t- R_EX_galur_e\n", + "\t- R_EX_gam_e\n", + "\t- R_EX_gam6p_e\n", + "\t- R_EX_gbbtn_e\n", + "\t- R_EX_gdp_e\n", + "\t- R_EX_3hoxpac_e\n", + "\t- R_EX_3ntym_e\n", + "\t- R_EX_4hoxpac_e\n", + "\t- R_EX_6apa_e\n", + "\t- R_EX_cellb_e\n", + "\t- R_EX_galam_e\n", + "\t- R_EX_lipidA_core_e\n", + "\t- R_EX_o6a4colipa_e\n", + "\t- R_EX_peng_e\n", + "\t- R_EX_raffin_e\n", + "\t- R_EX_salcn_e\n", + "\t- R_DM_4hba_c\n", + "\t- R_DM_hmfurn_c\n", + "\t- R_EDTXSCOF\n", + "\t- R_EX_arab__D_e\u001b[0m\n", + "File saved at: ../../data/sbml_corrected/iEC1344_C.xml\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: iAF1260b\n", + "\u001b[0;93mWARNING : \n", + " - R_CAT: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_FHL: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SPODM: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SPODMpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\u001b[0m\n", + "\n", + "\n", + "\u001b[0;36m############################################\n", + "############################################\n", + " WRITING SBML FILE \n", + "############################################\n", + "############################################\n", + "\u001b[0m\n", + "\u001b[0;93mWARNING : IMPORT REACTION REMOVED BY DEFAULT\u001b[0m\n", + "\u001b[0;93mWARNING : If you want to keep import reaction\n", + "use option -kir / --keep-import-reactions\u001b[0m\n", + "\u001b[0;93mWARNING : Import reaction removed:\n", + "\t- R_DM_4hba_c\n", + "\t- R_DM_5drib_c\n", + "\t- R_DM_aacald_c\n", + "\t- R_DM_hmfurn_c\n", + "\t- R_DM_oxam_c\n", + "\t- R_EX_cl_e\n", + "\t- R_EX_cmp_e\n", + "\t- R_EX_co2_e\n", + "\t- R_EX_cobalt2_e\n", + "\t- R_EX_colipa_e\n", + "\t- R_EX_cpgn_e\n", + "\t- R_EX_cpgn_un_e\n", + "\t- R_EX_crn_e\n", + "\t- R_EX_csn_e\n", + "\t- R_EX_cu2_e\n", + "\t- R_EX_cu_e\n", + "\t- R_EX_cyan_e\n", + "\t- R_EX_cynt_e\n", + "\t- R_EX_cys__D_e\n", + "\t- R_EX_cys__L_e\n", + "\t- R_EX_cytd_e\n", + "\t- R_EX_dad_2_e\n", + "\t- R_EX_damp_e\n", + "\t- R_EX_dca_e\n", + "\t- R_EX_dcmp_e\n", + "\t- R_EX_dcyt_e\n", + "\t- R_EX_ddca_e\n", + "\t- R_EX_dgmp_e\n", + "\t- R_EX_dgsn_e\n", + "\t- R_EX_dha_e\n", + "\t- R_EX_dimp_e\n", + "\t- R_EX_din_e\n", + "\t- R_EX_dms_e\n", + "\t- R_EX_dmso_e\n", + "\t- R_EX_dopa_e\n", + "\t- R_EX_dtmp_e\n", + "\t- R_EX_dump_e\n", + "\t- R_EX_duri_e\n", + "\t- R_EX_eca4colipa_e\n", + "\t- R_EX_enlipa_e\n", + "\t- R_EX_enter_e\n", + "\t- R_EX_etha_e\n", + "\t- R_EX_ethso3_e\n", + "\t- R_EX_glcur_e\n", + "\t- R_EX_etoh_e\n", + "\t- R_EX_gln__L_e\n", + "\t- R_EX_f6p_e\n", + "\t- R_EX_fald_e\n", + "\t- R_EX_fe2_e\n", + "\t- R_EX_glu__L_e\n", + "\t- R_EX_gly_e\n", + "\t- R_EX_glyald_e\n", + "\t- R_EX_glyb_e\n", + "\t- R_EX_fe3_e\n", + "\t- R_EX_fe3dcit_e\n", + "\t- R_EX_fe3dhbzs_e\n", + "\t- R_EX_fe3hox_e\n", + "\t- R_EX_fe3hox_un_e\n", + "\t- R_EX_fecrm_e\n", + "\t- R_EX_glyc2p_e\n", + "\t- R_EX_glyc3p_e\n", + "\t- R_EX_glyc_e\n", + "\t- R_EX_glyc__R_e\n", + "\t- R_EX_glyclt_e\n", + "\t- R_EX_gmp_e\n", + "\t- R_EX_gsn_e\n", + "\t- R_EX_gthox_e\n", + "\t- R_EX_gthrd_e\n", + "\t- R_EX_gtp_e\n", + "\t- R_EX_gua_e\n", + "\t- R_EX_fecrm_un_e\n", + "\t- R_EX_feenter_e\n", + "\t- R_EX_feoxam_e\n", + "\t- R_EX_feoxam_un_e\n", + "\t- R_EX_for_e\n", + "\t- R_EX_fru_e\n", + "\t- R_EX_frulys_e\n", + "\t- R_EX_h2_e\n", + "\t- R_EX_h2o2_e\n", + "\t- R_EX_h2o_e\n", + "\t- R_EX_fruur_e\n", + "\t- R_EX_fuc__L_e\n", + "\t- R_EX_h2s_e\n", + "\t- R_EX_fum_e\n", + "\t- R_EX_g1p_e\n", + "\t- R_EX_g3pc_e\n", + "\t- R_EX_h_e\n", + "\t- R_EX_hacolipa_e\n", + "\t- R_EX_halipa_e\n", + "\t- R_EX_hdca_e\n", + "\t- R_EX_g3pe_e\n", + "\t- R_EX_g3pg_e\n", + "\t- R_EX_hdcea_e\n", + "\t- R_EX_12ppd__R_e\n", + "\t- R_EX_hg2_e\n", + "\t- R_EX_his__L_e\n", + "\t- R_EX_hom__L_e\n", + "\t- R_EX_hxa_e\n", + "\t- R_EX_hxan_e\n", + "\t- R_EX_idon__L_e\n", + "\t- R_EX_g3pi_e\n", + "\t- R_EX_g3ps_e\n", + "\t- R_EX_g6p_e\n", + "\t- R_EX_gal1p_e\n", + "\t- R_EX_gal_e\n", + "\t- R_EX_gal_bD_e\n", + "\t- R_EX_galct__D_e\n", + "\t- R_EX_galctn__D_e\n", + "\t- R_EX_ile__L_e\n", + "\t- R_EX_galctn__L_e\n", + "\t- R_EX_12ppd__S_e\n", + "\t- R_EX_galt_e\n", + "\t- R_EX_galur_e\n", + "\t- R_EX_gam6p_e\n", + "\t- R_EX_gam_e\n", + "\t- R_EX_imp_e\n", + "\t- R_EX_indole_e\n", + "\t- R_EX_inost_e\n", + "\t- R_EX_ins_e\n", + "\t- R_EX_gbbtn_e\n", + "\t- R_EX_14glucan_e\n", + "\t- R_EX_gdp_e\n", + "\t- R_EX_glc__D_e\n", + "\t- R_EX_glcn_e\n", + "\t- R_EX_isetac_e\n", + "\t- R_EX_k_e\n", + "\t- R_EX_kdo2lipid4_e\n", + "\t- R_EX_glcr_e\n", + "\t- R_EX_glcur1p_e\n", + "\t- R_EX_lac__D_e\n", + "\t- R_EX_15dap_e\n", + "\t- R_EX_lac__L_e\n", + "\t- R_EX_23camp_e\n", + "\t- R_EX_lcts_e\n", + "\t- R_EX_leu__L_e\n", + "\t- R_EX_lipa_e\n", + "\t- R_EX_lipa_cold_e\n", + "\t- R_EX_lys__L_e\n", + "\t- R_EX_o2s_e\n", + "\t- R_EX_ocdca_e\n", + "\t- R_EX_ocdcea_e\n", + "\t- R_EX_octa_e\n", + "\t- R_EX_orn_e\n", + "\t- R_EX_orot_e\n", + "\t- R_EX_pacald_e\n", + "\t- R_EX_lyx__L_e\n", + "\t- R_EX_mal__D_e\n", + "\t- R_EX_mal__L_e\n", + "\t- R_EX_peamn_e\n", + "\t- R_EX_23ccmp_e\n", + "\t- R_EX_phe__L_e\n", + "\t- R_EX_pheme_e\n", + "\t- R_EX_pi_e\n", + "\t- R_EX_malt_e\n", + "\t- R_EX_malthx_e\n", + "\t- R_EX_maltpt_e\n", + "\t- R_EX_malttr_e\n", + "\t- R_EX_maltttr_e\n", + "\t- R_EX_pnto__R_e\n", + "\t- R_EX_ppa_e\n", + "\t- R_EX_ppal_e\n", + "\t- R_EX_man6p_e\n", + "\t- R_EX_23cgmp_e\n", + "\t- R_EX_man_e\n", + "\t- R_EX_manglyc_e\n", + "\t- R_EX_melib_e\n", + "\t- R_EX_pppn_e\n", + "\t- R_EX_ppt_e\n", + "\t- R_EX_pro__L_e\n", + "\t- R_EX_progly_e\n", + "\t- R_EX_met__D_e\n", + "\t- R_EX_met__L_e\n", + "\t- R_EX_metsox_R__L_e\n", + "\t- R_EX_metsox_S__L_e\n", + "\t- R_EX_psclys_e\n", + "\t- R_EX_pser__L_e\n", + "\t- R_EX_ptrc_e\n", + "\t- R_EX_pyr_e\n", + "\t- R_EX_mg2_e\n", + "\t- R_EX_minohp_e\n", + "\t- R_EX_r5p_e\n", + "\t- R_EX_mmet_e\n", + "\t- R_EX_23cump_e\n", + "\t- R_EX_mn2_e\n", + "\t- R_EX_mnl_e\n", + "\t- R_EX_rib__D_e\n", + "\t- R_EX_rmn_e\n", + "\t- R_EX_sbt__D_e\n", + "\t- R_EX_mobd_e\n", + "\t- R_EX_ser__D_e\n", + "\t- R_EX_mso3_e\n", + "\t- R_EX_23dappa_e\n", + "\t- R_EX_n2o_e\n", + "\t- R_EX_na1_e\n", + "\t- R_EX_ser__L_e\n", + "\t- R_EX_skm_e\n", + "\t- R_EX_nac_e\n", + "\t- R_EX_so2_e\n", + "\t- R_EX_26dap__M_e\n", + "\t- R_EX_so3_e\n", + "\t- R_EX_nh4_e\n", + "\t- R_EX_ni2_e\n", + "\t- R_EX_so4_e\n", + "\t- R_EX_nmn_e\n", + "\t- R_EX_2ddglcn_e\n", + "\t- R_EX_no2_e\n", + "\t- R_EX_no3_e\n", + "\t- R_EX_no_e\n", + "\t- R_EX_spmd_e\n", + "\t- R_EX_succ_e\n", + "\t- R_EX_sucr_e\n", + "\t- R_EX_sulfac_e\n", + "\t- R_EX_tartr__L_e\n", + "\t- R_EX_o16a4colipa_e\n", + "\t- R_EX_o2_e\n", + "\t- R_EX_taur_e\n", + "\t- R_EX_34dhpac_e\n", + "\t- R_EX_tcynt_e\n", + "\t- R_EX_thm_e\n", + "\t- R_EX_thr__L_e\n", + "\t- R_EX_thrp_e\n", + "\t- R_EX_3amp_e\n", + "\t- R_EX_thym_e\n", + "\t- R_EX_thymd_e\n", + "\t- R_EX_tma_e\n", + "\t- R_EX_tmao_e\n", + "\t- R_EX_tre_e\n", + "\t- R_EX_3cmp_e\n", + "\t- R_EX_trp__L_e\n", + "\t- R_EX_tsul_e\n", + "\t- R_EX_ttdca_e\n", + "\t- R_EX_ttdcea_e\n", + "\t- R_EX_tungs_e\n", + "\t- R_EX_tym_e\n", + "\t- R_EX_tyr__L_e\n", + "\t- R_EX_tyrp_e\n", + "\t- R_EX_3gmp_e\n", + "\t- R_EX_uacgam_e\n", + "\t- R_EX_udpacgal_e\n", + "\t- R_EX_udpg_e\n", + "\t- R_EX_udpgal_e\n", + "\t- R_EX_udpglcur_e\n", + "\t- R_EX_3hcinnm_e\n", + "\t- R_EX_ump_e\n", + "\t- R_EX_ura_e\n", + "\t- R_EX_urea_e\n", + "\t- R_EX_uri_e\n", + "\t- R_EX_val__L_e\n", + "\t- R_EX_xan_e\n", + "\t- R_EX_3hpppn_e\n", + "\t- R_EX_xmp_e\n", + "\t- R_EX_xtsn_e\n", + "\t- R_EX_xyl__D_e\n", + "\t- R_EX_xylu__L_e\n", + "\t- R_EX_zn2_e\n", + "\t- R_EX_3ump_e\n", + "\t- R_EX_4abut_e\n", + "\t- R_EX_4hoxpacd_e\n", + "\t- R_EX_5dglcn_e\n", + "\t- R_EX_LalaDgluMdapDala_e\n", + "\t- R_EX_LalaDgluMdap_e\n", + "\t- R_EX_ac_e\n", + "\t- R_EX_acac_e\n", + "\t- R_EX_acald_e\n", + "\t- R_EX_acgal1p_e\n", + "\t- R_EX_acgal_e\n", + "\t- R_EX_acgam1p_e\n", + "\t- R_EX_acgam_e\n", + "\t- R_EX_acmana_e\n", + "\t- R_EX_acmum_e\n", + "\t- R_EX_acnam_e\n", + "\t- R_EX_acolipa_e\n", + "\t- R_EX_acser_e\n", + "\t- R_EX_ade_e\n", + "\t- R_EX_adn_e\n", + "\t- R_EX_adocbl_e\n", + "\t- R_EX_ag_e\n", + "\t- R_EX_agm_e\n", + "\t- R_EX_akg_e\n", + "\t- R_EX_ala_B_e\n", + "\t- R_EX_ala__D_e\n", + "\t- R_EX_ala__L_e\n", + "\t- R_EX_alaala_e\n", + "\t- R_EX_all__D_e\n", + "\t- R_EX_alltn_e\n", + "\t- R_EX_amp_e\n", + "\t- R_EX_anhgm_e\n", + "\t- R_EX_arab__L_e\n", + "\t- R_EX_arbtn_e\n", + "\t- R_EX_arbtn_fe3_e\n", + "\t- R_EX_arg__L_e\n", + "\t- R_EX_ascb__L_e\n", + "\t- R_EX_asn__L_e\n", + "\t- R_EX_aso3_e\n", + "\t- R_EX_asp__L_e\n", + "\t- R_EX_but_e\n", + "\t- R_EX_butso3_e\n", + "\t- R_EX_ca2_e\n", + "\t- R_EX_cbi_e\n", + "\t- R_EX_cbl1_e\n", + "\t- R_EX_cd2_e\n", + "\t- R_EX_cgly_e\n", + "\t- R_EX_chol_e\n", + "\t- R_EX_cit_e\u001b[0m\n", + "File saved at: ../../data/sbml_corrected/iAF1260b.xml\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: iAM_Pv461\n", + "\u001b[0;93mWARNING : - R_ARGLYSex: Reactants and products exchanged.\n", + " Boundaries was: [-1000000.0 ; 0.0]\n", + " - R_RE1342C: Reactants and products exchanged.\n", + " Boundaries was: [-1000000.0 ; 0.0]\n", + "\u001b[0m\n", + "\n", + "\n", + "\u001b[0;36m############################################\n", + "############################################\n", + " WRITING SBML FILE \n", + "############################################\n", + "############################################\n", + "\u001b[0m\n", + "\u001b[0;93mWARNING : IMPORT REACTION REMOVED BY DEFAULT\u001b[0m\n", + "\u001b[0;93mWARNING : If you want to keep import reaction\n", + "use option -kir / --keep-import-reactions\u001b[0m\n", + "\u001b[0;93mWARNING : Reaction with tag reversible modified:\n", + "\t- R_ARGLYSex\n", + "\t- R_RE1342C\u001b[0m\n", + "\u001b[0;93mWARNING : Reaction with Reactants and Products exchanged:\n", + "\t- R_ARGLYSex\n", + "\t- R_RE1342C\u001b[0m\n", + "\u001b[0;93mWARNING : Import reaction removed:\n", + "\t- R_EX_pro__L_e\n", + "\t- R_EX_4abz_e\n", + "\t- R_EX_4ahmmp_e\n", + "\t- R_EX_5fthf_e\n", + "\t- R_EX_Hb_e\n", + "\t- R_EX_ac_e\n", + "\t- R_EX_ade_e\n", + "\t- R_EX_adn_e\n", + "\t- R_EX_adp_e\n", + "\t- R_EX_ala__L_e\n", + "\t- R_EX_amp_e\n", + "\t- R_EX_arachd_e\n", + "\t- R_EX_arg__L_e\n", + "\t- R_EX_asn__L_e\n", + "\t- R_EX_asp__L_e\n", + "\t- R_EX_atp_e\n", + "\t- R_EX_ca2_e\n", + "\t- R_EX_chol_e\n", + "\t- R_EX_cl_e\n", + "\t- R_EX_co2_e\n", + "\t- R_EX_cobalt2_e\n", + "\t- R_EX_csn_e\n", + "\t- R_EX_cu_e\n", + "\t- R_EX_cu2_e\n", + "\t- R_EX_cys__L_e\n", + "\t- R_EX_dha_e\n", + "\t- R_EX_dhpt_e\n", + "\t- R_EX_fe2_e\n", + "\t- R_EX_fol_e\n", + "\t- R_EX_for_e\n", + "\t- R_EX_fru_e\n", + "\t- R_EX_glc__D_e\n", + "\t- R_EX_gln__L_e\n", + "\t- R_EX_glu__L_e\n", + "\t- R_EX_gly_e\n", + "\t- R_EX_glyc_e\n", + "\t- R_EX_gsn_e\n", + "\t- R_EX_gthox_e\n", + "\t- R_EX_gthrd_e\n", + "\t- R_EX_gua_e\n", + "\t- R_EX_h_e\n", + "\t- R_EX_h2o_e\n", + "\t- R_EX_hco3_e\n", + "\t- R_EX_hdca_e\n", + "\t- R_EX_his__L_e\n", + "\t- R_EX_hxan_e\n", + "\t- R_EX_ile__L_e\n", + "\t- R_EX_ins_e\n", + "\t- R_EX_k_e\n", + "\t- R_EX_lac__D_e\n", + "\t- R_EX_lac__L_e\n", + "\t- R_EX_leu__L_e\n", + "\t- R_EX_lipoate_e\n", + "\t- R_EX_lnlc_e\n", + "\t- R_EX_lys__L_e\n", + "\t- R_EX_man_e\n", + "\t- R_EX_met__L_e\n", + "\t- R_EX_mg2_e\n", + "\t- R_EX_mthgxl_e\n", + "\t- R_EX_na1_e\n", + "\t- R_EX_nac_e\n", + "\t- R_EX_ncam_e\n", + "\t- R_EX_nh4_e\n", + "\t- R_EX_ni2_e\n", + "\t- R_EX_o2_e\n", + "\t- R_EX_ocdca_e\n", + "\t- R_EX_ocdcea_e\n", + "\t- R_EX_phe__L_e\n", + "\t- R_EX_pi_e\n", + "\t- R_EX_pnto__R_e\n", + "\t- R_DM_hmz_l\n", + "\t- R_SK_gcald_c\n", + "\t- R_DM_5mdr1p_c\n", + "\t- R_DM_hcys__L_c\n", + "\t- R_DM_saccrp__L_c\n", + "\t- R_EX_Lpipecol_e\n", + "\t- R_EX_4pyrdx_e\n", + "\t- R_DM_pail345p_pf_16_0_18_1_c\n", + "\t- R_DM_mi13456p_c\n", + "\t- R_DM_pail345p_pf_18_0_18_1_c\n", + "\t- R_EX_lgn_e\n", + "\t- R_EX_4hba_e\n", + "\t- R_SK_thbpt4acam_c\n", + "\t- R_SK_dhbpt_c\n", + "\t- R_SK_thbpt_c\n", + "\t- R_SK_no_c\n", + "\t- R_SK_citr__L_c\n", + "\t- R_SK_dxyl5p_c\n", + "\t- R_SK_accoa_h\n", + "\t- R_EX_ptrc_e\n", + "\t- R_EX_spmd_e\n", + "\t- R_DM_oxptn_c\n", + "\t- R_EX_orn_e\n", + "\t- R_EX_chsterol_e\n", + "\t- R_EX_acgam_e\n", + "\t- R_EX_pyr_e\n", + "\t- R_EX_ribflv_e\n", + "\t- R_EX_ser__L_e\n", + "\t- R_EX_thm_e\n", + "\t- R_EX_thr__L_e\n", + "\t- R_EX_thym_e\n", + "\t- R_EX_thymd_e\n", + "\t- R_EX_trp__L_e\n", + "\t- R_EX_ttdca_e\n", + "\t- R_EX_tyr__L_e\n", + "\t- R_EX_ura_e\n", + "\t- R_EX_uri_e\n", + "\t- R_EX_val__L_e\u001b[0m\n", + "File saved at: ../../data/sbml_corrected/iAM_Pv461.xml\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: iSSON_1240\n", + "\u001b[0;93mWARNING : \n", + " - R_CAT: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_DHPTDNR: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_DHPTDNRN: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCFUMtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCMALtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCTARTtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SPODM: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SPODMpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCASPtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_EX_chitob_e: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_CHITPH: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_CHITOBpts: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_URCN: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\u001b[0m\n", + "\n", + "\n", + "\u001b[0;36m############################################\n", + "############################################\n", + " WRITING SBML FILE \n", + "############################################\n", + "############################################\n", + "\u001b[0m\n", + "\u001b[0;93mWARNING : IMPORT REACTION REMOVED BY DEFAULT\u001b[0m\n", + "\u001b[0;93mWARNING : If you want to keep import reaction\n", + "use option -kir / --keep-import-reactions\u001b[0m\n", + "\u001b[0;93mWARNING : Import reaction removed:\n", + "\t- R_EX_cit_e\n", + "\t- R_EX_cm_e\n", + "\t- R_EX_cmp_e\n", + "\t- R_EX_glcr_e\n", + "\t- R_EX_cl_e\n", + "\t- R_EX_co2_e\n", + "\t- R_EX_cobalt2_e\n", + "\t- R_EX_colipa_e\n", + "\t- R_EX_colipap_e\n", + "\t- R_EX_chtbs_e\n", + "\t- R_DM_4crsol_c\n", + "\t- R_DM_5drib_c\n", + "\t- R_DM_aacald_c\n", + "\t- R_DM_amob_c\n", + "\t- R_EX_cpgn_e\n", + "\t- R_EX_cpgn_un_e\n", + "\t- R_DM_mththf_c\n", + "\t- R_EX_glcur_e\n", + "\t- R_DM_oxam_c\n", + "\t- R_EX_crn_e\n", + "\t- R_EX_crn__D_e\n", + "\t- R_EX_csn_e\n", + "\t- R_EX_cu_e\n", + "\t- R_EX_glcur1p_e\n", + "\t- R_EX_cu2_e\n", + "\t- R_EX_cyan_e\n", + "\t- R_EX_12ppd__R_e\n", + "\t- R_EX_12ppd__S_e\n", + "\t- R_EX_cynt_e\n", + "\t- R_EX_cys__D_e\n", + "\t- R_EX_14glucan_e\n", + "\t- R_EX_15dap_e\n", + "\t- R_EX_23camp_e\n", + "\t- R_EX_23ccmp_e\n", + "\t- R_EX_23cgmp_e\n", + "\t- R_EX_23cump_e\n", + "\t- R_EX_23dappa_e\n", + "\t- R_EX_26dap__M_e\n", + "\t- R_EX_2ddglcn_e\n", + "\t- R_EX_34dhpac_e\n", + "\t- R_EX_3amp_e\n", + "\t- R_EX_cys__L_e\n", + "\t- R_EX_cytd_e\n", + "\t- R_EX_dad_2_e\n", + "\t- R_EX_damp_e\n", + "\t- R_EX_dca_e\n", + "\t- R_EX_dcmp_e\n", + "\t- R_EX_dcyt_e\n", + "\t- R_EX_ddca_e\n", + "\t- R_EX_dgmp_e\n", + "\t- R_EX_dgsn_e\n", + "\t- R_EX_dha_e\n", + "\t- R_EX_dimp_e\n", + "\t- R_EX_din_e\n", + "\t- R_EX_3cmp_e\n", + "\t- R_EX_3gmp_e\n", + "\t- R_EX_3hcinnm_e\n", + "\t- R_EX_3hpp_e\n", + "\t- R_EX_3hpppn_e\n", + "\t- R_EX_dms_e\n", + "\t- R_EX_dmso_e\n", + "\t- R_EX_3ump_e\n", + "\t- R_EX_gln__L_e\n", + "\t- R_EX_4abut_e\n", + "\t- R_EX_4hoxpacd_e\n", + "\t- R_EX_dopa_e\n", + "\t- R_EX_doxrbcn_e\n", + "\t- R_EX_5dglcn_e\n", + "\t- R_EX_5mtr_e\n", + "\t- R_EX_dtmp_e\n", + "\t- R_EX_dump_e\n", + "\t- R_EX_duri_e\n", + "\t- R_EX_eca4colipa_e\n", + "\t- R_EX_enlipa_e\n", + "\t- R_EX_LalaDglu_e\n", + "\t- R_EX_LalaDgluMdap_e\n", + "\t- R_EX_LalaDgluMdapDala_e\n", + "\t- R_EX_LalaLglu_e\n", + "\t- R_EX_ac_e\n", + "\t- R_EX_acac_e\n", + "\t- R_EX_enter_e\n", + "\t- R_EX_etha_e\n", + "\t- R_EX_acald_e\n", + "\t- R_EX_ethso3_e\n", + "\t- R_EX_glu__L_e\n", + "\t- R_EX_etoh_e\n", + "\t- R_EX_f6p_e\n", + "\t- R_EX_fald_e\n", + "\t- R_EX_acgal_e\n", + "\t- R_EX_acgal1p_e\n", + "\t- R_EX_acgam_e\n", + "\t- R_EX_acgam1p_e\n", + "\t- R_EX_fe2_e\n", + "\t- R_EX_fe3_e\n", + "\t- R_EX_acmana_e\n", + "\t- R_EX_fe3dcit_e\n", + "\t- R_EX_acmum_e\n", + "\t- R_EX_gly_e\n", + "\t- R_EX_acnam_e\n", + "\t- R_EX_acolipa_e\n", + "\t- R_EX_fe3dhbzs_e\n", + "\t- R_EX_fe3hox_e\n", + "\t- R_EX_fe3hox_un_e\n", + "\t- R_EX_acser_e\n", + "\t- R_EX_fecrm_e\n", + "\t- R_EX_ade_e\n", + "\t- R_EX_adn_e\n", + "\t- R_EX_adocbl_e\n", + "\t- R_EX_ag_e\n", + "\t- R_EX_agm_e\n", + "\t- R_EX_fecrm_un_e\n", + "\t- R_EX_feenter_e\n", + "\t- R_EX_feoxam_e\n", + "\t- R_EX_feoxam_un_e\n", + "\t- R_EX_akg_e\n", + "\t- R_EX_ala_B_e\n", + "\t- R_EX_glyald_e\n", + "\t- R_EX_ala__D_e\n", + "\t- R_EX_ala__L_e\n", + "\t- R_EX_for_e\n", + "\t- R_EX_fru_e\n", + "\t- R_EX_frulys_e\n", + "\t- R_EX_alaala_e\n", + "\t- R_EX_glyb_e\n", + "\t- R_EX_all__D_e\n", + "\t- R_EX_fruur_e\n", + "\t- R_EX_fuc__L_e\n", + "\t- R_EX_alltn_e\n", + "\t- R_EX_fum_e\n", + "\t- R_EX_amp_e\n", + "\t- R_EX_glyc_e\n", + "\t- R_EX_anhgm_e\n", + "\t- R_EX_fusa_e\n", + "\t- R_EX_g1p_e\n", + "\t- R_EX_arab__L_e\n", + "\t- R_EX_glyc__R_e\n", + "\t- R_EX_arbt_e\n", + "\t- R_EX_g3pc_e\n", + "\t- R_EX_g3pe_e\n", + "\t- R_EX_arbtn_e\n", + "\t- R_EX_glyc2p_e\n", + "\t- R_EX_arbtn_fe3_e\n", + "\t- R_EX_g3pg_e\n", + "\t- R_EX_g3pi_e\n", + "\t- R_EX_g3ps_e\n", + "\t- R_EX_g6p_e\n", + "\t- R_EX_arg__L_e\n", + "\t- R_EX_ascb__L_e\n", + "\t- R_EX_asn__L_e\n", + "\t- R_EX_aso3_e\n", + "\t- R_EX_asp__L_e\n", + "\t- R_EX_btn_e\n", + "\t- R_EX_gal_e\n", + "\t- R_EX_gal_bD_e\n", + "\t- R_EX_but_e\n", + "\t- R_EX_glyc3p_e\n", + "\t- R_EX_butso3_e\n", + "\t- R_EX_ca2_e\n", + "\t- R_EX_cbi_e\n", + "\t- R_EX_gal1p_e\n", + "\t- R_EX_galct__D_e\n", + "\t- R_EX_galctn__D_e\n", + "\t- R_EX_galctn__L_e\n", + "\t- R_EX_galt_e\n", + "\t- R_EX_cbl1_e\n", + "\t- R_EX_cd2_e\n", + "\t- R_EX_cgly_e\n", + "\t- R_EX_galur_e\n", + "\t- R_EX_glyclt_e\n", + "\t- R_EX_gam_e\n", + "\t- R_EX_gam6p_e\n", + "\t- R_EX_gbbtn_e\n", + "\t- R_EX_gdp_e\n", + "\t- R_EX_chol_e\n", + "\t- R_EX_glc__D_e\n", + "\t- R_EX_gmp_e\n", + "\t- R_EX_glcn_e\n", + "\t- R_EX_gsn_e\n", + "\t- R_EX_gthox_e\n", + "\t- R_EX_gthrd_e\n", + "\t- R_EX_gtp_e\n", + "\t- R_EX_no_e\n", + "\t- R_EX_no2_e\n", + "\t- R_EX_no3_e\n", + "\t- R_EX_novbcn_e\n", + "\t- R_EX_gua_e\n", + "\t- R_EX_o16a4colipa_e\n", + "\t- R_EX_o2_e\n", + "\t- R_EX_h_e\n", + "\t- R_EX_o2s_e\n", + "\t- R_EX_xmp_e\n", + "\t- R_EX_xtsn_e\n", + "\t- R_EX_ocdca_e\n", + "\t- R_EX_ocdcea_e\n", + "\t- R_EX_xyl__D_e\n", + "\t- R_EX_h2_e\n", + "\t- R_EX_xylu__L_e\n", + "\t- R_EX_zn2_e\n", + "\t- R_EX_octa_e\n", + "\t- R_EX_orn_e\n", + "\t- R_EX_orot_e\n", + "\t- R_EX_pacald_e\n", + "\t- R_EX_h2o_e\n", + "\t- R_EX_peamn_e\n", + "\t- R_EX_phe__L_e\n", + "\t- R_EX_pheme_e\n", + "\t- R_EX_pi_e\n", + "\t- R_EX_pnto__R_e\n", + "\t- R_EX_h2o2_e\n", + "\t- R_EX_ppa_e\n", + "\t- R_EX_ppal_e\n", + "\t- R_EX_pppn_e\n", + "\t- R_EX_ppt_e\n", + "\t- R_EX_pro__L_e\n", + "\t- R_EX_progly_e\n", + "\t- R_EX_psclys_e\n", + "\t- R_EX_pser__L_e\n", + "\t- R_EX_h2s_e\n", + "\t- R_EX_ptrc_e\n", + "\t- R_EX_pydam_e\n", + "\t- R_EX_pydx_e\n", + "\t- R_EX_hacolipa_e\n", + "\t- R_EX_pydxn_e\n", + "\t- R_EX_pyr_e\n", + "\t- R_EX_quin_e\n", + "\t- R_EX_halipa_e\n", + "\t- R_EX_r5p_e\n", + "\t- R_EX_rfamp_e\n", + "\t- R_EX_rib__D_e\n", + "\t- R_EX_rmn_e\n", + "\t- R_EX_sbt__D_e\n", + "\t- R_EX_sel_e\n", + "\t- R_EX_ser__D_e\n", + "\t- R_EX_ser__L_e\n", + "\t- R_EX_hdca_e\n", + "\t- R_EX_skm_e\n", + "\t- R_EX_slnt_e\n", + "\t- R_EX_so2_e\n", + "\t- R_EX_so3_e\n", + "\t- R_EX_so4_e\n", + "\t- R_EX_hdcea_e\n", + "\t- R_EX_spmd_e\n", + "\t- R_EX_hg2_e\n", + "\t- R_EX_succ_e\n", + "\t- R_EX_sucr_e\n", + "\t- R_EX_his__L_e\n", + "\t- R_EX_sulfac_e\n", + "\t- R_EX_hom__L_e\n", + "\t- R_EX_tartr__D_e\n", + "\t- R_EX_tartr__L_e\n", + "\t- R_EX_taur_e\n", + "\t- R_EX_tcynt_e\n", + "\t- R_EX_thm_e\n", + "\t- R_EX_thr__L_e\n", + "\t- R_EX_thrp_e\n", + "\t- R_EX_thym_e\n", + "\t- R_EX_thymd_e\n", + "\t- R_EX_tma_e\n", + "\t- R_EX_tmao_e\n", + "\t- R_EX_hxa_e\n", + "\t- R_EX_tre_e\n", + "\t- R_EX_trp__L_e\n", + "\t- R_EX_tsul_e\n", + "\t- R_EX_hxan_e\n", + "\t- R_EX_ttdca_e\n", + "\t- R_EX_ttdcea_e\n", + "\t- R_EX_ttrcyc_e\n", + "\t- R_EX_idon__L_e\n", + "\t- R_EX_tungs_e\n", + "\t- R_EX_tym_e\n", + "\t- R_EX_ile__L_e\n", + "\t- R_EX_tyr__L_e\n", + "\t- R_EX_tyrp_e\n", + "\t- R_EX_uacgam_e\n", + "\t- R_EX_udpacgal_e\n", + "\t- R_EX_udpg_e\n", + "\t- R_EX_udpgal_e\n", + "\t- R_EX_udpglcur_e\n", + "\t- R_EX_ump_e\n", + "\t- R_EX_ura_e\n", + "\t- R_EX_imp_e\n", + "\t- R_EX_urea_e\n", + "\t- R_EX_uri_e\n", + "\t- R_EX_val__L_e\n", + "\t- R_EX_xan_e\n", + "\t- R_EX_indole_e\n", + "\t- R_EX_inost_e\n", + "\t- R_EX_ins_e\n", + "\t- R_EX_isetac_e\n", + "\t- R_EX_k_e\n", + "\t- R_EX_kdo2lipid4_e\n", + "\t- R_EX_lac__D_e\n", + "\t- R_EX_lac__L_e\n", + "\t- R_EX_lcts_e\n", + "\t- R_EX_leu__L_e\n", + "\t- R_EX_lipa_e\n", + "\t- R_EX_lipa_cold_e\n", + "\t- R_EX_lipoate_e\n", + "\t- R_EX_lys__L_e\n", + "\t- R_EX_lyx__L_e\n", + "\t- R_EX_mal__D_e\n", + "\t- R_EX_mal__L_e\n", + "\t- R_EX_malt_e\n", + "\t- R_EX_malthx_e\n", + "\t- R_EX_maltpt_e\n", + "\t- R_EX_malttr_e\n", + "\t- R_EX_maltttr_e\n", + "\t- R_EX_man_e\n", + "\t- R_EX_man6p_e\n", + "\t- R_EX_manglyc_e\n", + "\t- R_EX_melib_e\n", + "\t- R_EX_meoh_e\n", + "\t- R_EX_met__D_e\n", + "\t- R_EX_met__L_e\n", + "\t- R_EX_metsox_R__L_e\n", + "\t- R_EX_metsox_S__L_e\n", + "\t- R_EX_mg2_e\n", + "\t- R_EX_mincyc_e\n", + "\t- R_EX_minohp_e\n", + "\t- R_EX_mmet_e\n", + "\t- R_EX_mn2_e\n", + "\t- R_EX_mnl_e\n", + "\t- R_EX_mobd_e\n", + "\t- R_EX_mso3_e\n", + "\t- R_EX_n2o_e\n", + "\t- R_EX_na1_e\n", + "\t- R_EX_nac_e\n", + "\t- R_EX_nh4_e\n", + "\t- R_EX_ni2_e\n", + "\t- R_EX_nmn_e\n", + "\t- R_EX_feroxBfe_e\n", + "\t- R_EX_4hphac_e\n", + "\t- R_EX_4abz_e\n", + "\t- R_EX_feroxB_e\n", + "\t- R_EX_tcb_e\n", + "\t- R_EX_tton_e\n", + "\t- R_EX_4hthr_e\n", + "\t- R_EX_oaa_e\n", + "\t- R_EX_salchs2fe_e\n", + "\t- R_EX_feroxEfe_e\n", + "\t- R_EX_feroxE_e\n", + "\t- R_EX_acon_C_e\n", + "\t- R_EX_23dhbzs3_e\n", + "\t- R_EX_tet_e\n", + "\t- R_EX_cysi__L_e\n", + "\t- R_EX_airs_e\n", + "\t- R_EX_dxyl_e\n", + "\t- R_EX_3pg_e\n", + "\t- R_EX_feroxGfe_e\n", + "\t- R_EX_fe3dhbzs3_e\n", + "\t- R_EX_drib_e\n", + "\t- R_EX_udcpo4_e\n", + "\t- R_EX_salchs4fe_e\n", + "\t- R_EX_tag__D_e\n", + "\t- R_EX_2pg_e\n", + "\t- R_EX_guln__L_e\n", + "\t- R_EX_frmd_e\n", + "\t- R_EX_remnant1_e\n", + "\t- R_EX_colipaOA_e\n", + "\t- R_EX_salchsx_e\n", + "\t- R_EX_AEP_e\n", + "\t- R_EX_feroxG_e\n", + "\t- R_EX_icit_e\n", + "\t- R_EX_pep_e\n", + "\t- R_EX_colipa20Oag_e\n", + "\t- R_EX_diact_e\n", + "\t- R_EX_salchs4_e\n", + "\t- R_EX_udcpo5_e\n", + "\t- R_EX_btd_RR_e\n", + "\t- R_EX_rbt_e\n", + "\t- R_EX_3hoxpac_e\n", + "\t- R_EX_cellb_e\n", + "\t- R_EX_salchs2_e\n", + "\t- R_EX_bz_e\n", + "\t- R_EX_13ppd_e\n", + "\t- R_EX_abt__D_e\n", + "\t- R_EX_rnam_e\n", + "\t- R_DM_lipidA_core_e_p\n", + "\t- R_EX_fe_e\n", + "\t- R_EX_kokdolipidA_e\n", + "\t- R_EX_3ntym_e\n", + "\t- R_EX_6apa_e\n", + "\t- R_EX_4hoxpac_e\n", + "\t- R_EX_peng_e\n", + "\t- R_EX_pac_e\n", + "\t- R_EX_salcn_e\n", + "\t- R_EX_galam_e\n", + "\t- R_EX_raffin_e\n", + "\t- R_EX_o6a4colipa_e\n", + "\t- R_EX_arab__D_e\n", + "\t- R_SK_thf_c\u001b[0m\n", + "File saved at: ../../data/sbml_corrected/iSSON_1240.xml\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: iEK1008\n", + "\u001b[0;93mWARNING : \n", + " - R_ACODA: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_DURIK1: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + " - R_HSDy: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_KARA1: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_MME: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_PTAr: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_ALCD22_L: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_ADOCBIAH: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_PPAKr: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_GLYDHDA: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + "\u001b[0m\n", + "\n", + "\n", + "\u001b[0;36m############################################\n", + "############################################\n", + " WRITING SBML FILE \n", + "############################################\n", + "############################################\n", + "\u001b[0m\n", + "\u001b[0;93mWARNING : IMPORT REACTION REMOVED BY DEFAULT\u001b[0m\n", + "\u001b[0;93mWARNING : If you want to keep import reaction\n", + "use option -kir / --keep-import-reactions\u001b[0m\n", + "\u001b[0;93mWARNING : Reaction with tag reversible modified:\n", + "\t- R_HSDy\n", + "\t- R_KARA1\n", + "\t- R_MME\n", + "\t- R_PTAr\n", + "\t- R_ALCD22_L\n", + "\t- R_ADOCBIAH\n", + "\t- R_PPAKr\n", + "\t- R_GLYDHDA\u001b[0m\n", + "\u001b[0;93mWARNING : Reaction with Reactants and Products exchanged:\n", + "\t- R_HSDy\n", + "\t- R_KARA1\n", + "\t- R_MME\n", + "\t- R_PTAr\n", + "\t- R_ALCD22_L\n", + "\t- R_ADOCBIAH\n", + "\t- R_PPAKr\n", + "\t- R_GLYDHDA\u001b[0m\n", + "\u001b[0;93mWARNING : Import reaction removed:\n", + "\t- R_EX_lac__L_e\n", + "\t- R_EX_lcts_e\n", + "\t- R_EX_leu__L_e\n", + "\t- R_EX_lys__L_e\n", + "\t- R_EX_mal__L_e\n", + "\t- R_EX_malt_e\n", + "\t- R_EX_malthp_e\n", + "\t- R_EX_meoh_e\n", + "\t- R_EX_met__L_e\n", + "\t- R_EX_mg2_e\n", + "\t- R_EX_na1_e\n", + "\t- R_EX_nh4_e\n", + "\t- R_EX_no2_e\n", + "\t- R_EX_no3_e\n", + "\t- R_EX_no_e\n", + "\t- R_EX_o2_e\n", + "\t- R_EX_ocdca_e\n", + "\t- R_EX_orn_e\n", + "\t- R_EX_pac_e\n", + "\t- R_EX_pdima_e\n", + "\t- R_EX_phdca_e\n", + "\t- R_EX_acysbmn_e\n", + "\t- R_EX_phe__L_e\n", + "\t- R_EX_pi_e\n", + "\t- R_EX_ala_B_e\n", + "\t- R_EX_ala__D_e\n", + "\t- R_EX_ala__L_e\n", + "\t- R_EX_arab__D_e\n", + "\t- R_EX_arab__L_e\n", + "\t- R_EX_ppa_e\n", + "\t- R_EX_ppdima_e\n", + "\t- R_EX_pro__L_e\n", + "\t- R_EX_pyr_e\n", + "\t- R_EX_rib__D_e\n", + "\t- R_EX_ser__D_e\n", + "\t- R_EX_ser__L_e\n", + "\t- R_EX_arg__L_e\n", + "\t- R_EX_asn__L_e\n", + "\t- R_EX_asp__L_e\n", + "\t- R_EX_skm_e\n", + "\t- R_EX_so4_e\n", + "\t- R_EX_succ_e\n", + "\t- R_EX_tartr__L_e\n", + "\t- R_EX_atp_e\n", + "\t- R_EX_bmn_e\n", + "\t- R_EX_ca2_e\n", + "\t- R_EX_chol_e\n", + "\t- R_EX_cit_e\n", + "\t- R_EX_thr__L_e\n", + "\t- R_EX_tre_e\n", + "\t- R_EX_trp__L_e\n", + "\t- R_EX_cl_e\n", + "\t- R_EX_co2_e\n", + "\t- R_EX_tsul_e\n", + "\t- R_EX_ttdca_e\n", + "\t- R_EX_tyr__L_e\n", + "\t- R_EX_val__L_e\n", + "\t- R_EX_coa_e\n", + "\t- R_EX_cobalt2_e\n", + "\t- R_EX_crn_e\n", + "\t- R_EX_cu2_e\n", + "\t- R_EX_cytd_e\n", + "\t- R_EX_ac_e\n", + "\t- R_EX_4abz_e\n", + "\t- R_EX_for_e\n", + "\t- R_EX_etha_e\n", + "\t- R_EX_fe2_e\n", + "\t- R_EX_fe3_e\n", + "\t- R_EX_akg_e\n", + "\t- R_EX_gua_e\n", + "\t- R_EX_fum_e\n", + "\t- R_EX_gal_e\n", + "\t- R_EX_glc__D_e\n", + "\t- R_EX_ad_e\n", + "\t- R_EX_urea_e\n", + "\t- R_EX_glu__L_e\n", + "\t- R_EX_gly_e\n", + "\t- R_EX_glyb_e\n", + "\t- R_EX_glyc3p_e\n", + "\t- R_EX_glyc_e\n", + "\t- R_EX_gthrd_e\n", + "\t- R_EX_h2_e\n", + "\t- R_EX_h2co3_e\n", + "\t- R_EX_h2o_e\n", + "\t- R_EX_h2s_e\n", + "\t- R_EX_h_e\n", + "\t- R_EX_hdca_e\n", + "\t- R_EX_his__L_e\n", + "\t- R_EX_id3acald_e\n", + "\t- R_EX_ile__L_e\n", + "\t- R_EX_k_e\n", + "\t- R_EX_ni2_e\n", + "\t- R_EX_mn2_e\n", + "\t- R_EX_chsterol_e\n", + "\t- R_EX_nodcoa_e\n", + "\t- R_EX_gln__L_e\n", + "\t- R_EX_4hba_e\n", + "\t- R_EX_octscoa_e\n", + "\t- R_EX_pydxn_e\n", + "\t- R_EX_glyclt_e\n", + "\t- R_EX_co_e\n", + "\t- R_EX_pheme_e\n", + "\t- R_EX_pc_TB_e\n", + "\t- R_DM_ergoth_c\n", + "\t- R_DM_tbsinol_c\n", + "\t- R_DM_isotbsinol_13S_c\n", + "\t- R_DM_isotbsinol_13R_c\n", + "\t- R_DM_psd5p_c\n", + "\t- R_EX_hia_e\n", + "\t- R_EX_acald_e\n", + "\t- R_DM_cpppg1_c\n", + "\t- R_DM_scys__L_c\n", + "\t- R_DM_gmhep1p_c\n", + "\t- R_EX_ocdcea_e\n", + "\t- R_EX_etoh_e\n", + "\t- R_EX_adocbl_e\n", + "\t- R_EX_mobd_e\n", + "\t- R_EX_btn_e\u001b[0m\n", + "File saved at: ../../data/sbml_corrected/iEK1008.xml\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: iECP_1309\n", + "\u001b[0;93mWARNING : \n", + " - R_CAT: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_DHPTDNR: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_DHPTDNRN: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_FHL: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SPODM: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SPODMpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCASPtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCFUMtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCMALtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCTARTtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_EX_chitob_e: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_CHITOBpts: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_URCN: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_CHITPH: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\u001b[0m\n", + "\n", + "\n", + "\u001b[0;36m############################################\n", + "############################################\n", + " WRITING SBML FILE \n", + "############################################\n", + "############################################\n", + "\u001b[0m\n", + "\u001b[0;93mWARNING : IMPORT REACTION REMOVED BY DEFAULT\u001b[0m\n", + "\u001b[0;93mWARNING : If you want to keep import reaction\n", + "use option -kir / --keep-import-reactions\u001b[0m\n", + "\u001b[0;93mWARNING : Import reaction removed:\n", + "\t- R_EX_ade_e\n", + "\t- R_EX_adn_e\n", + "\t- R_EX_adocbl_e\n", + "\t- R_EX_agm_e\n", + "\t- R_EX_ag_e\n", + "\t- R_EX_acser_e\n", + "\t- R_DM_4crsol_c\n", + "\t- R_DM_5drib_c\n", + "\t- R_DM_aacald_c\n", + "\t- R_DM_amob_c\n", + "\t- R_EX_akg_e\n", + "\t- R_EX_ala_B_e\n", + "\t- R_DM_mththf_c\n", + "\t- R_EX_ala__D_e\n", + "\t- R_DM_oxam_c\n", + "\t- R_EX_ala__L_e\n", + "\t- R_EX_12ppd__R_e\n", + "\t- R_EX_alaala_e\n", + "\t- R_EX_12ppd__S_e\n", + "\t- R_EX_all__D_e\n", + "\t- R_EX_14glucan_e\n", + "\t- R_EX_alltn_e\n", + "\t- R_EX_15dap_e\n", + "\t- R_EX_23camp_e\n", + "\t- R_EX_fecrm_e\n", + "\t- R_EX_fecrm_un_e\n", + "\t- R_EX_23ccmp_e\n", + "\t- R_EX_amp_e\n", + "\t- R_EX_23cgmp_e\n", + "\t- R_EX_23cump_e\n", + "\t- R_EX_feenter_e\n", + "\t- R_EX_feoxam_e\n", + "\t- R_EX_feoxam_un_e\n", + "\t- R_EX_for_e\n", + "\t- R_EX_fru_e\n", + "\t- R_EX_frulys_e\n", + "\t- R_EX_fruur_e\n", + "\t- R_EX_fuc__L_e\n", + "\t- R_EX_fum_e\n", + "\t- R_EX_fusa_e\n", + "\t- R_EX_23dappa_e\n", + "\t- R_EX_26dap__M_e\n", + "\t- R_EX_2ddglcn_e\n", + "\t- R_EX_34dhpac_e\n", + "\t- R_EX_3amp_e\n", + "\t- R_EX_3cmp_e\n", + "\t- R_EX_3gmp_e\n", + "\t- R_EX_3hcinnm_e\n", + "\t- R_EX_3hpp_e\n", + "\t- R_EX_3hpppn_e\n", + "\t- R_EX_g1p_e\n", + "\t- R_EX_g3pc_e\n", + "\t- R_EX_g3pe_e\n", + "\t- R_EX_3ump_e\n", + "\t- R_EX_4abut_e\n", + "\t- R_EX_anhgm_e\n", + "\t- R_EX_4hoxpacd_e\n", + "\t- R_EX_5dglcn_e\n", + "\t- R_EX_5mtr_e\n", + "\t- R_EX_g3pg_e\n", + "\t- R_EX_g3pi_e\n", + "\t- R_EX_g3ps_e\n", + "\t- R_EX_LalaDglu_e\n", + "\t- R_EX_g6p_e\n", + "\t- R_EX_arab__L_e\n", + "\t- R_EX_gal_e\n", + "\t- R_EX_gal_bD_e\n", + "\t- R_EX_gal1p_e\n", + "\t- R_EX_LalaDgluMdap_e\n", + "\t- R_EX_LalaDgluMdapDala_e\n", + "\t- R_EX_LalaLglu_e\n", + "\t- R_EX_ac_e\n", + "\t- R_EX_acac_e\n", + "\t- R_EX_acald_e\n", + "\t- R_EX_galct__D_e\n", + "\t- R_EX_galctn__D_e\n", + "\t- R_EX_galctn__L_e\n", + "\t- R_EX_galt_e\n", + "\t- R_EX_galur_e\n", + "\t- R_EX_acgal_e\n", + "\t- R_EX_arbt_e\n", + "\t- R_EX_acgal1p_e\n", + "\t- R_EX_acgam_e\n", + "\t- R_EX_acgam1p_e\n", + "\t- R_EX_acmana_e\n", + "\t- R_EX_gam_e\n", + "\t- R_EX_gam6p_e\n", + "\t- R_EX_gbbtn_e\n", + "\t- R_EX_gdp_e\n", + "\t- R_EX_acmum_e\n", + "\t- R_EX_acnam_e\n", + "\t- R_EX_glc__D_e\n", + "\t- R_EX_arbtn_e\n", + "\t- R_EX_glcn_e\n", + "\t- R_EX_acolipa_e\n", + "\t- R_EX_glcr_e\n", + "\t- R_EX_arbtn_fe3_e\n", + "\t- R_EX_glcur_e\n", + "\t- R_EX_glcur1p_e\n", + "\t- R_EX_arg__L_e\n", + "\t- R_EX_gln__L_e\n", + "\t- R_EX_glu__L_e\n", + "\t- R_EX_ascb__L_e\n", + "\t- R_EX_gly_e\n", + "\t- R_EX_glyald_e\n", + "\t- R_EX_asn__L_e\n", + "\t- R_EX_glyb_e\n", + "\t- R_EX_glyc_e\n", + "\t- R_EX_glyc__R_e\n", + "\t- R_EX_glyc2p_e\n", + "\t- R_EX_glyc3p_e\n", + "\t- R_EX_glyclt_e\n", + "\t- R_EX_lipoate_e\n", + "\t- R_EX_lys__L_e\n", + "\t- R_EX_lyx__L_e\n", + "\t- R_EX_mal__D_e\n", + "\t- R_EX_gmp_e\n", + "\t- R_EX_gsn_e\n", + "\t- R_EX_gthox_e\n", + "\t- R_EX_mal__L_e\n", + "\t- R_EX_aso3_e\n", + "\t- R_EX_malt_e\n", + "\t- R_EX_malthx_e\n", + "\t- R_EX_maltpt_e\n", + "\t- R_EX_gthrd_e\n", + "\t- R_EX_gtp_e\n", + "\t- R_EX_gua_e\n", + "\t- R_EX_h_e\n", + "\t- R_EX_malttr_e\n", + "\t- R_EX_maltttr_e\n", + "\t- R_EX_h2_e\n", + "\t- R_EX_asp__L_e\n", + "\t- R_EX_h2o_e\n", + "\t- R_EX_h2o2_e\n", + "\t- R_EX_man_e\n", + "\t- R_EX_man6p_e\n", + "\t- R_EX_manglyc_e\n", + "\t- R_EX_h2s_e\n", + "\t- R_EX_melib_e\n", + "\t- R_EX_btn_e\n", + "\t- R_EX_meoh_e\n", + "\t- R_EX_met__D_e\n", + "\t- R_EX_hacolipa_e\n", + "\t- R_EX_halipa_e\n", + "\t- R_EX_hdca_e\n", + "\t- R_EX_hdcea_e\n", + "\t- R_EX_hg2_e\n", + "\t- R_EX_his__L_e\n", + "\t- R_EX_hom__L_e\n", + "\t- R_EX_met__L_e\n", + "\t- R_EX_metsox_R__L_e\n", + "\t- R_EX_metsox_S__L_e\n", + "\t- R_EX_mg2_e\n", + "\t- R_EX_mincyc_e\n", + "\t- R_EX_hxa_e\n", + "\t- R_EX_hxan_e\n", + "\t- R_EX_but_e\n", + "\t- R_EX_idon__L_e\n", + "\t- R_EX_ile__L_e\n", + "\t- R_EX_minohp_e\n", + "\t- R_EX_mmet_e\n", + "\t- R_EX_butso3_e\n", + "\t- R_EX_mn2_e\n", + "\t- R_EX_mnl_e\n", + "\t- R_EX_imp_e\n", + "\t- R_EX_mobd_e\n", + "\t- R_EX_indole_e\n", + "\t- R_EX_ca2_e\n", + "\t- R_EX_inost_e\n", + "\t- R_EX_ins_e\n", + "\t- R_EX_mso3_e\n", + "\t- R_EX_n2o_e\n", + "\t- R_EX_na1_e\n", + "\t- R_EX_nac_e\n", + "\t- R_EX_isetac_e\n", + "\t- R_EX_cbi_e\n", + "\t- R_EX_k_e\n", + "\t- R_EX_nh4_e\n", + "\t- R_EX_ni2_e\n", + "\t- R_EX_kdo2lipid4_e\n", + "\t- R_EX_cbl1_e\n", + "\t- R_EX_lac__D_e\n", + "\t- R_EX_lac__L_e\n", + "\t- R_EX_lcts_e\n", + "\t- R_EX_leu__L_e\n", + "\t- R_EX_lipa_e\n", + "\t- R_EX_nmn_e\n", + "\t- R_EX_no_e\n", + "\t- R_EX_no2_e\n", + "\t- R_EX_no3_e\n", + "\t- R_EX_novbcn_e\n", + "\t- R_EX_o16a4colipa_e\n", + "\t- R_EX_lipa_cold_e\n", + "\t- R_EX_o2_e\n", + "\t- R_EX_cd2_e\n", + "\t- R_EX_o2s_e\n", + "\t- R_EX_sulfac_e\n", + "\t- R_EX_ocdca_e\n", + "\t- R_EX_tartr__D_e\n", + "\t- R_EX_ocdcea_e\n", + "\t- R_EX_cgly_e\n", + "\t- R_EX_octa_e\n", + "\t- R_EX_orn_e\n", + "\t- R_EX_orot_e\n", + "\t- R_EX_tartr__L_e\n", + "\t- R_EX_taur_e\n", + "\t- R_EX_tcynt_e\n", + "\t- R_EX_thm_e\n", + "\t- R_EX_pacald_e\n", + "\t- R_EX_peamn_e\n", + "\t- R_EX_chol_e\n", + "\t- R_EX_phe__L_e\n", + "\t- R_EX_pheme_e\n", + "\t- R_EX_pi_e\n", + "\t- R_EX_thr__L_e\n", + "\t- R_EX_thrp_e\n", + "\t- R_EX_thym_e\n", + "\t- R_EX_pnto__R_e\n", + "\t- R_EX_ppa_e\n", + "\t- R_EX_thymd_e\n", + "\t- R_EX_tma_e\n", + "\t- R_EX_tmao_e\n", + "\t- R_EX_tre_e\n", + "\t- R_EX_ppal_e\n", + "\t- R_EX_pppn_e\n", + "\t- R_EX_ppt_e\n", + "\t- R_EX_pro__L_e\n", + "\t- R_EX_trp__L_e\n", + "\t- R_EX_chtbs_e\n", + "\t- R_EX_progly_e\n", + "\t- R_EX_cit_e\n", + "\t- R_EX_psclys_e\n", + "\t- R_EX_pser__L_e\n", + "\t- R_EX_tsul_e\n", + "\t- R_EX_ttdca_e\n", + "\t- R_EX_cl_e\n", + "\t- R_EX_ttdcea_e\n", + "\t- R_EX_ptrc_e\n", + "\t- R_EX_pydam_e\n", + "\t- R_EX_pydx_e\n", + "\t- R_EX_ttrcyc_e\n", + "\t- R_EX_tungs_e\n", + "\t- R_EX_pydxn_e\n", + "\t- R_EX_tym_e\n", + "\t- R_EX_cm_e\n", + "\t- R_EX_tyr__L_e\n", + "\t- R_EX_tyrp_e\n", + "\t- R_EX_pyr_e\n", + "\t- R_EX_quin_e\n", + "\t- R_EX_cmp_e\n", + "\t- R_EX_r5p_e\n", + "\t- R_EX_uacgam_e\n", + "\t- R_EX_udpacgal_e\n", + "\t- R_EX_rfamp_e\n", + "\t- R_EX_co2_e\n", + "\t- R_EX_rib__D_e\n", + "\t- R_EX_rmn_e\n", + "\t- R_EX_udpg_e\n", + "\t- R_EX_udpgal_e\n", + "\t- R_EX_udpglcur_e\n", + "\t- R_EX_ump_e\n", + "\t- R_EX_ura_e\n", + "\t- R_EX_sbt__D_e\n", + "\t- R_EX_sel_e\n", + "\t- R_EX_ser__D_e\n", + "\t- R_EX_ser__L_e\n", + "\t- R_EX_urea_e\n", + "\t- R_EX_skm_e\n", + "\t- R_EX_cobalt2_e\n", + "\t- R_EX_slnt_e\n", + "\t- R_EX_uri_e\n", + "\t- R_EX_val__L_e\n", + "\t- R_EX_so2_e\n", + "\t- R_EX_colipa_e\n", + "\t- R_EX_so3_e\n", + "\t- R_EX_so4_e\n", + "\t- R_EX_xan_e\n", + "\t- R_EX_xmp_e\n", + "\t- R_EX_xtsn_e\n", + "\t- R_EX_xyl__D_e\n", + "\t- R_EX_spmd_e\n", + "\t- R_EX_xylu__L_e\n", + "\t- R_EX_colipap_e\n", + "\t- R_EX_zn2_e\n", + "\t- R_EX_succ_e\n", + "\t- R_EX_sucr_e\n", + "\t- R_EX_cpgn_e\n", + "\t- R_EX_cpgn_un_e\n", + "\t- R_EX_crn_e\n", + "\t- R_EX_crn__D_e\n", + "\t- R_EX_csn_e\n", + "\t- R_EX_cu_e\n", + "\t- R_EX_cu2_e\n", + "\t- R_EX_cyan_e\n", + "\t- R_EX_cynt_e\n", + "\t- R_EX_cys__D_e\n", + "\t- R_EX_cys__L_e\n", + "\t- R_EX_cytd_e\n", + "\t- R_EX_dad_2_e\n", + "\t- R_EX_damp_e\n", + "\t- R_EX_dca_e\n", + "\t- R_EX_dcmp_e\n", + "\t- R_EX_dcyt_e\n", + "\t- R_EX_ddca_e\n", + "\t- R_EX_dgmp_e\n", + "\t- R_EX_dgsn_e\n", + "\t- R_EX_dha_e\n", + "\t- R_EX_dimp_e\n", + "\t- R_EX_din_e\n", + "\t- R_EX_dms_e\n", + "\t- R_EX_dmso_e\n", + "\t- R_EX_dopa_e\n", + "\t- R_EX_doxrbcn_e\n", + "\t- R_EX_dtmp_e\n", + "\t- R_EX_dump_e\n", + "\t- R_EX_duri_e\n", + "\t- R_EX_eca4colipa_e\n", + "\t- R_EX_enlipa_e\n", + "\t- R_EX_enter_e\n", + "\t- R_EX_etha_e\n", + "\t- R_EX_ethso3_e\n", + "\t- R_EX_etoh_e\n", + "\t- R_EX_f6p_e\n", + "\t- R_EX_fald_e\n", + "\t- R_EX_fe2_e\n", + "\t- R_EX_fe3_e\n", + "\t- R_EX_fe3dcit_e\n", + "\t- R_EX_fe3dhbzs_e\n", + "\t- R_EX_fe3hox_e\n", + "\t- R_EX_fe3hox_un_e\n", + "\t- R_EX_AEP_e\n", + "\t- R_EX_icit_e\n", + "\t- R_EX_cellb_e\n", + "\t- R_EX_bz_e\n", + "\t- R_EX_salchsx_e\n", + "\t- R_EX_acon_C_e\n", + "\t- R_EX_feroxB_e\n", + "\t- R_EX_tcb_e\n", + "\t- R_EX_tton_e\n", + "\t- R_EX_4hthr_e\n", + "\t- R_EX_feroxE_e\n", + "\t- R_EX_pep_e\n", + "\t- R_EX_colipa20Oag_e\n", + "\t- R_EX_23dhbzs3_e\n", + "\t- R_EX_4hphac_e\n", + "\t- R_EX_tet_e\n", + "\t- R_EX_4abz_e\n", + "\t- R_EX_salchs4_e\n", + "\t- R_EX_tag__D_e\n", + "\t- R_EX_abt__D_e\n", + "\t- R_EX_remnant1_e\n", + "\t- R_EX_oaa_e\n", + "\t- R_EX_salchs2fe_e\n", + "\t- R_EX_feroxEfe_e\n", + "\t- R_EX_cysi__L_e\n", + "\t- R_EX_fe3dhbzs3_e\n", + "\t- R_EX_fe_e\n", + "\t- R_EX_kokdolipidA_e\n", + "\t- R_EX_drib_e\n", + "\t- R_EX_3hoxpac_e\n", + "\t- R_EX_feroxBfe_e\n", + "\t- R_EX_salchs4fe_e\n", + "\t- R_EX_2pg_e\n", + "\t- R_EX_o6a4colipa_e\n", + "\t- R_EX_frmd_e\n", + "\t- R_EX_salchs2_e\n", + "\t- R_EX_colipaOA_e\n", + "\t- R_EX_3ntym_e\n", + "\t- R_EX_rnam_e\n", + "\t- R_EX_6apa_e\n", + "\t- R_EX_arab__D_e\n", + "\t- R_EX_airs_e\n", + "\t- R_EX_4hoxpac_e\n", + "\t- R_EX_peng_e\n", + "\t- R_EX_dxyl_e\n", + "\t- R_EX_pac_e\n", + "\t- R_EX_3pg_e\n", + "\t- R_EX_salcn_e\n", + "\t- R_EX_galam_e\n", + "\t- R_EX_raffin_e\n", + "\t- R_DM_lipidA_core_e_p\n", + "\t- R_EX_udcpo4_e\n", + "\t- R_EX_feroxGfe_e\n", + "\t- R_EX_udcpo5_e\n", + "\t- R_EX_guln__L_e\n", + "\t- R_EX_feroxG_e\n", + "\t- R_EX_diact_e\n", + "\t- R_EX_btd_RR_e\n", + "\t- R_EX_13ppd_e\n", + "\t- R_EX_rbt_e\u001b[0m\n", + "File saved at: ../../data/sbml_corrected/iECP_1309.xml\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: iB21_1397\n", + "\u001b[0;93mWARNING : \n", + " - R_CAT: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_DHPTDNR: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_DHPTDNRN: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_FHL: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SPODM: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SPODMpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCASPtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCFUMtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCMALtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCTARTtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_EX_chitob_e: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_CHITOBpts: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_CHITPH: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_URCN: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\u001b[0m\n", + "\n", + "\n", + "\u001b[0;36m############################################\n", + "############################################\n", + " WRITING SBML FILE \n", + "############################################\n", + "############################################\n", + "\u001b[0m\n", + "\u001b[0;93mWARNING : IMPORT REACTION REMOVED BY DEFAULT\u001b[0m\n", + "\u001b[0;93mWARNING : If you want to keep import reaction\n", + "use option -kir / --keep-import-reactions\u001b[0m\n", + "\u001b[0;93mWARNING : Import reaction removed:\n", + "\t- R_EX_6apa_e\n", + "\t- R_EX_4hoxpac_e\n", + "\t- R_EX_peng_e\n", + "\t- R_EX_pac_e\n", + "\t- R_EX_salcn_e\n", + "\t- R_DM_4crsol_c\n", + "\t- R_DM_5drib_c\n", + "\t- R_DM_aacald_c\n", + "\t- R_DM_amob_c\n", + "\t- R_DM_mththf_c\n", + "\t- R_DM_oxam_c\n", + "\t- R_EX_5mtr_e\n", + "\t- R_EX_LalaDglu_e\n", + "\t- R_EX_LalaDgluMdap_e\n", + "\t- R_EX_LalaDgluMdapDala_e\n", + "\t- R_EX_LalaLglu_e\n", + "\t- R_EX_ac_e\n", + "\t- R_EX_acac_e\n", + "\t- R_EX_acald_e\n", + "\t- R_EX_acgal_e\n", + "\t- R_EX_acgal1p_e\n", + "\t- R_EX_acgam_e\n", + "\t- R_EX_12ppd__R_e\n", + "\t- R_EX_12ppd__S_e\n", + "\t- R_EX_14glucan_e\n", + "\t- R_EX_15dap_e\n", + "\t- R_EX_23camp_e\n", + "\t- R_EX_23ccmp_e\n", + "\t- R_EX_23cgmp_e\n", + "\t- R_EX_23cump_e\n", + "\t- R_EX_23dappa_e\n", + "\t- R_EX_26dap__M_e\n", + "\t- R_EX_2ddglcn_e\n", + "\t- R_EX_34dhpac_e\n", + "\t- R_EX_3amp_e\n", + "\t- R_EX_acgam1p_e\n", + "\t- R_EX_acmana_e\n", + "\t- R_EX_acmum_e\n", + "\t- R_EX_acnam_e\n", + "\t- R_EX_3cmp_e\n", + "\t- R_EX_3gmp_e\n", + "\t- R_EX_3hcinnm_e\n", + "\t- R_EX_acolipa_e\n", + "\t- R_EX_acser_e\n", + "\t- R_EX_ade_e\n", + "\t- R_EX_adn_e\n", + "\t- R_EX_3hpp_e\n", + "\t- R_EX_3hpppn_e\n", + "\t- R_EX_3ump_e\n", + "\t- R_EX_4abut_e\n", + "\t- R_EX_4hoxpacd_e\n", + "\t- R_EX_adocbl_e\n", + "\t- R_EX_ag_e\n", + "\t- R_EX_agm_e\n", + "\t- R_EX_akg_e\n", + "\t- R_EX_ala_B_e\n", + "\t- R_EX_5dglcn_e\n", + "\t- R_EX_ala__D_e\n", + "\t- R_EX_ala__L_e\n", + "\t- R_EX_alaala_e\n", + "\t- R_EX_all__D_e\n", + "\t- R_EX_alltn_e\n", + "\t- R_EX_amp_e\n", + "\t- R_EX_anhgm_e\n", + "\t- R_EX_arab__L_e\n", + "\t- R_EX_arbt_e\n", + "\t- R_EX_arbtn_e\n", + "\t- R_EX_arbtn_fe3_e\n", + "\t- R_EX_arg__L_e\n", + "\t- R_EX_din_e\n", + "\t- R_EX_dms_e\n", + "\t- R_EX_ascb__L_e\n", + "\t- R_EX_asn__L_e\n", + "\t- R_EX_dmso_e\n", + "\t- R_EX_dopa_e\n", + "\t- R_EX_aso3_e\n", + "\t- R_EX_asp__L_e\n", + "\t- R_EX_btn_e\n", + "\t- R_EX_but_e\n", + "\t- R_EX_butso3_e\n", + "\t- R_EX_doxrbcn_e\n", + "\t- R_EX_dtmp_e\n", + "\t- R_EX_dump_e\n", + "\t- R_EX_duri_e\n", + "\t- R_EX_eca4colipa_e\n", + "\t- R_EX_enlipa_e\n", + "\t- R_EX_enter_e\n", + "\t- R_EX_ca2_e\n", + "\t- R_EX_cbi_e\n", + "\t- R_EX_cbl1_e\n", + "\t- R_EX_etha_e\n", + "\t- R_EX_ethso3_e\n", + "\t- R_EX_etoh_e\n", + "\t- R_EX_cd2_e\n", + "\t- R_EX_cgly_e\n", + "\t- R_EX_chol_e\n", + "\t- R_EX_f6p_e\n", + "\t- R_EX_fald_e\n", + "\t- R_EX_fe2_e\n", + "\t- R_EX_chtbs_e\n", + "\t- R_EX_cit_e\n", + "\t- R_EX_fe3_e\n", + "\t- R_EX_fe3dcit_e\n", + "\t- R_EX_cl_e\n", + "\t- R_EX_cm_e\n", + "\t- R_EX_fe3dhbzs_e\n", + "\t- R_EX_cmp_e\n", + "\t- R_EX_co2_e\n", + "\t- R_EX_cobalt2_e\n", + "\t- R_EX_fe3hox_e\n", + "\t- R_EX_fe3hox_un_e\n", + "\t- R_EX_fecrm_e\n", + "\t- R_EX_fecrm_un_e\n", + "\t- R_EX_feenter_e\n", + "\t- R_EX_colipa_e\n", + "\t- R_EX_colipap_e\n", + "\t- R_EX_cpgn_e\n", + "\t- R_EX_cpgn_un_e\n", + "\t- R_EX_crn_e\n", + "\t- R_EX_feoxam_e\n", + "\t- R_EX_feoxam_un_e\n", + "\t- R_EX_for_e\n", + "\t- R_EX_crn__D_e\n", + "\t- R_EX_csn_e\n", + "\t- R_EX_cu_e\n", + "\t- R_EX_cu2_e\n", + "\t- R_EX_fru_e\n", + "\t- R_EX_frulys_e\n", + "\t- R_EX_fruur_e\n", + "\t- R_EX_cyan_e\n", + "\t- R_EX_fuc__L_e\n", + "\t- R_EX_fum_e\n", + "\t- R_EX_cynt_e\n", + "\t- R_EX_cys__D_e\n", + "\t- R_EX_cys__L_e\n", + "\t- R_EX_fusa_e\n", + "\t- R_EX_g1p_e\n", + "\t- R_EX_cytd_e\n", + "\t- R_EX_dad_2_e\n", + "\t- R_EX_damp_e\n", + "\t- R_EX_dca_e\n", + "\t- R_EX_dcmp_e\n", + "\t- R_EX_g3pc_e\n", + "\t- R_EX_g3pe_e\n", + "\t- R_EX_g3pg_e\n", + "\t- R_EX_g3pi_e\n", + "\t- R_EX_dcyt_e\n", + "\t- R_EX_ddca_e\n", + "\t- R_EX_dgmp_e\n", + "\t- R_EX_dgsn_e\n", + "\t- R_EX_g3ps_e\n", + "\t- R_EX_psclys_e\n", + "\t- R_EX_g6p_e\n", + "\t- R_EX_gal_e\n", + "\t- R_EX_gal_bD_e\n", + "\t- R_EX_gal1p_e\n", + "\t- R_EX_galct__D_e\n", + "\t- R_EX_galctn__D_e\n", + "\t- R_EX_galctn__L_e\n", + "\t- R_EX_galt_e\n", + "\t- R_EX_galur_e\n", + "\t- R_EX_dha_e\n", + "\t- R_EX_dimp_e\n", + "\t- R_EX_halipa_e\n", + "\t- R_EX_hdca_e\n", + "\t- R_EX_hdcea_e\n", + "\t- R_EX_hg2_e\n", + "\t- R_EX_his__L_e\n", + "\t- R_EX_hom__L_e\n", + "\t- R_EX_hxa_e\n", + "\t- R_EX_gam_e\n", + "\t- R_EX_gam6p_e\n", + "\t- R_EX_gbbtn_e\n", + "\t- R_EX_hxan_e\n", + "\t- R_EX_pser__L_e\n", + "\t- R_EX_idon__L_e\n", + "\t- R_EX_ile__L_e\n", + "\t- R_EX_gdp_e\n", + "\t- R_EX_glc__D_e\n", + "\t- R_EX_glcn_e\n", + "\t- R_EX_glcr_e\n", + "\t- R_EX_glcur_e\n", + "\t- R_EX_glcur1p_e\n", + "\t- R_EX_imp_e\n", + "\t- R_EX_indole_e\n", + "\t- R_EX_inost_e\n", + "\t- R_EX_ins_e\n", + "\t- R_EX_isetac_e\n", + "\t- R_EX_k_e\n", + "\t- R_EX_gln__L_e\n", + "\t- R_EX_ptrc_e\n", + "\t- R_EX_glu__L_e\n", + "\t- R_EX_gly_e\n", + "\t- R_EX_glyald_e\n", + "\t- R_EX_kdo2lipid4_e\n", + "\t- R_EX_lac__D_e\n", + "\t- R_EX_glyb_e\n", + "\t- R_EX_lac__L_e\n", + "\t- R_EX_glyc_e\n", + "\t- R_EX_pydam_e\n", + "\t- R_EX_glyc__R_e\n", + "\t- R_EX_glyc2p_e\n", + "\t- R_EX_lcts_e\n", + "\t- R_EX_leu__L_e\n", + "\t- R_EX_lipa_e\n", + "\t- R_EX_lipa_cold_e\n", + "\t- R_EX_glyc3p_e\n", + "\t- R_EX_pydx_e\n", + "\t- R_EX_glyclt_e\n", + "\t- R_EX_lipoate_e\n", + "\t- R_EX_lys__L_e\n", + "\t- R_EX_lyx__L_e\n", + "\t- R_EX_gmp_e\n", + "\t- R_EX_gsn_e\n", + "\t- R_EX_pydxn_e\n", + "\t- R_EX_gthox_e\n", + "\t- R_EX_mal__D_e\n", + "\t- R_EX_mal__L_e\n", + "\t- R_EX_gthrd_e\n", + "\t- R_EX_gtp_e\n", + "\t- R_EX_gua_e\n", + "\t- R_EX_h_e\n", + "\t- R_EX_malt_e\n", + "\t- R_EX_malthx_e\n", + "\t- R_EX_maltpt_e\n", + "\t- R_EX_h2_e\n", + "\t- R_EX_pyr_e\n", + "\t- R_EX_h2o_e\n", + "\t- R_EX_h2o2_e\n", + "\t- R_EX_h2s_e\n", + "\t- R_EX_malttr_e\n", + "\t- R_EX_maltttr_e\n", + "\t- R_EX_man_e\n", + "\t- R_EX_man6p_e\n", + "\t- R_EX_hacolipa_e\n", + "\t- R_EX_quin_e\n", + "\t- R_EX_manglyc_e\n", + "\t- R_EX_melib_e\n", + "\t- R_EX_r5p_e\n", + "\t- R_EX_meoh_e\n", + "\t- R_EX_met__D_e\n", + "\t- R_EX_rfamp_e\n", + "\t- R_EX_met__L_e\n", + "\t- R_EX_rib__D_e\n", + "\t- R_EX_metsox_R__L_e\n", + "\t- R_EX_metsox_S__L_e\n", + "\t- R_EX_mg2_e\n", + "\t- R_EX_mincyc_e\n", + "\t- R_EX_rmn_e\n", + "\t- R_EX_minohp_e\n", + "\t- R_EX_mmet_e\n", + "\t- R_EX_mn2_e\n", + "\t- R_EX_mnl_e\n", + "\t- R_EX_mobd_e\n", + "\t- R_EX_mso3_e\n", + "\t- R_EX_n2o_e\n", + "\t- R_EX_na1_e\n", + "\t- R_EX_nac_e\n", + "\t- R_EX_sbt__D_e\n", + "\t- R_EX_nh4_e\n", + "\t- R_EX_ni2_e\n", + "\t- R_EX_nmn_e\n", + "\t- R_EX_sel_e\n", + "\t- R_EX_no_e\n", + "\t- R_EX_no2_e\n", + "\t- R_EX_no3_e\n", + "\t- R_EX_novbcn_e\n", + "\t- R_EX_ser__D_e\n", + "\t- R_EX_o16a4colipa_e\n", + "\t- R_EX_o2_e\n", + "\t- R_EX_o2s_e\n", + "\t- R_EX_ocdca_e\n", + "\t- R_EX_ocdcea_e\n", + "\t- R_EX_octa_e\n", + "\t- R_EX_orn_e\n", + "\t- R_EX_orot_e\n", + "\t- R_EX_pacald_e\n", + "\t- R_EX_ser__L_e\n", + "\t- R_EX_peamn_e\n", + "\t- R_EX_phe__L_e\n", + "\t- R_EX_pheme_e\n", + "\t- R_EX_skm_e\n", + "\t- R_EX_pi_e\n", + "\t- R_EX_pnto__R_e\n", + "\t- R_EX_ppa_e\n", + "\t- R_EX_slnt_e\n", + "\t- R_EX_ppal_e\n", + "\t- R_EX_so2_e\n", + "\t- R_EX_pppn_e\n", + "\t- R_EX_ppt_e\n", + "\t- R_EX_pro__L_e\n", + "\t- R_EX_progly_e\n", + "\t- R_EX_so3_e\n", + "\t- R_EX_so4_e\n", + "\t- R_EX_spmd_e\n", + "\t- R_EX_succ_e\n", + "\t- R_EX_sucr_e\n", + "\t- R_EX_sulfac_e\n", + "\t- R_EX_tartr__D_e\n", + "\t- R_EX_tartr__L_e\n", + "\t- R_EX_taur_e\n", + "\t- R_EX_tcynt_e\n", + "\t- R_EX_thm_e\n", + "\t- R_EX_thr__L_e\n", + "\t- R_EX_thrp_e\n", + "\t- R_EX_thym_e\n", + "\t- R_EX_thymd_e\n", + "\t- R_EX_tma_e\n", + "\t- R_EX_tmao_e\n", + "\t- R_EX_tre_e\n", + "\t- R_EX_trp__L_e\n", + "\t- R_EX_tsul_e\n", + "\t- R_EX_ttdca_e\n", + "\t- R_EX_ttdcea_e\n", + "\t- R_EX_ttrcyc_e\n", + "\t- R_EX_tungs_e\n", + "\t- R_EX_tym_e\n", + "\t- R_EX_tyr__L_e\n", + "\t- R_EX_tyrp_e\n", + "\t- R_EX_uacgam_e\n", + "\t- R_EX_udpacgal_e\n", + "\t- R_EX_udpg_e\n", + "\t- R_EX_udpgal_e\n", + "\t- R_EX_udpglcur_e\n", + "\t- R_EX_ump_e\n", + "\t- R_EX_ura_e\n", + "\t- R_EX_urea_e\n", + "\t- R_EX_uri_e\n", + "\t- R_EX_val__L_e\n", + "\t- R_EX_xan_e\n", + "\t- R_EX_xmp_e\n", + "\t- R_EX_xtsn_e\n", + "\t- R_EX_xyl__D_e\n", + "\t- R_EX_xylu__L_e\n", + "\t- R_EX_zn2_e\n", + "\t- R_EX_feroxBfe_e\n", + "\t- R_EX_4hphac_e\n", + "\t- R_EX_4abz_e\n", + "\t- R_EX_acon_C_e\n", + "\t- R_EX_cysi__L_e\n", + "\t- R_EX_feroxB_e\n", + "\t- R_EX_fe3dhbzs3_e\n", + "\t- R_EX_tcb_e\n", + "\t- R_EX_tton_e\n", + "\t- R_EX_4hthr_e\n", + "\t- R_EX_feroxEfe_e\n", + "\t- R_EX_drib_e\n", + "\t- R_EX_salchs4fe_e\n", + "\t- R_EX_2pg_e\n", + "\t- R_EX_feroxE_e\n", + "\t- R_EX_23dhbzs3_e\n", + "\t- R_EX_tet_e\n", + "\t- R_EX_frmd_e\n", + "\t- R_EX_salchs4_e\n", + "\t- R_EX_colipaOA_e\n", + "\t- R_EX_salchsx_e\n", + "\t- R_EX_AEP_e\n", + "\t- R_EX_icit_e\n", + "\t- R_EX_oaa_e\n", + "\t- R_EX_pep_e\n", + "\t- R_EX_salchs2fe_e\n", + "\t- R_EX_colipa20Oag_e\n", + "\t- R_EX_tag__D_e\n", + "\t- R_EX_remnant1_e\n", + "\t- R_EX_udcpo5_e\n", + "\t- R_EX_guln__L_e\n", + "\t- R_EX_salchs2_e\n", + "\t- R_EX_feroxG_e\n", + "\t- R_EX_rnam_e\n", + "\t- R_EX_airs_e\n", + "\t- R_EX_dxyl_e\n", + "\t- R_EX_3pg_e\n", + "\t- R_EX_feroxGfe_e\n", + "\t- R_EX_diact_e\n", + "\t- R_EX_cellb_e\n", + "\t- R_EX_btd_RR_e\n", + "\t- R_EX_13ppd_e\n", + "\t- R_EX_udcpo4_e\n", + "\t- R_EX_rbt_e\n", + "\t- R_EX_bz_e\n", + "\t- R_EX_abt__D_e\n", + "\t- R_EX_galam_e\n", + "\t- R_EX_raffin_e\n", + "\t- R_EX_o6a4colipa_e\n", + "\t- R_EX_fe_e\n", + "\t- R_EX_kokdolipidA_e\n", + "\t- R_EX_3hoxpac_e\n", + "\t- R_EX_arab__D_e\n", + "\t- R_DM_lipidA_core_e_p\n", + "\t- R_EX_3ntym_e\u001b[0m\n", + "File saved at: ../../data/sbml_corrected/iB21_1397.xml\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: iCN718\n", + "\u001b[0;93mWARNING : - R_ASAD: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_AGPR: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_ACOTA: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_NAPRT: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_CYSTA: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_KARA1: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + "\n", + " - R_NADTRHD2: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + " - R_SK_ACP_c: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_DNADRAIN deleted. No reactants and no products.\n", + " - R_FOLR2: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_NARK: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + "\u001b[0m\n", + "\n", + "\n", + "\u001b[0;36m############################################\n", + "############################################\n", + " WRITING SBML FILE \n", + "############################################\n", + "############################################\n", + "\u001b[0m\n", + "\u001b[0;93mWARNING : IMPORT REACTION REMOVED BY DEFAULT\u001b[0m\n", + "\u001b[0;93mWARNING : If you want to keep import reaction\n", + "use option -kir / --keep-import-reactions\u001b[0m\n", + "\u001b[0;93mWARNING : Reaction with tag reversible modified:\n", + "\t- R_ASAD\n", + "\t- R_AGPR\n", + "\t- R_ACOTA\n", + "\t- R_NAPRT\n", + "\t- R_CYSTA\n", + "\t- R_KARA1\n", + "\t- R_SK_ACP_c\n", + "\t- R_FOLR2\n", + "\t- R_NARK\u001b[0m\n", + "\u001b[0;93mWARNING : Reaction with Reactants and Products exchanged:\n", + "\t- R_ASAD\n", + "\t- R_AGPR\n", + "\t- R_ACOTA\n", + "\t- R_NAPRT\n", + "\t- R_CYSTA\n", + "\t- R_KARA1\n", + "\t- R_SK_ACP_c\n", + "\t- R_FOLR2\n", + "\t- R_NARK\u001b[0m\n", + "\u001b[0;93mWARNING : Import reaction removed:\n", + "\t- R_EX_tyr__L_e\n", + "\t- R_EX_val__L_e\n", + "\t- R_EX_ser__D_e\n", + "\t- R_EX_orn_e\n", + "\t- R_EX_ptrc_e\n", + "\t- R_EX_spmd_e\n", + "\t- R_EX_co2_e\n", + "\t- R_EX_na1_e\n", + "\t- R_EX_no2_e\n", + "\t- R_EX_o2_e\n", + "\t- R_EX_urea_e\n", + "\t- R_EX_chol_e\n", + "\t- R_EX_adn_e\n", + "\t- R_EX_cytd_e\n", + "\t- R_EX_dad_2_e\n", + "\t- R_EX_dcyt_e\n", + "\t- R_EX_dgsn_e\n", + "\t- R_EX_thymd_e\n", + "\t- R_EX_duri_e\n", + "\t- R_EX_h_e\n", + "\t- R_EX_malt_e\n", + "\t- R_EX_fru_e\n", + "\t- R_EX_glc__D_e\n", + "\t- R_EX_glcn__D_e\n", + "\t- R_EX_mnl_e\n", + "\t- R_EX_acgam_e\n", + "\t- R_EX_gsn_e\n", + "\t- R_EX_uri_e\n", + "\t- R_EX_csn_e\n", + "\t- R_EX_xan_e\n", + "\t- R_EX_ura_e\n", + "\t- R_EX_taur_e\n", + "\t- R_EX_etha_e\n", + "\t- R_EX_sucr_e\n", + "\t- R_EX_tre_e\n", + "\t- R_EX_2pg_e\n", + "\t- R_EX_glyb_e\n", + "\t- R_EX_bz_e\n", + "\t- R_EX_3pg_e\n", + "\t- R_EX_ac_e\n", + "\t- R_EX_akg_e\n", + "\t- R_EX_cit_e\n", + "\t- R_EX_for_e\n", + "\t- R_EX_fum_e\n", + "\t- R_EX_succ_e\n", + "\t- R_EX_icit_e\n", + "\t- R_EX_mal__L_e\n", + "\t- R_EX_glyclt_e\n", + "\t- R_EX_no3_e\n", + "\t- R_EX_pi_e\n", + "\t- R_EX_so4_e\n", + "\t- R_EX_ala__L_e\n", + "\t- R_EX_arg__L_e\n", + "\t- R_EX_asn__L_e\n", + "\t- R_EX_asp__L_e\n", + "\t- R_EX_cys__L_e\n", + "\t- R_EX_ala__D_e\n", + "\t- R_EX_glu__D_e\n", + "\t- R_EX_gln__L_e\n", + "\t- R_EX_glu__L_e\n", + "\t- R_EX_gly_e\n", + "\t- R_EX_his__L_e\n", + "\t- R_EX_ile__L_e\n", + "\t- R_EX_leu__L_e\n", + "\t- R_EX_lys__L_e\n", + "\t- R_EX_met__L_e\n", + "\t- R_EX_phe__L_e\n", + "\t- R_EX_pro__L_e\n", + "\t- R_EX_cl_e\n", + "\t- R_EX_ser__L_e\n", + "\t- R_EX_thr__L_e\n", + "\t- R_EX_trp__L_e\n", + "\t- R_EX_mg2_e\n", + "\t- R_EX_h2o_e\n", + "\t- R_SK_ACP_c\n", + "\t- R_EX_nh4_e\n", + "\t- R_EX_arab__L_e\n", + "\t- R_EX_glcr_e\n", + "\t- R_EX_gal_e\n", + "\t- R_EX_man_e\n", + "\t- R_EX_galt_e\n", + "\t- R_EX_sbt__D_e\n", + "\t- R_EX_glyc_e\n", + "\t- R_EX_fuc__L_e\n", + "\t- R_EX_glcn_e\n", + "\t- R_EX_glyc3p_e\n", + "\t- R_EX_xyl__D_e\n", + "\t- R_EX_lac__L_e\n", + "\t- R_EX_g6p_e\n", + "\t- R_EX_rib__D_e\n", + "\t- R_EX_rmn_e\n", + "\t- R_EX_melib_e\n", + "\t- R_DM_rna_c\n", + "\t- R_DM_cav_c\n", + "\t- R_DM_phospholipid_c\n", + "\t- R_EX_asp__D_e\n", + "\t- R_DM_lipids_c\n", + "\t- R_EX_gam_e\n", + "\t- R_EX_13ppd_e\n", + "\t- R_EX_2obut_e\n", + "\t- R_EX_tartr__L_e\n", + "\t- R_DM_lps_c\n", + "\t- R_EX_g1p_e\n", + "\t- R_EX_f6p_e\n", + "\t- R_EX_mbdg_e\n", + "\t- R_DM_biomass_c\n", + "\t- R_EX_rbt_e\n", + "\t- R_EX_malttr_e\n", + "\t- R_EX_inost_e\n", + "\t- R_EX_ppa_e\n", + "\t- R_EX_galct__D_e\n", + "\t- R_EX_glx_e\n", + "\t- R_EX_ins_e\n", + "\t- R_EX_gly_glu__L_e\n", + "\t- R_EX_acac_e\n", + "\t- R_EX_acmana_e\n", + "\t- R_EX_mal__D_e\n", + "\t- R_EX_4hoxpac_e\n", + "\t- R_EX_3hoxpac_e\n", + "\t- R_EX_tym_e\n", + "\t- R_EX_all__D_e\n", + "\t- R_EX_lyx__L_e\n", + "\t- R_EX_pyr_e\n", + "\t- R_EX_galur_e\n", + "\t- R_EX_peamn_e\n", + "\t- R_EX_lcts_e\n", + "\t- R_SK_met__L_c\n", + "\t- R_DM_protein_c\u001b[0m\n", + "File saved at: ../../data/sbml_corrected/iCN718.xml\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: iIS312\n", + "\u001b[0;93mWARNING : - R_r0202: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_SUCCtp: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_ALATA_L: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_EX_pro__L_e: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_EX_glu__L_e: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_EX_thr__L_e: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_EX_b_D_glucose_e: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_EX_a_D_glucose_e: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_EX_asp__L_e: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_EX_pi_e: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_CYSTA: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_MCPST: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_NITR: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_ALATA_Lm: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + "\u001b[0m\n", + "\n", + "\n", + "\u001b[0;36m############################################\n", + "############################################\n", + " WRITING SBML FILE \n", + "############################################\n", + "############################################\n", + "\u001b[0m\n", + "\u001b[0;93mWARNING : IMPORT REACTION REMOVED BY DEFAULT\u001b[0m\n", + "\u001b[0;93mWARNING : If you want to keep import reaction\n", + "use option -kir / --keep-import-reactions\u001b[0m\n", + "\u001b[0;93mWARNING : Reaction with tag reversible modified:\n", + "\t- R_r0202\n", + "\t- R_SUCCtp\n", + "\t- R_ALATA_L\n", + "\t- R_EX_pro__L_e\n", + "\t- R_EX_glu__L_e\n", + "\t- R_EX_thr__L_e\n", + "\t- R_EX_b_D_glucose_e\n", + "\t- R_EX_a_D_glucose_e\n", + "\t- R_EX_asp__L_e\n", + "\t- R_EX_pi_e\n", + "\t- R_CYSTA\n", + "\t- R_MCPST\n", + "\t- R_NITR\n", + "\t- R_ALATA_Lm\u001b[0m\n", + "\u001b[0;93mWARNING : Reaction with Reactants and Products exchanged:\n", + "\t- R_r0202\n", + "\t- R_SUCCtp\n", + "\t- R_ALATA_L\n", + "\t- R_EX_pro__L_e\n", + "\t- R_EX_glu__L_e\n", + "\t- R_EX_thr__L_e\n", + "\t- R_EX_b_D_glucose_e\n", + "\t- R_EX_a_D_glucose_e\n", + "\t- R_EX_asp__L_e\n", + "\t- R_EX_pi_e\n", + "\t- R_CYSTA\n", + "\t- R_MCPST\n", + "\t- R_NITR\n", + "\t- R_ALATA_Lm\u001b[0m\n", + "\u001b[0;93mWARNING : Import reaction removed:\n", + "\t- R_EX_h2o_e\n", + "\t- R_EX_pro__L_e\n", + "\t- R_EX_glu__L_e\n", + "\t- R_EX_co2_e\n", + "\t- R_EX_nh4_e\n", + "\t- R_EX_glyc_e\n", + "\t- R_EX_gly_e\n", + "\t- R_EX_o2_e\n", + "\t- R_EX_h_e\n", + "\t- R_EX_thr__L_e\n", + "\t- R_EX_ala__L_e\n", + "\t- R_EX_succ_e\n", + "\t- R_EX_b_D_glucose_e\n", + "\t- R_EX_a_D_glucose_e\n", + "\t- R_EX_ac_e\n", + "\t- R_EX_asp__L_e\n", + "\t- R_EX_pi_e\n", + "\t- R_EX_gln__L_e\n", + "\t- R_EX_arg__L_e\n", + "\t- R_EX_urea_e\n", + "\t- R_EX_phe__L_e\n", + "\t- R_EX_ile__L_e\n", + "\t- R_EX_his__L_e\n", + "\t- R_EX_val__L_e\n", + "\t- R_EX_leu__L_e\n", + "\t- R_EX_lys__L_e\n", + "\t- R_EX_ala__D_e\n", + "\t- R_EX_ergst_e\n", + "\t- R_EX_ser__L_e\n", + "\t- R_EX_ura_e\n", + "\t- R_EX_met__L_e\n", + "\t- R_EX_cys__L_e\n", + "\t- R_EX_asn__L_e\n", + "\t- R_EX_chol_e\n", + "\t- R_EX_cit_e\n", + "\t- R_EX_gthrd_e\n", + "\t- R_EX_h2s_e\n", + "\t- R_EX_nac_e\n", + "\t- R_EX_ncam_e\n", + "\t- R_EX_inost_e\n", + "\t- R_EX_nh3_e\n", + "\t- R_EX_gua_e\n", + "\t- R_EX_drib_e\n", + "\t- R_EX_thym_e\n", + "\t- R_EX_hxan_e\u001b[0m\n", + "File saved at: ../../data/sbml_corrected/iIS312.xml\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: RECON1\n", + "\u001b[0;93mWARNING : - R_AATA: Reactants and products exchanged.\n", + " Boundaries was: [-999999.0 ; 0.0]\n", + " - R_ABTt: Reactants and products exchanged.\n", + " Boundaries was: [-999999.0 ; 0.0]\n", + " - R_ACOAH: Reactants and products exchanged.\n", + " Boundaries was: [-999999.0 ; 0.0]\n", + " - R_LCARS: Reactants and products exchanged.\n", + " Boundaries was: [-999999.0 ; 0.0]\n", + " - R_HMGCOAS: Reactants and products exchanged.\n", + " Boundaries was: [-999999.0 ; 0.0]\n", + " - R_HMGCOASm: Reactants and products exchanged.\n", + " Boundaries was: [-999999.0 ; 0.0]\n", + "\u001b[0m\n", + "\n", + "\n", + "\u001b[0;36m############################################\n", + "############################################\n", + " WRITING SBML FILE \n", + "############################################\n", + "############################################\n", + "\u001b[0m\n", + "\u001b[0;93mWARNING : IMPORT REACTION REMOVED BY DEFAULT\u001b[0m\n", + "\u001b[0;93mWARNING : If you want to keep import reaction\n", + "use option -kir / --keep-import-reactions\u001b[0m\n", + "\u001b[0;93mWARNING : Reaction with tag reversible modified:\n", + "\t- R_AATA\n", + "\t- R_ABTt\n", + "\t- R_ACOAH\n", + "\t- R_LCARS\n", + "\t- R_HMGCOAS\n", + "\t- R_HMGCOASm\u001b[0m\n", + "\u001b[0;93mWARNING : Reaction with Reactants and Products exchanged:\n", + "\t- R_AATA\n", + "\t- R_ABTt\n", + "\t- R_ACOAH\n", + "\t- R_LCARS\n", + "\t- R_HMGCOAS\n", + "\t- R_HMGCOASm\u001b[0m\n", + "\u001b[0;93mWARNING : Import reaction removed:\n", + "\t- R_DM_13_cis_oretn_n\n", + "\t- R_DM_13_cis_retn_n\n", + "\t- R_DM_Asn_X_Ser_Thr_l\n", + "\t- R_DM_Ser_Thr_l\n", + "\t- R_DM_Ser_Gly_Ala_X_Gly_l\n", + "\t- R_DM_avite1_c\n", + "\t- R_DM_avite2_c\n", + "\t- R_DM_bvite_c\n", + "\t- R_DM_datp_m\n", + "\t- R_DM_datp_n\n", + "\t- R_DM_dctp_m\n", + "\t- R_DM_dctp_n\n", + "\t- R_DM_dgtp_m\n", + "\t- R_DM_dgtp_n\n", + "\t- R_DM_dsT_antigen_g\n", + "\t- R_DM_dttp_m\n", + "\t- R_DM_dttp_n\n", + "\t- R_DM_ethamp_r\n", + "\t- R_DM_gpi_sig_r\n", + "\t- R_DM_hretn_n\n", + "\t- R_DM_kdn_c\n", + "\t- R_DM_melanin_c\n", + "\t- R_DM_oretn_n\n", + "\t- R_DM_sTn_antigen_g\n", + "\t- R_DM_sprm_c\n", + "\t- R_DM_yvite_c\n", + "\t- R_EX_10fthf5glu_e\n", + "\t- R_EX_10fthf6glu_e\n", + "\t- R_EX_10fthf7glu_e\n", + "\t- R_EX_10fthf_e\n", + "\t- R_EX_11_cis_retfa_e\n", + "\t- R_EX_13_cis_retnglc_e\n", + "\t- R_EX_1glyc_hs_e\n", + "\t- R_EX_1mncam_e\n", + "\t- R_EX_2425dhvitd2_e\n", + "\t- R_EX_2425dhvitd3_e\n", + "\t- R_EX_24nph_e\n", + "\t- R_EX_25hvitd2_e\n", + "\t- R_EX_25hvitd3_e\n", + "\t- R_EX_2hb_e\n", + "\t- R_EX_2mcit_e\n", + "\t- R_EX_34dhoxpeg_e\n", + "\t- R_EX_34dhphe_e\n", + "\t- R_EX_35cgmp_e\n", + "\t- R_EX_3aib__D_e\n", + "\t- R_EX_3aib_e\n", + "\t- R_EX_3mlda_e\n", + "\t- R_EX_4abut_e\n", + "\t- R_EX_4hdebrisoquine_e\n", + "\t- R_EX_4hphac_e\n", + "\t- R_EX_4mtolbutamide_e\n", + "\t- R_EX_4nph_e\n", + "\t- R_EX_4nphsf_e\n", + "\t- R_EX_4pyrdx_e\n", + "\t- R_EX_5adtststerone_e\n", + "\t- R_EX_5adtststeroneglc_e\n", + "\t- R_EX_5adtststerones_e\n", + "\t- R_EX_5dhf_e\n", + "\t- R_EX_5fthf_e\n", + "\t- R_EX_5homeprazole_e\n", + "\t- R_EX_5htrp_e\n", + "\t- R_EX_5mthf_e\n", + "\t- R_EX_5thf_e\n", + "\t- R_EX_6dhf_e\n", + "\t- R_EX_6htststerone_e\n", + "\t- R_EX_6thf_e\n", + "\t- R_EX_7dhf_e\n", + "\t- R_EX_7thf_e\n", + "\t- R_EX_9_cis_retfa_e\n", + "\t- R_EX_clpnd_e\n", + "\t- R_EX_cysi__L_e\n", + "\t- R_EX_Rtotal2_e\n", + "\t- R_EX_Rtotal3_e\n", + "\t- R_EX_Rtotal_e\n", + "\t- R_EX_Tyr_ggn_e\n", + "\t- R_EX_abt_e\n", + "\t- R_EX_ac_e\n", + "\t- R_EX_acac_e\n", + "\t- R_EX_acald_e\n", + "\t- R_EX_acetone_e\n", + "\t- R_EX_acgalfucgalacgalfuc12gal14acglcgalgluside_hs_e\n", + "\t- R_EX_acgalfucgalacgalfucgalacglcgal14acglcgalgluside_hs_e\n", + "\t- R_EX_acgam_e\n", + "\t- R_EX_ach_e\n", + "\t- R_EX_acn13acngalgbside_hs_e\n", + "\t- R_EX_acn23acngalgbside_hs_e\n", + "\t- R_EX_acnacngal14acglcgalgluside_hs_e\n", + "\t- R_EX_acnacngalgbside_hs_e\n", + "\t- R_EX_acngalacglcgal14acglcgalgluside_hs_e\n", + "\t- R_EX_ade_e\n", + "\t- R_EX_adn_e\n", + "\t- R_EX_adp_e\n", + "\t- R_EX_adprbp_e\n", + "\t- R_EX_adprib_e\n", + "\t- R_EX_adrn_e\n", + "\t- R_EX_adrnl_e\n", + "\t- R_EX_aflatoxin_e\n", + "\t- R_EX_ahandrostanglc_e\n", + "\t- R_EX_ak2lgchol_hs_e\n", + "\t- R_EX_akg_e\n", + "\t- R_EX_ala_B_e\n", + "\t- R_EX_ala__D_e\n", + "\t- R_EX_ala__L_e\n", + "\t- R_EX_aldstrn_e\n", + "\t- R_EX_amp_e\n", + "\t- R_EX_andrstrn_e\n", + "\t- R_EX_andrstrnglc_e\n", + "\t- R_EX_antipyrene_e\n", + "\t- R_EX_apnnox_e\n", + "\t- R_EX_appnn_e\n", + "\t- R_EX_aprgstrn_e\n", + "\t- R_EX_aqcobal_e\n", + "\t- R_EX_arab__L_e\n", + "\t- R_EX_arach_e\n", + "\t- R_EX_arachd_e\n", + "\t- R_EX_arg__L_e\n", + "\t- R_EX_ascb__L_e\n", + "\t- R_EX_asn__L_e\n", + "\t- R_EX_asp__D_e\n", + "\t- R_EX_asp__L_e\n", + "\t- R_EX_camp_e\n", + "\t- R_EX_caro_e\n", + "\t- R_EX_carveol_e\n", + "\t- R_EX_cca_d3_e\n", + "\t- R_EX_cgly_e\n", + "\t- R_EX_chol_e\n", + "\t- R_EX_cholate_e\n", + "\t- R_EX_chsterol_e\n", + "\t- R_EX_chtn_e\n", + "\t- R_EX_cit_e\n", + "\t- R_EX_cl_e\n", + "\t- R_EX_cmp_e\n", + "\t- R_EX_co2_e\n", + "\t- R_EX_co_e\n", + "\t- R_EX_coumarin_e\n", + "\t- R_EX_creat_e\n", + "\t- R_EX_crmp_hs_e\n", + "\t- R_EX_crn_e\n", + "\t- R_EX_crvnc_e\n", + "\t- R_EX_csn_e\n", + "\t- R_EX_cspg_a_e\n", + "\t- R_EX_cspg_b_e\n", + "\t- R_EX_cspg_c_e\n", + "\t- R_EX_cspg_d_e\n", + "\t- R_EX_cspg_e_e\n", + "\t- R_EX_cyan_e\n", + "\t- R_EX_cys__L_e\n", + "\t- R_EX_cytd_e\n", + "\t- R_EX_dad_2_e\n", + "\t- R_EX_dad_5_e\n", + "\t- R_EX_dag_hs_e\n", + "\t- R_EX_dcsptn1_e\n", + "\t- R_EX_dcyt_e\n", + "\t- R_EX_debrisoquine_e\n", + "\t- R_EX_dgchol_e\n", + "\t- R_EX_dgsn_e\n", + "\t- R_EX_dhdascb_e\n", + "\t- R_EX_dheas_e\n", + "\t- R_EX_dhf_e\n", + "\t- R_EX_digalsgalside_hs_e\n", + "\t- R_EX_din_e\n", + "\t- R_EX_dlnlcg_e\n", + "\t- R_EX_dmantipyrine_e\n", + "\t- R_EX_dmhptcrn_e\n", + "\t- R_EX_dopa_e\n", + "\t- R_EX_dopasf_e\n", + "\t- R_EX_drib_e\n", + "\t- R_EX_atp_e\n", + "\t- R_EX_avite1_e\n", + "\t- R_EX_avite2_e\n", + "\t- R_EX_bhb_e\n", + "\t- R_EX_bildglcur_e\n", + "\t- R_EX_bilglcur_e\n", + "\t- R_EX_bilirub_e\n", + "\t- R_EX_biocyt_e\n", + "\t- R_EX_btn_e\n", + "\t- R_EX_but_e\n", + "\t- R_EX_bvite_e\n", + "\t- R_EX_bz_e\n", + "\t- R_EX_ca2_e\n", + "\t- R_EX_galgalfucfucgalacglcgalacglcgal14acglcgalgluside_hs_e\n", + "\t- R_EX_galgalgalthcrm_hs_e\n", + "\t- R_EX_gam_e\n", + "\t- R_EX_gbside_hs_e\n", + "\t- R_EX_gchola_e\n", + "\t- R_EX_duri_e\n", + "\t- R_EX_eaflatoxin_e\n", + "\t- R_EX_ebastine_e\n", + "\t- R_EX_ebastineoh_e\n", + "\t- R_EX_eicostet_e\n", + "\t- R_EX_elaid_e\n", + "\t- R_EX_estradiolglc_e\n", + "\t- R_EX_estriolglc_e\n", + "\t- R_EX_estroneglc_e\n", + "\t- R_EX_estrones_e\n", + "\t- R_EX_gd1b2_hs_e\n", + "\t- R_EX_gd1c_hs_e\n", + "\t- R_EX_etoh_e\n", + "\t- R_EX_fe2_e\n", + "\t- R_EX_fe3_e\n", + "\t- R_EX_gdchola_e\n", + "\t- R_EX_gdp_e\n", + "\t- R_EX_glc__D_e\n", + "\t- R_EX_gln__L_e\n", + "\t- R_EX_glu__L_e\n", + "\t- R_EX_fol_e\n", + "\t- R_EX_for_e\n", + "\t- R_EX_fru_e\n", + "\t- R_EX_fuc13galacglcgal14acglcgalgluside_hs_e\n", + "\t- R_EX_gluala_e\n", + "\t- R_EX_fuc14galacglcgalgluside_hs_e\n", + "\t- R_EX_fuc__L_e\n", + "\t- R_EX_fucacgalfucgalacglcgalgluside_hs_e\n", + "\t- R_EX_fucacngal14acglcgalgluside_hs_e\n", + "\t- R_EX_fucacngalacglcgalgluside_hs_e\n", + "\t- R_EX_gly_e\n", + "\t- R_EX_glyb_e\n", + "\t- R_EX_glyc_e\n", + "\t- R_EX_glyc__S_e\n", + "\t- R_EX_glygn2_e\n", + "\t- R_EX_fucfuc12gal14acglcgalgluside_hs_e\n", + "\t- R_EX_fucfuc132galacglcgal14acglcgalgluside_hs_e\n", + "\t- R_EX_fucfucfucgalacglc13galacglcgal14acglcgalgluside_hs_e\n", + "\t- R_EX_glygn4_e\n", + "\t- R_EX_glygn5_e\n", + "\t- R_EX_gmp_e\n", + "\t- R_EX_fucfucfucgalacglcgal14acglcgalgluside_hs_e\n", + "\t- R_EX_fucfucgalacglcgalgluside_hs_e\n", + "\t- R_EX_fucgal14acglcgalgluside_hs_e\n", + "\t- R_EX_fucgalfucgalacglcgalgluside_hs_e\n", + "\t- R_EX_gp1c_hs_e\n", + "\t- R_EX_gp1calpha_hs_e\n", + "\t- R_EX_gq1b_hs_e\n", + "\t- R_EX_gq1balpha_hs_e\n", + "\t- R_EX_gsn_e\n", + "\t- R_EX_gt1a_hs_e\n", + "\t- R_EX_gthox_e\n", + "\t- R_EX_fucgalgbside_hs_e\n", + "\t- R_EX_gal_e\n", + "\t- R_EX_galacglcgalgbside_hs_e\n", + "\t- R_EX_galfuc12gal14acglcgalgluside_hs_e\n", + "\t- R_EX_galfucgalacglcgal14acglcgalgluside_hs_e\n", + "\t- R_EX_gthrd_e\n", + "\t- R_EX_gtp_e\n", + "\t- R_EX_gua_e\n", + "\t- R_EX_h2o2_e\n", + "\t- R_EX_met__L_e\n", + "\t- R_EX_h2o_e\n", + "\t- R_EX_h_e\n", + "\t- R_EX_mthgxl_e\n", + "\t- R_EX_n2m2nmasn_e\n", + "\t- R_EX_na1_e\n", + "\t- R_EX_ha_e\n", + "\t- R_EX_ha_pre1_e\n", + "\t- R_EX_nac_e\n", + "\t- R_EX_nad_e\n", + "\t- R_EX_hco3_e\n", + "\t- R_EX_hcoumarin_e\n", + "\t- R_EX_hdca_e\n", + "\t- R_EX_nadp_e\n", + "\t- R_EX_hdcea_e\n", + "\t- R_EX_ncam_e\n", + "\t- R_EX_nh4_e\n", + "\t- R_EX_nifedipine_e\n", + "\t- R_EX_no_e\n", + "\t- R_EX_npthl_e\n", + "\t- R_EX_nrpphr_e\n", + "\t- R_EX_nrpphrsf_e\n", + "\t- R_EX_nrvnc_e\n", + "\t- R_EX_hestratriol_e\n", + "\t- R_EX_hexc_e\n", + "\t- R_EX_his__L_e\n", + "\t- R_EX_hista_e\n", + "\t- R_EX_hom__L_e\n", + "\t- R_EX_hpdca_e\n", + "\t- R_EX_hspg_e\n", + "\t- R_EX_htaxol_e\n", + "\t- R_EX_o2_e\n", + "\t- R_EX_o2s_e\n", + "\t- R_EX_oagd3_hs_e\n", + "\t- R_EX_oagt3_hs_e\n", + "\t- R_EX_hxan_e\n", + "\t- R_EX_i_e\n", + "\t- R_EX_idp_e\n", + "\t- R_EX_ile__L_e\n", + "\t- R_EX_ocdca_e\n", + "\t- R_EX_ocdcea_e\n", + "\t- R_EX_octa_e\n", + "\t- R_EX_oh1_e\n", + "\t- R_EX_imp_e\n", + "\t- R_EX_omeprazole_e\n", + "\t- R_EX_onpthl_e\n", + "\t- R_EX_orn_e\n", + "\t- R_EX_inost_e\n", + "\t- R_EX_ins_e\n", + "\t- R_EX_k_e\n", + "\t- R_EX_ksi_e\n", + "\t- R_EX_ksi_deg1_e\n", + "\t- R_EX_ksii_core2_e\n", + "\t- R_EX_ksii_core4_e\n", + "\t- R_EX_oxa_e\n", + "\t- R_EX_paf_hs_e\n", + "\t- R_EX_pchol_hs_e\n", + "\t- R_EX_pe_hs_e\n", + "\t- R_EX_peplys_e\n", + "\t- R_EX_perillyl_e\n", + "\t- R_EX_pglyc_hs_e\n", + "\t- R_EX_lac__D_e\n", + "\t- R_EX_lac__L_e\n", + "\t- R_EX_phe__L_e\n", + "\t- R_EX_lcts_e\n", + "\t- R_EX_leu__L_e\n", + "\t- R_EX_leuktrC4_e\n", + "\t- R_EX_lgnc_e\n", + "\t- R_EX_pheacgln_e\n", + "\t- R_EX_pheme_e\n", + "\t- R_EX_phllqne_e\n", + "\t- R_EX_phyt_e\n", + "\t- R_EX_limnen_e\n", + "\t- R_EX_pi_e\n", + "\t- R_EX_pnto__R_e\n", + "\t- R_EX_lipoate_e\n", + "\t- R_EX_lneldc_e\n", + "\t- R_EX_ppa_e\n", + "\t- R_EX_pro__D_e\n", + "\t- R_EX_lnlc_e\n", + "\t- R_EX_lnlnca_e\n", + "\t- R_EX_lnlncg_e\n", + "\t- R_EX_pro__L_e\n", + "\t- R_EX_prostgd2_e\n", + "\t- R_EX_prostge1_e\n", + "\t- R_EX_prostge2_e\n", + "\t- R_EX_prostgf2_e\n", + "\t- R_EX_lpchol_hs_e\n", + "\t- R_EX_lys__L_e\n", + "\t- R_EX_mag_hs_e\n", + "\t- R_EX_malt_e\n", + "\t- R_EX_ps_hs_e\n", + "\t- R_EX_malttr_e\n", + "\t- R_EX_ptdca_e\n", + "\t- R_EX_man_e\n", + "\t- R_EX_meoh_e\n", + "\t- R_EX_pydam_e\n", + "\t- R_EX_pydx_e\n", + "\t- R_EX_pydxn_e\n", + "\t- R_EX_mepi_e\n", + "\t- R_EX_mercplaccys_e\n", + "\t- R_EX_pyr_e\n", + "\t- R_EX_rbt_e\n", + "\t- R_EX_retfa_e\n", + "\t- R_EX_tag__D_e\n", + "\t- R_EX_retinol_9_cis_e\n", + "\t- R_EX_retinol_e\n", + "\t- R_EX_retinol_cis_11_e\n", + "\t- R_EX_taur_e\n", + "\t- R_EX_taxol_e\n", + "\t- R_EX_retn_e\n", + "\t- R_EX_retnglc_e\n", + "\t- R_EX_retpalm_e\n", + "\t- R_EX_tchola_e\n", + "\t- R_EX_tcynt_e\n", + "\t- R_EX_tdchola_e\n", + "\t- R_EX_tethex3_e\n", + "\t- R_EX_tetpent3_e\n", + "\t- R_EX_tetpent6_e\n", + "\t- R_EX_rib__D_e\n", + "\t- R_EX_ribflv_e\n", + "\t- R_EX_s2l2fn2m2masn_e\n", + "\t- R_EX_s2l2n2m2masn_e\n", + "\t- R_EX_tettet6_e\n", + "\t- R_EX_thf_e\n", + "\t- R_EX_thm_e\n", + "\t- R_EX_sarcs_e\n", + "\t- R_EX_sel_e\n", + "\t- R_EX_ser__D_e\n", + "\t- R_EX_thmmp_e\n", + "\t- R_EX_ser__L_e\n", + "\t- R_EX_thmtp_e\n", + "\t- R_EX_thr__L_e\n", + "\t- R_EX_thym_e\n", + "\t- R_EX_sl__L_e\n", + "\t- R_EX_so4_e\n", + "\t- R_EX_spc_hs_e\n", + "\t- R_EX_sph1p_e\n", + "\t- R_EX_thymd_e\n", + "\t- R_EX_thyox__L_e\n", + "\t- R_EX_sphs1p_e\n", + "\t- R_EX_srtn_e\n", + "\t- R_EX_strch1_e\n", + "\t- R_EX_tmndnc_e\n", + "\t- R_EX_tolbutamide_e\n", + "\t- R_EX_strch2_e\n", + "\t- R_EX_tre_e\n", + "\t- R_EX_strdnc_e\n", + "\t- R_EX_succ_e\n", + "\t- R_EX_sucr_e\n", + "\t- R_EX_tag_hs_e\n", + "\t- R_EX_triodthy_e\n", + "\t- R_EX_triodthysuf_e\n", + "\t- R_EX_trp__L_e\n", + "\t- R_EX_tststeroneglc_e\n", + "\t- R_EX_tststerones_e\n", + "\t- R_EX_tsul_e\n", + "\t- R_EX_ttdca_e\n", + "\t- R_EX_txa2_e\n", + "\t- R_EX_tymsf_e\n", + "\t- R_EX_tyr__L_e\n", + "\t- R_EX_udp_e\n", + "\t- R_EX_ump_e\n", + "\t- R_EX_ura_e\n", + "\t- R_EX_urate_e\n", + "\t- R_EX_urea_e\n", + "\t- R_EX_uri_e\n", + "\t- R_EX_utp_e\n", + "\t- R_EX_vacc_e\n", + "\t- R_EX_val__L_e\n", + "\t- R_EX_vitd2_e\n", + "\t- R_EX_vitd3_e\n", + "\t- R_EX_whddca_e\n", + "\t- R_EX_whhdca_e\n", + "\t- R_EX_whtststerone_e\n", + "\t- R_EX_whttdca_e\n", + "\t- R_EX_xolest2_hs_e\n", + "\t- R_EX_xolest_hs_e\n", + "\t- R_EX_xoltri24_e\n", + "\t- R_EX_xoltri25_e\n", + "\t- R_EX_xoltri27_e\n", + "\t- R_EX_xyl__D_e\n", + "\t- R_EX_xylt_e\n", + "\t- R_EX_yvite_e\u001b[0m\n", + "File saved at: ../../data/sbml_corrected/RECON1.xml\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: iECABU_c1320\n", + "\u001b[0;93mWARNING : \n", + " - R_CAT: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_DHPTDNR: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_DHPTDNRN: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_FHL: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SPODM: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SPODMpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCASPtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCFUMtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCMALtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCTARTtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_CHITOBpts: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_CHITPH: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_URCN: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_EX_chitob_e: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\u001b[0m\n", + "\n", + "\n", + "\u001b[0;36m############################################\n", + "############################################\n", + " WRITING SBML FILE \n", + "############################################\n", + "############################################\n", + "\u001b[0m\n", + "\u001b[0;93mWARNING : IMPORT REACTION REMOVED BY DEFAULT\u001b[0m\n", + "\u001b[0;93mWARNING : If you want to keep import reaction\n", + "use option -kir / --keep-import-reactions\u001b[0m\n", + "\u001b[0;93mWARNING : Import reaction removed:\n", + "\t- R_EX_3gmp_e\n", + "\t- R_EX_3hcinnm_e\n", + "\t- R_EX_3hpp_e\n", + "\t- R_EX_3hpppn_e\n", + "\t- R_EX_3cmp_e\n", + "\t- R_EX_ac_e\n", + "\t- R_EX_4abut_e\n", + "\t- R_EX_4hoxpacd_e\n", + "\t- R_EX_5dglcn_e\n", + "\t- R_EX_5mtr_e\n", + "\t- R_EX_LalaDglu_e\n", + "\t- R_EX_LalaDgluMdap_e\n", + "\t- R_EX_LalaDgluMdapDala_e\n", + "\t- R_EX_LalaLglu_e\n", + "\t- R_EX_3ump_e\n", + "\t- R_EX_acgam_e\n", + "\t- R_EX_acald_e\n", + "\t- R_EX_acgal1p_e\n", + "\t- R_EX_acac_e\n", + "\t- R_EX_acgal_e\n", + "\t- R_EX_acolipa_e\n", + "\t- R_EX_acgam1p_e\n", + "\t- R_EX_acmana_e\n", + "\t- R_EX_acmum_e\n", + "\t- R_EX_acnam_e\n", + "\t- R_EX_ala__L_e\n", + "\t- R_EX_adn_e\n", + "\t- R_EX_ag_e\n", + "\t- R_EX_akg_e\n", + "\t- R_EX_ala_B_e\n", + "\t- R_EX_acser_e\n", + "\t- R_EX_ade_e\n", + "\t- R_EX_adocbl_e\n", + "\t- R_EX_agm_e\n", + "\t- R_EX_ala__D_e\n", + "\t- R_EX_alaala_e\n", + "\t- R_EX_all__D_e\n", + "\t- R_EX_amp_e\n", + "\t- R_EX_alltn_e\n", + "\t- R_EX_arbt_e\n", + "\t- R_EX_anhgm_e\n", + "\t- R_EX_arab__L_e\n", + "\t- R_DM_5drib_c\n", + "\t- R_EX_arbtn_fe3_e\n", + "\t- R_EX_arg__L_e\n", + "\t- R_EX_ascb__L_e\n", + "\t- R_EX_asn__L_e\n", + "\t- R_EX_aso3_e\n", + "\t- R_EX_asp__L_e\n", + "\t- R_EX_btn_e\n", + "\t- R_EX_but_e\n", + "\t- R_EX_butso3_e\n", + "\t- R_EX_ca2_e\n", + "\t- R_EX_cbi_e\n", + "\t- R_EX_arbtn_e\n", + "\t- R_DM_4crsol_c\n", + "\t- R_DM_oxam_c\n", + "\t- R_DM_amob_c\n", + "\t- R_DM_aacald_c\n", + "\t- R_DM_mththf_c\n", + "\t- R_EX_cbl1_e\n", + "\t- R_EX_cd2_e\n", + "\t- R_EX_cgly_e\n", + "\t- R_EX_12ppd__S_e\n", + "\t- R_EX_12ppd__R_e\n", + "\t- R_EX_23cgmp_e\n", + "\t- R_EX_14glucan_e\n", + "\t- R_EX_23camp_e\n", + "\t- R_EX_23ccmp_e\n", + "\t- R_EX_2ddglcn_e\n", + "\t- R_EX_15dap_e\n", + "\t- R_EX_23cump_e\n", + "\t- R_EX_34dhpac_e\n", + "\t- R_EX_23dappa_e\n", + "\t- R_EX_3amp_e\n", + "\t- R_EX_chol_e\n", + "\t- R_EX_26dap__M_e\n", + "\t- R_EX_chtbs_e\n", + "\t- R_EX_cit_e\n", + "\t- R_EX_n2o_e\n", + "\t- R_EX_cl_e\n", + "\t- R_EX_cm_e\n", + "\t- R_EX_cmp_e\n", + "\t- R_EX_co2_e\n", + "\t- R_EX_cobalt2_e\n", + "\t- R_EX_gam_e\n", + "\t- R_EX_gam6p_e\n", + "\t- R_EX_gbbtn_e\n", + "\t- R_EX_gdp_e\n", + "\t- R_EX_glc__D_e\n", + "\t- R_EX_glcn_e\n", + "\t- R_EX_colipa_e\n", + "\t- R_EX_colipap_e\n", + "\t- R_EX_na1_e\n", + "\t- R_EX_glcr_e\n", + "\t- R_EX_glcur_e\n", + "\t- R_EX_cpgn_e\n", + "\t- R_EX_nac_e\n", + "\t- R_EX_glcur1p_e\n", + "\t- R_EX_gln__L_e\n", + "\t- R_EX_nh4_e\n", + "\t- R_EX_glu__L_e\n", + "\t- R_EX_cpgn_un_e\n", + "\t- R_EX_crn_e\n", + "\t- R_EX_ni2_e\n", + "\t- R_EX_crn__D_e\n", + "\t- R_EX_gly_e\n", + "\t- R_EX_glyald_e\n", + "\t- R_EX_glyb_e\n", + "\t- R_EX_glyc_e\n", + "\t- R_EX_glyc__R_e\n", + "\t- R_EX_csn_e\n", + "\t- R_EX_cu_e\n", + "\t- R_EX_cu2_e\n", + "\t- R_EX_cyan_e\n", + "\t- R_EX_glyc2p_e\n", + "\t- R_EX_cynt_e\n", + "\t- R_EX_nmn_e\n", + "\t- R_EX_cys__D_e\n", + "\t- R_EX_cys__L_e\n", + "\t- R_EX_glyc3p_e\n", + "\t- R_EX_glyclt_e\n", + "\t- R_EX_gmp_e\n", + "\t- R_EX_gsn_e\n", + "\t- R_EX_cytd_e\n", + "\t- R_EX_no_e\n", + "\t- R_EX_dad_2_e\n", + "\t- R_EX_gthox_e\n", + "\t- R_EX_damp_e\n", + "\t- R_EX_gthrd_e\n", + "\t- R_EX_no2_e\n", + "\t- R_EX_gtp_e\n", + "\t- R_EX_dca_e\n", + "\t- R_EX_dcmp_e\n", + "\t- R_EX_gua_e\n", + "\t- R_EX_no3_e\n", + "\t- R_EX_h_e\n", + "\t- R_EX_h2_e\n", + "\t- R_EX_dcyt_e\n", + "\t- R_EX_ddca_e\n", + "\t- R_EX_novbcn_e\n", + "\t- R_EX_dgmp_e\n", + "\t- R_EX_h2o_e\n", + "\t- R_EX_h2o2_e\n", + "\t- R_EX_h2s_e\n", + "\t- R_EX_dgsn_e\n", + "\t- R_EX_dha_e\n", + "\t- R_EX_hacolipa_e\n", + "\t- R_EX_halipa_e\n", + "\t- R_EX_hdca_e\n", + "\t- R_EX_hdcea_e\n", + "\t- R_EX_dimp_e\n", + "\t- R_EX_din_e\n", + "\t- R_EX_hg2_e\n", + "\t- R_EX_dms_e\n", + "\t- R_EX_o16a4colipa_e\n", + "\t- R_EX_dmso_e\n", + "\t- R_EX_his__L_e\n", + "\t- R_EX_hom__L_e\n", + "\t- R_EX_dopa_e\n", + "\t- R_EX_hxa_e\n", + "\t- R_EX_o2_e\n", + "\t- R_EX_hxan_e\n", + "\t- R_EX_doxrbcn_e\n", + "\t- R_EX_dtmp_e\n", + "\t- R_EX_dump_e\n", + "\t- R_EX_idon__L_e\n", + "\t- R_EX_o2s_e\n", + "\t- R_EX_ile__L_e\n", + "\t- R_EX_imp_e\n", + "\t- R_EX_duri_e\n", + "\t- R_EX_eca4colipa_e\n", + "\t- R_EX_enlipa_e\n", + "\t- R_EX_indole_e\n", + "\t- R_EX_ocdca_e\n", + "\t- R_EX_inost_e\n", + "\t- R_EX_ins_e\n", + "\t- R_EX_isetac_e\n", + "\t- R_EX_enter_e\n", + "\t- R_EX_etha_e\n", + "\t- R_EX_ethso3_e\n", + "\t- R_EX_k_e\n", + "\t- R_EX_kdo2lipid4_e\n", + "\t- R_EX_lac__D_e\n", + "\t- R_EX_etoh_e\n", + "\t- R_EX_f6p_e\n", + "\t- R_EX_fald_e\n", + "\t- R_EX_fe2_e\n", + "\t- R_EX_lac__L_e\n", + "\t- R_EX_ocdcea_e\n", + "\t- R_EX_lcts_e\n", + "\t- R_EX_leu__L_e\n", + "\t- R_EX_lipa_e\n", + "\t- R_EX_fe3_e\n", + "\t- R_EX_fe3dcit_e\n", + "\t- R_EX_fe3dhbzs_e\n", + "\t- R_EX_fe3hox_e\n", + "\t- R_EX_lipa_cold_e\n", + "\t- R_EX_fe3hox_un_e\n", + "\t- R_EX_lipoate_e\n", + "\t- R_EX_octa_e\n", + "\t- R_EX_lys__L_e\n", + "\t- R_EX_fecrm_e\n", + "\t- R_EX_fecrm_un_e\n", + "\t- R_EX_lyx__L_e\n", + "\t- R_EX_orn_e\n", + "\t- R_EX_mal__D_e\n", + "\t- R_EX_feenter_e\n", + "\t- R_EX_feoxam_e\n", + "\t- R_EX_mal__L_e\n", + "\t- R_EX_feoxam_un_e\n", + "\t- R_EX_orot_e\n", + "\t- R_EX_for_e\n", + "\t- R_EX_malt_e\n", + "\t- R_EX_malthx_e\n", + "\t- R_EX_fru_e\n", + "\t- R_EX_pacald_e\n", + "\t- R_EX_frulys_e\n", + "\t- R_EX_maltpt_e\n", + "\t- R_EX_malttr_e\n", + "\t- R_EX_fruur_e\n", + "\t- R_EX_fuc__L_e\n", + "\t- R_EX_fum_e\n", + "\t- R_EX_fusa_e\n", + "\t- R_EX_g1p_e\n", + "\t- R_EX_maltttr_e\n", + "\t- R_EX_man_e\n", + "\t- R_EX_man6p_e\n", + "\t- R_EX_manglyc_e\n", + "\t- R_EX_g3pc_e\n", + "\t- R_EX_g3pe_e\n", + "\t- R_EX_melib_e\n", + "\t- R_EX_peamn_e\n", + "\t- R_EX_meoh_e\n", + "\t- R_EX_g3pg_e\n", + "\t- R_EX_g3pi_e\n", + "\t- R_EX_met__D_e\n", + "\t- R_EX_met__L_e\n", + "\t- R_EX_g3ps_e\n", + "\t- R_EX_phe__L_e\n", + "\t- R_EX_g6p_e\n", + "\t- R_EX_metsox_R__L_e\n", + "\t- R_EX_gal_e\n", + "\t- R_EX_metsox_S__L_e\n", + "\t- R_EX_pheme_e\n", + "\t- R_EX_mg2_e\n", + "\t- R_EX_mincyc_e\n", + "\t- R_EX_minohp_e\n", + "\t- R_EX_gal_bD_e\n", + "\t- R_EX_gal1p_e\n", + "\t- R_EX_galct__D_e\n", + "\t- R_EX_mmet_e\n", + "\t- R_EX_pi_e\n", + "\t- R_EX_mn2_e\n", + "\t- R_EX_mnl_e\n", + "\t- R_EX_galctn__D_e\n", + "\t- R_EX_galctn__L_e\n", + "\t- R_EX_mobd_e\n", + "\t- R_EX_mso3_e\n", + "\t- R_EX_galt_e\n", + "\t- R_EX_galur_e\n", + "\t- R_EX_pnto__R_e\n", + "\t- R_EX_ump_e\n", + "\t- R_EX_ura_e\n", + "\t- R_EX_ppa_e\n", + "\t- R_EX_urea_e\n", + "\t- R_EX_uri_e\n", + "\t- R_EX_val__L_e\n", + "\t- R_EX_xan_e\n", + "\t- R_EX_ppal_e\n", + "\t- R_EX_xmp_e\n", + "\t- R_EX_xtsn_e\n", + "\t- R_EX_pppn_e\n", + "\t- R_EX_xyl__D_e\n", + "\t- R_EX_ppt_e\n", + "\t- R_EX_xylu__L_e\n", + "\t- R_EX_zn2_e\n", + "\t- R_EX_pro__L_e\n", + "\t- R_EX_progly_e\n", + "\t- R_EX_psclys_e\n", + "\t- R_EX_pser__L_e\n", + "\t- R_EX_ptrc_e\n", + "\t- R_EX_pydam_e\n", + "\t- R_EX_pydx_e\n", + "\t- R_EX_pydxn_e\n", + "\t- R_EX_pyr_e\n", + "\t- R_EX_quin_e\n", + "\t- R_EX_r5p_e\n", + "\t- R_EX_rfamp_e\n", + "\t- R_EX_rib__D_e\n", + "\t- R_EX_rmn_e\n", + "\t- R_EX_sbt__D_e\n", + "\t- R_EX_sel_e\n", + "\t- R_EX_ser__D_e\n", + "\t- R_EX_ser__L_e\n", + "\t- R_EX_skm_e\n", + "\t- R_EX_slnt_e\n", + "\t- R_EX_so2_e\n", + "\t- R_EX_so3_e\n", + "\t- R_EX_so4_e\n", + "\t- R_EX_spmd_e\n", + "\t- R_EX_succ_e\n", + "\t- R_EX_sucr_e\n", + "\t- R_EX_sulfac_e\n", + "\t- R_EX_tartr__D_e\n", + "\t- R_EX_tartr__L_e\n", + "\t- R_EX_taur_e\n", + "\t- R_EX_tcynt_e\n", + "\t- R_EX_thm_e\n", + "\t- R_EX_thr__L_e\n", + "\t- R_EX_thrp_e\n", + "\t- R_EX_thym_e\n", + "\t- R_EX_thymd_e\n", + "\t- R_EX_tma_e\n", + "\t- R_EX_tmao_e\n", + "\t- R_EX_tre_e\n", + "\t- R_EX_trp__L_e\n", + "\t- R_EX_tsul_e\n", + "\t- R_EX_ttdca_e\n", + "\t- R_EX_ttdcea_e\n", + "\t- R_EX_ttrcyc_e\n", + "\t- R_EX_tungs_e\n", + "\t- R_EX_tym_e\n", + "\t- R_EX_tyr__L_e\n", + "\t- R_EX_tyrp_e\n", + "\t- R_EX_uacgam_e\n", + "\t- R_EX_udpacgal_e\n", + "\t- R_EX_udpg_e\n", + "\t- R_EX_udpgal_e\n", + "\t- R_EX_udpglcur_e\n", + "\t- R_EX_feroxEfe_e\n", + "\t- R_EX_feroxE_e\n", + "\t- R_EX_pep_e\n", + "\t- R_EX_23dhbzs3_e\n", + "\t- R_EX_tet_e\n", + "\t- R_EX_salchs4_e\n", + "\t- R_EX_tag__D_e\n", + "\t- R_EX_oaa_e\n", + "\t- R_EX_salchs2fe_e\n", + "\t- R_EX_cysi__L_e\n", + "\t- R_EX_4hphac_e\n", + "\t- R_EX_4abz_e\n", + "\t- R_EX_fe3dhbzs3_e\n", + "\t- R_EX_drib_e\n", + "\t- R_EX_feroxBfe_e\n", + "\t- R_EX_salchs4fe_e\n", + "\t- R_EX_2pg_e\n", + "\t- R_EX_acon_C_e\n", + "\t- R_EX_frmd_e\n", + "\t- R_EX_colipaOA_e\n", + "\t- R_EX_salchsx_e\n", + "\t- R_EX_AEP_e\n", + "\t- R_EX_feroxB_e\n", + "\t- R_EX_tcb_e\n", + "\t- R_EX_tton_e\n", + "\t- R_EX_icit_e\n", + "\t- R_EX_4hthr_e\n", + "\t- R_EX_salchs2_e\n", + "\t- R_EX_colipa20Oag_e\n", + "\t- R_EX_remnant1_e\n", + "\t- R_EX_rnam_e\n", + "\t- R_EX_airs_e\n", + "\t- R_EX_dxyl_e\n", + "\t- R_EX_3pg_e\n", + "\t- R_EX_fe_e\n", + "\t- R_EX_udcpo4_e\n", + "\t- R_EX_kokdolipidA_e\n", + "\t- R_EX_feroxGfe_e\n", + "\t- R_EX_udcpo5_e\n", + "\t- R_EX_guln__L_e\n", + "\t- R_EX_3hoxpac_e\n", + "\t- R_EX_feroxG_e\n", + "\t- R_EX_o6a4colipa_e\n", + "\t- R_EX_diact_e\n", + "\t- R_EX_btd_RR_e\n", + "\t- R_EX_13ppd_e\n", + "\t- R_EX_rbt_e\n", + "\t- R_EX_cellb_e\n", + "\t- R_EX_bz_e\n", + "\t- R_EX_abt__D_e\n", + "\t- R_EX_3ntym_e\n", + "\t- R_EX_6apa_e\n", + "\t- R_EX_4hoxpac_e\n", + "\t- R_EX_peng_e\n", + "\t- R_EX_pac_e\n", + "\t- R_EX_salcn_e\n", + "\t- R_EX_galam_e\n", + "\t- R_EX_raffin_e\n", + "\t- R_DM_lipidA_core_e_p\n", + "\t- R_EX_arab__D_e\u001b[0m\n", + "File saved at: ../../data/sbml_corrected/iECABU_c1320.xml\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: iEC1368_DH5a\n", + "\u001b[0;93mWARNING : \n", + " - R_CAT: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_DHPTDNR: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_DHPTDNRN: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_FHL: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SPODM: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SPODMpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCASPtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCFUMtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCMALtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCTARTtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + " - R_EDTXSCOF is multiple (more than 1) metabolites import/export reaction. \n", + "\tDeleted as it is an import reaction.\n", + " - R_DURAD: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_CHOLSH: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_CHDLDH: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_DURAD2: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + "\u001b[0m\n", + "\n", + "\n", + "\u001b[0;36m############################################\n", + "############################################\n", + " WRITING SBML FILE \n", + "############################################\n", + "############################################\n", + "\u001b[0m\n", + "\u001b[0;93mWARNING : IMPORT REACTION REMOVED BY DEFAULT\u001b[0m\n", + "\u001b[0;93mWARNING : If you want to keep import reaction\n", + "use option -kir / --keep-import-reactions\u001b[0m\n", + "\u001b[0;93mWARNING : Reaction with tag reversible modified:\n", + "\t- R_DURAD\n", + "\t- R_CHOLSH\n", + "\t- R_CHDLDH\n", + "\t- R_DURAD2\u001b[0m\n", + "\u001b[0;93mWARNING : Reaction with Reactants and Products exchanged:\n", + "\t- R_DURAD\n", + "\t- R_CHOLSH\n", + "\t- R_CHDLDH\n", + "\t- R_DURAD2\u001b[0m\n", + "\u001b[0;93mWARNING : Import reaction removed:\n", + "\t- R_EX_idon__L_e\n", + "\t- R_EX_ile__L_e\n", + "\t- R_EX_imp_e\n", + "\t- R_EX_indole_e\n", + "\t- R_EX_feenter_e\n", + "\t- R_EX_galct__D_e\n", + "\t- R_EX_galctn__D_e\n", + "\t- R_EX_galctn__L_e\n", + "\t- R_EX_inost_e\n", + "\t- R_EX_feoxam_e\n", + "\t- R_EX_ins_e\n", + "\t- R_EX_feoxam_un_e\n", + "\t- R_EX_g3pc_e\n", + "\t- R_EX_galt_e\n", + "\t- R_EX_hxan_e\n", + "\t- R_EX_g3pe_e\n", + "\t- R_EX_g3pg_e\n", + "\t- R_EX_for_e\n", + "\t- R_EX_fru_e\n", + "\t- R_EX_frulys_e\n", + "\t- R_EX_fruur_e\n", + "\t- R_EX_fuc__L_e\n", + "\t- R_EX_g3pi_e\n", + "\t- R_EX_g3ps_e\n", + "\t- R_EX_isetac_e\n", + "\t- R_EX_g6p_e\n", + "\t- R_EX_fum_e\n", + "\t- R_EX_fusa_e\n", + "\t- R_EX_g1p_e\n", + "\t- R_EX_gal_e\n", + "\t- R_EX_k_e\n", + "\t- R_EX_gal_bD_e\n", + "\t- R_EX_gal1p_e\n", + "\t- R_EX_kdo2lipid4_e\n", + "\t- R_EX_galur_e\n", + "\t- R_EX_gam_e\n", + "\t- R_EX_gam6p_e\n", + "\t- R_EX_gbbtn_e\n", + "\t- R_EX_gdp_e\n", + "\t- R_EX_glc__D_e\n", + "\t- R_EX_glcn_e\n", + "\t- R_EX_glcr_e\n", + "\t- R_EX_glcur_e\n", + "\t- R_EX_glcur1p_e\n", + "\t- R_EX_gln__L_e\n", + "\t- R_EX_glu__L_e\n", + "\t- R_EX_mn2_e\n", + "\t- R_EX_mnl_e\n", + "\t- R_EX_metsox_R__L_e\n", + "\t- R_EX_mobd_e\n", + "\t- R_EX_metsox_S__L_e\n", + "\t- R_EX_mg2_e\n", + "\t- R_EX_mincyc_e\n", + "\t- R_EX_minohp_e\n", + "\t- R_EX_mmet_e\n", + "\t- R_EX_mso3_e\n", + "\t- R_EX_n2o_e\n", + "\t- R_EX_na1_e\n", + "\t- R_EX_nac_e\n", + "\t- R_EX_nh4_e\n", + "\t- R_EX_ni2_e\n", + "\t- R_EX_nmn_e\n", + "\t- R_EX_gly_e\n", + "\t- R_EX_glyald_e\n", + "\t- R_EX_glyb_e\n", + "\t- R_EX_glyc_e\n", + "\t- R_EX_glyc__R_e\n", + "\t- R_EX_glyc2p_e\n", + "\t- R_EX_no_e\n", + "\t- R_EX_lac__D_e\n", + "\t- R_EX_no2_e\n", + "\t- R_EX_no3_e\n", + "\t- R_EX_novbcn_e\n", + "\t- R_EX_o16a4colipa_e\n", + "\t- R_EX_o2_e\n", + "\t- R_EX_o2s_e\n", + "\t- R_EX_glyc3p_e\n", + "\t- R_EX_glyclt_e\n", + "\t- R_EX_gmp_e\n", + "\t- R_EX_gsn_e\n", + "\t- R_EX_gthox_e\n", + "\t- R_EX_gthrd_e\n", + "\t- R_EX_gtp_e\n", + "\t- R_EX_gua_e\n", + "\t- R_EX_ocdca_e\n", + "\t- R_EX_ocdcea_e\n", + "\t- R_EX_octa_e\n", + "\t- R_EX_h_e\n", + "\t- R_EX_h2_e\n", + "\t- R_EX_orn_e\n", + "\t- R_EX_lac__L_e\n", + "\t- R_EX_orot_e\n", + "\t- R_EX_pacald_e\n", + "\t- R_EX_h2o_e\n", + "\t- R_EX_h2o2_e\n", + "\t- R_EX_h2s_e\n", + "\t- R_EX_hacolipa_e\n", + "\t- R_EX_peamn_e\n", + "\t- R_EX_phe__L_e\n", + "\t- R_EX_halipa_e\n", + "\t- R_EX_lcts_e\n", + "\t- R_EX_hdca_e\n", + "\t- R_EX_pheme_e\n", + "\t- R_EX_leu__L_e\n", + "\t- R_EX_pi_e\n", + "\t- R_EX_pnto__R_e\n", + "\t- R_EX_hdcea_e\n", + "\t- R_EX_hg2_e\n", + "\t- R_EX_his__L_e\n", + "\t- R_EX_ppa_e\n", + "\t- R_EX_ppal_e\n", + "\t- R_EX_pppn_e\n", + "\t- R_EX_hom__L_e\n", + "\t- R_EX_hxa_e\n", + "\t- R_EX_ppt_e\n", + "\t- R_EX_pro__L_e\n", + "\t- R_EX_lipa_e\n", + "\t- R_EX_progly_e\n", + "\t- R_EX_lipa_cold_e\n", + "\t- R_EX_all__D_e\n", + "\t- R_EX_alltn_e\n", + "\t- R_EX_amp_e\n", + "\t- R_EX_alaala_e\n", + "\t- R_EX_cytd_e\n", + "\t- R_EX_lipoate_e\n", + "\t- R_EX_dad_2_e\n", + "\t- R_EX_damp_e\n", + "\t- R_EX_anhgm_e\n", + "\t- R_EX_arab__L_e\n", + "\t- R_EX_arbt_e\n", + "\t- R_EX_lys__L_e\n", + "\t- R_EX_arbtn_e\n", + "\t- R_EX_crn__D_e\n", + "\t- R_EX_csn_e\n", + "\t- R_EX_arbtn_fe3_e\n", + "\t- R_EX_cu_e\n", + "\t- R_EX_lyx__L_e\n", + "\t- R_EX_cu2_e\n", + "\t- R_EX_arg__L_e\n", + "\t- R_EX_mal__D_e\n", + "\t- R_EX_ascb__L_e\n", + "\t- R_EX_cyan_e\n", + "\t- R_EX_asn__L_e\n", + "\t- R_EX_cynt_e\n", + "\t- R_EX_mal__L_e\n", + "\t- R_EX_cys__D_e\n", + "\t- R_EX_cys__L_e\n", + "\t- R_EX_aso3_e\n", + "\t- R_EX_asp__L_e\n", + "\t- R_EX_btn_e\n", + "\t- R_EX_but_e\n", + "\t- R_EX_butso3_e\n", + "\t- R_EX_dca_e\n", + "\t- R_EX_dcmp_e\n", + "\t- R_EX_dcyt_e\n", + "\t- R_EX_ddca_e\n", + "\t- R_EX_ca2_e\n", + "\t- R_EX_dgmp_e\n", + "\t- R_EX_malt_e\n", + "\t- R_EX_dgsn_e\n", + "\t- R_EX_dha_e\n", + "\t- R_EX_cbi_e\n", + "\t- R_EX_cbl1_e\n", + "\t- R_EX_dimp_e\n", + "\t- R_EX_cd2_e\n", + "\t- R_EX_malthx_e\n", + "\t- R_EX_cgly_e\n", + "\t- R_EX_din_e\n", + "\t- R_EX_dms_e\n", + "\t- R_EX_chol_e\n", + "\t- R_EX_dmso_e\n", + "\t- R_EX_dopa_e\n", + "\t- R_EX_chtbs_e\n", + "\t- R_EX_cit_e\n", + "\t- R_EX_maltpt_e\n", + "\t- R_EX_cl_e\n", + "\t- R_EX_cm_e\n", + "\t- R_EX_doxrbcn_e\n", + "\t- R_EX_dtmp_e\n", + "\t- R_EX_dump_e\n", + "\t- R_EX_cmp_e\n", + "\t- R_EX_co2_e\n", + "\t- R_EX_malttr_e\n", + "\t- R_EX_cobalt2_e\n", + "\t- R_EX_colipa_e\n", + "\t- R_EX_duri_e\n", + "\t- R_EX_eca4colipa_e\n", + "\t- R_EX_enlipa_e\n", + "\t- R_EX_enter_e\n", + "\t- R_EX_etha_e\n", + "\t- R_EX_ethso3_e\n", + "\t- R_EX_colipap_e\n", + "\t- R_EX_cpgn_e\n", + "\t- R_EX_cpgn_un_e\n", + "\t- R_EX_etoh_e\n", + "\t- R_EX_maltttr_e\n", + "\t- R_EX_f6p_e\n", + "\t- R_EX_fald_e\n", + "\t- R_EX_fe2_e\n", + "\t- R_EX_crn_e\n", + "\t- R_EX_man_e\n", + "\t- R_EX_fe3_e\n", + "\t- R_EX_man6p_e\n", + "\t- R_EX_fe3dcit_e\n", + "\t- R_EX_fe3dhbzs_e\n", + "\t- R_EX_manglyc_e\n", + "\t- R_EX_fe3hox_e\n", + "\t- R_EX_melib_e\n", + "\t- R_EX_fe3hox_un_e\n", + "\t- R_EX_fecrm_e\n", + "\t- R_EX_fecrm_un_e\n", + "\t- R_EX_meoh_e\n", + "\t- R_EX_met__D_e\n", + "\t- R_EX_psclys_e\n", + "\t- R_EX_met__L_e\n", + "\t- R_EX_pser__L_e\n", + "\t- R_EX_acac_e\n", + "\t- R_EX_acald_e\n", + "\t- R_EX_acgal_e\n", + "\t- R_EX_acgal1p_e\n", + "\t- R_DM_4crsol_c\n", + "\t- R_EX_ptrc_e\n", + "\t- R_EX_pydam_e\n", + "\t- R_EX_pydx_e\n", + "\t- R_EX_pydxn_e\n", + "\t- R_EX_pyr_e\n", + "\t- R_EX_quin_e\n", + "\t- R_EX_r5p_e\n", + "\t- R_EX_rfamp_e\n", + "\t- R_EX_rib__D_e\n", + "\t- R_EX_rmn_e\n", + "\t- R_DM_5drib_c\n", + "\t- R_EX_sbt__D_e\n", + "\t- R_EX_sel_e\n", + "\t- R_EX_ser__D_e\n", + "\t- R_DM_aacald_c\n", + "\t- R_DM_amob_c\n", + "\t- R_DM_mththf_c\n", + "\t- R_EX_ser__L_e\n", + "\t- R_DM_oxam_c\n", + "\t- R_EX_12ppd__R_e\n", + "\t- R_EX_12ppd__S_e\n", + "\t- R_EX_14glucan_e\n", + "\t- R_EX_15dap_e\n", + "\t- R_EX_23camp_e\n", + "\t- R_EX_23ccmp_e\n", + "\t- R_EX_23cgmp_e\n", + "\t- R_EX_23cump_e\n", + "\t- R_EX_23dappa_e\n", + "\t- R_EX_26dap__M_e\n", + "\t- R_EX_skm_e\n", + "\t- R_EX_slnt_e\n", + "\t- R_EX_so2_e\n", + "\t- R_EX_so3_e\n", + "\t- R_EX_so4_e\n", + "\t- R_EX_spmd_e\n", + "\t- R_EX_succ_e\n", + "\t- R_EX_sucr_e\n", + "\t- R_EX_2ddglcn_e\n", + "\t- R_EX_34dhpac_e\n", + "\t- R_EX_3amp_e\n", + "\t- R_EX_3cmp_e\n", + "\t- R_EX_3gmp_e\n", + "\t- R_EX_sulfac_e\n", + "\t- R_EX_tartr__D_e\n", + "\t- R_EX_tartr__L_e\n", + "\t- R_EX_3hcinnm_e\n", + "\t- R_EX_3hpp_e\n", + "\t- R_EX_3hpppn_e\n", + "\t- R_EX_taur_e\n", + "\t- R_EX_tcynt_e\n", + "\t- R_EX_thm_e\n", + "\t- R_EX_thr__L_e\n", + "\t- R_EX_3ump_e\n", + "\t- R_EX_4abut_e\n", + "\t- R_EX_thrp_e\n", + "\t- R_EX_thym_e\n", + "\t- R_EX_thymd_e\n", + "\t- R_EX_4hoxpacd_e\n", + "\t- R_EX_5dglcn_e\n", + "\t- R_EX_5mtr_e\n", + "\t- R_EX_LalaDglu_e\n", + "\t- R_EX_LalaDgluMdap_e\n", + "\t- R_EX_tma_e\n", + "\t- R_EX_tmao_e\n", + "\t- R_EX_tre_e\n", + "\t- R_EX_trp__L_e\n", + "\t- R_EX_LalaDgluMdapDala_e\n", + "\t- R_EX_LalaLglu_e\n", + "\t- R_EX_ac_e\n", + "\t- R_EX_acgam_e\n", + "\t- R_EX_tsul_e\n", + "\t- R_EX_ttdca_e\n", + "\t- R_EX_ttdcea_e\n", + "\t- R_EX_ttrcyc_e\n", + "\t- R_EX_acgam1p_e\n", + "\t- R_EX_acmana_e\n", + "\t- R_EX_tungs_e\n", + "\t- R_EX_acmum_e\n", + "\t- R_EX_acnam_e\n", + "\t- R_EX_acolipa_e\n", + "\t- R_EX_acser_e\n", + "\t- R_EX_tym_e\n", + "\t- R_EX_tyr__L_e\n", + "\t- R_EX_tyrp_e\n", + "\t- R_EX_uacgam_e\n", + "\t- R_EX_ade_e\n", + "\t- R_EX_adn_e\n", + "\t- R_EX_adocbl_e\n", + "\t- R_EX_udpacgal_e\n", + "\t- R_EX_udpg_e\n", + "\t- R_EX_ag_e\n", + "\t- R_EX_agm_e\n", + "\t- R_EX_akg_e\n", + "\t- R_EX_ala_B_e\n", + "\t- R_EX_udpgal_e\n", + "\t- R_EX_udpglcur_e\n", + "\t- R_EX_ump_e\n", + "\t- R_EX_ura_e\n", + "\t- R_EX_ala__D_e\n", + "\t- R_EX_urea_e\n", + "\t- R_EX_uri_e\n", + "\t- R_EX_ala__L_e\n", + "\t- R_EX_val__L_e\n", + "\t- R_EX_xan_e\n", + "\t- R_EX_xmp_e\n", + "\t- R_EX_xtsn_e\n", + "\t- R_EX_xyl__D_e\n", + "\t- R_EX_xylu__L_e\n", + "\t- R_EX_zn2_e\n", + "\t- R_EDTXSCOF\n", + "\t- R_EX_arab__D_e\n", + "\t- R_EX_3hoxpac_e\n", + "\t- R_EX_3ntym_e\n", + "\t- R_EX_4hoxpac_e\n", + "\t- R_EX_6apa_e\n", + "\t- R_EX_cellb_e\n", + "\t- R_EX_galam_e\n", + "\t- R_EX_lipidA_core_e\n", + "\t- R_EX_o6a4colipa_e\n", + "\t- R_EX_peng_e\n", + "\t- R_EX_raffin_e\n", + "\t- R_EX_salcn_e\n", + "\t- R_DM_4hba_c\n", + "\t- R_DM_hmfurn_c\u001b[0m\n", + "File saved at: ../../data/sbml_corrected/iEC1368_DH5a.xml\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: iYS1720\n", + "\u001b[0;93mWARNING : \n", + " - R_EX_chitob_e: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_EX_pydxn_e: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_EX_pydx_e: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SPODM: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SPODMpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_CAT: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_CHITPH: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_CHITOBpts: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_OA4L_ST: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_OA4VL_ST: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_OAO4t3pp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_OAO4t3ex: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_URCN: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_HISDr: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_AB6PGH: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SARCOX: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_EX_pydam_e: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_FHL: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\u001b[0m\n", + "\n", + "\n", + "\u001b[0;36m############################################\n", + "############################################\n", + " WRITING SBML FILE \n", + "############################################\n", + "############################################\n", + "\u001b[0m\n", + "\u001b[0;93mWARNING : IMPORT REACTION REMOVED BY DEFAULT\u001b[0m\n", + "\u001b[0;93mWARNING : If you want to keep import reaction\n", + "use option -kir / --keep-import-reactions\u001b[0m\n", + "\u001b[0;93mWARNING : Import reaction removed:\n", + "\t- R_EX_cobalt2_e\n", + "\t- R_EX_cmp_e\n", + "\t- R_DM_4hba_c\n", + "\t- R_DM_5drib_c\n", + "\t- R_DM_hmfurn_c\n", + "\t- R_DM_oxam_c\n", + "\t- R_EX_12ppd__R_e\n", + "\t- R_EX_12ppd__S_e\n", + "\t- R_EX_14glucan_e\n", + "\t- R_EX_15dap_e\n", + "\t- R_EX_23camp_e\n", + "\t- R_EX_23ccmp_e\n", + "\t- R_EX_23cgmp_e\n", + "\t- R_EX_23cump_e\n", + "\t- R_EX_23dappa_e\n", + "\t- R_EX_26dap__M_e\n", + "\t- R_EX_2ddglcn_e\n", + "\t- R_EX_34dhpac_e\n", + "\t- R_EX_3amp_e\n", + "\t- R_EX_3cmp_e\n", + "\t- R_EX_3gmp_e\n", + "\t- R_EX_3hcinnm_e\n", + "\t- R_EX_3hpppn_e\n", + "\t- R_EX_3ump_e\n", + "\t- R_EX_4abut_e\n", + "\t- R_EX_4hoxpacd_e\n", + "\t- R_EX_5dglcn_e\n", + "\t- R_EX_LalaDgluMdapDala_e\n", + "\t- R_EX_LalaDgluMdap_e\n", + "\t- R_EX_ac_e\n", + "\t- R_EX_acac_e\n", + "\t- R_EX_acald_e\n", + "\t- R_EX_acgal1p_e\n", + "\t- R_EX_acgal_e\n", + "\t- R_EX_acgam1p_e\n", + "\t- R_EX_acgam_e\n", + "\t- R_EX_acmana_e\n", + "\t- R_EX_acmum_e\n", + "\t- R_EX_acnam_e\n", + "\t- R_EX_acolipa_e\n", + "\t- R_EX_acser_e\n", + "\t- R_EX_ade_e\n", + "\t- R_EX_adn_e\n", + "\t- R_EX_adocbl_e\n", + "\t- R_EX_ag_e\n", + "\t- R_EX_agm_e\n", + "\t- R_EX_akg_e\n", + "\t- R_EX_ala_B_e\n", + "\t- R_EX_ala__D_e\n", + "\t- R_EX_ala__L_e\n", + "\t- R_EX_alaala_e\n", + "\t- R_EX_all__D_e\n", + "\t- R_EX_alltn_e\n", + "\t- R_EX_amp_e\n", + "\t- R_EX_anhgm_e\n", + "\t- R_EX_arab__L_e\n", + "\t- R_EX_arbtn_e\n", + "\t- R_EX_arbtn_fe3_e\n", + "\t- R_EX_arg__L_e\n", + "\t- R_EX_ascb__L_e\n", + "\t- R_EX_asn__L_e\n", + "\t- R_EX_aso3_e\n", + "\t- R_EX_asp__L_e\n", + "\t- R_EX_but_e\n", + "\t- R_EX_butso3_e\n", + "\t- R_EX_ca2_e\n", + "\t- R_EX_cbi_e\n", + "\t- R_EX_cbl1_e\n", + "\t- R_EX_cd2_e\n", + "\t- R_EX_cgly_e\n", + "\t- R_EX_chol_e\n", + "\t- R_EX_cit_e\n", + "\t- R_EX_icit_e\n", + "\t- R_EX_acon_C_e\n", + "\t- R_EX_cl_e\n", + "\t- R_EX_gly_e\n", + "\t- R_EX_glyald_e\n", + "\t- R_EX_glyb_e\n", + "\t- R_EX_glyc2p_e\n", + "\t- R_EX_glyc3p_e\n", + "\t- R_EX_glyc__R_e\n", + "\t- R_EX_glyc_e\n", + "\t- R_EX_glyclt_e\n", + "\t- R_EX_gmp_e\n", + "\t- R_EX_gsn_e\n", + "\t- R_EX_gthox_e\n", + "\t- R_EX_gthrd_e\n", + "\t- R_EX_gtp_e\n", + "\t- R_EX_gua_e\n", + "\t- R_EX_h2_e\n", + "\t- R_EX_h2o2_e\n", + "\t- R_EX_h2o_e\n", + "\t- R_EX_h2s_e\n", + "\t- R_EX_h_e\n", + "\t- R_EX_hacolipa_e\n", + "\t- R_EX_halipa_e\n", + "\t- R_EX_hdca_e\n", + "\t- R_EX_hdcea_e\n", + "\t- R_EX_hg2_e\n", + "\t- R_EX_his__L_e\n", + "\t- R_EX_hom__L_e\n", + "\t- R_EX_hxa_e\n", + "\t- R_EX_hxan_e\n", + "\t- R_EX_idon__L_e\n", + "\t- R_EX_ile__L_e\n", + "\t- R_EX_imp_e\n", + "\t- R_EX_indole_e\n", + "\t- R_EX_inost_e\n", + "\t- R_EX_ins_e\n", + "\t- R_EX_isetac_e\n", + "\t- R_EX_k_e\n", + "\t- R_EX_kdo2lipid4_e\n", + "\t- R_EX_lac__D_e\n", + "\t- R_EX_lac__L_e\n", + "\t- R_EX_lcts_e\n", + "\t- R_EX_leu__L_e\n", + "\t- R_EX_lipa_cold_e\n", + "\t- R_EX_lipa_e\n", + "\t- R_EX_lys__L_e\n", + "\t- R_EX_lyx__L_e\n", + "\t- R_EX_mal__D_e\n", + "\t- R_EX_mal__L_e\n", + "\t- R_EX_malt_e\n", + "\t- R_EX_malthx_e\n", + "\t- R_EX_maltpt_e\n", + "\t- R_EX_malttr_e\n", + "\t- R_EX_maltttr_e\n", + "\t- R_EX_man6p_e\n", + "\t- R_EX_man_e\n", + "\t- R_EX_manglyc_e\n", + "\t- R_EX_melib_e\n", + "\t- R_EX_met__D_e\n", + "\t- R_EX_met__L_e\n", + "\t- R_EX_metsox_R__L_e\n", + "\t- R_EX_metsox_S__L_e\n", + "\t- R_EX_mg2_e\n", + "\t- R_EX_minohp_e\n", + "\t- R_EX_mmet_e\n", + "\t- R_EX_mn2_e\n", + "\t- R_EX_mnl_e\n", + "\t- R_EX_mobd_e\n", + "\t- R_EX_mso3_e\n", + "\t- R_EX_n2o_e\n", + "\t- R_EX_na1_e\n", + "\t- R_EX_nac_e\n", + "\t- R_EX_nh4_e\n", + "\t- R_EX_ni2_e\n", + "\t- R_EX_nmn_e\n", + "\t- R_EX_no2_e\n", + "\t- R_EX_no3_e\n", + "\t- R_EX_no_e\n", + "\t- R_EX_o2_e\n", + "\t- R_EX_o2s_e\n", + "\t- R_EX_ocdca_e\n", + "\t- R_EX_ocdcea_e\n", + "\t- R_EX_octa_e\n", + "\t- R_EX_AEP_e\n", + "\t- R_EX_4hoxpac_e\n", + "\t- R_EX_remnant1_e\n", + "\t- R_EX_colipa20Oag_e\n", + "\t- R_EX_salchs4_e\n", + "\t- R_EX_salchs4fe_e\n", + "\t- R_EX_salchsx_e\n", + "\t- R_EX_tag__D_e\n", + "\t- R_EX_airs_e\n", + "\t- R_EX_pep_e\n", + "\t- R_EX_2pg_e\n", + "\t- R_EX_3pg_e\n", + "\t- R_EX_tet_e\n", + "\t- R_EX_frmd_e\n", + "\t- R_EX_tartr__D_e\n", + "\t- R_EX_4abz_e\n", + "\t- R_EX_feroxB_e\n", + "\t- R_EX_feroxBfe_e\n", + "\t- R_EX_feroxE_e\n", + "\t- R_EX_feroxEfe_e\n", + "\t- R_EX_feroxG_e\n", + "\t- R_EX_feroxGfe_e\n", + "\t- R_EX_drib_e\n", + "\t- R_EX_rnam_e\n", + "\t- R_EX_udcpo5_e\n", + "\t- R_EX_tcb_e\n", + "\t- R_EX_tton_e\n", + "\t- R_EX_colipaOA_e\n", + "\t- R_EX_udcpo4_e\n", + "\t- R_EX_4hthr_e\n", + "\t- R_EX_23dhbzs3_e\n", + "\t- R_EX_fe3dhbzs3_e\n", + "\t- R_EX_salchs2_e\n", + "\t- R_EX_salchs2fe_e\n", + "\t- R_EX_guln__L_e\n", + "\t- R_EX_dxyl_e\n", + "\t- R_EX_udcpo4min_e\n", + "\t- R_EX_colipaOAmin_e\n", + "\t- R_EX_colipamin_e\n", + "\t- R_EX_23dhb_e\n", + "\t- R_EX_eca4colipamin_e\n", + "\t- R_EX_LPS51_ST_e\n", + "\t- R_EX_LPS57_ST_e\n", + "\t- R_EX_LPS18__L_e\n", + "\t- R_EX_LPS28ac_VL_e\n", + "\t- R_EX_LPS1_3_19__L_e\n", + "\t- R_EX_LPS21_ST_e\n", + "\t- R_DM_LPS55_ST_p\n", + "\t- R_EX_LPS38__L_e\n", + "\t- R_EX_LPS16__L_e\n", + "\t- R_EX_LPS9_46__L_e\n", + "\t- R_EX_LPS8__L_e\n", + "\t- R_EX_LPS40_VL_e\n", + "\t- R_EX_LPS57__L_e\n", + "\t- R_EX_LPS51__L_e\n", + "\t- R_EX_LPS13__L_e\n", + "\t- R_EX_LPS18_VL_e\n", + "\t- R_EX_LPS53__L_e\n", + "\t- R_DM_LPS55__L_p\n", + "\t- R_EX_LPS17_ST_e\n", + "\t- R_EX_LPS28ab_VL_e\n", + "\t- R_EX_LPS3_10_ST_e\n", + "\t- R_EX_LPS4_VL_e\n", + "\t- R_EX_LPS9__L_e\n", + "\t- R_EX_LPS62_ST_e\n", + "\t- R_EX_LPS7__L_e\n", + "\t- R_EX_LPS41_VL_e\n", + "\t- R_EX_LPS47_VL_e\n", + "\t- R_EX_LPS28ac_ST_e\n", + "\t- R_EX_LPS58_ST_e\n", + "\t- R_EX_LPS21__L_e\n", + "\t- R_EX_LPS1_3_19_VL_e\n", + "\t- R_EX_LPS59_ST_e\n", + "\t- R_EX_LPS39_VL_e\n", + "\t- R_EX_LPS38_ST_e\n", + "\t- R_EX_LPS65_ST_e\n", + "\t- R_EX_LPS7_ST_e\n", + "\t- R_EX_LPS21_VL_e\n", + "\t- R_EX_LPS30_ST_e\n", + "\t- R_EX_LPS44_ST_e\n", + "\t- R_EX_LPS42__L_e\n", + "\t- R_EX_LPS9_46_ST_e\n", + "\t- R_EX_LPS2_VL_e\n", + "\t- R_EX_LPS59_VL_e\n", + "\t- R_EX_LPS63_ST_e\n", + "\t- R_EX_LPS9_VL_e\n", + "\t- R_EX_LPS42_VL_e\n", + "\t- R_EX_LPS63__L_e\n", + "\t- R_EX_LPS65__L_e\n", + "\t- R_EX_LPS40__L_e\n", + "\t- R_EX_LPS6_14_VL_e\n", + "\t- R_EX_LPS44__L_e\n", + "\t- R_EX_LPS50_ST_e\n", + "\t- R_EX_LPS57_VL_e\n", + "\t- R_EX_LPS8_VL_e\n", + "\t- R_EX_LPS60_VL_e\n", + "\t- R_EX_LPS66_ST_e\n", + "\t- R_EX_LPS11_VL_e\n", + "\t- R_DM_LPS9_46_27_VL_p\n", + "\t- R_EX_LPS48_ST_e\n", + "\t- R_EX_LPS38_VL_e\n", + "\t- R_EX_LPS35_VL_e\n", + "\t- R_EX_LPS43_ST_e\n", + "\t- R_EX_LPS16_ST_e\n", + "\t- R_EX_LPS53_VL_e\n", + "\t- R_EX_LPS3_10_VL_e\n", + "\t- R_EX_LPS4_ST_e\n", + "\t- R_DM_LPS55_VL_p\n", + "\t- R_EX_LPS17_VL_e\n", + "\t- R_EX_LPS8_ST_e\n", + "\t- R_EX_LPS28ac__L_e\n", + "\t- R_EX_LPS43__L_e\n", + "\t- R_EX_LPS52_ST_e\n", + "\t- R_EX_LPS48_VL_e\n", + "\t- R_EX_LPS41__L_e\n", + "\t- R_EX_LPS60__L_e\n", + "\t- R_EX_LPS47__L_e\n", + "\t- R_EX_LPS45__L_e\n", + "\t- R_EX_LPS62__L_e\n", + "\t- R_EX_LPS65_VL_e\n", + "\t- R_EX_LPS28ab_ST_e\n", + "\t- R_EX_LPS30_VL_e\n", + "\t- R_EX_LPS48__L_e\n", + "\t- R_EX_LPS58_VL_e\n", + "\t- R_EX_LPS66_VL_e\n", + "\t- R_EX_LPS35_ST_e\n", + "\t- R_EX_LPS41_ST_e\n", + "\t- R_EX_LPS40_ST_e\n", + "\t- R_EX_LPS52_VL_e\n", + "\t- R_EX_LPS2_ST_e\n", + "\t- R_EX_LPS47_ST_e\n", + "\t- R_EX_LPS13_VL_e\n", + "\t- R_EX_LPS35__L_e\n", + "\t- R_EX_LPS7_VL_e\n", + "\t- R_EX_LPS39_ST_e\n", + "\t- R_EX_LPS43_VL_e\n", + "\t- R_EX_LPS16_VL_e\n", + "\t- R_DM_LPS56_ST_p\n", + "\t- R_EX_LPS9_46_VL_e\n", + "\t- R_EX_LPS42_ST_e\n", + "\t- R_DM_LPS56_VL_p\n", + "\t- R_EX_LPS6_14_ST_e\n", + "\t- R_EX_LPS50_VL_e\n", + "\t- R_EX_LPS28ab__L_e\n", + "\t- R_EX_LPS63_VL_e\n", + "\t- R_EX_LPS39__L_e\n", + "\t- R_EX_LPS17__L_e\n", + "\t- R_EX_LPS67_e\n", + "\t- R_DM_LPS56__L_p\n", + "\t- R_EX_LPS4__L_e\n", + "\t- R_EX_LPS54_e\n", + "\t- R_EX_LPS50__L_e\n", + "\t- R_EX_LPS6_14__L_e\n", + "\t- R_EX_LPS66__L_e\n", + "\t- R_EX_LPS52__L_e\n", + "\t- R_EX_LPS51_VL_e\n", + "\t- R_EX_LPS58__L_e\n", + "\t- R_EX_LPS44_VL_e\n", + "\t- R_EX_LPS11__L_e\n", + "\t- R_DM_LPS9_46_27__L_p\n", + "\t- R_EX_LPS1_3_19_ST_e\n", + "\t- R_EX_LPS53_ST_e\n", + "\t- R_EX_LPS18_ST_e\n", + "\t- R_EX_LPS3_10__L_e\n", + "\t- R_EX_LPS62_VL_e\n", + "\t- R_EX_LPS2__L_e\n", + "\t- R_EX_LPS45_ST_e\n", + "\t- R_EX_LPS59__L_e\n", + "\t- R_EX_LPS45_VL_e\n", + "\t- R_EX_LPS9_ST_e\n", + "\t- R_EX_LPS60_ST_e\n", + "\t- R_EX_LPS11_ST_e\n", + "\t- R_EX_LPS30__L_e\n", + "\t- R_DM_LPS9_46_27_ST_p\n", + "\t- R_EX_LPS13_ST_e\n", + "\t- R_EX_co2_e\n", + "\t- R_EX_colipa_e\n", + "\t- R_EX_cpgn_e\n", + "\t- R_EX_cpgn_un_e\n", + "\t- R_EX_crn_e\n", + "\t- R_EX_csn_e\n", + "\t- R_EX_cu2_e\n", + "\t- R_EX_cu_e\n", + "\t- R_EX_cyan_e\n", + "\t- R_EX_cynt_e\n", + "\t- R_EX_cys__D_e\n", + "\t- R_EX_cys__L_e\n", + "\t- R_EX_cysi__L_e\n", + "\t- R_EX_cytd_e\n", + "\t- R_EX_dad_2_e\n", + "\t- R_EX_damp_e\n", + "\t- R_EX_dca_e\n", + "\t- R_EX_dcmp_e\n", + "\t- R_EX_dcyt_e\n", + "\t- R_EX_ddca_e\n", + "\t- R_EX_dgmp_e\n", + "\t- R_EX_dgsn_e\n", + "\t- R_EX_dha_e\n", + "\t- R_EX_dimp_e\n", + "\t- R_EX_din_e\n", + "\t- R_EX_dms_e\n", + "\t- R_EX_dmso_e\n", + "\t- R_EX_dopa_e\n", + "\t- R_EX_dtmp_e\n", + "\t- R_EX_dump_e\n", + "\t- R_EX_duri_e\n", + "\t- R_EX_eca4colipa_e\n", + "\t- R_EX_enlipa_e\n", + "\t- R_EX_enter_e\n", + "\t- R_EX_etha_e\n", + "\t- R_EX_ethso3_e\n", + "\t- R_EX_etoh_e\n", + "\t- R_EX_f6p_e\n", + "\t- R_EX_fald_e\n", + "\t- R_EX_fe2_e\n", + "\t- R_EX_fe3_e\n", + "\t- R_EX_fe3dcit_e\n", + "\t- R_EX_fe3dhbzs_e\n", + "\t- R_EX_fe3hox_e\n", + "\t- R_EX_fe3hox_un_e\n", + "\t- R_EX_fecrm_e\n", + "\t- R_EX_fecrm_un_e\n", + "\t- R_EX_feenter_e\n", + "\t- R_EX_feoxam_e\n", + "\t- R_EX_feoxam_un_e\n", + "\t- R_EX_for_e\n", + "\t- R_EX_fru_e\n", + "\t- R_EX_frulys_e\n", + "\t- R_EX_fruur_e\n", + "\t- R_EX_fuc__L_e\n", + "\t- R_EX_fum_e\n", + "\t- R_EX_g1p_e\n", + "\t- R_EX_g3pc_e\n", + "\t- R_EX_g3pe_e\n", + "\t- R_EX_g3pg_e\n", + "\t- R_EX_g3pi_e\n", + "\t- R_EX_g3ps_e\n", + "\t- R_EX_g6p_e\n", + "\t- R_EX_gal1p_e\n", + "\t- R_EX_gal_bD_e\n", + "\t- R_EX_gal_e\n", + "\t- R_EX_galct__D_e\n", + "\t- R_EX_galctn__D_e\n", + "\t- R_EX_galctn__L_e\n", + "\t- R_EX_galt_e\n", + "\t- R_EX_galur_e\n", + "\t- R_EX_gam6p_e\n", + "\t- R_EX_gam_e\n", + "\t- R_EX_gbbtn_e\n", + "\t- R_EX_gdp_e\n", + "\t- R_EX_glc__D_e\n", + "\t- R_EX_glcn_e\n", + "\t- R_EX_glcr_e\n", + "\t- R_EX_glcur1p_e\n", + "\t- R_EX_glcur_e\n", + "\t- R_EX_gln__L_e\n", + "\t- R_EX_glu__L_e\n", + "\t- R_EX_orn_e\n", + "\t- R_EX_orot_e\n", + "\t- R_EX_oaa_e\n", + "\t- R_EX_pacald_e\n", + "\t- R_EX_peamn_e\n", + "\t- R_EX_phe__L_e\n", + "\t- R_EX_pheme_e\n", + "\t- R_EX_pi_e\n", + "\t- R_EX_pnto__R_e\n", + "\t- R_EX_ppa_e\n", + "\t- R_EX_ppal_e\n", + "\t- R_EX_pppn_e\n", + "\t- R_EX_ppt_e\n", + "\t- R_EX_pro__L_e\n", + "\t- R_EX_progly_e\n", + "\t- R_EX_psclys_e\n", + "\t- R_EX_pser__L_e\n", + "\t- R_EX_ptrc_e\n", + "\t- R_EX_pyr_e\n", + "\t- R_EX_r5p_e\n", + "\t- R_EX_rib__D_e\n", + "\t- R_EX_rmn_e\n", + "\t- R_EX_sbt__D_e\n", + "\t- R_EX_ser__D_e\n", + "\t- R_EX_ser__L_e\n", + "\t- R_EX_skm_e\n", + "\t- R_EX_so2_e\n", + "\t- R_EX_so3_e\n", + "\t- R_EX_so4_e\n", + "\t- R_EX_spmd_e\n", + "\t- R_EX_succ_e\n", + "\t- R_EX_sucr_e\n", + "\t- R_EX_sulfac_e\n", + "\t- R_EX_tartr__L_e\n", + "\t- R_EX_taur_e\n", + "\t- R_EX_tcynt_e\n", + "\t- R_EX_thm_e\n", + "\t- R_EX_thr__L_e\n", + "\t- R_EX_thrp_e\n", + "\t- R_EX_thym_e\n", + "\t- R_EX_thymd_e\n", + "\t- R_EX_tma_e\n", + "\t- R_EX_tmao_e\n", + "\t- R_EX_tre_e\n", + "\t- R_EX_trp__L_e\n", + "\t- R_EX_tsul_e\n", + "\t- R_EX_ttdca_e\n", + "\t- R_EX_ttdcea_e\n", + "\t- R_EX_tungs_e\n", + "\t- R_EX_tym_e\n", + "\t- R_EX_tyr__L_e\n", + "\t- R_EX_tyrp_e\n", + "\t- R_EX_uacgam_e\n", + "\t- R_EX_udpacgal_e\n", + "\t- R_EX_udpg_e\n", + "\t- R_EX_udpgal_e\n", + "\t- R_EX_udpglcur_e\n", + "\t- R_EX_ump_e\n", + "\t- R_EX_ura_e\n", + "\t- R_EX_urea_e\n", + "\t- R_EX_uri_e\n", + "\t- R_EX_val__L_e\n", + "\t- R_EX_xan_e\n", + "\t- R_EX_xmp_e\n", + "\t- R_EX_xtsn_e\n", + "\t- R_EX_xyl__D_e\n", + "\t- R_EX_xylu__L_e\n", + "\t- R_EX_zn2_e\u001b[0m\n", + "File saved at: ../../data/sbml_corrected/iYS1720.xml\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: iJN678\n", + "\u001b[0;93mWARNING : \n", + " - R_EX_photon_e: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + " - R_ASAD: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_ALAD_L: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_KARA1: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_CO2tpp: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_GLYCLTDx: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + "\u001b[0m\n", + "\n", + "\n", + "\u001b[0;36m############################################\n", + "############################################\n", + " WRITING SBML FILE \n", + "############################################\n", + "############################################\n", + "\u001b[0m\n", + "\u001b[0;93mWARNING : IMPORT REACTION REMOVED BY DEFAULT\u001b[0m\n", + "\u001b[0;93mWARNING : If you want to keep import reaction\n", + "use option -kir / --keep-import-reactions\u001b[0m\n", + "\u001b[0;93mWARNING : Reaction with tag reversible modified:\n", + "\t- R_ASAD\n", + "\t- R_ALAD_L\n", + "\t- R_KARA1\n", + "\t- R_CO2tpp\n", + "\t- R_GLYCLTDx\u001b[0m\n", + "\u001b[0;93mWARNING : Reaction with Reactants and Products exchanged:\n", + "\t- R_ASAD\n", + "\t- R_ALAD_L\n", + "\t- R_KARA1\n", + "\t- R_CO2tpp\n", + "\t- R_GLYCLTDx\u001b[0m\n", + "\u001b[0;93mWARNING : Import reaction removed:\n", + "\t- R_EX_ac_e\n", + "\t- R_EX_ca2_e\n", + "\t- R_EX_cobalt2_e\n", + "\t- R_EX_ni2_e\n", + "\t- R_EX_cynt_e\n", + "\t- R_EX_h_e\n", + "\t- R_EX_pyr_e\n", + "\t- R_EX_fum_e\n", + "\t- R_EX_succ_e\n", + "\t- R_EX_cit_e\n", + "\t- R_EX_h2_e\n", + "\t- R_EX_co_e\n", + "\t- R_SK_precyanphy_c\n", + "\t- R_SK_phbg_c\n", + "\t- R_SK_rdmbzi_c\n", + "\t- R_EX_h2o_e\n", + "\t- R_DM_PHB_c\n", + "\t- R_EX_o2_e\n", + "\t- R_EX_glc__D_e\n", + "\t- R_EX_fru_e\n", + "\t- R_EX_glu__L_e\n", + "\t- R_DM_cyanphy_c\n", + "\t- R_EX_hco3_e\n", + "\t- R_EX_co2_e\n", + "\t- R_EX_ala__L_e\n", + "\t- R_EX_arg__L_e\n", + "\t- R_EX_gly_e\n", + "\t- R_EX_gln__L_e\n", + "\t- R_EX_his__L_e\n", + "\t- R_EX_leu__L_e\n", + "\t- R_EX_lys__L_e\n", + "\t- R_EX_pro__L_e\n", + "\t- R_EX_ser__L_e\n", + "\t- R_EX_no3_e\n", + "\t- R_EX_urea_e\n", + "\t- R_EX_nh4_e\n", + "\t- R_EX_akg_e\n", + "\t- R_EX_mal__L_e\n", + "\t- R_EX_k_e\n", + "\t- R_EX_so4_e\n", + "\t- R_SK_dna_c\n", + "\t- R_SK_dna5mtc_c\n", + "\t- R_EX_pi_e\n", + "\t- R_EX_mobd_e\n", + "\t- R_EX_fe3_e\n", + "\t- R_EX_fe2_e\n", + "\t- R_EX_na1_e\n", + "\t- R_EX_mn2_e\n", + "\t- R_EX_mg2_e\n", + "\t- R_EX_ptrc_e\n", + "\t- R_EX_spmd_e\n", + "\t- R_DM_5mdru1p_c\n", + "\t- R_EX_cu2_e\n", + "\t- R_EX_zn2_e\n", + "\t- R_EX_glcglyc_e\n", + "\t- R_EX_sucr_e\u001b[0m\n", + "File saved at: ../../data/sbml_corrected/iJN678.xml\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: iECW_1372\n", + "\u001b[0;93mWARNING : \n", + " - R_CAT: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_DHPTDNR: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_DHPTDNRN: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_FHL: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SPODM: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SPODMpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCASPtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCFUMtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCMALtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCTARTtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_EX_chitob_e: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_CHITOBpts: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_CHITPH: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_URCN: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\u001b[0m\n", + "\n", + "\n", + "\u001b[0;36m############################################\n", + "############################################\n", + " WRITING SBML FILE \n", + "############################################\n", + "############################################\n", + "\u001b[0m\n", + "\u001b[0;93mWARNING : IMPORT REACTION REMOVED BY DEFAULT\u001b[0m\n", + "\u001b[0;93mWARNING : If you want to keep import reaction\n", + "use option -kir / --keep-import-reactions\u001b[0m\n", + "\u001b[0;93mWARNING : Import reaction removed:\n", + "\t- R_EX_acmum_e\n", + "\t- R_EX_acnam_e\n", + "\t- R_EX_acgam1p_e\n", + "\t- R_EX_acmana_e\n", + "\t- R_EX_acgal_e\n", + "\t- R_EX_acolipa_e\n", + "\t- R_EX_acald_e\n", + "\t- R_EX_acser_e\n", + "\t- R_EX_ade_e\n", + "\t- R_EX_acgam_e\n", + "\t- R_EX_acgal1p_e\n", + "\t- R_EX_fe3dhbzs_e\n", + "\t- R_DM_4crsol_c\n", + "\t- R_DM_5drib_c\n", + "\t- R_DM_aacald_c\n", + "\t- R_DM_amob_c\n", + "\t- R_DM_mththf_c\n", + "\t- R_EX_adn_e\n", + "\t- R_EX_adocbl_e\n", + "\t- R_EX_ag_e\n", + "\t- R_EX_agm_e\n", + "\t- R_EX_akg_e\n", + "\t- R_EX_ala_B_e\n", + "\t- R_EX_ala__D_e\n", + "\t- R_DM_oxam_c\n", + "\t- R_EX_fe3hox_e\n", + "\t- R_EX_12ppd__R_e\n", + "\t- R_EX_12ppd__S_e\n", + "\t- R_EX_ala__L_e\n", + "\t- R_EX_alaala_e\n", + "\t- R_EX_fe3hox_un_e\n", + "\t- R_EX_all__D_e\n", + "\t- R_EX_alltn_e\n", + "\t- R_EX_amp_e\n", + "\t- R_EX_anhgm_e\n", + "\t- R_EX_arab__L_e\n", + "\t- R_EX_arbt_e\n", + "\t- R_EX_arbtn_e\n", + "\t- R_EX_arbtn_fe3_e\n", + "\t- R_EX_arg__L_e\n", + "\t- R_EX_ascb__L_e\n", + "\t- R_EX_asn__L_e\n", + "\t- R_EX_14glucan_e\n", + "\t- R_EX_15dap_e\n", + "\t- R_EX_23camp_e\n", + "\t- R_EX_23ccmp_e\n", + "\t- R_EX_23cgmp_e\n", + "\t- R_EX_23cump_e\n", + "\t- R_EX_23dappa_e\n", + "\t- R_EX_26dap__M_e\n", + "\t- R_EX_2ddglcn_e\n", + "\t- R_EX_34dhpac_e\n", + "\t- R_EX_3amp_e\n", + "\t- R_EX_3cmp_e\n", + "\t- R_EX_3gmp_e\n", + "\t- R_EX_3hcinnm_e\n", + "\t- R_EX_3hpp_e\n", + "\t- R_EX_3hpppn_e\n", + "\t- R_EX_aso3_e\n", + "\t- R_EX_asp__L_e\n", + "\t- R_EX_btn_e\n", + "\t- R_EX_but_e\n", + "\t- R_EX_butso3_e\n", + "\t- R_EX_ca2_e\n", + "\t- R_EX_3ump_e\n", + "\t- R_EX_4abut_e\n", + "\t- R_EX_cbi_e\n", + "\t- R_EX_4hoxpacd_e\n", + "\t- R_EX_cbl1_e\n", + "\t- R_EX_cd2_e\n", + "\t- R_EX_cgly_e\n", + "\t- R_EX_chol_e\n", + "\t- R_EX_5dglcn_e\n", + "\t- R_EX_5mtr_e\n", + "\t- R_EX_LalaDglu_e\n", + "\t- R_EX_LalaDgluMdap_e\n", + "\t- R_EX_LalaDgluMdapDala_e\n", + "\t- R_EX_LalaLglu_e\n", + "\t- R_EX_ac_e\n", + "\t- R_EX_chtbs_e\n", + "\t- R_EX_cit_e\n", + "\t- R_EX_fecrm_e\n", + "\t- R_EX_cl_e\n", + "\t- R_EX_cm_e\n", + "\t- R_EX_acac_e\n", + "\t- R_EX_cmp_e\n", + "\t- R_EX_fecrm_un_e\n", + "\t- R_EX_co2_e\n", + "\t- R_EX_cobalt2_e\n", + "\t- R_EX_feenter_e\n", + "\t- R_EX_colipa_e\n", + "\t- R_EX_feoxam_e\n", + "\t- R_EX_colipap_e\n", + "\t- R_EX_feoxam_un_e\n", + "\t- R_EX_cpgn_e\n", + "\t- R_EX_leu__L_e\n", + "\t- R_EX_for_e\n", + "\t- R_EX_lipa_e\n", + "\t- R_EX_cpgn_un_e\n", + "\t- R_EX_crn_e\n", + "\t- R_EX_lipa_cold_e\n", + "\t- R_EX_crn__D_e\n", + "\t- R_EX_fru_e\n", + "\t- R_EX_csn_e\n", + "\t- R_EX_lipoate_e\n", + "\t- R_EX_lys__L_e\n", + "\t- R_EX_lyx__L_e\n", + "\t- R_EX_mal__D_e\n", + "\t- R_EX_mal__L_e\n", + "\t- R_EX_malt_e\n", + "\t- R_EX_malthx_e\n", + "\t- R_EX_cu_e\n", + "\t- R_EX_cu2_e\n", + "\t- R_EX_cyan_e\n", + "\t- R_EX_cynt_e\n", + "\t- R_EX_cys__D_e\n", + "\t- R_EX_maltpt_e\n", + "\t- R_EX_malttr_e\n", + "\t- R_EX_cys__L_e\n", + "\t- R_EX_frulys_e\n", + "\t- R_EX_cytd_e\n", + "\t- R_EX_dad_2_e\n", + "\t- R_EX_maltttr_e\n", + "\t- R_EX_man_e\n", + "\t- R_EX_man6p_e\n", + "\t- R_EX_damp_e\n", + "\t- R_EX_fruur_e\n", + "\t- R_EX_dca_e\n", + "\t- R_EX_dcmp_e\n", + "\t- R_EX_manglyc_e\n", + "\t- R_EX_melib_e\n", + "\t- R_EX_dcyt_e\n", + "\t- R_EX_meoh_e\n", + "\t- R_EX_fuc__L_e\n", + "\t- R_EX_met__D_e\n", + "\t- R_EX_ddca_e\n", + "\t- R_EX_dgmp_e\n", + "\t- R_EX_met__L_e\n", + "\t- R_EX_dgsn_e\n", + "\t- R_EX_fum_e\n", + "\t- R_EX_metsox_R__L_e\n", + "\t- R_EX_metsox_S__L_e\n", + "\t- R_EX_dha_e\n", + "\t- R_EX_mg2_e\n", + "\t- R_EX_mincyc_e\n", + "\t- R_EX_minohp_e\n", + "\t- R_EX_mmet_e\n", + "\t- R_EX_dimp_e\n", + "\t- R_EX_din_e\n", + "\t- R_EX_dms_e\n", + "\t- R_EX_dmso_e\n", + "\t- R_EX_dopa_e\n", + "\t- R_EX_mn2_e\n", + "\t- R_EX_fusa_e\n", + "\t- R_EX_doxrbcn_e\n", + "\t- R_EX_dtmp_e\n", + "\t- R_EX_mnl_e\n", + "\t- R_EX_mobd_e\n", + "\t- R_EX_dump_e\n", + "\t- R_EX_g1p_e\n", + "\t- R_EX_duri_e\n", + "\t- R_EX_eca4colipa_e\n", + "\t- R_EX_mso3_e\n", + "\t- R_EX_n2o_e\n", + "\t- R_EX_na1_e\n", + "\t- R_EX_enlipa_e\n", + "\t- R_EX_enter_e\n", + "\t- R_EX_g3pc_e\n", + "\t- R_EX_etha_e\n", + "\t- R_EX_nac_e\n", + "\t- R_EX_nh4_e\n", + "\t- R_EX_g3pe_e\n", + "\t- R_EX_ni2_e\n", + "\t- R_EX_nmn_e\n", + "\t- R_EX_ethso3_e\n", + "\t- R_EX_etoh_e\n", + "\t- R_EX_f6p_e\n", + "\t- R_EX_no_e\n", + "\t- R_EX_g3pg_e\n", + "\t- R_EX_no2_e\n", + "\t- R_EX_no3_e\n", + "\t- R_EX_fald_e\n", + "\t- R_EX_fe2_e\n", + "\t- R_EX_fe3_e\n", + "\t- R_EX_fe3dcit_e\n", + "\t- R_EX_novbcn_e\n", + "\t- R_EX_o16a4colipa_e\n", + "\t- R_EX_o2_e\n", + "\t- R_EX_o2s_e\n", + "\t- R_EX_ocdca_e\n", + "\t- R_EX_g3pi_e\n", + "\t- R_EX_ocdcea_e\n", + "\t- R_EX_octa_e\n", + "\t- R_EX_spmd_e\n", + "\t- R_EX_succ_e\n", + "\t- R_EX_g3ps_e\n", + "\t- R_EX_sucr_e\n", + "\t- R_EX_orn_e\n", + "\t- R_EX_orot_e\n", + "\t- R_EX_pacald_e\n", + "\t- R_EX_sulfac_e\n", + "\t- R_EX_tartr__D_e\n", + "\t- R_EX_peamn_e\n", + "\t- R_EX_g6p_e\n", + "\t- R_EX_phe__L_e\n", + "\t- R_EX_pheme_e\n", + "\t- R_EX_pi_e\n", + "\t- R_EX_tartr__L_e\n", + "\t- R_EX_taur_e\n", + "\t- R_EX_gal_e\n", + "\t- R_EX_tcynt_e\n", + "\t- R_EX_thm_e\n", + "\t- R_EX_pnto__R_e\n", + "\t- R_EX_ppa_e\n", + "\t- R_EX_ppal_e\n", + "\t- R_EX_thr__L_e\n", + "\t- R_EX_gal_bD_e\n", + "\t- R_EX_thrp_e\n", + "\t- R_EX_pppn_e\n", + "\t- R_EX_ppt_e\n", + "\t- R_EX_pro__L_e\n", + "\t- R_EX_progly_e\n", + "\t- R_EX_psclys_e\n", + "\t- R_EX_thym_e\n", + "\t- R_EX_thymd_e\n", + "\t- R_EX_tma_e\n", + "\t- R_EX_pser__L_e\n", + "\t- R_EX_tmao_e\n", + "\t- R_EX_gal1p_e\n", + "\t- R_EX_tre_e\n", + "\t- R_EX_ptrc_e\n", + "\t- R_EX_pydam_e\n", + "\t- R_EX_pydx_e\n", + "\t- R_EX_trp__L_e\n", + "\t- R_EX_galct__D_e\n", + "\t- R_EX_pydxn_e\n", + "\t- R_EX_galctn__D_e\n", + "\t- R_EX_pyr_e\n", + "\t- R_EX_quin_e\n", + "\t- R_EX_r5p_e\n", + "\t- R_EX_galctn__L_e\n", + "\t- R_EX_rfamp_e\n", + "\t- R_EX_galt_e\n", + "\t- R_EX_rib__D_e\n", + "\t- R_EX_rmn_e\n", + "\t- R_EX_sbt__D_e\n", + "\t- R_EX_galur_e\n", + "\t- R_EX_sel_e\n", + "\t- R_EX_ser__D_e\n", + "\t- R_EX_tsul_e\n", + "\t- R_EX_ser__L_e\n", + "\t- R_EX_gam_e\n", + "\t- R_EX_skm_e\n", + "\t- R_EX_slnt_e\n", + "\t- R_EX_ttdca_e\n", + "\t- R_EX_ttdcea_e\n", + "\t- R_EX_so2_e\n", + "\t- R_EX_so3_e\n", + "\t- R_EX_so4_e\n", + "\t- R_EX_ttrcyc_e\n", + "\t- R_EX_tungs_e\n", + "\t- R_EX_tym_e\n", + "\t- R_EX_gam6p_e\n", + "\t- R_EX_tyr__L_e\n", + "\t- R_EX_tyrp_e\n", + "\t- R_EX_uacgam_e\n", + "\t- R_EX_gbbtn_e\n", + "\t- R_EX_udpacgal_e\n", + "\t- R_EX_udpg_e\n", + "\t- R_EX_udpgal_e\n", + "\t- R_EX_gdp_e\n", + "\t- R_EX_udpglcur_e\n", + "\t- R_EX_ump_e\n", + "\t- R_EX_glc__D_e\n", + "\t- R_EX_ura_e\n", + "\t- R_EX_urea_e\n", + "\t- R_EX_glcn_e\n", + "\t- R_EX_uri_e\n", + "\t- R_EX_val__L_e\n", + "\t- R_EX_glcr_e\n", + "\t- R_EX_xan_e\n", + "\t- R_EX_xmp_e\n", + "\t- R_EX_xtsn_e\n", + "\t- R_EX_xyl__D_e\n", + "\t- R_EX_xylu__L_e\n", + "\t- R_EX_zn2_e\n", + "\t- R_EX_glcur_e\n", + "\t- R_EX_glcur1p_e\n", + "\t- R_EX_gln__L_e\n", + "\t- R_EX_glu__L_e\n", + "\t- R_EX_gly_e\n", + "\t- R_EX_glyald_e\n", + "\t- R_EX_glyb_e\n", + "\t- R_EX_glyc_e\n", + "\t- R_EX_glyc__R_e\n", + "\t- R_EX_glyc2p_e\n", + "\t- R_EX_glyc3p_e\n", + "\t- R_EX_glyclt_e\n", + "\t- R_EX_gmp_e\n", + "\t- R_EX_gsn_e\n", + "\t- R_EX_gthox_e\n", + "\t- R_EX_gthrd_e\n", + "\t- R_EX_gtp_e\n", + "\t- R_EX_gua_e\n", + "\t- R_EX_h_e\n", + "\t- R_EX_h2_e\n", + "\t- R_EX_h2o_e\n", + "\t- R_EX_h2o2_e\n", + "\t- R_EX_h2s_e\n", + "\t- R_EX_hacolipa_e\n", + "\t- R_EX_halipa_e\n", + "\t- R_EX_hdca_e\n", + "\t- R_EX_hdcea_e\n", + "\t- R_EX_hg2_e\n", + "\t- R_EX_his__L_e\n", + "\t- R_EX_hom__L_e\n", + "\t- R_EX_hxa_e\n", + "\t- R_EX_hxan_e\n", + "\t- R_EX_idon__L_e\n", + "\t- R_EX_ile__L_e\n", + "\t- R_EX_imp_e\n", + "\t- R_EX_indole_e\n", + "\t- R_EX_inost_e\n", + "\t- R_EX_ins_e\n", + "\t- R_EX_isetac_e\n", + "\t- R_EX_k_e\n", + "\t- R_EX_kdo2lipid4_e\n", + "\t- R_EX_lac__D_e\n", + "\t- R_EX_lac__L_e\n", + "\t- R_EX_lcts_e\n", + "\t- R_EX_feroxBfe_e\n", + "\t- R_EX_4hphac_e\n", + "\t- R_EX_4abz_e\n", + "\t- R_EX_acon_C_e\n", + "\t- R_EX_fe3dhbzs3_e\n", + "\t- R_EX_feroxB_e\n", + "\t- R_EX_tcb_e\n", + "\t- R_EX_drib_e\n", + "\t- R_EX_tton_e\n", + "\t- R_EX_4hthr_e\n", + "\t- R_EX_salchs4fe_e\n", + "\t- R_EX_2pg_e\n", + "\t- R_EX_feroxEfe_e\n", + "\t- R_EX_feroxE_e\n", + "\t- R_EX_guln__L_e\n", + "\t- R_EX_23dhbzs3_e\n", + "\t- R_EX_tet_e\n", + "\t- R_EX_salchs4_e\n", + "\t- R_EX_frmd_e\n", + "\t- R_EX_colipaOA_e\n", + "\t- R_EX_salchsx_e\n", + "\t- R_EX_AEP_e\n", + "\t- R_EX_icit_e\n", + "\t- R_EX_oaa_e\n", + "\t- R_EX_salchs2fe_e\n", + "\t- R_EX_pep_e\n", + "\t- R_EX_feroxG_e\n", + "\t- R_EX_colipa20Oag_e\n", + "\t- R_EX_cysi__L_e\n", + "\t- R_EX_remnant1_e\n", + "\t- R_EX_udcpo5_e\n", + "\t- R_EX_3ntym_e\n", + "\t- R_EX_salchs2_e\n", + "\t- R_EX_6apa_e\n", + "\t- R_EX_rnam_e\n", + "\t- R_EX_4hoxpac_e\n", + "\t- R_EX_peng_e\n", + "\t- R_EX_airs_e\n", + "\t- R_EX_dxyl_e\n", + "\t- R_EX_3pg_e\n", + "\t- R_EX_feroxGfe_e\n", + "\t- R_EX_pac_e\n", + "\t- R_EX_diact_e\n", + "\t- R_EX_salcn_e\n", + "\t- R_EX_cellb_e\n", + "\t- R_EX_udcpo4_e\n", + "\t- R_DM_lipidA_core_e_p\n", + "\t- R_EX_tag__D_e\n", + "\t- R_EX_galam_e\n", + "\t- R_EX_raffin_e\n", + "\t- R_EX_btd_RR_e\n", + "\t- R_EX_rbt_e\n", + "\t- R_EX_o6a4colipa_e\n", + "\t- R_EX_arab__D_e\n", + "\t- R_EX_bz_e\n", + "\t- R_EX_13ppd_e\n", + "\t- R_EX_abt__D_e\n", + "\t- R_EX_fe_e\n", + "\t- R_EX_kokdolipidA_e\n", + "\t- R_EX_3hoxpac_e\u001b[0m\n", + "File saved at: ../../data/sbml_corrected/iECW_1372.xml\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: iECBD_1354\n", + "\u001b[0;93mWARNING : \n", + " - R_CAT: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_DHPTDNR: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_DHPTDNRN: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_FHL: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCFUMtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCMALtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCTARTtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SPODM: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SPODMpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCASPtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_CHITPH: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_URCN: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_EX_chitob_e: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_CHITOBpts: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\u001b[0m\n", + "\n", + "\n", + "\u001b[0;36m############################################\n", + "############################################\n", + " WRITING SBML FILE \n", + "############################################\n", + "############################################\n", + "\u001b[0m\n", + "\u001b[0;93mWARNING : IMPORT REACTION REMOVED BY DEFAULT\u001b[0m\n", + "\u001b[0;93mWARNING : If you want to keep import reaction\n", + "use option -kir / --keep-import-reactions\u001b[0m\n", + "\u001b[0;93mWARNING : Import reaction removed:\n", + "\t- R_EX_arg__L_e\n", + "\t- R_EX_ascb__L_e\n", + "\t- R_EX_asn__L_e\n", + "\t- R_EX_aso3_e\n", + "\t- R_EX_asp__L_e\n", + "\t- R_EX_btn_e\n", + "\t- R_EX_but_e\n", + "\t- R_EX_butso3_e\n", + "\t- R_EX_ca2_e\n", + "\t- R_EX_cbi_e\n", + "\t- R_EX_cbl1_e\n", + "\t- R_EX_g3pe_e\n", + "\t- R_EX_g3pg_e\n", + "\t- R_EX_g3pi_e\n", + "\t- R_DM_4crsol_c\n", + "\t- R_DM_5drib_c\n", + "\t- R_EX_cd2_e\n", + "\t- R_DM_aacald_c\n", + "\t- R_EX_g3ps_e\n", + "\t- R_EX_g6p_e\n", + "\t- R_EX_gal_e\n", + "\t- R_DM_amob_c\n", + "\t- R_EX_gal_bD_e\n", + "\t- R_EX_cgly_e\n", + "\t- R_EX_gal1p_e\n", + "\t- R_DM_mththf_c\n", + "\t- R_DM_oxam_c\n", + "\t- R_EX_galct__D_e\n", + "\t- R_EX_chol_e\n", + "\t- R_EX_galctn__D_e\n", + "\t- R_EX_galctn__L_e\n", + "\t- R_EX_12ppd__R_e\n", + "\t- R_EX_12ppd__S_e\n", + "\t- R_EX_14glucan_e\n", + "\t- R_EX_15dap_e\n", + "\t- R_EX_23camp_e\n", + "\t- R_EX_23ccmp_e\n", + "\t- R_EX_23cgmp_e\n", + "\t- R_EX_galt_e\n", + "\t- R_EX_galur_e\n", + "\t- R_EX_gam_e\n", + "\t- R_EX_gam6p_e\n", + "\t- R_EX_gbbtn_e\n", + "\t- R_EX_gdp_e\n", + "\t- R_EX_glc__D_e\n", + "\t- R_EX_glcn_e\n", + "\t- R_EX_glcr_e\n", + "\t- R_EX_glcur_e\n", + "\t- R_EX_glcur1p_e\n", + "\t- R_EX_gln__L_e\n", + "\t- R_EX_glu__L_e\n", + "\t- R_EX_23cump_e\n", + "\t- R_EX_23dappa_e\n", + "\t- R_EX_26dap__M_e\n", + "\t- R_EX_2ddglcn_e\n", + "\t- R_EX_34dhpac_e\n", + "\t- R_EX_3amp_e\n", + "\t- R_EX_gly_e\n", + "\t- R_EX_glyald_e\n", + "\t- R_EX_glyb_e\n", + "\t- R_EX_3cmp_e\n", + "\t- R_EX_3gmp_e\n", + "\t- R_EX_glyc_e\n", + "\t- R_EX_chtbs_e\n", + "\t- R_EX_glyc__R_e\n", + "\t- R_EX_glyc2p_e\n", + "\t- R_EX_glyc3p_e\n", + "\t- R_EX_3hcinnm_e\n", + "\t- R_EX_3hpp_e\n", + "\t- R_EX_3hpppn_e\n", + "\t- R_EX_3ump_e\n", + "\t- R_EX_4abut_e\n", + "\t- R_EX_4hoxpacd_e\n", + "\t- R_EX_5dglcn_e\n", + "\t- R_EX_5mtr_e\n", + "\t- R_EX_LalaDglu_e\n", + "\t- R_EX_glyclt_e\n", + "\t- R_EX_gmp_e\n", + "\t- R_EX_gsn_e\n", + "\t- R_EX_gthox_e\n", + "\t- R_EX_gthrd_e\n", + "\t- R_EX_LalaDgluMdap_e\n", + "\t- R_EX_LalaDgluMdapDala_e\n", + "\t- R_EX_LalaLglu_e\n", + "\t- R_EX_gtp_e\n", + "\t- R_EX_gua_e\n", + "\t- R_EX_ac_e\n", + "\t- R_EX_cit_e\n", + "\t- R_EX_acac_e\n", + "\t- R_EX_h_e\n", + "\t- R_EX_h2_e\n", + "\t- R_EX_h2o_e\n", + "\t- R_EX_acald_e\n", + "\t- R_EX_cl_e\n", + "\t- R_EX_acgal_e\n", + "\t- R_EX_acgal1p_e\n", + "\t- R_EX_h2o2_e\n", + "\t- R_EX_h2s_e\n", + "\t- R_EX_hacolipa_e\n", + "\t- R_EX_acgam_e\n", + "\t- R_EX_cm_e\n", + "\t- R_EX_acgam1p_e\n", + "\t- R_EX_halipa_e\n", + "\t- R_EX_hdca_e\n", + "\t- R_EX_acmana_e\n", + "\t- R_EX_hdcea_e\n", + "\t- R_EX_cmp_e\n", + "\t- R_EX_hg2_e\n", + "\t- R_EX_his__L_e\n", + "\t- R_EX_hom__L_e\n", + "\t- R_EX_hxa_e\n", + "\t- R_EX_acmum_e\n", + "\t- R_EX_acnam_e\n", + "\t- R_EX_acolipa_e\n", + "\t- R_EX_acser_e\n", + "\t- R_EX_ade_e\n", + "\t- R_EX_adn_e\n", + "\t- R_EX_hxan_e\n", + "\t- R_EX_idon__L_e\n", + "\t- R_EX_adocbl_e\n", + "\t- R_EX_ile__L_e\n", + "\t- R_EX_ag_e\n", + "\t- R_EX_co2_e\n", + "\t- R_EX_agm_e\n", + "\t- R_EX_akg_e\n", + "\t- R_EX_ala_B_e\n", + "\t- R_EX_imp_e\n", + "\t- R_EX_indole_e\n", + "\t- R_EX_inost_e\n", + "\t- R_EX_ala__D_e\n", + "\t- R_EX_ins_e\n", + "\t- R_EX_ala__L_e\n", + "\t- R_EX_isetac_e\n", + "\t- R_EX_cobalt2_e\n", + "\t- R_EX_k_e\n", + "\t- R_EX_kdo2lipid4_e\n", + "\t- R_EX_lac__D_e\n", + "\t- R_EX_alaala_e\n", + "\t- R_EX_all__D_e\n", + "\t- R_EX_alltn_e\n", + "\t- R_EX_colipa_e\n", + "\t- R_EX_amp_e\n", + "\t- R_EX_anhgm_e\n", + "\t- R_EX_lac__L_e\n", + "\t- R_EX_lcts_e\n", + "\t- R_EX_leu__L_e\n", + "\t- R_EX_lipa_e\n", + "\t- R_EX_lipa_cold_e\n", + "\t- R_EX_lipoate_e\n", + "\t- R_EX_arab__L_e\n", + "\t- R_EX_arbt_e\n", + "\t- R_EX_arbtn_e\n", + "\t- R_EX_arbtn_fe3_e\n", + "\t- R_EX_lys__L_e\n", + "\t- R_EX_lyx__L_e\n", + "\t- R_EX_colipap_e\n", + "\t- R_EX_mal__D_e\n", + "\t- R_EX_mal__L_e\n", + "\t- R_EX_meoh_e\n", + "\t- R_EX_met__D_e\n", + "\t- R_EX_malt_e\n", + "\t- R_EX_cpgn_e\n", + "\t- R_EX_malthx_e\n", + "\t- R_EX_maltpt_e\n", + "\t- R_EX_met__L_e\n", + "\t- R_EX_metsox_R__L_e\n", + "\t- R_EX_metsox_S__L_e\n", + "\t- R_EX_mg2_e\n", + "\t- R_EX_malttr_e\n", + "\t- R_EX_mincyc_e\n", + "\t- R_EX_maltttr_e\n", + "\t- R_EX_cpgn_un_e\n", + "\t- R_EX_man_e\n", + "\t- R_EX_minohp_e\n", + "\t- R_EX_mmet_e\n", + "\t- R_EX_man6p_e\n", + "\t- R_EX_crn_e\n", + "\t- R_EX_manglyc_e\n", + "\t- R_EX_melib_e\n", + "\t- R_EX_mn2_e\n", + "\t- R_EX_mnl_e\n", + "\t- R_EX_crn__D_e\n", + "\t- R_EX_mobd_e\n", + "\t- R_EX_mso3_e\n", + "\t- R_EX_n2o_e\n", + "\t- R_EX_na1_e\n", + "\t- R_EX_nac_e\n", + "\t- R_EX_nh4_e\n", + "\t- R_EX_ni2_e\n", + "\t- R_EX_nmn_e\n", + "\t- R_EX_tsul_e\n", + "\t- R_EX_ttdca_e\n", + "\t- R_EX_ttdcea_e\n", + "\t- R_EX_ttrcyc_e\n", + "\t- R_EX_tungs_e\n", + "\t- R_EX_tym_e\n", + "\t- R_EX_no_e\n", + "\t- R_EX_no2_e\n", + "\t- R_EX_no3_e\n", + "\t- R_EX_tyr__L_e\n", + "\t- R_EX_csn_e\n", + "\t- R_EX_tyrp_e\n", + "\t- R_EX_uacgam_e\n", + "\t- R_EX_novbcn_e\n", + "\t- R_EX_o16a4colipa_e\n", + "\t- R_EX_o2_e\n", + "\t- R_EX_o2s_e\n", + "\t- R_EX_udpacgal_e\n", + "\t- R_EX_udpg_e\n", + "\t- R_EX_udpgal_e\n", + "\t- R_EX_ocdca_e\n", + "\t- R_EX_ocdcea_e\n", + "\t- R_EX_cu_e\n", + "\t- R_EX_octa_e\n", + "\t- R_EX_orn_e\n", + "\t- R_EX_udpglcur_e\n", + "\t- R_EX_ump_e\n", + "\t- R_EX_ura_e\n", + "\t- R_EX_orot_e\n", + "\t- R_EX_cu2_e\n", + "\t- R_EX_pacald_e\n", + "\t- R_EX_peamn_e\n", + "\t- R_EX_phe__L_e\n", + "\t- R_EX_pheme_e\n", + "\t- R_EX_pi_e\n", + "\t- R_EX_urea_e\n", + "\t- R_EX_uri_e\n", + "\t- R_EX_val__L_e\n", + "\t- R_EX_xan_e\n", + "\t- R_EX_xmp_e\n", + "\t- R_EX_xtsn_e\n", + "\t- R_EX_xyl__D_e\n", + "\t- R_EX_pnto__R_e\n", + "\t- R_EX_ppa_e\n", + "\t- R_EX_ppal_e\n", + "\t- R_EX_xylu__L_e\n", + "\t- R_EX_pppn_e\n", + "\t- R_EX_cyan_e\n", + "\t- R_EX_ppt_e\n", + "\t- R_EX_pro__L_e\n", + "\t- R_EX_zn2_e\n", + "\t- R_EX_progly_e\n", + "\t- R_EX_psclys_e\n", + "\t- R_EX_cynt_e\n", + "\t- R_EX_pser__L_e\n", + "\t- R_EX_ptrc_e\n", + "\t- R_EX_cys__D_e\n", + "\t- R_EX_pydam_e\n", + "\t- R_EX_pydx_e\n", + "\t- R_EX_pydxn_e\n", + "\t- R_EX_pyr_e\n", + "\t- R_EX_quin_e\n", + "\t- R_EX_r5p_e\n", + "\t- R_EX_rfamp_e\n", + "\t- R_EX_rib__D_e\n", + "\t- R_EX_rmn_e\n", + "\t- R_EX_cys__L_e\n", + "\t- R_EX_sbt__D_e\n", + "\t- R_EX_sel_e\n", + "\t- R_EX_ser__D_e\n", + "\t- R_EX_ser__L_e\n", + "\t- R_EX_skm_e\n", + "\t- R_EX_slnt_e\n", + "\t- R_EX_cytd_e\n", + "\t- R_EX_so2_e\n", + "\t- R_EX_so3_e\n", + "\t- R_EX_so4_e\n", + "\t- R_EX_spmd_e\n", + "\t- R_EX_succ_e\n", + "\t- R_EX_sucr_e\n", + "\t- R_EX_sulfac_e\n", + "\t- R_EX_tartr__D_e\n", + "\t- R_EX_tartr__L_e\n", + "\t- R_EX_taur_e\n", + "\t- R_EX_dad_2_e\n", + "\t- R_EX_tcynt_e\n", + "\t- R_EX_thm_e\n", + "\t- R_EX_thr__L_e\n", + "\t- R_EX_thrp_e\n", + "\t- R_EX_thym_e\n", + "\t- R_EX_thymd_e\n", + "\t- R_EX_damp_e\n", + "\t- R_EX_tma_e\n", + "\t- R_EX_tmao_e\n", + "\t- R_EX_tre_e\n", + "\t- R_EX_trp__L_e\n", + "\t- R_EX_dca_e\n", + "\t- R_EX_dcmp_e\n", + "\t- R_EX_dcyt_e\n", + "\t- R_EX_ddca_e\n", + "\t- R_EX_dgmp_e\n", + "\t- R_EX_dgsn_e\n", + "\t- R_EX_dha_e\n", + "\t- R_EX_dimp_e\n", + "\t- R_EX_din_e\n", + "\t- R_EX_dms_e\n", + "\t- R_EX_dmso_e\n", + "\t- R_EX_dopa_e\n", + "\t- R_EX_doxrbcn_e\n", + "\t- R_EX_dtmp_e\n", + "\t- R_EX_dump_e\n", + "\t- R_EX_duri_e\n", + "\t- R_EX_eca4colipa_e\n", + "\t- R_EX_enlipa_e\n", + "\t- R_EX_enter_e\n", + "\t- R_EX_etha_e\n", + "\t- R_EX_ethso3_e\n", + "\t- R_EX_etoh_e\n", + "\t- R_EX_f6p_e\n", + "\t- R_EX_fald_e\n", + "\t- R_EX_fe2_e\n", + "\t- R_EX_fe3_e\n", + "\t- R_EX_fe3dcit_e\n", + "\t- R_EX_fe3dhbzs_e\n", + "\t- R_EX_fe3hox_e\n", + "\t- R_EX_fe3hox_un_e\n", + "\t- R_EX_fecrm_e\n", + "\t- R_EX_fecrm_un_e\n", + "\t- R_EX_feenter_e\n", + "\t- R_EX_feoxam_e\n", + "\t- R_EX_feoxam_un_e\n", + "\t- R_EX_for_e\n", + "\t- R_EX_fru_e\n", + "\t- R_EX_frulys_e\n", + "\t- R_EX_fruur_e\n", + "\t- R_EX_fuc__L_e\n", + "\t- R_EX_fum_e\n", + "\t- R_EX_fusa_e\n", + "\t- R_EX_g1p_e\n", + "\t- R_EX_g3pc_e\n", + "\t- R_EX_23dhbzs3_e\n", + "\t- R_EX_tet_e\n", + "\t- R_EX_salchs4_e\n", + "\t- R_EX_oaa_e\n", + "\t- R_EX_salchs2fe_e\n", + "\t- R_EX_feroxBfe_e\n", + "\t- R_EX_cysi__L_e\n", + "\t- R_EX_4hphac_e\n", + "\t- R_EX_fe3dhbzs3_e\n", + "\t- R_EX_4abz_e\n", + "\t- R_EX_drib_e\n", + "\t- R_EX_salchs4fe_e\n", + "\t- R_EX_2pg_e\n", + "\t- R_EX_acon_C_e\n", + "\t- R_EX_frmd_e\n", + "\t- R_EX_colipaOA_e\n", + "\t- R_EX_salchsx_e\n", + "\t- R_EX_AEP_e\n", + "\t- R_EX_icit_e\n", + "\t- R_EX_feroxB_e\n", + "\t- R_EX_tcb_e\n", + "\t- R_EX_pep_e\n", + "\t- R_EX_tton_e\n", + "\t- R_EX_4hthr_e\n", + "\t- R_EX_colipa20Oag_e\n", + "\t- R_EX_remnant1_e\n", + "\t- R_EX_feroxEfe_e\n", + "\t- R_EX_feroxE_e\n", + "\t- R_EX_udcpo5_e\n", + "\t- R_EX_salchs2_e\n", + "\t- R_EX_airs_e\n", + "\t- R_EX_dxyl_e\n", + "\t- R_EX_3pg_e\n", + "\t- R_EX_feroxGfe_e\n", + "\t- R_EX_rnam_e\n", + "\t- R_DM_lipidA_core_e_p\n", + "\t- R_EX_3ntym_e\n", + "\t- R_EX_udcpo4_e\n", + "\t- R_EX_tag__D_e\n", + "\t- R_EX_6apa_e\n", + "\t- R_EX_4hoxpac_e\n", + "\t- R_EX_peng_e\n", + "\t- R_EX_guln__L_e\n", + "\t- R_EX_pac_e\n", + "\t- R_EX_feroxG_e\n", + "\t- R_EX_salcn_e\n", + "\t- R_EX_galam_e\n", + "\t- R_EX_raffin_e\n", + "\t- R_EX_o6a4colipa_e\n", + "\t- R_EX_diact_e\n", + "\t- R_EX_cellb_e\n", + "\t- R_EX_btd_RR_e\n", + "\t- R_EX_13ppd_e\n", + "\t- R_EX_rbt_e\n", + "\t- R_EX_bz_e\n", + "\t- R_EX_abt__D_e\n", + "\t- R_EX_fe_e\n", + "\t- R_EX_kokdolipidA_e\n", + "\t- R_EX_3hoxpac_e\n", + "\t- R_EX_arab__D_e\u001b[0m\n", + "File saved at: ../../data/sbml_corrected/iECBD_1354.xml\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: iYS854\n", + "\u001b[0;93mWARNING : - R_ACALD: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_AIRCr: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_ALAR: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_BTNTe: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_CYStec: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + "\n", + " - R_EX_for_e: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + " - R_G3PD2: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_sink_GCVHF: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_sink_GCVHLF: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_HMGCOAR: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_HMGCOAS: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_HSDxi: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_METSOXR1: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_PIt2r: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_BIOMASS_iYS_wild_type is multiple (more than 1) metabolites import/export reaction. \n", + "\tDeleted as it is an import reaction.\n", + " - R_SULR_1: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + "\n", + " - R_BIOMASS_iYS_reduced: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\u001b[0m\n", + "\n", + "\n", + "\u001b[0;36m############################################\n", + "############################################\n", + " WRITING SBML FILE \n", + "############################################\n", + "############################################\n", + "\u001b[0m\n", + "\u001b[0;93mWARNING : IMPORT REACTION REMOVED BY DEFAULT\u001b[0m\n", + "\u001b[0;93mWARNING : If you want to keep import reaction\n", + "use option -kir / --keep-import-reactions\u001b[0m\n", + "\u001b[0;93mWARNING : Reaction with tag reversible modified:\n", + "\t- R_ACALD\n", + "\t- R_AIRCr\n", + "\t- R_ALAR\n", + "\t- R_BTNTe\n", + "\t- R_CYStec\n", + "\t- R_G3PD2\n", + "\t- R_sink_GCVHF\n", + "\t- R_sink_GCVHLF\n", + "\t- R_HMGCOAR\n", + "\t- R_HMGCOAS\n", + "\t- R_HSDxi\n", + "\t- R_METSOXR1\n", + "\t- R_PIt2r\n", + "\t- R_SULR_1\u001b[0m\n", + "\u001b[0;93mWARNING : Reaction with Reactants and Products exchanged:\n", + "\t- R_ACALD\n", + "\t- R_AIRCr\n", + "\t- R_ALAR\n", + "\t- R_BTNTe\n", + "\t- R_CYStec\n", + "\t- R_G3PD2\n", + "\t- R_sink_GCVHF\n", + "\t- R_sink_GCVHLF\n", + "\t- R_HMGCOAR\n", + "\t- R_HMGCOAS\n", + "\t- R_HSDxi\n", + "\t- R_METSOXR1\n", + "\t- R_PIt2r\n", + "\t- R_SULR_1\u001b[0m\n", + "\u001b[0;93mWARNING : Import reaction removed:\n", + "\t- R_EX_4ahmmp_e\n", + "\t- R_EX_4hoxpacd_e\n", + "\t- R_DM_4hba_c\n", + "\t- R_DM_5drib_c\n", + "\t- R_EX_5mtr_e\n", + "\t- R_EX_Cit_Mg_e\n", + "\t- R_DM_amob_c\n", + "\t- R_EX_hemoglobin_e\n", + "\t- R_EX_istfrnA_e\n", + "\t- R_EX_istfrnB_e\n", + "\t- R_EX_stfrnA_e\n", + "\t- R_EX_stfrnB_e\n", + "\t- R_EX_xsiderophore_e\n", + "\t- R_EX_4abz_e\n", + "\t- R_EX_ac_e\n", + "\t- R_EX_acald_e\n", + "\t- R_EX_acgam_e\n", + "\t- R_EX_acmana_e\n", + "\t- R_EX_gly_asp__L_e\n", + "\t- R_EX_acnam_e\n", + "\t- R_EX_adn_e\n", + "\t- R_EX_aglaa_e\n", + "\t- R_EX_akg_e\n", + "\t- R_EX_gly_cys_e\n", + "\t- R_EX_gly_e\n", + "\t- R_EX_ala__D_e\n", + "\t- R_EX_ala_L_asp__L_e\n", + "\t- R_EX_ala__L_e\n", + "\t- R_EX_gly_glu__L_e\n", + "\t- R_EX_gly_met_e\n", + "\t- R_EX_ala_L_glu__L_e\n", + "\t- R_EX_gly_phe_e\n", + "\t- R_EX_ala_gln_e\n", + "\t- R_EX_ala_his_e\n", + "\t- R_EX_gly_tyr_e\n", + "\t- R_EX_glyald_e\n", + "\t- R_EX_ala_leu_e\n", + "\t- R_EX_glyb_e\n", + "\t- R_EX_glyc3p_e\n", + "\t- R_EX_glyc_e\n", + "\t- R_EX_alagly_e\n", + "\t- R_EX_alltn_e\n", + "\t- R_EX_arab__L_e\n", + "\t- R_EX_arbt_e\n", + "\t- R_EX_gsn_e\n", + "\t- R_EX_gthrd_e\n", + "\t- R_EX_gua_e\n", + "\t- R_EX_h2o_e\n", + "\t- R_EX_arg__L_e\n", + "\t- R_EX_arsbet_e\n", + "\t- R_EX_ascb__L_e\n", + "\t- R_EX_asn__L_e\n", + "\t- R_EX_aso3_e\n", + "\t- R_EX_h_e\n", + "\t- R_EX_hdca_e\n", + "\t- R_EX_hedACP_e\n", + "\t- R_EX_his__L_e\n", + "\t- R_EX_ile__L_e\n", + "\t- R_EX_ins_e\n", + "\t- R_EX_k_e\n", + "\t- R_EX_aso4_e\n", + "\t- R_EX_asp__L_e\n", + "\t- R_EX_btbet_e\n", + "\t- R_EX_lac__D_e\n", + "\t- R_EX_btn_e\n", + "\t- R_EX_butso3_e\n", + "\t- R_EX_ca2_e\n", + "\t- R_EX_cbl2_e\n", + "\t- R_EX_lac__L_e\n", + "\t- R_EX_lcts_e\n", + "\t- R_EX_leu__L_e\n", + "\t- R_EX_cgly_e\n", + "\t- R_EX_chol_e\n", + "\t- R_EX_lipoate_e\n", + "\t- R_EX_lys__L_e\n", + "\t- R_EX_mal__D_e\n", + "\t- R_EX_mal__L_e\n", + "\t- R_EX_malt_e\n", + "\t- R_EX_chols_e\n", + "\t- R_EX_cl_e\n", + "\t- R_EX_co2_e\n", + "\t- R_EX_cobalt2_e\n", + "\t- R_EX_crn_e\n", + "\t- R_EX_csn_e\n", + "\t- R_EX_malthx_e\n", + "\t- R_EX_malttr_e\n", + "\t- R_EX_man6p_e\n", + "\t- R_EX_man_e\n", + "\t- R_EX_met__D_e\n", + "\t- R_EX_ctbt_e\n", + "\t- R_EX_cu2_e\n", + "\t- R_EX_cys__L_e\n", + "\t- R_EX_cytd_e\n", + "\t- R_EX_met_L_ala__L_e\n", + "\t- R_EX_met__L_e\n", + "\t- R_EX_metsox_R__L_e\n", + "\t- R_EX_metsox_S__L_e\n", + "\t- R_EX_dad_2_e\n", + "\t- R_EX_dcyt_e\n", + "\t- R_EX_dgsn_e\n", + "\t- R_EX_din_e\n", + "\t- R_EX_mg2_e\n", + "\t- R_EX_mn2_e\n", + "\t- R_EX_drib_e\n", + "\t- R_EX_etoh_e\n", + "\t- R_EX_mnl_e\n", + "\t- R_EX_mobd_e\n", + "\t- R_EX_f6p_e\n", + "\t- R_EX_myrsACP_e\n", + "\t- R_EX_na1_e\n", + "\t- R_EX_fe2_e\n", + "\t- R_EX_fe3_e\n", + "\t- R_EX_nac_e\n", + "\t- R_EX_fe3dcit_e\n", + "\t- R_EX_fecrm_e\n", + "\t- R_EX_ncam_e\n", + "\t- R_EX_nh4_e\n", + "\t- R_EX_ni2_e\n", + "\t- R_EX_no2_e\n", + "\t- R_EX_no3_e\n", + "\t- R_EX_o2_e\n", + "\t- R_EX_fol_e\n", + "\t- R_EX_octACP_e\n", + "\t- R_EX_orn_e\n", + "\t- R_EX_peamn_e\n", + "\t- R_EX_fru_e\n", + "\t- R_EX_fuc__L_e\n", + "\t- R_EX_phe__L_e\n", + "\t- R_EX_fum_e\n", + "\t- R_EX_pheme_e\n", + "\t- R_EX_pi_e\n", + "\t- R_EX_g6p_e\n", + "\t- R_EX_gal_e\n", + "\t- R_EX_pnto__R_e\n", + "\t- R_EX_ppi_e\n", + "\t- R_EX_pro__L_e\n", + "\t- R_EX_galctn__D_e\n", + "\t- R_EX_galt_e\n", + "\t- R_EX_gam6p_e\n", + "\t- R_EX_ptrc_e\n", + "\t- R_EX_pydx_e\n", + "\t- R_EX_gam_e\n", + "\t- R_EX_glc__D_e\n", + "\t- R_EX_pyr_e\n", + "\t- R_EX_rib__D_e\n", + "\t- R_EX_glcn_e\n", + "\t- R_EX_glcr_e\n", + "\t- R_EX_glcur_e\n", + "\t- R_EX_gln__L_e\n", + "\t- R_EX_glu__L_e\n", + "\t- R_EX_gly_pro__L_e\n", + "\t- R_EX_ribflv_e\n", + "\t- R_EX_salcn_e\n", + "\t- R_EX_sbt__D_e\n", + "\t- R_EX_ser__D_e\n", + "\t- R_EX_ser__L_e\n", + "\t- R_EX_gly_asn__L_e\n", + "\t- R_EX_so4_e\n", + "\t- R_EX_spmd_e\n", + "\t- R_EX_sprm_e\n", + "\t- R_EX_tre_e\n", + "\t- R_EX_trp__L_e\n", + "\t- R_EX_tsul_e\n", + "\t- R_EX_succ_e\n", + "\t- R_EX_sucr_e\n", + "\t- R_EX_tyr__L_e\n", + "\t- R_EX_tartr__L_e\n", + "\t- R_EX_thm_e\n", + "\t- R_EX_ura_e\n", + "\t- R_EX_urea_e\n", + "\t- R_EX_uri_e\n", + "\t- R_EX_thr__L_e\n", + "\t- R_EX_val__L_e\n", + "\t- R_EX_thym_e\n", + "\t- R_EX_thymd_e\n", + "\t- R_EX_vegACP_e\n", + "\t- R_EX_xan_e\n", + "\t- R_EX_xyl__D_e\n", + "\t- R_EX_zn2_e\n", + "\t- R_EX_12methedec_e\n", + "\t- R_EX_12mtACP_e\n", + "\t- R_EX_12ppd__S_e\n", + "\t- R_EX_13mtdACP_e\n", + "\t- R_EX_14mhdACP_e\n", + "\t- R_EX_14mpACP_e\n", + "\t- R_EX_15mhdACP_e\n", + "\t- R_EX_16mhdACP_e\n", + "\t- R_EX_16mpACP_e\n", + "\t- R_EX_17mhdACP_e\n", + "\t- R_EX_26dap__M_e\n", + "\t- R_sink_GCVHF\n", + "\t- R_sink_GCVHLF\n", + "\t- R_EX_3amp_e\n", + "\t- R_EX_3gmp_e\n", + "\t- R_SK_ahcys_c\n", + "\t- R_BIOMASS_iYS_wild_type\n", + "\t- R_SK_apoACP_c\n", + "\t- R_SK_2fe1s_c\n", + "\t- R_EX_2obut_e\n", + "\t- R_EX_raffin_e\n", + "\t- R_EX_actn__R_e\n", + "\t- R_EX_amdglc_e\n", + "\t- R_EX_dcaACP_e\n", + "\t- R_EX_btd_RR_e\n", + "\t- R_EX_abt_e\n", + "\t- R_EX_4abutn_e\n", + "\t- R_EX_xylt_e\n", + "\t- R_EX_cellb_e\n", + "\t- R_EX_tre6p_e\n", + "\t- R_EX_forglu_e\n", + "\t- R_EX_mnl1p_e\n", + "\t- R_EX_oxa_e\n", + "\t- R_EX_butACP_e\n", + "\t- R_EX_ad_e\n", + "\t- R_EX_acglu_e\n", + "\t- R_EX_dhap_e\n", + "\t- R_EX_4hba_e\n", + "\t- R_EX_salc_e\n", + "\t- R_EX_citr__L_e\n", + "\t- R_EX_acgam1p_e\n", + "\t- R_EX_glx_e\n", + "\t- R_EX_arbt6p_e\n", + "\t- R_EX_4hpro_LT_e\n", + "\t- R_EX_g6p_B_e\n", + "\t- R_EX_g1p_e\n", + "\t- R_EX_ppap_e\n", + "\t- R_EX_hom__L_e\n", + "\t- R_EX_hxan_e\n", + "\t- R_EX_5dglcn_e\n", + "\t- R_EX_galur_e\n", + "\t- R_EX_all__D_e\n", + "\t- R_EX_inost_e\n", + "\t- R_EX_etha_e\n", + "\t- R_EX_lyx__L_e\n", + "\t- R_EX_glyclt_e\n", + "\t- R_EX_rmn_e\n", + "\t- R_EX_acgal_e\n", + "\t- R_EX_acac_e\n", + "\t- R_EX_rbl_B_e\n", + "\t- R_EX_galct__D_e\n", + "\t- R_EX_tartr__D_e\n", + "\t- R_EX_tym_e\n", + "\t- R_EX_12ppd__R_e\n", + "\t- R_EX_quin_e\n", + "\t- R_EX_melib_e\u001b[0m\n", + "File saved at: ../../data/sbml_corrected/iYS854.xml\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: iECS88_1305\n", + "\u001b[0;93mWARNING : \n", + " - R_CAT: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_DHPTDNR: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_DHPTDNRN: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_FHL: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SPODM: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SPODMpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCASPtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCFUMtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCMALtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCTARTtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_EX_chitob_e: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_CHITOBpts: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_CHITPH: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_URCN: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\u001b[0m\n", + "\n", + "\n", + "\u001b[0;36m############################################\n", + "############################################\n", + " WRITING SBML FILE \n", + "############################################\n", + "############################################\n", + "\u001b[0m\n", + "\u001b[0;93mWARNING : IMPORT REACTION REMOVED BY DEFAULT\u001b[0m\n", + "\u001b[0;93mWARNING : If you want to keep import reaction\n", + "use option -kir / --keep-import-reactions\u001b[0m\n", + "\u001b[0;93mWARNING : Import reaction removed:\n", + "\t- R_EX_acser_e\n", + "\t- R_EX_acnam_e\n", + "\t- R_EX_acolipa_e\n", + "\t- R_EX_adn_e\n", + "\t- R_EX_adocbl_e\n", + "\t- R_DM_4crsol_c\n", + "\t- R_EX_ade_e\n", + "\t- R_DM_5drib_c\n", + "\t- R_DM_aacald_c\n", + "\t- R_DM_amob_c\n", + "\t- R_EX_ag_e\n", + "\t- R_EX_agm_e\n", + "\t- R_DM_mththf_c\n", + "\t- R_EX_akg_e\n", + "\t- R_EX_fe3dhbzs_e\n", + "\t- R_EX_ala_B_e\n", + "\t- R_EX_ala__D_e\n", + "\t- R_EX_ala__L_e\n", + "\t- R_EX_alaala_e\n", + "\t- R_EX_all__D_e\n", + "\t- R_EX_alltn_e\n", + "\t- R_DM_oxam_c\n", + "\t- R_EX_12ppd__R_e\n", + "\t- R_EX_12ppd__S_e\n", + "\t- R_EX_amp_e\n", + "\t- R_EX_14glucan_e\n", + "\t- R_EX_15dap_e\n", + "\t- R_EX_anhgm_e\n", + "\t- R_EX_fe3hox_e\n", + "\t- R_EX_arab__L_e\n", + "\t- R_EX_arbt_e\n", + "\t- R_EX_arbtn_e\n", + "\t- R_EX_arbtn_fe3_e\n", + "\t- R_EX_arg__L_e\n", + "\t- R_EX_ascb__L_e\n", + "\t- R_EX_asn__L_e\n", + "\t- R_EX_aso3_e\n", + "\t- R_EX_asp__L_e\n", + "\t- R_EX_btn_e\n", + "\t- R_EX_23camp_e\n", + "\t- R_EX_23ccmp_e\n", + "\t- R_EX_23cgmp_e\n", + "\t- R_EX_23cump_e\n", + "\t- R_EX_23dappa_e\n", + "\t- R_EX_26dap__M_e\n", + "\t- R_EX_2ddglcn_e\n", + "\t- R_EX_34dhpac_e\n", + "\t- R_EX_3amp_e\n", + "\t- R_EX_3cmp_e\n", + "\t- R_EX_3gmp_e\n", + "\t- R_EX_3hcinnm_e\n", + "\t- R_EX_3hpp_e\n", + "\t- R_EX_but_e\n", + "\t- R_EX_butso3_e\n", + "\t- R_EX_ca2_e\n", + "\t- R_EX_cbi_e\n", + "\t- R_EX_3hpppn_e\n", + "\t- R_EX_cbl1_e\n", + "\t- R_EX_3ump_e\n", + "\t- R_EX_fe3hox_un_e\n", + "\t- R_EX_4abut_e\n", + "\t- R_EX_4hoxpacd_e\n", + "\t- R_EX_cd2_e\n", + "\t- R_EX_cgly_e\n", + "\t- R_EX_chol_e\n", + "\t- R_EX_5dglcn_e\n", + "\t- R_EX_5mtr_e\n", + "\t- R_EX_fecrm_e\n", + "\t- R_EX_LalaDglu_e\n", + "\t- R_EX_LalaDgluMdap_e\n", + "\t- R_EX_LalaDgluMdapDala_e\n", + "\t- R_EX_LalaLglu_e\n", + "\t- R_EX_ac_e\n", + "\t- R_EX_acac_e\n", + "\t- R_EX_chtbs_e\n", + "\t- R_EX_cit_e\n", + "\t- R_EX_cl_e\n", + "\t- R_EX_cm_e\n", + "\t- R_EX_cmp_e\n", + "\t- R_EX_co2_e\n", + "\t- R_EX_cobalt2_e\n", + "\t- R_EX_acald_e\n", + "\t- R_EX_acgal_e\n", + "\t- R_EX_acgal1p_e\n", + "\t- R_EX_colipa_e\n", + "\t- R_EX_colipap_e\n", + "\t- R_EX_acgam_e\n", + "\t- R_EX_fecrm_un_e\n", + "\t- R_EX_acgam1p_e\n", + "\t- R_EX_acmana_e\n", + "\t- R_EX_acmum_e\n", + "\t- R_EX_cpgn_e\n", + "\t- R_EX_cpgn_un_e\n", + "\t- R_EX_crn_e\n", + "\t- R_EX_feenter_e\n", + "\t- R_EX_crn__D_e\n", + "\t- R_EX_feoxam_e\n", + "\t- R_EX_csn_e\n", + "\t- R_EX_cu_e\n", + "\t- R_EX_leu__L_e\n", + "\t- R_EX_lipa_e\n", + "\t- R_EX_feoxam_un_e\n", + "\t- R_EX_lipa_cold_e\n", + "\t- R_EX_lipoate_e\n", + "\t- R_EX_lys__L_e\n", + "\t- R_EX_cu2_e\n", + "\t- R_EX_cyan_e\n", + "\t- R_EX_cynt_e\n", + "\t- R_EX_cys__D_e\n", + "\t- R_EX_cys__L_e\n", + "\t- R_EX_cytd_e\n", + "\t- R_EX_dad_2_e\n", + "\t- R_EX_lyx__L_e\n", + "\t- R_EX_mal__D_e\n", + "\t- R_EX_mal__L_e\n", + "\t- R_EX_malt_e\n", + "\t- R_EX_damp_e\n", + "\t- R_EX_dca_e\n", + "\t- R_EX_malthx_e\n", + "\t- R_EX_for_e\n", + "\t- R_EX_maltpt_e\n", + "\t- R_EX_malttr_e\n", + "\t- R_EX_dcmp_e\n", + "\t- R_EX_dcyt_e\n", + "\t- R_EX_maltttr_e\n", + "\t- R_EX_ddca_e\n", + "\t- R_EX_man_e\n", + "\t- R_EX_fru_e\n", + "\t- R_EX_man6p_e\n", + "\t- R_EX_dgmp_e\n", + "\t- R_EX_dgsn_e\n", + "\t- R_EX_dha_e\n", + "\t- R_EX_manglyc_e\n", + "\t- R_EX_frulys_e\n", + "\t- R_EX_melib_e\n", + "\t- R_EX_dimp_e\n", + "\t- R_EX_din_e\n", + "\t- R_EX_dms_e\n", + "\t- R_EX_meoh_e\n", + "\t- R_EX_fruur_e\n", + "\t- R_EX_met__D_e\n", + "\t- R_EX_met__L_e\n", + "\t- R_EX_dmso_e\n", + "\t- R_EX_dopa_e\n", + "\t- R_EX_doxrbcn_e\n", + "\t- R_EX_dtmp_e\n", + "\t- R_EX_dump_e\n", + "\t- R_EX_duri_e\n", + "\t- R_EX_metsox_R__L_e\n", + "\t- R_EX_metsox_S__L_e\n", + "\t- R_EX_mg2_e\n", + "\t- R_EX_mincyc_e\n", + "\t- R_EX_minohp_e\n", + "\t- R_EX_eca4colipa_e\n", + "\t- R_EX_mmet_e\n", + "\t- R_EX_mn2_e\n", + "\t- R_EX_enlipa_e\n", + "\t- R_EX_fuc__L_e\n", + "\t- R_EX_enter_e\n", + "\t- R_EX_mnl_e\n", + "\t- R_EX_mobd_e\n", + "\t- R_EX_mso3_e\n", + "\t- R_EX_etha_e\n", + "\t- R_EX_ethso3_e\n", + "\t- R_EX_fum_e\n", + "\t- R_EX_etoh_e\n", + "\t- R_EX_f6p_e\n", + "\t- R_EX_n2o_e\n", + "\t- R_EX_na1_e\n", + "\t- R_EX_nac_e\n", + "\t- R_EX_fald_e\n", + "\t- R_EX_nh4_e\n", + "\t- R_EX_fusa_e\n", + "\t- R_EX_ni2_e\n", + "\t- R_EX_fe2_e\n", + "\t- R_EX_nmn_e\n", + "\t- R_EX_fe3_e\n", + "\t- R_EX_g1p_e\n", + "\t- R_EX_fe3dcit_e\n", + "\t- R_EX_no_e\n", + "\t- R_EX_no2_e\n", + "\t- R_EX_g3pc_e\n", + "\t- R_EX_no3_e\n", + "\t- R_EX_novbcn_e\n", + "\t- R_EX_o16a4colipa_e\n", + "\t- R_EX_o2_e\n", + "\t- R_EX_spmd_e\n", + "\t- R_EX_succ_e\n", + "\t- R_EX_o2s_e\n", + "\t- R_EX_sucr_e\n", + "\t- R_EX_ocdca_e\n", + "\t- R_EX_g3pe_e\n", + "\t- R_EX_ocdcea_e\n", + "\t- R_EX_sulfac_e\n", + "\t- R_EX_tartr__D_e\n", + "\t- R_EX_tartr__L_e\n", + "\t- R_EX_octa_e\n", + "\t- R_EX_g3pg_e\n", + "\t- R_EX_orn_e\n", + "\t- R_EX_taur_e\n", + "\t- R_EX_tcynt_e\n", + "\t- R_EX_orot_e\n", + "\t- R_EX_thm_e\n", + "\t- R_EX_g3pi_e\n", + "\t- R_EX_thr__L_e\n", + "\t- R_EX_thrp_e\n", + "\t- R_EX_pacald_e\n", + "\t- R_EX_peamn_e\n", + "\t- R_EX_phe__L_e\n", + "\t- R_EX_thym_e\n", + "\t- R_EX_thymd_e\n", + "\t- R_EX_pheme_e\n", + "\t- R_EX_g3ps_e\n", + "\t- R_EX_pi_e\n", + "\t- R_EX_tma_e\n", + "\t- R_EX_tmao_e\n", + "\t- R_EX_g6p_e\n", + "\t- R_EX_tre_e\n", + "\t- R_EX_trp__L_e\n", + "\t- R_EX_pnto__R_e\n", + "\t- R_EX_ppa_e\n", + "\t- R_EX_ppal_e\n", + "\t- R_EX_pppn_e\n", + "\t- R_EX_tsul_e\n", + "\t- R_EX_ttdca_e\n", + "\t- R_EX_ttdcea_e\n", + "\t- R_EX_ttrcyc_e\n", + "\t- R_EX_ppt_e\n", + "\t- R_EX_tungs_e\n", + "\t- R_EX_pro__L_e\n", + "\t- R_EX_gal_e\n", + "\t- R_EX_progly_e\n", + "\t- R_EX_tym_e\n", + "\t- R_EX_tyr__L_e\n", + "\t- R_EX_psclys_e\n", + "\t- R_EX_gal_bD_e\n", + "\t- R_EX_pser__L_e\n", + "\t- R_EX_tyrp_e\n", + "\t- R_EX_gal1p_e\n", + "\t- R_EX_uacgam_e\n", + "\t- R_EX_udpacgal_e\n", + "\t- R_EX_udpg_e\n", + "\t- R_EX_ptrc_e\n", + "\t- R_EX_pydam_e\n", + "\t- R_EX_pydx_e\n", + "\t- R_EX_pydxn_e\n", + "\t- R_EX_udpgal_e\n", + "\t- R_EX_udpglcur_e\n", + "\t- R_EX_pyr_e\n", + "\t- R_EX_quin_e\n", + "\t- R_EX_galct__D_e\n", + "\t- R_EX_r5p_e\n", + "\t- R_EX_ump_e\n", + "\t- R_EX_ura_e\n", + "\t- R_EX_urea_e\n", + "\t- R_EX_rfamp_e\n", + "\t- R_EX_galctn__D_e\n", + "\t- R_EX_rib__D_e\n", + "\t- R_EX_rmn_e\n", + "\t- R_EX_sbt__D_e\n", + "\t- R_EX_sel_e\n", + "\t- R_EX_uri_e\n", + "\t- R_EX_val__L_e\n", + "\t- R_EX_xan_e\n", + "\t- R_EX_xmp_e\n", + "\t- R_EX_xtsn_e\n", + "\t- R_EX_ser__D_e\n", + "\t- R_EX_ser__L_e\n", + "\t- R_EX_xyl__D_e\n", + "\t- R_EX_skm_e\n", + "\t- R_EX_galctn__L_e\n", + "\t- R_EX_slnt_e\n", + "\t- R_EX_xylu__L_e\n", + "\t- R_EX_zn2_e\n", + "\t- R_EX_so2_e\n", + "\t- R_EX_galt_e\n", + "\t- R_EX_so3_e\n", + "\t- R_EX_so4_e\n", + "\t- R_EX_galur_e\n", + "\t- R_EX_gam_e\n", + "\t- R_EX_gam6p_e\n", + "\t- R_EX_gbbtn_e\n", + "\t- R_EX_gdp_e\n", + "\t- R_EX_glc__D_e\n", + "\t- R_EX_glcn_e\n", + "\t- R_EX_glcr_e\n", + "\t- R_EX_glcur_e\n", + "\t- R_EX_glcur1p_e\n", + "\t- R_EX_gln__L_e\n", + "\t- R_EX_glu__L_e\n", + "\t- R_EX_gly_e\n", + "\t- R_EX_glyald_e\n", + "\t- R_EX_glyb_e\n", + "\t- R_EX_glyc_e\n", + "\t- R_EX_glyc__R_e\n", + "\t- R_EX_glyc2p_e\n", + "\t- R_EX_glyc3p_e\n", + "\t- R_EX_glyclt_e\n", + "\t- R_EX_gmp_e\n", + "\t- R_EX_gsn_e\n", + "\t- R_EX_gthox_e\n", + "\t- R_EX_gthrd_e\n", + "\t- R_EX_gtp_e\n", + "\t- R_EX_gua_e\n", + "\t- R_EX_h_e\n", + "\t- R_EX_h2_e\n", + "\t- R_EX_h2o_e\n", + "\t- R_EX_h2o2_e\n", + "\t- R_EX_h2s_e\n", + "\t- R_EX_hacolipa_e\n", + "\t- R_EX_halipa_e\n", + "\t- R_EX_hdca_e\n", + "\t- R_EX_hdcea_e\n", + "\t- R_EX_hg2_e\n", + "\t- R_EX_his__L_e\n", + "\t- R_EX_hom__L_e\n", + "\t- R_EX_hxa_e\n", + "\t- R_EX_hxan_e\n", + "\t- R_EX_idon__L_e\n", + "\t- R_EX_ile__L_e\n", + "\t- R_EX_imp_e\n", + "\t- R_EX_indole_e\n", + "\t- R_EX_inost_e\n", + "\t- R_EX_ins_e\n", + "\t- R_EX_isetac_e\n", + "\t- R_EX_k_e\n", + "\t- R_EX_kdo2lipid4_e\n", + "\t- R_EX_lac__D_e\n", + "\t- R_EX_lac__L_e\n", + "\t- R_EX_lcts_e\n", + "\t- R_EX_icit_e\n", + "\t- R_EX_abt__D_e\n", + "\t- R_EX_4abz_e\n", + "\t- R_EX_acon_C_e\n", + "\t- R_EX_feroxB_e\n", + "\t- R_EX_tcb_e\n", + "\t- R_EX_tton_e\n", + "\t- R_EX_4hthr_e\n", + "\t- R_EX_feroxEfe_e\n", + "\t- R_EX_feroxE_e\n", + "\t- R_EX_pep_e\n", + "\t- R_EX_23dhbzs3_e\n", + "\t- R_EX_tet_e\n", + "\t- R_EX_salchs4_e\n", + "\t- R_EX_tag__D_e\n", + "\t- R_EX_oaa_e\n", + "\t- R_EX_4hphac_e\n", + "\t- R_EX_salchs2fe_e\n", + "\t- R_EX_cysi__L_e\n", + "\t- R_EX_fe3dhbzs3_e\n", + "\t- R_EX_colipa20Oag_e\n", + "\t- R_EX_drib_e\n", + "\t- R_EX_remnant1_e\n", + "\t- R_EX_feroxBfe_e\n", + "\t- R_EX_salchs4fe_e\n", + "\t- R_EX_2pg_e\n", + "\t- R_EX_salchs2_e\n", + "\t- R_EX_frmd_e\n", + "\t- R_EX_colipaOA_e\n", + "\t- R_EX_salchsx_e\n", + "\t- R_EX_AEP_e\n", + "\t- R_EX_rnam_e\n", + "\t- R_EX_airs_e\n", + "\t- R_EX_dxyl_e\n", + "\t- R_EX_3pg_e\n", + "\t- R_EX_arab__D_e\n", + "\t- R_EX_udcpo4_e\n", + "\t- R_EX_fe_e\n", + "\t- R_EX_feroxGfe_e\n", + "\t- R_EX_udcpo5_e\n", + "\t- R_EX_guln__L_e\n", + "\t- R_EX_kokdolipidA_e\n", + "\t- R_EX_feroxG_e\n", + "\t- R_EX_3hoxpac_e\n", + "\t- R_EX_o6a4colipa_e\n", + "\t- R_EX_diact_e\n", + "\t- R_EX_cellb_e\n", + "\t- R_EX_btd_RR_e\n", + "\t- R_EX_13ppd_e\n", + "\t- R_EX_rbt_e\n", + "\t- R_EX_bz_e\n", + "\t- R_EX_3ntym_e\n", + "\t- R_EX_6apa_e\n", + "\t- R_EX_4hoxpac_e\n", + "\t- R_EX_peng_e\n", + "\t- R_EX_pac_e\n", + "\t- R_EX_salcn_e\n", + "\t- R_EX_galam_e\n", + "\t- R_EX_raffin_e\n", + "\t- R_DM_lipidA_core_e_p\u001b[0m\n", + "File saved at: ../../data/sbml_corrected/iECS88_1305.xml\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: iMM1415\n", + "\u001b[0;93mWARNING : \n", + " - R_DM_13_cis_oretn_n: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_DM_13_cis_retn_n: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_EX_retpalm_SPACE_deleted_SPACE_10_09_2005_SPACE_SPACE_06_COLON_18_COLON_49_SPACE_PM_e: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SK_citr__L_c: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + " - R_EX_lnlc_e_copy2: Reactants and products exchanged.\n", + " Boundaries was: [-1.0 ; 0.0]\n", + " - R_AATA: Reactants and products exchanged.\n", + " Boundaries was: [-100000.0 ; 0.0]\n", + " - R_ABTt: Reactants and products exchanged.\n", + " Boundaries was: [-100000.0 ; 0.0]\n", + " - R_ACOAH: Reactants and products exchanged.\n", + " Boundaries was: [-100000.0 ; 0.0]\n", + "\n", + " - R_CYOOm3: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + " - R_HMGCOAS: Reactants and products exchanged.\n", + " Boundaries was: [-100000.0 ; 0.0]\n", + " - R_HMGCOASm: Reactants and products exchanged.\n", + " Boundaries was: [-100000.0 ; 0.0]\n", + "\n", + " - R_L_LACtcm: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_L_LACtm: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_PIt2m: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_THD1m: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\u001b[0m\n", + "\n", + "\n", + "\u001b[0;36m############################################\n", + "############################################\n", + " WRITING SBML FILE \n", + "############################################\n", + "############################################\n", + "\u001b[0m\n", + "\u001b[0;93mWARNING : IMPORT REACTION REMOVED BY DEFAULT\u001b[0m\n", + "\u001b[0;93mWARNING : If you want to keep import reaction\n", + "use option -kir / --keep-import-reactions\u001b[0m\n", + "\u001b[0;93mWARNING : Reaction with tag reversible modified:\n", + "\t- R_EX_lnlc_e_copy2\n", + "\t- R_AATA\n", + "\t- R_ABTt\n", + "\t- R_ACOAH\n", + "\t- R_HMGCOAS\n", + "\t- R_HMGCOASm\u001b[0m\n", + "\u001b[0;93mWARNING : Reaction with Reactants and Products exchanged:\n", + "\t- R_EX_lnlc_e_copy2\n", + "\t- R_AATA\n", + "\t- R_ABTt\n", + "\t- R_ACOAH\n", + "\t- R_HMGCOAS\n", + "\t- R_HMGCOASm\u001b[0m\n", + "\u001b[0;93mWARNING : Import reaction removed:\n", + "\t- R_EX_5fthf_e\n", + "\t- R_EX_5homeprazole_e\n", + "\t- R_EX_34dhoxpeg_e\n", + "\t- R_EX_5htrp_e\n", + "\t- R_EX_34dhphe_e\n", + "\t- R_EX_35cgmp_e\n", + "\t- R_EX_3aib_e\n", + "\t- R_EX_3aib__D_e\n", + "\t- R_EX_3mlda_e\n", + "\t- R_EX_4abut_e\n", + "\t- R_EX_4hdebrisoquine_e\n", + "\t- R_EX_acald_e\n", + "\t- R_DM_Ser_Thr_l\n", + "\t- R_EX_4hphac_e\n", + "\t- R_DM_avite1_c\n", + "\t- R_EX_acetone_e\n", + "\t- R_EX_acgalfucgalacgalfuc12gal14acglcgalgluside_hs_e\n", + "\t- R_EX_acgalfucgalacgalfucgalacglcgal14acglcgalgluside_hs_e\n", + "\t- R_DM_avite2_c\n", + "\t- R_DM_bvite_c\n", + "\t- R_EX_4mptnl_e\n", + "\t- R_DM_core5_g\n", + "\t- R_EX_acgam_e\n", + "\t- R_EX_ach_e\n", + "\t- R_EX_acn13acngalgbside_hs_e\n", + "\t- R_EX_acn23acngalgbside_hs_e\n", + "\t- R_EX_4mtolbutamide_e\n", + "\t- R_EX_acnacngal14acglcgalgluside_hs_e\n", + "\t- R_EX_acnacngalgbside_hs_e\n", + "\t- R_EX_acngalacglcgal14acglcgalgluside_hs_e\n", + "\t- R_EX_4nph_e\n", + "\t- R_EX_ade_e\n", + "\t- R_EX_adn_e\n", + "\t- R_EX_adp_e\n", + "\t- R_EX_adprbp_e\n", + "\t- R_EX_adprib_e\n", + "\t- R_EX_adrn_e\n", + "\t- R_EX_adrnl_e\n", + "\t- R_EX_aflatoxin_e\n", + "\t- R_EX_ahandrostanglc_e\n", + "\t- R_DM_core7_g\n", + "\t- R_DM_core8_g\n", + "\t- R_DM_datp_m\n", + "\t- R_DM_datp_n\n", + "\t- R_DM_dctp_m\n", + "\t- R_EX_ak2lgchol_hs_e\n", + "\t- R_EX_akg_e\n", + "\t- R_EX_ala_B_e\n", + "\t- R_DM_Asn_X_Ser_Thr_l\n", + "\t- R_EX_4nphsf_e\n", + "\t- R_DM_Ser_Gly_Ala_X_Gly_l\n", + "\t- R_DM_dctp_n\n", + "\t- R_DM_dem2emgacpail_prot_hs_r\n", + "\t- R_EX_ala__D_e\n", + "\t- R_EX_ala__L_e\n", + "\t- R_EX_aldstrn_e\n", + "\t- R_EX_amp_e\n", + "\t- R_DM_dgpi_prot_hs_r\n", + "\t- R_EX_4pyrdx_e\n", + "\t- R_DM_dgtp_m\n", + "\t- R_EX_andrstrn_e\n", + "\t- R_EX_andrstrnglc_e\n", + "\t- R_EX_5adtststerone_e\n", + "\t- R_EX_antipyrene_e\n", + "\t- R_DM_dgtp_n\n", + "\t- R_DM_dsT_antigen_g\n", + "\t- R_EX_5adtststeroneglc_e\n", + "\t- R_DM_dttp_m\n", + "\t- R_DM_dttp_n\n", + "\t- R_DM_ethamp_r\n", + "\t- R_DM_gncore2_g\n", + "\t- R_DM_gpi_sig_r\n", + "\t- R_EX_5adtststerones_e\n", + "\t- R_DM_hretn_n\n", + "\t- R_DM_kdn_c\n", + "\t- R_EX_apnnox_e\n", + "\t- R_EX_appnn_e\n", + "\t- R_DM_m_em_3gacpail_prot_hs_r\n", + "\t- R_EX_5dhf_e\n", + "\t- R_DM_melanin_c\n", + "\t- R_EX_aprgstrn_e\n", + "\t- R_EX_aqcobal_e\n", + "\t- R_EX_5mthf_e\n", + "\t- R_EX_arab__L_e\n", + "\t- R_EX_arach_e\n", + "\t- R_DM_mem2emgacpail_prot_hs_r\n", + "\t- R_DM_n5m2masn_g\n", + "\t- R_DM_oretn_n\n", + "\t- R_EX_arachd_e\n", + "\t- R_DM_sTn_antigen_g\n", + "\t- R_EX_5thf_e\n", + "\t- R_DM_sprm_c\n", + "\t- R_DM_yvite_c\n", + "\t- R_SK_T_antigen_g\n", + "\t- R_EX_arg__L_e\n", + "\t- R_EX_ascb__L_e\n", + "\t- R_EX_asn__L_e\n", + "\t- R_EX_asp__D_e\n", + "\t- R_EX_6dhf_e\n", + "\t- R_EX_asp__L_e\n", + "\t- R_EX_10fthf_e\n", + "\t- R_EX_10fthf5glu_e\n", + "\t- R_EX_6htststerone_e\n", + "\t- R_EX_atp_e\n", + "\t- R_EX_avite1_e\n", + "\t- R_EX_avite2_e\n", + "\t- R_EX_10fthf6glu_e\n", + "\t- R_EX_10fthf7glu_e\n", + "\t- R_EX_11_cis_retfa_e\n", + "\t- R_EX_bhb_e\n", + "\t- R_EX_6thf_e\n", + "\t- R_EX_13_cis_retnglc_e\n", + "\t- R_EX_1glyc_hs_e\n", + "\t- R_EX_1mncam_e\n", + "\t- R_EX_7dhf_e\n", + "\t- R_EX_2425dhvitd2_e\n", + "\t- R_EX_2425dhvitd3_e\n", + "\t- R_EX_7thf_e\n", + "\t- R_EX_24nph_e\n", + "\t- R_EX_9_cis_retfa_e\n", + "\t- R_EX_25hvitd2_e\n", + "\t- R_EX_25hvitd3_e\n", + "\t- R_EX_bildglcur_e\n", + "\t- R_EX_bilglcur_e\n", + "\t- R_EX_bilirub_e\n", + "\t- R_EX_clpnd_e\n", + "\t- R_EX_biocyt_e\n", + "\t- R_EX_cysi__L_e\n", + "\t- R_EX_2hb_e\n", + "\t- R_EX_2mcit_e\n", + "\t- R_EX_Rtotal_e\n", + "\t- R_EX_Rtotal2_e\n", + "\t- R_EX_Rtotal3_e\n", + "\t- R_EX_cspg_e_e\n", + "\t- R_EX_Tyr_ggn_e\n", + "\t- R_EX_btn_e\n", + "\t- R_EX_abt_e\n", + "\t- R_EX_but_e\n", + "\t- R_EX_bvite_e\n", + "\t- R_EX_cyan_e\n", + "\t- R_EX_cys__L_e\n", + "\t- R_EX_ac_e\n", + "\t- R_EX_cytd_e\n", + "\t- R_EX_dad_2_e\n", + "\t- R_EX_dad_5_e\n", + "\t- R_EX_dag_hs_e\n", + "\t- R_EX_bz_e\n", + "\t- R_EX_ca2_e\n", + "\t- R_EX_camp_e\n", + "\t- R_EX_caro_e\n", + "\t- R_EX_acac_e\n", + "\t- R_EX_carveol_e\n", + "\t- R_EX_cca_d3_e\n", + "\t- R_EX_dcsptn1_e\n", + "\t- R_EX_gq1b_hs_e\n", + "\t- R_EX_dcyt_e\n", + "\t- R_EX_debrisoquine_e\n", + "\t- R_EX_dgchol_e\n", + "\t- R_EX_dgsn_e\n", + "\t- R_EX_dhdascb_e\n", + "\t- R_EX_cgly_e\n", + "\t- R_EX_chol_e\n", + "\t- R_EX_cholate_e\n", + "\t- R_EX_chsterol_e\n", + "\t- R_EX_chtn_e\n", + "\t- R_EX_cit_e\n", + "\t- R_EX_cl_e\n", + "\t- R_EX_cmp_e\n", + "\t- R_EX_co_e\n", + "\t- R_EX_co2_e\n", + "\t- R_EX_coumarin_e\n", + "\t- R_EX_creat_e\n", + "\t- R_EX_crmp_hs_e\n", + "\t- R_EX_crn_e\n", + "\t- R_EX_crtsl_e\n", + "\t- R_EX_crtstrn_e\n", + "\t- R_EX_crvnc_e\n", + "\t- R_EX_csn_e\n", + "\t- R_EX_dheas_e\n", + "\t- R_EX_dhf_e\n", + "\t- R_EX_digalsgalside_hs_e\n", + "\t- R_EX_din_e\n", + "\t- R_EX_dlnlcg_e\n", + "\t- R_EX_dmantipyrine_e\n", + "\t- R_EX_dmhptcrn_e\n", + "\t- R_EX_dopa_e\n", + "\t- R_EX_cspg_a_e\n", + "\t- R_EX_cspg_b_e\n", + "\t- R_EX_cspg_c_e\n", + "\t- R_EX_cspg_d_e\n", + "\t- R_EX_dopasf_e\n", + "\t- R_EX_gq1balpha_hs_e\n", + "\t- R_EX_oagd3_hs_e\n", + "\t- R_EX_gsn_e\n", + "\t- R_EX_oagt3_hs_e\n", + "\t- R_EX_ocdca_e\n", + "\t- R_EX_ocdcea_e\n", + "\t- R_EX_octa_e\n", + "\t- R_EX_drib_e\n", + "\t- R_EX_duri_e\n", + "\t- R_EX_eaflatoxin_e\n", + "\t- R_EX_ebastine_e\n", + "\t- R_EX_ebastineoh_e\n", + "\t- R_EX_eicostet_e\n", + "\t- R_EX_oh1_e\n", + "\t- R_EX_omeprazole_e\n", + "\t- R_EX_gt1a_hs_e\n", + "\t- R_EX_onpthl_e\n", + "\t- R_EX_orn_e\n", + "\t- R_EX_elaid_e\n", + "\t- R_EX_estradiol_e\n", + "\t- R_EX_estradiolglc_e\n", + "\t- R_EX_estriolglc_e\n", + "\t- R_EX_gthox_e\n", + "\t- R_EX_estroneglc_e\n", + "\t- R_EX_estrones_e\n", + "\t- R_EX_gthrd_e\n", + "\t- R_EX_etoh_e\n", + "\t- R_EX_fe2_e\n", + "\t- R_EX_oxa_e\n", + "\t- R_EX_paf_hs_e\n", + "\t- R_EX_pchol_hs_e\n", + "\t- R_EX_pe_hs_e\n", + "\t- R_EX_peplys_e\n", + "\t- R_EX_perillyl_e\n", + "\t- R_EX_fe3_e\n", + "\t- R_EX_fol_e\n", + "\t- R_EX_for_e\n", + "\t- R_EX_pglyc_hs_e\n", + "\t- R_EX_phe__L_e\n", + "\t- R_EX_gtp_e\n", + "\t- R_EX_pheacgln_e\n", + "\t- R_EX_pheme_e\n", + "\t- R_EX_phllqne_e\n", + "\t- R_EX_fru_e\n", + "\t- R_EX_fuc__L_e\n", + "\t- R_EX_fuc13galacglcgal14acglcgalgluside_hs_e\n", + "\t- R_EX_fuc14galacglcgalgluside_hs_e\n", + "\t- R_EX_phyt_e\n", + "\t- R_EX_gua_e\n", + "\t- R_EX_pi_e\n", + "\t- R_EX_pnto__R_e\n", + "\t- R_EX_fucacgalfucgalacglcgalgluside_hs_e\n", + "\t- R_EX_fucacngal14acglcgalgluside_hs_e\n", + "\t- R_EX_fucacngalacglcgalgluside_hs_e\n", + "\t- R_EX_ppa_e\n", + "\t- R_EX_prgstrn_e\n", + "\t- R_EX_fucfuc12gal14acglcgalgluside_hs_e\n", + "\t- R_EX_pro__D_e\n", + "\t- R_EX_h_e\n", + "\t- R_EX_pro__L_e\n", + "\t- R_EX_fucfuc132galacglcgal14acglcgalgluside_hs_e\n", + "\t- R_EX_fucfucfucgalacglc13galacglcgal14acglcgalgluside_hs_e\n", + "\t- R_EX_fucfucfucgalacglcgal14acglcgalgluside_hs_e\n", + "\t- R_EX_prostgd2_e\n", + "\t- R_EX_h2o_e\n", + "\t- R_EX_prostge1_e\n", + "\t- R_EX_prostge2_e\n", + "\t- R_EX_prostgf2_e\n", + "\t- R_EX_ps_hs_e\n", + "\t- R_EX_ptdca_e\n", + "\t- R_EX_pydam_e\n", + "\t- R_EX_fucfucgalacglcgalgluside_hs_e\n", + "\t- R_EX_fucgal14acglcgalgluside_hs_e\n", + "\t- R_EX_fucgalfucgalacglcgalgluside_hs_e\n", + "\t- R_EX_fucgalgbside_hs_e\n", + "\t- R_EX_gal_e\n", + "\t- R_EX_galacglcgalgbside_hs_e\n", + "\t- R_EX_galfuc12gal14acglcgalgluside_hs_e\n", + "\t- R_EX_galfucgalacglcgal14acglcgalgluside_hs_e\n", + "\t- R_EX_pydx_e\n", + "\t- R_EX_pydxn_e\n", + "\t- R_EX_galgalfucfucgalacglcgalacglcgal14acglcgalgluside_hs_e\n", + "\t- R_EX_h2o2_e\n", + "\t- R_EX_galgalgalthcrm_hs_e\n", + "\t- R_EX_gam_e\n", + "\t- R_EX_ha_e\n", + "\t- R_EX_gbside_hs_e\n", + "\t- R_EX_gchola_e\n", + "\t- R_EX_gd1b2_hs_e\n", + "\t- R_EX_pyr_e\n", + "\t- R_EX_rbt_e\n", + "\t- R_EX_retfa_e\n", + "\t- R_EX_gd1c_hs_e\n", + "\t- R_EX_ha_pre1_e\n", + "\t- R_EX_gdchola_e\n", + "\t- R_EX_gdp_e\n", + "\t- R_EX_glc__D_e\n", + "\t- R_EX_gln__L_e\n", + "\t- R_EX_retinol_e\n", + "\t- R_EX_retinol_9_cis_e\n", + "\t- R_EX_retinol_cis_11_e\n", + "\t- R_EX_retn_e\n", + "\t- R_EX_retnglc_e\n", + "\t- R_EX_glu__L_e\n", + "\t- R_EX_gluala_e\n", + "\t- R_EX_gly_e\n", + "\t- R_EX_glyb_e\n", + "\t- R_EX_rib__D_e\n", + "\t- R_EX_hco3_e\n", + "\t- R_EX_glyc_e\n", + "\t- R_EX_glyc__S_e\n", + "\t- R_EX_glygn2_e\n", + "\t- R_EX_glygn4_e\n", + "\t- R_EX_ribflv_e\n", + "\t- R_EX_hcoumarin_e\n", + "\t- R_EX_glygn5_e\n", + "\t- R_EX_gmp_e\n", + "\t- R_EX_hdca_e\n", + "\t- R_EX_gp1c_hs_e\n", + "\t- R_EX_gp1calpha_hs_e\n", + "\t- R_EX_s2l2fn2m2masn_e\n", + "\t- R_EX_hdcea_e\n", + "\t- R_EX_s2l2n2m2masn_e\n", + "\t- R_EX_sarcs_e\n", + "\t- R_EX_sel_e\n", + "\t- R_EX_hestratriol_e\n", + "\t- R_EX_ser__D_e\n", + "\t- R_EX_thymd_e\n", + "\t- R_EX_ser__L_e\n", + "\t- R_EX_hexc_e\n", + "\t- R_EX_sl__L_e\n", + "\t- R_EX_thyox__L_e\n", + "\t- R_EX_tmndnc_e\n", + "\t- R_EX_so4_e\n", + "\t- R_EX_his__L_e\n", + "\t- R_EX_spc_hs_e\n", + "\t- R_EX_sph1p_e\n", + "\t- R_EX_sphs1p_e\n", + "\t- R_EX_srtn_e\n", + "\t- R_EX_tolbutamide_e\n", + "\t- R_EX_tre_e\n", + "\t- R_EX_triodthy_e\n", + "\t- R_EX_triodthysuf_e\n", + "\t- R_EX_trp__L_e\n", + "\t- R_EX_strch1_e\n", + "\t- R_EX_strch2_e\n", + "\t- R_EX_hista_e\n", + "\t- R_EX_strdnc_e\n", + "\t- R_EX_succ_e\n", + "\t- R_EX_tststerone_e\n", + "\t- R_EX_tststeroneglc_e\n", + "\t- R_EX_sucr_e\n", + "\t- R_EX_tststerones_e\n", + "\t- R_EX_hom__L_e\n", + "\t- R_EX_tsul_e\n", + "\t- R_EX_tag_hs_e\n", + "\t- R_EX_hpdca_e\n", + "\t- R_EX_tag__D_e\n", + "\t- R_EX_ttdca_e\n", + "\t- R_EX_txa2_e\n", + "\t- R_EX_taur_e\n", + "\t- R_EX_hspg_e\n", + "\t- R_EX_taxol_e\n", + "\t- R_EX_tchola_e\n", + "\t- R_EX_tymsf_e\n", + "\t- R_EX_tyr__L_e\n", + "\t- R_EX_udp_e\n", + "\t- R_EX_tcynt_e\n", + "\t- R_EX_tdchola_e\n", + "\t- R_EX_htaxol_e\n", + "\t- R_EX_tethex3_e\n", + "\t- R_EX_ump_e\n", + "\t- R_EX_ura_e\n", + "\t- R_EX_hxan_e\n", + "\t- R_EX_tetpent3_e\n", + "\t- R_EX_tetpent6_e\n", + "\t- R_EX_tettet6_e\n", + "\t- R_EX_thf_e\n", + "\t- R_EX_thm_e\n", + "\t- R_EX_urate_e\n", + "\t- R_EX_urea_e\n", + "\t- R_EX_uri_e\n", + "\t- R_EX_utp_e\n", + "\t- R_EX_thmmp_e\n", + "\t- R_EX_i_e\n", + "\t- R_EX_thmtp_e\n", + "\t- R_EX_vacc_e\n", + "\t- R_EX_val__L_e\n", + "\t- R_EX_vitd2_e\n", + "\t- R_EX_thr__L_e\n", + "\t- R_EX_idp_e\n", + "\t- R_EX_thym_e\n", + "\t- R_EX_vitd3_e\n", + "\t- R_EX_whddca_e\n", + "\t- R_EX_ile__L_e\n", + "\t- R_EX_whhdca_e\n", + "\t- R_EX_whtststerone_e\n", + "\t- R_EX_whttdca_e\n", + "\t- R_EX_imp_e\n", + "\t- R_EX_xolest2_hs_e\n", + "\t- R_EX_inost_e\n", + "\t- R_EX_xolest_hs_e\n", + "\t- R_EX_xoltri24_e\n", + "\t- R_EX_ins_e\n", + "\t- R_EX_xoltri25_e\n", + "\t- R_EX_xoltri27_e\n", + "\t- R_EX_k_e\n", + "\t- R_EX_xyl__D_e\n", + "\t- R_EX_ksi_e\n", + "\t- R_EX_xylt_e\n", + "\t- R_EX_yvite_e\n", + "\t- R_SK_pre_prot_r\n", + "\t- R_EX_ksi_deg1_e\n", + "\t- R_EX_ksii_core2_e\n", + "\t- R_EX_ksii_core4_e\n", + "\t- R_EX_lac__D_e\n", + "\t- R_EX_lac__L_e\n", + "\t- R_EX_lcts_e\n", + "\t- R_EX_leu__L_e\n", + "\t- R_EX_leuktrA4_e\n", + "\t- R_EX_leuktrB4_e\n", + "\t- R_EX_leuktrC4_e\n", + "\t- R_EX_leuktrD4_e\n", + "\t- R_EX_leuktrE4_e\n", + "\t- R_EX_leuktrF4_e\n", + "\t- R_EX_lgnc_e\n", + "\t- R_EX_limnen_e\n", + "\t- R_EX_lipoate_e\n", + "\t- R_EX_lneldc_e\n", + "\t- R_EX_lnlc_e_copy1\n", + "\t- R_EX_lnlc_e_copy2\n", + "\t- R_EX_lnlnca_e\n", + "\t- R_EX_lnlncg_e\n", + "\t- R_EX_lpchol_hs_e\n", + "\t- R_EX_lys__L_e\n", + "\t- R_EX_mag_hs_e\n", + "\t- R_EX_malt_e\n", + "\t- R_EX_malttr_e\n", + "\t- R_EX_man_e\n", + "\t- R_EX_meoh_e\n", + "\t- R_EX_mepi_e\n", + "\t- R_EX_mercplaccys_e\n", + "\t- R_EX_met__L_e\n", + "\t- R_EX_mthgxl_e\n", + "\t- R_EX_n2m2nmasn_e\n", + "\t- R_EX_na1_e\n", + "\t- R_EX_nac_e\n", + "\t- R_EX_nad_e\n", + "\t- R_EX_nadp_e\n", + "\t- R_EX_ncam_e\n", + "\t- R_EX_nh4_e\n", + "\t- R_EX_nifedipine_e\n", + "\t- R_EX_no_e\n", + "\t- R_EX_npthl_e\n", + "\t- R_EX_nrpphr_e\n", + "\t- R_EX_nrpphrsf_e\n", + "\t- R_EX_nrvnc_e\n", + "\t- R_EX_o2_e\n", + "\t- R_EX_o2s_e\u001b[0m\n", + "File saved at: ../../data/sbml_corrected/iMM1415.xml\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: iYL1228\n", + "\u001b[0;93mWARNING : - R_ACMANAtex: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_ABUTtex: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_AKGtex: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_DCAtex: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_FUMtex: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_HXAtex: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_FORtex: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_MALDtex: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_NO2tex: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_NO3tex: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_PEAMNtex: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_SUCCtex: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_THRtex: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_TARTRtex: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + "\u001b[0m\n", + "\n", + "\n", + "\u001b[0;36m############################################\n", + "############################################\n", + " WRITING SBML FILE \n", + "############################################\n", + "############################################\n", + "\u001b[0m\n", + "\u001b[0;93mWARNING : IMPORT REACTION REMOVED BY DEFAULT\u001b[0m\n", + "\u001b[0;93mWARNING : If you want to keep import reaction\n", + "use option -kir / --keep-import-reactions\u001b[0m\n", + "\u001b[0;93mWARNING : Reaction with tag reversible modified:\n", + "\t- R_ACMANAtex\n", + "\t- R_ABUTtex\n", + "\t- R_AKGtex\n", + "\t- R_DCAtex\n", + "\t- R_FUMtex\n", + "\t- R_HXAtex\n", + "\t- R_FORtex\n", + "\t- R_MALDtex\n", + "\t- R_NO2tex\n", + "\t- R_NO3tex\n", + "\t- R_PEAMNtex\n", + "\t- R_SUCCtex\n", + "\t- R_THRtex\n", + "\t- R_TARTRtex\u001b[0m\n", + "\u001b[0;93mWARNING : Reaction with Reactants and Products exchanged:\n", + "\t- R_ACMANAtex\n", + "\t- R_ABUTtex\n", + "\t- R_AKGtex\n", + "\t- R_DCAtex\n", + "\t- R_FUMtex\n", + "\t- R_HXAtex\n", + "\t- R_FORtex\n", + "\t- R_MALDtex\n", + "\t- R_NO2tex\n", + "\t- R_NO3tex\n", + "\t- R_PEAMNtex\n", + "\t- R_SUCCtex\n", + "\t- R_THRtex\n", + "\t- R_TARTRtex\u001b[0m\n", + "\u001b[0;93mWARNING : Import reaction removed:\n", + "\t- R_EX_cd2_e\n", + "\t- R_EX_cbi_e\n", + "\t- R_EX_cbl1_e\n", + "\t- R_EX_cgly_e\n", + "\t- R_EX_cellb_e\n", + "\t- R_EX_chol_e\n", + "\t- R_EX_ca2_e\n", + "\t- R_EX_12ppd__S_e\n", + "\t- R_EX_13ppd_e\n", + "\t- R_EX_14glucan_e\n", + "\t- R_EX_15dap_e\n", + "\t- R_EX_23camp_e\n", + "\t- R_EX_23ccmp_e\n", + "\t- R_EX_23cgmp_e\n", + "\t- R_EX_cit_e\n", + "\t- R_EX_cl_e\n", + "\t- R_EX_23cump_e\n", + "\t- R_EX_23dappa_e\n", + "\t- R_EX_26dap__M_e\n", + "\t- R_EX_2ddglcn_e\n", + "\t- R_EX_cmp_e\n", + "\t- R_EX_co2_e\n", + "\t- R_EX_34dhpac_e\n", + "\t- R_EX_glyc2p_e\n", + "\t- R_EX_3amp_e\n", + "\t- R_EX_3cmp_e\n", + "\t- R_EX_3gmp_e\n", + "\t- R_EX_3hcinnm_e\n", + "\t- R_EX_3hpppn_e\n", + "\t- R_EX_cobalt2_e\n", + "\t- R_EX_colipa_e\n", + "\t- R_EX_crn_e\n", + "\t- R_EX_csn_e\n", + "\t- R_EX_cu_e\n", + "\t- R_EX_cu2_e\n", + "\t- R_EX_cyan_e\n", + "\t- R_EX_cynt_e\n", + "\t- R_EX_3ump_e\n", + "\t- R_EX_4abut_e\n", + "\t- R_EX_glyc3p_e\n", + "\t- R_EX_4hoxpacd_e\n", + "\t- R_EX_cys__D_e\n", + "\t- R_EX_cys__L_e\n", + "\t- R_EX_cytd_e\n", + "\t- R_EX_glyclt_e\n", + "\t- R_EX_dad_2_e\n", + "\t- R_EX_4hphac_e\n", + "\t- R_EX_gmp_e\n", + "\t- R_EX_5dglcn_e\n", + "\t- R_EX_damp_e\n", + "\t- R_EX_dca_e\n", + "\t- R_EX_LalaDgluMdap_e\n", + "\t- R_EX_dcmp_e\n", + "\t- R_EX_gsn_e\n", + "\t- R_EX_dcyt_e\n", + "\t- R_EX_ddca_e\n", + "\t- R_EX_dgmp_e\n", + "\t- R_EX_dgsn_e\n", + "\t- R_EX_LalaDgluMdapDala_e\n", + "\t- R_EX_abt__D_e\n", + "\t- R_EX_ac_e\n", + "\t- R_EX_acald_e\n", + "\t- R_EX_acgal_e\n", + "\t- R_EX_acgal1p_e\n", + "\t- R_EX_dha_e\n", + "\t- R_EX_gthox_e\n", + "\t- R_EX_diact_e\n", + "\t- R_EX_dimp_e\n", + "\t- R_EX_acgam_e\n", + "\t- R_EX_acgam1p_e\n", + "\t- R_EX_din_e\n", + "\t- R_EX_acmana_e\n", + "\t- R_EX_gthrd_e\n", + "\t- R_EX_acmum_e\n", + "\t- R_EX_dms_e\n", + "\t- R_EX_dmso_e\n", + "\t- R_EX_acolipa_e\n", + "\t- R_EX_dopa_e\n", + "\t- R_EX_gtp_e\n", + "\t- R_EX_dtmp_e\n", + "\t- R_EX_acser_e\n", + "\t- R_EX_dump_e\n", + "\t- R_EX_ade_e\n", + "\t- R_EX_gua_e\n", + "\t- R_EX_adn_e\n", + "\t- R_EX_adocbl_e\n", + "\t- R_EX_duri_e\n", + "\t- R_EX_etha_e\n", + "\t- R_EX_ethso3_e\n", + "\t- R_EX_ag_e\n", + "\t- R_EX_etoh_e\n", + "\t- R_EX_h_e\n", + "\t- R_EX_f6p_e\n", + "\t- R_EX_fald_e\n", + "\t- R_EX_agm_e\n", + "\t- R_EX_akg_e\n", + "\t- R_EX_ala_B_e\n", + "\t- R_EX_ala__D_e\n", + "\t- R_EX_ala__L_e\n", + "\t- R_EX_alaala_e\n", + "\t- R_EX_fe2_e\n", + "\t- R_EX_fe3_e\n", + "\t- R_EX_fe3dhbzs_e\n", + "\t- R_EX_fe3hox_e\n", + "\t- R_EX_fecrm_e\n", + "\t- R_EX_all__D_e\n", + "\t- R_EX_amp_e\n", + "\t- R_EX_feenter_e\n", + "\t- R_EX_anhgm_e\n", + "\t- R_EX_feoxam_e\n", + "\t- R_EX_arab__L_e\n", + "\t- R_EX_h2_e\n", + "\t- R_EX_arg__L_e\n", + "\t- R_EX_for_e\n", + "\t- R_EX_ascb__L_e\n", + "\t- R_EX_fru_e\n", + "\t- R_EX_h2o_e\n", + "\t- R_EX_frulys_e\n", + "\t- R_EX_asn__L_e\n", + "\t- R_EX_aso3_e\n", + "\t- R_EX_h2o2_e\n", + "\t- R_EX_asp__L_e\n", + "\t- R_EX_fruur_e\n", + "\t- R_EX_fuc__L_e\n", + "\t- R_EX_btd_RR_e\n", + "\t- R_EX_h2s_e\n", + "\t- R_EX_but_e\n", + "\t- R_EX_fum_e\n", + "\t- R_EX_g1p_e\n", + "\t- R_EX_butso3_e\n", + "\t- R_EX_hacolipa_e\n", + "\t- R_EX_bz_e\n", + "\t- R_EX_g3pc_e\n", + "\t- R_EX_g3pe_e\n", + "\t- R_EX_halipa_e\n", + "\t- R_EX_g3pg_e\n", + "\t- R_EX_g3pi_e\n", + "\t- R_EX_g3ps_e\n", + "\t- R_EX_pi_e\n", + "\t- R_EX_pnto__R_e\n", + "\t- R_EX_g6p_e\n", + "\t- R_EX_ppa_e\n", + "\t- R_EX_gal_e\n", + "\t- R_EX_hdca_e\n", + "\t- R_EX_gal_bD_e\n", + "\t- R_EX_ppal_e\n", + "\t- R_EX_pppn_e\n", + "\t- R_EX_ppt_e\n", + "\t- R_EX_gal1p_e\n", + "\t- R_EX_hdcea_e\n", + "\t- R_EX_galct__D_e\n", + "\t- R_EX_galctn__D_e\n", + "\t- R_EX_galctn__L_e\n", + "\t- R_EX_galt_e\n", + "\t- R_EX_pro__L_e\n", + "\t- R_EX_progly_e\n", + "\t- R_EX_psclys_e\n", + "\t- R_EX_pser__L_e\n", + "\t- R_EX_galur_e\n", + "\t- R_EX_gam_e\n", + "\t- R_EX_ptrc_e\n", + "\t- R_EX_hg2_e\n", + "\t- R_EX_pyr_e\n", + "\t- R_EX_r5p_e\n", + "\t- R_EX_gam6p_e\n", + "\t- R_EX_gdp_e\n", + "\t- R_EX_rbt_e\n", + "\t- R_EX_his__L_e\n", + "\t- R_EX_rib__D_e\n", + "\t- R_EX_rmn_e\n", + "\t- R_EX_glc__D_e\n", + "\t- R_EX_glcn_e\n", + "\t- R_EX_glcr_e\n", + "\t- R_EX_sbt__D_e\n", + "\t- R_EX_glcur_e\n", + "\t- R_EX_hom__L_e\n", + "\t- R_EX_glcur1p_e\n", + "\t- R_EX_gln__L_e\n", + "\t- R_EX_glu__L_e\n", + "\t- R_EX_gly_e\n", + "\t- R_EX_ser__D_e\n", + "\t- R_EX_ser__L_e\n", + "\t- R_EX_skm_e\n", + "\t- R_EX_so2_e\n", + "\t- R_EX_so3_e\n", + "\t- R_EX_so4_e\n", + "\t- R_EX_glyald_e\n", + "\t- R_EX_glyb_e\n", + "\t- R_EX_spmd_e\n", + "\t- R_EX_hxa_e\n", + "\t- R_EX_succ_e\n", + "\t- R_EX_sucr_e\n", + "\t- R_EX_sulfac_e\n", + "\t- R_EX_glyc_e\n", + "\t- R_EX_glyc__R_e\n", + "\t- R_EX_hxan_e\n", + "\t- R_EX_tartr__L_e\n", + "\t- R_EX_taur_e\n", + "\t- R_EX_tcynt_e\n", + "\t- R_EX_idon__L_e\n", + "\t- R_EX_thm_e\n", + "\t- R_EX_thr__L_e\n", + "\t- R_EX_ile__L_e\n", + "\t- R_EX_thrp_e\n", + "\t- R_EX_thym_e\n", + "\t- R_EX_imp_e\n", + "\t- R_EX_thymd_e\n", + "\t- R_EX_indole_e\n", + "\t- R_EX_tma_e\n", + "\t- R_EX_tmao_e\n", + "\t- R_EX_tre_e\n", + "\t- R_EX_inost_e\n", + "\t- R_EX_trp__L_e\n", + "\t- R_EX_tsul_e\n", + "\t- R_EX_ins_e\n", + "\t- R_EX_ttdca_e\n", + "\t- R_EX_ttdcea_e\n", + "\t- R_EX_isetac_e\n", + "\t- R_EX_tungs_e\n", + "\t- R_EX_tym_e\n", + "\t- R_EX_tyr__L_e\n", + "\t- R_EX_tyrp_e\n", + "\t- R_EX_uacgam_e\n", + "\t- R_EX_udpacgal_e\n", + "\t- R_EX_k_e\n", + "\t- R_EX_udpg_e\n", + "\t- R_EX_udpgal_e\n", + "\t- R_EX_kdo2lipid4_e\n", + "\t- R_EX_udpglcur_e\n", + "\t- R_EX_ump_e\n", + "\t- R_EX_lac__D_e\n", + "\t- R_EX_ura_e\n", + "\t- R_EX_urea_e\n", + "\t- R_EX_lac__L_e\n", + "\t- R_EX_uri_e\n", + "\t- R_EX_val__L_e\n", + "\t- R_EX_lcts_e\n", + "\t- R_EX_xan_e\n", + "\t- R_EX_xmp_e\n", + "\t- R_EX_xtsn_e\n", + "\t- R_EX_leu__L_e\n", + "\t- R_EX_xyl__D_e\n", + "\t- R_EX_xylu__L_e\n", + "\t- R_EX_lipa_e\n", + "\t- R_EX_zn2_e\n", + "\t- R_EX_lipa_cold_e\n", + "\t- R_EX_lys__L_e\n", + "\t- R_SK_dna_c\n", + "\t- R_EX_lyx__L_e\n", + "\t- R_SK_dna5mtc_c\n", + "\t- R_EX_mal__D_e\n", + "\t- R_EX_mal__L_e\n", + "\t- R_EX_malt_e\n", + "\t- R_EX_malthx_e\n", + "\t- R_EX_maltpt_e\n", + "\t- R_EX_malttr_e\n", + "\t- R_EX_maltttr_e\n", + "\t- R_EX_man_e\n", + "\t- R_EX_man6p_e\n", + "\t- R_EX_manglyc_e\n", + "\t- R_EX_melib_e\n", + "\t- R_EX_met__D_e\n", + "\t- R_EX_met__L_e\n", + "\t- R_EX_metsox_R__L_e\n", + "\t- R_EX_metsox_S__L_e\n", + "\t- R_EX_mg2_e\n", + "\t- R_EX_mmet_e\n", + "\t- R_EX_mn2_e\n", + "\t- R_EX_mnl_e\n", + "\t- R_EX_mobd_e\n", + "\t- R_EX_mso3_e\n", + "\t- R_EX_n2o_e\n", + "\t- R_EX_na1_e\n", + "\t- R_EX_nac_e\n", + "\t- R_EX_nh4_e\n", + "\t- R_EX_ni2_e\n", + "\t- R_EX_nmn_e\n", + "\t- R_EX_no_e\n", + "\t- R_EX_no2_e\n", + "\t- R_EX_no3_e\n", + "\t- R_EX_o2_e\n", + "\t- R_EX_o2s_e\n", + "\t- R_EX_ocdca_e\n", + "\t- R_EX_ocdcea_e\n", + "\t- R_EX_octa_e\n", + "\t- R_EX_orn_e\n", + "\t- R_EX_orot_e\n", + "\t- R_EX_pacald_e\n", + "\t- R_EX_peamn_e\n", + "\t- R_EX_phe__L_e\n", + "\t- R_EX_pheme_e\u001b[0m\n", + "File saved at: ../../data/sbml_corrected/iYL1228.xml\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: iECD_1391\n", + "\u001b[0;93mWARNING : \n", + " - R_CAT: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_DHPTDNR: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_DHPTDNRN: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_FHL: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCTARTtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SPODM: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SPODMpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCASPtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCFUMtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCMALtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_CHITPH: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_URCN: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_EX_chitob_e: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_CHITOBpts: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\u001b[0m\n", + "\n", + "\n", + "\u001b[0;36m############################################\n", + "############################################\n", + " WRITING SBML FILE \n", + "############################################\n", + "############################################\n", + "\u001b[0m\n", + "\u001b[0;93mWARNING : IMPORT REACTION REMOVED BY DEFAULT\u001b[0m\n", + "\u001b[0;93mWARNING : If you want to keep import reaction\n", + "use option -kir / --keep-import-reactions\u001b[0m\n", + "\u001b[0;93mWARNING : Import reaction removed:\n", + "\t- R_EX_2ddglcn_e\n", + "\t- R_EX_3cmp_e\n", + "\t- R_EX_3hcinnm_e\n", + "\t- R_EX_34dhpac_e\n", + "\t- R_EX_3amp_e\n", + "\t- R_EX_3gmp_e\n", + "\t- R_EX_26dap__M_e\n", + "\t- R_EX_ddca_e\n", + "\t- R_DM_4crsol_c\n", + "\t- R_DM_5drib_c\n", + "\t- R_DM_aacald_c\n", + "\t- R_DM_amob_c\n", + "\t- R_DM_mththf_c\n", + "\t- R_EX_3hpp_e\n", + "\t- R_EX_3hpppn_e\n", + "\t- R_EX_3ump_e\n", + "\t- R_EX_4abut_e\n", + "\t- R_EX_4hoxpacd_e\n", + "\t- R_EX_5dglcn_e\n", + "\t- R_EX_5mtr_e\n", + "\t- R_DM_oxam_c\n", + "\t- R_EX_LalaDglu_e\n", + "\t- R_EX_dgmp_e\n", + "\t- R_EX_LalaDgluMdap_e\n", + "\t- R_EX_12ppd__R_e\n", + "\t- R_EX_12ppd__S_e\n", + "\t- R_EX_LalaDgluMdapDala_e\n", + "\t- R_EX_LalaLglu_e\n", + "\t- R_EX_14glucan_e\n", + "\t- R_EX_dgsn_e\n", + "\t- R_EX_15dap_e\n", + "\t- R_EX_23camp_e\n", + "\t- R_EX_23ccmp_e\n", + "\t- R_EX_23cgmp_e\n", + "\t- R_EX_23cump_e\n", + "\t- R_EX_23dappa_e\n", + "\t- R_EX_h2o_e\n", + "\t- R_EX_ac_e\n", + "\t- R_EX_acac_e\n", + "\t- R_EX_acald_e\n", + "\t- R_EX_acgal_e\n", + "\t- R_EX_acgal1p_e\n", + "\t- R_EX_acgam_e\n", + "\t- R_EX_acgam1p_e\n", + "\t- R_EX_acmana_e\n", + "\t- R_EX_acmum_e\n", + "\t- R_EX_acnam_e\n", + "\t- R_EX_acolipa_e\n", + "\t- R_EX_acser_e\n", + "\t- R_EX_ade_e\n", + "\t- R_EX_adn_e\n", + "\t- R_EX_h2o2_e\n", + "\t- R_EX_h2s_e\n", + "\t- R_EX_hacolipa_e\n", + "\t- R_EX_halipa_e\n", + "\t- R_EX_hdca_e\n", + "\t- R_EX_hdcea_e\n", + "\t- R_EX_adocbl_e\n", + "\t- R_EX_ag_e\n", + "\t- R_EX_agm_e\n", + "\t- R_EX_hg2_e\n", + "\t- R_EX_dha_e\n", + "\t- R_EX_his__L_e\n", + "\t- R_EX_hom__L_e\n", + "\t- R_EX_hxa_e\n", + "\t- R_EX_akg_e\n", + "\t- R_EX_ala_B_e\n", + "\t- R_EX_ala__D_e\n", + "\t- R_EX_ala__L_e\n", + "\t- R_EX_alaala_e\n", + "\t- R_EX_all__D_e\n", + "\t- R_EX_alltn_e\n", + "\t- R_EX_amp_e\n", + "\t- R_EX_hxan_e\n", + "\t- R_EX_anhgm_e\n", + "\t- R_EX_dimp_e\n", + "\t- R_EX_arab__L_e\n", + "\t- R_EX_arbt_e\n", + "\t- R_EX_idon__L_e\n", + "\t- R_EX_ile__L_e\n", + "\t- R_EX_din_e\n", + "\t- R_EX_imp_e\n", + "\t- R_EX_indole_e\n", + "\t- R_EX_arbtn_e\n", + "\t- R_EX_arbtn_fe3_e\n", + "\t- R_EX_dms_e\n", + "\t- R_EX_arg__L_e\n", + "\t- R_EX_ascb__L_e\n", + "\t- R_EX_inost_e\n", + "\t- R_EX_ins_e\n", + "\t- R_EX_isetac_e\n", + "\t- R_EX_asn__L_e\n", + "\t- R_EX_k_e\n", + "\t- R_EX_aso3_e\n", + "\t- R_EX_dmso_e\n", + "\t- R_EX_asp__L_e\n", + "\t- R_EX_btn_e\n", + "\t- R_EX_but_e\n", + "\t- R_EX_kdo2lipid4_e\n", + "\t- R_EX_lac__D_e\n", + "\t- R_EX_lac__L_e\n", + "\t- R_EX_lcts_e\n", + "\t- R_EX_butso3_e\n", + "\t- R_EX_dopa_e\n", + "\t- R_EX_ca2_e\n", + "\t- R_EX_cbi_e\n", + "\t- R_EX_cbl1_e\n", + "\t- R_EX_cd2_e\n", + "\t- R_EX_cgly_e\n", + "\t- R_EX_chol_e\n", + "\t- R_EX_leu__L_e\n", + "\t- R_EX_lipa_e\n", + "\t- R_EX_lipa_cold_e\n", + "\t- R_EX_lipoate_e\n", + "\t- R_EX_lys__L_e\n", + "\t- R_EX_lyx__L_e\n", + "\t- R_EX_mal__D_e\n", + "\t- R_EX_mal__L_e\n", + "\t- R_EX_malt_e\n", + "\t- R_EX_malthx_e\n", + "\t- R_EX_maltpt_e\n", + "\t- R_EX_malttr_e\n", + "\t- R_EX_chtbs_e\n", + "\t- R_EX_cit_e\n", + "\t- R_EX_cl_e\n", + "\t- R_EX_cm_e\n", + "\t- R_EX_cmp_e\n", + "\t- R_EX_co2_e\n", + "\t- R_EX_cobalt2_e\n", + "\t- R_EX_colipa_e\n", + "\t- R_EX_colipap_e\n", + "\t- R_EX_cpgn_e\n", + "\t- R_EX_maltttr_e\n", + "\t- R_EX_man_e\n", + "\t- R_EX_man6p_e\n", + "\t- R_EX_manglyc_e\n", + "\t- R_EX_melib_e\n", + "\t- R_EX_meoh_e\n", + "\t- R_EX_cpgn_un_e\n", + "\t- R_EX_crn_e\n", + "\t- R_EX_met__D_e\n", + "\t- R_EX_crn__D_e\n", + "\t- R_EX_doxrbcn_e\n", + "\t- R_EX_csn_e\n", + "\t- R_EX_cu_e\n", + "\t- R_EX_cu2_e\n", + "\t- R_EX_cyan_e\n", + "\t- R_EX_cynt_e\n", + "\t- R_EX_cys__D_e\n", + "\t- R_EX_met__L_e\n", + "\t- R_EX_metsox_R__L_e\n", + "\t- R_EX_metsox_S__L_e\n", + "\t- R_EX_mg2_e\n", + "\t- R_EX_mincyc_e\n", + "\t- R_EX_minohp_e\n", + "\t- R_EX_mmet_e\n", + "\t- R_EX_mn2_e\n", + "\t- R_EX_mnl_e\n", + "\t- R_EX_cys__L_e\n", + "\t- R_EX_cytd_e\n", + "\t- R_EX_dad_2_e\n", + "\t- R_EX_damp_e\n", + "\t- R_EX_dca_e\n", + "\t- R_EX_mobd_e\n", + "\t- R_EX_mso3_e\n", + "\t- R_EX_dtmp_e\n", + "\t- R_EX_n2o_e\n", + "\t- R_EX_na1_e\n", + "\t- R_EX_nac_e\n", + "\t- R_EX_dcmp_e\n", + "\t- R_EX_dcyt_e\n", + "\t- R_EX_nh4_e\n", + "\t- R_EX_dump_e\n", + "\t- R_EX_ni2_e\n", + "\t- R_EX_nmn_e\n", + "\t- R_EX_ppt_e\n", + "\t- R_EX_pro__L_e\n", + "\t- R_EX_no_e\n", + "\t- R_EX_duri_e\n", + "\t- R_EX_no2_e\n", + "\t- R_EX_no3_e\n", + "\t- R_EX_progly_e\n", + "\t- R_EX_psclys_e\n", + "\t- R_EX_pser__L_e\n", + "\t- R_EX_ptrc_e\n", + "\t- R_EX_pydam_e\n", + "\t- R_EX_novbcn_e\n", + "\t- R_EX_o16a4colipa_e\n", + "\t- R_EX_o2_e\n", + "\t- R_EX_o2s_e\n", + "\t- R_EX_ocdca_e\n", + "\t- R_EX_ocdcea_e\n", + "\t- R_EX_pydx_e\n", + "\t- R_EX_pydxn_e\n", + "\t- R_EX_octa_e\n", + "\t- R_EX_pyr_e\n", + "\t- R_EX_orn_e\n", + "\t- R_EX_eca4colipa_e\n", + "\t- R_EX_orot_e\n", + "\t- R_EX_pacald_e\n", + "\t- R_EX_peamn_e\n", + "\t- R_EX_quin_e\n", + "\t- R_EX_r5p_e\n", + "\t- R_EX_rfamp_e\n", + "\t- R_EX_rib__D_e\n", + "\t- R_EX_phe__L_e\n", + "\t- R_EX_rmn_e\n", + "\t- R_EX_pheme_e\n", + "\t- R_EX_enlipa_e\n", + "\t- R_EX_pi_e\n", + "\t- R_EX_sbt__D_e\n", + "\t- R_EX_sel_e\n", + "\t- R_EX_pnto__R_e\n", + "\t- R_EX_ser__D_e\n", + "\t- R_EX_ppa_e\n", + "\t- R_EX_enter_e\n", + "\t- R_EX_ppal_e\n", + "\t- R_EX_ser__L_e\n", + "\t- R_EX_skm_e\n", + "\t- R_EX_pppn_e\n", + "\t- R_EX_slnt_e\n", + "\t- R_EX_so2_e\n", + "\t- R_EX_so3_e\n", + "\t- R_EX_so4_e\n", + "\t- R_EX_spmd_e\n", + "\t- R_EX_etha_e\n", + "\t- R_EX_succ_e\n", + "\t- R_EX_sucr_e\n", + "\t- R_EX_ethso3_e\n", + "\t- R_EX_sulfac_e\n", + "\t- R_EX_tartr__D_e\n", + "\t- R_EX_tartr__L_e\n", + "\t- R_EX_taur_e\n", + "\t- R_EX_etoh_e\n", + "\t- R_EX_tcynt_e\n", + "\t- R_EX_thm_e\n", + "\t- R_EX_f6p_e\n", + "\t- R_EX_thr__L_e\n", + "\t- R_EX_thrp_e\n", + "\t- R_EX_thym_e\n", + "\t- R_EX_fald_e\n", + "\t- R_EX_thymd_e\n", + "\t- R_EX_tma_e\n", + "\t- R_EX_tmao_e\n", + "\t- R_EX_tre_e\n", + "\t- R_EX_trp__L_e\n", + "\t- R_EX_tsul_e\n", + "\t- R_EX_fe2_e\n", + "\t- R_EX_ttdca_e\n", + "\t- R_EX_ttdcea_e\n", + "\t- R_EX_ttrcyc_e\n", + "\t- R_EX_tungs_e\n", + "\t- R_EX_fe3_e\n", + "\t- R_EX_tym_e\n", + "\t- R_EX_tyr__L_e\n", + "\t- R_EX_tyrp_e\n", + "\t- R_EX_fe3dcit_e\n", + "\t- R_EX_uacgam_e\n", + "\t- R_EX_udpacgal_e\n", + "\t- R_EX_udpg_e\n", + "\t- R_EX_fe3dhbzs_e\n", + "\t- R_EX_udpgal_e\n", + "\t- R_EX_udpglcur_e\n", + "\t- R_EX_ump_e\n", + "\t- R_EX_ura_e\n", + "\t- R_EX_urea_e\n", + "\t- R_EX_uri_e\n", + "\t- R_EX_val__L_e\n", + "\t- R_EX_xan_e\n", + "\t- R_EX_xmp_e\n", + "\t- R_EX_xtsn_e\n", + "\t- R_EX_fe3hox_e\n", + "\t- R_EX_xyl__D_e\n", + "\t- R_EX_xylu__L_e\n", + "\t- R_EX_zn2_e\n", + "\t- R_EX_fe3hox_un_e\n", + "\t- R_EX_fecrm_e\n", + "\t- R_EX_fecrm_un_e\n", + "\t- R_EX_feenter_e\n", + "\t- R_EX_feoxam_e\n", + "\t- R_EX_feoxam_un_e\n", + "\t- R_EX_for_e\n", + "\t- R_EX_fru_e\n", + "\t- R_EX_frulys_e\n", + "\t- R_EX_fruur_e\n", + "\t- R_EX_fuc__L_e\n", + "\t- R_EX_fum_e\n", + "\t- R_EX_fusa_e\n", + "\t- R_EX_g1p_e\n", + "\t- R_EX_g3pc_e\n", + "\t- R_EX_g3pe_e\n", + "\t- R_EX_g3pg_e\n", + "\t- R_EX_g3pi_e\n", + "\t- R_EX_g3ps_e\n", + "\t- R_EX_g6p_e\n", + "\t- R_EX_gal_e\n", + "\t- R_EX_gal_bD_e\n", + "\t- R_EX_gal1p_e\n", + "\t- R_EX_galct__D_e\n", + "\t- R_EX_galctn__D_e\n", + "\t- R_EX_galctn__L_e\n", + "\t- R_EX_galt_e\n", + "\t- R_EX_galur_e\n", + "\t- R_EX_gam_e\n", + "\t- R_EX_gam6p_e\n", + "\t- R_EX_gbbtn_e\n", + "\t- R_EX_gdp_e\n", + "\t- R_EX_glc__D_e\n", + "\t- R_EX_glcn_e\n", + "\t- R_EX_glcr_e\n", + "\t- R_EX_glcur_e\n", + "\t- R_EX_glcur1p_e\n", + "\t- R_EX_gln__L_e\n", + "\t- R_EX_glu__L_e\n", + "\t- R_EX_gly_e\n", + "\t- R_EX_glyald_e\n", + "\t- R_EX_glyb_e\n", + "\t- R_EX_glyc_e\n", + "\t- R_EX_glyc__R_e\n", + "\t- R_EX_glyc2p_e\n", + "\t- R_EX_glyc3p_e\n", + "\t- R_EX_glyclt_e\n", + "\t- R_EX_gmp_e\n", + "\t- R_EX_gsn_e\n", + "\t- R_EX_gthox_e\n", + "\t- R_EX_gthrd_e\n", + "\t- R_EX_gtp_e\n", + "\t- R_EX_gua_e\n", + "\t- R_EX_h_e\n", + "\t- R_EX_h2_e\n", + "\t- R_EX_rnam_e\n", + "\t- R_EX_23dhbzs3_e\n", + "\t- R_EX_tet_e\n", + "\t- R_EX_salchs4_e\n", + "\t- R_EX_airs_e\n", + "\t- R_EX_dxyl_e\n", + "\t- R_EX_3pg_e\n", + "\t- R_EX_feroxGfe_e\n", + "\t- R_EX_oaa_e\n", + "\t- R_EX_salchs2fe_e\n", + "\t- R_EX_cysi__L_e\n", + "\t- R_EX_fe3dhbzs3_e\n", + "\t- R_EX_udcpo4_e\n", + "\t- R_EX_drib_e\n", + "\t- R_EX_tag__D_e\n", + "\t- R_EX_salchs4fe_e\n", + "\t- R_EX_guln__L_e\n", + "\t- R_EX_2pg_e\n", + "\t- R_EX_feroxG_e\n", + "\t- R_EX_frmd_e\n", + "\t- R_EX_colipaOA_e\n", + "\t- R_EX_salchsx_e\n", + "\t- R_EX_AEP_e\n", + "\t- R_EX_diact_e\n", + "\t- R_EX_icit_e\n", + "\t- R_EX_pep_e\n", + "\t- R_EX_colipa20Oag_e\n", + "\t- R_EX_cellb_e\n", + "\t- R_EX_remnant1_e\n", + "\t- R_EX_btd_RR_e\n", + "\t- R_EX_udcpo5_e\n", + "\t- R_EX_13ppd_e\n", + "\t- R_EX_rbt_e\n", + "\t- R_EX_bz_e\n", + "\t- R_EX_abt__D_e\n", + "\t- R_EX_salchs2_e\n", + "\t- R_EX_fe_e\n", + "\t- R_EX_kokdolipidA_e\n", + "\t- R_DM_lipidA_core_e_p\n", + "\t- R_EX_3hoxpac_e\n", + "\t- R_EX_3ntym_e\n", + "\t- R_EX_feroxBfe_e\n", + "\t- R_EX_6apa_e\n", + "\t- R_EX_4hoxpac_e\n", + "\t- R_EX_peng_e\n", + "\t- R_EX_4hphac_e\n", + "\t- R_EX_pac_e\n", + "\t- R_EX_salcn_e\n", + "\t- R_EX_4abz_e\n", + "\t- R_EX_galam_e\n", + "\t- R_EX_raffin_e\n", + "\t- R_EX_o6a4colipa_e\n", + "\t- R_EX_acon_C_e\n", + "\t- R_EX_arab__D_e\n", + "\t- R_EX_feroxB_e\n", + "\t- R_EX_tcb_e\n", + "\t- R_EX_tton_e\n", + "\t- R_EX_4hthr_e\n", + "\t- R_EX_feroxEfe_e\n", + "\t- R_EX_feroxE_e\u001b[0m\n", + "File saved at: ../../data/sbml_corrected/iECD_1391.xml\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: iEC55989_1330\n", + "\u001b[0;93mWARNING : \n", + " - R_CAT: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_DHPTDNR: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_DHPTDNRN: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_FHL: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SPODM: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SPODMpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCASPtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCFUMtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCMALtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCTARTtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_CHITPH: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_URCN: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_EX_chitob_e: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_CHITOBpts: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\u001b[0m\n", + "\n", + "\n", + "\u001b[0;36m############################################\n", + "############################################\n", + " WRITING SBML FILE \n", + "############################################\n", + "############################################\n", + "\u001b[0m\n", + "\u001b[0;93mWARNING : IMPORT REACTION REMOVED BY DEFAULT\u001b[0m\n", + "\u001b[0;93mWARNING : If you want to keep import reaction\n", + "use option -kir / --keep-import-reactions\u001b[0m\n", + "\u001b[0;93mWARNING : Import reaction removed:\n", + "\t- R_EX_12ppd__S_e\n", + "\t- R_EX_crn_e\n", + "\t- R_EX_crn__D_e\n", + "\t- R_EX_csn_e\n", + "\t- R_EX_cu_e\n", + "\t- R_EX_cu2_e\n", + "\t- R_EX_cyan_e\n", + "\t- R_EX_cynt_e\n", + "\t- R_EX_cys__D_e\n", + "\t- R_EX_cys__L_e\n", + "\t- R_EX_cytd_e\n", + "\t- R_EX_dad_2_e\n", + "\t- R_EX_damp_e\n", + "\t- R_EX_dca_e\n", + "\t- R_EX_dcmp_e\n", + "\t- R_EX_dcyt_e\n", + "\t- R_EX_ddca_e\n", + "\t- R_EX_dgmp_e\n", + "\t- R_EX_dgsn_e\n", + "\t- R_DM_4crsol_c\n", + "\t- R_DM_5drib_c\n", + "\t- R_DM_aacald_c\n", + "\t- R_DM_amob_c\n", + "\t- R_DM_mththf_c\n", + "\t- R_DM_oxam_c\n", + "\t- R_EX_12ppd__R_e\n", + "\t- R_EX_fe2_e\n", + "\t- R_EX_fe3_e\n", + "\t- R_EX_fe3dcit_e\n", + "\t- R_EX_fe3dhbzs_e\n", + "\t- R_EX_fe3hox_e\n", + "\t- R_EX_fe3hox_un_e\n", + "\t- R_EX_fecrm_e\n", + "\t- R_EX_fecrm_un_e\n", + "\t- R_EX_feenter_e\n", + "\t- R_EX_feoxam_e\n", + "\t- R_EX_feoxam_un_e\n", + "\t- R_EX_for_e\n", + "\t- R_EX_fru_e\n", + "\t- R_EX_frulys_e\n", + "\t- R_EX_fruur_e\n", + "\t- R_EX_fuc__L_e\n", + "\t- R_EX_fum_e\n", + "\t- R_EX_fusa_e\n", + "\t- R_EX_g1p_e\n", + "\t- R_EX_dha_e\n", + "\t- R_EX_dimp_e\n", + "\t- R_EX_din_e\n", + "\t- R_EX_dms_e\n", + "\t- R_EX_dmso_e\n", + "\t- R_EX_dopa_e\n", + "\t- R_EX_doxrbcn_e\n", + "\t- R_EX_dtmp_e\n", + "\t- R_EX_dump_e\n", + "\t- R_EX_duri_e\n", + "\t- R_EX_eca4colipa_e\n", + "\t- R_EX_enlipa_e\n", + "\t- R_EX_enter_e\n", + "\t- R_EX_etha_e\n", + "\t- R_EX_ethso3_e\n", + "\t- R_EX_g3pc_e\n", + "\t- R_EX_g3pe_e\n", + "\t- R_EX_g3pg_e\n", + "\t- R_EX_g3pi_e\n", + "\t- R_EX_g3ps_e\n", + "\t- R_EX_etoh_e\n", + "\t- R_EX_f6p_e\n", + "\t- R_EX_g6p_e\n", + "\t- R_EX_14glucan_e\n", + "\t- R_EX_gal_e\n", + "\t- R_EX_gal_bD_e\n", + "\t- R_EX_gal1p_e\n", + "\t- R_EX_galct__D_e\n", + "\t- R_EX_fald_e\n", + "\t- R_EX_lac__D_e\n", + "\t- R_EX_galctn__D_e\n", + "\t- R_EX_galctn__L_e\n", + "\t- R_EX_galt_e\n", + "\t- R_EX_galur_e\n", + "\t- R_EX_lac__L_e\n", + "\t- R_EX_lcts_e\n", + "\t- R_EX_gam_e\n", + "\t- R_EX_leu__L_e\n", + "\t- R_EX_gam6p_e\n", + "\t- R_EX_15dap_e\n", + "\t- R_EX_gbbtn_e\n", + "\t- R_EX_gdp_e\n", + "\t- R_EX_lipa_e\n", + "\t- R_EX_lipa_cold_e\n", + "\t- R_EX_lipoate_e\n", + "\t- R_EX_glc__D_e\n", + "\t- R_EX_lys__L_e\n", + "\t- R_EX_glcn_e\n", + "\t- R_EX_23camp_e\n", + "\t- R_EX_glcr_e\n", + "\t- R_EX_glcur_e\n", + "\t- R_EX_lyx__L_e\n", + "\t- R_EX_mal__D_e\n", + "\t- R_EX_mal__L_e\n", + "\t- R_EX_malt_e\n", + "\t- R_EX_malthx_e\n", + "\t- R_EX_glcur1p_e\n", + "\t- R_EX_gln__L_e\n", + "\t- R_EX_glu__L_e\n", + "\t- R_EX_maltpt_e\n", + "\t- R_EX_gly_e\n", + "\t- R_EX_23ccmp_e\n", + "\t- R_EX_glyald_e\n", + "\t- R_EX_glyb_e\n", + "\t- R_EX_malttr_e\n", + "\t- R_EX_maltttr_e\n", + "\t- R_EX_man_e\n", + "\t- R_EX_man6p_e\n", + "\t- R_EX_manglyc_e\n", + "\t- R_EX_glyc_e\n", + "\t- R_EX_glyc__R_e\n", + "\t- R_EX_glyc2p_e\n", + "\t- R_EX_glyc3p_e\n", + "\t- R_EX_glyclt_e\n", + "\t- R_EX_gmp_e\n", + "\t- R_EX_melib_e\n", + "\t- R_EX_meoh_e\n", + "\t- R_EX_met__D_e\n", + "\t- R_EX_gsn_e\n", + "\t- R_EX_met__L_e\n", + "\t- R_EX_23cgmp_e\n", + "\t- R_EX_metsox_R__L_e\n", + "\t- R_EX_metsox_S__L_e\n", + "\t- R_EX_gthox_e\n", + "\t- R_EX_gthrd_e\n", + "\t- R_EX_gtp_e\n", + "\t- R_EX_mg2_e\n", + "\t- R_EX_mincyc_e\n", + "\t- R_EX_gua_e\n", + "\t- R_EX_23cump_e\n", + "\t- R_EX_h_e\n", + "\t- R_EX_minohp_e\n", + "\t- R_EX_mmet_e\n", + "\t- R_EX_h2_e\n", + "\t- R_EX_h2o_e\n", + "\t- R_EX_23dappa_e\n", + "\t- R_EX_h2o2_e\n", + "\t- R_EX_mn2_e\n", + "\t- R_EX_mnl_e\n", + "\t- R_EX_h2s_e\n", + "\t- R_EX_hacolipa_e\n", + "\t- R_EX_mobd_e\n", + "\t- R_EX_26dap__M_e\n", + "\t- R_EX_mso3_e\n", + "\t- R_EX_n2o_e\n", + "\t- R_EX_na1_e\n", + "\t- R_EX_nac_e\n", + "\t- R_EX_halipa_e\n", + "\t- R_EX_hdca_e\n", + "\t- R_EX_hdcea_e\n", + "\t- R_EX_hg2_e\n", + "\t- R_EX_his__L_e\n", + "\t- R_EX_hom__L_e\n", + "\t- R_EX_hxa_e\n", + "\t- R_EX_nh4_e\n", + "\t- R_EX_ni2_e\n", + "\t- R_EX_nmn_e\n", + "\t- R_EX_hxan_e\n", + "\t- R_EX_2ddglcn_e\n", + "\t- R_EX_idon__L_e\n", + "\t- R_EX_ile__L_e\n", + "\t- R_EX_imp_e\n", + "\t- R_EX_no_e\n", + "\t- R_EX_no2_e\n", + "\t- R_EX_no3_e\n", + "\t- R_EX_novbcn_e\n", + "\t- R_EX_o16a4colipa_e\n", + "\t- R_EX_indole_e\n", + "\t- R_EX_inost_e\n", + "\t- R_EX_o2_e\n", + "\t- R_EX_34dhpac_e\n", + "\t- R_EX_o2s_e\n", + "\t- R_EX_ocdca_e\n", + "\t- R_EX_ins_e\n", + "\t- R_EX_isetac_e\n", + "\t- R_EX_k_e\n", + "\t- R_EX_kdo2lipid4_e\n", + "\t- R_EX_ocdcea_e\n", + "\t- R_EX_octa_e\n", + "\t- R_EX_3amp_e\n", + "\t- R_EX_orn_e\n", + "\t- R_EX_orot_e\n", + "\t- R_EX_pacald_e\n", + "\t- R_EX_peamn_e\n", + "\t- R_EX_phe__L_e\n", + "\t- R_EX_pheme_e\n", + "\t- R_EX_so2_e\n", + "\t- R_EX_so3_e\n", + "\t- R_EX_so4_e\n", + "\t- R_EX_spmd_e\n", + "\t- R_EX_succ_e\n", + "\t- R_EX_sucr_e\n", + "\t- R_EX_pi_e\n", + "\t- R_EX_pnto__R_e\n", + "\t- R_EX_sulfac_e\n", + "\t- R_EX_tartr__D_e\n", + "\t- R_EX_ppa_e\n", + "\t- R_EX_3cmp_e\n", + "\t- R_EX_ppal_e\n", + "\t- R_EX_pppn_e\n", + "\t- R_EX_tartr__L_e\n", + "\t- R_EX_taur_e\n", + "\t- R_EX_tcynt_e\n", + "\t- R_EX_ppt_e\n", + "\t- R_EX_pro__L_e\n", + "\t- R_EX_thm_e\n", + "\t- R_EX_thr__L_e\n", + "\t- R_EX_progly_e\n", + "\t- R_EX_psclys_e\n", + "\t- R_EX_thrp_e\n", + "\t- R_EX_3gmp_e\n", + "\t- R_EX_thym_e\n", + "\t- R_EX_pser__L_e\n", + "\t- R_EX_ptrc_e\n", + "\t- R_EX_pydam_e\n", + "\t- R_EX_thymd_e\n", + "\t- R_EX_3hcinnm_e\n", + "\t- R_EX_tma_e\n", + "\t- R_EX_tmao_e\n", + "\t- R_EX_tre_e\n", + "\t- R_EX_pydx_e\n", + "\t- R_EX_pydxn_e\n", + "\t- R_EX_pyr_e\n", + "\t- R_EX_quin_e\n", + "\t- R_EX_r5p_e\n", + "\t- R_EX_rfamp_e\n", + "\t- R_EX_rib__D_e\n", + "\t- R_EX_trp__L_e\n", + "\t- R_EX_tsul_e\n", + "\t- R_EX_ttdca_e\n", + "\t- R_EX_rmn_e\n", + "\t- R_EX_3hpp_e\n", + "\t- R_EX_sbt__D_e\n", + "\t- R_EX_sel_e\n", + "\t- R_EX_ser__D_e\n", + "\t- R_EX_ser__L_e\n", + "\t- R_EX_ttdcea_e\n", + "\t- R_EX_ttrcyc_e\n", + "\t- R_EX_tungs_e\n", + "\t- R_EX_tym_e\n", + "\t- R_EX_tyr__L_e\n", + "\t- R_EX_tyrp_e\n", + "\t- R_EX_skm_e\n", + "\t- R_EX_slnt_e\n", + "\t- R_EX_uacgam_e\n", + "\t- R_EX_3hpppn_e\n", + "\t- R_EX_udpacgal_e\n", + "\t- R_EX_udpg_e\n", + "\t- R_EX_udpgal_e\n", + "\t- R_EX_3ump_e\n", + "\t- R_EX_udpglcur_e\n", + "\t- R_EX_ump_e\n", + "\t- R_EX_ura_e\n", + "\t- R_EX_urea_e\n", + "\t- R_EX_uri_e\n", + "\t- R_EX_val__L_e\n", + "\t- R_EX_xan_e\n", + "\t- R_EX_xmp_e\n", + "\t- R_EX_xtsn_e\n", + "\t- R_EX_xyl__D_e\n", + "\t- R_EX_4abut_e\n", + "\t- R_EX_4hoxpacd_e\n", + "\t- R_EX_xylu__L_e\n", + "\t- R_EX_5dglcn_e\n", + "\t- R_EX_5mtr_e\n", + "\t- R_EX_zn2_e\n", + "\t- R_EX_LalaDglu_e\n", + "\t- R_EX_LalaDgluMdap_e\n", + "\t- R_EX_LalaDgluMdapDala_e\n", + "\t- R_EX_LalaLglu_e\n", + "\t- R_EX_ac_e\n", + "\t- R_EX_acac_e\n", + "\t- R_EX_acald_e\n", + "\t- R_EX_acgal_e\n", + "\t- R_EX_acgal1p_e\n", + "\t- R_EX_acgam_e\n", + "\t- R_EX_acgam1p_e\n", + "\t- R_EX_acmana_e\n", + "\t- R_EX_acmum_e\n", + "\t- R_EX_acnam_e\n", + "\t- R_EX_acolipa_e\n", + "\t- R_EX_acser_e\n", + "\t- R_EX_ade_e\n", + "\t- R_EX_adn_e\n", + "\t- R_EX_adocbl_e\n", + "\t- R_EX_ag_e\n", + "\t- R_EX_agm_e\n", + "\t- R_EX_akg_e\n", + "\t- R_EX_ala_B_e\n", + "\t- R_EX_ala__D_e\n", + "\t- R_EX_ala__L_e\n", + "\t- R_EX_alaala_e\n", + "\t- R_EX_all__D_e\n", + "\t- R_EX_alltn_e\n", + "\t- R_EX_amp_e\n", + "\t- R_EX_anhgm_e\n", + "\t- R_EX_arab__L_e\n", + "\t- R_EX_arbt_e\n", + "\t- R_EX_arbtn_e\n", + "\t- R_EX_arbtn_fe3_e\n", + "\t- R_EX_arg__L_e\n", + "\t- R_EX_ascb__L_e\n", + "\t- R_EX_asn__L_e\n", + "\t- R_EX_aso3_e\n", + "\t- R_EX_asp__L_e\n", + "\t- R_EX_btn_e\n", + "\t- R_EX_but_e\n", + "\t- R_EX_butso3_e\n", + "\t- R_EX_ca2_e\n", + "\t- R_EX_cbi_e\n", + "\t- R_EX_cbl1_e\n", + "\t- R_EX_cd2_e\n", + "\t- R_EX_cgly_e\n", + "\t- R_EX_chol_e\n", + "\t- R_EX_chtbs_e\n", + "\t- R_EX_cit_e\n", + "\t- R_EX_cl_e\n", + "\t- R_EX_cm_e\n", + "\t- R_EX_cmp_e\n", + "\t- R_EX_co2_e\n", + "\t- R_EX_cobalt2_e\n", + "\t- R_EX_colipa_e\n", + "\t- R_EX_colipap_e\n", + "\t- R_EX_cpgn_e\n", + "\t- R_EX_cpgn_un_e\n", + "\t- R_EX_4hphac_e\n", + "\t- R_EX_4abz_e\n", + "\t- R_EX_tag__D_e\n", + "\t- R_EX_oaa_e\n", + "\t- R_EX_salchs2fe_e\n", + "\t- R_EX_acon_C_e\n", + "\t- R_EX_cysi__L_e\n", + "\t- R_EX_fe3dhbzs3_e\n", + "\t- R_EX_feroxB_e\n", + "\t- R_EX_tcb_e\n", + "\t- R_EX_tton_e\n", + "\t- R_EX_4hthr_e\n", + "\t- R_EX_drib_e\n", + "\t- R_EX_feroxEfe_e\n", + "\t- R_EX_feroxE_e\n", + "\t- R_EX_pep_e\n", + "\t- R_EX_feroxBfe_e\n", + "\t- R_EX_salchs4fe_e\n", + "\t- R_EX_2pg_e\n", + "\t- R_EX_23dhbzs3_e\n", + "\t- R_EX_tet_e\n", + "\t- R_EX_salchs4_e\n", + "\t- R_EX_frmd_e\n", + "\t- R_EX_colipaOA_e\n", + "\t- R_EX_salchsx_e\n", + "\t- R_EX_AEP_e\n", + "\t- R_EX_udcpo4_e\n", + "\t- R_EX_icit_e\n", + "\t- R_EX_feroxGfe_e\n", + "\t- R_EX_colipa20Oag_e\n", + "\t- R_EX_udcpo5_e\n", + "\t- R_EX_guln__L_e\n", + "\t- R_EX_remnant1_e\n", + "\t- R_EX_feroxG_e\n", + "\t- R_EX_salchs2_e\n", + "\t- R_EX_rnam_e\n", + "\t- R_EX_airs_e\n", + "\t- R_EX_dxyl_e\n", + "\t- R_EX_3pg_e\n", + "\t- R_EX_diact_e\n", + "\t- R_EX_cellb_e\n", + "\t- R_EX_btd_RR_e\n", + "\t- R_EX_4hoxpac_e\n", + "\t- R_EX_rbt_e\n", + "\t- R_DM_lipidA_core_e_p\n", + "\t- R_EX_bz_e\n", + "\t- R_EX_13ppd_e\n", + "\t- R_EX_abt__D_e\n", + "\t- R_EX_3ntym_e\n", + "\t- R_EX_6apa_e\n", + "\t- R_EX_fe_e\n", + "\t- R_EX_kokdolipidA_e\n", + "\t- R_EX_peng_e\n", + "\t- R_EX_pac_e\n", + "\t- R_EX_3hoxpac_e\n", + "\t- R_EX_salcn_e\n", + "\t- R_EX_galam_e\n", + "\t- R_EX_raffin_e\n", + "\t- R_EX_o6a4colipa_e\n", + "\t- R_EX_arab__D_e\u001b[0m\n", + "File saved at: ../../data/sbml_corrected/iEC55989_1330.xml\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: iAM_Pc455\n", + "\u001b[0;93mWARNING : - R_ARGLYSex: Reactants and products exchanged.\n", + " Boundaries was: [-1000000.0 ; 0.0]\n", + " - R_RE1342C: Reactants and products exchanged.\n", + " Boundaries was: [-1000000.0 ; 0.0]\n", + "\u001b[0m\n", + "\n", + "\n", + "\u001b[0;36m############################################\n", + "############################################\n", + " WRITING SBML FILE \n", + "############################################\n", + "############################################\n", + "\u001b[0m\n", + "\u001b[0;93mWARNING : IMPORT REACTION REMOVED BY DEFAULT\u001b[0m\n", + "\u001b[0;93mWARNING : If you want to keep import reaction\n", + "use option -kir / --keep-import-reactions\u001b[0m\n", + "\u001b[0;93mWARNING : Reaction with tag reversible modified:\n", + "\t- R_ARGLYSex\n", + "\t- R_RE1342C\u001b[0m\n", + "\u001b[0;93mWARNING : Reaction with Reactants and Products exchanged:\n", + "\t- R_ARGLYSex\n", + "\t- R_RE1342C\u001b[0m\n", + "\u001b[0;93mWARNING : Import reaction removed:\n", + "\t- R_EX_gly_e\n", + "\t- R_EX_4abz_e\n", + "\t- R_EX_4ahmmp_e\n", + "\t- R_EX_5fthf_e\n", + "\t- R_EX_Hb_e\n", + "\t- R_EX_ac_e\n", + "\t- R_EX_ade_e\n", + "\t- R_EX_adn_e\n", + "\t- R_EX_adp_e\n", + "\t- R_EX_ala__L_e\n", + "\t- R_EX_amp_e\n", + "\t- R_EX_arachd_e\n", + "\t- R_EX_arg__L_e\n", + "\t- R_EX_asn__L_e\n", + "\t- R_EX_asp__L_e\n", + "\t- R_EX_atp_e\n", + "\t- R_EX_ca2_e\n", + "\t- R_EX_chol_e\n", + "\t- R_EX_cl_e\n", + "\t- R_EX_co2_e\n", + "\t- R_EX_cobalt2_e\n", + "\t- R_EX_csn_e\n", + "\t- R_EX_cu_e\n", + "\t- R_EX_cu2_e\n", + "\t- R_EX_cys__L_e\n", + "\t- R_EX_dha_e\n", + "\t- R_EX_dhpt_e\n", + "\t- R_EX_fe2_e\n", + "\t- R_EX_fol_e\n", + "\t- R_EX_for_e\n", + "\t- R_EX_fru_e\n", + "\t- R_EX_glc__D_e\n", + "\t- R_EX_gln__L_e\n", + "\t- R_EX_glu__L_e\n", + "\t- R_DM_hmz_l\n", + "\t- R_SK_gcald_c\n", + "\t- R_DM_5mdr1p_c\n", + "\t- R_DM_hcys__L_c\n", + "\t- R_EX_lgn_e\n", + "\t- R_DM_saccrp__L_c\n", + "\t- R_EX_Lpipecol_e\n", + "\t- R_EX_4pyrdx_e\n", + "\t- R_SK_citr__L_c\n", + "\t- R_DM_pail345p_pf_16_0_18_1_c\n", + "\t- R_DM_mi13456p_c\n", + "\t- R_DM_pail345p_pf_18_0_18_1_c\n", + "\t- R_EX_4hba_e\n", + "\t- R_SK_no_c\n", + "\t- R_SK_dxyl5p_c\n", + "\t- R_SK_accoa_h\n", + "\t- R_EX_ptrc_e\n", + "\t- R_EX_spmd_e\n", + "\t- R_DM_oxptn_c\n", + "\t- R_EX_orn_e\n", + "\t- R_EX_chsterol_e\n", + "\t- R_EX_acgam_e\n", + "\t- R_EX_glyc_e\n", + "\t- R_EX_gsn_e\n", + "\t- R_EX_gthox_e\n", + "\t- R_EX_gthrd_e\n", + "\t- R_EX_gua_e\n", + "\t- R_EX_h_e\n", + "\t- R_EX_h2o_e\n", + "\t- R_EX_hco3_e\n", + "\t- R_EX_hdca_e\n", + "\t- R_EX_his__L_e\n", + "\t- R_EX_hxan_e\n", + "\t- R_EX_ile__L_e\n", + "\t- R_EX_ins_e\n", + "\t- R_EX_k_e\n", + "\t- R_EX_lac__D_e\n", + "\t- R_EX_lac__L_e\n", + "\t- R_EX_leu__L_e\n", + "\t- R_EX_lipoate_e\n", + "\t- R_EX_lnlc_e\n", + "\t- R_EX_lys__L_e\n", + "\t- R_EX_man_e\n", + "\t- R_EX_met__L_e\n", + "\t- R_EX_mg2_e\n", + "\t- R_EX_mthgxl_e\n", + "\t- R_EX_na1_e\n", + "\t- R_EX_nac_e\n", + "\t- R_EX_ncam_e\n", + "\t- R_EX_nh4_e\n", + "\t- R_EX_ni2_e\n", + "\t- R_EX_o2_e\n", + "\t- R_EX_ocdca_e\n", + "\t- R_EX_ocdcea_e\n", + "\t- R_EX_phe__L_e\n", + "\t- R_EX_pi_e\n", + "\t- R_EX_pnto__R_e\n", + "\t- R_EX_pro__L_e\n", + "\t- R_EX_pyr_e\n", + "\t- R_EX_ribflv_e\n", + "\t- R_EX_ser__L_e\n", + "\t- R_EX_thm_e\n", + "\t- R_EX_thr__L_e\n", + "\t- R_EX_thym_e\n", + "\t- R_EX_thymd_e\n", + "\t- R_EX_trp__L_e\n", + "\t- R_EX_ttdca_e\n", + "\t- R_EX_tyr__L_e\n", + "\t- R_EX_ura_e\n", + "\t- R_EX_uri_e\n", + "\t- R_EX_val__L_e\u001b[0m\n", + "File saved at: ../../data/sbml_corrected/iAM_Pc455.xml\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: iJN1463\n", + "\u001b[0;93mWARNING : - R_CUt2pp: Reactants and products exchanged.\n", + " Boundaries was: [-999999.0 ; 0.0]\n", + " - R_EX_o2_e: Reactants and products exchanged.\n", + " Boundaries was: [-100.0 ; 0.0]\n", + " - R_HVCD: Reactants and products exchanged.\n", + " Boundaries was: [-999999.0 ; 0.0]\n", + " - R_VCACT: Reactants and products exchanged.\n", + " Boundaries was: [-999999.0 ; 0.0]\n", + "\n", + " - R_PIt7ipp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + " - R_SK_pqqA_kt_c: Reactants and products exchanged.\n", + " Boundaries was: [-1.0 ; 0.0]\n", + "\u001b[0m\n", + "\n", + "\n", + "\u001b[0;36m############################################\n", + "############################################\n", + " WRITING SBML FILE \n", + "############################################\n", + "############################################\n", + "\u001b[0m\n", + "\u001b[0;93mWARNING : IMPORT REACTION REMOVED BY DEFAULT\u001b[0m\n", + "\u001b[0;93mWARNING : If you want to keep import reaction\n", + "use option -kir / --keep-import-reactions\u001b[0m\n", + "\u001b[0;93mWARNING : Reaction with tag reversible modified:\n", + "\t- R_CUt2pp\n", + "\t- R_EX_o2_e\n", + "\t- R_HVCD\n", + "\t- R_VCACT\n", + "\t- R_SK_pqqA_kt_c\u001b[0m\n", + "\u001b[0;93mWARNING : Reaction with Reactants and Products exchanged:\n", + "\t- R_CUt2pp\n", + "\t- R_EX_o2_e\n", + "\t- R_HVCD\n", + "\t- R_VCACT\n", + "\t- R_SK_pqqA_kt_c\u001b[0m\n", + "\u001b[0;93mWARNING : Import reaction removed:\n", + "\t- R_DM_5drib_c\n", + "\t- R_DM_amob_c\n", + "\t- R_DM_C100aPHA_c\n", + "\t- R_DM_C100pPHA_c\n", + "\t- R_DM_C101PAH_c\n", + "\t- R_DM_C120aPHA_c\n", + "\t- R_DM_C121aPHA_c\n", + "\t- R_DM_C121d6PHA_c\n", + "\t- R_DM_C140aPHA_c\n", + "\t- R_DM_C141aPHA_c\n", + "\t- R_DM_C141d5PHA_c\n", + "\t- R_DM_C142PHA_c\n", + "\t- R_DM_C40aPHA_c\n", + "\t- R_DM_C40atPHA_c\n", + "\t- R_DM_C40pPHA_c\n", + "\t- R_DM_C50aPHA_c\n", + "\t- R_DM_C50pPHA_c\n", + "\t- R_DM_C60aPHA_c\n", + "\t- R_DM_C60atPHA_c\n", + "\t- R_DM_C60pPHA_c\n", + "\t- R_EX_12dgr160_e\n", + "\t- R_EX_12dgr180_e\n", + "\t- R_EX_13dampp_e\n", + "\t- R_EX_15dap_e\n", + "\t- R_EX_1ag160_e\n", + "\t- R_EX_1ag180_e\n", + "\t- R_EX_1ag181d9_e\n", + "\t- R_EX_1ag182d9d12_e\n", + "\t- R_EX_25dkglcn_e\n", + "\t- R_EX_26dap__M_e\n", + "\t- R_EX_AEP_e\n", + "\t- R_EX_2ddglcn_e\n", + "\t- R_EX_2dhglcn_e\n", + "\t- R_EX_2m35mdntha_e\n", + "\t- R_EX_34dhbz_e\n", + "\t- R_EX_34dhcinm_e\n", + "\t- R_EX_34dhphe_e\n", + "\t- R_EX_35dnta_e\n", + "\t- R_EX_3h4atb_e\n", + "\t- R_EX_3mb_e\n", + "\t- R_EX_btd_RR_e\n", + "\t- R_EX_btn_e\n", + "\t- R_EX_butso3_e\n", + "\t- R_EX_bz_e\n", + "\t- R_EX_ca2_e\n", + "\t- R_EX_carn_e\n", + "\t- R_EX_catechol_e\n", + "\t- R_EX_cbi_e\n", + "\t- R_EX_cbl1_e\n", + "\t- R_EX_cd2_e\n", + "\t- R_EX_cell4_e\n", + "\t- R_EX_cgly_e\n", + "\t- R_EX_chol_e\n", + "\t- R_EX_chols_e\n", + "\t- R_EX_chor_e\n", + "\t- R_EX_cinnm_e\n", + "\t- R_EX_cit_e\n", + "\t- R_EX_cl_e\n", + "\t- R_EX_cm_e\n", + "\t- R_EX_cmcbtt_e\n", + "\t- R_EX_co2_e\n", + "\t- R_EX_co_e\n", + "\t- R_EX_cobalt2_e\n", + "\t- R_EX_confrl_e\n", + "\t- R_EX_creat_e\n", + "\t- R_EX_crn_e\n", + "\t- R_EX_cro2_e\n", + "\t- R_EX_cro4_e\n", + "\t- R_EX_crtn_e\n", + "\t- R_EX_csn_e\n", + "\t- R_EX_cu2_e\n", + "\t- R_EX_cu_e\n", + "\t- R_EX_cyan_e\n", + "\t- R_EX_cys__D_e\n", + "\t- R_EX_cys__L_e\n", + "\t- R_EX_dag181d9_e\n", + "\t- R_EX_dag182d9d12_e\n", + "\t- R_EX_dca_e\n", + "\t- R_EX_ddca_e\n", + "\t- R_EX_dgudbutn_e\n", + "\t- R_EX_dmanur_e\n", + "\t- R_EX_dmgly_e\n", + "\t- R_EX_dmso2_e\n", + "\t- R_EX_dopa_e\n", + "\t- R_EX_ecto__L_e\n", + "\t- R_EX_enter_e\n", + "\t- R_EX_etha_e\n", + "\t- R_EX_ethso3_e\n", + "\t- R_EX_etoh_e\n", + "\t- R_EX_fald_e\n", + "\t- R_EX_fcmcbtt_e\n", + "\t- R_EX_fe2_e\n", + "\t- R_EX_fe3_e\n", + "\t- R_EX_fe3dcit_e\n", + "\t- R_EX_fe3mcbtt_e\n", + "\t- R_EX_fe3pyovd_e\n", + "\t- R_EX_fe3pyovd_kt_e\n", + "\t- R_EX_fecrm_e\n", + "\t- R_EX_fecrm_un_e\n", + "\t- R_EX_feenter_e\n", + "\t- R_EX_feoxam_e\n", + "\t- R_EX_feoxam_un_e\n", + "\t- R_EX_fer_e\n", + "\t- R_EX_for_e\n", + "\t- R_EX_fru_e\n", + "\t- R_EX_fum_e\n", + "\t- R_EX_ga_e\n", + "\t- R_EX_galct__D_e\n", + "\t- R_EX_galur_e\n", + "\t- R_EX_glc__D_e\n", + "\t- R_EX_glcn_e\n", + "\t- R_EX_glcr_e\n", + "\t- R_EX_glcur_e\n", + "\t- R_EX_gln__L_e\n", + "\t- R_EX_glu__L_e\n", + "\t- R_EX_glutar_e\n", + "\t- R_EX_gly_e\n", + "\t- R_EX_glyald_e\n", + "\t- R_EX_glyb_e\n", + "\t- R_EX_glyc__R_e\n", + "\t- R_EX_glyc_e\n", + "\t- R_EX_glyclt_e\n", + "\t- R_EX_glycol_e\n", + "\t- R_EX_glygln_e\n", + "\t- R_EX_glyglu_e\n", + "\t- R_EX_glygly_e\n", + "\t- R_EX_glymet_e\n", + "\t- R_EX_glyphe_e\n", + "\t- R_EX_glyser_e\n", + "\t- R_EX_gua_e\n", + "\t- R_EX_gudac_e\n", + "\t- R_EX_gudptn_e\n", + "\t- R_EX_h2o2_e\n", + "\t- R_EX_h2o_e\n", + "\t- R_EX_h2s_e\n", + "\t- R_EX_h_e\n", + "\t- R_EX_hco3_e\n", + "\t- R_EX_hcys__L_e\n", + "\t- R_EX_hdca_e\n", + "\t- R_EX_hdcea_e\n", + "\t- R_EX_hg2_e\n", + "\t- R_EX_hgentis_e\n", + "\t- R_EX_his__L_e\n", + "\t- R_EX_hisgly_e\n", + "\t- R_EX_hishis_e\n", + "\t- R_EX_hom__L_e\n", + "\t- R_EX_hpta_e\n", + "\t- R_EX_hxa_e\n", + "\t- R_EX_hxan_e\n", + "\t- R_EX_icit_e\n", + "\t- R_EX_ile__L_e\n", + "\t- R_EX_ind3ac_e\n", + "\t- R_EX_indole_e\n", + "\t- R_EX_ins_e\n", + "\t- R_EX_isetac_e\n", + "\t- R_EX_k_e\n", + "\t- R_EX_lac__D_e\n", + "\t- R_EX_lac__L_e\n", + "\t- R_EX_leu__D_e\n", + "\t- R_EX_leu__L_e\n", + "\t- R_EX_leuleu_e\n", + "\t- R_EX_lnlc_e\n", + "\t- R_EX_lpspput_e\n", + "\t- R_EX_lys__D_e\n", + "\t- R_EX_lys__L_e\n", + "\t- R_EX_m_xyl_e\n", + "\t- R_EX_mal__D_e\n", + "\t- R_EX_mal__L_e\n", + "\t- R_EX_man_e\n", + "\t- R_EX_manur_e\n", + "\t- R_EX_mcbtt_e\n", + "\t- R_EX_meoh_e\n", + "\t- R_EX_met__L_e\n", + "\t- R_EX_mg2_e\n", + "\t- R_EX_mmet_e\n", + "\t- R_EX_mn2_e\n", + "\t- R_EX_mobd_e\n", + "\t- R_EX_mso3_e\n", + "\t- R_EX_na1_e\n", + "\t- R_EX_nac_e\n", + "\t- R_EX_nh4_e\n", + "\t- R_EX_ni2_e\n", + "\t- R_EX_no2_e\n", + "\t- R_EX_no3_e\n", + "\t- R_EX_no_e\n", + "\t- R_EX_nona_e\n", + "\t- R_EX_o2_e\n", + "\t- R_EX_o_xyl_e\n", + "\t- R_EX_oaa_e\n", + "\t- R_EX_ocdca_e\n", + "\t- R_EX_ocdcea_e\n", + "\t- R_EX_octa_e\n", + "\t- R_EX_orn__D_e\n", + "\t- R_EX_orn_e\n", + "\t- R_EX_orot_e\n", + "\t- R_EX_oxa_e\n", + "\t- R_EX_5g2oxpt_e\n", + "\t- R_EX_p_xyl_e\n", + "\t- R_EX_pac_e\n", + "\t- R_EX_pacald_e\n", + "\t- R_EX_pb2_e\n", + "\t- R_EX_pea_e\n", + "\t- R_EX_peamn_e\n", + "\t- R_EX_pentso3_e\n", + "\t- R_EX_phe__D_e\n", + "\t- R_EX_phe__L_e\n", + "\t- R_EX_phedca_e\n", + "\t- R_EX_phehpa_e\n", + "\t- R_EX_phehxa_e\n", + "\t- R_EX_pheme_e\n", + "\t- R_EX_phenona_e\n", + "\t- R_EX_pheocta_e\n", + "\t- R_EX_phept_e\n", + "\t- R_EX_phpyr_e\n", + "\t- R_EX_pi_e\n", + "\t- R_EX_pnto__R_e\n", + "\t- R_EX_ppa_e\n", + "\t- R_EX_ppi_e\n", + "\t- R_EX_prealg_MG_14_e\n", + "\t- R_EX_prealg_MG_23_e\n", + "\t- R_EX_prealg_MG_32_e\n", + "\t- R_EX_prealg_MG_41_e\n", + "\t- R_EX_prealginate_G_e\n", + "\t- R_EX_pro__D_e\n", + "\t- R_EX_pro__L_e\n", + "\t- R_EX_progly_e\n", + "\t- R_EX_pta_e\n", + "\t- R_EX_ptrc_e\n", + "\t- R_EX_ptsla_e\n", + "\t- R_EX_pydam_e\n", + "\t- R_EX_pyovd_e\n", + "\t- R_EX_pyovd_kt_e\n", + "\t- R_EX_pyr_e\n", + "\t- R_EX_quin_e\n", + "\t- R_EX_rib__D_e\n", + "\t- R_EX_sbo3_e\n", + "\t- R_EX_sel_e\n", + "\t- R_EX_ser__D_e\n", + "\t- R_EX_ser__L_e\n", + "\t- R_EX_sheme_e\n", + "\t- R_EX_skm_e\n", + "\t- R_EX_so2_e\n", + "\t- R_EX_so3_e\n", + "\t- R_EX_so4_e\n", + "\t- R_EX_spmd_e\n", + "\t- R_EX_succ_e\n", + "\t- R_EX_sulfac_e\n", + "\t- R_EX_tag160_e\n", + "\t- R_EX_tag180_e\n", + "\t- R_EX_tag181d9_e\n", + "\t- R_EX_tag182d9d12_e\n", + "\t- R_EX_taur_e\n", + "\t- R_EX_tcynt_e\n", + "\t- R_EX_thr__L_e\n", + "\t- R_EX_thym_e\n", + "\t- R_EX_tmanur_e\n", + "\t- R_EX_tnt_e\n", + "\t- R_EX_tol_e\n", + "\t- R_EX_trp__L_e\n", + "\t- R_EX_tsul_e\n", + "\t- R_EX_ttdca_e\n", + "\t- R_EX_ttdcea_e\n", + "\t- R_EX_ttrcyc_e\n", + "\t- R_EX_tungs_e\n", + "\t- R_EX_tyr__D_e\n", + "\t- R_EX_tyr__L_e\n", + "\t- R_EX_ura_e\n", + "\t- R_EX_urea_e\n", + "\t- R_EX_uri_e\n", + "\t- R_EX_vacc_e\n", + "\t- R_EX_val__D_e\n", + "\t- R_EX_val__L_e\n", + "\t- R_EX_vanln_e\n", + "\t- R_EX_vanlt_e\n", + "\t- R_EX_xan_e\n", + "\t- R_EX_zn2_e\n", + "\t- R_SK_PHAg_c\n", + "\t- R_SK_pqqA_kt_c\n", + "\t- R_EX_4hptn_e\n", + "\t- R_EX_4oxptn_e\n", + "\t- R_EX_acmtsoxin_e\n", + "\t- R_EX_acpptrn_e\n", + "\t- R_EX_d2one_e\n", + "\t- R_EX_d3one_e\n", + "\t- R_EX_d4one_e\n", + "\t- R_EX_mtsoxin_e\n", + "\t- R_EX_n2one_e\n", + "\t- R_EX_pptrn_e\n", + "\t- R_EX_und2one_e\n", + "\t- R_DM_C70aPHA_c\n", + "\t- R_DM_C70pPHA_c\n", + "\t- R_DM_C80aPHA_c\n", + "\t- R_DM_C80pPHA_c\n", + "\t- R_DM_C90aPHA_c\n", + "\t- R_DM_C90pPHA_c\n", + "\t- R_DM_acmum6p_c\n", + "\t- R_DM_ppi50_c\n", + "\t- R_DM_acgam_c\n", + "\t- R_DM_doxopa_c\n", + "\t- R_DM_tripeptide_c\n", + "\t- R_EX_3oxoadp_e\n", + "\t- R_EX_4abut_e\n", + "\t- R_EX_4hbz_e\n", + "\t- R_EX_4hpro_DC_e\n", + "\t- R_EX_4hpro_LT_e\n", + "\t- R_EX_5aptn_e\n", + "\t- R_EX_5mcsn_e\n", + "\t- R_EX_5oxpro_e\n", + "\t- R_EX_6atha_e\n", + "\t- R_EX_6hnac_e\n", + "\t- R_EX_met__D_e\n", + "\t- R_EX_LalaLglu_e\n", + "\t- R_EX_Ncbmpts_e\n", + "\t- R_EX_R3hdec4e_e\n", + "\t- R_EX_R_3h4atba_e\n", + "\t- R_EX_R_3h6atha_e\n", + "\t- R_EX_R_3hcmrs7e_e\n", + "\t- R_EX_R_3hdcaa_e\n", + "\t- R_EX_R_3hdd5ea_e\n", + "\t- R_EX_R_3hdd6e_e\n", + "\t- R_EX_R_3hdda_e\n", + "\t- R_EX_R_3hhdca_e\n", + "\t- R_EX_R_3hhpa_e\n", + "\t- R_EX_R_3hhxa_e\n", + "\t- R_EX_R_3hnonaa_e\n", + "\t- R_EX_R_3hocta_e\n", + "\t- R_EX_R_3hpba_e\n", + "\t- R_EX_R_3hpdeca_e\n", + "\t- R_EX_R_3hphpa_e\n", + "\t- R_EX_R_3hphxa_e\n", + "\t- R_EX_R_3hpnona_e\n", + "\t- R_EX_R_3hpocta_e\n", + "\t- R_EX_R_3hppta_e\n", + "\t- R_EX_R_3hpt_e\n", + "\t- R_EX_R_3htd58e_e\n", + "\t- R_EX_R_3htd5e_e\n", + "\t- R_EX_R_3httdca_e\n", + "\t- R_EX_T4hcinnm_e\n", + "\t- R_EX_ac_e\n", + "\t- R_EX_acac_e\n", + "\t- R_EX_acald_e\n", + "\t- R_EX_acon_C_e\n", + "\t- R_EX_acser_e\n", + "\t- R_EX_actn__R_e\n", + "\t- R_EX_ade_e\n", + "\t- R_EX_adn_e\n", + "\t- R_EX_adocbl_e\n", + "\t- R_EX_ag_e\n", + "\t- R_EX_agm_e\n", + "\t- R_EX_akg_e\n", + "\t- R_EX_ala_B_e\n", + "\t- R_EX_ala__D_e\n", + "\t- R_EX_ala__L_e\n", + "\t- R_EX_alaala_e\n", + "\t- R_EX_alahis_e\n", + "\t- R_EX_alaleu_e\n", + "\t- R_EX_alathr_e\n", + "\t- R_EX_alatrp_e\n", + "\t- R_EX_algac_MG_14_e\n", + "\t- R_EX_algac_MG_23_e\n", + "\t- R_EX_algac_MG_32_e\n", + "\t- R_EX_algac_MG_41_e\n", + "\t- R_EX_algac__M_e\n", + "\t- R_EX_alltn_e\n", + "\t- R_EX_apc_e\n", + "\t- R_EX_arbtn_e\n", + "\t- R_EX_arbtn_fe3_e\n", + "\t- R_EX_arg__D_e\n", + "\t- R_EX_arg__L_e\n", + "\t- R_EX_asn__D_e\n", + "\t- R_EX_asn__L_e\n", + "\t- R_EX_aso3_e\n", + "\t- R_EX_aso4_e\n", + "\t- R_EX_asp__L_e\n", + "\t- R_EX_balaala_e\n", + "\t- R_EX_balabala_e\n", + "\t- R_EX_balagly_e\n", + "\t- R_EX_balaleu_e\n", + "\t- R_EX_balamd_e\n", + "\t- R_EX_bhb_e\n", + "\t- R_EX_biliverd_e\u001b[0m\n", + "File saved at: ../../data/sbml_corrected/iJN1463.xml\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: iSB619\n", + "\u001b[0;93mWARNING : - R_FMNRx2: Reactants and products exchanged.\n", + " Boundaries was: [-999999.0 ; 0.0]\n", + " - R_HMGCOAR: Reactants and products exchanged.\n", + " Boundaries was: [-999999.0 ; 0.0]\n", + " - R_HMGCOAS: Reactants and products exchanged.\n", + " Boundaries was: [-999999.0 ; 0.0]\n", + " - R_KARA1: Reactants and products exchanged.\n", + " Boundaries was: [-999999.0 ; 0.0]\n", + "\n", + " - R_BIOMASS_SA_1a: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_BIOMASS_SA_2a: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_BIOMASS_SA_2b: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_BIOMASS_SA_3a: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_BIOMASS_SA_3b: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_BIOMASS_SA_4a: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_BIOMASS_SA_5a: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_BIOMASS_SA_6a: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_BIOMASS_SA_6b: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_BIOMASS_SA_7a: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_BIOMASS_SA_7b: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_BIOMASS_SA_lipids_only: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_BIOMASS_SA_nuc_only: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_BIOMASS_SA_only_AA: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\u001b[0m\n", + "\n", + "\n", + "\u001b[0;36m############################################\n", + "############################################\n", + " WRITING SBML FILE \n", + "############################################\n", + "############################################\n", + "\u001b[0m\n", + "\u001b[0;93mWARNING : IMPORT REACTION REMOVED BY DEFAULT\u001b[0m\n", + "\u001b[0;93mWARNING : If you want to keep import reaction\n", + "use option -kir / --keep-import-reactions\u001b[0m\n", + "\u001b[0;93mWARNING : Reaction with tag reversible modified:\n", + "\t- R_FMNRx2\n", + "\t- R_HMGCOAR\n", + "\t- R_HMGCOAS\n", + "\t- R_KARA1\u001b[0m\n", + "\u001b[0;93mWARNING : Reaction with Reactants and Products exchanged:\n", + "\t- R_FMNRx2\n", + "\t- R_HMGCOAR\n", + "\t- R_HMGCOAS\n", + "\t- R_KARA1\u001b[0m\n", + "\u001b[0;93mWARNING : Import reaction removed:\n", + "\t- R_EX_26dap__M_e\n", + "\t- R_EX_Cit_Mg_e\n", + "\t- R_EX_acgam_e\n", + "\t- R_EX_acnam_e\n", + "\t- R_EX_adn_e\n", + "\t- R_EX_akg_e\n", + "\t- R_EX_ala__D_e\n", + "\t- R_EX_ala__L_e\n", + "\t- R_EX_arg__L_e\n", + "\t- R_EX_asp__L_e\n", + "\t- R_EX_btn_e\n", + "\t- R_EX_chol_e\n", + "\t- R_EX_co2_e\n", + "\t- R_EX_cobalt2_e\n", + "\t- R_EX_crn_e\n", + "\t- R_EX_csn_e\n", + "\t- R_EX_cu2_e\n", + "\t- R_EX_cys__L_e\n", + "\t- R_EX_cytd_e\n", + "\t- R_EX_etoh_e\n", + "\t- R_EX_fe2_e\n", + "\t- R_EX_for_e\n", + "\t- R_EX_fru_e\n", + "\t- R_EX_galt_e\n", + "\t- R_EX_glc__D_e\n", + "\t- R_EX_glcn_e\n", + "\t- R_EX_glu__L_e\n", + "\t- R_EX_gly_e\n", + "\t- R_EX_glyald_e\n", + "\t- R_EX_glyb_e\n", + "\t- R_EX_glyc3p_e\n", + "\t- R_EX_glyc_e\n", + "\t- R_EX_gua_e\n", + "\t- R_EX_h2o_e\n", + "\t- R_EX_h_e\n", + "\t- R_EX_hdca_e\n", + "\t- R_EX_his__L_e\n", + "\t- R_EX_ile__L_e\n", + "\t- R_EX_k_e\n", + "\t- R_EX_lac__D_e\n", + "\t- R_EX_lac__L_e\n", + "\t- R_EX_lcts_e\n", + "\t- R_EX_leu__L_e\n", + "\t- R_EX_lys__L_e\n", + "\t- R_EX_mal__L_e\n", + "\t- R_EX_malt_e\n", + "\t- R_EX_man_e\n", + "\t- R_EX_met__L_e\n", + "\t- R_EX_mg2_e\n", + "\t- R_EX_mn2_e\n", + "\t- R_EX_mnl_e\n", + "\t- R_EX_zn2_e\n", + "\t- R_EX_mobd_e\n", + "\t- R_EX_na1_e\n", + "\t- R_EX_nac_e\n", + "\t- R_EX_ncam_e\n", + "\t- R_EX_nh4_e\n", + "\t- R_EX_ni2_e\n", + "\t- R_EX_no2_e\n", + "\t- R_EX_no3_e\n", + "\t- R_EX_o2_e\n", + "\t- R_EX_orn_e\n", + "\t- R_EX_phe__L_e\n", + "\t- R_EX_pheme_e\n", + "\t- R_EX_pi_e\n", + "\t- R_EX_pro__L_e\n", + "\t- R_EX_ptrc_e\n", + "\t- R_EX_rib__D_e\n", + "\t- R_EX_salcn_e\n", + "\t- R_EX_ser__L_e\n", + "\t- R_EX_so4_e\n", + "\t- R_EX_spmd_e\n", + "\t- R_EX_sucr_e\n", + "\t- R_EX_thm_e\n", + "\t- R_EX_thr__L_e\n", + "\t- R_EX_thymd_e\n", + "\t- R_EX_tre_e\n", + "\t- R_EX_trp__L_e\n", + "\t- R_EX_tyr__L_e\n", + "\t- R_EX_ura_e\n", + "\t- R_EX_urea_e\n", + "\t- R_EX_uri_e\n", + "\t- R_EX_val__L_e\n", + "\t- R_EX_xan_e\n", + "\t- R_SK_ahcys_c\n", + "\t- R_SK_glyclt_c\u001b[0m\n", + "File saved at: ../../data/sbml_corrected/iSB619.xml\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: iG2583_1286\n", + "\u001b[0;93mWARNING : \n", + " - R_DHPTDNR: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_DHPTDNRN: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_CAT: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCFUMtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCMALtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCTARTtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SPODM: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SPODMpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCASPtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_CHITOBpts: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_CHITPH: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_URCN: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_EX_chitob_e: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\u001b[0m\n", + "\n", + "\n", + "\u001b[0;36m############################################\n", + "############################################\n", + " WRITING SBML FILE \n", + "############################################\n", + "############################################\n", + "\u001b[0m\n", + "\u001b[0;93mWARNING : IMPORT REACTION REMOVED BY DEFAULT\u001b[0m\n", + "\u001b[0;93mWARNING : If you want to keep import reaction\n", + "use option -kir / --keep-import-reactions\u001b[0m\n", + "\u001b[0;93mWARNING : Import reaction removed:\n", + "\t- R_EX_alaala_e\n", + "\t- R_EX_alltn_e\n", + "\t- R_EX_amp_e\n", + "\t- R_EX_anhgm_e\n", + "\t- R_EX_fum_e\n", + "\t- R_EX_all__D_e\n", + "\t- R_EX_arab__L_e\n", + "\t- R_EX_ala__L_e\n", + "\t- R_DM_4crsol_c\n", + "\t- R_DM_5drib_c\n", + "\t- R_EX_arbt_e\n", + "\t- R_EX_fusa_e\n", + "\t- R_EX_arbtn_e\n", + "\t- R_EX_arbtn_fe3_e\n", + "\t- R_DM_aacald_c\n", + "\t- R_DM_amob_c\n", + "\t- R_EX_g1p_e\n", + "\t- R_DM_mththf_c\n", + "\t- R_EX_arg__L_e\n", + "\t- R_DM_oxam_c\n", + "\t- R_EX_ascb__L_e\n", + "\t- R_EX_g3pc_e\n", + "\t- R_EX_asn__L_e\n", + "\t- R_EX_aso3_e\n", + "\t- R_EX_g3pe_e\n", + "\t- R_EX_12ppd__R_e\n", + "\t- R_EX_asp__L_e\n", + "\t- R_EX_g3pg_e\n", + "\t- R_EX_btn_e\n", + "\t- R_EX_but_e\n", + "\t- R_EX_butso3_e\n", + "\t- R_EX_12ppd__S_e\n", + "\t- R_EX_14glucan_e\n", + "\t- R_EX_15dap_e\n", + "\t- R_EX_23camp_e\n", + "\t- R_EX_23ccmp_e\n", + "\t- R_EX_23cgmp_e\n", + "\t- R_EX_23cump_e\n", + "\t- R_EX_23dappa_e\n", + "\t- R_EX_ca2_e\n", + "\t- R_EX_cbi_e\n", + "\t- R_EX_cbl1_e\n", + "\t- R_EX_cd2_e\n", + "\t- R_EX_cgly_e\n", + "\t- R_EX_chol_e\n", + "\t- R_EX_chtbs_e\n", + "\t- R_EX_cit_e\n", + "\t- R_EX_cl_e\n", + "\t- R_EX_26dap__M_e\n", + "\t- R_EX_2ddglcn_e\n", + "\t- R_EX_34dhpac_e\n", + "\t- R_EX_cm_e\n", + "\t- R_EX_cmp_e\n", + "\t- R_EX_3amp_e\n", + "\t- R_EX_g3pi_e\n", + "\t- R_EX_3cmp_e\n", + "\t- R_EX_3gmp_e\n", + "\t- R_EX_3hcinnm_e\n", + "\t- R_EX_co2_e\n", + "\t- R_EX_cobalt2_e\n", + "\t- R_EX_colipa_e\n", + "\t- R_EX_colipap_e\n", + "\t- R_EX_g3ps_e\n", + "\t- R_EX_cpgn_e\n", + "\t- R_EX_cpgn_un_e\n", + "\t- R_EX_3hpp_e\n", + "\t- R_EX_3hpppn_e\n", + "\t- R_EX_3ump_e\n", + "\t- R_EX_4abut_e\n", + "\t- R_EX_4hoxpacd_e\n", + "\t- R_EX_crn_e\n", + "\t- R_EX_crn__D_e\n", + "\t- R_EX_5dglcn_e\n", + "\t- R_EX_5mtr_e\n", + "\t- R_EX_csn_e\n", + "\t- R_EX_cu_e\n", + "\t- R_EX_g6p_e\n", + "\t- R_EX_cu2_e\n", + "\t- R_EX_cyan_e\n", + "\t- R_EX_cynt_e\n", + "\t- R_EX_LalaDglu_e\n", + "\t- R_EX_LalaDgluMdap_e\n", + "\t- R_EX_LalaDgluMdapDala_e\n", + "\t- R_EX_LalaLglu_e\n", + "\t- R_EX_ac_e\n", + "\t- R_EX_cys__D_e\n", + "\t- R_EX_cys__L_e\n", + "\t- R_EX_gal_e\n", + "\t- R_EX_cytd_e\n", + "\t- R_EX_acac_e\n", + "\t- R_EX_acald_e\n", + "\t- R_EX_acgal_e\n", + "\t- R_EX_gal_bD_e\n", + "\t- R_EX_acgal1p_e\n", + "\t- R_EX_acgam_e\n", + "\t- R_EX_dad_2_e\n", + "\t- R_EX_damp_e\n", + "\t- R_EX_acgam1p_e\n", + "\t- R_EX_gal1p_e\n", + "\t- R_EX_acmana_e\n", + "\t- R_EX_dca_e\n", + "\t- R_EX_acmum_e\n", + "\t- R_EX_dcmp_e\n", + "\t- R_EX_galct__D_e\n", + "\t- R_EX_dcyt_e\n", + "\t- R_EX_ddca_e\n", + "\t- R_EX_dgmp_e\n", + "\t- R_EX_dgsn_e\n", + "\t- R_EX_acnam_e\n", + "\t- R_EX_acolipa_e\n", + "\t- R_EX_acser_e\n", + "\t- R_EX_ade_e\n", + "\t- R_EX_adn_e\n", + "\t- R_EX_adocbl_e\n", + "\t- R_EX_dha_e\n", + "\t- R_EX_dimp_e\n", + "\t- R_EX_ag_e\n", + "\t- R_EX_galctn__D_e\n", + "\t- R_EX_agm_e\n", + "\t- R_EX_akg_e\n", + "\t- R_EX_ala_B_e\n", + "\t- R_EX_din_e\n", + "\t- R_EX_dms_e\n", + "\t- R_EX_galctn__L_e\n", + "\t- R_EX_dmso_e\n", + "\t- R_EX_dopa_e\n", + "\t- R_EX_ala__D_e\n", + "\t- R_EX_galt_e\n", + "\t- R_EX_doxrbcn_e\n", + "\t- R_EX_galur_e\n", + "\t- R_EX_dtmp_e\n", + "\t- R_EX_dump_e\n", + "\t- R_EX_man_e\n", + "\t- R_EX_man6p_e\n", + "\t- R_EX_manglyc_e\n", + "\t- R_EX_duri_e\n", + "\t- R_EX_gam_e\n", + "\t- R_EX_eca4colipa_e\n", + "\t- R_EX_enlipa_e\n", + "\t- R_EX_enter_e\n", + "\t- R_EX_melib_e\n", + "\t- R_EX_meoh_e\n", + "\t- R_EX_met__D_e\n", + "\t- R_EX_etha_e\n", + "\t- R_EX_ethso3_e\n", + "\t- R_EX_etoh_e\n", + "\t- R_EX_met__L_e\n", + "\t- R_EX_metsox_R__L_e\n", + "\t- R_EX_metsox_S__L_e\n", + "\t- R_EX_f6p_e\n", + "\t- R_EX_gam6p_e\n", + "\t- R_EX_fald_e\n", + "\t- R_EX_fe2_e\n", + "\t- R_EX_mg2_e\n", + "\t- R_EX_mincyc_e\n", + "\t- R_EX_fe3_e\n", + "\t- R_EX_gbbtn_e\n", + "\t- R_EX_fe3dcit_e\n", + "\t- R_EX_fe3dhbzs_e\n", + "\t- R_EX_minohp_e\n", + "\t- R_EX_mmet_e\n", + "\t- R_EX_mn2_e\n", + "\t- R_EX_mnl_e\n", + "\t- R_EX_fe3hox_e\n", + "\t- R_EX_gdp_e\n", + "\t- R_EX_fe3hox_un_e\n", + "\t- R_EX_fecrm_e\n", + "\t- R_EX_fecrm_un_e\n", + "\t- R_EX_mobd_e\n", + "\t- R_EX_mso3_e\n", + "\t- R_EX_n2o_e\n", + "\t- R_EX_feenter_e\n", + "\t- R_EX_na1_e\n", + "\t- R_EX_glc__D_e\n", + "\t- R_EX_nac_e\n", + "\t- R_EX_nh4_e\n", + "\t- R_EX_feoxam_e\n", + "\t- R_EX_feoxam_un_e\n", + "\t- R_EX_for_e\n", + "\t- R_EX_glcn_e\n", + "\t- R_EX_fru_e\n", + "\t- R_EX_frulys_e\n", + "\t- R_EX_fruur_e\n", + "\t- R_EX_fuc__L_e\n", + "\t- R_EX_ni2_e\n", + "\t- R_EX_nmn_e\n", + "\t- R_EX_no_e\n", + "\t- R_EX_no2_e\n", + "\t- R_EX_no3_e\n", + "\t- R_EX_novbcn_e\n", + "\t- R_EX_glcr_e\n", + "\t- R_EX_o16a4colipa_e\n", + "\t- R_EX_o2_e\n", + "\t- R_EX_o2s_e\n", + "\t- R_EX_tma_e\n", + "\t- R_EX_tmao_e\n", + "\t- R_EX_ocdca_e\n", + "\t- R_EX_glcur_e\n", + "\t- R_EX_ocdcea_e\n", + "\t- R_EX_tre_e\n", + "\t- R_EX_trp__L_e\n", + "\t- R_EX_glcur1p_e\n", + "\t- R_EX_tsul_e\n", + "\t- R_EX_octa_e\n", + "\t- R_EX_orn_e\n", + "\t- R_EX_ttdca_e\n", + "\t- R_EX_orot_e\n", + "\t- R_EX_ttdcea_e\n", + "\t- R_EX_gln__L_e\n", + "\t- R_EX_ttrcyc_e\n", + "\t- R_EX_pacald_e\n", + "\t- R_EX_peamn_e\n", + "\t- R_EX_glu__L_e\n", + "\t- R_EX_phe__L_e\n", + "\t- R_EX_pheme_e\n", + "\t- R_EX_tungs_e\n", + "\t- R_EX_tym_e\n", + "\t- R_EX_tyr__L_e\n", + "\t- R_EX_pi_e\n", + "\t- R_EX_pnto__R_e\n", + "\t- R_EX_ppa_e\n", + "\t- R_EX_ppal_e\n", + "\t- R_EX_tyrp_e\n", + "\t- R_EX_uacgam_e\n", + "\t- R_EX_udpacgal_e\n", + "\t- R_EX_pppn_e\n", + "\t- R_EX_ppt_e\n", + "\t- R_EX_udpg_e\n", + "\t- R_EX_gly_e\n", + "\t- R_EX_udpgal_e\n", + "\t- R_EX_udpglcur_e\n", + "\t- R_EX_pro__L_e\n", + "\t- R_EX_progly_e\n", + "\t- R_EX_psclys_e\n", + "\t- R_EX_glyald_e\n", + "\t- R_EX_pser__L_e\n", + "\t- R_EX_ptrc_e\n", + "\t- R_EX_ump_e\n", + "\t- R_EX_ura_e\n", + "\t- R_EX_glyb_e\n", + "\t- R_EX_urea_e\n", + "\t- R_EX_pydam_e\n", + "\t- R_EX_uri_e\n", + "\t- R_EX_pydx_e\n", + "\t- R_EX_glyc_e\n", + "\t- R_EX_pydxn_e\n", + "\t- R_EX_val__L_e\n", + "\t- R_EX_xan_e\n", + "\t- R_EX_pyr_e\n", + "\t- R_EX_glyc__R_e\n", + "\t- R_EX_quin_e\n", + "\t- R_EX_r5p_e\n", + "\t- R_EX_xmp_e\n", + "\t- R_EX_xtsn_e\n", + "\t- R_EX_xyl__D_e\n", + "\t- R_EX_glyc2p_e\n", + "\t- R_EX_xylu__L_e\n", + "\t- R_EX_zn2_e\n", + "\t- R_EX_rfamp_e\n", + "\t- R_EX_rib__D_e\n", + "\t- R_EX_rmn_e\n", + "\t- R_EX_sbt__D_e\n", + "\t- R_EX_sel_e\n", + "\t- R_EX_ser__D_e\n", + "\t- R_EX_ser__L_e\n", + "\t- R_EX_skm_e\n", + "\t- R_EX_glyc3p_e\n", + "\t- R_EX_slnt_e\n", + "\t- R_EX_so2_e\n", + "\t- R_EX_so3_e\n", + "\t- R_EX_glyclt_e\n", + "\t- R_EX_so4_e\n", + "\t- R_EX_gmp_e\n", + "\t- R_EX_spmd_e\n", + "\t- R_EX_succ_e\n", + "\t- R_EX_gsn_e\n", + "\t- R_EX_sucr_e\n", + "\t- R_EX_sulfac_e\n", + "\t- R_EX_tartr__D_e\n", + "\t- R_EX_tartr__L_e\n", + "\t- R_EX_taur_e\n", + "\t- R_EX_tcynt_e\n", + "\t- R_EX_thm_e\n", + "\t- R_EX_thr__L_e\n", + "\t- R_EX_thrp_e\n", + "\t- R_EX_thym_e\n", + "\t- R_EX_gthox_e\n", + "\t- R_EX_thymd_e\n", + "\t- R_EX_gthrd_e\n", + "\t- R_EX_gtp_e\n", + "\t- R_EX_gua_e\n", + "\t- R_EX_h_e\n", + "\t- R_EX_h2_e\n", + "\t- R_EX_h2o_e\n", + "\t- R_EX_h2o2_e\n", + "\t- R_EX_h2s_e\n", + "\t- R_EX_hacolipa_e\n", + "\t- R_EX_halipa_e\n", + "\t- R_EX_hdca_e\n", + "\t- R_EX_hdcea_e\n", + "\t- R_EX_hg2_e\n", + "\t- R_EX_his__L_e\n", + "\t- R_EX_hom__L_e\n", + "\t- R_EX_hxa_e\n", + "\t- R_EX_hxan_e\n", + "\t- R_EX_idon__L_e\n", + "\t- R_EX_ile__L_e\n", + "\t- R_EX_imp_e\n", + "\t- R_EX_indole_e\n", + "\t- R_EX_inost_e\n", + "\t- R_EX_ins_e\n", + "\t- R_EX_isetac_e\n", + "\t- R_EX_k_e\n", + "\t- R_EX_kdo2lipid4_e\n", + "\t- R_EX_lac__D_e\n", + "\t- R_EX_lac__L_e\n", + "\t- R_EX_lcts_e\n", + "\t- R_EX_leu__L_e\n", + "\t- R_EX_lipa_e\n", + "\t- R_EX_lipa_cold_e\n", + "\t- R_EX_lipoate_e\n", + "\t- R_EX_lys__L_e\n", + "\t- R_EX_lyx__L_e\n", + "\t- R_EX_mal__D_e\n", + "\t- R_EX_mal__L_e\n", + "\t- R_EX_malt_e\n", + "\t- R_EX_malthx_e\n", + "\t- R_EX_maltpt_e\n", + "\t- R_EX_malttr_e\n", + "\t- R_EX_maltttr_e\n", + "\t- R_EX_feroxEfe_e\n", + "\t- R_EX_feroxE_e\n", + "\t- R_EX_pep_e\n", + "\t- R_EX_23dhbzs3_e\n", + "\t- R_EX_tet_e\n", + "\t- R_EX_salchs4_e\n", + "\t- R_EX_tag__D_e\n", + "\t- R_EX_oaa_e\n", + "\t- R_EX_salchs2fe_e\n", + "\t- R_EX_cysi__L_e\n", + "\t- R_EX_fe3dhbzs3_e\n", + "\t- R_EX_drib_e\n", + "\t- R_EX_4hphac_e\n", + "\t- R_EX_feroxBfe_e\n", + "\t- R_EX_salchs4fe_e\n", + "\t- R_EX_2pg_e\n", + "\t- R_EX_4abz_e\n", + "\t- R_EX_acon_C_e\n", + "\t- R_EX_frmd_e\n", + "\t- R_EX_colipaOA_e\n", + "\t- R_EX_salchsx_e\n", + "\t- R_EX_AEP_e\n", + "\t- R_EX_icit_e\n", + "\t- R_EX_salchs2_e\n", + "\t- R_EX_feroxB_e\n", + "\t- R_EX_tcb_e\n", + "\t- R_EX_tton_e\n", + "\t- R_EX_4hthr_e\n", + "\t- R_EX_colipa20Oag_e\n", + "\t- R_EX_remnant1_e\n", + "\t- R_EX_fe_e\n", + "\t- R_EX_kokdolipidA_e\n", + "\t- R_EX_rnam_e\n", + "\t- R_EX_3hoxpac_e\n", + "\t- R_EX_airs_e\n", + "\t- R_EX_4hoxpac_e\n", + "\t- R_EX_dxyl_e\n", + "\t- R_EX_3pg_e\n", + "\t- R_EX_3ntym_e\n", + "\t- R_EX_6apa_e\n", + "\t- R_EX_udcpo4_e\n", + "\t- R_EX_feroxGfe_e\n", + "\t- R_EX_peng_e\n", + "\t- R_EX_udcpo5_e\n", + "\t- R_EX_pac_e\n", + "\t- R_EX_guln__L_e\n", + "\t- R_EX_salcn_e\n", + "\t- R_EX_galam_e\n", + "\t- R_DM_lipidA_core_e_p\n", + "\t- R_EX_raffin_e\n", + "\t- R_EX_o6a4colipa_e\n", + "\t- R_EX_feroxG_e\n", + "\t- R_EX_arab__D_e\n", + "\t- R_EX_diact_e\n", + "\t- R_EX_btd_RR_e\n", + "\t- R_EX_13ppd_e\n", + "\t- R_EX_rbt_e\n", + "\t- R_EX_cellb_e\n", + "\t- R_EX_bz_e\n", + "\t- R_EX_abt__D_e\u001b[0m\n", + "File saved at: ../../data/sbml_corrected/iG2583_1286.xml\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: iAF987\n", + "\u001b[0;93mWARNING : \n", + " - R_BIOMASS_Gm_GS15_core_79p20M: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + " - R_EX_ac_e: Reactants and products exchanged.\n", + " Boundaries was: [-8.88 ; -6.84]\n", + " - R_EX_fe3_e: Reactants and products exchanged.\n", + " Boundaries was: [-67.37 ; -49.21]\n", + "\n", + " - R_HDR3: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + " - R_G3PD2: Reactants and products exchanged.\n", + " Boundaries was: [-999999.0 ; 0.0]\n", + " - R_G3PD: Reactants and products exchanged.\n", + " Boundaries was: [-999999.0 ; 0.0]\n", + "\n", + " - R_NAD_H2: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_HYD4pp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\u001b[0m\n", + "\n", + "\n", + "\u001b[0;36m############################################\n", + "############################################\n", + " WRITING SBML FILE \n", + "############################################\n", + "############################################\n", + "\u001b[0m\n", + "\u001b[0;93mWARNING : IMPORT REACTION REMOVED BY DEFAULT\u001b[0m\n", + "\u001b[0;93mWARNING : If you want to keep import reaction\n", + "use option -kir / --keep-import-reactions\u001b[0m\n", + "\u001b[0;93mWARNING : Reaction with tag reversible modified:\n", + "\t- R_EX_ac_e\n", + "\t- R_EX_fe3_e\n", + "\t- R_G3PD2\n", + "\t- R_G3PD\u001b[0m\n", + "\u001b[0;93mWARNING : Reaction with Reactants and Products exchanged:\n", + "\t- R_EX_ac_e\n", + "\t- R_EX_fe3_e\n", + "\t- R_G3PD2\n", + "\t- R_G3PD\u001b[0m\n", + "\u001b[0;93mWARNING : Import reaction removed:\n", + "\t- R_EX_gm1lipb_e\n", + "\t- R_EX_gm2lipa_e\n", + "\t- R_EX_gua_e\n", + "\t- R_EX_h_e\n", + "\t- R_EX_h2_e\n", + "\t- R_EX_h2o_e\n", + "\t- R_EX_hdca_e\n", + "\t- R_EX_hdcea_e\n", + "\t- R_EX_hxan_e\n", + "\t- R_EX_ibt_e\n", + "\t- R_EX_ile__L_e\n", + "\t- R_EX_isetac_e\n", + "\t- R_EX_k_e\n", + "\t- R_EX_gm1lipa_e\n", + "\t- R_EX_kdo2lipid4_e\n", + "\t- R_DM_5drib_c\n", + "\t- R_DM_amob_c\n", + "\t- R_EX_kdo2lipid4b_e\n", + "\t- R_EX_leu__L_e\n", + "\t- R_EX_lipa_e\n", + "\t- R_EX_2obut_e\n", + "\t- R_EX_lipb_e\n", + "\t- R_EX_3httdca_e\n", + "\t- R_EX_3mb_e\n", + "\t- R_EX_4crsol_e\n", + "\t- R_EX_4hba_e\n", + "\t- R_EX_4hbald_e\n", + "\t- R_EX_lys__L_e\n", + "\t- R_EX_4hbz_e\n", + "\t- R_EX_meoh_e\n", + "\t- R_EX_Lcyst_e\n", + "\t- R_EX_mg2_e\n", + "\t- R_EX_ac_e\n", + "\t- R_EX_aso4_e\n", + "\t- R_EX_btoh_e\n", + "\t- R_EX_but_e\n", + "\t- R_EX_butso3_e\n", + "\t- R_EX_mn2_e\n", + "\t- R_EX_bz_e\n", + "\t- R_EX_bzal_e\n", + "\t- R_EX_bzalc_e\n", + "\t- R_EX_ca2_e\n", + "\t- R_EX_cd2_e\n", + "\t- R_EX_mn4_e\n", + "\t- R_EX_cl_e\n", + "\t- R_EX_co2_e\n", + "\t- R_EX_cobalt2_e\n", + "\t- R_EX_cro4_e\n", + "\t- R_EX_cu_e\n", + "\t- R_EX_cu2_e\n", + "\t- R_EX_ddca_e\n", + "\t- R_EX_mobd_e\n", + "\t- R_EX_elao_e\n", + "\t- R_EX_elar_e\n", + "\t- R_EX_elco_e\n", + "\t- R_EX_elcr_e\n", + "\t- R_EX_mso3_e\n", + "\t- R_EX_ethso3_e\n", + "\t- R_EX_etoh_e\n", + "\t- R_EX_fe2_e\n", + "\t- R_EX_fe3_e\n", + "\t- R_EX_n2_e\n", + "\t- R_EX_for_e\n", + "\t- R_EX_na1_e\n", + "\t- R_EX_galur_e\n", + "\t- R_EX_nh4_e\n", + "\t- R_EX_glcur_e\n", + "\t- R_EX_glyc_e\n", + "\t- R_EX_ni2_e\n", + "\t- R_EX_no2_e\n", + "\t- R_EX_no3_e\n", + "\t- R_EX_ocdca_e\n", + "\t- R_EX_ocdcea_e\n", + "\t- R_EX_oxa_e\n", + "\t- R_EX_phenol_e\n", + "\t- R_EX_pi_e\n", + "\t- R_EX_ppa_e\n", + "\t- R_EX_ppoh_e\n", + "\t- R_EX_pta_e\n", + "\t- R_EX_pyr_e\n", + "\t- R_EX_so3_e\n", + "\t- R_EX_so4_e\n", + "\t- R_EX_sulfac_e\n", + "\t- R_EX_taur_e\n", + "\t- R_EX_tc4_e\n", + "\t- R_EX_tc7_e\n", + "\t- R_EX_tol_e\n", + "\t- R_EX_tsul_e\n", + "\t- R_EX_ttdca_e\n", + "\t- R_EX_ttdcea_e\n", + "\t- R_EX_tungs_e\n", + "\t- R_EX_u4_e\n", + "\t- R_EX_u6_e\n", + "\t- R_EX_v4_e\n", + "\t- R_EX_v5_e\n", + "\t- R_EX_val__L_e\n", + "\t- R_EX_zn2_e\u001b[0m\n", + "File saved at: ../../data/sbml_corrected/iAF987.xml\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: iECO111_1330\n", + "\u001b[0;93mWARNING : \n", + " - R_DHPTDNR: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_DHPTDNRN: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_CAT: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_FHL: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCTARTtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SPODM: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SPODMpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCASPtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCFUMtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCMALtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_EX_chitob_e: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_CHITOBpts: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_CHITPH: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_URCN: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\u001b[0m\n", + "\n", + "\n", + "\u001b[0;36m############################################\n", + "############################################\n", + " WRITING SBML FILE \n", + "############################################\n", + "############################################\n", + "\u001b[0m\n", + "\u001b[0;93mWARNING : IMPORT REACTION REMOVED BY DEFAULT\u001b[0m\n", + "\u001b[0;93mWARNING : If you want to keep import reaction\n", + "use option -kir / --keep-import-reactions\u001b[0m\n", + "\u001b[0;93mWARNING : Import reaction removed:\n", + "\t- R_EX_cit_e\n", + "\t- R_EX_cl_e\n", + "\t- R_EX_cm_e\n", + "\t- R_EX_cmp_e\n", + "\t- R_EX_co2_e\n", + "\t- R_EX_cobalt2_e\n", + "\t- R_EX_colipa_e\n", + "\t- R_EX_colipap_e\n", + "\t- R_EX_cpgn_e\n", + "\t- R_EX_cpgn_un_e\n", + "\t- R_EX_crn_e\n", + "\t- R_EX_crn__D_e\n", + "\t- R_EX_gbbtn_e\n", + "\t- R_EX_csn_e\n", + "\t- R_EX_gdp_e\n", + "\t- R_EX_cu_e\n", + "\t- R_EX_glc__D_e\n", + "\t- R_EX_glcn_e\n", + "\t- R_EX_cu2_e\n", + "\t- R_EX_glcr_e\n", + "\t- R_EX_glcur_e\n", + "\t- R_DM_4crsol_c\n", + "\t- R_EX_glcur1p_e\n", + "\t- R_DM_5drib_c\n", + "\t- R_EX_cyan_e\n", + "\t- R_DM_aacald_c\n", + "\t- R_EX_gln__L_e\n", + "\t- R_EX_glu__L_e\n", + "\t- R_EX_cynt_e\n", + "\t- R_EX_gly_e\n", + "\t- R_DM_amob_c\n", + "\t- R_DM_mththf_c\n", + "\t- R_EX_glyald_e\n", + "\t- R_EX_cys__D_e\n", + "\t- R_EX_glyb_e\n", + "\t- R_EX_glyc_e\n", + "\t- R_EX_glyc__R_e\n", + "\t- R_EX_glyc2p_e\n", + "\t- R_EX_glyc3p_e\n", + "\t- R_EX_glyclt_e\n", + "\t- R_EX_gmp_e\n", + "\t- R_EX_gsn_e\n", + "\t- R_EX_gthox_e\n", + "\t- R_DM_oxam_c\n", + "\t- R_EX_12ppd__R_e\n", + "\t- R_EX_12ppd__S_e\n", + "\t- R_EX_14glucan_e\n", + "\t- R_EX_15dap_e\n", + "\t- R_EX_23camp_e\n", + "\t- R_EX_23ccmp_e\n", + "\t- R_EX_23cgmp_e\n", + "\t- R_EX_23cump_e\n", + "\t- R_EX_gthrd_e\n", + "\t- R_EX_gtp_e\n", + "\t- R_EX_gua_e\n", + "\t- R_EX_h_e\n", + "\t- R_EX_23dappa_e\n", + "\t- R_EX_26dap__M_e\n", + "\t- R_EX_h2_e\n", + "\t- R_EX_cys__L_e\n", + "\t- R_EX_h2o_e\n", + "\t- R_EX_h2o2_e\n", + "\t- R_EX_2ddglcn_e\n", + "\t- R_EX_34dhpac_e\n", + "\t- R_EX_3amp_e\n", + "\t- R_EX_h2s_e\n", + "\t- R_EX_hacolipa_e\n", + "\t- R_EX_3cmp_e\n", + "\t- R_EX_cytd_e\n", + "\t- R_EX_3gmp_e\n", + "\t- R_EX_3hcinnm_e\n", + "\t- R_EX_3hpp_e\n", + "\t- R_EX_3hpppn_e\n", + "\t- R_EX_halipa_e\n", + "\t- R_EX_hdca_e\n", + "\t- R_EX_hdcea_e\n", + "\t- R_EX_hg2_e\n", + "\t- R_EX_his__L_e\n", + "\t- R_EX_hom__L_e\n", + "\t- R_EX_3ump_e\n", + "\t- R_EX_4abut_e\n", + "\t- R_EX_4hoxpacd_e\n", + "\t- R_EX_hxa_e\n", + "\t- R_EX_5dglcn_e\n", + "\t- R_EX_dad_2_e\n", + "\t- R_EX_5mtr_e\n", + "\t- R_EX_LalaDglu_e\n", + "\t- R_EX_LalaDgluMdap_e\n", + "\t- R_EX_hxan_e\n", + "\t- R_EX_damp_e\n", + "\t- R_EX_LalaDgluMdapDala_e\n", + "\t- R_EX_LalaLglu_e\n", + "\t- R_EX_ac_e\n", + "\t- R_EX_dca_e\n", + "\t- R_EX_acac_e\n", + "\t- R_EX_acald_e\n", + "\t- R_EX_idon__L_e\n", + "\t- R_EX_dcmp_e\n", + "\t- R_EX_ile__L_e\n", + "\t- R_EX_imp_e\n", + "\t- R_EX_acgal_e\n", + "\t- R_EX_indole_e\n", + "\t- R_EX_dcyt_e\n", + "\t- R_EX_inost_e\n", + "\t- R_EX_ins_e\n", + "\t- R_EX_acgal1p_e\n", + "\t- R_EX_acgam_e\n", + "\t- R_EX_acgam1p_e\n", + "\t- R_EX_acmana_e\n", + "\t- R_EX_acmum_e\n", + "\t- R_EX_isetac_e\n", + "\t- R_EX_k_e\n", + "\t- R_EX_kdo2lipid4_e\n", + "\t- R_EX_lac__D_e\n", + "\t- R_EX_acnam_e\n", + "\t- R_EX_acolipa_e\n", + "\t- R_EX_ddca_e\n", + "\t- R_EX_acser_e\n", + "\t- R_EX_ade_e\n", + "\t- R_EX_lac__L_e\n", + "\t- R_EX_lcts_e\n", + "\t- R_EX_leu__L_e\n", + "\t- R_EX_dgmp_e\n", + "\t- R_EX_lipa_e\n", + "\t- R_EX_adn_e\n", + "\t- R_EX_dgsn_e\n", + "\t- R_EX_adocbl_e\n", + "\t- R_EX_ag_e\n", + "\t- R_EX_lipa_cold_e\n", + "\t- R_EX_lipoate_e\n", + "\t- R_EX_lys__L_e\n", + "\t- R_EX_lyx__L_e\n", + "\t- R_EX_agm_e\n", + "\t- R_EX_akg_e\n", + "\t- R_EX_ala_B_e\n", + "\t- R_EX_mal__D_e\n", + "\t- R_EX_dha_e\n", + "\t- R_EX_mal__L_e\n", + "\t- R_EX_malt_e\n", + "\t- R_EX_ala__D_e\n", + "\t- R_EX_ala__L_e\n", + "\t- R_EX_malthx_e\n", + "\t- R_EX_alaala_e\n", + "\t- R_EX_dimp_e\n", + "\t- R_EX_all__D_e\n", + "\t- R_EX_alltn_e\n", + "\t- R_EX_amp_e\n", + "\t- R_EX_anhgm_e\n", + "\t- R_EX_maltpt_e\n", + "\t- R_EX_malttr_e\n", + "\t- R_EX_maltttr_e\n", + "\t- R_EX_man_e\n", + "\t- R_EX_man6p_e\n", + "\t- R_EX_manglyc_e\n", + "\t- R_EX_melib_e\n", + "\t- R_EX_arab__L_e\n", + "\t- R_EX_arbt_e\n", + "\t- R_EX_arbtn_e\n", + "\t- R_EX_meoh_e\n", + "\t- R_EX_din_e\n", + "\t- R_EX_met__D_e\n", + "\t- R_EX_met__L_e\n", + "\t- R_EX_metsox_R__L_e\n", + "\t- R_EX_metsox_S__L_e\n", + "\t- R_EX_arbtn_fe3_e\n", + "\t- R_EX_arg__L_e\n", + "\t- R_EX_ascb__L_e\n", + "\t- R_EX_asn__L_e\n", + "\t- R_EX_aso3_e\n", + "\t- R_EX_mg2_e\n", + "\t- R_EX_mincyc_e\n", + "\t- R_EX_asp__L_e\n", + "\t- R_EX_minohp_e\n", + "\t- R_EX_btn_e\n", + "\t- R_EX_dms_e\n", + "\t- R_EX_but_e\n", + "\t- R_EX_butso3_e\n", + "\t- R_EX_mmet_e\n", + "\t- R_EX_mn2_e\n", + "\t- R_EX_mnl_e\n", + "\t- R_EX_dmso_e\n", + "\t- R_EX_mobd_e\n", + "\t- R_EX_ca2_e\n", + "\t- R_EX_cbi_e\n", + "\t- R_EX_cbl1_e\n", + "\t- R_EX_cd2_e\n", + "\t- R_EX_mso3_e\n", + "\t- R_EX_n2o_e\n", + "\t- R_EX_na1_e\n", + "\t- R_EX_nac_e\n", + "\t- R_EX_cgly_e\n", + "\t- R_EX_dopa_e\n", + "\t- R_EX_chol_e\n", + "\t- R_EX_chtbs_e\n", + "\t- R_EX_nh4_e\n", + "\t- R_EX_ni2_e\n", + "\t- R_EX_nmn_e\n", + "\t- R_EX_no_e\n", + "\t- R_EX_no2_e\n", + "\t- R_EX_doxrbcn_e\n", + "\t- R_EX_no3_e\n", + "\t- R_EX_dtmp_e\n", + "\t- R_EX_novbcn_e\n", + "\t- R_EX_o16a4colipa_e\n", + "\t- R_EX_dump_e\n", + "\t- R_EX_o2_e\n", + "\t- R_EX_duri_e\n", + "\t- R_EX_o2s_e\n", + "\t- R_EX_ocdca_e\n", + "\t- R_EX_urea_e\n", + "\t- R_EX_eca4colipa_e\n", + "\t- R_EX_uri_e\n", + "\t- R_EX_ocdcea_e\n", + "\t- R_EX_octa_e\n", + "\t- R_EX_orn_e\n", + "\t- R_EX_val__L_e\n", + "\t- R_EX_enlipa_e\n", + "\t- R_EX_xan_e\n", + "\t- R_EX_xmp_e\n", + "\t- R_EX_xtsn_e\n", + "\t- R_EX_orot_e\n", + "\t- R_EX_pacald_e\n", + "\t- R_EX_peamn_e\n", + "\t- R_EX_phe__L_e\n", + "\t- R_EX_pheme_e\n", + "\t- R_EX_pi_e\n", + "\t- R_EX_xyl__D_e\n", + "\t- R_EX_xylu__L_e\n", + "\t- R_EX_zn2_e\n", + "\t- R_EX_pnto__R_e\n", + "\t- R_EX_ppa_e\n", + "\t- R_EX_ppal_e\n", + "\t- R_EX_enter_e\n", + "\t- R_EX_pppn_e\n", + "\t- R_EX_ppt_e\n", + "\t- R_EX_pro__L_e\n", + "\t- R_EX_progly_e\n", + "\t- R_EX_psclys_e\n", + "\t- R_EX_etha_e\n", + "\t- R_EX_pser__L_e\n", + "\t- R_EX_ptrc_e\n", + "\t- R_EX_pydam_e\n", + "\t- R_EX_ethso3_e\n", + "\t- R_EX_pydx_e\n", + "\t- R_EX_pydxn_e\n", + "\t- R_EX_pyr_e\n", + "\t- R_EX_quin_e\n", + "\t- R_EX_r5p_e\n", + "\t- R_EX_rfamp_e\n", + "\t- R_EX_rib__D_e\n", + "\t- R_EX_rmn_e\n", + "\t- R_EX_sbt__D_e\n", + "\t- R_EX_sel_e\n", + "\t- R_EX_etoh_e\n", + "\t- R_EX_ser__D_e\n", + "\t- R_EX_ser__L_e\n", + "\t- R_EX_skm_e\n", + "\t- R_EX_slnt_e\n", + "\t- R_EX_so2_e\n", + "\t- R_EX_so3_e\n", + "\t- R_EX_f6p_e\n", + "\t- R_EX_so4_e\n", + "\t- R_EX_spmd_e\n", + "\t- R_EX_succ_e\n", + "\t- R_EX_sucr_e\n", + "\t- R_EX_sulfac_e\n", + "\t- R_EX_tartr__D_e\n", + "\t- R_EX_tartr__L_e\n", + "\t- R_EX_fald_e\n", + "\t- R_EX_taur_e\n", + "\t- R_EX_tcynt_e\n", + "\t- R_EX_thm_e\n", + "\t- R_EX_fe2_e\n", + "\t- R_EX_thr__L_e\n", + "\t- R_EX_thrp_e\n", + "\t- R_EX_fe3_e\n", + "\t- R_EX_thym_e\n", + "\t- R_EX_thymd_e\n", + "\t- R_EX_fe3dcit_e\n", + "\t- R_EX_tma_e\n", + "\t- R_EX_tmao_e\n", + "\t- R_EX_tre_e\n", + "\t- R_EX_fe3dhbzs_e\n", + "\t- R_EX_fe3hox_e\n", + "\t- R_EX_trp__L_e\n", + "\t- R_EX_tsul_e\n", + "\t- R_EX_ttdca_e\n", + "\t- R_EX_ttdcea_e\n", + "\t- R_EX_ttrcyc_e\n", + "\t- R_EX_tungs_e\n", + "\t- R_EX_tym_e\n", + "\t- R_EX_tyr__L_e\n", + "\t- R_EX_fe3hox_un_e\n", + "\t- R_EX_tyrp_e\n", + "\t- R_EX_uacgam_e\n", + "\t- R_EX_udpacgal_e\n", + "\t- R_EX_udpg_e\n", + "\t- R_EX_udpgal_e\n", + "\t- R_EX_fecrm_e\n", + "\t- R_EX_udpglcur_e\n", + "\t- R_EX_ump_e\n", + "\t- R_EX_ura_e\n", + "\t- R_EX_fecrm_un_e\n", + "\t- R_EX_feenter_e\n", + "\t- R_EX_feoxam_e\n", + "\t- R_EX_feoxam_un_e\n", + "\t- R_EX_for_e\n", + "\t- R_EX_fru_e\n", + "\t- R_EX_frulys_e\n", + "\t- R_EX_fruur_e\n", + "\t- R_EX_fuc__L_e\n", + "\t- R_EX_fum_e\n", + "\t- R_EX_fusa_e\n", + "\t- R_EX_g1p_e\n", + "\t- R_EX_g3pc_e\n", + "\t- R_EX_g3pe_e\n", + "\t- R_EX_g3pg_e\n", + "\t- R_EX_g3pi_e\n", + "\t- R_EX_g3ps_e\n", + "\t- R_EX_g6p_e\n", + "\t- R_EX_gal_e\n", + "\t- R_EX_gal_bD_e\n", + "\t- R_EX_gal1p_e\n", + "\t- R_EX_galct__D_e\n", + "\t- R_EX_galctn__D_e\n", + "\t- R_EX_galctn__L_e\n", + "\t- R_EX_galt_e\n", + "\t- R_EX_galur_e\n", + "\t- R_EX_gam_e\n", + "\t- R_EX_gam6p_e\n", + "\t- R_EX_salchsx_e\n", + "\t- R_EX_AEP_e\n", + "\t- R_EX_pac_e\n", + "\t- R_EX_peng_e\n", + "\t- R_EX_feroxB_e\n", + "\t- R_EX_tcb_e\n", + "\t- R_EX_tton_e\n", + "\t- R_EX_4hthr_e\n", + "\t- R_EX_feroxBfe_e\n", + "\t- R_EX_4hphac_e\n", + "\t- R_EX_feroxEfe_e\n", + "\t- R_EX_feroxE_e\n", + "\t- R_EX_23dhbzs3_e\n", + "\t- R_EX_4abz_e\n", + "\t- R_EX_tet_e\n", + "\t- R_EX_acon_C_e\n", + "\t- R_EX_salchs4_e\n", + "\t- R_EX_tag__D_e\n", + "\t- R_EX_icit_e\n", + "\t- R_EX_pep_e\n", + "\t- R_EX_oaa_e\n", + "\t- R_EX_salchs2fe_e\n", + "\t- R_EX_colipa20Oag_e\n", + "\t- R_EX_cysi__L_e\n", + "\t- R_EX_remnant1_e\n", + "\t- R_EX_fe3dhbzs3_e\n", + "\t- R_EX_feroxGfe_e\n", + "\t- R_EX_drib_e\n", + "\t- R_EX_salchs2_e\n", + "\t- R_EX_salchs4fe_e\n", + "\t- R_EX_2pg_e\n", + "\t- R_EX_frmd_e\n", + "\t- R_EX_colipaOA_e\n", + "\t- R_EX_rnam_e\n", + "\t- R_EX_airs_e\n", + "\t- R_EX_dxyl_e\n", + "\t- R_EX_3pg_e\n", + "\t- R_EX_udcpo4_e\n", + "\t- R_EX_btd_RR_e\n", + "\t- R_EX_diact_e\n", + "\t- R_EX_rbt_e\n", + "\t- R_EX_cellb_e\n", + "\t- R_EX_udcpo5_e\n", + "\t- R_EX_guln__L_e\n", + "\t- R_EX_feroxG_e\n", + "\t- R_EX_salcn_e\n", + "\t- R_EX_bz_e\n", + "\t- R_EX_13ppd_e\n", + "\t- R_EX_abt__D_e\n", + "\t- R_EX_fe_e\n", + "\t- R_EX_kokdolipidA_e\n", + "\t- R_EX_3hoxpac_e\n", + "\t- R_EX_galam_e\n", + "\t- R_EX_4hoxpac_e\n", + "\t- R_EX_3ntym_e\n", + "\t- R_EX_6apa_e\n", + "\t- R_EX_raffin_e\n", + "\t- R_EX_o6a4colipa_e\n", + "\t- R_EX_arab__D_e\n", + "\t- R_DM_lipidA_core_e_p\u001b[0m\n", + "File saved at: ../../data/sbml_corrected/iECO111_1330.xml\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: iECUMN_1333\n", + "\u001b[0;93mWARNING : \n", + " - R_CAT: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_DHPTDNR: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_DHPTDNRN: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_FHL: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SPODM: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SPODMpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCASPtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCFUMtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCMALtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCTARTtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_EX_chitob_e: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_CHITOBpts: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_CHITPH: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_URCN: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\u001b[0m\n", + "\n", + "\n", + "\u001b[0;36m############################################\n", + "############################################\n", + " WRITING SBML FILE \n", + "############################################\n", + "############################################\n", + "\u001b[0m\n", + "\u001b[0;93mWARNING : IMPORT REACTION REMOVED BY DEFAULT\u001b[0m\n", + "\u001b[0;93mWARNING : If you want to keep import reaction\n", + "use option -kir / --keep-import-reactions\u001b[0m\n", + "\u001b[0;93mWARNING : Import reaction removed:\n", + "\t- R_EX_asn__L_e\n", + "\t- R_EX_asp__L_e\n", + "\t- R_EX_btn_e\n", + "\t- R_EX_arbtn_fe3_e\n", + "\t- R_EX_aso3_e\n", + "\t- R_EX_ascb__L_e\n", + "\t- R_EX_but_e\n", + "\t- R_EX_arg__L_e\n", + "\t- R_EX_butso3_e\n", + "\t- R_EX_ca2_e\n", + "\t- R_DM_4crsol_c\n", + "\t- R_EX_cbi_e\n", + "\t- R_DM_5drib_c\n", + "\t- R_DM_aacald_c\n", + "\t- R_EX_gal_bD_e\n", + "\t- R_EX_gal1p_e\n", + "\t- R_EX_galct__D_e\n", + "\t- R_EX_cbl1_e\n", + "\t- R_EX_galctn__D_e\n", + "\t- R_DM_amob_c\n", + "\t- R_DM_mththf_c\n", + "\t- R_DM_oxam_c\n", + "\t- R_EX_galctn__L_e\n", + "\t- R_EX_cd2_e\n", + "\t- R_EX_galt_e\n", + "\t- R_EX_cgly_e\n", + "\t- R_EX_12ppd__R_e\n", + "\t- R_EX_12ppd__S_e\n", + "\t- R_EX_galur_e\n", + "\t- R_EX_gam_e\n", + "\t- R_EX_gam6p_e\n", + "\t- R_EX_gbbtn_e\n", + "\t- R_EX_gdp_e\n", + "\t- R_EX_glc__D_e\n", + "\t- R_EX_glcn_e\n", + "\t- R_EX_glcr_e\n", + "\t- R_EX_glcur_e\n", + "\t- R_EX_glcur1p_e\n", + "\t- R_EX_gln__L_e\n", + "\t- R_EX_glu__L_e\n", + "\t- R_EX_gly_e\n", + "\t- R_EX_14glucan_e\n", + "\t- R_EX_15dap_e\n", + "\t- R_EX_23camp_e\n", + "\t- R_EX_23ccmp_e\n", + "\t- R_EX_23cgmp_e\n", + "\t- R_EX_23cump_e\n", + "\t- R_EX_23dappa_e\n", + "\t- R_EX_26dap__M_e\n", + "\t- R_EX_2ddglcn_e\n", + "\t- R_EX_34dhpac_e\n", + "\t- R_EX_3amp_e\n", + "\t- R_EX_3cmp_e\n", + "\t- R_EX_3gmp_e\n", + "\t- R_EX_3hcinnm_e\n", + "\t- R_EX_3hpp_e\n", + "\t- R_EX_3hpppn_e\n", + "\t- R_EX_glyald_e\n", + "\t- R_EX_glyb_e\n", + "\t- R_EX_glyc_e\n", + "\t- R_EX_glyc__R_e\n", + "\t- R_EX_glyc2p_e\n", + "\t- R_EX_3ump_e\n", + "\t- R_EX_glyc3p_e\n", + "\t- R_EX_4abut_e\n", + "\t- R_EX_glyclt_e\n", + "\t- R_EX_4hoxpacd_e\n", + "\t- R_EX_chol_e\n", + "\t- R_EX_5dglcn_e\n", + "\t- R_EX_5mtr_e\n", + "\t- R_EX_LalaDglu_e\n", + "\t- R_EX_LalaDgluMdap_e\n", + "\t- R_EX_LalaDgluMdapDala_e\n", + "\t- R_EX_LalaLglu_e\n", + "\t- R_EX_gmp_e\n", + "\t- R_EX_gsn_e\n", + "\t- R_EX_gthox_e\n", + "\t- R_EX_gthrd_e\n", + "\t- R_EX_gtp_e\n", + "\t- R_EX_gua_e\n", + "\t- R_EX_h_e\n", + "\t- R_EX_h2_e\n", + "\t- R_EX_ac_e\n", + "\t- R_EX_acac_e\n", + "\t- R_EX_acald_e\n", + "\t- R_EX_h2o_e\n", + "\t- R_EX_acgal_e\n", + "\t- R_EX_h2o2_e\n", + "\t- R_EX_chtbs_e\n", + "\t- R_EX_h2s_e\n", + "\t- R_EX_hacolipa_e\n", + "\t- R_EX_acgal1p_e\n", + "\t- R_EX_acgam_e\n", + "\t- R_EX_acgam1p_e\n", + "\t- R_EX_acmana_e\n", + "\t- R_EX_halipa_e\n", + "\t- R_EX_cit_e\n", + "\t- R_EX_hdca_e\n", + "\t- R_EX_hdcea_e\n", + "\t- R_EX_hg2_e\n", + "\t- R_EX_acmum_e\n", + "\t- R_EX_acnam_e\n", + "\t- R_EX_acolipa_e\n", + "\t- R_EX_his__L_e\n", + "\t- R_EX_acser_e\n", + "\t- R_EX_cl_e\n", + "\t- R_EX_ade_e\n", + "\t- R_EX_adn_e\n", + "\t- R_EX_adocbl_e\n", + "\t- R_EX_ag_e\n", + "\t- R_EX_agm_e\n", + "\t- R_EX_hom__L_e\n", + "\t- R_EX_hxa_e\n", + "\t- R_EX_hxan_e\n", + "\t- R_EX_idon__L_e\n", + "\t- R_EX_ile__L_e\n", + "\t- R_EX_imp_e\n", + "\t- R_EX_akg_e\n", + "\t- R_EX_ala_B_e\n", + "\t- R_EX_indole_e\n", + "\t- R_EX_ala__D_e\n", + "\t- R_EX_inost_e\n", + "\t- R_EX_cm_e\n", + "\t- R_EX_ins_e\n", + "\t- R_EX_isetac_e\n", + "\t- R_EX_ala__L_e\n", + "\t- R_EX_alaala_e\n", + "\t- R_EX_all__D_e\n", + "\t- R_EX_alltn_e\n", + "\t- R_EX_k_e\n", + "\t- R_EX_amp_e\n", + "\t- R_EX_kdo2lipid4_e\n", + "\t- R_EX_cmp_e\n", + "\t- R_EX_lac__D_e\n", + "\t- R_EX_anhgm_e\n", + "\t- R_EX_arab__L_e\n", + "\t- R_EX_arbt_e\n", + "\t- R_EX_co2_e\n", + "\t- R_EX_arbtn_e\n", + "\t- R_EX_lac__L_e\n", + "\t- R_EX_lcts_e\n", + "\t- R_EX_leu__L_e\n", + "\t- R_EX_cobalt2_e\n", + "\t- R_EX_lipa_e\n", + "\t- R_EX_lipa_cold_e\n", + "\t- R_EX_lipoate_e\n", + "\t- R_EX_lys__L_e\n", + "\t- R_EX_lyx__L_e\n", + "\t- R_EX_mal__D_e\n", + "\t- R_EX_mincyc_e\n", + "\t- R_EX_minohp_e\n", + "\t- R_EX_mmet_e\n", + "\t- R_EX_mn2_e\n", + "\t- R_EX_mnl_e\n", + "\t- R_EX_mal__L_e\n", + "\t- R_EX_malt_e\n", + "\t- R_EX_colipa_e\n", + "\t- R_EX_malthx_e\n", + "\t- R_EX_maltpt_e\n", + "\t- R_EX_mobd_e\n", + "\t- R_EX_mso3_e\n", + "\t- R_EX_colipap_e\n", + "\t- R_EX_n2o_e\n", + "\t- R_EX_malttr_e\n", + "\t- R_EX_maltttr_e\n", + "\t- R_EX_man_e\n", + "\t- R_EX_na1_e\n", + "\t- R_EX_cpgn_e\n", + "\t- R_EX_nac_e\n", + "\t- R_EX_man6p_e\n", + "\t- R_EX_manglyc_e\n", + "\t- R_EX_nh4_e\n", + "\t- R_EX_cpgn_un_e\n", + "\t- R_EX_ni2_e\n", + "\t- R_EX_nmn_e\n", + "\t- R_EX_melib_e\n", + "\t- R_EX_meoh_e\n", + "\t- R_EX_met__D_e\n", + "\t- R_EX_no_e\n", + "\t- R_EX_no2_e\n", + "\t- R_EX_met__L_e\n", + "\t- R_EX_no3_e\n", + "\t- R_EX_novbcn_e\n", + "\t- R_EX_o16a4colipa_e\n", + "\t- R_EX_o2_e\n", + "\t- R_EX_o2s_e\n", + "\t- R_EX_metsox_R__L_e\n", + "\t- R_EX_metsox_S__L_e\n", + "\t- R_EX_mg2_e\n", + "\t- R_EX_ocdca_e\n", + "\t- R_EX_crn_e\n", + "\t- R_EX_ocdcea_e\n", + "\t- R_EX_octa_e\n", + "\t- R_EX_orn_e\n", + "\t- R_EX_tyr__L_e\n", + "\t- R_EX_tyrp_e\n", + "\t- R_EX_uacgam_e\n", + "\t- R_EX_orot_e\n", + "\t- R_EX_crn__D_e\n", + "\t- R_EX_pacald_e\n", + "\t- R_EX_udpacgal_e\n", + "\t- R_EX_udpg_e\n", + "\t- R_EX_udpgal_e\n", + "\t- R_EX_peamn_e\n", + "\t- R_EX_udpglcur_e\n", + "\t- R_EX_csn_e\n", + "\t- R_EX_ump_e\n", + "\t- R_EX_ura_e\n", + "\t- R_EX_phe__L_e\n", + "\t- R_EX_pheme_e\n", + "\t- R_EX_urea_e\n", + "\t- R_EX_pi_e\n", + "\t- R_EX_uri_e\n", + "\t- R_EX_cu_e\n", + "\t- R_EX_val__L_e\n", + "\t- R_EX_xan_e\n", + "\t- R_EX_pnto__R_e\n", + "\t- R_EX_ppa_e\n", + "\t- R_EX_xmp_e\n", + "\t- R_EX_ppal_e\n", + "\t- R_EX_pppn_e\n", + "\t- R_EX_ppt_e\n", + "\t- R_EX_xtsn_e\n", + "\t- R_EX_xyl__D_e\n", + "\t- R_EX_xylu__L_e\n", + "\t- R_EX_zn2_e\n", + "\t- R_EX_pro__L_e\n", + "\t- R_EX_progly_e\n", + "\t- R_EX_cu2_e\n", + "\t- R_EX_psclys_e\n", + "\t- R_EX_pser__L_e\n", + "\t- R_EX_ptrc_e\n", + "\t- R_EX_pydam_e\n", + "\t- R_EX_pydx_e\n", + "\t- R_EX_cyan_e\n", + "\t- R_EX_pydxn_e\n", + "\t- R_EX_pyr_e\n", + "\t- R_EX_quin_e\n", + "\t- R_EX_cynt_e\n", + "\t- R_EX_r5p_e\n", + "\t- R_EX_rfamp_e\n", + "\t- R_EX_rib__D_e\n", + "\t- R_EX_rmn_e\n", + "\t- R_EX_sbt__D_e\n", + "\t- R_EX_sel_e\n", + "\t- R_EX_ser__D_e\n", + "\t- R_EX_ser__L_e\n", + "\t- R_EX_skm_e\n", + "\t- R_EX_cys__D_e\n", + "\t- R_EX_slnt_e\n", + "\t- R_EX_so2_e\n", + "\t- R_EX_so3_e\n", + "\t- R_EX_cys__L_e\n", + "\t- R_EX_so4_e\n", + "\t- R_EX_spmd_e\n", + "\t- R_EX_succ_e\n", + "\t- R_EX_sucr_e\n", + "\t- R_EX_cytd_e\n", + "\t- R_EX_sulfac_e\n", + "\t- R_EX_tartr__D_e\n", + "\t- R_EX_tartr__L_e\n", + "\t- R_EX_taur_e\n", + "\t- R_EX_tcynt_e\n", + "\t- R_EX_thm_e\n", + "\t- R_EX_thr__L_e\n", + "\t- R_EX_thrp_e\n", + "\t- R_EX_thym_e\n", + "\t- R_EX_thymd_e\n", + "\t- R_EX_dad_2_e\n", + "\t- R_EX_tma_e\n", + "\t- R_EX_tmao_e\n", + "\t- R_EX_tre_e\n", + "\t- R_EX_damp_e\n", + "\t- R_EX_trp__L_e\n", + "\t- R_EX_tsul_e\n", + "\t- R_EX_ttdca_e\n", + "\t- R_EX_dca_e\n", + "\t- R_EX_ttdcea_e\n", + "\t- R_EX_dcmp_e\n", + "\t- R_EX_ttrcyc_e\n", + "\t- R_EX_tungs_e\n", + "\t- R_EX_tym_e\n", + "\t- R_EX_dcyt_e\n", + "\t- R_EX_ddca_e\n", + "\t- R_EX_dgmp_e\n", + "\t- R_EX_dgsn_e\n", + "\t- R_EX_dha_e\n", + "\t- R_EX_dimp_e\n", + "\t- R_EX_din_e\n", + "\t- R_EX_dms_e\n", + "\t- R_EX_dmso_e\n", + "\t- R_EX_dopa_e\n", + "\t- R_EX_doxrbcn_e\n", + "\t- R_EX_dtmp_e\n", + "\t- R_EX_dump_e\n", + "\t- R_EX_duri_e\n", + "\t- R_EX_eca4colipa_e\n", + "\t- R_EX_enlipa_e\n", + "\t- R_EX_enter_e\n", + "\t- R_EX_etha_e\n", + "\t- R_EX_ethso3_e\n", + "\t- R_EX_etoh_e\n", + "\t- R_EX_f6p_e\n", + "\t- R_EX_fald_e\n", + "\t- R_EX_fe2_e\n", + "\t- R_EX_fe3_e\n", + "\t- R_EX_fe3dcit_e\n", + "\t- R_EX_fe3dhbzs_e\n", + "\t- R_EX_fe3hox_e\n", + "\t- R_EX_fe3hox_un_e\n", + "\t- R_EX_fecrm_e\n", + "\t- R_EX_fecrm_un_e\n", + "\t- R_EX_feenter_e\n", + "\t- R_EX_feoxam_e\n", + "\t- R_EX_feoxam_un_e\n", + "\t- R_EX_for_e\n", + "\t- R_EX_fru_e\n", + "\t- R_EX_frulys_e\n", + "\t- R_EX_fruur_e\n", + "\t- R_EX_fuc__L_e\n", + "\t- R_EX_fum_e\n", + "\t- R_EX_fusa_e\n", + "\t- R_EX_g1p_e\n", + "\t- R_EX_g3pc_e\n", + "\t- R_EX_g3pe_e\n", + "\t- R_EX_g3pg_e\n", + "\t- R_EX_g3pi_e\n", + "\t- R_EX_g3ps_e\n", + "\t- R_EX_g6p_e\n", + "\t- R_EX_gal_e\n", + "\t- R_EX_feroxBfe_e\n", + "\t- R_EX_4hphac_e\n", + "\t- R_EX_4abz_e\n", + "\t- R_EX_acon_C_e\n", + "\t- R_EX_frmd_e\n", + "\t- R_EX_feroxB_e\n", + "\t- R_EX_tcb_e\n", + "\t- R_EX_tton_e\n", + "\t- R_EX_colipaOA_e\n", + "\t- R_EX_remnant1_e\n", + "\t- R_EX_feroxG_e\n", + "\t- R_EX_4hthr_e\n", + "\t- R_EX_feroxEfe_e\n", + "\t- R_EX_feroxE_e\n", + "\t- R_EX_salchsx_e\n", + "\t- R_EX_23dhbzs3_e\n", + "\t- R_EX_tet_e\n", + "\t- R_EX_salchs4_e\n", + "\t- R_EX_AEP_e\n", + "\t- R_EX_icit_e\n", + "\t- R_EX_diact_e\n", + "\t- R_EX_oaa_e\n", + "\t- R_EX_salchs2fe_e\n", + "\t- R_EX_pep_e\n", + "\t- R_EX_colipa20Oag_e\n", + "\t- R_EX_cysi__L_e\n", + "\t- R_EX_fe3dhbzs3_e\n", + "\t- R_EX_cellb_e\n", + "\t- R_EX_drib_e\n", + "\t- R_EX_btd_RR_e\n", + "\t- R_EX_salchs4fe_e\n", + "\t- R_EX_rbt_e\n", + "\t- R_EX_bz_e\n", + "\t- R_EX_2pg_e\n", + "\t- R_EX_peng_e\n", + "\t- R_EX_pac_e\n", + "\t- R_EX_13ppd_e\n", + "\t- R_EX_abt__D_e\n", + "\t- R_EX_udcpo5_e\n", + "\t- R_EX_salcn_e\n", + "\t- R_EX_galam_e\n", + "\t- R_EX_raffin_e\n", + "\t- R_EX_o6a4colipa_e\n", + "\t- R_EX_arab__D_e\n", + "\t- R_EX_fe_e\n", + "\t- R_EX_kokdolipidA_e\n", + "\t- R_EX_3hoxpac_e\n", + "\t- R_EX_4hoxpac_e\n", + "\t- R_DM_lipidA_core_e_p\n", + "\t- R_EX_salchs2_e\n", + "\t- R_EX_3ntym_e\n", + "\t- R_EX_6apa_e\n", + "\t- R_EX_rnam_e\n", + "\t- R_EX_airs_e\n", + "\t- R_EX_dxyl_e\n", + "\t- R_EX_3pg_e\n", + "\t- R_EX_feroxGfe_e\n", + "\t- R_EX_udcpo4_e\n", + "\t- R_EX_tag__D_e\n", + "\t- R_EX_guln__L_e\u001b[0m\n", + "File saved at: ../../data/sbml_corrected/iECUMN_1333.xml\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: iEcSMS35_1347\n", + "\u001b[0;93mWARNING : \n", + " - R_CAT: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_DHPTDNR: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_DHPTDNRN: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_FHL: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SPODM: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SPODMpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCASPtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCFUMtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCMALtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCTARTtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_EX_chitob_e: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_CHITOBpts: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_CHITPH: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_URCN: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\u001b[0m\n", + "\n", + "\n", + "\u001b[0;36m############################################\n", + "############################################\n", + " WRITING SBML FILE \n", + "############################################\n", + "############################################\n", + "\u001b[0m\n", + "\u001b[0;93mWARNING : IMPORT REACTION REMOVED BY DEFAULT\u001b[0m\n", + "\u001b[0;93mWARNING : If you want to keep import reaction\n", + "use option -kir / --keep-import-reactions\u001b[0m\n", + "\u001b[0;93mWARNING : Import reaction removed:\n", + "\t- R_EX_arab__L_e\n", + "\t- R_EX_arbtn_e\n", + "\t- R_EX_arg__L_e\n", + "\t- R_EX_arbt_e\n", + "\t- R_EX_anhgm_e\n", + "\t- R_EX_arbtn_fe3_e\n", + "\t- R_DM_4crsol_c\n", + "\t- R_EX_alltn_e\n", + "\t- R_EX_amp_e\n", + "\t- R_EX_all__D_e\n", + "\t- R_DM_5drib_c\n", + "\t- R_EX_ascb__L_e\n", + "\t- R_DM_aacald_c\n", + "\t- R_EX_asn__L_e\n", + "\t- R_EX_aso3_e\n", + "\t- R_EX_asp__L_e\n", + "\t- R_EX_g3pg_e\n", + "\t- R_EX_g3pi_e\n", + "\t- R_EX_btn_e\n", + "\t- R_EX_g3ps_e\n", + "\t- R_EX_but_e\n", + "\t- R_DM_amob_c\n", + "\t- R_EX_butso3_e\n", + "\t- R_EX_g6p_e\n", + "\t- R_EX_gal_e\n", + "\t- R_EX_ca2_e\n", + "\t- R_DM_mththf_c\n", + "\t- R_EX_cbi_e\n", + "\t- R_EX_gal_bD_e\n", + "\t- R_EX_cbl1_e\n", + "\t- R_EX_gal1p_e\n", + "\t- R_DM_oxam_c\n", + "\t- R_EX_galct__D_e\n", + "\t- R_EX_cd2_e\n", + "\t- R_EX_cgly_e\n", + "\t- R_EX_chol_e\n", + "\t- R_EX_chtbs_e\n", + "\t- R_EX_cit_e\n", + "\t- R_EX_cl_e\n", + "\t- R_EX_cm_e\n", + "\t- R_EX_cmp_e\n", + "\t- R_EX_co2_e\n", + "\t- R_EX_galctn__D_e\n", + "\t- R_EX_galctn__L_e\n", + "\t- R_EX_galt_e\n", + "\t- R_EX_galur_e\n", + "\t- R_EX_gam_e\n", + "\t- R_EX_gam6p_e\n", + "\t- R_EX_gbbtn_e\n", + "\t- R_EX_gdp_e\n", + "\t- R_EX_glc__D_e\n", + "\t- R_EX_glcn_e\n", + "\t- R_EX_glcr_e\n", + "\t- R_EX_glcur_e\n", + "\t- R_EX_cobalt2_e\n", + "\t- R_EX_colipa_e\n", + "\t- R_EX_colipap_e\n", + "\t- R_EX_cpgn_e\n", + "\t- R_EX_cpgn_un_e\n", + "\t- R_EX_glcur1p_e\n", + "\t- R_EX_gln__L_e\n", + "\t- R_EX_glu__L_e\n", + "\t- R_EX_crn_e\n", + "\t- R_EX_crn__D_e\n", + "\t- R_EX_gly_e\n", + "\t- R_EX_glyald_e\n", + "\t- R_EX_glyb_e\n", + "\t- R_EX_csn_e\n", + "\t- R_EX_cu_e\n", + "\t- R_EX_cu2_e\n", + "\t- R_EX_cyan_e\n", + "\t- R_EX_cynt_e\n", + "\t- R_EX_cys__D_e\n", + "\t- R_EX_cys__L_e\n", + "\t- R_EX_glyc_e\n", + "\t- R_EX_glyc__R_e\n", + "\t- R_EX_glyc2p_e\n", + "\t- R_EX_glyc3p_e\n", + "\t- R_EX_glyclt_e\n", + "\t- R_EX_gmp_e\n", + "\t- R_EX_gsn_e\n", + "\t- R_EX_cytd_e\n", + "\t- R_EX_dad_2_e\n", + "\t- R_EX_damp_e\n", + "\t- R_EX_gthox_e\n", + "\t- R_EX_dca_e\n", + "\t- R_EX_dcmp_e\n", + "\t- R_EX_dcyt_e\n", + "\t- R_EX_ddca_e\n", + "\t- R_EX_gthrd_e\n", + "\t- R_EX_gtp_e\n", + "\t- R_EX_gua_e\n", + "\t- R_EX_h_e\n", + "\t- R_EX_h2_e\n", + "\t- R_EX_h2o_e\n", + "\t- R_EX_h2o2_e\n", + "\t- R_EX_dgmp_e\n", + "\t- R_EX_dgsn_e\n", + "\t- R_EX_h2s_e\n", + "\t- R_EX_dha_e\n", + "\t- R_EX_12ppd__R_e\n", + "\t- R_EX_dimp_e\n", + "\t- R_EX_din_e\n", + "\t- R_EX_dms_e\n", + "\t- R_EX_hacolipa_e\n", + "\t- R_EX_halipa_e\n", + "\t- R_EX_hdca_e\n", + "\t- R_EX_hdcea_e\n", + "\t- R_EX_hg2_e\n", + "\t- R_EX_his__L_e\n", + "\t- R_EX_dmso_e\n", + "\t- R_EX_dopa_e\n", + "\t- R_EX_doxrbcn_e\n", + "\t- R_EX_dtmp_e\n", + "\t- R_EX_hom__L_e\n", + "\t- R_EX_hxa_e\n", + "\t- R_EX_dump_e\n", + "\t- R_EX_hxan_e\n", + "\t- R_EX_duri_e\n", + "\t- R_EX_12ppd__S_e\n", + "\t- R_EX_eca4colipa_e\n", + "\t- R_EX_enlipa_e\n", + "\t- R_EX_enter_e\n", + "\t- R_EX_idon__L_e\n", + "\t- R_EX_ile__L_e\n", + "\t- R_EX_imp_e\n", + "\t- R_EX_indole_e\n", + "\t- R_EX_inost_e\n", + "\t- R_EX_etha_e\n", + "\t- R_EX_ethso3_e\n", + "\t- R_EX_etoh_e\n", + "\t- R_EX_ins_e\n", + "\t- R_EX_isetac_e\n", + "\t- R_EX_f6p_e\n", + "\t- R_EX_fald_e\n", + "\t- R_EX_14glucan_e\n", + "\t- R_EX_fe2_e\n", + "\t- R_EX_fe3_e\n", + "\t- R_EX_k_e\n", + "\t- R_EX_kdo2lipid4_e\n", + "\t- R_EX_lac__D_e\n", + "\t- R_EX_fe3dcit_e\n", + "\t- R_EX_lac__L_e\n", + "\t- R_EX_15dap_e\n", + "\t- R_EX_lcts_e\n", + "\t- R_EX_leu__L_e\n", + "\t- R_EX_lipa_e\n", + "\t- R_EX_lipa_cold_e\n", + "\t- R_EX_fe3dhbzs_e\n", + "\t- R_EX_fe3hox_e\n", + "\t- R_EX_fe3hox_un_e\n", + "\t- R_EX_fecrm_e\n", + "\t- R_EX_fecrm_un_e\n", + "\t- R_EX_feenter_e\n", + "\t- R_EX_feoxam_e\n", + "\t- R_EX_lipoate_e\n", + "\t- R_EX_lys__L_e\n", + "\t- R_EX_lyx__L_e\n", + "\t- R_EX_mal__D_e\n", + "\t- R_EX_feoxam_un_e\n", + "\t- R_EX_for_e\n", + "\t- R_EX_mal__L_e\n", + "\t- R_EX_malt_e\n", + "\t- R_EX_23camp_e\n", + "\t- R_EX_malthx_e\n", + "\t- R_EX_maltpt_e\n", + "\t- R_EX_fru_e\n", + "\t- R_EX_frulys_e\n", + "\t- R_EX_fruur_e\n", + "\t- R_EX_malttr_e\n", + "\t- R_EX_fuc__L_e\n", + "\t- R_EX_maltttr_e\n", + "\t- R_EX_23ccmp_e\n", + "\t- R_EX_man_e\n", + "\t- R_EX_man6p_e\n", + "\t- R_EX_fum_e\n", + "\t- R_EX_fusa_e\n", + "\t- R_EX_g1p_e\n", + "\t- R_EX_g3pc_e\n", + "\t- R_EX_manglyc_e\n", + "\t- R_EX_melib_e\n", + "\t- R_EX_g3pe_e\n", + "\t- R_EX_23cgmp_e\n", + "\t- R_EX_23cump_e\n", + "\t- R_EX_met__D_e\n", + "\t- R_EX_met__L_e\n", + "\t- R_EX_23dappa_e\n", + "\t- R_EX_metsox_R__L_e\n", + "\t- R_EX_metsox_S__L_e\n", + "\t- R_EX_meoh_e\n", + "\t- R_EX_26dap__M_e\n", + "\t- R_EX_mg2_e\n", + "\t- R_EX_mincyc_e\n", + "\t- R_EX_2ddglcn_e\n", + "\t- R_EX_minohp_e\n", + "\t- R_EX_34dhpac_e\n", + "\t- R_EX_mmet_e\n", + "\t- R_EX_ttdca_e\n", + "\t- R_EX_ttdcea_e\n", + "\t- R_EX_3amp_e\n", + "\t- R_EX_ttrcyc_e\n", + "\t- R_EX_mn2_e\n", + "\t- R_EX_mnl_e\n", + "\t- R_EX_tungs_e\n", + "\t- R_EX_mobd_e\n", + "\t- R_EX_tym_e\n", + "\t- R_EX_3cmp_e\n", + "\t- R_EX_tyr__L_e\n", + "\t- R_EX_mso3_e\n", + "\t- R_EX_n2o_e\n", + "\t- R_EX_tyrp_e\n", + "\t- R_EX_na1_e\n", + "\t- R_EX_3gmp_e\n", + "\t- R_EX_nac_e\n", + "\t- R_EX_uacgam_e\n", + "\t- R_EX_udpacgal_e\n", + "\t- R_EX_udpg_e\n", + "\t- R_EX_nh4_e\n", + "\t- R_EX_ni2_e\n", + "\t- R_EX_udpgal_e\n", + "\t- R_EX_udpglcur_e\n", + "\t- R_EX_ump_e\n", + "\t- R_EX_ura_e\n", + "\t- R_EX_nmn_e\n", + "\t- R_EX_no_e\n", + "\t- R_EX_no2_e\n", + "\t- R_EX_no3_e\n", + "\t- R_EX_urea_e\n", + "\t- R_EX_uri_e\n", + "\t- R_EX_novbcn_e\n", + "\t- R_EX_o16a4colipa_e\n", + "\t- R_EX_val__L_e\n", + "\t- R_EX_3hcinnm_e\n", + "\t- R_EX_xan_e\n", + "\t- R_EX_xmp_e\n", + "\t- R_EX_o2_e\n", + "\t- R_EX_o2s_e\n", + "\t- R_EX_ocdca_e\n", + "\t- R_EX_3hpp_e\n", + "\t- R_EX_ocdcea_e\n", + "\t- R_EX_xtsn_e\n", + "\t- R_EX_xyl__D_e\n", + "\t- R_EX_xylu__L_e\n", + "\t- R_EX_octa_e\n", + "\t- R_EX_orn_e\n", + "\t- R_EX_zn2_e\n", + "\t- R_EX_3hpppn_e\n", + "\t- R_EX_orot_e\n", + "\t- R_EX_pacald_e\n", + "\t- R_EX_peamn_e\n", + "\t- R_EX_3ump_e\n", + "\t- R_EX_phe__L_e\n", + "\t- R_EX_pheme_e\n", + "\t- R_EX_pi_e\n", + "\t- R_EX_pnto__R_e\n", + "\t- R_EX_ppa_e\n", + "\t- R_EX_ppal_e\n", + "\t- R_EX_pppn_e\n", + "\t- R_EX_ppt_e\n", + "\t- R_EX_pro__L_e\n", + "\t- R_EX_progly_e\n", + "\t- R_EX_psclys_e\n", + "\t- R_EX_4abut_e\n", + "\t- R_EX_pser__L_e\n", + "\t- R_EX_ptrc_e\n", + "\t- R_EX_4hoxpacd_e\n", + "\t- R_EX_pydam_e\n", + "\t- R_EX_pydx_e\n", + "\t- R_EX_pydxn_e\n", + "\t- R_EX_pyr_e\n", + "\t- R_EX_quin_e\n", + "\t- R_EX_r5p_e\n", + "\t- R_EX_rfamp_e\n", + "\t- R_EX_5dglcn_e\n", + "\t- R_EX_rib__D_e\n", + "\t- R_EX_rmn_e\n", + "\t- R_EX_sbt__D_e\n", + "\t- R_EX_sel_e\n", + "\t- R_EX_ser__D_e\n", + "\t- R_EX_ser__L_e\n", + "\t- R_EX_5mtr_e\n", + "\t- R_EX_skm_e\n", + "\t- R_EX_slnt_e\n", + "\t- R_EX_so2_e\n", + "\t- R_EX_so3_e\n", + "\t- R_EX_so4_e\n", + "\t- R_EX_spmd_e\n", + "\t- R_EX_succ_e\n", + "\t- R_EX_sucr_e\n", + "\t- R_EX_sulfac_e\n", + "\t- R_EX_LalaDglu_e\n", + "\t- R_EX_tartr__D_e\n", + "\t- R_EX_tartr__L_e\n", + "\t- R_EX_taur_e\n", + "\t- R_EX_LalaDgluMdap_e\n", + "\t- R_EX_tcynt_e\n", + "\t- R_EX_thm_e\n", + "\t- R_EX_thr__L_e\n", + "\t- R_EX_thrp_e\n", + "\t- R_EX_thym_e\n", + "\t- R_EX_LalaDgluMdapDala_e\n", + "\t- R_EX_thymd_e\n", + "\t- R_EX_tma_e\n", + "\t- R_EX_tmao_e\n", + "\t- R_EX_tre_e\n", + "\t- R_EX_trp__L_e\n", + "\t- R_EX_tsul_e\n", + "\t- R_EX_LalaLglu_e\n", + "\t- R_EX_ac_e\n", + "\t- R_EX_acac_e\n", + "\t- R_EX_acald_e\n", + "\t- R_EX_acgal_e\n", + "\t- R_EX_acgal1p_e\n", + "\t- R_EX_acgam_e\n", + "\t- R_EX_acgam1p_e\n", + "\t- R_EX_acmana_e\n", + "\t- R_EX_acmum_e\n", + "\t- R_EX_acnam_e\n", + "\t- R_EX_acolipa_e\n", + "\t- R_EX_acser_e\n", + "\t- R_EX_ade_e\n", + "\t- R_EX_adn_e\n", + "\t- R_EX_adocbl_e\n", + "\t- R_EX_ag_e\n", + "\t- R_EX_agm_e\n", + "\t- R_EX_akg_e\n", + "\t- R_EX_ala_B_e\n", + "\t- R_EX_ala__D_e\n", + "\t- R_EX_ala__L_e\n", + "\t- R_EX_alaala_e\n", + "\t- R_EX_peng_e\n", + "\t- R_EX_pac_e\n", + "\t- R_EX_4hphac_e\n", + "\t- R_EX_4abz_e\n", + "\t- R_EX_acon_C_e\n", + "\t- R_EX_feroxB_e\n", + "\t- R_EX_tcb_e\n", + "\t- R_EX_tton_e\n", + "\t- R_EX_4hthr_e\n", + "\t- R_EX_salcn_e\n", + "\t- R_EX_feroxEfe_e\n", + "\t- R_EX_feroxE_e\n", + "\t- R_EX_fe3dhbzs3_e\n", + "\t- R_EX_pep_e\n", + "\t- R_EX_23dhbzs3_e\n", + "\t- R_EX_tet_e\n", + "\t- R_EX_salchs4_e\n", + "\t- R_EX_drib_e\n", + "\t- R_EX_feroxBfe_e\n", + "\t- R_EX_salchs4fe_e\n", + "\t- R_EX_2pg_e\n", + "\t- R_EX_tag__D_e\n", + "\t- R_EX_oaa_e\n", + "\t- R_EX_salchs2fe_e\n", + "\t- R_EX_frmd_e\n", + "\t- R_EX_cysi__L_e\n", + "\t- R_EX_colipaOA_e\n", + "\t- R_EX_salchsx_e\n", + "\t- R_EX_AEP_e\n", + "\t- R_EX_icit_e\n", + "\t- R_EX_colipa20Oag_e\n", + "\t- R_EX_udcpo5_e\n", + "\t- R_EX_remnant1_e\n", + "\t- R_EX_guln__L_e\n", + "\t- R_EX_feroxG_e\n", + "\t- R_EX_salchs2_e\n", + "\t- R_EX_rnam_e\n", + "\t- R_EX_galam_e\n", + "\t- R_EX_airs_e\n", + "\t- R_EX_dxyl_e\n", + "\t- R_EX_3pg_e\n", + "\t- R_EX_diact_e\n", + "\t- R_EX_raffin_e\n", + "\t- R_DM_lipidA_core_e_p\n", + "\t- R_EX_cellb_e\n", + "\t- R_EX_btd_RR_e\n", + "\t- R_EX_udcpo4_e\n", + "\t- R_EX_13ppd_e\n", + "\t- R_EX_rbt_e\n", + "\t- R_EX_bz_e\n", + "\t- R_EX_feroxGfe_e\n", + "\t- R_EX_abt__D_e\n", + "\t- R_EX_fe_e\n", + "\t- R_EX_kokdolipidA_e\n", + "\t- R_EX_3hoxpac_e\n", + "\t- R_EX_o6a4colipa_e\n", + "\t- R_EX_3ntym_e\n", + "\t- R_EX_6apa_e\n", + "\t- R_EX_4hoxpac_e\n", + "\t- R_EX_arab__D_e\u001b[0m\n", + "File saved at: ../../data/sbml_corrected/iEcSMS35_1347.xml\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: iCHOv1\n", + "\u001b[0;93mWARNING : - R_ABTt: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_ACOAH: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_LCARS: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + "\n", + " - R_DM_epo_g: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_DM_igg_g: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + " - R_FE3t: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_HMGCOAS: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_HMGCOASm: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_IMPC: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_XYLR: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_ACACT7m: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_SPHPL: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_HACD4p: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_ECOAH4p: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_HISt2m: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_ALAtmi: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_PHEt2m: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_Kt1: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_GTHRDt2: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + "\u001b[0m\n", + "\n", + "\n", + "\u001b[0;36m############################################\n", + "############################################\n", + " WRITING SBML FILE \n", + "############################################\n", + "############################################\n", + "\u001b[0m\n", + "\u001b[0;93mWARNING : IMPORT REACTION REMOVED BY DEFAULT\u001b[0m\n", + "\u001b[0;93mWARNING : If you want to keep import reaction\n", + "use option -kir / --keep-import-reactions\u001b[0m\n", + "\u001b[0;93mWARNING : Reaction with tag reversible modified:\n", + "\t- R_ABTt\n", + "\t- R_ACOAH\n", + "\t- R_LCARS\n", + "\t- R_FE3t\n", + "\t- R_HMGCOAS\n", + "\t- R_HMGCOASm\n", + "\t- R_IMPC\n", + "\t- R_XYLR\n", + "\t- R_ACACT7m\n", + "\t- R_SPHPL\n", + "\t- R_HACD4p\n", + "\t- R_ECOAH4p\n", + "\t- R_HISt2m\n", + "\t- R_ALAtmi\n", + "\t- R_PHEt2m\n", + "\t- R_Kt1\n", + "\t- R_GTHRDt2\u001b[0m\n", + "\u001b[0;93mWARNING : Reaction with Reactants and Products exchanged:\n", + "\t- R_ABTt\n", + "\t- R_ACOAH\n", + "\t- R_LCARS\n", + "\t- R_FE3t\n", + "\t- R_HMGCOAS\n", + "\t- R_HMGCOASm\n", + "\t- R_IMPC\n", + "\t- R_XYLR\n", + "\t- R_ACACT7m\n", + "\t- R_SPHPL\n", + "\t- R_HACD4p\n", + "\t- R_ECOAH4p\n", + "\t- R_HISt2m\n", + "\t- R_ALAtmi\n", + "\t- R_PHEt2m\n", + "\t- R_Kt1\n", + "\t- R_GTHRDt2\u001b[0m\n", + "\u001b[0;93mWARNING : Import reaction removed:\n", + "\t- R_DM_dgtp_n\n", + "\t- R_DM_dsT_antigen_g\n", + "\t- R_DM_dttp_m\n", + "\t- R_DM_dttp_n\n", + "\t- R_DM_ethamp_r\n", + "\t- R_DM_gncore2_g\n", + "\t- R_DM_gpi_sig_r\n", + "\t- R_DM_hretn_n\n", + "\t- R_DM_kdn_c\n", + "\t- R_DM_n5m2masn_g\n", + "\t- R_DM_oretn_n\n", + "\t- R_DM_sTn_antigen_g\n", + "\t- R_DM_sprm_c\n", + "\t- R_DM_yvite_c\n", + "\t- R_DM_13_cis_oretn_n\n", + "\t- R_DM_13_cis_retn_n\n", + "\t- R_DM_56iqcrbxlt_c\n", + "\t- R_DM_Asn_X_Ser_Thr_l\n", + "\t- R_DM_Ser_Thr_l\n", + "\t- R_DM_Ser_Gly_Ala_X_Gly_l\n", + "\t- R_SK_T_antigen_g\n", + "\t- R_DM_avite1_c\n", + "\t- R_DM_avite2_c\n", + "\t- R_DM_bvite_c\n", + "\t- R_DM_core5_g\n", + "\t- R_DM_core7_g\n", + "\t- R_DM_core8_g\n", + "\t- R_DM_datp_m\n", + "\t- R_DM_datp_n\n", + "\t- R_DM_dctp_m\n", + "\t- R_DM_dctp_n\n", + "\t- R_DM_dem2emgacpail_prot_cho_r\n", + "\t- R_DM_dgpi_prot_cho_r\n", + "\t- R_DM_dgtp_m\n", + "\t- R_EX_2hb_e\n", + "\t- R_EX_2mcit_e\n", + "\t- R_EX_34dhoxpeg_e\n", + "\t- R_EX_34dhphe_e\n", + "\t- R_EX_34hpp_e\n", + "\t- R_EX_35cgmp_e\n", + "\t- R_EX_3aib__D_e\n", + "\t- R_EX_3aib_e\n", + "\t- R_EX_3bcrn_e\n", + "\t- R_EX_3ddcrn_e\n", + "\t- R_EX_3deccrn_e\n", + "\t- R_EX_3hexdcrn_e\n", + "\t- R_EX_3mlda_e\n", + "\t- R_EX_3mob_e\n", + "\t- R_EX_3mop_e\n", + "\t- R_EX_3tdcrn_e\n", + "\t- R_EX_3ump_e\n", + "\t- R_EX_4abut_e\n", + "\t- R_EX_4abutn_e\n", + "\t- R_EX_4hdebrisoquine_e\n", + "\t- R_EX_4hphac_e\n", + "\t- R_EX_4hpro_LT_e\n", + "\t- R_EX_4mop_e\n", + "\t- R_EX_4mptnl_e\n", + "\t- R_EX_4mtolbutamide_e\n", + "\t- R_EX_4nph_e\n", + "\t- R_EX_4nphsf_e\n", + "\t- R_EX_4pyrdx_e\n", + "\t- R_EX_5adtststerone_e\n", + "\t- R_EX_5adtststeroneglc_e\n", + "\t- R_EX_5adtststerones_e\n", + "\t- R_EX_HC00229_e\n", + "\t- R_EX_HC00250_e\n", + "\t- R_EX_HC00822_e\n", + "\t- R_EX_5dhf_e\n", + "\t- R_EX_5fthf_e\n", + "\t- R_EX_HC00955_e\n", + "\t- R_EX_HC01104_e\n", + "\t- R_EX_HC01361_e\n", + "\t- R_EX_5homeprazole_e\n", + "\t- R_EX_5htrp_e\n", + "\t- R_EX_5mta_e\n", + "\t- R_EX_HC01440_e\n", + "\t- R_EX_5mthf_e\n", + "\t- R_EX_5oxpro_e\n", + "\t- R_EX_HC01441_e\n", + "\t- R_EX_HC01444_e\n", + "\t- R_EX_HC01446_e\n", + "\t- R_EX_HC01577_e\n", + "\t- R_EX_HC01609_e\n", + "\t- R_EX_5thf_e\n", + "\t- R_EX_6dhf_e\n", + "\t- R_EX_6htststerone_e\n", + "\t- R_EX_6thf_e\n", + "\t- R_EX_7dhf_e\n", + "\t- R_EX_cpppg1_e\n", + "\t- R_EX_HC01700_e\n", + "\t- R_EX_7thf_e\n", + "\t- R_EX_9_cis_retfa_e\n", + "\t- R_EX_C02470_e\n", + "\t- R_EX_HC01787_e\n", + "\t- R_EX_HC02154_e\n", + "\t- R_EX_HC02160_e\n", + "\t- R_EX_HC02161_e\n", + "\t- R_EX_C02528_e\n", + "\t- R_EX_C04849_e\n", + "\t- R_EX_HC02172_e\n", + "\t- R_EX_12HPET_e\n", + "\t- R_EX_CE0074_e\n", + "\t- R_EX_CE1925_e\n", + "\t- R_EX_ddca_e\n", + "\t- R_EX_wharachd_e\n", + "\t- R_EX_CE1926_e\n", + "\t- R_EX_CE1935_e\n", + "\t- R_EX_HC02180_e\n", + "\t- R_EX_HC02187_e\n", + "\t- R_EX_HC02191_e\n", + "\t- R_EX_HC02192_e\n", + "\t- R_EX_CE1936_e\n", + "\t- R_EX_CE1939_e\n", + "\t- R_EX_CE1940_e\n", + "\t- R_EX_CE1943_e\n", + "\t- R_EX_CE2011_e\n", + "\t- R_EX_CE2250_e\n", + "\t- R_EX_HC02193_e\n", + "\t- R_EX_HC02194_e\n", + "\t- R_EX_HC02195_e\n", + "\t- R_EX_HC02196_e\n", + "\t- R_EX_HC02197_e\n", + "\t- R_EX_CE2838_e\n", + "\t- R_EX_HC02198_e\n", + "\t- R_EX_CE2839_e\n", + "\t- R_EX_CE2915_e\n", + "\t- R_EX_CE2916_e\n", + "\t- R_EX_HC02199_e\n", + "\t- R_EX_HC02200_e\n", + "\t- R_EX_HC02201_e\n", + "\t- R_EX_HC02202_e\n", + "\t- R_EX_CE2917_e\n", + "\t- R_EX_CE4633_e\n", + "\t- R_EX_HC02203_e\n", + "\t- R_EX_HC02204_e\n", + "\t- R_EX_HC02205_e\n", + "\t- R_EX_CE4722_e\n", + "\t- R_EX_CE4723_e\n", + "\t- R_EX_CE4724_e\n", + "\t- R_EX_HC02206_e\n", + "\t- R_EX_HC02207_e\n", + "\t- R_EX_CE4881_e\n", + "\t- R_EX_CE5786_e\n", + "\t- R_EX_HC02208_e\n", + "\t- R_EX_HC02210_e\n", + "\t- R_EX_HC02213_e\n", + "\t- R_EX_HC02214_e\n", + "\t- R_EX_HC02216_e\n", + "\t- R_EX_HC02217_e\n", + "\t- R_EX_CE5788_e\n", + "\t- R_EX_CE5789_e\n", + "\t- R_EX_CE5797_e\n", + "\t- R_EX_CE5798_e\n", + "\t- R_EX_CE5853_e\n", + "\t- R_EX_CE5854_e\n", + "\t- R_EX_HC02220_e\n", + "\t- R_EX_cysi__L_e\n", + "\t- R_EX_CE5867_e\n", + "\t- R_EX_CE5868_e\n", + "\t- R_EX_CE5869_e\n", + "\t- R_EX_clpnd_e\n", + "\t- R_EX_Rtotal2_e\n", + "\t- R_EX_Rtotal3_e\n", + "\t- R_EX_Rtotal_e\n", + "\t- R_EX_HC00004_e\n", + "\t- R_EX_Tyr_ggn_e\n", + "\t- R_EX_abt_e\n", + "\t- R_EX_akg_e\n", + "\t- R_EX_ala_B_e\n", + "\t- R_EX_ala__D_e\n", + "\t- R_EX_ac_e\n", + "\t- R_EX_acac_e\n", + "\t- R_EX_ala__L_e\n", + "\t- R_EX_alaala_e\n", + "\t- R_EX_acald_e\n", + "\t- R_EX_acetone_e\n", + "\t- R_EX_acgalfucgalacgalfuc12gal14acglcgalgluside_cho_e\n", + "\t- R_EX_acgalfucgalacgalfucgalacglcgal14acglcgalgluside_cho_e\n", + "\t- R_EX_acgam_e\n", + "\t- R_EX_ach_e\n", + "\t- R_EX_aldstrn_e\n", + "\t- R_EX_amp_e\n", + "\t- R_EX_andrstrn_e\n", + "\t- R_EX_andrstrnglc_e\n", + "\t- R_EX_anth_e\n", + "\t- R_EX_acmana_e\n", + "\t- R_EX_acn13acngalgbside_cho_e\n", + "\t- R_EX_antipyrene_e\n", + "\t- R_EX_apnnox_e\n", + "\t- R_EX_acn23acngalgbside_cho_e\n", + "\t- R_EX_acnacngal14acglcgalgluside_cho_e\n", + "\t- R_EX_acnacngalgbside_cho_e\n", + "\t- R_EX_appnn_e\n", + "\t- R_EX_aprgstrn_e\n", + "\t- R_EX_acngalacglcgal14acglcgalgluside_cho_e\n", + "\t- R_EX_aqcobal_e\n", + "\t- R_EX_ade_e\n", + "\t- R_EX_adn_e\n", + "\t- R_EX_arab__L_e\n", + "\t- R_EX_arach_e\n", + "\t- R_EX_arachcoa_e\n", + "\t- R_EX_adp_e\n", + "\t- R_EX_adocbl_e\n", + "\t- R_EX_adprbp_e\n", + "\t- R_EX_adprib_e\n", + "\t- R_EX_arachd_e\n", + "\t- R_EX_arg__L_e\n", + "\t- R_EX_ascb__L_e\n", + "\t- R_EX_adrn_e\n", + "\t- R_EX_adrnl_e\n", + "\t- R_EX_aflatoxin_e\n", + "\t- R_EX_ahandrostanglc_e\n", + "\t- R_EX_ahcys_e\n", + "\t- R_EX_ahdt_e\n", + "\t- R_EX_asn__L_e\n", + "\t- R_EX_asp__D_e\n", + "\t- R_EX_asp__L_e\n", + "\t- R_EX_atp_e\n", + "\t- R_EX_aicar_e\n", + "\t- R_EX_ak2lgchol_cho_e\n", + "\t- R_EX_avite1_e\n", + "\t- R_EX_avite2_e\n", + "\t- R_EX_bglc_e\n", + "\t- R_EX_bhb_e\n", + "\t- R_EX_cys__L_e\n", + "\t- R_EX_cysam_e\n", + "\t- R_EX_bildglcur_e\n", + "\t- R_EX_bilglcur_e\n", + "\t- R_EX_bilirub_e\n", + "\t- R_EX_cytd_e\n", + "\t- R_EX_dad_2_e\n", + "\t- R_EX_dag_cho_e\n", + "\t- R_EX_biocyt_e\n", + "\t- R_EX_btn_e\n", + "\t- R_EX_but_e\n", + "\t- R_EX_dca_e\n", + "\t- R_EX_dchac_e\n", + "\t- R_EX_bvite_e\n", + "\t- R_EX_bz_e\n", + "\t- R_EX_dcsptn1_e\n", + "\t- R_EX_dcyt_e\n", + "\t- R_EX_c10dc_e\n", + "\t- R_EX_c4crn_e\n", + "\t- R_EX_c4dc_e\n", + "\t- R_EX_c51crn_e\n", + "\t- R_EX_c6dc_e\n", + "\t- R_EX_debrisoquine_e\n", + "\t- R_EX_dgchol_e\n", + "\t- R_EX_dgmp_e\n", + "\t- R_EX_dgsn_e\n", + "\t- R_EX_dgtp_e\n", + "\t- R_EX_c8crn_e\n", + "\t- R_EX_dha_e\n", + "\t- R_EX_c8dc_e\n", + "\t- R_EX_ca2_e\n", + "\t- R_EX_dhap_e\n", + "\t- R_EX_dhdascb_e\n", + "\t- R_EX_dheas_e\n", + "\t- R_EX_camp_e\n", + "\t- R_EX_carn_e\n", + "\t- R_EX_caro_e\n", + "\t- R_EX_dhf_e\n", + "\t- R_EX_digalsgalside_cho_e\n", + "\t- R_EX_carveol_e\n", + "\t- R_EX_cbasp_e\n", + "\t- R_EX_cca_d3_e\n", + "\t- R_EX_din_e\n", + "\t- R_EX_dlnlcg_e\n", + "\t- R_EX_dmantipyrine_e\n", + "\t- R_EX_dmhptcrn_e\n", + "\t- R_EX_docosac_e\n", + "\t- R_EX_cdp_e\n", + "\t- R_EX_cgly_e\n", + "\t- R_EX_chol_e\n", + "\t- R_EX_dopa_e\n", + "\t- R_EX_dopasf_e\n", + "\t- R_EX_dpcoa_e\n", + "\t- R_EX_drib_e\n", + "\t- R_EX_cholate_e\n", + "\t- R_EX_hspg_e\n", + "\t- R_EX_dtdp_e\n", + "\t- R_EX_chsterol_e\n", + "\t- R_EX_dtmp_e\n", + "\t- R_EX_dttp_e\n", + "\t- R_EX_duri_e\n", + "\t- R_EX_chtn_e\n", + "\t- R_EX_cit_e\n", + "\t- R_EX_eaflatoxin_e\n", + "\t- R_EX_ebastine_e\n", + "\t- R_EX_ebastineoh_e\n", + "\t- R_EX_citr__L_e\n", + "\t- R_EX_eicostet_e\n", + "\t- R_EX_cl_e\n", + "\t- R_EX_cmp_e\n", + "\t- R_EX_elaid_e\n", + "\t- R_EX_co2_e\n", + "\t- R_EX_co_e\n", + "\t- R_EX_coa_e\n", + "\t- R_EX_estradiol_e\n", + "\t- R_EX_estradiolglc_e\n", + "\t- R_EX_estriolglc_e\n", + "\t- R_EX_estroneglc_e\n", + "\t- R_EX_estrones_e\n", + "\t- R_EX_coumarin_e\n", + "\t- R_EX_creat_e\n", + "\t- R_EX_crm_cho_e\n", + "\t- R_EX_etoh_e\n", + "\t- R_EX_fad_e\n", + "\t- R_EX_crmp_cho_e\n", + "\t- R_EX_crn_e\n", + "\t- R_EX_crtsl_e\n", + "\t- R_EX_crtstrn_e\n", + "\t- R_EX_fald_e\n", + "\t- R_EX_fe2_e\n", + "\t- R_EX_fe3_e\n", + "\t- R_EX_crvnc_e\n", + "\t- R_EX_fmn_e\n", + "\t- R_EX_csn_e\n", + "\t- R_EX_cspg_a_e\n", + "\t- R_EX_fol_e\n", + "\t- R_EX_for_e\n", + "\t- R_EX_fru_e\n", + "\t- R_EX_fuc13galacglcgal14acglcgalgluside_cho_e\n", + "\t- R_EX_cspg_b_e\n", + "\t- R_EX_cspg_c_e\n", + "\t- R_EX_cspg_d_e\n", + "\t- R_EX_cspg_e_e\n", + "\t- R_EX_fuc14galacglcgalgluside_cho_e\n", + "\t- R_EX_fuc__L_e\n", + "\t- R_EX_10fthf5glu_e\n", + "\t- R_EX_fucacgalfucgalacglcgalgluside_cho_e\n", + "\t- R_EX_fucacngal14acglcgalgluside_cho_e\n", + "\t- R_EX_ctp_e\n", + "\t- R_EX_cyan_e\n", + "\t- R_EX_fucacngalacglcgalgluside_cho_e\n", + "\t- R_EX_10fthf6glu_e\n", + "\t- R_EX_fucfuc12gal14acglcgalgluside_cho_e\n", + "\t- R_EX_fucfuc132galacglcgal14acglcgalgluside_cho_e\n", + "\t- R_EX_glcur_e\n", + "\t- R_EX_10fthf7glu_e\n", + "\t- R_EX_fucfucfucgalacglc13galacglcgal14acglcgalgluside_cho_e\n", + "\t- R_EX_fucfucfucgalacglcgal14acglcgalgluside_cho_e\n", + "\t- R_EX_fucfucgalacglcgalgluside_cho_e\n", + "\t- R_EX_fucgal14acglcgalgluside_cho_e\n", + "\t- R_EX_fucgalfucgalacglcgalgluside_cho_e\n", + "\t- R_EX_glgchlo_e\n", + "\t- R_EX_gln__L_e\n", + "\t- R_EX_gltcho_e\n", + "\t- R_EX_gltdechol_e\n", + "\t- R_EX_glu__L_e\n", + "\t- R_EX_fucgalgbside_cho_e\n", + "\t- R_EX_g1p_e\n", + "\t- R_EX_10fthf_e\n", + "\t- R_EX_gal_e\n", + "\t- R_EX_gluala_e\n", + "\t- R_EX_11_cis_retfa_e\n", + "\t- R_EX_gly_e\n", + "\t- R_EX_glyb_e\n", + "\t- R_EX_galacglcgalgbside_cho_e\n", + "\t- R_EX_galfuc12gal14acglcgalgluside_cho_e\n", + "\t- R_EX_galfucgalacglcgal14acglcgalgluside_cho_e\n", + "\t- R_EX_galgalfucfucgalacglcgalacglcgal14acglcgalgluside_cho_e\n", + "\t- R_EX_glyc__S_e\n", + "\t- R_EX_glyc_e\n", + "\t- R_EX_galgalgalthcrm_cho_e\n", + "\t- R_EX_13_cis_retnglc_e\n", + "\t- R_EX_galside_cho_e\n", + "\t- R_EX_glygly_e\n", + "\t- R_EX_glygn2_e\n", + "\t- R_EX_gam_e\n", + "\t- R_EX_1glyc_cho_e\n", + "\t- R_EX_gbside_cho_e\n", + "\t- R_EX_glygn4_e\n", + "\t- R_EX_glygn5_e\n", + "\t- R_EX_gchola_e\n", + "\t- R_EX_1mncam_e\n", + "\t- R_EX_gd1b2_cho_e\n", + "\t- R_EX_gd1c_cho_e\n", + "\t- R_EX_gdp_e\n", + "\t- R_EX_glc__D_e\n", + "\t- R_EX_glyleu_e\n", + "\t- R_EX_glyphe_e\n", + "\t- R_EX_glypro_e\n", + "\t- R_EX_glysar_e\n", + "\t- R_EX_gmp_e\n", + "\t- R_EX_23cump_e\n", + "\t- R_EX_gp1c_cho_e\n", + "\t- R_EX_gp1calpha_cho_e\n", + "\t- R_EX_gq1b_cho_e\n", + "\t- R_EX_lnlc_e\n", + "\t- R_EX_lnlnca_e\n", + "\t- R_EX_lnlncg_e\n", + "\t- R_EX_lpchol_cho_e\n", + "\t- R_EX_gq1balpha_cho_e\n", + "\t- R_EX_gsn_e\n", + "\t- R_EX_lys__L_e\n", + "\t- R_EX_gt1a_cho_e\n", + "\t- R_EX_2425dhvitd2_e\n", + "\t- R_EX_gthox_e\n", + "\t- R_EX_gthrd_e\n", + "\t- R_EX_gtp_e\n", + "\t- R_EX_mag_cho_e\n", + "\t- R_EX_mal__L_e\n", + "\t- R_EX_malcoa_e\n", + "\t- R_EX_gua_e\n", + "\t- R_EX_2425dhvitd3_e\n", + "\t- R_EX_gum_e\n", + "\t- R_EX_gumdchac_e\n", + "\t- R_EX_malt_e\n", + "\t- R_EX_malthp_e\n", + "\t- R_EX_malthx_e\n", + "\t- R_EX_gumgchol_e\n", + "\t- R_EX_maltpt_e\n", + "\t- R_EX_gumtchol_e\n", + "\t- R_EX_24nph_e\n", + "\t- R_EX_h2o2_e\n", + "\t- R_EX_h2o_e\n", + "\t- R_EX_h_e\n", + "\t- R_EX_ha_e\n", + "\t- R_EX_ha_pre1_e\n", + "\t- R_EX_malttr_e\n", + "\t- R_EX_maltttr_e\n", + "\t- R_EX_man_e\n", + "\t- R_EX_meoh_e\n", + "\t- R_EX_mepi_e\n", + "\t- R_EX_mercplaccys_e\n", + "\t- R_EX_hco3_e\n", + "\t- R_EX_met__L_e\n", + "\t- R_EX_25hvitd2_e\n", + "\t- R_EX_mthgxl_e\n", + "\t- R_EX_hcoumarin_e\n", + "\t- R_EX_hdca_e\n", + "\t- R_EX_hdcea_e\n", + "\t- R_EX_n2m2nmasn_e\n", + "\t- R_EX_hestratriol_e\n", + "\t- R_EX_25hvitd3_e\n", + "\t- R_EX_na1_e\n", + "\t- R_EX_nac_e\n", + "\t- R_EX_psyltchol_e\n", + "\t- R_EX_nad_e\n", + "\t- R_EX_nadp_e\n", + "\t- R_EX_ncam_e\n", + "\t- R_EX_nh4_e\n", + "\t- R_EX_hexc_e\n", + "\t- R_EX_his__L_e\n", + "\t- R_EX_hista_e\n", + "\t- R_EX_hom__L_e\n", + "\t- R_EX_hpdca_e\n", + "\t- R_EX_htaxol_e\n", + "\t- R_EX_hxan_e\n", + "\t- R_EX_hyptaur_e\n", + "\t- R_EX_i_e\n", + "\t- R_EX_idour_e\n", + "\t- R_EX_nifedipine_e\n", + "\t- R_EX_no2_e\n", + "\t- R_EX_no_e\n", + "\t- R_EX_npthl_e\n", + "\t- R_EX_nrpphr_e\n", + "\t- R_EX_nrpphrsf_e\n", + "\t- R_EX_nrvnc_e\n", + "\t- R_EX_idp_e\n", + "\t- R_EX_ile__L_e\n", + "\t- R_EX_imp_e\n", + "\t- R_EX_inost_e\n", + "\t- R_EX_ins_e\n", + "\t- R_EX_o2_e\n", + "\t- R_EX_o2s_e\n", + "\t- R_EX_ivcrn_e\n", + "\t- R_EX_oagd3_cho_e\n", + "\t- R_EX_psyltdechol_e\n", + "\t- R_EX_oagt3_cho_e\n", + "\t- R_EX_ocdca_e\n", + "\t- R_EX_k_e\n", + "\t- R_EX_ksi_deg1_e\n", + "\t- R_EX_ksi_e\n", + "\t- R_EX_ocdcea_e\n", + "\t- R_EX_octa_e\n", + "\t- R_EX_ksii_core2_e\n", + "\t- R_EX_ptdca_e\n", + "\t- R_EX_ksii_core4_e\n", + "\t- R_EX_lac__D_e\n", + "\t- R_EX_oh1_e\n", + "\t- R_EX_omeprazole_e\n", + "\t- R_EX_onpthl_e\n", + "\t- R_EX_orn_e\n", + "\t- R_EX_lac__L_e\n", + "\t- R_EX_orot_e\n", + "\t- R_EX_ptrc_e\n", + "\t- R_EX_lcts_e\n", + "\t- R_EX_leu__L_e\n", + "\t- R_EX_oxa_e\n", + "\t- R_EX_ptth_e\n", + "\t- R_EX_paf_cho_e\n", + "\t- R_EX_pan4p_e\n", + "\t- R_EX_pchol_cho_e\n", + "\t- R_EX_pe_cho_e\n", + "\t- R_EX_pect_e\n", + "\t- R_EX_leugly_e\n", + "\t- R_EX_leuktrA4_e\n", + "\t- R_EX_leuktrB4_e\n", + "\t- R_EX_leuktrC4_e\n", + "\t- R_EX_leuktrD4_e\n", + "\t- R_EX_leuktrE4_e\n", + "\t- R_EX_pectindchac_e\n", + "\t- R_EX_pectingchol_e\n", + "\t- R_EX_pydam_e\n", + "\t- R_EX_pectintchol_e\n", + "\t- R_EX_peplys_e\n", + "\t- R_EX_leuktrF4_e\n", + "\t- R_EX_leuleu_e\n", + "\t- R_EX_lgnc_e\n", + "\t- R_EX_perillyl_e\n", + "\t- R_EX_pglyc_cho_e\n", + "\t- R_EX_pydx5p_e\n", + "\t- R_EX_phe__L_e\n", + "\t- R_EX_pheacgln_e\n", + "\t- R_EX_limnen_e\n", + "\t- R_EX_lipoate_e\n", + "\t- R_EX_lneldc_e\n", + "\t- R_EX_pheme_e\n", + "\t- R_EX_pydx_e\n", + "\t- R_EX_phllqne_e\n", + "\t- R_EX_phyt_e\n", + "\t- R_EX_pydxn_e\n", + "\t- R_EX_pi_e\n", + "\t- R_EX_pnto__R_e\n", + "\t- R_EX_ura_e\n", + "\t- R_EX_pyr_e\n", + "\t- R_EX_ppa_e\n", + "\t- R_EX_q10_e\n", + "\t- R_EX_ppi_e\n", + "\t- R_EX_prgstrn_e\n", + "\t- R_EX_pro__D_e\n", + "\t- R_EX_pro__L_e\n", + "\t- R_EX_progly_e\n", + "\t- R_EX_prostgd2_e\n", + "\t- R_EX_urate_e\n", + "\t- R_EX_urea_e\n", + "\t- R_EX_uri_e\n", + "\t- R_EX_utp_e\n", + "\t- R_EX_vacc_e\n", + "\t- R_EX_val__L_e\n", + "\t- R_EX_vitd2_e\n", + "\t- R_EX_prostge1_e\n", + "\t- R_EX_prostge2_e\n", + "\t- R_EX_prostgf2_e\n", + "\t- R_EX_q10h2_e\n", + "\t- R_EX_prostgh2_e\n", + "\t- R_EX_prostgi2_e\n", + "\t- R_EX_prpp_e\n", + "\t- R_EX_vitd3_e\n", + "\t- R_EX_whddca_e\n", + "\t- R_EX_whhdca_e\n", + "\t- R_EX_ps_cho_e\n", + "\t- R_EX_psyl_e\n", + "\t- R_EX_whtststerone_e\n", + "\t- R_EX_rbt_e\n", + "\t- R_EX_whttdca_e\n", + "\t- R_EX_psylchol_e\n", + "\t- R_EX_retfa_e\n", + "\t- R_EX_xolest2_cho_e\n", + "\t- R_EX_xolest_cho_e\n", + "\t- R_EX_retinol_9_cis_e\n", + "\t- R_EX_xoltri24_e\n", + "\t- R_EX_xoltri25_e\n", + "\t- R_EX_xoltri27_e\n", + "\t- R_EX_xyl__D_e\n", + "\t- R_EX_xylt_e\n", + "\t- R_EX_yvite_e\n", + "\t- R_EX_retinol_cis_11_e\n", + "\t- R_EX_retinol_e\n", + "\t- R_EX_retn_e\n", + "\t- R_EX_retnglc_e\n", + "\t- R_EX_rib__D_e\n", + "\t- R_EX_ribflv_e\n", + "\t- R_EX_s2l2fn2m2masn_e\n", + "\t- R_EX_s2l2n2m2masn_e\n", + "\t- R_EX_sarcs_e\n", + "\t- R_EX_sbt__D_e\n", + "\t- R_EX_sel_e\n", + "\t- R_EX_ser__D_e\n", + "\t- R_EX_ser__L_e\n", + "\t- R_EX_slfcys_e\n", + "\t- R_EX_sl__L_e\n", + "\t- R_EX_so4_e\n", + "\t- R_EX_spc_cho_e\n", + "\t- R_EX_sph1p_e\n", + "\t- R_EX_sphs1p_e\n", + "\t- R_EX_spmd_e\n", + "\t- R_EX_sprm_e\n", + "\t- R_EX_srtn_e\n", + "\t- R_EX_strch1_e\n", + "\t- R_EX_strch2_e\n", + "\t- R_EX_strdnc_e\n", + "\t- R_EX_succ_e\n", + "\t- R_EX_sucr_e\n", + "\t- R_EX_tag_cho_e\n", + "\t- R_EX_tag__D_e\n", + "\t- R_EX_taur_e\n", + "\t- R_EX_taxol_e\n", + "\t- R_EX_tchola_e\n", + "\t- R_EX_tcynt_e\n", + "\t- R_EX_tdchola_e\n", + "\t- R_EX_ttdcrn_e\n", + "\t- R_EX_tdechola_e\n", + "\t- R_EX_tethex3_e\n", + "\t- R_EX_tetpent3_e\n", + "\t- R_EX_tetpent6_e\n", + "\t- R_EX_tettet6_e\n", + "\t- R_EX_thf_e\n", + "\t- R_EX_thm_e\n", + "\t- R_EX_thmmp_e\n", + "\t- R_EX_thmtp_e\n", + "\t- R_EX_thr__L_e\n", + "\t- R_EX_thym_e\n", + "\t- R_EX_thymd_e\n", + "\t- R_EX_thyox__L_e\n", + "\t- R_EX_tmndnc_e\n", + "\t- R_EX_tolbutamide_e\n", + "\t- R_EX_tre_e\n", + "\t- R_EX_triodthy_e\n", + "\t- R_EX_triodthysuf_e\n", + "\t- R_EX_trp__L_e\n", + "\t- R_EX_tststerone_e\n", + "\t- R_EX_tststeroneglc_e\n", + "\t- R_EX_tststerones_e\n", + "\t- R_EX_tsul_e\n", + "\t- R_EX_ttdca_e\n", + "\t- R_EX_txa2_e\n", + "\t- R_EX_tymsf_e\n", + "\t- R_EX_tyr__L_e\n", + "\t- R_EX_udp_e\n", + "\t- R_EX_udpg_e\n", + "\t- R_EX_udpgal_e\n", + "\t- R_EX_ump_e\n", + "\t- R_SK_Asn_X_Ser_Thr_r\n", + "\t- R_SK_Ser_Thr_g\n", + "\t- R_SK_Tyr_ggn_c\n", + "\t- R_SK_pre_prot_r\u001b[0m\n", + "File saved at: ../../data/sbml_corrected/iCHOv1.xml\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: iUMNK88_1353\n", + "\u001b[0;93mWARNING : \n", + " - R_CAT: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_DHPTDNR: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_DHPTDNRN: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_FHL: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCASPtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCFUMtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCMALtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCTARTtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SPODM: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SPODMpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_EX_chitob_e: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_CHITOBpts: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_URCN: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_CHITPH: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\u001b[0m\n", + "\n", + "\n", + "\u001b[0;36m############################################\n", + "############################################\n", + " WRITING SBML FILE \n", + "############################################\n", + "############################################\n", + "\u001b[0m\n", + "\u001b[0;93mWARNING : IMPORT REACTION REMOVED BY DEFAULT\u001b[0m\n", + "\u001b[0;93mWARNING : If you want to keep import reaction\n", + "use option -kir / --keep-import-reactions\u001b[0m\n", + "\u001b[0;93mWARNING : Import reaction removed:\n", + "\t- R_EX_asn__L_e\n", + "\t- R_EX_aso3_e\n", + "\t- R_EX_asp__L_e\n", + "\t- R_EX_btn_e\n", + "\t- R_EX_butso3_e\n", + "\t- R_EX_arg__L_e\n", + "\t- R_EX_but_e\n", + "\t- R_DM_4crsol_c\n", + "\t- R_EX_ascb__L_e\n", + "\t- R_EX_ca2_e\n", + "\t- R_EX_cbi_e\n", + "\t- R_DM_5drib_c\n", + "\t- R_DM_aacald_c\n", + "\t- R_EX_cbl1_e\n", + "\t- R_EX_cd2_e\n", + "\t- R_DM_amob_c\n", + "\t- R_EX_cgly_e\n", + "\t- R_EX_chol_e\n", + "\t- R_DM_mththf_c\n", + "\t- R_EX_chtbs_e\n", + "\t- R_EX_cit_e\n", + "\t- R_DM_oxam_c\n", + "\t- R_EX_cl_e\n", + "\t- R_EX_cm_e\n", + "\t- R_EX_galct__D_e\n", + "\t- R_EX_cmp_e\n", + "\t- R_EX_galctn__D_e\n", + "\t- R_EX_galctn__L_e\n", + "\t- R_EX_galt_e\n", + "\t- R_EX_co2_e\n", + "\t- R_EX_cobalt2_e\n", + "\t- R_EX_galur_e\n", + "\t- R_EX_gam_e\n", + "\t- R_EX_gam6p_e\n", + "\t- R_EX_gbbtn_e\n", + "\t- R_EX_gdp_e\n", + "\t- R_EX_glc__D_e\n", + "\t- R_EX_glcn_e\n", + "\t- R_EX_glcr_e\n", + "\t- R_EX_colipa_e\n", + "\t- R_EX_colipap_e\n", + "\t- R_EX_cpgn_e\n", + "\t- R_EX_cpgn_un_e\n", + "\t- R_EX_crn_e\n", + "\t- R_EX_crn__D_e\n", + "\t- R_EX_csn_e\n", + "\t- R_EX_cu_e\n", + "\t- R_EX_cu2_e\n", + "\t- R_EX_glcur_e\n", + "\t- R_EX_glcur1p_e\n", + "\t- R_EX_gln__L_e\n", + "\t- R_EX_glu__L_e\n", + "\t- R_EX_cyan_e\n", + "\t- R_EX_cynt_e\n", + "\t- R_EX_gly_e\n", + "\t- R_EX_12ppd__R_e\n", + "\t- R_EX_glyald_e\n", + "\t- R_EX_glyb_e\n", + "\t- R_EX_cys__D_e\n", + "\t- R_EX_cys__L_e\n", + "\t- R_EX_cytd_e\n", + "\t- R_EX_dad_2_e\n", + "\t- R_EX_glyc_e\n", + "\t- R_EX_12ppd__S_e\n", + "\t- R_EX_glyc__R_e\n", + "\t- R_EX_damp_e\n", + "\t- R_EX_dca_e\n", + "\t- R_EX_glyc2p_e\n", + "\t- R_EX_14glucan_e\n", + "\t- R_EX_glyc3p_e\n", + "\t- R_EX_glyclt_e\n", + "\t- R_EX_gmp_e\n", + "\t- R_EX_gsn_e\n", + "\t- R_EX_dcmp_e\n", + "\t- R_EX_dcyt_e\n", + "\t- R_EX_ddca_e\n", + "\t- R_EX_dgmp_e\n", + "\t- R_EX_dgsn_e\n", + "\t- R_EX_gthox_e\n", + "\t- R_EX_gthrd_e\n", + "\t- R_EX_15dap_e\n", + "\t- R_EX_gtp_e\n", + "\t- R_EX_gua_e\n", + "\t- R_EX_h_e\n", + "\t- R_EX_dha_e\n", + "\t- R_EX_dimp_e\n", + "\t- R_EX_din_e\n", + "\t- R_EX_h2_e\n", + "\t- R_EX_dms_e\n", + "\t- R_EX_dmso_e\n", + "\t- R_EX_h2o_e\n", + "\t- R_EX_23camp_e\n", + "\t- R_EX_h2o2_e\n", + "\t- R_EX_h2s_e\n", + "\t- R_EX_dopa_e\n", + "\t- R_EX_doxrbcn_e\n", + "\t- R_EX_hacolipa_e\n", + "\t- R_EX_dtmp_e\n", + "\t- R_EX_halipa_e\n", + "\t- R_EX_dump_e\n", + "\t- R_EX_23ccmp_e\n", + "\t- R_EX_duri_e\n", + "\t- R_EX_hdca_e\n", + "\t- R_EX_hdcea_e\n", + "\t- R_EX_eca4colipa_e\n", + "\t- R_EX_23cgmp_e\n", + "\t- R_EX_hg2_e\n", + "\t- R_EX_enlipa_e\n", + "\t- R_EX_enter_e\n", + "\t- R_EX_etha_e\n", + "\t- R_EX_ethso3_e\n", + "\t- R_EX_his__L_e\n", + "\t- R_EX_hom__L_e\n", + "\t- R_EX_hxa_e\n", + "\t- R_EX_hxan_e\n", + "\t- R_EX_23cump_e\n", + "\t- R_EX_etoh_e\n", + "\t- R_EX_f6p_e\n", + "\t- R_EX_fald_e\n", + "\t- R_EX_idon__L_e\n", + "\t- R_EX_23dappa_e\n", + "\t- R_EX_ile__L_e\n", + "\t- R_EX_imp_e\n", + "\t- R_EX_indole_e\n", + "\t- R_EX_fe2_e\n", + "\t- R_EX_fe3_e\n", + "\t- R_EX_fe3dcit_e\n", + "\t- R_EX_fe3dhbzs_e\n", + "\t- R_EX_inost_e\n", + "\t- R_EX_ins_e\n", + "\t- R_EX_26dap__M_e\n", + "\t- R_EX_isetac_e\n", + "\t- R_EX_k_e\n", + "\t- R_EX_fe3hox_e\n", + "\t- R_EX_fe3hox_un_e\n", + "\t- R_EX_fecrm_e\n", + "\t- R_EX_kdo2lipid4_e\n", + "\t- R_EX_lac__D_e\n", + "\t- R_EX_fecrm_un_e\n", + "\t- R_EX_2ddglcn_e\n", + "\t- R_EX_feenter_e\n", + "\t- R_EX_feoxam_e\n", + "\t- R_EX_lac__L_e\n", + "\t- R_EX_lcts_e\n", + "\t- R_EX_feoxam_un_e\n", + "\t- R_EX_for_e\n", + "\t- R_EX_fru_e\n", + "\t- R_EX_leu__L_e\n", + "\t- R_EX_lipa_e\n", + "\t- R_EX_lipa_cold_e\n", + "\t- R_EX_lipoate_e\n", + "\t- R_EX_frulys_e\n", + "\t- R_EX_lys__L_e\n", + "\t- R_EX_34dhpac_e\n", + "\t- R_EX_lyx__L_e\n", + "\t- R_EX_mal__D_e\n", + "\t- R_EX_fruur_e\n", + "\t- R_EX_fuc__L_e\n", + "\t- R_EX_fum_e\n", + "\t- R_EX_mal__L_e\n", + "\t- R_EX_malt_e\n", + "\t- R_EX_fusa_e\n", + "\t- R_EX_3amp_e\n", + "\t- R_EX_g1p_e\n", + "\t- R_EX_malthx_e\n", + "\t- R_EX_maltpt_e\n", + "\t- R_EX_g3pc_e\n", + "\t- R_EX_3cmp_e\n", + "\t- R_EX_g3pe_e\n", + "\t- R_EX_malttr_e\n", + "\t- R_EX_g3pg_e\n", + "\t- R_EX_maltttr_e\n", + "\t- R_EX_3gmp_e\n", + "\t- R_EX_man_e\n", + "\t- R_EX_g3pi_e\n", + "\t- R_EX_g3ps_e\n", + "\t- R_EX_man6p_e\n", + "\t- R_EX_g6p_e\n", + "\t- R_EX_gal_e\n", + "\t- R_EX_manglyc_e\n", + "\t- R_EX_3hcinnm_e\n", + "\t- R_EX_melib_e\n", + "\t- R_EX_meoh_e\n", + "\t- R_EX_met__D_e\n", + "\t- R_EX_met__L_e\n", + "\t- R_EX_metsox_R__L_e\n", + "\t- R_EX_gal_bD_e\n", + "\t- R_EX_gal1p_e\n", + "\t- R_EX_mmet_e\n", + "\t- R_EX_metsox_S__L_e\n", + "\t- R_EX_mg2_e\n", + "\t- R_EX_mn2_e\n", + "\t- R_EX_mincyc_e\n", + "\t- R_EX_3hpp_e\n", + "\t- R_EX_minohp_e\n", + "\t- R_EX_mnl_e\n", + "\t- R_EX_mobd_e\n", + "\t- R_EX_mso3_e\n", + "\t- R_EX_3hpppn_e\n", + "\t- R_EX_n2o_e\n", + "\t- R_EX_na1_e\n", + "\t- R_EX_3ump_e\n", + "\t- R_EX_nac_e\n", + "\t- R_EX_nh4_e\n", + "\t- R_EX_ni2_e\n", + "\t- R_EX_uacgam_e\n", + "\t- R_EX_udpacgal_e\n", + "\t- R_EX_udpg_e\n", + "\t- R_EX_udpgal_e\n", + "\t- R_EX_nmn_e\n", + "\t- R_EX_4abut_e\n", + "\t- R_EX_no_e\n", + "\t- R_EX_udpglcur_e\n", + "\t- R_EX_ump_e\n", + "\t- R_EX_no2_e\n", + "\t- R_EX_4hoxpacd_e\n", + "\t- R_EX_no3_e\n", + "\t- R_EX_ura_e\n", + "\t- R_EX_urea_e\n", + "\t- R_EX_novbcn_e\n", + "\t- R_EX_o16a4colipa_e\n", + "\t- R_EX_o2_e\n", + "\t- R_EX_uri_e\n", + "\t- R_EX_val__L_e\n", + "\t- R_EX_xan_e\n", + "\t- R_EX_xmp_e\n", + "\t- R_EX_xtsn_e\n", + "\t- R_EX_o2s_e\n", + "\t- R_EX_xyl__D_e\n", + "\t- R_EX_ocdca_e\n", + "\t- R_EX_5dglcn_e\n", + "\t- R_EX_ocdcea_e\n", + "\t- R_EX_octa_e\n", + "\t- R_EX_xylu__L_e\n", + "\t- R_EX_zn2_e\n", + "\t- R_EX_orn_e\n", + "\t- R_EX_orot_e\n", + "\t- R_EX_5mtr_e\n", + "\t- R_EX_pacald_e\n", + "\t- R_EX_peamn_e\n", + "\t- R_EX_phe__L_e\n", + "\t- R_EX_LalaDglu_e\n", + "\t- R_EX_pheme_e\n", + "\t- R_EX_LalaDgluMdap_e\n", + "\t- R_EX_pi_e\n", + "\t- R_EX_pnto__R_e\n", + "\t- R_EX_ppa_e\n", + "\t- R_EX_ppal_e\n", + "\t- R_EX_pppn_e\n", + "\t- R_EX_LalaDgluMdapDala_e\n", + "\t- R_EX_ppt_e\n", + "\t- R_EX_pro__L_e\n", + "\t- R_EX_progly_e\n", + "\t- R_EX_psclys_e\n", + "\t- R_EX_LalaLglu_e\n", + "\t- R_EX_pser__L_e\n", + "\t- R_EX_ptrc_e\n", + "\t- R_EX_ac_e\n", + "\t- R_EX_pydam_e\n", + "\t- R_EX_pydx_e\n", + "\t- R_EX_acac_e\n", + "\t- R_EX_pydxn_e\n", + "\t- R_EX_pyr_e\n", + "\t- R_EX_quin_e\n", + "\t- R_EX_r5p_e\n", + "\t- R_EX_rfamp_e\n", + "\t- R_EX_rib__D_e\n", + "\t- R_EX_acald_e\n", + "\t- R_EX_rmn_e\n", + "\t- R_EX_sbt__D_e\n", + "\t- R_EX_acgal_e\n", + "\t- R_EX_sel_e\n", + "\t- R_EX_ser__D_e\n", + "\t- R_EX_ser__L_e\n", + "\t- R_EX_acgal1p_e\n", + "\t- R_EX_skm_e\n", + "\t- R_EX_slnt_e\n", + "\t- R_EX_acgam_e\n", + "\t- R_EX_so2_e\n", + "\t- R_EX_so3_e\n", + "\t- R_EX_acgam1p_e\n", + "\t- R_EX_so4_e\n", + "\t- R_EX_spmd_e\n", + "\t- R_EX_succ_e\n", + "\t- R_EX_acmana_e\n", + "\t- R_EX_sucr_e\n", + "\t- R_EX_sulfac_e\n", + "\t- R_EX_tartr__D_e\n", + "\t- R_EX_tartr__L_e\n", + "\t- R_EX_taur_e\n", + "\t- R_EX_tcynt_e\n", + "\t- R_EX_thm_e\n", + "\t- R_EX_acmum_e\n", + "\t- R_EX_thr__L_e\n", + "\t- R_EX_thrp_e\n", + "\t- R_EX_thym_e\n", + "\t- R_EX_thymd_e\n", + "\t- R_EX_acnam_e\n", + "\t- R_EX_acolipa_e\n", + "\t- R_EX_tma_e\n", + "\t- R_EX_tmao_e\n", + "\t- R_EX_tre_e\n", + "\t- R_EX_trp__L_e\n", + "\t- R_EX_tsul_e\n", + "\t- R_EX_ttdca_e\n", + "\t- R_EX_ttdcea_e\n", + "\t- R_EX_ttrcyc_e\n", + "\t- R_EX_tungs_e\n", + "\t- R_EX_acser_e\n", + "\t- R_EX_tym_e\n", + "\t- R_EX_tyr__L_e\n", + "\t- R_EX_tyrp_e\n", + "\t- R_EX_ade_e\n", + "\t- R_EX_adn_e\n", + "\t- R_EX_adocbl_e\n", + "\t- R_EX_ag_e\n", + "\t- R_EX_agm_e\n", + "\t- R_EX_akg_e\n", + "\t- R_EX_ala_B_e\n", + "\t- R_EX_ala__D_e\n", + "\t- R_EX_ala__L_e\n", + "\t- R_EX_alaala_e\n", + "\t- R_EX_all__D_e\n", + "\t- R_EX_alltn_e\n", + "\t- R_EX_amp_e\n", + "\t- R_EX_anhgm_e\n", + "\t- R_EX_arab__L_e\n", + "\t- R_EX_arbt_e\n", + "\t- R_EX_arbtn_e\n", + "\t- R_EX_arbtn_fe3_e\n", + "\t- R_EX_4hphac_e\n", + "\t- R_EX_4abz_e\n", + "\t- R_EX_frmd_e\n", + "\t- R_EX_colipaOA_e\n", + "\t- R_EX_acon_C_e\n", + "\t- R_EX_salchsx_e\n", + "\t- R_EX_AEP_e\n", + "\t- R_EX_icit_e\n", + "\t- R_EX_colipa20Oag_e\n", + "\t- R_EX_remnant1_e\n", + "\t- R_EX_feroxGfe_e\n", + "\t- R_EX_salchs2_e\n", + "\t- R_EX_tcb_e\n", + "\t- R_EX_feroxB_e\n", + "\t- R_EX_rnam_e\n", + "\t- R_EX_airs_e\n", + "\t- R_EX_dxyl_e\n", + "\t- R_EX_tton_e\n", + "\t- R_EX_3pg_e\n", + "\t- R_EX_4hthr_e\n", + "\t- R_EX_bz_e\n", + "\t- R_EX_udcpo4_e\n", + "\t- R_EX_cellb_e\n", + "\t- R_EX_feroxE_e\n", + "\t- R_EX_13ppd_e\n", + "\t- R_EX_abt__D_e\n", + "\t- R_EX_udcpo5_e\n", + "\t- R_EX_guln__L_e\n", + "\t- R_EX_pep_e\n", + "\t- R_EX_feroxG_e\n", + "\t- R_EX_rbt_e\n", + "\t- R_EX_3ntym_e\n", + "\t- R_EX_23dhbzs3_e\n", + "\t- R_EX_tet_e\n", + "\t- R_EX_diact_e\n", + "\t- R_EX_6apa_e\n", + "\t- R_EX_btd_RR_e\n", + "\t- R_EX_peng_e\n", + "\t- R_EX_pac_e\n", + "\t- R_EX_salchs4_e\n", + "\t- R_EX_fe_e\n", + "\t- R_EX_kokdolipidA_e\n", + "\t- R_EX_salcn_e\n", + "\t- R_EX_galam_e\n", + "\t- R_EX_raffin_e\n", + "\t- R_EX_3hoxpac_e\n", + "\t- R_EX_4hoxpac_e\n", + "\t- R_EX_o6a4colipa_e\n", + "\t- R_DM_lipidA_core_e_p\n", + "\t- R_EX_arab__D_e\n", + "\t- R_EX_tag__D_e\n", + "\t- R_EX_oaa_e\n", + "\t- R_EX_salchs2fe_e\n", + "\t- R_EX_feroxEfe_e\n", + "\t- R_EX_cysi__L_e\n", + "\t- R_EX_fe3dhbzs3_e\n", + "\t- R_EX_drib_e\n", + "\t- R_EX_feroxBfe_e\n", + "\t- R_EX_salchs4fe_e\n", + "\t- R_EX_2pg_e\u001b[0m\n", + "File saved at: ../../data/sbml_corrected/iUMNK88_1353.xml\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: iEKO11_1354\n", + "\u001b[0;93mWARNING : \n", + " - R_CAT: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_DHPTDNRN: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_DHPTDNR: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_FHL: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SPODM: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SPODMpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCASPtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCFUMtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCMALtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCTARTtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_EX_chitob_e: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_CHITOBpts: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_CHITPH: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_URCN: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\u001b[0m\n", + "\n", + "\n", + "\u001b[0;36m############################################\n", + "############################################\n", + " WRITING SBML FILE \n", + "############################################\n", + "############################################\n", + "\u001b[0m\n", + "\u001b[0;93mWARNING : IMPORT REACTION REMOVED BY DEFAULT\u001b[0m\n", + "\u001b[0;93mWARNING : If you want to keep import reaction\n", + "use option -kir / --keep-import-reactions\u001b[0m\n", + "\u001b[0;93mWARNING : Import reaction removed:\n", + "\t- R_EX_cm_e\n", + "\t- R_EX_cmp_e\n", + "\t- R_EX_co2_e\n", + "\t- R_EX_cobalt2_e\n", + "\t- R_EX_colipa_e\n", + "\t- R_EX_colipap_e\n", + "\t- R_EX_cpgn_e\n", + "\t- R_EX_cpgn_un_e\n", + "\t- R_EX_crn_e\n", + "\t- R_EX_crn__D_e\n", + "\t- R_EX_csn_e\n", + "\t- R_EX_cu_e\n", + "\t- R_EX_cu2_e\n", + "\t- R_EX_cyan_e\n", + "\t- R_EX_cynt_e\n", + "\t- R_EX_cys__D_e\n", + "\t- R_EX_cys__L_e\n", + "\t- R_DM_4crsol_c\n", + "\t- R_EX_glc__D_e\n", + "\t- R_EX_glcn_e\n", + "\t- R_EX_glcr_e\n", + "\t- R_EX_glcur_e\n", + "\t- R_DM_5drib_c\n", + "\t- R_EX_cytd_e\n", + "\t- R_DM_aacald_c\n", + "\t- R_DM_amob_c\n", + "\t- R_DM_mththf_c\n", + "\t- R_EX_glcur1p_e\n", + "\t- R_EX_gln__L_e\n", + "\t- R_EX_dad_2_e\n", + "\t- R_EX_glu__L_e\n", + "\t- R_EX_gly_e\n", + "\t- R_DM_oxam_c\n", + "\t- R_EX_12ppd__R_e\n", + "\t- R_EX_12ppd__S_e\n", + "\t- R_EX_14glucan_e\n", + "\t- R_EX_15dap_e\n", + "\t- R_EX_23camp_e\n", + "\t- R_EX_glyald_e\n", + "\t- R_EX_glyb_e\n", + "\t- R_EX_glyc_e\n", + "\t- R_EX_glyc__R_e\n", + "\t- R_EX_glyc2p_e\n", + "\t- R_EX_glyc3p_e\n", + "\t- R_EX_glyclt_e\n", + "\t- R_EX_gmp_e\n", + "\t- R_EX_gsn_e\n", + "\t- R_EX_gthox_e\n", + "\t- R_EX_gthrd_e\n", + "\t- R_EX_gtp_e\n", + "\t- R_EX_23ccmp_e\n", + "\t- R_EX_23cgmp_e\n", + "\t- R_EX_23cump_e\n", + "\t- R_EX_23dappa_e\n", + "\t- R_EX_26dap__M_e\n", + "\t- R_EX_gua_e\n", + "\t- R_EX_damp_e\n", + "\t- R_EX_h_e\n", + "\t- R_EX_h2_e\n", + "\t- R_EX_h2o_e\n", + "\t- R_EX_2ddglcn_e\n", + "\t- R_EX_34dhpac_e\n", + "\t- R_EX_3amp_e\n", + "\t- R_EX_3cmp_e\n", + "\t- R_EX_3gmp_e\n", + "\t- R_EX_h2o2_e\n", + "\t- R_EX_3hcinnm_e\n", + "\t- R_EX_dca_e\n", + "\t- R_EX_3hpp_e\n", + "\t- R_EX_h2s_e\n", + "\t- R_EX_hacolipa_e\n", + "\t- R_EX_halipa_e\n", + "\t- R_EX_hdca_e\n", + "\t- R_EX_3hpppn_e\n", + "\t- R_EX_3ump_e\n", + "\t- R_EX_4abut_e\n", + "\t- R_EX_4hoxpacd_e\n", + "\t- R_EX_5dglcn_e\n", + "\t- R_EX_5mtr_e\n", + "\t- R_EX_hdcea_e\n", + "\t- R_EX_hg2_e\n", + "\t- R_EX_his__L_e\n", + "\t- R_EX_LalaDglu_e\n", + "\t- R_EX_dcmp_e\n", + "\t- R_EX_LalaDgluMdap_e\n", + "\t- R_EX_LalaDgluMdapDala_e\n", + "\t- R_EX_hom__L_e\n", + "\t- R_EX_hxa_e\n", + "\t- R_EX_LalaLglu_e\n", + "\t- R_EX_dcyt_e\n", + "\t- R_EX_ac_e\n", + "\t- R_EX_hxan_e\n", + "\t- R_EX_acac_e\n", + "\t- R_EX_ddca_e\n", + "\t- R_EX_acald_e\n", + "\t- R_EX_idon__L_e\n", + "\t- R_EX_ile__L_e\n", + "\t- R_EX_acgal_e\n", + "\t- R_EX_dgmp_e\n", + "\t- R_EX_imp_e\n", + "\t- R_EX_indole_e\n", + "\t- R_EX_acgal1p_e\n", + "\t- R_EX_inost_e\n", + "\t- R_EX_dgsn_e\n", + "\t- R_EX_ins_e\n", + "\t- R_EX_acgam_e\n", + "\t- R_EX_dha_e\n", + "\t- R_EX_acgam1p_e\n", + "\t- R_EX_acmana_e\n", + "\t- R_EX_acmum_e\n", + "\t- R_EX_acnam_e\n", + "\t- R_EX_isetac_e\n", + "\t- R_EX_k_e\n", + "\t- R_EX_kdo2lipid4_e\n", + "\t- R_EX_lac__D_e\n", + "\t- R_EX_lac__L_e\n", + "\t- R_EX_acolipa_e\n", + "\t- R_EX_acser_e\n", + "\t- R_EX_ade_e\n", + "\t- R_EX_lcts_e\n", + "\t- R_EX_dimp_e\n", + "\t- R_EX_leu__L_e\n", + "\t- R_EX_adn_e\n", + "\t- R_EX_adocbl_e\n", + "\t- R_EX_lipa_e\n", + "\t- R_EX_lipa_cold_e\n", + "\t- R_EX_din_e\n", + "\t- R_EX_lipoate_e\n", + "\t- R_EX_ag_e\n", + "\t- R_EX_agm_e\n", + "\t- R_EX_lys__L_e\n", + "\t- R_EX_akg_e\n", + "\t- R_EX_dms_e\n", + "\t- R_EX_ala_B_e\n", + "\t- R_EX_ala__D_e\n", + "\t- R_EX_lyx__L_e\n", + "\t- R_EX_mal__D_e\n", + "\t- R_EX_dmso_e\n", + "\t- R_EX_mal__L_e\n", + "\t- R_EX_ala__L_e\n", + "\t- R_EX_alaala_e\n", + "\t- R_EX_malt_e\n", + "\t- R_EX_all__D_e\n", + "\t- R_EX_dopa_e\n", + "\t- R_EX_alltn_e\n", + "\t- R_EX_amp_e\n", + "\t- R_EX_anhgm_e\n", + "\t- R_EX_arab__L_e\n", + "\t- R_EX_malthx_e\n", + "\t- R_EX_maltpt_e\n", + "\t- R_EX_malttr_e\n", + "\t- R_EX_maltttr_e\n", + "\t- R_EX_man_e\n", + "\t- R_EX_arbt_e\n", + "\t- R_EX_arbtn_e\n", + "\t- R_EX_doxrbcn_e\n", + "\t- R_EX_arbtn_fe3_e\n", + "\t- R_EX_arg__L_e\n", + "\t- R_EX_man6p_e\n", + "\t- R_EX_manglyc_e\n", + "\t- R_EX_ascb__L_e\n", + "\t- R_EX_melib_e\n", + "\t- R_EX_dtmp_e\n", + "\t- R_EX_asn__L_e\n", + "\t- R_EX_aso3_e\n", + "\t- R_EX_meoh_e\n", + "\t- R_EX_dump_e\n", + "\t- R_EX_met__D_e\n", + "\t- R_EX_met__L_e\n", + "\t- R_EX_asp__L_e\n", + "\t- R_EX_duri_e\n", + "\t- R_EX_btn_e\n", + "\t- R_EX_metsox_R__L_e\n", + "\t- R_EX_metsox_S__L_e\n", + "\t- R_EX_but_e\n", + "\t- R_EX_eca4colipa_e\n", + "\t- R_EX_butso3_e\n", + "\t- R_EX_ca2_e\n", + "\t- R_EX_mg2_e\n", + "\t- R_EX_mincyc_e\n", + "\t- R_EX_cbi_e\n", + "\t- R_EX_enlipa_e\n", + "\t- R_EX_cbl1_e\n", + "\t- R_EX_cd2_e\n", + "\t- R_EX_cgly_e\n", + "\t- R_EX_minohp_e\n", + "\t- R_EX_mmet_e\n", + "\t- R_EX_mn2_e\n", + "\t- R_EX_mnl_e\n", + "\t- R_EX_mobd_e\n", + "\t- R_EX_chol_e\n", + "\t- R_EX_chtbs_e\n", + "\t- R_EX_mso3_e\n", + "\t- R_EX_enter_e\n", + "\t- R_EX_n2o_e\n", + "\t- R_EX_cit_e\n", + "\t- R_EX_cl_e\n", + "\t- R_EX_na1_e\n", + "\t- R_EX_etha_e\n", + "\t- R_EX_nac_e\n", + "\t- R_EX_ethso3_e\n", + "\t- R_EX_nh4_e\n", + "\t- R_EX_ni2_e\n", + "\t- R_EX_etoh_e\n", + "\t- R_EX_nmn_e\n", + "\t- R_EX_f6p_e\n", + "\t- R_EX_no_e\n", + "\t- R_EX_val__L_e\n", + "\t- R_EX_no2_e\n", + "\t- R_EX_fald_e\n", + "\t- R_EX_no3_e\n", + "\t- R_EX_xan_e\n", + "\t- R_EX_xmp_e\n", + "\t- R_EX_novbcn_e\n", + "\t- R_EX_o16a4colipa_e\n", + "\t- R_EX_xtsn_e\n", + "\t- R_EX_fe2_e\n", + "\t- R_EX_xyl__D_e\n", + "\t- R_EX_xylu__L_e\n", + "\t- R_EX_zn2_e\n", + "\t- R_EX_o2_e\n", + "\t- R_EX_o2s_e\n", + "\t- R_EX_ocdca_e\n", + "\t- R_EX_ocdcea_e\n", + "\t- R_EX_octa_e\n", + "\t- R_EX_orn_e\n", + "\t- R_EX_orot_e\n", + "\t- R_EX_pacald_e\n", + "\t- R_EX_peamn_e\n", + "\t- R_EX_phe__L_e\n", + "\t- R_EX_pheme_e\n", + "\t- R_EX_fe3_e\n", + "\t- R_EX_pi_e\n", + "\t- R_EX_pnto__R_e\n", + "\t- R_EX_ppa_e\n", + "\t- R_EX_ppal_e\n", + "\t- R_EX_fe3dcit_e\n", + "\t- R_EX_pppn_e\n", + "\t- R_EX_ppt_e\n", + "\t- R_EX_fe3dhbzs_e\n", + "\t- R_EX_pro__L_e\n", + "\t- R_EX_progly_e\n", + "\t- R_EX_psclys_e\n", + "\t- R_EX_pser__L_e\n", + "\t- R_EX_ptrc_e\n", + "\t- R_EX_fe3hox_e\n", + "\t- R_EX_pydam_e\n", + "\t- R_EX_pydx_e\n", + "\t- R_EX_pydxn_e\n", + "\t- R_EX_pyr_e\n", + "\t- R_EX_fe3hox_un_e\n", + "\t- R_EX_quin_e\n", + "\t- R_EX_fecrm_e\n", + "\t- R_EX_r5p_e\n", + "\t- R_EX_fecrm_un_e\n", + "\t- R_EX_rfamp_e\n", + "\t- R_EX_feenter_e\n", + "\t- R_EX_rib__D_e\n", + "\t- R_EX_rmn_e\n", + "\t- R_EX_sbt__D_e\n", + "\t- R_EX_sel_e\n", + "\t- R_EX_ser__D_e\n", + "\t- R_EX_ser__L_e\n", + "\t- R_EX_skm_e\n", + "\t- R_EX_slnt_e\n", + "\t- R_EX_feoxam_e\n", + "\t- R_EX_so2_e\n", + "\t- R_EX_so3_e\n", + "\t- R_EX_so4_e\n", + "\t- R_EX_spmd_e\n", + "\t- R_EX_feoxam_un_e\n", + "\t- R_EX_succ_e\n", + "\t- R_EX_sucr_e\n", + "\t- R_EX_sulfac_e\n", + "\t- R_EX_tartr__D_e\n", + "\t- R_EX_for_e\n", + "\t- R_EX_tartr__L_e\n", + "\t- R_EX_taur_e\n", + "\t- R_EX_tcynt_e\n", + "\t- R_EX_thm_e\n", + "\t- R_EX_thr__L_e\n", + "\t- R_EX_thrp_e\n", + "\t- R_EX_thym_e\n", + "\t- R_EX_thymd_e\n", + "\t- R_EX_fru_e\n", + "\t- R_EX_tma_e\n", + "\t- R_EX_tmao_e\n", + "\t- R_EX_tre_e\n", + "\t- R_EX_trp__L_e\n", + "\t- R_EX_frulys_e\n", + "\t- R_EX_tsul_e\n", + "\t- R_EX_ttdca_e\n", + "\t- R_EX_ttdcea_e\n", + "\t- R_EX_fruur_e\n", + "\t- R_EX_ttrcyc_e\n", + "\t- R_EX_tungs_e\n", + "\t- R_EX_tym_e\n", + "\t- R_EX_tyr__L_e\n", + "\t- R_EX_tyrp_e\n", + "\t- R_EX_fuc__L_e\n", + "\t- R_EX_uacgam_e\n", + "\t- R_EX_udpacgal_e\n", + "\t- R_EX_udpg_e\n", + "\t- R_EX_fum_e\n", + "\t- R_EX_udpgal_e\n", + "\t- R_EX_fusa_e\n", + "\t- R_EX_udpglcur_e\n", + "\t- R_EX_ump_e\n", + "\t- R_EX_ura_e\n", + "\t- R_EX_g1p_e\n", + "\t- R_EX_urea_e\n", + "\t- R_EX_uri_e\n", + "\t- R_EX_g3pc_e\n", + "\t- R_EX_g3pe_e\n", + "\t- R_EX_g3pg_e\n", + "\t- R_EX_g3pi_e\n", + "\t- R_EX_g3ps_e\n", + "\t- R_EX_g6p_e\n", + "\t- R_EX_gal_e\n", + "\t- R_EX_gal_bD_e\n", + "\t- R_EX_gal1p_e\n", + "\t- R_EX_galct__D_e\n", + "\t- R_EX_galctn__D_e\n", + "\t- R_EX_galctn__L_e\n", + "\t- R_EX_galt_e\n", + "\t- R_EX_galur_e\n", + "\t- R_EX_gam_e\n", + "\t- R_EX_gam6p_e\n", + "\t- R_EX_gbbtn_e\n", + "\t- R_EX_gdp_e\n", + "\t- R_EX_feroxBfe_e\n", + "\t- R_EX_4hphac_e\n", + "\t- R_EX_4abz_e\n", + "\t- R_EX_fe3dhbzs3_e\n", + "\t- R_EX_acon_C_e\n", + "\t- R_EX_drib_e\n", + "\t- R_EX_salchs4fe_e\n", + "\t- R_EX_2pg_e\n", + "\t- R_EX_feroxB_e\n", + "\t- R_EX_frmd_e\n", + "\t- R_EX_tcb_e\n", + "\t- R_EX_tton_e\n", + "\t- R_EX_4hthr_e\n", + "\t- R_EX_colipaOA_e\n", + "\t- R_EX_salchsx_e\n", + "\t- R_EX_AEP_e\n", + "\t- R_EX_tag__D_e\n", + "\t- R_EX_icit_e\n", + "\t- R_EX_pep_e\n", + "\t- R_EX_colipa20Oag_e\n", + "\t- R_EX_feroxEfe_e\n", + "\t- R_EX_feroxE_e\n", + "\t- R_EX_23dhbzs3_e\n", + "\t- R_EX_tet_e\n", + "\t- R_EX_salchs4_e\n", + "\t- R_EX_remnant1_e\n", + "\t- R_EX_udcpo5_e\n", + "\t- R_EX_oaa_e\n", + "\t- R_EX_salchs2_e\n", + "\t- R_EX_salchs2fe_e\n", + "\t- R_EX_cysi__L_e\n", + "\t- R_EX_rnam_e\n", + "\t- R_EX_airs_e\n", + "\t- R_EX_guln__L_e\n", + "\t- R_EX_dxyl_e\n", + "\t- R_EX_3pg_e\n", + "\t- R_EX_feroxGfe_e\n", + "\t- R_EX_3ntym_e\n", + "\t- R_EX_6apa_e\n", + "\t- R_EX_feroxG_e\n", + "\t- R_EX_4hoxpac_e\n", + "\t- R_EX_udcpo4_e\n", + "\t- R_EX_peng_e\n", + "\t- R_EX_pac_e\n", + "\t- R_EX_salcn_e\n", + "\t- R_EX_diact_e\n", + "\t- R_EX_cellb_e\n", + "\t- R_EX_btd_RR_e\n", + "\t- R_EX_rbt_e\n", + "\t- R_DM_lipidA_core_e_p\n", + "\t- R_EX_galam_e\n", + "\t- R_EX_raffin_e\n", + "\t- R_EX_bz_e\n", + "\t- R_EX_o6a4colipa_e\n", + "\t- R_EX_13ppd_e\n", + "\t- R_EX_arab__D_e\n", + "\t- R_EX_abt__D_e\n", + "\t- R_EX_fe_e\n", + "\t- R_EX_kokdolipidA_e\n", + "\t- R_EX_3hoxpac_e\u001b[0m\n", + "File saved at: ../../data/sbml_corrected/iEKO11_1354.xml\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: iNJ661\n", + "\u001b[0;93mWARNING : - R_KARA1: Reactants and products exchanged.\n", + " Boundaries was: [-999999.0 ; 0.0]\n", + "\n", + " - R_EX_co_e: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_DM_atp_c: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_CITL_copy2: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\u001b[0m\n", + "\n", + "\n", + "\u001b[0;36m############################################\n", + "############################################\n", + " WRITING SBML FILE \n", + "############################################\n", + "############################################\n", + "\u001b[0m\n", + "\u001b[0;93mWARNING : IMPORT REACTION REMOVED BY DEFAULT\u001b[0m\n", + "\u001b[0;93mWARNING : If you want to keep import reaction\n", + "use option -kir / --keep-import-reactions\u001b[0m\n", + "\u001b[0;93mWARNING : Reaction with tag reversible modified:\n", + "\t- R_KARA1\u001b[0m\n", + "\u001b[0;93mWARNING : Reaction with Reactants and Products exchanged:\n", + "\t- R_KARA1\u001b[0m\n", + "\u001b[0;93mWARNING : Import reaction removed:\n", + "\t- R_EX_mal__L_e\n", + "\t- R_EX_lys__L_e\n", + "\t- R_EX_leu__L_e\n", + "\t- R_EX_lcts_e\n", + "\t- R_EX_lac__L_e\n", + "\t- R_EX_k_e\n", + "\t- R_EX_ile__L_e\n", + "\t- R_EX_id3acald_e\n", + "\t- R_EX_his__L_e\n", + "\t- R_EX_hdca_e\n", + "\t- R_EX_h_e\n", + "\t- R_EX_h2s_e\n", + "\t- R_EX_h2o_e\n", + "\t- R_EX_h2co3_e\n", + "\t- R_EX_h2_e\n", + "\t- R_EX_gthrd_e\n", + "\t- R_EX_glyc_e\n", + "\t- R_EX_glyc3p_e\n", + "\t- R_EX_glyb_e\n", + "\t- R_EX_gly_e\n", + "\t- R_EX_glu__L_e\n", + "\t- R_EX_glc__D_e\n", + "\t- R_EX_gal_e\n", + "\t- R_EX_fum_e\n", + "\t- R_EX_fe3_e\n", + "\t- R_EX_fe2_e\n", + "\t- R_EX_etha_e\n", + "\t- R_EX_cytd_e\n", + "\t- R_EX_cu2_e\n", + "\t- R_EX_crn_e\n", + "\t- R_EX_cobalt2_e\n", + "\t- R_EX_coa_e\n", + "\t- R_EX_co2_e\n", + "\t- R_EX_cl_e\n", + "\t- R_EX_cit_e\n", + "\t- R_EX_chol_e\n", + "\t- R_EX_ca2_e\n", + "\t- R_EX_bmn_e\n", + "\t- R_EX_atp_e\n", + "\t- R_EX_asp__L_e\n", + "\t- R_EX_asn__L_e\n", + "\t- R_EX_arg__L_e\n", + "\t- R_EX_arab__L_e\n", + "\t- R_EX_arab__D_e\n", + "\t- R_EX_ala__L_e\n", + "\t- R_EX_ala__D_e\n", + "\t- R_EX_met__L_e\n", + "\t- R_EX_meoh_e\n", + "\t- R_EX_malthp_e\n", + "\t- R_EX_malt_e\n", + "\t- R_EX_ala_B_e\n", + "\t- R_EX_acysbmn_e\n", + "\t- R_EX_xyl__D_e\n", + "\t- R_EX_val__L_e\n", + "\t- R_EX_tyr__L_e\n", + "\t- R_EX_ttdca_e\n", + "\t- R_EX_tsul_e\n", + "\t- R_EX_trp__L_e\n", + "\t- R_EX_tre_e\n", + "\t- R_EX_thr__L_e\n", + "\t- R_EX_tartr__L_e\n", + "\t- R_EX_succ_e\n", + "\t- R_EX_so4_e\n", + "\t- R_EX_skm_e\n", + "\t- R_EX_ser__L_e\n", + "\t- R_EX_ser__D_e\n", + "\t- R_EX_rib__D_e\n", + "\t- R_EX_pyr_e\n", + "\t- R_EX_pro__L_e\n", + "\t- R_EX_ppdima_e\n", + "\t- R_EX_ppa_e\n", + "\t- R_EX_pi_e\n", + "\t- R_EX_phe__L_e\n", + "\t- R_EX_phdca_e\n", + "\t- R_EX_pdima_e\n", + "\t- R_EX_pac_e\n", + "\t- R_EX_orn_e\n", + "\t- R_EX_ocdca_e\n", + "\t- R_EX_o2_e\n", + "\t- R_EX_no_e\n", + "\t- R_EX_no3_e\n", + "\t- R_EX_no2_e\n", + "\t- R_EX_nh4_e\n", + "\t- R_EX_na1_e\n", + "\t- R_EX_mg2_e\u001b[0m\n", + "File saved at: ../../data/sbml_corrected/iNJ661.xml\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: iSFxv_1172\n", + "\u001b[0;93mWARNING : \n", + " - R_CAT: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_DHPTDNR: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_DHPTDNRN: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SPODM: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SPODMpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCASPtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCFUMtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCMALtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCTARTtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_CHITOBpts: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_URCN: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_EX_chitob_e: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\u001b[0m\n", + "\n", + "\n", + "\u001b[0;36m############################################\n", + "############################################\n", + " WRITING SBML FILE \n", + "############################################\n", + "############################################\n", + "\u001b[0m\n", + "\u001b[0;93mWARNING : IMPORT REACTION REMOVED BY DEFAULT\u001b[0m\n", + "\u001b[0;93mWARNING : If you want to keep import reaction\n", + "use option -kir / --keep-import-reactions\u001b[0m\n", + "\u001b[0;93mWARNING : Import reaction removed:\n", + "\t- R_DM_4crsol_c\n", + "\t- R_DM_5drib_c\n", + "\t- R_DM_aacald_c\n", + "\t- R_DM_amob_c\n", + "\t- R_DM_mththf_c\n", + "\t- R_DM_oxam_c\n", + "\t- R_EX_12ppd__R_e\n", + "\t- R_EX_12ppd__S_e\n", + "\t- R_EX_14glucan_e\n", + "\t- R_EX_15dap_e\n", + "\t- R_EX_23camp_e\n", + "\t- R_EX_23ccmp_e\n", + "\t- R_EX_23cgmp_e\n", + "\t- R_EX_23cump_e\n", + "\t- R_EX_23dappa_e\n", + "\t- R_EX_26dap__M_e\n", + "\t- R_EX_but_e\n", + "\t- R_EX_btn_e\n", + "\t- R_EX_2ddglcn_e\n", + "\t- R_EX_butso3_e\n", + "\t- R_EX_ca2_e\n", + "\t- R_EX_cbi_e\n", + "\t- R_EX_34dhpac_e\n", + "\t- R_EX_cbl1_e\n", + "\t- R_EX_3amp_e\n", + "\t- R_EX_cd2_e\n", + "\t- R_EX_gal_bD_e\n", + "\t- R_EX_gal1p_e\n", + "\t- R_EX_cgly_e\n", + "\t- R_EX_galct__D_e\n", + "\t- R_EX_3cmp_e\n", + "\t- R_EX_galctn__D_e\n", + "\t- R_EX_galctn__L_e\n", + "\t- R_EX_galt_e\n", + "\t- R_EX_galur_e\n", + "\t- R_EX_gam_e\n", + "\t- R_EX_gam6p_e\n", + "\t- R_EX_gbbtn_e\n", + "\t- R_EX_gdp_e\n", + "\t- R_EX_glc__D_e\n", + "\t- R_EX_chol_e\n", + "\t- R_EX_chtbs_e\n", + "\t- R_EX_cit_e\n", + "\t- R_EX_cl_e\n", + "\t- R_EX_cm_e\n", + "\t- R_EX_cmp_e\n", + "\t- R_EX_co2_e\n", + "\t- R_EX_cobalt2_e\n", + "\t- R_EX_colipa_e\n", + "\t- R_EX_colipap_e\n", + "\t- R_EX_cpgn_e\n", + "\t- R_EX_cpgn_un_e\n", + "\t- R_EX_crn_e\n", + "\t- R_EX_crn__D_e\n", + "\t- R_EX_csn_e\n", + "\t- R_EX_glcn_e\n", + "\t- R_EX_glcr_e\n", + "\t- R_EX_glcur_e\n", + "\t- R_EX_glcur1p_e\n", + "\t- R_EX_gln__L_e\n", + "\t- R_EX_glu__L_e\n", + "\t- R_EX_gly_e\n", + "\t- R_EX_cu_e\n", + "\t- R_EX_3gmp_e\n", + "\t- R_EX_cu2_e\n", + "\t- R_EX_cyan_e\n", + "\t- R_EX_cynt_e\n", + "\t- R_EX_glyald_e\n", + "\t- R_EX_glyb_e\n", + "\t- R_EX_glyc_e\n", + "\t- R_EX_glyc__R_e\n", + "\t- R_EX_glyc2p_e\n", + "\t- R_EX_glyc3p_e\n", + "\t- R_EX_glyclt_e\n", + "\t- R_EX_cys__D_e\n", + "\t- R_EX_cys__L_e\n", + "\t- R_EX_cytd_e\n", + "\t- R_EX_dad_2_e\n", + "\t- R_EX_damp_e\n", + "\t- R_EX_dca_e\n", + "\t- R_EX_gmp_e\n", + "\t- R_EX_gsn_e\n", + "\t- R_EX_gthox_e\n", + "\t- R_EX_dcmp_e\n", + "\t- R_EX_gthrd_e\n", + "\t- R_EX_3hcinnm_e\n", + "\t- R_EX_gtp_e\n", + "\t- R_EX_dcyt_e\n", + "\t- R_EX_ddca_e\n", + "\t- R_EX_dgmp_e\n", + "\t- R_EX_gua_e\n", + "\t- R_EX_h_e\n", + "\t- R_EX_dgsn_e\n", + "\t- R_EX_3hpp_e\n", + "\t- R_EX_dha_e\n", + "\t- R_EX_dimp_e\n", + "\t- R_EX_h2_e\n", + "\t- R_EX_h2o_e\n", + "\t- R_EX_h2o2_e\n", + "\t- R_EX_3hpppn_e\n", + "\t- R_EX_h2s_e\n", + "\t- R_EX_hacolipa_e\n", + "\t- R_EX_din_e\n", + "\t- R_EX_dms_e\n", + "\t- R_EX_dmso_e\n", + "\t- R_EX_halipa_e\n", + "\t- R_EX_3ump_e\n", + "\t- R_EX_hdca_e\n", + "\t- R_EX_hdcea_e\n", + "\t- R_EX_hg2_e\n", + "\t- R_EX_his__L_e\n", + "\t- R_EX_hom__L_e\n", + "\t- R_EX_hxa_e\n", + "\t- R_EX_hxan_e\n", + "\t- R_EX_dopa_e\n", + "\t- R_EX_doxrbcn_e\n", + "\t- R_EX_dtmp_e\n", + "\t- R_EX_dump_e\n", + "\t- R_EX_duri_e\n", + "\t- R_EX_eca4colipa_e\n", + "\t- R_EX_enlipa_e\n", + "\t- R_EX_idon__L_e\n", + "\t- R_EX_ile__L_e\n", + "\t- R_EX_imp_e\n", + "\t- R_EX_enter_e\n", + "\t- R_EX_etha_e\n", + "\t- R_EX_indole_e\n", + "\t- R_EX_ethso3_e\n", + "\t- R_EX_4abut_e\n", + "\t- R_EX_etoh_e\n", + "\t- R_EX_f6p_e\n", + "\t- R_EX_inost_e\n", + "\t- R_EX_ins_e\n", + "\t- R_EX_isetac_e\n", + "\t- R_EX_k_e\n", + "\t- R_EX_fald_e\n", + "\t- R_EX_fe2_e\n", + "\t- R_EX_kdo2lipid4_e\n", + "\t- R_EX_fe3_e\n", + "\t- R_EX_4hoxpacd_e\n", + "\t- R_EX_fe3dcit_e\n", + "\t- R_EX_fe3dhbzs_e\n", + "\t- R_EX_lac__D_e\n", + "\t- R_EX_lac__L_e\n", + "\t- R_EX_fe3hox_e\n", + "\t- R_EX_fe3hox_un_e\n", + "\t- R_EX_fecrm_e\n", + "\t- R_EX_fecrm_un_e\n", + "\t- R_EX_lcts_e\n", + "\t- R_EX_leu__L_e\n", + "\t- R_EX_lipa_e\n", + "\t- R_EX_lipa_cold_e\n", + "\t- R_EX_feenter_e\n", + "\t- R_EX_5dglcn_e\n", + "\t- R_EX_feoxam_e\n", + "\t- R_EX_feoxam_un_e\n", + "\t- R_EX_lipoate_e\n", + "\t- R_EX_lys__L_e\n", + "\t- R_EX_lyx__L_e\n", + "\t- R_EX_for_e\n", + "\t- R_EX_fru_e\n", + "\t- R_EX_mal__D_e\n", + "\t- R_EX_5mtr_e\n", + "\t- R_EX_mal__L_e\n", + "\t- R_EX_malt_e\n", + "\t- R_EX_frulys_e\n", + "\t- R_EX_fruur_e\n", + "\t- R_EX_fuc__L_e\n", + "\t- R_EX_malthx_e\n", + "\t- R_EX_maltpt_e\n", + "\t- R_EX_LalaDglu_e\n", + "\t- R_EX_malttr_e\n", + "\t- R_EX_fum_e\n", + "\t- R_EX_fusa_e\n", + "\t- R_EX_LalaDgluMdap_e\n", + "\t- R_EX_g1p_e\n", + "\t- R_EX_g3pc_e\n", + "\t- R_EX_maltttr_e\n", + "\t- R_EX_LalaDgluMdapDala_e\n", + "\t- R_EX_man_e\n", + "\t- R_EX_man6p_e\n", + "\t- R_EX_g3pe_e\n", + "\t- R_EX_g3pg_e\n", + "\t- R_EX_g3pi_e\n", + "\t- R_EX_g3ps_e\n", + "\t- R_EX_g6p_e\n", + "\t- R_EX_manglyc_e\n", + "\t- R_EX_melib_e\n", + "\t- R_EX_meoh_e\n", + "\t- R_EX_met__D_e\n", + "\t- R_EX_gal_e\n", + "\t- R_EX_LalaLglu_e\n", + "\t- R_EX_met__L_e\n", + "\t- R_EX_metsox_R__L_e\n", + "\t- R_EX_metsox_S__L_e\n", + "\t- R_EX_mg2_e\n", + "\t- R_EX_mincyc_e\n", + "\t- R_EX_minohp_e\n", + "\t- R_EX_ac_e\n", + "\t- R_EX_mmet_e\n", + "\t- R_EX_mn2_e\n", + "\t- R_EX_tyr__L_e\n", + "\t- R_EX_acac_e\n", + "\t- R_EX_tyrp_e\n", + "\t- R_EX_mnl_e\n", + "\t- R_EX_mobd_e\n", + "\t- R_EX_uacgam_e\n", + "\t- R_EX_acald_e\n", + "\t- R_EX_udpacgal_e\n", + "\t- R_EX_udpg_e\n", + "\t- R_EX_mso3_e\n", + "\t- R_EX_n2o_e\n", + "\t- R_EX_na1_e\n", + "\t- R_EX_udpgal_e\n", + "\t- R_EX_nac_e\n", + "\t- R_EX_acgal_e\n", + "\t- R_EX_nh4_e\n", + "\t- R_EX_ni2_e\n", + "\t- R_EX_udpglcur_e\n", + "\t- R_EX_ump_e\n", + "\t- R_EX_ura_e\n", + "\t- R_EX_urea_e\n", + "\t- R_EX_uri_e\n", + "\t- R_EX_val__L_e\n", + "\t- R_EX_nmn_e\n", + "\t- R_EX_no_e\n", + "\t- R_EX_no2_e\n", + "\t- R_EX_no3_e\n", + "\t- R_EX_novbcn_e\n", + "\t- R_EX_o16a4colipa_e\n", + "\t- R_EX_o2_e\n", + "\t- R_EX_xan_e\n", + "\t- R_EX_xmp_e\n", + "\t- R_EX_o2s_e\n", + "\t- R_EX_xtsn_e\n", + "\t- R_EX_acgal1p_e\n", + "\t- R_EX_xyl__D_e\n", + "\t- R_EX_xylu__L_e\n", + "\t- R_EX_ocdca_e\n", + "\t- R_EX_ocdcea_e\n", + "\t- R_EX_octa_e\n", + "\t- R_EX_zn2_e\n", + "\t- R_EX_orn_e\n", + "\t- R_EX_orot_e\n", + "\t- R_EX_acgam_e\n", + "\t- R_EX_pacald_e\n", + "\t- R_EX_peamn_e\n", + "\t- R_EX_acgam1p_e\n", + "\t- R_EX_phe__L_e\n", + "\t- R_EX_pheme_e\n", + "\t- R_EX_pi_e\n", + "\t- R_EX_pnto__R_e\n", + "\t- R_EX_ppa_e\n", + "\t- R_EX_ppal_e\n", + "\t- R_EX_pppn_e\n", + "\t- R_EX_ppt_e\n", + "\t- R_EX_pro__L_e\n", + "\t- R_EX_progly_e\n", + "\t- R_EX_acmana_e\n", + "\t- R_EX_psclys_e\n", + "\t- R_EX_pser__L_e\n", + "\t- R_EX_ptrc_e\n", + "\t- R_EX_acmum_e\n", + "\t- R_EX_pydam_e\n", + "\t- R_EX_pydx_e\n", + "\t- R_EX_acnam_e\n", + "\t- R_EX_pydxn_e\n", + "\t- R_EX_pyr_e\n", + "\t- R_EX_quin_e\n", + "\t- R_EX_r5p_e\n", + "\t- R_EX_rfamp_e\n", + "\t- R_EX_rib__D_e\n", + "\t- R_EX_rmn_e\n", + "\t- R_EX_sbt__D_e\n", + "\t- R_EX_sel_e\n", + "\t- R_EX_ser__D_e\n", + "\t- R_EX_acolipa_e\n", + "\t- R_EX_ser__L_e\n", + "\t- R_EX_skm_e\n", + "\t- R_EX_acser_e\n", + "\t- R_EX_slnt_e\n", + "\t- R_EX_so2_e\n", + "\t- R_EX_so3_e\n", + "\t- R_EX_so4_e\n", + "\t- R_EX_ade_e\n", + "\t- R_EX_spmd_e\n", + "\t- R_EX_succ_e\n", + "\t- R_EX_sucr_e\n", + "\t- R_EX_sulfac_e\n", + "\t- R_EX_tartr__D_e\n", + "\t- R_EX_tartr__L_e\n", + "\t- R_EX_taur_e\n", + "\t- R_EX_tcynt_e\n", + "\t- R_EX_adn_e\n", + "\t- R_EX_thm_e\n", + "\t- R_EX_thr__L_e\n", + "\t- R_EX_thrp_e\n", + "\t- R_EX_thym_e\n", + "\t- R_EX_thymd_e\n", + "\t- R_EX_adocbl_e\n", + "\t- R_EX_tma_e\n", + "\t- R_EX_tmao_e\n", + "\t- R_EX_ag_e\n", + "\t- R_EX_tre_e\n", + "\t- R_EX_trp__L_e\n", + "\t- R_EX_agm_e\n", + "\t- R_EX_tsul_e\n", + "\t- R_EX_ttdca_e\n", + "\t- R_EX_ttdcea_e\n", + "\t- R_EX_ttrcyc_e\n", + "\t- R_EX_tungs_e\n", + "\t- R_EX_tym_e\n", + "\t- R_EX_tton_e\n", + "\t- R_EX_akg_e\n", + "\t- R_EX_ala_B_e\n", + "\t- R_EX_ala__D_e\n", + "\t- R_EX_ala__L_e\n", + "\t- R_EX_alaala_e\n", + "\t- R_EX_all__D_e\n", + "\t- R_EX_alltn_e\n", + "\t- R_EX_amp_e\n", + "\t- R_EX_anhgm_e\n", + "\t- R_EX_arab__L_e\n", + "\t- R_EX_arbt_e\n", + "\t- R_EX_arbtn_e\n", + "\t- R_EX_arbtn_fe3_e\n", + "\t- R_EX_arg__L_e\n", + "\t- R_EX_ascb__L_e\n", + "\t- R_EX_asn__L_e\n", + "\t- R_EX_aso3_e\n", + "\t- R_EX_asp__L_e\n", + "\t- R_EX_4hthr_e\n", + "\t- R_SK_thf_c\n", + "\t- R_EX_feroxEfe_e\n", + "\t- R_EX_feroxE_e\n", + "\t- R_EX_cysi__L_e\n", + "\t- R_EX_23dhbzs3_e\n", + "\t- R_EX_tet_e\n", + "\t- R_EX_salchs4_e\n", + "\t- R_EX_feroxBfe_e\n", + "\t- R_EX_oaa_e\n", + "\t- R_EX_salchs2fe_e\n", + "\t- R_EX_4hphac_e\n", + "\t- R_EX_4abz_e\n", + "\t- R_EX_fe3dhbzs3_e\n", + "\t- R_EX_drib_e\n", + "\t- R_EX_acon_C_e\n", + "\t- R_EX_salchs4fe_e\n", + "\t- R_EX_2pg_e\n", + "\t- R_EX_frmd_e\n", + "\t- R_EX_colipaOA_e\n", + "\t- R_EX_feroxB_e\n", + "\t- R_EX_tcb_e\n", + "\t- R_EX_salchsx_e\n", + "\t- R_EX_AEP_e\n", + "\t- R_EX_icit_e\n", + "\t- R_EX_pep_e\n", + "\t- R_EX_salchs2_e\n", + "\t- R_EX_dxyl_e\n", + "\t- R_EX_colipa20Oag_e\n", + "\t- R_EX_rnam_e\n", + "\t- R_EX_remnant1_e\n", + "\t- R_EX_airs_e\n", + "\t- R_EX_3pg_e\n", + "\t- R_EX_udcpo5_e\n", + "\t- R_EX_feroxGfe_e\n", + "\t- R_EX_fe_e\n", + "\t- R_EX_udcpo4_e\n", + "\t- R_EX_kokdolipidA_e\n", + "\t- R_EX_3hoxpac_e\n", + "\t- R_EX_tag__D_e\n", + "\t- R_EX_guln__L_e\n", + "\t- R_EX_feroxG_e\n", + "\t- R_EX_4hoxpac_e\n", + "\t- R_DM_lipidA_core_e_p\n", + "\t- R_EX_3ntym_e\n", + "\t- R_EX_6apa_e\n", + "\t- R_EX_diact_e\n", + "\t- R_EX_peng_e\n", + "\t- R_EX_pac_e\n", + "\t- R_EX_salcn_e\n", + "\t- R_EX_galam_e\n", + "\t- R_EX_raffin_e\n", + "\t- R_EX_o6a4colipa_e\n", + "\t- R_EX_arab__D_e\n", + "\t- R_EX_btd_RR_e\n", + "\t- R_EX_rbt_e\n", + "\t- R_EX_cellb_e\n", + "\t- R_EX_bz_e\n", + "\t- R_EX_13ppd_e\n", + "\t- R_EX_abt__D_e\u001b[0m\n", + "File saved at: ../../data/sbml_corrected/iSFxv_1172.xml\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: iJO1366\n", + "\u001b[0;93mWARNING : \n", + " - R_CAT: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_DHPTDNR: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_DHPTDNRN: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_FHL: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SPODM: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SPODMpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCASPtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCFUMtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCMALtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCTARTtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\u001b[0m\n", + "\n", + "\n", + "\u001b[0;36m############################################\n", + "############################################\n", + " WRITING SBML FILE \n", + "############################################\n", + "############################################\n", + "\u001b[0m\n", + "\u001b[0;93mWARNING : IMPORT REACTION REMOVED BY DEFAULT\u001b[0m\n", + "\u001b[0;93mWARNING : If you want to keep import reaction\n", + "use option -kir / --keep-import-reactions\u001b[0m\n", + "\u001b[0;93mWARNING : Import reaction removed:\n", + "\t- R_EX_cm_e\n", + "\t- R_EX_cmp_e\n", + "\t- R_EX_co2_e\n", + "\t- R_EX_cobalt2_e\n", + "\t- R_DM_4crsol_c\n", + "\t- R_DM_5drib_c\n", + "\t- R_DM_aacald_c\n", + "\t- R_DM_amob_c\n", + "\t- R_DM_mththf_c\n", + "\t- R_EX_colipa_e\n", + "\t- R_DM_oxam_c\n", + "\t- R_EX_glc__D_e\n", + "\t- R_EX_glcn_e\n", + "\t- R_EX_glcr_e\n", + "\t- R_EX_colipap_e\n", + "\t- R_EX_glcur_e\n", + "\t- R_EX_glcur1p_e\n", + "\t- R_EX_12ppd__R_e\n", + "\t- R_EX_gln__L_e\n", + "\t- R_EX_cpgn_e\n", + "\t- R_EX_glu__L_e\n", + "\t- R_EX_gly_e\n", + "\t- R_EX_glyald_e\n", + "\t- R_EX_glyb_e\n", + "\t- R_EX_glyc_e\n", + "\t- R_EX_12ppd__S_e\n", + "\t- R_EX_14glucan_e\n", + "\t- R_EX_cpgn_un_e\n", + "\t- R_EX_15dap_e\n", + "\t- R_EX_glyc__R_e\n", + "\t- R_EX_glyc2p_e\n", + "\t- R_EX_23camp_e\n", + "\t- R_EX_23ccmp_e\n", + "\t- R_EX_23cgmp_e\n", + "\t- R_EX_23cump_e\n", + "\t- R_EX_23dappa_e\n", + "\t- R_EX_26dap__M_e\n", + "\t- R_EX_glyc3p_e\n", + "\t- R_EX_glyclt_e\n", + "\t- R_EX_gmp_e\n", + "\t- R_EX_gsn_e\n", + "\t- R_EX_gthox_e\n", + "\t- R_EX_gthrd_e\n", + "\t- R_EX_gtp_e\n", + "\t- R_EX_gua_e\n", + "\t- R_EX_h_e\n", + "\t- R_EX_h2_e\n", + "\t- R_EX_h2o_e\n", + "\t- R_EX_h2o2_e\n", + "\t- R_EX_h2s_e\n", + "\t- R_EX_hacolipa_e\n", + "\t- R_EX_halipa_e\n", + "\t- R_EX_hdca_e\n", + "\t- R_EX_hdcea_e\n", + "\t- R_EX_hg2_e\n", + "\t- R_EX_his__L_e\n", + "\t- R_EX_2ddglcn_e\n", + "\t- R_EX_34dhpac_e\n", + "\t- R_EX_3amp_e\n", + "\t- R_EX_3cmp_e\n", + "\t- R_EX_3gmp_e\n", + "\t- R_EX_3hcinnm_e\n", + "\t- R_EX_3hpp_e\n", + "\t- R_EX_3hpppn_e\n", + "\t- R_EX_hom__L_e\n", + "\t- R_EX_hxa_e\n", + "\t- R_EX_hxan_e\n", + "\t- R_EX_idon__L_e\n", + "\t- R_EX_3ump_e\n", + "\t- R_EX_4abut_e\n", + "\t- R_EX_4hoxpacd_e\n", + "\t- R_EX_5dglcn_e\n", + "\t- R_EX_5mtr_e\n", + "\t- R_EX_LalaDglu_e\n", + "\t- R_EX_ile__L_e\n", + "\t- R_EX_imp_e\n", + "\t- R_EX_indole_e\n", + "\t- R_EX_inost_e\n", + "\t- R_EX_ins_e\n", + "\t- R_EX_isetac_e\n", + "\t- R_EX_k_e\n", + "\t- R_EX_LalaDgluMdap_e\n", + "\t- R_EX_LalaDgluMdapDala_e\n", + "\t- R_EX_LalaLglu_e\n", + "\t- R_EX_kdo2lipid4_e\n", + "\t- R_EX_ac_e\n", + "\t- R_EX_crn_e\n", + "\t- R_EX_acac_e\n", + "\t- R_EX_acald_e\n", + "\t- R_EX_lac__D_e\n", + "\t- R_EX_lac__L_e\n", + "\t- R_EX_crn__D_e\n", + "\t- R_EX_lcts_e\n", + "\t- R_EX_acgal_e\n", + "\t- R_EX_acgal1p_e\n", + "\t- R_EX_acgam_e\n", + "\t- R_EX_leu__L_e\n", + "\t- R_EX_csn_e\n", + "\t- R_EX_acgam1p_e\n", + "\t- R_EX_acmana_e\n", + "\t- R_EX_acmum_e\n", + "\t- R_EX_lipa_e\n", + "\t- R_EX_cu_e\n", + "\t- R_EX_lipa_cold_e\n", + "\t- R_EX_acnam_e\n", + "\t- R_EX_acolipa_e\n", + "\t- R_EX_cu2_e\n", + "\t- R_EX_acser_e\n", + "\t- R_EX_ade_e\n", + "\t- R_EX_adn_e\n", + "\t- R_EX_adocbl_e\n", + "\t- R_EX_ag_e\n", + "\t- R_EX_lipoate_e\n", + "\t- R_EX_lys__L_e\n", + "\t- R_EX_lyx__L_e\n", + "\t- R_EX_mal__D_e\n", + "\t- R_EX_agm_e\n", + "\t- R_EX_akg_e\n", + "\t- R_EX_cyan_e\n", + "\t- R_EX_ala_B_e\n", + "\t- R_EX_ala__D_e\n", + "\t- R_EX_ala__L_e\n", + "\t- R_EX_alaala_e\n", + "\t- R_EX_all__D_e\n", + "\t- R_EX_alltn_e\n", + "\t- R_EX_amp_e\n", + "\t- R_EX_anhgm_e\n", + "\t- R_EX_arab__L_e\n", + "\t- R_EX_mal__L_e\n", + "\t- R_EX_malt_e\n", + "\t- R_EX_malthx_e\n", + "\t- R_EX_maltpt_e\n", + "\t- R_EX_malttr_e\n", + "\t- R_EX_maltttr_e\n", + "\t- R_EX_man_e\n", + "\t- R_EX_cynt_e\n", + "\t- R_EX_man6p_e\n", + "\t- R_EX_manglyc_e\n", + "\t- R_EX_melib_e\n", + "\t- R_EX_cys__D_e\n", + "\t- R_EX_meoh_e\n", + "\t- R_EX_met__D_e\n", + "\t- R_EX_arbt_e\n", + "\t- R_EX_arbtn_e\n", + "\t- R_EX_arbtn_fe3_e\n", + "\t- R_EX_met__L_e\n", + "\t- R_EX_cys__L_e\n", + "\t- R_EX_metsox_R__L_e\n", + "\t- R_EX_metsox_S__L_e\n", + "\t- R_EX_mg2_e\n", + "\t- R_EX_mincyc_e\n", + "\t- R_EX_minohp_e\n", + "\t- R_EX_mmet_e\n", + "\t- R_EX_arg__L_e\n", + "\t- R_EX_ascb__L_e\n", + "\t- R_EX_asn__L_e\n", + "\t- R_EX_aso3_e\n", + "\t- R_EX_asp__L_e\n", + "\t- R_EX_btn_e\n", + "\t- R_EX_but_e\n", + "\t- R_EX_mn2_e\n", + "\t- R_EX_mnl_e\n", + "\t- R_EX_mobd_e\n", + "\t- R_EX_mso3_e\n", + "\t- R_EX_n2o_e\n", + "\t- R_EX_na1_e\n", + "\t- R_EX_nac_e\n", + "\t- R_EX_nh4_e\n", + "\t- R_EX_butso3_e\n", + "\t- R_EX_ca2_e\n", + "\t- R_EX_cbi_e\n", + "\t- R_EX_cytd_e\n", + "\t- R_EX_cbl1_e\n", + "\t- R_EX_cd2_e\n", + "\t- R_EX_cgly_e\n", + "\t- R_EX_ni2_e\n", + "\t- R_EX_nmn_e\n", + "\t- R_EX_no_e\n", + "\t- R_EX_no2_e\n", + "\t- R_EX_no3_e\n", + "\t- R_EX_novbcn_e\n", + "\t- R_EX_o16a4colipa_e\n", + "\t- R_EX_o2_e\n", + "\t- R_EX_o2s_e\n", + "\t- R_EX_ocdca_e\n", + "\t- R_EX_ocdcea_e\n", + "\t- R_EX_octa_e\n", + "\t- R_EX_orn_e\n", + "\t- R_EX_orot_e\n", + "\t- R_EX_pacald_e\n", + "\t- R_EX_peamn_e\n", + "\t- R_EX_phe__L_e\n", + "\t- R_EX_pheme_e\n", + "\t- R_EX_chol_e\n", + "\t- R_EX_chtbs_e\n", + "\t- R_EX_cit_e\n", + "\t- R_EX_cl_e\n", + "\t- R_EX_pi_e\n", + "\t- R_EX_dad_2_e\n", + "\t- R_EX_pnto__R_e\n", + "\t- R_EX_ppa_e\n", + "\t- R_EX_damp_e\n", + "\t- R_EX_ppal_e\n", + "\t- R_EX_dca_e\n", + "\t- R_EX_pppn_e\n", + "\t- R_EX_ppt_e\n", + "\t- R_EX_dcmp_e\n", + "\t- R_EX_pro__L_e\n", + "\t- R_EX_dcyt_e\n", + "\t- R_EX_progly_e\n", + "\t- R_EX_psclys_e\n", + "\t- R_EX_ddca_e\n", + "\t- R_EX_pser__L_e\n", + "\t- R_EX_ptrc_e\n", + "\t- R_EX_dgmp_e\n", + "\t- R_EX_dgsn_e\n", + "\t- R_EX_val__L_e\n", + "\t- R_EX_xan_e\n", + "\t- R_EX_dha_e\n", + "\t- R_EX_xmp_e\n", + "\t- R_EX_dimp_e\n", + "\t- R_EX_din_e\n", + "\t- R_EX_xtsn_e\n", + "\t- R_EX_pydam_e\n", + "\t- R_EX_xyl__D_e\n", + "\t- R_EX_xylu__L_e\n", + "\t- R_EX_zn2_e\n", + "\t- R_EX_pydx_e\n", + "\t- R_EX_pydxn_e\n", + "\t- R_EX_pyr_e\n", + "\t- R_EX_quin_e\n", + "\t- R_EX_r5p_e\n", + "\t- R_EX_rfamp_e\n", + "\t- R_EX_rib__D_e\n", + "\t- R_EX_rmn_e\n", + "\t- R_EX_sbt__D_e\n", + "\t- R_EX_sel_e\n", + "\t- R_EX_ser__D_e\n", + "\t- R_EX_ser__L_e\n", + "\t- R_EX_skm_e\n", + "\t- R_EX_dms_e\n", + "\t- R_EX_slnt_e\n", + "\t- R_EX_so2_e\n", + "\t- R_EX_so3_e\n", + "\t- R_EX_dmso_e\n", + "\t- R_EX_so4_e\n", + "\t- R_EX_spmd_e\n", + "\t- R_EX_succ_e\n", + "\t- R_EX_sucr_e\n", + "\t- R_EX_sulfac_e\n", + "\t- R_EX_tartr__D_e\n", + "\t- R_EX_dopa_e\n", + "\t- R_EX_tartr__L_e\n", + "\t- R_EX_taur_e\n", + "\t- R_EX_tcynt_e\n", + "\t- R_EX_doxrbcn_e\n", + "\t- R_EX_thm_e\n", + "\t- R_EX_thr__L_e\n", + "\t- R_EX_dtmp_e\n", + "\t- R_EX_thrp_e\n", + "\t- R_EX_thym_e\n", + "\t- R_EX_dump_e\n", + "\t- R_EX_thymd_e\n", + "\t- R_EX_tma_e\n", + "\t- R_EX_duri_e\n", + "\t- R_EX_tmao_e\n", + "\t- R_EX_tre_e\n", + "\t- R_EX_trp__L_e\n", + "\t- R_EX_tsul_e\n", + "\t- R_EX_eca4colipa_e\n", + "\t- R_EX_ttdca_e\n", + "\t- R_EX_ttdcea_e\n", + "\t- R_EX_enlipa_e\n", + "\t- R_EX_ttrcyc_e\n", + "\t- R_EX_enter_e\n", + "\t- R_EX_etha_e\n", + "\t- R_EX_tungs_e\n", + "\t- R_EX_tym_e\n", + "\t- R_EX_ethso3_e\n", + "\t- R_EX_tyr__L_e\n", + "\t- R_EX_tyrp_e\n", + "\t- R_EX_uacgam_e\n", + "\t- R_EX_udpacgal_e\n", + "\t- R_EX_udpg_e\n", + "\t- R_EX_udpgal_e\n", + "\t- R_EX_etoh_e\n", + "\t- R_EX_udpglcur_e\n", + "\t- R_EX_ump_e\n", + "\t- R_EX_f6p_e\n", + "\t- R_EX_ura_e\n", + "\t- R_EX_urea_e\n", + "\t- R_EX_fald_e\n", + "\t- R_EX_uri_e\n", + "\t- R_EX_fe2_e\n", + "\t- R_EX_fe3_e\n", + "\t- R_EX_fe3dcit_e\n", + "\t- R_EX_fe3dhbzs_e\n", + "\t- R_EX_fe3hox_e\n", + "\t- R_EX_fe3hox_un_e\n", + "\t- R_EX_fecrm_e\n", + "\t- R_EX_fecrm_un_e\n", + "\t- R_EX_feenter_e\n", + "\t- R_EX_feoxam_e\n", + "\t- R_EX_feoxam_un_e\n", + "\t- R_EX_for_e\n", + "\t- R_EX_fru_e\n", + "\t- R_EX_frulys_e\n", + "\t- R_EX_fruur_e\n", + "\t- R_EX_fuc__L_e\n", + "\t- R_EX_fum_e\n", + "\t- R_EX_fusa_e\n", + "\t- R_EX_g1p_e\n", + "\t- R_EX_g3pc_e\n", + "\t- R_EX_g3pe_e\n", + "\t- R_EX_g3pg_e\n", + "\t- R_EX_g3pi_e\n", + "\t- R_EX_g3ps_e\n", + "\t- R_EX_g6p_e\n", + "\t- R_EX_gal_e\n", + "\t- R_EX_gal_bD_e\n", + "\t- R_EX_gal1p_e\n", + "\t- R_EX_galct__D_e\n", + "\t- R_EX_galctn__D_e\n", + "\t- R_EX_galctn__L_e\n", + "\t- R_EX_galt_e\n", + "\t- R_EX_galur_e\n", + "\t- R_EX_gam_e\n", + "\t- R_EX_gam6p_e\n", + "\t- R_EX_gbbtn_e\n", + "\t- R_EX_gdp_e\u001b[0m\n", + "File saved at: ../../data/sbml_corrected/iJO1366.xml\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: iMM904\n", + "\u001b[0;93mWARNING : - R_ASAD: Reactants and products exchanged.\n", + " Boundaries was: [-999999.0 ; 0.0]\n", + " - R_GLYAT: Reactants and products exchanged.\n", + " Boundaries was: [-999999.0 ; 0.0]\n", + " - R_GLUDy: Reactants and products exchanged.\n", + " Boundaries was: [-999999.0 ; 0.0]\n", + " - R_HSDy: Reactants and products exchanged.\n", + " Boundaries was: [-999999.0 ; 0.0]\n", + "\u001b[0m\n", + "\n", + "\n", + "\u001b[0;36m############################################\n", + "############################################\n", + " WRITING SBML FILE \n", + "############################################\n", + "############################################\n", + "\u001b[0m\n", + "\u001b[0;93mWARNING : IMPORT REACTION REMOVED BY DEFAULT\u001b[0m\n", + "\u001b[0;93mWARNING : If you want to keep import reaction\n", + "use option -kir / --keep-import-reactions\u001b[0m\n", + "\u001b[0;93mWARNING : Reaction with tag reversible modified:\n", + "\t- R_ASAD\n", + "\t- R_GLYAT\n", + "\t- R_GLUDy\n", + "\t- R_HSDy\u001b[0m\n", + "\u001b[0;93mWARNING : Reaction with Reactants and Products exchanged:\n", + "\t- R_ASAD\n", + "\t- R_GLYAT\n", + "\t- R_GLUDy\n", + "\t- R_HSDy\u001b[0m\n", + "\u001b[0;93mWARNING : Import reaction removed:\n", + "\t- R_EX_epistest_SC_e\n", + "\t- R_EX_epist_e\n", + "\t- R_EX_ergst_e\n", + "\t- R_EX_ergstest_SC_e\n", + "\t- R_EX_13BDglcn_e\n", + "\t- R_EX_etha_e\n", + "\t- R_EX_2hb_e\n", + "\t- R_EX_etoh_e\n", + "\t- R_EX_fe2_e\n", + "\t- R_EX_fecost_e\n", + "\t- R_EX_2mbac_e\n", + "\t- R_EX_2mbald_e\n", + "\t- R_EX_2mbtoh_e\n", + "\t- R_EX_2mppal_e\n", + "\t- R_EX_2phetoh_e\n", + "\t- R_EX_fecostest_SC_e\n", + "\t- R_EX_fmn_e\n", + "\t- R_EX_3c3hmp_e\n", + "\t- R_EX_3mbald_e\n", + "\t- R_EX_for_e\n", + "\t- R_EX_fru_e\n", + "\t- R_EX_3mop_e\n", + "\t- R_EX_4abut_e\n", + "\t- R_EX_fum_e\n", + "\t- R_EX_g3pc_e\n", + "\t- R_EX_4abz_e\n", + "\t- R_EX_5aop_e\n", + "\t- R_EX_g3pi_e\n", + "\t- R_EX_gal_e\n", + "\t- R_EX_8aonn_e\n", + "\t- R_EX_Nbfortyr_e\n", + "\t- R_EX_abt_e\n", + "\t- R_EX_ac_e\n", + "\t- R_EX_acald_e\n", + "\t- R_EX_aces_e\n", + "\t- R_EX_galur_e\n", + "\t- R_EX_gam6p_e\n", + "\t- R_EX_gcald_e\n", + "\t- R_EX_glc__D_e\n", + "\t- R_EX_gln__L_e\n", + "\t- R_EX_glu__L_e\n", + "\t- R_EX_glx_e\n", + "\t- R_EX_ade_e\n", + "\t- R_EX_adn_e\n", + "\t- R_EX_akg_e\n", + "\t- R_EX_ala__L_e\n", + "\t- R_EX_gly_e\n", + "\t- R_EX_glyc_e\n", + "\t- R_EX_gsn_e\n", + "\t- R_EX_gthox_e\n", + "\t- R_EX_gthrd_e\n", + "\t- R_EX_alltn_e\n", + "\t- R_EX_alltt_e\n", + "\t- R_EX_amet_e\n", + "\t- R_EX_arab__D_e\n", + "\t- R_EX_arab__L_e\n", + "\t- R_EX_gua_e\n", + "\t- R_EX_arg__L_e\n", + "\t- R_EX_h2o_e\n", + "\t- R_EX_h_e\n", + "\t- R_EX_hdca_e\n", + "\t- R_EX_hdcea_e\n", + "\t- R_EX_asn__L_e\n", + "\t- R_EX_asp__L_e\n", + "\t- R_EX_btd_RR_e\n", + "\t- R_EX_btn_e\n", + "\t- R_EX_hexc_e\n", + "\t- R_EX_his__L_e\n", + "\t- R_EX_hxan_e\n", + "\t- R_EX_iamac_e\n", + "\t- R_EX_iamoh_e\n", + "\t- R_EX_ibutac_e\n", + "\t- R_EX_chol_e\n", + "\t- R_EX_cit_e\n", + "\t- R_EX_co2_e\n", + "\t- R_EX_crn_e\n", + "\t- R_EX_csn_e\n", + "\t- R_EX_cys__L_e\n", + "\t- R_EX_cytd_e\n", + "\t- R_EX_dad_2_e\n", + "\t- R_EX_dann_e\n", + "\t- R_EX_ibutoh_e\n", + "\t- R_EX_id3acald_e\n", + "\t- R_EX_ile__L_e\n", + "\t- R_EX_ind3eth_e\n", + "\t- R_EX_inost_e\n", + "\t- R_EX_dca_e\n", + "\t- R_EX_dcyt_e\n", + "\t- R_EX_ddca_e\n", + "\t- R_EX_ins_e\n", + "\t- R_EX_k_e\n", + "\t- R_EX_dgsn_e\n", + "\t- R_EX_din_e\n", + "\t- R_EX_lac__D_e\n", + "\t- R_EX_lac__L_e\n", + "\t- R_EX_lanost_e\n", + "\t- R_EX_lanostest_SC_e\n", + "\t- R_EX_dttp_e\n", + "\t- R_EX_duri_e\n", + "\t- R_EX_leu__L_e\n", + "\t- R_EX_lys__L_e\n", + "\t- R_EX_mal__L_e\n", + "\t- R_EX_malt_e\n", + "\t- R_EX_ribflv_e\n", + "\t- R_EX_sbt__D_e\n", + "\t- R_EX_man_e\n", + "\t- R_EX_melib_e\n", + "\t- R_EX_met__L_e\n", + "\t- R_EX_mmet_e\n", + "\t- R_EX_na1_e\n", + "\t- R_EX_nac_e\n", + "\t- R_EX_sbt__L_e\n", + "\t- R_EX_ser__L_e\n", + "\t- R_EX_so3_e\n", + "\t- R_EX_so4_e\n", + "\t- R_EX_spmd_e\n", + "\t- R_EX_sprm_e\n", + "\t- R_EX_srb__L_e\n", + "\t- R_EX_succ_e\n", + "\t- R_EX_nadp_e\n", + "\t- R_EX_nh4_e\n", + "\t- R_EX_nmn_e\n", + "\t- R_EX_sucr_e\n", + "\t- R_EX_o2_e\n", + "\t- R_EX_taur_e\n", + "\t- R_EX_thm_e\n", + "\t- R_EX_oaa_e\n", + "\t- R_EX_ocdca_e\n", + "\t- R_EX_ocdcea_e\n", + "\t- R_EX_ocdcya_e\n", + "\t- R_EX_orn_e\n", + "\t- R_EX_thmmp_e\n", + "\t- R_EX_thmpp_e\n", + "\t- R_EX_thr__L_e\n", + "\t- R_EX_thym_e\n", + "\t- R_EX_pacald_e\n", + "\t- R_EX_pap_e\n", + "\t- R_EX_thymd_e\n", + "\t- R_EX_tre_e\n", + "\t- R_EX_trp__L_e\n", + "\t- R_EX_pc_SC_e\n", + "\t- R_EX_pectin_e\n", + "\t- R_EX_pepd_e\n", + "\t- R_EX_phe__L_e\n", + "\t- R_EX_pheac_e\n", + "\t- R_EX_pi_e\n", + "\t- R_EX_pnto__R_e\n", + "\t- R_EX_ttdca_e\n", + "\t- R_EX_tyr__L_e\n", + "\t- R_EX_ura_e\n", + "\t- R_EX_urea_e\n", + "\t- R_EX_uri_e\n", + "\t- R_EX_val__L_e\n", + "\t- R_EX_xan_e\n", + "\t- R_EX_xtsn_e\n", + "\t- R_EX_pro__L_e\n", + "\t- R_EX_ptd1ino_SC_e\n", + "\t- R_EX_ptrc_e\n", + "\t- R_EX_pyr_e\n", + "\t- R_EX_xyl__D_e\n", + "\t- R_EX_xylt_e\n", + "\t- R_EX_rib__D_e\n", + "\t- R_EX_zymst_e\n", + "\t- R_EX_zymstest_SC_e\u001b[0m\n", + "File saved at: ../../data/sbml_corrected/iMM904.xml\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: iSF_1195\n", + "\u001b[0;93mWARNING : \n", + " - R_CAT: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_DHPTDNR: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_DHPTDNRN: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCFUMtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCMALtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCTARTtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SPODM: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SPODMpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCASPtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_EX_chitob_e: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_CHITOBpts: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_CHITPH: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_URCN: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\u001b[0m\n", + "\n", + "\n", + "\u001b[0;36m############################################\n", + "############################################\n", + " WRITING SBML FILE \n", + "############################################\n", + "############################################\n", + "\u001b[0m\n", + "\u001b[0;93mWARNING : IMPORT REACTION REMOVED BY DEFAULT\u001b[0m\n", + "\u001b[0;93mWARNING : If you want to keep import reaction\n", + "use option -kir / --keep-import-reactions\u001b[0m\n", + "\u001b[0;93mWARNING : Import reaction removed:\n", + "\t- R_EX_eca4colipa_e\n", + "\t- R_EX_enlipa_e\n", + "\t- R_EX_enter_e\n", + "\t- R_EX_etha_e\n", + "\t- R_EX_ethso3_e\n", + "\t- R_EX_etoh_e\n", + "\t- R_EX_f6p_e\n", + "\t- R_EX_fald_e\n", + "\t- R_EX_fe2_e\n", + "\t- R_EX_fe3_e\n", + "\t- R_EX_acald_e\n", + "\t- R_EX_fe3dcit_e\n", + "\t- R_EX_acgal_e\n", + "\t- R_DM_4crsol_c\n", + "\t- R_EX_acgal1p_e\n", + "\t- R_DM_5drib_c\n", + "\t- R_EX_fe3dhbzs_e\n", + "\t- R_DM_aacald_c\n", + "\t- R_EX_acgam_e\n", + "\t- R_EX_acgam1p_e\n", + "\t- R_EX_acmana_e\n", + "\t- R_DM_amob_c\n", + "\t- R_EX_acmum_e\n", + "\t- R_EX_fe3hox_e\n", + "\t- R_EX_acnam_e\n", + "\t- R_EX_acolipa_e\n", + "\t- R_DM_mththf_c\n", + "\t- R_DM_oxam_c\n", + "\t- R_EX_acser_e\n", + "\t- R_EX_ade_e\n", + "\t- R_EX_adn_e\n", + "\t- R_EX_adocbl_e\n", + "\t- R_EX_ag_e\n", + "\t- R_EX_agm_e\n", + "\t- R_EX_akg_e\n", + "\t- R_EX_ala_B_e\n", + "\t- R_EX_ala__D_e\n", + "\t- R_EX_ala__L_e\n", + "\t- R_EX_12ppd__R_e\n", + "\t- R_EX_12ppd__S_e\n", + "\t- R_EX_14glucan_e\n", + "\t- R_EX_15dap_e\n", + "\t- R_EX_23camp_e\n", + "\t- R_EX_23ccmp_e\n", + "\t- R_EX_23cgmp_e\n", + "\t- R_EX_23cump_e\n", + "\t- R_EX_23dappa_e\n", + "\t- R_EX_26dap__M_e\n", + "\t- R_EX_2ddglcn_e\n", + "\t- R_EX_alaala_e\n", + "\t- R_EX_all__D_e\n", + "\t- R_EX_alltn_e\n", + "\t- R_EX_amp_e\n", + "\t- R_EX_anhgm_e\n", + "\t- R_EX_34dhpac_e\n", + "\t- R_EX_3amp_e\n", + "\t- R_EX_3cmp_e\n", + "\t- R_EX_arab__L_e\n", + "\t- R_EX_fe3hox_un_e\n", + "\t- R_EX_arbt_e\n", + "\t- R_EX_arbtn_e\n", + "\t- R_EX_arbtn_fe3_e\n", + "\t- R_EX_3gmp_e\n", + "\t- R_EX_3hcinnm_e\n", + "\t- R_EX_3hpp_e\n", + "\t- R_EX_3hpppn_e\n", + "\t- R_EX_3ump_e\n", + "\t- R_EX_arg__L_e\n", + "\t- R_EX_ascb__L_e\n", + "\t- R_EX_asn__L_e\n", + "\t- R_EX_aso3_e\n", + "\t- R_EX_asp__L_e\n", + "\t- R_EX_4abut_e\n", + "\t- R_EX_4hoxpacd_e\n", + "\t- R_EX_5dglcn_e\n", + "\t- R_EX_5mtr_e\n", + "\t- R_EX_LalaDglu_e\n", + "\t- R_EX_btn_e\n", + "\t- R_EX_but_e\n", + "\t- R_EX_butso3_e\n", + "\t- R_EX_ca2_e\n", + "\t- R_EX_LalaDgluMdap_e\n", + "\t- R_EX_fecrm_e\n", + "\t- R_EX_LalaDgluMdapDala_e\n", + "\t- R_EX_LalaLglu_e\n", + "\t- R_EX_ac_e\n", + "\t- R_EX_cbi_e\n", + "\t- R_EX_cbl1_e\n", + "\t- R_EX_cd2_e\n", + "\t- R_EX_acac_e\n", + "\t- R_EX_cgly_e\n", + "\t- R_EX_fecrm_un_e\n", + "\t- R_EX_feenter_e\n", + "\t- R_EX_chol_e\n", + "\t- R_EX_chtbs_e\n", + "\t- R_EX_cit_e\n", + "\t- R_EX_ile__L_e\n", + "\t- R_EX_feoxam_e\n", + "\t- R_EX_imp_e\n", + "\t- R_EX_indole_e\n", + "\t- R_EX_cl_e\n", + "\t- R_EX_cm_e\n", + "\t- R_EX_cmp_e\n", + "\t- R_EX_inost_e\n", + "\t- R_EX_ins_e\n", + "\t- R_EX_feoxam_un_e\n", + "\t- R_EX_isetac_e\n", + "\t- R_EX_k_e\n", + "\t- R_EX_kdo2lipid4_e\n", + "\t- R_EX_lac__D_e\n", + "\t- R_EX_co2_e\n", + "\t- R_EX_cobalt2_e\n", + "\t- R_EX_colipa_e\n", + "\t- R_EX_colipap_e\n", + "\t- R_EX_cpgn_e\n", + "\t- R_EX_cpgn_un_e\n", + "\t- R_EX_lac__L_e\n", + "\t- R_EX_lcts_e\n", + "\t- R_EX_leu__L_e\n", + "\t- R_EX_crn_e\n", + "\t- R_EX_for_e\n", + "\t- R_EX_crn__D_e\n", + "\t- R_EX_csn_e\n", + "\t- R_EX_lipa_e\n", + "\t- R_EX_lipa_cold_e\n", + "\t- R_EX_lipoate_e\n", + "\t- R_EX_cu_e\n", + "\t- R_EX_cu2_e\n", + "\t- R_EX_cyan_e\n", + "\t- R_EX_lys__L_e\n", + "\t- R_EX_lyx__L_e\n", + "\t- R_EX_cynt_e\n", + "\t- R_EX_fru_e\n", + "\t- R_EX_cys__D_e\n", + "\t- R_EX_cys__L_e\n", + "\t- R_EX_cytd_e\n", + "\t- R_EX_dad_2_e\n", + "\t- R_EX_mal__D_e\n", + "\t- R_EX_mal__L_e\n", + "\t- R_EX_malt_e\n", + "\t- R_EX_malthx_e\n", + "\t- R_EX_damp_e\n", + "\t- R_EX_frulys_e\n", + "\t- R_EX_dca_e\n", + "\t- R_EX_dcmp_e\n", + "\t- R_EX_dcyt_e\n", + "\t- R_EX_ddca_e\n", + "\t- R_EX_dgmp_e\n", + "\t- R_EX_maltpt_e\n", + "\t- R_EX_malttr_e\n", + "\t- R_EX_maltttr_e\n", + "\t- R_EX_man_e\n", + "\t- R_EX_man6p_e\n", + "\t- R_EX_manglyc_e\n", + "\t- R_EX_melib_e\n", + "\t- R_EX_dgsn_e\n", + "\t- R_EX_fruur_e\n", + "\t- R_EX_dha_e\n", + "\t- R_EX_dimp_e\n", + "\t- R_EX_din_e\n", + "\t- R_EX_meoh_e\n", + "\t- R_EX_met__D_e\n", + "\t- R_EX_met__L_e\n", + "\t- R_EX_metsox_R__L_e\n", + "\t- R_EX_dms_e\n", + "\t- R_EX_dmso_e\n", + "\t- R_EX_dopa_e\n", + "\t- R_EX_metsox_S__L_e\n", + "\t- R_EX_fuc__L_e\n", + "\t- R_EX_mg2_e\n", + "\t- R_EX_mincyc_e\n", + "\t- R_EX_doxrbcn_e\n", + "\t- R_EX_dtmp_e\n", + "\t- R_EX_minohp_e\n", + "\t- R_EX_dump_e\n", + "\t- R_EX_mmet_e\n", + "\t- R_EX_fum_e\n", + "\t- R_EX_mn2_e\n", + "\t- R_EX_mnl_e\n", + "\t- R_EX_duri_e\n", + "\t- R_EX_mobd_e\n", + "\t- R_EX_fusa_e\n", + "\t- R_EX_mso3_e\n", + "\t- R_EX_n2o_e\n", + "\t- R_EX_na1_e\n", + "\t- R_EX_nac_e\n", + "\t- R_EX_nh4_e\n", + "\t- R_EX_rib__D_e\n", + "\t- R_EX_rmn_e\n", + "\t- R_EX_sbt__D_e\n", + "\t- R_EX_sel_e\n", + "\t- R_EX_ser__D_e\n", + "\t- R_EX_ser__L_e\n", + "\t- R_EX_skm_e\n", + "\t- R_EX_slnt_e\n", + "\t- R_EX_ni2_e\n", + "\t- R_EX_nmn_e\n", + "\t- R_EX_no_e\n", + "\t- R_EX_no2_e\n", + "\t- R_EX_so2_e\n", + "\t- R_EX_so3_e\n", + "\t- R_EX_no3_e\n", + "\t- R_EX_g1p_e\n", + "\t- R_EX_novbcn_e\n", + "\t- R_EX_o16a4colipa_e\n", + "\t- R_EX_so4_e\n", + "\t- R_EX_spmd_e\n", + "\t- R_EX_succ_e\n", + "\t- R_EX_sucr_e\n", + "\t- R_EX_o2_e\n", + "\t- R_EX_o2s_e\n", + "\t- R_EX_g3pc_e\n", + "\t- R_EX_ocdca_e\n", + "\t- R_EX_ocdcea_e\n", + "\t- R_EX_octa_e\n", + "\t- R_EX_orn_e\n", + "\t- R_EX_sulfac_e\n", + "\t- R_EX_tartr__D_e\n", + "\t- R_EX_tartr__L_e\n", + "\t- R_EX_taur_e\n", + "\t- R_EX_tcynt_e\n", + "\t- R_EX_thm_e\n", + "\t- R_EX_thr__L_e\n", + "\t- R_EX_thrp_e\n", + "\t- R_EX_thym_e\n", + "\t- R_EX_orot_e\n", + "\t- R_EX_pacald_e\n", + "\t- R_EX_peamn_e\n", + "\t- R_EX_phe__L_e\n", + "\t- R_EX_pheme_e\n", + "\t- R_EX_pi_e\n", + "\t- R_EX_pnto__R_e\n", + "\t- R_EX_ppa_e\n", + "\t- R_EX_ppal_e\n", + "\t- R_EX_thymd_e\n", + "\t- R_EX_tma_e\n", + "\t- R_EX_tmao_e\n", + "\t- R_EX_tre_e\n", + "\t- R_EX_trp__L_e\n", + "\t- R_EX_pppn_e\n", + "\t- R_EX_ppt_e\n", + "\t- R_EX_g3pe_e\n", + "\t- R_EX_pro__L_e\n", + "\t- R_EX_progly_e\n", + "\t- R_EX_psclys_e\n", + "\t- R_EX_tsul_e\n", + "\t- R_EX_ttdca_e\n", + "\t- R_EX_ttdcea_e\n", + "\t- R_EX_pser__L_e\n", + "\t- R_EX_g3pg_e\n", + "\t- R_EX_ptrc_e\n", + "\t- R_EX_pydam_e\n", + "\t- R_EX_ttrcyc_e\n", + "\t- R_EX_tungs_e\n", + "\t- R_EX_tym_e\n", + "\t- R_EX_pydx_e\n", + "\t- R_EX_pydxn_e\n", + "\t- R_EX_tyr__L_e\n", + "\t- R_EX_g3pi_e\n", + "\t- R_EX_tyrp_e\n", + "\t- R_EX_uacgam_e\n", + "\t- R_EX_udpacgal_e\n", + "\t- R_EX_udpg_e\n", + "\t- R_EX_pyr_e\n", + "\t- R_EX_quin_e\n", + "\t- R_EX_r5p_e\n", + "\t- R_EX_rfamp_e\n", + "\t- R_EX_udpgal_e\n", + "\t- R_EX_g3ps_e\n", + "\t- R_EX_udpglcur_e\n", + "\t- R_EX_ump_e\n", + "\t- R_EX_ura_e\n", + "\t- R_EX_urea_e\n", + "\t- R_EX_uri_e\n", + "\t- R_EX_val__L_e\n", + "\t- R_EX_g6p_e\n", + "\t- R_EX_xan_e\n", + "\t- R_EX_xmp_e\n", + "\t- R_EX_xtsn_e\n", + "\t- R_EX_xyl__D_e\n", + "\t- R_EX_xylu__L_e\n", + "\t- R_EX_gal_e\n", + "\t- R_EX_zn2_e\n", + "\t- R_EX_gal_bD_e\n", + "\t- R_EX_gal1p_e\n", + "\t- R_EX_galct__D_e\n", + "\t- R_EX_galctn__D_e\n", + "\t- R_EX_galctn__L_e\n", + "\t- R_EX_galt_e\n", + "\t- R_EX_galur_e\n", + "\t- R_EX_gam_e\n", + "\t- R_EX_gam6p_e\n", + "\t- R_EX_gbbtn_e\n", + "\t- R_EX_gdp_e\n", + "\t- R_EX_glc__D_e\n", + "\t- R_EX_glcn_e\n", + "\t- R_EX_glcr_e\n", + "\t- R_EX_glcur_e\n", + "\t- R_EX_glcur1p_e\n", + "\t- R_EX_gln__L_e\n", + "\t- R_EX_glu__L_e\n", + "\t- R_EX_gly_e\n", + "\t- R_EX_glyald_e\n", + "\t- R_EX_glyb_e\n", + "\t- R_EX_glyc_e\n", + "\t- R_EX_glyc__R_e\n", + "\t- R_EX_glyc2p_e\n", + "\t- R_EX_glyc3p_e\n", + "\t- R_EX_glyclt_e\n", + "\t- R_EX_gmp_e\n", + "\t- R_EX_gsn_e\n", + "\t- R_EX_gthox_e\n", + "\t- R_EX_gthrd_e\n", + "\t- R_EX_gtp_e\n", + "\t- R_EX_gua_e\n", + "\t- R_EX_h_e\n", + "\t- R_EX_h2_e\n", + "\t- R_EX_h2o_e\n", + "\t- R_EX_h2o2_e\n", + "\t- R_EX_h2s_e\n", + "\t- R_EX_hacolipa_e\n", + "\t- R_EX_halipa_e\n", + "\t- R_EX_hdca_e\n", + "\t- R_EX_hdcea_e\n", + "\t- R_EX_hg2_e\n", + "\t- R_EX_his__L_e\n", + "\t- R_EX_hom__L_e\n", + "\t- R_EX_hxa_e\n", + "\t- R_EX_hxan_e\n", + "\t- R_EX_3ntym_e\n", + "\t- R_EX_idon__L_e\n", + "\t- R_EX_6apa_e\n", + "\t- R_EX_feroxBfe_e\n", + "\t- R_EX_4hphac_e\n", + "\t- R_EX_4abz_e\n", + "\t- R_EX_fe3dhbzs3_e\n", + "\t- R_EX_acon_C_e\n", + "\t- R_EX_drib_e\n", + "\t- R_EX_guln__L_e\n", + "\t- R_EX_feroxG_e\n", + "\t- R_EX_feroxB_e\n", + "\t- R_EX_tcb_e\n", + "\t- R_EX_tton_e\n", + "\t- R_EX_4hthr_e\n", + "\t- R_EX_salchs4fe_e\n", + "\t- R_EX_feroxEfe_e\n", + "\t- R_EX_diact_e\n", + "\t- R_EX_feroxE_e\n", + "\t- R_EX_cysi__L_e\n", + "\t- R_EX_23dhbzs3_e\n", + "\t- R_EX_2pg_e\n", + "\t- R_EX_tet_e\n", + "\t- R_EX_salchs4_e\n", + "\t- R_EX_btd_RR_e\n", + "\t- R_EX_rbt_e\n", + "\t- R_EX_cellb_e\n", + "\t- R_EX_bz_e\n", + "\t- R_EX_13ppd_e\n", + "\t- R_EX_oaa_e\n", + "\t- R_EX_salchs2fe_e\n", + "\t- R_EX_abt__D_e\n", + "\t- R_EX_peng_e\n", + "\t- R_EX_pac_e\n", + "\t- R_EX_frmd_e\n", + "\t- R_EX_fe_e\n", + "\t- R_EX_colipaOA_e\n", + "\t- R_EX_salcn_e\n", + "\t- R_EX_salchsx_e\n", + "\t- R_EX_galam_e\n", + "\t- R_EX_kokdolipidA_e\n", + "\t- R_EX_raffin_e\n", + "\t- R_EX_o6a4colipa_e\n", + "\t- R_EX_AEP_e\n", + "\t- R_EX_3hoxpac_e\n", + "\t- R_EX_arab__D_e\n", + "\t- R_EX_4hoxpac_e\n", + "\t- R_DM_lipidA_core_e_p\n", + "\t- R_EX_icit_e\n", + "\t- R_EX_pep_e\n", + "\t- R_EX_dxyl_e\n", + "\t- R_SK_thf_c\n", + "\t- R_EX_colipa20Oag_e\n", + "\t- R_EX_remnant1_e\n", + "\t- R_EX_udcpo5_e\n", + "\t- R_EX_salchs2_e\n", + "\t- R_EX_rnam_e\n", + "\t- R_EX_airs_e\n", + "\t- R_EX_3pg_e\n", + "\t- R_EX_feroxGfe_e\n", + "\t- R_EX_udcpo4_e\n", + "\t- R_EX_tag__D_e\u001b[0m\n", + "File saved at: ../../data/sbml_corrected/iSF_1195.xml\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: iECB_1328\n", + "\u001b[0;93mWARNING : \n", + " - R_CAT: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_FHL: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_DHPTDNR: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_DHPTDNRN: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SPODM: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SPODMpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCASPtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCFUMtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCMALtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCTARTtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_EX_chitob_e: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_CHITOBpts: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_CHITPH: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_URCN: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\u001b[0m\n", + "\n", + "\n", + "\u001b[0;36m############################################\n", + "############################################\n", + " WRITING SBML FILE \n", + "############################################\n", + "############################################\n", + "\u001b[0m\n", + "\u001b[0;93mWARNING : IMPORT REACTION REMOVED BY DEFAULT\u001b[0m\n", + "\u001b[0;93mWARNING : If you want to keep import reaction\n", + "use option -kir / --keep-import-reactions\u001b[0m\n", + "\u001b[0;93mWARNING : Import reaction removed:\n", + "\t- R_EX_all__D_e\n", + "\t- R_EX_alltn_e\n", + "\t- R_EX_anhgm_e\n", + "\t- R_EX_ala__L_e\n", + "\t- R_EX_amp_e\n", + "\t- R_EX_alaala_e\n", + "\t- R_EX_arab__L_e\n", + "\t- R_EX_arbtn_e\n", + "\t- R_EX_arbt_e\n", + "\t- R_EX_arg__L_e\n", + "\t- R_EX_arbtn_fe3_e\n", + "\t- R_EX_asn__L_e\n", + "\t- R_EX_ascb__L_e\n", + "\t- R_EX_g3ps_e\n", + "\t- R_EX_aso3_e\n", + "\t- R_EX_asp__L_e\n", + "\t- R_EX_btn_e\n", + "\t- R_EX_but_e\n", + "\t- R_EX_acmum_e\n", + "\t- R_DM_4crsol_c\n", + "\t- R_DM_5drib_c\n", + "\t- R_DM_aacald_c\n", + "\t- R_EX_butso3_e\n", + "\t- R_EX_ca2_e\n", + "\t- R_DM_amob_c\n", + "\t- R_EX_cbi_e\n", + "\t- R_DM_mththf_c\n", + "\t- R_EX_g6p_e\n", + "\t- R_DM_oxam_c\n", + "\t- R_EX_cbl1_e\n", + "\t- R_EX_cd2_e\n", + "\t- R_EX_cgly_e\n", + "\t- R_EX_12ppd__R_e\n", + "\t- R_EX_12ppd__S_e\n", + "\t- R_EX_14glucan_e\n", + "\t- R_EX_15dap_e\n", + "\t- R_EX_23camp_e\n", + "\t- R_EX_23ccmp_e\n", + "\t- R_EX_23cgmp_e\n", + "\t- R_EX_23cump_e\n", + "\t- R_EX_23dappa_e\n", + "\t- R_EX_chol_e\n", + "\t- R_EX_chtbs_e\n", + "\t- R_EX_cit_e\n", + "\t- R_EX_cl_e\n", + "\t- R_EX_cm_e\n", + "\t- R_EX_cmp_e\n", + "\t- R_EX_co2_e\n", + "\t- R_EX_cobalt2_e\n", + "\t- R_EX_colipa_e\n", + "\t- R_EX_colipap_e\n", + "\t- R_EX_26dap__M_e\n", + "\t- R_EX_2ddglcn_e\n", + "\t- R_EX_34dhpac_e\n", + "\t- R_EX_cpgn_e\n", + "\t- R_EX_3amp_e\n", + "\t- R_EX_gal_e\n", + "\t- R_EX_3cmp_e\n", + "\t- R_EX_3gmp_e\n", + "\t- R_EX_3hcinnm_e\n", + "\t- R_EX_cpgn_un_e\n", + "\t- R_EX_crn_e\n", + "\t- R_EX_crn__D_e\n", + "\t- R_EX_csn_e\n", + "\t- R_EX_3hpp_e\n", + "\t- R_EX_gal_bD_e\n", + "\t- R_EX_3hpppn_e\n", + "\t- R_EX_cu_e\n", + "\t- R_EX_cu2_e\n", + "\t- R_EX_3ump_e\n", + "\t- R_EX_4abut_e\n", + "\t- R_EX_4hoxpacd_e\n", + "\t- R_EX_cyan_e\n", + "\t- R_EX_cynt_e\n", + "\t- R_EX_cys__D_e\n", + "\t- R_EX_cys__L_e\n", + "\t- R_EX_5dglcn_e\n", + "\t- R_EX_5mtr_e\n", + "\t- R_EX_LalaDglu_e\n", + "\t- R_EX_cytd_e\n", + "\t- R_EX_dad_2_e\n", + "\t- R_EX_LalaDgluMdap_e\n", + "\t- R_EX_damp_e\n", + "\t- R_EX_gal1p_e\n", + "\t- R_EX_dca_e\n", + "\t- R_EX_LalaDgluMdapDala_e\n", + "\t- R_EX_LalaLglu_e\n", + "\t- R_EX_ac_e\n", + "\t- R_EX_dcmp_e\n", + "\t- R_EX_galct__D_e\n", + "\t- R_EX_dcyt_e\n", + "\t- R_EX_ddca_e\n", + "\t- R_EX_acac_e\n", + "\t- R_EX_acald_e\n", + "\t- R_EX_acgal_e\n", + "\t- R_EX_dgmp_e\n", + "\t- R_EX_dgsn_e\n", + "\t- R_EX_acgal1p_e\n", + "\t- R_EX_galctn__D_e\n", + "\t- R_EX_acgam_e\n", + "\t- R_EX_acgam1p_e\n", + "\t- R_EX_acmana_e\n", + "\t- R_EX_acnam_e\n", + "\t- R_EX_dha_e\n", + "\t- R_EX_dimp_e\n", + "\t- R_EX_din_e\n", + "\t- R_EX_dms_e\n", + "\t- R_EX_dmso_e\n", + "\t- R_EX_dopa_e\n", + "\t- R_EX_acolipa_e\n", + "\t- R_EX_acser_e\n", + "\t- R_EX_ade_e\n", + "\t- R_EX_adn_e\n", + "\t- R_EX_doxrbcn_e\n", + "\t- R_EX_dtmp_e\n", + "\t- R_EX_adocbl_e\n", + "\t- R_EX_ag_e\n", + "\t- R_EX_dump_e\n", + "\t- R_EX_galctn__L_e\n", + "\t- R_EX_duri_e\n", + "\t- R_EX_eca4colipa_e\n", + "\t- R_EX_agm_e\n", + "\t- R_EX_akg_e\n", + "\t- R_EX_ala_B_e\n", + "\t- R_EX_enlipa_e\n", + "\t- R_EX_ala__D_e\n", + "\t- R_EX_enter_e\n", + "\t- R_EX_galt_e\n", + "\t- R_EX_etha_e\n", + "\t- R_EX_galur_e\n", + "\t- R_EX_ethso3_e\n", + "\t- R_EX_etoh_e\n", + "\t- R_EX_gam_e\n", + "\t- R_EX_f6p_e\n", + "\t- R_EX_fald_e\n", + "\t- R_EX_fe2_e\n", + "\t- R_EX_gam6p_e\n", + "\t- R_EX_fe3_e\n", + "\t- R_EX_fe3dcit_e\n", + "\t- R_EX_fe3dhbzs_e\n", + "\t- R_EX_metsox_R__L_e\n", + "\t- R_EX_gbbtn_e\n", + "\t- R_EX_metsox_S__L_e\n", + "\t- R_EX_fe3hox_e\n", + "\t- R_EX_fe3hox_un_e\n", + "\t- R_EX_fecrm_e\n", + "\t- R_EX_fecrm_un_e\n", + "\t- R_EX_feenter_e\n", + "\t- R_EX_feoxam_e\n", + "\t- R_EX_feoxam_un_e\n", + "\t- R_EX_mg2_e\n", + "\t- R_EX_mincyc_e\n", + "\t- R_EX_minohp_e\n", + "\t- R_EX_mmet_e\n", + "\t- R_EX_mn2_e\n", + "\t- R_EX_mnl_e\n", + "\t- R_EX_mobd_e\n", + "\t- R_EX_for_e\n", + "\t- R_EX_fru_e\n", + "\t- R_EX_frulys_e\n", + "\t- R_EX_mso3_e\n", + "\t- R_EX_n2o_e\n", + "\t- R_EX_gdp_e\n", + "\t- R_EX_na1_e\n", + "\t- R_EX_nac_e\n", + "\t- R_EX_nh4_e\n", + "\t- R_EX_fruur_e\n", + "\t- R_EX_fuc__L_e\n", + "\t- R_EX_fum_e\n", + "\t- R_EX_fusa_e\n", + "\t- R_EX_ni2_e\n", + "\t- R_EX_nmn_e\n", + "\t- R_EX_g1p_e\n", + "\t- R_EX_glc__D_e\n", + "\t- R_EX_g3pc_e\n", + "\t- R_EX_g3pe_e\n", + "\t- R_EX_no_e\n", + "\t- R_EX_no2_e\n", + "\t- R_EX_g3pg_e\n", + "\t- R_EX_glcn_e\n", + "\t- R_EX_g3pi_e\n", + "\t- R_EX_no3_e\n", + "\t- R_EX_novbcn_e\n", + "\t- R_EX_o16a4colipa_e\n", + "\t- R_EX_o2_e\n", + "\t- R_EX_o2s_e\n", + "\t- R_EX_ocdca_e\n", + "\t- R_EX_glcr_e\n", + "\t- R_EX_ocdcea_e\n", + "\t- R_EX_octa_e\n", + "\t- R_EX_orn_e\n", + "\t- R_EX_orot_e\n", + "\t- R_EX_glcur_e\n", + "\t- R_EX_pacald_e\n", + "\t- R_EX_peamn_e\n", + "\t- R_EX_ttrcyc_e\n", + "\t- R_EX_phe__L_e\n", + "\t- R_EX_tungs_e\n", + "\t- R_EX_pheme_e\n", + "\t- R_EX_glcur1p_e\n", + "\t- R_EX_pi_e\n", + "\t- R_EX_tym_e\n", + "\t- R_EX_tyr__L_e\n", + "\t- R_EX_pnto__R_e\n", + "\t- R_EX_tyrp_e\n", + "\t- R_EX_gln__L_e\n", + "\t- R_EX_ppa_e\n", + "\t- R_EX_ppal_e\n", + "\t- R_EX_uacgam_e\n", + "\t- R_EX_glu__L_e\n", + "\t- R_EX_udpacgal_e\n", + "\t- R_EX_udpg_e\n", + "\t- R_EX_udpgal_e\n", + "\t- R_EX_pppn_e\n", + "\t- R_EX_ppt_e\n", + "\t- R_EX_pro__L_e\n", + "\t- R_EX_gly_e\n", + "\t- R_EX_progly_e\n", + "\t- R_EX_psclys_e\n", + "\t- R_EX_udpglcur_e\n", + "\t- R_EX_ump_e\n", + "\t- R_EX_ura_e\n", + "\t- R_EX_urea_e\n", + "\t- R_EX_uri_e\n", + "\t- R_EX_val__L_e\n", + "\t- R_EX_pser__L_e\n", + "\t- R_EX_ptrc_e\n", + "\t- R_EX_pydam_e\n", + "\t- R_EX_pydx_e\n", + "\t- R_EX_pydxn_e\n", + "\t- R_EX_pyr_e\n", + "\t- R_EX_xan_e\n", + "\t- R_EX_quin_e\n", + "\t- R_EX_xmp_e\n", + "\t- R_EX_glyald_e\n", + "\t- R_EX_xtsn_e\n", + "\t- R_EX_xyl__D_e\n", + "\t- R_EX_xylu__L_e\n", + "\t- R_EX_r5p_e\n", + "\t- R_EX_rfamp_e\n", + "\t- R_EX_rib__D_e\n", + "\t- R_EX_zn2_e\n", + "\t- R_EX_rmn_e\n", + "\t- R_EX_sbt__D_e\n", + "\t- R_EX_glyb_e\n", + "\t- R_EX_sel_e\n", + "\t- R_EX_ser__D_e\n", + "\t- R_EX_ser__L_e\n", + "\t- R_EX_skm_e\n", + "\t- R_EX_glyc_e\n", + "\t- R_EX_slnt_e\n", + "\t- R_EX_so2_e\n", + "\t- R_EX_so3_e\n", + "\t- R_EX_so4_e\n", + "\t- R_EX_spmd_e\n", + "\t- R_EX_succ_e\n", + "\t- R_EX_sucr_e\n", + "\t- R_EX_glyc__R_e\n", + "\t- R_EX_sulfac_e\n", + "\t- R_EX_tartr__D_e\n", + "\t- R_EX_glyc2p_e\n", + "\t- R_EX_tartr__L_e\n", + "\t- R_EX_taur_e\n", + "\t- R_EX_glyc3p_e\n", + "\t- R_EX_tcynt_e\n", + "\t- R_EX_thm_e\n", + "\t- R_EX_thr__L_e\n", + "\t- R_EX_thrp_e\n", + "\t- R_EX_thym_e\n", + "\t- R_EX_thymd_e\n", + "\t- R_EX_tma_e\n", + "\t- R_EX_tmao_e\n", + "\t- R_EX_tre_e\n", + "\t- R_EX_trp__L_e\n", + "\t- R_EX_tsul_e\n", + "\t- R_EX_glyclt_e\n", + "\t- R_EX_ttdca_e\n", + "\t- R_EX_ttdcea_e\n", + "\t- R_EX_gmp_e\n", + "\t- R_EX_gsn_e\n", + "\t- R_EX_gthox_e\n", + "\t- R_EX_gthrd_e\n", + "\t- R_EX_gtp_e\n", + "\t- R_EX_gua_e\n", + "\t- R_EX_h_e\n", + "\t- R_EX_h2_e\n", + "\t- R_EX_h2o_e\n", + "\t- R_EX_h2o2_e\n", + "\t- R_EX_h2s_e\n", + "\t- R_EX_hacolipa_e\n", + "\t- R_EX_halipa_e\n", + "\t- R_EX_hdca_e\n", + "\t- R_EX_hdcea_e\n", + "\t- R_EX_hg2_e\n", + "\t- R_EX_his__L_e\n", + "\t- R_EX_hom__L_e\n", + "\t- R_EX_hxa_e\n", + "\t- R_EX_hxan_e\n", + "\t- R_EX_idon__L_e\n", + "\t- R_EX_ile__L_e\n", + "\t- R_EX_imp_e\n", + "\t- R_EX_indole_e\n", + "\t- R_EX_inost_e\n", + "\t- R_EX_ins_e\n", + "\t- R_EX_isetac_e\n", + "\t- R_EX_k_e\n", + "\t- R_EX_kdo2lipid4_e\n", + "\t- R_EX_lac__D_e\n", + "\t- R_EX_lac__L_e\n", + "\t- R_EX_lcts_e\n", + "\t- R_EX_leu__L_e\n", + "\t- R_EX_lipa_e\n", + "\t- R_EX_lipa_cold_e\n", + "\t- R_EX_lipoate_e\n", + "\t- R_EX_lys__L_e\n", + "\t- R_EX_lyx__L_e\n", + "\t- R_EX_mal__D_e\n", + "\t- R_EX_mal__L_e\n", + "\t- R_EX_malt_e\n", + "\t- R_EX_malthx_e\n", + "\t- R_EX_maltpt_e\n", + "\t- R_EX_malttr_e\n", + "\t- R_EX_maltttr_e\n", + "\t- R_EX_man_e\n", + "\t- R_EX_man6p_e\n", + "\t- R_EX_manglyc_e\n", + "\t- R_EX_melib_e\n", + "\t- R_EX_meoh_e\n", + "\t- R_EX_met__D_e\n", + "\t- R_EX_met__L_e\n", + "\t- R_EX_salchsx_e\n", + "\t- R_EX_AEP_e\n", + "\t- R_EX_4abz_e\n", + "\t- R_EX_icit_e\n", + "\t- R_EX_pep_e\n", + "\t- R_EX_acon_C_e\n", + "\t- R_EX_colipa20Oag_e\n", + "\t- R_EX_remnant1_e\n", + "\t- R_EX_udcpo5_e\n", + "\t- R_EX_feroxB_e\n", + "\t- R_EX_tcb_e\n", + "\t- R_EX_tton_e\n", + "\t- R_EX_salchs2_e\n", + "\t- R_EX_4hthr_e\n", + "\t- R_EX_feroxEfe_e\n", + "\t- R_EX_feroxE_e\n", + "\t- R_EX_rnam_e\n", + "\t- R_EX_23dhbzs3_e\n", + "\t- R_EX_airs_e\n", + "\t- R_EX_tet_e\n", + "\t- R_EX_salchs4_e\n", + "\t- R_EX_dxyl_e\n", + "\t- R_EX_3pg_e\n", + "\t- R_EX_feroxGfe_e\n", + "\t- R_EX_oaa_e\n", + "\t- R_EX_salchs2fe_e\n", + "\t- R_EX_cysi__L_e\n", + "\t- R_EX_udcpo4_e\n", + "\t- R_EX_fe3dhbzs3_e\n", + "\t- R_EX_tag__D_e\n", + "\t- R_EX_drib_e\n", + "\t- R_EX_guln__L_e\n", + "\t- R_EX_salchs4fe_e\n", + "\t- R_EX_2pg_e\n", + "\t- R_EX_feroxG_e\n", + "\t- R_EX_frmd_e\n", + "\t- R_EX_colipaOA_e\n", + "\t- R_EX_btd_RR_e\n", + "\t- R_EX_diact_e\n", + "\t- R_EX_13ppd_e\n", + "\t- R_EX_rbt_e\n", + "\t- R_EX_bz_e\n", + "\t- R_EX_cellb_e\n", + "\t- R_EX_abt__D_e\n", + "\t- R_EX_fe_e\n", + "\t- R_EX_kokdolipidA_e\n", + "\t- R_EX_3hoxpac_e\n", + "\t- R_DM_lipidA_core_e_p\n", + "\t- R_EX_3ntym_e\n", + "\t- R_EX_6apa_e\n", + "\t- R_EX_4hoxpac_e\n", + "\t- R_EX_peng_e\n", + "\t- R_EX_pac_e\n", + "\t- R_EX_salcn_e\n", + "\t- R_EX_galam_e\n", + "\t- R_EX_raffin_e\n", + "\t- R_EX_o6a4colipa_e\n", + "\t- R_EX_arab__D_e\n", + "\t- R_EX_feroxBfe_e\n", + "\t- R_EX_4hphac_e\u001b[0m\n", + "File saved at: ../../data/sbml_corrected/iECB_1328.xml\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: iECO103_1326\n", + "\u001b[0;93mWARNING : \n", + " - R_CAT: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_FHL: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_DHPTDNR: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_DHPTDNRN: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SPODM: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SPODMpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCASPtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCFUMtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCMALtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCTARTtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_EX_chitob_e: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_CHITPH: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_CHITOBpts: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_URCN: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\u001b[0m\n", + "\n", + "\n", + "\u001b[0;36m############################################\n", + "############################################\n", + " WRITING SBML FILE \n", + "############################################\n", + "############################################\n", + "\u001b[0m\n", + "\u001b[0;93mWARNING : IMPORT REACTION REMOVED BY DEFAULT\u001b[0m\n", + "\u001b[0;93mWARNING : If you want to keep import reaction\n", + "use option -kir / --keep-import-reactions\u001b[0m\n", + "\u001b[0;93mWARNING : Import reaction removed:\n", + "\t- R_EX_acald_e\n", + "\t- R_EX_acgal_e\n", + "\t- R_EX_acgam_e\n", + "\t- R_EX_acgam1p_e\n", + "\t- R_EX_acolipa_e\n", + "\t- R_EX_acac_e\n", + "\t- R_EX_acmana_e\n", + "\t- R_EX_acnam_e\n", + "\t- R_EX_acgal1p_e\n", + "\t- R_EX_acmum_e\n", + "\t- R_DM_4crsol_c\n", + "\t- R_EX_acser_e\n", + "\t- R_EX_ac_e\n", + "\t- R_DM_5drib_c\n", + "\t- R_EX_fe3_e\n", + "\t- R_EX_fe3dcit_e\n", + "\t- R_EX_ade_e\n", + "\t- R_EX_adn_e\n", + "\t- R_EX_fe3dhbzs_e\n", + "\t- R_DM_aacald_c\n", + "\t- R_EX_fe3hox_e\n", + "\t- R_EX_adocbl_e\n", + "\t- R_EX_ag_e\n", + "\t- R_EX_fe3hox_un_e\n", + "\t- R_DM_amob_c\n", + "\t- R_EX_fecrm_e\n", + "\t- R_EX_fecrm_un_e\n", + "\t- R_EX_agm_e\n", + "\t- R_EX_akg_e\n", + "\t- R_EX_ala_B_e\n", + "\t- R_EX_feenter_e\n", + "\t- R_EX_ala__D_e\n", + "\t- R_DM_mththf_c\n", + "\t- R_EX_ala__L_e\n", + "\t- R_EX_alaala_e\n", + "\t- R_EX_all__D_e\n", + "\t- R_EX_alltn_e\n", + "\t- R_EX_amp_e\n", + "\t- R_EX_anhgm_e\n", + "\t- R_EX_arab__L_e\n", + "\t- R_EX_arbt_e\n", + "\t- R_EX_feoxam_e\n", + "\t- R_EX_feoxam_un_e\n", + "\t- R_EX_for_e\n", + "\t- R_EX_fru_e\n", + "\t- R_EX_frulys_e\n", + "\t- R_EX_fruur_e\n", + "\t- R_EX_fuc__L_e\n", + "\t- R_EX_fum_e\n", + "\t- R_EX_fusa_e\n", + "\t- R_EX_g1p_e\n", + "\t- R_EX_g3pc_e\n", + "\t- R_EX_g3pe_e\n", + "\t- R_EX_arbtn_e\n", + "\t- R_EX_arbtn_fe3_e\n", + "\t- R_EX_arg__L_e\n", + "\t- R_EX_g3pg_e\n", + "\t- R_EX_ascb__L_e\n", + "\t- R_DM_oxam_c\n", + "\t- R_EX_asn__L_e\n", + "\t- R_EX_g3pi_e\n", + "\t- R_EX_g3ps_e\n", + "\t- R_EX_g6p_e\n", + "\t- R_EX_aso3_e\n", + "\t- R_EX_asp__L_e\n", + "\t- R_EX_btn_e\n", + "\t- R_EX_gal_e\n", + "\t- R_EX_gal_bD_e\n", + "\t- R_EX_gal1p_e\n", + "\t- R_EX_but_e\n", + "\t- R_EX_butso3_e\n", + "\t- R_EX_ca2_e\n", + "\t- R_EX_galct__D_e\n", + "\t- R_EX_galctn__D_e\n", + "\t- R_EX_galctn__L_e\n", + "\t- R_EX_galt_e\n", + "\t- R_EX_galur_e\n", + "\t- R_EX_cbi_e\n", + "\t- R_EX_cbl1_e\n", + "\t- R_EX_gam_e\n", + "\t- R_EX_gam6p_e\n", + "\t- R_EX_cd2_e\n", + "\t- R_EX_cgly_e\n", + "\t- R_EX_gbbtn_e\n", + "\t- R_EX_12ppd__R_e\n", + "\t- R_EX_gdp_e\n", + "\t- R_EX_glc__D_e\n", + "\t- R_EX_chol_e\n", + "\t- R_EX_chtbs_e\n", + "\t- R_EX_glcn_e\n", + "\t- R_EX_cit_e\n", + "\t- R_EX_glcr_e\n", + "\t- R_EX_12ppd__S_e\n", + "\t- R_EX_glcur_e\n", + "\t- R_EX_glcur1p_e\n", + "\t- R_EX_cl_e\n", + "\t- R_EX_cm_e\n", + "\t- R_EX_cmp_e\n", + "\t- R_EX_14glucan_e\n", + "\t- R_EX_co2_e\n", + "\t- R_EX_cobalt2_e\n", + "\t- R_EX_gln__L_e\n", + "\t- R_EX_glu__L_e\n", + "\t- R_EX_colipa_e\n", + "\t- R_EX_15dap_e\n", + "\t- R_EX_colipap_e\n", + "\t- R_EX_gly_e\n", + "\t- R_EX_glyald_e\n", + "\t- R_EX_glyb_e\n", + "\t- R_EX_glyc_e\n", + "\t- R_EX_cpgn_e\n", + "\t- R_EX_cpgn_un_e\n", + "\t- R_EX_crn_e\n", + "\t- R_EX_glyc__R_e\n", + "\t- R_EX_crn__D_e\n", + "\t- R_EX_glyc2p_e\n", + "\t- R_EX_23camp_e\n", + "\t- R_EX_glyc3p_e\n", + "\t- R_EX_glyclt_e\n", + "\t- R_EX_csn_e\n", + "\t- R_EX_cu_e\n", + "\t- R_EX_cu2_e\n", + "\t- R_EX_gmp_e\n", + "\t- R_EX_cyan_e\n", + "\t- R_EX_gsn_e\n", + "\t- R_EX_23ccmp_e\n", + "\t- R_EX_gthox_e\n", + "\t- R_EX_gthrd_e\n", + "\t- R_EX_gtp_e\n", + "\t- R_EX_cynt_e\n", + "\t- R_EX_cys__D_e\n", + "\t- R_EX_cys__L_e\n", + "\t- R_EX_cytd_e\n", + "\t- R_EX_gua_e\n", + "\t- R_EX_23cgmp_e\n", + "\t- R_EX_h_e\n", + "\t- R_EX_h2_e\n", + "\t- R_EX_dad_2_e\n", + "\t- R_EX_damp_e\n", + "\t- R_EX_h2o_e\n", + "\t- R_EX_dca_e\n", + "\t- R_EX_23cump_e\n", + "\t- R_EX_dcmp_e\n", + "\t- R_EX_h2o2_e\n", + "\t- R_EX_h2s_e\n", + "\t- R_EX_hacolipa_e\n", + "\t- R_EX_halipa_e\n", + "\t- R_EX_hdca_e\n", + "\t- R_EX_hdcea_e\n", + "\t- R_EX_dcyt_e\n", + "\t- R_EX_ddca_e\n", + "\t- R_EX_dgmp_e\n", + "\t- R_EX_dgsn_e\n", + "\t- R_EX_hg2_e\n", + "\t- R_EX_his__L_e\n", + "\t- R_EX_dha_e\n", + "\t- R_EX_dimp_e\n", + "\t- R_EX_hom__L_e\n", + "\t- R_EX_din_e\n", + "\t- R_EX_23dappa_e\n", + "\t- R_EX_dms_e\n", + "\t- R_EX_dmso_e\n", + "\t- R_EX_hxa_e\n", + "\t- R_EX_hxan_e\n", + "\t- R_EX_idon__L_e\n", + "\t- R_EX_dopa_e\n", + "\t- R_EX_26dap__M_e\n", + "\t- R_EX_doxrbcn_e\n", + "\t- R_EX_ile__L_e\n", + "\t- R_EX_imp_e\n", + "\t- R_EX_2ddglcn_e\n", + "\t- R_EX_indole_e\n", + "\t- R_EX_inost_e\n", + "\t- R_EX_dtmp_e\n", + "\t- R_EX_dump_e\n", + "\t- R_EX_duri_e\n", + "\t- R_EX_ins_e\n", + "\t- R_EX_isetac_e\n", + "\t- R_EX_eca4colipa_e\n", + "\t- R_EX_k_e\n", + "\t- R_EX_34dhpac_e\n", + "\t- R_EX_kdo2lipid4_e\n", + "\t- R_EX_lac__D_e\n", + "\t- R_EX_lac__L_e\n", + "\t- R_EX_enlipa_e\n", + "\t- R_EX_enter_e\n", + "\t- R_EX_etha_e\n", + "\t- R_EX_ethso3_e\n", + "\t- R_EX_etoh_e\n", + "\t- R_EX_f6p_e\n", + "\t- R_EX_lcts_e\n", + "\t- R_EX_leu__L_e\n", + "\t- R_EX_lipa_e\n", + "\t- R_EX_fald_e\n", + "\t- R_EX_3amp_e\n", + "\t- R_EX_fe2_e\n", + "\t- R_EX_lipa_cold_e\n", + "\t- R_EX_lipoate_e\n", + "\t- R_EX_lys__L_e\n", + "\t- R_EX_3cmp_e\n", + "\t- R_EX_lyx__L_e\n", + "\t- R_EX_mal__D_e\n", + "\t- R_EX_so3_e\n", + "\t- R_EX_so4_e\n", + "\t- R_EX_spmd_e\n", + "\t- R_EX_mal__L_e\n", + "\t- R_EX_3gmp_e\n", + "\t- R_EX_malt_e\n", + "\t- R_EX_succ_e\n", + "\t- R_EX_sucr_e\n", + "\t- R_EX_malthx_e\n", + "\t- R_EX_3hcinnm_e\n", + "\t- R_EX_maltpt_e\n", + "\t- R_EX_malttr_e\n", + "\t- R_EX_sulfac_e\n", + "\t- R_EX_tartr__D_e\n", + "\t- R_EX_tartr__L_e\n", + "\t- R_EX_taur_e\n", + "\t- R_EX_tcynt_e\n", + "\t- R_EX_thm_e\n", + "\t- R_EX_maltttr_e\n", + "\t- R_EX_man_e\n", + "\t- R_EX_man6p_e\n", + "\t- R_EX_manglyc_e\n", + "\t- R_EX_melib_e\n", + "\t- R_EX_thr__L_e\n", + "\t- R_EX_thrp_e\n", + "\t- R_EX_thym_e\n", + "\t- R_EX_thymd_e\n", + "\t- R_EX_tma_e\n", + "\t- R_EX_tmao_e\n", + "\t- R_EX_meoh_e\n", + "\t- R_EX_met__D_e\n", + "\t- R_EX_tre_e\n", + "\t- R_EX_3hpp_e\n", + "\t- R_EX_trp__L_e\n", + "\t- R_EX_tsul_e\n", + "\t- R_EX_met__L_e\n", + "\t- R_EX_metsox_R__L_e\n", + "\t- R_EX_metsox_S__L_e\n", + "\t- R_EX_ttdca_e\n", + "\t- R_EX_ttdcea_e\n", + "\t- R_EX_3hpppn_e\n", + "\t- R_EX_ttrcyc_e\n", + "\t- R_EX_tungs_e\n", + "\t- R_EX_mg2_e\n", + "\t- R_EX_mincyc_e\n", + "\t- R_EX_minohp_e\n", + "\t- R_EX_mmet_e\n", + "\t- R_EX_tym_e\n", + "\t- R_EX_tyr__L_e\n", + "\t- R_EX_mn2_e\n", + "\t- R_EX_3ump_e\n", + "\t- R_EX_mnl_e\n", + "\t- R_EX_tyrp_e\n", + "\t- R_EX_mobd_e\n", + "\t- R_EX_uacgam_e\n", + "\t- R_EX_mso3_e\n", + "\t- R_EX_4abut_e\n", + "\t- R_EX_n2o_e\n", + "\t- R_EX_na1_e\n", + "\t- R_EX_nac_e\n", + "\t- R_EX_nh4_e\n", + "\t- R_EX_ni2_e\n", + "\t- R_EX_udpacgal_e\n", + "\t- R_EX_udpg_e\n", + "\t- R_EX_udpgal_e\n", + "\t- R_EX_udpglcur_e\n", + "\t- R_EX_ump_e\n", + "\t- R_EX_ura_e\n", + "\t- R_EX_urea_e\n", + "\t- R_EX_nmn_e\n", + "\t- R_EX_no_e\n", + "\t- R_EX_no2_e\n", + "\t- R_EX_no3_e\n", + "\t- R_EX_uri_e\n", + "\t- R_EX_val__L_e\n", + "\t- R_EX_4hoxpacd_e\n", + "\t- R_EX_xan_e\n", + "\t- R_EX_xmp_e\n", + "\t- R_EX_novbcn_e\n", + "\t- R_EX_o16a4colipa_e\n", + "\t- R_EX_xtsn_e\n", + "\t- R_EX_5dglcn_e\n", + "\t- R_EX_xyl__D_e\n", + "\t- R_EX_xylu__L_e\n", + "\t- R_EX_zn2_e\n", + "\t- R_EX_o2_e\n", + "\t- R_EX_o2s_e\n", + "\t- R_EX_ocdca_e\n", + "\t- R_EX_ocdcea_e\n", + "\t- R_EX_octa_e\n", + "\t- R_EX_5mtr_e\n", + "\t- R_EX_orn_e\n", + "\t- R_EX_orot_e\n", + "\t- R_EX_pacald_e\n", + "\t- R_EX_peamn_e\n", + "\t- R_EX_phe__L_e\n", + "\t- R_EX_pheme_e\n", + "\t- R_EX_pi_e\n", + "\t- R_EX_pnto__R_e\n", + "\t- R_EX_ppa_e\n", + "\t- R_EX_ppal_e\n", + "\t- R_EX_pppn_e\n", + "\t- R_EX_LalaDglu_e\n", + "\t- R_EX_ppt_e\n", + "\t- R_EX_pro__L_e\n", + "\t- R_EX_progly_e\n", + "\t- R_EX_psclys_e\n", + "\t- R_EX_pser__L_e\n", + "\t- R_EX_LalaDgluMdap_e\n", + "\t- R_EX_ptrc_e\n", + "\t- R_EX_pydam_e\n", + "\t- R_EX_pydx_e\n", + "\t- R_EX_pydxn_e\n", + "\t- R_EX_pyr_e\n", + "\t- R_EX_quin_e\n", + "\t- R_EX_r5p_e\n", + "\t- R_EX_rfamp_e\n", + "\t- R_EX_rib__D_e\n", + "\t- R_EX_rmn_e\n", + "\t- R_EX_sbt__D_e\n", + "\t- R_EX_LalaDgluMdapDala_e\n", + "\t- R_EX_sel_e\n", + "\t- R_EX_ser__D_e\n", + "\t- R_EX_LalaLglu_e\n", + "\t- R_EX_ser__L_e\n", + "\t- R_EX_skm_e\n", + "\t- R_EX_slnt_e\n", + "\t- R_EX_so2_e\n", + "\t- R_EX_3hoxpac_e\n", + "\t- R_EX_feroxBfe_e\n", + "\t- R_EX_4hphac_e\n", + "\t- R_EX_4abz_e\n", + "\t- R_EX_acon_C_e\n", + "\t- R_EX_salchs4_e\n", + "\t- R_EX_feroxB_e\n", + "\t- R_EX_tcb_e\n", + "\t- R_EX_tton_e\n", + "\t- R_EX_4hthr_e\n", + "\t- R_EX_feroxEfe_e\n", + "\t- R_EX_oaa_e\n", + "\t- R_EX_salchs2fe_e\n", + "\t- R_EX_feroxE_e\n", + "\t- R_EX_23dhbzs3_e\n", + "\t- R_EX_tet_e\n", + "\t- R_EX_cysi__L_e\n", + "\t- R_EX_fe3dhbzs3_e\n", + "\t- R_EX_airs_e\n", + "\t- R_EX_dxyl_e\n", + "\t- R_EX_3pg_e\n", + "\t- R_EX_feroxGfe_e\n", + "\t- R_EX_drib_e\n", + "\t- R_EX_salchs4fe_e\n", + "\t- R_EX_2pg_e\n", + "\t- R_EX_frmd_e\n", + "\t- R_EX_udcpo4_e\n", + "\t- R_EX_colipaOA_e\n", + "\t- R_EX_salchsx_e\n", + "\t- R_EX_AEP_e\n", + "\t- R_EX_icit_e\n", + "\t- R_EX_pep_e\n", + "\t- R_EX_tag__D_e\n", + "\t- R_EX_guln__L_e\n", + "\t- R_EX_feroxG_e\n", + "\t- R_EX_colipa20Oag_e\n", + "\t- R_EX_remnant1_e\n", + "\t- R_EX_udcpo5_e\n", + "\t- R_EX_salchs2_e\n", + "\t- R_EX_diact_e\n", + "\t- R_EX_cellb_e\n", + "\t- R_EX_rnam_e\n", + "\t- R_EX_btd_RR_e\n", + "\t- R_EX_rbt_e\n", + "\t- R_EX_bz_e\n", + "\t- R_EX_13ppd_e\n", + "\t- R_EX_abt__D_e\n", + "\t- R_EX_4hoxpac_e\n", + "\t- R_EX_fe_e\n", + "\t- R_EX_kokdolipidA_e\n", + "\t- R_EX_3ntym_e\n", + "\t- R_EX_6apa_e\n", + "\t- R_DM_lipidA_core_e_p\n", + "\t- R_EX_peng_e\n", + "\t- R_EX_pac_e\n", + "\t- R_EX_salcn_e\n", + "\t- R_EX_galam_e\n", + "\t- R_EX_raffin_e\n", + "\t- R_EX_o6a4colipa_e\n", + "\t- R_EX_arab__D_e\u001b[0m\n", + "File saved at: ../../data/sbml_corrected/iECO103_1326.xml\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: iEC1356_Bl21DE3\n", + "\u001b[0;93mWARNING : \n", + " - R_CAT: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_DHPTDNR: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_DHPTDNRN: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_FHL: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCMALtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCTARTtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCASPtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SPODM: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCFUMtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SPODMpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + " - R_EDTXSCOF is multiple (more than 1) metabolites import/export reaction. \n", + "\tDeleted as it is an import reaction.\n", + " - R_HPA3MOFAD: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + "\u001b[0m\n", + "\n", + "\n", + "\u001b[0;36m############################################\n", + "############################################\n", + " WRITING SBML FILE \n", + "############################################\n", + "############################################\n", + "\u001b[0m\n", + "\u001b[0;93mWARNING : IMPORT REACTION REMOVED BY DEFAULT\u001b[0m\n", + "\u001b[0;93mWARNING : If you want to keep import reaction\n", + "use option -kir / --keep-import-reactions\u001b[0m\n", + "\u001b[0;93mWARNING : Reaction with tag reversible modified:\n", + "\t- R_HPA3MOFAD\u001b[0m\n", + "\u001b[0;93mWARNING : Reaction with Reactants and Products exchanged:\n", + "\t- R_HPA3MOFAD\u001b[0m\n", + "\u001b[0;93mWARNING : Import reaction removed:\n", + "\t- R_DM_4crsol_c\n", + "\t- R_DM_5drib_c\n", + "\t- R_DM_aacald_c\n", + "\t- R_DM_amob_c\n", + "\t- R_DM_mththf_c\n", + "\t- R_DM_oxam_c\n", + "\t- R_EX_12ppd__R_e\n", + "\t- R_EX_12ppd__S_e\n", + "\t- R_EX_14glucan_e\n", + "\t- R_EX_15dap_e\n", + "\t- R_EX_23camp_e\n", + "\t- R_EX_23ccmp_e\n", + "\t- R_EX_ala__L_e\n", + "\t- R_EX_alaala_e\n", + "\t- R_EX_23cgmp_e\n", + "\t- R_EX_all__D_e\n", + "\t- R_EX_alltn_e\n", + "\t- R_EX_23cump_e\n", + "\t- R_EX_amp_e\n", + "\t- R_EX_anhgm_e\n", + "\t- R_EX_23dappa_e\n", + "\t- R_EX_arab__L_e\n", + "\t- R_EX_arbt_e\n", + "\t- R_EX_26dap__M_e\n", + "\t- R_EX_arbtn_e\n", + "\t- R_EX_arbtn_fe3_e\n", + "\t- R_EX_2ddglcn_e\n", + "\t- R_EX_arg__L_e\n", + "\t- R_EX_ascb__L_e\n", + "\t- R_EX_asn__L_e\n", + "\t- R_EX_aso3_e\n", + "\t- R_EX_asp__L_e\n", + "\t- R_EX_btn_e\n", + "\t- R_EX_but_e\n", + "\t- R_EX_butso3_e\n", + "\t- R_EX_ca2_e\n", + "\t- R_EX_feoxam_un_e\n", + "\t- R_EX_for_e\n", + "\t- R_EX_fru_e\n", + "\t- R_EX_frulys_e\n", + "\t- R_EX_fruur_e\n", + "\t- R_EX_fuc__L_e\n", + "\t- R_EX_fum_e\n", + "\t- R_EX_cbi_e\n", + "\t- R_EX_cbl1_e\n", + "\t- R_EX_cd2_e\n", + "\t- R_EX_fusa_e\n", + "\t- R_EX_34dhpac_e\n", + "\t- R_EX_g1p_e\n", + "\t- R_EX_g3pc_e\n", + "\t- R_EX_g3pe_e\n", + "\t- R_EX_g3pg_e\n", + "\t- R_EX_cgly_e\n", + "\t- R_EX_chol_e\n", + "\t- R_EX_chtbs_e\n", + "\t- R_EX_cit_e\n", + "\t- R_EX_3amp_e\n", + "\t- R_EX_cl_e\n", + "\t- R_EX_cm_e\n", + "\t- R_EX_cmp_e\n", + "\t- R_EX_g3pi_e\n", + "\t- R_EX_g3ps_e\n", + "\t- R_EX_g6p_e\n", + "\t- R_EX_co2_e\n", + "\t- R_EX_3cmp_e\n", + "\t- R_EX_cobalt2_e\n", + "\t- R_EX_colipa_e\n", + "\t- R_EX_colipap_e\n", + "\t- R_EX_cpgn_e\n", + "\t- R_EX_gal_e\n", + "\t- R_EX_gal_bD_e\n", + "\t- R_EX_gal1p_e\n", + "\t- R_EX_galct__D_e\n", + "\t- R_EX_galctn__D_e\n", + "\t- R_EX_galctn__L_e\n", + "\t- R_EX_galt_e\n", + "\t- R_EX_cpgn_un_e\n", + "\t- R_EX_crn_e\n", + "\t- R_EX_crn__D_e\n", + "\t- R_EX_csn_e\n", + "\t- R_EX_galur_e\n", + "\t- R_EX_3gmp_e\n", + "\t- R_EX_gam_e\n", + "\t- R_EX_gam6p_e\n", + "\t- R_EX_gbbtn_e\n", + "\t- R_EX_cu_e\n", + "\t- R_EX_cu2_e\n", + "\t- R_EX_cyan_e\n", + "\t- R_EX_cynt_e\n", + "\t- R_EX_gdp_e\n", + "\t- R_EX_glc__D_e\n", + "\t- R_EX_cys__D_e\n", + "\t- R_EX_3hcinnm_e\n", + "\t- R_EX_cys__L_e\n", + "\t- R_EX_cytd_e\n", + "\t- R_EX_glcn_e\n", + "\t- R_EX_glcr_e\n", + "\t- R_EX_glcur_e\n", + "\t- R_EX_dad_2_e\n", + "\t- R_EX_3hpp_e\n", + "\t- R_EX_damp_e\n", + "\t- R_EX_dca_e\n", + "\t- R_EX_glcur1p_e\n", + "\t- R_EX_gln__L_e\n", + "\t- R_EX_glu__L_e\n", + "\t- R_EX_gly_e\n", + "\t- R_EX_glyald_e\n", + "\t- R_EX_dcmp_e\n", + "\t- R_EX_dcyt_e\n", + "\t- R_EX_ddca_e\n", + "\t- R_EX_dgmp_e\n", + "\t- R_EX_dgsn_e\n", + "\t- R_EX_dha_e\n", + "\t- R_EX_glyb_e\n", + "\t- R_EX_glyc_e\n", + "\t- R_EX_glyc__R_e\n", + "\t- R_EX_dimp_e\n", + "\t- R_EX_glyc2p_e\n", + "\t- R_EX_din_e\n", + "\t- R_EX_3hpppn_e\n", + "\t- R_EX_dms_e\n", + "\t- R_EX_dmso_e\n", + "\t- R_EX_glyc3p_e\n", + "\t- R_EX_glyclt_e\n", + "\t- R_EX_gmp_e\n", + "\t- R_EX_dopa_e\n", + "\t- R_EX_gsn_e\n", + "\t- R_EX_doxrbcn_e\n", + "\t- R_EX_3ump_e\n", + "\t- R_EX_dtmp_e\n", + "\t- R_EX_dump_e\n", + "\t- R_EX_gthox_e\n", + "\t- R_EX_gthrd_e\n", + "\t- R_EX_gtp_e\n", + "\t- R_EX_duri_e\n", + "\t- R_EX_gua_e\n", + "\t- R_EX_4abut_e\n", + "\t- R_EX_h_e\n", + "\t- R_EX_eca4colipa_e\n", + "\t- R_EX_enlipa_e\n", + "\t- R_EX_h2_e\n", + "\t- R_EX_enter_e\n", + "\t- R_EX_etha_e\n", + "\t- R_EX_ethso3_e\n", + "\t- R_EX_etoh_e\n", + "\t- R_EX_h2o_e\n", + "\t- R_EX_h2o2_e\n", + "\t- R_EX_h2s_e\n", + "\t- R_EX_hacolipa_e\n", + "\t- R_EX_f6p_e\n", + "\t- R_EX_fald_e\n", + "\t- R_EX_halipa_e\n", + "\t- R_EX_4hoxpacd_e\n", + "\t- R_EX_hdca_e\n", + "\t- R_EX_hdcea_e\n", + "\t- R_EX_fe2_e\n", + "\t- R_EX_fe3_e\n", + "\t- R_EX_hg2_e\n", + "\t- R_EX_5dglcn_e\n", + "\t- R_EX_his__L_e\n", + "\t- R_EX_hom__L_e\n", + "\t- R_EX_hxa_e\n", + "\t- R_EX_fe3dcit_e\n", + "\t- R_EX_fe3dhbzs_e\n", + "\t- R_EX_5mtr_e\n", + "\t- R_EX_fe3hox_e\n", + "\t- R_EX_hxan_e\n", + "\t- R_EX_fe3hox_un_e\n", + "\t- R_EX_LalaDglu_e\n", + "\t- R_EX_fecrm_e\n", + "\t- R_EX_fecrm_un_e\n", + "\t- R_EX_feenter_e\n", + "\t- R_EX_idon__L_e\n", + "\t- R_EX_ile__L_e\n", + "\t- R_EX_feoxam_e\n", + "\t- R_EX_imp_e\n", + "\t- R_EX_LalaDgluMdap_e\n", + "\t- R_EX_indole_e\n", + "\t- R_EX_LalaDgluMdapDala_e\n", + "\t- R_EX_inost_e\n", + "\t- R_EX_ins_e\n", + "\t- R_EX_isetac_e\n", + "\t- R_EX_k_e\n", + "\t- R_EX_mal__L_e\n", + "\t- R_EX_malt_e\n", + "\t- R_EX_malthx_e\n", + "\t- R_EX_LalaLglu_e\n", + "\t- R_EX_maltpt_e\n", + "\t- R_EX_malttr_e\n", + "\t- R_EX_kdo2lipid4_e\n", + "\t- R_EX_lac__D_e\n", + "\t- R_EX_maltttr_e\n", + "\t- R_EX_man_e\n", + "\t- R_EX_lac__L_e\n", + "\t- R_EX_lcts_e\n", + "\t- R_EX_ac_e\n", + "\t- R_EX_leu__L_e\n", + "\t- R_EX_man6p_e\n", + "\t- R_EX_manglyc_e\n", + "\t- R_EX_lipa_e\n", + "\t- R_EX_lipa_cold_e\n", + "\t- R_EX_melib_e\n", + "\t- R_EX_lipoate_e\n", + "\t- R_EX_acac_e\n", + "\t- R_EX_lys__L_e\n", + "\t- R_EX_lyx__L_e\n", + "\t- R_EX_meoh_e\n", + "\t- R_EX_met__D_e\n", + "\t- R_EX_met__L_e\n", + "\t- R_EX_metsox_R__L_e\n", + "\t- R_EX_mal__D_e\n", + "\t- R_EX_acald_e\n", + "\t- R_EX_metsox_S__L_e\n", + "\t- R_EX_mg2_e\n", + "\t- R_EX_mincyc_e\n", + "\t- R_EX_minohp_e\n", + "\t- R_EX_mmet_e\n", + "\t- R_EX_mn2_e\n", + "\t- R_EX_mnl_e\n", + "\t- R_EX_tcynt_e\n", + "\t- R_EX_thm_e\n", + "\t- R_EX_thr__L_e\n", + "\t- R_EX_thrp_e\n", + "\t- R_EX_thym_e\n", + "\t- R_EX_thymd_e\n", + "\t- R_EX_tma_e\n", + "\t- R_EX_mobd_e\n", + "\t- R_EX_mso3_e\n", + "\t- R_EX_acgal_e\n", + "\t- R_EX_n2o_e\n", + "\t- R_EX_tmao_e\n", + "\t- R_EX_tre_e\n", + "\t- R_EX_na1_e\n", + "\t- R_EX_trp__L_e\n", + "\t- R_EX_nac_e\n", + "\t- R_EX_acgal1p_e\n", + "\t- R_EX_nh4_e\n", + "\t- R_EX_ni2_e\n", + "\t- R_EX_tsul_e\n", + "\t- R_EX_nmn_e\n", + "\t- R_EX_acgam_e\n", + "\t- R_EX_no_e\n", + "\t- R_EX_no2_e\n", + "\t- R_EX_no3_e\n", + "\t- R_EX_ttdca_e\n", + "\t- R_EX_ttdcea_e\n", + "\t- R_EX_ttrcyc_e\n", + "\t- R_EX_tungs_e\n", + "\t- R_EX_novbcn_e\n", + "\t- R_EX_tym_e\n", + "\t- R_EX_o16a4colipa_e\n", + "\t- R_EX_acgam1p_e\n", + "\t- R_EX_o2_e\n", + "\t- R_EX_o2s_e\n", + "\t- R_EX_ocdca_e\n", + "\t- R_EX_ocdcea_e\n", + "\t- R_EX_octa_e\n", + "\t- R_EX_orn_e\n", + "\t- R_EX_tyr__L_e\n", + "\t- R_EX_tyrp_e\n", + "\t- R_EX_uacgam_e\n", + "\t- R_EX_udpacgal_e\n", + "\t- R_EX_acmana_e\n", + "\t- R_EX_udpg_e\n", + "\t- R_EX_udpgal_e\n", + "\t- R_EX_orot_e\n", + "\t- R_EX_acmum_e\n", + "\t- R_EX_pacald_e\n", + "\t- R_EX_udpglcur_e\n", + "\t- R_EX_peamn_e\n", + "\t- R_EX_ump_e\n", + "\t- R_EX_phe__L_e\n", + "\t- R_EX_ura_e\n", + "\t- R_EX_acnam_e\n", + "\t- R_EX_urea_e\n", + "\t- R_EX_uri_e\n", + "\t- R_EX_val__L_e\n", + "\t- R_EX_pheme_e\n", + "\t- R_EX_pi_e\n", + "\t- R_EX_pnto__R_e\n", + "\t- R_EX_acolipa_e\n", + "\t- R_EX_ppa_e\n", + "\t- R_EX_xan_e\n", + "\t- R_EX_xmp_e\n", + "\t- R_EX_ppal_e\n", + "\t- R_EX_xtsn_e\n", + "\t- R_EX_acser_e\n", + "\t- R_EX_xyl__D_e\n", + "\t- R_EX_xylu__L_e\n", + "\t- R_EX_pppn_e\n", + "\t- R_EX_ppt_e\n", + "\t- R_EX_pro__L_e\n", + "\t- R_EX_progly_e\n", + "\t- R_EX_psclys_e\n", + "\t- R_EX_pser__L_e\n", + "\t- R_EX_ptrc_e\n", + "\t- R_EX_zn2_e\n", + "\t- R_EX_pydam_e\n", + "\t- R_EX_pydx_e\n", + "\t- R_EX_pydxn_e\n", + "\t- R_EX_ade_e\n", + "\t- R_EX_pyr_e\n", + "\t- R_EX_quin_e\n", + "\t- R_EX_r5p_e\n", + "\t- R_EX_rfamp_e\n", + "\t- R_EX_adn_e\n", + "\t- R_EX_rib__D_e\n", + "\t- R_EX_adocbl_e\n", + "\t- R_EX_rmn_e\n", + "\t- R_EX_sbt__D_e\n", + "\t- R_EX_sel_e\n", + "\t- R_EX_ag_e\n", + "\t- R_EX_ser__D_e\n", + "\t- R_EX_agm_e\n", + "\t- R_EX_ser__L_e\n", + "\t- R_EX_skm_e\n", + "\t- R_EX_akg_e\n", + "\t- R_EX_slnt_e\n", + "\t- R_EX_so2_e\n", + "\t- R_EX_so3_e\n", + "\t- R_EX_so4_e\n", + "\t- R_EX_spmd_e\n", + "\t- R_EX_succ_e\n", + "\t- R_EX_ala_B_e\n", + "\t- R_EX_sucr_e\n", + "\t- R_EX_sulfac_e\n", + "\t- R_EX_ala__D_e\n", + "\t- R_EX_tartr__D_e\n", + "\t- R_EX_tartr__L_e\n", + "\t- R_EX_taur_e\n", + "\t- R_EX_3hoxpac_e\n", + "\t- R_EX_3ntym_e\n", + "\t- R_EX_4hoxpac_e\n", + "\t- R_EX_6apa_e\n", + "\t- R_EX_cellb_e\n", + "\t- R_EX_galam_e\n", + "\t- R_EX_lipidA_core_e\n", + "\t- R_EX_o6a4colipa_e\n", + "\t- R_EX_peng_e\n", + "\t- R_EX_raffin_e\n", + "\t- R_EX_salcn_e\n", + "\t- R_DM_4hba_c\n", + "\t- R_DM_hmfurn_c\n", + "\t- R_EDTXSCOF\n", + "\t- R_EX_arab__D_e\u001b[0m\n", + "File saved at: ../../data/sbml_corrected/iEC1356_Bl21DE3.xml\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: iWFL_1372\n", + "\u001b[0;93mWARNING : \n", + " - R_CAT: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_FHL: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_DHPTDNR: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_DHPTDNRN: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCFUMtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCMALtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SPODM: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SPODMpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCTARTtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCASPtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_CHITOBpts: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_CHITPH: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_URCN: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_EX_chitob_e: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\u001b[0m\n", + "\n", + "\n", + "\u001b[0;36m############################################\n", + "############################################\n", + " WRITING SBML FILE \n", + "############################################\n", + "############################################\n", + "\u001b[0m\n", + "\u001b[0;93mWARNING : IMPORT REACTION REMOVED BY DEFAULT\u001b[0m\n", + "\u001b[0;93mWARNING : If you want to keep import reaction\n", + "use option -kir / --keep-import-reactions\u001b[0m\n", + "\u001b[0;93mWARNING : Import reaction removed:\n", + "\t- R_DM_mththf_c\n", + "\t- R_EX_12ppd__S_e\n", + "\t- R_EX_14glucan_e\n", + "\t- R_DM_4crsol_c\n", + "\t- R_DM_oxam_c\n", + "\t- R_EX_12ppd__R_e\n", + "\t- R_EX_15dap_e\n", + "\t- R_EX_23camp_e\n", + "\t- R_EX_23ccmp_e\n", + "\t- R_EX_23cgmp_e\n", + "\t- R_DM_5drib_c\n", + "\t- R_EX_23cump_e\n", + "\t- R_DM_aacald_c\n", + "\t- R_EX_23dappa_e\n", + "\t- R_EX_26dap__M_e\n", + "\t- R_DM_amob_c\n", + "\t- R_EX_2ddglcn_e\n", + "\t- R_EX_34dhpac_e\n", + "\t- R_EX_3amp_e\n", + "\t- R_EX_glyc_e\n", + "\t- R_EX_3cmp_e\n", + "\t- R_EX_3gmp_e\n", + "\t- R_EX_3hcinnm_e\n", + "\t- R_EX_3hpp_e\n", + "\t- R_EX_csn_e\n", + "\t- R_EX_cu_e\n", + "\t- R_EX_cu2_e\n", + "\t- R_EX_cyan_e\n", + "\t- R_EX_cynt_e\n", + "\t- R_EX_cys__D_e\n", + "\t- R_EX_3hpppn_e\n", + "\t- R_EX_3ump_e\n", + "\t- R_EX_4abut_e\n", + "\t- R_EX_4hoxpacd_e\n", + "\t- R_EX_5dglcn_e\n", + "\t- R_EX_5mtr_e\n", + "\t- R_EX_LalaDglu_e\n", + "\t- R_EX_LalaDgluMdap_e\n", + "\t- R_EX_LalaDgluMdapDala_e\n", + "\t- R_EX_LalaLglu_e\n", + "\t- R_EX_cys__L_e\n", + "\t- R_EX_cytd_e\n", + "\t- R_EX_dad_2_e\n", + "\t- R_EX_damp_e\n", + "\t- R_EX_dca_e\n", + "\t- R_EX_dcmp_e\n", + "\t- R_EX_dcyt_e\n", + "\t- R_EX_ddca_e\n", + "\t- R_EX_dgmp_e\n", + "\t- R_EX_dgsn_e\n", + "\t- R_EX_ac_e\n", + "\t- R_EX_acac_e\n", + "\t- R_EX_acald_e\n", + "\t- R_EX_acgal_e\n", + "\t- R_EX_dha_e\n", + "\t- R_EX_dimp_e\n", + "\t- R_EX_glyc__R_e\n", + "\t- R_EX_din_e\n", + "\t- R_EX_dms_e\n", + "\t- R_EX_acgal1p_e\n", + "\t- R_EX_acgam_e\n", + "\t- R_EX_acgam1p_e\n", + "\t- R_EX_dmso_e\n", + "\t- R_EX_dopa_e\n", + "\t- R_EX_glyc2p_e\n", + "\t- R_EX_doxrbcn_e\n", + "\t- R_EX_dtmp_e\n", + "\t- R_EX_dump_e\n", + "\t- R_EX_acmana_e\n", + "\t- R_EX_acmum_e\n", + "\t- R_EX_acnam_e\n", + "\t- R_EX_acolipa_e\n", + "\t- R_EX_acser_e\n", + "\t- R_EX_ade_e\n", + "\t- R_EX_adn_e\n", + "\t- R_EX_duri_e\n", + "\t- R_EX_eca4colipa_e\n", + "\t- R_EX_enlipa_e\n", + "\t- R_EX_enter_e\n", + "\t- R_EX_etha_e\n", + "\t- R_EX_adocbl_e\n", + "\t- R_EX_ag_e\n", + "\t- R_EX_ethso3_e\n", + "\t- R_EX_glyc3p_e\n", + "\t- R_EX_etoh_e\n", + "\t- R_EX_f6p_e\n", + "\t- R_EX_fald_e\n", + "\t- R_EX_agm_e\n", + "\t- R_EX_akg_e\n", + "\t- R_EX_ala_B_e\n", + "\t- R_EX_ala__D_e\n", + "\t- R_EX_ala__L_e\n", + "\t- R_EX_alaala_e\n", + "\t- R_EX_fe2_e\n", + "\t- R_EX_fe3_e\n", + "\t- R_EX_all__D_e\n", + "\t- R_EX_glyclt_e\n", + "\t- R_EX_alltn_e\n", + "\t- R_EX_amp_e\n", + "\t- R_EX_fe3dcit_e\n", + "\t- R_EX_fe3dhbzs_e\n", + "\t- R_EX_gmp_e\n", + "\t- R_EX_fe3hox_e\n", + "\t- R_EX_fe3hox_un_e\n", + "\t- R_EX_anhgm_e\n", + "\t- R_EX_arab__L_e\n", + "\t- R_EX_arbt_e\n", + "\t- R_EX_arbtn_e\n", + "\t- R_EX_arbtn_fe3_e\n", + "\t- R_EX_arg__L_e\n", + "\t- R_EX_fecrm_e\n", + "\t- R_EX_fecrm_un_e\n", + "\t- R_EX_feenter_e\n", + "\t- R_EX_feoxam_e\n", + "\t- R_EX_feoxam_un_e\n", + "\t- R_EX_for_e\n", + "\t- R_EX_fru_e\n", + "\t- R_EX_ascb__L_e\n", + "\t- R_EX_asn__L_e\n", + "\t- R_EX_aso3_e\n", + "\t- R_EX_frulys_e\n", + "\t- R_EX_asp__L_e\n", + "\t- R_EX_gsn_e\n", + "\t- R_EX_btn_e\n", + "\t- R_EX_but_e\n", + "\t- R_EX_butso3_e\n", + "\t- R_EX_ca2_e\n", + "\t- R_EX_cbi_e\n", + "\t- R_EX_fruur_e\n", + "\t- R_EX_fuc__L_e\n", + "\t- R_EX_fum_e\n", + "\t- R_EX_fusa_e\n", + "\t- R_EX_g1p_e\n", + "\t- R_EX_g3pc_e\n", + "\t- R_EX_g3pe_e\n", + "\t- R_EX_g3pg_e\n", + "\t- R_EX_cbl1_e\n", + "\t- R_EX_cd2_e\n", + "\t- R_EX_cgly_e\n", + "\t- R_EX_g3pi_e\n", + "\t- R_EX_chol_e\n", + "\t- R_EX_gthox_e\n", + "\t- R_EX_chtbs_e\n", + "\t- R_EX_cit_e\n", + "\t- R_EX_cl_e\n", + "\t- R_EX_cm_e\n", + "\t- R_EX_cmp_e\n", + "\t- R_EX_co2_e\n", + "\t- R_EX_g3ps_e\n", + "\t- R_EX_g6p_e\n", + "\t- R_EX_gal_e\n", + "\t- R_EX_gal_bD_e\n", + "\t- R_EX_gal1p_e\n", + "\t- R_EX_galct__D_e\n", + "\t- R_EX_galctn__D_e\n", + "\t- R_EX_cobalt2_e\n", + "\t- R_EX_colipa_e\n", + "\t- R_EX_colipap_e\n", + "\t- R_EX_galctn__L_e\n", + "\t- R_EX_galt_e\n", + "\t- R_EX_gthrd_e\n", + "\t- R_EX_galur_e\n", + "\t- R_EX_gam_e\n", + "\t- R_EX_cpgn_e\n", + "\t- R_EX_cpgn_un_e\n", + "\t- R_EX_crn_e\n", + "\t- R_EX_crn__D_e\n", + "\t- R_EX_gam6p_e\n", + "\t- R_EX_gtp_e\n", + "\t- R_EX_gbbtn_e\n", + "\t- R_EX_gdp_e\n", + "\t- R_EX_gua_e\n", + "\t- R_EX_glc__D_e\n", + "\t- R_EX_glcn_e\n", + "\t- R_EX_h_e\n", + "\t- R_EX_glcr_e\n", + "\t- R_EX_h2_e\n", + "\t- R_EX_glcur_e\n", + "\t- R_EX_glcur1p_e\n", + "\t- R_EX_h2o_e\n", + "\t- R_EX_gln__L_e\n", + "\t- R_EX_h2o2_e\n", + "\t- R_EX_glu__L_e\n", + "\t- R_EX_gly_e\n", + "\t- R_EX_glyald_e\n", + "\t- R_EX_glyb_e\n", + "\t- R_EX_ocdcea_e\n", + "\t- R_EX_octa_e\n", + "\t- R_EX_orn_e\n", + "\t- R_EX_orot_e\n", + "\t- R_EX_pacald_e\n", + "\t- R_EX_peamn_e\n", + "\t- R_EX_h2s_e\n", + "\t- R_EX_phe__L_e\n", + "\t- R_EX_pheme_e\n", + "\t- R_EX_pi_e\n", + "\t- R_EX_pnto__R_e\n", + "\t- R_EX_hacolipa_e\n", + "\t- R_EX_ppa_e\n", + "\t- R_EX_ppal_e\n", + "\t- R_EX_pppn_e\n", + "\t- R_EX_ppt_e\n", + "\t- R_EX_pro__L_e\n", + "\t- R_EX_progly_e\n", + "\t- R_EX_psclys_e\n", + "\t- R_EX_halipa_e\n", + "\t- R_EX_pser__L_e\n", + "\t- R_EX_ptrc_e\n", + "\t- R_EX_pydam_e\n", + "\t- R_EX_pydx_e\n", + "\t- R_EX_pydxn_e\n", + "\t- R_EX_pyr_e\n", + "\t- R_EX_quin_e\n", + "\t- R_EX_hdca_e\n", + "\t- R_EX_r5p_e\n", + "\t- R_EX_rfamp_e\n", + "\t- R_EX_rib__D_e\n", + "\t- R_EX_rmn_e\n", + "\t- R_EX_sbt__D_e\n", + "\t- R_EX_sel_e\n", + "\t- R_EX_hdcea_e\n", + "\t- R_EX_ser__D_e\n", + "\t- R_EX_ser__L_e\n", + "\t- R_EX_skm_e\n", + "\t- R_EX_slnt_e\n", + "\t- R_EX_so2_e\n", + "\t- R_EX_so3_e\n", + "\t- R_EX_so4_e\n", + "\t- R_EX_spmd_e\n", + "\t- R_EX_succ_e\n", + "\t- R_EX_hg2_e\n", + "\t- R_EX_sucr_e\n", + "\t- R_EX_sulfac_e\n", + "\t- R_EX_tartr__D_e\n", + "\t- R_EX_his__L_e\n", + "\t- R_EX_tartr__L_e\n", + "\t- R_EX_taur_e\n", + "\t- R_EX_tcynt_e\n", + "\t- R_EX_thm_e\n", + "\t- R_EX_hom__L_e\n", + "\t- R_EX_thr__L_e\n", + "\t- R_EX_thrp_e\n", + "\t- R_EX_thym_e\n", + "\t- R_EX_thymd_e\n", + "\t- R_EX_tma_e\n", + "\t- R_EX_tmao_e\n", + "\t- R_EX_tre_e\n", + "\t- R_EX_trp__L_e\n", + "\t- R_EX_tsul_e\n", + "\t- R_EX_ttdca_e\n", + "\t- R_EX_ttdcea_e\n", + "\t- R_EX_ttrcyc_e\n", + "\t- R_EX_hxa_e\n", + "\t- R_EX_tungs_e\n", + "\t- R_EX_tym_e\n", + "\t- R_EX_tyr__L_e\n", + "\t- R_EX_tyrp_e\n", + "\t- R_EX_uacgam_e\n", + "\t- R_EX_udpacgal_e\n", + "\t- R_EX_hxan_e\n", + "\t- R_EX_udpg_e\n", + "\t- R_EX_udpgal_e\n", + "\t- R_EX_udpglcur_e\n", + "\t- R_EX_ump_e\n", + "\t- R_EX_ura_e\n", + "\t- R_EX_urea_e\n", + "\t- R_EX_uri_e\n", + "\t- R_EX_val__L_e\n", + "\t- R_EX_idon__L_e\n", + "\t- R_EX_xan_e\n", + "\t- R_EX_xmp_e\n", + "\t- R_EX_xtsn_e\n", + "\t- R_EX_ile__L_e\n", + "\t- R_EX_xyl__D_e\n", + "\t- R_EX_xylu__L_e\n", + "\t- R_EX_zn2_e\n", + "\t- R_EX_imp_e\n", + "\t- R_EX_indole_e\n", + "\t- R_EX_inost_e\n", + "\t- R_EX_ins_e\n", + "\t- R_EX_isetac_e\n", + "\t- R_EX_k_e\n", + "\t- R_EX_kdo2lipid4_e\n", + "\t- R_EX_lac__D_e\n", + "\t- R_EX_lac__L_e\n", + "\t- R_EX_lcts_e\n", + "\t- R_EX_leu__L_e\n", + "\t- R_EX_lipa_e\n", + "\t- R_EX_lipa_cold_e\n", + "\t- R_EX_lipoate_e\n", + "\t- R_EX_lys__L_e\n", + "\t- R_EX_lyx__L_e\n", + "\t- R_EX_mal__D_e\n", + "\t- R_EX_mal__L_e\n", + "\t- R_EX_malt_e\n", + "\t- R_EX_malthx_e\n", + "\t- R_EX_maltpt_e\n", + "\t- R_EX_malttr_e\n", + "\t- R_EX_maltttr_e\n", + "\t- R_EX_man_e\n", + "\t- R_EX_man6p_e\n", + "\t- R_EX_manglyc_e\n", + "\t- R_EX_melib_e\n", + "\t- R_EX_meoh_e\n", + "\t- R_EX_met__D_e\n", + "\t- R_EX_met__L_e\n", + "\t- R_EX_metsox_R__L_e\n", + "\t- R_EX_metsox_S__L_e\n", + "\t- R_EX_mg2_e\n", + "\t- R_EX_mincyc_e\n", + "\t- R_EX_minohp_e\n", + "\t- R_EX_mmet_e\n", + "\t- R_EX_mn2_e\n", + "\t- R_EX_mnl_e\n", + "\t- R_EX_mobd_e\n", + "\t- R_EX_mso3_e\n", + "\t- R_EX_n2o_e\n", + "\t- R_EX_na1_e\n", + "\t- R_EX_nac_e\n", + "\t- R_EX_nh4_e\n", + "\t- R_EX_ni2_e\n", + "\t- R_EX_nmn_e\n", + "\t- R_EX_no_e\n", + "\t- R_EX_no2_e\n", + "\t- R_EX_no3_e\n", + "\t- R_EX_novbcn_e\n", + "\t- R_EX_o16a4colipa_e\n", + "\t- R_EX_o2_e\n", + "\t- R_EX_o2s_e\n", + "\t- R_EX_ocdca_e\n", + "\t- R_EX_fe_e\n", + "\t- R_EX_kokdolipidA_e\n", + "\t- R_EX_feroxBfe_e\n", + "\t- R_EX_4hphac_e\n", + "\t- R_EX_feroxB_e\n", + "\t- R_EX_tcb_e\n", + "\t- R_EX_tton_e\n", + "\t- R_EX_4hthr_e\n", + "\t- R_EX_4abz_e\n", + "\t- R_EX_feroxEfe_e\n", + "\t- R_EX_feroxE_e\n", + "\t- R_EX_23dhbzs3_e\n", + "\t- R_EX_acon_C_e\n", + "\t- R_EX_tet_e\n", + "\t- R_EX_salchs4_e\n", + "\t- R_EX_oaa_e\n", + "\t- R_EX_salchs2fe_e\n", + "\t- R_EX_cysi__L_e\n", + "\t- R_EX_fe3dhbzs3_e\n", + "\t- R_EX_salchs2_e\n", + "\t- R_EX_drib_e\n", + "\t- R_EX_rnam_e\n", + "\t- R_EX_salchs4fe_e\n", + "\t- R_EX_2pg_e\n", + "\t- R_EX_airs_e\n", + "\t- R_EX_dxyl_e\n", + "\t- R_EX_3pg_e\n", + "\t- R_EX_feroxGfe_e\n", + "\t- R_EX_frmd_e\n", + "\t- R_EX_colipaOA_e\n", + "\t- R_EX_salchsx_e\n", + "\t- R_EX_AEP_e\n", + "\t- R_EX_icit_e\n", + "\t- R_EX_pep_e\n", + "\t- R_EX_colipa20Oag_e\n", + "\t- R_EX_udcpo4_e\n", + "\t- R_EX_tag__D_e\n", + "\t- R_EX_remnant1_e\n", + "\t- R_EX_udcpo5_e\n", + "\t- R_EX_guln__L_e\n", + "\t- R_EX_o6a4colipa_e\n", + "\t- R_EX_feroxG_e\n", + "\t- R_EX_3hoxpac_e\n", + "\t- R_EX_diact_e\n", + "\t- R_EX_arab__D_e\n", + "\t- R_EX_cellb_e\n", + "\t- R_EX_btd_RR_e\n", + "\t- R_EX_rbt_e\n", + "\t- R_EX_bz_e\n", + "\t- R_EX_13ppd_e\n", + "\t- R_EX_abt__D_e\n", + "\t- R_EX_3ntym_e\n", + "\t- R_EX_6apa_e\n", + "\t- R_EX_4hoxpac_e\n", + "\t- R_EX_peng_e\n", + "\t- R_EX_pac_e\n", + "\t- R_EX_salcn_e\n", + "\t- R_DM_lipidA_core_e_p\n", + "\t- R_EX_galam_e\n", + "\t- R_EX_raffin_e\u001b[0m\n", + "File saved at: ../../data/sbml_corrected/iWFL_1372.xml\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: iECSE_1348\n", + "\u001b[0;93mWARNING : \n", + " - R_CAT: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_FHL: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_DHPTDNR: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_DHPTDNRN: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCASPtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCFUMtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCMALtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCTARTtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SPODM: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SPODMpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_EX_chitob_e: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_CHITOBpts: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_CHITPH: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_URCN: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\u001b[0m\n", + "\n", + "\n", + "\u001b[0;36m############################################\n", + "############################################\n", + " WRITING SBML FILE \n", + "############################################\n", + "############################################\n", + "\u001b[0m\n", + "\u001b[0;93mWARNING : IMPORT REACTION REMOVED BY DEFAULT\u001b[0m\n", + "\u001b[0;93mWARNING : If you want to keep import reaction\n", + "use option -kir / --keep-import-reactions\u001b[0m\n", + "\u001b[0;93mWARNING : Import reaction removed:\n", + "\t- R_EX_23cump_e\n", + "\t- R_EX_23dappa_e\n", + "\t- R_EX_26dap__M_e\n", + "\t- R_EX_2ddglcn_e\n", + "\t- R_EX_34dhpac_e\n", + "\t- R_EX_3amp_e\n", + "\t- R_EX_3cmp_e\n", + "\t- R_EX_3gmp_e\n", + "\t- R_EX_3hcinnm_e\n", + "\t- R_EX_3hpp_e\n", + "\t- R_EX_3hpppn_e\n", + "\t- R_EX_3ump_e\n", + "\t- R_EX_4abut_e\n", + "\t- R_EX_4hoxpacd_e\n", + "\t- R_DM_4crsol_c\n", + "\t- R_EX_5dglcn_e\n", + "\t- R_DM_5drib_c\n", + "\t- R_DM_aacald_c\n", + "\t- R_EX_5mtr_e\n", + "\t- R_DM_amob_c\n", + "\t- R_EX_LalaDglu_e\n", + "\t- R_DM_mththf_c\n", + "\t- R_DM_oxam_c\n", + "\t- R_EX_LalaDgluMdap_e\n", + "\t- R_EX_LalaDgluMdapDala_e\n", + "\t- R_EX_12ppd__R_e\n", + "\t- R_EX_LalaLglu_e\n", + "\t- R_EX_12ppd__S_e\n", + "\t- R_EX_14glucan_e\n", + "\t- R_EX_cynt_e\n", + "\t- R_EX_cys__D_e\n", + "\t- R_EX_cys__L_e\n", + "\t- R_EX_cytd_e\n", + "\t- R_EX_dad_2_e\n", + "\t- R_EX_damp_e\n", + "\t- R_EX_dca_e\n", + "\t- R_EX_dcmp_e\n", + "\t- R_EX_dcyt_e\n", + "\t- R_EX_ddca_e\n", + "\t- R_EX_dgmp_e\n", + "\t- R_EX_15dap_e\n", + "\t- R_EX_23camp_e\n", + "\t- R_EX_23ccmp_e\n", + "\t- R_EX_23cgmp_e\n", + "\t- R_EX_glyclt_e\n", + "\t- R_EX_gmp_e\n", + "\t- R_EX_gsn_e\n", + "\t- R_EX_gthox_e\n", + "\t- R_EX_gthrd_e\n", + "\t- R_EX_gtp_e\n", + "\t- R_EX_gua_e\n", + "\t- R_EX_dgsn_e\n", + "\t- R_EX_dha_e\n", + "\t- R_EX_dimp_e\n", + "\t- R_EX_h_e\n", + "\t- R_EX_h2_e\n", + "\t- R_EX_ac_e\n", + "\t- R_EX_h2o_e\n", + "\t- R_EX_h2o2_e\n", + "\t- R_EX_din_e\n", + "\t- R_EX_dms_e\n", + "\t- R_EX_dmso_e\n", + "\t- R_EX_acac_e\n", + "\t- R_EX_dopa_e\n", + "\t- R_EX_doxrbcn_e\n", + "\t- R_EX_dtmp_e\n", + "\t- R_EX_h2s_e\n", + "\t- R_EX_hacolipa_e\n", + "\t- R_EX_halipa_e\n", + "\t- R_EX_hdca_e\n", + "\t- R_EX_hdcea_e\n", + "\t- R_EX_hg2_e\n", + "\t- R_EX_his__L_e\n", + "\t- R_EX_hom__L_e\n", + "\t- R_EX_dump_e\n", + "\t- R_EX_duri_e\n", + "\t- R_EX_eca4colipa_e\n", + "\t- R_EX_enlipa_e\n", + "\t- R_EX_enter_e\n", + "\t- R_EX_etha_e\n", + "\t- R_EX_ethso3_e\n", + "\t- R_EX_etoh_e\n", + "\t- R_EX_hxa_e\n", + "\t- R_EX_hxan_e\n", + "\t- R_EX_idon__L_e\n", + "\t- R_EX_f6p_e\n", + "\t- R_EX_fald_e\n", + "\t- R_EX_ile__L_e\n", + "\t- R_EX_acald_e\n", + "\t- R_EX_imp_e\n", + "\t- R_EX_indole_e\n", + "\t- R_EX_fe2_e\n", + "\t- R_EX_fe3_e\n", + "\t- R_EX_fe3dcit_e\n", + "\t- R_EX_inost_e\n", + "\t- R_EX_fe3dhbzs_e\n", + "\t- R_EX_ins_e\n", + "\t- R_EX_acgal_e\n", + "\t- R_EX_isetac_e\n", + "\t- R_EX_k_e\n", + "\t- R_EX_kdo2lipid4_e\n", + "\t- R_EX_fe3hox_e\n", + "\t- R_EX_fe3hox_un_e\n", + "\t- R_EX_fecrm_e\n", + "\t- R_EX_fecrm_un_e\n", + "\t- R_EX_feenter_e\n", + "\t- R_EX_lac__D_e\n", + "\t- R_EX_lac__L_e\n", + "\t- R_EX_lcts_e\n", + "\t- R_EX_leu__L_e\n", + "\t- R_EX_lipa_e\n", + "\t- R_EX_feoxam_e\n", + "\t- R_EX_feoxam_un_e\n", + "\t- R_EX_for_e\n", + "\t- R_EX_fru_e\n", + "\t- R_EX_lipa_cold_e\n", + "\t- R_EX_frulys_e\n", + "\t- R_EX_acgal1p_e\n", + "\t- R_EX_fruur_e\n", + "\t- R_EX_fuc__L_e\n", + "\t- R_EX_fum_e\n", + "\t- R_EX_fusa_e\n", + "\t- R_EX_g1p_e\n", + "\t- R_EX_lipoate_e\n", + "\t- R_EX_lys__L_e\n", + "\t- R_EX_lyx__L_e\n", + "\t- R_EX_mal__D_e\n", + "\t- R_EX_mal__L_e\n", + "\t- R_EX_malt_e\n", + "\t- R_EX_g3pc_e\n", + "\t- R_EX_g3pe_e\n", + "\t- R_EX_malthx_e\n", + "\t- R_EX_g3pg_e\n", + "\t- R_EX_maltpt_e\n", + "\t- R_EX_acgam_e\n", + "\t- R_EX_malttr_e\n", + "\t- R_EX_maltttr_e\n", + "\t- R_EX_g3pi_e\n", + "\t- R_EX_g3ps_e\n", + "\t- R_EX_man_e\n", + "\t- R_EX_g6p_e\n", + "\t- R_EX_acgam1p_e\n", + "\t- R_EX_gal_e\n", + "\t- R_EX_man6p_e\n", + "\t- R_EX_manglyc_e\n", + "\t- R_EX_melib_e\n", + "\t- R_EX_meoh_e\n", + "\t- R_EX_met__D_e\n", + "\t- R_EX_met__L_e\n", + "\t- R_EX_gal_bD_e\n", + "\t- R_EX_gal1p_e\n", + "\t- R_EX_galct__D_e\n", + "\t- R_EX_galctn__D_e\n", + "\t- R_EX_galctn__L_e\n", + "\t- R_EX_metsox_R__L_e\n", + "\t- R_EX_galt_e\n", + "\t- R_EX_acmana_e\n", + "\t- R_EX_galur_e\n", + "\t- R_EX_gam_e\n", + "\t- R_EX_metsox_S__L_e\n", + "\t- R_EX_mg2_e\n", + "\t- R_EX_mincyc_e\n", + "\t- R_EX_minohp_e\n", + "\t- R_EX_mmet_e\n", + "\t- R_EX_gam6p_e\n", + "\t- R_EX_gbbtn_e\n", + "\t- R_EX_gdp_e\n", + "\t- R_EX_mn2_e\n", + "\t- R_EX_mnl_e\n", + "\t- R_EX_glc__D_e\n", + "\t- R_EX_acmum_e\n", + "\t- R_EX_glcn_e\n", + "\t- R_EX_glcr_e\n", + "\t- R_EX_mobd_e\n", + "\t- R_EX_mso3_e\n", + "\t- R_EX_n2o_e\n", + "\t- R_EX_glcur_e\n", + "\t- R_EX_glcur1p_e\n", + "\t- R_EX_na1_e\n", + "\t- R_EX_gln__L_e\n", + "\t- R_EX_acnam_e\n", + "\t- R_EX_glu__L_e\n", + "\t- R_EX_gly_e\n", + "\t- R_EX_glyald_e\n", + "\t- R_EX_glyb_e\n", + "\t- R_EX_glyc_e\n", + "\t- R_EX_glyc__R_e\n", + "\t- R_EX_nac_e\n", + "\t- R_EX_nh4_e\n", + "\t- R_EX_ni2_e\n", + "\t- R_EX_nmn_e\n", + "\t- R_EX_no_e\n", + "\t- R_EX_no2_e\n", + "\t- R_EX_no3_e\n", + "\t- R_EX_novbcn_e\n", + "\t- R_EX_o16a4colipa_e\n", + "\t- R_EX_glyc2p_e\n", + "\t- R_EX_glyc3p_e\n", + "\t- R_EX_o2_e\n", + "\t- R_EX_o2s_e\n", + "\t- R_EX_acolipa_e\n", + "\t- R_EX_ocdca_e\n", + "\t- R_EX_ocdcea_e\n", + "\t- R_EX_octa_e\n", + "\t- R_EX_orn_e\n", + "\t- R_EX_pacald_e\n", + "\t- R_EX_peamn_e\n", + "\t- R_EX_phe__L_e\n", + "\t- R_EX_pheme_e\n", + "\t- R_EX_orot_e\n", + "\t- R_EX_acser_e\n", + "\t- R_EX_pi_e\n", + "\t- R_EX_pnto__R_e\n", + "\t- R_EX_ppa_e\n", + "\t- R_EX_ppal_e\n", + "\t- R_EX_pppn_e\n", + "\t- R_EX_ade_e\n", + "\t- R_EX_ppt_e\n", + "\t- R_EX_pro__L_e\n", + "\t- R_EX_progly_e\n", + "\t- R_EX_psclys_e\n", + "\t- R_EX_pser__L_e\n", + "\t- R_EX_ptrc_e\n", + "\t- R_EX_pydam_e\n", + "\t- R_EX_adn_e\n", + "\t- R_EX_pydx_e\n", + "\t- R_EX_pydxn_e\n", + "\t- R_EX_pyr_e\n", + "\t- R_EX_quin_e\n", + "\t- R_EX_r5p_e\n", + "\t- R_EX_rfamp_e\n", + "\t- R_EX_rib__D_e\n", + "\t- R_EX_rmn_e\n", + "\t- R_EX_sbt__D_e\n", + "\t- R_EX_sel_e\n", + "\t- R_EX_ser__D_e\n", + "\t- R_EX_ser__L_e\n", + "\t- R_EX_skm_e\n", + "\t- R_EX_slnt_e\n", + "\t- R_EX_so2_e\n", + "\t- R_EX_so3_e\n", + "\t- R_EX_so4_e\n", + "\t- R_EX_adocbl_e\n", + "\t- R_EX_spmd_e\n", + "\t- R_EX_succ_e\n", + "\t- R_EX_ag_e\n", + "\t- R_EX_sucr_e\n", + "\t- R_EX_sulfac_e\n", + "\t- R_EX_tartr__D_e\n", + "\t- R_EX_agm_e\n", + "\t- R_EX_tartr__L_e\n", + "\t- R_EX_taur_e\n", + "\t- R_EX_tcynt_e\n", + "\t- R_EX_thm_e\n", + "\t- R_EX_thr__L_e\n", + "\t- R_EX_thrp_e\n", + "\t- R_EX_thym_e\n", + "\t- R_EX_thymd_e\n", + "\t- R_EX_akg_e\n", + "\t- R_EX_tma_e\n", + "\t- R_EX_tmao_e\n", + "\t- R_EX_tre_e\n", + "\t- R_EX_trp__L_e\n", + "\t- R_EX_tsul_e\n", + "\t- R_EX_ala_B_e\n", + "\t- R_EX_ttdca_e\n", + "\t- R_EX_ttdcea_e\n", + "\t- R_EX_ttrcyc_e\n", + "\t- R_EX_tungs_e\n", + "\t- R_EX_tym_e\n", + "\t- R_EX_tyr__L_e\n", + "\t- R_EX_tyrp_e\n", + "\t- R_EX_uacgam_e\n", + "\t- R_EX_udpacgal_e\n", + "\t- R_EX_udpg_e\n", + "\t- R_EX_ala__D_e\n", + "\t- R_EX_udpgal_e\n", + "\t- R_EX_udpglcur_e\n", + "\t- R_EX_ump_e\n", + "\t- R_EX_ura_e\n", + "\t- R_EX_urea_e\n", + "\t- R_EX_uri_e\n", + "\t- R_EX_ala__L_e\n", + "\t- R_EX_val__L_e\n", + "\t- R_EX_xan_e\n", + "\t- R_EX_xmp_e\n", + "\t- R_EX_alaala_e\n", + "\t- R_EX_xtsn_e\n", + "\t- R_EX_xyl__D_e\n", + "\t- R_EX_xylu__L_e\n", + "\t- R_EX_zn2_e\n", + "\t- R_EX_all__D_e\n", + "\t- R_EX_abt__D_e\n", + "\t- R_EX_alltn_e\n", + "\t- R_EX_amp_e\n", + "\t- R_EX_anhgm_e\n", + "\t- R_EX_arab__L_e\n", + "\t- R_EX_arbt_e\n", + "\t- R_EX_arbtn_e\n", + "\t- R_EX_arbtn_fe3_e\n", + "\t- R_EX_arg__L_e\n", + "\t- R_EX_ascb__L_e\n", + "\t- R_EX_asn__L_e\n", + "\t- R_EX_aso3_e\n", + "\t- R_EX_asp__L_e\n", + "\t- R_EX_btn_e\n", + "\t- R_EX_but_e\n", + "\t- R_EX_butso3_e\n", + "\t- R_EX_ca2_e\n", + "\t- R_EX_cbi_e\n", + "\t- R_EX_cbl1_e\n", + "\t- R_EX_cd2_e\n", + "\t- R_EX_cgly_e\n", + "\t- R_EX_chol_e\n", + "\t- R_EX_chtbs_e\n", + "\t- R_EX_cit_e\n", + "\t- R_EX_cl_e\n", + "\t- R_EX_cm_e\n", + "\t- R_EX_cmp_e\n", + "\t- R_EX_co2_e\n", + "\t- R_EX_cobalt2_e\n", + "\t- R_EX_colipa_e\n", + "\t- R_EX_colipap_e\n", + "\t- R_EX_cpgn_e\n", + "\t- R_EX_cpgn_un_e\n", + "\t- R_EX_crn_e\n", + "\t- R_EX_crn__D_e\n", + "\t- R_EX_csn_e\n", + "\t- R_EX_cu_e\n", + "\t- R_EX_cu2_e\n", + "\t- R_EX_cyan_e\n", + "\t- R_EX_13ppd_e\n", + "\t- R_EX_feroxBfe_e\n", + "\t- R_EX_4hphac_e\n", + "\t- R_EX_4abz_e\n", + "\t- R_EX_acon_C_e\n", + "\t- R_EX_feroxB_e\n", + "\t- R_EX_tcb_e\n", + "\t- R_EX_tton_e\n", + "\t- R_EX_4hthr_e\n", + "\t- R_EX_colipa20Oag_e\n", + "\t- R_EX_remnant1_e\n", + "\t- R_EX_udcpo5_e\n", + "\t- R_EX_feroxEfe_e\n", + "\t- R_EX_feroxE_e\n", + "\t- R_EX_salchs2_e\n", + "\t- R_EX_23dhbzs3_e\n", + "\t- R_EX_tet_e\n", + "\t- R_EX_salchs4_e\n", + "\t- R_EX_rnam_e\n", + "\t- R_EX_oaa_e\n", + "\t- R_EX_salchs2fe_e\n", + "\t- R_EX_airs_e\n", + "\t- R_EX_dxyl_e\n", + "\t- R_EX_3pg_e\n", + "\t- R_EX_feroxGfe_e\n", + "\t- R_EX_cysi__L_e\n", + "\t- R_EX_fe3dhbzs3_e\n", + "\t- R_EX_drib_e\n", + "\t- R_EX_salchs4fe_e\n", + "\t- R_EX_2pg_e\n", + "\t- R_EX_udcpo4_e\n", + "\t- R_EX_tag__D_e\n", + "\t- R_EX_frmd_e\n", + "\t- R_EX_guln__L_e\n", + "\t- R_EX_colipaOA_e\n", + "\t- R_EX_salchsx_e\n", + "\t- R_EX_AEP_e\n", + "\t- R_EX_icit_e\n", + "\t- R_EX_feroxG_e\n", + "\t- R_EX_pep_e\n", + "\t- R_EX_diact_e\n", + "\t- R_EX_arab__D_e\n", + "\t- R_EX_cellb_e\n", + "\t- R_EX_btd_RR_e\n", + "\t- R_EX_fe_e\n", + "\t- R_EX_rbt_e\n", + "\t- R_EX_kokdolipidA_e\n", + "\t- R_EX_bz_e\n", + "\t- R_EX_3hoxpac_e\n", + "\t- R_EX_4hoxpac_e\n", + "\t- R_EX_3ntym_e\n", + "\t- R_EX_6apa_e\n", + "\t- R_DM_lipidA_core_e_p\n", + "\t- R_EX_peng_e\n", + "\t- R_EX_pac_e\n", + "\t- R_EX_salcn_e\n", + "\t- R_EX_galam_e\n", + "\t- R_EX_raffin_e\n", + "\t- R_EX_o6a4colipa_e\u001b[0m\n", + "File saved at: ../../data/sbml_corrected/iECSE_1348.xml\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: iHN637\n", + "\u001b[0;93mWARNING : \n", + " - R_ACACT1r: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + " - R_ACALD: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + "\n", + " - R_ACOAD1z: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_BTOHt: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_BUTKr: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_BUTt: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + " - R_ALCD2y: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_ACAFDOR: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + "\n", + " - R_ECOAH1: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + " - R_LDAPAT: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + "\n", + " - R_HACD1: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_HYDFDi: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_PBUTT: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_ALCD4: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_BTCOARx: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_HYDFDN: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\u001b[0m\n", + "\n", + "\n", + "\u001b[0;36m############################################\n", + "############################################\n", + " WRITING SBML FILE \n", + "############################################\n", + "############################################\n", + "\u001b[0m\n", + "\u001b[0;93mWARNING : IMPORT REACTION REMOVED BY DEFAULT\u001b[0m\n", + "\u001b[0;93mWARNING : If you want to keep import reaction\n", + "use option -kir / --keep-import-reactions\u001b[0m\n", + "\u001b[0;93mWARNING : Reaction with tag reversible modified:\n", + "\t- R_ACALD\n", + "\t- R_ALCD2y\n", + "\t- R_ACAFDOR\n", + "\t- R_LDAPAT\u001b[0m\n", + "\u001b[0;93mWARNING : Reaction with Reactants and Products exchanged:\n", + "\t- R_ACALD\n", + "\t- R_ALCD2y\n", + "\t- R_ACAFDOR\n", + "\t- R_LDAPAT\u001b[0m\n", + "\u001b[0;93mWARNING : Import reaction removed:\n", + "\t- R_EX_h2_e\n", + "\t- R_EX_h2o_e\n", + "\t- R_EX_his__L_e\n", + "\t- R_EX_ile__L_e\n", + "\t- R_EX_lac__D_e\n", + "\t- R_EX_murein4p3p_e\n", + "\t- R_EX_murein4p4p_e\n", + "\t- R_EX_murein4px4p_e\n", + "\t- R_EX_k_e\n", + "\t- R_EX_murein4px4px4p_e\n", + "\t- R_EX_alaala_e\n", + "\t- R_EX_murein5p3p_e\n", + "\t- R_EX_murein5p4p_e\n", + "\t- R_EX_murein5p5p_e\n", + "\t- R_EX_murein5p5p5p_e\n", + "\t- R_EX_lac__L_e\n", + "\t- R_EX_leu__L_e\n", + "\t- R_EX_lys__L_e\n", + "\t- R_EX_alltn_e\n", + "\t- R_EX_malt_e\n", + "\t- R_EX_murein5px3p_e\n", + "\t- R_EX_murein5px4p_e\n", + "\t- R_EX_malthx_e\n", + "\t- R_EX_murein5px4px4p_e\n", + "\t- R_EX_arab__L_e\n", + "\t- R_EX_malttr_e\n", + "\t- R_EX_man_e\n", + "\t- R_EX_araban__L_e\n", + "\t- R_EX_met__L_e\n", + "\t- R_EX_mg2_e\n", + "\t- R_EX_mn2_e\n", + "\t- R_EX_na1_e\n", + "\t- R_EX_nh4_e\n", + "\t- R_EX_ni2_e\n", + "\t- R_EX_no2_e\n", + "\t- R_EX_no3_e\n", + "\t- R_EX_pi_e\n", + "\t- R_EX_pro__L_e\n", + "\t- R_EX_mobd_e\n", + "\t- R_EX_xan_e\n", + "\t- R_EX_xyl__D_e\n", + "\t- R_EX_ptrc_e\n", + "\t- R_EX_arg__L_e\n", + "\t- R_EX_pydxn_e\n", + "\t- R_EX_pyr_e\n", + "\t- R_EX_zn2_e\n", + "\t- R_EX_rib__D_e\n", + "\t- R_DM_mththf_c\n", + "\t- R_DM_succ_c\n", + "\t- R_EX_ac_e\n", + "\t- R_EX_ala__D_e\n", + "\t- R_EX_asn__L_e\n", + "\t- R_EX_asp__L_e\n", + "\t- R_EX_ribflv_e\n", + "\t- R_EX_rmn_e\n", + "\t- R_EX_ser__D_e\n", + "\t- R_EX_btd_RR_e\n", + "\t- R_EX_ser__L_e\n", + "\t- R_EX_btn_e\n", + "\t- R_EX_so4_e\n", + "\t- R_EX_btoh_e\n", + "\t- R_EX_spmd_e\n", + "\t- R_EX_taur_e\n", + "\t- R_EX_thm_e\n", + "\t- R_EX_but_e\n", + "\t- R_EX_trp__L_e\n", + "\t- R_EX_uaagmda_e\n", + "\t- R_EX_udcpdp_e\n", + "\t- R_EX_udcpp_e\n", + "\t- R_EX_ura_e\n", + "\t- R_EX_ca2_e\n", + "\t- R_EX_val__L_e\n", + "\t- R_EX_citr__L_e\n", + "\t- R_EX_cl_e\n", + "\t- R_EX_co_e\n", + "\t- R_EX_co2_e\n", + "\t- R_EX_cobalt2_e\n", + "\t- R_EX_csn_e\n", + "\t- R_EX_cu2_e\n", + "\t- R_EX_cys__L_e\n", + "\t- R_EX_etoh_e\n", + "\t- R_EX_fe2_e\n", + "\t- R_EX_fol_e\n", + "\t- R_EX_for_e\n", + "\t- R_EX_fru_e\n", + "\t- R_EX_gal_e\n", + "\t- R_EX_galctn__D_e\n", + "\t- R_EX_galur_e\n", + "\t- R_EX_glc__D_e\n", + "\t- R_EX_glcn_e\n", + "\t- R_EX_glcur_e\n", + "\t- R_EX_gln__L_e\n", + "\t- R_EX_glu__L_e\n", + "\t- R_EX_gly_e\n", + "\t- R_EX_glyb_e\n", + "\t- R_EX_glyc_e\n", + "\t- R_EX_h_e\u001b[0m\n", + "File saved at: ../../data/sbml_corrected/iHN637.xml\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: iSynCJ816\n", + "\u001b[0;93mWARNING : - R_MAN1PT2: Reactants and products exchanged.\n", + " Boundaries was: [-999999.0 ; 0.0]\n", + " - R_HSDy: Reactants and products exchanged.\n", + " Boundaries was: [-999999.0 ; 0.0]\n", + " - R_ASAD: Reactants and products exchanged.\n", + " Boundaries was: [-999999.0 ; 0.0]\n", + " - R_KARA1: Reactants and products exchanged.\n", + " Boundaries was: [-999999.0 ; 0.0]\n", + " - R_ALCD2y: Reactants and products exchanged.\n", + " Boundaries was: [-999999.0 ; 0.0]\n", + " - R_CO2tpp: Reactants and products exchanged.\n", + " Boundaries was: [-999999.0 ; 0.0]\n", + " - R_GLYCLTDx: Reactants and products exchanged.\n", + " Boundaries was: [-999999.0 ; 0.0]\n", + "\n", + " - R_EX_photon_e: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + " - R_PGMT_B: Reactants and products exchanged.\n", + " Boundaries was: [-999999.0 ; 0.0]\n", + " - R_DNADRAIN deleted. No reactants and no products.\n", + " - R_HCO3E1 is multiple (more than 1) metabolites import/export reaction. \n", + "\tDeleted as it is an import reaction.\n", + " - R_GTHOr: Reactants and products exchanged.\n", + " Boundaries was: [-999999.0 ; 0.0]\n", + " - R_PYDXO: Reactants and products exchanged.\n", + " Boundaries was: [-999999.0 ; 0.0]\n", + "\u001b[0m\n", + "\n", + "\n", + "\u001b[0;36m############################################\n", + "############################################\n", + " WRITING SBML FILE \n", + "############################################\n", + "############################################\n", + "\u001b[0m\n", + "\u001b[0;93mWARNING : IMPORT REACTION REMOVED BY DEFAULT\u001b[0m\n", + "\u001b[0;93mWARNING : If you want to keep import reaction\n", + "use option -kir / --keep-import-reactions\u001b[0m\n", + "\u001b[0;93mWARNING : Reaction with tag reversible modified:\n", + "\t- R_MAN1PT2\n", + "\t- R_HSDy\n", + "\t- R_ASAD\n", + "\t- R_KARA1\n", + "\t- R_ALCD2y\n", + "\t- R_CO2tpp\n", + "\t- R_GLYCLTDx\n", + "\t- R_PGMT_B\n", + "\t- R_GTHOr\n", + "\t- R_PYDXO\u001b[0m\n", + "\u001b[0;93mWARNING : Reaction with Reactants and Products exchanged:\n", + "\t- R_MAN1PT2\n", + "\t- R_HSDy\n", + "\t- R_ASAD\n", + "\t- R_KARA1\n", + "\t- R_ALCD2y\n", + "\t- R_CO2tpp\n", + "\t- R_GLYCLTDx\n", + "\t- R_PGMT_B\n", + "\t- R_GTHOr\n", + "\t- R_PYDXO\u001b[0m\n", + "\u001b[0;93mWARNING : Import reaction removed:\n", + "\t- R_EX_ac_e\n", + "\t- R_EX_akg_e\n", + "\t- R_EX_ala__L_e\n", + "\t- R_EX_ca2_e\n", + "\t- R_EX_cit_e\n", + "\t- R_EX_co_e\n", + "\t- R_EX_co2_e\n", + "\t- R_EX_cobalt2_e\n", + "\t- R_EX_cu2_e\n", + "\t- R_EX_cynt_e\n", + "\t- R_EX_fe2_e\n", + "\t- R_EX_fe3_e\n", + "\t- R_EX_fru_e\n", + "\t- R_EX_fum_e\n", + "\t- R_EX_glc__D_e\n", + "\t- R_EX_glcglyc_e\n", + "\t- R_EX_gln__L_e\n", + "\t- R_EX_glu__L_e\n", + "\t- R_EX_gly_e\n", + "\t- R_EX_h_e\n", + "\t- R_EX_h2_e\n", + "\t- R_EX_h2o_e\n", + "\t- R_EX_hco3_e\n", + "\t- R_EX_his__L_e\n", + "\t- R_EX_k_e\n", + "\t- R_EX_leu__L_e\n", + "\t- R_EX_lys__L_e\n", + "\t- R_EX_mal__L_e\n", + "\t- R_EX_mg2_e\n", + "\t- R_EX_mn2_e\n", + "\t- R_EX_mobd_e\n", + "\t- R_EX_na1_e\n", + "\t- R_EX_nh4_e\n", + "\t- R_EX_ni2_e\n", + "\t- R_EX_no3_e\n", + "\t- R_EX_o2_e\n", + "\t- R_EX_ptrc_e\n", + "\t- R_EX_pi_e\n", + "\t- R_EX_pyr_e\n", + "\t- R_EX_ser__L_e\n", + "\t- R_EX_pro__L_e\n", + "\t- R_EX_so4_e\n", + "\t- R_EX_spmd_e\n", + "\t- R_EX_succ_e\n", + "\t- R_EX_sucr_e\n", + "\t- R_EX_urea_e\n", + "\t- R_EX_zn2_e\n", + "\t- R_DM_5mdru1p_c\n", + "\t- R_DM_cyanphy_c\n", + "\t- R_SK_dna5mtc_c\n", + "\t- R_SK_glycogen_c\n", + "\t- R_SK_phb_c\n", + "\t- R_SK_precyanphy_c\n", + "\t- R_SK_rdmbzi_c\n", + "\t- R_EX_arg__L_e\n", + "\t- R_HCO3E1\u001b[0m\n", + "File saved at: ../../data/sbml_corrected/iSynCJ816.xml\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: iECDH1ME8569_1439\n", + "\u001b[0;93mWARNING : \n", + " - R_CAT: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_DHPTDNR: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_DHPTDNRN: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_FHL: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SPODM: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SPODMpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCASPtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCFUMtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCMALtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCTARTtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_EX_chitob_e: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_CHITOBpts: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_CHITPH: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_URCN: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\u001b[0m\n", + "\n", + "\n", + "\u001b[0;36m############################################\n", + "############################################\n", + " WRITING SBML FILE \n", + "############################################\n", + "############################################\n", + "\u001b[0m\n", + "\u001b[0;93mWARNING : IMPORT REACTION REMOVED BY DEFAULT\u001b[0m\n", + "\u001b[0;93mWARNING : If you want to keep import reaction\n", + "use option -kir / --keep-import-reactions\u001b[0m\n", + "\u001b[0;93mWARNING : Import reaction removed:\n", + "\t- R_EX_ala_B_e\n", + "\t- R_EX_ala__D_e\n", + "\t- R_EX_ala__L_e\n", + "\t- R_EX_akg_e\n", + "\t- R_EX_all__D_e\n", + "\t- R_EX_alaala_e\n", + "\t- R_EX_agm_e\n", + "\t- R_EX_anhgm_e\n", + "\t- R_EX_alltn_e\n", + "\t- R_EX_amp_e\n", + "\t- R_DM_4crsol_c\n", + "\t- R_EX_arbt_e\n", + "\t- R_EX_adocbl_e\n", + "\t- R_EX_ag_e\n", + "\t- R_EX_arab__L_e\n", + "\t- R_EX_arbtn_e\n", + "\t- R_EX_arbtn_fe3_e\n", + "\t- R_DM_5drib_c\n", + "\t- R_EX_arg__L_e\n", + "\t- R_DM_aacald_c\n", + "\t- R_EX_ascb__L_e\n", + "\t- R_EX_asn__L_e\n", + "\t- R_DM_amob_c\n", + "\t- R_EX_aso3_e\n", + "\t- R_EX_asp__L_e\n", + "\t- R_DM_mththf_c\n", + "\t- R_EX_btn_e\n", + "\t- R_EX_but_e\n", + "\t- R_EX_butso3_e\n", + "\t- R_EX_g1p_e\n", + "\t- R_DM_oxam_c\n", + "\t- R_EX_g3pc_e\n", + "\t- R_EX_g3pe_e\n", + "\t- R_EX_ca2_e\n", + "\t- R_EX_cbi_e\n", + "\t- R_EX_cbl1_e\n", + "\t- R_EX_cd2_e\n", + "\t- R_EX_cgly_e\n", + "\t- R_EX_chol_e\n", + "\t- R_EX_chtbs_e\n", + "\t- R_EX_g3pg_e\n", + "\t- R_EX_g3pi_e\n", + "\t- R_EX_g3ps_e\n", + "\t- R_EX_g6p_e\n", + "\t- R_EX_gal_e\n", + "\t- R_EX_gal_bD_e\n", + "\t- R_EX_gal1p_e\n", + "\t- R_EX_galct__D_e\n", + "\t- R_EX_galctn__D_e\n", + "\t- R_EX_galctn__L_e\n", + "\t- R_EX_cit_e\n", + "\t- R_EX_cl_e\n", + "\t- R_EX_cm_e\n", + "\t- R_EX_cmp_e\n", + "\t- R_EX_co2_e\n", + "\t- R_EX_galt_e\n", + "\t- R_EX_cobalt2_e\n", + "\t- R_EX_galur_e\n", + "\t- R_EX_colipa_e\n", + "\t- R_EX_colipap_e\n", + "\t- R_EX_cpgn_e\n", + "\t- R_EX_cpgn_un_e\n", + "\t- R_EX_crn_e\n", + "\t- R_EX_gam_e\n", + "\t- R_EX_gam6p_e\n", + "\t- R_EX_gbbtn_e\n", + "\t- R_EX_gdp_e\n", + "\t- R_EX_glc__D_e\n", + "\t- R_EX_crn__D_e\n", + "\t- R_EX_csn_e\n", + "\t- R_EX_cu_e\n", + "\t- R_EX_cu2_e\n", + "\t- R_EX_cyan_e\n", + "\t- R_EX_cynt_e\n", + "\t- R_EX_cys__D_e\n", + "\t- R_EX_cys__L_e\n", + "\t- R_EX_glcn_e\n", + "\t- R_EX_glcr_e\n", + "\t- R_EX_glcur_e\n", + "\t- R_EX_glcur1p_e\n", + "\t- R_EX_gln__L_e\n", + "\t- R_EX_glu__L_e\n", + "\t- R_EX_gly_e\n", + "\t- R_EX_glyald_e\n", + "\t- R_EX_glyb_e\n", + "\t- R_EX_glyc_e\n", + "\t- R_EX_cytd_e\n", + "\t- R_EX_dad_2_e\n", + "\t- R_EX_damp_e\n", + "\t- R_EX_dca_e\n", + "\t- R_EX_glyc__R_e\n", + "\t- R_EX_glyc2p_e\n", + "\t- R_EX_dcmp_e\n", + "\t- R_EX_dcyt_e\n", + "\t- R_EX_glyc3p_e\n", + "\t- R_EX_ddca_e\n", + "\t- R_EX_glyclt_e\n", + "\t- R_EX_12ppd__R_e\n", + "\t- R_EX_gmp_e\n", + "\t- R_EX_gsn_e\n", + "\t- R_EX_gthox_e\n", + "\t- R_EX_dgmp_e\n", + "\t- R_EX_dgsn_e\n", + "\t- R_EX_dha_e\n", + "\t- R_EX_gthrd_e\n", + "\t- R_EX_gtp_e\n", + "\t- R_EX_dimp_e\n", + "\t- R_EX_din_e\n", + "\t- R_EX_dms_e\n", + "\t- R_EX_dmso_e\n", + "\t- R_EX_dopa_e\n", + "\t- R_EX_doxrbcn_e\n", + "\t- R_EX_gua_e\n", + "\t- R_EX_h_e\n", + "\t- R_EX_h2_e\n", + "\t- R_EX_h2o_e\n", + "\t- R_EX_h2o2_e\n", + "\t- R_EX_h2s_e\n", + "\t- R_EX_hacolipa_e\n", + "\t- R_EX_dtmp_e\n", + "\t- R_EX_dump_e\n", + "\t- R_EX_duri_e\n", + "\t- R_EX_halipa_e\n", + "\t- R_EX_hdca_e\n", + "\t- R_EX_12ppd__S_e\n", + "\t- R_EX_hdcea_e\n", + "\t- R_EX_hg2_e\n", + "\t- R_EX_eca4colipa_e\n", + "\t- R_EX_enlipa_e\n", + "\t- R_EX_enter_e\n", + "\t- R_EX_etha_e\n", + "\t- R_EX_his__L_e\n", + "\t- R_EX_hom__L_e\n", + "\t- R_EX_ethso3_e\n", + "\t- R_EX_14glucan_e\n", + "\t- R_EX_etoh_e\n", + "\t- R_EX_f6p_e\n", + "\t- R_EX_hxa_e\n", + "\t- R_EX_hxan_e\n", + "\t- R_EX_fald_e\n", + "\t- R_EX_15dap_e\n", + "\t- R_EX_fe2_e\n", + "\t- R_EX_idon__L_e\n", + "\t- R_EX_ile__L_e\n", + "\t- R_EX_fe3_e\n", + "\t- R_EX_23camp_e\n", + "\t- R_EX_fe3dcit_e\n", + "\t- R_EX_fe3dhbzs_e\n", + "\t- R_EX_fe3hox_e\n", + "\t- R_EX_imp_e\n", + "\t- R_EX_indole_e\n", + "\t- R_EX_inost_e\n", + "\t- R_EX_ins_e\n", + "\t- R_EX_isetac_e\n", + "\t- R_EX_fe3hox_un_e\n", + "\t- R_EX_fecrm_e\n", + "\t- R_EX_fecrm_un_e\n", + "\t- R_EX_k_e\n", + "\t- R_EX_kdo2lipid4_e\n", + "\t- R_EX_23ccmp_e\n", + "\t- R_EX_lac__D_e\n", + "\t- R_EX_lac__L_e\n", + "\t- R_EX_feenter_e\n", + "\t- R_EX_feoxam_e\n", + "\t- R_EX_feoxam_un_e\n", + "\t- R_EX_for_e\n", + "\t- R_EX_lcts_e\n", + "\t- R_EX_23cgmp_e\n", + "\t- R_EX_leu__L_e\n", + "\t- R_EX_lipa_e\n", + "\t- R_EX_fru_e\n", + "\t- R_EX_frulys_e\n", + "\t- R_EX_lipa_cold_e\n", + "\t- R_EX_fruur_e\n", + "\t- R_EX_23cump_e\n", + "\t- R_EX_fuc__L_e\n", + "\t- R_EX_fum_e\n", + "\t- R_EX_lipoate_e\n", + "\t- R_EX_lys__L_e\n", + "\t- R_EX_lyx__L_e\n", + "\t- R_EX_mal__D_e\n", + "\t- R_EX_fusa_e\n", + "\t- R_EX_23dappa_e\n", + "\t- R_EX_mal__L_e\n", + "\t- R_EX_malt_e\n", + "\t- R_EX_malthx_e\n", + "\t- R_EX_maltpt_e\n", + "\t- R_EX_manglyc_e\n", + "\t- R_EX_melib_e\n", + "\t- R_EX_meoh_e\n", + "\t- R_EX_met__D_e\n", + "\t- R_EX_met__L_e\n", + "\t- R_EX_malttr_e\n", + "\t- R_EX_maltttr_e\n", + "\t- R_EX_26dap__M_e\n", + "\t- R_EX_man_e\n", + "\t- R_EX_man6p_e\n", + "\t- R_EX_metsox_R__L_e\n", + "\t- R_EX_metsox_S__L_e\n", + "\t- R_EX_mg2_e\n", + "\t- R_EX_2ddglcn_e\n", + "\t- R_EX_mincyc_e\n", + "\t- R_EX_minohp_e\n", + "\t- R_EX_tre_e\n", + "\t- R_EX_34dhpac_e\n", + "\t- R_EX_trp__L_e\n", + "\t- R_EX_mmet_e\n", + "\t- R_EX_mn2_e\n", + "\t- R_EX_3amp_e\n", + "\t- R_EX_mnl_e\n", + "\t- R_EX_tsul_e\n", + "\t- R_EX_mobd_e\n", + "\t- R_EX_3cmp_e\n", + "\t- R_EX_mso3_e\n", + "\t- R_EX_ttdca_e\n", + "\t- R_EX_ttdcea_e\n", + "\t- R_EX_n2o_e\n", + "\t- R_EX_na1_e\n", + "\t- R_EX_ttrcyc_e\n", + "\t- R_EX_nac_e\n", + "\t- R_EX_tungs_e\n", + "\t- R_EX_tym_e\n", + "\t- R_EX_tyr__L_e\n", + "\t- R_EX_tyrp_e\n", + "\t- R_EX_nh4_e\n", + "\t- R_EX_ni2_e\n", + "\t- R_EX_nmn_e\n", + "\t- R_EX_no_e\n", + "\t- R_EX_no2_e\n", + "\t- R_EX_uacgam_e\n", + "\t- R_EX_udpacgal_e\n", + "\t- R_EX_udpg_e\n", + "\t- R_EX_no3_e\n", + "\t- R_EX_3gmp_e\n", + "\t- R_EX_novbcn_e\n", + "\t- R_EX_o16a4colipa_e\n", + "\t- R_EX_o2_e\n", + "\t- R_EX_o2s_e\n", + "\t- R_EX_udpgal_e\n", + "\t- R_EX_udpglcur_e\n", + "\t- R_EX_ump_e\n", + "\t- R_EX_ura_e\n", + "\t- R_EX_ocdca_e\n", + "\t- R_EX_ocdcea_e\n", + "\t- R_EX_urea_e\n", + "\t- R_EX_3hcinnm_e\n", + "\t- R_EX_uri_e\n", + "\t- R_EX_val__L_e\n", + "\t- R_EX_octa_e\n", + "\t- R_EX_orn_e\n", + "\t- R_EX_orot_e\n", + "\t- R_EX_xan_e\n", + "\t- R_EX_xmp_e\n", + "\t- R_EX_pacald_e\n", + "\t- R_EX_3hpp_e\n", + "\t- R_EX_peamn_e\n", + "\t- R_EX_xtsn_e\n", + "\t- R_EX_phe__L_e\n", + "\t- R_EX_xyl__D_e\n", + "\t- R_EX_xylu__L_e\n", + "\t- R_EX_zn2_e\n", + "\t- R_EX_pheme_e\n", + "\t- R_EX_pi_e\n", + "\t- R_EX_pnto__R_e\n", + "\t- R_EX_ppa_e\n", + "\t- R_EX_ppal_e\n", + "\t- R_EX_3hpppn_e\n", + "\t- R_EX_pppn_e\n", + "\t- R_EX_ppt_e\n", + "\t- R_EX_pro__L_e\n", + "\t- R_EX_progly_e\n", + "\t- R_EX_psclys_e\n", + "\t- R_EX_pser__L_e\n", + "\t- R_EX_ptrc_e\n", + "\t- R_EX_pydam_e\n", + "\t- R_EX_pydx_e\n", + "\t- R_EX_3ump_e\n", + "\t- R_EX_pydxn_e\n", + "\t- R_EX_pyr_e\n", + "\t- R_EX_quin_e\n", + "\t- R_EX_r5p_e\n", + "\t- R_EX_rfamp_e\n", + "\t- R_EX_rib__D_e\n", + "\t- R_EX_rmn_e\n", + "\t- R_EX_4abut_e\n", + "\t- R_EX_sbt__D_e\n", + "\t- R_EX_sel_e\n", + "\t- R_EX_ser__D_e\n", + "\t- R_EX_4hoxpacd_e\n", + "\t- R_EX_ser__L_e\n", + "\t- R_EX_skm_e\n", + "\t- R_EX_slnt_e\n", + "\t- R_EX_so2_e\n", + "\t- R_EX_so3_e\n", + "\t- R_EX_so4_e\n", + "\t- R_EX_spmd_e\n", + "\t- R_EX_succ_e\n", + "\t- R_EX_sucr_e\n", + "\t- R_EX_sulfac_e\n", + "\t- R_EX_tartr__D_e\n", + "\t- R_EX_5dglcn_e\n", + "\t- R_EX_tartr__L_e\n", + "\t- R_EX_taur_e\n", + "\t- R_EX_tcynt_e\n", + "\t- R_EX_5mtr_e\n", + "\t- R_EX_thm_e\n", + "\t- R_EX_thr__L_e\n", + "\t- R_EX_LalaDglu_e\n", + "\t- R_EX_thrp_e\n", + "\t- R_EX_LalaDgluMdap_e\n", + "\t- R_EX_thym_e\n", + "\t- R_EX_thymd_e\n", + "\t- R_EX_LalaDgluMdapDala_e\n", + "\t- R_EX_tma_e\n", + "\t- R_EX_tmao_e\n", + "\t- R_EX_LalaLglu_e\n", + "\t- R_EX_ac_e\n", + "\t- R_EX_acac_e\n", + "\t- R_EX_acald_e\n", + "\t- R_EX_acgal_e\n", + "\t- R_EX_acgal1p_e\n", + "\t- R_EX_acgam_e\n", + "\t- R_EX_acgam1p_e\n", + "\t- R_EX_acmana_e\n", + "\t- R_EX_acmum_e\n", + "\t- R_EX_acnam_e\n", + "\t- R_EX_acolipa_e\n", + "\t- R_EX_acser_e\n", + "\t- R_EX_ade_e\n", + "\t- R_EX_adn_e\n", + "\t- R_EX_cysi__L_e\n", + "\t- R_EX_4hphac_e\n", + "\t- R_EX_fe3dhbzs3_e\n", + "\t- R_EX_4abz_e\n", + "\t- R_EX_drib_e\n", + "\t- R_EX_acon_C_e\n", + "\t- R_EX_feroxBfe_e\n", + "\t- R_EX_salchs4fe_e\n", + "\t- R_EX_2pg_e\n", + "\t- R_EX_feroxB_e\n", + "\t- R_EX_tcb_e\n", + "\t- R_EX_tton_e\n", + "\t- R_EX_4hthr_e\n", + "\t- R_EX_frmd_e\n", + "\t- R_EX_feroxEfe_e\n", + "\t- R_EX_feroxE_e\n", + "\t- R_EX_colipaOA_e\n", + "\t- R_EX_salchsx_e\n", + "\t- R_EX_AEP_e\n", + "\t- R_EX_icit_e\n", + "\t- R_EX_pep_e\n", + "\t- R_EX_23dhbzs3_e\n", + "\t- R_EX_tet_e\n", + "\t- R_EX_salchs4_e\n", + "\t- R_EX_colipa20Oag_e\n", + "\t- R_EX_remnant1_e\n", + "\t- R_EX_oaa_e\n", + "\t- R_EX_salchs2fe_e\n", + "\t- R_EX_salchs2_e\n", + "\t- R_EX_rnam_e\n", + "\t- R_EX_feroxG_e\n", + "\t- R_EX_airs_e\n", + "\t- R_EX_dxyl_e\n", + "\t- R_EX_3pg_e\n", + "\t- R_EX_feroxGfe_e\n", + "\t- R_EX_udcpo4_e\n", + "\t- R_EX_diact_e\n", + "\t- R_EX_tag__D_e\n", + "\t- R_EX_udcpo5_e\n", + "\t- R_EX_cellb_e\n", + "\t- R_EX_guln__L_e\n", + "\t- R_EX_btd_RR_e\n", + "\t- R_EX_rbt_e\n", + "\t- R_EX_bz_e\n", + "\t- R_EX_13ppd_e\n", + "\t- R_EX_abt__D_e\n", + "\t- R_EX_raffin_e\n", + "\t- R_EX_fe_e\n", + "\t- R_EX_kokdolipidA_e\n", + "\t- R_EX_3hoxpac_e\n", + "\t- R_EX_o6a4colipa_e\n", + "\t- R_EX_arab__D_e\n", + "\t- R_EX_4hoxpac_e\n", + "\t- R_DM_lipidA_core_e_p\n", + "\t- R_EX_3ntym_e\n", + "\t- R_EX_6apa_e\n", + "\t- R_EX_peng_e\n", + "\t- R_EX_pac_e\n", + "\t- R_EX_salcn_e\n", + "\t- R_EX_galam_e\u001b[0m\n", + "File saved at: ../../data/sbml_corrected/iECDH1ME8569_1439.xml\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: iE2348C_1286\n", + "\u001b[0;93mWARNING : \n", + " - R_CAT: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_DHPTDNR: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_DHPTDNRN: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_FHL: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCFUMtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCASPtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCMALtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCTARTtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SPODM: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SPODMpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_CHITOBpts: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_URCN: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_CHITPH: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_EX_chitob_e: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\u001b[0m\n", + "\n", + "\n", + "\u001b[0;36m############################################\n", + "############################################\n", + " WRITING SBML FILE \n", + "############################################\n", + "############################################\n", + "\u001b[0m\n", + "\u001b[0;93mWARNING : IMPORT REACTION REMOVED BY DEFAULT\u001b[0m\n", + "\u001b[0;93mWARNING : If you want to keep import reaction\n", + "use option -kir / --keep-import-reactions\u001b[0m\n", + "\u001b[0;93mWARNING : Import reaction removed:\n", + "\t- R_EX_ile__L_e\n", + "\t- R_EX_amp_e\n", + "\t- R_EX_anhgm_e\n", + "\t- R_EX_arab__L_e\n", + "\t- R_EX_arbt_e\n", + "\t- R_EX_arbtn_e\n", + "\t- R_EX_arbtn_fe3_e\n", + "\t- R_EX_arg__L_e\n", + "\t- R_EX_imp_e\n", + "\t- R_EX_ascb__L_e\n", + "\t- R_EX_asn__L_e\n", + "\t- R_EX_aso3_e\n", + "\t- R_EX_indole_e\n", + "\t- R_EX_asp__L_e\n", + "\t- R_EX_fru_e\n", + "\t- R_EX_btn_e\n", + "\t- R_EX_but_e\n", + "\t- R_EX_butso3_e\n", + "\t- R_EX_frulys_e\n", + "\t- R_EX_ca2_e\n", + "\t- R_EX_cbi_e\n", + "\t- R_EX_cbl1_e\n", + "\t- R_EX_acser_e\n", + "\t- R_EX_fruur_e\n", + "\t- R_EX_cd2_e\n", + "\t- R_EX_fuc__L_e\n", + "\t- R_EX_cgly_e\n", + "\t- R_EX_fum_e\n", + "\t- R_EX_chol_e\n", + "\t- R_EX_chtbs_e\n", + "\t- R_EX_cit_e\n", + "\t- R_EX_ade_e\n", + "\t- R_EX_fusa_e\n", + "\t- R_EX_g1p_e\n", + "\t- R_EX_cl_e\n", + "\t- R_EX_cm_e\n", + "\t- R_EX_adn_e\n", + "\t- R_EX_g3pc_e\n", + "\t- R_EX_adocbl_e\n", + "\t- R_EX_ag_e\n", + "\t- R_EX_agm_e\n", + "\t- R_EX_akg_e\n", + "\t- R_EX_cmp_e\n", + "\t- R_EX_co2_e\n", + "\t- R_EX_cobalt2_e\n", + "\t- R_EX_colipa_e\n", + "\t- R_EX_colipap_e\n", + "\t- R_EX_ala_B_e\n", + "\t- R_EX_ala__D_e\n", + "\t- R_EX_ala__L_e\n", + "\t- R_EX_g3pe_e\n", + "\t- R_EX_alaala_e\n", + "\t- R_EX_all__D_e\n", + "\t- R_EX_alltn_e\n", + "\t- R_EX_cpgn_e\n", + "\t- R_EX_cpgn_un_e\n", + "\t- R_EX_crn_e\n", + "\t- R_EX_crn__D_e\n", + "\t- R_EX_csn_e\n", + "\t- R_EX_g3pg_e\n", + "\t- R_EX_cu_e\n", + "\t- R_EX_cu2_e\n", + "\t- R_EX_cyan_e\n", + "\t- R_EX_cynt_e\n", + "\t- R_DM_4crsol_c\n", + "\t- R_EX_g3pi_e\n", + "\t- R_DM_5drib_c\n", + "\t- R_EX_cys__D_e\n", + "\t- R_EX_cys__L_e\n", + "\t- R_EX_cytd_e\n", + "\t- R_DM_aacald_c\n", + "\t- R_EX_g3ps_e\n", + "\t- R_DM_amob_c\n", + "\t- R_DM_mththf_c\n", + "\t- R_EX_dad_2_e\n", + "\t- R_EX_damp_e\n", + "\t- R_EX_dca_e\n", + "\t- R_EX_dcmp_e\n", + "\t- R_EX_dcyt_e\n", + "\t- R_EX_ddca_e\n", + "\t- R_DM_oxam_c\n", + "\t- R_EX_12ppd__R_e\n", + "\t- R_EX_12ppd__S_e\n", + "\t- R_EX_14glucan_e\n", + "\t- R_EX_dgmp_e\n", + "\t- R_EX_dgsn_e\n", + "\t- R_EX_g6p_e\n", + "\t- R_EX_dha_e\n", + "\t- R_EX_dimp_e\n", + "\t- R_EX_din_e\n", + "\t- R_EX_15dap_e\n", + "\t- R_EX_23camp_e\n", + "\t- R_EX_23ccmp_e\n", + "\t- R_EX_23cgmp_e\n", + "\t- R_EX_23cump_e\n", + "\t- R_EX_dms_e\n", + "\t- R_EX_dmso_e\n", + "\t- R_EX_dopa_e\n", + "\t- R_EX_23dappa_e\n", + "\t- R_EX_26dap__M_e\n", + "\t- R_EX_gal_e\n", + "\t- R_EX_2ddglcn_e\n", + "\t- R_EX_34dhpac_e\n", + "\t- R_EX_doxrbcn_e\n", + "\t- R_EX_dtmp_e\n", + "\t- R_EX_gal_bD_e\n", + "\t- R_EX_dump_e\n", + "\t- R_EX_3amp_e\n", + "\t- R_EX_3cmp_e\n", + "\t- R_EX_duri_e\n", + "\t- R_EX_eca4colipa_e\n", + "\t- R_EX_enlipa_e\n", + "\t- R_EX_enter_e\n", + "\t- R_EX_3gmp_e\n", + "\t- R_EX_3hcinnm_e\n", + "\t- R_EX_3hpp_e\n", + "\t- R_EX_3hpppn_e\n", + "\t- R_EX_3ump_e\n", + "\t- R_EX_4abut_e\n", + "\t- R_EX_etha_e\n", + "\t- R_EX_ethso3_e\n", + "\t- R_EX_4hoxpacd_e\n", + "\t- R_EX_etoh_e\n", + "\t- R_EX_gal1p_e\n", + "\t- R_EX_f6p_e\n", + "\t- R_EX_fald_e\n", + "\t- R_EX_5dglcn_e\n", + "\t- R_EX_5mtr_e\n", + "\t- R_EX_LalaDglu_e\n", + "\t- R_EX_LalaDgluMdap_e\n", + "\t- R_EX_fe2_e\n", + "\t- R_EX_galct__D_e\n", + "\t- R_EX_fe3_e\n", + "\t- R_EX_LalaDgluMdapDala_e\n", + "\t- R_EX_galctn__D_e\n", + "\t- R_EX_LalaLglu_e\n", + "\t- R_EX_ac_e\n", + "\t- R_EX_fe3dcit_e\n", + "\t- R_EX_fe3dhbzs_e\n", + "\t- R_EX_fe3hox_e\n", + "\t- R_EX_acac_e\n", + "\t- R_EX_galctn__L_e\n", + "\t- R_EX_acald_e\n", + "\t- R_EX_acgal_e\n", + "\t- R_EX_fe3hox_un_e\n", + "\t- R_EX_fecrm_e\n", + "\t- R_EX_fecrm_un_e\n", + "\t- R_EX_feenter_e\n", + "\t- R_EX_feoxam_e\n", + "\t- R_EX_feoxam_un_e\n", + "\t- R_EX_acgal1p_e\n", + "\t- R_EX_acgam_e\n", + "\t- R_EX_acgam1p_e\n", + "\t- R_EX_acmana_e\n", + "\t- R_EX_acmum_e\n", + "\t- R_EX_for_e\n", + "\t- R_EX_acnam_e\n", + "\t- R_EX_galt_e\n", + "\t- R_EX_acolipa_e\n", + "\t- R_EX_galur_e\n", + "\t- R_EX_lipa_cold_e\n", + "\t- R_EX_lipoate_e\n", + "\t- R_EX_sucr_e\n", + "\t- R_EX_lys__L_e\n", + "\t- R_EX_gam_e\n", + "\t- R_EX_lyx__L_e\n", + "\t- R_EX_sulfac_e\n", + "\t- R_EX_gam6p_e\n", + "\t- R_EX_tartr__D_e\n", + "\t- R_EX_mal__D_e\n", + "\t- R_EX_gbbtn_e\n", + "\t- R_EX_mal__L_e\n", + "\t- R_EX_malt_e\n", + "\t- R_EX_tartr__L_e\n", + "\t- R_EX_taur_e\n", + "\t- R_EX_tcynt_e\n", + "\t- R_EX_malthx_e\n", + "\t- R_EX_gdp_e\n", + "\t- R_EX_maltpt_e\n", + "\t- R_EX_malttr_e\n", + "\t- R_EX_thm_e\n", + "\t- R_EX_thr__L_e\n", + "\t- R_EX_thrp_e\n", + "\t- R_EX_maltttr_e\n", + "\t- R_EX_thym_e\n", + "\t- R_EX_thymd_e\n", + "\t- R_EX_tma_e\n", + "\t- R_EX_tmao_e\n", + "\t- R_EX_man_e\n", + "\t- R_EX_man6p_e\n", + "\t- R_EX_manglyc_e\n", + "\t- R_EX_melib_e\n", + "\t- R_EX_tre_e\n", + "\t- R_EX_trp__L_e\n", + "\t- R_EX_meoh_e\n", + "\t- R_EX_glc__D_e\n", + "\t- R_EX_met__D_e\n", + "\t- R_EX_met__L_e\n", + "\t- R_EX_metsox_R__L_e\n", + "\t- R_EX_tsul_e\n", + "\t- R_EX_ttdca_e\n", + "\t- R_EX_glcn_e\n", + "\t- R_EX_ttdcea_e\n", + "\t- R_EX_metsox_S__L_e\n", + "\t- R_EX_glcr_e\n", + "\t- R_EX_mg2_e\n", + "\t- R_EX_ttrcyc_e\n", + "\t- R_EX_tungs_e\n", + "\t- R_EX_tym_e\n", + "\t- R_EX_tyr__L_e\n", + "\t- R_EX_mincyc_e\n", + "\t- R_EX_minohp_e\n", + "\t- R_EX_tyrp_e\n", + "\t- R_EX_glcur_e\n", + "\t- R_EX_uacgam_e\n", + "\t- R_EX_udpacgal_e\n", + "\t- R_EX_udpg_e\n", + "\t- R_EX_mmet_e\n", + "\t- R_EX_mn2_e\n", + "\t- R_EX_mnl_e\n", + "\t- R_EX_udpgal_e\n", + "\t- R_EX_glcur1p_e\n", + "\t- R_EX_udpglcur_e\n", + "\t- R_EX_ump_e\n", + "\t- R_EX_ura_e\n", + "\t- R_EX_urea_e\n", + "\t- R_EX_uri_e\n", + "\t- R_EX_val__L_e\n", + "\t- R_EX_xan_e\n", + "\t- R_EX_mobd_e\n", + "\t- R_EX_mso3_e\n", + "\t- R_EX_n2o_e\n", + "\t- R_EX_na1_e\n", + "\t- R_EX_nac_e\n", + "\t- R_EX_nh4_e\n", + "\t- R_EX_ni2_e\n", + "\t- R_EX_nmn_e\n", + "\t- R_EX_xmp_e\n", + "\t- R_EX_xtsn_e\n", + "\t- R_EX_xyl__D_e\n", + "\t- R_EX_gln__L_e\n", + "\t- R_EX_xylu__L_e\n", + "\t- R_EX_zn2_e\n", + "\t- R_EX_no_e\n", + "\t- R_EX_no2_e\n", + "\t- R_EX_no3_e\n", + "\t- R_EX_novbcn_e\n", + "\t- R_EX_glu__L_e\n", + "\t- R_EX_o16a4colipa_e\n", + "\t- R_EX_o2_e\n", + "\t- R_EX_o2s_e\n", + "\t- R_EX_gly_e\n", + "\t- R_EX_ocdca_e\n", + "\t- R_EX_ocdcea_e\n", + "\t- R_EX_octa_e\n", + "\t- R_EX_orn_e\n", + "\t- R_EX_orot_e\n", + "\t- R_EX_pacald_e\n", + "\t- R_EX_peamn_e\n", + "\t- R_EX_glyald_e\n", + "\t- R_EX_phe__L_e\n", + "\t- R_EX_pheme_e\n", + "\t- R_EX_pi_e\n", + "\t- R_EX_pnto__R_e\n", + "\t- R_EX_ppa_e\n", + "\t- R_EX_ppal_e\n", + "\t- R_EX_glyb_e\n", + "\t- R_EX_pppn_e\n", + "\t- R_EX_ppt_e\n", + "\t- R_EX_glyc_e\n", + "\t- R_EX_pro__L_e\n", + "\t- R_EX_progly_e\n", + "\t- R_EX_psclys_e\n", + "\t- R_EX_glyc__R_e\n", + "\t- R_EX_pser__L_e\n", + "\t- R_EX_ptrc_e\n", + "\t- R_EX_pydam_e\n", + "\t- R_EX_pydx_e\n", + "\t- R_EX_pydxn_e\n", + "\t- R_EX_pyr_e\n", + "\t- R_EX_quin_e\n", + "\t- R_EX_r5p_e\n", + "\t- R_EX_glyc2p_e\n", + "\t- R_EX_rfamp_e\n", + "\t- R_EX_rib__D_e\n", + "\t- R_EX_rmn_e\n", + "\t- R_EX_sbt__D_e\n", + "\t- R_EX_sel_e\n", + "\t- R_EX_glyc3p_e\n", + "\t- R_EX_ser__D_e\n", + "\t- R_EX_ser__L_e\n", + "\t- R_EX_skm_e\n", + "\t- R_EX_glyclt_e\n", + "\t- R_EX_slnt_e\n", + "\t- R_EX_so2_e\n", + "\t- R_EX_so3_e\n", + "\t- R_EX_so4_e\n", + "\t- R_EX_spmd_e\n", + "\t- R_EX_succ_e\n", + "\t- R_EX_gmp_e\n", + "\t- R_EX_gsn_e\n", + "\t- R_EX_gthox_e\n", + "\t- R_EX_gthrd_e\n", + "\t- R_EX_gtp_e\n", + "\t- R_EX_gua_e\n", + "\t- R_EX_h_e\n", + "\t- R_EX_h2_e\n", + "\t- R_EX_h2o_e\n", + "\t- R_EX_h2o2_e\n", + "\t- R_EX_h2s_e\n", + "\t- R_EX_hacolipa_e\n", + "\t- R_EX_halipa_e\n", + "\t- R_EX_hdca_e\n", + "\t- R_EX_hdcea_e\n", + "\t- R_EX_hg2_e\n", + "\t- R_EX_his__L_e\n", + "\t- R_EX_hom__L_e\n", + "\t- R_EX_hxa_e\n", + "\t- R_EX_hxan_e\n", + "\t- R_EX_idon__L_e\n", + "\t- R_EX_inost_e\n", + "\t- R_EX_ins_e\n", + "\t- R_EX_isetac_e\n", + "\t- R_EX_k_e\n", + "\t- R_EX_kdo2lipid4_e\n", + "\t- R_EX_feroxE_e\n", + "\t- R_EX_lac__D_e\n", + "\t- R_EX_lac__L_e\n", + "\t- R_EX_lcts_e\n", + "\t- R_EX_leu__L_e\n", + "\t- R_EX_lipa_e\n", + "\t- R_EX_pep_e\n", + "\t- R_EX_23dhbzs3_e\n", + "\t- R_EX_tet_e\n", + "\t- R_EX_salchs4_e\n", + "\t- R_EX_oaa_e\n", + "\t- R_EX_salchs2fe_e\n", + "\t- R_EX_rnam_e\n", + "\t- R_EX_airs_e\n", + "\t- R_EX_cysi__L_e\n", + "\t- R_EX_dxyl_e\n", + "\t- R_EX_3pg_e\n", + "\t- R_EX_fe3dhbzs3_e\n", + "\t- R_EX_feroxGfe_e\n", + "\t- R_EX_udcpo4_e\n", + "\t- R_EX_tag__D_e\n", + "\t- R_EX_udcpo5_e\n", + "\t- R_EX_drib_e\n", + "\t- R_EX_feroxBfe_e\n", + "\t- R_EX_guln__L_e\n", + "\t- R_EX_salchs4fe_e\n", + "\t- R_EX_2pg_e\n", + "\t- R_EX_remnant1_e\n", + "\t- R_EX_feroxG_e\n", + "\t- R_EX_4hphac_e\n", + "\t- R_EX_frmd_e\n", + "\t- R_EX_colipaOA_e\n", + "\t- R_EX_colipa20Oag_e\n", + "\t- R_EX_salchsx_e\n", + "\t- R_EX_AEP_e\n", + "\t- R_EX_icit_e\n", + "\t- R_EX_4abz_e\n", + "\t- R_EX_salchs2_e\n", + "\t- R_EX_acon_C_e\n", + "\t- R_EX_bz_e\n", + "\t- R_EX_cellb_e\n", + "\t- R_EX_13ppd_e\n", + "\t- R_EX_abt__D_e\n", + "\t- R_EX_rbt_e\n", + "\t- R_EX_diact_e\n", + "\t- R_EX_feroxB_e\n", + "\t- R_EX_tcb_e\n", + "\t- R_EX_tton_e\n", + "\t- R_EX_4hthr_e\n", + "\t- R_EX_feroxEfe_e\n", + "\t- R_EX_btd_RR_e\n", + "\t- R_EX_kokdolipidA_e\n", + "\t- R_EX_fe_e\n", + "\t- R_EX_3hoxpac_e\n", + "\t- R_EX_4hoxpac_e\n", + "\t- R_EX_3ntym_e\n", + "\t- R_EX_6apa_e\n", + "\t- R_EX_peng_e\n", + "\t- R_EX_pac_e\n", + "\t- R_EX_salcn_e\n", + "\t- R_EX_galam_e\n", + "\t- R_DM_lipidA_core_e_p\n", + "\t- R_EX_raffin_e\n", + "\t- R_EX_o6a4colipa_e\n", + "\t- R_EX_arab__D_e\u001b[0m\n", + "File saved at: ../../data/sbml_corrected/iE2348C_1286.xml\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: iLB1027_lipid\n", + "\u001b[0;93mWARNING : - R_ALCD2y: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_EX_photon_e: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_ACKr: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_ECOAH8p: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_HACD8p: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_ECOAH7p: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_HACD7p: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_ECOAH6p: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_HACD6p: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_ECOAH5p: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_HACD5p: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_ECOAH4p: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_HACD4p: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_ECOAH7m: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_HACD7m: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_ACACT7m: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_ECOAH6m: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_HACD6m: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_ACACT6m: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_ECOAH5m: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_HACD5m: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_ACACT5m: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_ECOAH4m: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_HACD4m: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_ACACT4m: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_ECOAH3m: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_HACD3m: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_ACACT3m: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_ECOAH2m: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_HACD2m: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_ACACT2m: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_ECOAH1m: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_HACD1m: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_DCIm: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_NTP10: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_HMGCOASm: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_EX_co2_e: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + "\n", + " - R_EX_hco3_e: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_EX_no2_e: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + " - R_AATA: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_EX_no3_e: Reactants and products exchanged.\n", + " Boundaries was: [-1.76 ; 0.0]\n", + "\n", + " - R_EX_nh4_e: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + " - R_HMGCOAR: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_EX_pi_e: Reactants and products exchanged.\n", + " Boundaries was: [-0.22 ; 0.0]\n", + " - R_EX_so4_e: Reactants and products exchanged.\n", + " Boundaries was: [-28.8 ; 0.0]\n", + " - R_GLYCLTth: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_GLYCLTt: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_SUCCtp: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_AHCYStm: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_5MTAth: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_ADEtm: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_MGt5: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_ASPSAth: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_NADPth: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_ACtm: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_RIBFLVth: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_FADth: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_FADH2th: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_NAt3_1: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + "\n", + " - R_NDHPQR_h: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_PTOX_h: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_MEHLER_h: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_ATPt_h: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + " - R_QULNtm: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_NADPtxu: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_NADtx: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + "\u001b[0m\n", + "\n", + "\n", + "\u001b[0;36m############################################\n", + "############################################\n", + " WRITING SBML FILE \n", + "############################################\n", + "############################################\n", + "\u001b[0m\n", + "\u001b[0;93mWARNING : IMPORT REACTION REMOVED BY DEFAULT\u001b[0m\n", + "\u001b[0;93mWARNING : If you want to keep import reaction\n", + "use option -kir / --keep-import-reactions\u001b[0m\n", + "\u001b[0;93mWARNING : Reaction with tag reversible modified:\n", + "\t- R_ALCD2y\n", + "\t- R_EX_photon_e\n", + "\t- R_ACKr\n", + "\t- R_ECOAH8p\n", + "\t- R_HACD8p\n", + "\t- R_ECOAH7p\n", + "\t- R_HACD7p\n", + "\t- R_ECOAH6p\n", + "\t- R_HACD6p\n", + "\t- R_ECOAH5p\n", + "\t- R_HACD5p\n", + "\t- R_ECOAH4p\n", + "\t- R_HACD4p\n", + "\t- R_ECOAH7m\n", + "\t- R_HACD7m\n", + "\t- R_ACACT7m\n", + "\t- R_ECOAH6m\n", + "\t- R_HACD6m\n", + "\t- R_ACACT6m\n", + "\t- R_ECOAH5m\n", + "\t- R_HACD5m\n", + "\t- R_ACACT5m\n", + "\t- R_ECOAH4m\n", + "\t- R_HACD4m\n", + "\t- R_ACACT4m\n", + "\t- R_ECOAH3m\n", + "\t- R_HACD3m\n", + "\t- R_ACACT3m\n", + "\t- R_ECOAH2m\n", + "\t- R_HACD2m\n", + "\t- R_ACACT2m\n", + "\t- R_ECOAH1m\n", + "\t- R_HACD1m\n", + "\t- R_DCIm\n", + "\t- R_NTP10\n", + "\t- R_HMGCOASm\n", + "\t- R_EX_co2_e\n", + "\t- R_AATA\n", + "\t- R_EX_no3_e\n", + "\t- R_HMGCOAR\n", + "\t- R_EX_pi_e\n", + "\t- R_EX_so4_e\n", + "\t- R_GLYCLTth\n", + "\t- R_GLYCLTt\n", + "\t- R_SUCCtp\n", + "\t- R_AHCYStm\n", + "\t- R_5MTAth\n", + "\t- R_ADEtm\n", + "\t- R_MGt5\n", + "\t- R_ASPSAth\n", + "\t- R_NADPth\n", + "\t- R_ACtm\n", + "\t- R_RIBFLVth\n", + "\t- R_FADth\n", + "\t- R_FADH2th\n", + "\t- R_NAt3_1\n", + "\t- R_QULNtm\n", + "\t- R_NADPtxu\n", + "\t- R_NADtx\u001b[0m\n", + "\u001b[0;93mWARNING : Reaction with Reactants and Products exchanged:\n", + "\t- R_ALCD2y\n", + "\t- R_EX_photon_e\n", + "\t- R_ACKr\n", + "\t- R_ECOAH8p\n", + "\t- R_HACD8p\n", + "\t- R_ECOAH7p\n", + "\t- R_HACD7p\n", + "\t- R_ECOAH6p\n", + "\t- R_HACD6p\n", + "\t- R_ECOAH5p\n", + "\t- R_HACD5p\n", + "\t- R_ECOAH4p\n", + "\t- R_HACD4p\n", + "\t- R_ECOAH7m\n", + "\t- R_HACD7m\n", + "\t- R_ACACT7m\n", + "\t- R_ECOAH6m\n", + "\t- R_HACD6m\n", + "\t- R_ACACT6m\n", + "\t- R_ECOAH5m\n", + "\t- R_HACD5m\n", + "\t- R_ACACT5m\n", + "\t- R_ECOAH4m\n", + "\t- R_HACD4m\n", + "\t- R_ACACT4m\n", + "\t- R_ECOAH3m\n", + "\t- R_HACD3m\n", + "\t- R_ACACT3m\n", + "\t- R_ECOAH2m\n", + "\t- R_HACD2m\n", + "\t- R_ACACT2m\n", + "\t- R_ECOAH1m\n", + "\t- R_HACD1m\n", + "\t- R_DCIm\n", + "\t- R_NTP10\n", + "\t- R_HMGCOASm\n", + "\t- R_EX_co2_e\n", + "\t- R_AATA\n", + "\t- R_EX_no3_e\n", + "\t- R_HMGCOAR\n", + "\t- R_EX_pi_e\n", + "\t- R_EX_so4_e\n", + "\t- R_GLYCLTth\n", + "\t- R_GLYCLTt\n", + "\t- R_SUCCtp\n", + "\t- R_AHCYStm\n", + "\t- R_5MTAth\n", + "\t- R_ADEtm\n", + "\t- R_MGt5\n", + "\t- R_ASPSAth\n", + "\t- R_NADPth\n", + "\t- R_ACtm\n", + "\t- R_RIBFLVth\n", + "\t- R_FADth\n", + "\t- R_FADH2th\n", + "\t- R_NAt3_1\n", + "\t- R_QULNtm\n", + "\t- R_NADPtxu\n", + "\t- R_NADtx\u001b[0m\n", + "\u001b[0;93mWARNING : Import reaction removed:\n", + "\t- R_EX_photon_e\n", + "\t- R_EX_co2_e\n", + "\t- R_DM_m2masn_c\n", + "\t- R_EX_no3_e\n", + "\t- R_EX_btn_e\n", + "\t- R_EX_fe2_e\n", + "\t- R_EX_h_e\n", + "\t- R_EX_h2o_e\n", + "\t- R_EX_o2_e\n", + "\t- R_EX_pi_e\n", + "\t- R_EX_na1_e\n", + "\t- R_EX_so4_e\n", + "\t- R_EX_so3_e\n", + "\t- R_EX_mg2_e\n", + "\t- R_EX_glyclt_e\n", + "\t- R_EX_slnt_e\n", + "\t- R_EX_glc__D_e\n", + "\t- R_EX_cl_e\n", + "\t- R_EX_h2_e\n", + "\t- R_DM_minohp_c\n", + "\t- R_DM_thmtp_c\n", + "\t- R_DM_tre_c\n", + "\t- R_EX_fol_e\n", + "\t- R_DM_5drib_c\n", + "\t- R_DM_2mop_m\n", + "\t- R_DM_fald_m\n", + "\t- R_EX_co_e\n", + "\t- R_SK_Asn_X_Ser_Thr_c\n", + "\t- R_SK_5mthglu_c\n", + "\t- R_EX_cyan_e\n", + "\t- R_EX_cynt_e\n", + "\t- R_EX_tcynt_e\n", + "\t- R_EX_lac__D_e\n", + "\t- R_EX_etoh_e\n", + "\t- R_DM_biomass_c\n", + "\t- R_DM_no3_c\n", + "\t- R_DM_dmsp_c\n", + "\t- R_DM_indole_c\n", + "\t- R_SK_for_c\n", + "\t- R_EX_urea_e\n", + "\t- R_EX_thm_e\u001b[0m\n", + "File saved at: ../../data/sbml_corrected/iLB1027_lipid.xml\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: iEC042_1314\n", + "\u001b[0;93mWARNING : \n", + " - R_CAT: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_DHPTDNR: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_DHPTDNRN: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_FHL: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCFUMtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCMALtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCTARTtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SPODM: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SPODMpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCASPtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_CHITOBpts: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_CHITPH: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_URCN: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_EX_chitob_e: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\u001b[0m\n", + "\n", + "\n", + "\u001b[0;36m############################################\n", + "############################################\n", + " WRITING SBML FILE \n", + "############################################\n", + "############################################\n", + "\u001b[0m\n", + "\u001b[0;93mWARNING : IMPORT REACTION REMOVED BY DEFAULT\u001b[0m\n", + "\u001b[0;93mWARNING : If you want to keep import reaction\n", + "use option -kir / --keep-import-reactions\u001b[0m\n", + "\u001b[0;93mWARNING : Import reaction removed:\n", + "\t- R_DM_4crsol_c\n", + "\t- R_DM_5drib_c\n", + "\t- R_DM_aacald_c\n", + "\t- R_DM_amob_c\n", + "\t- R_DM_mththf_c\n", + "\t- R_DM_oxam_c\n", + "\t- R_EX_acgam_e\n", + "\t- R_EX_acgam1p_e\n", + "\t- R_EX_acmana_e\n", + "\t- R_EX_3cmp_e\n", + "\t- R_EX_3gmp_e\n", + "\t- R_EX_3hcinnm_e\n", + "\t- R_EX_acmum_e\n", + "\t- R_EX_3hpp_e\n", + "\t- R_EX_acnam_e\n", + "\t- R_EX_3hpppn_e\n", + "\t- R_EX_3ump_e\n", + "\t- R_EX_4abut_e\n", + "\t- R_EX_acolipa_e\n", + "\t- R_EX_4hoxpacd_e\n", + "\t- R_EX_5dglcn_e\n", + "\t- R_EX_12ppd__R_e\n", + "\t- R_EX_5mtr_e\n", + "\t- R_EX_acser_e\n", + "\t- R_EX_LalaDglu_e\n", + "\t- R_EX_LalaDgluMdap_e\n", + "\t- R_EX_12ppd__S_e\n", + "\t- R_EX_14glucan_e\n", + "\t- R_EX_15dap_e\n", + "\t- R_EX_LalaDgluMdapDala_e\n", + "\t- R_EX_ade_e\n", + "\t- R_EX_LalaLglu_e\n", + "\t- R_EX_ac_e\n", + "\t- R_EX_acac_e\n", + "\t- R_EX_acald_e\n", + "\t- R_EX_acgal_e\n", + "\t- R_EX_acgal1p_e\n", + "\t- R_EX_ag_e\n", + "\t- R_EX_23camp_e\n", + "\t- R_EX_23ccmp_e\n", + "\t- R_EX_23cgmp_e\n", + "\t- R_EX_23cump_e\n", + "\t- R_EX_23dappa_e\n", + "\t- R_EX_26dap__M_e\n", + "\t- R_EX_2ddglcn_e\n", + "\t- R_EX_34dhpac_e\n", + "\t- R_EX_3amp_e\n", + "\t- R_EX_agm_e\n", + "\t- R_EX_akg_e\n", + "\t- R_EX_ala_B_e\n", + "\t- R_EX_ala__D_e\n", + "\t- R_EX_ala__L_e\n", + "\t- R_EX_asn__L_e\n", + "\t- R_EX_aso3_e\n", + "\t- R_EX_alaala_e\n", + "\t- R_EX_asp__L_e\n", + "\t- R_EX_adn_e\n", + "\t- R_EX_btn_e\n", + "\t- R_EX_all__D_e\n", + "\t- R_EX_alltn_e\n", + "\t- R_EX_amp_e\n", + "\t- R_EX_but_e\n", + "\t- R_EX_butso3_e\n", + "\t- R_EX_anhgm_e\n", + "\t- R_EX_adocbl_e\n", + "\t- R_EX_arab__L_e\n", + "\t- R_EX_ca2_e\n", + "\t- R_EX_cbi_e\n", + "\t- R_EX_cbl1_e\n", + "\t- R_EX_cd2_e\n", + "\t- R_EX_cgly_e\n", + "\t- R_EX_chol_e\n", + "\t- R_EX_arbt_e\n", + "\t- R_EX_arbtn_e\n", + "\t- R_EX_arbtn_fe3_e\n", + "\t- R_EX_chtbs_e\n", + "\t- R_EX_arg__L_e\n", + "\t- R_EX_damp_e\n", + "\t- R_EX_ascb__L_e\n", + "\t- R_EX_g3pg_e\n", + "\t- R_EX_cit_e\n", + "\t- R_EX_cl_e\n", + "\t- R_EX_cm_e\n", + "\t- R_EX_cmp_e\n", + "\t- R_EX_g3pi_e\n", + "\t- R_EX_co2_e\n", + "\t- R_EX_g3ps_e\n", + "\t- R_EX_dca_e\n", + "\t- R_EX_g6p_e\n", + "\t- R_EX_gal_e\n", + "\t- R_EX_cobalt2_e\n", + "\t- R_EX_colipa_e\n", + "\t- R_EX_colipap_e\n", + "\t- R_EX_gal_bD_e\n", + "\t- R_EX_dcmp_e\n", + "\t- R_EX_gal1p_e\n", + "\t- R_EX_galct__D_e\n", + "\t- R_EX_cpgn_e\n", + "\t- R_EX_cpgn_un_e\n", + "\t- R_EX_galctn__D_e\n", + "\t- R_EX_dcyt_e\n", + "\t- R_EX_crn_e\n", + "\t- R_EX_ddca_e\n", + "\t- R_EX_crn__D_e\n", + "\t- R_EX_galctn__L_e\n", + "\t- R_EX_galt_e\n", + "\t- R_EX_dgmp_e\n", + "\t- R_EX_galur_e\n", + "\t- R_EX_gam_e\n", + "\t- R_EX_gam6p_e\n", + "\t- R_EX_gbbtn_e\n", + "\t- R_EX_gdp_e\n", + "\t- R_EX_csn_e\n", + "\t- R_EX_cu_e\n", + "\t- R_EX_cu2_e\n", + "\t- R_EX_cyan_e\n", + "\t- R_EX_cynt_e\n", + "\t- R_EX_cys__D_e\n", + "\t- R_EX_cys__L_e\n", + "\t- R_EX_cytd_e\n", + "\t- R_EX_dad_2_e\n", + "\t- R_EX_glc__D_e\n", + "\t- R_EX_glcn_e\n", + "\t- R_EX_glcr_e\n", + "\t- R_EX_glcur_e\n", + "\t- R_EX_dgsn_e\n", + "\t- R_EX_glcur1p_e\n", + "\t- R_EX_gln__L_e\n", + "\t- R_EX_gly_e\n", + "\t- R_EX_dha_e\n", + "\t- R_EX_glyald_e\n", + "\t- R_EX_glyb_e\n", + "\t- R_EX_glu__L_e\n", + "\t- R_EX_halipa_e\n", + "\t- R_EX_glyc_e\n", + "\t- R_EX_dimp_e\n", + "\t- R_EX_glyc__R_e\n", + "\t- R_EX_glyc2p_e\n", + "\t- R_EX_glyc3p_e\n", + "\t- R_EX_hdca_e\n", + "\t- R_EX_hdcea_e\n", + "\t- R_EX_hg2_e\n", + "\t- R_EX_his__L_e\n", + "\t- R_EX_glyclt_e\n", + "\t- R_EX_din_e\n", + "\t- R_EX_gmp_e\n", + "\t- R_EX_gsn_e\n", + "\t- R_EX_gthox_e\n", + "\t- R_EX_gthrd_e\n", + "\t- R_EX_gtp_e\n", + "\t- R_EX_gua_e\n", + "\t- R_EX_hom__L_e\n", + "\t- R_EX_hxa_e\n", + "\t- R_EX_hxan_e\n", + "\t- R_EX_idon__L_e\n", + "\t- R_EX_ile__L_e\n", + "\t- R_EX_imp_e\n", + "\t- R_EX_indole_e\n", + "\t- R_EX_h_e\n", + "\t- R_EX_h2_e\n", + "\t- R_EX_h2o_e\n", + "\t- R_EX_h2o2_e\n", + "\t- R_EX_inost_e\n", + "\t- R_EX_h2s_e\n", + "\t- R_EX_dms_e\n", + "\t- R_EX_hacolipa_e\n", + "\t- R_EX_ins_e\n", + "\t- R_EX_isetac_e\n", + "\t- R_EX_k_e\n", + "\t- R_EX_dmso_e\n", + "\t- R_EX_kdo2lipid4_e\n", + "\t- R_EX_lac__D_e\n", + "\t- R_EX_maltttr_e\n", + "\t- R_EX_dopa_e\n", + "\t- R_EX_man_e\n", + "\t- R_EX_man6p_e\n", + "\t- R_EX_manglyc_e\n", + "\t- R_EX_lac__L_e\n", + "\t- R_EX_lcts_e\n", + "\t- R_EX_leu__L_e\n", + "\t- R_EX_lipa_e\n", + "\t- R_EX_lipa_cold_e\n", + "\t- R_EX_melib_e\n", + "\t- R_EX_meoh_e\n", + "\t- R_EX_met__D_e\n", + "\t- R_EX_met__L_e\n", + "\t- R_EX_metsox_R__L_e\n", + "\t- R_EX_lipoate_e\n", + "\t- R_EX_lys__L_e\n", + "\t- R_EX_lyx__L_e\n", + "\t- R_EX_metsox_S__L_e\n", + "\t- R_EX_doxrbcn_e\n", + "\t- R_EX_mg2_e\n", + "\t- R_EX_mincyc_e\n", + "\t- R_EX_mal__D_e\n", + "\t- R_EX_mal__L_e\n", + "\t- R_EX_dtmp_e\n", + "\t- R_EX_malt_e\n", + "\t- R_EX_minohp_e\n", + "\t- R_EX_mmet_e\n", + "\t- R_EX_malthx_e\n", + "\t- R_EX_mn2_e\n", + "\t- R_EX_maltpt_e\n", + "\t- R_EX_dump_e\n", + "\t- R_EX_malttr_e\n", + "\t- R_EX_duri_e\n", + "\t- R_EX_mnl_e\n", + "\t- R_EX_mobd_e\n", + "\t- R_EX_mso3_e\n", + "\t- R_EX_ocdcea_e\n", + "\t- R_EX_octa_e\n", + "\t- R_EX_n2o_e\n", + "\t- R_EX_eca4colipa_e\n", + "\t- R_EX_na1_e\n", + "\t- R_EX_nac_e\n", + "\t- R_EX_orn_e\n", + "\t- R_EX_orot_e\n", + "\t- R_EX_pacald_e\n", + "\t- R_EX_nh4_e\n", + "\t- R_EX_enlipa_e\n", + "\t- R_EX_ni2_e\n", + "\t- R_EX_nmn_e\n", + "\t- R_EX_no_e\n", + "\t- R_EX_peamn_e\n", + "\t- R_EX_phe__L_e\n", + "\t- R_EX_pheme_e\n", + "\t- R_EX_pi_e\n", + "\t- R_EX_pnto__R_e\n", + "\t- R_EX_no2_e\n", + "\t- R_EX_no3_e\n", + "\t- R_EX_novbcn_e\n", + "\t- R_EX_ppa_e\n", + "\t- R_EX_enter_e\n", + "\t- R_EX_ppal_e\n", + "\t- R_EX_o16a4colipa_e\n", + "\t- R_EX_o2_e\n", + "\t- R_EX_o2s_e\n", + "\t- R_EX_ocdca_e\n", + "\t- R_EX_pppn_e\n", + "\t- R_EX_ppt_e\n", + "\t- R_EX_etha_e\n", + "\t- R_EX_pro__L_e\n", + "\t- R_EX_ethso3_e\n", + "\t- R_EX_progly_e\n", + "\t- R_EX_ser__L_e\n", + "\t- R_EX_skm_e\n", + "\t- R_EX_psclys_e\n", + "\t- R_EX_etoh_e\n", + "\t- R_EX_pser__L_e\n", + "\t- R_EX_ptrc_e\n", + "\t- R_EX_slnt_e\n", + "\t- R_EX_so2_e\n", + "\t- R_EX_so3_e\n", + "\t- R_EX_pydam_e\n", + "\t- R_EX_pydx_e\n", + "\t- R_EX_f6p_e\n", + "\t- R_EX_pydxn_e\n", + "\t- R_EX_so4_e\n", + "\t- R_EX_spmd_e\n", + "\t- R_EX_succ_e\n", + "\t- R_EX_sucr_e\n", + "\t- R_EX_pyr_e\n", + "\t- R_EX_quin_e\n", + "\t- R_EX_r5p_e\n", + "\t- R_EX_rfamp_e\n", + "\t- R_EX_sulfac_e\n", + "\t- R_EX_tartr__D_e\n", + "\t- R_EX_tartr__L_e\n", + "\t- R_EX_rib__D_e\n", + "\t- R_EX_taur_e\n", + "\t- R_EX_rmn_e\n", + "\t- R_EX_fald_e\n", + "\t- R_EX_sbt__D_e\n", + "\t- R_EX_sel_e\n", + "\t- R_EX_ser__D_e\n", + "\t- R_EX_tcynt_e\n", + "\t- R_EX_thm_e\n", + "\t- R_EX_thr__L_e\n", + "\t- R_EX_fe2_e\n", + "\t- R_EX_thrp_e\n", + "\t- R_EX_thymd_e\n", + "\t- R_EX_fe3_e\n", + "\t- R_EX_tma_e\n", + "\t- R_EX_thym_e\n", + "\t- R_EX_fe3dcit_e\n", + "\t- R_EX_tmao_e\n", + "\t- R_EX_tre_e\n", + "\t- R_EX_trp__L_e\n", + "\t- R_EX_tsul_e\n", + "\t- R_EX_ttdca_e\n", + "\t- R_EX_fe3dhbzs_e\n", + "\t- R_EX_ttdcea_e\n", + "\t- R_EX_ttrcyc_e\n", + "\t- R_EX_fe3hox_e\n", + "\t- R_EX_tungs_e\n", + "\t- R_EX_fe3hox_un_e\n", + "\t- R_EX_tym_e\n", + "\t- R_EX_tyr__L_e\n", + "\t- R_EX_tyrp_e\n", + "\t- R_EX_uacgam_e\n", + "\t- R_EX_udpacgal_e\n", + "\t- R_EX_udpg_e\n", + "\t- R_EX_udpgal_e\n", + "\t- R_EX_udpglcur_e\n", + "\t- R_EX_ump_e\n", + "\t- R_EX_ura_e\n", + "\t- R_EX_fecrm_e\n", + "\t- R_EX_urea_e\n", + "\t- R_EX_uri_e\n", + "\t- R_EX_val__L_e\n", + "\t- R_EX_fecrm_un_e\n", + "\t- R_EX_xan_e\n", + "\t- R_EX_xmp_e\n", + "\t- R_EX_feenter_e\n", + "\t- R_EX_xtsn_e\n", + "\t- R_EX_xyl__D_e\n", + "\t- R_EX_xylu__L_e\n", + "\t- R_EX_zn2_e\n", + "\t- R_EX_feoxam_e\n", + "\t- R_EX_feoxam_un_e\n", + "\t- R_EX_for_e\n", + "\t- R_EX_fru_e\n", + "\t- R_EX_frulys_e\n", + "\t- R_EX_fruur_e\n", + "\t- R_EX_fuc__L_e\n", + "\t- R_EX_fum_e\n", + "\t- R_EX_fusa_e\n", + "\t- R_EX_g1p_e\n", + "\t- R_EX_g3pc_e\n", + "\t- R_EX_g3pe_e\n", + "\t- R_EX_feroxB_e\n", + "\t- R_EX_tcb_e\n", + "\t- R_EX_tton_e\n", + "\t- R_EX_4hthr_e\n", + "\t- R_EX_4hphac_e\n", + "\t- R_EX_feroxEfe_e\n", + "\t- R_EX_4abz_e\n", + "\t- R_EX_feroxE_e\n", + "\t- R_EX_pep_e\n", + "\t- R_EX_23dhbzs3_e\n", + "\t- R_EX_tet_e\n", + "\t- R_EX_salchs4_e\n", + "\t- R_EX_tag__D_e\n", + "\t- R_EX_acon_C_e\n", + "\t- R_EX_oaa_e\n", + "\t- R_EX_salchs2fe_e\n", + "\t- R_EX_cysi__L_e\n", + "\t- R_EX_fe3dhbzs3_e\n", + "\t- R_EX_drib_e\n", + "\t- R_EX_feroxBfe_e\n", + "\t- R_EX_salchs4fe_e\n", + "\t- R_EX_2pg_e\n", + "\t- R_EX_salchs2_e\n", + "\t- R_EX_rnam_e\n", + "\t- R_EX_frmd_e\n", + "\t- R_EX_colipaOA_e\n", + "\t- R_EX_salchsx_e\n", + "\t- R_EX_airs_e\n", + "\t- R_EX_dxyl_e\n", + "\t- R_EX_3pg_e\n", + "\t- R_EX_AEP_e\n", + "\t- R_EX_icit_e\n", + "\t- R_EX_colipa20Oag_e\n", + "\t- R_EX_remnant1_e\n", + "\t- R_EX_udcpo4_e\n", + "\t- R_EX_feroxGfe_e\n", + "\t- R_EX_fe_e\n", + "\t- R_EX_kokdolipidA_e\n", + "\t- R_EX_3hoxpac_e\n", + "\t- R_EX_udcpo5_e\n", + "\t- R_EX_guln__L_e\n", + "\t- R_EX_4hoxpac_e\n", + "\t- R_EX_feroxG_e\n", + "\t- R_EX_3ntym_e\n", + "\t- R_EX_6apa_e\n", + "\t- R_EX_peng_e\n", + "\t- R_EX_pac_e\n", + "\t- R_EX_diact_e\n", + "\t- R_EX_salcn_e\n", + "\t- R_EX_cellb_e\n", + "\t- R_EX_galam_e\n", + "\t- R_DM_lipidA_core_e_p\n", + "\t- R_EX_btd_RR_e\n", + "\t- R_EX_13ppd_e\n", + "\t- R_EX_rbt_e\n", + "\t- R_EX_bz_e\n", + "\t- R_EX_raffin_e\n", + "\t- R_EX_o6a4colipa_e\n", + "\t- R_EX_arab__D_e\n", + "\t- R_EX_abt__D_e\u001b[0m\n", + "File saved at: ../../data/sbml_corrected/iEC042_1314.xml\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: iECNA114_1301\n", + "\u001b[0;93mWARNING : \n", + " - R_DHPTDNR: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_DHPTDNRN: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_CAT: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_FHL: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SPODM: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SPODMpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCASPtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCFUMtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCMALtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_SUCTARTtpp: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_EX_chitob_e: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_CHITOBpts: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_CHITPH: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\n", + " - R_URCN: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + "\u001b[0m\n", + "\n", + "\n", + "\u001b[0;36m############################################\n", + "############################################\n", + " WRITING SBML FILE \n", + "############################################\n", + "############################################\n", + "\u001b[0m\n", + "\u001b[0;93mWARNING : IMPORT REACTION REMOVED BY DEFAULT\u001b[0m\n", + "\u001b[0;93mWARNING : If you want to keep import reaction\n", + "use option -kir / --keep-import-reactions\u001b[0m\n", + "\u001b[0;93mWARNING : Import reaction removed:\n", + "\t- R_EX_acac_e\n", + "\t- R_DM_4crsol_c\n", + "\t- R_DM_5drib_c\n", + "\t- R_DM_aacald_c\n", + "\t- R_EX_ac_e\n", + "\t- R_EX_acald_e\n", + "\t- R_DM_amob_c\n", + "\t- R_DM_mththf_c\n", + "\t- R_EX_acgal_e\n", + "\t- R_DM_oxam_c\n", + "\t- R_EX_acgal1p_e\n", + "\t- R_EX_acgam_e\n", + "\t- R_EX_acgam1p_e\n", + "\t- R_EX_acmana_e\n", + "\t- R_EX_acmum_e\n", + "\t- R_EX_12ppd__R_e\n", + "\t- R_EX_acnam_e\n", + "\t- R_EX_acolipa_e\n", + "\t- R_EX_12ppd__S_e\n", + "\t- R_EX_acser_e\n", + "\t- R_EX_ade_e\n", + "\t- R_EX_adn_e\n", + "\t- R_EX_enlipa_e\n", + "\t- R_EX_enter_e\n", + "\t- R_EX_14glucan_e\n", + "\t- R_EX_etha_e\n", + "\t- R_EX_ethso3_e\n", + "\t- R_EX_adocbl_e\n", + "\t- R_EX_ag_e\n", + "\t- R_EX_agm_e\n", + "\t- R_EX_etoh_e\n", + "\t- R_EX_f6p_e\n", + "\t- R_EX_fald_e\n", + "\t- R_EX_fe2_e\n", + "\t- R_EX_fe3_e\n", + "\t- R_EX_fe3dcit_e\n", + "\t- R_EX_fe3dhbzs_e\n", + "\t- R_EX_fe3hox_e\n", + "\t- R_EX_fe3hox_un_e\n", + "\t- R_EX_akg_e\n", + "\t- R_EX_ala_B_e\n", + "\t- R_EX_ala__D_e\n", + "\t- R_EX_ala__L_e\n", + "\t- R_EX_alaala_e\n", + "\t- R_EX_all__D_e\n", + "\t- R_EX_alltn_e\n", + "\t- R_EX_amp_e\n", + "\t- R_EX_anhgm_e\n", + "\t- R_EX_arab__L_e\n", + "\t- R_EX_fecrm_e\n", + "\t- R_EX_fecrm_un_e\n", + "\t- R_EX_feenter_e\n", + "\t- R_EX_arbt_e\n", + "\t- R_EX_15dap_e\n", + "\t- R_EX_arbtn_e\n", + "\t- R_EX_arbtn_fe3_e\n", + "\t- R_EX_feoxam_e\n", + "\t- R_EX_feoxam_un_e\n", + "\t- R_EX_23camp_e\n", + "\t- R_EX_for_e\n", + "\t- R_EX_fru_e\n", + "\t- R_EX_arg__L_e\n", + "\t- R_EX_ascb__L_e\n", + "\t- R_EX_asn__L_e\n", + "\t- R_EX_frulys_e\n", + "\t- R_EX_23ccmp_e\n", + "\t- R_EX_fruur_e\n", + "\t- R_EX_fuc__L_e\n", + "\t- R_EX_fum_e\n", + "\t- R_EX_fusa_e\n", + "\t- R_EX_g1p_e\n", + "\t- R_EX_g3pc_e\n", + "\t- R_EX_aso3_e\n", + "\t- R_EX_asp__L_e\n", + "\t- R_EX_btn_e\n", + "\t- R_EX_but_e\n", + "\t- R_EX_butso3_e\n", + "\t- R_EX_ca2_e\n", + "\t- R_EX_cbi_e\n", + "\t- R_EX_g3pe_e\n", + "\t- R_EX_g3pg_e\n", + "\t- R_EX_g3pi_e\n", + "\t- R_EX_cbl1_e\n", + "\t- R_EX_23cgmp_e\n", + "\t- R_EX_cd2_e\n", + "\t- R_EX_cgly_e\n", + "\t- R_EX_chol_e\n", + "\t- R_EX_g3ps_e\n", + "\t- R_EX_g6p_e\n", + "\t- R_EX_gal_e\n", + "\t- R_EX_gal_bD_e\n", + "\t- R_EX_gal1p_e\n", + "\t- R_EX_chtbs_e\n", + "\t- R_EX_cit_e\n", + "\t- R_EX_cl_e\n", + "\t- R_EX_galct__D_e\n", + "\t- R_EX_23cump_e\n", + "\t- R_EX_galctn__D_e\n", + "\t- R_EX_galctn__L_e\n", + "\t- R_EX_cm_e\n", + "\t- R_EX_cmp_e\n", + "\t- R_EX_co2_e\n", + "\t- R_EX_23dappa_e\n", + "\t- R_EX_cobalt2_e\n", + "\t- R_EX_galt_e\n", + "\t- R_EX_galur_e\n", + "\t- R_EX_colipa_e\n", + "\t- R_EX_colipap_e\n", + "\t- R_EX_cpgn_e\n", + "\t- R_EX_gam_e\n", + "\t- R_EX_gam6p_e\n", + "\t- R_EX_gbbtn_e\n", + "\t- R_EX_gdp_e\n", + "\t- R_EX_cpgn_un_e\n", + "\t- R_EX_glc__D_e\n", + "\t- R_EX_26dap__M_e\n", + "\t- R_EX_glcn_e\n", + "\t- R_EX_crn_e\n", + "\t- R_EX_crn__D_e\n", + "\t- R_EX_csn_e\n", + "\t- R_EX_glcr_e\n", + "\t- R_EX_2ddglcn_e\n", + "\t- R_EX_glcur_e\n", + "\t- R_EX_glcur1p_e\n", + "\t- R_EX_cu_e\n", + "\t- R_EX_cu2_e\n", + "\t- R_EX_cyan_e\n", + "\t- R_EX_gln__L_e\n", + "\t- R_EX_34dhpac_e\n", + "\t- R_EX_cynt_e\n", + "\t- R_EX_cys__D_e\n", + "\t- R_EX_cys__L_e\n", + "\t- R_EX_glu__L_e\n", + "\t- R_EX_gly_e\n", + "\t- R_EX_cytd_e\n", + "\t- R_EX_glyald_e\n", + "\t- R_EX_3amp_e\n", + "\t- R_EX_glyb_e\n", + "\t- R_EX_glyc_e\n", + "\t- R_EX_dad_2_e\n", + "\t- R_EX_damp_e\n", + "\t- R_EX_glyc__R_e\n", + "\t- R_EX_3cmp_e\n", + "\t- R_EX_glyc2p_e\n", + "\t- R_EX_dca_e\n", + "\t- R_EX_dcmp_e\n", + "\t- R_EX_dcyt_e\n", + "\t- R_EX_ddca_e\n", + "\t- R_EX_dgmp_e\n", + "\t- R_EX_glyc3p_e\n", + "\t- R_EX_glyclt_e\n", + "\t- R_EX_gmp_e\n", + "\t- R_EX_dgsn_e\n", + "\t- R_EX_gsn_e\n", + "\t- R_EX_3gmp_e\n", + "\t- R_EX_gthox_e\n", + "\t- R_EX_gthrd_e\n", + "\t- R_EX_dha_e\n", + "\t- R_EX_dimp_e\n", + "\t- R_EX_din_e\n", + "\t- R_EX_gtp_e\n", + "\t- R_EX_gua_e\n", + "\t- R_EX_dms_e\n", + "\t- R_EX_3hcinnm_e\n", + "\t- R_EX_dmso_e\n", + "\t- R_EX_dopa_e\n", + "\t- R_EX_h_e\n", + "\t- R_EX_h2_e\n", + "\t- R_EX_3hpp_e\n", + "\t- R_EX_h2o_e\n", + "\t- R_EX_h2o2_e\n", + "\t- R_EX_doxrbcn_e\n", + "\t- R_EX_dtmp_e\n", + "\t- R_EX_dump_e\n", + "\t- R_EX_3hpppn_e\n", + "\t- R_EX_duri_e\n", + "\t- R_EX_h2s_e\n", + "\t- R_EX_hacolipa_e\n", + "\t- R_EX_eca4colipa_e\n", + "\t- R_EX_3ump_e\n", + "\t- R_EX_halipa_e\n", + "\t- R_EX_hdca_e\n", + "\t- R_EX_hdcea_e\n", + "\t- R_EX_hg2_e\n", + "\t- R_EX_his__L_e\n", + "\t- R_EX_4abut_e\n", + "\t- R_EX_hom__L_e\n", + "\t- R_EX_hxa_e\n", + "\t- R_EX_hxan_e\n", + "\t- R_EX_4hoxpacd_e\n", + "\t- R_EX_imp_e\n", + "\t- R_EX_5dglcn_e\n", + "\t- R_EX_indole_e\n", + "\t- R_EX_idon__L_e\n", + "\t- R_EX_ile__L_e\n", + "\t- R_EX_inost_e\n", + "\t- R_EX_5mtr_e\n", + "\t- R_EX_ins_e\n", + "\t- R_EX_isetac_e\n", + "\t- R_EX_LalaDglu_e\n", + "\t- R_EX_k_e\n", + "\t- R_EX_kdo2lipid4_e\n", + "\t- R_EX_rmn_e\n", + "\t- R_EX_LalaDgluMdap_e\n", + "\t- R_EX_sbt__D_e\n", + "\t- R_EX_lac__D_e\n", + "\t- R_EX_sel_e\n", + "\t- R_EX_LalaDgluMdapDala_e\n", + "\t- R_EX_ser__D_e\n", + "\t- R_EX_lac__L_e\n", + "\t- R_EX_lcts_e\n", + "\t- R_EX_ser__L_e\n", + "\t- R_EX_skm_e\n", + "\t- R_EX_leu__L_e\n", + "\t- R_EX_LalaLglu_e\n", + "\t- R_EX_lipa_e\n", + "\t- R_EX_lipa_cold_e\n", + "\t- R_EX_slnt_e\n", + "\t- R_EX_so2_e\n", + "\t- R_EX_so3_e\n", + "\t- R_EX_so4_e\n", + "\t- R_EX_spmd_e\n", + "\t- R_EX_lipoate_e\n", + "\t- R_EX_lys__L_e\n", + "\t- R_EX_lyx__L_e\n", + "\t- R_EX_mal__D_e\n", + "\t- R_EX_mal__L_e\n", + "\t- R_EX_succ_e\n", + "\t- R_EX_sucr_e\n", + "\t- R_EX_malt_e\n", + "\t- R_EX_sulfac_e\n", + "\t- R_EX_tartr__D_e\n", + "\t- R_EX_tartr__L_e\n", + "\t- R_EX_taur_e\n", + "\t- R_EX_tcynt_e\n", + "\t- R_EX_thm_e\n", + "\t- R_EX_thr__L_e\n", + "\t- R_EX_thrp_e\n", + "\t- R_EX_malthx_e\n", + "\t- R_EX_maltpt_e\n", + "\t- R_EX_malttr_e\n", + "\t- R_EX_maltttr_e\n", + "\t- R_EX_man_e\n", + "\t- R_EX_man6p_e\n", + "\t- R_EX_manglyc_e\n", + "\t- R_EX_melib_e\n", + "\t- R_EX_meoh_e\n", + "\t- R_EX_thym_e\n", + "\t- R_EX_thymd_e\n", + "\t- R_EX_tma_e\n", + "\t- R_EX_tmao_e\n", + "\t- R_EX_tre_e\n", + "\t- R_EX_met__D_e\n", + "\t- R_EX_met__L_e\n", + "\t- R_EX_metsox_R__L_e\n", + "\t- R_EX_trp__L_e\n", + "\t- R_EX_tsul_e\n", + "\t- R_EX_ttdca_e\n", + "\t- R_EX_ttdcea_e\n", + "\t- R_EX_metsox_S__L_e\n", + "\t- R_EX_mg2_e\n", + "\t- R_EX_mincyc_e\n", + "\t- R_EX_minohp_e\n", + "\t- R_EX_mmet_e\n", + "\t- R_EX_ttrcyc_e\n", + "\t- R_EX_tungs_e\n", + "\t- R_EX_mn2_e\n", + "\t- R_EX_tym_e\n", + "\t- R_EX_tyr__L_e\n", + "\t- R_EX_tyrp_e\n", + "\t- R_EX_mnl_e\n", + "\t- R_EX_mobd_e\n", + "\t- R_EX_mso3_e\n", + "\t- R_EX_n2o_e\n", + "\t- R_EX_uacgam_e\n", + "\t- R_EX_udpacgal_e\n", + "\t- R_EX_na1_e\n", + "\t- R_EX_nac_e\n", + "\t- R_EX_udpg_e\n", + "\t- R_EX_udpgal_e\n", + "\t- R_EX_nh4_e\n", + "\t- R_EX_udpglcur_e\n", + "\t- R_EX_ni2_e\n", + "\t- R_EX_ump_e\n", + "\t- R_EX_nmn_e\n", + "\t- R_EX_no_e\n", + "\t- R_EX_ura_e\n", + "\t- R_EX_urea_e\n", + "\t- R_EX_uri_e\n", + "\t- R_EX_val__L_e\n", + "\t- R_EX_xan_e\n", + "\t- R_EX_no2_e\n", + "\t- R_EX_no3_e\n", + "\t- R_EX_novbcn_e\n", + "\t- R_EX_o16a4colipa_e\n", + "\t- R_EX_o2_e\n", + "\t- R_EX_o2s_e\n", + "\t- R_EX_ocdca_e\n", + "\t- R_EX_xmp_e\n", + "\t- R_EX_xtsn_e\n", + "\t- R_EX_xyl__D_e\n", + "\t- R_EX_ocdcea_e\n", + "\t- R_EX_xylu__L_e\n", + "\t- R_EX_octa_e\n", + "\t- R_EX_orn_e\n", + "\t- R_EX_orot_e\n", + "\t- R_EX_zn2_e\n", + "\t- R_EX_pacald_e\n", + "\t- R_EX_peamn_e\n", + "\t- R_EX_phe__L_e\n", + "\t- R_EX_pheme_e\n", + "\t- R_EX_pi_e\n", + "\t- R_EX_pnto__R_e\n", + "\t- R_EX_ppa_e\n", + "\t- R_EX_ppal_e\n", + "\t- R_EX_pppn_e\n", + "\t- R_EX_ppt_e\n", + "\t- R_EX_pro__L_e\n", + "\t- R_EX_progly_e\n", + "\t- R_EX_psclys_e\n", + "\t- R_EX_pser__L_e\n", + "\t- R_EX_ptrc_e\n", + "\t- R_EX_pydam_e\n", + "\t- R_EX_pydx_e\n", + "\t- R_EX_pydxn_e\n", + "\t- R_EX_pyr_e\n", + "\t- R_EX_quin_e\n", + "\t- R_EX_r5p_e\n", + "\t- R_EX_rfamp_e\n", + "\t- R_EX_rib__D_e\n", + "\t- R_EX_guln__L_e\n", + "\t- R_DM_lipidA_core_e_p\n", + "\t- R_EX_4hphac_e\n", + "\t- R_EX_4abz_e\n", + "\t- R_EX_drib_e\n", + "\t- R_EX_acon_C_e\n", + "\t- R_EX_feroxBfe_e\n", + "\t- R_EX_salchs4fe_e\n", + "\t- R_EX_feroxB_e\n", + "\t- R_EX_2pg_e\n", + "\t- R_EX_tcb_e\n", + "\t- R_EX_tton_e\n", + "\t- R_EX_4hthr_e\n", + "\t- R_EX_feroxEfe_e\n", + "\t- R_EX_feroxE_e\n", + "\t- R_EX_feroxG_e\n", + "\t- R_EX_pep_e\n", + "\t- R_EX_23dhbzs3_e\n", + "\t- R_EX_tet_e\n", + "\t- R_EX_salchs4_e\n", + "\t- R_EX_tag__D_e\n", + "\t- R_EX_diact_e\n", + "\t- R_EX_frmd_e\n", + "\t- R_EX_oaa_e\n", + "\t- R_EX_salchs2fe_e\n", + "\t- R_EX_colipaOA_e\n", + "\t- R_EX_cysi__L_e\n", + "\t- R_EX_cellb_e\n", + "\t- R_EX_salchsx_e\n", + "\t- R_EX_fe3dhbzs3_e\n", + "\t- R_EX_AEP_e\n", + "\t- R_EX_btd_RR_e\n", + "\t- R_EX_13ppd_e\n", + "\t- R_EX_rbt_e\n", + "\t- R_EX_raffin_e\n", + "\t- R_EX_icit_e\n", + "\t- R_EX_o6a4colipa_e\n", + "\t- R_EX_bz_e\n", + "\t- R_EX_arab__D_e\n", + "\t- R_EX_abt__D_e\n", + "\t- R_EX_fe_e\n", + "\t- R_EX_kokdolipidA_e\n", + "\t- R_EX_colipa20Oag_e\n", + "\t- R_EX_3hoxpac_e\n", + "\t- R_EX_4hoxpac_e\n", + "\t- R_EX_remnant1_e\n", + "\t- R_EX_3ntym_e\n", + "\t- R_EX_6apa_e\n", + "\t- R_EX_peng_e\n", + "\t- R_EX_pac_e\n", + "\t- R_EX_salcn_e\n", + "\t- R_EX_galam_e\n", + "\t- R_EX_salchs2_e\n", + "\t- R_EX_rnam_e\n", + "\t- R_EX_airs_e\n", + "\t- R_EX_dxyl_e\n", + "\t- R_EX_3pg_e\n", + "\t- R_EX_udcpo4_e\n", + "\t- R_EX_feroxGfe_e\n", + "\t- R_EX_udcpo5_e\u001b[0m\n", + "File saved at: ../../data/sbml_corrected/iECNA114_1301.xml\n" + ] + } + ], + "source": [ + "run_normalisation(sbml_dir, norm_sbml_dir)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### **List of output files**\n", + "\n", + "The sbml_corrected directory (initially \"../../data/sbml_corrected\") contains:\n", + "- Directory logs: one log file for each sbml file. It shows what modifications were performed\n", + "- List of sbml file taking into these corrections (deletion or reaction, switching reactant and products for reaction with boundaries negative or null, import reactions when launched by default)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "test", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.14" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/notebook/run/05_run_netseed.ipynb b/notebook/run/05_run_netseed.ipynb new file mode 100644 index 0000000..22d0708 --- /dev/null +++ b/notebook/run/05_run_netseed.ipynb @@ -0,0 +1,369 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# NetSeed: Get tool, run tool, get data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "NetSeed [Carr and Borenstein, 2012] is a tool from [Borenstein Lab]https://borensteinlab.sites.tau.ac.il/, usually available [here](https://borensteinlab.sites.tau.ac.il/items-1/netseed). The Perl version download link is not available anymore. This is a seed searching tool based on graph analyses.\n", + "\n", + "We downloaded the tool on 2022-01-19 and the tool is avaiblable in N2PComp archive: N2Pcomp/NetSeedPerl" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## BEFORE STARTING\n", + "\n", + "For the paper, NetSeed had been downloaded and integrated to a third part tool, called N2PComp, to make it easier to benchmark. N2PComp is used for launching NetSeed (and precursor) then generate output results file. \n", + "\n", + "N2Pcomp is available: [https://doi.org/10.57745/OS1JND](https://doi.org/10.57745/OS1JND)\n", + "\n", + "After downloading and unzipping the package, go to analyses/tools/N2PComp\n", + "\n", + "When N2PComp is downloaded, install it on a conda environment (advice for cluster scripts: call it s2lp)\n", + "\n", + "**STEPS**\n", + "- On the console, go on the root of N2PComp directory: `cd [downloaded N2PComp direcotry]`\n", + "- Install N2PComp using `pip install .`\n", + "\n", + "\n", + "\n", + "For the N2PComp to work, it is necessary to install precursor additionally.\n", + "\n", + "Go to analyses/tools/precursor/\n", + "\n", + "When precursor is downloaded, install it on the same conda environment \n", + "\n", + "**STEPS**\n", + "- On the console, go on the root of precursor directory: `cd [downloaded precursor directory]`\n", + "- Install precursor using `pip install .`\n", + "\n", + "\n", + "\n", + "N2PComp will execute NetSeed, and from NetSeed results it will produce result files partly compatible with Seed2LP, by performing a scalar product of sets composing sets of seed results." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## **WARNING**\n", + "This notebook will run NetSeed using N2PComp with default parameters, limiting to 10 min or 30 solutions obtained.\n", + "\n", + "In the paper, the time limit is set to 45 min and number of solutions limit to 1000.\n", + "\n", + "To avoid a long running time in the notebook execution, the notebook will copy the normalised e_coli_core in a sbml directory on a path that you can change.\n", + "\n", + "> Note:\n", + ">\n", + "> - All result files from NetSeed search are available: [https://doi.org/10.57745/OS1JND](https://doi.org/10.57745/OS1JND)\n", + "> - go to analyses/results/netseed\n", + "> - All formated result files from NetSeed search are availabl: [https://doi.org/10.57745/OS1JND](https://doi.org/10.57745/OS1JND)\n", + "> - go to analyses/results/netseed_formated_results\n", + "\n", + "Because of temporary file management reasons, it is best **not** to parallelise the NetSeed search through N2PComp." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Requirements\n", + "The Network must have been normalised before starting. See [04_normalise_network.ipynb](04_normalise_network.ipynb)\n", + "\n", + "N2PComp and precursor have to be installed on env conv: \n", + "\n", + "- Copy the downloaded N2PComp (which include NetSeedPerl package on the root of the direcotry, \"NetSeedPerl\" directory must be present)\n", + "- Go on root of directory and install N2PComp using `pip install .`\n", + "- Copy the downloaded precursor\n", + "- Go on root of directory and install precursor using `pip install .`\n", + "\n", + "Seed2LP has to be installed.\n", + "\n", + "> Advice:\n", + "> \n", + "> Use a conda env called s2lp" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## **Slurm-based cluster**: Reproducing paper data\n", + "Slurm-based scripts for cluster are available:\n", + "- Launch if needed \n", + " - [04_job_run_sbml_normalisation.sh](../../scripts/plafrim_cluster/04_job_run_sbml_normalisation.sh): `sbatch 04_job_run_sbml_normalisation.sh`\n", + " - or copy your local files into you cluster\n", + "- Change **_source_** variable by the path of your conda environment with tools-comparison installed in files: [05_1_job_run_n2pcomp_netseed.sh](../../scripts/plafrim_cluster/05_1_job_run_n2pcomp_netseed.sh) and on [05_2_job_format_netseed_results.sh](../../scripts/plafrim_cluster/05_2_job_format_netseed_results.sh)\n", + "- launch [05_1_job_run_n2pcomp_netseed.sh](../../scripts/plafrim_cluster/05_1_job_run_n2pcomp_netseed.sh): `sbatch 05_1_job_run_n2pcomp_netseed.sh`\n", + "- When the job is done, you can launch [05_2_job_format_netseed_results.sh](../../scripts/plafrim_cluster/05_2_job_format_netseed_results.sh): `sbatch 05_2_job_format_netseed_results.sh`" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## **LAUNCH**" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Variable to change (if wanted)" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "analyse_dir = \"../../analyses\"\n", + "data_dir = f\"{analyse_dir}/data/\"\n", + "result_dir=f\"{analyse_dir}/results\"\n", + "result_dir=f\"{analyse_dir}/tools/N2PComp\"\n", + "#n2pcomp_dir=\"../../../N2PComp\"\n", + "\n", + "time_limit = 10 # time limit\n", + "number_solution = 30 # number solutions" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Execute" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "from os import path, makedirs, listdir\n", + "from shutil import copyfile" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "sbml_dir = f\"{data_dir}/bigg/sbml\"\n", + "norm_sbml_dir=f\"{data_dir}/sbml_corrected\"\n", + "netseed_result_dir=f\"{result_dir}/netseed\"\n", + "netseed_form_result_dir=f\"{result_dir}/netseed_formated_results\"\n", + "objecive_dir = f\"{data_dir}/objective\"\n", + "\n", + "e_coli_norm_dir =f\"{data_dir}/sbml_corrected_e_coli_core\"\n", + "e_coli_dir =f\"{data_dir}/bigg/sbml_e_coli_core\"" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "if not path.isdir(e_coli_norm_dir):\n", + " makedirs(e_coli_norm_dir)\n", + "\n", + "copyfile(path.join(norm_sbml_dir, \"e_coli_core.xml\"), path.join(e_coli_norm_dir, \"e_coli_core.xml\"))\n", + "\n", + "if not path.isdir(netseed_result_dir):\n", + " makedirs(netseed_result_dir)\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "def run_netseed(normalised_sbml_dir, source_sbml_dir:str, out_dir:str):\n", + " conf_file=path.join(n2pcomp_dir,\"config_netseed.yaml\")\n", + " \n", + " for filename in listdir(normalised_sbml_dir):\n", + " species = f'{path.splitext(path.basename(filename))[0]}'\n", + " sbml_normalised_path = path.join(normalised_sbml_dir,filename)\n", + " sbml_path = path.join(source_sbml_dir,filename)\n", + " species_result_dir=path.join(out_dir,species)\n", + " if not path.isdir(species_result_dir):\n", + " makedirs(species_result_dir)\n", + "\n", + " # Netseed has to be run with the normalise sbml\n", + " netseed_command=f\"-m n2pcomp run {sbml_normalised_path} --output {species_result_dir} -c {conf_file} -nbs {number_solution} -tl {time_limit}\"\n", + "\n", + " !python {netseed_command}\n", + "\n", + " # Finish formating results\n", + " file = \"../../scripts/05_format_results.py\"\n", + " objective_path = path.join(objecive_dir,f\"{species}_target.txt\")\n", + " netseed_result_file=path.join(species_result_dir,\"results.json\")\n", + " form_result_path=path.join(netseed_form_result_dir,species)\n", + " if not path.isdir(form_result_path):\n", + " makedirs(form_result_path)\n", + " netseed_form_result_file=path.join(form_result_path,f\"{species}_netseed_results.json\")\n", + " with open(objective_path) as f:\n", + " objective = f.readline()\n", + " \n", + " format_command=f\"{file} {netseed_result_file} {species} {objective} {netseed_form_result_file} NETSEED\"\n", + " !python {format_command}\n", + "\n", + " # Execute check_flux with Seed2LP with the orginal path because seed2lp needs the complete\n", + " # network to fin the import reaction that have been shut down. Seed2lp will always normalise\n", + " # the network befor calculating flux.\n", + " flux_command=f\"flux {sbml_path} {netseed_form_result_file} {form_result_path}\"\n", + " !seed2lp {flux_command}" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Directory ../../results/netseed/e_coli_core/ already exists. Overwritting directory...\n", + " Netseed Directory given : /home/cghassem/miniconda3/envs/test/lib/python3.10/site-packages/n2pcomp/NetSeedPerl\n", + "/home/cghassem/miniconda3/envs/test/lib/python3.10/site-packages/n2pcomp/netseed.py\n", + "NETSEED OUTPUT: M_fum_e\n", + "M_fru_e\n", + "M_gln__L_e\n", + "M_mal__L_e\n", + "M_o2_e,M_o2_c\n", + "M_glc__D_e\n", + "\n", + "NETSEED ERR: NO ERROR\n", + "NETSEED ENUMERATION \n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: e_coli_core\n", + "____________________________________________\n", + "\n", + "\u001b[0;96m\n", + "############################################\n", + "############################################\n", + " \u001b[1mCHECK FLUX\u001b[0;96m\n", + "############################################\n", + "############################################\n", + "\u001b[0m\n", + "---------------- FLUX INIT -----------------\n", + "\n", + "{'BIOMASS_Ecoli_core_w_GAM': np.float64(0.8739215069684295)}\n", + "\n", + "\n", + "--------------- MEDIUM INIT ----------------\n", + "\n", + "EX_co2_e -1000.0 1000.0\n", + "EX_glc__D_e -10.0 1000.0\n", + "EX_h_e -1000.0 1000.0\n", + "EX_h2o_e -1000.0 1000.0\n", + "EX_nh4_e -1000.0 1000.0\n", + "EX_o2_e -1000.0 1000.0\n", + "EX_pi_e -1000.0 1000.0\n", + "\n", + "\n", + "---------- STOP IMPORT FLUX -------------\n", + "\n", + "{'BIOMASS_Ecoli_core_w_GAM': 0.0}\n", + "\n", + "\n", + "\u001b[0;94m\n", + "____________________________________________\n", + "____________________________________________\n", + "\u001b[0m\n", + " RESULTS \n", + "\u001b[0;94m____________________________________________\n", + "____________________________________________\n", + "\u001b[0m\n", + " \u001b[1m \"Cobra (seeds)\" \u001b[0m indicates the maximum flux \n", + "obtained in FBA from the seeds after shutting \n", + "off all other exchange reactions. If the maximum \n", + "flux is null, a test is performed opening demand \n", + "reactions for the objective reaction's products, \n", + "in order to test the effect of their accumulation \n", + "(\u001b[1m\"cobra (demands)\"\u001b[0m ). If this test is not performed, \n", + "\"NA\" value is indicated.\n", + "\u001b[0;93m\n", + "____________________________________________\n", + "____________________________________________\n", + "\u001b[0m\n", + " Other \n", + "\u001b[0;93m--------------------------------------------\u001b[0m\n", + " NETSEED \n", + "\u001b[0;93m . . . . . . . . . . \u001b[0m\n", + "name | cobra (seeds) | cobra (demands)\n", + "-----|---------------|-----------------\n", + "model_1 | \u001b[0;91m0.0\u001b[0m | \u001b[0;91m-8.568626548908283e-17\u001b[0m\n", + "model_2 | \u001b[0;91m2.35850022798956e-16\u001b[0m | \u001b[0;91m6.401841185396951e-16\u001b[0m\n", + "\u001b[0;93m\n", + "____________________________________________\n", + "\u001b[0m\n" + ] + } + ], + "source": [ + "run_netseed(e_coli_norm_dir, e_coli_dir, netseed_result_dir)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### **List of output files**\n", + "\n", + "The netseed_formated_result directory contains:\n", + "- Directory logs: one log by sbml file. It shows modification performed on the original metabolic network.\n", + "- a file *_results.json containing the formatted results compatible with Seed2LP. This file is used to launch the check-flux mode of Seed2LP.\n", + "- a file *_fluxes_from_result.tsv: This is the output of check-flux while using the check flux mode from a s2lp file or result file formatted to be compatible with Seed2LP results files." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "test", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.14" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/notebook/run/06_run_precursor.ipynb b/notebook/run/06_run_precursor.ipynb new file mode 100644 index 0000000..ea93383 --- /dev/null +++ b/notebook/run/06_run_precursor.ipynb @@ -0,0 +1,610 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Precursor: Get tool, run tool, get data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Precursor is a seed searching tool using a different network expansion definition than seed2lp. This is not a released tool but its code is freely available on [Github](https://github.com/bioasp/precursor). We made some changes to make the tool compatible with our benchmark: the code is available in the archive associated to the paper. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## BEFORE STARTING\n", + "\n", + "Such as [Netseed notebook](05_run_netseed.ipynb), N2PComp is used for launching precursor then generate output results file. \n", + "\n", + "N2Pcomp is available: [https://doi.org/10.57745/OS1JND](https://doi.org/10.57745/OS1JND)\n", + "\n", + "After downloadind and unzipping the package, go to analyses/tools/N2PComp\n", + "\n", + "\n", + "### IF N2PComp not already install, follow these steps\n", + "When N2PComp is downloaded, install it on a conda environment (advice for cluster scripts: called s2lp)\n", + "\n", + "**STEPS**\n", + "- On the console, go on the root of N2PComp directory: `cd [downloaded N2PComp direcotry]`\n", + "- Install N2PComp using `pip install .`\n", + "\n", + "\n", + "\n", + "For the N2PComp to work, it is necessary to install precursor also.\n", + "\n", + "Go to analyses/tools/precursor/\n", + "\n", + "When precursor is downloaded, install it on the same conda environment \n", + "\n", + "**STEPS**\n", + "- On the console, go on the root of precursor directory: `cd [downloaded precursor direcotry]`\n", + "- Install precursor using `pip install .`\n", + "\n", + "\n", + "\n", + "N2PComp will perform netseed, and from netseed results it will produce result files partly compatible with Seed2LP, by performing a scalar product of sets composing sets of seed results." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## **WARNING**\n", + "This notebook will run precursor using N2PComp for Full Network and Target mode, limiting to 10 min/30 solutions.\n", + "\n", + "In the paper, the time limit is set to 45 min and number of solutions limit to 1000.\n", + "\n", + "To avoid a long running time running in the notebook, the notebook will copy the normalised e_coli_core in an sbml directory on a path that you can change.\n", + "\n", + "> Note:\n", + ">\n", + "> - All result files from precursor search are available: [https://doi.org/10.57745/OS1JND](https://doi.org/10.57745/OS1JND)\n", + "> - go to analyses/results/precursor\n", + "> - All formated results file from precursor search are available: [https://doi.org/10.57745/OS1JND](https://doi.org/10.57745/OS1JND)\n", + "> - go to analyses/results/precursor_formated_results\n", + "\n", + "Because of temporary file management reasons, it is best **not** to parallelise the precursor search through N2PComp." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Requirements\n", + "The Network must have been normalised before starting. See [04_normalise_network.ipynb](04_normalise_network.ipynb)\n", + "\n", + "N2PComp and precursor have to be installed on env conv: \n", + "\n", + "- Copy the downloaded N2PComp (which include NetSeedPerl package on the root of the direcotry, \"NetSeedPerl\" directory must be present)\n", + "- Go on root of directory and install N2PComp using `pip install .`\n", + "- Copy the downloaded precursor\n", + "- Go on root of directory and install precursor using `pip install .`\n", + "\n", + "Seed2LP has to be installed.\n", + "\n", + "> Advice:\n", + "> \n", + "> Use a conda env called s2lp" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## **Slurm-based cluster**: Reproducing paper data\n", + "Slurm-based scripts for cluster are available:\n", + "- Launch if needed \n", + " - [04_job_run_sbml_normalisation.sh](../../scripts/plafrim_cluster/04_job_run_sbml_normalisation.sh): `sbatch 04_job_run_sbml_normalisation.sh`\n", + " - or copy your local files into you cluster\n", + "- Change **_source_** variable by the path of your conda environement with tools-comparison installed in files: [06_1_job_run_n2pcomp_precursor.sh](../../scripts/plafrim_cluster/06_1_job_run_n2pcomp_precursor.sh) and on [06_2_job_format_precursor_results.sh](../../scripts/plafrim_cluster/06_2_job_format_precursor_results.sh)\n", + "- launch [06_1_job_run_n2pcomp_precursor.sh](../../scripts/plafrim_cluster/06_1_job_run_n2pcomp_precursor.sh): `sbatch 06_1_job_run_n2pcomp_precursor.sh`\n", + "- When the job is done, you can launch [06_2_job_format_precursor_results.sh](../../scripts/plafrim_cluster/06_2_job_format_precursor_results.sh): `sbatch 06_2_job_format_precursor_results.sh`" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## **LAUNCH**" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Variable to change (if wanted)" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "analyse_dir = \"../../analyses\"\n", + "data_dir = f\"{analyse_dir}/data/\"\n", + "result_dir=f\"{analyse_dir}/results\"\n", + "result_dir=f\"{analyse_dir}/tools/N2PComp\"\n", + "\n", + "time_limit = 10 # time limit\n", + "number_solution = 30 # number solutions" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Execute" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "from os import path, makedirs, listdir\n", + "from shutil import copyfile" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "sbml_dir = f\"{data_dir}/bigg/sbml\"\n", + "norm_sbml_dir=f\"{data_dir}/sbml_corrected\"\n", + "precursor_result_dir=f\"{result_dir}/precursor\"\n", + "precursor_form_result_dir=f\"{result_dir}/precursor_formated_results\"\n", + "objective_dir = f\"{data_dir}/objective\"\n", + "target_dir=f\"{data_dir}/target\"\n", + "\n", + "e_coli_norm_dir =f\"{data_dir}/sbml_corrected_e_coli_core\"\n", + "e_coli_dir =f\"{data_dir}/bigg/sbml_e_coli_core\"" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "if not path.isdir(e_coli_norm_dir):\n", + " makedirs(e_coli_norm_dir)\n", + "\n", + "copyfile(path.join(norm_sbml_dir, \"e_coli_core.xml\"), path.join(e_coli_norm_dir, \"e_coli_core.xml\"))\n", + "\n", + "if not path.isdir(precursor_result_dir):\n", + " makedirs(precursor_result_dir)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "def run_precursor(normalised_sbml_dir, source_sbml_dir:str, out_dir:str):\n", + " conf_file=path.join(n2pcomp_dir,\"config_precursor.yaml\")\n", + "\n", + " \n", + " for filename in listdir(normalised_sbml_dir):\n", + " species = f'{path.splitext(path.basename(filename))[0]}'\n", + " sbml_normalised_path = path.join(normalised_sbml_dir,filename)\n", + " sbml_path = path.join(source_sbml_dir,filename)\n", + "\n", + " species_result_dir=path.join(out_dir,species)\n", + " if not path.isdir(species_result_dir):\n", + " makedirs(species_result_dir)\n", + " result_fn_dir=path.join(species_result_dir,\"full_network\")\n", + " if not path.isdir(result_fn_dir):\n", + " makedirs(result_fn_dir)\n", + " result_tgt_dir=path.join(species_result_dir,\"target\")\n", + " if not path.isdir(result_tgt_dir):\n", + " makedirs(result_tgt_dir)\n", + "\n", + " objective_path = path.join(objective_dir,f\"{species}_target.txt\")\n", + " with open(objective_path) as f:\n", + " objective = f.readline()\n", + "\n", + " # Get targetted metabolites\n", + " # Precursor has a target mode and needs a target fil compose of targetted metabolite \n", + " target_dir=path.join(data_dir,\"target\")\n", + " if not path.isdir(target_dir):\n", + " makedirs(target_dir)\n", + " target_command=f\"objective_targets {sbml_normalised_path} {target_dir} -o {objective}\"\n", + " !seed2lp {target_command}\n", + " target_file=path.join(target_dir,f\"{species}_targets.txt\")\n", + "\n", + " # Precursor has to be run with the normalise sbml\n", + " precursor_command_tgt=f\"-m n2pcomp run {sbml_normalised_path} --output {result_tgt_dir} -c {conf_file} -nbs {number_solution} -tl {time_limit} -t {target_file}\"\n", + " !python {precursor_command_tgt}\n", + "\n", + " precursor_command_fn=f\"-m n2pcomp run {sbml_normalised_path} --output {result_fn_dir} -c {conf_file} -nbs {number_solution} -tl {time_limit}\"\n", + " !python {precursor_command_fn}\n", + "\n", + " # Finish formating results\n", + " file = \"../../scripts/05_format_results.py\"\n", + "\n", + " precursor_tgt_result_file=path.join(result_tgt_dir,\"results.json\")\n", + " precursor_fn_result_file=path.join(result_fn_dir,\"results.json\")\n", + "\n", + " form_result_path=path.join(precursor_form_result_dir,species)\n", + " if not path.isdir(form_result_path):\n", + " makedirs(form_result_path)\n", + "\n", + " precursor_form_tgt_result_file=path.join(form_result_path,f\"{species}_precursor_tgt_results.json\")\n", + " precursor_form_fn_result_file=path.join(form_result_path,f\"{species}_precursor_fn_results.json\")\n", + " \n", + "\n", + " format_tgt_command=f\"{file} {precursor_tgt_result_file} {species} {objective} {precursor_form_tgt_result_file} PRECURSOR\"\n", + " !python {format_tgt_command}\n", + "\n", + " format_fn_command=f\"{file} {precursor_fn_result_file} {species} {objective} {precursor_form_fn_result_file} PRECURSOR\"\n", + " !python {format_fn_command}\n", + "\n", + " # Execute check_flux with Seed2LP with the orginal path because seed2lp needs the complete\n", + " # network to fin the import reaction that have been shut down. Seed2lp will always normalise\n", + " # the network befor calculating flux.\n", + " flux_tgt_command=f\"flux {sbml_path} {precursor_form_tgt_result_file} {form_result_path}\"\n", + " !seed2lp {flux_tgt_command}\n", + "\n", + " flux_fn_command=f\"flux {sbml_path} {precursor_form_fn_result_file} {form_result_path}\"\n", + " !seed2lp {flux_fn_command}" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Directory ../../results/precursor/e_coli_core/target/ already exists. Overwritting directory...\n", + " Netseed Directory given : /home/cghassem/miniconda3/envs/test/lib/python3.10/site-packages/n2pcomp/NetSeedPerl\n", + "Reading network from ../../data/sbml_corrected_e_coli_core/e_coli_core.xml ...done.\n", + "Reading inputs from ../../results/precursor/e_coli_core/target/inputs/precursor_input.xml ...\n", + "done.\n", + " 16 target metabolites.\n", + " 0 possible seed metabolites.\n", + "\n", + "Autocompute possible seeds ...done.\n", + " 17 possible seeds found.\n", + "\n", + "Testing targets for producibility ...done.\n", + " 16 targets can be produced:\n", + " \"M_gln__L_c\"\n", + " \"M_h2o_c\"\n", + " \"M_r5p_c\"\n", + " \"M_atp_c\"\n", + " \"M_e4p_c\"\n", + " \"M_nadph_c\"\n", + " \"M_3pg_c\"\n", + " \"M_oaa_c\"\n", + " \"M_g6p_c\"\n", + " \"M_pep_c\"\n", + " \"M_glu__L_c\"\n", + " \"M_g3p_c\"\n", + " \"M_nad_c\"\n", + " \"M_f6p_c\"\n", + " \"M_accoa_c\"\n", + " \"M_pyr_c\"\n", + "\n", + "Compute subset minimal precursor sets ...done.\n", + "17 subset minimal precursor sets found.\n", + "Solution 1: \"M_h2o_e\" \n", + "Solution 2: \"M_nh4_e\" \n", + "Solution 3: \"M_ac_e\" \n", + "Solution 4: \"M_pi_e\" \n", + "Solution 5: \"M_lac__D_e\" \n", + "Solution 6: \"M_pyr_e\" \n", + "Solution 7: \"M_glu__L_e\" \n", + "Solution 8: \"M_acald_e\" \n", + "Solution 9: \"M_co2_e\" \n", + "Solution 10: \"M_etoh_e\" \n", + "Solution 11: \"M_akg_e\" \n", + "Solution 12: \"M_o2_e\" \n", + "Solution 13: \"M_fru_e\" \n", + "Solution 14: \"M_glc__D_e\" \n", + "Solution 15: \"M_fum_e\" \n", + "Solution 16: \"M_mal__L_e\" \n", + "Solution 17: \"M_gln__L_e\" \n", + "Directory ../../results/precursor/e_coli_core/full_network/ already exists. Overwritting directory...\n", + " Netseed Directory given : /home/cghassem/miniconda3/envs/test/lib/python3.10/site-packages/n2pcomp/NetSeedPerl\n", + "Reading network from ../../data/sbml_corrected_e_coli_core/e_coli_core.xml ...done.\n", + "Reading inputs from ../../results/precursor/e_coli_core/full_network/inputs/precursor_input.xml ...\n", + "done.\n", + " 72 target metabolites.\n", + " 0 possible seed metabolites.\n", + "\n", + "Autocompute possible seeds ...done.\n", + " 17 possible seeds found.\n", + "\n", + "Testing targets for producibility ...done.\n", + " 72 targets can be produced:\n", + " \"M_xu5p__D_c\"\n", + " \"M_dhap_c\"\n", + " \"M_acald_c\"\n", + " \"M_acald_e\"\n", + " \"M_pyr_c\"\n", + " \"M_6pgl_c\"\n", + " \"M_h2o_c\"\n", + " \"M_atp_c\"\n", + " \"M_akg_c\"\n", + " \"M_ac_e\"\n", + " \"M_6pgc_c\"\n", + " \"M_nadph_c\"\n", + " \"M_succ_e\"\n", + " \"M_glx_c\"\n", + " \"M_nadh_c\"\n", + " \"M_h_c\"\n", + " \"M_fru_e\"\n", + " \"M_o2_e\"\n", + " \"M_gln__L_c\"\n", + " \"M_amp_c\"\n", + " \"M_nh4_e\"\n", + " \"M_ac_c\"\n", + " \"M_mal__L_c\"\n", + " \"M_succoa_c\"\n", + " \"M_succ_c\"\n", + " \"M_gln__L_e\"\n", + " \"M_3pg_c\"\n", + " \"M_etoh_e\"\n", + " \"M_for_e\"\n", + " \"M_lac__D_e\"\n", + " \"M_fdp_c\"\n", + " \"M_coa_c\"\n", + " \"M_h2o_e\"\n", + " \"M_pi_e\"\n", + " \"M_mal__L_e\"\n", + " \"M_r5p_c\"\n", + " \"M_akg_e\"\n", + " \"M_pi_c\"\n", + " \"M_fum_c\"\n", + " \"M_q8h2_c\"\n", + " \"M_actp_c\"\n", + " \"M_glu__L_c\"\n", + " \"M_ru5p__D_c\"\n", + " \"M_g6p_c\"\n", + " \"M_pep_c\"\n", + " \"M_e4p_c\"\n", + " \"M_accoa_c\"\n", + " \"M_acon_C_c\"\n", + " \"M_oaa_c\"\n", + " \"M_2pg_c\"\n", + " \"M_13dpg_c\"\n", + " \"M_nh4_c\"\n", + " \"M_lac__D_c\"\n", + " \"M_cit_c\"\n", + " \"M_pyr_e\"\n", + " \"M_fum_e\"\n", + " \"M_glu__L_e\"\n", + " \"M_h_e\"\n", + " \"M_co2_e\"\n", + " \"M_icit_c\"\n", + " \"M_s7p_c\"\n", + " \"M_g3p_c\"\n", + " \"M_co2_c\"\n", + " \"M_glc__D_e\"\n", + " \"M_nad_c\"\n", + " \"M_nadp_c\"\n", + " \"M_o2_c\"\n", + " \"M_adp_c\"\n", + " \"M_q8_c\"\n", + " \"M_for_c\"\n", + " \"M_f6p_c\"\n", + " \"M_etoh_c\"\n", + "\n", + "Compute subset minimal precursor sets ...done.\n", + "1 subset minimal precursor sets found.\n", + "Solution 1: \"M_mal__L_e\" \"M_o2_e\" \"M_glc__D_e\" \"M_fru_e\" \"M_fum_e\" \"M_gln__L_e\" \n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: e_coli_core\n", + "____________________________________________\n", + "\n", + "\u001b[0;96m\n", + "############################################\n", + "############################################\n", + " \u001b[1mCHECK FLUX\u001b[0;96m\n", + "############################################\n", + "############################################\n", + "\u001b[0m\n", + "---------------- FLUX INIT -----------------\n", + "\n", + "{'BIOMASS_Ecoli_core_w_GAM': np.float64(0.8739215069684295)}\n", + "\n", + "\n", + "--------------- MEDIUM INIT ----------------\n", + "\n", + "EX_co2_e -1000.0 1000.0\n", + "EX_glc__D_e -10.0 1000.0\n", + "EX_h_e -1000.0 1000.0\n", + "EX_h2o_e -1000.0 1000.0\n", + "EX_nh4_e -1000.0 1000.0\n", + "EX_o2_e -1000.0 1000.0\n", + "EX_pi_e -1000.0 1000.0\n", + "\n", + "\n", + "---------- STOP IMPORT FLUX -------------\n", + "\n", + "{'BIOMASS_Ecoli_core_w_GAM': 0.0}\n", + "\n", + "\n", + "\u001b[0;94m\n", + "____________________________________________\n", + "____________________________________________\n", + "\u001b[0m\n", + " RESULTS \n", + "\u001b[0;94m____________________________________________\n", + "____________________________________________\n", + "\u001b[0m\n", + " \u001b[1m \"Cobra (seeds)\" \u001b[0m indicates the maximum flux \n", + "obtained in FBA from the seeds after shutting \n", + "off all other exchange reactions. If the maximum \n", + "flux is null, a test is performed opening demand \n", + "reactions for the objective reaction's products, \n", + "in order to test the effect of their accumulation \n", + "(\u001b[1m\"cobra (demands)\"\u001b[0m ). If this test is not performed, \n", + "\"NA\" value is indicated.\n", + "\u001b[0;93m\n", + "____________________________________________\n", + "____________________________________________\n", + "\u001b[0m\n", + " Other \n", + "\u001b[0;93m--------------------------------------------\u001b[0m\n", + " PRECURSOR \n", + "\u001b[0;93m . . . . . . . . . . \u001b[0m\n", + "name | cobra (seeds) | cobra (demands)\n", + "-----|---------------|-----------------\n", + "model_1 | Infeasible | Infeasible\n", + "model_2 | Infeasible | Infeasible\n", + "model_3 | Infeasible | Infeasible\n", + "model_4 | Infeasible | Infeasible\n", + "model_5 | Infeasible | Infeasible\n", + "model_6 | Infeasible | Infeasible\n", + "model_7 | Infeasible | Infeasible\n", + "model_8 | Infeasible | Infeasible\n", + "model_9 | Infeasible | Infeasible\n", + "model_10 | Infeasible | Infeasible\n", + "model_11 | Infeasible | Infeasible\n", + "model_12 | Infeasible | Infeasible\n", + "model_13 | \u001b[0;91m6.822823432538421e-32\u001b[0m | \u001b[0;91m0.0\u001b[0m\n", + "model_14 | \u001b[0;91m-1.3006688324330317e-32\u001b[0m | \u001b[0;91m0.0\u001b[0m\n", + "model_15 | Infeasible | Infeasible\n", + "model_16 | \u001b[0;91m-4.512440482989953e-31\u001b[0m | \u001b[0;91m0.0\u001b[0m\n", + "model_17 | Infeasible | Infeasible\n", + "\u001b[0;93m\n", + "____________________________________________\n", + "\u001b[0m\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: e_coli_core\n", + "____________________________________________\n", + "\n", + "\u001b[0;96m\n", + "############################################\n", + "############################################\n", + " \u001b[1mCHECK FLUX\u001b[0;96m\n", + "############################################\n", + "############################################\n", + "\u001b[0m\n", + "---------------- FLUX INIT -----------------\n", + "\n", + "{'BIOMASS_Ecoli_core_w_GAM': np.float64(0.8739215069684295)}\n", + "\n", + "\n", + "--------------- MEDIUM INIT ----------------\n", + "\n", + "EX_co2_e -1000.0 1000.0\n", + "EX_glc__D_e -10.0 1000.0\n", + "EX_h_e -1000.0 1000.0\n", + "EX_h2o_e -1000.0 1000.0\n", + "EX_nh4_e -1000.0 1000.0\n", + "EX_o2_e -1000.0 1000.0\n", + "EX_pi_e -1000.0 1000.0\n", + "\n", + "\n", + "---------- STOP IMPORT FLUX -------------\n", + "\n", + "{'BIOMASS_Ecoli_core_w_GAM': 0.0}\n", + "\n", + "\n", + "\u001b[0;94m\n", + "____________________________________________\n", + "____________________________________________\n", + "\u001b[0m\n", + " RESULTS \n", + "\u001b[0;94m____________________________________________\n", + "____________________________________________\n", + "\u001b[0m\n", + " \u001b[1m \"Cobra (seeds)\" \u001b[0m indicates the maximum flux \n", + "obtained in FBA from the seeds after shutting \n", + "off all other exchange reactions. If the maximum \n", + "flux is null, a test is performed opening demand \n", + "reactions for the objective reaction's products, \n", + "in order to test the effect of their accumulation \n", + "(\u001b[1m\"cobra (demands)\"\u001b[0m ). If this test is not performed, \n", + "\"NA\" value is indicated.\n", + "\u001b[0;93m\n", + "____________________________________________\n", + "____________________________________________\n", + "\u001b[0m\n", + " Other \n", + "\u001b[0;93m--------------------------------------------\u001b[0m\n", + " PRECURSOR \n", + "\u001b[0;93m . . . . . . . . . . \u001b[0m\n", + "name | cobra (seeds) | cobra (demands)\n", + "-----|---------------|-----------------\n", + "model_1 | \u001b[0;91m0.0\u001b[0m | \u001b[0;91m-8.568626548908283e-17\u001b[0m\n", + "\u001b[0;93m\n", + "____________________________________________\n", + "\u001b[0m\n" + ] + } + ], + "source": [ + "run_precursor(e_coli_norm_dir, e_coli_dir, precursor_result_dir)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### **List of output files**\n", + "\n", + "The precursor_formated_result directory contains:\n", + "- Directory logs: one log file by sbml metabolic network. It shows modification performed on the original file.\n", + "- 2 files *_results.json containing the formatted results compatible with Seed2LP. This file is used to launch the check-flux mode of Seed2LP.\n", + "- 2 files *_fluxes_from_result.tsv: This is the output of check-flux while using the check flux mode from a s2lp file or result file formatted to be like Seed2LP results file" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "test", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.14" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/notebook/run/07_run_seed2lp_iCN718.ipynb b/notebook/run/07_run_seed2lp_iCN718.ipynb new file mode 100644 index 0000000..a74f427 --- /dev/null +++ b/notebook/run/07_run_seed2lp_iCN718.ipynb @@ -0,0 +1,2223 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Seed2LP: Run for iCN718" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This notebook explain how to run Seed2lp for iCN718, and must be run **AFTER** retrieving BiGG SBML file (see notebook [01_get_sbml_BiGG.ipynb](./01_get_sbml_BiGG.ipynb)) ant getting objective (see notebook [02_get_objectives.ipynb](./02_get_objectives.ipynb))\n", + "\n", + "> Note:\n", + ">\n", + "> The Seed2lp (seed searching and flux) result files for iCN718 are available: [https://doi.org/10.57745/OS1JND](https://doi.org/10.57745/OS1JND)\n", + ">\n", + "> After downloadind and unzipping the package, go to analyses/results/iCN718_2000" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## **WARNING**\n", + "This notebook will run Seed2LP for iCN718 in target seed searching mode, using methods: *Reasoning*, *Hybrid-Filter*, *Hybrid-GC*, *Hybrid-GCDiv*.\n", + "\n", + "No accumulation allowed with NE seed inference, using subset minimal. Seed2LP is set to find 30 solutions and will stop if exceed 10 min.\n", + "\n", + "On the paper, the number of solutions limit is set to 2000, with no time limit." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Requirements\n", + "Module *seed2lp* needed\n", + "\n", + "> Advice:\n", + "> \n", + "> Use a conda env called s2lp with python 3.10 for plafrim cluster scripts" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!pip install seed2lp" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## **Slurm-based cluster**: Reproducing paper data\n", + "Slurm-based scripts for cluster are available with no time limit and 2000 solution for iCN718:\n", + "- Launch if needed \n", + " - [01_job_retrieve_bigg_sbml.sh](../../scripts/plafrim_cluster/01_job_retrieve_bigg_sbml.sh): `sbatch 01_job_retrieve_bigg_sbml.sh`\n", + " - [02_job_get_objective.sh](../../scripts/plafrim_cluster/02_job_get_objective.sh): `sbatch 02_job_get_objective.sh`\n", + " - or copy your local files into you cluster\n", + "- Change **_source_** variable by the path of your conda environement with seed2lp installed in files: [03_execute_workflow_search.sh](../../scripts/plafrim_cluster/03_execute_workflow_search.sh) and [03_job_run_s2lp.sh](../../scripts/plafrim_cluster/01_job_run_s2lp.sh) \n", + "- launch [03_execute_workflow_search.sh](../../scripts/plafrim_cluster/03_execute_workflow_search.sh): `sbatch 03_execute_workflow_search.sh`\n", + "\n", + "> Warning:\n", + ">\n", + "> The run might take more than a week per mode to find 2000 solutions" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## **LAUNCH**" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Variable to change (if wanted)" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "analyse_dir = \"../../analyses\"\n", + "data_dir = f\"{analyse_dir}/data/\"\n", + "result_dir=f\"{analyse_dir}/results\"\n", + "temp_dir = \"../../tmp/\"\n", + "\n", + "time_limit = 10 # time limit\n", + "number_solution = 30 # number solutions" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Execute" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "from os import path" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "sbml_dir = f\"{data_dir}/bigg/sbml\"\n", + "result_dir = f\"{result_dir}/iCN718_2000\"\n", + "objecive_dir = f\"{data_dir}/objective\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This function will execute seed2lp for iCN718:\n", + "- Target\n", + "- subset minimal\n", + "- *Reasoning*, *Hybrid-Filter*, *Hybrid-GC* and *Hybrid-GCDiv*\n", + "- no accumulation\n", + "- maximisation (of flux in Objective reaction)\n", + "- Limitations: 30 solutions and 10 min\n", + "\n", + "Also, it will check the flux for each solution and write it into files." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "def run_s2lp(in_dir:str):\n", + " species = f'iCN718'\n", + " sbml_path = path.join(in_dir,f\"{species}.xml\")\n", + " objective_path = path.join(objecive_dir,f\"{species}_target.txt\")\n", + " result_path = path.join(result_dir,species)\n", + "\n", + " command = f\"target {sbml_path} {result_path} --temp {temp_dir} -tl {time_limit} -nbs {number_solution} -cf -max -tf {objective_path}\"\n", + "\n", + " command_reasoning=command+' -so reasoning'\n", + " command_filter=command+' -so filter'\n", + " command_guess_check=command+' -so guess_check'\n", + " command_guess_check_div=command+' -so guess_check_div'\n", + "\n", + " !seed2lp {command_reasoning}\n", + " !seed2lp {command_filter}\n", + " !seed2lp {command_guess_check}\n", + " !seed2lp {command_guess_check_div}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The execution might take more than 1h15min due to :\n", + "- The size of the network\n", + "- 30 solutions asked (it can calculate more for Filter, Guess-Check and Guess Chack with diversity to fin 30 ok)\n", + "- Cobra flux calculation during Filter, Guess-Check and Guess Chack with diversity" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: iCN718\n", + "\n", + "____________________________________________\n", + "\n", + " TARGETS \n", + " FOR TARGET MODE AND FBA \n", + "____________________________________________\n", + "\n", + "Targets set:\n", + " Reactant of objective reaction\n", + " from target file\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " OBJECTVE \n", + " FOR HYBRID \n", + "____________________________________________\n", + "\n", + "Objective set:\n", + " Objective reaction from target file\n", + "\n", + "\n", + "Objective : R_BIOMASS__3\n", + "\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " NETWORK \n", + "____________________________________________\n", + "\n", + "Import reaction: Removed\n", + "Targets can be seeds: No\n", + "Accumulation: Forbidden\n", + "\n", + "\n", + "\u001b[0;93mWARNING : - R_ASAD: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_AGPR: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_ACOTA: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_NAPRT: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_CYSTA: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_KARA1: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + "\n", + " - R_NADTRHD2: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + " - R_SK_ACP_c: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_DNADRAIN deleted. No reactants and no products.\n", + " - R_FOLR2: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_NARK: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + "\u001b[0m\n", + "\u001b[0;96m\n", + "############################################\n", + "############################################\n", + " \u001b[1mREASONING\u001b[0;96m\n", + "############################################\n", + "############################################\n", + "\u001b[0m\n", + "Mode : TARGET\n", + "Option: TARGETS ARE FORBIDDEN SEEDS\n", + "ACCUMULATION: Forbidden\n", + "Time limit: 10.0 minutes\n", + "Solution number limit: 30\n", + "\u001b[0;94m\n", + "____________________________________________\u001b[0m\n", + "\u001b[0;94m____________________________________________\n", + "\u001b[0m\n", + " Sub Mode: \u001b[1mSUBSET MINIMAL\u001b[0m \n", + "\u001b[0;94m____________________________________________\u001b[0m\n", + "\u001b[0;94m____________________________________________\n", + "\u001b[0m\n", + "\n", + "················ \u001b[1mClassic mode\u001b[0m ···············\n", + "\n", + "~~~~~~~~~~~~~~~~ \u001b[1mEnumeration\u001b[0m ~~~~~~~~~~~~~~~\n", + "SOLVING...\n", + "\n", + "Answer: 1 (9 seeds) \n", + "M_4izp_c, M_ACP_c, M_adocbi_c, M_cys__L_c, M_dtmp_c, M_glutcoa_c, M_gtp_c, M_rdmbzi_c, M_thm_c\n", + "\n", + "Answer: 2 (9 seeds) \n", + "M_ACP_c, M_adocbi_c, M_cys__L_c, M_dtmp_c, M_glutcoa_c, M_gtp_c, M_rdmbzi_c, M_thm_c, M_urcan_c\n", + "\n", + "Answer: 3 (9 seeds) \n", + "M_ACP_c, M_adocbi_c, M_cys__L_c, M_dtmp_c, M_glu__L_c, M_glutcoa_c, M_gtp_c, M_rdmbzi_c, M_thm_c\n", + "\n", + "Answer: 4 (9 seeds) \n", + "M_4izp_c, M_ACP_c, M_accoa_c, M_adocbi_c, M_cys__L_c, M_dtmp_c, M_gtp_c, M_rdmbzi_c, M_thm_c\n", + "\n", + "Answer: 5 (9 seeds) \n", + "M_ACP_c, M_accoa_c, M_adocbi_c, M_cys__L_c, M_dtmp_c, M_gtp_c, M_rdmbzi_c, M_thm_c, M_urcan_c\n", + "\n", + "Answer: 6 (9 seeds) \n", + "M_ACP_c, M_accoa_c, M_adocbi_c, M_cys__L_c, M_dtmp_c, M_glu__L_c, M_gtp_c, M_rdmbzi_c, M_thm_c\n", + "\n", + "Answer: 7 (9 seeds) \n", + "M_ACP_c, M_adocbi_c, M_cys__L_c, M_dtmp_c, M_gln__D_c, M_glutcoa_c, M_gtp_c, M_rdmbzi_c, M_thm_c\n", + "\n", + "Answer: 8 (10 seeds) \n", + "M_2mcit_c, M_ACP_c, M_adocbi_c, M_cys__L_c, M_dtmp_c, M_glutcoa_c, M_gtp_c, M_nadph_c, M_rdmbzi_c, M_thm_c\n", + "\n", + "Answer: 9 (10 seeds) \n", + "M_ACP_c, M_adocbi_c, M_asn__L_c, M_cys__L_c, M_dtmp_c, M_glutcoa_c, M_gtp_c, M_nadph_c, M_rdmbzi_c, M_thm_c\n", + "\n", + "Answer: 10 (10 seeds) \n", + "M_ACP_c, M_adocbi_c, M_cys__L_c, M_dtmp_c, M_glu5sa_c, M_glutcoa_c, M_gtp_c, M_nadph_c, M_rdmbzi_c, M_thm_c\n", + "\n", + "Answer: 11 (10 seeds) \n", + "M_1pyr5c_c, M_ACP_c, M_adocbi_c, M_cys__L_c, M_dtmp_c, M_glutcoa_c, M_gtp_c, M_nadph_c, M_rdmbzi_c, M_thm_c\n", + "\n", + "Answer: 12 (10 seeds) \n", + "M_ACP_c, M_adocbi_c, M_asp__D_c, M_cys__L_c, M_dtmp_c, M_glutcoa_c, M_gtp_c, M_nadph_c, M_rdmbzi_c, M_thm_c\n", + "\n", + "Answer: 13 (10 seeds) \n", + "M_ACP_c, M_adocbi_c, M_cys__L_c, M_dtmp_c, M_glutcoa_c, M_gtp_c, M_micit_c, M_nadph_c, M_rdmbzi_c, M_thm_c\n", + "\n", + "Answer: 14 (10 seeds) \n", + "M_ACP_c, M_adocbi_c, M_cbasp_c, M_cys__L_c, M_dtmp_c, M_glutcoa_c, M_gtp_c, M_nadph_c, M_rdmbzi_c, M_thm_c\n", + "\n", + "Answer: 15 (10 seeds) \n", + "M_ACP_c, M_adocbi_c, M_cys__L_c, M_dhor__S_c, M_dtmp_c, M_glutcoa_c, M_gtp_c, M_nadph_c, M_rdmbzi_c, M_thm_c\n", + "\n", + "Answer: 16 (9 seeds) \n", + "M_ACP_c, M_accoa_c, M_adocbi_c, M_cys__L_c, M_dtmp_c, M_gln__D_c, M_gtp_c, M_rdmbzi_c, M_thm_c\n", + "\n", + "Answer: 17 (9 seeds) \n", + "M_ACP_c, M_accoa_c, M_adocbi_c, M_cys__L_c, M_dtmp_c, M_gtp_c, M_nadph_c, M_rdmbzi_c, M_thm_c\n", + "\n", + "Answer: 18 (9 seeds) \n", + "M_ACP_c, M_adocbi_c, M_cys__L_c, M_dtmp_c, M_gtp_c, M_orot5p_c, M_rdmbzi_c, M_thm_c, M_urcan_c\n", + "\n", + "Answer: 19 (9 seeds) \n", + "M_ACP_c, M_adocbi_c, M_cys__L_c, M_dtmp_c, M_gln__D_c, M_gtp_c, M_orot5p_c, M_rdmbzi_c, M_thm_c\n", + "\n", + "Answer: 20 (9 seeds) \n", + "M_ACP_c, M_adocbi_c, M_cys__L_c, M_dtmp_c, M_glu__L_c, M_gtp_c, M_orot5p_c, M_rdmbzi_c, M_thm_c\n", + "\n", + "Answer: 21 (9 seeds) \n", + "M_4izp_c, M_ACP_c, M_adocbi_c, M_cys__L_c, M_dtmp_c, M_gtp_c, M_orot5p_c, M_rdmbzi_c, M_thm_c\n", + "\n", + "Answer: 22 (11 seeds) \n", + "M_4h2oglt_c, M_ACP_c, M_adocbi_c, M_cys__L_c, M_dtmp_c, M_gtp_c, M_micit_c, M_nadph_c, M_orot5p_c, M_rdmbzi_c, M_thm_c\n", + "\n", + "Answer: 23 (11 seeds) \n", + "M_ACP_c, M_adocbi_c, M_cys__L_c, M_dtmp_c, M_glyclt_e, M_gtp_c, M_micit_c, M_nadph_c, M_orot5p_c, M_rdmbzi_c, M_thm_c\n", + "\n", + "Answer: 24 (11 seeds) \n", + "M_4h2oglt_c, M_ACP_c, M_adocbi_c, M_asn__L_c, M_cys__L_c, M_dtmp_c, M_gtp_c, M_nadph_c, M_orot5p_c, M_rdmbzi_c, M_thm_c\n", + "\n", + "Answer: 25 (11 seeds) \n", + "M_ACP_c, M_adocbi_c, M_asn__L_c, M_cys__L_c, M_dtmp_c, M_glyclt_e, M_gtp_c, M_nadph_c, M_orot5p_c, M_rdmbzi_c, M_thm_c\n", + "\n", + "Answer: 26 (11 seeds) \n", + "M_2mcit_c, M_4h2oglt_c, M_ACP_c, M_adocbi_c, M_cys__L_c, M_dtmp_c, M_gtp_c, M_nadph_c, M_orot5p_c, M_rdmbzi_c, M_thm_c\n", + "\n", + "Answer: 27 (11 seeds) \n", + "M_2mcit_c, M_ACP_c, M_adocbi_c, M_cys__L_c, M_dtmp_c, M_glyclt_e, M_gtp_c, M_nadph_c, M_orot5p_c, M_rdmbzi_c, M_thm_c\n", + "\n", + "Answer: 28 (10 seeds) \n", + "M_ACP_c, M_adocbi_c, M_cys__L_c, M_dtmp_c, M_glu5sa_c, M_gtp_c, M_nadph_c, M_orot5p_c, M_rdmbzi_c, M_thm_c\n", + "\n", + "Answer: 29 (11 seeds) \n", + "M_4h2oglt_c, M_ACP_c, M_adocbi_c, M_asp__D_c, M_cys__L_c, M_dtmp_c, M_gtp_c, M_nadph_c, M_orot5p_c, M_rdmbzi_c, M_thm_c\n", + "\n", + "Answer: 30 (11 seeds) \n", + "M_ACP_c, M_adocbi_c, M_asp__D_c, M_cys__L_c, M_dtmp_c, M_glyclt_e, M_gtp_c, M_nadph_c, M_orot5p_c, M_rdmbzi_c, M_thm_c\n", + "\n", + "\u001b[0;94m\n", + "____________________________________________\u001b[0m\n", + "\u001b[0;94m____________________________________________\n", + "\u001b[0m\n", + " Sub Mode: \u001b[1mMINIMIZE\u001b[0m \n", + "\u001b[0;94m____________________________________________\u001b[0m\n", + "\u001b[0;94m____________________________________________\n", + "\u001b[0m\n", + "\n", + "················ \u001b[1mClassic mode\u001b[0m ···············\n", + "Finding optimum...\n", + "SOLVING...\n", + "\n", + "Optimum found.\n", + "Minimal size of seed set is 7\n", + "\n", + "\n", + "~~~~~~~~~~~~~~~~ \u001b[1mEnumeration\u001b[0m ~~~~~~~~~~~~~~~\n", + "SOLVING...\n", + "\n", + "Answer: 1 (7 seeds) \n", + "M_3hbcoa__R_c, M_4mpetz_c, M_adcobdam_c, M_dcdp_c, M_gthox_c, M_gtp_c, M_tetacp_c\n", + "\n", + "Answer: 2 (7 seeds) \n", + "M_3hpcoa_c, M_4mpetz_c, M_adcobdam_c, M_dcdp_c, M_gthox_c, M_gtp_c, M_tetacp_c\n", + "\n", + "Answer: 3 (7 seeds) \n", + "M_3hbcoa_c, M_4mpetz_c, M_adcobdam_c, M_dcdp_c, M_gthox_c, M_gtp_c, M_tetacp_c\n", + "\n", + "Answer: 4 (7 seeds) \n", + "M_3hbcoa__R_c, M_4mpetz_c, M_ACP_c, M_adcobdam_c, M_dcdp_c, M_gthox_c, M_gtp_c\n", + "\n", + "Answer: 5 (7 seeds) \n", + "M_3hbcoa__R_c, M_4mpetz_c, M_adcobdam_c, M_dcdp_c, M_dodecacp_c, M_gthox_c, M_gtp_c\n", + "\n", + "Answer: 6 (7 seeds) \n", + "M_3hbcoa_c, M_4mpetz_c, M_ACP_c, M_adcobdam_c, M_dcdp_c, M_gthox_c, M_gtp_c\n", + "\n", + "Answer: 7 (7 seeds) \n", + "M_3hpcoa_c, M_4mpetz_c, M_ACP_c, M_adcobdam_c, M_dcdp_c, M_gthox_c, M_gtp_c\n", + "\n", + "Answer: 8 (7 seeds) \n", + "M_3hbcoa_c, M_4mpetz_c, M_adcobdam_c, M_dcdp_c, M_dodecacp_c, M_gthox_c, M_gtp_c\n", + "\n", + "Answer: 9 (7 seeds) \n", + "M_3hpcoa_c, M_4mpetz_c, M_adcobdam_c, M_dcdp_c, M_dodecacp_c, M_gthox_c, M_gtp_c\n", + "\n", + "Answer: 10 (7 seeds) \n", + "M_3hbcoa__R_c, M_ACP_c, M_adcobdam_c, M_dcdp_c, M_gthox_c, M_gtp_c, M_thm_c\n", + "\n", + "Answer: 11 (7 seeds) \n", + "M_3hbcoa_c, M_ACP_c, M_adcobdam_c, M_dcdp_c, M_gthox_c, M_gtp_c, M_thm_c\n", + "\n", + "Answer: 12 (7 seeds) \n", + "M_3hpcoa_c, M_ACP_c, M_adcobdam_c, M_dcdp_c, M_gthox_c, M_gtp_c, M_thm_c\n", + "\n", + "Answer: 13 (7 seeds) \n", + "M_3hbcoa__R_c, M_adcobdam_c, M_dcdp_c, M_dodecacp_c, M_gthox_c, M_gtp_c, M_thm_c\n", + "\n", + "Answer: 14 (7 seeds) \n", + "M_3hpcoa_c, M_adcobdam_c, M_dcdp_c, M_dodecacp_c, M_gthox_c, M_gtp_c, M_thm_c\n", + "\n", + "Answer: 15 (7 seeds) \n", + "M_3hbcoa_c, M_adcobdam_c, M_dcdp_c, M_dodecacp_c, M_gthox_c, M_gtp_c, M_thm_c\n", + "\n", + "Answer: 16 (7 seeds) \n", + "M_3hbcoa__R_c, M_4mhetz_c, M_ACP_c, M_adcobdam_c, M_dcdp_c, M_gthox_c, M_gtp_c\n", + "\n", + "Answer: 17 (7 seeds) \n", + "M_3hpcoa_c, M_4mhetz_c, M_ACP_c, M_adcobdam_c, M_dcdp_c, M_gthox_c, M_gtp_c\n", + "\n", + "Answer: 18 (7 seeds) \n", + "M_3hbcoa_c, M_4mhetz_c, M_ACP_c, M_adcobdam_c, M_dcdp_c, M_gthox_c, M_gtp_c\n", + "\n", + "Answer: 19 (7 seeds) \n", + "M_3hbcoa__R_c, M_4mhetz_c, M_adcobdam_c, M_dcdp_c, M_dodecacp_c, M_gthox_c, M_gtp_c\n", + "\n", + "Answer: 20 (7 seeds) \n", + "M_3hpcoa_c, M_4mhetz_c, M_adcobdam_c, M_dcdp_c, M_dodecacp_c, M_gthox_c, M_gtp_c\n", + "\n", + "Answer: 21 (7 seeds) \n", + "M_3hbcoa_c, M_4mhetz_c, M_adcobdam_c, M_dcdp_c, M_dodecacp_c, M_gthox_c, M_gtp_c\n", + "\n", + "Answer: 22 (7 seeds) \n", + "M_3hbcoa__R_c, M_adcobdam_c, M_dcdp_c, M_gthox_c, M_gtp_c, M_tetacp_c, M_thm_c\n", + "\n", + "Answer: 23 (7 seeds) \n", + "M_3hpcoa_c, M_adcobdam_c, M_dcdp_c, M_gthox_c, M_gtp_c, M_tetacp_c, M_thm_c\n", + "\n", + "Answer: 24 (7 seeds) \n", + "M_3hbcoa_c, M_adcobdam_c, M_dcdp_c, M_gthox_c, M_gtp_c, M_tetacp_c, M_thm_c\n", + "\n", + "Answer: 25 (7 seeds) \n", + "M_3hbcoa__R_c, M_4mhetz_c, M_adcobdam_c, M_dcdp_c, M_gthox_c, M_gtp_c, M_tetacp_c\n", + "\n", + "Answer: 26 (7 seeds) \n", + "M_3hpcoa_c, M_4mhetz_c, M_adcobdam_c, M_dcdp_c, M_gthox_c, M_gtp_c, M_tetacp_c\n", + "\n", + "Answer: 27 (7 seeds) \n", + "M_3hbcoa_c, M_4mhetz_c, M_adcobdam_c, M_dcdp_c, M_gthox_c, M_gtp_c, M_tetacp_c\n", + "\n", + "Answer: 28 (7 seeds) \n", + "M_3hpcoa_c, M_ACP_c, M_adcobdam_c, M_dcdp_c, M_glucys_c, M_gtp_c, M_thm_c\n", + "\n", + "Answer: 29 (7 seeds) \n", + "M_3hbcoa__R_c, M_ACP_c, M_adcobdam_c, M_dcdp_c, M_glucys_c, M_gtp_c, M_thm_c\n", + "\n", + "Answer: 30 (7 seeds) \n", + "M_3hbcoa_c, M_ACP_c, M_adcobdam_c, M_dcdp_c, M_glucys_c, M_gtp_c, M_thm_c\n", + "\n", + "\u001b[0;96m############################################\n", + "\n", + "\u001b[0m\n", + "\n", + "TIME DATA EXTRACTION : 0.524s\n", + "TIME TOTAL SEED SEARCH: 150.637s\n", + "TIME TOTAL : 151.161s\n", + "\n", + "\n", + "\u001b[0;96m\n", + "############################################\n", + "############################################\n", + " \u001b[1mCHECK FLUX\u001b[0;96m\n", + "############################################\n", + "############################################\n", + "\u001b[0m\n", + "---------------- FLUX INIT -----------------\n", + "\n", + "{'BIOMASS__3': np.float64(1.3135912923645483)}\n", + "\n", + "\n", + "--------------- MEDIUM INIT ----------------\n", + "\n", + "EX_tyr__L_e -10.0 1000.0\n", + "EX_val__L_e -10.0 1000.0\n", + "EX_ser__D_e -10.0 1000.0\n", + "EX_orn_e -10.0 1000.0\n", + "EX_ptrc_e -10.0 1000.0\n", + "EX_spmd_e -10.0 1000.0\n", + "EX_co2_e -10.0 1000.0\n", + "EX_na1_e -10.0 1000.0\n", + "EX_no2_e -10.0 1000.0\n", + "EX_o2_e -10.0 1000.0\n", + "EX_urea_e -10.0 1000.0\n", + "EX_chol_e -10.0 1000.0\n", + "EX_adn_e -10.0 1000.0\n", + "EX_cytd_e -10.0 1000.0\n", + "EX_dad_2_e -10.0 1000.0\n", + "EX_dcyt_e -10.0 1000.0\n", + "EX_dgsn_e -10.0 1000.0\n", + "EX_thymd_e -10.0 1000.0\n", + "EX_duri_e -10.0 1000.0\n", + "EX_h_e -10.0 1000.0\n", + "EX_malt_e -10.0 1000.0\n", + "EX_fru_e -10.0 1000.0\n", + "EX_glc__D_e -10.0 1000.0\n", + "EX_glcn__D_e -10.0 1000.0\n", + "EX_mnl_e -10.0 1000.0\n", + "EX_acgam_e -10.0 1000.0\n", + "EX_gsn_e -10.0 1000.0\n", + "EX_uri_e -10.0 1000.0\n", + "EX_csn_e -10.0 1000.0\n", + "EX_xan_e -10.0 1000.0\n", + "EX_ura_e -10.0 1000.0\n", + "EX_taur_e -10.0 1000.0\n", + "EX_etha_e -10.0 1000.0\n", + "EX_sucr_e -10.0 1000.0\n", + "EX_tre_e -10.0 1000.0\n", + "EX_2pg_e -10.0 1000.0\n", + "EX_glyb_e -10.0 1000.0\n", + "EX_bz_e -10.0 1000.0\n", + "EX_3pg_e -10.0 1000.0\n", + "EX_ac_e -10.0 1000.0\n", + "EX_akg_e -10.0 1000.0\n", + "EX_cit_e -10.0 1000.0\n", + "EX_for_e -10.0 1000.0\n", + "EX_fum_e -10.0 1000.0\n", + "EX_succ_e -10.0 1000.0\n", + "EX_icit_e -10.0 1000.0\n", + "EX_mal__L_e -10.0 1000.0\n", + "EX_glyclt_e -10.0 1000.0\n", + "EX_no3_e -10.0 1000.0\n", + "EX_pi_e -10.0 1000.0\n", + "EX_so4_e -10.0 1000.0\n", + "EX_ala__L_e -10.0 1000.0\n", + "EX_arg__L_e -10.0 1000.0\n", + "EX_asn__L_e -10.0 1000.0\n", + "EX_asp__L_e -10.0 1000.0\n", + "EX_cys__L_e -10.0 1000.0\n", + "EX_ala__D_e -10.0 1000.0\n", + "EX_glu__D_e -10.0 1000.0\n", + "EX_gln__L_e -10.0 1000.0\n", + "EX_glu__L_e -10.0 1000.0\n", + "EX_gly_e -10.0 1000.0\n", + "EX_his__L_e -10.0 1000.0\n", + "EX_ile__L_e -10.0 1000.0\n", + "EX_leu__L_e -10.0 1000.0\n", + "EX_lys__L_e -10.0 1000.0\n", + "EX_met__L_e -10.0 1000.0\n", + "EX_phe__L_e -10.0 1000.0\n", + "EX_pro__L_e -10.0 1000.0\n", + "EX_cl_e -10.0 1000.0\n", + "EX_ser__L_e -10.0 1000.0\n", + "EX_thr__L_e -10.0 1000.0\n", + "EX_trp__L_e -10.0 1000.0\n", + "EX_mg2_e -10.0 1000.0\n", + "EX_h2o_e -10.0 1000.0\n", + "EX_nh4_e -10.0 1000.0\n", + "EX_arab__L_e -10.0 1000.0\n", + "EX_glcr_e -10.0 1000.0\n", + "EX_gal_e -10.0 1000.0\n", + "EX_man_e -10.0 1000.0\n", + "EX_galt_e -10.0 1000.0\n", + "EX_sbt__D_e -10.0 1000.0\n", + "EX_glyc_e -10.0 1000.0\n", + "EX_fuc__L_e -10.0 1000.0\n", + "EX_glcn_e -10.0 1000.0\n", + "EX_glyc3p_e -10.0 1000.0\n", + "EX_xyl__D_e -10.0 1000.0\n", + "EX_lac__L_e -10.0 1000.0\n", + "EX_g6p_e -10.0 1000.0\n", + "EX_rib__D_e -10.0 1000.0\n", + "EX_rmn_e -10.0 1000.0\n", + "EX_melib_e -10.0 1000.0\n", + "EX_asp__D_e -10.0 1000.0\n", + "EX_gam_e -10.0 1000.0\n", + "EX_13ppd_e -10.0 1000.0\n", + "EX_2obut_e -10.0 1000.0\n", + "EX_tartr__L_e -10.0 1000.0\n", + "EX_g1p_e -10.0 1000.0\n", + "EX_f6p_e -10.0 1000.0\n", + "EX_mbdg_e -10.0 1000.0\n", + "EX_rbt_e -10.0 1000.0\n", + "EX_malttr_e -10.0 1000.0\n", + "EX_inost_e -10.0 1000.0\n", + "EX_ppa_e -10.0 1000.0\n", + "EX_galct__D_e -10.0 1000.0\n", + "EX_glx_e -10.0 1000.0\n", + "EX_ins_e -10.0 1000.0\n", + "EX_gly_glu__L_e -10.0 1000.0\n", + "EX_acac_e -10.0 1000.0\n", + "EX_acmana_e -10.0 1000.0\n", + "EX_mal__D_e -10.0 1000.0\n", + "EX_4hoxpac_e -10.0 1000.0\n", + "EX_3hoxpac_e -10.0 1000.0\n", + "EX_tym_e -10.0 1000.0\n", + "EX_all__D_e -10.0 1000.0\n", + "EX_lyx__L_e -10.0 1000.0\n", + "EX_pyr_e -10.0 1000.0\n", + "EX_galur_e -10.0 1000.0\n", + "EX_peamn_e -10.0 1000.0\n", + "EX_lcts_e -10.0 1000.0\n", + "\n", + "\n", + "---------- STOP IMPORT FLUX -------------\n", + "\n", + "{'BIOMASS__3': np.float64(0.0)}\n", + "\n", + "\n", + "\u001b[0;94m\n", + "____________________________________________\n", + "____________________________________________\n", + "\u001b[0m\n", + " RESULTS \n", + "\u001b[0;94m____________________________________________\n", + "____________________________________________\n", + "\u001b[0m\n", + " \u001b[1m \"Cobra (seeds)\" \u001b[0m indicates the maximum flux \n", + "obtained in FBA from the seeds after shutting \n", + "off all other exchange reactions. If the maximum \n", + "flux is null, a test is performed opening demand \n", + "reactions for the objective reaction's products, \n", + "in order to test the effect of their accumulation \n", + "(\u001b[1m\"cobra (demands)\"\u001b[0m ). If this test is not performed, \n", + "\"NA\" value is indicated.\n", + "\u001b[0;93m\n", + "____________________________________________\n", + "____________________________________________\n", + "\u001b[0m\n", + " Subset Minimal \n", + "\u001b[0;93m--------------------------------------------\u001b[0m\n", + " REASONING \n", + "\u001b[0;93m . . . . . . . . . . \u001b[0m\n", + "name | cobra (seeds) | cobra (demands)\n", + "-----|---------------|-----------------\n", + "model_1 | \u001b[0;91m0.0\u001b[0m | \u001b[0;91m0.0\u001b[0m\n", + "model_2 | \u001b[0;91m0.0\u001b[0m | \u001b[0;91m0.0\u001b[0m\n", + "model_3 | \u001b[0;91m0.0\u001b[0m | \u001b[0;91m0.0\u001b[0m\n", + "model_4 | \u001b[0;91m0.0\u001b[0m | \u001b[0;91m0.0\u001b[0m\n", + "model_5 | \u001b[0;91m0.0\u001b[0m | \u001b[0;91m0.0\u001b[0m\n", + "model_6 | \u001b[0;91m0.0\u001b[0m | \u001b[0;91m0.0\u001b[0m\n", + "model_7 | \u001b[0;91m0.0\u001b[0m | \u001b[0;91m0.0\u001b[0m\n", + "model_8 | \u001b[0;91m0.0\u001b[0m | \u001b[0;91m0.0\u001b[0m\n", + "model_9 | \u001b[0;91m0.0\u001b[0m | \u001b[0;91m0.0\u001b[0m\n", + "model_10 | \u001b[0;91m0.0\u001b[0m | \u001b[0;91m0.0\u001b[0m\n", + "model_11 | \u001b[0;91m0.0\u001b[0m | \u001b[0;91m0.0\u001b[0m\n", + "model_12 | \u001b[0;91m0.0\u001b[0m | \u001b[0;91m0.0\u001b[0m\n", + "model_13 | \u001b[0;91m0.0\u001b[0m | \u001b[0;91m0.0\u001b[0m\n", + "model_14 | \u001b[0;91m0.0\u001b[0m | \u001b[0;91m0.0\u001b[0m\n", + "model_15 | \u001b[0;91m0.0\u001b[0m | \u001b[0;91m0.0\u001b[0m\n", + "model_16 | \u001b[0;91m0.0\u001b[0m | \u001b[0;91m0.0\u001b[0m\n", + "model_17 | \u001b[0;91m0.0\u001b[0m | \u001b[0;91m0.0\u001b[0m\n", + "model_18 | \u001b[0;91m0.0\u001b[0m | \u001b[0;91m0.0\u001b[0m\n", + "model_19 | \u001b[0;91m0.0\u001b[0m | \u001b[0;91m0.0\u001b[0m\n", + "model_20 | \u001b[0;91m0.0\u001b[0m | \u001b[0;91m0.0\u001b[0m\n", + "model_21 | \u001b[0;91m0.0\u001b[0m | \u001b[0;91m0.0\u001b[0m\n", + "model_22 | \u001b[0;91m0.0\u001b[0m | \u001b[0;91m0.0\u001b[0m\n", + "model_23 | \u001b[0;91m0.0\u001b[0m | \u001b[0;91m0.0\u001b[0m\n", + "model_24 | \u001b[0;91m0.0\u001b[0m | \u001b[0;91m0.0\u001b[0m\n", + "model_25 | \u001b[0;91m0.0\u001b[0m | \u001b[0;91m0.0\u001b[0m\n", + "model_26 | \u001b[0;91m0.0\u001b[0m | \u001b[0;91m0.0\u001b[0m\n", + "model_27 | \u001b[0;91m0.0\u001b[0m | \u001b[0;91m0.0\u001b[0m\n", + "model_28 | \u001b[0;91m0.0\u001b[0m | \u001b[0;91m0.0\u001b[0m\n", + "model_29 | \u001b[0;91m0.0\u001b[0m | \u001b[0;91m0.0\u001b[0m\n", + "model_30 | \u001b[0;91m0.0\u001b[0m | \u001b[0;91m0.0\u001b[0m\n", + "\u001b[0;93m\n", + "____________________________________________\n", + "____________________________________________\n", + "\u001b[0m\n", + " Minimize \n", + "\u001b[0;93m--------------------------------------------\u001b[0m\n", + " REASONING \n", + "\u001b[0;93m . . . . . . . . . . \u001b[0m\n", + "name | cobra (seeds) | cobra (demands)\n", + "-----|---------------|-----------------\n", + "model_1 | \u001b[0;91m0.0\u001b[0m | \u001b[0;91m0.0\u001b[0m\n", + "model_2 | \u001b[0;91m0.0\u001b[0m | \u001b[0;91m0.0\u001b[0m\n", + "model_3 | \u001b[0;91m0.0\u001b[0m | \u001b[0;91m0.0\u001b[0m\n", + "model_4 | \u001b[0;91m0.0\u001b[0m | \u001b[0;91m0.0\u001b[0m\n", + "model_5 | \u001b[0;91m0.0\u001b[0m | \u001b[0;91m0.0\u001b[0m\n", + "model_6 | \u001b[0;91m0.0\u001b[0m | \u001b[0;91m0.0\u001b[0m\n", + "model_7 | \u001b[0;91m0.0\u001b[0m | \u001b[0;91m0.0\u001b[0m\n", + "model_8 | \u001b[0;91m0.0\u001b[0m | \u001b[0;91m0.0\u001b[0m\n", + "model_9 | \u001b[0;91m0.0\u001b[0m | \u001b[0;91m0.0\u001b[0m\n", + "model_10 | \u001b[0;91m0.0\u001b[0m | \u001b[0;91m0.0\u001b[0m\n", + "model_11 | \u001b[0;91m0.0\u001b[0m | \u001b[0;91m0.0\u001b[0m\n", + "model_12 | \u001b[0;91m0.0\u001b[0m | \u001b[0;91m0.0\u001b[0m\n", + "model_13 | \u001b[0;91m0.0\u001b[0m | \u001b[0;91m0.0\u001b[0m\n", + "model_14 | \u001b[0;91m0.0\u001b[0m | \u001b[0;91m0.0\u001b[0m\n", + "model_15 | \u001b[0;91m0.0\u001b[0m | \u001b[0;91m0.0\u001b[0m\n", + "model_16 | \u001b[0;91m0.0\u001b[0m | \u001b[0;91m0.0\u001b[0m\n", + "model_17 | \u001b[0;91m0.0\u001b[0m | \u001b[0;91m0.0\u001b[0m\n", + "model_18 | \u001b[0;91m0.0\u001b[0m | \u001b[0;91m0.0\u001b[0m\n", + "model_19 | \u001b[0;91m0.0\u001b[0m | \u001b[0;91m0.0\u001b[0m\n", + "model_20 | \u001b[0;91m0.0\u001b[0m | \u001b[0;91m0.0\u001b[0m\n", + "model_21 | \u001b[0;91m0.0\u001b[0m | \u001b[0;91m0.0\u001b[0m\n", + "model_22 | \u001b[0;91m0.0\u001b[0m | \u001b[0;91m0.0\u001b[0m\n", + "model_23 | \u001b[0;91m0.0\u001b[0m | \u001b[0;91m0.0\u001b[0m\n", + "model_24 | \u001b[0;91m0.0\u001b[0m | \u001b[0;91m0.0\u001b[0m\n", + "model_25 | \u001b[0;91m0.0\u001b[0m | \u001b[0;91m0.0\u001b[0m\n", + "model_26 | \u001b[0;91m0.0\u001b[0m | \u001b[0;91m0.0\u001b[0m\n", + "model_27 | \u001b[0;91m0.0\u001b[0m | \u001b[0;91m0.0\u001b[0m\n", + "model_28 | \u001b[0;91m0.0\u001b[0m | \u001b[0;91m0.0\u001b[0m\n", + "model_29 | \u001b[0;91m0.0\u001b[0m | \u001b[0;91m0.0\u001b[0m\n", + "model_30 | \u001b[0;91m0.0\u001b[0m | \u001b[0;91m0.0\u001b[0m\n", + "\u001b[0;93m\n", + "____________________________________________\n", + "\u001b[0m\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: iCN718\n", + "\n", + "____________________________________________\n", + "\n", + " TARGETS \n", + " FOR TARGET MODE AND FBA \n", + "____________________________________________\n", + "\n", + "Targets set:\n", + " Reactant of objective reaction\n", + " from target file\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " OBJECTVE \n", + " FOR HYBRID \n", + "____________________________________________\n", + "\n", + "Objective set:\n", + " Objective reaction from target file\n", + "\n", + "\n", + "Objective : R_BIOMASS__3\n", + "\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " NETWORK \n", + "____________________________________________\n", + "\n", + "Import reaction: Removed\n", + "Targets can be seeds: No\n", + "Accumulation: Forbidden\n", + "\n", + "\n", + "\u001b[0;93mWARNING : - R_ASAD: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_AGPR: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_ACOTA: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_NAPRT: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_CYSTA: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_KARA1: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + "\n", + " - R_NADTRHD2: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + " - R_SK_ACP_c: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_DNADRAIN deleted. No reactants and no products.\n", + " - R_FOLR2: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_NARK: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + "\u001b[0m\n", + "\u001b[0;96m\n", + "############################################\n", + "############################################\n", + " \u001b[1mREASONING\u001b[0;96m\n", + "############################################\n", + "############################################\n", + "\u001b[0m\n", + "Mode : TARGET\n", + "Option: TARGETS ARE FORBIDDEN SEEDS\n", + "ACCUMULATION: Forbidden\n", + "Time limit: 10.0 minutes\n", + "Solution number limit: 30\n", + "\u001b[0;94m\n", + "____________________________________________\u001b[0m\n", + "\u001b[0;94m____________________________________________\n", + "\u001b[0m\n", + " Sub Mode: \u001b[1mSUBSET MINIMAL\u001b[0m \n", + "\u001b[0;94m____________________________________________\u001b[0m\n", + "\u001b[0;94m____________________________________________\n", + "\u001b[0m\n", + "\n", + "················ \u001b[1mFilter mode\u001b[0m ···············\n", + "\n", + "~~~~~~~~~~~~~~~~ \u001b[1mEnumeration\u001b[0m ~~~~~~~~~~~~~~~\n", + "SOLVING...\n", + "\n", + "Answer: 1 (11 seeds)\n", + "M_ACP_c, M_amp_c, M_co1dam_c, M_cys__L_c, M_dopa_c, M_dtmp_c, M_glu__L_c, M_glutcoa_c, M_gtp_c, M_prpp_c, M_thm_c\n", + "\n", + "Answer: 2 (10 seeds)\n", + "M_ACP_c, M_accoa_c, M_amp_c, M_co1dam_c, M_cys__L_c, M_dtmp_c, M_glu__L_c, M_gtp_c, M_prpp_c, M_thm_c\n", + "\n", + "Answer: 3 (13 seeds)\n", + "M_ACP_c, M_ap4a_c, M_co1dam_c, M_dhpmp_c, M_fe2_c, M_g3pe_c, M_glu1sa_c, M_glu__D_e, M_hLkynr_c, M_ptth_c, M_so4_c, M_thm_c, M_trdox_c\n", + "\n", + "Answer: 4 (12 seeds)\n", + "M_3pg_e, M_ACP_c, M_ap4a_c, M_co1dam_c, M_dhpmp_c, M_fe2_c, M_glu1sa_c, M_glu__D_e, M_ptth_c, M_so4_c, M_thm_c, M_trdox_c\n", + "\n", + "Answer: 5 (13 seeds)\n", + "M_ACP_c, M_ap4a_c, M_carn_c, M_co1dam_c, M_dhpmp_c, M_fe2_c, M_g3pe_c, M_glu1sa_c, M_hLkynr_c, M_ptth_c, M_so4_c, M_thm_c, M_trdox_c\n", + "\n", + "Answer: 6 (12 seeds)\n", + "M_3pg_e, M_ACP_c, M_ap4a_c, M_co1dam_c, M_dhpmp_c, M_fe2_c, M_glu1sa_c, M_glu__D_e, M_ptcys_c, M_so4_c, M_thm_c, M_trdox_c\n", + "\n", + "Answer: 7 (13 seeds)\n", + "M_ACP_c, M_ap4a_c, M_co1dam_c, M_dhpmp_c, M_fe2_c, M_g3pe_c, M_glu1sa_c, M_glu__D_e, M_hLkynr_c, M_ptcys_c, M_so4_c, M_thm_c, M_trdox_c\n", + "\n", + "Answer: 8 (13 seeds)\n", + "M_ACP_c, M_ap4a_c, M_carn_c, M_co1dam_c, M_dhpmp_c, M_fe2_c, M_g3pe_c, M_glu1sa_c, M_hLkynr_c, M_ptcys_c, M_so4_c, M_thm_c, M_trdox_c\n", + "\n", + "Answer: 9 (13 seeds)\n", + "M_ACP_c, M_ap4a_c, M_carn_c, M_co1dam_c, M_dhpmp_c, M_fe2_c, M_g3pe_c, M_hLkynr_c, M_hmbil_c, M_ptth_c, M_so4_c, M_thm_c, M_trdox_c\n", + "\n", + "Answer: 10 (13 seeds)\n", + "M_5aop_c, M_ACP_c, M_ap4a_c, M_carn_c, M_co1dam_c, M_dhpmp_c, M_fe2_c, M_g3pe_c, M_hLkynr_c, M_ptth_c, M_so4_c, M_thm_c, M_trdox_c\n", + "\n", + "Answer: 11 (12 seeds)\n", + "M_3pg_e, M_ACP_c, M_ap4a_c, M_co1dam_c, M_dhpmp_c, M_fe2_c, M_glu__D_e, M_hmbil_c, M_ptth_c, M_so4_c, M_thm_c, M_trdox_c\n", + "\n", + "Answer: 12 (12 seeds)\n", + "M_3pg_e, M_5aop_c, M_ACP_c, M_ap4a_c, M_co1dam_c, M_dhpmp_c, M_fe2_c, M_glu__D_e, M_ptth_c, M_so4_c, M_thm_c, M_trdox_c\n", + "\n", + "Answer: 13 (13 seeds)\n", + "M_5aop_c, M_ACP_c, M_ap4a_c, M_co1dam_c, M_dhpmp_c, M_fe2_c, M_g3pe_c, M_glu__D_e, M_hLkynr_c, M_ptth_c, M_so4_c, M_thm_c, M_trdox_c\n", + "\n", + "Answer: 14 (13 seeds)\n", + "M_ACP_c, M_ap4a_c, M_co1dam_c, M_dhpmp_c, M_fe2_c, M_g3pe_c, M_glu__D_e, M_hLkynr_c, M_hmbil_c, M_ptth_c, M_so4_c, M_thm_c, M_trdox_c\n", + "\n", + "Answer: 15 (12 seeds)\n", + "M_3pg_e, M_5aop_c, M_ACP_c, M_ap4a_c, M_co1dam_c, M_dhpmp_c, M_fe2_c, M_glu__D_e, M_ptcys_c, M_so4_c, M_thm_c, M_trdox_c\n", + "\n", + "Answer: 16 (13 seeds)\n", + "M_5aop_c, M_ACP_c, M_ap4a_c, M_co1dam_c, M_dhpmp_c, M_fe2_c, M_g3pe_c, M_glu__D_e, M_hLkynr_c, M_ptcys_c, M_so4_c, M_thm_c, M_trdox_c\n", + "\n", + "Answer: 17 (13 seeds)\n", + "M_5aop_c, M_ACP_c, M_ap4a_c, M_carn_c, M_co1dam_c, M_dhpmp_c, M_fe2_c, M_g3pe_c, M_hLkynr_c, M_ptcys_c, M_so4_c, M_thm_c, M_trdox_c\n", + "\n", + "Answer: 18 (13 seeds)\n", + "M_ACP_c, M_ap4a_c, M_carn_c, M_co1dam_c, M_dhpmp_c, M_fe2_c, M_g3pe_c, M_hLkynr_c, M_hmbil_c, M_ptcys_c, M_so4_c, M_thm_c, M_trdox_c\n", + "\n", + "Answer: 19 (12 seeds)\n", + "M_3pg_e, M_ACP_c, M_ap4a_c, M_co1dam_c, M_dhpmp_c, M_fe2_c, M_glu__D_e, M_hmbil_c, M_ptcys_c, M_so4_c, M_thm_c, M_trdox_c\n", + "\n", + "Answer: 20 (13 seeds)\n", + "M_ACP_c, M_ap4a_c, M_co1dam_c, M_dhpmp_c, M_fe2_c, M_g3pe_c, M_glu__D_e, M_hLkynr_c, M_hmbil_c, M_ptcys_c, M_so4_c, M_thm_c, M_trdox_c\n", + "\n", + "Answer: 21 (13 seeds)\n", + "M_ACP_c, M_ap4a_c, M_carn_c, M_co1dam_c, M_fe2_c, M_g3pe_c, M_hLkynr_c, M_hmbil_c, M_ptth_c, M_so4_c, M_thm_c, M_trdox_c, M_xan_e\n", + "\n", + "Answer: 22 (13 seeds)\n", + "M_5aop_c, M_ACP_c, M_ap4a_c, M_carn_c, M_co1dam_c, M_fe2_c, M_g3pe_c, M_hLkynr_c, M_ptth_c, M_so4_c, M_thm_c, M_trdox_c, M_xan_e\n", + "\n", + "Answer: 23 (12 seeds)\n", + "M_3pg_e, M_5aop_c, M_ACP_c, M_ap4a_c, M_co1dam_c, M_fe2_c, M_glu__D_e, M_ptth_c, M_so4_c, M_thm_c, M_trdox_c, M_xan_e\n", + "\n", + "Answer: 24 (13 seeds)\n", + "M_5aop_c, M_ACP_c, M_ap4a_c, M_co1dam_c, M_fe2_c, M_g3pe_c, M_glu__D_e, M_hLkynr_c, M_ptth_c, M_so4_c, M_thm_c, M_trdox_c, M_xan_e\n", + "\n", + "Answer: 25 (12 seeds)\n", + "M_3pg_e, M_ACP_c, M_ap4a_c, M_co1dam_c, M_fe2_c, M_glu__D_e, M_hmbil_c, M_ptth_c, M_so4_c, M_thm_c, M_trdox_c, M_xan_e\n", + "\n", + "Answer: 26 (13 seeds)\n", + "M_ACP_c, M_ap4a_c, M_co1dam_c, M_fe2_c, M_g3pe_c, M_glu__D_e, M_hLkynr_c, M_hmbil_c, M_ptth_c, M_so4_c, M_thm_c, M_trdox_c, M_xan_e\n", + "\n", + "Answer: 27 (13 seeds)\n", + "M_5aop_c, M_ACP_c, M_ap4a_c, M_carn_c, M_co1dam_c, M_fe2_c, M_g3pe_c, M_hLkynr_c, M_ptcys_c, M_so4_c, M_thm_c, M_trdox_c, M_xan_e\n", + "\n", + "Answer: 28 (12 seeds)\n", + "M_3pg_e, M_5aop_c, M_ACP_c, M_ap4a_c, M_co1dam_c, M_fe2_c, M_glu__D_e, M_ptcys_c, M_so4_c, M_thm_c, M_trdox_c, M_xan_e\n", + "\n", + "Answer: 29 (13 seeds)\n", + "M_5aop_c, M_ACP_c, M_ap4a_c, M_co1dam_c, M_fe2_c, M_g3pe_c, M_glu__D_e, M_hLkynr_c, M_ptcys_c, M_so4_c, M_thm_c, M_trdox_c, M_xan_e\n", + "\n", + "Answer: 30 (13 seeds)\n", + "M_ACP_c, M_ap4a_c, M_carn_c, M_co1dam_c, M_fe2_c, M_g3pe_c, M_hLkynr_c, M_hmbil_c, M_ptcys_c, M_so4_c, M_thm_c, M_trdox_c, M_xan_e\n", + "\n", + "Rejected solution during process: 375 \n", + "\n", + "\u001b[0;94m\n", + "____________________________________________\u001b[0m\n", + "\u001b[0;94m____________________________________________\n", + "\u001b[0m\n", + " Sub Mode: \u001b[1mMINIMIZE\u001b[0m \n", + "\u001b[0;94m____________________________________________\u001b[0m\n", + "\u001b[0;94m____________________________________________\n", + "\u001b[0m\n", + "\n", + "················ \u001b[1mFilter mode\u001b[0m ···············\n", + "Finding optimum...\n", + "SOLVING...\n", + "\n", + "Optimum found.\n", + "Minimal size of seed set is 7\n", + "\n", + "\n", + "~~~~~~~~~~~~~~~~ \u001b[1mEnumeration\u001b[0m ~~~~~~~~~~~~~~~\n", + "SOLVING...\n", + "\n", + "Answer: 1 (7 seeds)\n", + "M_adcobdam_c, M_ahdt_c, M_gthox_c, M_succoa_c, M_tetacp_c, M_thm_c, M_trdrd_c\n", + "\n", + "Answer: 2 (7 seeds)\n", + "M_4mpetz_c, M_adcobdam_c, M_ahdt_c, M_gthox_c, M_succoa_c, M_tetacp_c, M_trdrd_c\n", + "\n", + "Answer: 3 (7 seeds)\n", + "M_4mhetz_c, M_adcobdam_c, M_ahdt_c, M_gthox_c, M_succoa_c, M_tetacp_c, M_trdrd_c\n", + "\n", + "Time out: 10.0 min expired\n", + "\u001b[0;91mERROR : Time out: 10.0 min expired\u001b[0m\n", + "Rejected solution during process: at least 400 \n", + "\n", + "\u001b[0;96m############################################\n", + "\n", + "\u001b[0m\n", + "\n", + "TIME DATA EXTRACTION : 0.512s\n", + "TIME TOTAL SEED SEARCH: 1223.782s\n", + "TIME TOTAL : 1224.294s\n", + "\n", + "\n", + "\u001b[0;96m\n", + "############################################\n", + "############################################\n", + " \u001b[1mCHECK FLUX\u001b[0;96m\n", + "############################################\n", + "############################################\n", + "\u001b[0m\n", + "---------------- FLUX INIT -----------------\n", + "\n", + "{'BIOMASS__3': np.float64(1.3135912923645483)}\n", + "\n", + "\n", + "--------------- MEDIUM INIT ----------------\n", + "\n", + "EX_tyr__L_e -10.0 1000.0\n", + "EX_val__L_e -10.0 1000.0\n", + "EX_ser__D_e -10.0 1000.0\n", + "EX_orn_e -10.0 1000.0\n", + "EX_ptrc_e -10.0 1000.0\n", + "EX_spmd_e -10.0 1000.0\n", + "EX_co2_e -10.0 1000.0\n", + "EX_na1_e -10.0 1000.0\n", + "EX_no2_e -10.0 1000.0\n", + "EX_o2_e -10.0 1000.0\n", + "EX_urea_e -10.0 1000.0\n", + "EX_chol_e -10.0 1000.0\n", + "EX_adn_e -10.0 1000.0\n", + "EX_cytd_e -10.0 1000.0\n", + "EX_dad_2_e -10.0 1000.0\n", + "EX_dcyt_e -10.0 1000.0\n", + "EX_dgsn_e -10.0 1000.0\n", + "EX_thymd_e -10.0 1000.0\n", + "EX_duri_e -10.0 1000.0\n", + "EX_h_e -10.0 1000.0\n", + "EX_malt_e -10.0 1000.0\n", + "EX_fru_e -10.0 1000.0\n", + "EX_glc__D_e -10.0 1000.0\n", + "EX_glcn__D_e -10.0 1000.0\n", + "EX_mnl_e -10.0 1000.0\n", + "EX_acgam_e -10.0 1000.0\n", + "EX_gsn_e -10.0 1000.0\n", + "EX_uri_e -10.0 1000.0\n", + "EX_csn_e -10.0 1000.0\n", + "EX_xan_e -10.0 1000.0\n", + "EX_ura_e -10.0 1000.0\n", + "EX_taur_e -10.0 1000.0\n", + "EX_etha_e -10.0 1000.0\n", + "EX_sucr_e -10.0 1000.0\n", + "EX_tre_e -10.0 1000.0\n", + "EX_2pg_e -10.0 1000.0\n", + "EX_glyb_e -10.0 1000.0\n", + "EX_bz_e -10.0 1000.0\n", + "EX_3pg_e -10.0 1000.0\n", + "EX_ac_e -10.0 1000.0\n", + "EX_akg_e -10.0 1000.0\n", + "EX_cit_e -10.0 1000.0\n", + "EX_for_e -10.0 1000.0\n", + "EX_fum_e -10.0 1000.0\n", + "EX_succ_e -10.0 1000.0\n", + "EX_icit_e -10.0 1000.0\n", + "EX_mal__L_e -10.0 1000.0\n", + "EX_glyclt_e -10.0 1000.0\n", + "EX_no3_e -10.0 1000.0\n", + "EX_pi_e -10.0 1000.0\n", + "EX_so4_e -10.0 1000.0\n", + "EX_ala__L_e -10.0 1000.0\n", + "EX_arg__L_e -10.0 1000.0\n", + "EX_asn__L_e -10.0 1000.0\n", + "EX_asp__L_e -10.0 1000.0\n", + "EX_cys__L_e -10.0 1000.0\n", + "EX_ala__D_e -10.0 1000.0\n", + "EX_glu__D_e -10.0 1000.0\n", + "EX_gln__L_e -10.0 1000.0\n", + "EX_glu__L_e -10.0 1000.0\n", + "EX_gly_e -10.0 1000.0\n", + "EX_his__L_e -10.0 1000.0\n", + "EX_ile__L_e -10.0 1000.0\n", + "EX_leu__L_e -10.0 1000.0\n", + "EX_lys__L_e -10.0 1000.0\n", + "EX_met__L_e -10.0 1000.0\n", + "EX_phe__L_e -10.0 1000.0\n", + "EX_pro__L_e -10.0 1000.0\n", + "EX_cl_e -10.0 1000.0\n", + "EX_ser__L_e -10.0 1000.0\n", + "EX_thr__L_e -10.0 1000.0\n", + "EX_trp__L_e -10.0 1000.0\n", + "EX_mg2_e -10.0 1000.0\n", + "EX_h2o_e -10.0 1000.0\n", + "EX_nh4_e -10.0 1000.0\n", + "EX_arab__L_e -10.0 1000.0\n", + "EX_glcr_e -10.0 1000.0\n", + "EX_gal_e -10.0 1000.0\n", + "EX_man_e -10.0 1000.0\n", + "EX_galt_e -10.0 1000.0\n", + "EX_sbt__D_e -10.0 1000.0\n", + "EX_glyc_e -10.0 1000.0\n", + "EX_fuc__L_e -10.0 1000.0\n", + "EX_glcn_e -10.0 1000.0\n", + "EX_glyc3p_e -10.0 1000.0\n", + "EX_xyl__D_e -10.0 1000.0\n", + "EX_lac__L_e -10.0 1000.0\n", + "EX_g6p_e -10.0 1000.0\n", + "EX_rib__D_e -10.0 1000.0\n", + "EX_rmn_e -10.0 1000.0\n", + "EX_melib_e -10.0 1000.0\n", + "EX_asp__D_e -10.0 1000.0\n", + "EX_gam_e -10.0 1000.0\n", + "EX_13ppd_e -10.0 1000.0\n", + "EX_2obut_e -10.0 1000.0\n", + "EX_tartr__L_e -10.0 1000.0\n", + "EX_g1p_e -10.0 1000.0\n", + "EX_f6p_e -10.0 1000.0\n", + "EX_mbdg_e -10.0 1000.0\n", + "EX_rbt_e -10.0 1000.0\n", + "EX_malttr_e -10.0 1000.0\n", + "EX_inost_e -10.0 1000.0\n", + "EX_ppa_e -10.0 1000.0\n", + "EX_galct__D_e -10.0 1000.0\n", + "EX_glx_e -10.0 1000.0\n", + "EX_ins_e -10.0 1000.0\n", + "EX_gly_glu__L_e -10.0 1000.0\n", + "EX_acac_e -10.0 1000.0\n", + "EX_acmana_e -10.0 1000.0\n", + "EX_mal__D_e -10.0 1000.0\n", + "EX_4hoxpac_e -10.0 1000.0\n", + "EX_3hoxpac_e -10.0 1000.0\n", + "EX_tym_e -10.0 1000.0\n", + "EX_all__D_e -10.0 1000.0\n", + "EX_lyx__L_e -10.0 1000.0\n", + "EX_pyr_e -10.0 1000.0\n", + "EX_galur_e -10.0 1000.0\n", + "EX_peamn_e -10.0 1000.0\n", + "EX_lcts_e -10.0 1000.0\n", + "\n", + "\n", + "---------- STOP IMPORT FLUX -------------\n", + "\n", + "{'BIOMASS__3': np.float64(0.0)}\n", + "\n", + "\n", + "\u001b[0;94m\n", + "____________________________________________\n", + "____________________________________________\n", + "\u001b[0m\n", + " RESULTS \n", + "\u001b[0;94m____________________________________________\n", + "____________________________________________\n", + "\u001b[0m\n", + " \u001b[1m \"Cobra (seeds)\" \u001b[0m indicates the maximum flux \n", + "obtained in FBA from the seeds after shutting \n", + "off all other exchange reactions. If the maximum \n", + "flux is null, a test is performed opening demand \n", + "reactions for the objective reaction's products, \n", + "in order to test the effect of their accumulation \n", + "(\u001b[1m\"cobra (demands)\"\u001b[0m ). If this test is not performed, \n", + "\"NA\" value is indicated.\n", + "\u001b[0;93m\n", + "____________________________________________\n", + "____________________________________________\n", + "\u001b[0m\n", + " Subset Minimal \n", + "\u001b[0;93m--------------------------------------------\u001b[0m\n", + " REASONING FILTER \n", + "\u001b[0;93m . . . . . . . . . . \u001b[0m\n", + "name | cobra (seeds) | cobra (demands)\n", + "-----|---------------|-----------------\n", + "model_1 | \u001b[0;96m8.644737239404844\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_2 | \u001b[0;96m9.682662691827007\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_3 | \u001b[0;96m11.489068583370964\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_4 | \u001b[0;96m0.08461766164480758\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_5 | \u001b[0;96m11.4890685834513\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_6 | \u001b[0;96m0.08461393294340747\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_7 | \u001b[0;96m11.484058932906027\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_8 | \u001b[0;96m11.484058932765363\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_9 | \u001b[0;96m11.489068583373669\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_10 | \u001b[0;96m11.489068583230493\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_11 | \u001b[0;96m0.08461766164480718\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_12 | \u001b[0;96m0.08461766164480758\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_13 | \u001b[0;96m11.48906858337037\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_14 | \u001b[0;96m11.489068583370885\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_15 | \u001b[0;96m0.08461393294340802\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_16 | \u001b[0;96m11.484058932906027\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_17 | \u001b[0;96m11.484058932975437\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_18 | \u001b[0;96m11.484058932908756\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_19 | \u001b[0;96m0.08461393294341225\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_20 | \u001b[0;96m11.484058932904434\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_21 | \u001b[0;96m11.604014853098676\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_22 | \u001b[0;96m11.604014853219997\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_23 | \u001b[0;96m0.08589834596710179\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_24 | \u001b[0;96m11.600136904714242\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_25 | \u001b[0;96m0.08589834596709892\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_26 | \u001b[0;96m11.600136904715077\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_27 | \u001b[0;96m11.598904482057142\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_28 | \u001b[0;96m0.08589450354670379\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_29 | \u001b[0;96m11.595029947961246\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_30 | \u001b[0;96m11.598904482020295\u001b[0m | \u001b[0mNA\u001b[0m\n", + "\u001b[0;93m\n", + "____________________________________________\n", + "____________________________________________\n", + "\u001b[0m\n", + " Minimize \n", + "\u001b[0;93m--------------------------------------------\u001b[0m\n", + " REASONING FILTER \n", + "\u001b[0;93m . . . . . . . . . . \u001b[0m\n", + "name | cobra (seeds) | cobra (demands)\n", + "-----|---------------|-----------------\n", + "\u001b[0;93m--------------------------------------------\u001b[0m\n", + " REASONING FILTER \n", + "\u001b[0;93m . . . . . . . . . . \u001b[0m\n", + "name | cobra (seeds) | cobra (demands)\n", + "-----|---------------|-----------------\n", + "model_1 | \u001b[0;96m0.16060075544324376\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_2 | \u001b[0;96m0.16060075544307983\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_3 | \u001b[0;96m0.16060075544317312\u001b[0m | \u001b[0mNA\u001b[0m\n", + "\u001b[0;93m\n", + "____________________________________________\n", + "\u001b[0m\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: iCN718\n", + "\n", + "____________________________________________\n", + "\n", + " TARGETS \n", + " FOR TARGET MODE AND FBA \n", + "____________________________________________\n", + "\n", + "Targets set:\n", + " Reactant of objective reaction\n", + " from target file\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " OBJECTVE \n", + " FOR HYBRID \n", + "____________________________________________\n", + "\n", + "Objective set:\n", + " Objective reaction from target file\n", + "\n", + "\n", + "Objective : R_BIOMASS__3\n", + "\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " NETWORK \n", + "____________________________________________\n", + "\n", + "Import reaction: Removed\n", + "Targets can be seeds: No\n", + "Accumulation: Forbidden\n", + "\n", + "\n", + "\u001b[0;93mWARNING : - R_ASAD: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_AGPR: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_ACOTA: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_NAPRT: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_CYSTA: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_KARA1: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + "\n", + " - R_NADTRHD2: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + " - R_SK_ACP_c: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_DNADRAIN deleted. No reactants and no products.\n", + " - R_FOLR2: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_NARK: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + "\u001b[0m\n", + "\u001b[0;96m\n", + "############################################\n", + "############################################\n", + " \u001b[1mREASONING\u001b[0;96m\n", + "############################################\n", + "############################################\n", + "\u001b[0m\n", + "Mode : TARGET\n", + "Option: TARGETS ARE FORBIDDEN SEEDS\n", + "ACCUMULATION: Forbidden\n", + "Time limit: 10.0 minutes\n", + "Solution number limit: 30\n", + "\u001b[0;94m\n", + "____________________________________________\u001b[0m\n", + "\u001b[0;94m____________________________________________\n", + "\u001b[0m\n", + " Sub Mode: \u001b[1mSUBSET MINIMAL\u001b[0m \n", + "\u001b[0;94m____________________________________________\u001b[0m\n", + "\u001b[0;94m____________________________________________\n", + "\u001b[0m\n", + "\n", + "·············· \u001b[1mGuess-Check mode\u001b[0m ············\n", + "\n", + "~~~~~~~~~~~~~~~~ \u001b[1mEnumeration\u001b[0m ~~~~~~~~~~~~~~~\n", + "SOLVING...\n", + "\n", + "Answer: 1 (14 seeds)\n", + "M_3sala_c, M_ACP_c, M_akg_e, M_alatrna_c, M_ap4a_c, M_co1dam_c, M_fe2_c, M_g3pe_c, M_glu1sa_c, M_ptcys_c, M_so4_c, M_thm_c, M_trdox_c, M_xan_e\n", + "\n", + "Answer: 2 (16 seeds)\n", + "M_ACP_c, M_acetone_c, M_adocbi_c, M_alatrna_c, M_dhpmp_c, M_fe2_c, M_g3pe_c, M_glu__L_e, M_hphaccoa_c, M_no2_e, M_ppp9_c, M_rdmbzi_c, M_so4_e, M_thm_c, M_trdox_c, M_uppg3_c\n", + "\n", + "Answer: 3 (19 seeds)\n", + "M_2ohph_c, M_ACP_c, M_adocbi_c, M_amob_c, M_ap4a_c, M_apocarb_c, M_dann_c, M_g3pe_c, M_gbdp_c, M_hcarn_c, M_holocarb_c, M_na1_e, M_pi_e, M_ptth_c, M_rdmbzi_c, M_s_c, M_thm_c, M_trdox_c, M_xan_e\n", + "\n", + "Answer: 4 (19 seeds)\n", + "M_2ohph_c, M_ACP_c, M_adocbi_c, M_ala__L_e, M_amob_c, M_ap4a_c, M_apocarb_c, M_dann_c, M_g3pe_c, M_hcarn_c, M_holocarb_c, M_na1_e, M_nformanth_c, M_ptth_c, M_rdmbzi_c, M_s_c, M_thm_c, M_trdox_c, M_xan_e\n", + "\n", + "Answer: 5 (19 seeds)\n", + "M_2ombz_c, M_ACP_c, M_adocbi_c, M_ala__L_e, M_amob_c, M_ap4a_c, M_apocarb_c, M_dann_c, M_g3pe_c, M_hcarn_c, M_holocarb_c, M_na1_e, M_nformanth_c, M_ptth_c, M_rdmbzi_c, M_s_c, M_thm_c, M_trdox_c, M_xan_e\n", + "\n", + "Answer: 6 (14 seeds)\n", + "M_24dab_c, M_3pg_e, M_ACP_c, M_adocbi_c, M_adprib_c, M_akg_e, M_fe2_c, M_glu1sa_c, M_ptcys_c, M_rdmbzi_c, M_so4_e, M_thm_c, M_trdox_c, M_xtp_c\n", + "\n", + "Answer: 7 (14 seeds)\n", + "M_24dab_c, M_2pg_e, M_ACP_c, M_adocbi_c, M_adprib_c, M_akg_e, M_fe2_c, M_glu1sa_c, M_ptcys_c, M_rdmbzi_c, M_so4_e, M_thm_c, M_trdox_c, M_xtp_c\n", + "\n", + "Answer: 8 (17 seeds)\n", + "M_4mhetz_c, M_8aonn_c, M_ACP_c, M_adocbi_c, M_apocarb_c, M_co2_c, M_cys__L_e, M_dhpmp_c, M_gly_e, M_hcarn_c, M_holocarb_c, M_hphaccoa_c, M_na1_e, M_nformanth_c, M_rdmbzi_c, M_s_c, M_trdox_c\n", + "\n", + "Answer: 9 (14 seeds)\n", + "M_4mhetz_c, M_ACP_c, M_adcobdam_c, M_adprib_c, M_ala__L_e, M_co2_c, M_duri_e, M_glu__D_e, M_id3acald_c, M_mercppyr_c, M_na1_e, M_nformanth_c, M_trdox_c, M_xtp_c\n", + "\n", + "Answer: 10 (18 seeds)\n", + "M_4mhetz_c, M_8aonn_c, M_ACP_c, M_adcobdam_c, M_adprib_c, M_apocarb_c, M_co2_c, M_cysi__L_c, M_glu__D_e, M_gly_e, M_holocarb_c, M_hphaccoa_c, M_met__L_e, M_na1_e, M_nformanth_c, M_s_c, M_trdox_c, M_xtp_c\n", + "\n", + "Answer: 11 (14 seeds)\n", + "M_4mhetz_c, M_ACP_c, M_adcobdam_c, M_adprib_c, M_ala__L_e, M_co2_c, M_cpppg1_c, M_duri_e, M_glu__D_e, M_mercppyr_c, M_na1_e, M_nformanth_c, M_trdox_c, M_xtp_c\n", + "\n", + "Answer: 12 (14 seeds)\n", + "M_4mhetz_c, M_ACP_c, M_adcobdam_c, M_ala__L_e, M_ap4a_c, M_co2_c, M_cpppg1_c, M_duri_e, M_glu__L_e, M_idp_c, M_mercppyr_c, M_na1_e, M_nformanth_c, M_trdox_c\n", + "\n", + "Answer: 13 (14 seeds)\n", + "M_4mhetz_c, M_ACP_c, M_adcobdam_c, M_ala__L_e, M_ap4a_c, M_carn_c, M_co2_c, M_cpppg1_c, M_duri_e, M_idp_c, M_mercppyr_c, M_na1_e, M_nformanth_c, M_trdox_c\n", + "\n", + "Answer: 14 (14 seeds)\n", + "M_4mhetz_c, M_ACP_c, M_adcobdam_c, M_ala__L_e, M_ap4a_c, M_carn_c, M_co2_c, M_cpppg1_c, M_cyst__L_c, M_idp_c, M_na1_e, M_nformanth_c, M_ptth_c, M_trdox_c\n", + "\n", + "Answer: 15 (16 seeds)\n", + "M_4mhetz_c, M_8aonn_c, M_ACP_c, M_adcobdam_c, M_apocarb_c, M_co2_c, M_cys__L_e, M_dhpmp_c, M_gly_e, M_hcarn_c, M_holocarb_c, M_hphaccoa_c, M_na1_e, M_nformanth_c, M_s_c, M_trdox_c\n", + "\n", + "Answer: 16 (15 seeds)\n", + "M_4mhetz_c, M_ACP_c, M_adocbi_c, M_adprib_c, M_ala__L_e, M_carn_c, M_co2_c, M_cpppg1_c, M_cyst__L_c, M_na1_e, M_nformanth_c, M_ptcys_c, M_rdmbzi_c, M_trdox_c, M_xtp_c\n", + "\n", + "Answer: 17 (17 seeds)\n", + "M_4mhetz_c, M_8aonn_c, M_ACP_c, M_adocbi_c, M_adprib_c, M_apocarb_c, M_carn_c, M_fe2_c, M_g3pe_c, M_glu1sa_c, M_holocarb_c, M_met__L_e, M_ptcys_c, M_rdmbzi_c, M_s_c, M_trdox_c, M_xtp_c\n", + "\n", + "Answer: 18 (15 seeds)\n", + "M_24dab_c, M_4mhetz_c, M_ACP_c, M_adcobdam_c, M_akg_e, M_ala__L_e, M_ap4a_c, M_co2_c, M_cpppg1_c, M_duri_e, M_idp_c, M_mercppyr_c, M_na1_e, M_nformanth_c, M_trdox_c\n", + "\n", + "Answer: 19 (15 seeds)\n", + "M_4mhetz_c, M_ACP_c, M_adcobdam_c, M_akg_e, M_ala__L_e, M_ap4a_c, M_co2_c, M_cpppg1_c, M_duri_e, M_idp_c, M_leu__L_e, M_mercppyr_c, M_na1_e, M_nformanth_c, M_trdox_c\n", + "\n", + "Answer: 20 (14 seeds)\n", + "M_4mhetz_c, M_ACP_c, M_adocbi_c, M_ala__L_e, M_co2_c, M_cpppg1_c, M_csn_e, M_cys__L_e, M_dhpmp_c, M_glu__D_e, M_na1_e, M_nformanth_c, M_rdmbzi_c, M_trdox_c\n", + "\n", + "Answer: 21 (16 seeds)\n", + "M_4mhetz_c, M_8aonn_c, M_ACP_c, M_adocbi_c, M_apocarb_c, M_dhpmp_c, M_fe2_c, M_g3pe_c, M_glu1sa_c, M_glu__D_e, M_holocarb_c, M_ptth_c, M_rdmbzi_c, M_s_c, M_so4_e, M_trdox_c\n", + "\n", + "Answer: 22 (13 seeds)\n", + "M_3pg_e, M_4mhetz_c, M_ACP_c, M_adcobdam_c, M_ala__L_e, M_carn_c, M_dhpmp_c, M_h_c, M_met__L_e, M_na1_e, M_nformanth_c, M_ptth_c, M_trdox_c\n", + "\n", + "Answer: 23 (13 seeds)\n", + "M_2pg_e, M_4mhetz_c, M_ACP_c, M_adcobdam_c, M_ala__L_e, M_carn_c, M_dhpmp_c, M_h_c, M_met__L_e, M_na1_e, M_nformanth_c, M_ptth_c, M_trdox_c\n", + "\n", + "Answer: 24 (14 seeds)\n", + "M_4mhetz_c, M_ACP_c, M_adcobdam_c, M_ap4a_c, M_co2_c, M_csn_e, M_cys__L_e, M_gbdp_c, M_glu__D_e, M_id3acald_c, M_idp_c, M_na1_e, M_pi_e, M_trdox_c\n", + "\n", + "Answer: 25 (13 seeds)\n", + "M_3hanthrn_c, M_4mhetz_c, M_ACP_c, M_adocbi_c, M_ala__L_e, M_dhpmp_c, M_f1p_c, M_glu__D_e, M_mercppyr_c, M_na1_e, M_ptcys_c, M_rdmbzi_c, M_trdox_c\n", + "\n", + "Answer: 26 (12 seeds)\n", + "M_3pg_e, M_4mhetz_c, M_ACP_c, M_adocbi_c, M_dhpmp_c, M_fe2_c, M_glu1sa_c, M_glu__L_e, M_met__L_e, M_ptcys_c, M_rdmbzi_c, M_trdox_c\n", + "\n", + "Answer: 27 (12 seeds)\n", + "M_2pg_e, M_4mhetz_c, M_ACP_c, M_adocbi_c, M_dhpmp_c, M_fe2_c, M_glu1sa_c, M_glu__L_e, M_met__L_e, M_ptcys_c, M_rdmbzi_c, M_trdox_c\n", + "\n", + "Answer: 28 (13 seeds)\n", + "M_4mhetz_c, M_ACP_c, M_Lfmkynr_c, M_adocbi_c, M_dhpmp_c, M_fe2_c, M_g3pe_c, M_glu1sa_c, M_glu__L_e, M_met__L_e, M_ptcys_c, M_rdmbzi_c, M_trdox_c\n", + "\n", + "Answer: 29 (15 seeds)\n", + "M_4mhetz_c, M_ACP_c, M_adprib_c, M_co1dam_c, M_co2_c, M_cysi__L_c, M_gbdp_c, M_glu__D_e, M_id3acald_c, M_na1_e, M_pi_e, M_ptth_c, M_so4_e, M_trdox_c, M_xtp_c\n", + "\n", + "Answer: 30 (15 seeds)\n", + "M_4mhetz_c, M_ACP_c, M_adprib_c, M_co1dam_c, M_co2_c, M_cysi__L_c, M_gbdp_c, M_glu__D_e, M_id3acald_c, M_met__L_e, M_na1_e, M_pi_e, M_ptth_c, M_trdox_c, M_xtp_c\n", + "\n", + "Rejected solution during process: 218 \n", + "\n", + "\u001b[0;94m\n", + "____________________________________________\u001b[0m\n", + "\u001b[0;94m____________________________________________\n", + "\u001b[0m\n", + " Sub Mode: \u001b[1mMINIMIZE\u001b[0m \n", + "\u001b[0;94m____________________________________________\u001b[0m\n", + "\u001b[0;94m____________________________________________\n", + "\u001b[0m\n", + "\n", + "·············· \u001b[1mGuess-Check mode\u001b[0m ············\n", + "Finding optimum...\n", + "SOLVING...\n", + "\n", + "Optimum found.\n", + "Minimal size of seed set is 7\n", + "\n", + "Rejected solution during process: 91 \n", + "\n", + "\n", + "~~~~~~~~~~~~~~~~ \u001b[1mEnumeration\u001b[0m ~~~~~~~~~~~~~~~\n", + "SOLVING...\n", + "\n", + "Answer: 1 (7 seeds)\n", + "M_adcobdam_c, M_ahdt_c, M_gthox_c, M_succoa_c, M_tetacp_c, M_thm_c, M_trdrd_c\n", + "\n", + "Answer: 2 (7 seeds)\n", + "M_4mpetz_c, M_adcobdam_c, M_ahdt_c, M_gthox_c, M_succoa_c, M_tetacp_c, M_trdrd_c\n", + "\n", + "Answer: 3 (7 seeds)\n", + "M_4mhetz_c, M_adcobdam_c, M_ahdt_c, M_gthox_c, M_succoa_c, M_tetacp_c, M_trdrd_c\n", + "\n", + "Answer: 4 (7 seeds)\n", + "M_4mhetz_c, M_adcobdam_c, M_dtdpgal_c, M_glc__bD_c, M_gthox_c, M_gtp_c, M_tetacp_c\n", + "\n", + "Answer: 5 (7 seeds)\n", + "M_4mhetz_c, M_adcobdam_c, M_dtdpgal_c, M_glc__D_c, M_gthox_c, M_gtp_c, M_tetacp_c\n", + "\n", + "Answer: 6 (7 seeds)\n", + "M_4mhetz_c, M_adcobdam_c, M_dtdpgal_c, M_gthox_c, M_gtp_c, M_tetacp_c, M_tre6p_c\n", + "\n", + "Answer: 7 (7 seeds)\n", + "M_4mhetz_c, M_adcobdam_c, M_dtdpgal_c, M_glc__D_e, M_gthox_c, M_gtp_c, M_tetacp_c\n", + "\n", + "Time out: 10.0 min expired\n", + "\u001b[0;91mERROR : Time out: 10.0 min expired\u001b[0m\n", + "Rejected solution during process: at least 243 \n", + "\n", + "\u001b[0;96m############################################\n", + "\n", + "\u001b[0m\n", + "\n", + "TIME DATA EXTRACTION : 0.502s\n", + "TIME TOTAL SEED SEARCH: 1450.992s\n", + "TIME TOTAL : 1451.494s\n", + "\n", + "\n", + "\u001b[0;96m\n", + "############################################\n", + "############################################\n", + " \u001b[1mCHECK FLUX\u001b[0;96m\n", + "############################################\n", + "############################################\n", + "\u001b[0m\n", + "---------------- FLUX INIT -----------------\n", + "\n", + "{'BIOMASS__3': np.float64(1.3135912923645483)}\n", + "\n", + "\n", + "--------------- MEDIUM INIT ----------------\n", + "\n", + "EX_tyr__L_e -10.0 1000.0\n", + "EX_val__L_e -10.0 1000.0\n", + "EX_ser__D_e -10.0 1000.0\n", + "EX_orn_e -10.0 1000.0\n", + "EX_ptrc_e -10.0 1000.0\n", + "EX_spmd_e -10.0 1000.0\n", + "EX_co2_e -10.0 1000.0\n", + "EX_na1_e -10.0 1000.0\n", + "EX_no2_e -10.0 1000.0\n", + "EX_o2_e -10.0 1000.0\n", + "EX_urea_e -10.0 1000.0\n", + "EX_chol_e -10.0 1000.0\n", + "EX_adn_e -10.0 1000.0\n", + "EX_cytd_e -10.0 1000.0\n", + "EX_dad_2_e -10.0 1000.0\n", + "EX_dcyt_e -10.0 1000.0\n", + "EX_dgsn_e -10.0 1000.0\n", + "EX_thymd_e -10.0 1000.0\n", + "EX_duri_e -10.0 1000.0\n", + "EX_h_e -10.0 1000.0\n", + "EX_malt_e -10.0 1000.0\n", + "EX_fru_e -10.0 1000.0\n", + "EX_glc__D_e -10.0 1000.0\n", + "EX_glcn__D_e -10.0 1000.0\n", + "EX_mnl_e -10.0 1000.0\n", + "EX_acgam_e -10.0 1000.0\n", + "EX_gsn_e -10.0 1000.0\n", + "EX_uri_e -10.0 1000.0\n", + "EX_csn_e -10.0 1000.0\n", + "EX_xan_e -10.0 1000.0\n", + "EX_ura_e -10.0 1000.0\n", + "EX_taur_e -10.0 1000.0\n", + "EX_etha_e -10.0 1000.0\n", + "EX_sucr_e -10.0 1000.0\n", + "EX_tre_e -10.0 1000.0\n", + "EX_2pg_e -10.0 1000.0\n", + "EX_glyb_e -10.0 1000.0\n", + "EX_bz_e -10.0 1000.0\n", + "EX_3pg_e -10.0 1000.0\n", + "EX_ac_e -10.0 1000.0\n", + "EX_akg_e -10.0 1000.0\n", + "EX_cit_e -10.0 1000.0\n", + "EX_for_e -10.0 1000.0\n", + "EX_fum_e -10.0 1000.0\n", + "EX_succ_e -10.0 1000.0\n", + "EX_icit_e -10.0 1000.0\n", + "EX_mal__L_e -10.0 1000.0\n", + "EX_glyclt_e -10.0 1000.0\n", + "EX_no3_e -10.0 1000.0\n", + "EX_pi_e -10.0 1000.0\n", + "EX_so4_e -10.0 1000.0\n", + "EX_ala__L_e -10.0 1000.0\n", + "EX_arg__L_e -10.0 1000.0\n", + "EX_asn__L_e -10.0 1000.0\n", + "EX_asp__L_e -10.0 1000.0\n", + "EX_cys__L_e -10.0 1000.0\n", + "EX_ala__D_e -10.0 1000.0\n", + "EX_glu__D_e -10.0 1000.0\n", + "EX_gln__L_e -10.0 1000.0\n", + "EX_glu__L_e -10.0 1000.0\n", + "EX_gly_e -10.0 1000.0\n", + "EX_his__L_e -10.0 1000.0\n", + "EX_ile__L_e -10.0 1000.0\n", + "EX_leu__L_e -10.0 1000.0\n", + "EX_lys__L_e -10.0 1000.0\n", + "EX_met__L_e -10.0 1000.0\n", + "EX_phe__L_e -10.0 1000.0\n", + "EX_pro__L_e -10.0 1000.0\n", + "EX_cl_e -10.0 1000.0\n", + "EX_ser__L_e -10.0 1000.0\n", + "EX_thr__L_e -10.0 1000.0\n", + "EX_trp__L_e -10.0 1000.0\n", + "EX_mg2_e -10.0 1000.0\n", + "EX_h2o_e -10.0 1000.0\n", + "EX_nh4_e -10.0 1000.0\n", + "EX_arab__L_e -10.0 1000.0\n", + "EX_glcr_e -10.0 1000.0\n", + "EX_gal_e -10.0 1000.0\n", + "EX_man_e -10.0 1000.0\n", + "EX_galt_e -10.0 1000.0\n", + "EX_sbt__D_e -10.0 1000.0\n", + "EX_glyc_e -10.0 1000.0\n", + "EX_fuc__L_e -10.0 1000.0\n", + "EX_glcn_e -10.0 1000.0\n", + "EX_glyc3p_e -10.0 1000.0\n", + "EX_xyl__D_e -10.0 1000.0\n", + "EX_lac__L_e -10.0 1000.0\n", + "EX_g6p_e -10.0 1000.0\n", + "EX_rib__D_e -10.0 1000.0\n", + "EX_rmn_e -10.0 1000.0\n", + "EX_melib_e -10.0 1000.0\n", + "EX_asp__D_e -10.0 1000.0\n", + "EX_gam_e -10.0 1000.0\n", + "EX_13ppd_e -10.0 1000.0\n", + "EX_2obut_e -10.0 1000.0\n", + "EX_tartr__L_e -10.0 1000.0\n", + "EX_g1p_e -10.0 1000.0\n", + "EX_f6p_e -10.0 1000.0\n", + "EX_mbdg_e -10.0 1000.0\n", + "EX_rbt_e -10.0 1000.0\n", + "EX_malttr_e -10.0 1000.0\n", + "EX_inost_e -10.0 1000.0\n", + "EX_ppa_e -10.0 1000.0\n", + "EX_galct__D_e -10.0 1000.0\n", + "EX_glx_e -10.0 1000.0\n", + "EX_ins_e -10.0 1000.0\n", + "EX_gly_glu__L_e -10.0 1000.0\n", + "EX_acac_e -10.0 1000.0\n", + "EX_acmana_e -10.0 1000.0\n", + "EX_mal__D_e -10.0 1000.0\n", + "EX_4hoxpac_e -10.0 1000.0\n", + "EX_3hoxpac_e -10.0 1000.0\n", + "EX_tym_e -10.0 1000.0\n", + "EX_all__D_e -10.0 1000.0\n", + "EX_lyx__L_e -10.0 1000.0\n", + "EX_pyr_e -10.0 1000.0\n", + "EX_galur_e -10.0 1000.0\n", + "EX_peamn_e -10.0 1000.0\n", + "EX_lcts_e -10.0 1000.0\n", + "\n", + "\n", + "---------- STOP IMPORT FLUX -------------\n", + "\n", + "{'BIOMASS__3': np.float64(0.0)}\n", + "\n", + "\n", + "\u001b[0;94m\n", + "____________________________________________\n", + "____________________________________________\n", + "\u001b[0m\n", + " RESULTS \n", + "\u001b[0;94m____________________________________________\n", + "____________________________________________\n", + "\u001b[0m\n", + " \u001b[1m \"Cobra (seeds)\" \u001b[0m indicates the maximum flux \n", + "obtained in FBA from the seeds after shutting \n", + "off all other exchange reactions. If the maximum \n", + "flux is null, a test is performed opening demand \n", + "reactions for the objective reaction's products, \n", + "in order to test the effect of their accumulation \n", + "(\u001b[1m\"cobra (demands)\"\u001b[0m ). If this test is not performed, \n", + "\"NA\" value is indicated.\n", + "\u001b[0;93m\n", + "____________________________________________\n", + "____________________________________________\n", + "\u001b[0m\n", + " Subset Minimal \n", + "\u001b[0;93m--------------------------------------------\u001b[0m\n", + " REASONING GUESS-CHECK \n", + "\u001b[0;93m . . . . . . . . . . \u001b[0m\n", + "name | cobra (seeds) | cobra (demands)\n", + "-----|---------------|-----------------\n", + "model_1 | \u001b[0;96m11.595029947739947\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_2 | \u001b[0;96m15.634417929802696\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_3 | \u001b[0;96m11.580646155224125\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_4 | \u001b[0;96m11.606436733023573\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_5 | \u001b[0;96m11.606436733024909\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_6 | \u001b[0;96m0.4132078191383604\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_7 | \u001b[0;96m0.4132078191383604\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_8 | \u001b[0;96m4.015556932938674\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_9 | \u001b[0;96m9.394503181911226\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_10 | \u001b[0;96m9.236657843763487\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_11 | \u001b[0;96m9.394503181911192\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_12 | \u001b[0;96m3.250652067641271\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_13 | \u001b[0;96m3.053240363235924\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_14 | \u001b[0;96m6.060479763094121\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_15 | \u001b[0;96m4.015556932938037\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_16 | \u001b[0;96m6.156957583809918\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_17 | \u001b[0;96m11.902549374238427\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_18 | \u001b[0;96m4.843521817120871\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_19 | \u001b[0;96m4.792599142118797\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_20 | \u001b[0;96m8.822659893532345\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_21 | \u001b[0;96m11.068508573138756\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_22 | \u001b[0;96m0.17874086681938026\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_23 | \u001b[0;96m0.1787408668181123\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_24 | \u001b[0;96m9.049461884098273\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_25 | \u001b[0;96m0.37899577976448096\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_26 | \u001b[0;96m0.08281888694293614\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_27 | \u001b[0;96m0.08281888694292752\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_28 | \u001b[0;96m11.111362594195253\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_29 | \u001b[0;96m9.058963118809023\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_30 | \u001b[0;96m9.12056673643139\u001b[0m | \u001b[0mNA\u001b[0m\n", + "\u001b[0;93m\n", + "____________________________________________\n", + "____________________________________________\n", + "\u001b[0m\n", + " Minimize \n", + "\u001b[0;93m--------------------------------------------\u001b[0m\n", + " REASONING GUESS-CHECK \n", + "\u001b[0;93m . . . . . . . . . . \u001b[0m\n", + "name | cobra (seeds) | cobra (demands)\n", + "-----|---------------|-----------------\n", + "\u001b[0;93m--------------------------------------------\u001b[0m\n", + " REASONING GUESS-CHECK \n", + "\u001b[0;93m . . . . . . . . . . \u001b[0m\n", + "name | cobra (seeds) | cobra (demands)\n", + "-----|---------------|-----------------\n", + "model_1 | \u001b[0;96m0.16060075544313768\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_2 | \u001b[0;96m0.16060075544304922\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_3 | \u001b[0;96m0.1606007554430786\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_4 | \u001b[0;96m1.3049143437751831\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_5 | \u001b[0;96m1.304914343772125\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_6 | \u001b[0;96m1.3584159226282044\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_7 | \u001b[0;96m0.06067032018730024\u001b[0m | \u001b[0mNA\u001b[0m\n", + "\u001b[0;93m\n", + "____________________________________________\n", + "\u001b[0m\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: iCN718\n", + "\n", + "____________________________________________\n", + "\n", + " TARGETS \n", + " FOR TARGET MODE AND FBA \n", + "____________________________________________\n", + "\n", + "Targets set:\n", + " Reactant of objective reaction\n", + " from target file\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " OBJECTVE \n", + " FOR HYBRID \n", + "____________________________________________\n", + "\n", + "Objective set:\n", + " Objective reaction from target file\n", + "\n", + "\n", + "Objective : R_BIOMASS__3\n", + "\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " NETWORK \n", + "____________________________________________\n", + "\n", + "Import reaction: Removed\n", + "Targets can be seeds: No\n", + "Accumulation: Forbidden\n", + "\n", + "\n", + "\u001b[0;93mWARNING : - R_ASAD: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_AGPR: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_ACOTA: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_NAPRT: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_CYSTA: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_KARA1: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + "\n", + " - R_NADTRHD2: Deleted.\n", + " Boundaries was: [0.0 ; 0.0]\n", + " - R_SK_ACP_c: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_DNADRAIN deleted. No reactants and no products.\n", + " - R_FOLR2: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + " - R_NARK: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + "\u001b[0m\n", + "\u001b[0;96m\n", + "############################################\n", + "############################################\n", + " \u001b[1mREASONING\u001b[0;96m\n", + "############################################\n", + "############################################\n", + "\u001b[0m\n", + "Mode : TARGET\n", + "Option: TARGETS ARE FORBIDDEN SEEDS\n", + "ACCUMULATION: Forbidden\n", + "Time limit: 10.0 minutes\n", + "Solution number limit: 30\n", + "\u001b[0;94m\n", + "____________________________________________\u001b[0m\n", + "\u001b[0;94m____________________________________________\n", + "\u001b[0m\n", + " Sub Mode: \u001b[1mSUBSET MINIMAL\u001b[0m \n", + "\u001b[0;94m____________________________________________\u001b[0m\n", + "\u001b[0;94m____________________________________________\n", + "\u001b[0m\n", + "\n", + "····· \u001b[1mGuess-Check with diversity mode\u001b[0m ······\n", + "\n", + "~~~~~~~~~~~~~~~~ \u001b[1mEnumeration\u001b[0m ~~~~~~~~~~~~~~~\n", + "SOLVING...\n", + "\n", + "Answer: 1 (24 seeds)\n", + "M_2ombzl_c, M_4mhetz_c, M_acetone_c, M_amob_c, M_apocarb_c, M_carn_c, M_cbi_c, M_cpppg3_c, M_dann_c, M_e4hglu_c, M_f1p_c, M_holocarb_c, M_itp_c, M_mal__L_e, M_mhpglu_c, M_mql7_c, M_no2_e, M_paps_c, M_pmcoa_c, M_rdmbzi_c, M_s_c, M_taur_c, M_tetacp_c, M_trdox_c\n", + "\n", + "Answer: 2 (26 seeds)\n", + "M_2ohph_c, M_3pg_e, M_4mhetz_c, M_amob_c, M_apocarb_c, M_asn__L_e, M_carn_c, M_cbi_c, M_cpppg3_c, M_dann_c, M_gly_e, M_holocarb_c, M_hpglu_c, M_hphaccoa_c, M_idp_c, M_laur_kdo2lipid4_c, M_mql7_c, M_na1_e, M_nformanth_c, M_paps_c, M_rdmbzi_c, M_rdxo_c, M_s_c, M_tetacp_c, M_trdrd_c, M_urdglyc_c\n", + "\n", + "Answer: 3 (25 seeds)\n", + "M_2ombzl_c, M_3pg_e, M_4mhetz_c, M_ACP_c, M_amob_c, M_apocarb_c, M_asn__L_e, M_btamp_c, M_carn_c, M_cbi_c, M_cpppg3_c, M_dann_c, M_g3pc_c, M_gly_e, M_holocarb_c, M_hpglu_c, M_hphaccoa_c, M_idp_c, M_na1_e, M_nformanth_c, M_rdmbzi_c, M_rdxo_c, M_s_c, M_trdox_c, M_urdglyc_c\n", + "\n", + "Answer: 4 (16 seeds)\n", + "M_4mhetz_c, M_acACP_c, M_adprib_c, M_ala__L_e, M_carn_c, M_co1dam_c, M_dad_2_e, M_dcdp_c, M_fe2_c, M_glc__D_e, M_malACP_c, M_mercppyr_c, M_na1_e, M_nformanth_c, M_ppp9_c, M_xtp_c\n", + "\n", + "Answer: 5 (15 seeds)\n", + "M_24dab_c, M_3hanthrn_c, M_4mhetz_c, M_adocbi_c, M_akg_e, M_ala__L_e, M_dad_2_e, M_dcdp_c, M_dhpmp_c, M_f1p_c, M_mercppyr_c, M_na1_e, M_ptth_c, M_rdmbzi_c, M_tetacp_c\n", + "\n", + "Answer: 6 (25 seeds)\n", + "M_2oph_c, M_3pg_e, M_5aop_c, M_ACP_c, M_adprib_c, M_akg_e, M_amob_c, M_apocarb_c, M_asn__L_e, M_bgly_c, M_cbi_c, M_dann_c, M_fe2_c, M_g3pc_c, M_holocarb_c, M_hpglu_c, M_hphaccoa_c, M_idp_c, M_na1_e, M_rdmbzi_c, M_rdxo_c, M_s_c, M_thm_c, M_trdrd_c, M_urdglyc_c\n", + "\n", + "Answer: 7 (14 seeds)\n", + "M_3hanthrn_c, M_3sala_c, M_4mpetz_c, M_adcobdam_c, M_akg_e, M_ala__L_e, M_dhpmp_c, M_dodecacp_c, M_f1p_c, M_hmn4_c, M_mercppyr_c, M_na1_e, M_ptth_c, M_trdox_c\n", + "\n", + "Answer: 8 (17 seeds)\n", + "M_24dab_c, M_4mhetz_c, M_acetone_c, M_adocbi_c, M_agdpcbi_c, M_akg_e, M_ala__L_e, M_cysi__L_c, M_dhpmp_c, M_dodecacp_c, M_na1_e, M_nformanth_c, M_no3_e, M_ptth_c, M_rdmbzi_c, M_so4_e, M_trdox_c\n", + "\n", + "Answer: 9 (25 seeds)\n", + "M_2oph_c, M_3pg_e, M_4mpetz_c, M_ACP_c, M_adocbi_c, M_adprib_c, M_amob_c, M_apocarb_c, M_asn__L_e, M_bgly_c, M_carn_c, M_dann_c, M_fe2_c, M_g3pc_c, M_glu1sa_c, M_holocarb_c, M_hpglu_c, M_hphaccoa_c, M_idp_c, M_na1_e, M_rdmbzi_c, M_rdxo_c, M_s_c, M_trdrd_c, M_urdglyc_c\n", + "\n", + "Answer: 10 (15 seeds)\n", + "M_24dab_c, M_4mpetz_c, M_acetone_c, M_akg_e, M_ap4a_c, M_co1dam_c, M_cyst__L_c, M_dhpmp_c, M_dodecacp_c, M_h_c, M_nh4_e, M_no3_e, M_ptcys_c, M_trdox_c, M_uppg1_c\n", + "\n", + "Answer: 11 (14 seeds)\n", + "M_3sala_c, M_4mhetz_c, M_acetone_c, M_akg_e, M_ap4a_c, M_co1dam_c, M_csn_e, M_dodecacp_c, M_fe2_c, M_glu1sa_c, M_mercppyr_c, M_no3_e, M_trdrd_c, M_xtp_c\n", + "\n", + "Answer: 12 (16 seeds)\n", + "M_3hanthrn_c, M_3sala_c, M_4mpetz_c, M_acetone_c, M_akg_e, M_ala__L_e, M_cbi_c, M_csn_e, M_dodecacp_c, M_mercppyr_c, M_na1_e, M_no3_e, M_pap_c, M_rdmbzi_c, M_trdox_c, M_xtp_c\n", + "\n", + "Answer: 13 (19 seeds)\n", + "M_4mpetz_c, M_adcobdam_c, M_amob_c, M_ap4a_c, M_apocarb_c, M_cpppg3_c, M_dann_c, M_dcmp_c, M_din_c, M_glu__D_e, M_gly_e, M_holocarb_c, M_hphaccoa_c, M_na1_e, M_nformanth_c, M_s_c, M_selcyst_c, M_tetacp_c, M_xtp_c\n", + "\n", + "Answer: 14 (13 seeds)\n", + "M_3sala_c, M_acetone_c, M_adcobdam_c, M_akg_e, M_ap4a_c, M_cyst__L_c, M_dodecacp_c, M_etha_c, M_no3_e, M_ptth_c, M_thm_c, M_trdox_c, M_xan_c\n", + "\n", + "Answer: 15 (19 seeds)\n", + "M_3sala_c, M_4mpetz_c, M_ACP_c, M_adocbi_c, M_akg_e, M_amob_c, M_ap4a_c, M_apocarb_c, M_cpppg3_c, M_dann_c, M_g3pe_c, M_holocarb_c, M_hphaccoa_c, M_igly_c, M_nformanth_c, M_rdmbzi_c, M_s_c, M_trdox_c, M_xan_e\n", + "\n", + "Answer: 16 (22 seeds)\n", + "M_13dampp_c, M_2ombz_c, M_2oph_c, M_3pg_e, M_3sala_c, M_4mhetz_c, M_ACP_c, M_acetone_c, M_adprib_c, M_akg_e, M_amob_c, M_apocarb_c, M_co1dam_c, M_dann_c, M_g3pc_c, M_holocarb_c, M_idp_c, M_no2_e, M_ptth_c, M_s_c, M_trdrd_c, M_urdglyc_c\n", + "\n", + "Answer: 17 (13 seeds)\n", + "M_5aop_c, M_ap4a_c, M_carn_c, M_cbi_c, M_cyst__L_c, M_dctp_c, M_din_c, M_fe2_c, M_ptcys_c, M_rdmbzi_c, M_tetacp_c, M_thm_c, M_xan_e\n", + "\n", + "Answer: 18 (22 seeds)\n", + "M_2ombz_c, M_3sala_c, M_4mhetz_c, M_acetone_c, M_adprib_c, M_akg_e, M_amob_c, M_apocarb_c, M_co1dam_c, M_dad_2_e, M_dann_c, M_dctp_c, M_dhpmp_c, M_dodecacp_c, M_gbdp_c, M_holocarb_c, M_na1_e, M_no3_e, M_pi_e, M_ptth_c, M_s_c, M_selcyst_c\n", + "\n", + "Answer: 19 (14 seeds)\n", + "M_3sala_c, M_4mpetz_c, M_acetone_c, M_akg_e, M_ap4a_c, M_co1dam_c, M_cyst__L_c, M_dhpmp_c, M_dodecacp_c, M_fe2_c, M_glu1sa_c, M_no3_e, M_ptcys_c, M_trdrd_c\n", + "\n", + "Answer: 20 (17 seeds)\n", + "M_3sala_c, M_acetone_c, M_akg_e, M_ap4a_c, M_cbi_c, M_cyst__L_c, M_dodecacp_c, M_gbdp_c, M_h_c, M_na1_e, M_no3_e, M_pi_e, M_ptth_c, M_rdmbzi_c, M_thm_c, M_trdox_c, M_xtp_c\n", + "\n", + "Answer: 21 (14 seeds)\n", + "M_3hanthrn_c, M_4mhetz_c, M_ala__L_e, M_ap4a_c, M_carn_c, M_cbi_c, M_dctp_c, M_din_c, M_mercppyr_c, M_na1_e, M_ptth_c, M_rdmbzi_c, M_tetacp_c, M_xtp_c\n", + "\n", + "Answer: 22 (18 seeds)\n", + "M_3hanthrn_c, M_4mhetz_c, M_acetone_c, M_ala__L_e, M_apocarb_c, M_btamp_c, M_carn_c, M_cbi_c, M_cysi__L_c, M_dodecacp_c, M_holocarb_c, M_itp_c, M_na1_e, M_no3_e, M_ptcys_c, M_rdmbzi_c, M_so4_e, M_trdrd_c\n", + "\n", + "Answer: 23 (14 seeds)\n", + "M_3sala_c, M_4mhetz_c, M_acetone_c, M_adcobdam_c, M_adprib_c, M_akg_e, M_cyst__L_c, M_dodecacp_c, M_fe2_c, M_glu1sa_c, M_itp_c, M_no3_e, M_ptth_c, M_trdrd_c\n", + "\n", + "Answer: 24 (18 seeds)\n", + "M_3hanthrn_c, M_4mpetz_c, M_8aonn_c, M_acetone_c, M_adcobdam_c, M_apocarb_c, M_carn_c, M_cyst__L_c, M_dhpmp_c, M_dodecacp_c, M_h_c, M_holocarb_c, M_hphaccoa_c, M_igly_c, M_indpyr_c, M_no3_e, M_s_c, M_trdrd_c\n", + "\n", + "Answer: 25 (14 seeds)\n", + "M_4mpetz_c, M_5aop_c, M_acetone_c, M_adprib_c, M_carn_c, M_cbi_c, M_cyst__L_c, M_dodecacp_c, M_fe2_c, M_no3_e, M_ptcys_c, M_rdmbzi_c, M_trdox_c, M_xtp_c\n", + "\n", + "Answer: 26 (15 seeds)\n", + "M_4mhetz_c, M_5aop_c, M_acetone_c, M_adprib_c, M_carn_c, M_cbi_c, M_cysi__L_c, M_dodecacp_c, M_fe2_c, M_no3_e, M_ptcys_c, M_rdmbzi_c, M_so4_e, M_trdrd_c, M_xtp_c\n", + "\n", + "Answer: 27 (14 seeds)\n", + "M_3sala_c, M_4mpetz_c, M_acetone_c, M_adcobdam_c, M_akg_e, M_ap4a_c, M_co2_c, M_cyst__L_c, M_dodecacp_c, M_etha_c, M_no3_e, M_ptth_c, M_trdox_c, M_xtp_c\n", + "\n", + "Answer: 28 (15 seeds)\n", + "M_3hanthrn_c, M_acetone_c, M_adocbi_c, M_ala__L_e, M_carn_c, M_cyst__L_c, M_dad_2_e, M_dhpmp_c, M_dodecacp_c, M_dump_c, M_na1_e, M_no3_e, M_ptth_c, M_rdmbzi_c, M_thm_c\n", + "\n", + "Answer: 29 (16 seeds)\n", + "M_3sala_c, M_4mhetz_c, M_acetone_c, M_adocbi_c, M_akg_e, M_cyst__L_c, M_dhpmp_c, M_dodecacp_c, M_gbdp_c, M_h_c, M_na1_e, M_no3_e, M_pi_e, M_ptth_c, M_rdmbzi_c, M_trdox_c\n", + "\n", + "Answer: 30 (16 seeds)\n", + "M_3hanthrn_c, M_3sala_c, M_acetone_c, M_adprib_c, M_akg_e, M_ala__L_e, M_cbi_c, M_cyst__L_c, M_dodecacp_c, M_na1_e, M_no3_e, M_ptth_c, M_rdmbzi_c, M_thm_c, M_trdrd_c, M_xtp_c\n", + "\n", + "Rejected solution during process: 217 \n", + "\n", + "\u001b[0;94m\n", + "____________________________________________\u001b[0m\n", + "\u001b[0;94m____________________________________________\n", + "\u001b[0m\n", + " Sub Mode: \u001b[1mMINIMIZE\u001b[0m \n", + "\u001b[0;94m____________________________________________\u001b[0m\n", + "\u001b[0;94m____________________________________________\n", + "\u001b[0m\n", + "\n", + "····· \u001b[1mGuess-Check with diversity mode\u001b[0m ······\n", + "Finding optimum...\n", + "SOLVING...\n", + "\n", + "Optimum found.\n", + "Minimal size of seed set is 7\n", + "\n", + "Rejected solution during process: 20 \n", + "\n", + "\n", + "~~~~~~~~~~~~~~~~ \u001b[1mEnumeration\u001b[0m ~~~~~~~~~~~~~~~\n", + "SOLVING...\n", + "\n", + "Answer: 1 (7 seeds)\n", + "M_4mhetz_c, M_adcobdam_c, M_dodecacp_c, M_dtdpglu_c, M_glc__D_c, M_gthox_c, M_gtp_c\n", + "\n", + "Answer: 2 (7 seeds)\n", + "M_4mhetz_c, M_adcobdam_c, M_dodecacp_c, M_dtdpglu_c, M_gthox_c, M_gtp_c, M_succoa_c\n", + "\n", + "Answer: 3 (7 seeds)\n", + "M_4mhetz_c, M_adcobdam_c, M_dodecacp_c, M_dtdpglu_c, M_glc__bD_c, M_gthox_c, M_gtp_c\n", + "\n", + "Answer: 4 (7 seeds)\n", + "M_4mhetz_c, M_adcobdam_c, M_dodecacp_c, M_dtdpglu_c, M_gthox_c, M_gtp_c, M_tre6p_c\n", + "\n", + "Answer: 5 (7 seeds)\n", + "M_4mhetz_c, M_adcobdam_c, M_dodecacp_c, M_dtdpglu_c, M_glc__D_e, M_gthox_c, M_gtp_c\n", + "\n", + "Answer: 6 (7 seeds)\n", + "M_4mhetz_c, M_ACP_c, M_adcobdam_c, M_dtdpglu_c, M_glc__D_c, M_gthox_c, M_gtp_c\n", + "\n", + "Answer: 7 (7 seeds)\n", + "M_4mhetz_c, M_ACP_c, M_adcobdam_c, M_dtdpglu_c, M_glc__bD_c, M_gthox_c, M_gtp_c\n", + "\n", + "Answer: 8 (7 seeds)\n", + "M_4mhetz_c, M_ACP_c, M_adcobdam_c, M_dtdpglu_c, M_gthox_c, M_gtp_c, M_tre6p_c\n", + "\n", + "Answer: 9 (7 seeds)\n", + "M_4mhetz_c, M_ACP_c, M_adcobdam_c, M_dtdpglu_c, M_glc__D_e, M_gthox_c, M_gtp_c\n", + "\n", + "Answer: 10 (7 seeds)\n", + "M_4mhetz_c, M_ACP_c, M_adcobdam_c, M_dtdpglu_c, M_glc__D_c, M_gtp_c, M_lgt__S_c\n", + "\n", + "Answer: 11 (7 seeds)\n", + "M_4mhetz_c, M_ACP_c, M_adcobdam_c, M_dtdpglu_c, M_glc__bD_c, M_gtp_c, M_lgt__S_c\n", + "\n", + "Answer: 12 (7 seeds)\n", + "M_4mhetz_c, M_ACP_c, M_adcobdam_c, M_dtdpglu_c, M_gtp_c, M_lgt__S_c, M_tre6p_c\n", + "\n", + "Answer: 13 (7 seeds)\n", + "M_4mhetz_c, M_ACP_c, M_adcobdam_c, M_dtdpglu_c, M_gthox_c, M_gtp_c, M_succoa_c\n", + "\n", + "Answer: 14 (7 seeds)\n", + "M_4mhetz_c, M_ACP_c, M_adcobdam_c, M_dtdpglu_c, M_glc__D_c, M_gthrd_c, M_gtp_c\n", + "\n", + "Answer: 15 (7 seeds)\n", + "M_4mhetz_c, M_ACP_c, M_adcobdam_c, M_dtdpglu_c, M_glc__bD_c, M_gthrd_c, M_gtp_c\n", + "\n", + "Answer: 16 (7 seeds)\n", + "M_4mhetz_c, M_ACP_c, M_adcobdam_c, M_dtdpglu_c, M_gthrd_c, M_gtp_c, M_tre6p_c\n", + "\n", + "Answer: 17 (7 seeds)\n", + "M_4mhetz_c, M_ACP_c, M_adcobdam_c, M_dtdpglu_c, M_glc__D_c, M_glucys_c, M_gtp_c\n", + "\n", + "Answer: 18 (7 seeds)\n", + "M_4mhetz_c, M_ACP_c, M_adcobdam_c, M_dtdpglu_c, M_glc__bD_c, M_glucys_c, M_gtp_c\n", + "\n", + "Answer: 19 (7 seeds)\n", + "M_4mhetz_c, M_ACP_c, M_adcobdam_c, M_dtdpglu_c, M_glucys_c, M_gtp_c, M_tre6p_c\n", + "\n", + "Answer: 20 (7 seeds)\n", + "M_4mhetz_c, M_adcobdam_c, M_dtdpglu_c, M_glc__D_c, M_gthrd_c, M_gtp_c, M_tetacp_c\n", + "\n", + "Answer: 21 (7 seeds)\n", + "M_4mhetz_c, M_adcobdam_c, M_dtdpglu_c, M_glc__bD_c, M_gthrd_c, M_gtp_c, M_tetacp_c\n", + "\n", + "Answer: 22 (7 seeds)\n", + "M_4mhetz_c, M_adcobdam_c, M_dtdpglu_c, M_gthrd_c, M_gtp_c, M_tetacp_c, M_tre6p_c\n", + "\n", + "Time out: 10.0 min expired\n", + "\u001b[0;91mERROR : Time out: 10.0 min expired\u001b[0m\n", + "Rejected solution during process: at least 400 \n", + "\n", + "\u001b[0;96m############################################\n", + "\n", + "\u001b[0m\n", + "\n", + "TIME DATA EXTRACTION : 0.502s\n", + "TIME TOTAL SEED SEARCH: 1375.956s\n", + "TIME TOTAL : 1376.458s\n", + "\n", + "\n", + "\u001b[0;96m\n", + "############################################\n", + "############################################\n", + " \u001b[1mCHECK FLUX\u001b[0;96m\n", + "############################################\n", + "############################################\n", + "\u001b[0m\n", + "---------------- FLUX INIT -----------------\n", + "\n", + "{'BIOMASS__3': np.float64(1.3135912923645483)}\n", + "\n", + "\n", + "--------------- MEDIUM INIT ----------------\n", + "\n", + "EX_tyr__L_e -10.0 1000.0\n", + "EX_val__L_e -10.0 1000.0\n", + "EX_ser__D_e -10.0 1000.0\n", + "EX_orn_e -10.0 1000.0\n", + "EX_ptrc_e -10.0 1000.0\n", + "EX_spmd_e -10.0 1000.0\n", + "EX_co2_e -10.0 1000.0\n", + "EX_na1_e -10.0 1000.0\n", + "EX_no2_e -10.0 1000.0\n", + "EX_o2_e -10.0 1000.0\n", + "EX_urea_e -10.0 1000.0\n", + "EX_chol_e -10.0 1000.0\n", + "EX_adn_e -10.0 1000.0\n", + "EX_cytd_e -10.0 1000.0\n", + "EX_dad_2_e -10.0 1000.0\n", + "EX_dcyt_e -10.0 1000.0\n", + "EX_dgsn_e -10.0 1000.0\n", + "EX_thymd_e -10.0 1000.0\n", + "EX_duri_e -10.0 1000.0\n", + "EX_h_e -10.0 1000.0\n", + "EX_malt_e -10.0 1000.0\n", + "EX_fru_e -10.0 1000.0\n", + "EX_glc__D_e -10.0 1000.0\n", + "EX_glcn__D_e -10.0 1000.0\n", + "EX_mnl_e -10.0 1000.0\n", + "EX_acgam_e -10.0 1000.0\n", + "EX_gsn_e -10.0 1000.0\n", + "EX_uri_e -10.0 1000.0\n", + "EX_csn_e -10.0 1000.0\n", + "EX_xan_e -10.0 1000.0\n", + "EX_ura_e -10.0 1000.0\n", + "EX_taur_e -10.0 1000.0\n", + "EX_etha_e -10.0 1000.0\n", + "EX_sucr_e -10.0 1000.0\n", + "EX_tre_e -10.0 1000.0\n", + "EX_2pg_e -10.0 1000.0\n", + "EX_glyb_e -10.0 1000.0\n", + "EX_bz_e -10.0 1000.0\n", + "EX_3pg_e -10.0 1000.0\n", + "EX_ac_e -10.0 1000.0\n", + "EX_akg_e -10.0 1000.0\n", + "EX_cit_e -10.0 1000.0\n", + "EX_for_e -10.0 1000.0\n", + "EX_fum_e -10.0 1000.0\n", + "EX_succ_e -10.0 1000.0\n", + "EX_icit_e -10.0 1000.0\n", + "EX_mal__L_e -10.0 1000.0\n", + "EX_glyclt_e -10.0 1000.0\n", + "EX_no3_e -10.0 1000.0\n", + "EX_pi_e -10.0 1000.0\n", + "EX_so4_e -10.0 1000.0\n", + "EX_ala__L_e -10.0 1000.0\n", + "EX_arg__L_e -10.0 1000.0\n", + "EX_asn__L_e -10.0 1000.0\n", + "EX_asp__L_e -10.0 1000.0\n", + "EX_cys__L_e -10.0 1000.0\n", + "EX_ala__D_e -10.0 1000.0\n", + "EX_glu__D_e -10.0 1000.0\n", + "EX_gln__L_e -10.0 1000.0\n", + "EX_glu__L_e -10.0 1000.0\n", + "EX_gly_e -10.0 1000.0\n", + "EX_his__L_e -10.0 1000.0\n", + "EX_ile__L_e -10.0 1000.0\n", + "EX_leu__L_e -10.0 1000.0\n", + "EX_lys__L_e -10.0 1000.0\n", + "EX_met__L_e -10.0 1000.0\n", + "EX_phe__L_e -10.0 1000.0\n", + "EX_pro__L_e -10.0 1000.0\n", + "EX_cl_e -10.0 1000.0\n", + "EX_ser__L_e -10.0 1000.0\n", + "EX_thr__L_e -10.0 1000.0\n", + "EX_trp__L_e -10.0 1000.0\n", + "EX_mg2_e -10.0 1000.0\n", + "EX_h2o_e -10.0 1000.0\n", + "EX_nh4_e -10.0 1000.0\n", + "EX_arab__L_e -10.0 1000.0\n", + "EX_glcr_e -10.0 1000.0\n", + "EX_gal_e -10.0 1000.0\n", + "EX_man_e -10.0 1000.0\n", + "EX_galt_e -10.0 1000.0\n", + "EX_sbt__D_e -10.0 1000.0\n", + "EX_glyc_e -10.0 1000.0\n", + "EX_fuc__L_e -10.0 1000.0\n", + "EX_glcn_e -10.0 1000.0\n", + "EX_glyc3p_e -10.0 1000.0\n", + "EX_xyl__D_e -10.0 1000.0\n", + "EX_lac__L_e -10.0 1000.0\n", + "EX_g6p_e -10.0 1000.0\n", + "EX_rib__D_e -10.0 1000.0\n", + "EX_rmn_e -10.0 1000.0\n", + "EX_melib_e -10.0 1000.0\n", + "EX_asp__D_e -10.0 1000.0\n", + "EX_gam_e -10.0 1000.0\n", + "EX_13ppd_e -10.0 1000.0\n", + "EX_2obut_e -10.0 1000.0\n", + "EX_tartr__L_e -10.0 1000.0\n", + "EX_g1p_e -10.0 1000.0\n", + "EX_f6p_e -10.0 1000.0\n", + "EX_mbdg_e -10.0 1000.0\n", + "EX_rbt_e -10.0 1000.0\n", + "EX_malttr_e -10.0 1000.0\n", + "EX_inost_e -10.0 1000.0\n", + "EX_ppa_e -10.0 1000.0\n", + "EX_galct__D_e -10.0 1000.0\n", + "EX_glx_e -10.0 1000.0\n", + "EX_ins_e -10.0 1000.0\n", + "EX_gly_glu__L_e -10.0 1000.0\n", + "EX_acac_e -10.0 1000.0\n", + "EX_acmana_e -10.0 1000.0\n", + "EX_mal__D_e -10.0 1000.0\n", + "EX_4hoxpac_e -10.0 1000.0\n", + "EX_3hoxpac_e -10.0 1000.0\n", + "EX_tym_e -10.0 1000.0\n", + "EX_all__D_e -10.0 1000.0\n", + "EX_lyx__L_e -10.0 1000.0\n", + "EX_pyr_e -10.0 1000.0\n", + "EX_galur_e -10.0 1000.0\n", + "EX_peamn_e -10.0 1000.0\n", + "EX_lcts_e -10.0 1000.0\n", + "\n", + "\n", + "---------- STOP IMPORT FLUX -------------\n", + "\n", + "{'BIOMASS__3': np.float64(0.0)}\n", + "\n", + "\n", + "\u001b[0;94m\n", + "____________________________________________\n", + "____________________________________________\n", + "\u001b[0m\n", + " RESULTS \n", + "\u001b[0;94m____________________________________________\n", + "____________________________________________\n", + "\u001b[0m\n", + " \u001b[1m \"Cobra (seeds)\" \u001b[0m indicates the maximum flux \n", + "obtained in FBA from the seeds after shutting \n", + "off all other exchange reactions. If the maximum \n", + "flux is null, a test is performed opening demand \n", + "reactions for the objective reaction's products, \n", + "in order to test the effect of their accumulation \n", + "(\u001b[1m\"cobra (demands)\"\u001b[0m ). If this test is not performed, \n", + "\"NA\" value is indicated.\n", + "\u001b[0;93m\n", + "____________________________________________\n", + "____________________________________________\n", + "\u001b[0m\n", + " Subset Minimal \n", + "\u001b[0;93m--------------------------------------------\u001b[0m\n", + " REASONING GUESS-CHECK DIVERSITY \n", + "\u001b[0;93m . . . . . . . . . . \u001b[0m\n", + "name | cobra (seeds) | cobra (demands)\n", + "-----|---------------|-----------------\n", + "model_1 | \u001b[0;96m9.937765597630253\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_2 | \u001b[0;96m9.792132367328103\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_3 | \u001b[0;96m9.778762898729909\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_4 | \u001b[0;96m0.025348903824010713\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_5 | \u001b[0;96m5.313490116741963\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_6 | \u001b[0;96m9.754700850896379\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_7 | \u001b[0;96m0.37899577976485394\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_8 | \u001b[0;96m0.7221255364279596\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_9 | \u001b[0;96m9.775407884435483\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_10 | \u001b[0;96m0.7140751422168274\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_11 | \u001b[0;96m0.5884177331998813\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_12 | \u001b[0;96m0.7505542433691159\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_13 | \u001b[0;96m2.2186401435980967\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_14 | \u001b[0;96m0.7258262664497575\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_15 | \u001b[0;96m12.925660218006431\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_16 | \u001b[0;96m4.5149703201069995\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_17 | \u001b[0;96m2.0227594586863504\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_18 | \u001b[0;96m0.5688814604929476\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_19 | \u001b[0;96m0.7069339044064468\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_20 | \u001b[0;96m0.7205811251567793\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_21 | \u001b[0;96m2.1954626612658137\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_22 | \u001b[0;96m0.7373332790486674\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_23 | \u001b[0;96m0.7413284705165132\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_24 | \u001b[0;96m0.665592704706559\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_25 | \u001b[0;96m0.7106932321170875\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_26 | \u001b[0;96m0.15744381467888485\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_27 | \u001b[0;96m10.3887448741306\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_28 | \u001b[0;96m0.7241467773351008\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_29 | \u001b[0;96m0.6834345119791333\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_30 | \u001b[0;96m0.7705169209282983\u001b[0m | \u001b[0mNA\u001b[0m\n", + "\u001b[0;93m\n", + "____________________________________________\n", + "____________________________________________\n", + "\u001b[0m\n", + " Minimize \n", + "\u001b[0;93m--------------------------------------------\u001b[0m\n", + " REASONING GUESS-CHECK DIVERSITY \n", + "\u001b[0;93m . . . . . . . . . . \u001b[0m\n", + "name | cobra (seeds) | cobra (demands)\n", + "-----|---------------|-----------------\n", + "\u001b[0;93m--------------------------------------------\u001b[0m\n", + " REASONING GUESS-CHECK-DIVERSITY \n", + "\u001b[0;93m . . . . . . . . . . \u001b[0m\n", + "name | cobra (seeds) | cobra (demands)\n", + "-----|---------------|-----------------\n", + "model_1 | \u001b[0;96m1.3049143438060105\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_2 | \u001b[0;96m0.16561548831040854\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_3 | \u001b[0;96m1.3049143437862722\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_4 | \u001b[0;96m1.3584159225680004\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_5 | \u001b[0;96m0.060670320187294825\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_6 | \u001b[0;96m1.304914343779634\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_7 | \u001b[0;96m1.304914343771887\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_8 | \u001b[0;96m1.3584159226110215\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_9 | \u001b[0;96m0.06067032018731216\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_10 | \u001b[0;96m0.059823086070294434\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_11 | \u001b[0;96m0.059823086070295066\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_12 | \u001b[0;96m0.14054134363746762\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_13 | \u001b[0;96m0.16561548831036807\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_14 | \u001b[0;96m0.1193637376054799\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_15 | \u001b[0;96m0.11936373760547944\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_16 | \u001b[0;96m0.3827648059804168\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_17 | \u001b[0;96m0.05641838099581737\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_18 | \u001b[0;96m0.0564183809958193\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_19 | \u001b[0;96m0.13281633576523577\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_20 | \u001b[0;96m0.11936373760634239\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_21 | \u001b[0;96m0.11936373760421234\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_22 | \u001b[0;96m0.3827648059719823\u001b[0m | \u001b[0mNA\u001b[0m\n", + "\u001b[0;93m\n", + "____________________________________________\n", + "\u001b[0m\n" + ] + } + ], + "source": [ + "run_s2lp(sbml_dir)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### **List of output files**\n", + "\n", + "In the result directory (initially \"../../results/iCN718\") you will find 8 files.\n", + "\n", + "Seed2lp results files:\n", + "- iCN718_rm_rxn_tgt_taf_reas_max_no_accu_results.json -> Reasoning\n", + "- iCN718_rm_rxn_tgt_taf_fil_max_no_accu_results.json -> Filter\n", + "- iCN718_rm_rxn_tgt_taf_gc_max_no_accu_results.json -> Guess-Check\n", + "- iCN718_rm_rxn_tgt_taf_gcd_max_no_accu_results.json -> Guess-Check and Diversity\n", + "\n", + "Fluxes files:\n", + "- iCN718_rm_rxn_tgt_taf_reas_max_no_accu_fluxes.tsv -> Reasoning\n", + "- iCN718_rm_rxn_tgt_taf_fil_max_no_accu_fluxes.tsv -> Filter\n", + "- iCN718_rm_rxn_tgt_taf_gc_max_no_accu_fluxes.tsv -> Guess-Check\n", + "- iCN718_rm_rxn_tgt_taf_gcd_max_no_accu_fluxes.tsv -> Guess-Check and Diversity" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "> Note:\n", + ">\n", + "> You will find log files in [result_directory]/logs\n", + ">\n", + "> Example: ../../results/iCN718/logs" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "s2lp", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.14" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/notebook/run/08_run_seed2lp_one_solution.ipynb b/notebook/run/08_run_seed2lp_one_solution.ipynb new file mode 100644 index 0000000..96e48a5 --- /dev/null +++ b/notebook/run/08_run_seed2lp_one_solution.ipynb @@ -0,0 +1,1531 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Seed2LP: Run for one solution per network to get time for first value" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This notebook explain how to run Seed2lp to get the first solution, and must be run **AFTER** retrieving BiGG SBML file (see notebook [01_get_sbml_BiGG.ipynb](./01_get_sbml_BiGG.ipynb)) ant getting objective (see notebook [02_get_objectives.ipynb](./02_get_objectives.ipynb))\n", + "\n", + "> Note:\n", + ">\n", + "> The Seed2lp (seed searching and flux) result files for one solution are available: [https://doi.org/10.57745/OS1JND](https://doi.org/10.57745/OS1JND)\n", + ">\n", + "> After downloadind and unzipping the package, go to analyses/results/one_solution" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## **WARNING**\n", + "This notebook will run Seed2LP for all 107 GSMNs from BiGG the target seed searching mode, using methods: *Reasoning*, *Hybrid-Filter*, *Hybrid-GC*, *Hybrid-GCDiv*.\n", + "\n", + "No accumulation allowed NE seed inference, using subset minimal and minimize otptimization. Seed2LP is set to find 1 solution and will stop if exceed 10 min.\n", + "\n", + "On the paper, the time limit is set to 45 min and number of solutions limit to 1.\n", + "\n", + "To avoid a long time running within the notebook, the notebook will use a copy of e_coli_core in an sbml directory already created in notebook [03_run_seed2lp](03_run_seed2lp.ipynb)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Requirements\n", + "Module *seed2lp* needed\n", + "\n", + "> Advice:\n", + "> \n", + "> Use a conda env called s2lp with python 3.10 for plafrim cluster scripts" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!pip install seed2lp" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## **Slurm-based cluster**: Reproducing paper data\n", + "Slurm-based scripts for cluster are available with 45 min and 1000 for all networks:\n", + "- Launch if needed \n", + " - [01_job_retrieve_bigg_sbml.sh](../../scripts/plafrim_cluster/01_job_retrieve_bigg_sbml.sh): `sbatch 01_job_retrieve_bigg_sbml.sh`\n", + " - [02_job_get_objective.sh](../../scripts/plafrim_cluster/02_job_get_objective.sh): `sbatch 02_job_get_objective.sh`\n", + " - or copy your local files into you cluster\n", + "- Change **_source_** variable by the path of your conda environement with seed2lp installed in files: [03_execute_workflow_search.sh](../../scripts/plafrim_cluster/03_execute_workflow_search.sh) and [03_job_run_s2lp.sh](../../scripts/plafrim_cluster/01_job_run_s2lp.sh) \n", + "- launch [03_execute_workflow_search.sh](../../scripts/plafrim_cluster/03_execute_workflow_search.sh): `sbatch 03_execute_workflow_search.sh`" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## **LAUNCH**" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Variable to change (if wanted)" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "analyse_dir = \"../../analyses\"\n", + "data_dir = f\"{analyse_dir}/data/\"\n", + "result_dir=f\"{analyse_dir}/results\"\n", + "temp_dir = \"../../tmp/\"\n", + "\n", + "time_limit = 10 # time limit\n", + "number_solution = 30 # number solutions" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Execute" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "from os import path, listdir, makedirs\n", + "from shutil import copyfile" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "sbml_dir = f\"{data_dir}/bigg/sbml\"\n", + "e_coli_dir = f\"{data_dir}/bigg/sbml_e_coli_core\"\n", + "result_dir = f\"{result_dir}/one_solution\"\n", + "objecive_dir = f\"{data_dir}/objective\"" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "if not path.isdir(e_coli_dir):\n", + " makedirs(e_coli_dir)\n", + " copyfile(path.join(sbml_dir, \"e_coli_core.xml\"), path.join(e_coli_dir, \"e_coli_core.xml\"))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This function will execute seed2lp for iCN718:\n", + "- Target\n", + "- subset minimal\n", + "- *Reasoning*, *Hybrid-Filter*, *Hybrid-GC* and *Hybrid-GCDiv*\n", + "- no accumulation\n", + "- maximisation (of flux in Objective reaction)\n", + "- Limitations: 30 solutions and 10 min\n", + "\n", + "Also, it will check the flux for each solution and write it into files." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "def run_s2lp(in_dir:str):\n", + " for filename in listdir(in_dir):\n", + " species = f'{path.splitext(path.basename(filename))[0]}'\n", + " sbml_path = path.join(in_dir,f\"{species}.xml\")\n", + " objective_path = path.join(objecive_dir,f\"{species}_target.txt\")\n", + " result_path = path.join(result_dir,species)\n", + "\n", + " command = f\"target {sbml_path} {result_path} --temp {temp_dir} -tl {time_limit} -nbs {number_solution} -cf -max -tf {objective_path}\"\n", + "\n", + " command_reasoning=command+' -so reasoning'\n", + " command_filter=command+' -so filter'\n", + " command_guess_check=command+' -so guess_check'\n", + " command_guess_check_div=command+' -so guess_check_div'\n", + "\n", + " !seed2lp {command_reasoning}\n", + " !seed2lp {command_filter}\n", + " !seed2lp {command_guess_check}\n", + " !seed2lp {command_guess_check_div}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The execution might take more than 3min due to finding minimal solutions and due to *Hybrid-lpx* mode (requires lot of time to calculate fluxes)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: e_coli_core\n", + "\n", + "____________________________________________\n", + "\n", + " TARGETS \n", + " FOR TARGET MODE AND FBA \n", + "____________________________________________\n", + "\n", + "Targets set:\n", + " Reactant of objective reaction\n", + " from target file\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " OBJECTVE \n", + " FOR HYBRID \n", + "____________________________________________\n", + "\n", + "Objective set:\n", + " Objective reaction from target file\n", + "\n", + "\n", + "Objective : R_BIOMASS_Ecoli_core_w_GAM\n", + "\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " NETWORK \n", + "____________________________________________\n", + "\n", + "Import reaction: Removed\n", + "Targets can be seeds: No\n", + "Accumulation: Forbidden\n", + "\n", + "\n", + "\u001b[0;93mWARNING : - R_FORt: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + "\u001b[0m\n", + "\u001b[0;96m\n", + "############################################\n", + "############################################\n", + " \u001b[1mREASONING\u001b[0;96m\n", + "############################################\n", + "############################################\n", + "\u001b[0m\n", + "Mode : TARGET\n", + "Option: TARGETS ARE FORBIDDEN SEEDS\n", + "ACCUMULATION: Forbidden\n", + "Time limit: 10.0 minutes\n", + "Solution number limit: 30\n", + "\u001b[0;94m\n", + "____________________________________________\u001b[0m\n", + "\u001b[0;94m____________________________________________\n", + "\u001b[0m\n", + " Sub Mode: \u001b[1mSUBSET MINIMAL\u001b[0m \n", + "\u001b[0;94m____________________________________________\u001b[0m\n", + "\u001b[0;94m____________________________________________\n", + "\u001b[0m\n", + "\n", + "················ \u001b[1mClassic mode\u001b[0m ···············\n", + "\n", + "~~~~~~~~~~~~~~~~ \u001b[1mEnumeration\u001b[0m ~~~~~~~~~~~~~~~\n", + "SOLVING...\n", + "\n", + "Answer: 1 (7 seeds) \n", + "M_adp_c, M_coa_c, M_glu__L_e, M_h2o_e, M_nadh_c, M_nadp_c, M_pyr_e\n", + "\n", + "Answer: 2 (7 seeds) \n", + "M_acald_e, M_adp_c, M_coa_c, M_glu__L_e, M_h2o_e, M_nadh_c, M_nadp_c\n", + "\n", + "Answer: 3 (7 seeds) \n", + "M_actp_c, M_adp_c, M_coa_c, M_glu__L_e, M_h2o_e, M_nadh_c, M_nadp_c\n", + "\n", + "Answer: 4 (7 seeds) \n", + "M_adp_c, M_cit_c, M_coa_c, M_glu__L_e, M_nadh_c, M_nadp_c, M_pyr_e\n", + "\n", + "Answer: 5 (7 seeds) \n", + "M_actp_c, M_adp_c, M_cit_c, M_coa_c, M_glu__L_e, M_nadh_c, M_nadp_c\n", + "\n", + "Answer: 6 (7 seeds) \n", + "M_acald_e, M_adp_c, M_cit_c, M_coa_c, M_glu__L_e, M_nadh_c, M_nadp_c\n", + "\n", + "Answer: 7 (7 seeds) \n", + "M_adp_c, M_coa_c, M_fum_c, M_glu__L_e, M_h2o_e, M_nadh_c, M_nadp_c\n", + "\n", + "Answer: 8 (7 seeds) \n", + "M_adp_c, M_cit_c, M_coa_c, M_fum_c, M_glu__L_e, M_nadh_c, M_nadp_c\n", + "\n", + "Answer: 9 (6 seeds) \n", + "M_adp_c, M_coa_c, M_glu__L_e, M_mal__L_c, M_nadh_c, M_nadp_c\n", + "\n", + "Answer: 10 (7 seeds) \n", + "M_adp_c, M_coa_c, M_etoh_c, M_glu__L_e, M_h2o_e, M_nadh_c, M_nadp_c\n", + "\n", + "Answer: 11 (7 seeds) \n", + "M_adp_c, M_cit_c, M_coa_c, M_etoh_c, M_glu__L_e, M_nadh_c, M_nadp_c\n", + "\n", + "Answer: 12 (7 seeds) \n", + "M_adp_c, M_coa_c, M_glu__L_e, M_h2o_e, M_lac__D_e, M_nadh_c, M_nadp_c\n", + "\n", + "Answer: 13 (7 seeds) \n", + "M_adp_c, M_cit_c, M_coa_c, M_glu__L_e, M_lac__D_e, M_nadh_c, M_nadp_c\n", + "\n", + "Answer: 14 (7 seeds) \n", + "M_adp_c, M_coa_c, M_etoh_e, M_glu__L_e, M_h2o_e, M_nadh_c, M_nadp_c\n", + "\n", + "Answer: 15 (7 seeds) \n", + "M_adp_c, M_cit_c, M_coa_c, M_etoh_e, M_glu__L_e, M_nadh_c, M_nadp_c\n", + "\n", + "Answer: 16 (8 seeds) \n", + "M_adp_c, M_akg_c, M_coa_c, M_fdp_c, M_glu__L_e, M_nadh_c, M_nadp_c, M_s7p_c\n", + "\n", + "Answer: 17 (8 seeds) \n", + "M_adp_c, M_coa_c, M_fdp_c, M_glu__L_e, M_nadh_c, M_nadp_c, M_pi_c, M_s7p_c\n", + "\n", + "Answer: 18 (8 seeds) \n", + "M_actp_c, M_adp_c, M_coa_c, M_fdp_c, M_glu__L_e, M_nadh_c, M_nadp_c, M_s7p_c\n", + "\n", + "Answer: 19 (9 seeds) \n", + "M_acald_e, M_adp_c, M_coa_c, M_fdp_c, M_glu__L_e, M_nadh_c, M_nadp_c, M_pi_e, M_s7p_c\n", + "\n", + "Answer: 20 (7 seeds) \n", + "M_adp_c, M_coa_c, M_fdp_c, M_glu__L_e, M_h2o_e, M_nadh_c, M_nadp_c\n", + "\n", + "Answer: 21 (9 seeds) \n", + "M_adp_c, M_coa_c, M_etoh_c, M_fdp_c, M_glu__L_e, M_nadh_c, M_nadp_c, M_pi_e, M_s7p_c\n", + "\n", + "Answer: 22 (8 seeds) \n", + "M_acald_e, M_adp_c, M_coa_c, M_glu__L_e, M_h_c, M_nadh_c, M_nadp_c, M_pi_e\n", + "\n", + "Answer: 23 (8 seeds) \n", + "M_adp_c, M_akg_c, M_coa_c, M_glu__L_e, M_h_c, M_nadh_c, M_nadp_c, M_pyr_e\n", + "\n", + "Answer: 24 (8 seeds) \n", + "M_adp_c, M_akg_c, M_coa_c, M_dhap_c, M_glu__L_e, M_h_c, M_nadh_c, M_nadp_c\n", + "\n", + "Answer: 25 (8 seeds) \n", + "M_acald_e, M_adp_c, M_akg_c, M_coa_c, M_glu__L_e, M_h_c, M_nadh_c, M_nadp_c\n", + "\n", + "Answer: 26 (8 seeds) \n", + "M_adp_c, M_coa_c, M_glu__L_e, M_h_c, M_nadh_c, M_nadp_c, M_pi_c, M_pyr_e\n", + "\n", + "Answer: 27 (8 seeds) \n", + "M_adp_c, M_coa_c, M_dhap_c, M_glu__L_e, M_h_c, M_nadh_c, M_nadp_c, M_pi_c\n", + "\n", + "Answer: 28 (8 seeds) \n", + "M_acald_e, M_adp_c, M_coa_c, M_glu__L_e, M_h_c, M_nadh_c, M_nadp_c, M_pi_c\n", + "\n", + "Answer: 29 (8 seeds) \n", + "M_adp_c, M_coa_c, M_fum_c, M_glu__L_e, M_h_c, M_nadh_c, M_nadp_c, M_pi_c\n", + "\n", + "Answer: 30 (8 seeds) \n", + "M_adp_c, M_akg_c, M_coa_c, M_fum_c, M_glu__L_e, M_h_c, M_nadh_c, M_nadp_c\n", + "\n", + "\u001b[0;94m\n", + "____________________________________________\u001b[0m\n", + "\u001b[0;94m____________________________________________\n", + "\u001b[0m\n", + " Sub Mode: \u001b[1mMINIMIZE\u001b[0m \n", + "\u001b[0;94m____________________________________________\u001b[0m\n", + "\u001b[0;94m____________________________________________\n", + "\u001b[0m\n", + "\n", + "················ \u001b[1mClassic mode\u001b[0m ···············\n", + "Finding optimum...\n", + "SOLVING...\n", + "\n", + "Optimum found.\n", + "Minimal size of seed set is 6\n", + "\n", + "\n", + "~~~~~~~~~~~~~~~~ \u001b[1mEnumeration\u001b[0m ~~~~~~~~~~~~~~~\n", + "SOLVING...\n", + "\n", + "Answer: 1 (6 seeds) \n", + "M_2pg_c, M_adp_c, M_glu__L_e, M_nadh_c, M_nadp_c, M_succoa_c\n", + "\n", + "Answer: 2 (6 seeds) \n", + "M_2pg_c, M_adp_c, M_nadh_c, M_nadp_c, M_nh4_e, M_succoa_c\n", + "\n", + "Answer: 3 (6 seeds) \n", + "M_adp_c, M_mal__L_c, M_nadh_c, M_nadp_c, M_nh4_e, M_succoa_c\n", + "\n", + "Answer: 4 (6 seeds) \n", + "M_13dpg_c, M_adp_c, M_nadh_c, M_nadp_c, M_nh4_e, M_succoa_c\n", + "\n", + "Answer: 5 (6 seeds) \n", + "M_2pg_c, M_adp_c, M_gln__L_e, M_nadh_c, M_nadp_c, M_succoa_c\n", + "\n", + "Answer: 6 (6 seeds) \n", + "M_adp_c, M_gln__L_e, M_mal__L_c, M_nadh_c, M_nadp_c, M_succoa_c\n", + "\n", + "Answer: 7 (6 seeds) \n", + "M_13dpg_c, M_adp_c, M_gln__L_e, M_nadh_c, M_nadp_c, M_succoa_c\n", + "\n", + "Answer: 8 (6 seeds) \n", + "M_13dpg_c, M_adp_c, M_glu__L_e, M_nadh_c, M_nadp_c, M_succoa_c\n", + "\n", + "Answer: 9 (6 seeds) \n", + "M_adp_c, M_glu__L_e, M_mal__L_c, M_nadh_c, M_nadp_c, M_succoa_c\n", + "\n", + "Answer: 10 (6 seeds) \n", + "M_13dpg_c, M_adp_c, M_coa_c, M_glu__L_e, M_nadh_c, M_nadp_c\n", + "\n", + "Answer: 11 (6 seeds) \n", + "M_adp_c, M_coa_c, M_glu__L_e, M_mal__L_c, M_nadh_c, M_nadp_c\n", + "\n", + "Answer: 12 (6 seeds) \n", + "M_2pg_c, M_adp_c, M_coa_c, M_glu__L_e, M_nadh_c, M_nadp_c\n", + "\n", + "Answer: 13 (6 seeds) \n", + "M_13dpg_c, M_adp_c, M_coa_c, M_nadh_c, M_nadp_c, M_nh4_e\n", + "\n", + "Answer: 14 (6 seeds) \n", + "M_13dpg_c, M_adp_c, M_coa_c, M_gln__L_e, M_nadh_c, M_nadp_c\n", + "\n", + "Answer: 15 (6 seeds) \n", + "M_adp_c, M_coa_c, M_mal__L_c, M_nadh_c, M_nadp_c, M_nh4_e\n", + "\n", + "Answer: 16 (6 seeds) \n", + "M_2pg_c, M_adp_c, M_coa_c, M_nadh_c, M_nadp_c, M_nh4_e\n", + "\n", + "Answer: 17 (6 seeds) \n", + "M_2pg_c, M_adp_c, M_coa_c, M_gln__L_e, M_nadh_c, M_nadp_c\n", + "\n", + "Answer: 18 (6 seeds) \n", + "M_adp_c, M_coa_c, M_gln__L_e, M_mal__L_c, M_nadh_c, M_nadp_c\n", + "\n", + "\u001b[0;96m############################################\n", + "\n", + "\u001b[0m\n", + "\n", + "TIME DATA EXTRACTION : 0.101s\n", + "TIME TOTAL SEED SEARCH: 0.437s\n", + "TIME TOTAL : 0.539s\n", + "\n", + "\n", + "\u001b[0;96m\n", + "############################################\n", + "############################################\n", + " \u001b[1mCHECK FLUX\u001b[0;96m\n", + "############################################\n", + "############################################\n", + "\u001b[0m\n", + "---------------- FLUX INIT -----------------\n", + "\n", + "{'BIOMASS_Ecoli_core_w_GAM': np.float64(0.8739215069684295)}\n", + "\n", + "\n", + "--------------- MEDIUM INIT ----------------\n", + "\n", + "EX_co2_e -1000.0 1000.0\n", + "EX_glc__D_e -10.0 1000.0\n", + "EX_h_e -1000.0 1000.0\n", + "EX_h2o_e -1000.0 1000.0\n", + "EX_nh4_e -1000.0 1000.0\n", + "EX_o2_e -1000.0 1000.0\n", + "EX_pi_e -1000.0 1000.0\n", + "\n", + "\n", + "---------- STOP IMPORT FLUX -------------\n", + "\n", + "{'BIOMASS_Ecoli_core_w_GAM': 0.0}\n", + "\n", + "\n", + "\u001b[0;94m\n", + "____________________________________________\n", + "____________________________________________\n", + "\u001b[0m\n", + " RESULTS \n", + "\u001b[0;94m____________________________________________\n", + "____________________________________________\n", + "\u001b[0m\n", + " \u001b[1m \"Cobra (seeds)\" \u001b[0m indicates the maximum flux \n", + "obtained in FBA from the seeds after shutting \n", + "off all other exchange reactions. If the maximum \n", + "flux is null, a test is performed opening demand \n", + "reactions for the objective reaction's products, \n", + "in order to test the effect of their accumulation \n", + "(\u001b[1m\"cobra (demands)\"\u001b[0m ). If this test is not performed, \n", + "\"NA\" value is indicated.\n", + "\u001b[0;93m\n", + "____________________________________________\n", + "____________________________________________\n", + "\u001b[0m\n", + " Subset Minimal \n", + "\u001b[0;93m--------------------------------------------\u001b[0m\n", + " REASONING \n", + "\u001b[0;93m . . . . . . . . . . \u001b[0m\n", + "name | cobra (seeds) | cobra (demands)\n", + "-----|---------------|-----------------\n", + "model_1 | \u001b[0;91m-7.86166742663191e-17\u001b[0m | \u001b[0;91m4.232985273912001e-15\u001b[0m\n", + "model_2 | \u001b[0;91m-6.359795915135753e-15\u001b[0m | \u001b[0;91m-3.132829936350669e-16\u001b[0m\n", + "model_3 | \u001b[0;96m12.18876131827227\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_4 | \u001b[0;91m6.113009953421643e-15\u001b[0m | \u001b[0;91m2.0094165626068292e-16\u001b[0m\n", + "model_5 | \u001b[0;91m-7.671877442968764e-16\u001b[0m | \u001b[0;91m2.1925482217146207e-16\u001b[0m\n", + "model_6 | \u001b[0;91m8.010758262783273e-15\u001b[0m | \u001b[0;91m3.542992415462609e-16\u001b[0m\n", + "model_7 | \u001b[0;91m2.336066763024368e-15\u001b[0m | \u001b[0;91m2.6582641914797665e-16\u001b[0m\n", + "model_8 | Infeasible | Infeasible\n", + "model_9 | \u001b[0;91m9.895839948359787e-17\u001b[0m | \u001b[0;91m3.1981673001529087e-16\u001b[0m\n", + "model_10 | \u001b[0;91m0.0\u001b[0m | \u001b[0;91m0.0\u001b[0m\n", + "model_11 | \u001b[0;91m-8.059888334404866e-17\u001b[0m | \u001b[0;91m0.0\u001b[0m\n", + "model_12 | \u001b[0;91m-1.0671296118196804e-15\u001b[0m | \u001b[0;91m-2.04482478772909e-15\u001b[0m\n", + "model_13 | \u001b[0;91m1.764675099286478e-14\u001b[0m | \u001b[0;91m1.401199030435258e-15\u001b[0m\n", + "model_14 | Infeasible | Infeasible\n", + "model_15 | Infeasible | Infeasible\n", + "model_16 | \u001b[0;96m18.991034830032238\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_17 | \u001b[0;96m19.049540798163616\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_18 | \u001b[0;96m16.31693176867618\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_19 | \u001b[0;96m16.316931768676206\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_20 | \u001b[0;96m31.320746297127958\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_21 | \u001b[0;96m17.72710498102588\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_22 | Infeasible | Infeasible\n", + "model_23 | \u001b[0;91m3.0367318785771547e-16\u001b[0m | \u001b[0;91m-1.1740341664808016e-16\u001b[0m\n", + "model_24 | \u001b[0;91m6.424784538198656e-16\u001b[0m | \u001b[0;91m3.756138359665475e-17\u001b[0m\n", + "model_25 | \u001b[0;91m5.556451161671328e-18\u001b[0m | \u001b[0;91m-4.02391907128601e-16\u001b[0m\n", + "model_26 | \u001b[0;91m-6.1365336365489405e-16\u001b[0m | \u001b[0;91m1.3865196427072677e-16\u001b[0m\n", + "model_27 | \u001b[0;91m5.666584191074749e-16\u001b[0m | \u001b[0;91m-9.18801314400082e-17\u001b[0m\n", + "model_28 | \u001b[0;91m-9.443691818151632e-17\u001b[0m | \u001b[0;91m-3.6775698751518996e-17\u001b[0m\n", + "model_29 | \u001b[0;91m-1.8250154686436248e-16\u001b[0m | \u001b[0;91m0.0\u001b[0m\n", + "model_30 | \u001b[0;91m5.4122018009124276e-17\u001b[0m | \u001b[0;91m-4.02391907128601e-16\u001b[0m\n", + "\u001b[0;93m\n", + "____________________________________________\n", + "____________________________________________\n", + "\u001b[0m\n", + " Minimize \n", + "\u001b[0;93m--------------------------------------------\u001b[0m\n", + " REASONING \n", + "\u001b[0;93m . . . . . . . . . . \u001b[0m\n", + "name | cobra (seeds) | cobra (demands)\n", + "-----|---------------|-----------------\n", + "model_1 | \u001b[0;91m-9.196485181120621e-16\u001b[0m | \u001b[0;91m-9.208326484467427e-16\u001b[0m\n", + "model_2 | \u001b[0;96m17.924992173791278\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_3 | \u001b[0;91m9.25209265848178e-16\u001b[0m | \u001b[0;91m-9.163605184929316e-17\u001b[0m\n", + "model_4 | Infeasible | Infeasible\n", + "model_5 | \u001b[0;91m-5.33709546052286e-16\u001b[0m | \u001b[0;91m3.9397682599516265e-17\u001b[0m\n", + "model_6 | \u001b[0;91m3.0217866279511706e-16\u001b[0m | \u001b[0;91m0.0\u001b[0m\n", + "model_7 | Infeasible | Infeasible\n", + "model_8 | Infeasible | Infeasible\n", + "model_9 | \u001b[0;91m5.236162733319999e-16\u001b[0m | \u001b[0;91m1.190016087135862e-15\u001b[0m\n", + "model_10 | Infeasible | Infeasible\n", + "model_11 | \u001b[0;91m9.895839948359787e-17\u001b[0m | \u001b[0;91m3.1981673001529087e-16\u001b[0m\n", + "model_12 | \u001b[0;91m-2.3353677926587578e-15\u001b[0m | \u001b[0;91m2.4005332370708894e-15\u001b[0m\n", + "model_13 | Infeasible | Infeasible\n", + "model_14 | Infeasible | Infeasible\n", + "model_15 | \u001b[0;91m2.844701110419275e-16\u001b[0m | \u001b[0;91m-5.055241681445326e-16\u001b[0m\n", + "model_16 | \u001b[0;96m17.92499217379125\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_17 | \u001b[0;91m-1.3553091859961835e-15\u001b[0m | \u001b[0;91m2.005455751743571e-16\u001b[0m\n", + "model_18 | \u001b[0;91m4.1887802369222987e-16\u001b[0m | \u001b[0;91m0.0\u001b[0m\n", + "\u001b[0;93m\n", + "____________________________________________\n", + "\u001b[0m\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: e_coli_core\n", + "\n", + "____________________________________________\n", + "\n", + " TARGETS \n", + " FOR TARGET MODE AND FBA \n", + "____________________________________________\n", + "\n", + "Targets set:\n", + " Reactant of objective reaction\n", + " from target file\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " OBJECTVE \n", + " FOR HYBRID \n", + "____________________________________________\n", + "\n", + "Objective set:\n", + " Objective reaction from target file\n", + "\n", + "\n", + "Objective : R_BIOMASS_Ecoli_core_w_GAM\n", + "\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " NETWORK \n", + "____________________________________________\n", + "\n", + "Import reaction: Removed\n", + "Targets can be seeds: No\n", + "Accumulation: Forbidden\n", + "\n", + "\n", + "\u001b[0;93mWARNING : - R_FORt: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + "\u001b[0m\n", + "\u001b[0;96m\n", + "############################################\n", + "############################################\n", + " \u001b[1mREASONING\u001b[0;96m\n", + "############################################\n", + "############################################\n", + "\u001b[0m\n", + "Mode : TARGET\n", + "Option: TARGETS ARE FORBIDDEN SEEDS\n", + "ACCUMULATION: Forbidden\n", + "Time limit: 10.0 minutes\n", + "Solution number limit: 30\n", + "\u001b[0;94m\n", + "____________________________________________\u001b[0m\n", + "\u001b[0;94m____________________________________________\n", + "\u001b[0m\n", + " Sub Mode: \u001b[1mSUBSET MINIMAL\u001b[0m \n", + "\u001b[0;94m____________________________________________\u001b[0m\n", + "\u001b[0;94m____________________________________________\n", + "\u001b[0m\n", + "\n", + "················ \u001b[1mFilter mode\u001b[0m ···············\n", + "\n", + "~~~~~~~~~~~~~~~~ \u001b[1mEnumeration\u001b[0m ~~~~~~~~~~~~~~~\n", + "SOLVING...\n", + "\n", + "Answer: 1 (7 seeds)\n", + "M_actp_c, M_adp_c, M_icit_c, M_nadh_c, M_nadp_c, M_nh4_e, M_succoa_c\n", + "\n", + "Answer: 2 (8 seeds)\n", + "M_actp_c, M_adp_c, M_akg_c, M_h_e, M_nadh_c, M_nadp_c, M_nh4_e, M_succoa_c\n", + "\n", + "Answer: 3 (7 seeds)\n", + "M_actp_c, M_adp_c, M_coa_c, M_icit_c, M_nadh_c, M_nadp_c, M_nh4_e\n", + "\n", + "Answer: 4 (8 seeds)\n", + "M_actp_c, M_adp_c, M_akg_c, M_coa_c, M_h_e, M_nadh_c, M_nadp_c, M_nh4_e\n", + "\n", + "Answer: 5 (8 seeds)\n", + "M_actp_c, M_adp_c, M_akg_c, M_h_c, M_nadh_c, M_nadp_c, M_nh4_e, M_succoa_c\n", + "\n", + "Answer: 6 (8 seeds)\n", + "M_actp_c, M_adp_c, M_akg_c, M_coa_c, M_h_c, M_nadh_c, M_nadp_c, M_nh4_e\n", + "\n", + "Answer: 7 (8 seeds)\n", + "M_acon_C_c, M_actp_c, M_adp_c, M_h2o_e, M_nadh_c, M_nadp_c, M_nh4_e, M_succoa_c\n", + "\n", + "Answer: 8 (8 seeds)\n", + "M_actp_c, M_adp_c, M_glx_c, M_h2o_e, M_nadh_c, M_nadp_c, M_nh4_e, M_succoa_c\n", + "\n", + "Answer: 9 (8 seeds)\n", + "M_actp_c, M_adp_c, M_akg_c, M_h2o_e, M_nadh_c, M_nadp_c, M_nh4_e, M_succoa_c\n", + "\n", + "Answer: 10 (7 seeds)\n", + "M_actp_c, M_adp_c, M_glu__L_e, M_h2o_e, M_nadh_c, M_nadp_c, M_succoa_c\n", + "\n", + "Answer: 11 (8 seeds)\n", + "M_actp_c, M_adp_c, M_akg_e, M_h2o_e, M_nadh_c, M_nadp_c, M_nh4_e, M_succoa_c\n", + "\n", + "Answer: 12 (8 seeds)\n", + "M_acon_C_c, M_actp_c, M_adp_c, M_coa_c, M_h2o_e, M_nadh_c, M_nadp_c, M_nh4_e\n", + "\n", + "Answer: 13 (8 seeds)\n", + "M_actp_c, M_adp_c, M_coa_c, M_glx_c, M_h2o_e, M_nadh_c, M_nadp_c, M_nh4_e\n", + "\n", + "Answer: 14 (8 seeds)\n", + "M_actp_c, M_adp_c, M_akg_c, M_coa_c, M_h2o_e, M_nadh_c, M_nadp_c, M_nh4_e\n", + "\n", + "Answer: 15 (7 seeds)\n", + "M_actp_c, M_adp_c, M_coa_c, M_glu__L_e, M_h2o_e, M_nadh_c, M_nadp_c\n", + "\n", + "Answer: 16 (8 seeds)\n", + "M_actp_c, M_adp_c, M_akg_e, M_coa_c, M_h2o_e, M_nadh_c, M_nadp_c, M_nh4_e\n", + "\n", + "Answer: 17 (7 seeds)\n", + "M_actp_c, M_adp_c, M_cit_c, M_nadh_c, M_nadp_c, M_nh4_e, M_succoa_c\n", + "\n", + "Answer: 18 (7 seeds)\n", + "M_actp_c, M_adp_c, M_cit_c, M_coa_c, M_nadh_c, M_nadp_c, M_nh4_e\n", + "\n", + "Answer: 19 (8 seeds)\n", + "M_adp_c, M_h_e, M_lac__D_e, M_nadh_c, M_nadp_c, M_nh4_e, M_pi_c, M_succoa_c\n", + "\n", + "Answer: 20 (8 seeds)\n", + "M_adp_c, M_h_e, M_lac__D_e, M_nadh_c, M_nadp_c, M_nh4_e, M_pi_e, M_succoa_c\n", + "\n", + "Answer: 21 (8 seeds)\n", + "M_adp_c, M_h_c, M_lac__D_e, M_nadh_c, M_nadp_c, M_nh4_e, M_pi_c, M_succoa_c\n", + "\n", + "Answer: 22 (9 seeds)\n", + "M_adp_c, M_etoh_c, M_h_c, M_lac__D_e, M_nadh_c, M_nadp_c, M_nh4_e, M_pi_e, M_succoa_c\n", + "\n", + "Answer: 23 (9 seeds)\n", + "M_acald_e, M_adp_c, M_h_c, M_lac__D_e, M_nadh_c, M_nadp_c, M_nh4_e, M_pi_e, M_succoa_c\n", + "\n", + "Answer: 24 (9 seeds)\n", + "M_adp_c, M_coa_c, M_etoh_c, M_h_c, M_lac__D_e, M_nadh_c, M_nadp_c, M_nh4_e, M_pi_e\n", + "\n", + "Answer: 25 (9 seeds)\n", + "M_acald_e, M_adp_c, M_coa_c, M_h_c, M_lac__D_e, M_nadh_c, M_nadp_c, M_nh4_e, M_pi_e\n", + "\n", + "Answer: 26 (8 seeds)\n", + "M_adp_c, M_coa_c, M_h_c, M_lac__D_e, M_nadh_c, M_nadp_c, M_nh4_e, M_pi_c\n", + "\n", + "Answer: 27 (8 seeds)\n", + "M_adp_c, M_coa_c, M_h_e, M_lac__D_e, M_nadh_c, M_nadp_c, M_nh4_e, M_pi_e\n", + "\n", + "Answer: 28 (8 seeds)\n", + "M_adp_c, M_coa_c, M_h_e, M_lac__D_e, M_nadh_c, M_nadp_c, M_nh4_e, M_pi_c\n", + "\n", + "Answer: 29 (8 seeds)\n", + "M_adp_c, M_coa_c, M_glu__L_e, M_h_c, M_lac__D_e, M_nadh_c, M_nadp_c, M_pi_c\n", + "\n", + "Answer: 30 (8 seeds)\n", + "M_adp_c, M_glu__L_e, M_h_c, M_lac__D_e, M_nadh_c, M_nadp_c, M_pi_c, M_succoa_c\n", + "\n", + "Rejected solution during process: 197 \n", + "\n", + "\u001b[0;94m\n", + "____________________________________________\u001b[0m\n", + "\u001b[0;94m____________________________________________\n", + "\u001b[0m\n", + " Sub Mode: \u001b[1mMINIMIZE\u001b[0m \n", + "\u001b[0;94m____________________________________________\u001b[0m\n", + "\u001b[0;94m____________________________________________\n", + "\u001b[0m\n", + "\n", + "················ \u001b[1mFilter mode\u001b[0m ···············\n", + "Finding optimum...\n", + "SOLVING...\n", + "\n", + "Optimum found.\n", + "Minimal size of seed set is 6\n", + "\n", + "\n", + "~~~~~~~~~~~~~~~~ \u001b[1mEnumeration\u001b[0m ~~~~~~~~~~~~~~~\n", + "SOLVING...\n", + "\n", + "Answer: 1 (6 seeds)\n", + "M_2pg_c, M_adp_c, M_coa_c, M_nadh_c, M_nadp_c, M_nh4_e\n", + "\n", + "Answer: 2 (6 seeds)\n", + "M_2pg_c, M_adp_c, M_nadh_c, M_nadp_c, M_nh4_e, M_succoa_c\n", + "\n", + "Rejected solution during process: 16 \n", + "\n", + "\u001b[0;96m############################################\n", + "\n", + "\u001b[0m\n", + "\n", + "TIME DATA EXTRACTION : 0.093s\n", + "TIME TOTAL SEED SEARCH: 43.483s\n", + "TIME TOTAL : 43.576s\n", + "\n", + "\n", + "\u001b[0;96m\n", + "############################################\n", + "############################################\n", + " \u001b[1mCHECK FLUX\u001b[0;96m\n", + "############################################\n", + "############################################\n", + "\u001b[0m\n", + "---------------- FLUX INIT -----------------\n", + "\n", + "{'BIOMASS_Ecoli_core_w_GAM': np.float64(0.8739215069684295)}\n", + "\n", + "\n", + "--------------- MEDIUM INIT ----------------\n", + "\n", + "EX_co2_e -1000.0 1000.0\n", + "EX_glc__D_e -10.0 1000.0\n", + "EX_h_e -1000.0 1000.0\n", + "EX_h2o_e -1000.0 1000.0\n", + "EX_nh4_e -1000.0 1000.0\n", + "EX_o2_e -1000.0 1000.0\n", + "EX_pi_e -1000.0 1000.0\n", + "\n", + "\n", + "---------- STOP IMPORT FLUX -------------\n", + "\n", + "{'BIOMASS_Ecoli_core_w_GAM': 0.0}\n", + "\n", + "\n", + "\u001b[0;94m\n", + "____________________________________________\n", + "____________________________________________\n", + "\u001b[0m\n", + " RESULTS \n", + "\u001b[0;94m____________________________________________\n", + "____________________________________________\n", + "\u001b[0m\n", + " \u001b[1m \"Cobra (seeds)\" \u001b[0m indicates the maximum flux \n", + "obtained in FBA from the seeds after shutting \n", + "off all other exchange reactions. If the maximum \n", + "flux is null, a test is performed opening demand \n", + "reactions for the objective reaction's products, \n", + "in order to test the effect of their accumulation \n", + "(\u001b[1m\"cobra (demands)\"\u001b[0m ). If this test is not performed, \n", + "\"NA\" value is indicated.\n", + "\u001b[0;93m\n", + "____________________________________________\n", + "____________________________________________\n", + "\u001b[0m\n", + " Subset Minimal \n", + "\u001b[0;93m--------------------------------------------\u001b[0m\n", + " REASONING FILTER \n", + "\u001b[0;93m . . . . . . . . . . \u001b[0m\n", + "name | cobra (seeds) | cobra (demands)\n", + "-----|---------------|-----------------\n", + "model_1 | \u001b[0;96m16.834862348370596\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_2 | \u001b[0;96m1.999108877835602\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_3 | \u001b[0;96m16.834862348370596\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_4 | \u001b[0;96m1.999108877835598\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_5 | \u001b[0;96m2.075985128450594\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_6 | \u001b[0;96m2.0759851284505304\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_7 | \u001b[0;96m17.006099936628743\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_8 | \u001b[0;96m13.935327681972675\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_9 | \u001b[0;96m14.642986340274444\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_10 | \u001b[0;96m12.188761318272235\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_11 | \u001b[0;96m12.18876131827224\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_12 | \u001b[0;96m17.006099936627766\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_13 | \u001b[0;96m13.935327681972707\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_14 | \u001b[0;96m14.64298634027443\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_15 | \u001b[0;96m12.18876131827227\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_16 | \u001b[0;96m12.188761318272231\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_17 | \u001b[0;96m16.749549580740705\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_18 | \u001b[0;96m16.749549580740783\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_19 | \u001b[0;96m5.152488440937438\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_20 | \u001b[0;96m2.532277073726985\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_21 | \u001b[0;96m2.858013136143328\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_22 | \u001b[0;96m2.7977445276309836\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_23 | \u001b[0;96m2.8580131361434518\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_24 | \u001b[0;96m2.797744527630954\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_25 | \u001b[0;96m2.858013136143486\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_26 | \u001b[0;96m2.8580131361433225\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_27 | \u001b[0;96m2.532277073726926\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_28 | \u001b[0;96m5.152488440937373\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_29 | \u001b[0;96m2.8661695958375812\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_30 | \u001b[0;96m2.866169595837584\u001b[0m | \u001b[0mNA\u001b[0m\n", + "\u001b[0;93m\n", + "____________________________________________\n", + "____________________________________________\n", + "\u001b[0m\n", + " Minimize \n", + "\u001b[0;93m--------------------------------------------\u001b[0m\n", + " REASONING FILTER \n", + "\u001b[0;93m . . . . . . . . . . \u001b[0m\n", + "name | cobra (seeds) | cobra (demands)\n", + "-----|---------------|-----------------\n", + "model_1 | \u001b[0;96m17.92499217379125\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_2 | \u001b[0;96m17.924992173791278\u001b[0m | \u001b[0mNA\u001b[0m\n", + "\u001b[0;93m\n", + "____________________________________________\n", + "\u001b[0m\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: e_coli_core\n", + "\n", + "____________________________________________\n", + "\n", + " TARGETS \n", + " FOR TARGET MODE AND FBA \n", + "____________________________________________\n", + "\n", + "Targets set:\n", + " Reactant of objective reaction\n", + " from target file\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " OBJECTVE \n", + " FOR HYBRID \n", + "____________________________________________\n", + "\n", + "Objective set:\n", + " Objective reaction from target file\n", + "\n", + "\n", + "Objective : R_BIOMASS_Ecoli_core_w_GAM\n", + "\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " NETWORK \n", + "____________________________________________\n", + "\n", + "Import reaction: Removed\n", + "Targets can be seeds: No\n", + "Accumulation: Forbidden\n", + "\n", + "\n", + "\u001b[0;93mWARNING : - R_FORt: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + "\u001b[0m\n", + "\u001b[0;96m\n", + "############################################\n", + "############################################\n", + " \u001b[1mREASONING\u001b[0;96m\n", + "############################################\n", + "############################################\n", + "\u001b[0m\n", + "Mode : TARGET\n", + "Option: TARGETS ARE FORBIDDEN SEEDS\n", + "ACCUMULATION: Forbidden\n", + "Time limit: 10.0 minutes\n", + "Solution number limit: 30\n", + "\u001b[0;94m\n", + "____________________________________________\u001b[0m\n", + "\u001b[0;94m____________________________________________\n", + "\u001b[0m\n", + " Sub Mode: \u001b[1mSUBSET MINIMAL\u001b[0m \n", + "\u001b[0;94m____________________________________________\u001b[0m\n", + "\u001b[0;94m____________________________________________\n", + "\u001b[0m\n", + "\n", + "·············· \u001b[1mGuess-Check mode\u001b[0m ············\n", + "\n", + "~~~~~~~~~~~~~~~~ \u001b[1mEnumeration\u001b[0m ~~~~~~~~~~~~~~~\n", + "SOLVING...\n", + "\n", + "Answer: 1 (8 seeds)\n", + "M_adp_c, M_coa_c, M_gln__L_e, M_h_c, M_mal__L_e, M_nadh_c, M_nadp_c, M_pi_c\n", + "\n", + "Answer: 2 (8 seeds)\n", + "M_adp_c, M_gln__L_e, M_h_c, M_mal__L_e, M_nadh_c, M_nadp_c, M_pi_c, M_succoa_c\n", + "\n", + "Answer: 3 (8 seeds)\n", + "M_adp_c, M_glu__L_e, M_h_c, M_mal__L_e, M_nadh_c, M_nadp_c, M_pi_c, M_succoa_c\n", + "\n", + "Answer: 4 (8 seeds)\n", + "M_adp_c, M_coa_c, M_glu__L_e, M_h_c, M_mal__L_e, M_nadh_c, M_nadp_c, M_pi_c\n", + "\n", + "Answer: 5 (8 seeds)\n", + "M_adp_c, M_coa_c, M_h_c, M_mal__L_e, M_nadh_c, M_nadp_c, M_nh4_e, M_pi_c\n", + "\n", + "Answer: 6 (8 seeds)\n", + "M_adp_c, M_h_c, M_mal__L_e, M_nadh_c, M_nadp_c, M_nh4_e, M_pi_c, M_succoa_c\n", + "\n", + "Answer: 7 (8 seeds)\n", + "M_ac_c, M_adp_c, M_gln__L_e, M_h_e, M_nadh_c, M_nadp_c, M_pi_e, M_succoa_c\n", + "\n", + "Answer: 8 (8 seeds)\n", + "M_ac_c, M_adp_c, M_gln__L_e, M_h_e, M_nadh_c, M_nadp_c, M_pi_c, M_succoa_c\n", + "\n", + "Answer: 9 (8 seeds)\n", + "M_ac_c, M_adp_c, M_gln__L_e, M_h_c, M_nadh_c, M_nadp_c, M_pi_e, M_succoa_c\n", + "\n", + "Answer: 10 (8 seeds)\n", + "M_actp_c, M_adp_c, M_gln__L_e, M_h_c, M_mal__L_e, M_nadh_c, M_nadp_c, M_succoa_c\n", + "\n", + "Answer: 11 (8 seeds)\n", + "M_ac_e, M_adp_c, M_gln__L_e, M_h_c, M_nadh_c, M_nadp_c, M_pi_c, M_succoa_c\n", + "\n", + "Answer: 12 (8 seeds)\n", + "M_ac_c, M_adp_c, M_gln__L_e, M_h_c, M_nadh_c, M_nadp_c, M_pi_c, M_succoa_c\n", + "\n", + "Answer: 13 (8 seeds)\n", + "M_ac_e, M_adp_c, M_gln__L_e, M_h_e, M_nadh_c, M_nadp_c, M_pi_c, M_succoa_c\n", + "\n", + "Answer: 14 (9 seeds)\n", + "M_ac_e, M_adp_c, M_akg_c, M_gln__L_e, M_h_c, M_nadh_c, M_nadp_c, M_pi_e, M_succoa_c\n", + "\n", + "Answer: 15 (7 seeds)\n", + "M_actp_c, M_adp_c, M_gln__L_e, M_h2o_e, M_nadh_c, M_nadp_c, M_succoa_c\n", + "\n", + "Answer: 16 (8 seeds)\n", + "M_ac_e, M_adp_c, M_coa_c, M_gln__L_e, M_h_c, M_nadh_c, M_nadp_c, M_pi_c\n", + "\n", + "Answer: 17 (7 seeds)\n", + "M_actp_c, M_adp_c, M_coa_c, M_gln__L_e, M_h2o_e, M_nadh_c, M_nadp_c\n", + "\n", + "Answer: 18 (9 seeds)\n", + "M_ac_c, M_adp_c, M_glx_c, M_h_c, M_nadh_c, M_nadp_c, M_nh4_e, M_pi_e, M_succoa_c\n", + "\n", + "Answer: 19 (9 seeds)\n", + "M_ac_c, M_acon_C_c, M_adp_c, M_h_c, M_nadh_c, M_nadp_c, M_nh4_e, M_pi_e, M_succoa_c\n", + "\n", + "Answer: 20 (9 seeds)\n", + "M_ac_c, M_adp_c, M_fum_e, M_h_c, M_nadh_c, M_nadp_c, M_nh4_e, M_pi_e, M_succoa_c\n", + "\n", + "Answer: 21 (9 seeds)\n", + "M_ac_c, M_adp_c, M_fum_c, M_h_c, M_nadh_c, M_nadp_c, M_nh4_e, M_pi_e, M_succoa_c\n", + "\n", + "Answer: 22 (8 seeds)\n", + "M_actp_c, M_adp_c, M_h_c, M_mal__L_e, M_nadh_c, M_nadp_c, M_nh4_e, M_succoa_c\n", + "\n", + "Answer: 23 (8 seeds)\n", + "M_actp_c, M_adp_c, M_akg_c, M_h_c, M_nadh_c, M_nadp_c, M_nh4_e, M_succoa_c\n", + "\n", + "Answer: 24 (8 seeds)\n", + "M_actp_c, M_adp_c, M_akg_c, M_h_e, M_nadh_c, M_nadp_c, M_nh4_e, M_succoa_c\n", + "\n", + "Answer: 25 (9 seeds)\n", + "M_ac_e, M_acon_C_c, M_adp_c, M_h_e, M_nadh_c, M_nadp_c, M_nh4_e, M_pi_e, M_succoa_c\n", + "\n", + "Answer: 26 (9 seeds)\n", + "M_ac_c, M_adp_c, M_glx_c, M_h_e, M_nadh_c, M_nadp_c, M_nh4_e, M_pi_e, M_succoa_c\n", + "\n", + "Answer: 27 (9 seeds)\n", + "M_ac_c, M_acon_C_c, M_adp_c, M_h_e, M_nadh_c, M_nadp_c, M_nh4_e, M_pi_e, M_succoa_c\n", + "\n", + "Answer: 28 (9 seeds)\n", + "M_ac_e, M_adp_c, M_glx_c, M_h_c, M_nadh_c, M_nadp_c, M_nh4_e, M_pi_c, M_succoa_c\n", + "\n", + "Answer: 29 (9 seeds)\n", + "M_ac_e, M_adp_c, M_coa_c, M_glx_c, M_h_c, M_nadh_c, M_nadp_c, M_nh4_e, M_pi_c\n", + "\n", + "Answer: 30 (9 seeds)\n", + "M_ac_e, M_acon_C_c, M_adp_c, M_coa_c, M_h_c, M_nadh_c, M_nadp_c, M_nh4_e, M_pi_c\n", + "\n", + "Rejected solution during process: 376 \n", + "\n", + "\u001b[0;94m\n", + "____________________________________________\u001b[0m\n", + "\u001b[0;94m____________________________________________\n", + "\u001b[0m\n", + " Sub Mode: \u001b[1mMINIMIZE\u001b[0m \n", + "\u001b[0;94m____________________________________________\u001b[0m\n", + "\u001b[0;94m____________________________________________\n", + "\u001b[0m\n", + "\n", + "·············· \u001b[1mGuess-Check mode\u001b[0m ············\n", + "Finding optimum...\n", + "SOLVING...\n", + "\n", + "Optimum found.\n", + "Minimal size of seed set is 6\n", + "\n", + "Rejected solution during process: 11 \n", + "\n", + "\n", + "~~~~~~~~~~~~~~~~ \u001b[1mEnumeration\u001b[0m ~~~~~~~~~~~~~~~\n", + "SOLVING...\n", + "\n", + "Answer: 1 (6 seeds)\n", + "M_2pg_c, M_adp_c, M_coa_c, M_nadh_c, M_nadp_c, M_nh4_e\n", + "\n", + "Answer: 2 (6 seeds)\n", + "M_2pg_c, M_adp_c, M_nadh_c, M_nadp_c, M_nh4_e, M_succoa_c\n", + "\n", + "Rejected solution during process: 16 \n", + "\n", + "\u001b[0;96m############################################\n", + "\n", + "\u001b[0m\n", + "\n", + "TIME DATA EXTRACTION : 0.091s\n", + "TIME TOTAL SEED SEARCH: 77.048s\n", + "TIME TOTAL : 77.138s\n", + "\n", + "\n", + "\u001b[0;96m\n", + "############################################\n", + "############################################\n", + " \u001b[1mCHECK FLUX\u001b[0;96m\n", + "############################################\n", + "############################################\n", + "\u001b[0m\n", + "---------------- FLUX INIT -----------------\n", + "\n", + "{'BIOMASS_Ecoli_core_w_GAM': np.float64(0.8739215069684295)}\n", + "\n", + "\n", + "--------------- MEDIUM INIT ----------------\n", + "\n", + "EX_co2_e -1000.0 1000.0\n", + "EX_glc__D_e -10.0 1000.0\n", + "EX_h_e -1000.0 1000.0\n", + "EX_h2o_e -1000.0 1000.0\n", + "EX_nh4_e -1000.0 1000.0\n", + "EX_o2_e -1000.0 1000.0\n", + "EX_pi_e -1000.0 1000.0\n", + "\n", + "\n", + "---------- STOP IMPORT FLUX -------------\n", + "\n", + "{'BIOMASS_Ecoli_core_w_GAM': 0.0}\n", + "\n", + "\n", + "\u001b[0;94m\n", + "____________________________________________\n", + "____________________________________________\n", + "\u001b[0m\n", + " RESULTS \n", + "\u001b[0;94m____________________________________________\n", + "____________________________________________\n", + "\u001b[0m\n", + " \u001b[1m \"Cobra (seeds)\" \u001b[0m indicates the maximum flux \n", + "obtained in FBA from the seeds after shutting \n", + "off all other exchange reactions. If the maximum \n", + "flux is null, a test is performed opening demand \n", + "reactions for the objective reaction's products, \n", + "in order to test the effect of their accumulation \n", + "(\u001b[1m\"cobra (demands)\"\u001b[0m ). If this test is not performed, \n", + "\"NA\" value is indicated.\n", + "\u001b[0;93m\n", + "____________________________________________\n", + "____________________________________________\n", + "\u001b[0m\n", + " Subset Minimal \n", + "\u001b[0;93m--------------------------------------------\u001b[0m\n", + " REASONING GUESS-CHECK \n", + "\u001b[0;93m . . . . . . . . . . \u001b[0m\n", + "name | cobra (seeds) | cobra (demands)\n", + "-----|---------------|-----------------\n", + "model_1 | \u001b[0;96m7.6132093089297905\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_2 | \u001b[0;96m7.613209308929621\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_3 | \u001b[0;96m7.718756894709091\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_4 | \u001b[0;96m7.718756894709114\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_5 | \u001b[0;96m7.59197315149503\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_6 | \u001b[0;96m7.591973151495022\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_7 | \u001b[0;96m1.4935652934354573\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_8 | \u001b[0;96m3.0389952150398627\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_9 | \u001b[0;96m1.4751626112228777\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_10 | \u001b[0;96m9.069599000351898\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_11 | \u001b[0;96m1.4751626112228184\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_12 | \u001b[0;96m3.0015508104103894\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_13 | \u001b[0;96m1.4935652934354127\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_14 | \u001b[0;96m2.0723410952847257\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_15 | \u001b[0;96m9.866945125890751\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_16 | \u001b[0;96m1.4751626112228235\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_17 | \u001b[0;96m9.866945125890755\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_18 | \u001b[0;96m2.6397083767393887\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_19 | \u001b[0;96m4.3544455429244975\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_20 | \u001b[0;96m2.594634531830012\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_21 | \u001b[0;96m2.82597561790438\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_22 | \u001b[0;96m9.93390213175503\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_23 | \u001b[0;96m2.075985128450594\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_24 | \u001b[0;96m1.9991088778356052\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_25 | \u001b[0;96m4.34491641862442\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_26 | \u001b[0;96m2.7117327673274443\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_27 | \u001b[0;96m6.554450611696359\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_28 | \u001b[0;96m2.6397083767394043\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_29 | \u001b[0;96m2.639708376739409\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_30 | \u001b[0;96m4.354445542924756\u001b[0m | \u001b[0mNA\u001b[0m\n", + "\u001b[0;93m\n", + "____________________________________________\n", + "____________________________________________\n", + "\u001b[0m\n", + " Minimize \n", + "\u001b[0;93m--------------------------------------------\u001b[0m\n", + " REASONING GUESS-CHECK \n", + "\u001b[0;93m . . . . . . . . . . \u001b[0m\n", + "name | cobra (seeds) | cobra (demands)\n", + "-----|---------------|-----------------\n", + "model_1 | \u001b[0;96m17.92499217379125\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_2 | \u001b[0;96m17.924992173791278\u001b[0m | \u001b[0mNA\u001b[0m\n", + "\u001b[0;93m\n", + "____________________________________________\n", + "\u001b[0m\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: e_coli_core\n", + "\n", + "____________________________________________\n", + "\n", + " TARGETS \n", + " FOR TARGET MODE AND FBA \n", + "____________________________________________\n", + "\n", + "Targets set:\n", + " Reactant of objective reaction\n", + " from target file\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " OBJECTVE \n", + " FOR HYBRID \n", + "____________________________________________\n", + "\n", + "Objective set:\n", + " Objective reaction from target file\n", + "\n", + "\n", + "Objective : R_BIOMASS_Ecoli_core_w_GAM\n", + "\n", + "\n", + "\n", + "____________________________________________\n", + "\n", + " NETWORK \n", + "____________________________________________\n", + "\n", + "Import reaction: Removed\n", + "Targets can be seeds: No\n", + "Accumulation: Forbidden\n", + "\n", + "\n", + "\u001b[0;93mWARNING : - R_FORt: Reactants and products exchanged.\n", + " Boundaries was: [-1000.0 ; 0.0]\n", + "\u001b[0m\n", + "\u001b[0;96m\n", + "############################################\n", + "############################################\n", + " \u001b[1mREASONING\u001b[0;96m\n", + "############################################\n", + "############################################\n", + "\u001b[0m\n", + "Mode : TARGET\n", + "Option: TARGETS ARE FORBIDDEN SEEDS\n", + "ACCUMULATION: Forbidden\n", + "Time limit: 10.0 minutes\n", + "Solution number limit: 30\n", + "\u001b[0;94m\n", + "____________________________________________\u001b[0m\n", + "\u001b[0;94m____________________________________________\n", + "\u001b[0m\n", + " Sub Mode: \u001b[1mSUBSET MINIMAL\u001b[0m \n", + "\u001b[0;94m____________________________________________\u001b[0m\n", + "\u001b[0;94m____________________________________________\n", + "\u001b[0m\n", + "\n", + "····· \u001b[1mGuess-Check with diversity mode\u001b[0m ······\n", + "\n", + "~~~~~~~~~~~~~~~~ \u001b[1mEnumeration\u001b[0m ~~~~~~~~~~~~~~~\n", + "SOLVING...\n", + "\n", + "Answer: 1 (6 seeds)\n", + "M_2pg_c, M_adp_c, M_coa_c, M_nadh_c, M_nadp_c, M_nh4_e\n", + "\n", + "Answer: 2 (8 seeds)\n", + "M_adp_c, M_coa_c, M_glu__L_e, M_h_c, M_mal__L_e, M_nadh_c, M_nadp_c, M_pi_c\n", + "\n", + "Answer: 3 (6 seeds)\n", + "M_2pg_c, M_adp_c, M_nadh_c, M_nadp_c, M_nh4_e, M_succoa_c\n", + "\n", + "Answer: 4 (8 seeds)\n", + "M_adp_c, M_glu__L_e, M_h_c, M_mal__L_e, M_nadh_c, M_nadp_c, M_pi_c, M_succoa_c\n", + "\n", + "Answer: 5 (8 seeds)\n", + "M_adp_c, M_gln__L_e, M_h_c, M_mal__L_e, M_nadh_c, M_nadp_c, M_pi_c, M_succoa_c\n", + "\n", + "Answer: 6 (8 seeds)\n", + "M_adp_c, M_coa_c, M_gln__L_e, M_h_c, M_mal__L_e, M_nadh_c, M_nadp_c, M_pi_c\n", + "\n", + "Answer: 7 (8 seeds)\n", + "M_adp_c, M_coa_c, M_h_c, M_mal__L_e, M_nadh_c, M_nadp_c, M_nh4_e, M_pi_c\n", + "\n", + "Answer: 8 (8 seeds)\n", + "M_adp_c, M_h_c, M_mal__L_e, M_nadh_c, M_nadp_c, M_nh4_e, M_pi_c, M_succoa_c\n", + "\n", + "Answer: 9 (7 seeds)\n", + "M_actp_c, M_adp_c, M_gln__L_e, M_h2o_e, M_nadh_c, M_nadp_c, M_succoa_c\n", + "\n", + "Answer: 10 (8 seeds)\n", + "M_actp_c, M_adp_c, M_gln__L_e, M_h_c, M_mal__L_e, M_nadh_c, M_nadp_c, M_succoa_c\n", + "\n", + "Answer: 11 (9 seeds)\n", + "M_ac_c, M_adp_c, M_glx_c, M_h_e, M_nadh_c, M_nadp_c, M_nh4_e, M_pi_c, M_succoa_c\n", + "\n", + "Answer: 12 (7 seeds)\n", + "M_actp_c, M_adp_c, M_coa_c, M_gln__L_e, M_h2o_e, M_nadh_c, M_nadp_c\n", + "\n", + "Answer: 13 (8 seeds)\n", + "M_actp_c, M_adp_c, M_h_c, M_mal__L_e, M_nadh_c, M_nadp_c, M_nh4_e, M_succoa_c\n", + "\n", + "Answer: 14 (7 seeds)\n", + "M_actp_c, M_adp_c, M_glu__L_e, M_h2o_e, M_nadh_c, M_nadp_c, M_succoa_c\n", + "\n", + "Answer: 15 (8 seeds)\n", + "M_ac_c, M_adp_c, M_gln__L_e, M_h_e, M_nadh_c, M_nadp_c, M_pi_c, M_succoa_c\n", + "\n", + "Answer: 16 (9 seeds)\n", + "M_ac_c, M_acon_C_c, M_adp_c, M_h_e, M_nadh_c, M_nadp_c, M_nh4_e, M_pi_c, M_succoa_c\n", + "\n", + "Answer: 17 (8 seeds)\n", + "M_ac_e, M_adp_c, M_glu__L_e, M_h_e, M_nadh_c, M_nadp_c, M_pi_c, M_succoa_c\n", + "\n", + "Answer: 18 (8 seeds)\n", + "M_ac_c, M_adp_c, M_gln__L_e, M_h_c, M_nadh_c, M_nadp_c, M_pi_e, M_succoa_c\n", + "\n", + "Answer: 19 (8 seeds)\n", + "M_ac_e, M_adp_c, M_gln__L_e, M_h_e, M_nadh_c, M_nadp_c, M_pi_c, M_succoa_c\n", + "\n", + "Answer: 20 (8 seeds)\n", + "M_ac_e, M_adp_c, M_gln__L_e, M_h_c, M_nadh_c, M_nadp_c, M_pi_c, M_succoa_c\n", + "\n", + "Answer: 21 (8 seeds)\n", + "M_ac_c, M_adp_c, M_gln__L_e, M_h_c, M_nadh_c, M_nadp_c, M_pi_c, M_succoa_c\n", + "\n", + "Answer: 22 (9 seeds)\n", + "M_ac_c, M_adp_c, M_fum_e, M_h_c, M_nadh_c, M_nadp_c, M_nh4_e, M_pi_e, M_succoa_c\n", + "\n", + "Answer: 23 (9 seeds)\n", + "M_ac_e, M_adp_c, M_glx_c, M_h_c, M_nadh_c, M_nadp_c, M_nh4_e, M_pi_c, M_succoa_c\n", + "\n", + "Answer: 24 (8 seeds)\n", + "M_ac_c, M_adp_c, M_gln__L_e, M_h_e, M_nadh_c, M_nadp_c, M_pi_e, M_succoa_c\n", + "\n", + "Answer: 25 (9 seeds)\n", + "M_ac_c, M_acon_C_c, M_adp_c, M_h_c, M_nadh_c, M_nadp_c, M_nh4_e, M_pi_e, M_succoa_c\n", + "\n", + "Answer: 26 (8 seeds)\n", + "M_actp_c, M_adp_c, M_glu__L_e, M_h_c, M_mal__L_e, M_nadh_c, M_nadp_c, M_succoa_c\n", + "\n", + "Answer: 27 (8 seeds)\n", + "M_ac_c, M_adp_c, M_glu__L_e, M_h_c, M_nadh_c, M_nadp_c, M_pi_e, M_succoa_c\n", + "\n", + "Answer: 28 (8 seeds)\n", + "M_ac_e, M_adp_c, M_coa_c, M_gln__L_e, M_h_c, M_nadh_c, M_nadp_c, M_pi_c\n", + "\n", + "Answer: 29 (8 seeds)\n", + "M_ac_e, M_adp_c, M_coa_c, M_glu__L_e, M_h_c, M_nadh_c, M_nadp_c, M_pi_c\n", + "\n", + "Answer: 30 (9 seeds)\n", + "M_ac_e, M_adp_c, M_akg_c, M_gln__L_e, M_h_c, M_nadh_c, M_nadp_c, M_pi_e, M_succoa_c\n", + "\n", + "Rejected solution during process: 515 \n", + "\n", + "\u001b[0;94m\n", + "____________________________________________\u001b[0m\n", + "\u001b[0;94m____________________________________________\n", + "\u001b[0m\n", + " Sub Mode: \u001b[1mMINIMIZE\u001b[0m \n", + "\u001b[0;94m____________________________________________\u001b[0m\n", + "\u001b[0;94m____________________________________________\n", + "\u001b[0m\n", + "\n", + "····· \u001b[1mGuess-Check with diversity mode\u001b[0m ······\n", + "Finding optimum...\n", + "SOLVING...\n", + "\n", + "Optimum found.\n", + "Minimal size of seed set is 6\n", + "\n", + "Rejected solution during process: 8 \n", + "\n", + "\n", + "~~~~~~~~~~~~~~~~ \u001b[1mEnumeration\u001b[0m ~~~~~~~~~~~~~~~\n", + "SOLVING...\n", + "\n", + "Answer: 1 (6 seeds)\n", + "M_2pg_c, M_adp_c, M_nadh_c, M_nadp_c, M_nh4_e, M_succoa_c\n", + "\n", + "Answer: 2 (6 seeds)\n", + "M_2pg_c, M_adp_c, M_coa_c, M_nadh_c, M_nadp_c, M_nh4_e\n", + "\n", + "Rejected solution during process: 16 \n", + "\n", + "\u001b[0;96m############################################\n", + "\n", + "\u001b[0m\n", + "\n", + "TIME DATA EXTRACTION : 0.088s\n", + "TIME TOTAL SEED SEARCH: 94.713s\n", + "TIME TOTAL : 94.802s\n", + "\n", + "\n", + "\u001b[0;96m\n", + "############################################\n", + "############################################\n", + " \u001b[1mCHECK FLUX\u001b[0;96m\n", + "############################################\n", + "############################################\n", + "\u001b[0m\n", + "---------------- FLUX INIT -----------------\n", + "\n", + "{'BIOMASS_Ecoli_core_w_GAM': np.float64(0.8739215069684295)}\n", + "\n", + "\n", + "--------------- MEDIUM INIT ----------------\n", + "\n", + "EX_co2_e -1000.0 1000.0\n", + "EX_glc__D_e -10.0 1000.0\n", + "EX_h_e -1000.0 1000.0\n", + "EX_h2o_e -1000.0 1000.0\n", + "EX_nh4_e -1000.0 1000.0\n", + "EX_o2_e -1000.0 1000.0\n", + "EX_pi_e -1000.0 1000.0\n", + "\n", + "\n", + "---------- STOP IMPORT FLUX -------------\n", + "\n", + "{'BIOMASS_Ecoli_core_w_GAM': 0.0}\n", + "\n", + "\n", + "\u001b[0;94m\n", + "____________________________________________\n", + "____________________________________________\n", + "\u001b[0m\n", + " RESULTS \n", + "\u001b[0;94m____________________________________________\n", + "____________________________________________\n", + "\u001b[0m\n", + " \u001b[1m \"Cobra (seeds)\" \u001b[0m indicates the maximum flux \n", + "obtained in FBA from the seeds after shutting \n", + "off all other exchange reactions. If the maximum \n", + "flux is null, a test is performed opening demand \n", + "reactions for the objective reaction's products, \n", + "in order to test the effect of their accumulation \n", + "(\u001b[1m\"cobra (demands)\"\u001b[0m ). If this test is not performed, \n", + "\"NA\" value is indicated.\n", + "\u001b[0;93m\n", + "____________________________________________\n", + "____________________________________________\n", + "\u001b[0m\n", + " Subset Minimal \n", + "\u001b[0;93m--------------------------------------------\u001b[0m\n", + " REASONING GUESS-CHECK DIVERSITY \n", + "\u001b[0;93m . . . . . . . . . . \u001b[0m\n", + "name | cobra (seeds) | cobra (demands)\n", + "-----|---------------|-----------------\n", + "model_1 | \u001b[0;96m17.924992173791306\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_2 | \u001b[0;96m7.718756894709114\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_3 | \u001b[0;96m17.924992173791278\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_4 | \u001b[0;96m7.718756894709091\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_5 | \u001b[0;96m7.613209308929621\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_6 | \u001b[0;96m7.61320930892982\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_7 | \u001b[0;96m7.59197315149503\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_8 | \u001b[0;96m7.591973151495022\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_9 | \u001b[0;96m9.866945125890751\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_10 | \u001b[0;96m9.069599000351898\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_11 | \u001b[0;96m5.517631495988731\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_12 | \u001b[0;96m9.866945125890755\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_13 | \u001b[0;96m9.93390213175503\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_14 | \u001b[0;96m12.188761318272235\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_15 | \u001b[0;96m3.0389952150398627\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_16 | \u001b[0;96m8.763984804768173\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_17 | \u001b[0;96m2.179025314338004\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_18 | \u001b[0;96m1.475162611222834\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_19 | \u001b[0;96m1.4935652934354127\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_20 | \u001b[0;96m1.4751626112228184\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_21 | \u001b[0;96m3.0015508104103894\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_22 | \u001b[0;96m2.594634531830016\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_23 | \u001b[0;96m2.6397083767394043\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_24 | \u001b[0;96m1.4935652934354149\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_25 | \u001b[0;96m4.354445542924616\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_26 | \u001b[0;96m9.515765567084044\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_27 | \u001b[0;96m2.1582719353259945\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_28 | \u001b[0;96m1.4751626112228235\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_29 | \u001b[0;96m2.1582719353259825\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_30 | \u001b[0;96m2.0723410952847257\u001b[0m | \u001b[0mNA\u001b[0m\n", + "\u001b[0;93m\n", + "____________________________________________\n", + "____________________________________________\n", + "\u001b[0m\n", + " Minimize \n", + "\u001b[0;93m--------------------------------------------\u001b[0m\n", + " REASONING GUESS-CHECK DIVERSITY \n", + "\u001b[0;93m . . . . . . . . . . \u001b[0m\n", + "name | cobra (seeds) | cobra (demands)\n", + "-----|---------------|-----------------\n", + "model_1 | \u001b[0;96m17.924992173791278\u001b[0m | \u001b[0mNA\u001b[0m\n", + "model_2 | \u001b[0;96m17.92499217379125\u001b[0m | \u001b[0mNA\u001b[0m\n", + "\u001b[0;93m\n", + "____________________________________________\n", + "\u001b[0m\n" + ] + } + ], + "source": [ + "run_s2lp(e_coli_dir)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### **List of output files**\n", + "\n", + "In the result directory (initially \"../../results/one_solution/e_coli_core\") you will find 8 files.\n", + "\n", + "Seed2lp results files:\n", + "- e_coli_core_rm_rxn_tgt_taf_reas_max_no_accu_results.json -> Reasoning\n", + "- e_coli_core_rm_rxn_tgt_taf_fil_max_no_accu_results.json -> Filter\n", + "- e_coli_core_rm_rxn_tgt_taf_gc_max_no_accu_results.json -> Guess-Check\n", + "- e_coli_core_rm_rxn_tgt_taf_gcd_max_no_accu_results.json -> Guess-Check and Diversity\n", + "\n", + "Fluxes files:\n", + "- e_coli_core_rm_rxn_tgt_taf_reas_max_no_accu_fluxes.tsv -> Reasoning\n", + "- e_coli_core_rm_rxn_tgt_taf_fil_max_no_accu_fluxes.tsv -> Filter\n", + "- e_coli_core_rm_rxn_tgt_taf_gc_max_no_accu_fluxes.tsv -> Guess-Check\n", + "- e_coli_core_rm_rxn_tgt_taf_gcd_max_no_accu_fluxes.tsv -> Guess-Check and Diversity" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "> Note:\n", + ">\n", + "> You will find log files in [result_directory]/one_solution/e_coli_core/logs\n", + ">\n", + "> Example: ../../results/one_solution/e_coli_core/logs" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "s2lp", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.14" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/notebook/run/09_run_scopes.ipynb b/notebook/run/09_run_scopes.ipynb new file mode 100644 index 0000000..d5931e7 --- /dev/null +++ b/notebook/run/09_run_scopes.ipynb @@ -0,0 +1,58329 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Seed2LP: Run for scope mode with set of seeds solutions" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This notebook explain how to run Seed2lp to compute scopes from detected set of seeds, for Seed2LP, NetSeed and Precursor. Therefore it must be run **AFTER** previous notebooks (number 01 to 08) or after retrieving all solutions: [https://doi.org/10.57745/OS1JND](https://doi.org/10.57745/OS1JND)\n", + "\n", + "\n", + "> Note:\n", + ">\n", + "> The Seed2lp scopes result files are available[https://doi.org/10.57745/OS1JND](https://doi.org/10.57745/OS1JND)\n", + ">\n", + "> After downloadind and unzipping the package\n", + "> - analyses/results/scopes_s2lp\n", + "> - analyses/results/scopes_netseed\n", + "> - analyses/results/scopes_iCN718_2000\n", + "> - there is a supplement repository analyses/results/scopes_iCN718_1000 for Target mode for iCN718" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## **WARNING**\n", + "This notebook will run Seed2LP scopes for all solutions found in repository.\n", + "\n", + "On the paper, the scope was computed for:\n", + "- All 107 Seed2LP results files (up to 1000 solutions), Full Network mode, submin, no accumulation, *Reasoning*\n", + "- All 107 NetSeed formated resulted files (up to 1000 solutions)\n", + "- iCN718 Seed2LP results files (up to 2000), Target mode, submin, no accumulation, *Reasoning*, *Hybrid-Filter*, *Hybrid-GC*, *Hybrid-GCDiv*.\n", + "\n", + "The scope uses another tool called **menetools** that does not normalize the network before computing the scope. Therefore the deleted reactions such as reaction with boundaries[0;0] are still present. This is why we need to give the normalised network file.\n", + "\n", + "To avoid a long time running within the notebook, the notebook will use a copy of e_coli_core in an sbml normalised directory already created in notebook [05_run_netseed](05_run_netseed.ipynb). Also we will add the iCN718 normalised network" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Requirements\n", + "Module *seed2lp* needed\n", + "\n", + "> Advice:\n", + "> \n", + "> Use a conda env called s2lp with python 3.10 for plafrim cluster scripts" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!pip install seed2lp" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## **Slurm-based cluster**: Reproducing paper data\n", + "Slurm-based scripts for cluster are available:\n", + "- Launch if needed \n", + " - all previous scripts (numer 01 to 08)\n", + "- Change **_source_** variable by the path of your conda environement with seed2lp installed in files: \n", + " - [09_job_run_scope_iCN718](../../scripts/plafrim_cluster/09_job_run_scope_iCN718.sh)\n", + " - [09_job_run_scope_netseed](../../scripts/plafrim_cluster/09_job_run_scope_netseed.sh)\n", + " - [09_job_run_scope_s2lp](../../scripts/plafrim_cluster/09_job_run_scope_s2lp.sh)\n", + "- launch \n", + " - [09_job_run_scope_iCN718](../../scripts/plafrim_cluster/09_job_run_scope_iCN718.sh): `sbatch 09_job_run_scope_iCN718.sh`\n", + " - [09_job_run_scope_netseed](../../scripts/plafrim_cluster/09_job_run_scope_netseed.sh): `sbatch 09_job_run_scope_netseed.sh`\n", + " - [09_job_run_scope_s2lp](../../scripts/plafrim_cluster/09_job_run_scope_s2lp.sh): `sbatch 09_job_run_scope_s2lp.sh`\n", + " - When all three jobs are done, you can launch [10_2_job_run_scope_analyse_concat](../../scripts/slurm_cluster/10_2_job_run_scope_analyse_concat.sh): `sbatch 10_2_job_run_scope_analyse_concat.sh`\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## **LAUNCH**" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Variable to change (if wanted)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "analyse_dir = \"../../analyses\"\n", + "data_dir = f\"{analyse_dir}/data/\"\n", + "result_dir=f\"{analyse_dir}/results\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Execute" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "from os import path, listdir, makedirs\n", + "from shutil import copyfile" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "sbml_dir = f\"{data_dir}/sbml_corrected\"\n", + "e_coli_dir = f\"{data_dir}/sbml_corrected_e_coli_core\"\n", + "\n", + "scope_dir = f\"{result_dir}/scopes\"\n", + "s2lp_solution=f\"{result_dir}/s2lp\"\n", + "netseed_solution=f\"{result_dir}/netseed_formated_results\"\n", + "iCN718_solution=f\"{result_dir}/iCN718_2000\"\n" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'../../data/sbml_corrected_e_coli_core/iCN718.xml'" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "if not path.isdir(e_coli_dir):\n", + " makedirs(e_coli_dir)\n", + " copyfile(path.join(sbml_dir, \"e_coli_core.xml\"), path.join(e_coli_dir, \"e_coli_core.xml\"))\n", + "copyfile(path.join(sbml_dir, \"iCN718.xml\"), path.join(e_coli_dir, \"iCN718.xml\"))\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This notebook compute scope for:\n", + "- e_coli_core Seed2LP results files (up to 30 solutions), Full Network mode, submin, no accumulation, *Reasoning*\n", + "- e_coli_core NetSeed formated resulted files (up to 30 solutions)\n", + "- iCN718 Seed2LP results files (up to 30), Target mode, submin, no accumulation, *Reasoning*, *Hybrid-Filter*, *Hybrid-GC*, *Hybrid-GCDiv*." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "def run_scope(solution_dir:str, suffix_dir:str, mode:str=None):\n", + " for filename in listdir(e_coli_dir):\n", + " species = f'{path.splitext(path.basename(filename))[0]}'\n", + " sbml_path = path.join(e_coli_dir,filename)\n", + "\n", + " dir = list(map(lambda x: x.split('.')[0], listdir(solution_dir)))\n", + " if species in dir:\n", + " solution_path=path.join(solution_dir, species)\n", + " for filename_solution in listdir(solution_path):\n", + " if species in filename_solution and \"results.json\" in filename_solution:\n", + " scope_path = path.join(f\"{scope_dir}_{suffix_dir}\",species)\n", + " if (mode==\"Target\" and \"_tgt_\" in filename_solution) \\\n", + " or mode == None:\n", + " _file_solution_path=path.join(solution_path,filename_solution)\n", + "\n", + " command = f\"scope {sbml_path} {_file_solution_path} {scope_path}\"\n", + " !seed2lp {command}" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: e_coli_core\n", + "____________________________________________\n", + "\n", + "TARGET\n", + "REASONING\n", + "Subset Minimal\n", + "model_1\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acald_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etoh_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "size of scope 66\n", + "size of all metabolites 72 \n", + "\n", + "\n", + "TARGET\n", + "REASONING\n", + "Subset Minimal\n", + "model_2\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acald_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etoh_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "size of scope 66\n", + "size of all metabolites 72 \n", + "\n", + "\n", + "TARGET\n", + "REASONING\n", + "Subset Minimal\n", + "model_3\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acald_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etoh_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "size of scope 66\n", + "size of all metabolites 72 \n", + "\n", + "\n", + "TARGET\n", + "REASONING\n", + "Subset Minimal\n", + "model_4\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acald_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etoh_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "size of scope 66\n", + "size of all metabolites 72 \n", + "\n", + "\n", + "TARGET\n", + "REASONING\n", + "Subset Minimal\n", + "model_5\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acald_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etoh_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "size of scope 66\n", + "size of all metabolites 72 \n", + "\n", + "\n", + "TARGET\n", + "REASONING\n", + "Subset Minimal\n", + "model_6\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acald_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etoh_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "size of scope 66\n", + "size of all metabolites 72 \n", + "\n", + "\n", + "TARGET\n", + "REASONING\n", + "Subset Minimal\n", + "model_7\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acald_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etoh_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "size of scope 66\n", + "size of all metabolites 72 \n", + "\n", + "\n", + "TARGET\n", + "REASONING\n", + "Subset Minimal\n", + "model_8\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acald_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etoh_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "size of scope 66\n", + "size of all metabolites 72 \n", + "\n", + "\n", + "TARGET\n", + "REASONING\n", + "Subset Minimal\n", + "model_9\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acald_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etoh_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "size of scope 66\n", + "size of all metabolites 72 \n", + "\n", + "\n", + "TARGET\n", + "REASONING\n", + "Subset Minimal\n", + "model_10\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acald_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etoh_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "size of scope 66\n", + "size of all metabolites 72 \n", + "\n", + "\n", + "TARGET\n", + "REASONING\n", + "Subset Minimal\n", + "model_11\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acald_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etoh_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "size of scope 66\n", + "size of all metabolites 72 \n", + "\n", + "\n", + "TARGET\n", + "REASONING\n", + "Subset Minimal\n", + "model_12\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acald_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etoh_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "size of scope 66\n", + "size of all metabolites 72 \n", + "\n", + "\n", + "TARGET\n", + "REASONING\n", + "Subset Minimal\n", + "model_13\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acald_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etoh_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "size of scope 66\n", + "size of all metabolites 72 \n", + "\n", + "\n", + "TARGET\n", + "REASONING\n", + "Subset Minimal\n", + "model_14\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acald_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etoh_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "size of scope 66\n", + "size of all metabolites 72 \n", + "\n", + "\n", + "TARGET\n", + "REASONING\n", + "Subset Minimal\n", + "model_15\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acald_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etoh_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "size of scope 66\n", + "size of all metabolites 72 \n", + "\n", + "\n", + "TARGET\n", + "REASONING\n", + "Subset Minimal\n", + "model_16\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acald_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etoh_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "size of scope 66\n", + "size of all metabolites 72 \n", + "\n", + "\n", + "TARGET\n", + "REASONING\n", + "Subset Minimal\n", + "model_17\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acald_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etoh_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "size of scope 66\n", + "size of all metabolites 72 \n", + "\n", + "\n", + "TARGET\n", + "REASONING\n", + "Subset Minimal\n", + "model_18\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acald_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etoh_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "size of scope 66\n", + "size of all metabolites 72 \n", + "\n", + "\n", + "TARGET\n", + "REASONING\n", + "Subset Minimal\n", + "model_19\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acald_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etoh_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "size of scope 66\n", + "size of all metabolites 72 \n", + "\n", + "\n", + "TARGET\n", + "REASONING\n", + "Subset Minimal\n", + "model_20\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acald_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etoh_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "size of scope 66\n", + "size of all metabolites 72 \n", + "\n", + "\n", + "TARGET\n", + "REASONING\n", + "Subset Minimal\n", + "model_21\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acald_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etoh_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "size of scope 66\n", + "size of all metabolites 72 \n", + "\n", + "\n", + "TARGET\n", + "REASONING\n", + "Subset Minimal\n", + "model_22\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acald_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etoh_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "size of scope 66\n", + "size of all metabolites 72 \n", + "\n", + "\n", + "TARGET\n", + "REASONING\n", + "Subset Minimal\n", + "model_23\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acald_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etoh_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "size of scope 66\n", + "size of all metabolites 72 \n", + "\n", + "\n", + "TARGET\n", + "REASONING\n", + "Subset Minimal\n", + "model_24\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acald_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etoh_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "size of scope 66\n", + "size of all metabolites 72 \n", + "\n", + "\n", + "TARGET\n", + "REASONING\n", + "Subset Minimal\n", + "model_25\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acald_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etoh_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "size of scope 66\n", + "size of all metabolites 72 \n", + "\n", + "\n", + "TARGET\n", + "REASONING\n", + "Subset Minimal\n", + "model_26\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acald_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etoh_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "size of scope 66\n", + "size of all metabolites 72 \n", + "\n", + "\n", + "TARGET\n", + "REASONING\n", + "Subset Minimal\n", + "model_27\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acald_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etoh_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "size of scope 66\n", + "size of all metabolites 72 \n", + "\n", + "\n", + "TARGET\n", + "REASONING\n", + "Subset Minimal\n", + "model_28\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acald_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etoh_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "size of scope 66\n", + "size of all metabolites 72 \n", + "\n", + "\n", + "TARGET\n", + "REASONING\n", + "Subset Minimal\n", + "model_29\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acald_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etoh_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "size of scope 66\n", + "size of all metabolites 72 \n", + "\n", + "\n", + "TARGET\n", + "REASONING\n", + "Subset Minimal\n", + "model_30\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acald_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etoh_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "size of scope 66\n", + "size of all metabolites 72 \n", + "\n", + "\n", + "TARGET\n", + "REASONING FILTER\n", + "Subset Minimal\n", + "model_1\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acald_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etoh_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "size of scope 66\n", + "size of all metabolites 72 \n", + "\n", + "\n", + "TARGET\n", + "REASONING FILTER\n", + "Subset Minimal\n", + "model_2\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acald_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etoh_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "size of scope 66\n", + "size of all metabolites 72 \n", + "\n", + "\n", + "TARGET\n", + "REASONING FILTER\n", + "Subset Minimal\n", + "model_3\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acald_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etoh_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "size of scope 66\n", + "size of all metabolites 72 \n", + "\n", + "\n", + "TARGET\n", + "REASONING FILTER\n", + "Subset Minimal\n", + "model_4\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acald_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etoh_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "size of scope 66\n", + "size of all metabolites 72 \n", + "\n", + "\n", + "TARGET\n", + "REASONING FILTER\n", + "Subset Minimal\n", + "model_5\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acald_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etoh_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "size of scope 66\n", + "size of all metabolites 72 \n", + "\n", + "\n", + "TARGET\n", + "REASONING FILTER\n", + "Subset Minimal\n", + "model_6\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acald_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etoh_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "size of scope 66\n", + "size of all metabolites 72 \n", + "\n", + "\n", + "TARGET\n", + "REASONING FILTER\n", + "Subset Minimal\n", + "model_7\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acald_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etoh_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "size of scope 66\n", + "size of all metabolites 72 \n", + "\n", + "\n", + "TARGET\n", + "REASONING FILTER\n", + "Subset Minimal\n", + "model_8\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acald_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etoh_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "size of scope 66\n", + "size of all metabolites 72 \n", + "\n", + "\n", + "TARGET\n", + "REASONING FILTER\n", + "Subset Minimal\n", + "model_9\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acald_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etoh_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "size of scope 66\n", + "size of all metabolites 72 \n", + "\n", + "\n", + "TARGET\n", + "REASONING FILTER\n", + "Subset Minimal\n", + "model_10\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acald_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etoh_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "size of scope 66\n", + "size of all metabolites 72 \n", + "\n", + "\n", + "TARGET\n", + "REASONING FILTER\n", + "Subset Minimal\n", + "model_11\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acald_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etoh_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "size of scope 66\n", + "size of all metabolites 72 \n", + "\n", + "\n", + "TARGET\n", + "REASONING FILTER\n", + "Subset Minimal\n", + "model_12\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acald_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etoh_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "size of scope 66\n", + "size of all metabolites 72 \n", + "\n", + "\n", + "TARGET\n", + "REASONING FILTER\n", + "Subset Minimal\n", + "model_13\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acald_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etoh_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "size of scope 66\n", + "size of all metabolites 72 \n", + "\n", + "\n", + "TARGET\n", + "REASONING FILTER\n", + "Subset Minimal\n", + "model_14\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acald_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etoh_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "size of scope 66\n", + "size of all metabolites 72 \n", + "\n", + "\n", + "TARGET\n", + "REASONING FILTER\n", + "Subset Minimal\n", + "model_15\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acald_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etoh_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "size of scope 66\n", + "size of all metabolites 72 \n", + "\n", + "\n", + "TARGET\n", + "REASONING FILTER\n", + "Subset Minimal\n", + "model_16\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acald_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etoh_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "size of scope 66\n", + "size of all metabolites 72 \n", + "\n", + "\n", + "TARGET\n", + "REASONING FILTER\n", + "Subset Minimal\n", + "model_17\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acald_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etoh_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "size of scope 66\n", + "size of all metabolites 72 \n", + "\n", + "\n", + "TARGET\n", + "REASONING FILTER\n", + "Subset Minimal\n", + "model_18\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acald_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etoh_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "size of scope 66\n", + "size of all metabolites 72 \n", + "\n", + "\n", + "TARGET\n", + "REASONING FILTER\n", + "Subset Minimal\n", + "model_19\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acald_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etoh_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "size of scope 66\n", + "size of all metabolites 72 \n", + "\n", + "\n", + "TARGET\n", + "REASONING FILTER\n", + "Subset Minimal\n", + "model_20\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acald_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etoh_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "size of scope 66\n", + "size of all metabolites 72 \n", + "\n", + "\n", + "TARGET\n", + "REASONING FILTER\n", + "Subset Minimal\n", + "model_21\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acald_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etoh_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "size of scope 66\n", + "size of all metabolites 72 \n", + "\n", + "\n", + "TARGET\n", + "REASONING FILTER\n", + "Subset Minimal\n", + "model_22\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acald_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etoh_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "size of scope 66\n", + "size of all metabolites 72 \n", + "\n", + "\n", + "TARGET\n", + "REASONING FILTER\n", + "Subset Minimal\n", + "model_23\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acald_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etoh_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "size of scope 66\n", + "size of all metabolites 72 \n", + "\n", + "\n", + "TARGET\n", + "REASONING FILTER\n", + "Subset Minimal\n", + "model_24\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acald_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etoh_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "size of scope 66\n", + "size of all metabolites 72 \n", + "\n", + "\n", + "TARGET\n", + "REASONING FILTER\n", + "Subset Minimal\n", + "model_25\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acald_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etoh_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "size of scope 66\n", + "size of all metabolites 72 \n", + "\n", + "\n", + "TARGET\n", + "REASONING FILTER\n", + "Subset Minimal\n", + "model_26\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acald_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etoh_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "size of scope 66\n", + "size of all metabolites 72 \n", + "\n", + "\n", + "TARGET\n", + "REASONING FILTER\n", + "Subset Minimal\n", + "model_27\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acald_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etoh_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "size of scope 66\n", + "size of all metabolites 72 \n", + "\n", + "\n", + "TARGET\n", + "REASONING FILTER\n", + "Subset Minimal\n", + "model_28\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acald_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etoh_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "size of scope 66\n", + "size of all metabolites 72 \n", + "\n", + "\n", + "TARGET\n", + "REASONING FILTER\n", + "Subset Minimal\n", + "model_29\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acald_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etoh_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "size of scope 66\n", + "size of all metabolites 72 \n", + "\n", + "\n", + "TARGET\n", + "REASONING FILTER\n", + "Subset Minimal\n", + "model_30\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acald_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etoh_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "size of scope 66\n", + "size of all metabolites 72 \n", + "\n", + "\n", + "TARGET\n", + "REASONING GUESS-CHECK\n", + "Subset Minimal\n", + "model_1\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acald_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etoh_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "size of scope 68\n", + "size of all metabolites 72 \n", + "\n", + "\n", + "TARGET\n", + "REASONING GUESS-CHECK\n", + "Subset Minimal\n", + "model_2\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acald_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etoh_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "size of scope 68\n", + "size of all metabolites 72 \n", + "\n", + "\n", + "TARGET\n", + "REASONING GUESS-CHECK\n", + "Subset Minimal\n", + "model_3\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acald_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etoh_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "size of scope 67\n", + "size of all metabolites 72 \n", + "\n", + "\n", + "TARGET\n", + "REASONING GUESS-CHECK\n", + "Subset Minimal\n", + "model_4\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acald_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etoh_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "size of scope 67\n", + "size of all metabolites 72 \n", + "\n", + "\n", + "TARGET\n", + "REASONING GUESS-CHECK\n", + "Subset Minimal\n", + "model_5\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acald_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etoh_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "size of scope 67\n", + "size of all metabolites 72 \n", + "\n", + "\n", + "TARGET\n", + "REASONING GUESS-CHECK\n", + "Subset Minimal\n", + "model_6\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acald_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etoh_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "size of scope 67\n", + "size of all metabolites 72 \n", + "\n", + "\n", + "TARGET\n", + "REASONING GUESS-CHECK\n", + "Subset Minimal\n", + "model_7\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acald_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etoh_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "size of scope 67\n", + "size of all metabolites 72 \n", + "\n", + "\n", + "TARGET\n", + "REASONING GUESS-CHECK\n", + "Subset Minimal\n", + "model_8\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acald_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etoh_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "size of scope 67\n", + "size of all metabolites 72 \n", + "\n", + "\n", + "TARGET\n", + "REASONING GUESS-CHECK\n", + "Subset Minimal\n", + "model_9\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acald_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etoh_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "size of scope 67\n", + "size of all metabolites 72 \n", + "\n", + "\n", + "TARGET\n", + "REASONING GUESS-CHECK\n", + "Subset Minimal\n", + "model_10\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acald_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etoh_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "size of scope 68\n", + "size of all metabolites 72 \n", + "\n", + "\n", + "TARGET\n", + "REASONING GUESS-CHECK\n", + "Subset Minimal\n", + "model_11\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acald_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etoh_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "size of scope 67\n", + "size of all metabolites 72 \n", + "\n", + "\n", + "TARGET\n", + "REASONING GUESS-CHECK\n", + "Subset Minimal\n", + "model_12\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acald_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etoh_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "size of scope 67\n", + "size of all metabolites 72 \n", + "\n", + "\n", + "TARGET\n", + "REASONING GUESS-CHECK\n", + "Subset Minimal\n", + "model_13\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acald_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etoh_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "size of scope 67\n", + "size of all metabolites 72 \n", + "\n", + "\n", + "TARGET\n", + "REASONING GUESS-CHECK\n", + "Subset Minimal\n", + "model_14\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acald_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etoh_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "size of scope 67\n", + "size of all metabolites 72 \n", + "\n", + "\n", + "TARGET\n", + "REASONING GUESS-CHECK\n", + "Subset Minimal\n", + "model_15\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acald_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etoh_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "size of scope 67\n", + "size of all metabolites 72 \n", + "\n", + "\n", + "TARGET\n", + "REASONING GUESS-CHECK\n", + "Subset Minimal\n", + "model_16\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acald_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etoh_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "size of scope 67\n", + "size of all metabolites 72 \n", + "\n", + "\n", + "TARGET\n", + "REASONING GUESS-CHECK\n", + "Subset Minimal\n", + "model_17\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acald_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etoh_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "size of scope 67\n", + "size of all metabolites 72 \n", + "\n", + "\n", + "TARGET\n", + "REASONING GUESS-CHECK\n", + "Subset Minimal\n", + "model_18\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acald_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etoh_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "size of scope 66\n", + "size of all metabolites 72 \n", + "\n", + "\n", + "TARGET\n", + "REASONING GUESS-CHECK\n", + "Subset Minimal\n", + "model_19\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acald_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etoh_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "size of scope 66\n", + "size of all metabolites 72 \n", + "\n", + "\n", + "TARGET\n", + "REASONING GUESS-CHECK\n", + "Subset Minimal\n", + "model_20\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acald_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etoh_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "size of scope 67\n", + "size of all metabolites 72 \n", + "\n", + "\n", + "TARGET\n", + "REASONING GUESS-CHECK\n", + "Subset Minimal\n", + "model_21\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acald_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etoh_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "size of scope 66\n", + "size of all metabolites 72 \n", + "\n", + "\n", + "TARGET\n", + "REASONING GUESS-CHECK\n", + "Subset Minimal\n", + "model_22\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acald_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etoh_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "size of scope 67\n", + "size of all metabolites 72 \n", + "\n", + "\n", + "TARGET\n", + "REASONING GUESS-CHECK\n", + "Subset Minimal\n", + "model_23\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acald_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etoh_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "size of scope 66\n", + "size of all metabolites 72 \n", + "\n", + "\n", + "TARGET\n", + "REASONING GUESS-CHECK\n", + "Subset Minimal\n", + "model_24\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acald_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etoh_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "size of scope 66\n", + "size of all metabolites 72 \n", + "\n", + "\n", + "TARGET\n", + "REASONING GUESS-CHECK\n", + "Subset Minimal\n", + "model_25\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acald_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etoh_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "size of scope 66\n", + "size of all metabolites 72 \n", + "\n", + "\n", + "TARGET\n", + "REASONING GUESS-CHECK\n", + "Subset Minimal\n", + "model_26\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acald_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etoh_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "size of scope 66\n", + "size of all metabolites 72 \n", + "\n", + "\n", + "TARGET\n", + "REASONING GUESS-CHECK\n", + "Subset Minimal\n", + "model_27\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acald_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etoh_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "size of scope 66\n", + "size of all metabolites 72 \n", + "\n", + "\n", + "TARGET\n", + "REASONING GUESS-CHECK\n", + "Subset Minimal\n", + "model_28\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acald_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etoh_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "size of scope 66\n", + "size of all metabolites 72 \n", + "\n", + "\n", + "TARGET\n", + "REASONING GUESS-CHECK\n", + "Subset Minimal\n", + "model_29\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acald_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etoh_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "size of scope 66\n", + "size of all metabolites 72 \n", + "\n", + "\n", + "TARGET\n", + "REASONING GUESS-CHECK\n", + "Subset Minimal\n", + "model_30\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acald_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etoh_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "size of scope 66\n", + "size of all metabolites 72 \n", + "\n", + "\n", + "TARGET\n", + "REASONING GUESS-CHECK DIVERSITY\n", + "Subset Minimal\n", + "model_1\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acald_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etoh_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "size of scope 66\n", + "size of all metabolites 72 \n", + "\n", + "\n", + "TARGET\n", + "REASONING GUESS-CHECK DIVERSITY\n", + "Subset Minimal\n", + "model_2\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acald_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etoh_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "size of scope 67\n", + "size of all metabolites 72 \n", + "\n", + "\n", + "TARGET\n", + "REASONING GUESS-CHECK DIVERSITY\n", + "Subset Minimal\n", + "model_3\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acald_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etoh_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "size of scope 68\n", + "size of all metabolites 72 \n", + "\n", + "\n", + "TARGET\n", + "REASONING GUESS-CHECK DIVERSITY\n", + "Subset Minimal\n", + "model_4\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acald_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etoh_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "size of scope 66\n", + "size of all metabolites 72 \n", + "\n", + "\n", + "TARGET\n", + "REASONING GUESS-CHECK DIVERSITY\n", + "Subset Minimal\n", + "model_5\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acald_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etoh_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "size of scope 67\n", + "size of all metabolites 72 \n", + "\n", + "\n", + "TARGET\n", + "REASONING GUESS-CHECK DIVERSITY\n", + "Subset Minimal\n", + "model_6\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acald_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etoh_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "size of scope 68\n", + "size of all metabolites 72 \n", + "\n", + "\n", + "TARGET\n", + "REASONING GUESS-CHECK DIVERSITY\n", + "Subset Minimal\n", + "model_7\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acald_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etoh_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "size of scope 67\n", + "size of all metabolites 72 \n", + "\n", + "\n", + "TARGET\n", + "REASONING GUESS-CHECK DIVERSITY\n", + "Subset Minimal\n", + "model_8\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acald_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etoh_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "size of scope 67\n", + "size of all metabolites 72 \n", + "\n", + "\n", + "TARGET\n", + "REASONING GUESS-CHECK DIVERSITY\n", + "Subset Minimal\n", + "model_9\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acald_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etoh_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "size of scope 66\n", + "size of all metabolites 72 \n", + "\n", + "\n", + "TARGET\n", + "REASONING GUESS-CHECK DIVERSITY\n", + "Subset Minimal\n", + "model_10\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acald_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etoh_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "size of scope 68\n", + "size of all metabolites 72 \n", + "\n", + "\n", + "TARGET\n", + "REASONING GUESS-CHECK DIVERSITY\n", + "Subset Minimal\n", + "model_11\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acald_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etoh_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "size of scope 67\n", + "size of all metabolites 72 \n", + "\n", + "\n", + "TARGET\n", + "REASONING GUESS-CHECK DIVERSITY\n", + "Subset Minimal\n", + "model_12\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acald_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etoh_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "size of scope 67\n", + "size of all metabolites 72 \n", + "\n", + "\n", + "TARGET\n", + "REASONING GUESS-CHECK DIVERSITY\n", + "Subset Minimal\n", + "model_13\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acald_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etoh_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "size of scope 67\n", + "size of all metabolites 72 \n", + "\n", + "\n", + "TARGET\n", + "REASONING GUESS-CHECK DIVERSITY\n", + "Subset Minimal\n", + "model_14\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acald_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etoh_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "size of scope 66\n", + "size of all metabolites 72 \n", + "\n", + "\n", + "TARGET\n", + "REASONING GUESS-CHECK DIVERSITY\n", + "Subset Minimal\n", + "model_15\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acald_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etoh_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "size of scope 67\n", + "size of all metabolites 72 \n", + "\n", + "\n", + "TARGET\n", + "REASONING GUESS-CHECK DIVERSITY\n", + "Subset Minimal\n", + "model_16\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acald_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etoh_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "size of scope 67\n", + "size of all metabolites 72 \n", + "\n", + "\n", + "TARGET\n", + "REASONING GUESS-CHECK DIVERSITY\n", + "Subset Minimal\n", + "model_17\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acald_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etoh_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "size of scope 66\n", + "size of all metabolites 72 \n", + "\n", + "\n", + "TARGET\n", + "REASONING GUESS-CHECK DIVERSITY\n", + "Subset Minimal\n", + "model_18\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acald_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etoh_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "size of scope 67\n", + "size of all metabolites 72 \n", + "\n", + "\n", + "TARGET\n", + "REASONING GUESS-CHECK DIVERSITY\n", + "Subset Minimal\n", + "model_19\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acald_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etoh_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "size of scope 67\n", + "size of all metabolites 72 \n", + "\n", + "\n", + "TARGET\n", + "REASONING GUESS-CHECK DIVERSITY\n", + "Subset Minimal\n", + "model_20\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acald_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etoh_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "size of scope 67\n", + "size of all metabolites 72 \n", + "\n", + "\n", + "TARGET\n", + "REASONING GUESS-CHECK DIVERSITY\n", + "Subset Minimal\n", + "model_21\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acald_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etoh_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "size of scope 67\n", + "size of all metabolites 72 \n", + "\n", + "\n", + "TARGET\n", + "REASONING GUESS-CHECK DIVERSITY\n", + "Subset Minimal\n", + "model_22\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acald_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etoh_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "size of scope 66\n", + "size of all metabolites 72 \n", + "\n", + "\n", + "TARGET\n", + "REASONING GUESS-CHECK DIVERSITY\n", + "Subset Minimal\n", + "model_23\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acald_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etoh_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "size of scope 67\n", + "size of all metabolites 72 \n", + "\n", + "\n", + "TARGET\n", + "REASONING GUESS-CHECK DIVERSITY\n", + "Subset Minimal\n", + "model_24\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acald_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etoh_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "size of scope 66\n", + "size of all metabolites 72 \n", + "\n", + "\n", + "TARGET\n", + "REASONING GUESS-CHECK DIVERSITY\n", + "Subset Minimal\n", + "model_25\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acald_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etoh_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "size of scope 66\n", + "size of all metabolites 72 \n", + "\n", + "\n", + "TARGET\n", + "REASONING GUESS-CHECK DIVERSITY\n", + "Subset Minimal\n", + "model_26\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acald_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etoh_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "size of scope 67\n", + "size of all metabolites 72 \n", + "\n", + "\n", + "TARGET\n", + "REASONING GUESS-CHECK DIVERSITY\n", + "Subset Minimal\n", + "model_27\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acald_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etoh_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "size of scope 66\n", + "size of all metabolites 72 \n", + "\n", + "\n", + "TARGET\n", + "REASONING GUESS-CHECK DIVERSITY\n", + "Subset Minimal\n", + "model_28\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acald_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etoh_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "size of scope 66\n", + "size of all metabolites 72 \n", + "\n", + "\n", + "TARGET\n", + "REASONING GUESS-CHECK DIVERSITY\n", + "Subset Minimal\n", + "model_29\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acald_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etoh_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "size of scope 66\n", + "size of all metabolites 72 \n", + "\n", + "\n", + "TARGET\n", + "REASONING GUESS-CHECK DIVERSITY\n", + "Subset Minimal\n", + "model_30\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acald_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etoh_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "size of scope 66\n", + "size of all metabolites 72 \n", + "\n", + "\n", + "TARGET\n", + "REASONING\n", + "Minimize\n", + "model_one_solution\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acald_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etoh_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "size of scope 67\n", + "size of all metabolites 72 \n", + "\n", + "\n", + "TARGET\n", + "REASONING\n", + "Minimize\n", + "model_1\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acald_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etoh_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "size of scope 66\n", + "size of all metabolites 72 \n", + "\n", + "\n", + "TARGET\n", + "REASONING\n", + "Minimize\n", + "model_2\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acald_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etoh_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "size of scope 66\n", + "size of all metabolites 72 \n", + "\n", + "\n", + "TARGET\n", + "REASONING\n", + "Minimize\n", + "model_3\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acald_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etoh_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "size of scope 66\n", + "size of all metabolites 72 \n", + "\n", + "\n", + "TARGET\n", + "REASONING\n", + "Minimize\n", + "model_4\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acald_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etoh_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "size of scope 66\n", + "size of all metabolites 72 \n", + "\n", + "\n", + "TARGET\n", + "REASONING\n", + "Minimize\n", + "model_5\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acald_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etoh_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "size of scope 67\n", + "size of all metabolites 72 \n", + "\n", + "\n", + "TARGET\n", + "REASONING\n", + "Minimize\n", + "model_6\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acald_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etoh_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "size of scope 67\n", + "size of all metabolites 72 \n", + "\n", + "\n", + "TARGET\n", + "REASONING\n", + "Minimize\n", + "model_7\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acald_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etoh_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "size of scope 67\n", + "size of all metabolites 72 \n", + "\n", + "\n", + "TARGET\n", + "REASONING\n", + "Minimize\n", + "model_8\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acald_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etoh_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "size of scope 66\n", + "size of all metabolites 72 \n", + "\n", + "\n", + "TARGET\n", + "REASONING\n", + "Minimize\n", + "model_9\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acald_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etoh_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "size of scope 66\n", + "size of all metabolites 72 \n", + "\n", + "\n", + "TARGET\n", + "REASONING\n", + "Minimize\n", + "model_10\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acald_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etoh_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "size of scope 66\n", + "size of all metabolites 72 \n", + "\n", + "\n", + "TARGET\n", + "REASONING\n", + "Minimize\n", + "model_11\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acald_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etoh_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "size of scope 66\n", + "size of all metabolites 72 \n", + "\n", + "\n", + "TARGET\n", + "REASONING\n", + "Minimize\n", + "model_12\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acald_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etoh_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "size of scope 66\n", + "size of all metabolites 72 \n", + "\n", + "\n", + "TARGET\n", + "REASONING\n", + "Minimize\n", + "model_13\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acald_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etoh_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "size of scope 66\n", + "size of all metabolites 72 \n", + "\n", + "\n", + "TARGET\n", + "REASONING\n", + "Minimize\n", + "model_14\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acald_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etoh_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "size of scope 67\n", + "size of all metabolites 72 \n", + "\n", + "\n", + "TARGET\n", + "REASONING\n", + "Minimize\n", + "model_15\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acald_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etoh_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "size of scope 66\n", + "size of all metabolites 72 \n", + "\n", + "\n", + "TARGET\n", + "REASONING\n", + "Minimize\n", + "model_16\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acald_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etoh_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "size of scope 66\n", + "size of all metabolites 72 \n", + "\n", + "\n", + "TARGET\n", + "REASONING\n", + "Minimize\n", + "model_17\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acald_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etoh_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "size of scope 67\n", + "size of all metabolites 72 \n", + "\n", + "\n", + "TARGET\n", + "REASONING\n", + "Minimize\n", + "model_18\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acald_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etoh_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "size of scope 67\n", + "size of all metabolites 72 \n", + "\n", + "\n", + "TARGET\n", + "REASONING FILTER\n", + "Other\n", + "model_one_solution\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acald_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etoh_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "size of scope 66\n", + "size of all metabolites 72 \n", + "\n", + "\n", + "TARGET\n", + "REASONING FILTER\n", + "Minimize\n", + "model_1\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acald_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etoh_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "size of scope 66\n", + "size of all metabolites 72 \n", + "\n", + "\n", + "TARGET\n", + "REASONING FILTER\n", + "Minimize\n", + "model_2\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acald_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etoh_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "size of scope 66\n", + "size of all metabolites 72 \n", + "\n", + "\n", + "TARGET\n", + "REASONING GUESS-CHECK\n", + "Other\n", + "model_one_solution\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acald_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etoh_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "size of scope 66\n", + "size of all metabolites 72 \n", + "\n", + "\n", + "TARGET\n", + "REASONING GUESS-CHECK\n", + "Minimize\n", + "model_1\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acald_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etoh_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "size of scope 66\n", + "size of all metabolites 72 \n", + "\n", + "\n", + "TARGET\n", + "REASONING GUESS-CHECK\n", + "Minimize\n", + "model_2\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acald_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etoh_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "size of scope 66\n", + "size of all metabolites 72 \n", + "\n", + "\n", + "TARGET\n", + "REASONING GUESS-CHECK DIVERSITY\n", + "Other\n", + "model_one_solution\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acald_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etoh_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "size of scope 66\n", + "size of all metabolites 72 \n", + "\n", + "\n", + "TARGET\n", + "REASONING GUESS-CHECK DIVERSITY\n", + "Minimize\n", + "model_1\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acald_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etoh_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "size of scope 66\n", + "size of all metabolites 72 \n", + "\n", + "\n", + "TARGET\n", + "REASONING GUESS-CHECK DIVERSITY\n", + "Minimize\n", + "model_2\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acald_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etoh_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "size of scope 66\n", + "size of all metabolites 72 \n", + "\n", + "\n", + "TARGET\n", + "HYBRID\n", + "Minimize\n", + "model_one_solution\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acald_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etoh_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "size of scope 66\n", + "size of all metabolites 72 \n", + "\n", + "\n", + "TARGET\n", + "HYBRID\n", + "Minimize\n", + "model_1\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acald_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etoh_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "size of scope 66\n", + "size of all metabolites 72 \n", + "\n", + "\n", + "TARGET\n", + "HYBRID\n", + "Minimize\n", + "model_2\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acald_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etoh_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "size of scope 66\n", + "size of all metabolites 72 \n", + "\n", + "\n" + ] + } + ], + "source": [ + "run_scope(s2lp_solution, \"s2lp\", \"Target\")" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: e_coli_core\n", + "____________________________________________\n", + "\n", + "NETSEED\n", + "NETSEED\n", + "Other\n", + "model_1\n", + "Accumulation: True\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acald_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etoh_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "size of scope 19\n", + "size of all metabolites 72 \n", + "\n", + "\n", + "NETSEED\n", + "NETSEED\n", + "Other\n", + "model_2\n", + "Accumulation: True\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acald_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etoh_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "size of scope 19\n", + "size of all metabolites 72 \n", + "\n", + "\n" + ] + } + ], + "source": [ + "run_scope(netseed_solution, \"netseed\")" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: iCN718\n", + "____________________________________________\n", + "\n", + "\u001b[0;93mWARNING : Reaction R_SK_ACP_c deleted, boudaries [0,0]\u001b[0m\n", + "TARGET\n", + "REASONING FILTER\n", + "Subset Minimal\n", + "model_1\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tyr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_val__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_orn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ptrc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_spmd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_na1_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_urea_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_chol_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_adn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cytd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dad_2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dcyt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dgsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thymd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_duri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mnl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acgam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_uri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_csn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xan_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ura_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_taur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etha_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sucr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tre_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyb_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_bz_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_icit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyclt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no3_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_so4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arg__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asn__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_his__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ile__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_leu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_met__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_phe__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pro__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_trp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mg2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arab__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gal_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_man_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sbt__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fuc__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc3p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xyl__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rib__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rmn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_melib_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_rna_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_cav_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_phospholipid_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lipids_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_13ppd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2obut_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tartr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lps_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g1p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_f6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mbdg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_biomass_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rbt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malttr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_inost_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ppa_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galct__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glx_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ins_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acmana_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_4hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tym_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_all__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lyx__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_peamn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lcts_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_SK_met__L_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_protein_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfReactants=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfProducts=None\u001b[0m\n", + "size of scope 625\n", + "size of all metabolites 884 \n", + "\n", + "\n", + "TARGET\n", + "REASONING FILTER\n", + "Subset Minimal\n", + "model_2\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tyr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_val__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_orn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ptrc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_spmd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_na1_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_urea_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_chol_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_adn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cytd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dad_2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dcyt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dgsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thymd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_duri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mnl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acgam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_uri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_csn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xan_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ura_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_taur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etha_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sucr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tre_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyb_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_bz_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_icit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyclt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no3_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_so4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arg__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asn__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_his__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ile__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_leu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_met__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_phe__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pro__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_trp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mg2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arab__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gal_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_man_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sbt__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fuc__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc3p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xyl__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rib__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rmn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_melib_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_rna_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_cav_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_phospholipid_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lipids_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_13ppd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2obut_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tartr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lps_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g1p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_f6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mbdg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_biomass_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rbt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malttr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_inost_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ppa_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galct__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glx_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ins_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acmana_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_4hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tym_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_all__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lyx__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_peamn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lcts_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_SK_met__L_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_protein_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfReactants=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfProducts=None\u001b[0m\n", + "size of scope 625\n", + "size of all metabolites 884 \n", + "\n", + "\n", + "TARGET\n", + "REASONING FILTER\n", + "Subset Minimal\n", + "model_3\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tyr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_val__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_orn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ptrc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_spmd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_na1_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_urea_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_chol_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_adn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cytd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dad_2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dcyt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dgsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thymd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_duri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mnl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acgam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_uri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_csn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xan_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ura_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_taur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etha_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sucr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tre_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyb_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_bz_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_icit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyclt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no3_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_so4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arg__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asn__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_his__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ile__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_leu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_met__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_phe__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pro__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_trp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mg2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arab__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gal_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_man_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sbt__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fuc__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc3p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xyl__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rib__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rmn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_melib_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_rna_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_cav_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_phospholipid_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lipids_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_13ppd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2obut_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tartr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lps_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g1p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_f6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mbdg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_biomass_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rbt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malttr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_inost_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ppa_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galct__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glx_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ins_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acmana_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_4hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tym_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_all__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lyx__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_peamn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lcts_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_SK_met__L_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_protein_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfReactants=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfProducts=None\u001b[0m\n", + "size of scope 655\n", + "size of all metabolites 884 \n", + "\n", + "\n", + "TARGET\n", + "REASONING FILTER\n", + "Subset Minimal\n", + "model_4\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tyr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_val__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_orn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ptrc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_spmd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_na1_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_urea_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_chol_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_adn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cytd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dad_2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dcyt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dgsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thymd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_duri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mnl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acgam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_uri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_csn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xan_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ura_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_taur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etha_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sucr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tre_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyb_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_bz_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_icit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyclt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no3_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_so4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arg__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asn__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_his__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ile__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_leu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_met__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_phe__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pro__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_trp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mg2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arab__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gal_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_man_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sbt__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fuc__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc3p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xyl__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rib__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rmn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_melib_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_rna_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_cav_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_phospholipid_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lipids_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_13ppd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2obut_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tartr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lps_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g1p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_f6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mbdg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_biomass_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rbt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malttr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_inost_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ppa_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galct__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glx_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ins_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acmana_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_4hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tym_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_all__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lyx__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_peamn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lcts_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_SK_met__L_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_protein_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfReactants=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfProducts=None\u001b[0m\n", + "size of scope 651\n", + "size of all metabolites 884 \n", + "\n", + "\n", + "TARGET\n", + "REASONING FILTER\n", + "Subset Minimal\n", + "model_5\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tyr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_val__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_orn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ptrc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_spmd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_na1_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_urea_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_chol_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_adn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cytd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dad_2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dcyt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dgsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thymd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_duri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mnl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acgam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_uri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_csn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xan_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ura_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_taur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etha_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sucr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tre_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyb_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_bz_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_icit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyclt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no3_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_so4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arg__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asn__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_his__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ile__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_leu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_met__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_phe__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pro__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_trp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mg2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arab__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gal_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_man_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sbt__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fuc__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc3p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xyl__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rib__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rmn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_melib_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_rna_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_cav_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_phospholipid_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lipids_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_13ppd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2obut_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tartr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lps_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g1p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_f6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mbdg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_biomass_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rbt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malttr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_inost_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ppa_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galct__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glx_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ins_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acmana_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_4hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tym_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_all__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lyx__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_peamn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lcts_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_SK_met__L_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_protein_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfReactants=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfProducts=None\u001b[0m\n", + "size of scope 656\n", + "size of all metabolites 884 \n", + "\n", + "\n", + "TARGET\n", + "REASONING FILTER\n", + "Subset Minimal\n", + "model_6\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tyr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_val__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_orn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ptrc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_spmd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_na1_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_urea_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_chol_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_adn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cytd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dad_2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dcyt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dgsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thymd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_duri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mnl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acgam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_uri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_csn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xan_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ura_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_taur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etha_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sucr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tre_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyb_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_bz_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_icit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyclt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no3_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_so4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arg__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asn__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_his__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ile__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_leu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_met__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_phe__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pro__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_trp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mg2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arab__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gal_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_man_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sbt__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fuc__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc3p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xyl__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rib__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rmn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_melib_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_rna_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_cav_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_phospholipid_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lipids_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_13ppd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2obut_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tartr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lps_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g1p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_f6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mbdg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_biomass_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rbt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malttr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_inost_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ppa_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galct__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glx_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ins_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acmana_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_4hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tym_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_all__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lyx__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_peamn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lcts_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_SK_met__L_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_protein_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfReactants=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfProducts=None\u001b[0m\n", + "size of scope 651\n", + "size of all metabolites 884 \n", + "\n", + "\n", + "TARGET\n", + "REASONING FILTER\n", + "Subset Minimal\n", + "model_7\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tyr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_val__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_orn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ptrc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_spmd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_na1_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_urea_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_chol_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_adn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cytd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dad_2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dcyt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dgsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thymd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_duri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mnl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acgam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_uri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_csn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xan_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ura_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_taur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etha_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sucr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tre_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyb_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_bz_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_icit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyclt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no3_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_so4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arg__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asn__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_his__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ile__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_leu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_met__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_phe__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pro__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_trp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mg2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arab__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gal_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_man_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sbt__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fuc__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc3p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xyl__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rib__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rmn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_melib_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_rna_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_cav_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_phospholipid_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lipids_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_13ppd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2obut_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tartr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lps_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g1p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_f6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mbdg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_biomass_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rbt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malttr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_inost_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ppa_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galct__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glx_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ins_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acmana_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_4hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tym_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_all__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lyx__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_peamn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lcts_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_SK_met__L_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_protein_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfReactants=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfProducts=None\u001b[0m\n", + "size of scope 655\n", + "size of all metabolites 884 \n", + "\n", + "\n", + "TARGET\n", + "REASONING FILTER\n", + "Subset Minimal\n", + "model_8\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tyr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_val__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_orn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ptrc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_spmd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_na1_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_urea_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_chol_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_adn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cytd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dad_2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dcyt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dgsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thymd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_duri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mnl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acgam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_uri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_csn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xan_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ura_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_taur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etha_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sucr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tre_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyb_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_bz_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_icit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyclt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no3_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_so4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arg__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asn__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_his__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ile__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_leu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_met__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_phe__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pro__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_trp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mg2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arab__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gal_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_man_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sbt__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fuc__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc3p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xyl__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rib__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rmn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_melib_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_rna_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_cav_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_phospholipid_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lipids_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_13ppd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2obut_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tartr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lps_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g1p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_f6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mbdg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_biomass_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rbt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malttr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_inost_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ppa_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galct__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glx_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ins_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acmana_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_4hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tym_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_all__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lyx__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_peamn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lcts_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_SK_met__L_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_protein_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfReactants=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfProducts=None\u001b[0m\n", + "size of scope 656\n", + "size of all metabolites 884 \n", + "\n", + "\n", + "TARGET\n", + "REASONING FILTER\n", + "Subset Minimal\n", + "model_9\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tyr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_val__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_orn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ptrc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_spmd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_na1_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_urea_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_chol_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_adn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cytd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dad_2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dcyt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dgsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thymd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_duri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mnl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acgam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_uri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_csn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xan_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ura_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_taur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etha_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sucr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tre_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyb_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_bz_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_icit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyclt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no3_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_so4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arg__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asn__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_his__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ile__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_leu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_met__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_phe__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pro__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_trp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mg2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arab__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gal_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_man_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sbt__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fuc__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc3p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xyl__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rib__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rmn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_melib_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_rna_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_cav_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_phospholipid_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lipids_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_13ppd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2obut_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tartr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lps_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g1p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_f6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mbdg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_biomass_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rbt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malttr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_inost_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ppa_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galct__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glx_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ins_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acmana_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_4hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tym_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_all__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lyx__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_peamn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lcts_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_SK_met__L_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_protein_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfReactants=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfProducts=None\u001b[0m\n", + "size of scope 653\n", + "size of all metabolites 884 \n", + "\n", + "\n", + "TARGET\n", + "REASONING FILTER\n", + "Subset Minimal\n", + "model_10\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tyr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_val__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_orn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ptrc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_spmd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_na1_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_urea_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_chol_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_adn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cytd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dad_2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dcyt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dgsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thymd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_duri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mnl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acgam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_uri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_csn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xan_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ura_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_taur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etha_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sucr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tre_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyb_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_bz_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_icit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyclt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no3_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_so4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arg__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asn__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_his__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ile__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_leu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_met__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_phe__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pro__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_trp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mg2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arab__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gal_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_man_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sbt__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fuc__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc3p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xyl__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rib__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rmn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_melib_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_rna_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_cav_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_phospholipid_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lipids_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_13ppd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2obut_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tartr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lps_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g1p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_f6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mbdg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_biomass_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rbt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malttr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_inost_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ppa_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galct__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glx_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ins_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acmana_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_4hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tym_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_all__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lyx__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_peamn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lcts_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_SK_met__L_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_protein_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfReactants=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfProducts=None\u001b[0m\n", + "size of scope 655\n", + "size of all metabolites 884 \n", + "\n", + "\n", + "TARGET\n", + "REASONING FILTER\n", + "Subset Minimal\n", + "model_11\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tyr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_val__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_orn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ptrc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_spmd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_na1_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_urea_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_chol_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_adn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cytd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dad_2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dcyt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dgsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thymd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_duri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mnl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acgam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_uri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_csn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xan_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ura_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_taur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etha_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sucr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tre_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyb_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_bz_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_icit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyclt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no3_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_so4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arg__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asn__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_his__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ile__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_leu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_met__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_phe__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pro__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_trp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mg2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arab__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gal_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_man_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sbt__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fuc__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc3p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xyl__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rib__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rmn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_melib_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_rna_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_cav_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_phospholipid_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lipids_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_13ppd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2obut_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tartr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lps_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g1p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_f6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mbdg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_biomass_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rbt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malttr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_inost_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ppa_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galct__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glx_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ins_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acmana_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_4hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tym_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_all__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lyx__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_peamn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lcts_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_SK_met__L_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_protein_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfReactants=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfProducts=None\u001b[0m\n", + "size of scope 648\n", + "size of all metabolites 884 \n", + "\n", + "\n", + "TARGET\n", + "REASONING FILTER\n", + "Subset Minimal\n", + "model_12\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tyr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_val__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_orn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ptrc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_spmd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_na1_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_urea_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_chol_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_adn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cytd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dad_2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dcyt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dgsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thymd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_duri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mnl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acgam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_uri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_csn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xan_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ura_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_taur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etha_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sucr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tre_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyb_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_bz_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_icit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyclt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no3_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_so4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arg__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asn__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_his__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ile__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_leu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_met__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_phe__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pro__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_trp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mg2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arab__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gal_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_man_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sbt__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fuc__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc3p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xyl__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rib__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rmn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_melib_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_rna_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_cav_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_phospholipid_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lipids_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_13ppd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2obut_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tartr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lps_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g1p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_f6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mbdg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_biomass_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rbt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malttr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_inost_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ppa_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galct__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glx_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ins_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acmana_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_4hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tym_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_all__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lyx__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_peamn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lcts_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_SK_met__L_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_protein_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfReactants=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfProducts=None\u001b[0m\n", + "size of scope 650\n", + "size of all metabolites 884 \n", + "\n", + "\n", + "TARGET\n", + "REASONING FILTER\n", + "Subset Minimal\n", + "model_13\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tyr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_val__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_orn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ptrc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_spmd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_na1_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_urea_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_chol_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_adn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cytd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dad_2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dcyt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dgsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thymd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_duri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mnl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acgam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_uri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_csn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xan_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ura_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_taur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etha_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sucr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tre_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyb_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_bz_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_icit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyclt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no3_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_so4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arg__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asn__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_his__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ile__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_leu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_met__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_phe__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pro__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_trp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mg2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arab__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gal_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_man_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sbt__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fuc__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc3p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xyl__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rib__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rmn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_melib_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_rna_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_cav_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_phospholipid_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lipids_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_13ppd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2obut_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tartr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lps_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g1p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_f6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mbdg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_biomass_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rbt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malttr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_inost_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ppa_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galct__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glx_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ins_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acmana_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_4hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tym_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_all__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lyx__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_peamn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lcts_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_SK_met__L_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_protein_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfReactants=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfProducts=None\u001b[0m\n", + "size of scope 654\n", + "size of all metabolites 884 \n", + "\n", + "\n", + "TARGET\n", + "REASONING FILTER\n", + "Subset Minimal\n", + "model_14\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tyr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_val__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_orn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ptrc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_spmd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_na1_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_urea_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_chol_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_adn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cytd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dad_2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dcyt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dgsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thymd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_duri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mnl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acgam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_uri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_csn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xan_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ura_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_taur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etha_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sucr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tre_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyb_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_bz_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_icit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyclt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no3_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_so4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arg__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asn__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_his__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ile__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_leu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_met__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_phe__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pro__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_trp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mg2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arab__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gal_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_man_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sbt__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fuc__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc3p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xyl__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rib__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rmn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_melib_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_rna_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_cav_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_phospholipid_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lipids_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_13ppd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2obut_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tartr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lps_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g1p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_f6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mbdg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_biomass_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rbt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malttr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_inost_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ppa_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galct__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glx_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ins_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acmana_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_4hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tym_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_all__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lyx__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_peamn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lcts_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_SK_met__L_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_protein_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfReactants=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfProducts=None\u001b[0m\n", + "size of scope 652\n", + "size of all metabolites 884 \n", + "\n", + "\n", + "TARGET\n", + "REASONING FILTER\n", + "Subset Minimal\n", + "model_15\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tyr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_val__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_orn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ptrc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_spmd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_na1_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_urea_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_chol_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_adn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cytd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dad_2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dcyt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dgsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thymd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_duri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mnl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acgam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_uri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_csn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xan_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ura_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_taur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etha_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sucr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tre_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyb_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_bz_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_icit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyclt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no3_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_so4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arg__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asn__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_his__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ile__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_leu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_met__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_phe__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pro__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_trp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mg2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arab__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gal_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_man_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sbt__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fuc__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc3p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xyl__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rib__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rmn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_melib_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_rna_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_cav_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_phospholipid_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lipids_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_13ppd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2obut_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tartr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lps_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g1p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_f6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mbdg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_biomass_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rbt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malttr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_inost_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ppa_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galct__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glx_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ins_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acmana_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_4hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tym_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_all__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lyx__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_peamn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lcts_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_SK_met__L_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_protein_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfReactants=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfProducts=None\u001b[0m\n", + "size of scope 650\n", + "size of all metabolites 884 \n", + "\n", + "\n", + "TARGET\n", + "REASONING FILTER\n", + "Subset Minimal\n", + "model_16\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tyr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_val__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_orn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ptrc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_spmd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_na1_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_urea_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_chol_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_adn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cytd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dad_2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dcyt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dgsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thymd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_duri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mnl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acgam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_uri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_csn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xan_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ura_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_taur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etha_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sucr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tre_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyb_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_bz_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_icit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyclt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no3_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_so4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arg__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asn__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_his__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ile__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_leu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_met__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_phe__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pro__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_trp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mg2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arab__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gal_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_man_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sbt__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fuc__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc3p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xyl__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rib__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rmn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_melib_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_rna_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_cav_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_phospholipid_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lipids_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_13ppd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2obut_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tartr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lps_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g1p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_f6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mbdg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_biomass_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rbt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malttr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_inost_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ppa_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galct__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glx_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ins_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acmana_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_4hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tym_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_all__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lyx__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_peamn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lcts_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_SK_met__L_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_protein_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfReactants=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfProducts=None\u001b[0m\n", + "size of scope 654\n", + "size of all metabolites 884 \n", + "\n", + "\n", + "TARGET\n", + "REASONING FILTER\n", + "Subset Minimal\n", + "model_17\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tyr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_val__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_orn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ptrc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_spmd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_na1_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_urea_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_chol_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_adn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cytd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dad_2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dcyt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dgsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thymd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_duri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mnl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acgam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_uri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_csn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xan_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ura_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_taur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etha_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sucr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tre_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyb_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_bz_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_icit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyclt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no3_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_so4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arg__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asn__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_his__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ile__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_leu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_met__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_phe__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pro__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_trp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mg2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arab__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gal_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_man_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sbt__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fuc__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc3p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xyl__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rib__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rmn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_melib_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_rna_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_cav_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_phospholipid_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lipids_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_13ppd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2obut_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tartr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lps_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g1p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_f6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mbdg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_biomass_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rbt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malttr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_inost_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ppa_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galct__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glx_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ins_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acmana_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_4hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tym_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_all__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lyx__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_peamn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lcts_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_SK_met__L_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_protein_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfReactants=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfProducts=None\u001b[0m\n", + "size of scope 655\n", + "size of all metabolites 884 \n", + "\n", + "\n", + "TARGET\n", + "REASONING FILTER\n", + "Subset Minimal\n", + "model_18\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tyr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_val__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_orn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ptrc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_spmd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_na1_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_urea_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_chol_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_adn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cytd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dad_2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dcyt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dgsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thymd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_duri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mnl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acgam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_uri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_csn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xan_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ura_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_taur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etha_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sucr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tre_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyb_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_bz_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_icit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyclt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no3_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_so4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arg__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asn__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_his__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ile__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_leu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_met__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_phe__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pro__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_trp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mg2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arab__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gal_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_man_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sbt__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fuc__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc3p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xyl__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rib__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rmn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_melib_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_rna_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_cav_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_phospholipid_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lipids_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_13ppd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2obut_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tartr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lps_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g1p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_f6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mbdg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_biomass_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rbt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malttr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_inost_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ppa_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galct__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glx_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ins_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acmana_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_4hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tym_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_all__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lyx__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_peamn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lcts_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_SK_met__L_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_protein_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfReactants=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfProducts=None\u001b[0m\n", + "size of scope 653\n", + "size of all metabolites 884 \n", + "\n", + "\n", + "TARGET\n", + "REASONING FILTER\n", + "Subset Minimal\n", + "model_19\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tyr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_val__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_orn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ptrc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_spmd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_na1_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_urea_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_chol_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_adn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cytd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dad_2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dcyt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dgsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thymd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_duri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mnl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acgam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_uri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_csn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xan_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ura_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_taur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etha_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sucr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tre_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyb_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_bz_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_icit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyclt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no3_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_so4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arg__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asn__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_his__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ile__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_leu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_met__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_phe__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pro__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_trp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mg2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arab__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gal_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_man_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sbt__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fuc__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc3p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xyl__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rib__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rmn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_melib_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_rna_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_cav_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_phospholipid_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lipids_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_13ppd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2obut_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tartr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lps_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g1p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_f6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mbdg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_biomass_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rbt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malttr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_inost_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ppa_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galct__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glx_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ins_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acmana_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_4hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tym_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_all__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lyx__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_peamn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lcts_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_SK_met__L_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_protein_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfReactants=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfProducts=None\u001b[0m\n", + "size of scope 648\n", + "size of all metabolites 884 \n", + "\n", + "\n", + "TARGET\n", + "REASONING FILTER\n", + "Subset Minimal\n", + "model_20\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tyr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_val__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_orn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ptrc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_spmd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_na1_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_urea_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_chol_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_adn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cytd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dad_2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dcyt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dgsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thymd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_duri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mnl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acgam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_uri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_csn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xan_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ura_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_taur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etha_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sucr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tre_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyb_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_bz_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_icit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyclt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no3_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_so4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arg__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asn__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_his__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ile__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_leu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_met__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_phe__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pro__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_trp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mg2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arab__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gal_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_man_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sbt__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fuc__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc3p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xyl__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rib__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rmn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_melib_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_rna_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_cav_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_phospholipid_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lipids_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_13ppd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2obut_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tartr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lps_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g1p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_f6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mbdg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_biomass_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rbt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malttr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_inost_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ppa_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galct__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glx_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ins_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acmana_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_4hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tym_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_all__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lyx__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_peamn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lcts_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_SK_met__L_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_protein_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfReactants=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfProducts=None\u001b[0m\n", + "size of scope 652\n", + "size of all metabolites 884 \n", + "\n", + "\n", + "TARGET\n", + "REASONING FILTER\n", + "Subset Minimal\n", + "model_21\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tyr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_val__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_orn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ptrc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_spmd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_na1_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_urea_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_chol_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_adn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cytd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dad_2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dcyt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dgsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thymd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_duri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mnl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acgam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_uri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_csn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xan_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ura_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_taur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etha_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sucr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tre_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyb_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_bz_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_icit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyclt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no3_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_so4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arg__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asn__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_his__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ile__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_leu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_met__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_phe__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pro__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_trp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mg2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arab__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gal_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_man_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sbt__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fuc__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc3p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xyl__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rib__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rmn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_melib_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_rna_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_cav_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_phospholipid_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lipids_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_13ppd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2obut_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tartr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lps_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g1p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_f6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mbdg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_biomass_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rbt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malttr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_inost_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ppa_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galct__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glx_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ins_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acmana_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_4hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tym_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_all__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lyx__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_peamn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lcts_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_SK_met__L_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_protein_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfReactants=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfProducts=None\u001b[0m\n", + "size of scope 652\n", + "size of all metabolites 884 \n", + "\n", + "\n", + "TARGET\n", + "REASONING FILTER\n", + "Subset Minimal\n", + "model_22\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tyr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_val__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_orn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ptrc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_spmd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_na1_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_urea_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_chol_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_adn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cytd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dad_2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dcyt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dgsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thymd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_duri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mnl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acgam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_uri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_csn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xan_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ura_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_taur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etha_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sucr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tre_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyb_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_bz_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_icit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyclt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no3_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_so4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arg__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asn__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_his__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ile__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_leu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_met__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_phe__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pro__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_trp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mg2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arab__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gal_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_man_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sbt__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fuc__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc3p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xyl__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rib__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rmn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_melib_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_rna_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_cav_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_phospholipid_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lipids_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_13ppd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2obut_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tartr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lps_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g1p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_f6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mbdg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_biomass_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rbt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malttr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_inost_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ppa_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galct__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glx_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ins_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acmana_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_4hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tym_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_all__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lyx__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_peamn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lcts_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_SK_met__L_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_protein_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfReactants=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfProducts=None\u001b[0m\n", + "size of scope 654\n", + "size of all metabolites 884 \n", + "\n", + "\n", + "TARGET\n", + "REASONING FILTER\n", + "Subset Minimal\n", + "model_23\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tyr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_val__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_orn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ptrc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_spmd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_na1_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_urea_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_chol_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_adn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cytd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dad_2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dcyt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dgsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thymd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_duri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mnl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acgam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_uri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_csn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xan_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ura_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_taur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etha_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sucr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tre_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyb_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_bz_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_icit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyclt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no3_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_so4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arg__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asn__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_his__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ile__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_leu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_met__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_phe__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pro__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_trp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mg2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arab__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gal_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_man_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sbt__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fuc__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc3p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xyl__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rib__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rmn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_melib_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_rna_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_cav_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_phospholipid_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lipids_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_13ppd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2obut_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tartr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lps_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g1p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_f6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mbdg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_biomass_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rbt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malttr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_inost_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ppa_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galct__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glx_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ins_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acmana_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_4hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tym_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_all__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lyx__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_peamn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lcts_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_SK_met__L_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_protein_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfReactants=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfProducts=None\u001b[0m\n", + "size of scope 649\n", + "size of all metabolites 884 \n", + "\n", + "\n", + "TARGET\n", + "REASONING FILTER\n", + "Subset Minimal\n", + "model_24\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tyr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_val__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_orn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ptrc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_spmd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_na1_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_urea_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_chol_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_adn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cytd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dad_2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dcyt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dgsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thymd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_duri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mnl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acgam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_uri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_csn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xan_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ura_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_taur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etha_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sucr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tre_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyb_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_bz_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_icit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyclt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no3_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_so4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arg__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asn__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_his__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ile__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_leu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_met__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_phe__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pro__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_trp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mg2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arab__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gal_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_man_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sbt__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fuc__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc3p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xyl__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rib__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rmn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_melib_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_rna_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_cav_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_phospholipid_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lipids_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_13ppd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2obut_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tartr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lps_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g1p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_f6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mbdg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_biomass_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rbt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malttr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_inost_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ppa_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galct__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glx_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ins_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acmana_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_4hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tym_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_all__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lyx__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_peamn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lcts_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_SK_met__L_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_protein_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfReactants=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfProducts=None\u001b[0m\n", + "size of scope 653\n", + "size of all metabolites 884 \n", + "\n", + "\n", + "TARGET\n", + "REASONING FILTER\n", + "Subset Minimal\n", + "model_25\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tyr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_val__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_orn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ptrc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_spmd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_na1_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_urea_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_chol_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_adn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cytd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dad_2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dcyt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dgsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thymd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_duri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mnl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acgam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_uri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_csn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xan_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ura_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_taur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etha_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sucr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tre_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyb_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_bz_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_icit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyclt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no3_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_so4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arg__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asn__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_his__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ile__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_leu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_met__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_phe__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pro__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_trp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mg2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arab__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gal_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_man_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sbt__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fuc__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc3p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xyl__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rib__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rmn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_melib_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_rna_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_cav_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_phospholipid_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lipids_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_13ppd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2obut_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tartr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lps_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g1p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_f6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mbdg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_biomass_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rbt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malttr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_inost_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ppa_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galct__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glx_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ins_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acmana_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_4hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tym_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_all__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lyx__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_peamn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lcts_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_SK_met__L_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_protein_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfReactants=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfProducts=None\u001b[0m\n", + "size of scope 647\n", + "size of all metabolites 884 \n", + "\n", + "\n", + "TARGET\n", + "REASONING FILTER\n", + "Subset Minimal\n", + "model_26\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tyr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_val__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_orn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ptrc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_spmd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_na1_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_urea_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_chol_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_adn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cytd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dad_2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dcyt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dgsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thymd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_duri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mnl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acgam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_uri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_csn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xan_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ura_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_taur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etha_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sucr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tre_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyb_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_bz_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_icit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyclt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no3_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_so4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arg__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asn__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_his__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ile__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_leu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_met__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_phe__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pro__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_trp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mg2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arab__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gal_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_man_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sbt__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fuc__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc3p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xyl__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rib__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rmn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_melib_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_rna_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_cav_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_phospholipid_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lipids_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_13ppd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2obut_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tartr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lps_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g1p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_f6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mbdg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_biomass_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rbt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malttr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_inost_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ppa_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galct__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glx_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ins_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acmana_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_4hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tym_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_all__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lyx__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_peamn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lcts_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_SK_met__L_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_protein_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfReactants=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfProducts=None\u001b[0m\n", + "size of scope 651\n", + "size of all metabolites 884 \n", + "\n", + "\n", + "TARGET\n", + "REASONING FILTER\n", + "Subset Minimal\n", + "model_27\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tyr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_val__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_orn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ptrc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_spmd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_na1_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_urea_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_chol_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_adn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cytd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dad_2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dcyt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dgsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thymd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_duri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mnl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acgam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_uri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_csn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xan_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ura_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_taur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etha_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sucr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tre_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyb_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_bz_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_icit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyclt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no3_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_so4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arg__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asn__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_his__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ile__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_leu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_met__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_phe__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pro__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_trp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mg2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arab__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gal_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_man_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sbt__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fuc__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc3p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xyl__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rib__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rmn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_melib_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_rna_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_cav_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_phospholipid_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lipids_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_13ppd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2obut_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tartr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lps_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g1p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_f6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mbdg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_biomass_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rbt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malttr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_inost_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ppa_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galct__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glx_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ins_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acmana_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_4hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tym_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_all__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lyx__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_peamn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lcts_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_SK_met__L_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_protein_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfReactants=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfProducts=None\u001b[0m\n", + "size of scope 654\n", + "size of all metabolites 884 \n", + "\n", + "\n", + "TARGET\n", + "REASONING FILTER\n", + "Subset Minimal\n", + "model_28\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tyr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_val__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_orn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ptrc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_spmd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_na1_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_urea_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_chol_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_adn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cytd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dad_2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dcyt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dgsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thymd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_duri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mnl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acgam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_uri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_csn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xan_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ura_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_taur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etha_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sucr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tre_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyb_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_bz_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_icit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyclt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no3_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_so4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arg__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asn__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_his__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ile__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_leu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_met__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_phe__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pro__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_trp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mg2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arab__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gal_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_man_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sbt__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fuc__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc3p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xyl__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rib__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rmn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_melib_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_rna_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_cav_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_phospholipid_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lipids_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_13ppd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2obut_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tartr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lps_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g1p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_f6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mbdg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_biomass_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rbt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malttr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_inost_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ppa_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galct__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glx_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ins_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acmana_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_4hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tym_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_all__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lyx__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_peamn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lcts_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_SK_met__L_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_protein_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfReactants=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfProducts=None\u001b[0m\n", + "size of scope 649\n", + "size of all metabolites 884 \n", + "\n", + "\n", + "TARGET\n", + "REASONING FILTER\n", + "Subset Minimal\n", + "model_29\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tyr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_val__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_orn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ptrc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_spmd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_na1_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_urea_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_chol_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_adn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cytd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dad_2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dcyt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dgsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thymd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_duri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mnl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acgam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_uri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_csn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xan_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ura_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_taur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etha_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sucr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tre_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyb_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_bz_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_icit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyclt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no3_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_so4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arg__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asn__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_his__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ile__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_leu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_met__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_phe__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pro__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_trp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mg2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arab__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gal_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_man_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sbt__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fuc__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc3p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xyl__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rib__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rmn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_melib_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_rna_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_cav_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_phospholipid_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lipids_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_13ppd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2obut_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tartr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lps_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g1p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_f6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mbdg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_biomass_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rbt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malttr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_inost_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ppa_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galct__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glx_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ins_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acmana_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_4hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tym_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_all__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lyx__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_peamn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lcts_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_SK_met__L_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_protein_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfReactants=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfProducts=None\u001b[0m\n", + "size of scope 653\n", + "size of all metabolites 884 \n", + "\n", + "\n", + "TARGET\n", + "REASONING FILTER\n", + "Subset Minimal\n", + "model_30\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tyr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_val__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_orn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ptrc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_spmd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_na1_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_urea_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_chol_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_adn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cytd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dad_2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dcyt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dgsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thymd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_duri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mnl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acgam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_uri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_csn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xan_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ura_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_taur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etha_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sucr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tre_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyb_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_bz_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_icit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyclt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no3_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_so4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arg__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asn__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_his__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ile__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_leu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_met__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_phe__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pro__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_trp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mg2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arab__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gal_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_man_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sbt__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fuc__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc3p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xyl__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rib__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rmn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_melib_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_rna_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_cav_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_phospholipid_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lipids_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_13ppd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2obut_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tartr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lps_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g1p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_f6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mbdg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_biomass_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rbt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malttr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_inost_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ppa_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galct__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glx_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ins_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acmana_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_4hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tym_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_all__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lyx__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_peamn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lcts_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_SK_met__L_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_protein_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfReactants=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfProducts=None\u001b[0m\n", + "size of scope 652\n", + "size of all metabolites 884 \n", + "\n", + "\n", + "TARGET\n", + "REASONING FILTER\n", + "Other\n", + "model_one_solution\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tyr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_val__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_orn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ptrc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_spmd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_na1_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_urea_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_chol_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_adn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cytd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dad_2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dcyt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dgsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thymd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_duri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mnl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acgam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_uri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_csn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xan_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ura_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_taur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etha_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sucr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tre_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyb_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_bz_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_icit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyclt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no3_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_so4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arg__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asn__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_his__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ile__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_leu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_met__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_phe__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pro__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_trp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mg2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arab__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gal_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_man_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sbt__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fuc__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc3p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xyl__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rib__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rmn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_melib_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_rna_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_cav_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_phospholipid_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lipids_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_13ppd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2obut_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tartr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lps_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g1p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_f6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mbdg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_biomass_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rbt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malttr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_inost_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ppa_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galct__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glx_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ins_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acmana_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_4hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tym_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_all__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lyx__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_peamn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lcts_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_SK_met__L_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_protein_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfReactants=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfProducts=None\u001b[0m\n", + "size of scope 625\n", + "size of all metabolites 884 \n", + "\n", + "\n", + "TARGET\n", + "REASONING FILTER\n", + "Minimize\n", + "model_one_solution\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tyr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_val__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_orn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ptrc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_spmd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_na1_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_urea_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_chol_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_adn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cytd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dad_2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dcyt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dgsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thymd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_duri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mnl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acgam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_uri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_csn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xan_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ura_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_taur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etha_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sucr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tre_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyb_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_bz_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_icit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyclt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no3_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_so4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arg__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asn__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_his__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ile__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_leu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_met__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_phe__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pro__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_trp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mg2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arab__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gal_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_man_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sbt__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fuc__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc3p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xyl__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rib__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rmn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_melib_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_rna_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_cav_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_phospholipid_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lipids_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_13ppd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2obut_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tartr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lps_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g1p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_f6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mbdg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_biomass_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rbt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malttr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_inost_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ppa_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galct__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glx_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ins_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acmana_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_4hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tym_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_all__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lyx__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_peamn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lcts_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_SK_met__L_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_protein_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfReactants=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfProducts=None\u001b[0m\n", + "size of scope 625\n", + "size of all metabolites 884 \n", + "\n", + "\n", + "TARGET\n", + "REASONING FILTER\n", + "Minimize\n", + "model_1\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tyr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_val__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_orn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ptrc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_spmd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_na1_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_urea_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_chol_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_adn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cytd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dad_2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dcyt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dgsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thymd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_duri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mnl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acgam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_uri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_csn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xan_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ura_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_taur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etha_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sucr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tre_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyb_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_bz_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_icit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyclt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no3_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_so4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arg__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asn__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_his__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ile__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_leu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_met__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_phe__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pro__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_trp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mg2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arab__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gal_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_man_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sbt__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fuc__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc3p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xyl__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rib__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rmn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_melib_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_rna_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_cav_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_phospholipid_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lipids_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_13ppd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2obut_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tartr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lps_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g1p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_f6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mbdg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_biomass_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rbt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malttr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_inost_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ppa_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galct__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glx_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ins_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acmana_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_4hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tym_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_all__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lyx__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_peamn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lcts_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_SK_met__L_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_protein_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfReactants=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfProducts=None\u001b[0m\n", + "size of scope 636\n", + "size of all metabolites 884 \n", + "\n", + "\n", + "TARGET\n", + "REASONING FILTER\n", + "Minimize\n", + "model_2\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tyr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_val__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_orn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ptrc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_spmd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_na1_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_urea_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_chol_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_adn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cytd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dad_2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dcyt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dgsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thymd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_duri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mnl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acgam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_uri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_csn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xan_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ura_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_taur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etha_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sucr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tre_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyb_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_bz_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_icit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyclt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no3_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_so4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arg__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asn__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_his__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ile__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_leu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_met__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_phe__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pro__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_trp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mg2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arab__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gal_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_man_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sbt__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fuc__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc3p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xyl__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rib__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rmn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_melib_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_rna_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_cav_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_phospholipid_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lipids_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_13ppd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2obut_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tartr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lps_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g1p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_f6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mbdg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_biomass_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rbt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malttr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_inost_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ppa_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galct__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glx_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ins_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acmana_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_4hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tym_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_all__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lyx__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_peamn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lcts_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_SK_met__L_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_protein_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfReactants=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfProducts=None\u001b[0m\n", + "size of scope 636\n", + "size of all metabolites 884 \n", + "\n", + "\n", + "TARGET\n", + "REASONING FILTER\n", + "Minimize\n", + "model_3\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tyr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_val__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_orn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ptrc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_spmd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_na1_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_urea_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_chol_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_adn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cytd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dad_2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dcyt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dgsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thymd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_duri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mnl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acgam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_uri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_csn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xan_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ura_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_taur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etha_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sucr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tre_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyb_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_bz_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_icit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyclt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no3_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_so4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arg__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asn__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_his__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ile__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_leu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_met__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_phe__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pro__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_trp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mg2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arab__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gal_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_man_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sbt__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fuc__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc3p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xyl__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rib__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rmn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_melib_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_rna_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_cav_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_phospholipid_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lipids_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_13ppd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2obut_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tartr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lps_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g1p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_f6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mbdg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_biomass_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rbt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malttr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_inost_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ppa_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galct__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glx_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ins_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acmana_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_4hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tym_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_all__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lyx__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_peamn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lcts_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_SK_met__L_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_protein_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfReactants=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfProducts=None\u001b[0m\n", + "size of scope 636\n", + "size of all metabolites 884 \n", + "\n", + "\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: iCN718\n", + "____________________________________________\n", + "\n", + "\u001b[0;93mWARNING : Reaction R_SK_ACP_c deleted, boudaries [0,0]\u001b[0m\n", + "TARGET\n", + "REASONING GUESS-CHECK DIVERSITY\n", + "Subset Minimal\n", + "model_1\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tyr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_val__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_orn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ptrc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_spmd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_na1_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_urea_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_chol_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_adn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cytd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dad_2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dcyt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dgsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thymd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_duri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mnl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acgam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_uri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_csn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xan_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ura_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_taur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etha_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sucr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tre_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyb_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_bz_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_icit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyclt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no3_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_so4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arg__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asn__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_his__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ile__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_leu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_met__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_phe__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pro__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_trp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mg2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arab__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gal_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_man_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sbt__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fuc__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc3p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xyl__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rib__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rmn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_melib_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_rna_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_cav_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_phospholipid_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lipids_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_13ppd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2obut_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tartr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lps_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g1p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_f6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mbdg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_biomass_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rbt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malttr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_inost_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ppa_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galct__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glx_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ins_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acmana_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_4hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tym_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_all__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lyx__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_peamn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lcts_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_SK_met__L_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_protein_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfReactants=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfProducts=None\u001b[0m\n", + "size of scope 669\n", + "size of all metabolites 884 \n", + "\n", + "\n", + "TARGET\n", + "REASONING GUESS-CHECK DIVERSITY\n", + "Subset Minimal\n", + "model_2\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tyr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_val__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_orn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ptrc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_spmd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_na1_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_urea_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_chol_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_adn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cytd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dad_2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dcyt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dgsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thymd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_duri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mnl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acgam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_uri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_csn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xan_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ura_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_taur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etha_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sucr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tre_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyb_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_bz_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_icit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyclt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no3_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_so4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arg__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asn__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_his__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ile__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_leu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_met__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_phe__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pro__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_trp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mg2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arab__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gal_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_man_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sbt__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fuc__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc3p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xyl__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rib__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rmn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_melib_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_rna_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_cav_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_phospholipid_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lipids_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_13ppd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2obut_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tartr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lps_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g1p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_f6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mbdg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_biomass_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rbt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malttr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_inost_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ppa_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galct__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glx_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ins_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acmana_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_4hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tym_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_all__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lyx__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_peamn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lcts_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_SK_met__L_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_protein_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfReactants=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfProducts=None\u001b[0m\n", + "size of scope 677\n", + "size of all metabolites 884 \n", + "\n", + "\n", + "TARGET\n", + "REASONING GUESS-CHECK DIVERSITY\n", + "Subset Minimal\n", + "model_3\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tyr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_val__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_orn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ptrc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_spmd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_na1_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_urea_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_chol_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_adn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cytd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dad_2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dcyt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dgsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thymd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_duri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mnl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acgam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_uri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_csn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xan_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ura_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_taur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etha_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sucr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tre_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyb_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_bz_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_icit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyclt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no3_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_so4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arg__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asn__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_his__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ile__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_leu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_met__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_phe__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pro__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_trp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mg2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arab__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gal_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_man_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sbt__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fuc__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc3p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xyl__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rib__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rmn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_melib_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_rna_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_cav_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_phospholipid_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lipids_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_13ppd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2obut_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tartr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lps_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g1p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_f6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mbdg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_biomass_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rbt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malttr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_inost_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ppa_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galct__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glx_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ins_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acmana_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_4hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tym_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_all__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lyx__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_peamn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lcts_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_SK_met__L_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_protein_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfReactants=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfProducts=None\u001b[0m\n", + "size of scope 673\n", + "size of all metabolites 884 \n", + "\n", + "\n", + "TARGET\n", + "REASONING GUESS-CHECK DIVERSITY\n", + "Subset Minimal\n", + "model_4\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tyr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_val__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_orn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ptrc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_spmd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_na1_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_urea_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_chol_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_adn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cytd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dad_2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dcyt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dgsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thymd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_duri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mnl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acgam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_uri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_csn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xan_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ura_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_taur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etha_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sucr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tre_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyb_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_bz_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_icit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyclt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no3_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_so4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arg__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asn__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_his__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ile__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_leu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_met__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_phe__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pro__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_trp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mg2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arab__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gal_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_man_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sbt__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fuc__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc3p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xyl__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rib__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rmn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_melib_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_rna_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_cav_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_phospholipid_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lipids_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_13ppd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2obut_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tartr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lps_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g1p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_f6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mbdg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_biomass_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rbt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malttr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_inost_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ppa_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galct__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glx_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ins_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acmana_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_4hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tym_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_all__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lyx__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_peamn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lcts_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_SK_met__L_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_protein_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfReactants=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfProducts=None\u001b[0m\n", + "size of scope 641\n", + "size of all metabolites 884 \n", + "\n", + "\n", + "TARGET\n", + "REASONING GUESS-CHECK DIVERSITY\n", + "Subset Minimal\n", + "model_5\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tyr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_val__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_orn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ptrc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_spmd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_na1_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_urea_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_chol_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_adn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cytd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dad_2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dcyt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dgsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thymd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_duri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mnl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acgam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_uri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_csn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xan_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ura_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_taur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etha_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sucr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tre_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyb_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_bz_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_icit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyclt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no3_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_so4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arg__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asn__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_his__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ile__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_leu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_met__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_phe__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pro__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_trp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mg2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arab__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gal_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_man_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sbt__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fuc__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc3p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xyl__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rib__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rmn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_melib_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_rna_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_cav_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_phospholipid_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lipids_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_13ppd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2obut_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tartr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lps_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g1p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_f6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mbdg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_biomass_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rbt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malttr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_inost_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ppa_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galct__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glx_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ins_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acmana_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_4hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tym_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_all__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lyx__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_peamn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lcts_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_SK_met__L_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_protein_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfReactants=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfProducts=None\u001b[0m\n", + "size of scope 642\n", + "size of all metabolites 884 \n", + "\n", + "\n", + "TARGET\n", + "REASONING GUESS-CHECK DIVERSITY\n", + "Subset Minimal\n", + "model_6\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tyr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_val__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_orn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ptrc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_spmd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_na1_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_urea_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_chol_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_adn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cytd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dad_2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dcyt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dgsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thymd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_duri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mnl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acgam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_uri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_csn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xan_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ura_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_taur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etha_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sucr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tre_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyb_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_bz_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_icit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyclt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no3_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_so4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arg__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asn__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_his__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ile__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_leu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_met__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_phe__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pro__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_trp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mg2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arab__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gal_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_man_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sbt__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fuc__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc3p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xyl__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rib__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rmn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_melib_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_rna_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_cav_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_phospholipid_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lipids_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_13ppd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2obut_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tartr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lps_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g1p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_f6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mbdg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_biomass_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rbt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malttr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_inost_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ppa_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galct__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glx_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ins_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acmana_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_4hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tym_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_all__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lyx__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_peamn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lcts_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_SK_met__L_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_protein_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfReactants=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfProducts=None\u001b[0m\n", + "size of scope 682\n", + "size of all metabolites 884 \n", + "\n", + "\n", + "TARGET\n", + "REASONING GUESS-CHECK DIVERSITY\n", + "Subset Minimal\n", + "model_7\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tyr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_val__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_orn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ptrc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_spmd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_na1_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_urea_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_chol_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_adn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cytd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dad_2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dcyt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dgsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thymd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_duri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mnl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acgam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_uri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_csn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xan_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ura_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_taur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etha_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sucr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tre_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyb_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_bz_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_icit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyclt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no3_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_so4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arg__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asn__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_his__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ile__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_leu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_met__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_phe__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pro__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_trp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mg2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arab__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gal_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_man_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sbt__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fuc__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc3p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xyl__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rib__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rmn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_melib_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_rna_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_cav_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_phospholipid_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lipids_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_13ppd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2obut_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tartr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lps_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g1p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_f6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mbdg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_biomass_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rbt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malttr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_inost_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ppa_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galct__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glx_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ins_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acmana_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_4hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tym_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_all__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lyx__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_peamn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lcts_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_SK_met__L_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_protein_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfReactants=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfProducts=None\u001b[0m\n", + "size of scope 646\n", + "size of all metabolites 884 \n", + "\n", + "\n", + "TARGET\n", + "REASONING GUESS-CHECK DIVERSITY\n", + "Subset Minimal\n", + "model_8\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tyr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_val__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_orn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ptrc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_spmd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_na1_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_urea_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_chol_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_adn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cytd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dad_2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dcyt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dgsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thymd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_duri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mnl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acgam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_uri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_csn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xan_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ura_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_taur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etha_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sucr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tre_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyb_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_bz_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_icit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyclt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no3_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_so4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arg__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asn__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_his__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ile__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_leu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_met__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_phe__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pro__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_trp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mg2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arab__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gal_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_man_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sbt__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fuc__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc3p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xyl__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rib__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rmn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_melib_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_rna_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_cav_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_phospholipid_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lipids_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_13ppd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2obut_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tartr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lps_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g1p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_f6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mbdg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_biomass_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rbt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malttr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_inost_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ppa_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galct__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glx_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ins_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acmana_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_4hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tym_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_all__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lyx__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_peamn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lcts_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_SK_met__L_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_protein_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfReactants=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfProducts=None\u001b[0m\n", + "size of scope 649\n", + "size of all metabolites 884 \n", + "\n", + "\n", + "TARGET\n", + "REASONING GUESS-CHECK DIVERSITY\n", + "Subset Minimal\n", + "model_9\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tyr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_val__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_orn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ptrc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_spmd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_na1_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_urea_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_chol_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_adn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cytd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dad_2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dcyt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dgsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thymd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_duri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mnl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acgam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_uri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_csn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xan_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ura_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_taur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etha_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sucr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tre_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyb_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_bz_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_icit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyclt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no3_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_so4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arg__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asn__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_his__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ile__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_leu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_met__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_phe__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pro__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_trp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mg2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arab__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gal_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_man_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sbt__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fuc__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc3p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xyl__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rib__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rmn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_melib_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_rna_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_cav_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_phospholipid_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lipids_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_13ppd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2obut_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tartr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lps_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g1p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_f6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mbdg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_biomass_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rbt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malttr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_inost_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ppa_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galct__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glx_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ins_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acmana_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_4hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tym_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_all__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lyx__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_peamn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lcts_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_SK_met__L_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_protein_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfReactants=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfProducts=None\u001b[0m\n", + "size of scope 684\n", + "size of all metabolites 884 \n", + "\n", + "\n", + "TARGET\n", + "REASONING GUESS-CHECK DIVERSITY\n", + "Subset Minimal\n", + "model_10\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tyr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_val__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_orn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ptrc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_spmd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_na1_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_urea_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_chol_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_adn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cytd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dad_2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dcyt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dgsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thymd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_duri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mnl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acgam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_uri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_csn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xan_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ura_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_taur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etha_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sucr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tre_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyb_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_bz_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_icit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyclt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no3_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_so4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arg__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asn__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_his__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ile__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_leu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_met__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_phe__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pro__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_trp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mg2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arab__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gal_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_man_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sbt__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fuc__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc3p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xyl__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rib__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rmn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_melib_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_rna_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_cav_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_phospholipid_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lipids_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_13ppd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2obut_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tartr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lps_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g1p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_f6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mbdg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_biomass_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rbt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malttr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_inost_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ppa_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galct__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glx_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ins_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acmana_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_4hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tym_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_all__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lyx__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_peamn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lcts_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_SK_met__L_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_protein_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfReactants=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfProducts=None\u001b[0m\n", + "size of scope 645\n", + "size of all metabolites 884 \n", + "\n", + "\n", + "TARGET\n", + "REASONING GUESS-CHECK DIVERSITY\n", + "Subset Minimal\n", + "model_11\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tyr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_val__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_orn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ptrc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_spmd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_na1_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_urea_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_chol_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_adn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cytd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dad_2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dcyt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dgsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thymd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_duri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mnl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acgam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_uri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_csn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xan_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ura_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_taur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etha_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sucr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tre_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyb_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_bz_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_icit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyclt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no3_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_so4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arg__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asn__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_his__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ile__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_leu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_met__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_phe__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pro__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_trp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mg2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arab__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gal_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_man_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sbt__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fuc__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc3p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xyl__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rib__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rmn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_melib_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_rna_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_cav_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_phospholipid_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lipids_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_13ppd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2obut_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tartr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lps_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g1p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_f6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mbdg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_biomass_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rbt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malttr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_inost_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ppa_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galct__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glx_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ins_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acmana_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_4hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tym_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_all__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lyx__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_peamn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lcts_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_SK_met__L_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_protein_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfReactants=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfProducts=None\u001b[0m\n", + "size of scope 655\n", + "size of all metabolites 884 \n", + "\n", + "\n", + "TARGET\n", + "REASONING GUESS-CHECK DIVERSITY\n", + "Subset Minimal\n", + "model_12\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tyr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_val__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_orn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ptrc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_spmd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_na1_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_urea_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_chol_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_adn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cytd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dad_2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dcyt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dgsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thymd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_duri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mnl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acgam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_uri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_csn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xan_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ura_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_taur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etha_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sucr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tre_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyb_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_bz_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_icit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyclt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no3_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_so4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arg__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asn__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_his__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ile__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_leu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_met__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_phe__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pro__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_trp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mg2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arab__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gal_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_man_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sbt__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fuc__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc3p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xyl__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rib__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rmn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_melib_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_rna_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_cav_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_phospholipid_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lipids_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_13ppd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2obut_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tartr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lps_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g1p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_f6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mbdg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_biomass_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rbt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malttr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_inost_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ppa_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galct__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glx_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ins_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acmana_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_4hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tym_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_all__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lyx__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_peamn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lcts_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_SK_met__L_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_protein_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfReactants=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfProducts=None\u001b[0m\n", + "size of scope 649\n", + "size of all metabolites 884 \n", + "\n", + "\n", + "TARGET\n", + "REASONING GUESS-CHECK DIVERSITY\n", + "Subset Minimal\n", + "model_13\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tyr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_val__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_orn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ptrc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_spmd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_na1_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_urea_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_chol_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_adn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cytd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dad_2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dcyt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dgsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thymd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_duri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mnl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acgam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_uri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_csn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xan_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ura_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_taur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etha_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sucr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tre_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyb_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_bz_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_icit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyclt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no3_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_so4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arg__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asn__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_his__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ile__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_leu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_met__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_phe__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pro__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_trp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mg2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arab__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gal_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_man_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sbt__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fuc__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc3p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xyl__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rib__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rmn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_melib_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_rna_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_cav_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_phospholipid_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lipids_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_13ppd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2obut_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tartr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lps_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g1p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_f6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mbdg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_biomass_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rbt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malttr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_inost_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ppa_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galct__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glx_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ins_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acmana_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_4hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tym_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_all__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lyx__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_peamn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lcts_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_SK_met__L_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_protein_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfReactants=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfProducts=None\u001b[0m\n", + "size of scope 653\n", + "size of all metabolites 884 \n", + "\n", + "\n", + "TARGET\n", + "REASONING GUESS-CHECK DIVERSITY\n", + "Subset Minimal\n", + "model_14\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tyr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_val__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_orn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ptrc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_spmd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_na1_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_urea_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_chol_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_adn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cytd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dad_2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dcyt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dgsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thymd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_duri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mnl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acgam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_uri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_csn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xan_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ura_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_taur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etha_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sucr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tre_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyb_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_bz_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_icit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyclt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no3_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_so4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arg__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asn__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_his__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ile__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_leu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_met__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_phe__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pro__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_trp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mg2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arab__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gal_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_man_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sbt__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fuc__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc3p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xyl__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rib__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rmn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_melib_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_rna_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_cav_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_phospholipid_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lipids_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_13ppd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2obut_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tartr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lps_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g1p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_f6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mbdg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_biomass_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rbt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malttr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_inost_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ppa_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galct__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glx_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ins_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acmana_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_4hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tym_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_all__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lyx__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_peamn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lcts_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_SK_met__L_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_protein_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfReactants=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfProducts=None\u001b[0m\n", + "size of scope 643\n", + "size of all metabolites 884 \n", + "\n", + "\n", + "TARGET\n", + "REASONING GUESS-CHECK DIVERSITY\n", + "Subset Minimal\n", + "model_15\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tyr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_val__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_orn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ptrc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_spmd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_na1_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_urea_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_chol_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_adn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cytd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dad_2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dcyt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dgsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thymd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_duri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mnl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acgam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_uri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_csn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xan_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ura_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_taur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etha_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sucr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tre_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyb_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_bz_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_icit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyclt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no3_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_so4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arg__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asn__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_his__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ile__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_leu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_met__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_phe__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pro__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_trp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mg2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arab__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gal_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_man_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sbt__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fuc__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc3p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xyl__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rib__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rmn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_melib_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_rna_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_cav_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_phospholipid_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lipids_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_13ppd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2obut_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tartr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lps_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g1p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_f6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mbdg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_biomass_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rbt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malttr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_inost_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ppa_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galct__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glx_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ins_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acmana_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_4hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tym_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_all__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lyx__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_peamn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lcts_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_SK_met__L_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_protein_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfReactants=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfProducts=None\u001b[0m\n", + "size of scope 661\n", + "size of all metabolites 884 \n", + "\n", + "\n", + "TARGET\n", + "REASONING GUESS-CHECK DIVERSITY\n", + "Subset Minimal\n", + "model_16\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tyr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_val__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_orn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ptrc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_spmd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_na1_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_urea_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_chol_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_adn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cytd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dad_2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dcyt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dgsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thymd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_duri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mnl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acgam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_uri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_csn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xan_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ura_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_taur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etha_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sucr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tre_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyb_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_bz_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_icit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyclt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no3_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_so4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arg__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asn__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_his__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ile__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_leu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_met__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_phe__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pro__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_trp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mg2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arab__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gal_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_man_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sbt__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fuc__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc3p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xyl__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rib__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rmn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_melib_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_rna_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_cav_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_phospholipid_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lipids_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_13ppd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2obut_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tartr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lps_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g1p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_f6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mbdg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_biomass_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rbt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malttr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_inost_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ppa_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galct__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glx_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ins_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acmana_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_4hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tym_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_all__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lyx__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_peamn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lcts_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_SK_met__L_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_protein_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfReactants=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfProducts=None\u001b[0m\n", + "size of scope 669\n", + "size of all metabolites 884 \n", + "\n", + "\n", + "TARGET\n", + "REASONING GUESS-CHECK DIVERSITY\n", + "Subset Minimal\n", + "model_17\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tyr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_val__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_orn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ptrc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_spmd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_na1_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_urea_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_chol_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_adn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cytd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dad_2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dcyt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dgsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thymd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_duri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mnl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acgam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_uri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_csn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xan_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ura_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_taur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etha_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sucr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tre_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyb_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_bz_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_icit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyclt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no3_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_so4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arg__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asn__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_his__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ile__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_leu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_met__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_phe__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pro__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_trp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mg2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arab__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gal_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_man_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sbt__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fuc__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc3p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xyl__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rib__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rmn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_melib_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_rna_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_cav_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_phospholipid_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lipids_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_13ppd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2obut_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tartr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lps_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g1p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_f6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mbdg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_biomass_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rbt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malttr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_inost_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ppa_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galct__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glx_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ins_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acmana_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_4hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tym_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_all__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lyx__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_peamn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lcts_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_SK_met__L_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_protein_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfReactants=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfProducts=None\u001b[0m\n", + "size of scope 650\n", + "size of all metabolites 884 \n", + "\n", + "\n", + "TARGET\n", + "REASONING GUESS-CHECK DIVERSITY\n", + "Subset Minimal\n", + "model_18\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tyr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_val__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_orn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ptrc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_spmd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_na1_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_urea_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_chol_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_adn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cytd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dad_2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dcyt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dgsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thymd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_duri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mnl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acgam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_uri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_csn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xan_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ura_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_taur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etha_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sucr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tre_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyb_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_bz_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_icit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyclt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no3_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_so4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arg__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asn__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_his__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ile__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_leu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_met__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_phe__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pro__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_trp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mg2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arab__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gal_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_man_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sbt__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fuc__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc3p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xyl__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rib__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rmn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_melib_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_rna_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_cav_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_phospholipid_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lipids_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_13ppd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2obut_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tartr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lps_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g1p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_f6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mbdg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_biomass_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rbt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malttr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_inost_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ppa_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galct__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glx_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ins_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acmana_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_4hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tym_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_all__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lyx__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_peamn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lcts_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_SK_met__L_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_protein_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfReactants=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfProducts=None\u001b[0m\n", + "size of scope 654\n", + "size of all metabolites 884 \n", + "\n", + "\n", + "TARGET\n", + "REASONING GUESS-CHECK DIVERSITY\n", + "Subset Minimal\n", + "model_19\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tyr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_val__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_orn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ptrc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_spmd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_na1_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_urea_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_chol_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_adn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cytd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dad_2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dcyt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dgsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thymd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_duri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mnl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acgam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_uri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_csn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xan_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ura_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_taur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etha_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sucr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tre_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyb_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_bz_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_icit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyclt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no3_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_so4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arg__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asn__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_his__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ile__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_leu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_met__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_phe__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pro__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_trp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mg2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arab__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gal_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_man_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sbt__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fuc__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc3p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xyl__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rib__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rmn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_melib_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_rna_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_cav_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_phospholipid_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lipids_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_13ppd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2obut_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tartr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lps_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g1p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_f6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mbdg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_biomass_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rbt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malttr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_inost_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ppa_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galct__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glx_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ins_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acmana_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_4hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tym_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_all__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lyx__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_peamn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lcts_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_SK_met__L_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_protein_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfReactants=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfProducts=None\u001b[0m\n", + "size of scope 656\n", + "size of all metabolites 884 \n", + "\n", + "\n", + "TARGET\n", + "REASONING GUESS-CHECK DIVERSITY\n", + "Subset Minimal\n", + "model_20\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tyr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_val__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_orn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ptrc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_spmd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_na1_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_urea_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_chol_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_adn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cytd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dad_2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dcyt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dgsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thymd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_duri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mnl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acgam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_uri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_csn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xan_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ura_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_taur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etha_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sucr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tre_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyb_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_bz_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_icit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyclt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no3_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_so4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arg__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asn__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_his__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ile__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_leu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_met__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_phe__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pro__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_trp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mg2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arab__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gal_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_man_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sbt__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fuc__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc3p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xyl__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rib__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rmn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_melib_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_rna_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_cav_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_phospholipid_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lipids_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_13ppd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2obut_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tartr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lps_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g1p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_f6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mbdg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_biomass_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rbt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malttr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_inost_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ppa_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galct__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glx_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ins_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acmana_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_4hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tym_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_all__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lyx__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_peamn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lcts_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_SK_met__L_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_protein_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfReactants=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfProducts=None\u001b[0m\n", + "size of scope 649\n", + "size of all metabolites 884 \n", + "\n", + "\n", + "TARGET\n", + "REASONING GUESS-CHECK DIVERSITY\n", + "Subset Minimal\n", + "model_21\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tyr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_val__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_orn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ptrc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_spmd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_na1_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_urea_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_chol_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_adn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cytd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dad_2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dcyt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dgsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thymd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_duri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mnl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acgam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_uri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_csn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xan_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ura_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_taur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etha_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sucr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tre_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyb_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_bz_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_icit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyclt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no3_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_so4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arg__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asn__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_his__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ile__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_leu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_met__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_phe__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pro__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_trp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mg2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arab__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gal_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_man_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sbt__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fuc__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc3p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xyl__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rib__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rmn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_melib_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_rna_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_cav_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_phospholipid_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lipids_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_13ppd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2obut_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tartr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lps_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g1p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_f6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mbdg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_biomass_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rbt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malttr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_inost_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ppa_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galct__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glx_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ins_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acmana_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_4hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tym_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_all__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lyx__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_peamn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lcts_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_SK_met__L_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_protein_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfReactants=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfProducts=None\u001b[0m\n", + "size of scope 643\n", + "size of all metabolites 884 \n", + "\n", + "\n", + "TARGET\n", + "REASONING GUESS-CHECK DIVERSITY\n", + "Subset Minimal\n", + "model_22\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tyr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_val__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_orn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ptrc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_spmd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_na1_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_urea_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_chol_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_adn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cytd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dad_2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dcyt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dgsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thymd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_duri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mnl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acgam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_uri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_csn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xan_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ura_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_taur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etha_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sucr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tre_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyb_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_bz_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_icit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyclt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no3_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_so4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arg__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asn__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_his__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ile__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_leu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_met__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_phe__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pro__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_trp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mg2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arab__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gal_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_man_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sbt__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fuc__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc3p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xyl__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rib__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rmn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_melib_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_rna_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_cav_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_phospholipid_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lipids_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_13ppd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2obut_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tartr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lps_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g1p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_f6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mbdg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_biomass_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rbt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malttr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_inost_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ppa_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galct__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glx_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ins_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acmana_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_4hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tym_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_all__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lyx__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_peamn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lcts_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_SK_met__L_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_protein_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfReactants=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfProducts=None\u001b[0m\n", + "size of scope 654\n", + "size of all metabolites 884 \n", + "\n", + "\n", + "TARGET\n", + "REASONING GUESS-CHECK DIVERSITY\n", + "Subset Minimal\n", + "model_23\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tyr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_val__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_orn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ptrc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_spmd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_na1_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_urea_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_chol_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_adn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cytd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dad_2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dcyt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dgsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thymd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_duri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mnl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acgam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_uri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_csn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xan_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ura_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_taur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etha_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sucr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tre_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyb_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_bz_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_icit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyclt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no3_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_so4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arg__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asn__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_his__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ile__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_leu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_met__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_phe__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pro__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_trp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mg2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arab__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gal_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_man_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sbt__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fuc__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc3p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xyl__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rib__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rmn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_melib_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_rna_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_cav_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_phospholipid_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lipids_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_13ppd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2obut_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tartr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lps_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g1p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_f6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mbdg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_biomass_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rbt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malttr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_inost_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ppa_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galct__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glx_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ins_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acmana_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_4hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tym_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_all__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lyx__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_peamn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lcts_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_SK_met__L_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_protein_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfReactants=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfProducts=None\u001b[0m\n", + "size of scope 657\n", + "size of all metabolites 884 \n", + "\n", + "\n", + "TARGET\n", + "REASONING GUESS-CHECK DIVERSITY\n", + "Subset Minimal\n", + "model_24\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tyr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_val__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_orn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ptrc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_spmd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_na1_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_urea_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_chol_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_adn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cytd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dad_2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dcyt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dgsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thymd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_duri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mnl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acgam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_uri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_csn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xan_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ura_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_taur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etha_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sucr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tre_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyb_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_bz_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_icit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyclt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no3_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_so4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arg__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asn__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_his__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ile__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_leu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_met__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_phe__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pro__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_trp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mg2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arab__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gal_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_man_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sbt__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fuc__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc3p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xyl__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rib__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rmn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_melib_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_rna_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_cav_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_phospholipid_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lipids_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_13ppd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2obut_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tartr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lps_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g1p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_f6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mbdg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_biomass_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rbt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malttr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_inost_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ppa_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galct__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glx_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ins_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acmana_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_4hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tym_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_all__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lyx__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_peamn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lcts_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_SK_met__L_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_protein_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfReactants=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfProducts=None\u001b[0m\n", + "size of scope 660\n", + "size of all metabolites 884 \n", + "\n", + "\n", + "TARGET\n", + "REASONING GUESS-CHECK DIVERSITY\n", + "Subset Minimal\n", + "model_25\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tyr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_val__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_orn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ptrc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_spmd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_na1_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_urea_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_chol_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_adn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cytd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dad_2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dcyt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dgsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thymd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_duri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mnl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acgam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_uri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_csn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xan_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ura_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_taur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etha_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sucr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tre_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyb_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_bz_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_icit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyclt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no3_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_so4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arg__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asn__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_his__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ile__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_leu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_met__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_phe__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pro__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_trp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mg2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arab__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gal_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_man_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sbt__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fuc__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc3p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xyl__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rib__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rmn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_melib_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_rna_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_cav_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_phospholipid_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lipids_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_13ppd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2obut_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tartr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lps_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g1p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_f6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mbdg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_biomass_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rbt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malttr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_inost_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ppa_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galct__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glx_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ins_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acmana_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_4hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tym_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_all__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lyx__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_peamn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lcts_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_SK_met__L_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_protein_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfReactants=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfProducts=None\u001b[0m\n", + "size of scope 658\n", + "size of all metabolites 884 \n", + "\n", + "\n", + "TARGET\n", + "REASONING GUESS-CHECK DIVERSITY\n", + "Subset Minimal\n", + "model_26\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tyr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_val__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_orn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ptrc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_spmd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_na1_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_urea_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_chol_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_adn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cytd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dad_2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dcyt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dgsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thymd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_duri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mnl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acgam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_uri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_csn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xan_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ura_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_taur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etha_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sucr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tre_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyb_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_bz_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_icit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyclt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no3_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_so4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arg__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asn__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_his__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ile__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_leu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_met__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_phe__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pro__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_trp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mg2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arab__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gal_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_man_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sbt__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fuc__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc3p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xyl__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rib__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rmn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_melib_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_rna_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_cav_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_phospholipid_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lipids_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_13ppd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2obut_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tartr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lps_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g1p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_f6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mbdg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_biomass_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rbt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malttr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_inost_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ppa_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galct__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glx_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ins_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acmana_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_4hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tym_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_all__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lyx__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_peamn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lcts_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_SK_met__L_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_protein_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfReactants=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfProducts=None\u001b[0m\n", + "size of scope 659\n", + "size of all metabolites 884 \n", + "\n", + "\n", + "TARGET\n", + "REASONING GUESS-CHECK DIVERSITY\n", + "Subset Minimal\n", + "model_27\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tyr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_val__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_orn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ptrc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_spmd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_na1_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_urea_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_chol_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_adn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cytd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dad_2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dcyt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dgsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thymd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_duri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mnl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acgam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_uri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_csn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xan_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ura_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_taur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etha_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sucr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tre_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyb_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_bz_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_icit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyclt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no3_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_so4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arg__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asn__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_his__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ile__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_leu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_met__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_phe__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pro__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_trp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mg2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arab__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gal_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_man_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sbt__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fuc__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc3p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xyl__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rib__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rmn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_melib_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_rna_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_cav_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_phospholipid_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lipids_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_13ppd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2obut_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tartr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lps_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g1p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_f6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mbdg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_biomass_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rbt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malttr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_inost_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ppa_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galct__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glx_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ins_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acmana_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_4hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tym_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_all__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lyx__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_peamn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lcts_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_SK_met__L_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_protein_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfReactants=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfProducts=None\u001b[0m\n", + "size of scope 644\n", + "size of all metabolites 884 \n", + "\n", + "\n", + "TARGET\n", + "REASONING GUESS-CHECK DIVERSITY\n", + "Subset Minimal\n", + "model_28\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tyr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_val__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_orn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ptrc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_spmd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_na1_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_urea_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_chol_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_adn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cytd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dad_2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dcyt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dgsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thymd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_duri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mnl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acgam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_uri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_csn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xan_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ura_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_taur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etha_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sucr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tre_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyb_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_bz_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_icit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyclt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no3_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_so4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arg__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asn__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_his__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ile__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_leu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_met__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_phe__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pro__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_trp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mg2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arab__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gal_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_man_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sbt__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fuc__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc3p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xyl__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rib__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rmn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_melib_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_rna_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_cav_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_phospholipid_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lipids_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_13ppd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2obut_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tartr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lps_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g1p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_f6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mbdg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_biomass_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rbt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malttr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_inost_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ppa_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galct__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glx_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ins_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acmana_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_4hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tym_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_all__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lyx__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_peamn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lcts_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_SK_met__L_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_protein_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfReactants=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfProducts=None\u001b[0m\n", + "size of scope 641\n", + "size of all metabolites 884 \n", + "\n", + "\n", + "TARGET\n", + "REASONING GUESS-CHECK DIVERSITY\n", + "Subset Minimal\n", + "model_29\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tyr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_val__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_orn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ptrc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_spmd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_na1_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_urea_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_chol_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_adn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cytd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dad_2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dcyt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dgsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thymd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_duri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mnl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acgam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_uri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_csn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xan_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ura_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_taur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etha_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sucr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tre_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyb_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_bz_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_icit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyclt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no3_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_so4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arg__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asn__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_his__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ile__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_leu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_met__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_phe__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pro__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_trp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mg2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arab__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gal_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_man_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sbt__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fuc__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc3p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xyl__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rib__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rmn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_melib_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_rna_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_cav_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_phospholipid_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lipids_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_13ppd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2obut_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tartr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lps_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g1p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_f6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mbdg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_biomass_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rbt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malttr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_inost_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ppa_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galct__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glx_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ins_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acmana_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_4hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tym_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_all__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lyx__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_peamn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lcts_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_SK_met__L_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_protein_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfReactants=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfProducts=None\u001b[0m\n", + "size of scope 648\n", + "size of all metabolites 884 \n", + "\n", + "\n", + "TARGET\n", + "REASONING GUESS-CHECK DIVERSITY\n", + "Subset Minimal\n", + "model_30\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tyr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_val__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_orn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ptrc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_spmd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_na1_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_urea_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_chol_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_adn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cytd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dad_2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dcyt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dgsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thymd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_duri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mnl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acgam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_uri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_csn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xan_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ura_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_taur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etha_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sucr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tre_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyb_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_bz_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_icit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyclt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no3_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_so4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arg__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asn__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_his__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ile__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_leu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_met__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_phe__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pro__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_trp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mg2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arab__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gal_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_man_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sbt__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fuc__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc3p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xyl__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rib__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rmn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_melib_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_rna_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_cav_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_phospholipid_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lipids_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_13ppd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2obut_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tartr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lps_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g1p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_f6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mbdg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_biomass_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rbt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malttr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_inost_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ppa_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galct__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glx_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ins_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acmana_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_4hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tym_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_all__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lyx__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_peamn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lcts_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_SK_met__L_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_protein_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfReactants=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfProducts=None\u001b[0m\n", + "size of scope 651\n", + "size of all metabolites 884 \n", + "\n", + "\n", + "TARGET\n", + "REASONING GUESS-CHECK DIVERSITY\n", + "Other\n", + "model_one_solution\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tyr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_val__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_orn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ptrc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_spmd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_na1_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_urea_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_chol_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_adn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cytd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dad_2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dcyt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dgsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thymd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_duri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mnl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acgam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_uri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_csn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xan_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ura_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_taur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etha_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sucr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tre_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyb_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_bz_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_icit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyclt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no3_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_so4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arg__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asn__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_his__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ile__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_leu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_met__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_phe__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pro__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_trp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mg2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arab__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gal_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_man_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sbt__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fuc__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc3p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xyl__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rib__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rmn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_melib_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_rna_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_cav_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_phospholipid_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lipids_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_13ppd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2obut_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tartr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lps_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g1p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_f6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mbdg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_biomass_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rbt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malttr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_inost_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ppa_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galct__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glx_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ins_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acmana_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_4hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tym_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_all__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lyx__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_peamn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lcts_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_SK_met__L_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_protein_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfReactants=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfProducts=None\u001b[0m\n", + "size of scope 629\n", + "size of all metabolites 884 \n", + "\n", + "\n", + "TARGET\n", + "REASONING GUESS-CHECK DIVERSITY\n", + "Minimize\n", + "model_one_solution\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tyr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_val__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_orn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ptrc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_spmd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_na1_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_urea_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_chol_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_adn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cytd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dad_2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dcyt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dgsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thymd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_duri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mnl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acgam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_uri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_csn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xan_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ura_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_taur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etha_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sucr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tre_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyb_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_bz_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_icit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyclt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no3_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_so4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arg__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asn__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_his__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ile__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_leu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_met__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_phe__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pro__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_trp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mg2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arab__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gal_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_man_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sbt__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fuc__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc3p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xyl__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rib__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rmn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_melib_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_rna_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_cav_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_phospholipid_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lipids_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_13ppd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2obut_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tartr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lps_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g1p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_f6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mbdg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_biomass_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rbt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malttr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_inost_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ppa_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galct__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glx_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ins_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acmana_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_4hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tym_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_all__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lyx__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_peamn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lcts_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_SK_met__L_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_protein_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfReactants=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfProducts=None\u001b[0m\n", + "size of scope 629\n", + "size of all metabolites 884 \n", + "\n", + "\n", + "TARGET\n", + "REASONING GUESS-CHECK DIVERSITY\n", + "Minimize\n", + "model_1\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tyr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_val__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_orn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ptrc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_spmd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_na1_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_urea_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_chol_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_adn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cytd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dad_2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dcyt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dgsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thymd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_duri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mnl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acgam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_uri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_csn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xan_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ura_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_taur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etha_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sucr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tre_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyb_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_bz_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_icit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyclt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no3_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_so4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arg__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asn__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_his__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ile__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_leu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_met__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_phe__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pro__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_trp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mg2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arab__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gal_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_man_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sbt__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fuc__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc3p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xyl__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rib__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rmn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_melib_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_rna_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_cav_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_phospholipid_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lipids_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_13ppd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2obut_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tartr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lps_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g1p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_f6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mbdg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_biomass_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rbt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malttr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_inost_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ppa_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galct__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glx_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ins_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acmana_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_4hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tym_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_all__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lyx__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_peamn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lcts_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_SK_met__L_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_protein_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfReactants=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfProducts=None\u001b[0m\n", + "size of scope 625\n", + "size of all metabolites 884 \n", + "\n", + "\n", + "TARGET\n", + "REASONING GUESS-CHECK DIVERSITY\n", + "Minimize\n", + "model_2\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tyr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_val__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_orn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ptrc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_spmd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_na1_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_urea_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_chol_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_adn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cytd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dad_2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dcyt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dgsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thymd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_duri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mnl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acgam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_uri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_csn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xan_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ura_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_taur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etha_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sucr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tre_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyb_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_bz_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_icit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyclt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no3_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_so4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arg__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asn__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_his__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ile__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_leu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_met__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_phe__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pro__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_trp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mg2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arab__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gal_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_man_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sbt__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fuc__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc3p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xyl__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rib__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rmn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_melib_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_rna_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_cav_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_phospholipid_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lipids_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_13ppd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2obut_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tartr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lps_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g1p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_f6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mbdg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_biomass_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rbt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malttr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_inost_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ppa_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galct__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glx_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ins_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acmana_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_4hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tym_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_all__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lyx__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_peamn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lcts_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_SK_met__L_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_protein_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfReactants=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfProducts=None\u001b[0m\n", + "size of scope 625\n", + "size of all metabolites 884 \n", + "\n", + "\n", + "TARGET\n", + "REASONING GUESS-CHECK DIVERSITY\n", + "Minimize\n", + "model_3\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tyr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_val__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_orn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ptrc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_spmd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_na1_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_urea_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_chol_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_adn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cytd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dad_2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dcyt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dgsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thymd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_duri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mnl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acgam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_uri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_csn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xan_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ura_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_taur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etha_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sucr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tre_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyb_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_bz_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_icit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyclt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no3_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_so4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arg__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asn__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_his__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ile__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_leu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_met__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_phe__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pro__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_trp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mg2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arab__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gal_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_man_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sbt__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fuc__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc3p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xyl__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rib__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rmn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_melib_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_rna_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_cav_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_phospholipid_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lipids_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_13ppd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2obut_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tartr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lps_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g1p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_f6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mbdg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_biomass_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rbt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malttr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_inost_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ppa_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galct__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glx_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ins_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acmana_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_4hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tym_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_all__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lyx__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_peamn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lcts_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_SK_met__L_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_protein_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfReactants=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfProducts=None\u001b[0m\n", + "size of scope 625\n", + "size of all metabolites 884 \n", + "\n", + "\n", + "TARGET\n", + "REASONING GUESS-CHECK DIVERSITY\n", + "Minimize\n", + "model_4\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tyr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_val__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_orn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ptrc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_spmd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_na1_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_urea_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_chol_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_adn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cytd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dad_2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dcyt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dgsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thymd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_duri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mnl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acgam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_uri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_csn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xan_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ura_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_taur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etha_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sucr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tre_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyb_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_bz_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_icit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyclt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no3_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_so4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arg__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asn__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_his__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ile__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_leu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_met__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_phe__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pro__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_trp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mg2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arab__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gal_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_man_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sbt__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fuc__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc3p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xyl__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rib__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rmn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_melib_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_rna_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_cav_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_phospholipid_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lipids_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_13ppd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2obut_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tartr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lps_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g1p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_f6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mbdg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_biomass_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rbt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malttr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_inost_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ppa_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galct__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glx_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ins_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acmana_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_4hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tym_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_all__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lyx__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_peamn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lcts_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_SK_met__L_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_protein_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfReactants=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfProducts=None\u001b[0m\n", + "size of scope 625\n", + "size of all metabolites 884 \n", + "\n", + "\n", + "TARGET\n", + "REASONING GUESS-CHECK DIVERSITY\n", + "Minimize\n", + "model_5\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tyr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_val__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_orn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ptrc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_spmd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_na1_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_urea_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_chol_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_adn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cytd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dad_2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dcyt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dgsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thymd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_duri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mnl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acgam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_uri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_csn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xan_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ura_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_taur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etha_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sucr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tre_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyb_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_bz_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_icit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyclt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no3_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_so4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arg__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asn__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_his__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ile__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_leu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_met__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_phe__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pro__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_trp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mg2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arab__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gal_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_man_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sbt__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fuc__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc3p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xyl__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rib__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rmn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_melib_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_rna_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_cav_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_phospholipid_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lipids_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_13ppd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2obut_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tartr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lps_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g1p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_f6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mbdg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_biomass_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rbt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malttr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_inost_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ppa_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galct__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glx_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ins_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acmana_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_4hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tym_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_all__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lyx__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_peamn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lcts_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_SK_met__L_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_protein_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfReactants=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfProducts=None\u001b[0m\n", + "size of scope 625\n", + "size of all metabolites 884 \n", + "\n", + "\n", + "TARGET\n", + "REASONING GUESS-CHECK DIVERSITY\n", + "Minimize\n", + "model_6\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tyr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_val__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_orn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ptrc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_spmd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_na1_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_urea_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_chol_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_adn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cytd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dad_2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dcyt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dgsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thymd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_duri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mnl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acgam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_uri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_csn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xan_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ura_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_taur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etha_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sucr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tre_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyb_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_bz_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_icit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyclt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no3_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_so4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arg__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asn__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_his__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ile__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_leu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_met__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_phe__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pro__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_trp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mg2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arab__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gal_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_man_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sbt__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fuc__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc3p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xyl__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rib__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rmn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_melib_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_rna_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_cav_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_phospholipid_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lipids_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_13ppd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2obut_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tartr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lps_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g1p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_f6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mbdg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_biomass_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rbt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malttr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_inost_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ppa_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galct__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glx_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ins_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acmana_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_4hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tym_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_all__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lyx__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_peamn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lcts_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_SK_met__L_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_protein_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfReactants=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfProducts=None\u001b[0m\n", + "size of scope 625\n", + "size of all metabolites 884 \n", + "\n", + "\n", + "TARGET\n", + "REASONING GUESS-CHECK DIVERSITY\n", + "Minimize\n", + "model_7\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tyr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_val__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_orn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ptrc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_spmd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_na1_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_urea_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_chol_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_adn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cytd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dad_2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dcyt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dgsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thymd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_duri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mnl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acgam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_uri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_csn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xan_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ura_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_taur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etha_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sucr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tre_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyb_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_bz_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_icit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyclt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no3_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_so4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arg__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asn__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_his__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ile__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_leu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_met__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_phe__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pro__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_trp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mg2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arab__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gal_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_man_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sbt__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fuc__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc3p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xyl__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rib__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rmn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_melib_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_rna_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_cav_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_phospholipid_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lipids_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_13ppd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2obut_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tartr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lps_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g1p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_f6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mbdg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_biomass_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rbt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malttr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_inost_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ppa_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galct__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glx_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ins_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acmana_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_4hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tym_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_all__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lyx__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_peamn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lcts_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_SK_met__L_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_protein_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfReactants=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfProducts=None\u001b[0m\n", + "size of scope 625\n", + "size of all metabolites 884 \n", + "\n", + "\n", + "TARGET\n", + "REASONING GUESS-CHECK DIVERSITY\n", + "Minimize\n", + "model_8\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tyr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_val__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_orn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ptrc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_spmd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_na1_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_urea_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_chol_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_adn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cytd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dad_2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dcyt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dgsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thymd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_duri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mnl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acgam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_uri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_csn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xan_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ura_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_taur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etha_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sucr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tre_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyb_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_bz_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_icit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyclt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no3_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_so4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arg__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asn__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_his__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ile__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_leu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_met__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_phe__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pro__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_trp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mg2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arab__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gal_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_man_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sbt__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fuc__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc3p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xyl__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rib__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rmn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_melib_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_rna_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_cav_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_phospholipid_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lipids_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_13ppd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2obut_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tartr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lps_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g1p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_f6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mbdg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_biomass_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rbt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malttr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_inost_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ppa_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galct__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glx_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ins_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acmana_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_4hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tym_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_all__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lyx__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_peamn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lcts_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_SK_met__L_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_protein_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfReactants=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfProducts=None\u001b[0m\n", + "size of scope 625\n", + "size of all metabolites 884 \n", + "\n", + "\n", + "TARGET\n", + "REASONING GUESS-CHECK DIVERSITY\n", + "Minimize\n", + "model_9\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tyr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_val__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_orn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ptrc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_spmd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_na1_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_urea_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_chol_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_adn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cytd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dad_2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dcyt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dgsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thymd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_duri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mnl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acgam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_uri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_csn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xan_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ura_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_taur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etha_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sucr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tre_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyb_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_bz_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_icit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyclt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no3_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_so4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arg__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asn__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_his__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ile__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_leu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_met__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_phe__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pro__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_trp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mg2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arab__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gal_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_man_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sbt__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fuc__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc3p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xyl__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rib__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rmn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_melib_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_rna_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_cav_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_phospholipid_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lipids_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_13ppd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2obut_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tartr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lps_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g1p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_f6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mbdg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_biomass_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rbt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malttr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_inost_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ppa_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galct__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glx_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ins_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acmana_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_4hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tym_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_all__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lyx__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_peamn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lcts_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_SK_met__L_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_protein_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfReactants=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfProducts=None\u001b[0m\n", + "size of scope 625\n", + "size of all metabolites 884 \n", + "\n", + "\n", + "TARGET\n", + "REASONING GUESS-CHECK DIVERSITY\n", + "Minimize\n", + "model_10\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tyr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_val__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_orn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ptrc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_spmd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_na1_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_urea_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_chol_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_adn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cytd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dad_2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dcyt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dgsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thymd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_duri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mnl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acgam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_uri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_csn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xan_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ura_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_taur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etha_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sucr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tre_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyb_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_bz_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_icit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyclt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no3_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_so4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arg__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asn__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_his__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ile__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_leu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_met__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_phe__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pro__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_trp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mg2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arab__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gal_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_man_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sbt__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fuc__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc3p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xyl__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rib__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rmn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_melib_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_rna_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_cav_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_phospholipid_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lipids_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_13ppd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2obut_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tartr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lps_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g1p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_f6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mbdg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_biomass_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rbt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malttr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_inost_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ppa_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galct__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glx_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ins_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acmana_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_4hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tym_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_all__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lyx__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_peamn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lcts_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_SK_met__L_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_protein_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfReactants=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfProducts=None\u001b[0m\n", + "size of scope 629\n", + "size of all metabolites 884 \n", + "\n", + "\n", + "TARGET\n", + "REASONING GUESS-CHECK DIVERSITY\n", + "Minimize\n", + "model_11\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tyr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_val__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_orn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ptrc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_spmd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_na1_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_urea_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_chol_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_adn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cytd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dad_2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dcyt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dgsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thymd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_duri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mnl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acgam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_uri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_csn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xan_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ura_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_taur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etha_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sucr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tre_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyb_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_bz_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_icit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyclt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no3_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_so4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arg__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asn__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_his__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ile__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_leu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_met__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_phe__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pro__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_trp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mg2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arab__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gal_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_man_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sbt__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fuc__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc3p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xyl__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rib__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rmn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_melib_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_rna_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_cav_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_phospholipid_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lipids_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_13ppd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2obut_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tartr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lps_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g1p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_f6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mbdg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_biomass_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rbt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malttr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_inost_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ppa_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galct__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glx_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ins_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acmana_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_4hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tym_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_all__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lyx__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_peamn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lcts_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_SK_met__L_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_protein_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfReactants=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfProducts=None\u001b[0m\n", + "size of scope 629\n", + "size of all metabolites 884 \n", + "\n", + "\n", + "TARGET\n", + "REASONING GUESS-CHECK DIVERSITY\n", + "Minimize\n", + "model_12\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tyr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_val__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_orn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ptrc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_spmd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_na1_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_urea_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_chol_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_adn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cytd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dad_2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dcyt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dgsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thymd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_duri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mnl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acgam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_uri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_csn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xan_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ura_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_taur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etha_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sucr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tre_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyb_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_bz_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_icit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyclt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no3_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_so4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arg__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asn__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_his__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ile__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_leu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_met__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_phe__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pro__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_trp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mg2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arab__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gal_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_man_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sbt__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fuc__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc3p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xyl__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rib__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rmn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_melib_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_rna_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_cav_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_phospholipid_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lipids_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_13ppd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2obut_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tartr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lps_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g1p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_f6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mbdg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_biomass_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rbt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malttr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_inost_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ppa_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galct__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glx_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ins_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acmana_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_4hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tym_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_all__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lyx__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_peamn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lcts_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_SK_met__L_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_protein_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfReactants=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfProducts=None\u001b[0m\n", + "size of scope 629\n", + "size of all metabolites 884 \n", + "\n", + "\n", + "TARGET\n", + "REASONING GUESS-CHECK DIVERSITY\n", + "Minimize\n", + "model_13\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tyr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_val__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_orn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ptrc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_spmd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_na1_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_urea_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_chol_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_adn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cytd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dad_2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dcyt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dgsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thymd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_duri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mnl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acgam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_uri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_csn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xan_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ura_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_taur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etha_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sucr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tre_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyb_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_bz_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_icit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyclt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no3_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_so4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arg__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asn__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_his__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ile__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_leu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_met__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_phe__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pro__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_trp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mg2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arab__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gal_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_man_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sbt__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fuc__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc3p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xyl__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rib__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rmn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_melib_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_rna_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_cav_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_phospholipid_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lipids_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_13ppd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2obut_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tartr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lps_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g1p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_f6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mbdg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_biomass_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rbt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malttr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_inost_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ppa_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galct__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glx_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ins_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acmana_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_4hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tym_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_all__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lyx__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_peamn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lcts_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_SK_met__L_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_protein_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfReactants=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfProducts=None\u001b[0m\n", + "size of scope 625\n", + "size of all metabolites 884 \n", + "\n", + "\n", + "TARGET\n", + "REASONING GUESS-CHECK DIVERSITY\n", + "Minimize\n", + "model_14\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tyr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_val__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_orn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ptrc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_spmd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_na1_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_urea_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_chol_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_adn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cytd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dad_2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dcyt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dgsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thymd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_duri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mnl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acgam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_uri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_csn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xan_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ura_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_taur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etha_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sucr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tre_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyb_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_bz_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_icit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyclt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no3_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_so4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arg__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asn__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_his__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ile__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_leu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_met__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_phe__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pro__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_trp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mg2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arab__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gal_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_man_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sbt__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fuc__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc3p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xyl__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rib__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rmn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_melib_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_rna_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_cav_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_phospholipid_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lipids_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_13ppd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2obut_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tartr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lps_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g1p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_f6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mbdg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_biomass_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rbt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malttr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_inost_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ppa_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galct__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glx_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ins_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acmana_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_4hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tym_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_all__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lyx__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_peamn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lcts_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_SK_met__L_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_protein_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfReactants=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfProducts=None\u001b[0m\n", + "size of scope 625\n", + "size of all metabolites 884 \n", + "\n", + "\n", + "TARGET\n", + "REASONING GUESS-CHECK DIVERSITY\n", + "Minimize\n", + "model_15\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tyr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_val__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_orn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ptrc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_spmd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_na1_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_urea_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_chol_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_adn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cytd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dad_2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dcyt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dgsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thymd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_duri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mnl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acgam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_uri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_csn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xan_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ura_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_taur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etha_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sucr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tre_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyb_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_bz_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_icit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyclt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no3_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_so4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arg__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asn__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_his__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ile__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_leu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_met__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_phe__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pro__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_trp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mg2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arab__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gal_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_man_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sbt__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fuc__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc3p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xyl__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rib__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rmn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_melib_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_rna_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_cav_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_phospholipid_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lipids_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_13ppd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2obut_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tartr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lps_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g1p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_f6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mbdg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_biomass_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rbt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malttr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_inost_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ppa_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galct__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glx_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ins_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acmana_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_4hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tym_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_all__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lyx__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_peamn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lcts_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_SK_met__L_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_protein_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfReactants=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfProducts=None\u001b[0m\n", + "size of scope 625\n", + "size of all metabolites 884 \n", + "\n", + "\n", + "TARGET\n", + "REASONING GUESS-CHECK DIVERSITY\n", + "Minimize\n", + "model_16\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tyr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_val__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_orn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ptrc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_spmd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_na1_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_urea_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_chol_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_adn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cytd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dad_2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dcyt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dgsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thymd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_duri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mnl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acgam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_uri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_csn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xan_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ura_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_taur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etha_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sucr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tre_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyb_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_bz_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_icit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyclt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no3_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_so4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arg__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asn__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_his__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ile__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_leu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_met__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_phe__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pro__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_trp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mg2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arab__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gal_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_man_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sbt__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fuc__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc3p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xyl__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rib__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rmn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_melib_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_rna_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_cav_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_phospholipid_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lipids_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_13ppd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2obut_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tartr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lps_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g1p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_f6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mbdg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_biomass_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rbt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malttr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_inost_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ppa_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galct__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glx_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ins_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acmana_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_4hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tym_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_all__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lyx__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_peamn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lcts_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_SK_met__L_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_protein_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfReactants=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfProducts=None\u001b[0m\n", + "size of scope 625\n", + "size of all metabolites 884 \n", + "\n", + "\n", + "TARGET\n", + "REASONING GUESS-CHECK DIVERSITY\n", + "Minimize\n", + "model_17\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tyr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_val__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_orn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ptrc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_spmd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_na1_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_urea_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_chol_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_adn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cytd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dad_2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dcyt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dgsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thymd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_duri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mnl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acgam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_uri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_csn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xan_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ura_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_taur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etha_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sucr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tre_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyb_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_bz_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_icit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyclt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no3_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_so4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arg__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asn__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_his__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ile__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_leu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_met__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_phe__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pro__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_trp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mg2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arab__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gal_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_man_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sbt__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fuc__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc3p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xyl__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rib__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rmn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_melib_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_rna_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_cav_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_phospholipid_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lipids_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_13ppd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2obut_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tartr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lps_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g1p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_f6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mbdg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_biomass_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rbt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malttr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_inost_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ppa_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galct__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glx_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ins_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acmana_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_4hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tym_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_all__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lyx__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_peamn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lcts_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_SK_met__L_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_protein_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfReactants=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfProducts=None\u001b[0m\n", + "size of scope 625\n", + "size of all metabolites 884 \n", + "\n", + "\n", + "TARGET\n", + "REASONING GUESS-CHECK DIVERSITY\n", + "Minimize\n", + "model_18\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tyr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_val__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_orn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ptrc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_spmd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_na1_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_urea_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_chol_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_adn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cytd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dad_2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dcyt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dgsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thymd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_duri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mnl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acgam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_uri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_csn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xan_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ura_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_taur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etha_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sucr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tre_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyb_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_bz_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_icit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyclt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no3_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_so4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arg__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asn__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_his__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ile__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_leu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_met__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_phe__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pro__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_trp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mg2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arab__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gal_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_man_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sbt__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fuc__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc3p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xyl__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rib__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rmn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_melib_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_rna_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_cav_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_phospholipid_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lipids_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_13ppd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2obut_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tartr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lps_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g1p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_f6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mbdg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_biomass_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rbt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malttr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_inost_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ppa_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galct__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glx_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ins_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acmana_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_4hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tym_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_all__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lyx__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_peamn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lcts_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_SK_met__L_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_protein_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfReactants=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfProducts=None\u001b[0m\n", + "size of scope 625\n", + "size of all metabolites 884 \n", + "\n", + "\n", + "TARGET\n", + "REASONING GUESS-CHECK DIVERSITY\n", + "Minimize\n", + "model_19\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tyr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_val__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_orn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ptrc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_spmd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_na1_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_urea_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_chol_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_adn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cytd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dad_2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dcyt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dgsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thymd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_duri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mnl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acgam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_uri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_csn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xan_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ura_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_taur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etha_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sucr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tre_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyb_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_bz_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_icit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyclt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no3_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_so4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arg__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asn__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_his__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ile__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_leu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_met__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_phe__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pro__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_trp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mg2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arab__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gal_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_man_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sbt__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fuc__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc3p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xyl__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rib__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rmn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_melib_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_rna_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_cav_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_phospholipid_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lipids_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_13ppd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2obut_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tartr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lps_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g1p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_f6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mbdg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_biomass_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rbt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malttr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_inost_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ppa_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galct__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glx_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ins_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acmana_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_4hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tym_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_all__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lyx__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_peamn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lcts_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_SK_met__L_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_protein_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfReactants=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfProducts=None\u001b[0m\n", + "size of scope 625\n", + "size of all metabolites 884 \n", + "\n", + "\n", + "TARGET\n", + "REASONING GUESS-CHECK DIVERSITY\n", + "Minimize\n", + "model_20\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tyr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_val__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_orn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ptrc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_spmd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_na1_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_urea_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_chol_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_adn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cytd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dad_2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dcyt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dgsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thymd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_duri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mnl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acgam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_uri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_csn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xan_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ura_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_taur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etha_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sucr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tre_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyb_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_bz_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_icit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyclt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no3_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_so4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arg__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asn__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_his__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ile__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_leu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_met__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_phe__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pro__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_trp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mg2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arab__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gal_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_man_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sbt__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fuc__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc3p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xyl__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rib__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rmn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_melib_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_rna_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_cav_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_phospholipid_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lipids_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_13ppd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2obut_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tartr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lps_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g1p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_f6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mbdg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_biomass_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rbt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malttr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_inost_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ppa_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galct__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glx_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ins_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acmana_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_4hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tym_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_all__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lyx__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_peamn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lcts_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_SK_met__L_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_protein_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfReactants=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfProducts=None\u001b[0m\n", + "size of scope 625\n", + "size of all metabolites 884 \n", + "\n", + "\n", + "TARGET\n", + "REASONING GUESS-CHECK DIVERSITY\n", + "Minimize\n", + "model_21\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tyr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_val__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_orn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ptrc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_spmd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_na1_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_urea_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_chol_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_adn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cytd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dad_2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dcyt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dgsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thymd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_duri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mnl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acgam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_uri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_csn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xan_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ura_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_taur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etha_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sucr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tre_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyb_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_bz_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_icit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyclt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no3_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_so4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arg__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asn__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_his__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ile__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_leu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_met__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_phe__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pro__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_trp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mg2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arab__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gal_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_man_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sbt__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fuc__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc3p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xyl__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rib__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rmn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_melib_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_rna_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_cav_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_phospholipid_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lipids_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_13ppd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2obut_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tartr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lps_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g1p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_f6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mbdg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_biomass_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rbt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malttr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_inost_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ppa_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galct__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glx_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ins_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acmana_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_4hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tym_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_all__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lyx__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_peamn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lcts_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_SK_met__L_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_protein_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfReactants=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfProducts=None\u001b[0m\n", + "size of scope 625\n", + "size of all metabolites 884 \n", + "\n", + "\n", + "TARGET\n", + "REASONING GUESS-CHECK DIVERSITY\n", + "Minimize\n", + "model_22\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tyr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_val__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_orn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ptrc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_spmd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_na1_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_urea_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_chol_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_adn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cytd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dad_2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dcyt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dgsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thymd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_duri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mnl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acgam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_uri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_csn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xan_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ura_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_taur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etha_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sucr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tre_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyb_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_bz_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_icit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyclt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no3_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_so4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arg__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asn__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_his__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ile__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_leu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_met__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_phe__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pro__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_trp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mg2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arab__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gal_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_man_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sbt__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fuc__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc3p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xyl__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rib__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rmn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_melib_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_rna_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_cav_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_phospholipid_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lipids_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_13ppd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2obut_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tartr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lps_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g1p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_f6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mbdg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_biomass_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rbt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malttr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_inost_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ppa_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galct__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glx_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ins_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acmana_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_4hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tym_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_all__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lyx__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_peamn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lcts_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_SK_met__L_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_protein_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfReactants=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfProducts=None\u001b[0m\n", + "size of scope 625\n", + "size of all metabolites 884 \n", + "\n", + "\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: iCN718\n", + "____________________________________________\n", + "\n", + "\u001b[0;93mWARNING : Reaction R_SK_ACP_c deleted, boudaries [0,0]\u001b[0m\n", + "TARGET\n", + "REASONING\n", + "Subset Minimal\n", + "model_1\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tyr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_val__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_orn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ptrc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_spmd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_na1_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_urea_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_chol_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_adn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cytd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dad_2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dcyt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dgsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thymd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_duri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mnl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acgam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_uri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_csn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xan_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ura_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_taur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etha_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sucr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tre_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyb_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_bz_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_icit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyclt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no3_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_so4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arg__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asn__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_his__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ile__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_leu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_met__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_phe__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pro__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_trp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mg2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arab__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gal_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_man_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sbt__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fuc__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc3p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xyl__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rib__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rmn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_melib_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_rna_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_cav_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_phospholipid_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lipids_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_13ppd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2obut_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tartr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lps_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g1p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_f6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mbdg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_biomass_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rbt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malttr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_inost_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ppa_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galct__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glx_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ins_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acmana_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_4hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tym_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_all__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lyx__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_peamn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lcts_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_SK_met__L_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_protein_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfReactants=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfProducts=None\u001b[0m\n", + "size of scope 629\n", + "size of all metabolites 884 \n", + "\n", + "\n", + "TARGET\n", + "REASONING\n", + "Subset Minimal\n", + "model_2\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tyr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_val__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_orn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ptrc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_spmd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_na1_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_urea_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_chol_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_adn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cytd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dad_2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dcyt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dgsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thymd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_duri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mnl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acgam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_uri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_csn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xan_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ura_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_taur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etha_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sucr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tre_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyb_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_bz_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_icit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyclt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no3_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_so4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arg__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asn__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_his__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ile__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_leu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_met__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_phe__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pro__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_trp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mg2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arab__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gal_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_man_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sbt__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fuc__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc3p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xyl__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rib__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rmn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_melib_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_rna_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_cav_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_phospholipid_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lipids_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_13ppd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2obut_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tartr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lps_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g1p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_f6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mbdg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_biomass_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rbt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malttr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_inost_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ppa_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galct__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glx_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ins_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acmana_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_4hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tym_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_all__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lyx__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_peamn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lcts_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_SK_met__L_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_protein_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfReactants=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfProducts=None\u001b[0m\n", + "size of scope 629\n", + "size of all metabolites 884 \n", + "\n", + "\n", + "TARGET\n", + "REASONING\n", + "Subset Minimal\n", + "model_3\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tyr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_val__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_orn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ptrc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_spmd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_na1_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_urea_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_chol_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_adn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cytd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dad_2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dcyt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dgsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thymd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_duri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mnl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acgam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_uri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_csn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xan_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ura_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_taur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etha_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sucr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tre_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyb_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_bz_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_icit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyclt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no3_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_so4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arg__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asn__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_his__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ile__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_leu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_met__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_phe__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pro__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_trp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mg2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arab__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gal_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_man_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sbt__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fuc__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc3p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xyl__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rib__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rmn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_melib_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_rna_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_cav_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_phospholipid_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lipids_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_13ppd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2obut_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tartr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lps_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g1p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_f6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mbdg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_biomass_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rbt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malttr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_inost_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ppa_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galct__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glx_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ins_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acmana_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_4hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tym_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_all__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lyx__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_peamn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lcts_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_SK_met__L_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_protein_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfReactants=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfProducts=None\u001b[0m\n", + "size of scope 629\n", + "size of all metabolites 884 \n", + "\n", + "\n", + "TARGET\n", + "REASONING\n", + "Subset Minimal\n", + "model_4\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tyr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_val__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_orn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ptrc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_spmd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_na1_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_urea_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_chol_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_adn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cytd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dad_2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dcyt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dgsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thymd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_duri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mnl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acgam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_uri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_csn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xan_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ura_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_taur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etha_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sucr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tre_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyb_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_bz_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_icit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyclt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no3_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_so4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arg__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asn__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_his__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ile__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_leu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_met__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_phe__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pro__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_trp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mg2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arab__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gal_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_man_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sbt__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fuc__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc3p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xyl__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rib__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rmn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_melib_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_rna_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_cav_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_phospholipid_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lipids_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_13ppd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2obut_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tartr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lps_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g1p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_f6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mbdg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_biomass_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rbt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malttr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_inost_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ppa_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galct__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glx_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ins_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acmana_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_4hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tym_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_all__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lyx__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_peamn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lcts_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_SK_met__L_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_protein_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfReactants=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfProducts=None\u001b[0m\n", + "size of scope 629\n", + "size of all metabolites 884 \n", + "\n", + "\n", + "TARGET\n", + "REASONING\n", + "Subset Minimal\n", + "model_5\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tyr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_val__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_orn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ptrc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_spmd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_na1_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_urea_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_chol_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_adn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cytd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dad_2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dcyt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dgsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thymd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_duri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mnl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acgam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_uri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_csn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xan_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ura_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_taur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etha_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sucr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tre_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyb_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_bz_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_icit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyclt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no3_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_so4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arg__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asn__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_his__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ile__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_leu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_met__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_phe__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pro__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_trp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mg2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arab__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gal_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_man_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sbt__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fuc__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc3p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xyl__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rib__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rmn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_melib_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_rna_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_cav_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_phospholipid_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lipids_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_13ppd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2obut_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tartr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lps_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g1p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_f6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mbdg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_biomass_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rbt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malttr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_inost_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ppa_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galct__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glx_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ins_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acmana_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_4hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tym_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_all__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lyx__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_peamn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lcts_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_SK_met__L_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_protein_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfReactants=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfProducts=None\u001b[0m\n", + "size of scope 629\n", + "size of all metabolites 884 \n", + "\n", + "\n", + "TARGET\n", + "REASONING\n", + "Subset Minimal\n", + "model_6\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tyr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_val__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_orn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ptrc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_spmd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_na1_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_urea_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_chol_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_adn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cytd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dad_2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dcyt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dgsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thymd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_duri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mnl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acgam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_uri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_csn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xan_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ura_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_taur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etha_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sucr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tre_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyb_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_bz_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_icit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyclt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no3_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_so4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arg__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asn__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_his__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ile__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_leu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_met__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_phe__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pro__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_trp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mg2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arab__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gal_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_man_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sbt__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fuc__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc3p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xyl__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rib__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rmn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_melib_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_rna_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_cav_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_phospholipid_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lipids_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_13ppd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2obut_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tartr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lps_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g1p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_f6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mbdg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_biomass_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rbt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malttr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_inost_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ppa_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galct__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glx_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ins_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acmana_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_4hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tym_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_all__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lyx__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_peamn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lcts_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_SK_met__L_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_protein_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfReactants=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfProducts=None\u001b[0m\n", + "size of scope 629\n", + "size of all metabolites 884 \n", + "\n", + "\n", + "TARGET\n", + "REASONING\n", + "Subset Minimal\n", + "model_7\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tyr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_val__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_orn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ptrc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_spmd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_na1_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_urea_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_chol_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_adn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cytd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dad_2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dcyt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dgsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thymd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_duri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mnl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acgam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_uri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_csn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xan_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ura_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_taur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etha_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sucr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tre_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyb_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_bz_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_icit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyclt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no3_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_so4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arg__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asn__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_his__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ile__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_leu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_met__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_phe__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pro__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_trp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mg2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arab__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gal_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_man_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sbt__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fuc__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc3p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xyl__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rib__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rmn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_melib_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_rna_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_cav_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_phospholipid_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lipids_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_13ppd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2obut_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tartr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lps_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g1p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_f6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mbdg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_biomass_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rbt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malttr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_inost_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ppa_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galct__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glx_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ins_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acmana_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_4hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tym_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_all__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lyx__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_peamn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lcts_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_SK_met__L_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_protein_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfReactants=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfProducts=None\u001b[0m\n", + "size of scope 630\n", + "size of all metabolites 884 \n", + "\n", + "\n", + "TARGET\n", + "REASONING\n", + "Subset Minimal\n", + "model_8\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tyr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_val__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_orn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ptrc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_spmd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_na1_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_urea_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_chol_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_adn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cytd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dad_2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dcyt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dgsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thymd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_duri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mnl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acgam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_uri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_csn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xan_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ura_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_taur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etha_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sucr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tre_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyb_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_bz_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_icit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyclt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no3_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_so4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arg__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asn__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_his__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ile__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_leu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_met__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_phe__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pro__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_trp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mg2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arab__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gal_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_man_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sbt__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fuc__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc3p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xyl__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rib__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rmn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_melib_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_rna_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_cav_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_phospholipid_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lipids_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_13ppd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2obut_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tartr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lps_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g1p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_f6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mbdg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_biomass_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rbt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malttr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_inost_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ppa_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galct__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glx_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ins_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acmana_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_4hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tym_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_all__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lyx__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_peamn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lcts_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_SK_met__L_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_protein_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfReactants=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfProducts=None\u001b[0m\n", + "size of scope 629\n", + "size of all metabolites 884 \n", + "\n", + "\n", + "TARGET\n", + "REASONING\n", + "Subset Minimal\n", + "model_9\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tyr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_val__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_orn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ptrc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_spmd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_na1_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_urea_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_chol_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_adn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cytd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dad_2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dcyt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dgsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thymd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_duri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mnl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acgam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_uri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_csn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xan_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ura_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_taur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etha_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sucr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tre_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyb_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_bz_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_icit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyclt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no3_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_so4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arg__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asn__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_his__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ile__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_leu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_met__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_phe__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pro__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_trp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mg2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arab__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gal_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_man_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sbt__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fuc__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc3p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xyl__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rib__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rmn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_melib_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_rna_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_cav_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_phospholipid_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lipids_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_13ppd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2obut_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tartr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lps_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g1p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_f6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mbdg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_biomass_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rbt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malttr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_inost_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ppa_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galct__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glx_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ins_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acmana_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_4hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tym_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_all__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lyx__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_peamn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lcts_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_SK_met__L_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_protein_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfReactants=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfProducts=None\u001b[0m\n", + "size of scope 629\n", + "size of all metabolites 884 \n", + "\n", + "\n", + "TARGET\n", + "REASONING\n", + "Subset Minimal\n", + "model_10\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tyr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_val__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_orn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ptrc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_spmd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_na1_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_urea_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_chol_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_adn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cytd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dad_2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dcyt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dgsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thymd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_duri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mnl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acgam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_uri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_csn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xan_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ura_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_taur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etha_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sucr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tre_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyb_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_bz_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_icit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyclt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no3_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_so4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arg__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asn__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_his__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ile__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_leu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_met__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_phe__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pro__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_trp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mg2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arab__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gal_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_man_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sbt__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fuc__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc3p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xyl__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rib__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rmn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_melib_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_rna_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_cav_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_phospholipid_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lipids_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_13ppd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2obut_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tartr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lps_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g1p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_f6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mbdg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_biomass_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rbt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malttr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_inost_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ppa_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galct__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glx_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ins_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acmana_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_4hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tym_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_all__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lyx__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_peamn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lcts_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_SK_met__L_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_protein_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfReactants=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfProducts=None\u001b[0m\n", + "size of scope 629\n", + "size of all metabolites 884 \n", + "\n", + "\n", + "TARGET\n", + "REASONING\n", + "Subset Minimal\n", + "model_11\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tyr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_val__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_orn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ptrc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_spmd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_na1_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_urea_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_chol_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_adn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cytd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dad_2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dcyt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dgsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thymd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_duri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mnl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acgam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_uri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_csn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xan_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ura_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_taur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etha_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sucr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tre_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyb_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_bz_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_icit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyclt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no3_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_so4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arg__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asn__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_his__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ile__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_leu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_met__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_phe__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pro__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_trp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mg2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arab__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gal_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_man_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sbt__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fuc__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc3p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xyl__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rib__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rmn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_melib_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_rna_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_cav_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_phospholipid_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lipids_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_13ppd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2obut_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tartr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lps_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g1p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_f6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mbdg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_biomass_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rbt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malttr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_inost_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ppa_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galct__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glx_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ins_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acmana_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_4hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tym_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_all__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lyx__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_peamn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lcts_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_SK_met__L_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_protein_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfReactants=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfProducts=None\u001b[0m\n", + "size of scope 629\n", + "size of all metabolites 884 \n", + "\n", + "\n", + "TARGET\n", + "REASONING\n", + "Subset Minimal\n", + "model_12\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tyr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_val__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_orn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ptrc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_spmd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_na1_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_urea_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_chol_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_adn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cytd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dad_2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dcyt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dgsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thymd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_duri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mnl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acgam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_uri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_csn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xan_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ura_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_taur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etha_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sucr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tre_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyb_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_bz_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_icit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyclt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no3_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_so4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arg__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asn__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_his__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ile__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_leu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_met__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_phe__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pro__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_trp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mg2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arab__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gal_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_man_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sbt__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fuc__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc3p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xyl__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rib__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rmn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_melib_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_rna_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_cav_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_phospholipid_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lipids_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_13ppd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2obut_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tartr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lps_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g1p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_f6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mbdg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_biomass_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rbt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malttr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_inost_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ppa_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galct__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glx_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ins_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acmana_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_4hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tym_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_all__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lyx__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_peamn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lcts_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_SK_met__L_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_protein_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfReactants=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfProducts=None\u001b[0m\n", + "size of scope 629\n", + "size of all metabolites 884 \n", + "\n", + "\n", + "TARGET\n", + "REASONING\n", + "Subset Minimal\n", + "model_13\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tyr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_val__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_orn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ptrc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_spmd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_na1_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_urea_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_chol_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_adn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cytd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dad_2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dcyt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dgsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thymd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_duri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mnl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acgam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_uri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_csn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xan_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ura_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_taur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etha_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sucr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tre_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyb_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_bz_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_icit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyclt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no3_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_so4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arg__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asn__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_his__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ile__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_leu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_met__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_phe__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pro__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_trp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mg2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arab__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gal_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_man_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sbt__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fuc__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc3p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xyl__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rib__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rmn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_melib_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_rna_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_cav_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_phospholipid_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lipids_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_13ppd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2obut_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tartr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lps_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g1p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_f6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mbdg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_biomass_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rbt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malttr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_inost_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ppa_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galct__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glx_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ins_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acmana_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_4hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tym_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_all__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lyx__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_peamn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lcts_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_SK_met__L_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_protein_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfReactants=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfProducts=None\u001b[0m\n", + "size of scope 629\n", + "size of all metabolites 884 \n", + "\n", + "\n", + "TARGET\n", + "REASONING\n", + "Subset Minimal\n", + "model_14\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tyr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_val__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_orn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ptrc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_spmd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_na1_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_urea_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_chol_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_adn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cytd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dad_2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dcyt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dgsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thymd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_duri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mnl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acgam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_uri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_csn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xan_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ura_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_taur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etha_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sucr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tre_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyb_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_bz_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_icit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyclt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no3_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_so4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arg__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asn__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_his__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ile__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_leu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_met__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_phe__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pro__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_trp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mg2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arab__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gal_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_man_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sbt__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fuc__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc3p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xyl__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rib__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rmn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_melib_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_rna_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_cav_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_phospholipid_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lipids_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_13ppd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2obut_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tartr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lps_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g1p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_f6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mbdg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_biomass_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rbt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malttr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_inost_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ppa_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galct__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glx_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ins_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acmana_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_4hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tym_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_all__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lyx__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_peamn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lcts_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_SK_met__L_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_protein_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfReactants=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfProducts=None\u001b[0m\n", + "size of scope 629\n", + "size of all metabolites 884 \n", + "\n", + "\n", + "TARGET\n", + "REASONING\n", + "Subset Minimal\n", + "model_15\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tyr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_val__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_orn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ptrc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_spmd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_na1_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_urea_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_chol_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_adn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cytd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dad_2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dcyt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dgsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thymd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_duri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mnl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acgam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_uri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_csn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xan_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ura_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_taur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etha_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sucr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tre_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyb_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_bz_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_icit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyclt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no3_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_so4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arg__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asn__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_his__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ile__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_leu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_met__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_phe__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pro__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_trp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mg2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arab__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gal_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_man_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sbt__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fuc__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc3p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xyl__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rib__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rmn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_melib_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_rna_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_cav_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_phospholipid_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lipids_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_13ppd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2obut_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tartr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lps_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g1p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_f6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mbdg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_biomass_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rbt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malttr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_inost_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ppa_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galct__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glx_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ins_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acmana_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_4hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tym_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_all__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lyx__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_peamn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lcts_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_SK_met__L_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_protein_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfReactants=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfProducts=None\u001b[0m\n", + "size of scope 629\n", + "size of all metabolites 884 \n", + "\n", + "\n", + "TARGET\n", + "REASONING\n", + "Subset Minimal\n", + "model_16\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tyr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_val__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_orn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ptrc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_spmd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_na1_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_urea_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_chol_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_adn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cytd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dad_2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dcyt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dgsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thymd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_duri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mnl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acgam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_uri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_csn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xan_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ura_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_taur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etha_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sucr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tre_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyb_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_bz_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_icit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyclt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no3_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_so4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arg__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asn__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_his__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ile__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_leu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_met__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_phe__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pro__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_trp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mg2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arab__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gal_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_man_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sbt__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fuc__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc3p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xyl__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rib__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rmn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_melib_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_rna_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_cav_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_phospholipid_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lipids_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_13ppd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2obut_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tartr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lps_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g1p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_f6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mbdg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_biomass_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rbt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malttr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_inost_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ppa_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galct__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glx_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ins_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acmana_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_4hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tym_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_all__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lyx__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_peamn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lcts_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_SK_met__L_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_protein_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfReactants=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfProducts=None\u001b[0m\n", + "size of scope 630\n", + "size of all metabolites 884 \n", + "\n", + "\n", + "TARGET\n", + "REASONING\n", + "Subset Minimal\n", + "model_17\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tyr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_val__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_orn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ptrc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_spmd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_na1_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_urea_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_chol_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_adn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cytd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dad_2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dcyt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dgsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thymd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_duri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mnl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acgam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_uri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_csn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xan_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ura_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_taur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etha_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sucr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tre_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyb_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_bz_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_icit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyclt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no3_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_so4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arg__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asn__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_his__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ile__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_leu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_met__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_phe__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pro__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_trp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mg2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arab__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gal_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_man_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sbt__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fuc__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc3p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xyl__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rib__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rmn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_melib_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_rna_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_cav_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_phospholipid_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lipids_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_13ppd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2obut_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tartr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lps_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g1p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_f6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mbdg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_biomass_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rbt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malttr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_inost_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ppa_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galct__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glx_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ins_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acmana_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_4hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tym_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_all__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lyx__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_peamn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lcts_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_SK_met__L_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_protein_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfReactants=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfProducts=None\u001b[0m\n", + "size of scope 629\n", + "size of all metabolites 884 \n", + "\n", + "\n", + "TARGET\n", + "REASONING\n", + "Subset Minimal\n", + "model_18\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tyr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_val__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_orn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ptrc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_spmd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_na1_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_urea_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_chol_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_adn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cytd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dad_2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dcyt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dgsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thymd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_duri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mnl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acgam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_uri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_csn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xan_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ura_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_taur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etha_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sucr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tre_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyb_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_bz_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_icit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyclt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no3_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_so4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arg__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asn__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_his__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ile__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_leu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_met__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_phe__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pro__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_trp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mg2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arab__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gal_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_man_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sbt__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fuc__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc3p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xyl__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rib__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rmn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_melib_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_rna_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_cav_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_phospholipid_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lipids_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_13ppd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2obut_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tartr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lps_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g1p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_f6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mbdg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_biomass_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rbt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malttr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_inost_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ppa_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galct__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glx_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ins_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acmana_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_4hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tym_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_all__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lyx__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_peamn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lcts_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_SK_met__L_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_protein_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfReactants=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfProducts=None\u001b[0m\n", + "size of scope 629\n", + "size of all metabolites 884 \n", + "\n", + "\n", + "TARGET\n", + "REASONING\n", + "Subset Minimal\n", + "model_19\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tyr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_val__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_orn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ptrc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_spmd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_na1_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_urea_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_chol_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_adn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cytd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dad_2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dcyt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dgsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thymd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_duri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mnl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acgam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_uri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_csn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xan_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ura_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_taur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etha_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sucr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tre_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyb_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_bz_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_icit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyclt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no3_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_so4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arg__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asn__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_his__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ile__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_leu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_met__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_phe__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pro__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_trp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mg2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arab__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gal_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_man_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sbt__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fuc__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc3p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xyl__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rib__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rmn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_melib_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_rna_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_cav_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_phospholipid_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lipids_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_13ppd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2obut_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tartr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lps_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g1p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_f6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mbdg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_biomass_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rbt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malttr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_inost_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ppa_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galct__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glx_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ins_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acmana_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_4hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tym_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_all__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lyx__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_peamn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lcts_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_SK_met__L_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_protein_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfReactants=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfProducts=None\u001b[0m\n", + "size of scope 630\n", + "size of all metabolites 884 \n", + "\n", + "\n", + "TARGET\n", + "REASONING\n", + "Subset Minimal\n", + "model_20\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tyr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_val__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_orn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ptrc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_spmd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_na1_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_urea_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_chol_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_adn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cytd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dad_2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dcyt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dgsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thymd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_duri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mnl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acgam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_uri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_csn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xan_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ura_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_taur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etha_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sucr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tre_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyb_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_bz_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_icit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyclt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no3_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_so4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arg__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asn__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_his__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ile__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_leu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_met__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_phe__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pro__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_trp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mg2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arab__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gal_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_man_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sbt__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fuc__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc3p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xyl__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rib__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rmn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_melib_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_rna_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_cav_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_phospholipid_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lipids_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_13ppd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2obut_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tartr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lps_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g1p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_f6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mbdg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_biomass_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rbt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malttr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_inost_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ppa_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galct__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glx_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ins_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acmana_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_4hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tym_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_all__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lyx__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_peamn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lcts_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_SK_met__L_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_protein_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfReactants=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfProducts=None\u001b[0m\n", + "size of scope 629\n", + "size of all metabolites 884 \n", + "\n", + "\n", + "TARGET\n", + "REASONING\n", + "Subset Minimal\n", + "model_21\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tyr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_val__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_orn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ptrc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_spmd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_na1_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_urea_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_chol_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_adn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cytd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dad_2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dcyt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dgsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thymd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_duri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mnl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acgam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_uri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_csn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xan_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ura_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_taur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etha_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sucr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tre_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyb_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_bz_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_icit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyclt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no3_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_so4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arg__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asn__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_his__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ile__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_leu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_met__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_phe__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pro__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_trp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mg2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arab__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gal_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_man_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sbt__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fuc__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc3p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xyl__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rib__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rmn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_melib_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_rna_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_cav_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_phospholipid_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lipids_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_13ppd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2obut_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tartr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lps_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g1p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_f6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mbdg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_biomass_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rbt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malttr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_inost_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ppa_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galct__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glx_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ins_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acmana_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_4hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tym_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_all__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lyx__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_peamn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lcts_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_SK_met__L_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_protein_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfReactants=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfProducts=None\u001b[0m\n", + "size of scope 629\n", + "size of all metabolites 884 \n", + "\n", + "\n", + "TARGET\n", + "REASONING\n", + "Subset Minimal\n", + "model_22\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tyr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_val__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_orn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ptrc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_spmd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_na1_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_urea_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_chol_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_adn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cytd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dad_2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dcyt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dgsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thymd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_duri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mnl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acgam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_uri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_csn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xan_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ura_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_taur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etha_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sucr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tre_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyb_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_bz_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_icit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyclt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no3_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_so4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arg__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asn__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_his__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ile__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_leu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_met__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_phe__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pro__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_trp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mg2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arab__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gal_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_man_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sbt__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fuc__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc3p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xyl__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rib__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rmn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_melib_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_rna_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_cav_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_phospholipid_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lipids_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_13ppd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2obut_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tartr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lps_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g1p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_f6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mbdg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_biomass_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rbt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malttr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_inost_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ppa_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galct__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glx_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ins_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acmana_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_4hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tym_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_all__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lyx__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_peamn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lcts_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_SK_met__L_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_protein_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfReactants=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfProducts=None\u001b[0m\n", + "size of scope 629\n", + "size of all metabolites 884 \n", + "\n", + "\n", + "TARGET\n", + "REASONING\n", + "Subset Minimal\n", + "model_23\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tyr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_val__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_orn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ptrc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_spmd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_na1_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_urea_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_chol_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_adn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cytd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dad_2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dcyt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dgsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thymd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_duri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mnl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acgam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_uri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_csn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xan_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ura_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_taur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etha_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sucr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tre_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyb_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_bz_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_icit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyclt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no3_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_so4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arg__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asn__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_his__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ile__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_leu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_met__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_phe__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pro__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_trp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mg2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arab__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gal_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_man_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sbt__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fuc__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc3p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xyl__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rib__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rmn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_melib_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_rna_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_cav_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_phospholipid_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lipids_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_13ppd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2obut_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tartr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lps_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g1p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_f6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mbdg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_biomass_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rbt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malttr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_inost_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ppa_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galct__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glx_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ins_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acmana_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_4hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tym_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_all__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lyx__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_peamn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lcts_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_SK_met__L_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_protein_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfReactants=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfProducts=None\u001b[0m\n", + "size of scope 629\n", + "size of all metabolites 884 \n", + "\n", + "\n", + "TARGET\n", + "REASONING\n", + "Subset Minimal\n", + "model_24\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tyr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_val__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_orn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ptrc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_spmd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_na1_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_urea_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_chol_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_adn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cytd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dad_2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dcyt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dgsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thymd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_duri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mnl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acgam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_uri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_csn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xan_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ura_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_taur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etha_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sucr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tre_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyb_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_bz_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_icit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyclt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no3_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_so4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arg__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asn__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_his__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ile__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_leu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_met__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_phe__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pro__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_trp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mg2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arab__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gal_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_man_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sbt__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fuc__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc3p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xyl__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rib__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rmn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_melib_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_rna_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_cav_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_phospholipid_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lipids_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_13ppd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2obut_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tartr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lps_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g1p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_f6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mbdg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_biomass_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rbt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malttr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_inost_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ppa_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galct__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glx_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ins_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acmana_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_4hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tym_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_all__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lyx__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_peamn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lcts_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_SK_met__L_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_protein_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfReactants=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfProducts=None\u001b[0m\n", + "size of scope 629\n", + "size of all metabolites 884 \n", + "\n", + "\n", + "TARGET\n", + "REASONING\n", + "Subset Minimal\n", + "model_25\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tyr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_val__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_orn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ptrc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_spmd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_na1_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_urea_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_chol_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_adn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cytd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dad_2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dcyt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dgsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thymd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_duri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mnl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acgam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_uri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_csn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xan_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ura_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_taur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etha_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sucr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tre_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyb_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_bz_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_icit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyclt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no3_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_so4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arg__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asn__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_his__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ile__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_leu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_met__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_phe__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pro__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_trp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mg2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arab__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gal_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_man_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sbt__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fuc__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc3p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xyl__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rib__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rmn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_melib_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_rna_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_cav_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_phospholipid_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lipids_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_13ppd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2obut_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tartr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lps_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g1p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_f6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mbdg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_biomass_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rbt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malttr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_inost_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ppa_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galct__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glx_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ins_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acmana_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_4hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tym_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_all__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lyx__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_peamn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lcts_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_SK_met__L_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_protein_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfReactants=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfProducts=None\u001b[0m\n", + "size of scope 629\n", + "size of all metabolites 884 \n", + "\n", + "\n", + "TARGET\n", + "REASONING\n", + "Subset Minimal\n", + "model_26\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tyr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_val__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_orn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ptrc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_spmd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_na1_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_urea_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_chol_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_adn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cytd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dad_2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dcyt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dgsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thymd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_duri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mnl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acgam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_uri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_csn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xan_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ura_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_taur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etha_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sucr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tre_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyb_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_bz_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_icit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyclt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no3_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_so4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arg__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asn__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_his__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ile__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_leu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_met__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_phe__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pro__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_trp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mg2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arab__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gal_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_man_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sbt__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fuc__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc3p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xyl__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rib__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rmn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_melib_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_rna_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_cav_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_phospholipid_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lipids_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_13ppd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2obut_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tartr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lps_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g1p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_f6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mbdg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_biomass_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rbt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malttr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_inost_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ppa_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galct__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glx_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ins_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acmana_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_4hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tym_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_all__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lyx__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_peamn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lcts_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_SK_met__L_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_protein_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfReactants=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfProducts=None\u001b[0m\n", + "size of scope 629\n", + "size of all metabolites 884 \n", + "\n", + "\n", + "TARGET\n", + "REASONING\n", + "Subset Minimal\n", + "model_27\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tyr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_val__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_orn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ptrc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_spmd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_na1_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_urea_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_chol_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_adn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cytd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dad_2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dcyt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dgsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thymd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_duri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mnl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acgam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_uri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_csn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xan_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ura_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_taur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etha_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sucr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tre_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyb_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_bz_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_icit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyclt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no3_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_so4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arg__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asn__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_his__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ile__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_leu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_met__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_phe__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pro__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_trp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mg2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arab__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gal_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_man_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sbt__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fuc__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc3p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xyl__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rib__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rmn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_melib_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_rna_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_cav_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_phospholipid_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lipids_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_13ppd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2obut_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tartr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lps_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g1p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_f6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mbdg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_biomass_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rbt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malttr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_inost_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ppa_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galct__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glx_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ins_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acmana_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_4hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tym_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_all__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lyx__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_peamn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lcts_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_SK_met__L_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_protein_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfReactants=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfProducts=None\u001b[0m\n", + "size of scope 629\n", + "size of all metabolites 884 \n", + "\n", + "\n", + "TARGET\n", + "REASONING\n", + "Subset Minimal\n", + "model_28\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tyr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_val__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_orn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ptrc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_spmd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_na1_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_urea_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_chol_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_adn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cytd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dad_2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dcyt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dgsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thymd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_duri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mnl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acgam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_uri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_csn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xan_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ura_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_taur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etha_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sucr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tre_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyb_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_bz_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_icit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyclt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no3_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_so4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arg__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asn__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_his__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ile__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_leu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_met__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_phe__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pro__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_trp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mg2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arab__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gal_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_man_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sbt__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fuc__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc3p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xyl__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rib__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rmn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_melib_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_rna_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_cav_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_phospholipid_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lipids_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_13ppd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2obut_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tartr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lps_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g1p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_f6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mbdg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_biomass_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rbt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malttr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_inost_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ppa_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galct__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glx_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ins_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acmana_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_4hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tym_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_all__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lyx__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_peamn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lcts_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_SK_met__L_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_protein_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfReactants=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfProducts=None\u001b[0m\n", + "size of scope 629\n", + "size of all metabolites 884 \n", + "\n", + "\n", + "TARGET\n", + "REASONING\n", + "Subset Minimal\n", + "model_29\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tyr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_val__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_orn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ptrc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_spmd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_na1_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_urea_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_chol_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_adn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cytd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dad_2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dcyt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dgsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thymd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_duri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mnl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acgam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_uri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_csn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xan_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ura_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_taur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etha_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sucr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tre_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyb_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_bz_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_icit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyclt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no3_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_so4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arg__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asn__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_his__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ile__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_leu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_met__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_phe__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pro__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_trp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mg2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arab__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gal_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_man_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sbt__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fuc__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc3p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xyl__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rib__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rmn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_melib_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_rna_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_cav_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_phospholipid_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lipids_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_13ppd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2obut_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tartr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lps_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g1p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_f6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mbdg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_biomass_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rbt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malttr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_inost_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ppa_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galct__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glx_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ins_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acmana_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_4hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tym_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_all__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lyx__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_peamn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lcts_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_SK_met__L_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_protein_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfReactants=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfProducts=None\u001b[0m\n", + "size of scope 629\n", + "size of all metabolites 884 \n", + "\n", + "\n", + "TARGET\n", + "REASONING\n", + "Subset Minimal\n", + "model_30\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tyr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_val__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_orn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ptrc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_spmd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_na1_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_urea_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_chol_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_adn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cytd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dad_2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dcyt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dgsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thymd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_duri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mnl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acgam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_uri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_csn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xan_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ura_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_taur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etha_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sucr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tre_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyb_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_bz_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_icit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyclt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no3_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_so4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arg__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asn__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_his__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ile__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_leu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_met__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_phe__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pro__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_trp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mg2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arab__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gal_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_man_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sbt__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fuc__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc3p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xyl__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rib__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rmn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_melib_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_rna_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_cav_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_phospholipid_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lipids_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_13ppd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2obut_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tartr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lps_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g1p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_f6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mbdg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_biomass_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rbt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malttr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_inost_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ppa_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galct__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glx_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ins_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acmana_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_4hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tym_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_all__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lyx__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_peamn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lcts_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_SK_met__L_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_protein_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfReactants=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfProducts=None\u001b[0m\n", + "size of scope 629\n", + "size of all metabolites 884 \n", + "\n", + "\n", + "TARGET\n", + "REASONING\n", + "Minimize\n", + "model_one_solution\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tyr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_val__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_orn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ptrc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_spmd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_na1_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_urea_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_chol_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_adn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cytd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dad_2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dcyt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dgsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thymd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_duri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mnl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acgam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_uri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_csn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xan_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ura_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_taur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etha_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sucr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tre_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyb_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_bz_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_icit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyclt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no3_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_so4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arg__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asn__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_his__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ile__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_leu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_met__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_phe__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pro__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_trp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mg2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arab__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gal_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_man_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sbt__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fuc__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc3p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xyl__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rib__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rmn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_melib_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_rna_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_cav_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_phospholipid_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lipids_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_13ppd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2obut_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tartr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lps_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g1p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_f6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mbdg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_biomass_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rbt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malttr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_inost_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ppa_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galct__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glx_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ins_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acmana_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_4hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tym_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_all__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lyx__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_peamn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lcts_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_SK_met__L_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_protein_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfReactants=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfProducts=None\u001b[0m\n", + "size of scope 636\n", + "size of all metabolites 884 \n", + "\n", + "\n", + "TARGET\n", + "REASONING\n", + "Minimize\n", + "model_1\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tyr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_val__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_orn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ptrc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_spmd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_na1_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_urea_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_chol_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_adn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cytd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dad_2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dcyt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dgsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thymd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_duri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mnl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acgam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_uri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_csn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xan_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ura_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_taur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etha_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sucr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tre_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyb_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_bz_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_icit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyclt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no3_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_so4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arg__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asn__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_his__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ile__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_leu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_met__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_phe__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pro__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_trp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mg2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arab__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gal_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_man_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sbt__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fuc__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc3p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xyl__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rib__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rmn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_melib_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_rna_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_cav_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_phospholipid_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lipids_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_13ppd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2obut_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tartr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lps_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g1p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_f6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mbdg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_biomass_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rbt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malttr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_inost_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ppa_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galct__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glx_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ins_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acmana_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_4hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tym_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_all__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lyx__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_peamn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lcts_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_SK_met__L_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_protein_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfReactants=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfProducts=None\u001b[0m\n", + "size of scope 631\n", + "size of all metabolites 884 \n", + "\n", + "\n", + "TARGET\n", + "REASONING\n", + "Minimize\n", + "model_2\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tyr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_val__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_orn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ptrc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_spmd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_na1_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_urea_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_chol_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_adn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cytd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dad_2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dcyt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dgsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thymd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_duri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mnl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acgam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_uri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_csn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xan_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ura_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_taur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etha_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sucr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tre_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyb_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_bz_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_icit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyclt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no3_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_so4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arg__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asn__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_his__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ile__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_leu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_met__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_phe__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pro__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_trp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mg2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arab__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gal_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_man_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sbt__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fuc__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc3p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xyl__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rib__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rmn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_melib_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_rna_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_cav_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_phospholipid_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lipids_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_13ppd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2obut_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tartr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lps_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g1p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_f6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mbdg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_biomass_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rbt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malttr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_inost_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ppa_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galct__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glx_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ins_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acmana_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_4hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tym_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_all__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lyx__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_peamn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lcts_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_SK_met__L_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_protein_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfReactants=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfProducts=None\u001b[0m\n", + "size of scope 631\n", + "size of all metabolites 884 \n", + "\n", + "\n", + "TARGET\n", + "REASONING\n", + "Minimize\n", + "model_3\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tyr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_val__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_orn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ptrc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_spmd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_na1_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_urea_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_chol_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_adn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cytd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dad_2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dcyt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dgsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thymd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_duri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mnl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acgam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_uri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_csn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xan_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ura_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_taur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etha_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sucr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tre_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyb_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_bz_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_icit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyclt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no3_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_so4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arg__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asn__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_his__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ile__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_leu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_met__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_phe__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pro__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_trp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mg2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arab__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gal_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_man_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sbt__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fuc__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc3p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xyl__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rib__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rmn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_melib_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_rna_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_cav_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_phospholipid_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lipids_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_13ppd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2obut_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tartr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lps_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g1p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_f6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mbdg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_biomass_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rbt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malttr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_inost_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ppa_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galct__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glx_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ins_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acmana_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_4hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tym_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_all__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lyx__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_peamn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lcts_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_SK_met__L_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_protein_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfReactants=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfProducts=None\u001b[0m\n", + "size of scope 631\n", + "size of all metabolites 884 \n", + "\n", + "\n", + "TARGET\n", + "REASONING\n", + "Minimize\n", + "model_4\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tyr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_val__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_orn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ptrc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_spmd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_na1_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_urea_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_chol_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_adn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cytd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dad_2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dcyt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dgsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thymd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_duri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mnl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acgam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_uri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_csn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xan_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ura_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_taur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etha_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sucr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tre_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyb_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_bz_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_icit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyclt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no3_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_so4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arg__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asn__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_his__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ile__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_leu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_met__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_phe__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pro__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_trp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mg2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arab__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gal_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_man_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sbt__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fuc__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc3p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xyl__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rib__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rmn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_melib_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_rna_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_cav_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_phospholipid_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lipids_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_13ppd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2obut_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tartr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lps_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g1p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_f6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mbdg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_biomass_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rbt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malttr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_inost_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ppa_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galct__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glx_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ins_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acmana_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_4hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tym_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_all__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lyx__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_peamn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lcts_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_SK_met__L_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_protein_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfReactants=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfProducts=None\u001b[0m\n", + "size of scope 631\n", + "size of all metabolites 884 \n", + "\n", + "\n", + "TARGET\n", + "REASONING\n", + "Minimize\n", + "model_5\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tyr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_val__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_orn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ptrc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_spmd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_na1_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_urea_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_chol_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_adn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cytd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dad_2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dcyt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dgsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thymd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_duri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mnl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acgam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_uri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_csn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xan_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ura_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_taur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etha_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sucr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tre_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyb_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_bz_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_icit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyclt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no3_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_so4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arg__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asn__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_his__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ile__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_leu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_met__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_phe__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pro__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_trp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mg2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arab__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gal_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_man_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sbt__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fuc__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc3p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xyl__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rib__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rmn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_melib_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_rna_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_cav_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_phospholipid_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lipids_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_13ppd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2obut_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tartr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lps_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g1p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_f6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mbdg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_biomass_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rbt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malttr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_inost_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ppa_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galct__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glx_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ins_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acmana_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_4hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tym_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_all__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lyx__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_peamn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lcts_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_SK_met__L_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_protein_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfReactants=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfProducts=None\u001b[0m\n", + "size of scope 631\n", + "size of all metabolites 884 \n", + "\n", + "\n", + "TARGET\n", + "REASONING\n", + "Minimize\n", + "model_6\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tyr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_val__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_orn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ptrc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_spmd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_na1_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_urea_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_chol_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_adn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cytd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dad_2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dcyt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dgsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thymd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_duri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mnl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acgam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_uri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_csn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xan_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ura_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_taur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etha_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sucr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tre_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyb_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_bz_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_icit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyclt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no3_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_so4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arg__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asn__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_his__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ile__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_leu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_met__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_phe__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pro__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_trp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mg2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arab__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gal_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_man_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sbt__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fuc__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc3p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xyl__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rib__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rmn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_melib_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_rna_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_cav_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_phospholipid_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lipids_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_13ppd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2obut_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tartr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lps_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g1p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_f6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mbdg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_biomass_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rbt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malttr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_inost_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ppa_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galct__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glx_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ins_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acmana_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_4hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tym_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_all__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lyx__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_peamn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lcts_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_SK_met__L_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_protein_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfReactants=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfProducts=None\u001b[0m\n", + "size of scope 631\n", + "size of all metabolites 884 \n", + "\n", + "\n", + "TARGET\n", + "REASONING\n", + "Minimize\n", + "model_7\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tyr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_val__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_orn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ptrc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_spmd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_na1_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_urea_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_chol_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_adn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cytd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dad_2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dcyt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dgsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thymd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_duri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mnl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acgam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_uri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_csn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xan_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ura_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_taur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etha_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sucr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tre_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyb_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_bz_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_icit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyclt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no3_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_so4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arg__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asn__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_his__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ile__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_leu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_met__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_phe__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pro__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_trp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mg2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arab__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gal_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_man_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sbt__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fuc__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc3p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xyl__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rib__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rmn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_melib_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_rna_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_cav_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_phospholipid_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lipids_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_13ppd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2obut_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tartr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lps_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g1p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_f6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mbdg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_biomass_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rbt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malttr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_inost_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ppa_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galct__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glx_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ins_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acmana_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_4hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tym_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_all__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lyx__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_peamn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lcts_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_SK_met__L_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_protein_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfReactants=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfProducts=None\u001b[0m\n", + "size of scope 631\n", + "size of all metabolites 884 \n", + "\n", + "\n", + "TARGET\n", + "REASONING\n", + "Minimize\n", + "model_8\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tyr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_val__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_orn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ptrc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_spmd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_na1_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_urea_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_chol_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_adn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cytd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dad_2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dcyt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dgsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thymd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_duri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mnl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acgam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_uri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_csn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xan_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ura_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_taur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etha_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sucr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tre_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyb_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_bz_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_icit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyclt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no3_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_so4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arg__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asn__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_his__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ile__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_leu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_met__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_phe__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pro__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_trp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mg2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arab__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gal_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_man_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sbt__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fuc__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc3p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xyl__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rib__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rmn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_melib_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_rna_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_cav_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_phospholipid_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lipids_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_13ppd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2obut_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tartr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lps_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g1p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_f6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mbdg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_biomass_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rbt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malttr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_inost_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ppa_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galct__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glx_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ins_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acmana_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_4hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tym_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_all__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lyx__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_peamn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lcts_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_SK_met__L_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_protein_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfReactants=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfProducts=None\u001b[0m\n", + "size of scope 631\n", + "size of all metabolites 884 \n", + "\n", + "\n", + "TARGET\n", + "REASONING\n", + "Minimize\n", + "model_9\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tyr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_val__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_orn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ptrc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_spmd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_na1_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_urea_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_chol_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_adn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cytd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dad_2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dcyt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dgsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thymd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_duri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mnl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acgam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_uri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_csn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xan_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ura_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_taur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etha_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sucr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tre_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyb_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_bz_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_icit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyclt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no3_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_so4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arg__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asn__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_his__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ile__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_leu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_met__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_phe__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pro__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_trp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mg2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arab__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gal_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_man_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sbt__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fuc__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc3p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xyl__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rib__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rmn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_melib_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_rna_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_cav_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_phospholipid_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lipids_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_13ppd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2obut_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tartr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lps_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g1p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_f6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mbdg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_biomass_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rbt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malttr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_inost_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ppa_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galct__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glx_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ins_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acmana_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_4hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tym_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_all__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lyx__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_peamn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lcts_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_SK_met__L_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_protein_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfReactants=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfProducts=None\u001b[0m\n", + "size of scope 631\n", + "size of all metabolites 884 \n", + "\n", + "\n", + "TARGET\n", + "REASONING\n", + "Minimize\n", + "model_10\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tyr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_val__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_orn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ptrc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_spmd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_na1_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_urea_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_chol_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_adn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cytd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dad_2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dcyt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dgsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thymd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_duri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mnl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acgam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_uri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_csn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xan_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ura_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_taur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etha_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sucr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tre_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyb_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_bz_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_icit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyclt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no3_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_so4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arg__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asn__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_his__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ile__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_leu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_met__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_phe__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pro__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_trp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mg2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arab__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gal_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_man_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sbt__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fuc__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc3p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xyl__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rib__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rmn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_melib_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_rna_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_cav_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_phospholipid_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lipids_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_13ppd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2obut_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tartr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lps_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g1p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_f6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mbdg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_biomass_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rbt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malttr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_inost_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ppa_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galct__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glx_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ins_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acmana_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_4hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tym_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_all__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lyx__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_peamn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lcts_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_SK_met__L_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_protein_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfReactants=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfProducts=None\u001b[0m\n", + "size of scope 631\n", + "size of all metabolites 884 \n", + "\n", + "\n", + "TARGET\n", + "REASONING\n", + "Minimize\n", + "model_11\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tyr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_val__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_orn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ptrc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_spmd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_na1_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_urea_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_chol_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_adn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cytd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dad_2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dcyt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dgsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thymd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_duri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mnl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acgam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_uri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_csn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xan_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ura_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_taur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etha_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sucr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tre_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyb_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_bz_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_icit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyclt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no3_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_so4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arg__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asn__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_his__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ile__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_leu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_met__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_phe__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pro__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_trp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mg2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arab__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gal_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_man_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sbt__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fuc__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc3p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xyl__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rib__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rmn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_melib_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_rna_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_cav_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_phospholipid_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lipids_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_13ppd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2obut_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tartr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lps_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g1p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_f6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mbdg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_biomass_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rbt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malttr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_inost_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ppa_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galct__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glx_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ins_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acmana_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_4hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tym_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_all__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lyx__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_peamn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lcts_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_SK_met__L_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_protein_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfReactants=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfProducts=None\u001b[0m\n", + "size of scope 631\n", + "size of all metabolites 884 \n", + "\n", + "\n", + "TARGET\n", + "REASONING\n", + "Minimize\n", + "model_12\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tyr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_val__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_orn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ptrc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_spmd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_na1_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_urea_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_chol_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_adn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cytd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dad_2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dcyt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dgsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thymd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_duri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mnl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acgam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_uri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_csn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xan_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ura_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_taur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etha_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sucr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tre_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyb_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_bz_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_icit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyclt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no3_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_so4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arg__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asn__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_his__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ile__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_leu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_met__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_phe__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pro__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_trp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mg2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arab__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gal_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_man_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sbt__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fuc__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc3p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xyl__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rib__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rmn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_melib_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_rna_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_cav_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_phospholipid_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lipids_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_13ppd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2obut_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tartr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lps_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g1p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_f6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mbdg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_biomass_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rbt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malttr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_inost_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ppa_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galct__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glx_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ins_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acmana_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_4hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tym_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_all__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lyx__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_peamn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lcts_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_SK_met__L_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_protein_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfReactants=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfProducts=None\u001b[0m\n", + "size of scope 631\n", + "size of all metabolites 884 \n", + "\n", + "\n", + "TARGET\n", + "REASONING\n", + "Minimize\n", + "model_13\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tyr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_val__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_orn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ptrc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_spmd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_na1_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_urea_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_chol_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_adn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cytd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dad_2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dcyt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dgsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thymd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_duri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mnl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acgam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_uri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_csn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xan_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ura_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_taur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etha_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sucr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tre_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyb_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_bz_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_icit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyclt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no3_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_so4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arg__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asn__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_his__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ile__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_leu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_met__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_phe__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pro__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_trp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mg2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arab__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gal_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_man_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sbt__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fuc__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc3p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xyl__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rib__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rmn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_melib_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_rna_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_cav_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_phospholipid_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lipids_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_13ppd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2obut_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tartr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lps_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g1p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_f6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mbdg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_biomass_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rbt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malttr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_inost_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ppa_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galct__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glx_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ins_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acmana_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_4hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tym_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_all__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lyx__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_peamn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lcts_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_SK_met__L_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_protein_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfReactants=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfProducts=None\u001b[0m\n", + "size of scope 631\n", + "size of all metabolites 884 \n", + "\n", + "\n", + "TARGET\n", + "REASONING\n", + "Minimize\n", + "model_14\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tyr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_val__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_orn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ptrc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_spmd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_na1_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_urea_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_chol_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_adn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cytd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dad_2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dcyt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dgsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thymd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_duri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mnl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acgam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_uri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_csn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xan_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ura_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_taur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etha_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sucr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tre_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyb_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_bz_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_icit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyclt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no3_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_so4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arg__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asn__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_his__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ile__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_leu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_met__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_phe__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pro__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_trp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mg2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arab__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gal_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_man_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sbt__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fuc__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc3p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xyl__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rib__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rmn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_melib_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_rna_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_cav_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_phospholipid_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lipids_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_13ppd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2obut_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tartr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lps_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g1p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_f6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mbdg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_biomass_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rbt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malttr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_inost_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ppa_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galct__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glx_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ins_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acmana_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_4hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tym_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_all__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lyx__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_peamn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lcts_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_SK_met__L_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_protein_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfReactants=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfProducts=None\u001b[0m\n", + "size of scope 631\n", + "size of all metabolites 884 \n", + "\n", + "\n", + "TARGET\n", + "REASONING\n", + "Minimize\n", + "model_15\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tyr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_val__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_orn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ptrc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_spmd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_na1_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_urea_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_chol_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_adn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cytd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dad_2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dcyt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dgsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thymd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_duri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mnl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acgam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_uri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_csn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xan_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ura_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_taur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etha_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sucr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tre_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyb_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_bz_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_icit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyclt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no3_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_so4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arg__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asn__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_his__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ile__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_leu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_met__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_phe__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pro__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_trp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mg2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arab__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gal_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_man_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sbt__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fuc__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc3p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xyl__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rib__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rmn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_melib_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_rna_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_cav_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_phospholipid_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lipids_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_13ppd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2obut_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tartr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lps_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g1p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_f6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mbdg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_biomass_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rbt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malttr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_inost_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ppa_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galct__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glx_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ins_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acmana_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_4hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tym_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_all__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lyx__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_peamn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lcts_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_SK_met__L_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_protein_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfReactants=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfProducts=None\u001b[0m\n", + "size of scope 631\n", + "size of all metabolites 884 \n", + "\n", + "\n", + "TARGET\n", + "REASONING\n", + "Minimize\n", + "model_16\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tyr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_val__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_orn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ptrc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_spmd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_na1_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_urea_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_chol_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_adn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cytd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dad_2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dcyt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dgsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thymd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_duri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mnl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acgam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_uri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_csn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xan_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ura_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_taur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etha_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sucr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tre_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyb_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_bz_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_icit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyclt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no3_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_so4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arg__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asn__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_his__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ile__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_leu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_met__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_phe__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pro__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_trp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mg2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arab__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gal_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_man_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sbt__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fuc__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc3p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xyl__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rib__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rmn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_melib_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_rna_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_cav_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_phospholipid_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lipids_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_13ppd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2obut_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tartr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lps_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g1p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_f6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mbdg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_biomass_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rbt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malttr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_inost_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ppa_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galct__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glx_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ins_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acmana_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_4hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tym_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_all__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lyx__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_peamn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lcts_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_SK_met__L_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_protein_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfReactants=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfProducts=None\u001b[0m\n", + "size of scope 631\n", + "size of all metabolites 884 \n", + "\n", + "\n", + "TARGET\n", + "REASONING\n", + "Minimize\n", + "model_17\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tyr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_val__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_orn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ptrc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_spmd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_na1_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_urea_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_chol_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_adn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cytd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dad_2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dcyt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dgsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thymd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_duri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mnl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acgam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_uri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_csn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xan_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ura_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_taur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etha_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sucr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tre_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyb_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_bz_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_icit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyclt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no3_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_so4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arg__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asn__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_his__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ile__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_leu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_met__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_phe__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pro__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_trp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mg2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arab__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gal_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_man_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sbt__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fuc__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc3p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xyl__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rib__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rmn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_melib_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_rna_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_cav_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_phospholipid_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lipids_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_13ppd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2obut_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tartr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lps_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g1p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_f6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mbdg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_biomass_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rbt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malttr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_inost_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ppa_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galct__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glx_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ins_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acmana_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_4hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tym_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_all__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lyx__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_peamn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lcts_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_SK_met__L_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_protein_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfReactants=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfProducts=None\u001b[0m\n", + "size of scope 631\n", + "size of all metabolites 884 \n", + "\n", + "\n", + "TARGET\n", + "REASONING\n", + "Minimize\n", + "model_18\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tyr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_val__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_orn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ptrc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_spmd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_na1_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_urea_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_chol_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_adn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cytd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dad_2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dcyt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dgsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thymd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_duri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mnl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acgam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_uri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_csn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xan_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ura_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_taur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etha_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sucr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tre_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyb_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_bz_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_icit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyclt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no3_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_so4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arg__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asn__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_his__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ile__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_leu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_met__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_phe__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pro__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_trp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mg2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arab__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gal_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_man_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sbt__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fuc__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc3p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xyl__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rib__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rmn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_melib_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_rna_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_cav_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_phospholipid_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lipids_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_13ppd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2obut_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tartr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lps_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g1p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_f6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mbdg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_biomass_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rbt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malttr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_inost_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ppa_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galct__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glx_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ins_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acmana_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_4hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tym_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_all__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lyx__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_peamn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lcts_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_SK_met__L_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_protein_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfReactants=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfProducts=None\u001b[0m\n", + "size of scope 631\n", + "size of all metabolites 884 \n", + "\n", + "\n", + "TARGET\n", + "REASONING\n", + "Minimize\n", + "model_19\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tyr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_val__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_orn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ptrc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_spmd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_na1_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_urea_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_chol_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_adn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cytd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dad_2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dcyt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dgsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thymd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_duri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mnl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acgam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_uri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_csn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xan_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ura_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_taur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etha_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sucr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tre_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyb_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_bz_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_icit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyclt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no3_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_so4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arg__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asn__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_his__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ile__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_leu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_met__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_phe__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pro__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_trp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mg2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arab__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gal_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_man_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sbt__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fuc__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc3p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xyl__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rib__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rmn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_melib_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_rna_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_cav_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_phospholipid_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lipids_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_13ppd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2obut_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tartr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lps_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g1p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_f6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mbdg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_biomass_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rbt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malttr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_inost_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ppa_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galct__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glx_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ins_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acmana_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_4hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tym_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_all__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lyx__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_peamn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lcts_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_SK_met__L_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_protein_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfReactants=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfProducts=None\u001b[0m\n", + "size of scope 631\n", + "size of all metabolites 884 \n", + "\n", + "\n", + "TARGET\n", + "REASONING\n", + "Minimize\n", + "model_20\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tyr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_val__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_orn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ptrc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_spmd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_na1_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_urea_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_chol_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_adn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cytd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dad_2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dcyt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dgsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thymd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_duri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mnl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acgam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_uri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_csn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xan_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ura_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_taur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etha_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sucr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tre_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyb_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_bz_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_icit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyclt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no3_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_so4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arg__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asn__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_his__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ile__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_leu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_met__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_phe__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pro__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_trp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mg2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arab__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gal_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_man_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sbt__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fuc__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc3p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xyl__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rib__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rmn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_melib_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_rna_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_cav_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_phospholipid_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lipids_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_13ppd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2obut_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tartr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lps_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g1p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_f6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mbdg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_biomass_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rbt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malttr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_inost_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ppa_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galct__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glx_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ins_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acmana_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_4hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tym_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_all__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lyx__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_peamn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lcts_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_SK_met__L_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_protein_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfReactants=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfProducts=None\u001b[0m\n", + "size of scope 631\n", + "size of all metabolites 884 \n", + "\n", + "\n", + "TARGET\n", + "REASONING\n", + "Minimize\n", + "model_21\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tyr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_val__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_orn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ptrc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_spmd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_na1_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_urea_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_chol_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_adn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cytd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dad_2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dcyt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dgsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thymd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_duri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mnl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acgam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_uri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_csn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xan_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ura_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_taur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etha_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sucr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tre_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyb_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_bz_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_icit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyclt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no3_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_so4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arg__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asn__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_his__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ile__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_leu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_met__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_phe__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pro__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_trp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mg2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arab__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gal_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_man_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sbt__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fuc__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc3p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xyl__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rib__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rmn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_melib_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_rna_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_cav_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_phospholipid_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lipids_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_13ppd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2obut_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tartr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lps_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g1p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_f6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mbdg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_biomass_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rbt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malttr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_inost_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ppa_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galct__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glx_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ins_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acmana_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_4hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tym_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_all__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lyx__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_peamn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lcts_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_SK_met__L_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_protein_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfReactants=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfProducts=None\u001b[0m\n", + "size of scope 631\n", + "size of all metabolites 884 \n", + "\n", + "\n", + "TARGET\n", + "REASONING\n", + "Minimize\n", + "model_22\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tyr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_val__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_orn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ptrc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_spmd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_na1_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_urea_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_chol_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_adn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cytd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dad_2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dcyt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dgsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thymd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_duri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mnl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acgam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_uri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_csn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xan_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ura_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_taur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etha_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sucr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tre_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyb_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_bz_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_icit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyclt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no3_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_so4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arg__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asn__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_his__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ile__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_leu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_met__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_phe__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pro__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_trp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mg2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arab__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gal_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_man_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sbt__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fuc__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc3p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xyl__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rib__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rmn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_melib_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_rna_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_cav_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_phospholipid_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lipids_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_13ppd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2obut_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tartr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lps_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g1p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_f6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mbdg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_biomass_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rbt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malttr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_inost_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ppa_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galct__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glx_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ins_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acmana_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_4hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tym_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_all__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lyx__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_peamn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lcts_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_SK_met__L_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_protein_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfReactants=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfProducts=None\u001b[0m\n", + "size of scope 631\n", + "size of all metabolites 884 \n", + "\n", + "\n", + "TARGET\n", + "REASONING\n", + "Minimize\n", + "model_23\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tyr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_val__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_orn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ptrc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_spmd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_na1_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_urea_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_chol_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_adn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cytd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dad_2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dcyt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dgsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thymd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_duri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mnl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acgam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_uri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_csn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xan_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ura_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_taur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etha_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sucr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tre_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyb_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_bz_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_icit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyclt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no3_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_so4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arg__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asn__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_his__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ile__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_leu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_met__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_phe__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pro__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_trp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mg2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arab__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gal_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_man_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sbt__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fuc__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc3p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xyl__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rib__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rmn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_melib_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_rna_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_cav_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_phospholipid_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lipids_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_13ppd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2obut_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tartr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lps_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g1p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_f6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mbdg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_biomass_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rbt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malttr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_inost_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ppa_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galct__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glx_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ins_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acmana_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_4hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tym_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_all__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lyx__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_peamn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lcts_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_SK_met__L_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_protein_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfReactants=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfProducts=None\u001b[0m\n", + "size of scope 631\n", + "size of all metabolites 884 \n", + "\n", + "\n", + "TARGET\n", + "REASONING\n", + "Minimize\n", + "model_24\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tyr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_val__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_orn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ptrc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_spmd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_na1_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_urea_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_chol_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_adn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cytd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dad_2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dcyt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dgsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thymd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_duri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mnl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acgam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_uri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_csn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xan_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ura_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_taur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etha_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sucr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tre_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyb_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_bz_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_icit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyclt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no3_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_so4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arg__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asn__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_his__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ile__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_leu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_met__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_phe__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pro__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_trp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mg2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arab__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gal_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_man_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sbt__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fuc__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc3p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xyl__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rib__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rmn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_melib_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_rna_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_cav_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_phospholipid_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lipids_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_13ppd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2obut_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tartr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lps_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g1p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_f6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mbdg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_biomass_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rbt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malttr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_inost_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ppa_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galct__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glx_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ins_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acmana_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_4hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tym_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_all__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lyx__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_peamn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lcts_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_SK_met__L_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_protein_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfReactants=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfProducts=None\u001b[0m\n", + "size of scope 631\n", + "size of all metabolites 884 \n", + "\n", + "\n", + "TARGET\n", + "REASONING\n", + "Minimize\n", + "model_25\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tyr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_val__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_orn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ptrc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_spmd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_na1_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_urea_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_chol_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_adn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cytd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dad_2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dcyt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dgsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thymd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_duri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mnl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acgam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_uri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_csn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xan_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ura_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_taur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etha_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sucr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tre_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyb_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_bz_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_icit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyclt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no3_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_so4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arg__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asn__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_his__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ile__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_leu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_met__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_phe__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pro__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_trp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mg2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arab__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gal_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_man_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sbt__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fuc__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc3p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xyl__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rib__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rmn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_melib_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_rna_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_cav_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_phospholipid_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lipids_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_13ppd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2obut_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tartr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lps_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g1p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_f6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mbdg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_biomass_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rbt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malttr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_inost_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ppa_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galct__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glx_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ins_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acmana_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_4hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tym_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_all__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lyx__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_peamn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lcts_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_SK_met__L_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_protein_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfReactants=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfProducts=None\u001b[0m\n", + "size of scope 631\n", + "size of all metabolites 884 \n", + "\n", + "\n", + "TARGET\n", + "REASONING\n", + "Minimize\n", + "model_26\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tyr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_val__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_orn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ptrc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_spmd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_na1_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_urea_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_chol_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_adn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cytd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dad_2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dcyt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dgsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thymd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_duri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mnl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acgam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_uri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_csn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xan_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ura_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_taur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etha_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sucr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tre_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyb_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_bz_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_icit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyclt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no3_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_so4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arg__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asn__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_his__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ile__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_leu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_met__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_phe__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pro__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_trp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mg2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arab__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gal_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_man_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sbt__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fuc__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc3p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xyl__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rib__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rmn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_melib_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_rna_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_cav_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_phospholipid_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lipids_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_13ppd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2obut_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tartr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lps_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g1p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_f6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mbdg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_biomass_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rbt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malttr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_inost_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ppa_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galct__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glx_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ins_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acmana_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_4hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tym_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_all__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lyx__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_peamn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lcts_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_SK_met__L_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_protein_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfReactants=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfProducts=None\u001b[0m\n", + "size of scope 631\n", + "size of all metabolites 884 \n", + "\n", + "\n", + "TARGET\n", + "REASONING\n", + "Minimize\n", + "model_27\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tyr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_val__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_orn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ptrc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_spmd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_na1_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_urea_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_chol_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_adn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cytd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dad_2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dcyt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dgsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thymd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_duri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mnl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acgam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_uri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_csn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xan_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ura_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_taur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etha_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sucr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tre_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyb_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_bz_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_icit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyclt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no3_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_so4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arg__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asn__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_his__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ile__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_leu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_met__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_phe__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pro__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_trp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mg2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arab__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gal_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_man_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sbt__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fuc__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc3p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xyl__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rib__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rmn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_melib_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_rna_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_cav_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_phospholipid_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lipids_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_13ppd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2obut_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tartr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lps_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g1p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_f6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mbdg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_biomass_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rbt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malttr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_inost_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ppa_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galct__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glx_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ins_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acmana_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_4hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tym_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_all__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lyx__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_peamn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lcts_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_SK_met__L_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_protein_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfReactants=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfProducts=None\u001b[0m\n", + "size of scope 631\n", + "size of all metabolites 884 \n", + "\n", + "\n", + "TARGET\n", + "REASONING\n", + "Minimize\n", + "model_28\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tyr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_val__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_orn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ptrc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_spmd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_na1_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_urea_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_chol_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_adn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cytd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dad_2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dcyt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dgsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thymd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_duri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mnl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acgam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_uri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_csn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xan_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ura_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_taur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etha_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sucr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tre_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyb_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_bz_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_icit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyclt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no3_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_so4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arg__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asn__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_his__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ile__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_leu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_met__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_phe__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pro__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_trp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mg2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arab__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gal_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_man_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sbt__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fuc__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc3p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xyl__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rib__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rmn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_melib_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_rna_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_cav_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_phospholipid_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lipids_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_13ppd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2obut_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tartr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lps_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g1p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_f6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mbdg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_biomass_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rbt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malttr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_inost_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ppa_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galct__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glx_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ins_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acmana_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_4hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tym_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_all__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lyx__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_peamn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lcts_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_SK_met__L_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_protein_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfReactants=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfProducts=None\u001b[0m\n", + "size of scope 631\n", + "size of all metabolites 884 \n", + "\n", + "\n", + "TARGET\n", + "REASONING\n", + "Minimize\n", + "model_29\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tyr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_val__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_orn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ptrc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_spmd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_na1_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_urea_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_chol_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_adn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cytd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dad_2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dcyt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dgsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thymd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_duri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mnl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acgam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_uri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_csn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xan_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ura_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_taur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etha_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sucr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tre_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyb_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_bz_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_icit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyclt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no3_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_so4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arg__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asn__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_his__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ile__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_leu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_met__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_phe__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pro__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_trp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mg2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arab__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gal_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_man_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sbt__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fuc__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc3p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xyl__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rib__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rmn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_melib_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_rna_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_cav_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_phospholipid_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lipids_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_13ppd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2obut_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tartr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lps_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g1p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_f6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mbdg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_biomass_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rbt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malttr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_inost_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ppa_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galct__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glx_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ins_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acmana_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_4hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tym_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_all__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lyx__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_peamn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lcts_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_SK_met__L_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_protein_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfReactants=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfProducts=None\u001b[0m\n", + "size of scope 631\n", + "size of all metabolites 884 \n", + "\n", + "\n", + "TARGET\n", + "REASONING\n", + "Minimize\n", + "model_30\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tyr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_val__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_orn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ptrc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_spmd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_na1_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_urea_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_chol_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_adn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cytd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dad_2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dcyt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dgsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thymd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_duri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mnl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acgam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_uri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_csn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xan_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ura_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_taur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etha_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sucr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tre_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyb_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_bz_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_icit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyclt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no3_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_so4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arg__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asn__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_his__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ile__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_leu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_met__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_phe__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pro__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_trp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mg2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arab__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gal_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_man_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sbt__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fuc__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc3p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xyl__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rib__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rmn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_melib_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_rna_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_cav_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_phospholipid_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lipids_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_13ppd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2obut_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tartr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lps_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g1p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_f6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mbdg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_biomass_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rbt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malttr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_inost_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ppa_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galct__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glx_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ins_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acmana_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_4hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tym_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_all__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lyx__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_peamn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lcts_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_SK_met__L_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_protein_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfReactants=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfProducts=None\u001b[0m\n", + "size of scope 631\n", + "size of all metabolites 884 \n", + "\n", + "\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "Network name: iCN718\n", + "____________________________________________\n", + "\n", + "\u001b[0;93mWARNING : Reaction R_SK_ACP_c deleted, boudaries [0,0]\u001b[0m\n", + "TARGET\n", + "REASONING GUESS-CHECK\n", + "Subset Minimal\n", + "model_1\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tyr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_val__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_orn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ptrc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_spmd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_na1_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_urea_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_chol_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_adn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cytd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dad_2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dcyt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dgsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thymd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_duri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mnl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acgam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_uri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_csn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xan_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ura_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_taur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etha_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sucr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tre_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyb_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_bz_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_icit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyclt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no3_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_so4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arg__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asn__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_his__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ile__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_leu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_met__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_phe__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pro__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_trp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mg2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arab__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gal_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_man_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sbt__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fuc__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc3p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xyl__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rib__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rmn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_melib_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_rna_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_cav_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_phospholipid_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lipids_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_13ppd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2obut_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tartr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lps_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g1p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_f6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mbdg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_biomass_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rbt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malttr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_inost_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ppa_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galct__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glx_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ins_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acmana_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_4hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tym_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_all__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lyx__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_peamn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lcts_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_SK_met__L_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_protein_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfReactants=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfProducts=None\u001b[0m\n", + "size of scope 655\n", + "size of all metabolites 884 \n", + "\n", + "\n", + "TARGET\n", + "REASONING GUESS-CHECK\n", + "Subset Minimal\n", + "model_2\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tyr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_val__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_orn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ptrc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_spmd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_na1_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_urea_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_chol_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_adn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cytd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dad_2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dcyt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dgsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thymd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_duri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mnl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acgam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_uri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_csn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xan_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ura_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_taur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etha_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sucr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tre_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyb_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_bz_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_icit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyclt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no3_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_so4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arg__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asn__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_his__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ile__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_leu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_met__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_phe__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pro__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_trp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mg2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arab__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gal_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_man_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sbt__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fuc__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc3p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xyl__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rib__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rmn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_melib_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_rna_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_cav_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_phospholipid_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lipids_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_13ppd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2obut_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tartr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lps_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g1p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_f6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mbdg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_biomass_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rbt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malttr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_inost_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ppa_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galct__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glx_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ins_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acmana_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_4hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tym_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_all__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lyx__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_peamn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lcts_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_SK_met__L_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_protein_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfReactants=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfProducts=None\u001b[0m\n", + "size of scope 659\n", + "size of all metabolites 884 \n", + "\n", + "\n", + "TARGET\n", + "REASONING GUESS-CHECK\n", + "Subset Minimal\n", + "model_3\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tyr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_val__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_orn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ptrc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_spmd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_na1_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_urea_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_chol_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_adn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cytd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dad_2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dcyt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dgsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thymd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_duri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mnl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acgam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_uri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_csn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xan_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ura_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_taur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etha_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sucr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tre_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyb_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_bz_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_icit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyclt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no3_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_so4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arg__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asn__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_his__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ile__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_leu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_met__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_phe__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pro__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_trp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mg2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arab__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gal_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_man_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sbt__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fuc__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc3p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xyl__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rib__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rmn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_melib_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_rna_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_cav_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_phospholipid_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lipids_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_13ppd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2obut_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tartr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lps_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g1p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_f6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mbdg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_biomass_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rbt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malttr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_inost_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ppa_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galct__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glx_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ins_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acmana_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_4hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tym_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_all__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lyx__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_peamn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lcts_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_SK_met__L_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_protein_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfReactants=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfProducts=None\u001b[0m\n", + "size of scope 665\n", + "size of all metabolites 884 \n", + "\n", + "\n", + "TARGET\n", + "REASONING GUESS-CHECK\n", + "Subset Minimal\n", + "model_4\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tyr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_val__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_orn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ptrc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_spmd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_na1_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_urea_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_chol_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_adn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cytd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dad_2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dcyt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dgsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thymd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_duri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mnl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acgam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_uri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_csn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xan_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ura_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_taur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etha_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sucr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tre_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyb_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_bz_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_icit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyclt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no3_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_so4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arg__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asn__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_his__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ile__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_leu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_met__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_phe__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pro__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_trp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mg2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arab__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gal_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_man_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sbt__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fuc__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc3p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xyl__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rib__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rmn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_melib_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_rna_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_cav_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_phospholipid_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lipids_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_13ppd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2obut_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tartr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lps_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g1p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_f6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mbdg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_biomass_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rbt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malttr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_inost_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ppa_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galct__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glx_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ins_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acmana_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_4hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tym_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_all__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lyx__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_peamn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lcts_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_SK_met__L_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_protein_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfReactants=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfProducts=None\u001b[0m\n", + "size of scope 666\n", + "size of all metabolites 884 \n", + "\n", + "\n", + "TARGET\n", + "REASONING GUESS-CHECK\n", + "Subset Minimal\n", + "model_5\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tyr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_val__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_orn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ptrc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_spmd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_na1_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_urea_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_chol_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_adn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cytd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dad_2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dcyt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dgsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thymd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_duri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mnl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acgam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_uri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_csn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xan_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ura_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_taur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etha_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sucr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tre_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyb_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_bz_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_icit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyclt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no3_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_so4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arg__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asn__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_his__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ile__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_leu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_met__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_phe__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pro__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_trp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mg2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arab__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gal_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_man_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sbt__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fuc__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc3p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xyl__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rib__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rmn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_melib_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_rna_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_cav_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_phospholipid_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lipids_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_13ppd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2obut_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tartr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lps_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g1p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_f6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mbdg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_biomass_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rbt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malttr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_inost_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ppa_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galct__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glx_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ins_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acmana_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_4hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tym_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_all__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lyx__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_peamn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lcts_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_SK_met__L_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_protein_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfReactants=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfProducts=None\u001b[0m\n", + "size of scope 661\n", + "size of all metabolites 884 \n", + "\n", + "\n", + "TARGET\n", + "REASONING GUESS-CHECK\n", + "Subset Minimal\n", + "model_6\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tyr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_val__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_orn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ptrc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_spmd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_na1_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_urea_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_chol_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_adn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cytd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dad_2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dcyt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dgsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thymd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_duri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mnl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acgam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_uri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_csn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xan_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ura_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_taur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etha_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sucr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tre_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyb_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_bz_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_icit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyclt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no3_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_so4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arg__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asn__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_his__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ile__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_leu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_met__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_phe__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pro__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_trp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mg2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arab__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gal_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_man_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sbt__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fuc__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc3p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xyl__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rib__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rmn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_melib_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_rna_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_cav_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_phospholipid_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lipids_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_13ppd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2obut_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tartr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lps_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g1p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_f6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mbdg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_biomass_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rbt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malttr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_inost_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ppa_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galct__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glx_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ins_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acmana_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_4hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tym_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_all__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lyx__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_peamn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lcts_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_SK_met__L_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_protein_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfReactants=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfProducts=None\u001b[0m\n", + "size of scope 656\n", + "size of all metabolites 884 \n", + "\n", + "\n", + "TARGET\n", + "REASONING GUESS-CHECK\n", + "Subset Minimal\n", + "model_7\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tyr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_val__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_orn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ptrc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_spmd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_na1_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_urea_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_chol_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_adn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cytd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dad_2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dcyt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dgsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thymd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_duri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mnl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acgam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_uri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_csn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xan_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ura_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_taur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etha_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sucr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tre_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyb_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_bz_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_icit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyclt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no3_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_so4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arg__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asn__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_his__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ile__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_leu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_met__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_phe__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pro__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_trp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mg2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arab__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gal_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_man_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sbt__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fuc__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc3p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xyl__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rib__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rmn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_melib_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_rna_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_cav_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_phospholipid_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lipids_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_13ppd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2obut_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tartr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lps_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g1p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_f6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mbdg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_biomass_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rbt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malttr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_inost_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ppa_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galct__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glx_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ins_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acmana_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_4hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tym_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_all__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lyx__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_peamn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lcts_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_SK_met__L_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_protein_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfReactants=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfProducts=None\u001b[0m\n", + "size of scope 656\n", + "size of all metabolites 884 \n", + "\n", + "\n", + "TARGET\n", + "REASONING GUESS-CHECK\n", + "Subset Minimal\n", + "model_8\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tyr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_val__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_orn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ptrc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_spmd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_na1_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_urea_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_chol_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_adn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cytd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dad_2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dcyt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dgsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thymd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_duri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mnl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acgam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_uri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_csn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xan_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ura_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_taur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etha_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sucr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tre_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyb_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_bz_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_icit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyclt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no3_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_so4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arg__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asn__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_his__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ile__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_leu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_met__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_phe__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pro__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_trp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mg2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arab__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gal_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_man_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sbt__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fuc__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc3p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xyl__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rib__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rmn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_melib_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_rna_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_cav_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_phospholipid_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lipids_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_13ppd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2obut_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tartr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lps_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g1p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_f6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mbdg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_biomass_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rbt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malttr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_inost_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ppa_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galct__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glx_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ins_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acmana_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_4hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tym_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_all__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lyx__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_peamn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lcts_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_SK_met__L_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_protein_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfReactants=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfProducts=None\u001b[0m\n", + "size of scope 659\n", + "size of all metabolites 884 \n", + "\n", + "\n", + "TARGET\n", + "REASONING GUESS-CHECK\n", + "Subset Minimal\n", + "model_9\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tyr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_val__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_orn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ptrc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_spmd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_na1_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_urea_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_chol_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_adn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cytd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dad_2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dcyt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dgsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thymd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_duri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mnl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acgam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_uri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_csn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xan_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ura_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_taur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etha_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sucr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tre_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyb_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_bz_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_icit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyclt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no3_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_so4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arg__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asn__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_his__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ile__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_leu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_met__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_phe__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pro__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_trp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mg2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arab__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gal_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_man_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sbt__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fuc__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc3p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xyl__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rib__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rmn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_melib_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_rna_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_cav_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_phospholipid_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lipids_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_13ppd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2obut_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tartr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lps_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g1p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_f6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mbdg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_biomass_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rbt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malttr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_inost_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ppa_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galct__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glx_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ins_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acmana_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_4hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tym_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_all__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lyx__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_peamn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lcts_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_SK_met__L_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_protein_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfReactants=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfProducts=None\u001b[0m\n", + "size of scope 645\n", + "size of all metabolites 884 \n", + "\n", + "\n", + "TARGET\n", + "REASONING GUESS-CHECK\n", + "Subset Minimal\n", + "model_10\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tyr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_val__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_orn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ptrc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_spmd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_na1_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_urea_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_chol_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_adn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cytd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dad_2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dcyt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dgsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thymd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_duri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mnl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acgam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_uri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_csn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xan_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ura_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_taur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etha_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sucr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tre_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyb_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_bz_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_icit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyclt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no3_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_so4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arg__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asn__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_his__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ile__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_leu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_met__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_phe__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pro__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_trp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mg2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arab__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gal_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_man_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sbt__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fuc__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc3p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xyl__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rib__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rmn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_melib_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_rna_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_cav_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_phospholipid_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lipids_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_13ppd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2obut_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tartr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lps_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g1p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_f6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mbdg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_biomass_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rbt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malttr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_inost_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ppa_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galct__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glx_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ins_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acmana_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_4hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tym_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_all__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lyx__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_peamn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lcts_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_SK_met__L_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_protein_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfReactants=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfProducts=None\u001b[0m\n", + "size of scope 654\n", + "size of all metabolites 884 \n", + "\n", + "\n", + "TARGET\n", + "REASONING GUESS-CHECK\n", + "Subset Minimal\n", + "model_11\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tyr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_val__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_orn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ptrc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_spmd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_na1_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_urea_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_chol_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_adn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cytd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dad_2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dcyt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dgsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thymd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_duri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mnl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acgam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_uri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_csn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xan_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ura_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_taur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etha_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sucr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tre_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyb_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_bz_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_icit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyclt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no3_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_so4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arg__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asn__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_his__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ile__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_leu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_met__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_phe__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pro__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_trp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mg2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arab__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gal_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_man_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sbt__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fuc__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc3p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xyl__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rib__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rmn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_melib_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_rna_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_cav_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_phospholipid_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lipids_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_13ppd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2obut_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tartr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lps_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g1p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_f6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mbdg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_biomass_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rbt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malttr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_inost_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ppa_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galct__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glx_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ins_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acmana_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_4hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tym_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_all__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lyx__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_peamn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lcts_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_SK_met__L_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_protein_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfReactants=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfProducts=None\u001b[0m\n", + "size of scope 643\n", + "size of all metabolites 884 \n", + "\n", + "\n", + "TARGET\n", + "REASONING GUESS-CHECK\n", + "Subset Minimal\n", + "model_12\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tyr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_val__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_orn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ptrc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_spmd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_na1_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_urea_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_chol_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_adn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cytd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dad_2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dcyt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dgsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thymd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_duri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mnl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acgam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_uri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_csn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xan_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ura_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_taur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etha_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sucr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tre_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyb_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_bz_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_icit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyclt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no3_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_so4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arg__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asn__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_his__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ile__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_leu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_met__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_phe__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pro__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_trp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mg2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arab__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gal_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_man_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sbt__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fuc__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc3p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xyl__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rib__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rmn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_melib_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_rna_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_cav_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_phospholipid_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lipids_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_13ppd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2obut_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tartr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lps_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g1p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_f6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mbdg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_biomass_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rbt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malttr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_inost_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ppa_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galct__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glx_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ins_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acmana_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_4hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tym_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_all__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lyx__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_peamn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lcts_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_SK_met__L_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_protein_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfReactants=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfProducts=None\u001b[0m\n", + "size of scope 644\n", + "size of all metabolites 884 \n", + "\n", + "\n", + "TARGET\n", + "REASONING GUESS-CHECK\n", + "Subset Minimal\n", + "model_13\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tyr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_val__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_orn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ptrc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_spmd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_na1_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_urea_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_chol_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_adn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cytd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dad_2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dcyt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dgsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thymd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_duri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mnl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acgam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_uri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_csn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xan_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ura_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_taur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etha_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sucr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tre_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyb_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_bz_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_icit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyclt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no3_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_so4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arg__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asn__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_his__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ile__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_leu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_met__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_phe__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pro__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_trp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mg2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arab__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gal_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_man_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sbt__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fuc__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc3p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xyl__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rib__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rmn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_melib_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_rna_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_cav_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_phospholipid_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lipids_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_13ppd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2obut_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tartr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lps_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g1p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_f6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mbdg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_biomass_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rbt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malttr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_inost_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ppa_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galct__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glx_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ins_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acmana_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_4hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tym_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_all__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lyx__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_peamn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lcts_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_SK_met__L_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_protein_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfReactants=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfProducts=None\u001b[0m\n", + "size of scope 645\n", + "size of all metabolites 884 \n", + "\n", + "\n", + "TARGET\n", + "REASONING GUESS-CHECK\n", + "Subset Minimal\n", + "model_14\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tyr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_val__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_orn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ptrc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_spmd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_na1_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_urea_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_chol_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_adn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cytd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dad_2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dcyt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dgsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thymd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_duri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mnl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acgam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_uri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_csn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xan_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ura_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_taur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etha_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sucr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tre_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyb_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_bz_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_icit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyclt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no3_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_so4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arg__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asn__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_his__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ile__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_leu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_met__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_phe__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pro__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_trp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mg2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arab__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gal_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_man_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sbt__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fuc__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc3p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xyl__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rib__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rmn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_melib_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_rna_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_cav_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_phospholipid_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lipids_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_13ppd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2obut_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tartr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lps_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g1p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_f6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mbdg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_biomass_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rbt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malttr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_inost_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ppa_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galct__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glx_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ins_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acmana_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_4hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tym_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_all__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lyx__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_peamn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lcts_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_SK_met__L_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_protein_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfReactants=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfProducts=None\u001b[0m\n", + "size of scope 646\n", + "size of all metabolites 884 \n", + "\n", + "\n", + "TARGET\n", + "REASONING GUESS-CHECK\n", + "Subset Minimal\n", + "model_15\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tyr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_val__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_orn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ptrc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_spmd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_na1_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_urea_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_chol_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_adn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cytd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dad_2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dcyt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dgsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thymd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_duri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mnl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acgam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_uri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_csn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xan_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ura_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_taur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etha_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sucr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tre_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyb_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_bz_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_icit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyclt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no3_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_so4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arg__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asn__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_his__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ile__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_leu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_met__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_phe__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pro__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_trp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mg2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arab__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gal_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_man_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sbt__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fuc__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc3p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xyl__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rib__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rmn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_melib_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_rna_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_cav_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_phospholipid_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lipids_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_13ppd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2obut_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tartr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lps_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g1p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_f6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mbdg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_biomass_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rbt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malttr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_inost_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ppa_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galct__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glx_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ins_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acmana_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_4hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tym_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_all__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lyx__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_peamn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lcts_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_SK_met__L_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_protein_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfReactants=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfProducts=None\u001b[0m\n", + "size of scope 655\n", + "size of all metabolites 884 \n", + "\n", + "\n", + "TARGET\n", + "REASONING GUESS-CHECK\n", + "Subset Minimal\n", + "model_16\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tyr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_val__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_orn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ptrc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_spmd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_na1_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_urea_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_chol_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_adn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cytd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dad_2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dcyt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dgsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thymd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_duri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mnl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acgam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_uri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_csn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xan_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ura_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_taur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etha_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sucr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tre_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyb_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_bz_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_icit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyclt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no3_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_so4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arg__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asn__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_his__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ile__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_leu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_met__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_phe__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pro__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_trp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mg2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arab__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gal_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_man_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sbt__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fuc__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc3p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xyl__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rib__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rmn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_melib_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_rna_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_cav_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_phospholipid_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lipids_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_13ppd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2obut_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tartr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lps_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g1p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_f6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mbdg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_biomass_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rbt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malttr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_inost_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ppa_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galct__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glx_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ins_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acmana_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_4hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tym_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_all__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lyx__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_peamn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lcts_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_SK_met__L_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_protein_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfReactants=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfProducts=None\u001b[0m\n", + "size of scope 649\n", + "size of all metabolites 884 \n", + "\n", + "\n", + "TARGET\n", + "REASONING GUESS-CHECK\n", + "Subset Minimal\n", + "model_17\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tyr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_val__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_orn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ptrc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_spmd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_na1_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_urea_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_chol_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_adn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cytd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dad_2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dcyt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dgsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thymd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_duri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mnl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acgam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_uri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_csn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xan_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ura_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_taur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etha_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sucr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tre_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyb_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_bz_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_icit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyclt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no3_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_so4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arg__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asn__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_his__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ile__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_leu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_met__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_phe__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pro__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_trp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mg2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arab__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gal_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_man_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sbt__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fuc__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc3p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xyl__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rib__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rmn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_melib_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_rna_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_cav_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_phospholipid_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lipids_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_13ppd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2obut_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tartr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lps_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g1p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_f6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mbdg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_biomass_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rbt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malttr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_inost_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ppa_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galct__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glx_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ins_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acmana_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_4hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tym_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_all__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lyx__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_peamn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lcts_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_SK_met__L_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_protein_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfReactants=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfProducts=None\u001b[0m\n", + "size of scope 667\n", + "size of all metabolites 884 \n", + "\n", + "\n", + "TARGET\n", + "REASONING GUESS-CHECK\n", + "Subset Minimal\n", + "model_18\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tyr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_val__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_orn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ptrc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_spmd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_na1_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_urea_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_chol_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_adn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cytd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dad_2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dcyt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dgsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thymd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_duri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mnl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acgam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_uri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_csn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xan_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ura_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_taur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etha_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sucr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tre_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyb_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_bz_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_icit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyclt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no3_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_so4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arg__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asn__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_his__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ile__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_leu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_met__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_phe__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pro__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_trp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mg2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arab__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gal_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_man_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sbt__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fuc__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc3p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xyl__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rib__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rmn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_melib_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_rna_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_cav_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_phospholipid_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lipids_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_13ppd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2obut_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tartr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lps_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g1p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_f6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mbdg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_biomass_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rbt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malttr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_inost_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ppa_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galct__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glx_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ins_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acmana_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_4hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tym_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_all__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lyx__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_peamn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lcts_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_SK_met__L_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_protein_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfReactants=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfProducts=None\u001b[0m\n", + "size of scope 645\n", + "size of all metabolites 884 \n", + "\n", + "\n", + "TARGET\n", + "REASONING GUESS-CHECK\n", + "Subset Minimal\n", + "model_19\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tyr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_val__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_orn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ptrc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_spmd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_na1_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_urea_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_chol_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_adn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cytd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dad_2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dcyt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dgsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thymd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_duri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mnl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acgam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_uri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_csn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xan_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ura_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_taur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etha_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sucr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tre_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyb_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_bz_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_icit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyclt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no3_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_so4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arg__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asn__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_his__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ile__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_leu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_met__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_phe__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pro__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_trp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mg2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arab__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gal_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_man_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sbt__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fuc__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc3p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xyl__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rib__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rmn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_melib_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_rna_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_cav_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_phospholipid_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lipids_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_13ppd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2obut_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tartr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lps_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g1p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_f6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mbdg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_biomass_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rbt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malttr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_inost_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ppa_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galct__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glx_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ins_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acmana_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_4hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tym_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_all__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lyx__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_peamn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lcts_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_SK_met__L_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_protein_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfReactants=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfProducts=None\u001b[0m\n", + "size of scope 644\n", + "size of all metabolites 884 \n", + "\n", + "\n", + "TARGET\n", + "REASONING GUESS-CHECK\n", + "Subset Minimal\n", + "model_20\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tyr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_val__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_orn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ptrc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_spmd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_na1_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_urea_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_chol_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_adn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cytd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dad_2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dcyt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dgsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thymd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_duri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mnl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acgam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_uri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_csn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xan_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ura_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_taur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etha_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sucr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tre_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyb_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_bz_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_icit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyclt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no3_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_so4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arg__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asn__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_his__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ile__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_leu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_met__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_phe__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pro__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_trp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mg2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arab__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gal_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_man_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sbt__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fuc__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc3p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xyl__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rib__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rmn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_melib_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_rna_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_cav_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_phospholipid_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lipids_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_13ppd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2obut_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tartr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lps_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g1p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_f6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mbdg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_biomass_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rbt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malttr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_inost_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ppa_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galct__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glx_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ins_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acmana_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_4hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tym_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_all__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lyx__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_peamn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lcts_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_SK_met__L_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_protein_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfReactants=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfProducts=None\u001b[0m\n", + "size of scope 645\n", + "size of all metabolites 884 \n", + "\n", + "\n", + "TARGET\n", + "REASONING GUESS-CHECK\n", + "Subset Minimal\n", + "model_21\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tyr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_val__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_orn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ptrc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_spmd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_na1_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_urea_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_chol_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_adn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cytd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dad_2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dcyt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dgsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thymd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_duri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mnl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acgam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_uri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_csn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xan_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ura_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_taur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etha_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sucr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tre_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyb_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_bz_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_icit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyclt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no3_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_so4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arg__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asn__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_his__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ile__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_leu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_met__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_phe__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pro__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_trp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mg2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arab__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gal_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_man_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sbt__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fuc__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc3p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xyl__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rib__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rmn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_melib_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_rna_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_cav_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_phospholipid_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lipids_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_13ppd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2obut_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tartr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lps_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g1p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_f6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mbdg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_biomass_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rbt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malttr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_inost_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ppa_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galct__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glx_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ins_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acmana_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_4hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tym_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_all__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lyx__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_peamn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lcts_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_SK_met__L_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_protein_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfReactants=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfProducts=None\u001b[0m\n", + "size of scope 665\n", + "size of all metabolites 884 \n", + "\n", + "\n", + "TARGET\n", + "REASONING GUESS-CHECK\n", + "Subset Minimal\n", + "model_22\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tyr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_val__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_orn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ptrc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_spmd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_na1_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_urea_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_chol_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_adn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cytd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dad_2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dcyt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dgsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thymd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_duri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mnl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acgam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_uri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_csn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xan_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ura_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_taur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etha_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sucr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tre_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyb_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_bz_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_icit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyclt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no3_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_so4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arg__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asn__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_his__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ile__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_leu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_met__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_phe__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pro__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_trp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mg2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arab__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gal_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_man_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sbt__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fuc__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc3p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xyl__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rib__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rmn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_melib_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_rna_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_cav_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_phospholipid_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lipids_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_13ppd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2obut_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tartr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lps_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g1p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_f6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mbdg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_biomass_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rbt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malttr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_inost_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ppa_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galct__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glx_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ins_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acmana_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_4hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tym_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_all__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lyx__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_peamn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lcts_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_SK_met__L_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_protein_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfReactants=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfProducts=None\u001b[0m\n", + "size of scope 641\n", + "size of all metabolites 884 \n", + "\n", + "\n", + "TARGET\n", + "REASONING GUESS-CHECK\n", + "Subset Minimal\n", + "model_23\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tyr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_val__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_orn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ptrc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_spmd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_na1_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_urea_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_chol_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_adn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cytd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dad_2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dcyt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dgsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thymd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_duri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mnl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acgam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_uri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_csn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xan_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ura_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_taur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etha_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sucr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tre_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyb_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_bz_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_icit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyclt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no3_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_so4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arg__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asn__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_his__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ile__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_leu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_met__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_phe__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pro__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_trp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mg2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arab__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gal_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_man_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sbt__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fuc__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc3p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xyl__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rib__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rmn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_melib_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_rna_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_cav_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_phospholipid_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lipids_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_13ppd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2obut_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tartr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lps_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g1p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_f6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mbdg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_biomass_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rbt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malttr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_inost_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ppa_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galct__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glx_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ins_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acmana_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_4hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tym_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_all__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lyx__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_peamn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lcts_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_SK_met__L_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_protein_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfReactants=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfProducts=None\u001b[0m\n", + "size of scope 641\n", + "size of all metabolites 884 \n", + "\n", + "\n", + "TARGET\n", + "REASONING GUESS-CHECK\n", + "Subset Minimal\n", + "model_24\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tyr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_val__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_orn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ptrc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_spmd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_na1_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_urea_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_chol_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_adn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cytd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dad_2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dcyt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dgsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thymd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_duri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mnl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acgam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_uri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_csn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xan_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ura_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_taur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etha_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sucr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tre_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyb_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_bz_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_icit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyclt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no3_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_so4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arg__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asn__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_his__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ile__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_leu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_met__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_phe__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pro__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_trp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mg2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arab__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gal_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_man_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sbt__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fuc__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc3p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xyl__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rib__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rmn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_melib_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_rna_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_cav_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_phospholipid_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lipids_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_13ppd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2obut_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tartr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lps_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g1p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_f6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mbdg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_biomass_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rbt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malttr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_inost_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ppa_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galct__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glx_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ins_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acmana_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_4hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tym_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_all__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lyx__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_peamn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lcts_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_SK_met__L_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_protein_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfReactants=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfProducts=None\u001b[0m\n", + "size of scope 644\n", + "size of all metabolites 884 \n", + "\n", + "\n", + "TARGET\n", + "REASONING GUESS-CHECK\n", + "Subset Minimal\n", + "model_25\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tyr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_val__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_orn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ptrc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_spmd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_na1_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_urea_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_chol_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_adn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cytd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dad_2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dcyt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dgsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thymd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_duri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mnl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acgam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_uri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_csn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xan_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ura_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_taur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etha_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sucr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tre_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyb_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_bz_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_icit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyclt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no3_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_so4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arg__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asn__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_his__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ile__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_leu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_met__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_phe__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pro__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_trp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mg2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arab__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gal_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_man_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sbt__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fuc__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc3p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xyl__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rib__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rmn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_melib_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_rna_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_cav_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_phospholipid_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lipids_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_13ppd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2obut_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tartr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lps_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g1p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_f6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mbdg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_biomass_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rbt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malttr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_inost_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ppa_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galct__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glx_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ins_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acmana_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_4hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tym_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_all__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lyx__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_peamn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lcts_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_SK_met__L_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_protein_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfReactants=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfProducts=None\u001b[0m\n", + "size of scope 646\n", + "size of all metabolites 884 \n", + "\n", + "\n", + "TARGET\n", + "REASONING GUESS-CHECK\n", + "Subset Minimal\n", + "model_26\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tyr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_val__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_orn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ptrc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_spmd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_na1_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_urea_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_chol_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_adn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cytd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dad_2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dcyt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dgsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thymd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_duri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mnl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acgam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_uri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_csn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xan_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ura_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_taur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etha_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sucr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tre_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyb_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_bz_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_icit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyclt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no3_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_so4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arg__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asn__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_his__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ile__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_leu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_met__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_phe__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pro__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_trp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mg2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arab__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gal_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_man_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sbt__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fuc__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc3p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xyl__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rib__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rmn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_melib_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_rna_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_cav_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_phospholipid_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lipids_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_13ppd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2obut_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tartr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lps_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g1p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_f6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mbdg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_biomass_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rbt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malttr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_inost_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ppa_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galct__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glx_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ins_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acmana_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_4hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tym_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_all__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lyx__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_peamn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lcts_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_SK_met__L_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_protein_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfReactants=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfProducts=None\u001b[0m\n", + "size of scope 654\n", + "size of all metabolites 884 \n", + "\n", + "\n", + "TARGET\n", + "REASONING GUESS-CHECK\n", + "Subset Minimal\n", + "model_27\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tyr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_val__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_orn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ptrc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_spmd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_na1_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_urea_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_chol_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_adn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cytd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dad_2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dcyt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dgsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thymd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_duri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mnl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acgam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_uri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_csn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xan_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ura_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_taur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etha_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sucr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tre_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyb_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_bz_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_icit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyclt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no3_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_so4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arg__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asn__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_his__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ile__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_leu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_met__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_phe__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pro__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_trp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mg2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arab__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gal_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_man_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sbt__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fuc__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc3p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xyl__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rib__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rmn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_melib_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_rna_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_cav_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_phospholipid_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lipids_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_13ppd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2obut_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tartr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lps_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g1p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_f6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mbdg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_biomass_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rbt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malttr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_inost_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ppa_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galct__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glx_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ins_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acmana_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_4hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tym_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_all__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lyx__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_peamn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lcts_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_SK_met__L_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_protein_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfReactants=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfProducts=None\u001b[0m\n", + "size of scope 654\n", + "size of all metabolites 884 \n", + "\n", + "\n", + "TARGET\n", + "REASONING GUESS-CHECK\n", + "Subset Minimal\n", + "model_28\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tyr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_val__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_orn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ptrc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_spmd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_na1_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_urea_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_chol_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_adn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cytd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dad_2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dcyt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dgsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thymd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_duri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mnl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acgam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_uri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_csn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xan_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ura_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_taur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etha_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sucr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tre_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyb_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_bz_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_icit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyclt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no3_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_so4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arg__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asn__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_his__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ile__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_leu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_met__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_phe__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pro__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_trp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mg2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arab__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gal_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_man_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sbt__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fuc__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc3p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xyl__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rib__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rmn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_melib_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_rna_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_cav_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_phospholipid_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lipids_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_13ppd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2obut_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tartr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lps_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g1p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_f6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mbdg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_biomass_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rbt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malttr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_inost_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ppa_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galct__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glx_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ins_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acmana_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_4hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tym_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_all__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lyx__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_peamn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lcts_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_SK_met__L_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_protein_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfReactants=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfProducts=None\u001b[0m\n", + "size of scope 657\n", + "size of all metabolites 884 \n", + "\n", + "\n", + "TARGET\n", + "REASONING GUESS-CHECK\n", + "Subset Minimal\n", + "model_29\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tyr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_val__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_orn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ptrc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_spmd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_na1_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_urea_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_chol_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_adn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cytd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dad_2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dcyt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dgsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thymd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_duri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mnl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acgam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_uri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_csn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xan_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ura_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_taur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etha_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sucr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tre_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyb_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_bz_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_icit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyclt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no3_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_so4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arg__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asn__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_his__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ile__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_leu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_met__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_phe__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pro__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_trp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mg2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arab__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gal_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_man_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sbt__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fuc__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc3p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xyl__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rib__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rmn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_melib_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_rna_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_cav_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_phospholipid_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lipids_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_13ppd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2obut_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tartr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lps_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g1p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_f6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mbdg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_biomass_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rbt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malttr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_inost_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ppa_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galct__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glx_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ins_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acmana_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_4hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tym_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_all__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lyx__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_peamn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lcts_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_SK_met__L_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_protein_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfReactants=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfProducts=None\u001b[0m\n", + "size of scope 646\n", + "size of all metabolites 884 \n", + "\n", + "\n", + "TARGET\n", + "REASONING GUESS-CHECK\n", + "Subset Minimal\n", + "model_30\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tyr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_val__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_orn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ptrc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_spmd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_na1_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_urea_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_chol_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_adn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cytd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dad_2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dcyt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dgsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thymd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_duri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mnl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acgam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_uri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_csn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xan_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ura_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_taur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etha_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sucr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tre_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyb_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_bz_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_icit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyclt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no3_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_so4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arg__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asn__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_his__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ile__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_leu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_met__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_phe__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pro__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_trp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mg2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arab__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gal_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_man_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sbt__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fuc__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc3p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xyl__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rib__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rmn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_melib_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_rna_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_cav_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_phospholipid_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lipids_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_13ppd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2obut_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tartr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lps_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g1p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_f6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mbdg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_biomass_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rbt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malttr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_inost_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ppa_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galct__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glx_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ins_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acmana_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_4hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tym_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_all__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lyx__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_peamn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lcts_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_SK_met__L_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_protein_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfReactants=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfProducts=None\u001b[0m\n", + "size of scope 646\n", + "size of all metabolites 884 \n", + "\n", + "\n", + "TARGET\n", + "REASONING GUESS-CHECK\n", + "Other\n", + "model_one_solution\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tyr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_val__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_orn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ptrc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_spmd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_na1_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_urea_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_chol_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_adn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cytd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dad_2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dcyt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dgsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thymd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_duri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mnl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acgam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_uri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_csn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xan_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ura_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_taur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etha_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sucr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tre_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyb_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_bz_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_icit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyclt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no3_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_so4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arg__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asn__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_his__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ile__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_leu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_met__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_phe__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pro__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_trp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mg2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arab__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gal_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_man_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sbt__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fuc__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc3p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xyl__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rib__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rmn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_melib_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_rna_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_cav_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_phospholipid_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lipids_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_13ppd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2obut_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tartr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lps_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g1p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_f6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mbdg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_biomass_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rbt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malttr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_inost_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ppa_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galct__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glx_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ins_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acmana_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_4hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tym_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_all__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lyx__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_peamn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lcts_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_SK_met__L_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_protein_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfReactants=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfProducts=None\u001b[0m\n", + "size of scope 625\n", + "size of all metabolites 884 \n", + "\n", + "\n", + "TARGET\n", + "REASONING GUESS-CHECK\n", + "Minimize\n", + "model_one_solution\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tyr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_val__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_orn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ptrc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_spmd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_na1_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_urea_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_chol_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_adn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cytd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dad_2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dcyt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dgsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thymd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_duri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mnl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acgam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_uri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_csn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xan_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ura_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_taur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etha_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sucr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tre_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyb_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_bz_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_icit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyclt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no3_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_so4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arg__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asn__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_his__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ile__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_leu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_met__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_phe__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pro__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_trp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mg2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arab__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gal_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_man_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sbt__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fuc__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc3p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xyl__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rib__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rmn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_melib_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_rna_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_cav_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_phospholipid_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lipids_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_13ppd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2obut_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tartr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lps_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g1p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_f6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mbdg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_biomass_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rbt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malttr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_inost_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ppa_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galct__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glx_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ins_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acmana_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_4hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tym_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_all__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lyx__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_peamn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lcts_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_SK_met__L_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_protein_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfReactants=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfProducts=None\u001b[0m\n", + "size of scope 625\n", + "size of all metabolites 884 \n", + "\n", + "\n", + "TARGET\n", + "REASONING GUESS-CHECK\n", + "Minimize\n", + "model_1\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tyr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_val__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_orn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ptrc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_spmd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_na1_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_urea_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_chol_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_adn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cytd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dad_2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dcyt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dgsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thymd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_duri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mnl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acgam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_uri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_csn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xan_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ura_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_taur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etha_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sucr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tre_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyb_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_bz_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_icit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyclt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no3_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_so4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arg__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asn__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_his__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ile__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_leu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_met__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_phe__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pro__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_trp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mg2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arab__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gal_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_man_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sbt__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fuc__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc3p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xyl__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rib__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rmn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_melib_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_rna_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_cav_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_phospholipid_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lipids_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_13ppd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2obut_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tartr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lps_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g1p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_f6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mbdg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_biomass_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rbt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malttr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_inost_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ppa_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galct__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glx_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ins_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acmana_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_4hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tym_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_all__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lyx__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_peamn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lcts_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_SK_met__L_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_protein_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfReactants=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfProducts=None\u001b[0m\n", + "size of scope 636\n", + "size of all metabolites 884 \n", + "\n", + "\n", + "TARGET\n", + "REASONING GUESS-CHECK\n", + "Minimize\n", + "model_2\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tyr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_val__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_orn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ptrc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_spmd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_na1_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_urea_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_chol_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_adn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cytd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dad_2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dcyt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dgsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thymd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_duri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mnl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acgam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_uri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_csn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xan_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ura_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_taur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etha_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sucr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tre_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyb_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_bz_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_icit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyclt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no3_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_so4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arg__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asn__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_his__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ile__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_leu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_met__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_phe__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pro__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_trp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mg2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arab__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gal_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_man_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sbt__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fuc__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc3p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xyl__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rib__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rmn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_melib_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_rna_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_cav_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_phospholipid_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lipids_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_13ppd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2obut_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tartr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lps_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g1p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_f6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mbdg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_biomass_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rbt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malttr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_inost_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ppa_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galct__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glx_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ins_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acmana_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_4hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tym_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_all__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lyx__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_peamn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lcts_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_SK_met__L_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_protein_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfReactants=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfProducts=None\u001b[0m\n", + "size of scope 636\n", + "size of all metabolites 884 \n", + "\n", + "\n", + "TARGET\n", + "REASONING GUESS-CHECK\n", + "Minimize\n", + "model_3\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tyr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_val__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_orn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ptrc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_spmd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_na1_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_urea_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_chol_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_adn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cytd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dad_2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dcyt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dgsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thymd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_duri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mnl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acgam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_uri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_csn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xan_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ura_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_taur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etha_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sucr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tre_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyb_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_bz_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_icit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyclt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no3_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_so4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arg__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asn__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_his__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ile__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_leu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_met__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_phe__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pro__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_trp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mg2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arab__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gal_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_man_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sbt__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fuc__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc3p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xyl__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rib__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rmn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_melib_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_rna_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_cav_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_phospholipid_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lipids_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_13ppd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2obut_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tartr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lps_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g1p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_f6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mbdg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_biomass_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rbt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malttr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_inost_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ppa_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galct__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glx_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ins_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acmana_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_4hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tym_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_all__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lyx__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_peamn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lcts_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_SK_met__L_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_protein_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfReactants=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfProducts=None\u001b[0m\n", + "size of scope 636\n", + "size of all metabolites 884 \n", + "\n", + "\n", + "TARGET\n", + "REASONING GUESS-CHECK\n", + "Minimize\n", + "model_4\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tyr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_val__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_orn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ptrc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_spmd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_na1_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_urea_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_chol_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_adn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cytd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dad_2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dcyt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dgsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thymd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_duri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mnl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acgam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_uri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_csn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xan_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ura_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_taur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etha_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sucr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tre_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyb_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_bz_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_icit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyclt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no3_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_so4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arg__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asn__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_his__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ile__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_leu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_met__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_phe__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pro__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_trp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mg2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arab__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gal_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_man_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sbt__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fuc__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc3p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xyl__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rib__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rmn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_melib_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_rna_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_cav_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_phospholipid_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lipids_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_13ppd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2obut_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tartr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lps_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g1p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_f6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mbdg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_biomass_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rbt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malttr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_inost_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ppa_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galct__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glx_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ins_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acmana_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_4hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tym_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_all__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lyx__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_peamn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lcts_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_SK_met__L_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_protein_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfReactants=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfProducts=None\u001b[0m\n", + "size of scope 625\n", + "size of all metabolites 884 \n", + "\n", + "\n", + "TARGET\n", + "REASONING GUESS-CHECK\n", + "Minimize\n", + "model_5\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tyr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_val__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_orn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ptrc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_spmd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_na1_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_urea_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_chol_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_adn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cytd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dad_2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dcyt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dgsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thymd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_duri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mnl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acgam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_uri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_csn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xan_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ura_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_taur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etha_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sucr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tre_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyb_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_bz_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_icit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyclt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no3_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_so4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arg__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asn__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_his__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ile__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_leu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_met__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_phe__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pro__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_trp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mg2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arab__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gal_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_man_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sbt__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fuc__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc3p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xyl__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rib__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rmn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_melib_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_rna_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_cav_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_phospholipid_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lipids_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_13ppd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2obut_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tartr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lps_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g1p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_f6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mbdg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_biomass_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rbt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malttr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_inost_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ppa_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galct__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glx_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ins_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acmana_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_4hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tym_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_all__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lyx__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_peamn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lcts_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_SK_met__L_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_protein_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfReactants=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfProducts=None\u001b[0m\n", + "size of scope 625\n", + "size of all metabolites 884 \n", + "\n", + "\n", + "TARGET\n", + "REASONING GUESS-CHECK\n", + "Minimize\n", + "model_6\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tyr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_val__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_orn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ptrc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_spmd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_na1_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_urea_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_chol_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_adn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cytd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dad_2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dcyt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dgsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thymd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_duri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mnl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acgam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_uri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_csn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xan_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ura_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_taur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etha_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sucr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tre_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyb_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_bz_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_icit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyclt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no3_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_so4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arg__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asn__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_his__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ile__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_leu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_met__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_phe__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pro__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_trp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mg2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arab__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gal_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_man_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sbt__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fuc__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc3p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xyl__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rib__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rmn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_melib_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_rna_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_cav_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_phospholipid_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lipids_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_13ppd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2obut_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tartr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lps_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g1p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_f6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mbdg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_biomass_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rbt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malttr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_inost_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ppa_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galct__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glx_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ins_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acmana_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_4hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tym_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_all__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lyx__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_peamn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lcts_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_SK_met__L_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_protein_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfReactants=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfProducts=None\u001b[0m\n", + "size of scope 625\n", + "size of all metabolites 884 \n", + "\n", + "\n", + "TARGET\n", + "REASONING GUESS-CHECK\n", + "Minimize\n", + "model_7\n", + "Accumulation: False\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tyr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_val__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_orn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ptrc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_spmd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_co2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_na1_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_o2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_urea_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_chol_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_adn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cytd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dad_2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dcyt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_dgsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thymd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_duri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fru_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glc__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mnl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acgam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gsn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_uri_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_csn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xan_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ura_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_taur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_etha_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sucr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tre_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyb_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_bz_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3pg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_akg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_for_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fum_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_succ_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_icit_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyclt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_no3_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pi_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_so4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arg__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asn__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ala__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gln__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_his__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ile__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_leu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lys__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_met__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_phe__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pro__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_cl_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ser__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_thr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_trp__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mg2_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_h2o_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_nh4_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_arab__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gal_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_man_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_sbt__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_fuc__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glcn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glyc3p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_xyl__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lac__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rib__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rmn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_melib_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_rna_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_cav_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_phospholipid_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_asp__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lipids_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gam_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_13ppd_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_2obut_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tartr__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_lps_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_g1p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_f6p_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mbdg_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_biomass_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_rbt_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_malttr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_inost_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ppa_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galct__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_glx_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_ins_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_gly_glu__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_acmana_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_mal__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_4hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_3hoxpac_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_tym_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_all__D_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lyx__L_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_pyr_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_galur_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_peamn_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_EX_lcts_e listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_SK_met__L_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DM_protein_c listOfProducts=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfReactants=None\u001b[0m\n", + "\u001b[0;93mWARNING : \n", + " Warning: R_DNADRAIN listOfProducts=None\u001b[0m\n", + "size of scope 625\n", + "size of all metabolites 884 \n", + "\n", + "\n" + ] + } + ], + "source": [ + "run_scope(iCN718_solution, \"iCN718_2000\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### **List of output files**\n", + "\n", + "In each of scope directories you'll find\n", + "\n", + "- A directory for with the sepcies conaitning all result for the current species (needed when multiple species are launched), and in each:\n", + " - logs directory\n", + " - sbml direcotory and in each:\n", + " - a directory \"target\" (or full wwhen Full Network used but not in our example) or \"netseed\" and in each:\n", + " - a directory for each mode (reasoning, filter, Guess-Check or Guess-Check-Div) or \"netseed\" and in each:\n", + " - a directory for \"submin\" (and \"minimize\" when used) or \"other\" and in each:\n", + " - a diretory \"no_accu\" or \"accu\" and in each:\n", + " -1 sbml file per solutions containing the list of seeds\n", + " - scope direcotory and in each:\n", + " - a directory \"target\" (or full wwhen Full Network used but not in our example) or \"netseed\" and in each:\n", + " - a directory for each mode (reasoning, filter, Guess-Check or Guess-Check-Div) or \"netseed\" and in each:\n", + " - a directory for \"submin\" (and \"minimize\" when used) or \"other\" and in each:\n", + " - a diretory \"no_accu\" or \"accu\" and in each:\n", + " -1 json file per solutions containing the list of metaoblite in the scope, the size of the scope and the size of all used metabolites in source file (metabolite linked to any reaction are not counted) " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "s2lp", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.14" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/notebook/run/10_get_data_for_analyse.ipynb b/notebook/run/10_get_data_for_analyse.ipynb new file mode 100644 index 0000000..f78aaca --- /dev/null +++ b/notebook/run/10_get_data_for_analyse.ipynb @@ -0,0 +1,1050 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# PREPARE DATA FOR ANALYSES: Get and format results data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This notebook explain how to \n", + "It must be run **AFTER** :\n", + "- All previous notebooks (number 01 to 08) (extract of solutions)\n", + "- After retrieving all solutions: [https://doi.org/10.57745/OS1JND](https://doi.org/10.57745/OS1JND)\n", + "\n", + "> Note:\n", + ">\n", + "> The Seed2lp supllement data are available[https://doi.org/10.57745/OS1JND](https://doi.org/10.57745/OS1JND)\n", + ">\n", + "> After downloadind and unzipping the package\n", + "> - analyses/results/metabolites_iCN718\n", + "> - analyses/results/supp_data\n", + "> - analyses/results/timer_one_sol" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Requirements\n", + "Module *seed2lp* needed\n", + "\n", + "> Advice:\n", + "> \n", + "> Use a conda env called s2lp" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!pip install seed2lp" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# GET AND CONCAT SCOPES DATA" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## **Slurm-based cluster**: Reproducing paper data\n", + "Slurm-based scripts for cluster are available:\n", + "- Launch if needed \n", + " - all previous scripts to get results, see notebook: \n", + " - [03_run_seed2lp](03_run_seed2lp.ipynb)\n", + " - [05_run_netseed](05_run_netseed.ipynb)\n", + " - [07_run_seed2lp_iCN718](07_run_seed2lp_iCN718.ipynb)\n", + "- Change **_source_** variable by the path of your conda environement with seed2lp installed in files: \n", + " - [10_1_job_run_scope_analyse_iCN718](../../scripts/plafrim_cluster/10_1_job_run_scope_analyse_iCN718.sh)\n", + " - [10_1_job_run_scope_analyse_netseed](../../scripts/plafrim_cluster/10_1_job_run_scope_analyse_netseed.sh)\n", + " - [10_1_job_run_scope_analyse_s2lp](../../scripts/plafrim_cluster/10_1_job_run_scope_analyse_s2lp.sh)\n", + "- launch \n", + " - [10_1_job_run_scope_analyse_iCN718](../../scripts/plafrim_cluster/10_1_job_run_scope_analyse_iCN718.sh): `sbatch 10_1_job_run_scope_analyse_iCN718.`\n", + " - [10_1_job_run_scope_analyse_netseed](../../scripts/plafrim_cluster/10_1_job_run_scope_analyse_netseed.sh): `sbatch 10_1_job_run_scope_analyse_netseed.`\n", + " - [10_1_job_run_scope_analyse_s2lp](../../scripts/plafrim_cluster/10_1_job_run_scope_analyse_s2lp.sh): `sbatch 10_1_job_run_scope_analyse_s2lp.`" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Function" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "from os import path, listdir, makedirs, walk\n", + "from shutil import copyfile" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "analyse_dir = \"../../analyses\"\n", + "data_dir = f\"{analyse_dir}/data/\"\n", + "result_dir=f\"{analyse_dir}/results\"\n", + "\n", + "objective_dir=f\"{data_dir}/objective\"\n", + "sbml_dir=f\"{data_dir}/bigg/sbml\"\n", + "e_coli_dir=f\"{data_dir}/bigg/sbml_e_coli_core\"" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'../../data/bigg/sbml_e_coli_core/iCN718.xml'" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "if not path.isdir(e_coli_dir):\n", + " makedirs(e_coli_dir)\n", + " copyfile(path.join(sbml_dir, \"e_coli_core.xml\"), path.join(e_coli_dir, \"e_coli_core.xml\"))\n", + "copyfile(path.join(sbml_dir, \"iCN718.xml\"), path.join(e_coli_dir, \"iCN718.xml\"))" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "def run_scope_analyses(sbml_dir:str, scope_dir:str, obj_dir:str,\n", + " lev1:list, lev2:list, lev3:list, lev4:list, \n", + " given_species:str=None):\n", + " file = \"../../scripts/10_1_scope_analyse.py\"\n", + " for filename in listdir(sbml_dir):\n", + " species = f'{path.splitext(path.basename(filename))[0]}'\n", + " if not given_species or species==given_species:\n", + " sbml_path = path.join(sbml_dir, filename)\n", + " objective_path = path.join(obj_dir,f\"{species}_target.txt\")\n", + " dir = list(map(lambda x: x.split('.')[0], listdir(scope_dir)))\n", + " if species in dir:\n", + " path_seed=path.join(scope_dir, species, 'sbml')\n", + " path_scope=path.join(scope_dir, species, 'scope')\n", + " for l1 in lev1:\n", + " for l2 in lev2:\n", + " for l3 in lev3:\n", + " for l4 in lev4:\n", + " modes_info = path.join(l1, l2, l3, l4)\n", + " full_path_seed = path.join(path_seed, modes_info)\n", + " full_path_scope = path.join(path_scope, modes_info)\n", + " if path.isdir(full_path_scope):\n", + " command=f\"{species} {sbml_path} {full_path_scope} {full_path_seed} {objective_path} {modes_info}\"\n", + " !python {file} {command}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Seed2LP" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "list_dir_lev1=[\"full\", \"target\"]\n", + "list_dir_lev2=[\"reasoning\", \"reasoning_filter\", \"reasoning_guess_check\", \"reasoning_guess_check_diversity\"]\n", + "list_dir_lev3=[\"minimize\", \"subset_minimal\"]\n", + "list_dir_lev4=[\"accu\", \"no_accu\"]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Variable to change (if wanted)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "scope_dir_s2lp=f\"{result_dir}/scopes_s2lp\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### RUN" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "/home/cghassem/Projets/seed-2-lp/notebook/run/../../scripts/10_1_scope_analyse.py:247: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", + " comparison_scope_df = pd.concat([comparison_scope_df, current_df], ignore_index=True)\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "/home/cghassem/Projets/seed-2-lp/notebook/run/../../scripts/10_1_scope_analyse.py:247: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", + " comparison_scope_df = pd.concat([comparison_scope_df, current_df], ignore_index=True)\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "/home/cghassem/Projets/seed-2-lp/notebook/run/../../scripts/10_1_scope_analyse.py:247: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", + " comparison_scope_df = pd.concat([comparison_scope_df, current_df], ignore_index=True)\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "/home/cghassem/Projets/seed-2-lp/notebook/run/../../scripts/10_1_scope_analyse.py:247: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", + " comparison_scope_df = pd.concat([comparison_scope_df, current_df], ignore_index=True)\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "/home/cghassem/Projets/seed-2-lp/notebook/run/../../scripts/10_1_scope_analyse.py:247: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", + " comparison_scope_df = pd.concat([comparison_scope_df, current_df], ignore_index=True)\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "/home/cghassem/Projets/seed-2-lp/notebook/run/../../scripts/10_1_scope_analyse.py:247: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", + " comparison_scope_df = pd.concat([comparison_scope_df, current_df], ignore_index=True)\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "/home/cghassem/Projets/seed-2-lp/notebook/run/../../scripts/10_1_scope_analyse.py:247: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", + " comparison_scope_df = pd.concat([comparison_scope_df, current_df], ignore_index=True)\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "/home/cghassem/Projets/seed-2-lp/notebook/run/../../scripts/10_1_scope_analyse.py:247: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", + " comparison_scope_df = pd.concat([comparison_scope_df, current_df], ignore_index=True)\n" + ] + } + ], + "source": [ + "run_scope_analyses(e_coli_dir, scope_dir_s2lp, objective_dir, list_dir_lev1, list_dir_lev2, list_dir_lev3, list_dir_lev4, \"e_coli_core\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## NetSeed" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": {}, + "outputs": [], + "source": [ + "list_dir_lev1=[\"netseed\"]\n", + "list_dir_lev2=[\"netseed\"]\n", + "list_dir_lev3=[\"other\"]\n", + "list_dir_lev4=[\"accu\"]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Variable to change (if wanted)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "scope_dir_netseed=f\"{result_dir}/scopes_netseed\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### RUN" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "/home/cghassem/Projets/seed-2-lp/notebook/run/../../scripts/10_1_scope_analyse.py:224: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", + " comparison_scope_df = pd.concat([comparison_scope_df, current_df], ignore_index=True)\n" + ] + } + ], + "source": [ + "run_scope_analyses(e_coli_dir, scope_dir_netseed, objective_dir, list_dir_lev1, list_dir_lev2, list_dir_lev3, list_dir_lev4, \"e_coli_core\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## iCN718" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [], + "source": [ + "list_dir_lev1=[\"target\"]\n", + "list_dir_lev2=[\"reasoning\", \"reasoning_filter\", \"reasoning_guess_check\", \"reasoning_guess_check_diversity\"]\n", + "list_dir_lev3=[\"subset_minimal\"]\n", + "list_dir_lev4=[\"no_accu\"]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Variable to change (if wanted)" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [], + "source": [ + "scope_dir_iCN718=f\"{result_dir}/scopes_iCN718\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### RUN" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "/home/cghassem/Projets/seed-2-lp/notebook/run/../../scripts/10_1_scope_analyse.py:247: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", + " comparison_scope_df = pd.concat([comparison_scope_df, current_df], ignore_index=True)\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "/home/cghassem/Projets/seed-2-lp/notebook/run/../../scripts/10_1_scope_analyse.py:247: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", + " comparison_scope_df = pd.concat([comparison_scope_df, current_df], ignore_index=True)\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "/home/cghassem/Projets/seed-2-lp/notebook/run/../../scripts/10_1_scope_analyse.py:247: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", + " comparison_scope_df = pd.concat([comparison_scope_df, current_df], ignore_index=True)\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "/home/cghassem/Projets/seed-2-lp/notebook/run/../../scripts/10_1_scope_analyse.py:247: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", + " comparison_scope_df = pd.concat([comparison_scope_df, current_df], ignore_index=True)\n" + ] + } + ], + "source": [ + "run_scope_analyses(e_coli_dir, scope_dir_s2lp, objective_dir, list_dir_lev1, list_dir_lev2, list_dir_lev3, list_dir_lev4, \"e_coli_core\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### RUN" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "/home/cghassem/Projets/seed-2-lp/notebook/run/../../scripts/10_1_scope_analyse.py:224: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", + " comparison_scope_df = pd.concat([comparison_scope_df, current_df], ignore_index=True)\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "/home/cghassem/Projets/seed-2-lp/notebook/run/../../scripts/10_1_scope_analyse.py:224: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", + " comparison_scope_df = pd.concat([comparison_scope_df, current_df], ignore_index=True)\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "/home/cghassem/Projets/seed-2-lp/notebook/run/../../scripts/10_1_scope_analyse.py:224: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", + " comparison_scope_df = pd.concat([comparison_scope_df, current_df], ignore_index=True)\n", + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "/home/cghassem/Projets/seed-2-lp/notebook/run/../../scripts/10_1_scope_analyse.py:224: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", + " comparison_scope_df = pd.concat([comparison_scope_df, current_df], ignore_index=True)\n" + ] + } + ], + "source": [ + "run_scope_analyses(e_coli_dir, scope_dir_iCN718, objective_dir, list_dir_lev1, list_dir_lev2, list_dir_lev3, list_dir_lev4, \"iCN718\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### **List of output files**\n", + "\n", + "In each of scope directories you'll find\n", + "\n", + "- logs directory\n", + "- sbml direcotory\n", + "- scope direcotory and in each:\n", + " - a directory \"target\" (or full wwhen Full Network used but not in our example) or \"netseed\" and in each:\n", + " - a directory for each mode (reasoning, filter, Guess-Check or Guess-Check-Div) or \"netseed\" and in each:\n", + " - a directory for \"submin\" (and \"minimize\" when used) or \"other\" and in each:\n", + " - a file *_compare.tsv which contains all scope of the directory" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Concat by scopes directory (al sub repository)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## **Slurm-based cluster**: Reproducing paper data\n", + "Slurm-based scripts for cluster are available:\n", + "- Launch if needed \n", + " - all previous scripts to get results for iCN718 and scope, see notebbok: [07_run_seed2lp_iCN718](07_run_seed2lp_iCN718.ipynb)\n", + "- Change **_source_** variable by the path of your conda environement with seed2lp installed in files: \n", + " - [10_2_run_scope_analyse_concat](../../scripts/plafrim_cluster/10_2_run_scope_analyse_concat.sh)\n", + "- launch \n", + " - [10_2_run_scope_analyse_concat](../../scripts/plafrim_cluster/10_2_run_scope_analyse_concat.sh): `sbatch 10_2_run_scope_analyse_concat.`" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### RUN" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": {}, + "outputs": [], + "source": [ + "def concat_scopes_by_dir(scope_dir):\n", + " for dir in listdir(scope_dir):\n", + " species=dir\n", + " path_scope=path.join(scope_dir, species, 'scope')\n", + " files_list=list()\n", + " for dirpath, dirnames, filenames in walk(path_scope):\n", + " for filename in [f for f in filenames if f.endswith(\"_compare.tsv\")]:\n", + " files_list.append(path.join(dirpath, filename))\n", + " \n", + " output_file=path.join(scope_dir, f'{species}_scope_compare.tsv')\n", + " first_line=None\n", + " with open(output_file, 'w') as outfile:\n", + " for file in files_list:\n", + " with open(file) as infile:\n", + " if not first_line:\n", + " first_line = infile.readline()\n", + " outfile.write(first_line)\n", + " else:\n", + " next(infile)\n", + " for line in infile:\n", + " outfile.write(line)" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "metadata": {}, + "outputs": [], + "source": [ + "concat_scopes_by_dir(scope_dir_s2lp)" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": {}, + "outputs": [], + "source": [ + "concat_scopes_by_dir(scope_dir_netseed)" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "metadata": {}, + "outputs": [], + "source": [ + "concat_scopes_by_dir(scope_dir_iCN718)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### **List of output files**\n", + "\n", + "In each of scope directories you'll find\n", + "\n", + "- logs directory\n", + "- sbml direcotory\n", + "- scope direcotory and in each:\n", + " - a file *_scope_compare.tsv which contains all scope of subdirectories" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# METABOLITES ANALYSE FOR iCN718" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this section we will count the occurences of metabolites in solutions by Search method Reasoning / Filter / Gues Check and Guess Check Diversity." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## **WARNING**\n", + "This section of the notebook will run the metabolites occurences on solution only for iCN718 with 30 solutions.\n", + "\n", + "On the paper, the metabolites occurences was computed for:\n", + "- iCN718 Seed2LP results files (up to 2000), Target mode, submin, no accumulation, *Reasoning*, *Hybrid-Filter*, *Hybrid-GC*, *Hybrid-GCDiv*.\n", + "\n", + "\n", + "> Note:\n", + ">\n", + "> - All results file from Seed2LP iCN metabolites occurences are available here: ????? (LINK)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Requirements\n", + "Module *seed2lp* needed\n", + "\n", + "> Advice:\n", + "> \n", + "> Use a conda env called s2lp with python 3.10 for plafrim cluster scripts" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## **Slurm-based cluster**: Reproducing paper data\n", + "Slurm-based scripts for cluster are available:\n", + "- Launch if needed \n", + " - previous scripts to get iCN718 results. Seed notebook : [07_run_seed2lp_iCN718](07_run_seed2lp_iCN718.ipynb)\n", + "- Change **_source_** variable by the path of your conda environement with seed2lp installed in files: \n", + " - [10_3_job_iCN718_metabolite_analysis](../../scripts/slurm_cluster/10_3_job_iCN718_metabolite_analysis.sh)\n", + "- launch \n", + " - [10_3_job_iCN718_metabolite_analysis](../../scripts/slurm_cluster/10_3_job_iCN718_metabolite_analysis.sh): `sbatch 10_3_job_iCN718_metabolite_analysis.sh`\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from os import path, makedirs" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Variable to change (if wanted)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "analyse_dir = \"../../analyses\"\n", + "data_dir = f\"{analyse_dir}/data/\"\n", + "result_dir=f\"{analyse_dir}/results\"\n", + "\n", + "species=\"iCN718\"\n", + "sbml_dir = f\"{data_dir}/bigg/sbml\"\n", + "sbml_file = f\"{sbml_dir}/{species}.xml\"\n", + "species_result_dir=f\"{result_dir}/{species}_2000/{species}\"\n", + "output_dir=f\"{result_dir}/metabolites_{species}\"" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "if not path.isdir(output_dir):\n", + " makedirs(output_dir)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### RUN" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n" + ] + } + ], + "source": [ + "file = \"../../scripts/10_3_iCN718_metabolite_analyses.py\"\n", + "\n", + "command=f\"{file} {species_result_dir} {sbml_file} {output_dir}\"\n", + "!python {command}\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### **List of output files**\n", + "\n", + "In the metabolite output directory you will find a file called \"metabolites_occurences.tsv\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# GET SOLUTIONS SUPPLEMENTARY DATA" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## **Slurm-based cluster**: Reproducing paper data\n", + "Slurm-based scripts for cluster are available:\n", + "- Launch if needed \n", + " - all previous scripts to get results, see notebook: \n", + " - [03_run_seed2lp](03_run_seed2lp.ipynb)\n", + " - [05_run_netseed](05_run_netseed.ipynb)\n", + " - [07_run_seed2lp_iCN718](07_run_seed2lp_iCN718.ipynb)\n", + "- Change **_source_** variable by the path of your conda environement with seed2lp installed in files: \n", + " - [10_4_job_get_supp_data](../../scripts/plafrim_cluster/10_4_job_get_supp_data.sh)\n", + "- launch \n", + " - [10_4_job_get_supp_data](../../scripts/plafrim_cluster/10_4_job_get_supp_data.sh): `sbatch 10_4_job_get_supp_data.sh`\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Variable to change (if wanted)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "analyse_dir = \"../../analyses\"\n", + "data_dir = f\"{analyse_dir}/data/\"\n", + "result_dir=f\"{analyse_dir}/results\"\n", + "\n", + "sbml_dir=f\"{data_dir}/sbml_corrected\"\n", + "s2lp_result_dir=f\"{result_dir}/s2lp\"\n", + "iCN718_result_dir=f\"{result_dir}/iCN718\"\n", + "onesol_result_dir=f\"{result_dir}/one_solution\"\n", + "out_dir=f\"{result_dir}/supp_data\"" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "if not path.isdir(out_dir):\n", + " makedirs(out_dir)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "file = \"../../scripts/10_4_get_supp_data.py\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Seed2LP" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "/home/cghassem/Projets/seed-2-lp/notebook/run/../../scripts/10_4_get_supp_data.py:152: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", + " results=pd.concat([results, current_df], ignore_index=True)\n" + ] + } + ], + "source": [ + "s2lp_command = f\"{file} {s2lp_result_dir} {sbml_dir} {out_dir} 'seed2lp'\"\n", + "!python {s2lp_command}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## iCN718" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "/home/cghassem/Projets/seed-2-lp/notebook/run/../../scripts/10_4_get_supp_data.py:152: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", + " results=pd.concat([results, current_df], ignore_index=True)\n" + ] + } + ], + "source": [ + "iCN718_command = f\"{file} {iCN718_result_dir} {sbml_dir} {out_dir} 'iCN718_2000'\"\n", + "!python {iCN718_command}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## One solution" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n", + "/home/cghassem/Projets/seed-2-lp/notebook/run/../../scripts/10_4_get_supp_data.py:152: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", + " results=pd.concat([results, current_df], ignore_index=True)\n" + ] + } + ], + "source": [ + "onesol_command = f\"{file} {onesol_result_dir} {sbml_dir} {out_dir} 'one_solution'\"\n", + "!python {onesol_command}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# GET EXCHANGED METABOLITES WITHOUT PREFIX AND SUFFIX\n", + "For iCN 718 metabolites analyses it is needed to retrieve exchanged metabolites without the prefix \"M_\" and compartment suffix. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## **Slurm-based cluster**: Reproducing paper data\n", + "Slurm-based scripts for cluster are available:\n", + "- Launch if needed \n", + " - get sbml file from BiGG, see notebook: [01_get_sbml_BiGG](01_get_sbml_BiGG.ipynb)\n", + "- Change **_source_** variable by the path of your conda environement with seed2lp installed in files: \n", + " - [10_5_job_get_iCN718_exchange](../../scripts/slurm_cluster/10_5_job_get_iCN718_exchange.sh)\n", + "- launch \n", + " - [10_5_job_get_iCN718_exchange](../../scripts/plafrim_cluster/10_5_job_get_iCN718_exchange.sh): `sbatch 10_5_job_get_iCN718_exchange.sh`\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "file = \"../../scripts/10_5_get_exchange.py\"" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "species=\"iCN718\"\n", + "data_dir=\"../../data\"\n", + "sbml_dir = f\"{data_dir}/bigg/sbml\"\n", + "sbml_file = f\"{sbml_dir}/{species}.xml\"\n", + "result_dir=\"../../results\"\n", + "output_dir=f\"{result_dir}/metabolites_{species}\"" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[0;96m\u001b[1m \n", + " _ ___ _ \n", + " ___ ___ ___ __| | |_ \\ | | _ __ \n", + " / __| / _ \\ / _ \\ / _` | ) | | || '_ \\ \n", + " \\__ \\| __/| __/| (_| | / /_ | || |_) |\n", + " |___/ \\___| \\___| \\__,_| |____| |_|| .__/ \n", + " |_| \n", + " \u001b[0m\n" + ] + } + ], + "source": [ + "command_exch = f\"{file} {species} {sbml_file} {output_dir}\"\n", + "\n", + "!python {command_exch}" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "test", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.14" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/scripts/01_retrieve_bigg_sbml.py b/scripts/01_retrieve_bigg_sbml.py new file mode 100644 index 0000000..0a5772c --- /dev/null +++ b/scripts/01_retrieve_bigg_sbml.py @@ -0,0 +1,34 @@ +import requests +from os import path, mkdir +import json +from sys import argv + + +def bigg_data_import(out_dir): + """Import BIGG models and save them to a directory. + + Args: + dir_ (str, optional): Name of the directory where files will be stored. Defaults to 'BIGG'. + """ + print('>>> BIGG data import...', end='\t') + dir_= path.join(out_dir,'bigg') + db_version = requests.get('http://bigg.ucsd.edu/api/v2/database_version').json() + data = requests.get('http://bigg.ucsd.edu/api/v2/models').json() + if not path.exists(dir_) : mkdir(dir_) + mkdir(dir_+'/data') + mkdir(dir_+'/sbml') + + with open(f'{dir_}/data/db_version.json', 'w') as db_version_f: json.dump(db_version, db_version_f, indent=4) + + for model in data['results']: + id_ = model['bigg_id'] + r = requests.get(f'http://bigg.ucsd.edu/static/models/{id_}.xml') + with open(f'{dir_}/sbml/{id_}.xml', 'wb') as f : f.write(r.content) + print('Done.') + return db_version + + + +if __name__ == '__main__': + out_dir = argv[1] + bigg_data_import(out_dir) diff --git a/scripts/02_get_objective.py b/scripts/02_get_objective.py new file mode 100644 index 0000000..0b619d8 --- /dev/null +++ b/scripts/02_get_objective.py @@ -0,0 +1,91 @@ +from sys import argv +from os import listdir,path +import xml.etree.ElementTree as etree +import pandas as pd + +in_dir = argv[1] +out_dir = argv[2] + +def get_sbml_tag(element) -> str: + "Return tag associated with given SBML element" + if element.tag[0] == "{": + _, tag = element.tag[1:].split("}") # uri is not used + else: + tag = element.tag + return tag + +def get_fbc(sbml): + """ + return the fbc namespace of a SBML + """ + fbc = None + for nss in etree._namespaces(sbml): + for key in nss: + if key is not None and 'fbc' in key: + fbc=key + break + return fbc + +def get_model(sbml): + """ + return the model of a SBML + """ + model_element = None + for e in sbml: + tag = get_sbml_tag(e) + if tag == "model": + model_element = e + break + return model_element + + +if __name__ == '__main__': + + special_dict = {"iMM1415": "R_BIOMASS_mm_1_no_glygln", + "iAF987": "R_BIOMASS_Gm_GS15_WT_79p20M", + "iSynCJ816": "R_BIOMASS_Ec_SynAuto_1", + "iRC1080": "R_BIOMASS_Chlamy_auto"} + + listOfFluxObjectives = pd.DataFrame(columns=["species","reaction","coeffficient"]) + for file in listdir( in_dir ): + found = False + file_path = path.join(in_dir,file) + species = f'{path.splitext(path.basename(file_path))[0]}' + print(species) + tree = etree.parse(file_path) + sbml = tree.getroot() + model = get_model(sbml) + fbc = get_fbc(sbml) + + for e in model: + tag = get_sbml_tag(e) + if tag == "listOfObjectives": + for lo in e[0]: + if lo: + for o in lo: + reac = o.attrib.get('{'+fbc+'}reaction') + coef = o.attrib.get('{'+fbc+'}coefficient') + if float(coef) == 1: + current_df = pd.DataFrame([[species,reac,coef]], + columns=["species","reaction","coeffficient"]) + listOfFluxObjectives=pd.concat([listOfFluxObjectives, current_df], + ignore_index=True) + + found = True + if not found: + reac = special_dict[species] + current_df = pd.DataFrame([[species, reac, 0]], + columns=["species","reaction","coeffficient"]) + listOfFluxObjectives=pd.concat([listOfFluxObjectives, current_df], + ignore_index=True) + break + + tgt_path = path.join(out_dir,f"{species}_target.txt") + with open(tgt_path, 'w') as tf: + tf.write(f'{reac}') + tf.close() + + out_file_path = path.join(out_dir,"list_objectives.tsv") + with open(out_file_path, 'w') as f: + listOfFluxObjectives.to_csv(out_file_path, sep="\t") + f.close() diff --git a/scripts/05_format_results.py b/scripts/05_format_results.py new file mode 100644 index 0000000..b63fdb0 --- /dev/null +++ b/scripts/05_format_results.py @@ -0,0 +1,66 @@ +from sys import argv +import json + +if __name__ == '__main__': + # User data + tool_result_file = argv[1] + species = argv[2] + objective = argv[3] + result_path = argv[4] + tool = argv[5] + + if tool=="PRECURSOR": + if "target" in tool_result_file: + mode = "Precursor_tgt" + else: + mode = "Precursor_fn" + else: + mode = tool + + + results=dict() + options=dict() + net=dict() + enumeration=dict() + tool_enum=dict() + tool_result=dict() + + options['REACTION'] = "Remove Import Reaction" + #Netseed or Precursor doesn't take into account the accumulation, by default its allowed + options['ACCUMULATION'] = "Allowed" + options['FLUX'] = "Maximization" + results["OPTIONS"] = options + + net["NAME"] = species + net["SEARCH_MODE"] = mode + net["OBJECTIVE"] = [objective] + net["SOLVE"] = tool + results["NETWORK"] = net + + with open(tool_result_file, 'r') as j: + net_solutions = json.loads(j.read()) + + if tool=="NETSEED": + del net_solutions['netseed']['solutions']['union'] + + tool.lower() + solutions = {f"model_{key}": val for key, val in net_solutions[tool.lower()]['solutions'].items()} + for key,val in solutions.items(): + sol=[ + "size", + len(val), + "Set of seeds", + val + ] + solutions[key]=sol + + + enumeration['solutions']=solutions + tool_enum['ENUMERATION']=enumeration + + tool_result[tool]=tool_enum + results['RESULTS']=tool_result + + + with open(result_path, 'w') as f: + json.dump(results, f, indent="\t") diff --git a/scripts/10_1_scope_analyse.py b/scripts/10_1_scope_analyse.py new file mode 100644 index 0000000..c89da58 --- /dev/null +++ b/scripts/10_1_scope_analyse.py @@ -0,0 +1,255 @@ +import os +import pandas as pd +from sys import argv +from seed2lp import sbml +import xml.etree.ElementTree as etree +import json + +def get_listOfProducts_from_name(model, reaction_name) -> list: + """return list of products of a reaction""" + reactions_list = sbml.get_listOfReactions(model) + for reaction in reactions_list: + if reaction_name == reaction.attrib['id']: + for e in reaction: + tag = sbml.get_sbml_tag(e) + if tag == "listOfProducts": + listOfProducts = e + break + return listOfProducts + +def trim(meta): + meta_trim = str.strip(meta) + meta_trim = meta_trim.rsplit('_', 1)[0] + meta_trim = meta_trim.split('_', 1)[1] + return meta_trim + + +if __name__ == '__main__': + # User data + species_name = argv[1] + sbml_file = argv[2] + species_scope_dir = argv[3] + species_seeds_dir = argv[4] + objective_file = argv[5] + modes_info = argv[6] + + if modes_info == "netseed": + run = mode = "netseed" + optim = "submin" + accu = True + else: + #'run','mode','optim', accu + modes_split = modes_info.split("/") + run = modes_split[0] + mode = modes_split[1] + optim = modes_split[2] + if modes_split[3] == "no_accu": + accu=False + else: + accu=True + + + modes_info=modes_info.replace("/", "_") + + tree = etree.parse(sbml_file) + net = tree.getroot() + model = sbml.get_model(net) + + species_set = sbml.get_used_metabolites(sbml_file, False) + + with open(objective_file) as fd: + objective = fd.readline() + + reactants=list() + biomass_reactants = sbml.get_listOfReactants_from_name(model,objective) + for reactant in biomass_reactants: + reactants.append(reactant.attrib.get('species')) + biomass_reactants_set=set(reactants) + + exch=list() + exch_trim=list() + SBML, sbml_first_line = sbml.get_root(sbml_file) + reactions_list = sbml.get_listOfReactions(model) + parameters = sbml.get_listOfParameters(model) + fbc = sbml.get_fbc(SBML) + + for r in reactions_list: + reactants = sbml.get_listOfReactants(r) + products = sbml.get_listOfProducts(r) + reaction_name = r.attrib.get("id") + + lower_bound = parameters[r.attrib.get('{'+fbc+'}lowerFluxBound')] \ + if type(r.attrib.get('{'+fbc+'}lowerFluxBound')) is not float \ + else '"'+r.attrib.get('{'+fbc+'}lowerFluxBound')+'"' + lbound = round(float(lower_bound),10) + upper_bound = parameters[r.attrib.get('{'+fbc+'}upperFluxBound')] \ + if type(r.attrib.get('{'+fbc+'}upperFluxBound')) is not float \ + else '"'+r.attrib.get('{'+fbc+'}upperFluxBound')+'"' + ubound = round(float(upper_bound),10) + + # uses the definition of boundaries as cobra + # a reaction is in boundaries (so exchange reaction) + # when a reaction has only one metabolite and + # does not have reactants or products + if not (reactants and products): + exchange=None + if reactants and len(reactants) == 1: + if lbound >= 0: + continue + else: + #print ("reactant ",reaction_name, lbound, ubound) + exchange=sbml.get_listOfReactants_from_name(model,reaction_name) + elif (products and len(products) == 1): + if ubound <= 0: + continue + else: + #print ("product ",reaction_name, lbound, ubound) + exchange=get_listOfProducts_from_name(model,reaction_name) + if exchange: + for metabolite in exchange: + meta = metabolite.attrib.get('species') + meta_trim = trim(meta) + exch.append(meta) + exch_trim.append(meta_trim) + exchange_set=set(exch) + exchange_trim_set=set(exch_trim) + + + + solutions_set=list() + + dtypes = {'species':'str', + 'run':'str', + 'mode':'str', + 'optim':'str', + 'accu':'str', + 'model':'str', + 'is_equal_union_species':'bool', + 'missing':'str', + 'is_biomass_included':'bool', + 'missing_biomass':'str', + 'percentage_missing_biomass':'float', + 'is_exchange_included':'bool', + 'missing_exchange':'str', + 'percentage_missing_exchange':'float', + 'is_seed_included_to_exchange':'bool', + 'missing_seed_into_exchange':'str', + 'percentage_missing_seed_into_exchange':'float', + 'is_exchange_included_to_seed':'bool', + 'missing_exchange_into_seed':'str', + 'percentage_missing_exchange_into_seeds':'float'} + + comparison_scope_df=pd.DataFrame(columns=['species','run', 'mode', 'optim', 'accu', 'model', + 'is_equal_union_species', 'missing', 'percentage_missing', + 'is_biomass_included', 'missing_biomass', 'percentage_missing_biomass', + 'is_exchange_included', 'missing_exchange', 'percentage_missing_exchange', + 'is_seed_included_to_exchange', 'missing_seed_into_exchange', 'percentage_missing_seed_into_exchange', + 'is_exchange_included_to_seed', 'missing_exchange_into_seed', 'percentage_missing_exchange_into_seeds']) + comparison_scope_df = comparison_scope_df.astype(dtypes) + + # Loop into all solutions of a specie + for filename in os.listdir(species_scope_dir): + scope_file = os.path.join(species_scope_dir, filename) + model_name=os.path.splitext(filename)[0] + + # Get Scope + f = open(scope_file) + scope = json.load(f) + + scope_set = set(scope['scope']) + + # Get Seeds + seeds=list() + seeds_trim=list() + seed_file=f"{species_seeds_dir}/{model_name}.sbml" + tree = etree.parse(seed_file) + net = tree.getroot() + model = sbml.get_model(net) + seeds_list = sbml.get_listOfSpecies(model) + for e in seeds_list: + seed = e.attrib['id'] + seed_trim = trim(seed) + seeds.append(seed) + seeds_trim.append(seed_trim) + seed_set = set(seeds) + seed_trim_set = set(seeds_trim) + + # compare scope with union of metabolite + diff_scope = None + is_equal_union_species=False + if(scope_set == species_set): + is_equal_union_species=True + else: + diff_scope = list(species_set.difference(scope_set)) + diff_scope_bigger = list(scope_set.difference(species_set)) + #print(diff_scope_bigger) + + # Compare scope with biomass reactant + diff_biomass = None + is_biomass_included = biomass_reactants_set.issubset(scope_set) + if not is_biomass_included: + diff_biomass=list(biomass_reactants_set.difference(scope_set)) + + # Compare scope with exchange metabolites + diff_exchange = None + is_exchange_included = exchange_set.issubset(scope_set) + if not is_exchange_included: + diff_exchange=list(exchange_set.difference(scope_set)) + + # Compare seed with exchange metabolites: seeds include into exchange + diff_seed_into_exchange = None + is_seed_included_to_exchange = seed_trim_set.issubset(exchange_trim_set) + if not is_seed_included_to_exchange: + diff_seed_into_exchange=list(seed_trim_set.difference(exchange_trim_set)) + + # Compare seed with exchange metabolites: exchange included into seeds + diff_exchange_into_seeds = None + is_exchange_included_to_seeds = exchange_trim_set.issubset(seed_trim_set) + if not is_exchange_included_to_seeds: + diff_exchange_into_seeds=list(exchange_trim_set.difference(seed_trim_set)) + + + if diff_scope: + percentage_missing = len(diff_scope)*100/len(species_set) + else: + percentage_missing = 0 + if diff_biomass: + percentage_missing_biomass = len(diff_biomass)*100/len(biomass_reactants_set) + else: + percentage_missing_biomass = 0 + if diff_exchange: + percentage_missing_exchange = len(diff_exchange)*100/len(exchange_set) + else: + percentage_missing_exchange = 0 + if diff_seed_into_exchange: + percentage_missing_seed_into_exchange = len(diff_seed_into_exchange)*100/len(seed_set) + else: + percentage_missing_seed_into_exchange = 0 + if diff_exchange_into_seeds: + percentage_missing_exchange_into_seeds = len(diff_exchange_into_seeds)*100/len(exchange_set) + else: + percentage_missing_exchange_into_seeds = 0 + + current_df = pd.DataFrame([[species_name, run, mode, optim, accu, model_name, + str(is_equal_union_species), diff_scope, percentage_missing, + str(is_biomass_included), diff_biomass, percentage_missing_biomass, + str(is_exchange_included), diff_exchange, percentage_missing_exchange, + str(is_seed_included_to_exchange), diff_seed_into_exchange, percentage_missing_exchange, + str(is_exchange_included_to_seeds), diff_exchange_into_seeds, percentage_missing_exchange_into_seeds]], + columns=['species','run', 'mode', 'optim', 'accu', 'model', + 'is_equal_union_species', 'missing', 'percentage_missing', + 'is_biomass_included', 'missing_biomass', 'percentage_missing_biomass', + 'is_exchange_included', 'missing_exchange', 'percentage_missing_exchange', + 'is_seed_included_to_exchange', 'missing_seed_into_exchange', 'percentage_missing_seed_into_exchange', + 'is_exchange_included_to_seed', 'missing_exchange_into_seed', 'percentage_missing_exchange_into_seeds']) + current_df = current_df.astype(dtypes) + + comparison_scope_df = pd.concat([comparison_scope_df, current_df], ignore_index=True) + + + + # saving as tsv file + species_out_dir = os.path.dirname(species_scope_dir) + comparison_file=os.path.join(species_out_dir, f"{species_name}_{modes_info}_compare.tsv") + comparison_scope_df.to_csv(comparison_file, sep="\t") + \ No newline at end of file diff --git a/scripts/10_3_iCN718_metabolite_analyses.py b/scripts/10_3_iCN718_metabolite_analyses.py new file mode 100644 index 0000000..15376b3 --- /dev/null +++ b/scripts/10_3_iCN718_metabolite_analyses.py @@ -0,0 +1,49 @@ +import os +from sys import argv +import pandas as pd +from seed2lp.file import load_json +from seed2lp import sbml + + +solve_dict={"reas": "", + "fil": " FILTER", + "gc": " GUESS-CHECK", + "gcd": " GUESS-CHECK-DIVERSITY"} + +if __name__ == '__main__': + # User data + results_dir = argv[1] + sbml_file_path = argv[2] + out_dir = argv[3] + + species_set = sbml.get_used_metabolites(sbml_file_path, False) + + + df_count = pd.DataFrame(0,columns=["nb_reasoning", "nb_filter", "nb_gc", "nb_gcd"], index = list(species_set)) + + for filename in os.listdir(results_dir): + if "_results.json" in filename: + #Before, when tested for paper, filenames had _log_ and not _reas_ + if "_reas_" in filename or "_log_" in filename: + solve="reas" + col="nb_reasoning" + elif "_fil_" in filename: + solve="fil" + col="nb_filter" + elif "_gc_" in filename: + solve="gc" + col="nb_gc" + elif "_gcd_" in filename: + solve="gcd" + col="nb_gcd" + result_path=os.path.join(results_dir, filename) + data = load_json(result_path) + solutions = data["RESULTS"]["REASONING"][f"SUBSET MINIMAL ENUMERATION{solve_dict[solve]}"]["solutions"] + + for sol in solutions: + list_seed=solutions[sol][3] + for seed in list_seed: + df_count.loc[seed,col] +=1 + + df_count.to_csv(os.path.join(out_dir,"metabolites_occurences.tsv"), sep="\t") + diff --git a/scripts/10_4_get_supp_data.py b/scripts/10_4_get_supp_data.py new file mode 100644 index 0000000..95db819 --- /dev/null +++ b/scripts/10_4_get_supp_data.py @@ -0,0 +1,183 @@ +import sys +import json +import pathlib +import pandas as pd +from seed2lp import sbml + +def compute_union_intersection(data, mode, solve_mode, time_solve): + list_solutions=list() + if 'solutions' in data['RESULTS'][mode][solve_mode]: + for solution in data['RESULTS'][mode][solve_mode]['solutions']: + list_solutions.append(set(data['RESULTS'][mode][solve_mode]['solutions'][solution][3])) + + if list_solutions: + intersection = int(len(set.intersection(*list_solutions))) + union = int(len(set.union(*list_solutions))) + else: + if time_solve == "Time out" or time_solve =="time out": + intersection = union = "Time out" + else: + intersection = union = "unsat" + return intersection, union + +def get_union_intersection(data, mode, solve_mode, time_solve, is_reasoning): + if is_reasoning: + if time_solve == "unsat": + intersection = union = "unsat" + else: + intersection, union = compute_union_intersection(data, mode, solve_mode, time_solve) + else: + intersection, union = compute_union_intersection(data, mode, solve_mode, time_solve) + return intersection, union + + + +def get_datas(directory, sbml_path, modes_list, results, get_suffix:bool=True): + for filepath in directory.rglob('*_results.json'): + organism=filepath.parent.name + # get total number of metabolite + sbml_file = f'{sbml_path}/{organism}.xml' + species_list=list() + reaction_list=list() + for specie in sbml.read_SBML_species(sbml_file)['Metabolites']: + species_list.append(specie) + for reaction in sbml.read_SBML_species(sbml_file)['Reactions']: + reaction_list.append(reaction) + total_number_metabolite = len(species_list) + total_number_reaction = len(reaction_list) + + # Opening JSON file + result = open(filepath) + # returns JSON object as + # a dictionary + data = json.load(result) + + # SUBMIN data + submin_solve = "unsat" + submin_rejected = None + submin_memory = None + submin_intersection = "unsat" + submin_union = "unsat" + + # MINIMIZE OPTIMUM data + minimize_opti_solve = "unsat" + minimize_opti_memory = None + minimize_opti_rejected = None + + # MINIMIZE data + minimize_solve = "unsat" + minimize_rejected = None + min_intersection = "unsat" + min_union = "unsat" + minimize_memory = None + + is_reasoning = False + + search_mode = data['NETWORK']['SEARCH_MODE'] + if data['OPTIONS']['ACCUMULATION'] == "Allowed": + accumulation = True + else: + accumulation = False + + has_rejected=False + for mode in modes_list: + if mode in data['RESULTS']: + if get_suffix: + if 'GUESS-CHECK-DIVERSITY' in str(data['RESULTS'][mode]): + suffix=' GUESS-CHECK-DIVERSITY' + has_rejected = True + elif 'GUESS-CHECK' in str(data['RESULTS'][mode]): + suffix=' GUESS-CHECK' + has_rejected = True + elif 'FILTER' in str(data['RESULTS'][mode]): + suffix=' FILTER' + has_rejected = True + else: + suffix='' + is_reasoning=True + else: + suffix='' + is_reasoning=True + + if 'Timer' in data['RESULTS'][mode][f'SUBSET MINIMAL ENUMERATION{suffix}']: + submin_solve = data['RESULTS'][mode][f'SUBSET MINIMAL ENUMERATION{suffix}']['Timer']['Solving time'] + submin_intersection, submin_union = get_union_intersection(data, mode, f'SUBSET MINIMAL ENUMERATION{suffix}', submin_solve, is_reasoning) + + if 'rejected' in data['RESULTS'][mode][f'SUBSET MINIMAL ENUMERATION{suffix}']: + submin_rejected = data['RESULTS'][mode][f'SUBSET MINIMAL ENUMERATION{suffix}']['rejected'] + + if "MINIMIZE OPTIMUM" in data['RESULTS'][mode]: + if 'Timer' in data['RESULTS'][mode][f'MINIMIZE OPTIMUM{suffix}']: + minimize_opti_solve = data['RESULTS'][mode][f'MINIMIZE OPTIMUM{suffix}']['Timer']['Solving time'] + if has_rejected and 'rejected' in data['RESULTS'][mode][f'MINIMIZE OPTIMUM{suffix}']: + minimize_opti_rejected = data['RESULTS'][mode][f'MINIMIZE OPTIMUM{suffix}']['rejected'] + + if mode == 'HYBRID': + submin_memory = data['RESULTS'][mode][f'SUBSET MINIMAL ENUMERATION']["Memory (GB)"] + minimize_opti_memory = data['RESULTS'][mode][f'MINIMIZE OPTIMUM']["Memory (GB)"] + + + if('MINIMIZE ENUMERATION' in data['RESULTS'][mode]): + + if 'timer' in data['RESULTS'][mode][f'MINIMIZE ENUMERATION{suffix}']: + minimize_solve = data['RESULTS'][mode][f'MINIMIZE ENUMERATION{suffix}']['Timer']['Solving time'] + + min_intersection, min_union = get_union_intersection(data, mode, f'MINIMIZE ENUMERATION{suffix}', minimize_solve, is_reasoning) + + if has_rejected and 'rejected' in data['RESULTS'][mode][f'MINIMIZE ENUMERATION{suffix}']: + minimize_rejected = data['RESULTS'][mode][f'MINIMIZE ENUMERATION{suffix}']['rejected'] + if mode == 'HYBRID': + minimize_memory = data['RESULTS'][mode][f'MINIMIZE ENUMERATION{suffix}']["Memory (GB)"] + + list_data = [[organism, search_mode, f'{mode}{suffix}', accumulation, 'Solving (sec)', + submin_solve, minimize_opti_solve, minimize_solve, None, None], + [organism, search_mode, f'{mode}{suffix}', accumulation, 'Intersection', + submin_intersection, None, min_intersection, total_number_metabolite, total_number_reaction], + [organism, search_mode, f'{mode}{suffix}', accumulation, 'Union', + submin_union, None, min_union, total_number_metabolite, total_number_reaction]] + + if mode == 'HYBRID' or mode == 'FBA': + list_data.append([organism, search_mode, f'{mode}{suffix}', accumulation, 'Memory (GB)', + submin_memory, minimize_opti_memory, minimize_memory, None, None]) + + if has_rejected: + list_data.append([organism, search_mode, f'{mode}{suffix}', accumulation, 'Rejected', + submin_rejected, minimize_opti_rejected, minimize_rejected, None, None]) + + current_df = pd.DataFrame(list_data, + columns=['network', 'search_mode', 'mode', 'accumulation', 'type_data', + 'submin', 'minimize_opti', 'minimize', 'number_metabolites', 'number_reactions']) + current_df['accumulation'] = current_df['accumulation'].astype('bool') + + results=pd.concat([results, current_df], ignore_index=True) + result.close() + return results + + + +if __name__ == '__main__': + solutions_dir_path = sys.argv[1] + sbml_path = sys.argv[2] + result_dir_path = sys.argv[3] + prefix = sys.argv[4] + + results = pd.DataFrame(columns=['network', 'search_mode', 'mode', 'accumulation', 'type_data', + 'submin', 'minimize_opti', 'minimize', 'number_metabolites', 'number_reactions']) + + results['accumulation'] = results['accumulation'].astype('bool') + # search_mode: Full network / Target + # mode : REASONING / HYBRID /FBA + # type_data: Grounding (sec) / Solving (sec) + + results_dir = pathlib.Path(solutions_dir_path) + + modes_list = ['REASONING'] + results = get_datas(results_dir, sbml_path, modes_list, results, True) + + + results=results.set_index(['network', 'search_mode', 'mode']) + + results.to_csv(f'{result_dir_path}/{prefix}_supp_data.tsv', index=True, sep ='\t') + + + \ No newline at end of file diff --git a/scripts/10_5_get_exchange.py b/scripts/10_5_get_exchange.py new file mode 100644 index 0000000..cefefa0 --- /dev/null +++ b/scripts/10_5_get_exchange.py @@ -0,0 +1,90 @@ +import os +from sys import argv +from seed2lp import sbml +import xml.etree.ElementTree as etree + +def get_listOfProducts_from_name(model, reaction_name) -> list: + """return list of products of a reaction""" + reactions_list = sbml.get_listOfReactions(model) + for reaction in reactions_list: + if reaction_name == reaction.attrib['id']: + for e in reaction: + tag = sbml.get_sbml_tag(e) + if tag == "listOfProducts": + listOfProducts = e + break + return listOfProducts + +def trim(meta): + meta_trim = str.strip(meta) + meta_trim = meta_trim.rsplit('_', 1)[0] + meta_trim = meta_trim.split('_', 1)[1] + return meta_trim + +if __name__ == '__main__': + # User data + species_name = argv[1] + sbml_file = argv[2] + output=argv[3] + + tree = etree.parse(sbml_file) + net = tree.getroot() + model = sbml.get_model(net) + + exch=list() + exch_trim=list() + SBML, sbml_first_line = sbml.get_root(sbml_file) + reactions_list = sbml.get_listOfReactions(model) + parameters = sbml.get_listOfParameters(model) + fbc = sbml.get_fbc(SBML) + + for r in reactions_list: + reactants = sbml.get_listOfReactants(r) + products = sbml.get_listOfProducts(r) + reaction_name = r.attrib.get("id") + + lower_bound = parameters[r.attrib.get('{'+fbc+'}lowerFluxBound')] \ + if type(r.attrib.get('{'+fbc+'}lowerFluxBound')) is not float \ + else '"'+r.attrib.get('{'+fbc+'}lowerFluxBound')+'"' + lbound = round(float(lower_bound),10) + upper_bound = parameters[r.attrib.get('{'+fbc+'}upperFluxBound')] \ + if type(r.attrib.get('{'+fbc+'}upperFluxBound')) is not float \ + else '"'+r.attrib.get('{'+fbc+'}upperFluxBound')+'"' + ubound = round(float(upper_bound),10) + + # uses the definition of boundaries as cobra + # a reaction is in boundaries (so exchange reaction) + # when a reaction has only one metabolite and + # does not have reactants or products + if not (reactants and products): + exchange=None + if reactants and len(reactants) == 1: + if lbound >= 0: + continue + else: + #print ("reactant ",reaction_name, lbound, ubound) + exchange=sbml.get_listOfReactants_from_name(model,reaction_name) + elif (products and len(products) == 1): + if ubound <= 0: + continue + else: + #print ("product ",reaction_name, lbound, ubound) + exchange=get_listOfProducts_from_name(model,reaction_name) + if exchange: + for metabolite in exchange: + meta=metabolite.attrib.get('species') + meta_trim = trim(meta) + exch_trim.append(meta_trim) + exch.append(meta) + + + + # saving as tsv file + exch_trim_file=os.path.join(output, f"{species_name}_exchanges.txt") + exch_file=os.path.join(output, f"{species_name}_exchanges_pre_suffix.txt") + + with open(exch_file, "w") as outfile: + outfile.write("\n".join(str(item) for item in exch)) + with open(exch_trim_file, "w") as trimfile: + trimfile.write("\n".join(str(item) for item in exch_trim)) + \ No newline at end of file diff --git a/scripts/slurm_cluster/01_job_retrieve_bigg_sbml.sh b/scripts/slurm_cluster/01_job_retrieve_bigg_sbml.sh new file mode 100755 index 0000000..26be632 --- /dev/null +++ b/scripts/slurm_cluster/01_job_retrieve_bigg_sbml.sh @@ -0,0 +1,20 @@ +#!/bin/bash +#SBATCH --job-name=job_get_objective # Job name +#SBATCH --output=../output/objective/objective-%A_%a.out +#SBATCH -e ../error/objective/objective-%A_%a.err +#SBATCH --cpus-per-task=1 # Request that ncpus be allocated per process. +#SBATCH --mem-per-cpu=10gb +#SBATCH --time=5:00:00 # Time limit hrs:min:sec +#SBATCH --mail-user=chabname.ghassemi-nedjad@inria.fr #Receive email on this adress when the job is begin,over,or fail +#SBATCH --mail-type=END,FAIL #Define what we want to receive by email about the job statut +#SBATCH --exclude=arm01 + +source /home/cghassem/miniconda3/etc/profile.d/conda.sh +conda activate s2lp + +RESULT_DIR="../../data/" + +# Cluster +python ../01_retrieve_bigg_sbml.py $RESULT_DIR + +rm "${RESULT_DIR}/bigg/sbml/iAT_PLT_636.xml" \ No newline at end of file diff --git a/scripts/slurm_cluster/02_01_job_get_objective.sh b/scripts/slurm_cluster/02_01_job_get_objective.sh new file mode 100755 index 0000000..0edd7a6 --- /dev/null +++ b/scripts/slurm_cluster/02_01_job_get_objective.sh @@ -0,0 +1,20 @@ +#!/bin/bash +#SBATCH --job-name=job_get_objective # Job name +#SBATCH --output=../output/objective/objective-%A_%a.out +#SBATCH -e ../error/objective/objective-%A_%a.err +#SBATCH --cpus-per-task=1 # Request that ncpus be allocated per process. +#SBATCH --mem-per-cpu=10gb +#SBATCH --time=5:00:00 # Time limit hrs:min:sec +#SBATCH --mail-user=chabname.ghassemi-nedjad@inria.fr #Receive email on this adress when the job is begin,over,or fail +#SBATCH --mail-type=END,FAIL #Define what we want to receive by email about the job statut +#SBATCH --exclude=arm01 + +source /home/cghassem/miniconda3/etc/profile.d/conda.sh +conda activate s2lp + +DATA_DIR="../../data" +SBML_DIR="${DATA_DIR}/bigg/sbml" +OBJECTIVE_DIR="${DATA_DIR}/objective" + +# Cluster +python ../02_get_objective.py $SBML_DIR $OBJECTIVE_DIR \ No newline at end of file diff --git a/scripts/slurm_cluster/02_02_get_targets.sh b/scripts/slurm_cluster/02_02_get_targets.sh new file mode 100644 index 0000000..7d4ae74 --- /dev/null +++ b/scripts/slurm_cluster/02_02_get_targets.sh @@ -0,0 +1,26 @@ +#!/bin/bash + +# Get arguments +while getopts s:o:t: flag +do + case "${flag}" in + s) SBML_DIR=${OPTARG};; + o) OBJECTIVE_DIR=${OPTARG};; + t) TARGET_DIR=${OPTARG};; + *) echo "Invalid OPTION" && exit 1;; + esac +done + +#SLURM_ARRAY_TASK_ID=1 +CURRENT_FILE=$(ls "$SBML_DIR" | head -n "$SLURM_ARRAY_TASK_ID" | tail -n 1) + +CURRENT_SPECIES=$(basename "$CURRENT_FILE" | sed 's/\.[^.]*$//') +OBJECTIVE=$(head -n 1 $OBJECTIVES_DIR/${SPECIES}_target.txt) + +# Create directories if needed +if [[ ! -d "$TARGET_DIR" ]] +then + mkdir -p "$TARGET_DIR" +fi + +seed2lp objective_targets $SBML_DIR/$CURRENT_FILE $TARGET_DIR -o $OBJECTIVE diff --git a/scripts/slurm_cluster/02_02_job_get_targets.sh b/scripts/slurm_cluster/02_02_job_get_targets.sh new file mode 100755 index 0000000..43e9e1e --- /dev/null +++ b/scripts/slurm_cluster/02_02_job_get_targets.sh @@ -0,0 +1,22 @@ +#!/bin/bash +#SBATCH --job-name=job_get_objective # Job name +#SBATCH --output=../output/objective/objective-%A_%a.out +#SBATCH -e ../error/objective/objective-%A_%a.err +#SBATCH --cpus-per-task=1 # Request that ncpus be allocated per process. +#SBATCH --mem-per-cpu=10gb +#SBATCH --time=5:00:00 # Time limit hrs:min:sec +#SBATCH --mail-user=chabname.ghassemi-nedjad@inria.fr #Receive email on this adress when the job is begin,over,or fail +#SBATCH --mail-type=END,FAIL #Define what we want to receive by email about the job statut +#SBATCH --exclude=arm01 +#SBATCH --array=1-107%55 #107 networks + +source /home/cghassem/miniconda3/etc/profile.d/conda.sh +conda activate s2lp + +DATA_DIR="../../data" +SBML_DIR="${DATA_DIR}/bigg/sbml" +OBJECTIVE_DIR="${DATA_DIR}/objective" +TARGET_DIR="${DATA_DIR}/target" + +# Cluster +sbattch ../02_02_get_targets.sh -s $SBML_DIR -o $OBJECTIVE_DIR -t $TARGET_DIR diff --git a/scripts/slurm_cluster/03_01_execute_workflow_search.sh b/scripts/slurm_cluster/03_01_execute_workflow_search.sh new file mode 100755 index 0000000..7a24865 --- /dev/null +++ b/scripts/slurm_cluster/03_01_execute_workflow_search.sh @@ -0,0 +1,51 @@ +#!/bin/bash +#SBATCH --job-name=job_s2lp # Job name +#SBATCH --output=../output/workflow/s2lp-%A_%a.out +#SBATCH -e ../error/workflow/s2lp-%A_%a.err +#SBATCH --mem-per-cpu=10gb +#SBATCH --ntasks-per-node=1 #Number of tasks per node +#SBATCH --time=72:00:00 # Time limit hrs:min:sec +#SBATCH --mail-user=chabname.ghassemi-nedjad@inria.fr #Receive email on this adress when the job is begin,over,or fail +#SBATCH --mail-type=END,FAIL #Define what we want to receive by email about the job statut +#SBATCH --exclude=arm01 + +source /home/cghassem/miniconda3/etc/profile.d/conda.sh +conda activate s2lp + +# Cluster +FILE="03_01_job_run_s2lp" +###### EXECUTE FULL NETWORK ####### +### ALL ### +# Accumulation Forbidden +#sbatch $FILE -c full -m m +# Accumulation Allowed +#sbatch $FILE -c full -m m -a a + +### SPECIFIC ### +sbatch $FILE -c full -m m -s filter +sbatch $FILE -c full -m m -s guess_check +sbatch $FILE -c full -m m -s guess_check_div + + +sbatch $FILE -c full -m m -s filter -a a +sbatch $FILE -c full -m m -s guess_check -a a +sbatch $FILE -c full -m m -s guess_check_div -a a +################################### + +########## EXECUTE TARGET ########## +### ALL ### +# Accumulation Forbidden +#sbatch $FILE -c target -m m +# Accumulation Allowed +#sbatch $FILE -c target -m m -a a + +### SPECIFIC ### +sbatch $FILE -c target -m m -s filter +sbatch $FILE -c target -m m -s guess_check +sbatch $FILE -c target -m m -s guess_check_div + + +sbatch $FILE -c target -m m -s filter -a a +sbatch $FILE -c target -m m -s guess_check -a a +sbatch $FILE -c target -m m -s guess_check_div -a a +################################### diff --git a/scripts/slurm_cluster/03_01_job_run_s2lp.sh b/scripts/slurm_cluster/03_01_job_run_s2lp.sh new file mode 100755 index 0000000..db47fcc --- /dev/null +++ b/scripts/slurm_cluster/03_01_job_run_s2lp.sh @@ -0,0 +1,62 @@ +#!/bin/bash +#SBATCH --job-name=job_s2lp # Job name +#SBATCH --output=../output/s2lp/s2lp-%A_%a.out +#SBATCH -e ../error/s2lp/s2lp-%A_%a.err +#SBATCH --cpus-per-task=1 #Request that ncpus be allocated per process. + +#SBATCH -C zonda +#SBATCH --ntasks-per-node=1 #Number of tasks per node +#SBATCH --time=72:00:00 # Time limit hrs:min:sec +#SBATCH --mail-user=chabname.ghassemi-nedjad@inria.fr #Receive email on this adress when the job is begin,over,or fail +#SBATCH --mail-type=END,FAIL #Define what we want to receive by email about the job statut +#SBATCH --exclude=arm01 +#SBATCH --array=1-107%10 #108 networks + + +source /home/cghassem/miniconda3/etc/profile.d/conda.sh +conda activate s2lp + + +DATA_DIR="../../data" +S2LP_RESULT_DIR="../../results/s2lp" +SBML_DIR="${DATA_DIR}/bigg/sbml" +OBJECTIVE_DIR="${DATA_DIR}/objective" +TEMP_DIR="../../tmp/" + +while getopts c:m:a:s: flag +do + case "${flag}" in + c) COMMAND=${OPTARG};; + m) + if [[ ! -z ${OPTARG} ]]; then + MAXIMIZATION=${OPTARG} + fi + ;; + a) + if [[ ! -z ${OPTARG} ]]; then + ACCUMULATION=${OPTARG} + fi + ;; + s) + if [[ ! -z ${OPTARG} ]]; then + SOLVE=${OPTARG} + fi + ;; + esac +done + +OPTION="" +if [[ ! -z ${MAXIMIZATION} ]]; then + OPTION="-m m" +fi +if [[ ! -z ${ACCUMULATION} ]]; then + OPTION="${OPTION} -a a" +fi +if [[ ! -z ${SOLVE} ]]; then +OPTION="${OPTION} -s ${SOLVE}" +fi + +########### ALL SBML source in RESULTS dir ########### + +./03_run_s2lp.sh -i $SBML_DIR -r $S2LP_RESULT_DIR \ + -c $COMMAND -t $TEMP_DIR -o $OBJECTIVE_DIR $OPTION \ No newline at end of file diff --git a/scripts/slurm_cluster/03_02_execute_workflow_search_exclusive.sh b/scripts/slurm_cluster/03_02_execute_workflow_search_exclusive.sh new file mode 100755 index 0000000..aabb4a0 --- /dev/null +++ b/scripts/slurm_cluster/03_02_execute_workflow_search_exclusive.sh @@ -0,0 +1,36 @@ +#!/bin/bash +#SBATCH --job-name=job_s2lp # Job name +#SBATCH --output=../output/workflow/s2lp-%A_%a.out +#SBATCH -e ../error/workflow/s2lp-%A_%a.err +#SBATCH --mem-per-cpu=10gb +#SBATCH --ntasks-per-node=1 #Number of tasks per node +#SBATCH --time=72:00:00 # Time limit hrs:min:sec +#SBATCH --mail-user=chabname.ghassemi-nedjad@inria.fr #Receive email on this adress when the job is begin,over,or fail +#SBATCH --mail-type=END,FAIL #Define what we want to receive by email about the job statut +#SBATCH --exclude=arm01 + +source /home/cghassem/miniconda3/etc/profile.d/conda.sh +conda activate s2lp + +# Cluster +FILE="03_02_job_run_s2lp_exclusive" +###### EXECUTE FULL NETWORK ####### + +### SPECIFIC ### +sbatch $FILE -c full -m m -s hybrid + +sbatch $FILE -c full -m m -s hybrid -a a +################################### + +########## EXECUTE TARGET ########## + + +### SPECIFIC ### +sbatch $FILE -c target -m m -s hybrid + + +sbatch $FILE -c target -m m -s hybrid -a a +################################### + +########## EXECUTE FBA ########## + sbatch $FILE -c fba -m m diff --git a/scripts/slurm_cluster/03_02_job_run_s2lp_exclusive.sh b/scripts/slurm_cluster/03_02_job_run_s2lp_exclusive.sh new file mode 100755 index 0000000..f44a26d --- /dev/null +++ b/scripts/slurm_cluster/03_02_job_run_s2lp_exclusive.sh @@ -0,0 +1,63 @@ +#!/bin/bash +#SBATCH --job-name=job_s2lp # Job name +#SBATCH --output=../output/s2lp/s2lp-%A_%a.out +#SBATCH -e ../error/s2lp/s2lp-%A_%a.err +#SBATCH --cpus-per-task=1 #Request that ncpus be allocated per process. + +#SBATCH -C zonda +#SBATCH --ntasks-per-node=1 #Number of tasks per node +#SBATCH --time=72:00:00 # Time limit hrs:min:sec +#SBATCH --mail-user=chabname.ghassemi-nedjad@inria.fr #Receive email on this adress when the job is begin,over,or fail +#SBATCH --mail-type=END,FAIL #Define what we want to receive by email about the job statut +#SBATCH --exclude=arm01 +#SBATCH --array=1-107%1 #108 networks +#SBAYCH --exclusive + + +source /home/cghassem/miniconda3/etc/profile.d/conda.sh +conda activate s2lp + + +DATA_DIR="../../data" +S2LP_RESULT_DIR="../../results/s2lp" +SBML_DIR="${DATA_DIR}/bigg/sbml" +OBJECTIVE_DIR="${DATA_DIR}/objective" +TEMP_DIR="../../tmp/" + +while getopts c:m:a:s: flag +do + case "${flag}" in + c) COMMAND=${OPTARG};; + m) + if [[ ! -z ${OPTARG} ]]; then + MAXIMIZATION=${OPTARG} + fi + ;; + a) + if [[ ! -z ${OPTARG} ]]; then + ACCUMULATION=${OPTARG} + fi + ;; + s) + if [[ ! -z ${OPTARG} ]]; then + SOLVE=${OPTARG} + fi + ;; + esac +done + +OPTION="" +if [[ ! -z ${MAXIMIZATION} ]]; then + OPTION="-m m" +fi +if [[ ! -z ${ACCUMULATION} ]]; then + OPTION="${OPTION} -a a" +fi +if [[ ! -z ${SOLVE} ]]; then +OPTION="${OPTION} -s ${SOLVE}" +fi + +########### ALL SBML source in RESULTS dir ########### + +./03_run_s2lp.sh -i $SBML_DIR -r $S2LP_RESULT_DIR \ + -c $COMMAND -t $TEMP_DIR -o $OBJECTIVE_DIR $OPTION \ No newline at end of file diff --git a/scripts/slurm_cluster/03_run_s2lp.sh b/scripts/slurm_cluster/03_run_s2lp.sh new file mode 100755 index 0000000..bb2fc2f --- /dev/null +++ b/scripts/slurm_cluster/03_run_s2lp.sh @@ -0,0 +1,85 @@ +#!/bin/bash + +# Get arguments +while getopts i:r:c:t:o:m:a:s: flag +do + case "${flag}" in + i) IN_DIRECTORY=${OPTARG};; + r) RESULT_DIR=${OPTARG};; + c) COMMAND=${OPTARG};; + t) + if [[ ! -z ${OPTARG} ]]; then + TEMP=${OPTARG} + fi + ;; + o) OBJECTIVE_DIR=${OPTARG};; + m) + if [[ ! -z ${OPTARG} ]]; then + MAXIMIZATION=${OPTARG} + fi + ;; + n) + if [[ ! -z ${OPTARG} ]]; then + NUMBER_SOLUTION=${OPTARG} + else + NUMBER_SOLUTION=1000 + fi + ;; + a) + if [[ ! -z ${OPTARG} ]]; then + ACCUMULATION=${OPTARG} + fi + ;; + s) + if [[ ! -z ${OPTARG} ]]; then + SOLVE=${OPTARG} + fi + ;; + *) echo "Invalid OPTION" && exit 1;; + esac +done + +#SLURM_ARRAY_TASK_ID=1 +CURRENT_FILE=$(ls "$IN_DIRECTORY" | head -n "$SLURM_ARRAY_TASK_ID" | tail -n 1) + +CURRENT_SPECIES=$(basename "$CURRENT_FILE" | sed 's/\.[^.]*$//') +OBJECTIVE_PATH="$OBJECTIVE_DIR/${CURRENT_SPECIES}_target.txt" + +FULL_PATH=$RESULT_DIR +# Create directories if needed +if [[ ! -d "$FULL_PATH" ]] +then + mkdir -p "$FULL_PATH" +fi + +FULL_PATH=$FULL_PATH/$CURRENT_SPECIES +if [[ ! -d "$FULL_PATH" ]] +then + mkdir -p "$FULL_PATH" +fi + +OPTION="" +if [[ "$COMMAND" = "target" ]] +then + OPTION="-tf $OBJECTIVE_PATH" +else + read -r OBJECTIVE < $OBJECTIVE_PATH + OPTION=" -o $OBJECTIVE" +fi + +if [[ ! -z ${MAXIMIZATION} ]]; then + OPTION="${OPTION} -max" +fi + +if [[ ! -z ${ACCUMULATION} ]]; then + OPTION="${OPTION} -accu" +fi + +if [[ ! -z ${SOLVE} ]]; then + OPTION="${OPTION} -so ${SOLVE}" +fi + +seed2lp $COMMAND "$IN_DIRECTORY/$CURRENT_FILE" \ + "$FULL_PATH" \ + --temp $TEMP \ + -tl 45 -nbs $NUMBER_SOLUTION -cf $OPTION \ \ No newline at end of file diff --git a/scripts/slurm_cluster/04_job_run_sbml_normalisation.sh b/scripts/slurm_cluster/04_job_run_sbml_normalisation.sh new file mode 100755 index 0000000..948555d --- /dev/null +++ b/scripts/slurm_cluster/04_job_run_sbml_normalisation.sh @@ -0,0 +1,21 @@ +#!/bin/bash +#SBATCH --job-name=job_sbml_rewrite # Job name +#SBATCH --output=../output/sbml_rewrite/sbml_rewrite-%A_%a.out +#SBATCH -e ../error/sbml_rewrite/sbml_rewrite-%A_%a.err +#SBATCH --cpus-per-task=1 #Request that ncpus be allocated per process. +#SBATCH --ntasks-per-node=1 #Number of tasks per node +#SBATCH --time=24:00:00 # Time limit hrs:min:sec +#SBATCH --mail-user=chabname.ghassemi-nedjad@inria.fr #Receive email on this adress when the job is begin,over,or fail +#SBATCH --mail-type=END,FAIL #Define what we want to receive by email about the job statut +#SBATCH --exclude=arm01 +#SBATCH --array=1-107%55 #108 networks + + +source /home/cghassem/miniconda3/etc/profile.d/conda.sh +conda activate s2lp + +DATA_DIR="../../data" +SBML_DIR="${DATA_DIR}/bigg/sbml" +NORM_SBML_DIR="${DATA_DIR}/sbml_corrected" + +./04_run_sbml_normalisation.sh -i $SBML_DIR -r $NORM_SBML_DIR diff --git a/scripts/slurm_cluster/04_run_sbml_normalisation.sh b/scripts/slurm_cluster/04_run_sbml_normalisation.sh new file mode 100755 index 0000000..2d6cfc3 --- /dev/null +++ b/scripts/slurm_cluster/04_run_sbml_normalisation.sh @@ -0,0 +1,27 @@ +#!/bin/bash + +# Get arguments +while getopts i:r: flag +do + case "${flag}" in + i) IN_DIRECTORY=${OPTARG};; + r) RESULT_DIR=${OPTARG};; + *) echo "Invalid OPTION" && exit 1;; + esac +done + +#SLURM_ARRAY_TASK_ID=1 +CURRENT_FILE=$(ls "$IN_DIRECTORY" | head -n "$SLURM_ARRAY_TASK_ID" | tail -n 1) + + +CURRENT_SPECIES=$(basename "$CURRENT_FILE" | sed 's/\.[^.]*$//') + + +# Create directories if needed +if [[ ! -d "$RESULT_DIR" ]] +then + mkdir -p "$RESULT_DIR" +fi + + +seed2lp network "$IN_DIRECTORY/$CURRENT_FILE" "$RESULT_DIR" -wf diff --git a/scripts/slurm_cluster/05_1_job_run_n2pcomp_netseed.sh b/scripts/slurm_cluster/05_1_job_run_n2pcomp_netseed.sh new file mode 100755 index 0000000..f3bd42a --- /dev/null +++ b/scripts/slurm_cluster/05_1_job_run_n2pcomp_netseed.sh @@ -0,0 +1,25 @@ +#!/bin/bash +#SBATCH --job-name=job_netseed # Job name +#SBATCH --output=../output/netseed/netseed-%A_%a.out +#SBATCH -e ../error/netseed/netseed-%A_%a.err +#SBATCH -c 1 #Number of cores +#SBATCH --nodes=1 #Number of nodes +#SBATCH --cpus-per-task=1 #Request that ncpus be allocated per process. +#SBATCH --mem-per-cpu=10gb +#SBATCH --ntasks-per-node=1 #Number of tasks per node +#SBATCH --time=1:00:00 # Time limit hrs:min:sec +#SBATCH --mail-user=chabname.ghassemi-nedjad@inria.fr #Receive email on this adress when the job is begin,over,or fail +#SBATCH --mail-type=END,FAIL #Define what we want to receive by email about the job statut +#SBATCH --exclude=arm01 + + +source /home/cghassem/miniconda3/etc/profile.d/conda.sh +conda activate s2lp + +DATA_DIR="../../data" +NORM_SBML_DIR="${DATA_DIR}/sbml_corrected" +N2PCOMP_DIR="../../N2PComp" +NETSEED_RESULT_DIR="../../results/netseed" +TOOL="NETSEED" + +./05_1_run_n2pcom.sh -i $NORM_SBML_DIR -n $N2PCOMP_DIR -r $NETSEED_RESULT_DIR -t $TOOL diff --git a/scripts/slurm_cluster/05_1_run_n2pcom.sh b/scripts/slurm_cluster/05_1_run_n2pcom.sh new file mode 100755 index 0000000..880febc --- /dev/null +++ b/scripts/slurm_cluster/05_1_run_n2pcom.sh @@ -0,0 +1,83 @@ +#!/bin/bash + +# Get arguments +while getopts d:o:i:n:r:t: flag +do + case "${flag}" in + d) + if [[ ! -z ${OPTARG} ]]; then + TARGET_DIR="${OPTARG}/target" + fi + ;; + o) + if [[ ! -z ${OPTARG} ]]; then + read -r OBJECTIVE < ${OPTARG} + fi + ;; + i) SBML_DIR=${OPTARG};; + n) N2PCOMP_DIR=${OPTARG};; + r) RESULT_DIR=${OPTARG};; + t) TOOL=${OPTARG};; + esac +done + + + +if [[ "$TOOL" = "NETSEED" ]] +then + CONF_PATH="${N2PCOMP_DIR}/config_netseed.yaml" +else + CONF_PATH="${N2PCOMP_DIR}/config_precursor.yaml" + # Create directories if needed + if [[ ! -d "$TARGET_DIR" ]] + then + mkdir -p "$TARGET_DIR" + fi +fi + +# Create directories if needed +if [[ ! -d "$RESULT_DIR" ]] +then + mkdir -p "$RESULT_DIR" +fi + +for file in $(find "$SBML_DIR"/ -maxdepth 1 -mindepth 1 -type f -print); +do + CURRENT_SPECIES=$(basename $file | sed 's/\.[^.]*$//') + + if [[ ! -d "$RESULT_DIR/$CURRENT_SPECIES" ]] + then + mkdir -p "$RESULT_DIR/$CURRENT_SPECIES" + fi + + if [[ "$TOOL" = "NETSEED" ]] + then + python -m n2pcomp run "$file" \ + --output "${RESULT_DIR}/$CURRENT_SPECIES/" \ + -c $CONF_PATH -nbs 1000 -tl 45 + else + if [[ ! -d "$RESULT_DIR/$CURRENT_SPECIES/full_network" ]] + then + mkdir -p "$RESULT_DIR/$CURRENT_SPECIES/full_network" + fi + + if [[ ! -d "$RESULT_DIR/$CURRENT_SPECIES/target" ]] + then + mkdir -p "$RESULT_DIR/$CURRENT_SPECIES/target" + fi + + #Split Array + python seed2lp $file $TARGET_DIR -o $OBJECTIVE + + + python -m n2pcomp run $file \ + --output "${RESULT_DIR}/$CURRENT_SPECIES/target" \ + -c $CONF_FILE -nbs 1000 -tl 45 \ + -t "$TARGET_DIR/${CURRENT_SPECIES}_targets.txt" + + python -m n2pcomp run $file \ + --output "${RESULT_DIR}/$CURRENT_SPECIES/full_network" \ + -c $CONF_FILE -nbs 1000 -tl 45 + fi + +done \ No newline at end of file diff --git a/scripts/slurm_cluster/05_2_format_results.sh b/scripts/slurm_cluster/05_2_format_results.sh new file mode 100755 index 0000000..54bb489 --- /dev/null +++ b/scripts/slurm_cluster/05_2_format_results.sh @@ -0,0 +1,44 @@ +#!/bin/bash + +# Get arguments +while getopts i:r:o:s:t: flag +do + case "${flag}" in + i) RESULT_DIR=${OPTARG};; + r) FORM_RESULT_DIR=${OPTARG};; + o) OBJECTIVES_DIR=${OPTARG};; + s) SBML_DIR=${OPTARG};; + t) TOOL=${OPTARG};; + *) echo "Invalid OPTION" && exit 1;; + esac +done + +#SLURM_ARRAY_TASK_ID="1" +SPECIES=$(ls "$RESULT_DIR" | head -n "$SLURM_ARRAY_TASK_ID" | tail -n 1) +OBJECTIVE=$(head -n 1 $OBJECTIVES_DIR/${SPECIES}_target.txt) + +$PYTHON_FILE="../05_format_results.py" + +if [[ "$TOOL" = "NETSEED" ]] +then + RESULT_FILE="$RESULT_DIR/$SPECIES/results.json" + FORM_RESULT_FILE="$FORM_RESULT_DIR/${SPECIES}_netseed_results.json" + python $PYTHON_FILE $RESULT_FILE $SPECIES $OBJECTIVE $FORM_RESULT_FILE $TOOL + seed2lp flux "$SBML_DIR/${SPECIES}.xml" $FORM_RESULT_FILE $FORM_RESULT_DIR +else + RESULTS_FILE_FN="$RESULT_DIR/$SPECIES/full_network/results.json" + RESULTS_FILE_TGT="$RESULT_DIR/$SPECIES/biomass_target/results.json" + FORM_RESULT_FILE_FN="$FORM_RESULT_DIR/${SPECIES}_precursor_fn_results.json" + FORM_RESULT_FILE_TGT="$FORM_RESULT_DIR/${SPECIES}_precursor_tgt_results.json" + python $PYTHON_FILE $RESULTS_FILE_FN $SPECIES $OBJECTIVE $FORM_RESULT_FILE_FN $TOOL + python $PYTHON_FILE $RESULTS_FILE_TGT $SPECIES $OBJECTIVE $FORM_RESULT_FILE_TGT $TOOL + + seed2lp flux "$SBML_DIR/${SPECIES}.xml" $FORM_RESULT_FILE_FN $FORM_RESULT_DIR + seed2lp flux "$SBML_DIR/${SPECIES}.xml" $FORM_RESULT_FILE_TGT $FORM_RESULT_DIR +fi + + + + + + diff --git a/scripts/slurm_cluster/05_2_job_format_netseed_results.sh b/scripts/slurm_cluster/05_2_job_format_netseed_results.sh new file mode 100755 index 0000000..be51a74 --- /dev/null +++ b/scripts/slurm_cluster/05_2_job_format_netseed_results.sh @@ -0,0 +1,25 @@ +#!/bin/bash +#SBATCH --job-name=job_netseed # Job name +#SBATCH --output=../output/netseed/netseed-%A_%a.out +#SBATCH -e ../error/netseed/netseed-%A_%a.err +#SBATCH --cpus-per-task=1 #Request that ncpus be allocated per process. +#SBATCH --ntasks-per-node=1 #Number of tasks per node +#SBATCH --time=24:00:00 # Time limit hrs:min:sec +#SBATCH --mail-user=chabname.ghassemi-nedjad@inria.fr #Receive email on this adress when the job is begin,over,or fail +#SBATCH --mail-type=END,FAIL #Define what we want to receive by email about the job statut +#SBATCH --exclude=arm01 +#SBATCH --array=1-107%55 #108 networks + + +source /home/cghassem/miniconda3/etc/profile.d/conda.sh +conda activate s2lp + +DATA_DIR="../../data" +OBJECTIVE_DIR="${DATA_DIR}/objective" +SBML_DIR="${DATA_DIR}/bigg/sbml" +RESULT_DIR="../../results" +NETSEED_RESULT_DIR="${RESULT_DIR}/netseed" +NETSEED_FORM_RESULT_DIR="${RESULT_DIR}/netseed_formated_results" +TOOL="NETSEED" + +./05_2_results.sh -i $NETSEED_RESULT_DIR -r $NETSEED_FORM_RESULT_DIR -o $OBJECTIVE_DIR -s $SBML_DIR $TOOL diff --git a/scripts/slurm_cluster/06_1_job_run_n2pcomp_precursor.sh b/scripts/slurm_cluster/06_1_job_run_n2pcomp_precursor.sh new file mode 100755 index 0000000..248fbe0 --- /dev/null +++ b/scripts/slurm_cluster/06_1_job_run_n2pcomp_precursor.sh @@ -0,0 +1,25 @@ +#!/bin/bash +#SBATCH --job-name=job_netseed # Job name +#SBATCH --output=../output/netseed/netseed-%A_%a.out +#SBATCH -e ../error/netseed/netseed-%A_%a.err +#SBATCH -c 1 #Number of cores +#SBATCH --nodes=1 #Number of nodes +#SBATCH --cpus-per-task=1 #Request that ncpus be allocated per process. +#SBATCH --mem-per-cpu=10gb +#SBATCH --ntasks-per-node=1 #Number of tasks per node +#SBATCH --time=1:00:00 # Time limit hrs:min:sec +#SBATCH --mail-user=chabname.ghassemi-nedjad@inria.fr #Receive email on this adress when the job is begin,over,or fail +#SBATCH --mail-type=END,FAIL #Define what we want to receive by email about the job statut +#SBATCH --exclude=arm01 + + +source /home/cghassem/miniconda3/etc/profile.d/conda.sh +conda activate s2lp + +DATA_DIR="../../data" +NORM_SBML_DIR="${DATA_DIR}/sbml_corrected" +N2PCOMP_DIR="../../N2PComp" +PRECURSOR_RESULT_DIR="../../results/precursor" +TOOL="PRECURSOR" + +./05_1_run_n2pcom.sh -d $DATA_DIR -i $NORM_SBML_DIR -n $N2PCOMP_DIR -r $PRECURSOR_RESULT_DIR -t $TOOL diff --git a/scripts/slurm_cluster/06_2_job_format_precursor_results.sh b/scripts/slurm_cluster/06_2_job_format_precursor_results.sh new file mode 100755 index 0000000..a960284 --- /dev/null +++ b/scripts/slurm_cluster/06_2_job_format_precursor_results.sh @@ -0,0 +1,25 @@ +#!/bin/bash +#SBATCH --job-name=job_netseed # Job name +#SBATCH --output=../output/netseed/netseed-%A_%a.out +#SBATCH -e ../error/netseed/netseed-%A_%a.err +#SBATCH --cpus-per-task=1 #Request that ncpus be allocated per process. +#SBATCH --ntasks-per-node=1 #Number of tasks per node +#SBATCH --time=24:00:00 # Time limit hrs:min:sec +#SBATCH --mail-user=chabname.ghassemi-nedjad@inria.fr #Receive email on this adress when the job is begin,over,or fail +#SBATCH --mail-type=END,FAIL #Define what we want to receive by email about the job statut +#SBATCH --exclude=arm01 +#SBATCH --array=1-107%55 #108 networks + + +source /home/cghassem/miniconda3/etc/profile.d/conda.sh +conda activate s2lp + +DATA_DIR="../../data" +OBJECTIVE_DIR="${DATA_DIR}/objective" +SBML_DIR="${DATA_DIR}/bigg/sbml" +RESULT_DIR="../../results" +NETSEED_RESULT_DIR="${RESULT_DIR}/precursor" +NETSEED_FORM_RESULT_DIR="${RESULT_DIR}/precursor_formated_results" +TOOL="PRECURSOR" + +./05_2_results.sh -i $NETSEED_RESULT_DIR -r $NETSEED_FORM_RESULT_DIR -o $OBJECTIVE_DIR -s $SBML_DIR $TOOL diff --git a/scripts/slurm_cluster/07_execute_s2lp_iCN718.sh b/scripts/slurm_cluster/07_execute_s2lp_iCN718.sh new file mode 100755 index 0000000..8a61e1c --- /dev/null +++ b/scripts/slurm_cluster/07_execute_s2lp_iCN718.sh @@ -0,0 +1,20 @@ +#!/bin/bash +#SBATCH --job-name=job_s2lp # Job name +#SBATCH --output=../output/workflow/s2lp-%A_%a.out +#SBATCH -e ../error/workflow/s2lp-%A_%a.err +#SBATCH --mem-per-cpu=10gb +#SBATCH --ntasks-per-node=1 #Number of tasks per node +#SBATCH --time=72:00:00 # Time limit hrs:min:sec +#SBATCH --mail-user=chabname.ghassemi-nedjad@inria.fr #Receive email on this adress when the job is begin,over,or fail +#SBATCH --mail-type=END,FAIL #Define what we want to receive by email about the job statut +#SBATCH --exclude=arm01 + +source /home/cghassem/miniconda3/etc/profile.d/conda.sh +conda activate s2lp + +########## EXECUTE TARGET ########## +### SPECIFIC ### +sbatch 07_job_run_s2lp_iCN718.sh -c target -m m -s reasoning +sbatch 07_job_run_s2lp_iCN718.sh -c target -m m -s filter +sbatch 07_job_run_s2lp_iCN718.sh -c target -m m -s guess_check +sbatch 07_job_run_s2lp_iCN718.sh -c target -m m -s guess_check_div diff --git a/scripts/slurm_cluster/07_job_run_s2lp_iCN718.sh b/scripts/slurm_cluster/07_job_run_s2lp_iCN718.sh new file mode 100755 index 0000000..2bc3d8a --- /dev/null +++ b/scripts/slurm_cluster/07_job_run_s2lp_iCN718.sh @@ -0,0 +1,61 @@ +#!/bin/bash +#SBATCH --job-name=job_s2lp # Job name +#SBATCH --output=../output/s2lp/s2lp-%A_%a.out +#SBATCH -e ../error/s2lp/s2lp-%A_%a.err +#SBATCH --cpus-per-task=1 #Request that ncpus be allocated per process. +#SBATCH -C zonda +#SBATCH --ntasks-per-node=1 #Number of tasks per node +#SBATCH --time=72:00:00 # Time limit hrs:min:sec +#SBATCH --mail-user=chabname.ghassemi-nedjad@inria.fr #Receive email on this adress when the job is begin,over,or fail +#SBATCH --mail-type=END,FAIL #Define what we want to receive by email about the job statut +#SBATCH --exclude=arm01 + + +source /home/cghassem/miniconda3/etc/profile.d/conda.sh +conda activate s2lp + +########## SERVER ########## +DATA_DIR="../../data" +RESULT_DIR="../../results/iCN718_2000" +SBML_DIR="${DATA_DIR}/bigg/sbml" +OBJECTIVE_DIR="${DATA_DIR}/objective" +TEMP_DIR="../../tmp/" + + +while getopts c:m:a:s: flag +do + case "${flag}" in + c) COMMAND=${OPTARG};; + m) + if [[ ! -z ${OPTARG} ]]; then + MAXIMIZATION=${OPTARG} + fi + ;; + a) + if [[ ! -z ${OPTARG} ]]; then + ACCUMULATION=${OPTARG} + fi + ;; + s) + if [[ ! -z ${OPTARG} ]]; then + SOLVE=${OPTARG} + fi + ;; + esac +done + +OPTION="" +if [[ ! -z ${MAXIMIZATION} ]]; then + OPTION="-m m" +fi +if [[ ! -z ${ACCUMULATION} ]]; then + OPTION="${OPTION} -a a" +fi +if [[ ! -z ${SOLVE} ]]; then + OPTION="${OPTION} -s ${SOLVE}" +fi + + + +./07_run_s2lp_iCN718.sh -i $SBML_DIR -r $RESULT_DIR \ + -c $COMMAND -t $TEMP_DIR -o $OBJECTIVE_DIR $OPTION \ No newline at end of file diff --git a/scripts/slurm_cluster/07_run_s2lp_iCN718.sh b/scripts/slurm_cluster/07_run_s2lp_iCN718.sh new file mode 100755 index 0000000..49e065b --- /dev/null +++ b/scripts/slurm_cluster/07_run_s2lp_iCN718.sh @@ -0,0 +1,78 @@ +#!/bin/bash + +# Get arguments +while getopts i:r:c:t:o:m:a:s: flag +do + case "${flag}" in + i) IN_DIRECTORY=${OPTARG};; + r) RESULT_DIR=${OPTARG};; + c) COMMAND=${OPTARG};; + t) + if [[ ! -z ${OPTARG} ]]; then + TEMP=${OPTARG} + fi + ;; + o) OBJECTIVE_DIR=${OPTARG};; + m) + if [[ ! -z ${OPTARG} ]]; then + MAXIMIZATION=${OPTARG} + fi + ;; + a) + if [[ ! -z ${OPTARG} ]]; then + ACCUMULATION=${OPTARG} + fi + ;; + s) + if [[ ! -z ${OPTARG} ]]; then + SOLVE=${OPTARG} + fi + ;; + *) echo "Invalid OPTION" && exit 1;; + esac +done + +CURRENT_SPECIES="iCN718" + +CURRENT_FILE="${CURRENT_SPECIES}.xml" +OBJECTIVE_PATH="$OBJECTIVE_DIR/${CURRENT_SPECIES}_target.txt" + +FULL_PATH=$RESULT_DIR +# Create directories if needed +if [[ ! -d "$FULL_PATH" ]] +then + mkdir -p "$FULL_PATH" +fi + +FULL_PATH=$FULL_PATH/$CURRENT_SPECIES +if [[ ! -d "$FULL_PATH" ]] +then + mkdir -p "$FULL_PATH" +fi + +OPTION="" +if [[ "$COMMAND" = "target" ]] +then + OPTION="-tf $OBJECTIVE_PATH" +else + read -r OBJECTIVE < $OBJECTIVE_PATH + OPTION=" -o $OBJECTIVE" +fi + +if [[ ! -z ${MAXIMIZATION} ]]; then + OPTION="${OPTION} -max" +fi + +if [[ ! -z ${ACCUMULATION} ]]; then + OPTION="${OPTION} -accu" +fi + +if [[ ! -z ${SOLVE} ]]; then + OPTION="${OPTION} -so ${SOLVE}" +fi + +seed2lp $COMMAND "$IN_DIRECTORY/$CURRENT_FILE" \ + "$FULL_PATH" \ + --temp $TEMP \ + -tl 0 -nbs 2000 -cf $OPTION \ + -m subsetmin \ No newline at end of file diff --git a/scripts/slurm_cluster/08_execute_s2lp_one_solution.sh b/scripts/slurm_cluster/08_execute_s2lp_one_solution.sh new file mode 100755 index 0000000..9880bb1 --- /dev/null +++ b/scripts/slurm_cluster/08_execute_s2lp_one_solution.sh @@ -0,0 +1,20 @@ +#!/bin/bash +#SBATCH --job-name=job_s2lp # Job name +#SBATCH --output=../output/workflow/s2lp-%A_%a.out +#SBATCH -e ../error/workflow/s2lp-%A_%a.err +#SBATCH --mem-per-cpu=10gb +#SBATCH --ntasks-per-node=1 #Number of tasks per node +#SBATCH --time=72:00:00 # Time limit hrs:min:sec +#SBATCH --mail-user=chabname.ghassemi-nedjad@inria.fr #Receive email on this adress when the job is begin,over,or fail +#SBATCH --mail-type=END,FAIL #Define what we want to receive by email about the job statut +#SBATCH --exclude=arm01 + +source /home/cghassem/miniconda3/etc/profile.d/conda.sh +conda activate s2lp + +########## EXECUTE TARGET ########## +### SPECIFIC ### +sbatch 08_job_run_s2lp_one_solution.sh -c target -m m -s reasoning +sbatch 08_job_run_s2lp_one_solution.sh -c target -m m -s filter +sbatch 08_job_run_s2lp_one_solution.sh -c target -m m -s guess_check +sbatch 08_job_run_s2lp_one_solution.sh -c target -m m -s guess_check_div \ No newline at end of file diff --git a/scripts/slurm_cluster/08_job_run_s2lp_one_solution.sh b/scripts/slurm_cluster/08_job_run_s2lp_one_solution.sh new file mode 100755 index 0000000..a0ee4a5 --- /dev/null +++ b/scripts/slurm_cluster/08_job_run_s2lp_one_solution.sh @@ -0,0 +1,62 @@ +#!/bin/bash +#SBATCH --job-name=job_s2lp # Job name +#SBATCH --output=../output/s2lp/s2lp-%A_%a.out +#SBATCH -e ../error/s2lp/s2lp-%A_%a.err +#SBATCH --cpus-per-task=1 #Request that ncpus be allocated per process. + +#SBATCH -C zonda +#SBATCH --ntasks-per-node=1 #Number of tasks per node +#SBATCH --time=72:00:00 # Time limit hrs:min:sec +#SBATCH --mail-user=chabname.ghassemi-nedjad@inria.fr #Receive email on this adress when the job is begin,over,or fail +#SBATCH --mail-type=END,FAIL #Define what we want to receive by email about the job statut +#SBATCH --exclude=arm01 +#SBATCH --array=1-107%10 #108 networks + + +source /home/cghassem/miniconda3/etc/profile.d/conda.sh +conda activate s2lp + + +DATA_DIR="../../data" +S2LP_RESULT_DIR="../../results/one_solution" +SBML_DIR="${DATA_DIR}/bigg/sbml" +OBJECTIVE_DIR="${DATA_DIR}/objective" +TEMP_DIR="../../tmp/" + +while getopts c:m:a:s: flag +do + case "${flag}" in + c) COMMAND=${OPTARG};; + m) + if [[ ! -z ${OPTARG} ]]; then + MAXIMIZATION=${OPTARG} + fi + ;; + a) + if [[ ! -z ${OPTARG} ]]; then + ACCUMULATION=${OPTARG} + fi + ;; + s) + if [[ ! -z ${OPTARG} ]]; then + SOLVE=${OPTARG} + fi + ;; + esac +done + +OPTION="" +if [[ ! -z ${MAXIMIZATION} ]]; then + OPTION="-m m" +fi +if [[ ! -z ${ACCUMULATION} ]]; then + OPTION="${OPTION} -a a" +fi +if [[ ! -z ${SOLVE} ]]; then +OPTION="${OPTION} -s ${SOLVE}" +fi + +########### ALL SBML source in RESULTS dir ########### + +./03_run_s2lp.sh -i $SBML_DIR -r $S2LP_RESULT_DIR \ + -c $COMMAND -t $TEMP_DIR -n 1 -o $OBJECTIVE_DIR $OPTION \ No newline at end of file diff --git a/scripts/slurm_cluster/09_job_run_scope_iCN718.sh b/scripts/slurm_cluster/09_job_run_scope_iCN718.sh new file mode 100755 index 0000000..8f3eddf --- /dev/null +++ b/scripts/slurm_cluster/09_job_run_scope_iCN718.sh @@ -0,0 +1,25 @@ +#!/bin/bash +#SBATCH --job-name=job_scope_iCN718 # Job name +#SBATCH --output=../output/scope_iCN718/scope_iCN718-%A_%a.out +#SBATCH -e ../error/scope_iCN718/scope_iCN718-%A_%a.err +#SBATCH --cpus-per-task=1 #Request that ncpus be allocated per process. +#SBATCH --ntasks-per-node=1 #Number of tasks per node +#SBATCH --time=24:00:00 # Time limit hrs:min:sec +#SBATCH --mail-user=chabname.ghassemi-nedjad@inria.fr #Receive email on this adress when the job is begin,over,or fail +#SBATCH --mail-type=END,FAIL #Define what we want to receive by email about the job statut +#SBATCH --exclude=arm01 + + +source /home/cghassem/miniconda3/etc/profile.d/conda.sh +conda activate s2lp + + +DATA_DIR="../../data" +NORM_SBML_DIR="${DATA_DIR}/sbml_corrected" +RESULT_DIR="../../results" +SOLUTION_DIR="$RESULT_DIR/iCN718" +SCOPE_DIR="$RESULT_DIR/scopes_iCN718" +$SPECIES="iCN718" + + +./09_run_scope.sh -i $SOLUTION_DIR -s $SCOPE_DIR -n $NORM_SBML_DIR -b $SPECIES \ No newline at end of file diff --git a/scripts/slurm_cluster/09_job_run_scope_netseed.sh b/scripts/slurm_cluster/09_job_run_scope_netseed.sh new file mode 100755 index 0000000..8a1f478 --- /dev/null +++ b/scripts/slurm_cluster/09_job_run_scope_netseed.sh @@ -0,0 +1,25 @@ +#!/bin/bash +#SBATCH --job-name=job_scope_netseed # Job name +#SBATCH --output=../output/scope_netseed/scope_netseed-%A_%a.out +#SBATCH -e ../error/scope_netseed/scope_netseed-%A_%a.err +#SBATCH --cpus-per-task=1 #Request that ncpus be allocated per process. +#SBATCH --ntasks-per-node=1 #Number of tasks per node +#SBATCH --time=24:00:00 # Time limit hrs:min:sec +#SBATCH --mail-user=chabname.ghassemi-nedjad@inria.fr #Receive email on this adress when the job is begin,over,or fail +#SBATCH --mail-type=END,FAIL #Define what we want to receive by email about the job statut +#SBATCH --exclude=arm01 +#SBATCH --array=1-107%25 #107 networks + +source /home/cghassem/miniconda3/etc/profile.d/conda.sh +conda activate s2lp + + +DATA_DIR="../../data" +NORM_SBML_DIR="${DATA_DIR}/sbml_corrected" +RESULT_DIR="../../results" +SOLUTION_DIR="$RESULT_DIR/netseed_formated_results" +SCOPE_DIR="$RESULT_DIR/scopes_netseed" + + + +./09_run_scope.sh -i $SOLUTION_DIR -s $SCOPE_DIR -n $NORM_SBML_DIR \ No newline at end of file diff --git a/scripts/slurm_cluster/09_job_run_scope_s2lp.sh b/scripts/slurm_cluster/09_job_run_scope_s2lp.sh new file mode 100755 index 0000000..984c8b1 --- /dev/null +++ b/scripts/slurm_cluster/09_job_run_scope_s2lp.sh @@ -0,0 +1,25 @@ +#!/bin/bash +#SBATCH --job-name=job_scope_s2lp # Job name +#SBATCH --output=../output/scope_s2lp/scope_s2lp-%A_%a.out +#SBATCH -e ../error/scope_s2lp/scope_s2lp-%A_%a.err +#SBATCH --cpus-per-task=1 #Request that ncpus be allocated per process. +#SBATCH --ntasks-per-node=1 #Number of tasks per node +#SBATCH --time=24:00:00 # Time limit hrs:min:sec +#SBATCH --mail-user=chabname.ghassemi-nedjad@inria.fr #Receive email on this adress when the job is begin,over,or fail +#SBATCH --mail-type=END,FAIL #Define what we want to receive by email about the job statut +#SBATCH --exclude=arm01 +#SBATCH --array=1-107%25 #107 networks + +source /home/cghassem/miniconda3/etc/profile.d/conda.sh +conda activate s2lp + + +DATA_DIR="../../data" +NORM_SBML_DIR="${DATA_DIR}/sbml_corrected" +RESULT_DIR="../../results" +SOLUTION_DIR="$RESULT_DIR/s2lp" +SCOPE_DIR="$RESULT_DIR/scopes_s2lp" + + + +./09_run_scope.sh -i $SOLUTION_DIR -s $SCOPE_DIR -n $NORM_SBML_DIR \ No newline at end of file diff --git a/scripts/slurm_cluster/09_run_scope.sh b/scripts/slurm_cluster/09_run_scope.sh new file mode 100755 index 0000000..945087b --- /dev/null +++ b/scripts/slurm_cluster/09_run_scope.sh @@ -0,0 +1,42 @@ +#!/bin/bash + +# Get arguments +while getopts i:s:n:b: flag +do + case "${flag}" in + i) SOLUTION_DIR=${OPTARG};; + s) SCOPE_DIR=${OPTARG};; + n) NORM_SBML_DIR=${OPTARG};; + b) + if [[ ! -z ${OPTARG} ]]; then + SPECIES=${OPTARG} + fi + ;; + *) echo "Invalid OPTION" && exit 1;; + esac +done + +# SERVER + + +if [[ ! -z ${SPECIES} ]]; then + CURRENT_SPECIES=$SPECIES + CURRENT_SBML_FILE="$NORM_SBML_DIR/${SPECIES}.xml" +else + CURRENT_SBML_FILE=$(ls "$NORM_SBML_DIR" | head -n "$SLURM_ARRAY_TASK_ID" | tail -n 1) + CURRENT_SPECIES=$(basename "$CURRENT_SBML_FILE" | sed 's/\.[^.]*$//') +fi + + +SPECIES_SOLUTION_DIR="$SOLUTION_DIR/$CURRENT_SPECIES/" + +FULL_PATH=$SCOPE_DIR/$CURRENT_SPECIES +if [[ ! -d "$FULL_PATH" ]] +then + mkdir -p "$FULL_PATH" +fi + +for solution_file in "$SPECIES_SOLUTION_DIR"/*results.json +do + seed2lp scope "$NORM_SBML_DIR/$CURRENT_SBML_FILE" $solution_file $FULL_PATH +done \ No newline at end of file diff --git a/scripts/slurm_cluster/10_1_job_run_scope_analyse_iCN718.sh b/scripts/slurm_cluster/10_1_job_run_scope_analyse_iCN718.sh new file mode 100755 index 0000000..292ec38 --- /dev/null +++ b/scripts/slurm_cluster/10_1_job_run_scope_analyse_iCN718.sh @@ -0,0 +1,32 @@ +#!/bin/bash +#SBATCH --job-name=job_scope_s2lp # Job name +#SBATCH --output=../output/scope_s2lp/scope_s2lp-%A_%a.out +#SBATCH -e ../error/scope_s2lp/scope_s2lp-%A_%a.err +#SBATCH --cpus-per-task=1 #Request that ncpus be allocated per process. +#SBATCH --ntasks-per-node=1 #Number of tasks per node +#SBATCH --time=24:00:00 # Time limit hrs:min:sec +#SBATCH --mail-user=chabname.ghassemi-nedjad@inria.fr #Receive email on this adress when the job is begin,over,or fail +#SBATCH --mail-type=END,FAIL #Define what we want to receive by email about the job statut +#SBATCH --exclude=arm01 + + +source /home/cghassem/miniconda3/etc/profile.d/conda.sh +conda activate s2lp + +DATA_DIR="../../data" +OBJECTIVE_DIR="${DATA_DIR}/objective" +SBML_DIR="${DATA_DIR}/bigg/sbml" +RESULT_DIR="../../results" +SCOPE_DIR="$RESULT_DIR/scopes_iCN718" +$SPECIES="iCN718" + + +LIST_DIR_LEV1=("target") +LIST_DIR_LEV2=("reasoning" "reasoning_filter" "reasoning_guess_check" "reasoning_guess_check_diversity") +LIST_DIR_LEV3=("subset_minimal") +LIST_DIR_LEV4=("no_accu") + +./10_01_run_scope_analyse.sh -r $SCOPE_DIR -s $SBML_DIR -o $OBJECTIVE_DIR \ + -a $LIST_DIR_LEV1 -b $LIST_DIR_LEV2 -c $LIST_DIR_LEV3 -d $LIST_DIR_LEV4 \ + -n $SPECIES + diff --git a/scripts/slurm_cluster/10_1_job_run_scope_analyse_netseed.sh b/scripts/slurm_cluster/10_1_job_run_scope_analyse_netseed.sh new file mode 100755 index 0000000..c613682 --- /dev/null +++ b/scripts/slurm_cluster/10_1_job_run_scope_analyse_netseed.sh @@ -0,0 +1,30 @@ +#!/bin/bash +#SBATCH --job-name=job_scope_s2lp # Job name +#SBATCH --output=../output/scope_s2lp/scope_s2lp-%A_%a.out +#SBATCH -e ../error/scope_s2lp/scope_s2lp-%A_%a.err +#SBATCH --cpus-per-task=1 #Request that ncpus be allocated per process. +#SBATCH --ntasks-per-node=1 #Number of tasks per node +#SBATCH --time=24:00:00 # Time limit hrs:min:sec +#SBATCH --mail-user=chabname.ghassemi-nedjad@inria.fr #Receive email on this adress when the job is begin,over,or fail +#SBATCH --mail-type=END,FAIL #Define what we want to receive by email about the job statut +#SBATCH --exclude=arm01 +#SBATCH --array=1-107%55 #108 networks + + +source /home/cghassem/miniconda3/etc/profile.d/conda.sh +conda activate s2lp + +DATA_DIR="../../data" +OBJECTIVE_DIR="${DATA_DIR}/objective" +SBML_DIR="${DATA_DIR}/bigg/sbml" +RESULT_DIR="../../results" +SCOPE_DIR="$RESULT_DIR/scopes_netseed" + + +LIST_DIR_LEV1=("netseed") +LIST_DIR_LEV2=("netseed") +LIST_DIR_LEV3=("other") +LIST_DIR_LEV4=("accu") + +./10_01_run_scope_analyse.sh -r $SCOPE_DIR -s $SBML_DIR -o $OBJECTIVE_DIR \ + -a $LIST_DIR_LEV1 -b $LIST_DIR_LEV2 -c $LIST_DIR_LEV3 -d $LIST_DIR_LEV4 \ No newline at end of file diff --git a/scripts/slurm_cluster/10_1_job_run_scope_analyse_s2lp.sh b/scripts/slurm_cluster/10_1_job_run_scope_analyse_s2lp.sh new file mode 100755 index 0000000..6277d43 --- /dev/null +++ b/scripts/slurm_cluster/10_1_job_run_scope_analyse_s2lp.sh @@ -0,0 +1,30 @@ +#!/bin/bash +#SBATCH --job-name=job_scope_s2lp # Job name +#SBATCH --output=../output/scope_s2lp/scope_s2lp-%A_%a.out +#SBATCH -e ../error/scope_s2lp/scope_s2lp-%A_%a.err +#SBATCH --cpus-per-task=1 #Request that ncpus be allocated per process. +#SBATCH --ntasks-per-node=1 #Number of tasks per node +#SBATCH --time=24:00:00 # Time limit hrs:min:sec +#SBATCH --mail-user=chabname.ghassemi-nedjad@inria.fr #Receive email on this adress when the job is begin,over,or fail +#SBATCH --mail-type=END,FAIL #Define what we want to receive by email about the job statut +#SBATCH --exclude=arm01 +#SBATCH --array=1-107%55 #108 networks + + +source /home/cghassem/miniconda3/etc/profile.d/conda.sh +conda activate s2lp + +DATA_DIR="../../data" +OBJECTIVE_DIR="${DATA_DIR}/objective" +SBML_DIR="${DATA_DIR}/bigg/sbml" +RESULT_DIR="../../results" +SCOPE_DIR="$RESULT_DIR/scopes_s2lp" + + +LIST_DIR_LEV1=("full" "target") +LIST_DIR_LEV2=("reasoning" "reasoning_filter" "reasoning_guess_check" "reasoning_guess_check_diversity") +LIST_DIR_LEV3=("minimize" "subset_minimal") +LIST_DIR_LEV4=("accu" "no_accu") + +./10_01_run_scope_analyse.sh -r $SCOPE_DIR -s $SBML_DIR -o $OBJECTIVE_DIR \ + -a $LIST_DIR_LEV1 -b $LIST_DIR_LEV2 -c $LIST_DIR_LEV3 -d $LIST_DIR_LEV4 \ No newline at end of file diff --git a/scripts/slurm_cluster/10_1_run_scope_analyse.sh b/scripts/slurm_cluster/10_1_run_scope_analyse.sh new file mode 100755 index 0000000..a54a321 --- /dev/null +++ b/scripts/slurm_cluster/10_1_run_scope_analyse.sh @@ -0,0 +1,53 @@ +#!/bin/bash + +# Get arguments +while getopts r:s:o:a:b:c:d:n: flag +do + case "${flag}" in + r) SCOPE_DIR=${OPTARG};; + s) SBML_DIR=${OPTARG};; + o) OBJECTIVE_DIR=${OPTARG};; + a) LIST_DIR_LEV1=${OPTARG};; + b) LIST_DIR_LEV2=${OPTARG};; + c) LIST_DIR_LEV3=${OPTARG};; + d) LIST_DIR_LEV4=${OPTARG};; + n) + if [[ ! -z ${OPTARG} ]]; then + SPECIES=${OPTARG} + fi + ;; + *) echo "Invalid OPTION" && exit 1;; + esac +done + + +if [[ ! -z ${SPECIES} ]]; then + CURRENT_SPECIES=$SPECIES + CURRENT_SBML_FILE="$SBML_DIR/${SPECIES}.xml" +else + CURRENT_FILE=$(ls "$SBML_DIR" | head -n "$SLURM_ARRAY_TASK_ID" | tail -n 1) + CURRENT_SPECIES=$(basename "$CURRENT_FILE" | sed 's/\.[^.]*$//') +fi + +FULL_PATH_SCOPE=$SCOPE_DIR/$CURRENT_SPECIES/scope +FULL_PATH_SEEDS=$SCOPE_DIR/$CURRENT_SPECIES/sbml + + + +for dir_lev1 in ${LIST_DIR_LEV1[@]}; +do + for dir_lev2 in ${LIST_DIR_LEV2[@]}; + do + for dir_lev3 in ${LIST_DIR_LEV3[@]}; + do + for dir_lev4 in ${LIST_DIR_LEV4[@]}; + do + modes_info=$dir_lev1/$dir_lev2/$dir_lev3/$dir_lev4 + CURRENT_PATH_SEED=$FULL_PATH_SEEDS/$modes_info/ + CURRENT_PATH_SCOPE=$FULL_PATH_SCOPE/$modes_info/ + python ../10_1_scope_analyse.py ${CURRENT_SPECIES} "${SBML_DIR}/${CURRENT_FILE}" ${CURRENT_PATH_SCOPE} ${CURRENT_PATH_SEED} "${OBJECTIVE_DIR}/${CURRENT_SPECIES}_target.txt" ${modes_info} + done + done + done +done + diff --git a/scripts/slurm_cluster/10_2_job_run_scope_analyse_concat.sh b/scripts/slurm_cluster/10_2_job_run_scope_analyse_concat.sh new file mode 100755 index 0000000..66acdb8 --- /dev/null +++ b/scripts/slurm_cluster/10_2_job_run_scope_analyse_concat.sh @@ -0,0 +1,25 @@ +#!/bin/bash +#SBATCH --job-name=job_scope_s2lp # Job name +#SBATCH --output=../output/scope_s2lp/scope_s2lp-%A_%a.out +#SBATCH -e ../error/scope_s2lp/scope_s2lp-%A_%a.err +#SBATCH --cpus-per-task=1 #Request that ncpus be allocated per process. +#SBATCH --ntasks-per-node=1 #Number of tasks per node +#SBATCH --time=24:00:00 # Time limit hrs:min:sec +#SBATCH --mail-user=chabname.ghassemi-nedjad@inria.fr #Receive email on this adress when the job is begin,over,or fail +#SBATCH --mail-type=END,FAIL #Define what we want to receive by email about the job statut +#SBATCH --exclude=arm01 + + + +source /home/cghassem/miniconda3/etc/profile.d/conda.sh +conda activate seed2lp + + +RESULT_DIR="../../results" +S2LP_SCOPE_DIR="$RESULT_DIR/scopes_s2lp" +NETSEED_SCOPE_DIR="$RESULT_DIR/scopes_netseed" +ICN718_SCOPE_DIR="$RESULT_DIR/scopes_iCN718" + +./10_2_run_scope_analyse_concat.sh -r $S2LP_SCOPE_DIR +./10_2_run_scope_analyse_concat.sh -r $NETSEED_SCOPE_DIR +./10_2_run_scope_analyse_concat.sh -r $ICN718_SCOPE_DIR diff --git a/scripts/slurm_cluster/10_2_run_scope_analyse_concat.sh b/scripts/slurm_cluster/10_2_run_scope_analyse_concat.sh new file mode 100755 index 0000000..ed840e7 --- /dev/null +++ b/scripts/slurm_cluster/10_2_run_scope_analyse_concat.sh @@ -0,0 +1,26 @@ +#!/bin/bash + +while getopts r: flag +do + case "${flag}" in + r) SCOPE_DIR=${OPTARG};; + *) echo "Invalid OPTION" && exit 1;; + esac +done + +#################### +###### SERVER ###### +#################### + +first_line="\tspecies\trun\tmode\toptim\taccu\tmodel\tis_equal_union_species\tmissing\tpercentage_missing\tis_biomass_included\tmissing_biomass\tpercentage_missing_biomass\tis_exchange_included\tmissing_exchange\tpercentage_missing_exchange\tis_seed_included_to_exchange\tmissing_seed_into_exchange\tpercentage_missing_seed_into_exchange\tis_exchange_included_to_seed\tmissing_exchange_into_seed\tpercentage_missing_exchange_into_seeds" +for dir in $SCOPE_DIR/* +do + CURRENT_SPECIES=$(basename "$dir" | sed 's/\.[^.]*$//') + file_list=$(find "$SCOPE_DIR/$CURRENT_SPECIES/scope"/ -maxdepth 5 -mindepth 5 -type f -name "*_compare.tsv" -print) + + echo -e $first_line >> $SCOPE_DIR/$CURRENT_SPECIES/"${CURRENT_SPECIES}_scope_compare.tsv" + for i in $file_list + do + awk FNR!=1 $i >> $SCOPE_DIR/$CURRENT_SPECIES/"${CURRENT_SPECIES}_scope_compare.tsv" + done +done diff --git a/scripts/slurm_cluster/10_3_job_iCN718_metabolite_analysis.sh b/scripts/slurm_cluster/10_3_job_iCN718_metabolite_analysis.sh new file mode 100755 index 0000000..4c89ab3 --- /dev/null +++ b/scripts/slurm_cluster/10_3_job_iCN718_metabolite_analysis.sh @@ -0,0 +1,28 @@ +#!/bin/bash +#SBATCH --job-name=job_scope_s2lp # Job name +#SBATCH --output=../output/scope_s2lp/scope_s2lp-%A_%a.out +#SBATCH -e ../error/scope_s2lp/scope_s2lp-%A_%a.err +#SBATCH --cpus-per-task=1 #Request that ncpus be allocated per process. +#SBATCH --ntasks-per-node=1 #Number of tasks per node +#SBATCH --time=24:00:00 # Time limit hrs:min:sec +#SBATCH --mail-user=chabname.ghassemi-nedjad@inria.fr #Receive email on this adress when the job is begin,over,or fail +#SBATCH --mail-type=END,FAIL #Define what we want to receive by email about the job statut +#SBATCH --exclude=arm01 + +source /home/cghassem/miniconda3/etc/profile.d/conda.sh +conda activate s2lp + +DATA_DIR="../../data" +SBML_DIR="${DATA_DIR}/bigg/sbml" +SPECIES="iCN718" +SBML_FILE="${SBML_DIR}/${SPECIES}.xml" +RESULT_DIR="../../results" +SPECIES_RESULT_DIR="$RESULT_DIR/${SPECIES}/${SPECIES}" +OUT_DIR="${RESULT_DIR}/metabolites_${SPECIES}" + +if [[ ! -d "$OUT_DIR" ]] +then + mkdir -p "$OUT_DIR" +fi + +python ../10_3_iCN718_metabolite_analyses.py $SPECIES_RESULT_DIR $SBML_FILE $OUT_DIR \ No newline at end of file diff --git a/scripts/slurm_cluster/10_4_job_get_supp_data.sh b/scripts/slurm_cluster/10_4_job_get_supp_data.sh new file mode 100755 index 0000000..19eff1e --- /dev/null +++ b/scripts/slurm_cluster/10_4_job_get_supp_data.sh @@ -0,0 +1,31 @@ +#!/bin/bash +#SBATCH --job-name=job_timers # Job name +#SBATCH --output=../output/timers/timers-%A_%a.out +#SBATCH -e ../error/timers/timers-%A_%a.err +#SBATCH #SBATCH --nodes=1 #Number of nodes +#SBATCH --cpus-per-task=1 #Request that ncpus be allocated per process. +#SBATCH --mem-per-cpu=10gb +#SBATCH --ntasks-per-node=1 #Number of tasks per node +#SBATCH --time=10:00:00 # Time limit hrs:min:sec +#SBATCH --mail-user=chabname.ghassemi-nedjad@inria.fr #Receive email on this adress when the job is begin,over,or fail +#SBATCH --mail-type=END,FAIL #Define what we want to receive by email about the job statut +#SBATCH --exclude=arm01 + + +source /home/cghassem/miniconda3/etc/profile.d/conda.sh +conda activate s2lp + +DATA_DIR="../../data" +NORM_SBML_DIR="${DATA_DIR}/sbml_corrected" +RESULT_DIR="../../results" +S2LP_RESULT_DIR="${RESULT_DIR}/s2lp" +iCN718_RESULT_DIR="${RESULT_DIR}/iCN718" +ONESOL_RESULT_DIR="${RESULT_DIR}/one_solution" +OUT_DIR="${RESULT_DIR}/supp_data" +FILE="../10_4_get_supp_data.py" + + +# SERVER +python $FILE $S2LP_RESULT_DIR $NORM_SBML_DIR $OUT_DIR "seed2lp" +python $FILE $iCN718_RESULT_DIR $NORM_SBML_DIR $OUT_DIR "iCN718_2000" +python $FILE $ONESOL_RESULT_DIR $NORM_SBML_DIR $OUT_DIR "one_solution" diff --git a/scripts/slurm_cluster/10_5_job_get_iCN718_exchange.sh b/scripts/slurm_cluster/10_5_job_get_iCN718_exchange.sh new file mode 100755 index 0000000..cd00f2b --- /dev/null +++ b/scripts/slurm_cluster/10_5_job_get_iCN718_exchange.sh @@ -0,0 +1,26 @@ +#!/bin/bash +#SBATCH --job-name=job_timers # Job name +#SBATCH --output=../output/timers/timers-%A_%a.out +#SBATCH -e ../error/timers/timers-%A_%a.err +#SBATCH #SBATCH --nodes=1 #Number of nodes +#SBATCH --cpus-per-task=1 #Request that ncpus be allocated per process. +#SBATCH --mem-per-cpu=10gb +#SBATCH --ntasks-per-node=1 #Number of tasks per node +#SBATCH --time=10:00:00 # Time limit hrs:min:sec +#SBATCH --mail-user=chabname.ghassemi-nedjad@inria.fr #Receive email on this adress when the job is begin,over,or fail +#SBATCH --mail-type=END,FAIL #Define what we want to receive by email about the job statut +#SBATCH --exclude=arm01 + + +source /home/cghassem/miniconda3/etc/profile.d/conda.sh +conda activate s2lp + +DATA_DIR="../../data" +SBML_DIR="${DATA_DIR}/bigg/sbml" +SPECIES="iCN718" +SBML_FILE="${SBML_DIR}/${SPECIES}.xml" +RESULT_DIR="../../results" +OUT_DIR="${RESULT_DIR}/metabolites_${SPECIES}" + +# SERVER +python ../10_5_get_exchange.py $SPECIES $SBML_FILE $OUT_DIR diff --git a/seed2lp/__init__.py b/seed2lp/__init__.py new file mode 100644 index 0000000..02ddbf7 --- /dev/null +++ b/seed2lp/__init__.py @@ -0,0 +1,12 @@ +from . import color +from ._version import __version__ + + +print(color.cyan_light+color.bold+""" + _ ___ _ + ___ ___ ___ __| | |_ \ | | _ __ + / __| / _ \ / _ \ / _` | ) | | || '_ \ + \__ \| __/| __/| (_| | / /_ | || |_) | + |___/ \___| \___| \__,_| |____| |_|| .__/ + |_| + """+color.reset) \ No newline at end of file diff --git a/seed2lp/__main__.py b/seed2lp/__main__.py new file mode 100644 index 0000000..873af7c --- /dev/null +++ b/seed2lp/__main__.py @@ -0,0 +1,582 @@ +"""entry point for predator. + +""" + + +import argparse + +from time import time +from sys import exit +from os import path +from shutil import copyfile +from .file import is_valid_dir + +from . import utils, argument, file +from .sbml import read_SBML_species +from .network import Network +from .reasoning import Reasoning +from .linear import Hybrid, FBA +from .description import Description +from .file import load_json +from pathlib import Path +from .scope import Scope +from . import logger + +#Global variable needed +PROJECT_DIR = path.dirname(path.abspath(__file__)) +CLEAN_TEMP=True + + +####################### FUNCTIONS ########################## +def get_reaction_options(keep_import_reactions:bool, topological_injection:bool, + targets_as_seeds:bool, maximization:bool, mode:str, accumulation:bool, + solve:str=""): + """Get full options informations into a dictionnary + + Args: + keep_import_reactions (bool): Import reactions are not removed if True + topological_injection (bool): Topological injection is used if True + targets_as_seeds (bool): Target are forbidden seed if True + maximization (bool): Execute an objective flux maximization if True (Hybrid or FBA) + full_network (bool): Use Full Network mode + reasoning_option(str): Runing reasoning or guess_check or filter + + Returns: + dict: list of option used (short value is for filename differentiation) + """ + options = dict() + + if keep_import_reactions: + if topological_injection: + reaction_option = "Topological Injection" + short_option = "import_rxn_ti" + else: + reaction_option = "No Topological Injection" + short_option = "import_rxn_nti" + else: + reaction_option = "Remove Import Reaction" + short_option = "rm_rxn" + + target_option="" + match mode: + case "target": + network_option = "Target" + short_option += "_tgt" + if targets_as_seeds: + target_option = "Targets are allowed seeds" + else: + target_option = "Targets are forbidden seeds" + short_option += "_taf" + case "full": + network_option = "Full network" + short_option += "_fn" + case "fba": + network_option = "FBA" + short_option += "_fba" + if targets_as_seeds: + target_option = "Targets are allowed seeds" + else: + target_option = "Targets are forbidden seeds" + short_option += "_taf" + case _: + network_option = mode + short_option += f"_{mode.lower()}" + + match solve: + case 'reasoning': + solve_option = "REASONING" + short_option += "_reas" + case 'hybrid': + solve_option = "HYBRID" + short_option += "_hyb" + case 'guess_check': + solve_option = "REASONING GUESS-CHECK" + short_option += "_gc" + case 'guess_check_div': + solve_option = "REASONING GUESS-CHECK DIVERSITY" + short_option += "_gcd" + case 'filter': + solve_option = "REASONING FILTER" + short_option += "_fil" + case 'all': + if mode != "fba": + solve_option = "ALL" + short_option += "_all" + else: + solve_option = "" + short_option += "" + + if maximization: + flux_option = "Maximization" + short_option += "_max" + else: + flux_option = "With flux" + + if accumulation: + accu_option = "Allowed" + short_option += "_accu" + else: + accu_option = "Forbidden" + short_option += "_no_accu" + + options["short"] = short_option + options["reaction"] = reaction_option + options["network"] = network_option + options["target"] = target_option + options["flux"] = flux_option + options["accumulation"] = accu_option + options["solve"] = solve_option + + return options + + +def chek_inputs(sbml_file:str, input_dict:dict): + """ Checks the presence of elements in the sbml file + + Args: + sbml_file (str): Network sbml file + input_dict (dict): Input data ordered in dictionnary + + Raises: + ValueError: A reaction does not exist in network file + ValueError: A ùetabolite does not exist in network file + """ + model_dict = read_SBML_species(sbml_file) + for key, list_element in input_dict.items(): + if key == "Objective": + for reaction in list_element: + if f'{reaction}' not in model_dict["Reactions"]: + raise ValueError(f"Reaction {reaction} does not exist in network file {sbml_file}\n") + else: + for metabolite in list_element: + if metabolite not in model_dict["Metabolites"]: + raise ValueError(f"Metabolite {metabolite} does not exist in network file {sbml_file}\n") + + +def get_input_datas(seeds_file:str=None, + forbidden_seeds:str=None, possible_seeds:str=None): + """Get data from files given by user + + Args: + seeds_file (str, optional): Files containing mandatory seeds. Defaults to None. + forbidden_seeds (str, optional): Files containing forbidden seeds . Defaults to None. + possible_seeds (str, optional): Files containing possible seeds. Defaults to None. + + Returns: + _type_: _description_ + """ + input_dict = dict() + if seeds_file: + if file.file_is_empty(seeds_file): + logger.log.warning(f"\n{seeds_file} is empty.\nPlease check your file and launch again\n") + else: + input_dict["Seeds"] = utils.get_ids_from_file(seeds_file, 'seed_user') + if forbidden_seeds: + if file.file_is_empty(forbidden_seeds): + logger.log.warning(f"\n{forbidden_seeds} is empty.\nPlease check your file and launch again\n") + else: + input_dict["Forbidden seeds"] = utils.get_ids_from_file(forbidden_seeds, 'forbidden') + if possible_seeds: + if file.file_is_empty(possible_seeds): + logger.log.warning(f"\n{possible_seeds} is empty.\nPlease check your file and launch again\n") + possible_seeds=None + else: + input_dict["Possible seeds"] = utils.get_ids_from_file(possible_seeds, 'sub_seed') + return input_dict + + +def get_targets(targets_file:str, input_dict:dict) -> dict : + """Get metabolites target and objective reaction from file + Check if the given data exist into SBML file + ONLY USED WITH TARGET MODE + + Args: + targets_file (str): Path of target file + input_dict (dict): Constructed dictionnary of inputs + + Returns: + dict: Dictionnary of inputs completed + """ + + if file.file_is_empty(targets_file): + logger.log.warning(f"\n{targets_file} is empty.\nPlease check your file and launch again\n") + exit(1) + try: + input_dict["Targets"], input_dict["Objective"] = utils.get_targets_from_file(targets_file) + except ValueError as ve: + logger.log.error(str(ve)) + logger.log.warning("Please check your file and launch again\n") + exit(1) + except NotImplementedError as nie: + print(str(nie)) + exit(1) + + return input_dict + +def get_objective(objective:str, input_dict:dict): + """Get metabolites objective reaction from command line + Check if the given data exist into SBML file + ONLY USED WITH FULL NETWORK MODE + + Args: + objective (str): Name of new objective reaction from command line + input_dict (dict): Constructed dictionnary of inputs + + Returns: + dict: Dictionnary of inputs completed + """ + #Working with one objective for now + objectives = list() + objectives.append(objective) + input_dict["Objective"] = objectives + return input_dict + + + +############################################################ + + +############################################################ +####################### COMMANDS ########################### +############################################################ + + +#----------------------- SEED2LP --------------------------- +def run_seed2lp(args:dict, run_mode): + """Launch seed searching + + Args: + args (argparse): List or arguments + """ + minimize=False + subset_minimal=False + results=dict() + res_option = dict() + user_data = dict() + solutions = dict() + net = dict() + + options = \ + get_reaction_options(args['keep_import_reactions'], args['topological_injection'], + args['targets_as_seeds'], args['maximize_flux'], + run_mode, args['accumulation'], args['solve']) + + logger.get_logger(args['infile'], options["short"],args['verbose']) + + if args['temp']: + temp = Path(args['temp']).resolve() + else: + temp = path.join(PROJECT_DIR,'tmp') + file.is_valid_dir(temp) + + out_dir = args['output_dir'] + file.is_valid_dir(out_dir) + + # Getting the networks from sbml file + # Extract the ASP representation + time_data_extraction = time() + input_dict = get_input_datas(args['seeds_file'], args['forbidden_seeds_file'], + args['possible_seeds_file']) + if 'targets_file' in args and args['targets_file']: # only in target mode + input_dict = get_targets(args['targets_file'], input_dict) + if 'objective' in args and args['objective']: # only in full network mode + input_dict = get_objective(args['objective'], input_dict) + + # Verify if input data exist into sbml file + try: + chek_inputs(args['infile'], input_dict) + except ValueError as e : + logger.log.error(str(e)) + exit(1) + + network = Network(args['infile'], run_mode, args['targets_as_seeds'], + args['topological_injection'], args['keep_import_reactions'], + input_dict, args['accumulation']) + + + time_data_extraction = time() - time_data_extraction + + + if network.targets: + user_data['TARGETS'] = network.targets + if network.seeds: + user_data['SEEDS'] = network.seeds + if network.forbidden_seeds: + user_data['FORBIDDEN SEEDS'] = network.forbidden_seeds + if network.possible_seeds: + if args['mode'] == 'minimize' or args['mode'] == 'all': + user_data['POSSIBLE SEEDS'] = network.possible_seeds + else: + logger.log.error("Possible seed can be used only with minimize mode") + exit(1) + + res_option['REACTION'] = options['reaction'] + if run_mode == "target" or run_mode == 'fba': + res_option['TARGET'] = options['target'] + res_option['FLUX'] = options['flux'] + res_option['ACCUMULATION'] = options['accumulation'] + results["OPTIONS"]=res_option + net["NAME"] = network.name + net["OBJECTIVE"] = network.objectives + net["SEARCH_MODE"] = options['network'] + net["SOLVE"] = options["solve"] + results["NETWORK"] = net + + results["USER DATA"] = user_data + + if not args['targets_as_seeds']: + network.forbidden_seeds += network.targets + + + network.convert_to_facts() + + if args['instance']: + with open(args['instance'], "w") as f: + f.write(network.facts) + exit(1) + + network.simplify() + + if args['mode'] == 'minimize' or args['mode'] == 'all': + minimize = True + if args['mode'] == 'subsetmin' or args['mode'] == 'all': + subset_minimal = True + + # Global seed searching time + time_seed_search = time() + + match run_mode: + case "target" | "full": + run_solve = args['solve'] + if run_solve != "hybrid" or run_solve == 'all': + if network.is_objective_error and (run_solve != "reasoning" or run_mode == "target"): + end_message = " aborted! No Objective found.\n" + match run_solve,run_mode: + case _,"target": + logger.log.error(f"Mode Target {end_message}") + case 'filter','full': + logger.log.error(f"Solve Filter {end_message}") + case 'guess_check','full': + logger.log.error(f"Solve Guess Check {end_message}") + case 'guess_check_div','full': + logger.log.error(f"Solve Guess Check Diversity {end_message}") + # In reasoning classic, in Full Network, no need to have an objective reaction (event it is deleted) + case 'all','full': + model = Reasoning(run_mode, "reasoning", network, args['time_limit'], args['number_solution'], + args['clingo_configuration'], args['clingo_strategy'], + args['intersection'], args['union'], minimize, subset_minimal, + temp, options['short'], + args['verbose']) + model.search_seed() + solutions['REASONING'] = model.output + results["RESULTS"] = solutions + # Intermediar saving in case of hybrid mode fails + file.save(f'{network.name}_{options["short"]}_results',out_dir, results, 'json') + logger.log.error(f"Solve Filter / Guess Check / Guess Check Diversity {end_message}") + solutions['REASONING-OTHER'] = "No objective found" + elif run_mode == "target" and not network.targets: + logger.log.error(f"Mode REASONING aborted! No target found") + solutions['REASONING'] = "No target found" + else: + model = Reasoning(run_mode, run_solve, network, args['time_limit'], args['number_solution'], + args['clingo_configuration'], args['clingo_strategy'], + args['intersection'], args['union'], minimize, subset_minimal, + temp, options['short'], + args['verbose']) + model.search_seed() + solutions['REASONING'] = model.output + results["RESULTS"] = solutions + # Intermediar saving in case of hybrid mode fails + file.save(f'{network.name}_{options["short"]}_results',out_dir, results, 'json') + + if run_solve == "hybrid" or run_solve == 'all': + if not network.objectives or network.is_objective_error: + logger.log.error(f"Mode HYBRID aborted! No objective found") + solutions['HYBRID'] = "No objective found" + else: + model = Hybrid(run_mode, run_solve, network, args['time_limit'], args['number_solution'], + args['clingo_configuration'], args['clingo_strategy'], + args['intersection'], args['union'], minimize, subset_minimal, + args['maximize_flux'], temp, + options['short'], args['verbose']) + model.search_seed() + solutions['HYBRID'] = model.output + results["RESULTS"] = solutions + case "fba": + if not network.objectives or network.is_objective_error: + logger.log.error(f"Mode HYBFBARID aborted! No objective found") + solutions['FBA'] = "No objective found" + else: + model = FBA(run_mode, network, args['time_limit'], args['number_solution'], + args['clingo_configuration'], args['clingo_strategy'], + args['intersection'], args['union'], minimize, subset_minimal, + args['maximize_flux'], temp, + options['short'], args['verbose']) + model.search_seed() + solutions['FBA'] = model.output + results["RESULTS"] = solutions + time_seed_search = time() - time_seed_search + + # Show the timers + timers = {'DATA EXTRACTION': time_data_extraction} |\ + {'TOTAL SEED SEARCH': time_seed_search, + 'TOTAL': time_data_extraction + time_seed_search + } + + namewidth = max(map(len, timers)) + time_mess="" + for name, value in timers.items(): + value = value if isinstance(value, str) else f'{round(value, 3)}s' + time_mess += f'\nTIME {name.center(namewidth)}: {value}' + + print(time_mess) + logger.log.info(time_mess) + print("\n") + + # Save the result into json file + file.save(f'{network.name}_{options["short"]}_results',out_dir, results, 'json') + + # Save all fluxes into tsv file + if args['check_flux']: + network.check_fluxes(args['maximize_flux']) + file.save(f'{network.name}_{options["short"]}_fluxes', out_dir, network.fluxes, 'tsv') + + if CLEAN_TEMP: + file.delete(network.instance_file) + + return results, timers + + + +#---------------------- NETWORK --------------------------- +def network_rendering(args:argparse): + """Launch rendering as Network description (reaction formula) + or as Graphs + + Args: + args (argparse): List or arguments + """ + if args["keep_import_reactions"]: + reac_status="import_rxn_" + else: + reac_status="rm_rxn_" + logger.get_logger(args['infile'], f"{reac_status}network_render", args['verbose']) + + network = Description(args['infile'], args['keep_import_reactions'], + args['output_dir'], details = args['network_details'], + visu = args['visualize'], + visu_no_reaction = args['visualize_without_reactions'], + write_file = args['write_file'],) + + if network.details: + network.get_details() + if network.visu or network.visu_no_reaction: + time_rendering = time() + print('Rendering…') + network.render_network() + time_rendering = time() - time_rendering + if network.write_file: + network.rewrite_sbml_file() + + + + +#---------------------- FLUX --------------------------- +def network_flux(args:argparse): + """Check the Network flux using cobra from a seed2lp result file. + Needs the sbml file of the network. + Write the file in output directory. + + Args: + args (argparse): List or arguments + """ + logger.get_logger(args['infile'], "check_fluxes", args['verbose']) + + network = Network(args['infile'], to_print=False) + data = load_json(args['result_file']) + maximize, solve = network.convert_data_to_resmod(data) + network.check_fluxes(maximize) + + options = \ + get_reaction_options(network.keep_import_reactions, network.use_topological_injections, + network.targets_as_seeds, maximize, network.run_mode, network.accumulation, solve) + if args['output_dir']: + file.save(f'{network.name}_{options["short"]}_fluxes_from_result', args['output_dir'], network.fluxes, 'tsv') + + + + +#---------------------- SCOPE --------------------------- +def scope(args:argparse): + """Check the Network flux using cobra from a seed2lp result file. + Needs the sbml file of the network. + Write the file in output directory. + + Args: + args (argparse): List or arguments + """ + logger.get_logger(args['infile'], "scope", args['verbose']) + network = Network(args['infile'], to_print=False) + data = load_json(args['result_file']) + network.convert_data_to_resmod(data) + scope = Scope(args['infile'], network, args['output_dir']) + scope.execute() + + + +#------------------- CONF FILE ------------------------ +def save_conf(args:argparse): + conf_path = path.join(PROJECT_DIR,'config.yaml') + new_pah = path.join(args['output_dir'], 'config.yaml') + copyfile(conf_path, new_pah) + + +#------------------ WRITE TARGETS ----------------------- +def get_objective_targets(args:argparse): + """Get the metabolites reactant of objective reaction or found + + Args: + args (argparse): List or arguments + """ + logger.get_logger(args['infile'], "objective_targets", args['verbose']) + input_dict = get_input_datas() + if 'objective' in args and args['objective']: # only in full network mode + input_dict = get_objective(args['objective'], input_dict) + try: + chek_inputs(args['infile'], input_dict) + except ValueError as e : + logger.log.error(str(e)) + exit(1) + network = Network(args['infile'], run_mode="target", input_dict=input_dict, to_print=False) + + print("List of targets: ",network.targets) + file.save(f"{network.name}_targets", args['output_dir'],network.targets,"txt") + + +############################################################ + + +def main(): + args = argument.parse_args() + cfg = argument.get_config(args, PROJECT_DIR) + + logger.set_log_dir(path.join(args.output_dir,"logs")) + is_valid_dir(logger.LOG_DIR) + + match args.cmd: + case "target" | "full"| "fba": + run_seed2lp(cfg, args.cmd) + case "network": + network_rendering(cfg) + case "flux": + network_flux(cfg) + case "scope": + scope(cfg) + case "conf": + save_conf(cfg) + case "objective_targets": + get_objective_targets(cfg) + +if __name__ == '__main__': + main() + \ No newline at end of file diff --git a/seed2lp/_version.py b/seed2lp/_version.py new file mode 100644 index 0000000..27272b3 --- /dev/null +++ b/seed2lp/_version.py @@ -0,0 +1,2 @@ +__version_info__ = ('1', '0', '0') +__version__ = '.'.join(__version_info__) \ No newline at end of file diff --git a/seed2lp/argument.py b/seed2lp/argument.py new file mode 100644 index 0000000..3dd4e6e --- /dev/null +++ b/seed2lp/argument.py @@ -0,0 +1,517 @@ +import argparse +import yaml +from sys import argv +from os import path +from ._version import __version__ +from .file import existant_path, is_valid_dir + +DESCRIPTION = """ +Seed detection in metabolic networks. +""" +LICENSE = """ + GNU GENERAL PUBLIC LICENSE + Version 3, 29 June 2007 + """ + +############################################################ +##################### COMMAND PARSER ####################### +############################################################ + +def cli_parser() -> argparse.ArgumentParser: + parser = argparse.ArgumentParser( + "Seed2LP", + description=DESCRIPTION, + formatter_class=argparse.RawTextHelpFormatter + ) + + parser.add_argument( + "-v", + "--version", + action="version", + version="%(prog)s " + f"{__version__} \n{LICENSE}") + + + pp_verbose = argparse.ArgumentParser(add_help=False) + pp_verbose.add_argument( + '--verbose', '-v', action='store_true', + help="Print every process steps. Debug mode." + ) + + #----------------------- POSITIONNAL --------------------------- + + # run / Calculate flux / Network representations + pp_network = argparse.ArgumentParser(add_help=False) + pp_network.add_argument( + dest="infile", + help="SBML or ASP file containing the graph data.", + type=existant_path + ) + # Calculate flux + pp_result = argparse.ArgumentParser(add_help=False) + pp_result.add_argument( + dest="result_file", + help="Seed2lp result file containing results.", + type=existant_path + ) + # Network representations + pp_output_dir = argparse.ArgumentParser(add_help=False) + pp_output_dir.add_argument( + dest="output_dir", + help="Output directory path", + type=is_valid_dir + ) + + #--------------------- PARSERS ------------------------- + + #------------------------------------------------------- + # Supplementary data given by user + #------------------------------------------------------- + pp_targets_file = argparse.ArgumentParser(add_help=False) + pp_targets_file.add_argument( + "-tf", "--targets-file", dest="targets_file", + type=existant_path, default=None, + help="file containing one target per line", + required=False + ) + pp_seeds_file = argparse.ArgumentParser(add_help=False) + pp_seeds_file.add_argument( + "-sf","--seeds-file", dest="seeds_file", + type=existant_path, default=None, + help="file containing one seed per line", + required=False + ) + pp_possible_seeds_file = argparse.ArgumentParser(add_help=False) + pp_possible_seeds_file.add_argument( + '-psf', '--possible-seeds-file', dest="possible_seeds_file", + type=existant_path, default=None, + help="file containing a set of compounds in which seed selection will be performed (greedy mode only)", + required=False + ) + pp_forbidden_seeds_file = argparse.ArgumentParser(add_help=False) + pp_forbidden_seeds_file.add_argument( + '-fsf', '--forbidden-seeds-file', dest="forbidden_seeds_file", + type=existant_path, default=None, + help="file containing one forbidden seed per line", + required=False + ) + pp_objective = argparse.ArgumentParser(add_help=False) + pp_objective.add_argument( + "-o","--objective", dest="objective", + type=str, default=[], + help="objective reaction to activate in the graph", + required=False + ) + + #------------------------------------------------------- + # Set mode + #------------------------------------------------------- + pp_mode = argparse.ArgumentParser(add_help=False, formatter_class=argparse.RawTextHelpFormatter) + pp_mode.add_argument( + '-m', '--mode', dest="mode", + type=str, default='subsetmin', choices=['minimize', 'subsetmin', 'all'], + help="""Choose a mode for comuting solutions: \n \ + - minimize : The smallest set of seed \n \ + - subsetmin : All the minimal solution included into solutions (subset minimal)\n \ + - all: Compute subsetmin then minimize""", + required=False + ) + pp_solve = argparse.ArgumentParser(add_help=False, formatter_class=argparse.RawTextHelpFormatter) + pp_solve.add_argument( + '-so', '--solve', dest="solve", + type=str, default='reasoning', choices=['reasoning', 'filter', 'guess_check', 'guess_check_div', 'hybrid', 'all'], + help="Select the solving mode\n \ + - reasoning : Only reasoning, no linear calcul \n \ + - hybrid : Reasoning and linar calcul\n \ + - guess_check : Only reasoning with guess and check results using cobra (adapts rules) \n \ + - guess_check_div : Only reasoning with guess and check results using cobra (adapts rules) and add diversity \n \ + - filter : Only reasoning with a cobra filter validation during search (do not adapt rules) \n \ + - all : Compute reasoning then hybrid then fba", + required=False + ) + pp_intersection = argparse.ArgumentParser(add_help=False) + pp_intersection.add_argument( + '-i', '--intersection', dest="intersection", + action='store_true', + help="Compute intersection of solutions", + required=False + ) + pp_union = argparse.ArgumentParser(add_help=False) + pp_union.add_argument( + '-u', '--union', dest="union", + action='store_true', + help="Compute union of solutions", + required=False + ) + + #------------------------------------------------------- + # Set of seed restrictions + #------------------------------------------------------- + pp_targets_as_seeds = argparse.ArgumentParser(add_help=False) + pp_targets_as_seeds.add_argument( + '-tas', '--targets-as-seeds', dest="targets_as_seeds", + action='store_true', + help="Targets are allowed as seeds", + required=False + ) + pp_topological_injection = argparse.ArgumentParser(add_help=False) + pp_topological_injection.add_argument( + '-ti', '--topological-injection', dest="topological_injection", + action='store_true', + help="Use topological injection found in sbml data", + required=False + ) + pp_keep_import_reactions = argparse.ArgumentParser(add_help=False) + pp_keep_import_reactions.add_argument( + '-kir', '--keep-import-reactions', dest="keep_import_reactions", + action='store_true', + help="Keep import reactions found in sbml file", + required=False + ) + pp_accumulation = argparse.ArgumentParser(add_help=False) + pp_accumulation.add_argument( + '-accu', '--accumulation', dest="accumulation", + action='store_true', + help="Accumulation allowed", + required=False + ) + + + #------------------------------------------------------- + # Flux options + #------------------------------------------------------- + pp_check_flux= argparse.ArgumentParser(add_help=False) + pp_check_flux.add_argument( + '-cf', '--check-flux', dest="check_flux", + action='store_true', + help="Run a flux check on a resulted set of seeds using cobra.py", + required=False + ) + pp_maximize_flux = argparse.ArgumentParser(add_help=False) + pp_maximize_flux.add_argument( + '-max', '--maximize-flux', dest="maximize_flux", + action='store_true', + help="Maximize the flux of objective reaction", + required=False + ) + + #------------------------------------------------------- + # clingo parameters for solving + #------------------------------------------------------- + pp_clingo_configuration = argparse.ArgumentParser(add_help=False) + pp_clingo_configuration.add_argument( + '-cc', '--clingo-configuration', dest="clingo_configuration", + type=str, default='jumpy', choices=['jumpy', 'none'], + help="Changing clingo configuration: jumpy / none", + required=False + ) + pp_clingo_strategy = argparse.ArgumentParser(add_help=False) + pp_clingo_strategy.add_argument( + '-cs', '--clingo-strategy', dest="clingo_strategy", + type=str, default='none', choices=['usc,oll', 'none'], + help="Changing clingo strategy: usc,oll / none", + required=False + ) + pp_time_limit = argparse.ArgumentParser(add_help=False) + pp_time_limit.add_argument( + '-tl', '--time-limit', dest="time_limit", + type=float, default=0, + help="Add a time limit in minutes for finding seeds. By default 0, meaning no limit", + required=False + ) + pp_number_solution = argparse.ArgumentParser(add_help=False) + pp_number_solution.add_argument( + '-nbs', '--number-solution', dest="number_solution", + type=int, default=-1, + help="Change the number of solution limit. By default:-1. \ + \n0 solution means no limit. \ + \n-1 means no enumeration", + required=False + ) + + #------------------------------------------------------- + # ASP Facts storage + #------------------------------------------------------- + pp_instance = argparse.ArgumentParser(add_help=False) + pp_instance.add_argument( + '-in', '--instance', dest="instance", + type=str, default=None, + help="export the ASP instance of input data and quit", + required=False + ) + pp_temp = argparse.ArgumentParser(add_help=False) + pp_temp.add_argument( + '-tmp', '--temp', dest="temp", + type=str, + help="Temporary directory for hybrid or fba mode.", + required=False + ) + + #------------------------------------------------------- + # Network description + #------------------------------------------------------- + pp_visualize = argparse.ArgumentParser(add_help=False) + pp_visualize.add_argument( + '-vi', '--visualize', dest="visualize", + action='store_true', + help="Render the input graph in png (default: don't render)", + required=False + ) + + pp_visualize_without_reactions = argparse.ArgumentParser(add_help=False) + pp_visualize_without_reactions.add_argument( + '-vir', '--visualize-without-reactions', dest="visualize_without_reactions", + action='store_true', + help="Render the input graph without reactions in png (default: don't render)", + required=False + ) + pp_network_details = argparse.ArgumentParser(add_help=False) + pp_network_details.add_argument( + '-nd', '--network-details', dest="network_details", + action='store_true', + help="Render the description fo the network from lp and with cobra", + required=False + ) + pp_write_file = argparse.ArgumentParser(add_help=False) + pp_write_file.add_argument( + '-wf', '--write-file', dest="write_file", + action='store_true', + help="Write the network into a sbml file after applying modifications on it for simplifying reasoning.", + required=False + ) + + #------------------------------------------------------- + # Config file + #------------------------------------------------------- + pp_config = argparse.ArgumentParser(add_help=False) + pp_config.add_argument( + '-conf', '--config-file', dest="config_file", + type=str, + help="Configuration file to use.", + required=False + ) + + #------------------------------------------------------- + # Commands commposition + #------------------------------------------------------- + subparsers = parser.add_subparsers( + title='subcommands', + description='valid subcommands:', + dest="cmd",) + + subparsers.add_parser( + "target", + help="Run seeds detection focusing on targets.", + parents=[ + pp_verbose, + pp_network, pp_output_dir, + pp_targets_file, + pp_seeds_file, + pp_possible_seeds_file, + pp_forbidden_seeds_file, + pp_mode, pp_solve, pp_intersection, pp_union, + pp_targets_as_seeds, pp_topological_injection, pp_keep_import_reactions, + pp_clingo_configuration, pp_clingo_strategy, pp_time_limit, pp_number_solution, + pp_instance, pp_temp, pp_check_flux, pp_maximize_flux, + pp_config, pp_accumulation + ], + description= + """ + TODO + """, + usage=""" + seed2lp target network_file \n + """ + ) + + subparsers.add_parser( + "full", + help="Run seeds detection focusing on full network.", + parents=[ + pp_verbose, + pp_network, pp_output_dir, + pp_objective, + pp_seeds_file, + pp_possible_seeds_file, + pp_forbidden_seeds_file, + pp_mode, pp_solve, pp_intersection, pp_union, + pp_topological_injection, pp_keep_import_reactions, + pp_clingo_configuration, pp_clingo_strategy, pp_time_limit, pp_number_solution, + pp_instance, pp_temp, pp_check_flux, pp_maximize_flux, + pp_config, pp_accumulation + ], + description= + #TODO + """ + """, + usage=""" + seed2lp full network_file \n + """ + ) + + subparsers.add_parser( + "fba", + help="Run seeds detection in aleatory way focusing on fba.", + parents=[ + pp_verbose, + pp_network, pp_output_dir, + pp_objective, + pp_seeds_file, + pp_possible_seeds_file, + pp_forbidden_seeds_file, + pp_mode, pp_intersection, pp_union, + pp_targets_as_seeds, pp_topological_injection, pp_keep_import_reactions, + pp_clingo_configuration, pp_clingo_strategy, pp_time_limit, pp_number_solution, + pp_instance, pp_temp, pp_check_flux, pp_maximize_flux, + pp_config + ], + description= + #TODO + """ + + """, + usage=""" + seed2lp fba network_file \n + """ + ) + + subparsers.add_parser( + "network", + help="Give Network Details with reactions, create graph of Network, rewrite network sbml file", + parents=[ + pp_verbose, + pp_network, pp_output_dir, + pp_keep_import_reactions, + pp_visualize, pp_visualize_without_reactions, + pp_network_details, pp_write_file + ], + description= + #TODO + """ + + """, + usage=""" + seed2lp network [network_file] [output_dir] --visualize \n + """ + ) + + subparsers.add_parser( + "flux", + help="Calculate cobra flux from seed2lp result file, using sbml file", + parents=[ + pp_verbose, pp_network, pp_result, pp_output_dir + ], + description= + #TODO + """ + + """, + usage=""" + seed2lp flux [sbml_file] [seed2lp_result_file] [output_directory] \n + """ + ) + + subparsers.add_parser( + "scope", + help="From seeds determine scope of the network. ", + parents=[ + pp_verbose, pp_network, pp_result, pp_output_dir, pp_temp + ], + description= + #TODO + """ + + """, + usage=""" + seed2lp scope [sbml_file] [seed2lp_result_file] [output_directory] \n + """ + ) + + subparsers.add_parser( + "conf", + help="Copy and save a conf file. ", + parents=[ + pp_output_dir + ], + description= + #TODO + """ + + """, + usage=""" + seed2lp conf [output_directory] \n + """ + ) + + subparsers.add_parser( + "objective_targets", + help="Get the objective reaction reactants metabolites and write them into a file", + parents=[ + pp_verbose, pp_network, pp_objective, pp_output_dir + ], + description= + #TODO + """ + + """, + usage=""" + seed2lp conf [output_directory] \n + """ + ) + + return parser + +def parse_args(args: iter = None) -> dict: + return cli_parser().parse_args(args) + + +####################### FUNCTIONS ########################## + + +def get_config(args:argparse.Namespace, project_source): + dict_check = {"--verbose" : "-v", + "--mode" : "-m", + "--solve" : "-so", + "--intersection" : "-i", + "--union" : "-u", + "--targets-as-seeds" : "-tas", + "--topological-injection" : "-ti", + "--keep-import-reactions" : "-kir", + "--check-flux" : "-cf", + "--maximize-flux" : "-max", + "--clingo-configuration" : "-cc", + "--clingo-strategy" : "-cs", + "--time-limit" : "-tl", + "--number-solution" : "-nbs", + "--temp" : "-tmp", + "--accumulation" : "-accu"} + + conf_argparse = vars(args) + conf_file=None + # Get config file path + if "config_file" not in args: + cfg = conf_argparse + elif args.config_file == None: + conf_file = path.join(project_source,'config.yaml') + else: + conf_file = args.config_file + + # Get configs from file + if conf_file is not None: + with open(conf_file, "r") as ymlfile: + cfg_file = yaml.load(ymlfile, Loader=yaml.FullLoader) + + # Overwrite configs with cli argument + match args.cmd: + case "target" | "full" | "fba": + for key, value in conf_argparse.items(): + cfg = cfg_file['seed2lp'] + if key not in cfg: + cfg[key] = value + else: + for long , short in dict_check.items(): + if long in argv or short in argv: + long = long.lstrip("-") + long = long.replace("-","_") + cfg[long] = conf_argparse[long] + + return cfg \ No newline at end of file diff --git a/seed2lp/asp/enum-cc.lp b/seed2lp/asp/enum-cc.lp new file mode 100644 index 0000000..3dbc2a5 --- /dev/null +++ b/seed2lp/asp/enum-cc.lp @@ -0,0 +1,50 @@ +% Enumeration of Connected Components. +% INPUTS: +% - reaction(R): R is a reaction. +% - reactant(T,R): T is a reactant of reaction R. +% - product(P,R): P is a product of reaction R. +reaction(R) :- dreaction(R). +% OUTPUTS: +#show scc/2. % nodes in each SCC +#show noinput/1. % root SCC +#show sccedge/2. % link between SCC + + +% the edges are linking reactants to products of each reaction. +oedge(T,P) :- reactant(T,R) ; product(P,R). + +% the nodes +node(M) :- reactant(M,_). +node(M) :- product(M,_). + +% #show oedge/2. +% #show node/1. + + +% recursive definition of link between nodes +link(A,B) :- oedge(A,B). +link(A,C) :- oedge(A,B) ; link(B,C). + +% definitions of cycle +% A is in a cycle +cycle(A) :- link(A,A). +% A and B are in the same cycle +cycle(A,B) :- link(A,B) ; link(B,A) ; cycle(A) ; cycle(B) ; A<=B. + +% defining strongly connected components (scc) +scc_(A,C) :- cycle(A,C) ; not cycle(B,A) : cycle(B), B 0 +&sum{ R :objective(R) } >= "0.0001". +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \ No newline at end of file diff --git a/seed2lp/asp/maximize_flux.lp b/seed2lp/asp/maximize_flux.lp new file mode 100644 index 0000000..fb70c65 --- /dev/null +++ b/seed2lp/asp/maximize_flux.lp @@ -0,0 +1,2 @@ +% Maximization +&maximize{ R : objective(R) }. \ No newline at end of file diff --git a/seed2lp/asp/maximize_produced_target.lp b/seed2lp/asp/maximize_produced_target.lp new file mode 100644 index 0000000..565cd2d --- /dev/null +++ b/seed2lp/asp/maximize_produced_target.lp @@ -0,0 +1,7 @@ +% Maximization +% When possible seeds given by the user, a subset of these seeds is choosen +% by maximizing the targets into the scope +nb_produced_targets(N) :- N = #count{ M : target(M), activated(M) }. + +% maximize number produced target has a higher priority than minimize seed (from minimize.lp) +#maximize{N@2, N:nb_produced_targets(N)}. \ No newline at end of file diff --git a/seed2lp/asp/minimize.lp b/seed2lp/asp/minimize.lp new file mode 100644 index 0000000..3d6b03d --- /dev/null +++ b/seed2lp/asp/minimize.lp @@ -0,0 +1,8 @@ +% Add minimze option and show the total number of seeds in a set +% Ensure the minimality of the set of seeds + +nb_seed(N) :- N=#count{X,M : seed(X,M)}. + +% minimize seed has a lower priority than maximize number produced target (from maximize_produced_targets.lp) +#minimize{N@1, N:nb_seed(N)}. +%#show nb_seed/1. diff --git a/seed2lp/asp/seed-solving.lp b/seed2lp/asp/seed-solving.lp new file mode 100644 index 0000000..a3c2aad --- /dev/null +++ b/seed2lp/asp/seed-solving.lp @@ -0,0 +1,174 @@ +% Search of the minimal set of seeds in all graph activating all targets or specific targets. +% The +% +% INPUTS: +% - seed_user(S): node S is a seed given by users +% - forbidden(S): node S cannot be a seed +% - target(T): node T must be activated +% - reaction(R): R is a reaction. +% - reactant(T,_,R,_): T is a reactant of reaction R. +% - product(P,_,R,_): P is a product of reaction R. +% OUTPUTS: one model for each set of seed that activate all metabolites +% - seed(S,_): node S is a seed +#const run_mode=target. % full/target/fba +#const accu=0. +#const subseed=0. % mode select sub seed amoung possible seeds given by user + +% A metabolite is a reactant or product. +metabolite(M,X) :- reactant(M,_,_,X). +metabolite(M,X) :- product(M,_,_,X). + + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%% INITIAL COMPUTATION %%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% An initial seed is activated +activated_initial(S) :- seed_user(S). +activated_initial(S) :- seed_external(S). + +% Compute the initial scope from intial seeds +activated_initial(M) :- metabolite(M,_) ; product(M,_,R,_) ; + activated_initial(T): reactant(T,_,R,_); + % We reject de reaction created for flux mode to not have + % as activated initial the seed and the reaction + % can creates problems for cycles + not reac_import(M), not reac_export(M), + not import_exch(R), not import_exch_created(M), not export_exch_created(M). + + +% Define which non exchange metabolite is an imported metabolite +% meaning, there is a reation that transport the metabolite frome extracellular (tagued exchange) +% into intracellular (tagued other) +transported_meta(M) :- product(M,_,_,"transport"). +transported_meta(M) :- reactant(M,_,_,"transport"). +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +%%%%%%%%%%%%%%%%%%%%%%% POSSIBLE SEEDS WHEN GIVEN BY USER %%%%%%%%%%%%%%%%%%%%%%% +% When user gives a file of possible seeds, we want to find a subset of this seeds +% that respects the constraints (in any mode) +p_seed(M) :- sub_seed(M), subseed=1. +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + + +%%%%%%%%%%%%%%%%%%%%%%%%% TARGET SPECIFIC SEEDS SEARCH %%%%%%%%%%%%%%%%%%%%%%%%% +% Try to cut the set of possible seeds +can_reach(M) :- target(M) ; run_mode=target, subseed=0. +can_reach(M) :- reactant(M,_,R,_) ; product(P,_,R,_) ; can_reach(P) ; run_mode=target, subseed=0. + +% Possible seed detection when the user do not give them + +% Determine possible seeds when accumulation allowed +% Eliminate the metabolite imported (having an import reaction) +% Eliminate for example all the combination : +% M_S1_e, M_S2_e, M_S3_e with M_S1_C, M_S2_c, M_S3_c +% keep only M_S1_e, M_S2_e, M_S3_e +p_seed(M) :- can_reach(M), metabolite(M,_), not seed_user(M), + not transported_meta(M); + run_mode=target, accu=1, subseed=0. + + +% Case accumulation not allowed, we need to reach more metabolite +% to access to solution without accumulation +p_seed(M) :- metabolite(M,_), not seed_user(M), + not transported_meta(M); + run_mode=target, accu=0, subseed=0. + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +%%%%%%%%%%%%%%%%%%%%%%%%%% FULL NETWORK SEEDS SEARCH %%%%%%%%%%%%%%%%%%%%%%%%%% +% A metabolite which is not a product of any reaction except +% except his own exchange reaction is an external seed and must be a seed +% when the search is full network +% Also there is some cases when we have a reaction reversible where there is no +% exchange reaction. Such as B_m <-> B_c, then None -> A_c and B_c + A_c -> C_c +% We need to select one of the metabolite B as seed, but because of the reversibility +% it is not selected if we don't take the reversible reaction into account. +seed_external(M) :- reactant(M,_,_,_), + not product(M,_,R,_): reaction(R), + not exchange(R), not reversible(_,R); + not transported_meta(M); + run_mode=full, subseed=0. +seed_external(M) :- reactant(M,_,_,_), + not product(M,_,R,_): reaction(R), + not exchange(R), not reversible(R,_); + not transported_meta(M); + run_mode=full, subseed=0. + +% A metabolite which is only product of reaction cannot be a seed +impossible_seed(M) :- product(M,_,_,_) ; not reactant(M,_,_,_) ; run_mode=full, subseed=0. + +target(M) :- metabolite(M,_) ; run_mode=full. + +% Determine possible seeds all the time (with or without accumulation) +p_seed(M) :- metabolite(M,_); not transported_meta(M) ; + not impossible_seed(M) ; not activated_initial(M) ; + run_mode=full, subseed=0. +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% FBA SEEDS SEARCH %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Determine possible seeds for FBA : All metabolites are possible seeds +% Randomly choose metabolites, excepts the metabolite that are never consumed +% otherwise the flux will create an import/Export reaction to avoid accumulation +p_seed(M) :- metabolite(M,_), not transported_meta(M), + not activated_initial(M), not authorized_accu(M), + run_mode=fba, subseed=0. +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%% SCOPE DETERMINATION %%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% A metabolite is activated if produced by a reaction +% with all its reactants activated. +activated(M) :- activated_initial(M). +activated(S) :- new_seed(S). + +scopeR(R) :- reaction(R) ; activated(M): reactant(M,_,R,_). +activated(M) :- product(M,_,R,_) ; scopeR(R). +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% ACCUMULATION %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Avoid metabolite accumulation for target and full network mode +% The authorized accumulation of metabolites are for compounds that are never +% consumed by any reaction in the sbml file +% If FBA mode, all compounds are allowed to be accumulated, the fba will select +% the model without accumulation by itself with flux calculation +authorized_accu(M) :- product(M,_,_,_), not reactant(M,_,R,_): reaction(R), + R != reac_export(M), R!= export_exch_created(M). + +% Consumed at least one time +%%%%%%% TARGET OR FULL NETWORK %%%%% +% Target or Full mode, when accumulation not allowed +is_conso(M) :- reactant(M,_,R,_), scopeR(R), activated(M), accu=0, + R != reac_export(M), R!= export_exch_created(M); run_mode!=fba. +:- product(M,_,R,_), activated(M), not is_conso(M), + accu=0, not authorized_accu(M), run_mode!=fba. + + +%%%%%%% FBA %%%%% +% FBA mode, for checking if a seed is here to create export reaction and avoid +% accumulation +is_conso(M) :- reactant(M,_,R,_), scopeR(R), activated(M), + R != reac_export(M), R!= export_exch_created(M); run_mode=fba. +seed_accu(M) :- not is_conso(M), seed(M,_); run_mode=fba. + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + + + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% SEED SELECTION %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Choose a set of seed from the possible seeds +{ new_seed(M): p_seed(M), not forbidden(M) }. + +% Discard any model that is not activating all targets when not possible seeds +% given by the user. +:- target(M) ; not activated(M), run_mode!=fba, subseed=0. + + +% A seed is a seed given by the user, or an external seed +% or a new seed coming from the combinations of possible seeds +seed(M,X) :- seed_user(M), metabolite(M,X). +seed(M,X) :- seed_external(M), metabolite(M,X). +seed(M,X) :- new_seed(M), metabolite(M,X). +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +#show seed/2. +#show seed_accu/1. \ No newline at end of file diff --git a/seed2lp/clingo_lpx.py b/seed2lp/clingo_lpx.py new file mode 100644 index 0000000..3794240 --- /dev/null +++ b/seed2lp/clingo_lpx.py @@ -0,0 +1,235 @@ +""" +Clingo lpx functions for launching command and extract results. +""" + +import subprocess +import json +from resource import getrusage, RUSAGE_CHILDREN +from .utils import repair_json +from . import logger, color + + +def command(files:list, options:list, nb_model:int=0, time_limit:int=0) -> iter: + """Create the Clingo-lpx command to run for solving + + Args: + files (list): List of ASP files used for sovling. + options (list): Clingo options + nb_model (int, optional): Limit number of solutions to find. Defaults to 0 meaning unlimited. + time_limit (int, optional): Time limit given by user in minutes. Defaults to 0 meaning unlimited. + + Raises: + ValueError: If the number of model is negative, the command is not correct + + Returns: + iter: Composition of the command to run + """ + + CMD = ['python', '-m', 'clingolpx'] + options = list(filter(None, options)) + + if time_limit: + time_limit = int(time_limit) + options.append(f'--time-limit={str(time_limit)}') + if nb_model: + nb_model = int(nb_model) + if nb_model < 0: + raise ValueError("Number of model must be >= 0.") + options.append(f'-n {str(nb_model)}') + else: + options.append(f'-n 0') + + options.append("--warn=none") + + if files: + cmd = [*CMD, *files, *options] + else: + cmd = [*CMD, *options] + + return cmd + + +def solve(cmd:list, time_limit:int): + """Launch the Clingo-lpx command + + Args: + cmd (list): Clingo-lpx command + time_limit (int): Time limit given by user in minutes. + + Returns: + proc_output (str), err_output (str), error_code (int), memory (float), is_killed (bool) + """ + + cmd.append(f'--outf=2') + is_killed=False + try: + process = subprocess.Popen(cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE) + if time_limit: + process.wait(timeout=time_limit+60) + except subprocess.TimeoutExpired: + logger.log.error(f'Timeout: {time_limit/60} min expired') + process.kill() + process.wait() + is_killed=True + + proc_output, err_output = process.communicate() + memory=getrusage(RUSAGE_CHILDREN).ru_maxrss / (1024 * 1024) + error_code=process.returncode + if is_killed: + error_code=0 + if error_code==1: + logger.log.error(f'Timeout: {time_limit/60} min expired') + return str(proc_output, 'UTF-8'), err_output, error_code, round(memory,3), is_killed + + + +def result_convert(proc_output:str, objectives:list=None, enum_mode:str="", + is_killed:bool=False, to_print:bool=True): + """Convert the output result into constructed and limited output (JSON type) + + Args: + proc_output (str): Output from the solver + objectives (list, optional): List of objective reactions to show on output. Defaults to None. + enum_mode (str, optional): Enumeration mode to detect intersection or enumeration. Defaults to "". + is_killed (bool, optional): Detects if the process was killed. Defaults to False. + to_print (bool, optional): Print the solution into consol. Defaults to True. + + Returns: + result (dict), unsatisfiable (bool), has_optimum (bool), time (dict), costs (list) + """ + result={} + seed_accu={} + time={} + has_optimum = False + unsatisfiable = True + model_number=1 + seeds_list=list() + reaction_list=list() + costs=None + if proc_output: + if is_killed: + proc_output=repair_json(proc_output, True) + result_data = json.loads(proc_output) + + if 'Result' in result_data: + if result_data['Result'] == 'SATISFIABLE': + unsatisfiable = False + if result_data['Result'] == 'OPTIMUM FOUND': + has_optimum = True + unsatisfiable = False + elif enum_mode=="domRec": + unsatisfiable = False + if 'Time' in result_data: + time = result_data['Time'] + + if 'Call' in result_data: + if 'Witnesses' in result_data['Call'][0]: + #Case minimize finding optimum or case intersection + if (has_optimum and enum_mode != "enumeration") or enum_mode == "cautious": + model = result_data['Call'][0]['Witnesses'][-1] + reaction_list, seeds_list, objective_str, seeds_accu_list = \ + get_model_data(model, objectives) + nb_seed=len(seeds_list) + nb_seed_accu=len(seeds_accu_list) + result[f'model_{model_number}']=["size", nb_seed] + \ + ["Set of seeds", seeds_list.copy()] + \ + ['reaction_flux', reaction_list.copy()] + if nb_seed_accu or nb_seed_accu>0: + seed_accu["size"] = nb_seed_accu + seed_accu["Set of seeds"] = seeds_accu_list.copy() + result[f'model_{model_number}']=result[f'model_{model_number}'] +\ + ["accumulation avoid with seeds", seed_accu] + if "Costs" in model: + costs = model["Costs"] + if to_print: + print_data(model_number, objective_str, seeds_list, seeds_accu_list) + elif not unsatisfiable: + for model in result_data['Call'][0]['Witnesses']: + reaction_list, seeds_list, objective_str, seeds_accu_list = \ + get_model_data(model, objectives) + nb_seed=len(seeds_list) + nb_seed_accu=len(seeds_accu_list) + print_data(model_number, objective_str, seeds_list, seeds_accu_list) + result[f'model_{model_number}']=["size", nb_seed] + \ + ["Set of seeds", seeds_list.copy()] + \ + ['reaction_flux', reaction_list.copy()] + if nb_seed_accu or nb_seed_accu>0: + seed_accu["size"] = nb_seed_accu + seed_accu["Set of seeds"] = seeds_accu_list.copy() + result[f'model_{model_number}']=result[f'model_{model_number}'] +\ + ["accumulation avoid with seeds", seed_accu] + model_number+=1 + + return result, unsatisfiable, has_optimum, time, costs + + +def get_model_data(model:dict, objectives:list=None): + """Get clingo results and convert into lists + + Args: + model (dict): output of clingo + objectives (list, optional): List of objective reactions to show on output. Defaults to None. + + Returns: + reaction_list (list), seeds_list (list), objective_str (str), seeds_accu_list (list) + """ + objective_str='' + reaction_list=list() + seeds_list=list() + seeds_accu_list=list() + for answer in model['Value']: + if 'seed("' in answer: + seed=answer.replace('seed("','',1).replace('")','',1) + seed=seed.split(',')[0].replace('"','',2) + seeds_list.append(seed) + elif 'seed_accu(' in answer: + seed_accu=answer.replace('seed_accu("','',1).replace('")','',1) + seed_accu=seed_accu.split(',')[0].replace('"','',2) + seeds_accu_list.append(seed_accu) + else: + answer = answer.replace('__lpx(','',1).replace(')','',1) + reaction = answer.split(',')[0].replace('"','',2) + flux = answer.split(',')[1].replace('"','',2).replace(')','',1) + if '/' in flux: + numerator=int(flux.split('/')[0]) + denominator=int(flux.split('/')[1]) + flux=round(float(numerator/denominator),10) + else: + flux=round(float(flux),10) + if reaction in objectives: + objective_str+=f'"{reaction}" = {flux}\n' + reaction_list.append((reaction,flux)) + + seeds_list=list(sorted(seeds_list)) + seeds_accu_list=list(sorted(seeds_accu_list)) + return reaction_list, seeds_list, objective_str, seeds_accu_list + +def print_data(model_number:int, objective_str:str, seeds_list:list, + seeds_accu_list:list): + """Data written into terminal + + Args: + model_number (int): name of the solution + objective_str (str): Reaction name and flux + seeds_list (list): Seeds list + seeds_accu_list (list): Seeds used for accumulation list + """ + plural_seed="" + plural_accu="" + nb_seed = len(seeds_list) + nb_seed_accu = len(seeds_accu_list) + if nb_seed>=2: + plural_seed="s" + if nb_seed_accu>=2: + plural_accu="s" + #is_accu = seeds_accu_str or seeds_accu_str != "" + print(f"Answer: {model_number} ({nb_seed} seed{plural_seed}) ") + seeds_str = ', '.join(map(str, seeds_list)) + print(seeds_str) + if nb_seed_accu > 0: + seeds_accu_str = ', '.join(map(str, seeds_accu_list)) + print(f"{color.red_bright} Seed{plural_accu} for which export reaction \navoids accumulation ({nb_seed_accu} seed{plural_accu})") + print(seeds_accu_str+color.reset) + print('Assignment:') + print(objective_str) + \ No newline at end of file diff --git a/seed2lp/color.py b/seed2lp/color.py new file mode 100644 index 0000000..4bbe097 --- /dev/null +++ b/seed2lp/color.py @@ -0,0 +1,34 @@ +reset = '\033[0m' + +black_bright = "\033[0;90m" +black_pure = "\033[0;30m" +blue_bright = "\033[0;94m" +blue_dark = "\033[0;34m" +cyan_dark = "\033[0;36m" +cyan_light = "\033[0;96m" +green_dark = "\033[0;32m" +green_light = "\033[0;92m" +grey = '\033[38;4;236m' +magenta = "\033[0;95m" +purple = "\033[0;94m" +purple_bright = "\033[0;35m" +red_bright = "\033[0;91m" +yellow = "\033[0;93m" +white_bright = "\033[0;97m" + + +blue_back = "\033[0;44m" +cyan_back = "\033[0;46m" +green_back = "\033[0;42m" +orange_back = "\033[0;43m" +pink_back = "\033[0;41m" +purple_back = "\033[0;45m" +white_back = "\033[0;47m" + + +bold = "\033[1m" +darken = "\033[2m" +invisible = '\033[08m' +italic = "\033[3m" +reverse_colour = '\033[07m' +underline = "\033[4m" \ No newline at end of file diff --git a/seed2lp/config.yaml b/seed2lp/config.yaml new file mode 100644 index 0000000..124b89d --- /dev/null +++ b/seed2lp/config.yaml @@ -0,0 +1,26 @@ +############################### +# Seed2lp configuration # +############################### + +seed2lp: + # Options + verbose : False + # Searching mode + mode : "all" # 'minimize', 'subsetmin', 'all' + solve : "all" # 'reasoning', 'hybrid', 'fba', 'all' + intersection : False + union : False + targets_as_seeds : False + topological_injection : False + keep_import_reactions : False + accumulation : False + # Flux + check_flux : False + maximize_flux : False + # Clingo + clingo_configuration : 'jumpy' + clingo_strategy : 'none' + time_limit : 45 + number_solution : 10 + # Directories + temp : "tmp/" diff --git a/seed2lp/description.py b/seed2lp/description.py new file mode 100644 index 0000000..5c6ead9 --- /dev/null +++ b/seed2lp/description.py @@ -0,0 +1,497 @@ +# Object Description, herit from Network, added properties: +# - file (str): Path of input network file (sbml) +# - out_dir (str): Output directory +# - details (bool): Reaction Details performed if True. + +import pandas as pd +import re +from os import path +from seed2lp.network import Network +from . import flux, logger, color +import warnings +import difflib +import seed2lp.sbml as SBML +import copy + + +SRC_DIR = path.dirname(path.abspath(__file__)) +ASP_SRC_ENUM_CC = path.join(SRC_DIR, 'asp/enum-cc.lp') + +BISEAU_VIZ = """ +#defined reactant/4. +#defined product/4. +#defined reaction/1. +link(T,R) :- reactant(T,_,R,_). +link(R,P) :- product(P,_,R,_). +shape(R,rectangle) :- reaction(R). +obj_property(edge,arrowhead,vee). +""" +BISEAU_VIZ_NOREACTION = """ +link(M,P) :- product(P,_,R,_) ; reactant(M,_,R,_). +link(P,P) :- product(P,_,R,_) ; not reactant(_,_,R,_). +link(M,M) :- reactant(M,_,R,_), not product(_,_,R,_). +obj_property(edge,arrowhead,vee). +""" + + + +class Description(Network): + def __init__(self, file:str, keep_import_reactions:bool, out_dir:str, + details:bool=False, visu:bool=False, visu_no_reaction:bool=False, + write_file:bool=False): + """Initialize Object Description, herit from Network + + Args: + file (str): Path of input network file (sbml) + keep_import_reactions (bool): Import reactions are not removed + out_dir (str): Output directory + details (bool, optional): Reaction Details performed if True. Defaults to False. + visu (bool, optional): Graph of Network performed if True. Defaults to False. + visu_no_reaction (bool, optional): Graph of Network without the reaction performed if True. Defaults to False. + write_file (bool, optional): Write corrected network into SBML file if True. Defaults to False. + """ + super().__init__(file, keep_import_reactions=keep_import_reactions, write_sbml=write_file) + self.out_dir = out_dir + self.details = details + self.visu = visu + self.visu_no_reaction = visu_no_reaction + self.write_file = write_file + self.convert_to_facts() + if self.keep_import_reactions: + self.short_option="import_rxn" + else: + self.short_option="rm_rxn" + self.cobra_details="" + self.lp_details="" + + + ######################## METHODS ######################## + def get_details(self): + """Reconstruct reaction details from asp facts + Describe the reaction formula and boundaries + """ + + print(f"\n\n{color.cyan_dark}############################################") + print("############################################") + print(" DETAILS ") + print("############################################") + print(f"############################################\n{color.reset}") + + print(f"\n{color.purple}____________________________________________{color.reset}") + print("\n LP FACTS ") + print(f"{color.purple}____________________________________________\n{color.reset}") + print("Getting details from lp facts ...\n") + self.details_from_lp() + print(" ---> done\n") + + print(f"\n{color.purple}____________________________________________{color.reset}") + print("\n COBRA ") + print(f"{color.purple}____________________________________________\n{color.reset}") + print("Getting details from cobra ...\n") + self.details_from_cobra() + print(" ---> done\n") + + print(f"\n{color.purple}____________________________________________{color.reset}") + print("\n DIFF ") + print(f"{color.purple}____________________________________________\n{color.reset}") + print("Searching diff ...\n") + self.details_diff() + print(" ---> done\n") + + + def details_from_lp(self): + """Get the network description from lp facts and save it + """ + logger.log.info("Start Getting Details from LP file") + reactions_composition_df = pd.DataFrame(columns=['reaction', 'metabolite', 'type_metabolite', 'stoichiometry']) + reaction_df = pd.DataFrame(columns=['reaction', 'low_bound', 'up_bound','is_forward', 'is_reverse', 'is_low_set']) + only_forward=list() + full_details="" + lis_obj=list() + + lines=re.split("\n", self.facts) + # Getting the network and the reaction direction + for line in lines: + if re.search("reaction",line): + reaction = re.split("\"", line)[1] + if "rev_" in reaction: + reaction = re.split("rev_R_", reaction)[1] + df=pd.DataFrame(data=[[reaction, 0.0, 0.0, False, True, False]],columns=['reaction', 'low_bound', 'up_bound','is_forward', 'is_reverse', 'is_low_set']) + reaction_df=pd.concat([reaction_df,df], ignore_index = True) + else: + reaction = re.split("R_", reaction)[1] + try: + index=reaction_df[reaction_df["reaction"]==reaction].index[0] + reaction_df.at[index,'is_forward']=True + except Exception: + only_forward.append(f"R_{reaction}") + df=pd.DataFrame(data=[[reaction, 0.0, 0.0, True, False, False]],columns=['reaction', 'low_bound', 'up_bound','is_forward', 'is_reverse', 'is_low_set']) + reaction_df=pd.concat([reaction_df,df], ignore_index = True) + elif re.search("bounds",line): + split_line = re.split(",", line) + reaction=re.split("\"", split_line[0])[1] + lower=re.split("\"", split_line[1])[1] + lower=float(lower) + upper=re.split("\"", split_line[2])[1] + upper=float(upper) + if "rev_" in reaction: + reaction = re.split("rev_R_", reaction)[1] + try: + index=reaction_df[reaction_df["reaction"]==reaction].index[0] + reaction_df.at[index,'low_bound'] = -upper + reaction_df.at[index,'is_low_set'] = True + except Exception: + df=pd.DataFrame(data=[[reaction, -upper, 0.0, False, True, True]],columns=['reaction', 'low_bound', 'up_bound','is_forward', 'is_reverse', 'is_low_set']) + reaction_df=pd.concat([reaction_df,df], ignore_index = True) + reaction_df.at[index,'is_low_set'] = True + else: + reaction = re.split("R_", reaction)[1] + try: + index=reaction_df[reaction_df["reaction"]==reaction].index[0] + reaction_df.at[index,'up_bound'] = upper + if not reaction_df.at[index,'is_low_set']: + reaction_df.at[index,'low_bound'] = lower + reaction_df.at[index,'is_low_set'] = True + except Exception: + only_forward.append(f"R_{reaction}") + df=pd.DataFrame(data=[[reaction, lower, upper, True, False, True]],columns=['reaction', 'low_bound', 'up_bound','is_forward', 'is_reverse', 'is_low_set']) + reaction_df=pd.concat([reaction_df,df], ignore_index = True) + elif re.search("reactant",line): + split_line = re.split(",", line) + reaction=re.split("\"", split_line[2])[1] + metabolite = re.split("\"", split_line[0])[1] + stoichiometry=round(float(re.split("\"", split_line[1])[1]),6) + if "rev_" in reaction: + reaction = re.split("rev_R_", reaction)[1] + df=pd.DataFrame(data=[[reaction, metabolite, "product", stoichiometry]], + columns=['reaction', 'metabolite', 'type_metabolite', 'stoichiometry']) + reactions_composition_df=pd.concat([reactions_composition_df,df], ignore_index = True) + elif reaction in only_forward: + reaction = re.split("R_", reaction)[1] + df=pd.DataFrame(data=[[reaction, metabolite, "reactant", stoichiometry]], + columns=['reaction', 'metabolite', 'type_metabolite', 'stoichiometry']) + reactions_composition_df=pd.concat([reactions_composition_df,df], ignore_index = True) + elif re.search("product",line): + split_line = re.split(",", line) + reaction=re.split("\"", split_line[2])[1] + metabolite = re.split("\"", split_line[0])[1] + stoichiometry=round(float(re.split("\"", split_line[1])[1]),6) + if "rev_" in reaction: + reaction = re.split("rev_R_", reaction)[1] + df=pd.DataFrame(data=[[reaction, metabolite, "reactant", stoichiometry]], + columns=['reaction', 'metabolite', 'type_metabolite', 'stoichiometry']) + reactions_composition_df=pd.concat([reactions_composition_df,df], ignore_index = True) + elif reaction in only_forward: + reaction = re.split("R_", reaction)[1] + df=pd.DataFrame(data=[[reaction, metabolite, "product", stoichiometry]], + columns=['reaction', 'metabolite', 'type_metabolite', 'stoichiometry']) + reactions_composition_df=pd.concat([reactions_composition_df,df], ignore_index = True) + reaction_df.sort_values(by=['reaction']) + reactions_composition_df.sort_values(by=['reaction', 'metabolite']) + self.objectives = lis_obj + + #reaction_without_reactant=[] + #reaction_without_product=[] + # Writting the network description + for _,row in reaction_df.iterrows(): + reaction=row[0] + lower=row[1] + upper=row[2] + forward=row[3] + reverse=row[4] + symbol="" + compo_line=f'{reaction}:' + match ([forward, reverse]): + case [True, False]: + symbol=" -->" + if upper == 0: + symbol=" <--" + case [False, True]: + symbol=" <--" + if lower == 0: + symbol=" -->" + case [True, True]: + symbol=" <=>" + sub_reaction=reactions_composition_df[reactions_composition_df["reaction"]==reaction] + sub_reactant=sub_reaction[sub_reaction["type_metabolite"]=="reactant"] + sub_product=sub_reaction[sub_reaction["type_metabolite"]=="product"] + it=0 + if sub_reactant.empty: + #reaction_without_reactant.append(reaction) + compo_line += " " + else: + for r_metabolite in sub_reactant.iterrows(): + it += 1 + if it!=1: + compo_line += " +" + stoic="" + if r_metabolite[1]["stoichiometry"] != 1.0: + stoic=f' {r_metabolite[1]["stoichiometry"]}' + compo_line += f'{stoic} {re.sub("^M_", "", r_metabolite[1]["metabolite"])}' + compo_line += symbol + it=0 + if sub_product.empty: + #reaction_without_product.append(reaction) + compo_line += " " + else: + for p_metabolite in sub_product.iterrows(): + it += 1 + if it!=1: + compo_line += " +" + stoic="" + if p_metabolite[1]["stoichiometry"] != 1.0: + stoic=f' {p_metabolite[1]["stoichiometry"]}' + compo_line += f'{stoic} {re.sub("^M_", "", p_metabolite[1]["metabolite"])}' + compo_line += f'\t[{lower}, {upper}]' + #print(compo_line) + full_details += f'{compo_line}\n' + + self.lp_details = path.join(self.out_dir, f"{self.name}_{self.short_option}_details_from_lp.txt") + #print("REACTION WITHOUT REACTANT") + #print(reaction_without_reactant) + #print("REACTION WITHOUT PRODUCT") + #print(reaction_without_product) + save(self.lp_details , full_details) + + + def details_from_cobra(self): + """Get the network description from sbml file by using cobra and save it + """ + logger.log.info("Start Getting Details from Cobra file") + warnings.filterwarnings("error") + model = flux.get_model(self.file) + if not self.keep_import_reactions: + flux.stop_flux(model) + full_details="" + for reaction in model.reactions: + full_details += f"{reaction}\t[{reaction.lower_bound}, {reaction.upper_bound}]\n" + + self.cobra_details = path.join(self.out_dir, f"{self.name}_{self.short_option}_details_from_cobra.txt") + save(self.cobra_details, full_details) + + + def details_diff(self): + """Compare the Network description from cobra and lp facts + save the diff information into file + """ + logger.log.info("Start checking diff between Cobra and LP Network") + diff = "" + with open(self.cobra_details) as cobra_details: + cobra_details_text = cobra_details.readlines() + + with open(self.lp_details) as lp_details: + lp_details_text = lp_details.readlines() + + # Find the diff: + for line in difflib.unified_diff( + cobra_details_text, lp_details_text, fromfile=self.cobra_details, + tofile=self.lp_details, lineterm=''): + diff += f'{line}\n' + + cobra_details.close() + lp_details.close() + + diff_path = path.join(self.out_dir, f"{self.name}_{self.short_option}_details_diff.txt") + save(diff_path, diff) + + def render_network(self): + """From lp facts render the network graph with or without reaction + """ + import biseau + out_file = path.join(self.out_dir, f"{self.name}_{self.short_option}_visu") + + print(f"\n\n{color.cyan_dark}############################################") + print("############################################") + print(" GRAPH ") + print("############################################") + print(f"############################################\n{color.reset}") + if self.visu: + print(f"\n{color.purple}____________________________________________{color.reset}") + print(f"\n GRAPH WITH REACTIONS ") + print(f"{color.purple}____________________________________________{color.reset}\n") + print("Generating graph ...\n") + viz = BISEAU_VIZ + out_file_visu = f"{out_file}.png" + try: + img_visu = biseau.compile_to_single_image(self.facts + viz, outfile=out_file_visu) + img_visu.close() + print(f'-> Input graph rendered in {out_file_visu}\n') + except KeyboardInterrupt: + print(f'-> Aborted! file :{out_file_visu}\n') + if self.visu_no_reaction: + print(f"\n{color.purple}____________________________________________{color.reset}") + print("\n GRAPH WITHOUT REACTIONS ") + print(f"{color.purple}____________________________________________\n{color.reset}") + print("Generating graph ...\n") + viz = BISEAU_VIZ_NOREACTION + out_file_visu_no_reaction = f"{out_file}_no_reactions.png" + try: + img_visu_no_reaction = biseau.compile_to_single_image(self.facts + viz, outfile=out_file_visu_no_reaction) + img_visu_no_reaction.close() + print(f'-> Input graph rendered in {out_file_visu_no_reaction}\n') + except KeyboardInterrupt: + print(f'-> Aborted! file :{out_file_visu_no_reaction}\n') + + + def rewrite_sbml_file(self): + """SBML file is written from corrected network + """ + print(f"\n\n{color.cyan_dark}############################################") + print("############################################") + print(" WRITING SBML FILE ") + print("############################################") + print(f"############################################\n{color.reset}") + + if self.keep_import_reactions: + logger.log.warning("IMPORT REACTION KEPT") + else: + logger.log.warning("IMPORT REACTION REMOVED BY DEFAULT") + logger.log.warning("If you want to keep import reaction\nuse option -kir / --keep-import-reactions") + model = SBML.get_model(self.sbml) + original_reactions = SBML.get_listOfReactions(model) + + # Need Two loop to remove first then two do modification + # If removing while looping, then the loop misses some reactions + # because the index of the reaction changes + rm_reac_message = "Reaction removed:" + is_rm = False + for reaction in original_reactions: + reaction_name = reaction.attrib.get("id") + if reaction_name in self.deleted_reactions: + SBML.remove_reaction(model,reaction) + is_rm = True + rm_reac_message+=f"\n\t- {reaction_name}" + if is_rm: + logger.log.info(rm_reac_message) + + modif_rev_message = "Reaction with tag reversible modified:" + is_modif_rev = False + exch_meta_message = "Reaction with Reactants and Products exchanged:" + is_exch_meta = False + rm_import_message = "Import reaction removed:" + is_rm_import = False + for reaction in original_reactions: + reaction_name = reaction.attrib.get("id") + # Change the reversibility + if reaction_name in self.reversible_modified_reactions: + index = self.reversible_modified_reactions[reaction_name] + reaction.attrib["reversible"] = str(self.reactions[index].reversible).lower() + is_modif_rev = True + modif_rev_message+=f"\n\t- {reaction_name}" + + # Exchange list of reactants and product if they are tagged as modified + # The modification tag is only on exchanging reactants and products + # Reaction written backward will be wrote forward + if reaction_name in self.meta_modified_reactions: + index = self.meta_modified_reactions[reaction_name] + has_reactant=False + has_product=False + reactants=list() + products=list() + # loop into source + for element in reaction: + # copy and remove list of reactant and products from source + if SBML.get_sbml_tag(element) == "listOfReactants": + reactants = copy.copy(element) + has_reactant = True + SBML.remove_sub_elements(element) + elif SBML.get_sbml_tag(element) == "listOfProducts": + products = copy.copy(element) + has_product=True + SBML.remove_sub_elements(element) + + # add the new element into source node + # put products into reactant and reactant into products + recreate_other_node=True + for element in reaction: + if SBML.get_sbml_tag(element) == "listOfReactants": + #check if node listOfProducts exist and copy element + if has_product: + SBML.add_metabolites(element, products) + elif recreate_other_node: + # the node listOfProducts doesnt exist it needs to be created + SBML.create_sub_element(reaction, "listOfProducts") + #copy the element of reactant (exchanging reactant and products) + SBML.add_metabolites(element, reactants) + recreate_other_node=False + SBML.remove_sub_elements(element) + elif SBML.get_sbml_tag(element) == "listOfProducts" and has_reactant: + if has_reactant: + SBML.add_metabolites(element, reactants) + elif recreate_other_node: + SBML.create_sub_element(reaction, "listOfReactants") + SBML.add_metabolites(element, products) + recreate_other_node=False + SBML.remove_sub_elements(element) + # MODIFIER LES BOUNDARIES + if self.parameters: + self.parameters[f'{reaction_name}_lower_bound'] = self.reactions[index].lbound + self.parameters[f'{reaction_name}_upper_bound'] = self.reactions[index].ubound + reaction.attrib['{'+self.fbc+'}lowerFluxBound']= f'{reaction_name}_lower_bound' + reaction.attrib['{'+self.fbc+'}upperFluxBound']= f'{reaction_name}_upper_bound' + else: + reaction.attrib['{'+self.fbc+'}lowerFluxBound']= self.reactions[index].lbound + reaction.attrib['{'+self.fbc+'}upperFluxBound']= self.reactions[index].ubound + + is_exch_meta = True + exch_meta_message+=f"\n\t- {reaction_name}" + + + if not self.keep_import_reactions and reaction_name in self.exchanged_reactions: + index = self.exchanged_reactions[reaction_name] + self.parameters[f'{reaction_name}_lower_bound'] = self.reactions[index].lbound + self.parameters[f'{reaction_name}_upper_bound'] = self.reactions[index].ubound + reaction.attrib['{'+self.fbc+'}lowerFluxBound']= f'{reaction_name}_lower_bound' + reaction.attrib['{'+self.fbc+'}upperFluxBound']= f'{reaction_name}_upper_bound' + is_rm_import = True + rm_import_message+=f"\n\t- {reaction_name}" + + if is_modif_rev: + logger.log.warning(modif_rev_message) + if is_exch_meta: + logger.log.warning(exch_meta_message) + if is_rm_import: + logger.log.warning(rm_import_message) + + # Replace list of parameters because we added new specific parameters for the exchange reactions + parameters_copy = copy.copy(self.parameters) + + for el in model: + tag = SBML.get_sbml_tag(el) + if tag == "listOfParameters": + node = copy.deepcopy(el[0]) + for param in el: + id = param.attrib.get('id') + # Corrects the already existant parameters + if id in parameters_copy: + param.attrib['value'] = str(parameters_copy[id]) + # delete the existant parameter from the list of parameter to keep + # only the new parameters + parameters_copy.pop(id) + # create new paramaters node + for key, value in parameters_copy.items(): + new_node = copy.deepcopy(node) + new_node.attrib['id'] = key + new_node.attrib['value'] = str(value) + el.append(new_node) + + file_path = path.join(self.out_dir, self.name+".xml") + str_model = self.sbml_first_line+SBML.etree_to_string(self.sbml) + print(f"File saved at: {file_path}") + save(file_path, str_model) + + ######################################################## + + +######################## FUNCTIONS ######################## +def save(out_file:str, data): + """Save file of Network description or graphs + + Args: + out_file (str): Output file path + data: Graph or Network details data + """ + with open(out_file, 'w') as f: + f.write(data) + f.close() + logger.log.info(f"File saved at: {out_file}") \ No newline at end of file diff --git a/seed2lp/file.py b/seed2lp/file.py new file mode 100644 index 0000000..3f6e463 --- /dev/null +++ b/seed2lp/file.py @@ -0,0 +1,151 @@ +from os import path, makedirs, stat, remove +from json import dump, load +from csv import writer, reader +import argparse +from . import logger + + +def existant_path(inpath:str) -> str: + """Argparse type, raising an error if given file does not exists + + Args: + inpath (str): Network input path + + Raises: + argparse.ArgumentTypeError: Error if the file doesn't exist + + Returns: + str: Network input path + """ + if not path.exists(inpath): + raise argparse.ArgumentTypeError("file {} doesn't exists".format(inpath)) + return inpath + +def is_valid_dir(dirpath): + """Return True if directory exists or can be created (then create it) + + Args: + dirpath (str): path of directory + + Returns: + bool: True if dir exists, False otherwise + """ + if not path.isdir(dirpath): + try: + makedirs(dirpath) + return dirpath + except OSError as e: + logger.log.error(e) + return None + else: + return dirpath + +def existing_file(filepath): + """Return True if file exists + + Args: + filepath (str): path of file + + Returns: + bool: True if dir exists, False otherwise + """ + if not path.isfile(filepath): + return False + else: + return True + + +def save(filename:str, directory:str, results, type:str, is_result_temp=False): + """Save data into file dependinf on type (json / tsv) + + Args: + filename (str): Filename of saved file + directory (str): Output directory where to save file + results: Results data in dictionnary (json) or datatrame (tsv) + type (str): Type of output fils (json or tsv or txt) + """ + + out_file_path = path.join(directory,filename) + try: + match type: + case 'json': + out_file_path += '.json' + with open(out_file_path, 'w') as f: + dump(results, f, indent="\t") + f.close() + case 'tsv': + if not ".tsv" in out_file_path: + out_file_path += '.tsv' + # List is given an we want to append data at the end + if is_result_temp: + with open(out_file_path, 'a') as f: + tsv_output = writer(f, delimiter='\t') + tsv_output.writerow(results) + # Dataframe is given, and all results are written at once + else: + with open(out_file_path, 'w') as f: + results.to_csv(out_file_path, sep="\t") + f.close() + case 'txt': + if not ".txt" in out_file_path: + out_file_path += '.txt' + with open(out_file_path, "w") as f: + f.write("\n".join(results)) + except Exception as e: + logger.log.error(f"while saving file: {e}") + + + +def file_is_empty(file_path:str): + """Check if the file is empty + + Args: + file_path (str): Path of file to check + + Returns: + bool: True if the file is empty + """ + return stat(file_path).st_size==0 + + +def write_instance_file(instance_file:str, facts:str): + """Write and Save instance file + """ + with open(instance_file, 'w') as f: + f.write(facts) + f.close() + +def delete(filepath:str): + """Delete a file + + Args: + filepath (str): Path of File to delete + """ + remove(filepath) + +def load_json(filepath:str): + """Load a json file into a variable + + Args: + filepath (str): Path of json file to load + + Returns: + result (dict): Data formated into a dictionnary + """ + file = open(filepath) + result = load(file) + file.close() + return result + +def load_tsv(filepath:str): + """Load a tsv file into a variable + + Args: + filepath (str): Path of json file to load + + Returns: + result (list): Data formated into a list + """ + file = open(filepath) + result = reader(file, delimiter='\t') + return list(result) diff --git a/seed2lp/flux.py b/seed2lp/flux.py new file mode 100644 index 0000000..f370be4 --- /dev/null +++ b/seed2lp/flux.py @@ -0,0 +1,260 @@ +import cobra +from re import sub +from cobra.core import Model +import warnings +from . import logger, color + +def get_model(model_file:str): + """Get cobra model + + Args: + model_file (str): Sbml file path of the network + + Returns: + Model: The model generated from cobra + """ + model = cobra.io.read_sbml_model(model_file) + return model + + +def get_list_fluxes(model:Model, list_objective:list, show_messages:bool=True): + """Get the objective reactions fluxe. + Can generate the flux for multiple objective reactions + + Args: + model (Model): Cobra model + list_objective (list): List of objective reaction names + show_messages (bool, optional): Write messages into console if True. Default True. + + Returns: + dict: Dictionnary of objective reaction and their respective fluxes + """ + warnings.filterwarnings("error") + fluxes_dict = dict() + for objective_reaction in list_objective: + objective_reaction = sub("^R_","",objective_reaction) + # get flux of objective reaction + model.objective = objective_reaction + try: + objective_flux = model.optimize().fluxes[objective_reaction] + except UserWarning: + objective_flux = 0.0 + fluxes_dict[objective_reaction]=objective_flux + if show_messages: + print(fluxes_dict) + print('\n') + return fluxes_dict + + +def get_flux(model:Model, objective_reaction:str): + """Calculate the flux of the objective reaction chosen using cobra + + Args: + model (Model): Cobra model + objective_reaction (str): Objective reaction chosen + + Returns: + float: The value of the objective flux + """ + warnings.filterwarnings("error") + try: + #with open('/home/cghassem/Projets/seed-2-lp/analyses/test.lp', 'w') as out: + # #print(str(model.solver)) + # out.write(str(model.solver)) + objective_flux = model.optimize().fluxes[objective_reaction] + infeasible = False + except UserWarning: + objective_flux = 0.0 + infeasible = True + logger.log.info("Model infeasible") + return objective_flux, infeasible + + +def get_init(model:Model, list_objective:list, show_messages:bool=True): + """Get initial flux of all objective reactions using cobra + + Args: + model (Model): Cobra model + list_objective (list): List of objective reaction names + show_messages (bool, optional): Write messages into console if True. Default True. + + Returns: + dic: Dictionnary of objective reaction and their respective fluxes + """ + if show_messages: + title_mess = "\n############################################\n" \ + "############################################\n" \ + f" {color.bold}CHECK FLUX{color.cyan_light}\n"\ + "############################################\n" \ + "############################################\n" + logger.print_log(title_mess, "info", color.cyan_light) + + + print("---------------- FLUX INIT -----------------\n") + fluxes_init = get_list_fluxes(model, list_objective, show_messages) + + if show_messages: + print("--------------- MEDIUM INIT ----------------\n") + for reaction in model.medium: + print(reaction, model.reactions.get_by_id(reaction).lower_bound, model.reactions.get_by_id(reaction).upper_bound) + print(f"\n") + return fluxes_init + + +def stop_flux(model:Model, list_objective:list=None, show_messages:bool=True): + """Stop the import reaction flux + + Args: + model (Model): Cobra model + list_objective (list): List of objective reaction names + show_messages (bool, optional): Write messages into console if True. Default True. + + Returns: + dic: Dictionnary of objective reaction and their respective fluxes + """ + if show_messages: + print("---------- STOP IMPORT FLUX -------------\n") + logger.log.info("Shutting down import flux ...") + + for elem in model.boundary: + if not elem.reactants and elem.upper_bound > 0: + if elem.lower_bound > 0: + elem.lower_bound = 0 + elem.upper_bound = 0.0 + if not elem.products and elem.lower_bound < 0: + if elem.upper_bound < 0: + elem.upper_bound = 0 + elem.lower_bound = 0.0 + + logger.log.info("... DONE") + + if list_objective is not None: + fluxes_no_import = get_list_fluxes(model, list_objective, show_messages) + return fluxes_no_import + + +def calculate(model:Model, list_objective:list, list_seeds:list, + fluxes_lp=dict, try_demands:bool=True): + """Calculate the flux by adding seeds and import for them using Cobra. + Calculate on the objective list, the first having flux stop the calculation + + Args: + model (Model): Cobra model + list_objective (list): List of objective reaction names + list_seeds (list): One result set of seed + fluxes_lp (dict): Dictionnary of all reaction and their associated LP flux + try_demands (bool, optional): Option to try to add demands if seeds failed. + Defaults to True. False for hybrid with cobra. + + Returns: + dict, str: result (containing the data), objective_reaction (chosen, the first having flux) + """ + warnings.filterwarnings("error") + logger.log.info("Starting calculate Flux...") + if not list_objective: + logger.log.error("No objective found, abort") + return None, None + + #cobra.flux_analysis.add_loopless(model) + #model.solver = 'cplex' + + species = model.id + objective_flux_seeds=None + objective_flux_demands=None + ok_result=False + ok_seeds=None + ok_demands=None + objective_reaction=None + + meta_exchange_list=dict() + + for reaction in model.boundary: + for key in reaction.metabolites.keys(): + meta_exchange_list[str(key)]=reaction.id + + for objective_reaction in list_objective: + if fluxes_lp: + lp_flux = fluxes_lp[objective_reaction] + else: + lp_flux=None + + # get flux of objective reaction + objective_reaction = sub("^R_","",objective_reaction) + model.objective = objective_reaction + created_sinks = [] + logger.log.info("Opening Import flux from seeds (Exchange) or add Sinks ...") + for seed in list_seeds: + seed = sub("^M_","",seed) + #compartment = model.metabolites.get_by_id(seed).compartment + #if compartment == 'e': + if seed in meta_exchange_list.keys(): + reaction_exchange = model.reactions.get_by_id(meta_exchange_list[seed]) + # For a seed in the exchange metabolites list, we allow both import and export + # on the "maximum" flux (1000) + + if not reaction_exchange.reactants: + reaction_exchange.upper_bound = float(1000) + # Do not change the lower bound when there is an export + # Because in ASP we can not change the value of an already + # exisiting bounds atom + if reaction_exchange.lower_bound >= 0: + reaction_exchange.lower_bound = float(-1000) + if not reaction_exchange.products: + reaction_exchange.lower_bound = float(-1000) + # Do not change the lower bound when there is an export + # Because in ASP we can not change the value of an already + # exisiting bounds atom + if reaction_exchange.upper_bound <= 0: + reaction_exchange.upper_bound = float(1000) + else: + if not f"SK_{seed}" in created_sinks: + model.add_boundary(metabolite=model.metabolites.get_by_id(seed), + type='sink', + ub=float(1000), + lb=float(-1000)) + created_sinks.append(f"SK_{seed}") + + logger.log.info("Opening Import flux: Done") + + logger.log.info("Checking objective flux on seeds ...") + objective_flux_seeds, infeasible_seeds = get_flux(model, objective_reaction) + ok_seeds = objective_flux_seeds > 10e-5 if objective_flux_seeds else False + + objective_flux_demands=None + infeasible_demands=None + if ok_seeds: + ok_result = True + logger.log.info("... OK") + elif try_demands: + logger.log.info("... KO - Checking objective flux on demands ...") + # create a demand reaction for all products of the biomass reaction + products = [m.id for m in model.reactions.get_by_id(objective_reaction).products] + for m in products: + try: + model.add_boundary(model.metabolites.get_by_id(m), type="demand") + except: + low = model.reactions.get_by_id(f"DM_{m}").lower_bound + up = model.reactions.get_by_id(f"DM_{m}").upper_bound + if up <= 0: + model.reactions.get_by_id(f"DM_{m}").upper_bound = float(1000) + if low < 0 : + model.reactions.get_by_id(f"DM_{m}").lower_bound = float(0) + + objective_flux_demands, infeasible_demands = get_flux(model, objective_reaction) + ok_demands = objective_flux_demands > 10e-5 if objective_flux_demands else False + if ok_demands: + ok_result = True + logger.log.info("... OK") + else: + logger.log.info("... KO") + + result = {'id' : species, + 'objective_flux_seeds': objective_flux_seeds, + 'objective_flux_demands': objective_flux_demands, + 'OK_seeds': ok_seeds, + 'OK_demands': ok_demands, + 'OK': ok_result, + 'infeasible_seeds': infeasible_seeds, + 'infeasible_demands': infeasible_demands} + + return result, objective_reaction, lp_flux diff --git a/seed2lp/linear.py b/seed2lp/linear.py new file mode 100644 index 0000000..f32e366 --- /dev/null +++ b/seed2lp/linear.py @@ -0,0 +1,429 @@ +# Object Hybrid, herit from Solver, added properties: +# - temp_dir (str): temporary directory for saving instance file and clingo outputs +# - instance_file (str): Path of instance file for solving +# 1 -> Atom output +# 2 -> Json output + +# Object FBA, herit from Hybrid, no property added + +from seed2lp.network import Network +from seed2lp.solver import Solver +from . import clingo_lpx, color, logger + + +################################################################### +################### Class Hybrid : herit Solver ################### +################################################################### +class Hybrid(Solver): + def __init__(self, run_mode:str, run_solve:str, network:Network, + time_limit_minute:float=None, number_solution:int=None, + clingo_configuration:str=None, clingo_strategy:str=None, + intersection:bool=False, union:bool=False, + minimize:bool=False, subset_minimal:bool=False, + maximize_flux:bool=False, + temp_dir:str=None, short_option:str=None, + verbose:bool=False): + """Initialize Object Hybrid, herit from Solver + + Args: + run_mode (str): Running command used (full or target or fba) + run_solve (str): Solving command used (Hybrid) + network (Network): Network constructed + time_limit_minute (float, optional): Time limit given by user in minutes . Defaults to None. + number_solution (int, optional): Limit number of solutions to find. Defaults to None. + clingo_configuration (str, optional): Configuration for clingo resolution . Defaults to None. + clingo_strategy (str, optional): Strategy for clingo resolution. Defaults to None. + enumeration (bool, optional): Enumerate the solutions limited by the number of solutions. Defaults to True. + intersection (bool, optional): Find the intersection of all solutions without limitation (give one solution). Defaults to False. + union (bool, optional): Find the union of all solutions without limitation (give one solution). Defaults to False. + minimize (bool, optional): Search the minimal carinality of solutions. Defaults to False. + subset_minimal (bool, optional): Search the subset minimal solutions. Defaults to False. + maximize_flux (bool, optional): Use maximization of flux for calculation. Defaults to False. + temp_dir (str, optional): Temporary directory for saving instance file and clingo outputs. Defaults to None. + short_option (str, optional): Short way to write option on filename. Defaults to None. + verbose (bool, optional): Set debug mode. Defaults to False. + """ + super().__init__(run_mode, network, time_limit_minute, number_solution, clingo_configuration, + clingo_strategy, intersection, union, minimize, subset_minimal, temp_dir, short_option, run_solve, verbose) + + self.is_linear = True + self.maximize_flux = maximize_flux + self.temp_dir = temp_dir + self.short_option = short_option + self.get_init_message() + self._init_clingo_constant() + self._set_instance_file() + + + ######################## METHODS ######################## + def get_init_message(self): + """Get init message to put on terminal + """ + title_mess = "\n############################################\n" \ + "############################################\n" \ + f" {color.bold}HYBRID{color.cyan_light}\n"\ + "############################################\n" \ + "############################################\n" + logger.print_log(title_mess, "info", color.cyan_light) + + + def _init_clingo_constant(self): + """Init the list of ASP constant command for resolution + """ + self.init_const() + + if self.maximize_flux: + logger.print_log('Flux: MAXIMIZATION', "info") + else: + logger.print_log('Flux: NO MAXIMIZATION', "info") + logger.print_log(f"Time limit: {self.time_limit_minute} minutes", 'info') + logger.print_log(f"Solution number limit: {self.number_solution}", 'info') + + + def search_seed(self): + """Launch seed searching + """ + if not self.network.objectives: + self.get_message('optimum error') + self.get_message('end') + return + files = [self.network.instance_file, self.asp.ASP_SRC_SEED_SOLVING, self.asp.ASP_SRC_FLUX] + if self.maximize_flux: + files.append(self.asp.ASP_SRC_MAXIMIZE_FLUX) + if self.subset_minimal: + self.get_message('subsetmin') + #logger.print_log("GROUNDING...", "info") + #self.grounded, timer, err_output, error_code, memory = self.ground(files, self.clingo_constant, self.is_linear, self.verbose) + self.search_subsetmin(files) + if self.minimize: + self.get_message('minimize') + if self.network.is_subseed: + files.append(self.asp.ASP_SRC_MAXIMIZE_PRODUCED_TARGET) + logger.print_log('POSSIBLE SEED: Given', "info") + logger.print_log(' A subset of possible seed is search \n maximising the number of produced target', "info") + self.clingo_constant.append('-c') + self.clingo_constant.append('subseed=1') + files.append(self.asp.ASP_SRC_MINIMIZE) + #logger.print_log("GROUNDING...", "info") + #self.grounded, timer, err_output, error_code, memory = self.ground(files, self.clingo_constant, self.is_linear, self.verbose) + self.search_minimize(files) + self.get_message('end') + + + def search_minimize(self, asp_files): + """Launch seed searching with minimze options + """ + logger.print_log("Finding optimum...", "info") + self.solve(asp_files, "minimize-one-model") + + if self.one_model_unsat: + return + + ok_opti = self.optimum_found and (self.opt_size > 0) + #ok_opti = (self.optimum is not None) and (self.optimum > 0) + if self.optimum is None: + opti_message = "Optimum not found." + elif self.optimum == 0: + opti_message = "Optimum is 0." + + if self.enumeration: + if ok_opti: + self.get_message('enumeration') + self.solve(asp_files, "minimize-enumeration") + else: + self.get_message('enumeration') + logger.print_log(f"\nNot computed: {opti_message}", "info") + + if self.intersection: + if ok_opti: + self.get_message('intersection') + self.solve(asp_files, "minimize-intersection") + else: + self.get_message('intersection') + logger.print_log(f"\nNot computed: {opti_message}", "info") + + + def search_subsetmin(self, asp_files): + """Launch seed searching with subset minimal options + """ + if self.enumeration: + self.get_message('enumeration') + self.solve(asp_files, "submin-enumeration") + else: + self.number_solution = 1 + logger.print_log("\n--------------- One solution ---------------", "info") + self.solve(asp_files, "submin-enumeration") + + + if self.intersection: + self.get_message('intersection') + self.solve(asp_files, "submin-intersection") + + + def add_result_seeds(self, search_mode:str, model_name:str, len:int, seeds:list, flux_dict:dict): + """Add a formated resulted set of seeds into the network object + + Args: + search_mode (str): search mode type + model_name (str): model name + len (int): length of a set of seed + seeds (list): list of seeds + flux_dict(dict): Dictionnary of all reaction with their LP flux + """ + self.network.add_result_seeds('HYBRID', search_mode, model_name, len, seeds, flux_dict) + + + def get_objectives_flux(self, flux_list): + """From a list of all reaction and associated LP flux, find objective reactions + and create a dictionnary to stock the LP flux + + Args: + flux_list (list): List of all reaction name and associated LP flux + + Returns: + Dict: A dictionnary of Objective reaction with it associated flux + """ + obj_flux_dict = dict() + for line in flux_list: + reaction = line[0] + if reaction in self.network.objectives: + obj_flux_dict[reaction] = line[1] + return obj_flux_dict + + + + def solve(self, asp_files:list=[], search_mode:str=""): + """Solve the seed seraching using the launch mode + + Args: + asp_files (list, optional): List of ASP files used for sovling. Defaults to []. + search_mode (str, optional): Describe the launch mode. . Defaults to "". + """ + logger.print_log("SOLVING...\n", "info") + results = dict() + solution_list = dict() + timer = dict() + + full_option, _, model_type, output_type = self.get_solutions_infos(search_mode) + full_option = self.clingo_constant + full_option + + if self.optimum: + if self.opt_prod_tgt is not None: + full_option[-1]=full_option[-1]+f",{self.opt_prod_tgt},{self.opt_size}" + else: + full_option[-1]=full_option[-1]+f",{self.opt_size}" + + + match search_mode: + case "minimize-one-model": + cmd = clingo_lpx.command(files=asp_files, options=full_option, + time_limit=self.time_limit) + cmd_str = ' '.join(cmd) + proc_output, err, error_code, \ + memory, is_killed = clingo_lpx.solve(cmd, self.time_limit) + self.get_message("command") + logger.print_log(f'{cmd_str}', 'debug') + + self.get_error(error_code, err) + output_full_list, unsatisfiable, self.optimum_found, full_timers, opt = clingo_lpx.result_convert(proc_output, + self.network.objectives, + "", is_killed, False) + if unsatisfiable: + logger.print_log('Unsatisfiable problem', "error") + + elif self.optimum_found: + logger.print_log("Optimum Found", "info") + self.one_model_unsat = False + one_model_list=output_full_list[list(output_full_list.keys())[-1]] + self.optimum=opt + self.get_separate_optimum() + #TODO Corrects the producible targets count + #if self.network.is_subseed: + # logger.print_log((f"Number of producible targets: {- self.opt_prod_tgt}"), 'info') + #TODO END + logger.print_log(f"Minimal size of seed set is {self.opt_size}\n", 'info') + if self.optimum is not None and self.network.keep_import_reactions: + logger.print_log("Try with the option remove import reactions.", "info") + solution_list[model_type] = one_model_list + #Get obejctives fluxes and add results seeds to network object + obj_flux_dict = self.get_objectives_flux(one_model_list[5]) + self.add_result_seeds(search_mode, model_type, one_model_list[1], one_model_list[3], obj_flux_dict) + # Satisfiable probleme but optimum not found in given time + else: + logger.print_log('Optimum not found', "error") + + case "minimize-enumeration": + cmd = clingo_lpx.command(files=asp_files, options=full_option, nb_model= self.number_solution, + time_limit=self.time_limit) + cmd_str = ' '.join(cmd) + proc_output, err, error_code, \ + memory, is_killed = clingo_lpx.solve(cmd, self.time_limit) + self.get_message("command") + logger.print_log(f'{cmd_str}', 'debug') + self.get_error(error_code, err) + solution_list, unsatisfiable, _, full_timers, _ = clingo_lpx.result_convert(proc_output, + self.network.objectives, "enumeration", is_killed) + if unsatisfiable: + logger.print_log('Unsatisfiable problem', "error") + else: + for model_name, solution in solution_list.items(): + #Get obejctives fluxes and add results seeds to network object + obj_flux_dict = self.get_objectives_flux(solution[5]) + self.add_result_seeds(search_mode, model_name, solution[1], solution[3], obj_flux_dict) + + case "submin-enumeration": + cmd = clingo_lpx.command(files=asp_files, options=full_option, nb_model= self.number_solution, + time_limit=self.time_limit) + cmd_str = ' '.join(cmd) + proc_output, err, error_code, \ + memory, is_killed = clingo_lpx.solve(cmd, self.time_limit) + self.get_message("command") + logger.print_log(f'{cmd_str}', 'debug') + self.get_error(error_code, err) + solution_list, unsatisfiable, _, full_timers,_ = clingo_lpx.result_convert(proc_output, + self.network.objectives, "enumeration", is_killed) + if unsatisfiable: + logger.print_log('Unsatisfiable problem', "error") + else: + for model_name, solution in solution_list.items(): + #Get obejctives fluxes and add results seeds to network object + obj_flux_dict = self.get_objectives_flux(solution[5]) + self.add_result_seeds(search_mode, model_name, solution[1], solution[3], obj_flux_dict) + + case "minimize-intersection": + cmd = clingo_lpx.command(files=asp_files, options=full_option, + time_limit=self.time_limit) + cmd_str = ' '.join(cmd) + proc_output, err, error_code, \ + memory, is_killed = clingo_lpx.solve(cmd, self.time_limit) + self.get_message("command") + logger.print_log(f'{cmd_str}', 'debug') + self.get_error(error_code, err) + output_full_list, unsatisfiable, _, full_timers = clingo_lpx.result_convert(proc_output, + self.network.objectives, 'cautious', is_killed) + if unsatisfiable: + logger.print_log('Unsatisfiable problem', "error") + elif output_full_list: + model = output_full_list[list(output_full_list.keys())[-1]] + solution_list[model_type ] = model + #Get obejctives fluxes and add results seeds to network object + obj_flux_dict = self.get_objectives_flux(model[5]) + self.add_result_seeds(search_mode, model_type, model[1], model[3], obj_flux_dict) + + + case "submin-intersection": + cmd = clingo_lpx.command(files=asp_files, options=full_option, + time_limit=self.time_limit) + cmd_str = ' '.join(cmd) + proc_output, err, error_code, \ + memory, is_killed = clingo_lpx.solve(files=asp_files, options=full_option, + time_limit=self.time_limit) + self.get_message("command") + logger.print_log(f'{cmd_str}', 'debug') + self.get_error(error_code, err) + output_full_list, unsatisfiable, _, full_timers = clingo_lpx.result_convert(proc_output, + self.network.objectives, 'cautious', is_killed) + if unsatisfiable: + logger.print_log('Unsatisfiable problem', "error") + elif output_full_list: + model = output_full_list[list(output_full_list.keys())[-1]] + solution_list[model_type] = model + #Get obejctives fluxes and add results seeds to network object + obj_flux_dict = self.get_objectives_flux(model[5]) + self.add_result_seeds(search_mode, model_type, model[1], model[3], obj_flux_dict) + if 'Total' in full_timers: + timer["Grounding time"] = round(full_timers['Total'] - full_timers['Solve'], 3) + timer["Solving time"] = round(full_timers['Solve'], 3) + else: + timer["Grounding time"] = "Time out" + timer["Solving time"] = "Time out" + results ["Timer"] = timer + results ["Memory (GB)"] = memory + results['solutions']= solution_list + self.output[output_type] = results + + + def get_error(self,error_code:int, err:bytes): + """Get and print error + + Args: + error_code (int): Code error from Python process + err (bytes): Error text from python process + + Raises: + ValueError: Raise an error and stop the program when value of code < 0 + """ + if error_code and error_code<0: + print(f'error_code = {error_code}') + raise ValueError(err.decode()) + elif err: + logger.print_log(err.decode(), 'debug') + + ######################################################## + + +################################################################### +##################### Class FBA : herit Hybrid #################### +################################################################### +class FBA(Hybrid): + def __init__(self, run_mode:str, network:Network, + time_limit_minute:float=None, number_solution:int=None, + clingo_configuration:str=None, clingo_strategy:str=None, + intersection:bool=False, union:bool=False, + minimize:bool=False, subset_minimal:bool=False, + maximize_flux:bool=False, + temp_dir:str=None, short_option:str=None, + verbose:bool=False): + """Initialize Object Hybrid, herit from Solver + + Args: + run_mode (str): Running command used (full or target or fba) + network (Network): Network constructed + time_limit_minute (float, optional): Time limit given by user in minutes . Defaults to None. + number_solution (int, optional): Limit number of solutions to find. Defaults to None. + clingo_configuration (str, optional): Configuration for clingo resolution . Defaults to None. + clingo_strategy (str, optional): Strategy for clingo resolution. Defaults to None. + enumeration (bool, optional): Enumerate the solutions limited by the number of solutions. Defaults to True. + intersection (bool, optional): Find the intersection of all solutions without limitation (give one solution). Defaults to False. + union (bool, optional): Find the union of all solutions without limitation (give one solution). Defaults to False. + minimize (bool, optional): Search the minimal carinality of solutions. Defaults to False. + subset_minimal (bool, optional): Search the subset minimal solutions. Defaults to False. + maximize_flux (bool, optional): Use maximization of flux for calculation. Defaults to False. + temp_dir (str, optional): Temporary directory for saving instance file and clingo outputs. Defaults to None. + short_option (str, optional): Short way to write option on filename. Defaults to None. + verbose (bool, optional): Set debug mode. Defaults to False. + """ + super().__init__(run_mode, None, network, + time_limit_minute, number_solution, + clingo_configuration, clingo_strategy, + intersection, union, minimize, + subset_minimal, maximize_flux, + temp_dir, short_option, + verbose) + + ######################## METHODS ######################## + + def get_init_message(self): + """Get init message to put on terminal + """ + title_mess = "\n############################################\n" \ + "############################################\n" \ + f" {color.bold}FBA{color.cyan_light}\n"\ + "############################################\n" \ + "############################################\n" + logger.print_log(title_mess, "info", color.cyan_light) + + def add_result_seeds(self, search_mode:str, model_name:str, len:int, seeds:list, flux_list:list): + """Add a formated resulted set of seeds into the network object + + Args: + search_mode (str): search mode type + model_name (str): model name + len (int): length of a set of seed + seeds (list): list of seeds + """ + self.network.add_result_seeds('FBA', search_mode, model_name, len, seeds, flux_list) + ######################################################## + + + diff --git a/seed2lp/log_conf.yaml b/seed2lp/log_conf.yaml new file mode 100644 index 0000000..40f4628 --- /dev/null +++ b/seed2lp/log_conf.yaml @@ -0,0 +1,25 @@ +############################### +# Logger configuration # +############################### + +version: 1 +disable_existing_loggers: False +formatters: + simple: + format: '%(levelname)s : %(message)s' + () : seed2lp.logger.ColoredFormatter + use_color: true +handlers: + console: + class: logging.StreamHandler + level: WARNING + formatter: simple + stream: ext://sys.stdout +loggers: + s2lp: + level: DEBUG + handlers: [console] + propagate: no +root: + level: DEBUG + handlers: [console] diff --git a/seed2lp/logger.py b/seed2lp/logger.py new file mode 100644 index 0000000..1a60972 --- /dev/null +++ b/seed2lp/logger.py @@ -0,0 +1,116 @@ +import logging, logging.config +from os import path +from . import color, file +import yaml +from ._version import __version__ + +ROJECT_DIR = path.dirname(path.abspath(__file__)) +LOG_DIR:str +log:logging.Logger +verbose:bool + +COLORS = { + "WARNING": color.yellow, + "INFO": color.grey, + "DEBUG": color.reverse_colour, + "CRITICAL": color.orange_back, + "ERROR": color.red_bright, + "RESET": color.reset +} + + +class ColoredFormatter(logging.Formatter): + def __init__(self, *, format:str, use_color:bool): + logging.Formatter.__init__(self, fmt=format) + self.use_color = use_color + + def format(self, record): + msg = super().format(record) + if self.use_color: + levelname = record.levelname + if hasattr(record, "color"): + return f"{record.color}{msg}{COLORS['RESET']}" + if levelname in COLORS: + return f"{COLORS[levelname]}{msg}{COLORS['RESET']}" + return msg + + + +def __init_logger__(log_path:str): + """_summary_ + + Args: + log_path (str): _description_ + debug (bool): _description_ + + Returns: + _type_: _description_ + """ + global log + #logging.config.fileConfig(path.join(ROJECT_DIR,'log_conf.yaml')) + with open(path.join(ROJECT_DIR,'log_conf.yaml'), "rt") as f: + config = yaml.safe_load(f.read()) + if verbose: + config['handlers']['console']['level']='DEBUG' + logging.config.dictConfig(config) + formatter = logging.Formatter('%(levelname)s %(asctime)s: %(message)s') + file_handler = logging.FileHandler(log_path) + + if verbose: + file_handler.setLevel(logging.DEBUG) + else: + file_handler.setLevel(logging.INFO) + file_handler.setFormatter(formatter) + + # create logger + log = logging.getLogger('s2lp') + log.addHandler(file_handler) + + +def print_log(message:str, level:str, col=None): + """Print and log messages + + Args: + message (str): Message + level (str): level of the message (info, error or debug) + """ + if not verbose and level != "debug": + if col: + print(col + message + color.reset) + else: + print(message) + else: + pass + + match level: + case "info": + if col: + log.info(message, extra={"color": col}) + else: + log.info(message) + case "debug": + log.debug(message) + case "warning": + log.warning(message) + case "error": + log.error(message) + + + +def get_logger(sbml_file:str, short_option:str, debug:bool=False): + global log + global verbose + verbose=debug + net_name = f'{path.splitext(path.basename(sbml_file))[0]}' + filename = f'{net_name}_{short_option}.log' + log_path = path.join(LOG_DIR, filename) + if file.existing_file(log_path): + file.delete(log_path) + __init_logger__(log_path) + log.info(f"Seed2LP version: {__version__}") + + +def set_log_dir(value): + global LOG_DIR + LOG_DIR = value + \ No newline at end of file diff --git a/seed2lp/metabolite.py b/seed2lp/metabolite.py new file mode 100644 index 0000000..cdaffd8 --- /dev/null +++ b/seed2lp/metabolite.py @@ -0,0 +1,60 @@ +# Object Metabolite constitued of: +# - name (str): Name of the metabolite +# - stoichiometry (int): Stoiciometry of the metabolite in the reaction eaquation if linked into a reaction +# (not set for targets or possible seeds or forbidden seeds) +# - compartment (str): character of the compartment of metabolite + +class Metabolite: + def __init__(self, name:str, stoichiometry:float=None, meta_type:str=""): + """Initialize Object Metabolite + + Args: + name (str): Name / ID of the Metabolite + stoichiometry (int, optional): Stoichiometry of the metabolite if into a reactions. Defaults to None. + """ + self.name = name + self.stoichiometry = stoichiometry + self.type = meta_type + + ######################## GETTER ######################## + def _get_name(self,): + return self.name + + def _get_stoichiometry(self,): + return self.stoichiometry + ######################################################## + + ######################## SETTER ######################## + def _set_name(self, name:str): + self.name = name + + def _set_stoichiometry(self, stoichiometry:float): + self.stoichiometry = stoichiometry + ######################################################## + + + ######################## METHODS ######################## + def convert_to_facts(self, metabolite_type:str, reaction_name:str=None): + """Convert metabolite into ASP facts : an atom reactant or product, associated to a reaction + and having a stoichiometry value + + Args: + metabolite_type (str): Type of metabolite (exchange, transport, other) + reaction_name (str, optional): Name of the reaction associated to the metabolite. Defaults to None. + + Returns: + facts (str): The ASP atom created + """ + facts = "" + match metabolite_type: + case "reactant"|"product": + + facts += f'{metabolite_type}("{self.name}","{"{:.10f}".format(self.stoichiometry)}","{reaction_name}","{self.type}").\n' + case "seed": + facts += f'{metabolite_type}("{self.name}","{self.type}").\n' + case _: + facts += f'{metabolite_type}("{self.name}","{"{:.10f}".format(self.stoichiometry)}","{reaction_name}","{self.type}").\n' + return facts + + + ######################################################## diff --git a/seed2lp/network.py b/seed2lp/network.py new file mode 100644 index 0000000..b88a828 --- /dev/null +++ b/seed2lp/network.py @@ -0,0 +1,909 @@ +# Object Network constitued of +# - file (str): Path of input network file (sbml) +# - run_mode (str): Running command used (full or target) +# - name (str): Species name/ID from file name +# - targets_as_seeds (bool): Targets can't be seeds and are noted as forbidden +# - use_topological_injections (bool): Metabolite of import reaction are seeds +# - keep_import_reactions (bool): Import reactions are removed +# - reactions (list): List of reactions (object Reaction) +# - targets (list): List of target (object Metabolite) +# - seeds (list): List of seed given by the user (object Metabolite) +# - possible_seeds (list): List of possible seeds given by the user (object Metabolite) +# - forbiddend_seed (list): List of forbidden seeds (object Metabolite) +# - facts (str): Conversion sbml into asp facts +# - fluxes (list): List of flux check on all set of seeds + +import os +import pandas as pd +from .reaction import Reaction +import seed2lp.sbml as SBML +from .utils import quoted +from . import flux +from .resmod import Resmod +from time import time +from . import color +from . import logger + + +FLUX_MESSAGE=\ +f""" {color.bold} "Cobra (seeds)" {color.reset} indicates the maximum flux +obtained in FBA from the seeds after shutting +off all other exchange reactions. If the maximum +flux is null, a test is performed opening demand +reactions for the objective reaction's products, +in order to test the effect of their accumulation +({color.bold}"cobra (demands)"{color.reset} ). If this test is not performed, +"NA" value is indicated.""" + +WARNING_MESSAGE_LP_COBRA=\ +f"""Cobra flux and LP flux might be +different because the option -max/--maximize +is not used""" + + +class Network: + def __init__(self, file:str, run_mode:str=None, targets_as_seeds:bool=False, use_topological_injections:bool=False, + keep_import_reactions:bool=True, input_dict:dict=None, accumulation:bool=False, to_print:bool=True, + write_sbml:bool=False): + """Initialize Object Network + + Args: + run_mode (str): Running command used (full or target) + file (str): Path of input network file (sbml) + targets_as_seeds (bool): Targets can't be seeds and are noted as forbidden + use_topological_injections (bool): Metabolite of import reaction are seeds + keep_import_reactions (bool): Import reactions are not removed + accumulation (bool, optional): Is accumulation authorized. Defaults to False. + to_print (bool, optional): Write messages into console if True. Defaults to True. + write_sbml (bool, optional): Is a writing SBML file mode or not. Defaults to False. + """ + self.file = file + self.run_mode = run_mode + self.file_extension = "" + self._set_file_extension(file) + self._set_name() + + self.sbml, self.sbml_first_line = SBML.get_root(self.file) + self.model = SBML.get_model(self.sbml) + self.fbc = SBML.get_fbc(self.sbml) + self.parameters = SBML.get_listOfParameters(self.model) + + self.targets_as_seeds = targets_as_seeds + self.use_topological_injections = use_topological_injections + self.keep_import_reactions = keep_import_reactions + self.reactions = list() + # list of reaction having reactants and products switched + self.meta_modified_reactions = dict() + # list of reaction having the reversibility changed + self.reversible_modified_reactions = dict() + # list of reaction deleted because boudaries [0,0] + self.deleted_reactions = dict() + # list of exchange reaction + self.exchanged_reactions = dict() + + + self.objectives = list() + self.is_objective_error = False + self.targets=list() + self.seeds = list() + self.possible_seeds = list() + self.is_subseed = False + self.forbidden_seeds = list() + self.facts = "" + self.meta_exchange_list = list() + self.meta_transport_list = list() + # metabolite of import reaction having multiple metabolite such as None -> A+B + self.meta_multiple_import_list = list() + self.accumulation = accumulation + + self.instance_file=str() + + if self.run_mode is not None: + self.init_with_inputs(input_dict) + self.get_network(to_print, write_sbml) + self.result_seeds=list() + self.fluxes = pd.DataFrame() + + + ######################## GETTER ######################## + def _get_reactions(self): + return self.reactions + + def _get_objectives(self): + return self.objectives + + def _get_result_seeds(self): + return self.result_seeds + ######################################################## + + ######################## SETTER ######################## + def _set_file_extension(self, file:str): + ext = os.path.splitext(file)[1] + self.file_extension = ext + + def _set_name(self): + n = f'{os.path.splitext(os.path.basename(self.file))[0]}' + self.name = n + print(f"Network name: {n}") + + def _set_reactions(self, reactions:list): + self.reactions = reactions + + def _set_result_seeds(self, result_seeds:list): + self.result_seeds = result_seeds + ######################################################## + + ######################## METHODS ######################## + def get_objective_reactant(self): + """Get the objective reactants from SBML file + """ + logger.log.info("Finding list of reactants from opbjective reaction...") + for obj in self.objectives: + reactants = SBML.get_listOfReactants_from_name(self.model, obj) + for reactant in reactants: + react_name = reactant.attrib.get('species') + if react_name not in self.targets: + self.targets.append(react_name) + logger.log.info("... DONE") + + def get_boundaries(self, reaction): + """Get Boundaries of a reaction + + Args: + reaction (etree line): Reaction from etree package + + Returns: + lbound (float), ubound (float): lower and uppper boundaries value + """ + lower_bound = self.parameters[reaction.attrib.get('{'+self.fbc+'}lowerFluxBound')] \ + if type(reaction.attrib.get('{'+self.fbc+'}lowerFluxBound')) is not float \ + else '"'+reaction.attrib.get('{'+self.fbc+'}lowerFluxBound')+'"' + lbound = round(float(lower_bound),10) + upper_bound = self.parameters[reaction.attrib.get('{'+self.fbc+'}upperFluxBound')] \ + if type(reaction.attrib.get('{'+self.fbc+'}upperFluxBound')) is not float \ + else '"'+reaction.attrib.get('{'+self.fbc+'}upperFluxBound')+'"' + ubound = round(float(upper_bound),10) + return lbound, ubound + + def find_objectives(self, input_dict:dict): + """Find the objective reaction from SBML file + If mode Target and no target set : put reactant of objective + as targets + + Args: + input_dict (dict): Constructed dictionnary of inputs + + Raises: + ValueError: Multiple objective reaction with coefficient 1 found + ValueError: No objective reaction found or none has coefficient 1 + """ + logger.print_log("\n Finding objective ...", "info") + objectives = SBML.get_listOfFluxObjectives(self.model, self.fbc) + obj_found = None + is_reactant_found = False + for obj in objectives: + coef=float(obj[1]) + if obj_found is None: + #For now works with only one objective + if coef == 1: + obj_found = obj[0] + reaction = obj[2] + lbound, ubound = self.get_boundaries(reaction) + # multiple objectives found with coefficient 1 + else: + if coef == 1: + objectives = None + obj_found = None + self.is_objective_error = True + raise ValueError(f"Multiple objective reaction with coefficient 1 found\n") + + if not obj_found: + self.is_objective_error = True + raise ValueError(f"No objective reaction found or none has coefficient 1\n") + else: + # Works for 1 objective reation for now + logger.print_log(f'Objective found : {color.bold}{obj_found}{color.reset}\n', "info") + if lbound == 0 and ubound == 0: + self.is_objective_error = True + raise ValueError(f"Lower and upper boundaries are [0,0] \nfor objetive reaction {obj_found}\n") + self.objectives = [obj_found] + if (self.run_mode == "target" or self.run_mode == "fba")\ + and ('Targets' not in input_dict or not input_dict["Targets"]): + self.get_objective_reactant() + is_reactant_found = True + return is_reactant_found + + + def init_with_inputs(self, input_dict:dict): + """Init Network object with user inputs data + Construct the init messages + + Args: + input_dict (dict): Constructed dictionnary of inputs + """ + tgt_message = "" + if self.run_mode == "target": + tgt_message = "Targets set:\n" + elif self.run_mode == "fba": + tgt_message = "Targets detected for option \"target as seeds\":\n" + else: + tgt_message = "Targets set:\nAll metabolites as target\n" + obj_message = f"Objective set:\n" + kir_mess = "Import reaction: " + ti_inject = "Product of import reaction set as seed: " + tas_mess = "Targets can be seeds: " + accu_mess = "Accumulation: " + + # init network with input data + if input_dict: + if "Targets" in input_dict and input_dict["Targets"]: + self.targets = input_dict["Targets"] + tgt_message += " Metabolites from target file\n" + if "Seeds" in input_dict: + self.seeds = input_dict["Seeds"] + if "Possible seeds" in input_dict: + self.possible_seeds = input_dict["Possible seeds"] + self.is_subseed = True + if "Forbidden seeds" in input_dict: + self.forbidden_seeds = input_dict["Forbidden seeds"] + # Objective given in target file (mode target) + # or command line (mode full) + if "Objective" in input_dict and input_dict["Objective"]: + self.objectives = input_dict["Objective"] + if self.run_mode == "target": + obj_message += " Objective reaction from target file\n" + else: + obj_message += " Objective reaction from command line\n" + obj_string = " ".join([str(item) for item in self.objectives]) + obj_message += f'\n\nObjective : {obj_string}' + # Reactant of objective set as target on target mode + # No needed on full mode (all metabolite are set as target) + if self.run_mode != "full": + self.get_objective_reactant() + tgt_message += " Reactant of objective reaction\n from target file\n" + + # Find objective into sbml file if not given by user + if self.objectives is None or not self.objectives: + try: + is_reactant_found = self.find_objectives(input_dict) + obj_message += " Objective reaction from SBML file" + obj_string = " ".join([str(item) for item in self.objectives]) + obj_message += f'\n\nObjective : {obj_string}' + if self.run_mode != "full" \ + and is_reactant_found: + tgt_message += " Reactant of objective reaction\n from SBML file\n" + except ValueError as e: + if self.run_mode == "target" \ + and (self.targets is None or not self.targets): + tgt_message += " No target found" + obj_message += " No objective reaction found" + logger.log.error(str(e)) + + if self.keep_import_reactions: + kir_mess += " Kept" + else: + kir_mess += " Removed" + + if self.use_topological_injections: + ti_inject += " Yes" + else: + ti_inject += " No" + + if self.run_mode != "full" and self.targets_as_seeds: + tas_mess += " Yes" + elif self.run_mode == "full": + tas_mess = None + else: + tas_mess += " No" + + if self.accumulation: + accu_mess += " Allowed" + else: + accu_mess += " Forbidden" + + self.write_cases_messages(tgt_message, obj_message, [kir_mess, ti_inject, tas_mess, accu_mess]) + + + def add_reaction(self, reaction:Reaction): + """Add a reaction into the Network list of reaction + + Args: + reaction (Reaction): Object reaction + """ + reactions_list = self._get_reactions() + reactions_list.append(reaction) + self._set_reactions(reactions_list) + + def add_objective(self, reaction_name:str): + """Add a reaction into objecive list + + Args: + reaction_name (str): Reaction to add as objective + """ + objectives_list = self._get_objectives() + objectives_list.append(reaction_name) + self.objectives = objectives_list + + def get_network(self, to_print:bool=True, write_sbml:bool=False): + """Get the description of the Network from SBML file + Construct list of reactants and products + Correct the reversibility based on boundaries + For import or export reaction, if reversibilité is corrected + correct also reactants and products by exchanging them + When writing SBML, delete Import reaction + + Args: + to_print (bool, optional): _description_. Defaults to True. + write_sbml (bool, optional): Is a writing SBML file mode or not. Defaults to False. + """ + reactions_list = SBML.get_listOfReactions(self.model) + warning_message = "" + info_message = "" + for r in reactions_list: + reaction = Reaction(r.attrib.get("id")) + reaction.is_exchange=False + source_reversible = False if r.attrib.get('reversible') == 'false' else True + # Treating reverserbility separately, lower bound can stay to 0 + reaction.lbound, reaction.ubound = self.get_boundaries(r) + + # If the reaction can never have flux, meaning lower_bound = 0 and upper_bound = 0 + # The reaction is deleted from the network + if reaction.lbound == 0 and reaction.ubound == 0: + self.deleted_reactions[reaction.name] = len(self.reactions) + warning_message += f"\n - {reaction.name}: Deleted.\n Boundaries was: [{reaction.lbound} ; {reaction.ubound}]\n" + # Not added not reaction list of the network + continue + + reactants = SBML.get_listOfReactants(r) + products = SBML.get_listOfProducts(r) + + # uses the definition of boundaries as cobra + # a reaction is in boundaries (so exchange reaction) + # when a reaction has only one metabolite and + # does not have reactants or products + if not (reactants and products): + if (reactants and len(reactants) == 1) \ + or (products and len(products) == 1): + # Cobra deifinition of exchange reaction + reaction.is_exchange=True + self.exchanged_reactions[reaction.name] = len(self.reactions) + elif not reactants and not products: + # Reaction with boundaries [0,0] is deleted on the network for reasoning part. + # But it is not deleted on sbml file when rewritten (command network otion rewrite file). + warning_message += f" - {reaction.name} deleted. No reactants and no products.\n" + continue + else: + self.exchanged_reactions[reaction.name] = len(self.reactions) + # A reaction is multiple exchange + # when None -> A + B || A + B -> None || None <-> A + B || A + B <-> None + warning_message += f" - {reaction.name} is multiple (more than 1) metabolites import/export reaction. " + if not self.keep_import_reactions: + warning_message += "\n\tDeleted as it is an import reaction.\n" + else: + warning_message += "\n" + + + # Check if transport reactions + if len(reactants)==1 and len(products)==1: + if reactants[0][0].rsplit('_', 1)[0] == products[0][0].rsplit('_', 1)[0]: + reaction.is_transport=True + + + # import reactions to remove + + # For each reaction check if the lower and upper bounds + # have the same sign (not reversible) + # Cases : [-1000,-10] , [-1000,0], [0, 1000], [10,1000] + if reaction.lbound*reaction.ubound >= 0: + reaction.reversible=False + # Reaction written backwards + # M -> None, with boundaries [-1000, -10] is import reaction case + # R -> P, with boundaries [-1000, -10] is P -> R + # None -> M, with boundaries [-1000, -10] is export reaction case + # import reactions removed, needs no reactant + # exchange reactants and products + if reaction.ubound <= 0: + warning_message += f" - {reaction.name}: Reactants and products switched.\n Boundaries was: [{reaction.lbound} ; {reaction.ubound}]\n" + meta = products + products = reactants + reactants = meta + reaction.is_meta_modified = True + # Index of reaction needed for network rewriting + self.meta_modified_reactions[reaction.name] = len(self.reactions) + + # Change the bounds + if reaction.ubound != 0: + bound = - reaction.ubound + else: + bound = reaction.ubound + if reaction.lbound != 0: + reaction.ubound = - reaction.lbound + else: + reaction.ubound = reaction.lbound + reaction.lbound = bound + # The upper and lower bound does not have the same sign + # The reaction is reversible + # Cases: [-1000,10], [-10,1000], ... + else: + reaction.reversible = True + react_exchange_list,react_transport_list = reaction.add_metabolites_from_list(reactants,"reactant", + self.meta_exchange_list, self.meta_transport_list) + self.meta_exchange_list.extend(y for y in react_exchange_list if y not in self.meta_exchange_list) + self.meta_transport_list.extend(y for y in react_transport_list if y not in self.meta_transport_list) + + prod_exchange_list, prod_transpor_list = reaction.add_metabolites_from_list(products,"product", + self.meta_exchange_list, self.meta_transport_list) + self.meta_exchange_list.extend(y for y in prod_exchange_list if y not in self.meta_exchange_list) + self.meta_transport_list.extend(y for y in prod_transpor_list if y not in self.meta_transport_list) + + reaction.is_reversible_modified = source_reversible != reaction.reversible + + if reaction.is_reversible_modified: + # Index of reaction needed for network rewriting + self.reversible_modified_reactions[reaction.name] = len(self.reactions) + info_message += f"\n - {reaction.name}: Reversibility modified." + self.add_reaction(reaction) + + # Because of the order of reaction, the metabolites can be found as exchanged + # after another reaction, it is needed to correct that + for reaction in self.reactions: + for metabolite in reaction.products: + if metabolite.name in self.meta_exchange_list and not metabolite.type == "exchange": + metabolite.type = "exchange" + if metabolite.name in self.meta_transport_list and not metabolite.type == "transport": + metabolite.type = "transport" + for metabolite in reaction.reactants: + if metabolite.name in self.meta_exchange_list and not metabolite.type == "exchange": + metabolite.type = "exchange" + if metabolite.name in self.meta_transport_list and not metabolite.type == "transport": + metabolite.type = "transport" + # Change boundaries for rewritting sbml + # this part is not needed for the ASP writing because we need the original value of boundaries + # for hybrid-lpx to reopen the import boundaries to its origin value and not max + if write_sbml and reaction.is_exchange and not self.keep_import_reactions: + if len(reaction.reactants) == 0: + reaction.ubound=0 + if len(reaction.products) == 0: + reaction.lbound=0 + if (to_print): + if warning_message: + logger.log.warning(warning_message) + if info_message: + logger.log.info(info_message) + else: + logger.log.info(warning_message) + logger.log.info(info_message) + print("____________________________________________\n") + + + def convert_to_facts(self): + """Convert the corected Network into ASP facts + """ + logger.log.info("Converting Network into ASP facts ...") + facts = "" + # Upper bound does not change on forward reaction + + for reaction in self.reactions: + facts += reaction.convert_to_facts(self.keep_import_reactions, + self.use_topological_injections) + + + for objective in self.objectives: + facts += '\nobjective("'+objective+'").' + for seed in self.seeds: + facts += f'\nseed_user({quoted(seed)}).' + for target in self.targets: + facts += f'\ntarget({quoted(target)}).' + for forbidden in self.forbidden_seeds: + facts += f'\nforbidden({quoted(forbidden)}).' + for possible in self.possible_seeds: + facts += f'\np_seed({quoted(possible)}).' + + self.facts = facts + logger.log.info("... DONE") + + def simplify(self): + """Lighten the Network Object, only facts needed + """ + self.model = None + self.sbml = None + self.reactions = None + self.seeds = None + self.forbidden_seeds = None + + + def add_result_seeds(self, solver_type:str, search_info:str, model_name:str, + size:int, seeds:list, flux_lp:dict=None, flux_cobra:float=None): + """Add a formated resulted set of seeds into a list + + Args: + solver_type (str): Type of solver (Reasoning / FBA / Hybrid) + search_mode (str): search mode type (Minimize / Submin + containing search type enumeration / union /intersection) + model_name (str): model name + len (int): length of a set of seed + seeds (list): list of seeds + flux_lp (dict, optional): Dictionnary of all reaction with their LP flux. Defaults to None. + flux_cobra (float, optional): Cobra flux calculated (mode Filter, Guess Check). Defaults to None. + """ + result_seeds_list = self._get_result_seeds() + match search_info: + # FROM SOLVER + case "minimize-one-model": + search_mode="Minimize" + search_type="Optimum" + case "minimize-intersection": + search_mode="Minimize" + search_type="Intersection" + case "minimize-union": + search_mode="Minimize" + search_type="Union" + case "minimize-enumeration": + search_mode="Minimize" + search_type="Enumeration" + case "submin-enumeration": + search_mode="Subset Minimal" + search_type="Enumeration" + case "submin-intersection": + search_mode="Subset Minimal" + search_type="Intersection" + # FROM RESULT FILE + case'MINIMIZE OPTIMUM': + search_mode = 'Minimize' + search_type = 'Optimum' + case 'MINIMIZE INTERSECTION' \ + | 'MINIMIZE INTERSECTION FILTER' \ + | 'MINIMIZE INTERSECTION GUESS-CHECK' \ + | 'MINIMIZE INTERSECTION GUESS-CHECK-DIVERSITY': + search_mode = 'Minimize' + search_type = 'Intersection' + case 'MINIMIZE UNION' \ + | 'MINIMIZE UNION FILTER' \ + | 'MINIMIZE UNION GUESS-CHECK' \ + | 'MINIMIZE UNION GUESS-CHECK-DIVERSITY': + search_mode = 'Minimize' + search_type = 'Union' + case 'MINIMIZE ENUMERATION'\ + | 'MINIMIZE ENUMERATION FILTER' \ + | 'MINIMIZE ENUMERATION GUESS-CHECK' \ + | 'MINIMIZE ENUMERATION GUESS-CHECK-DIVERSITY': + search_mode = 'Minimize' + search_type = 'Enumeration' + case 'SUBSET MINIMAL ENUMERATION' \ + | 'SUBSET MINIMAL ENUMERATION FILTER' \ + | 'SUBSET MINIMAL ENUMERATION GUESS-CHECK' \ + | 'SUBSET MINIMAL ENUMERATION GUESS-CHECK-DIVERSITY': + search_mode = 'Subset Minimal' + search_type = 'Enumeration' + case 'SUBSET MINIMAL INTERSECTION'\ + | 'SUBSET MINIMAL INTERSECTION FILTER' \ + | 'SUBSET MINIMAL INTERSECTION GUESS-CHECK' \ + | 'SUBSET MINIMAL INTERSECTION GUESS-CHECK-DIVERSITY': + search_mode = 'Subset Minimal' + search_type = 'Intersection' + case _: + search_mode = 'Other' + search_type = 'Enumeration' + result = Resmod(model_name, self.objectives, solver_type, search_mode, search_type, size, seeds, flux_lp, flux_cobra, self.run_mode, self.accumulation) + result_seeds_list.append(result) + self._set_result_seeds(result_seeds_list) + + + + def check_fluxes(self, maximize:bool): + """Calculate the flux using Cobra and get the flux from lp for all solutions (set of seed). + Store information and data into dataframe + + Args: + maximize (bool): Determine if Maximize option is used + """ + dtypes = {'species':'str', + 'biomass_reaction':'str', + 'solver_type':'str', + 'search_mode':'str', + 'search_type':'str', + 'accumulation':'str', + 'model':'str', + 'size':'int', + 'lp_flux':'float', + 'cobra_flux_init':'float', + 'cobra_flux_no_import':'float', + 'cobra_flux_seeds':'float', + 'cobra_flux_demands':'float', + 'has_flux':'str', + 'has_flux_seeds':'str', + 'has_flux_demands':'str', + 'timer':'float' + } + fluxes = pd.DataFrame(columns=['species','biomass_reaction', 'solver_type', 'search_mode', 'search_type', + 'accumulation', 'model', 'size', 'lp_flux', 'cobra_flux_init', 'cobra_flux_no_import', + 'cobra_flux_seeds', 'cobra_flux_demands', 'has_flux','has_flux_seeds', + 'has_flux_demands', 'timer']) + fluxes = fluxes.astype(dtypes) + + if self.objectives: + if self.result_seeds: + logger.log.info("Check fluxes Starting") + model = flux.get_model(self.file) + fluxes_init = flux.get_init(model, self.objectives) + if not self.keep_import_reactions: + fluxes_no_import = flux.stop_flux(model, self.objectives) + self.model = model + print(color.purple+"\n____________________________________________") + print("____________________________________________\n"+color.reset) + print("RESULTS".center(44)) + print(color.purple+"____________________________________________") + print("____________________________________________\n"+color.reset) + + print(FLUX_MESSAGE) + + prev_solver_type=None + prev_search_mode=None + has_warning=False + + for result in self.result_seeds : + if prev_search_mode == None or result.search_mode != prev_search_mode: + if has_warning: + print("\n") + logger.log.warning(WARNING_MESSAGE_LP_COBRA) + print(color.yellow+"\n____________________________________________") + print("____________________________________________\n"+color.reset) + print(result.search_mode.center(44)) + prev_search_mode = result.search_mode + prev_solver_type=None + if prev_solver_type == None or result.solver_type != prev_solver_type: + print(color.yellow+"--------------------------------------------"+color.reset) + print(result.solver_type.center(44)) + print(color.yellow+". . . . . . . . . . ".center(44)+color.reset) + prev_solver_type=result.solver_type + type_column = "name | cobra (seeds) | cobra (demands)" + separate_line="-----|---------------|-----------------" + has_warning=False + if result.solver_type=="HYBRID" or result.solver_type=="FBA": + type_column+=" | LP" + separate_line+="|----" + print(type_column) + print(separate_line) + + flux_time = time() + result.check_flux(self.model) + + warn = print_flux(result,maximize) + if warn: + has_warning=True + + + flux_time = time() - flux_time + flux_time=round(flux_time, 3) + flux_init = fluxes_init[result.tested_objective] + if self.keep_import_reactions: + flux_no_import = None + else: + flux_no_import = fluxes_no_import[result.tested_objective] + result_flux = pd.DataFrame([[self.name, result.tested_objective, result.solver_type, result.search_mode, + result.search_type, str(self.accumulation), result.name, result.size, result.chosen_lp, flux_init, + flux_no_import, result.objective_flux_seeds, result.objective_flux_demands, + str(result.OK), str(result.OK_seeds), str(result.OK_demands), flux_time]], + columns=['species','biomass_reaction', 'solver_type', 'search_mode', + 'search_type', 'accumulation', 'model', 'size', 'lp_flux', 'cobra_flux_init', + 'cobra_flux_no_import', 'cobra_flux_seeds', 'cobra_flux_demands', + 'has_flux','has_flux_seeds', 'has_flux_demands', 'timer']) + result_flux = result_flux.astype(dtypes) + fluxes = pd.concat([fluxes, result_flux], ignore_index=True) + + if has_warning: + print("\n") + logger.log.warning(WARNING_MESSAGE_LP_COBRA) + print(color.yellow+"\n____________________________________________\n"+color.reset) + else: + print(color.red_bright+"No solution found"+color.reset) + else: + print(color.red_bright+"No objective found, can't run cobra optimization"+color.reset) + self.fluxes = fluxes + + + def convert_data_to_resmod(self, data): + """Convert json data into Resmod object in order to add the list to Netork object. + + Args: + data (dict): Json data from previous seed2lp result file + """ + logger.log.info("Converting data from result file ...") + reaction_option = data["OPTIONS"]["REACTION"] + match reaction_option: + case "Remove Import Reaction": + self.keep_import_reactions = False + self.use_topological_injections = False + case "Topological Injection": + self.keep_import_reactions = True + self.use_topological_injections = True + case "No Topological Injection": + self.keep_import_reactions = True + self.use_topological_injections = False + + if data["OPTIONS"]["ACCUMULATION"] == "Allowed": + self.accumulation = True + else: + self.accumulation = False + + self.objectives = data["NETWORK"]["OBJECTIVE"] + + match data["NETWORK"]["SEARCH_MODE"]: + case "Target": + self.run_mode = "target" + case "FBA": + self.run_mode = "fba" + case "Full network": + self.run_mode = "full" + case _ : + self.run_mode = data["NETWORK"]["SEARCH_MODE"] + + if data["OPTIONS"]["FLUX"] == "Maximization": + maximize = True + else: + maximize = False + + match data["NETWORK"]["SOLVE"]: + case "REASONING": + solve = 'reasoning' + case "HYBRID": + solve = 'hybrid' + case "REASONING GUESS-CHECK": + solve = 'guess_check' + case "REASONING GUESS-CHECK DIVERSITY": + solve = 'guess_check_div' + case "REASONING FILTER": + solve = 'filter' + case "ALL" | _: + solve = 'all' + + for solver_type in data["RESULTS"]: + for search_info in data["RESULTS"][solver_type]: + solver_type_transmetted = solver_type + if data["NETWORK"]["SOLVE"] !="ALL": + if "REASONING" in solver_type: + solver_type_transmetted = data["NETWORK"]["SOLVE"] + elif solver_type == "REASONING": + if "DIVERSITY" in search_info: + solver_type_transmetted = "REASONING GUESS-CHECK DIVERSITY" + elif 'GUESS-CHECK' in search_info: + solver_type_transmetted = "REASONING GUESS-CHECK" + elif 'FILTER' in search_info: + solver_type_transmetted = "REASONING FILTER" + + if "solutions" in data["RESULTS"][solver_type][search_info]: + for solution in data["RESULTS"][solver_type][search_info]["solutions"]: + name = solution + size = data["RESULTS"][solver_type][search_info]["solutions"][solution][1] + seeds_list = data["RESULTS"][solver_type][search_info]["solutions"][solution][3] + obj_flux_lp = dict() + if solver_type == "FBA" or solver_type == "HYBRID": + for flux in data["RESULTS"][solver_type][search_info]["solutions"][solution][5]: + reaction = flux[0] + if reaction in self.objectives: + obj_flux_lp[reaction] = flux[1] + self.add_result_seeds(solver_type_transmetted, search_info, name, size, seeds_list, + obj_flux_lp) + logger.log.info("... DONE") + return maximize, solve + + + def write_cases_messages(self, tgt_message:str, obj_message:str, + net_mess:list): + """Write terminal messages depending on + - target file data for target mode + - command line for full mode + + Args: + tgt_message (str): The message to show for target + obj_message (str): The message to show for objective + net_mess ([str]): The message to show for network + """ + print("\n____________________________________________\n") + print(f"TARGETS".center(44)) + print(f"FOR TARGET MODE AND FBA".center(44)) + print("____________________________________________\n") + logger.print_log(tgt_message, "info") + + print("\n____________________________________________\n") + print(f"OBJECTVE".center(44)) + print(f"FOR HYBRID".center(44)) + print("____________________________________________\n") + logger.print_log(obj_message, "info") + print("\n") + + + print("\n____________________________________________\n") + print(f"NETWORK".center(44)) + print("____________________________________________\n") + logger.print_log(net_mess[0], "info") + if self.keep_import_reactions: + logger.print_log(net_mess[1], "info") + if self.run_mode != "full": + logger.print_log(net_mess[2], "info") + if self.run_mode != "fba": + logger.print_log(net_mess[3], "info") + print("\n") + + + + def check_seeds(self, seeds:list): + """Check flux into objective reaction for a set of seeds. + + Args: + seeds (list): Set of seeds to test + + Returns: + bool: Return if the objective reaction has flux (True) or not (False) + """ + + model = flux.get_model(self.file) + flux.get_init(model, self.objectives, False) + flux.stop_flux(model, self.objectives, False) + + result = Resmod(None, self.objectives, + None, None, None, len(seeds), seeds, None, None) + + # This mode has to work with the seeds directly + # that's whiy wi do not want to try "on demands" the flux + result.check_flux(model, False) + return result.OK, result.objective_flux_seeds + + +def print_flux(result:Resmod, maximize:bool): + """Print fluxes data as a table + + Args: + result (Resmod): Current result to write + maximize (bool): Determine if Maximize option is used + + Returns: + warning (bool): Is a warning has to be raised or not + """ + warning=False + if result.name != "model_one_solution": + if result.OK_seeds: + if (result.solver_type=="HYBRID" or result.solver_type=="FBA") \ + and abs(result.chosen_lp - result.objective_flux_seeds) < 0.1: + color_seeds = color_lp =color.green_light + else: + color_seeds = color_lp =color.cyan_light + if (result.solver_type=="HYBRID" or result.solver_type=="FBA") \ + and not maximize and abs(result.chosen_lp - result.objective_flux_seeds) > 0.1: + warning = True + else: + color_seeds=color.red_bright + + if result.OK_demands: + if (result.solver_type=="HYBRID" or result.solver_type=="FBA") \ + and abs(result.chosen_lp - result.objective_flux_seeds) < 0.1: + color_demands=color.green_light + else: + color_demands=color.cyan_light + if (result.solver_type=="HYBRID" or result.solver_type=="FBA") \ + and not maximize and abs(result.chosen_lp - result.objective_flux_seeds) > 0.1: + warning = True + elif not result.OK_seeds: + color_demands=color.red_bright + else: + color_demands=color.reset + concat_result = f"{result.name} | " + + if not result.infeasible_seeds: + concat_result += color_seeds + f"{result.objective_flux_seeds}" + color.reset + " | " + else: + concat_result += f"Infeasible" + " | " + if not result.infeasible_demands: + flux_demand = result.objective_flux_demands + if flux_demand == None: + flux_demand = "NA" + + concat_result += color_demands + f"{flux_demand}" + color.reset + else: + concat_result += f"Infeasible" + + if result.solver_type=="HYBRID" or result.solver_type=="FBA": + lp_flux_rounded = round(result.chosen_lp,4) + concat_result += " | " + color_lp + f"{lp_flux_rounded}" + color.reset + print(concat_result) + return warning \ No newline at end of file diff --git a/seed2lp/reaction.py b/seed2lp/reaction.py new file mode 100644 index 0000000..43cd815 --- /dev/null +++ b/seed2lp/reaction.py @@ -0,0 +1,201 @@ +# Object Reaction constitued of +# - name: name/id of reaction +# - reversible (bool): Set if the reaction is reversible +# - lbound (float): Lower bound of a reaction +# - ubound (float): Upper bound of a reaction +# - Reactants (list): List of reactants (object Metabolite) +# - Products (list): List of list of products (object Metabolite) + +from seed2lp.metabolite import Metabolite +from . import logger + +class Reaction: + def __init__(self, name:str, reversible:bool=False, lbound:float=None, ubound:float=None): + """Initialize Object Reaction + + Args: + name (str): name/id of reaction + reversible (bool, optional): Set if the reaction is reversible. Defaults to False. + lbound (float, optional): Lower bound of a reaction. Defaults to None. + ubound (float, optional): Upper bound of a reaction. Defaults to None. + """ + self.name = name + self.reversible = reversible + self.lbound = lbound + self.ubound = ubound + self.reactants = list() + self.products = list() + self.is_exchange = False + self.is_transport = False + self.is_meta_modified = False + self.is_reversible_modified = False + + + ######################## SETTER ######################## + def _set_reactants(self, reactants_list:list): + self.reactants = reactants_list + + def _set_products(self, products_list:list): + self.products = products_list + ######################################################## + + ######################## METHODS ######################## + def add_metabolites_from_list(self, metabolites_list:list, metabolite_type:str, + meta_exchange_list:list, meta_transport_list:list): + """Add all Metabolite from a list as a Reactant or Product list + + Args: + metabolites_list (list): List of metabolite + metabolite_type (str): Type of metabolite to construc object list (Reactant or Product) + meta_exchange_list (list): List of exchanged metabolite + meta_transport_list (list): List of transport metabolite + """ + meta_list=[] + for meta in metabolites_list: + metabolite = Metabolite(meta[0],round(float(meta[1]),10)) + # A reaction is an exchange reaction + # Exchange reaction involving multiple metabolites are not considered as exchange + # but will me removed into write_facts() function + if self.is_exchange: + # The metabolite is tagged as exchange + meta_exchange_list.append(metabolite.name) + if metabolite.name in meta_transport_list: + meta_transport_list.remove(metabolite.name) + # we do not want to change exchange tag into transport tag + # The reaction has to be taggued transport + # if the reaction is not reversible, only the product is taggued transport + # if the reaction is reversible both can be transported + elif self.is_transport \ + and (metabolite.name not in meta_exchange_list) \ + and metabolite_type == "product": + # The metabolite is tagged as exchange + meta_transport_list.append(metabolite.name) + metabolite.type = "transport" + # A reaction is not an exchange reaction nor transport reaction + # Exchange reactions involving multiple metabolites are treated like intern metabolite + else: + # Check if the metabolite already existe in list of network + if metabolite.name in meta_exchange_list: + metabolite.type = "exchange" + elif metabolite.name in meta_transport_list: + metabolite.type = "transport" + else: + metabolite.type = "other" + meta_list.append(metabolite) + + meta_list.sort(key=lambda x: x.name) + match metabolite_type: + case "reactant": + self._set_reactants(meta_list) + case "product": + self._set_products(meta_list) + return meta_exchange_list, meta_transport_list + + + def convert_to_facts(self, keep_import_reactions, use_topological_injections): + """Correcting the Network an convert into facts + + Args: + keep_import_reactions (bool): Import reactions are not removed + use_topological_injections (bool): Metabolite of import reaction are seeds + """ + upper = self.ubound + rev_upper = -self.lbound + lower = round(float(0),10) + facts = "" + if self.reversible: + # When the reaction is reversible, the reaction is splitted + # Create 2 different reactions going in one way for reversible reaction + # Cases : [-1000, 1000] | [-8, 1000] | [-1000, 8] + # divided in : 2*[0,1000] | [0,1000] and rev_[0,8] | [0,8] and rev_[0,1000] + #lower= round(float(0),10) + facts += self.write_facts(lower, + rev_upper, + self.products, + self.reactants, + keep_import_reactions, + use_topological_injections, + self.reversible, + True) + + + # Upper bound does not change on forward reaction + facts += self.write_facts(lower, + upper, + self.reactants, + self.products, + keep_import_reactions, + use_topological_injections, + self.reversible, + False) + return facts + + + + def write_facts(self, lbound:float, ubound:float, reactants:list, products:list, + keep_import_reactions:bool, use_topological_injections:bool, + reversible:bool, is_reversed:bool): + """Convert the description of a reaction into ASP facts after correcting the Network + + Args: + lbound (float): Lower bound of a reaction + ubound (float): Upper bound of a reaction + reactants (list): List of Reactants (Metabolite) + products (list): List of Products (Metabolite) + keep_import_reactions (bool): Import reactions are not removed + use_topological_injections (bool): Metabolite of import reaction are seeds + reversible (bool): Define if the reaction is reversible. + is_reversed (bool): Define if the reaction is the reversed writtent as "Rev_R_*" + + Returns: + str: Reaction converted into ASP Facts + """ + + #Add reverse reaction + # If mode remove_rxn, the import reactions (none -> metabolite) + # are removed + facts="" + + # Delete both exchange reaction and multiple metabolite exchange reaction + # A reaction is multiple exchange + # when None -> A + B || A + B -> None || None <-> A + B || A + B <-> None + is_import_reaction = not reactants + + if not is_reversed: + name = self.name + if reversible: + facts += f'reversible("{name}","rev_{self.name}").\n' + else: + name = f'rev_{self.name}' + + # only one metabolite involved exchange reaction are tagued as exchange + # metabolite of exchange reaction involving multipele metabolite are managed like internal metabolites + if self.is_exchange: + facts += f'exchange("{name}").\n' + + prefix="" + if not keep_import_reactions and is_import_reaction: + prefix = "rm_" + logger.log.info(f"Reaction {self.name} artificially removed into lp facts with a prefix 'rm_'") + + facts += f'{prefix}reaction("{name}").\n' + + + + facts += f'{prefix}bounds("{name}","{"{:.10f}".format(lbound)}","{"{:.10f}".format(ubound)}").\n' + + if self.is_transport: + facts += f'{prefix}transport("{prefix}{name}","{reactants[0].name}","{products[0].name}").\n' + + + for metabolite in reactants: + facts += metabolite.convert_to_facts(f"{prefix}reactant", name) + + for metabolite in products: + facts += metabolite.convert_to_facts(f"{prefix}product", name) + # comme reversible, appelé 2 fois, donc suffit pour uniquement les produits + if use_topological_injections and is_import_reaction: # this reaction is a generator of seed + facts += metabolite.convert_to_facts("seed") + + return facts + ######################################################## diff --git a/seed2lp/reasoning.py b/seed2lp/reasoning.py new file mode 100644 index 0000000..bc11c51 --- /dev/null +++ b/seed2lp/reasoning.py @@ -0,0 +1,825 @@ +# Object Reasoning, herit from Solver, added properties: +# - grounded (str): All rules grounded before solving to save time + +import clyngor +import clingo +from time import time +from .network import Network +from .solver import Solver +import random +from multiprocessing import Process, Queue +from .file import save, delete, load_tsv, existing_file +from os import path +from . import color, logger + + +#TODO: CLEAN GROUND MODE IF NOT USED +GROUND = False + + +################################################################### +################# Class Reasoning : herit Solver ################## +################################################################### +class Reasoning(Solver): + def __init__(self, run_mode:str, run_solve:str, network:Network, + time_limit_minute:float=None, number_solution:int=None, + clingo_configuration:str=None, clingo_strategy:str=None, + intersection:bool=False, union:bool=False, + minimize:bool=False, subset_minimal:bool=False, + temp_dir:str=None, short_option:str=None, + verbose:bool=False): + """Initialize Object Reasoning, herit from Solver + + Args: + run_mode (str): Running command used (full or target) + network (Network): Network constructed + time_limit_minute (float, optional): Time limit given by user in minutes. Defaults to None. + number_solution (int, optional): Limit number of solutions to find. Defaults to None. + clingo_configuration (str, optional): Configuration for clingo resolution. Defaults to None. + clingo_strategy (str, optional): Strategy for clingo resolution. Defaults to None. + intersection (bool, optional): Find the intersection of all solutions without limitation (give one solution). Defaults to False. + union (bool, optional): Find the union of all solutions without limitation (give one solution). Defaults to False. + minimize (bool, optional): Search the minimal carinality of solutions. Defaults to False. + subset_minimal (bool, optional): Search the subset minimal solutions. Defaults to False. + temp_dir (str, optional): Temporary directory for saving instance file and clingo outputs. Defaults to None. + short_option (str, optional): Short way to write option on filename. Defaults to None. + verbose (bool, optional): Set debug mode. Defaults to False. + """ + super().__init__(run_mode, network, time_limit_minute, number_solution, clingo_configuration, + clingo_strategy, intersection, union, minimize, subset_minimal, + temp_dir, short_option, run_solve, verbose) + + self.is_linear = False + title_mess = "\n############################################\n" \ + "############################################\n" \ + f" {color.bold}REASONING{color.cyan_light}\n"\ + "############################################\n" \ + "############################################\n" + logger.print_log(title_mess, "info", color.cyan_light) + self._set_clingo_constant() + self._set_instance_file() + self._set_temp_result_file() + + + + ######################## SETTER ######################## + def _set_clingo_constant(self): + """Prepare ASP constant command for resolution + """ + self.init_const() + logger.print_log(f"Time limit: {self.time_limit_minute} minutes", "info") + logger.print_log( f"Solution number limit: {self.number_solution}", "info") + ######################################################## + + + ######################## METHODS ######################## + def reinit_optimum(self): + """Reinit optimum data to launch all modes + """ + self.optimum = None + self.optimum_found = False + self.opt_prod_tgt = None + self.opt_size = None + + def search_seed(self): + """Launch seed searching + """ + files = [self.network.instance_file, self.asp.ASP_SRC_SEED_SOLVING] + timer=dict() + # Subset minimal mode: By default the sub_seeds search from possible seed given is deactivated + if self.subset_minimal: + self.get_message('subsetmin') + if GROUND: + timer = self.reasoning_ground(files) + if self.run_solve == "reasoning" or self.run_solve == "all": + self.get_message('classic') + self.search_subsetmin(timer, files, 'classic') + + if self.run_solve == "filter" or self.run_solve == "all": + self.get_message('filter') + self.search_subsetmin(timer, files, 'filter') + + if self.run_solve == "guess_check" or self.run_solve == "all": + self.get_message('guess_check') + self.search_subsetmin(timer, files, 'guess_check') + + if self.run_solve == "guess_check_div" or self.run_solve == "all": + self.get_message('guess_check_div') + self.search_subsetmin(timer, files, 'guess_check_div') + + + if self.minimize: + self.get_message('minimize') + if self.network.is_subseed: + files.append(self.asp.ASP_SRC_MAXIMIZE_PRODUCED_TARGET) + logger.print_log('POSSIBLE SEED: Given\n A subset of possible seed is search \n maximising the number of produced target', 'info') + self.clingo_constant.append('-c') + self.clingo_constant.append('subseed=1') + files.append(self.asp.ASP_SRC_MINIMIZE) + + if GROUND: + timer = self.reasoning_ground(files) + + if self.run_solve == "reasoning" or self.run_solve == "all": + self.get_message('classic') + self.search_minimize(timer, files, 'classic') + self.reinit_optimum() + + if self.run_solve == "filter" or self.run_solve == "all": + self.get_message('filter') + self.search_minimize(timer, files, 'filter') + self.reinit_optimum() + + if self.run_solve == "guess_check" or self.run_solve == "all": + self.get_message('guess_check') + self.search_minimize(timer, files, 'guess_check') + self.reinit_optimum() + + + if self.run_solve == "guess_check_div" or self.run_solve == "all": + self.get_message('guess_check_div') + self.search_minimize(timer, files, 'guess_check_div') + self.reinit_optimum() + self.get_message('end') + + + + def reasoning_ground(self, asp_files:list): + """Ground the ASP files to create all facts from rules + + Args: + asp_files (list): List of ASP files, included th network asp file saved in temp directory + """ + timer = dict() + logger.print_log('GROUNDING...', 'info') + time_ground = time() + const_option = "" + const_option = ' '.join(self.clingo_constant) + self.grounded = clyngor.grounded_program(asp_files, options=const_option) + time_ground = time() - time_ground + timer["Grounding time"] = round(time_ground, 3) + return timer + + + + def write_one_model_solution(self, one_model:dict): + """Construct the outpu for minimize one model solution (finding optimum step) + + Args: + one_model (dict): Solution of finding opimum step + + Returns: + solution_list (dict): Constructed output + """ + solution_list = dict() + seeds = list() + + if self.optimum is None: + logger.print_log('\tNo seed found', 'info') + else: + self.get_separate_optimum() + logger.print_log(f"Optimum found.", "info") + if self.network.is_subseed: + logger.print_log((f"Number of producible targets: {- self.opt_prod_tgt}"), 'info') + logger.print_log(f"Minimal size of seed set is {self.opt_size}\n", 'info') + if self.opt_size > 0: + seeds = [args[0] for args in one_model.get('seed', ())] + seeds=list(sorted(seeds)) + else: + seeds = [] + if self.network.keep_import_reactions: + logger.print_log("Try with the option remove import reactions.", 'info') + #logger.print_log(f"\nOne solution:\n{', '.join(map(str, seeds))}\n", 'info') + solution_list['model_one_solution'] = ["size", self.opt_size] + \ + ["Set of seeds", seeds] + + return solution_list, seeds + + + def search_minimize(self, timer:dict, asp_files:list=None, step:str="classic"): + """Launch seed searching with minimze options + + Args: + timer (dict): Timer dictionnary containing grouding time + """ + logger.print_log("Finding optimum...", "info") + self.solve("minimize-one-model", timer, asp_files, step, True) + + if not self.optimum_found: + return + + if self.optimum == 0: + opti_message = "Optimum is 0." + + ok_opti = self.optimum_found and (self.opt_size > 0) + if self.intersection: + if ok_opti: + self.get_message('intersection') + self.solve("minimize-intersection", timer, asp_files, step) + else: + self.get_message('intersection') + logger.print_log(f"\nNot computed: {opti_message}", "error") + + if self.union: + if ok_opti: + self.get_message('union') + self.solve("minimize-union", timer, asp_files, step) + else: + self.get_message('union') + logger.print_log(f"\nNot computed: {opti_message}", "error") + + if self.enumeration: + if ok_opti: + self.get_message('enumeration') + self.solve("minimize-enumeration", timer, asp_files, step) + else: + self.get_message('enumeration') + logger.print_log(f"\nNot computed: {opti_message}", "error") + + + def search_subsetmin(self, timer:dict, asp_files:list=None, step:str="classic"): + """Launch seed searching with subset minimal options + + Args: + timer (dict): Timer dictionnary containing grouding time + """ + if self.enumeration: + self.get_message('enumeration') + self.solve("submin-enumeration", timer, asp_files, step) + else: + self.number_solution = 1 + self.get_message('One solution') + self.solve("submin-enumeration", timer, asp_files, step) + + if self.intersection: + self.get_message('intersection') + logger.print_log("SOLVING...\n", "info") + self.solve("submin-intersection", timer, asp_files, step) + + + + def solve(self, search_mode:str, timer:dict, asp_files:list=None, step:str="classic", is_one_model:bool=False): + """Solve the seed searching using the launch mode + + Args: + search_mode (str, optional): Describe the launch mode. + timer (dict): Timer dictionnary containing grouding time + """ + logger.print_log("SOLVING...\n", "info") + results = dict() + one_model = None + number_rejected = None + solution_list = dict() + full_option, mode_message, model_type, output_type = self.get_solutions_infos(search_mode) + + if not GROUND: + full_option = self.clingo_constant + full_option + + if self.optimum: + if self.opt_prod_tgt is not None: + full_option[-1]=full_option[-1]+f",{self.opt_prod_tgt},{self.opt_size}" + else: + full_option[-1]=full_option[-1]+f",{self.opt_size}" + + str_option = ' '.join(full_option) + suffix="" + if step == "filter": + suffix = " FILTER" + elif step == "guess_check": + suffix = " GUESS-CHECK" + match search_mode, step: + # CLASSIC MODE (NO FILTER, NO GUESS-CHECK) + case "minimize-one-model", "classic": + time_solve = time() + if GROUND: + models = clyngor.solve_from_grounded(self.grounded, options=str_option, + time_limit=self.time_limit).discard_quotes.by_predicate + else: + models = clyngor.solve(files=asp_files, options=str_option, + time_limit=self.time_limit).discard_quotes.by_predicate + time_solve = time() - time_solve + self.get_message("command") + logger.print_log(f'{models.command}', 'debug') + for model, opt, optimum_found in models.by_arity.with_optimality: + if optimum_found: + self.optimum_found = True + one_model = model + if one_model.get('seed'): + self.optimum = opt + else: + self.optimum = 0 + if not self.optimum_found: + logger.print_log('Optimum not found', "error") + else: + solution_list, seeds = self.write_one_model_solution(one_model) + self.network.add_result_seeds('REASONING', search_mode, model_type, len(seeds), seeds) + + case "minimize-enumeration" | "submin-enumeration", "classic": + time_solve = time() + solution_list = self.solve_enumeration(str_option, solution_list, + search_mode, asp_files) + time_solve = time() - time_solve + + + case _, "classic": + time_solve = time() + if GROUND: + models = clyngor.solve_from_grounded(self.grounded, options=str_option, + time_limit=self.time_limit).discard_quotes.by_predicate + else: + models = clyngor.solve(files=asp_files, options=str_option, + time_limit=self.time_limit).discard_quotes.by_predicate + time_solve = time() - time_solve + self.get_message("command") + logger.print_log(f'{models.command}', 'debug') + has_solution=False + for model in models: + has_solution=True + _models = [model] + if has_solution: + models = _models + seeds = [args[0] for args in models[0].get('seed', ())] + seeds=list(sorted(seeds)) + size = len(seeds) + print(f"Answer: {mode_message} ({size} seeds) \n{', '.join(map(str, seeds))}\n") + solution_list[model_type] = ["size", len(seeds)] + \ + ["Set of seeds", seeds] + self.network.add_result_seeds('REASONING', search_mode, model_type, len(seeds), seeds) + else: + logger.print_log('Unsatisfiable problem', "error") + + # FILTER OR GUESS-CHECK mode + #TODO redo intersection and union mode + case _, "filter"| "guess_check" | "guess_check_div": + # api clingo doesn't have time_limit option + # to add a time out, it is needed to call the function into a process + queue = Queue() + start=time() + if step == "filter": + suffix = " FILTER" + full_path = path.join(self.temp_dir,f"{self.temp_result_file}.tsv") + p = Process(target=self.filter, args=(queue, full_option, asp_files, search_mode, full_path, is_one_model)) + elif "guess_check" in step: + suffix = " GUESS-CHECK" + self.diversity=False + if step == "guess_check_div": + suffix += "-DIVERSITY" + self.diversity=True + full_path = path.join(self.temp_dir,f"{self.temp_result_file}.tsv") + p = Process(target=self.guess_check, args=(queue, full_option, asp_files, search_mode, full_path, is_one_model)) + + p.start() + try: + # the time out limit is added here + obj, solution_list, time_ground, time_solve, number_rejected = queue.get(timeout=self.time_limit) + # Because of the process, the object is not change (encapsulated and isolated) + # it is needed to give get the output object and modify the current object + if "minimize" in search_mode: + self.optimum_found = obj.optimum_found + self.optimum = obj.optimum + self.get_separate_optimum() + self.network.result_seeds = obj.network.result_seeds + if not is_one_model: + delete(full_path) + except: + time_process=time() - start + time_ground = time_solve = -1 + unsat = False + time_out = False + if not self.time_limit or time_process < self.time_limit: + unsat = True + else: + time_out = True + if time_out: + logger.print_log(f'Time out: {self.time_limit_minute} min expired', "error") + solution_list, number_rejected = self.get_solution_from_temp(unsat, is_one_model, full_path, suffix, search_mode) + p.terminate() + queue.close() + + if is_one_model: + if not self.optimum_found: + logger.print_log('Optimum not found', "error") + else: + if not any(solution_list): + logger.print_log('Unsatisfiable problem', "error") + + + #TODO: Intersection and union not needed with filter and guess check but + # do with python the union and intersection of resulted soluion of filer or guess check + + if step == "filter" or "guess_check" in step: + if time_ground != -1: + timer["Grounding time"] = round(time_ground, 3) + else: + timer["Grounding time"] = "Time out" + if time_solve != -1: + timer["Solving time"] = round(time_solve, 3) + else: + timer["Solving time"] = "Time out" + results["Timer"] = timer.copy() + results['solutions'] = solution_list + if number_rejected: + results['rejected'] = number_rejected + self.output[output_type+suffix] = results + + + def get_solution_from_temp(self, unsat:bool, is_one_model:bool, full_path:str, suffix:str, search_mode:str): + """Get the solution written in temporary file during execution fo seed searching while using + multiprocessing. + + Args: + unsat (bool): Determine if the model is unsat + is_one_model (bool): Determine if the model is the optimum finding model for minimize case + full_path (str): Path of temporary file + suffix (str): suffix to add for solution enumeration (filter or guess-check) + search_mode (str): search_mode needed to add to results (subset minimal or minimize) + + Returns: + List: list of solutions + """ + solution_list = dict() + number_rejected = None + + if not unsat and existing_file(full_path): + # in case of enumeration it is needed to get the results back from the saved temporary + # file which is saved during the called + if not is_one_model: + try: + temp_list = load_tsv(full_path) + for solution in temp_list: + if len(solution) == 5: + # some line has no data value onlu the number of rejected solution + if solution[0]: + seeds = solution[2].replace(" ", "") + seeds = seeds.replace("\'", "") + seeds_list = seeds[1:-1].split(',') + solution_list[solution[0]] = ["size", solution[1]] + \ + ["Set of seeds",seeds_list] + ["Cobra flux", float(solution[4])] + #get the last occurence pf rejected solutions number + number_rejected = solution[3] + logger.print_log(f'Rejected solution during process: at least {number_rejected} \n', 'info') + except Exception as e: + logger.print_log(f"An error occured while reading temporary file\n {full_path}:\n {e}", 'error') + + if any(solution_list): + for name in solution_list: + seeds = solution_list[name][3] + self.network.add_result_seeds('REASONING '+suffix, search_mode, name, len(seeds), seeds) + delete(full_path) + return solution_list, number_rejected + + + def solve_enumeration(self, construct_option:str, solution_list:dict, + search_mode:str, asp_files:list=None): + """Solve enumeration for Reasoning Classic mode using Clyngor + + Args: + construct_option (str): Constructed option for clingo + solution_list (dict): A dictionnary of all found solutions + search_mode (str): Optimization selected for the search (submin-enumeration / minimze-enumeration) + asp_files (list, optional): List of needed ASP files to solve ASP with Clyngor + + Returns: + solution_list (dict): a dictionnary of all found solutions + """ + if GROUND: + models = clyngor.solve_from_grounded(self.grounded, options=construct_option, + time_limit=self.time_limit, nb_model=self.number_solution).discard_quotes.by_predicate + else: + models = clyngor.solve(files=asp_files, options=construct_option, + time_limit=self.time_limit, nb_model=self.number_solution).discard_quotes.by_predicate + self.get_message("command") + logger.print_log(f'{models.command}', 'debug') + idx = 1 + m = models + models_list = list(m).copy() + size_answers = len(models_list) + if size_answers != 0: + for model in models_list: + seeds = [args[0] for args in model.get('seed', ())] + seeds=list(sorted(seeds)) + repr_seeds = ', '.join(map(str, seeds)) + size = len(seeds) + + print(f"Answer: {idx} ({size} seeds) \n{repr_seeds}\n") + solution_list['model_'+str(idx)] = ["size", size] + \ + ["Set of seeds", seeds] + self.network.add_result_seeds('REASONING', search_mode, 'model_'+str(idx), size, seeds) + idx += 1 + else: + logger.print_log('Unsatisfiable problem', "error") + return solution_list + + + + def guess_check(self, queue:Queue, full_option:list, asp_files:list, search_mode:str, full_path:str, is_one_model:bool=False): + """Guess and Check mode. Find a solution with Clingo package, check if the solution has flux on objective reaction. + This function works with multiprocessing in order to manage time limit. + Interacts with ASP solver and exclude supersets of the current tested solution. + If diversity is asked, the function add_diversity is called. + + Args: + queue (Queue): Queue for multiprocessing program (managing time limit) + full_option (list): All Clingo option + asp_files (list): List of needed ASP files to solve ASP (Clingo package) + search_mode (str): Optimization selected for the search (submin/minmize and enumeration/optimum) + full_path (str): Full path for temp file needed to get back solution when time out + is_one_model (bool, optional): Define if the solution we want is to fin the optimum when minimize is used (before enumration). + Defaults to False. + """ + solution_list = dict() + avoided = [] + all_time_solve = 0 + all_time_ground = 0 + + # No limit on number of solution + no_limit_solution = False + if self.number_solution == 0: + no_limit_solution = True + number_solution = self.number_solution + + ctrl = self.control_init(full_option, asp_files, True) + solution_idx = 1 + number_rejected = 0 + while ((len(solution_list) < number_solution \ + and not is_one_model and not no_limit_solution) \ + or is_one_model or no_limit_solution )\ + and \ + (self.time_limit and float(all_time_ground + all_time_solve) < float(self.time_limit) + or not self.time_limit): + with ctrl.solve(yield_=True) as h: + seeds = None + for model in h: + atoms = model.symbols(shown=True) + seeds = {a.arguments[0].string for a in atoms} + seeds=list(sorted(seeds)) + size = len(seeds) + if not is_one_model: + break + else: + self.optimum=model.cost + if not seeds: + if is_one_model: + name = 'model_one_solution' + solution_list[name] = ["size", 0] + \ + ["Set of seeds", []] + self.optimum_found = True + break + # Sum all grounding time together and all solving time together + stats = ctrl.statistics + total_time = stats["summary"]["times"]["total"] + time_solve = stats["summary"]["times"]["solve"] + time_ground = total_time - time_solve + + all_time_solve += float(time_solve) + all_time_ground += float(time_ground) + res = self.network.check_seeds(seeds) + if res[0]: + logger.print_log(f'CHECK Solution {size} seeds -> OK\n', 'debug') + # valid solution + if not is_one_model: + message = f"Answer: {solution_idx} ({size} seeds)\n" + for s in seeds: + message += f"{s}, " + message=message.rstrip(', ') + print(message + "\n") + name = 'model_'+str(solution_idx) + solution_list[name] = ["size", size] + \ + ["Set of seeds", seeds] + ["Cobra flux", res[1]] + solution_temp = [name, size, seeds, number_rejected, res[1]] + save(full_path, "", solution_temp, "tsv", True) + # exclude solutions and its supersets + ctrl.add("skip", [], f":- {','.join(map(str,atoms))}.") + mode = 'REASONING GUESS-CHECK' + if self.diversity: + ctrl, avoided = self.add_diversity(ctrl, seeds, avoided) + mode = 'REASONING GUESS-CHECK DIVERSITY' + ctrl.ground([("skip",[])]) + self.network.add_result_seeds(mode, search_mode, name, size, seeds, flux_cobra=res[1]) + solution_idx +=1 + else: + name = 'model_one_solution' + solution_list[name] = ["size", size] + \ + ["Set of seeds", seeds] + ["Cobra flux", res[1]] + logger.print_log(f"Optimum found.", "info") + self.optimum_found = True + if self.diversity: + ctrl, avoided = self.add_diversity(ctrl, seeds, avoided) + solution_temp = [name, size, seeds, number_rejected, res[1]] + save(full_path, self.temp_dir, solution_temp, "tsv", True) + mode = 'REASONING GUESS-CHECK' + if self.diversity: + mode = 'REASONING GUESS-CHECK DIVERSITY' + self.network.add_result_seeds(mode, search_mode, name, size, seeds, flux_cobra=res[1]) + self.get_separate_optimum() + if self.network.is_subseed: + logger.print_log(f"Number of producible targets: {- self.opt_prod_tgt}", "info") + logger.print_log(f"Minimal size of seed set is {self.opt_size}\n", "info") + break + + else: + logger.print_log(f'CHECK Solution {size} seeds -> KO\n', 'debug') + if self.diversity: + ctrl, avoided = self.add_diversity(ctrl, seeds, avoided) + + # exclude solution and its superset + ctrl.add("skip", [], f":- {','.join(map(str,atoms))}.") + + ################################################## + # exclude only solution, keep superset + # not used because superset are so much that it founds less solutions + # than when we delete the superset (more networks work, more solution + # found per network) + # code kept in case it is needed + + #ctrl.add("skip", [], f":- {','.join(map(str,atoms))}, #count{{M: seed(M,_)}} = {len(atoms)}.") + ################################################## + + ctrl.ground([("skip",[])]) + number_rejected +=1 + + if number_rejected%100 == 0: + solution_temp = [None, None, None, number_rejected, None] + save(full_path, "", solution_temp, "tsv", True) + + if number_rejected > 0: + logger.print_log(f'Rejected solution during process: {number_rejected} \n', 'info') + + ctrl.cleanup() + ctrl.interrupt() + queue.put([self, solution_list, all_time_ground, all_time_solve, number_rejected]) + + + def filter(self, queue:Queue, full_option:list, asp_files:list, search_mode:str, full_path:str, is_one_model:bool=False): + """Filter mode. Find a solution with Clingo package, check if the solution has flux on objective reaction. + This function works with multiprocessing in order to manage time limit. + It does not interact with the solver, only filter the solutions. + + Args: + queue (Queue): Queue for multiprocessing program (managing time limit) + full_option (list): All Clingo option + asp_files (list): List of needed ASP files to solve ASP (Clingo package) + search_mode (str): Optimization selected for the search (submin/minmize and enumeration/optimum) + full_path (str): Full path for temp file needed to get back solution when time out + is_one_model (bool, optional): Define if the solution we want is to fin the optimum when minimize is used (before enumration). + Defaults to False. + """ + solution_list = dict() + + no_limit_solution = False + if self.number_solution == 0: + no_limit_solution = True + full_option.append(f'-n 0') + number_solution = self.number_solution + + ctrl = self.control_init(full_option, asp_files) + + solution_idx = 1 + number_rejected = 0 + with ctrl.solve(yield_=True) as h: + for model in h: + seeds = None + if (len(solution_list) < number_solution \ + and not is_one_model and not no_limit_solution) \ + or is_one_model or no_limit_solution: + atoms = model.symbols(shown=True) + seeds = {a.arguments[0].string for a in atoms} + seeds=list(sorted(seeds)) + size = len(seeds) + #if not seeds: + # print("No seeds") + # if is_one_model: + # name = 'model_one_solution' + # solution_list[name] = ["size", 0] + \ + # ["Set of seeds", []] + # self.optimum_found = True + # break + + if not is_one_model: + res = self.network.check_seeds(seeds) + if res[0]: + # valid solution + logger.print_log(f'CHECK Solution {size} seeds -> OK\n', 'debug') + message = f"Answer: {solution_idx} ({size} seeds)\n" + for s in seeds: + message += f"{s}, " + message=message.rstrip(', ') + print(message + "\n") + name = 'model_'+str(solution_idx) + solution_list[name] = ["size", size] + \ + ["Set of seeds", seeds] + ["Cobra flux", res[1]] + solution_temp = [name, size, seeds, number_rejected, res[1]] + save(full_path, self.temp_dir, solution_temp, "tsv", True) + self.network.add_result_seeds('REASONING FILTER', search_mode, name, size, seeds, flux_cobra=res[1]) + solution_idx +=1 + else: + logger.print_log(f'CHECK Solution {size} seeds -> KO\n', 'debug') + number_rejected +=1 + if number_rejected%100 == 0: + solution_temp = [None, None, None, number_rejected, None] + save(full_path, "", solution_temp, "tsv", True) + else: + res = self.network.check_seeds(seeds) + self.optimum=model.cost + self.get_separate_optimum() + name = 'model_one_solution' + solution_list[name] = ["size", self.opt_size] + \ + ["Set of seeds", seeds] + ["Cobra flux", res[1]] + self.optimum_found = True + else: + break + + if number_rejected > 0: + logger.print_log(f'Rejected solution during process: {number_rejected} \n', "info") + + stats = ctrl.statistics + total_time = stats["summary"]["times"]["total"] + time_solve = stats["summary"]["times"]["solve"] + time_ground = total_time - time_solve + + # Because it is needed to get all answers from clingo to have optimum, we save it after + if is_one_model and self.optimum_found: + logger.print_log(f"Optimum found.", "info") + if self.network.is_subseed: + logger.print_log(f"Number of producible targets: {- self.opt_prod_tgt}", "info") + logger.print_log(f"Minimal size of seed set is {self.opt_size}\n", "info") + solution_temp = [name, size, seeds, number_rejected, res[1]] + save(full_path, self.temp_dir, solution_temp, "tsv", True) + self.network.add_result_seeds('REASONING FILTER', search_mode, name, size, seeds, flux_cobra=res[1]) + + ctrl.cleanup() + ctrl.interrupt() + queue.put([self, solution_list, time_ground, time_solve, number_rejected]) + + + + def control_init(self, full_option:list, asp_files:list, is_guess_check:bool=False): + """Initiate Clingo control for package Clingo + + Args: + full_option (list): All Clingo option + asp_files (list): List of needed ASP files to solve ASP (Clingo package) + is_guess_check (bool, optional): Determine if it is a Guess Check (True) or a Filter (Fale). + Defaults to False. + + Returns: + ctrl (clingo.Control): Return Clingo control for solving + """ + full_option.append("--warn=none") + ctrl = clingo.Control(full_option) + + for file in asp_files: + ctrl.load(file) + + + ctrl.ground([("base",[])]) + if self.diversity and is_guess_check: + ctrl.add("diversity", [], """ + #program diversity. + #heuristic new_seed(M) : avoidseed(M). [10,false] + #heuristic new_seed(M). [1,false] % subset + #external avoidseed(M) : metabolite(M,_). + """) + ctrl.ground([("diversity",[])]) + + self.get_message("command") + logger.print_log('clingo ' + ' '.join(full_option) + ' ' + ' '.join(asp_files), 'debug') + return ctrl + + + + def add_diversity(self, ctrl:clingo.Control, seeds:list, avoided:list): + """This function add diversity for the Gess Check mode by avoiding some metabolites + from previous solution. For each iteration, half of the avoided metabolites is + deleted randomly, and half of metabolites as seeds of the current solution is added randomly + + Args: + ctrl (clingo.Control): Clingo Control initiated + seeds (list): List of seeds (one solution) + avoided (list): List of already avoided metabolites + + Returns: + ctrl (clingo.Control), avoided (list): Return Clingo control for solving + and the new list of avoided metabolites for next iteration + """ + forget = 50 # 0..100: percentage of heuristics to forget at each iteration + + # tune heuristics for diversity + random.shuffle(avoided) + clue_to_forget = (len(avoided)*forget)//100 + for a in avoided[:clue_to_forget]: + ctrl.assign_external(a, False) + avoided = avoided[clue_to_forget:] + + + random.shuffle(seeds) + seed_to_forget = (len(seeds)*forget)//100 + seeds = seeds[seed_to_forget:] + + clues = [clingo.Function("avoidseed", [clingo.String(s)]) for s in seeds] + + for a in clues: + ctrl.assign_external(a, True) + avoided.extend(clues) + + return ctrl, avoided diff --git a/seed2lp/resmod.py b/seed2lp/resmod.py new file mode 100644 index 0000000..9782535 --- /dev/null +++ b/seed2lp/resmod.py @@ -0,0 +1,68 @@ +from . import flux +from cobra.core import Model + +class Resmod: + def __init__(self, name:str, objectives:list, solver_type:str, search_mode:str, search_type:str, + size:int, seeds_list:list, flux_lp:dict, flux_cobra:float=None, run_mode:str=None, accu:bool=False): + """Initialize Object Resmod + + Args: + name (str): Name of the solution + objectives (list): List of objective reaction names + solver_type (str): Type of solver (Reasoning / FBA / Hybrid) + search_mode (str): search mode type (Minimize / Submin) + search_type (str): search type (enumeration / union /intersection) + size (int): Size of set of seeds + seeds_list (list): List of seeds + flux_lp (dict): Dictionnary of all reaction with their LP flux + flux_cobra (float, optional): Cobra flux calculated (mode Filter, Guess Check). Defaults to None. + run_mode (str, optional): Running command used (full or target). Defaults to None. + accu (bool, optional): Is accumulation allowed. Defaults to False. + """ + self.name = name + self.objectives = objectives + self.solver_type = solver_type + self.search_mode = search_mode + self.search_type = search_type + self.size = size + self.seeds_list = seeds_list + self.flux_lp = flux_lp + self.chosen_lp = None + self.tested_objective = None + self.objective_flux_seeds = None + self.objective_flux_demands = None + self.OK_seeds = False + self.OK_demands = False + self.OK = False + self.run_mode = run_mode + self.accu = accu + self.flux_cobra = flux_cobra + + + + ######################## METHODS ######################## + def check_flux(self, model_cobra:Model, try_demands:bool=True): + """Execute flux calculation usng cobra and store data + + Args: + model_cobra (Model): Cobra model + show_messages (bool, optional): Option to show the messages on console. + Defaults to True. False for hybrid with cobra. + try_demands (bool, optional): Option to try to add demands if seeds failed. + Defaults to True. False for hybrid with cobra. + """ + with model_cobra as m: + flux_output, objective, lp_flux = \ + flux.calculate(m, self.objectives, self.seeds_list, self.flux_lp, try_demands) + if flux_output: + self.tested_objective = objective + self.chosen_lp = lp_flux + self.objective_flux_seeds = flux_output['objective_flux_seeds'] + self.objective_flux_demands = flux_output['objective_flux_demands'] + self.OK_seeds = flux_output['OK_seeds'] + self.OK_demands = flux_output['OK_demands'] + self.OK = flux_output['OK'] + self.infeasible_seeds = flux_output['infeasible_seeds'] + self.infeasible_demands = flux_output['infeasible_demands'] + + ######################################################## \ No newline at end of file diff --git a/seed2lp/sbml.py b/seed2lp/sbml.py new file mode 100644 index 0000000..fba815e --- /dev/null +++ b/seed2lp/sbml.py @@ -0,0 +1,246 @@ +"""Routines to extract information from SBML files. + +""" +import xml.etree.ElementTree as ET +from re import sub +from . import logger + +def register_all_namespaces(file): + """Get namespaces for rewriting SBML file + + Args: + file (str): SBML file path + """ + namespaces = dict([node for _, node in ET.iterparse(file, events=['start-ns'])]) + for ns in namespaces: + #print(ns, namespaces[ns]) + ET.register_namespace(ns, namespaces[ns]) + +def get_root(file): + """Get etree root + + Args: + file (str): SBML file path + + Returns: + sbml (etree Element), first_line (str) : Return an etree elemnt of the network, and the first line of the sbml file + """ + register_all_namespaces(file) + with open(file) as f: + first_line = f.readline() + xmlstring = f.read() + f.close() + + # Remove the default namespace definition (xmlns="http://some/namespace") + xmlstring = sub(r'\sxmlns="[^"]+"', '', xmlstring, count=1) + + sbml = ET.fromstring(xmlstring) + #tree = ET.parse(file) + #sbml = tree.getroot() + return sbml, first_line + +def get_sbml_tag(element) -> str: + "Return tag associated with given SBML element" + if element.tag[0] == "{": + _, tag = element.tag[1:].split("}") # uri is not used + else: + tag = element.tag + return tag + + +def get_model(sbml): + """ + return the model of a SBML + """ + model_element = None + for e in sbml: + tag = get_sbml_tag(e) + if tag == "model": + model_element = e + break + return model_element + +def get_listOfSpecies(model): + """ + return list of species of a SBML model + """ + listOfSpecies = None + for e in model: + tag = get_sbml_tag(e) + if tag == "listOfSpecies": + listOfSpecies = e + break + return listOfSpecies + + +def get_listOfReactions(model) -> list: + """return list of reactions of a SBML model""" + listOfReactions = [] + for e in model: + tag = get_sbml_tag(e) + if tag == "listOfReactions": + listOfReactions = e + break + return listOfReactions + + +def get_listOfReactants(reaction) -> list: + """return list of reactants of a reaction""" + listOfReactants = [] + for e in reaction: + tag = get_sbml_tag(e) + if tag == "listOfReactants": + for meta in e: + listOfReactants.append([meta.attrib.get('species'), meta.attrib.get('stoichiometry')]) + break + return listOfReactants + +def get_listOfReactants_from_name(model, reaction_name) -> list: + """return list of reactants of a reaction""" + reactions_list = get_listOfReactions(model) + for reaction in reactions_list: + if reaction_name == reaction.attrib['id']: + for e in reaction: + tag = get_sbml_tag(e) + if tag == "listOfReactants": + listOfReactants = e + break + return listOfReactants + +def get_listOfProducts(reaction) -> list: + """return list of products of a reaction""" + listOfProducts = [] + for e in reaction: + tag = get_sbml_tag(e) + if tag == "listOfProducts": + for meta in e: + listOfProducts.append([meta.attrib.get('species'), meta.attrib.get('stoichiometry')]) + break + return listOfProducts + +def get_reaction_from_name(model, fbc, reaction_name) -> list: + """return list of reactants of a reaction""" + reactions_list = get_listOfReactions(model) + for reaction in reactions_list: + if reaction_name == reaction.attrib['id']: + return reaction + raise ValueError(f"ERROR: No reaction {reaction_name} found in list of reactions \n") + +def get_fbc(sbml): + """ + return the fbc namespace of a SBML + """ + fbc = None + for nss in ET._namespaces(sbml): + for key in nss: + if key is not None and 'fbc' in key: + fbc=key + break + return fbc + +def get_listOfParameters(model)-> dict: + """return list of reactions of a SBML model""" + listOfParameters = dict() + for e in model: + tag = get_sbml_tag(e) + if tag == "listOfParameters": + for param in e: + listOfParameters[param.get('id')]=param.get('value') + break + return listOfParameters + +def get_listOfFluxObjectives(model,fbc)-> list: + """return list of reactions of a SBML model""" + listOfFluxObjectives = list() + + for e in model: + tag = get_sbml_tag(e) + if tag == "listOfObjectives": + for lo in e[0]: + if lo: + for o in lo: + name = o.attrib.get('{'+fbc+'}reaction') + coef = o.attrib.get('{'+fbc+'}coefficient') + reaction = get_reaction_from_name(model, fbc, name) + listOfFluxObjectives.append([name,coef,reaction]) + break + return listOfFluxObjectives + + +def read_SBML_species(filename): + """Yield names of species listed in given SBML file""" + model_dict = dict() + tree = ET.parse(filename) + sbml = tree.getroot() + model = get_model(sbml) + species_list = list() + reactions_list = list() + + for species in get_listOfSpecies(model): + species_list.append(species.attrib['id']) + for reaction in get_listOfReactions(model): + reactions_list.append(f"{reaction.attrib['id']}") + + model_dict['Metabolites'] = species_list + model_dict['Reactions'] = reactions_list + return model_dict + +def get_used_metabolites(filename, call_log=True): + """Determine from source file the truly used metabolite (and not the list of species) + + Args: + filename (str): Path to thie SBML file + + Returns: + used_metabolites (set): Set of used metabolites + """ + tree = ET.parse(filename) + sbml = tree.getroot() + model = get_model(sbml) + # SBML has species that are never used on any reactions + # but are present into species list + # Also, there is some reaction that involves species but + # having boundaries to [0,0], so we are not taken into account + # the species of the species of these reactions into the used metabolites + used_metabolites = set() + + fbc = get_fbc(get_root(filename)[0]) + parameters = get_listOfParameters(model) + + for reaction in get_listOfReactions(model): + ubound = parameters[reaction.attrib.get('{'+fbc+'}upperFluxBound')] + lbound = parameters[reaction.attrib.get('{'+fbc+'}lowerFluxBound')] + if float(ubound) == 0 and float(lbound) == 0 and call_log: + logger.log.warning(f"Reaction {reaction.attrib['id']} deleted, boudaries [0,0]") + continue + else: + reactants = get_listOfReactants(reaction) + products = get_listOfProducts(reaction) + for reactant in reactants: + used_metabolites.add(reactant[0]) + for product in products: + used_metabolites.add(product[0]) + + return used_metabolites + + + +def etree_to_string(model): + return str(ET.tostring(model, encoding='utf-8', method='xml'),'UTF-8') + +def create_sub_element(element:ET.Element, sub_element:str): + ET.SubElement(element,sub_element) + +def remove_sub_elements(element:ET.Element): + for metabolite in list(element): + element.remove(metabolite) + +def add_metabolites(element:ET.Element, metabolites_list): + for metabolite in metabolites_list: + element.append(metabolite) + +def remove_reaction(model:ET.Element, element:ET.Element): + for e in model: + tag = get_sbml_tag(e) + if tag == "listOfReactions": + e.remove(element) \ No newline at end of file diff --git a/seed2lp/scope.py b/seed2lp/scope.py new file mode 100644 index 0000000..08fd61f --- /dev/null +++ b/seed2lp/scope.py @@ -0,0 +1,122 @@ +from .network import Network +from menetools import run_menescope +from .file import is_valid_dir, save +from os.path import join +from .sbml import get_used_metabolites +import libsbml +from padmet.utils.sbmlPlugin import convert_from_coded_id +from padmet.utils.connection import sbmlGenerator +import sys +from . import logger + + +class Scope: + def __init__(self, file:str, network:Network, output_dir:str): + """Initialize Object Scope + + Args: + file (str): SBML File (Needed to detect source used metabolites on the network) + network (Network): Corrected Network + """ + self.file = file + self.network = network + self.output_dir = output_dir + self.dir_seeds_sbml = is_valid_dir(join(output_dir,'sbml')) + self.dir_scope = is_valid_dir(join(output_dir,'scope')) + + ######################## METHODS ######################## + def execute(self): + """Execute the scope from seeds solution for each solution and save it into file. + Creates an intermediate seed sbml file. + """ + # Get global data on the network + set_used_metabolites = get_used_metabolites(self.file) + + # run the scope for each solutions of the result + for result in self.network.result_seeds: + #TODO : print better in TABLE + print(result.run_mode.upper()) + print(result.solver_type) + print(result.search_mode) + print(result.name) + print("Accumulation:", result.accu) + + if result.accu == True: + accu="accu" + else: + accu="no_accu" + + run_mode = result.run_mode.lower().replace(" ","_").replace("-", "_") + solver = result.solver_type.lower().replace(" ","_").replace("-", "_") + search_mode = result.search_mode.lower().replace(" ","_").replace("-", "_") + seeds=set(result.seeds_list) + + seeds_sbml_complete_dir_path = is_valid_dir(join(self.dir_seeds_sbml, run_mode, solver, search_mode, accu)) + seeds_sbml_path=join(seeds_sbml_complete_dir_path, f'{result.name}.sbml') + scope_dir_path = is_valid_dir(join(self.dir_scope, run_mode, solver, search_mode, accu)) + + # Write the seed into sbl format for each solutions + create_species_sbml(seeds, seeds_sbml_path) + logger.log.info(f"Seeds sbml file created: {seeds_sbml_path}") + + # Run menescope from seed to get the scope + logger.log.info(f"Scope running for {seeds_sbml_path}...") + scope_model = run_menescope(self.file, seeds_sbml_path) + logger.log.info(f"Scope terminated.") + scope_model["size_scope"] = len(scope_model["scope"]) + scope_model["size_all_metabolites"] = len(set_used_metabolites) + + + + print("size of scope", scope_model["size_scope"]) + print("size of all metabolites", scope_model["size_all_metabolites"],"\n\n") + save(f'{result.name}', scope_dir_path, scope_model, "json") + logger.log.info(f"Scope saved in: {scope_dir_path}/{result.name}.json.") + + + +def create_species_sbml(metabolites, outputfile): + """Create a SBML files with a list of species containing metabolites of the input set. + Check if there are forbidden SBML characters in the metabolite IDs/ If yes, exit. + + Args: + metabolites (set): set of metabolites + outputfile (str): SBML file to be written + """ + document = libsbml.SBMLDocument(2, 1) + model = document.createModel("metabolites") + forbidden_charlist = ['-', '|', '/', '(', ')', + "'", '=', '#', '*', '.', ':', '!', '+', '[', + ']', ',', ' '] + forbidden_character_in_metabolites = None + issue_trying_to_add_species = None + for compound in metabolites: + compound = compound.strip('"') + _, _, comp = convert_from_coded_id(compound) + s = model.createSpecies() + sbmlGenerator.check(s, 'create species') + forbidden_characters_detacted = [char for char in forbidden_charlist if char in compound] + if len(forbidden_characters_detacted) > 0: + logger.log.warning("Forbidden character ({0}) in {1}. SBML creation will failed.".format(' '.join(forbidden_characters_detacted), compound)) + forbidden_character_in_metabolites = True + try: + sbmlGenerator.check(s.setId(compound), 'set species id') + except: + issue_trying_to_add_species = True + logger.log.warning("Issue when trying to add compound {0}.".format(compound)) + + if comp is not None: + sbmlGenerator.check(s.setCompartment(comp), 'set species compartment') + elif comp is None: + logger.log.warning("No compartment for " + compound) + + if issue_trying_to_add_species is True and forbidden_character_in_metabolites is True: + logger.log.warning("Forbidden character in compound ID, SBML creation will failed.") + logger.log.warning("Modify the metabolic networks SBMl file by renaming these metabolites and removing the forbidden character.") + sys.exit(1) + if issue_trying_to_add_species is True and forbidden_character_in_metabolites is None: + logger.log.warning("Issue when trying to add metabolite into SBML file, potential issue with SBML format.") + logger.log.warning("Modify the metabolic networks SBMl file by renaming these metabolites and removing the forbidden character.") + sys.exit(1) + + libsbml.writeSBMLToFile(document, outputfile) \ No newline at end of file diff --git a/seed2lp/solver.py b/seed2lp/solver.py new file mode 100644 index 0000000..449a8f0 --- /dev/null +++ b/seed2lp/solver.py @@ -0,0 +1,301 @@ +# Object Solver constitued of +# - run_mode (str): Running command used (full or target) +# - network (Network): Object Network +# - time_limit_minute (float): Time limit given by user in minutes +# - time_limit (int): Time limit converted in seconds for resolutions +# - number_solution (int): Limit number of solutions to find +# - clingo_configuration (str): Configuration for clingo resolution (jumpy or not, or other) +# - clingo_strategy (str): Strategy for clingo resolution (usc,oll or not, other) +# - enumeration (bool): Enumerate the solutions limited by the number of solutions +# - intersection (bool): Find the intersection of all solutions without limitation (give one solution) +# - union (bool): Find the union of all solutions without limitation (give one solution) +# - minimize (bool): Search the minimal carinality of solutions +# - subset_minimal (bool): Search the subset minimal solutions +# - clingo_constant (str): Set the value of constant in lp file for search +# - one_model_unsat (bool): Set if the minimze solution is unsatisfiable +# - optimum (int): Value of minimal cardinality if not unsatisfiable +# - output (dict): List of all solutions +# - timer_list (dict): List of all timers to find solution +# - verbose (bool): Set debug mode + +from os import path +from .network import Network +from .file import write_instance_file +from dataclasses import dataclass +from . import logger +from . import color + + + +@dataclass +class ASP_CLINGO: + SRC_DIR = path.dirname(path.abspath(__file__)) + ASP_SRC_SEED_SOLVING = path.join(SRC_DIR, 'asp/seed-solving.lp') + ASP_SRC_MINIMIZE = path.join(SRC_DIR, 'asp/minimize.lp') + ASP_SRC_FLUX = path.join(SRC_DIR, 'asp/flux.lp') + ASP_SRC_MAXIMIZE_FLUX = path.join(SRC_DIR, 'asp/maximize_flux.lp') + ASP_SRC_MAXIMIZE_PRODUCED_TARGET = path.join(SRC_DIR, 'asp/maximize_produced_target.lp') + CLINGO_CONFIGURATION = { + 'minimize-enumeration': ['--project=show', '--opt-mode=enum'], + 'minimize-union': ['--enum-mode=brave', '--opt-mode=enum'], + 'minimize-intersection': ['--enum-mode=cautious', '--opt-mode=enum'], + 'minimize-one-model': None, + 'submin-enumeration': ['--heuristic=Domain', '--enum-mode=domRec', '--dom-mod=5,16'], + 'submin-intersection': ['--heuristic=Domain', '--enum-mode=cautious', '--dom-mod=5,16'], + } + ASW_FLAG, OPT_FLAG, OPT_FOUND = 'Answer: ', 'Optimization: ', 'OPTIMUM FOUND' + +################################################################### +########################## Class Solver ########################## +################################################################### +class Solver: + def __init__(self, run_mode:str, network:Network, + time_limit_minute:float=None, number_solution:int=None, + clingo_configuration:str=None, clingo_strategy:str=None, + intersection:bool=False, union:bool=False, + minimize:bool=False, subset_minimal:bool=False, + temp_dir:str=None, short_option:str=None, run_solve:str=None, + verbose:bool=False): + """"Initialize Object Solver + + Args: + run_mode (str): Running command used (full or target) + network (Network): Network constructed + time_limit_minute (float, optional): Time limit given by user in minutes . Defaults to None. + number_solution (int, optional): Limit number of solutions to find. Defaults to 100. if -1, no enumeration + clingo_configuration (str, optional): Configuration for clingo resolution . Defaults to None. + clingo_strategy (str, optional): Strategy for clingo resolution. Defaults to None. + intersection (bool, optional): Find the intersection of all solutions without limitation (give one solution). Defaults to False. + union (bool, optional): Find the union of all solutions without limitation (give one solution). Defaults to False. + minimize (bool, optional): Search the minimal carinality of solutions. Defaults to False. + subset_minimal (bool, optional): Search the subset minimal solutions. Defaults to False. + temp_dir (str, optional): Temporary directory for saving instance file and clingo outputs. Defaults to None. + short_option (str, optional): Short way to write option on filename. Defaults to None. + run_solve (str, optional): Solving run used (reasoning, filter, guess-check, guess-check-div) + verbose (bool, optional): Set debug mode. Defaults to False. + """ + + self.is_linear:bool + self.asp = ASP_CLINGO() + self.run_mode = run_mode + self.network = network + self.time_limit_minute = time_limit_minute + self.set_time_limit() + self.number_solution = number_solution + self._set_clingo_configuration(clingo_configuration) + self._set_clingo_strategy(clingo_strategy) + self.enumeration = True + if(number_solution == -1): + self.enumeration = False + self.intersection = intersection + self.union = union + self.minimize = minimize + self.subset_minimal = subset_minimal + self.clingo_constant = list() + self.one_model_unsat = True + self.optimum = tuple() + self.optimum_found = False + self.opt_prod_tgt = None + self.opt_size = None + self.output = dict() + self.timer_list = dict() + self.verbose = verbose + self.messages = list() + self.temp_dir = temp_dir + self.short_option = short_option + self.run_solve = run_solve + if self.run_solve == "guess_check_div": + self.diversity = True + else: + self.diversity = False + self.grounded = str() + self.temp_result_file = str() + + + + ######################## SETTER ######################## + def set_time_limit(self): + """Convert time limit minute into seconds for resolutions + """ + if self.time_limit_minute != 0: + self.time_limit=self.time_limit_minute*60 + else: + self.time_limit=None + + + def _set_clingo_configuration(self, clingo_configuration:str): + """Prepare configuration command option for resolution + + Args: + clingo_configuration (str): configuration mode + """ + if clingo_configuration != "none": + self.clingo_configuration = f"--configuration={clingo_configuration}" + else: + self.clingo_configuration = "" + + def _set_clingo_strategy(self, clingo_strategy:str): + """Prepare strategy command option for resolution + + Args: + clingo_strategy (str): strategy mode + """ + if clingo_strategy != "none": + self.clingo_strategy = f"--opt-strategy={clingo_strategy}" + else: + self.clingo_strategy = "" + + def _set_instance_file(self): + """Prepare ASP instance filename for saving into temporary directory file + """ + filename = f'instance_{self.network.name}_{self.short_option}' + self.network.instance_file = path.join(self.temp_dir,f'{filename}.lp') + write_instance_file(self.network.instance_file, self.network.facts) + logger.log.info(f"Instance file written: {self.network.instance_file}") + + + def _set_temp_result_file(self): + self.temp_result_file = f'temp_{self.network.name}_{self.short_option}' + + ######################################################## + + ######################## METHODS ######################## + + + def get_solutions_infos(self, search_mode:str=""): + """Get infos of solving mode for messsages and outputs. + + Args: + search_mode (str, optional): Describe the mode of resolution. Defaults to "". + + Returns: + str, str, str: mode_message, model_type, output_type + """ + mode_message = "" + model_type = "" + output_type = "" + match search_mode: + case "minimize-one-model": + mode_message="Minimize optimum" + model_type="model_one_solution" + output_type = 'MINIMIZE OPTIMUM' + case "minimize-intersection": + mode_message="Minimize intersection" + model_type="model_intersection" + output_type = 'MINIMIZE INTERSECTION' + case "minimize-union": + mode_message="Minimize union" + model_type="model_union" + output_type = 'MINIMIZE UNION' + case "minimize-enumeration": + output_type = 'MINIMIZE ENUMERATION' + case "submin-enumeration": + output_type = 'SUBSET MINIMAL ENUMERATION' + case "submin-intersection": + mode_message="Subset Minimal intersection" + model_type="model_intersection" + output_type = 'SUBSET MINIMAL INTERSECTION' + + + full_option=[self.clingo_configuration, self.clingo_strategy] + greedy_clingo_option=self.asp.CLINGO_CONFIGURATION[search_mode] + if greedy_clingo_option: + full_option = [*full_option, *greedy_clingo_option] + full_option = list(filter(None, full_option)) + return full_option, mode_message, model_type, output_type + + + def init_const(self): + """ Inititate ASP constants + """ + self.clingo_constant = ['-c'] + match self.run_mode: + case 'target': + logger.print_log("Mode : TARGET", "info") + if not self.network.targets_as_seeds: + logger.print_log('Option: TARGETS ARE FORBIDDEN SEEDS', "info") + logger.print_log(f'Search seeds validating the {len(self.network.targets)} targets…','debug') + self.clingo_constant.append('run_mode=target') + case 'full': + logger.print_log("Mode : FULL NETWORK", "info") + logger.print_log("Search seeds validating all metabolites as targets…",'debug') + self.clingo_constant.append('run_mode=full') + case 'fba': + logger.print_log("Mode : FBA", "info") + if not self.network.targets_as_seeds: + logger.print_log('Option: TARGETS ARE FORBIDDEN SEEDS', "info") + logger.print_log('Info: Targets are reactant of objective', "info") + logger.print_log("Search seeds aleatory…",'debug') + self.clingo_constant.append('run_mode=fba') + + if self.network.accumulation: + logger.print_log('ACCUMULATION: Authorized', "info") + self.clingo_constant.append('-c') + self.clingo_constant.append('accu=1') + else: + logger.print_log('ACCUMULATION: Forbidden', "info") + self.clingo_constant.append('-c') + self.clingo_constant.append('accu=0') + + def get_separate_optimum(self): + """This function separate the optimisations if possible: + Maximization of produced target (rank 2) + Minimization of set of seeds (rank 1) + When using multiple optimization with clingo, the solver gives back + a list of optimality found in order of importance in the asp files . + A higher rank means a higher importance. + """ + if len(self.optimum)==1: + self.opt_prod_tgt = None + self.opt_size = self.optimum[0] + else: + self.opt_prod_tgt = self.optimum[0] + self.opt_size = self.optimum[1] + + def get_message(self, mode:str=None): + """Get messages to put on terminal + + Args: + mode (str, optional): witch kind of messages to chose. Defaults to None. + """ + match mode: + case 'subsetmin': + logger.print_log("\n____________________________________________","info",color.purple) + logger.print_log("____________________________________________\n", "info",color.purple) + logger.print_log(f"Sub Mode: {color.bold}SUBSET MINIMAL{color.reset}".center(55), "info") + logger.print_log("____________________________________________", "info",color.purple) + logger.print_log("____________________________________________\n", "info",color.purple) + case "minimize": + logger.print_log("\n____________________________________________","info",color.purple) + logger.print_log("____________________________________________\n", "info",color.purple) + logger.print_log(f"Sub Mode: {color.bold}MINIMIZE{color.reset}".center(55), "info") + logger.print_log("____________________________________________", "info",color.purple) + logger.print_log("____________________________________________\n", "info",color.purple) + case "one solution": + logger.print_log(f"\n~~~~~~~~~~~~~~~ {color.bold}One solution{color.reset} ~~~~~~~~~~~~~~~", "info") + case "intersection": + logger.print_log(f"\n~~~~~~~~~~~~~~~ {color.bold}Intersection{color.reset} ~~~~~~~~~~~~~~~", "info") + case "enumeration": + logger.print_log(f"\n~~~~~~~~~~~~~~~~ {color.bold}Enumeration{color.reset} ~~~~~~~~~~~~~~~", "info") + case "union": + logger.print_log(f"\n~~~~~~~~~~~~~~~~~~~ {color.bold}Union{color.reset} ~~~~~~~~~~~~~~~~~~", "info") + case "end": + logger.print_log('############################################\n\n', "info", color.cyan_light) + case "optimum error": + logger.print_log("\n____________________________________________","info") + logger.print_log('ABORTED: No objective funcion found \ + \nPlease correct the SBML file to contain either \ + \n - a function with "BIOMASS" (not case sensiive) in the name \ + \n - a function in the objective list', "error") + case "command": + logger.print_log(" Command", "debug") + case "classic": + logger.print_log(f"\n················ {color.bold}Classic mode{color.reset} ···············", "info") + case "filter": + logger.print_log(f"\n················ {color.bold}Filter mode{color.reset} ···············", "info") + case "guess_check": + logger.print_log(f"\n·············· {color.bold}Guess-Check mode{color.reset} ············", "info") + case "guess_check_div": + logger.print_log(f"\n····· {color.bold}Guess-Check with diversity mode{color.reset} ······", "info") + + diff --git a/seed2lp/utils.py b/seed2lp/utils.py new file mode 100644 index 0000000..4c496dd --- /dev/null +++ b/seed2lp/utils.py @@ -0,0 +1,183 @@ +"""Utilitaries""" +import os +import clyngor +import re +from re import findall +from . import logger + + +def solve(*args, **kwargs): + "Wrapper around clyngor.solve" + kwargs.setdefault('use_clingo_module', False) + try: + return clyngor.solve(*args, **kwargs) + except FileNotFoundError as err: + if 'clingo' in err.filename: + logger.log.error('Binary file clingo is not accessible in the PATH.') + exit(1) + else: raise err + + +def get_ids_from_file(fname:str, asp_atome_type:str=None) -> [str]: + """Get metabolites id from seeds file, forbidden seeds file or possible seeds file + + Args: + fname (str): file path + asp_atome_type (str, optional): Type of atome for facts. Defaults to None. + + Raises: + NotImplementedError: Target file of extension [ext] not implemented + + Returns: + [str]: List of metabolite + """ + "Yield identifiers of seeds/targets/metabolites found in given sbml or text or lp file" + metabolit_list = list() + ext = os.path.splitext(fname)[1] + #if ext in {'.sbml', '.xml'}: # sbml data + # from .sbml import read_SBML_species + # yield from read_SBML_species(fname) + if ext in {'.lp'}: # ASP data + for model in solve(fname).by_arity: + for line, in model.get(f'{asp_atome_type}/1', ()): + line = unquoted(line) + if re.search("^M_*",line): + metabolit_list.append(line) + else: + metabolit_list.append(f'M_{line}') + elif ext in {'.txt', ''}: # file, one line per metabolite + with open(fname) as fd: + for line in map(str.strip, fd): + if line: + if re.search("^M_*",line): + metabolit_list.append(line) + else: + metabolit_list.append(f'M_{line}') + else: + raise NotImplementedError(f"Target file of ext {ext}: {fname}") + return metabolit_list + + +def get_targets_from_file(fname:str) -> [str]: + """Get metabolites id or reactions id from target file + + Args: + fname (str): Target file path + + Raises: + ValueError: The element [element] misses prefix M_ or R_" + NotImplementedError: The [file name] extension has to be ".txt". + ValueError: Multiple objective reaction found + + Returns: + [str],[str]: List of target and list of objective reaction + """ + + target_list=list() + objective_reaction_list = list() + ext = os.path.splitext(fname)[1] + if ext in {'.txt', ''}: # file, one line per metabolite + with open(fname) as fd: + for line in map(str.strip, fd): # remove white spaces + if line: + if re.search("^M_*",line): + target_list.append(line) + elif re.search("^R_*",line): + objective_reaction_list.append(line) + else: + raise ValueError(f"\n{fname} : The element {line} misses prefix M_ or R_") + else: + raise NotImplementedError(f'\nThe {fname} extension has to be ".txt". Given: {ext}') + + if len(objective_reaction_list) >1 : + raise ValueError(f"\nMultiple objective reaction found in {fname}\n") + + return target_list, objective_reaction_list + + +def quoted(string:str) -> str: + r"""Return string, double quoted + + >>> quoted('"a').replace('\\', '$') + '"$"a"' + >>> quoted('"a b"').replace('\\', '$') + '"a b"' + >>> quoted('a b').replace('\\', '$') + '"a b"' + >>> quoted('a\\"').replace('\\', '$') + '"a$""' + >>> quoted('a"').replace('\\', '$') + '"a$""' + >>> quoted('\\"a"').replace('\\', '$') + '"$"a$""' + >>> quoted('"').replace('\\', '$') + '"$""' + + """ + if len(string) > 1 and string[0] == '"' and string[-2] != '\\' and string[-1] == '"': + return string + else: + return '"' + string.replace('\\"', '"').replace('"', '\\"') + '"' + + +def unquoted(string:str) -> str: + r"""Remove surrounding double quotes if they are acting as such + + >>> unquoted('"a').replace('\\', '$') + '$"a' + >>> unquoted('"a b"') + 'a b' + >>> unquoted('b"').replace('\\', '$') + 'b$"' + >>> unquoted('"b\\"').replace('\\', '$') + '$"b$"' + + """ + if string[0] == '"' and string[-2] != '\\' and string[-1] == '"': + return string[1:-1] + else: + return string.replace('\\"', '"').replace('"', '\\"') + + +def quoted_data(asp:str) -> str: + "Return the same atoms as found in given asp code, but with all arguments quoted" + def gen(): + for model in clyngor.solve(inline=asp): + for pred, args in model: + yield f'{pred}(' + ','.join(quoted(str(arg)) for arg in args) + ').' + return ' '.join(gen()) + + +def repair_json(json_str:str, is_clingo_lpx:bool=False): + """Function to add closing ] or } to the json after the process has been killed + delete also the last element of the json which can be not finished + + Args: + proc_output (str): process output + + Returns: + str: complete output on json format + """ + close = {'{': '}', + '[': ']'} + if is_clingo_lpx: + output = json_str.rsplit('{', 1)[0] + output = output.rsplit(',', 1)[0] + else: + output = json_str.rsplit('\"model', 1)[0] + # get the list of caracter "{" "[" "]" "}" in the order of apparition + list_open_close=findall("{|\[|\]|}", output) + missing_list=list() + for car in list_open_close: + size=len(missing_list) + # delete the opening element when the closing element appear right after + if size!= 0 and ((missing_list[size -1] == "{" and car == "}") + or (missing_list[size -1] == "[" and car == "]")): + missing_list.pop(size -1) + else: + missing_list.append(car) + close_str="" + for i, open in reversed(list(enumerate(missing_list))): + close_str += "\n" + i * "\t" + close[open] + logger.log.warning("Output not totally recovered. Json has been repaired but might miss results") + return output+close_str \ No newline at end of file diff --git a/setup.cfg b/setup.cfg new file mode 100644 index 0000000..10d6bec --- /dev/null +++ b/setup.cfg @@ -0,0 +1,49 @@ +[metadata] +name = seed2lp +version = attr: seed2lp.__version__ +description = Optimizable seed extraction from metabolic networks +long_description = file: README.mkd +long_description_content_type = text/markdown +author = Chabname Ghassemi Nedjad +author_email = chabname.ghassemi-nedjad@inria.fr +url = https://github.com/ +license = GPL +keywords = Answer Set Programming, wrapper, clingo +classifiers = + Development Status :: 4 - Beta + Intended Audience :: Science/Research + Programming Language :: Python :: 3 + Programming Language :: Python :: 3.5 + Programming Language :: Python :: 3.6 + Programming Language :: Python :: 3.7 + Programming Language :: Python :: 3.10 + Programming Language :: ASP + +[options] +zip_safe = False +include_package_data = True +packages = seed2lp +install_requires = + clyngor>=0.3.18 + pytest>=4.4.0 + biseau>=0.0.16 + cobra + clyngor-with-clingo + clingo-lpx + pyyaml + menetools + padmet +[options.package_data] +seed2lp = asp/*.lp + +[zest.releaser] +create-wheel = yes +python-file-with-version = seed2lp/__init__.py + +[options.entry_points] +console_scripts = + seed2lp=seed2lp.__main__:main + +[options.extras_require] +tests = + pytest \ No newline at end of file diff --git a/setup.py b/setup.py new file mode 100644 index 0000000..d6b8cae --- /dev/null +++ b/setup.py @@ -0,0 +1,32 @@ +from setuptools import setup, find_packages + +exec(open('seed2lp/_version.py').read()) +setup( + name='seed2lp', + version=__version__, + description='Seed searching from network as SBML using Logic programming', + url='http://github.com/*/*', + author='Chabname Ghassemi Nedjad', + author_email='chabname.ghassemi-nedjad@inria.fr', + license='GPL', + classifiers =[ + "Development Status :: 4 - Beta", + "Intended Audience :: Science/Research", + "Programming Language :: Python :: 3", + "Programming Language :: Python :: 3.5", + "Programming Language :: Python :: 3.6", + "Programming Language :: Python :: 3.7", + "Programming Language :: Python :: 3.10", + "Programming Language :: ASP", + ], + + packages=find_packages(), + package_data={ + "": ["*.yaml"], + }, + entry_points = { + 'console_scripts': ['seed2lp=seed2lp.__main__:main'], + }, + zip_safe=False, + include_package_data = True +) \ No newline at end of file diff --git a/tests/fba.py b/tests/fba.py new file mode 100644 index 0000000..1a27e8a --- /dev/null +++ b/tests/fba.py @@ -0,0 +1,147 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- + +""" +Description: +Test seed2lp +""" +from os import path +from tests.utils import search_seed + +##### ###### ##### DIRECTORIES AND FILES ################### +TEST_DIR = path.dirname(path.abspath(__file__)) + +# All reaction are not reversible +# except import/export reaction for exchange metabolite S1, S2 C and G +INFILE = path.join(TEST_DIR,'network/sbml/toy_paper_no_reversible.sbml') +######################################################## + + +################### FIXED VARIABLES #################### +RUN_MODE="fba" +######################################################## + + +################# EXPECTED SOLUTIONS ################### + +# Seeds for Reasoning, filter, Gues-Check, Guess-Check Diversty and Hybrid +SEEDS_SUBMIN_TAF= [{"M_B_c", "M_D_c", "M_E_c"}, + {"M_B_c", "M_D_c", "M_I_c"}, + {"M_B_c", "M_D_c", "M_H_c"}, + {"M_B_c", "M_I_c", "M_S2_e"}, + {"M_B_c", "M_E_c", "M_S2_e"}, + {"M_B_c", "M_H_c", "M_S2_e"}, + {"M_A_c", "M_D_c", "M_I_c", "M_S1_e"}, + {"M_D_c", "M_I_c", "M_J_c", "M_S1_e"}, + {"M_A_c", "M_D_c", "M_E_c", "M_S1_e"}, + {"M_A_c", "M_D_c", "M_H_c", "M_S1_e"}, + {"M_A_c", "M_E_c", "M_S1_e", "M_S2_e"}, + {"M_A_c", "M_H_c", "M_S1_e", "M_S2_e"}, + {"M_H_c", "M_J_c", "M_S1_e", "M_S2_e"}, + {"M_E_c", "M_J_c", "M_S1_e", "M_S2_e"}, + {"M_A_c", "M_I_c", "M_S1_e", "M_S2_e"}, + {"M_I_c", "M_J_c", "M_S1_e", "M_S2_e"}, + {"M_D_c", "M_H_c", "M_J_c", "M_S1_e"}, + {"M_D_c", "M_E_c", "M_J_c", "M_S1_e"} + ] +SIZE_SUBMIN_TAF=len(SEEDS_SUBMIN_TAF) + +SEEDS_MIN_TAF = [{"M_B_c", "M_D_c", "M_E_c"} , + {"M_B_c", "M_D_c", "M_I_c"} , + {"M_B_c", "M_D_c", "M_H_c"} , + {"M_B_c", "M_I_c", "M_S2_e"} , + {"M_B_c", "M_E_c", "M_S2_e"} , + {"M_B_c", "M_H_c", "M_S2_e"} + ] +SIZE_MIN_TAF=len(SEEDS_MIN_TAF) + + +SEEDS_SUBMIN_TAS= [{"M_F_c"}, + {"M_B_c", "M_D_c", "M_E_c"}, + {"M_B_c", "M_D_c", "M_I_c"}, + {"M_B_c", "M_D_c", "M_H_c"}, + {"M_B_c", "M_I_c", "M_S2_e"}, + {"M_B_c", "M_E_c", "M_S2_e"}, + {"M_B_c", "M_H_c", "M_S2_e"}, + {"M_A_c", "M_D_c", "M_I_c", "M_S1_e"}, + {"M_D_c", "M_I_c", "M_J_c", "M_S1_e"}, + {"M_A_c", "M_D_c", "M_E_c", "M_S1_e"}, + {"M_A_c", "M_D_c", "M_H_c", "M_S1_e"}, + {"M_A_c", "M_E_c", "M_S1_e", "M_S2_e"}, + {"M_A_c", "M_H_c", "M_S1_e", "M_S2_e"}, + {"M_H_c", "M_J_c", "M_S1_e", "M_S2_e"}, + {"M_E_c", "M_J_c", "M_S1_e", "M_S2_e"}, + {"M_A_c", "M_I_c", "M_S1_e", "M_S2_e"}, + {"M_I_c", "M_J_c", "M_S1_e", "M_S2_e"}, + {"M_D_c", "M_H_c", "M_J_c", "M_S1_e"}, + {"M_D_c", "M_E_c", "M_J_c", "M_S1_e"} + ] +SIZE_SUBMIN_TAS=len(SEEDS_SUBMIN_TAS) + +SEEDS_MIN_TAS = [{"M_F_c"}] +SIZE_MIN_TAS=len(SEEDS_MIN_TAS) + +######################################################## + + + + + + +######################### TESTS ######################## +#TODO: +# - test with accumulation +# - test keep import reaction and topological injection +# - find Network to have different value in Reasoning / Filter / Gues_check / Guess_Check_Div +# - find Network to have different value with accumulation authorized +# - test without maximization for hybrid +# - test unsat +# - test possible_seeds (subset of seed that maximize number of producible targets) +# - test changing objective +# - test target file + +# solve values: 'all' +# optim values: 'submin', 'min' + +# ---------------------- TARGET ARE FORBIDDEN ---------------------- +# ----------- SUBSETMIN ----------- +def test_submin_taf(): + solve="all" + optim="submin" + list_solution=search_seed(INFILE, RUN_MODE, solve, optim) + for solution in list_solution: + assert set(solution) in SEEDS_SUBMIN_TAF + assert len(list_solution) == SIZE_SUBMIN_TAF + + +# ----------- MINIMIZE ----------- +def test_min_taf(): + solve="all" + optim="min" + list_solution=search_seed(INFILE, RUN_MODE, solve, optim) + for solution in list_solution: + assert set(solution) in SEEDS_MIN_TAF + assert len(list_solution) == SIZE_MIN_TAF + + +# ---------------------- TARGET AS SEEDS ---------------------- +# ----------- SUBSETMIN ----------- +def test_submin_tas(): + solve="all" + optim="submin" + targets_as_seeds=True + list_solution=search_seed(INFILE, RUN_MODE, solve, optim, targets_as_seeds) + for solution in list_solution: + assert set(solution) in SEEDS_SUBMIN_TAS + assert len(list_solution) == SIZE_SUBMIN_TAS + + +# ----------- MINIMIZE ----------- +def test_min_tas(): + solve="all" + optim="min" + targets_as_seeds=True + list_solution=search_seed(INFILE, RUN_MODE, solve, optim, targets_as_seeds) + for solution in list_solution: + assert set(solution) in SEEDS_MIN_TAS + assert len(list_solution) == SIZE_MIN_TAS diff --git a/tests/full_network.py b/tests/full_network.py new file mode 100644 index 0000000..f35cdfe --- /dev/null +++ b/tests/full_network.py @@ -0,0 +1,166 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- + +""" +Description: +Test seed2lp +""" +from os import path +from tests.utils import search_seed + +################ DIRECTORIES AND FILES ################### +TEST_DIR = path.dirname(path.abspath(__file__)) + +# In This Network, The reaction R2 J + A -> C is deleted +# C is alone in the network, with an import/export reaction, +# therefore C is always a seed by design in Full Network +# J is alone never involved in any reaction, J is never a seed +# All reaction are not reversible +# except import/export reaction for exchange metabolite S1, S2 C and G +INFILE = path.join(TEST_DIR,'network/sbml/toy_paper_no_rev_rm_R2.sbml') +######################################################## + + +################### FIXED VARIABLES #################### +RUN_MODE="full" +######################################################## + + +################ EXPECTED SOLUTIONS ################## +# Same results for subset minimal and minimize +SEEDS_REASONING = [{'M_C_c', 'M_F_c', 'M_S1_e', 'M_S2_e'}, + {'M_C_c', 'M_I_c', 'M_S1_e', 'M_S2_e'}, + {'M_C_c', 'M_H_c', 'M_S1_e', 'M_S2_e'}, + {'M_C_c', 'M_E_c', 'M_S1_e', 'M_S2_e'} + ] +SIZE_REASONING=len(SEEDS_REASONING) + +# Same results for Filter, Guess-Check and Guess-Check Diversity +# Same results for subset minimal and minimize +# F is an objective reactant (therefore a target), and it is choosen +# event if target is forbidden because in Full Network mode, +# all metabolite are Target and can't be forbidden +SEEDS_HYB_COBRA = [{'M_C_c', 'M_F_c', 'M_S1_e', 'M_S2_e'}] +SIZE_HYB_COBRA=len(SEEDS_HYB_COBRA) + +# metabolite A is a seed for hybrid only to create an export reaction +# When B is a seed, The reaction S1 -> A+B is not activated in FBA +# A is therefore not accumlated +SEEDS_SUBMIN_HYBRID = [{'M_C_c', 'M_F_c', 'M_S1_e', 'M_S2_e'}, + {'M_A_c', 'M_C_c', 'M_I_c', 'M_S1_e', 'M_S2_e'}, + {'M_B_c', 'M_C_c', 'M_I_c', 'M_S1_e', 'M_S2_e'}, + {'M_A_c', 'M_C_c', 'M_H_c', 'M_S1_e', 'M_S2_e'}, + {'M_B_c', 'M_C_c', 'M_H_c', 'M_S1_e', 'M_S2_e'}, + {'M_A_c', 'M_C_c', 'M_E_c', 'M_S1_e', 'M_S2_e'}, + {'M_B_c', 'M_C_c', 'M_E_c', 'M_S1_e', 'M_S2_e'} + ] +SIZE_SUBMIN_HYBRID=len(SEEDS_SUBMIN_HYBRID) + +# F is an objective reactant (therefore a target), and it is choosen +# event if target is forbidden because in Full Network mode, +# all metabolite are Target and can't be forbidden +SEEDS_MIN_HYBRID = [{'M_C_c', 'M_F_c', 'M_S1_e', 'M_S2_e'}] +SIZE_MIN_HYBRID=len(SEEDS_MIN_HYBRID) + +######################################################## + + + +######################### TESTS ######################## +#TODO: +# - test with accumulation +# - test keep import reaction and topological injection +# - find Network to have different value in Filter / Gues_check / Guess_Check_Div +# - find Network to have different value with accumulation authorized +# - test without maximization for hybrid +# - test unsat +# - test possible_seeds (subset of seed that maximize number of producible targets) +# - test changing objective + +# solve values: 'reasoning', 'filter', 'guess_check', 'guess_check_div', 'hybrid' +# optim values: 'submin', 'min' + +# ----------- SUBSETMIN ----------- +def test_reasoning_submin(): + solve="reasoning" + optim="submin" + list_solution=search_seed(INFILE, RUN_MODE, solve, optim) + for solution in list_solution: + assert set(solution) in SEEDS_REASONING + assert len(list_solution) == SIZE_REASONING + +def test_filter_submin(): + solve="filter" + optim="submin" + list_solution=search_seed(INFILE, RUN_MODE, solve, optim) + for solution in list_solution: + assert set(solution) in SEEDS_HYB_COBRA + assert len(list_solution) == SIZE_HYB_COBRA + +def test_gc_submin(): + solve="guess_check" + optim="submin" + list_solution=search_seed(INFILE, RUN_MODE, solve, optim) + for solution in list_solution: + assert set(solution) in SEEDS_HYB_COBRA + assert len(list_solution) == SIZE_HYB_COBRA + +def test_gcd_submin(): + solve="guess_check_div" + optim="submin" + list_solution=search_seed(INFILE, RUN_MODE, solve, optim) + for solution in list_solution: + assert set(solution) in SEEDS_HYB_COBRA + assert len(list_solution) == SIZE_HYB_COBRA + + +def test_hybrid_submin(): + solve="hybrid" + optim="submin" + list_solution=search_seed(INFILE, RUN_MODE, solve, optim) + for solution in list_solution: + assert set(solution) in SEEDS_SUBMIN_HYBRID + assert len(list_solution) == SIZE_SUBMIN_HYBRID + + +# ----------- MINIMIZE ----------- +def test_reasoning_min(): + solve="reasoning" + optim="min" + list_solution=search_seed(INFILE, RUN_MODE, solve, optim) + for solution in list_solution: + assert set(solution) in SEEDS_REASONING + assert len(list_solution) == SIZE_REASONING + +def test_filter_min(): + solve="filter" + optim="min" + list_solution=search_seed(INFILE, RUN_MODE, solve, optim) + for solution in list_solution: + assert set(solution) in SEEDS_HYB_COBRA + assert len(list_solution) == SIZE_HYB_COBRA + +def test_gc_min(): + solve="guess_check" + optim="min" + list_solution=search_seed(INFILE, RUN_MODE, solve, optim) + for solution in list_solution: + assert set(solution) in SEEDS_HYB_COBRA + assert len(list_solution) == SIZE_HYB_COBRA + +def test_gcd_min(): + solve="guess_check_div" + optim="min" + list_solution=search_seed(INFILE, RUN_MODE, solve, optim) + for solution in list_solution: + assert set(solution) in SEEDS_HYB_COBRA + assert len(list_solution) == SIZE_HYB_COBRA + + +def test_hybrid_min(): + solve="hybrid" + optim="min" + list_solution=search_seed(INFILE, RUN_MODE, solve, optim) + for solution in list_solution: + assert set(solution) in SEEDS_MIN_HYBRID + assert len(list_solution) == SIZE_MIN_HYBRID \ No newline at end of file diff --git a/tests/network/graph/logs/toy_stoichiometry_cycle_import_rxn_network_render.log b/tests/network/graph/logs/toy_stoichiometry_cycle_import_rxn_network_render.log new file mode 100644 index 0000000..33a7ac8 --- /dev/null +++ b/tests/network/graph/logs/toy_stoichiometry_cycle_import_rxn_network_render.log @@ -0,0 +1,16 @@ +INFO 2024-09-06 08:03:04,300: Seed2LP version: 1.0.0 +INFO 2024-09-06 08:03:04,301: + - R_EX_S1: Reversibility modified. + - R_S1: Reversibility modified. + - R_EX_S2: Reversibility modified. + - R_S2: Reversibility modified. + - R_R1: Reversibility modified. + - R_R2: Reversibility modified. + - R_R3: Reversibility modified. + - R_BIOMASS_1: Reversibility modified. + - R_R5: Reversibility modified. + - R_BIOMASS_2: Reversibility modified. + - R_F: Reversibility modified. + - R_EX_F: Reversibility modified. +INFO 2024-09-06 08:03:04,302: Converting Network into ASP facts ... +INFO 2024-09-06 08:03:04,302: ... DONE diff --git a/tests/network/graph/logs/toy_stoichiometry_cycle_rm_rxn_network_render.log b/tests/network/graph/logs/toy_stoichiometry_cycle_rm_rxn_network_render.log new file mode 100644 index 0000000..c540474 --- /dev/null +++ b/tests/network/graph/logs/toy_stoichiometry_cycle_rm_rxn_network_render.log @@ -0,0 +1,18 @@ +INFO 2024-09-06 08:03:17,338: Seed2LP version: 1.0.0 +INFO 2024-09-06 08:03:17,340: + - R_EX_S1: Reversibility modified. + - R_S1: Reversibility modified. + - R_EX_S2: Reversibility modified. + - R_S2: Reversibility modified. + - R_R1: Reversibility modified. + - R_R2: Reversibility modified. + - R_R3: Reversibility modified. + - R_BIOMASS_1: Reversibility modified. + - R_R5: Reversibility modified. + - R_BIOMASS_2: Reversibility modified. + - R_F: Reversibility modified. + - R_EX_F: Reversibility modified. +INFO 2024-09-06 08:03:17,340: Converting Network into ASP facts ... +INFO 2024-09-06 08:03:17,340: Reaction R_EX_S1 artificially removed into lp facts with a prefix 'rm_' +INFO 2024-09-06 08:03:17,340: Reaction R_EX_S2 artificially removed into lp facts with a prefix 'rm_' +INFO 2024-09-06 08:03:17,340: ... DONE diff --git a/tests/network/graph/toy_example_import_rxn_visu.png b/tests/network/graph/toy_example_import_rxn_visu.png new file mode 100644 index 0000000000000000000000000000000000000000..1a3a05ba1981bae82b8d84f5114e3c1e21e6b005 GIT binary patch literal 89849 zcmZ^L2{@MT_VtU%EQ+EKq6}$NNRc6u3Q-zlN{J$(Oqnu^Qc0tkP#GJLSrSc@N~1ZF z3aQBStxe}YT;KQo&NMKQa7V_L7tT zBrUgGzj^8pP1qz8S!VF_&yUtSbM@+}iSZG>g9i`lZQdNDEy=_v_V~%>)LhESuse8g z{noA7<;^_Y+=lu4H#gqgIa5SLB;W)ueT*p&eX`2q+S-Z|uRnd=4N__+{?6JOzpnac z*Yp0tFWVkeS4*g<2;-9lU2cuG75R+)wQEf0H`u({%HE!H#flXduUtvqnZLjO#f!`{ z4*dAX)XlzkV6(P~v$c(l^YDP}o)S;N|-~KYZB3(D?T5j+M1_K#|+m z$_EdoJ$(2u-6)xjo11%|Z_n4ST03^kzI^#IgK_xqVLJy07WW-j57^o=xv#!)EiNu< zaL_d+C1t*}G;2mihTg`F>#eON1q1~8+N!x0ELbpKR+fV?Um(oV(J|`6;@y=mUI^g3 zw61TtW?1HPf~j;Dy>!f6ymZp?y2p>Jetmu8{P~Hn_J$2@FH$#!Mn(n|6{(7>cH};K z^ytdN?`FlH_$~b8>C=XEBdMKMH|^fOed|8Z!C`G}-PhNu^)`NMoZ9-md&NXn+A-$N zo!j*8UFEZ9ycXX5tc{J0(`xncT_!to5@aOhf*w53zPj#&i0^3sbWKgozJbn7jalY% zu_haCY`;~XX)1r)u7UaQ-@lr=i=$UNeVVU|r*QhDZDKOZ@l$cwj_kdOTdt|*+`AW2 zQnL2%udgAhZawUxF$fjCFZ2a}13NnQT~+fC7#|-WYCo+$KSsZy$j!m^$0yNMt5)UY zYC%cvlaK0-*I?z=E{`_d~C(jr#xE0-2P!jJnWuR zbLFQ`Vlx+R(ViF|duw_XAC|dNs8L({hsW@M^j)XssBiD@NhvDw-o1PG$gyJ^{(S#< z!+_uX>XRo1^jtNk#cQe~W;f(mX}NoNSXBmc>=D>PVJbjjS}dP(@6g)-?3vc)%{;$< z|L*JS^D8VYtZz8~;^ph;z2%bFCB~HyFfM06Ed`U^>tgNh|-|wBQEG_4|D#V;Qdp2P6l~t{eqNTdu-I4b` zae`tk(S}Y^Ei3)5F+Mgp*QO>3J7sqFkg&qy#X(UiXC0eMBZq%>wZFQtqbdIY9~@liuvP6@=_5yu%uqFsacO(NG1^zfH)omkv}4~t z)Rx>pjOf{h2J&iXFW8Eu`2PL7@`=AIS2=w;_3YXjF42rS;iZ<0Q>RWXJ@MByB{j9R zueHi>M;Eg!9xroLS!=Vl+|!zx>2p^*KES)xXPMu3IUO4v?T;4}bnks*;j43H)!}u2 zi`~Ayw)*j@ByR$%x4gKxSl{(2eqM6LihS)Z#XSWxPEO}jY$yyr_N&K~qUGJ~gJ!wb zLG{j9_RzY>ZPT<<@_6Sd`zblRwnUH$8XK@^+_8T(Lr{=4*G$3d(EGcG#ZNr@`X;;e z#}7URURJ}z#3U@@Afv#0IIq`pt=DfHKa*&2MC_8P11ZX{U%wt2ANi4M|4MG&I&ZUX zWvrWa=fi-@p(ZbG?8si~F|dzYeBHdLs3@1h;JbHM^}NYG-PdAdqa-ej30h0ERJejK}`Ce!pr$0{G4 zmoHzQymxQ)U7Ru`ti*Pd7ycMwM4_>Yb?%&9yLNHD72C9F6IZhC>0pExc7t=?<42Fm zD^5;d<}>PE*tO`!n`{f4rZ||W(^Eb7%9Pi$} zQ}!H8|B$sFE4aW(Aw5j5Gql=zu=nju{1sIOZ_OCL7&YYR?;K74OV(PBlGgOcD zhNf-4vc2ifv$(Yp+i&d>9P0b|Q~ssd#*G_~96zpWXgK5ZlS?PnCq`!?2X@ydvW)*J zo2V7Zs6C0jC_4T_@r?t$PAw&WW0iZ3A2ksGg2&dP%AA^~BH-cS(NuVJA!EM0JQo)2 z;!-QkpuIQJ6`Qp}co)AkQy(9aLP1-0;MqJ%s-B*n>83Y?5R~<)n_@Yinx9`%I>d|* zJv-t?;nxwws>Gjh{?sY%1q&A@JAN$UEXlfme+@Se5C5!Lf!DXjS2j0`Q})l_|8&;n z`Bu@`$V-<9_-u6@FVO0aXUSk&gc6&8&>V)lwsdh~$>m zR{waPzZ|>o9;#^eorpvpk?nZ*^Yc^rUq@YBT>3sgos0UZwRP)O-i&p(Jb(3^(N9Ii z^hXAoFFbijPSUA2?rF?miPj5@U1A|O`mQR3FQl||T|huU{mq>#P?a=Wh4uBZ=O|8( zo;};(=~*;iSYYPN#EgtEDnc$N@OViB)_^5)I$ByRGBPru5fR&l?)>`otM$tljuR(N z;Bd;0{!RV$YKH|AV|?`IbIqDP=H|Jczw8V5s-qVC>UniCI5>DuwiboMlF4wOMMjmH z(eaLo$-aFHN{8CG)@NCWY`eZi=Oi0X4t{%2!4ak}U%t5ax3OMOG!Mt~*NutwaLras z&^;~YQo@Gaj#kUqm};C+FA*9lXN-)?#v>Alt<+L{T8n@(ONidy_3T>i;rDl~pF6$E zG)=7e{PfC>w(lRr_wq3)0*`h*i&5y)Ek^BFbF8=g(uxBFGzdfCfYqdIJZHIo|Mu_w z5$DdF@mqJ|Z_9(QnVfHzP}R{BL(zz?tF5hFkN9uEiCM1HD>=M7Zy!fxRaO6wvI$L` zq^TrgTPfX)bBhX_<2~#$G&0hT1EpD$$fxE#G@~X;vk+yl?Wu*qDaVGPt_tByy}>3d65755giD=8@@N?{599pIe+h)YymyBWK8@4ji4 ztJ$p_G|SEDS4eS5N&nN;&5NoFI;CUuDZU8|4Rk&p>dk*X%X4yf!zYv6E`R_)kLidf zgKMfWE0UO_^;6BteAfDm{n|m`ro{7?fT{q#(WtOr43Bvbkn-WD60g>;Un6izB$q7N z+O2201%OFt@vcCY>AaT6?h0xDqnbF0iHR*;y0rX^gcd<01@od=Y;0_rHJJj&#>Pv1 z$G!KIdJAJgP9`TO*GE$b%W=ngqc-!4iA70h1#18o08eS6egsCPyci$u5`S`O#q^$@ zp1wbSLT=r<_3xKvP5x3t>ZaKr3Y=LEzq?%l0GeY}#kOz%eooO%o%{Cfv#bi{nX_U) zHzhIDi2RE(i1U~SSpH0x6l2-DjvvDdk9H;Px?_J)$w8?1+qa;oRDpn#Cv%T|+eJH3 z9WK~6FkmgEj9)X}c{NccNlscyie=%}xCcNu(Xp{1$G*L@dVO;zl~t6@>7_Mht7b3R z8yFHYegD(TL0GqT{O6*wvt-0U`iz5<=R#Uq+6tfv)ygkAnt#upJ+w|YEy~1@vqDdu z3JeS5C>{H~+uhy0rT1-qFWX*b#EP=lZ-ro9(eVEEn&kB-*$Q002mokU9Xh1os)mo2 zENnmPgs8$l?3|ndW(pgQ99g<@<;waSJC-P`s8qatE9}a&;J^Xk*xi25H9ShZhXn|~A=LTB#rdXBpH85`uDP_NzJYdXRyd`^Q&@oc^XD^A zPNJiufg=5{T@yt%XzlGi)${5`tlia-KR;|net#oS^QqY5Bywj3Amv)mK`P!IqdON} zM+K@LrJjR!NHVQIpR3PS6{BN6pAcKB5c-Ysat)LgB$o2}`WdLr zM_gPqflU*yU*GXLH$N|LzEDK}*X*)3#L0{>ZNgwkl&nDWbA*LWTMi_7pFMYuUsyP- zzTOa9qa_(#4v3F1J>u-_kB^%I%+soY0_3iBciZ8+2!!9&y$hJ@~C@M-qLq!cz zpF^qVFHTyd)~XQa@llVC&!4G9A&7qGjt(NX&a_zmB;M~svBylTPl0>CWH zT=6#l0Kb4hKue3|hcaJrG=EE1uhvDSp$@@p=gveR5f|C-@|s(=@S?LhGV=Q{eZ7&9 zk^5jb{{jJy%d4HDklf0#4Il3xM|CKzcb;q$2$dV_0|DYuXGBz^rY}N`Y)IKSlfvfJ zE5U1Pj&Hxb_!4!3{cSpLYXDZGFJ0o`;o-S>`Euol5A!x(UP(>_?vH%j$ z$33C~L|__=)sE*sqqm^`)vKU8ckUcY`}pu78+w3P2Stiq(+Vv8`G+Gqx68`Z(Fj`F z+OqE0v4bAq-Md}gq0(p3M}aqQD!lbTK(EIJ86|JvnKNh3qw$qX zm-^`~IIE=bvavBRD@&0vf5nQK)Ij$+ZvBRyH6<-ga;oRdD=R~!WW|1%-u7475d$)iwZI z&6_um!H|q*$7TZ|)CBX)EqJqvK2A8DOVlqkGZ_8n(BHq6KzAE9Y@pW8z<~RL!p?xM z=kZc%YV+2uTZi@|9hEk(tn2e9NHm+E`I&` z_4IJOyg&wQbR-upT&TQeO){DUtQ+{0U=fD>o2*EjG(pcj$ooK353$D$8QZSAn4d(# zNzZKIR^m5JN5-*4jCH?AsCZ>lwyoN zzo;k=O5`aKB~I)ObBYu;GII~5lfr6X(eQ4W&6g<&+P%9i%PXq7Kt4$~YF-#p7nkUr zItOICLr%SXe;&QAulEB&Ei??EbOEBC8H9+UHH-om!c; zPuoNxOgjKSf$*NU)^k>FZm!*n6ppJ`ueS8`grls@_E1+eEe^NWmxbiAe z=xA!{V&wb(H2Ws&LKcWX9l2R2=@8#fBW&{bYS2# zm*c(H$w+HHIJB zl{&E%t5=8IxudipQX~jvy*AvDmQGHL%RWhN0f;pOXCMkLpcQ&=D;t}W(wi=ngO9-x zpQ$P-t8xF>H%3DJ6&c{ihlyGtEnmKbIlReQudAyY>P~AE!v-jmQIL|d;NR&(G63#i zW*mBVo1?$KpPi4d=Dh4SGlLxxPp_;FiHhO{tzpvQ40PDrl*{rwZf#Y#p!_ub$7z37Q9&2HySAZkM&$KZ&$h6{GG0{oEe&{y2jDm5#Y4$m8kKza{5jvBAI-YS8zPsY zHNLs0PyqPCPwzpj(uvF1EX253Z$Gxn3avGMe;wGBP|%5TOGPP`Iuj7gshT14liD^e z5CB}(R1j+KZb$}wsB=zca|_ZaDv;~<55o5L_L?;dRB--T7=ZOV9s#9z819&Gx2P*F zR&)LOng=CDM_YRu(3V`sJ9^*DfNeXq^}EL;-=Ra~;NZx~%d0GKZpVi{*kEMh7%!Lh z^ZDD-6KjyLa-G{Z7{sqLXR4hFPBj1eU`J*qCMHrfc5a&n)J*jdQC@KWepO|qW_PFw z>m4wSSUIzTBN87zd{|dxKfqXVOgT{+PIaX1Ed!&Z11Q&N4cAWW4)uJB2}P$wE`$7iF->t5j}DmyLZn8 zLf4F6cj5?erLwXzyC~raJz0uBrn6_y&X_f8TCUkCAQtc&2}tokZIrt#39@P%yJ$W$ zEQ@bNlTvVEWEGkt88cowR<`|LvQ}!F$bgouKv^Mt-*{{H>`$LQG5pN$A8Q5hX6NR9 z5Xdpp%wSt;?!lKb)Yt*l&Qj&1H!yBF>y*Sf>;HioFD|#`{QKtz-`cfn6Qu-VE&*l> zDehqx7Z>NAyXsWr0IgshP;`<@cvKW|fwkurI)j%Yl6UOyuah7&{tkX&S-f~L){xUM zSh~6V{^F0+j5%m=xKVI8>F?3v9 zN=iz>EQfLQ^OFiR%MAt2Hd9@y?7S;yjbexf%*k1bYBFuwwE1h-iV{!k;t~)M!37#V zC`!+?98?vOD$lIgvVzZfsv%XggYum1)Sc-DI|VcRScw&ijg!k%@iH zJPRO?PLvBc0??kCnq|wDnV}tzQ}>MmPd79)lygxgCMZr8AIP@#X0G0+W-aXy-v!5^v=A zMn;YgmGDRD2=9vhPc#t9#1OpA1vZ$a>f2ct(^TxS0*v~??oVAvztt$A%e;OcLY~(I zh{{o*unK>WwlBqyQa3gSecqnL_6@qaTF9Bm69t~X+iTVSD3Hth`M{ z04p`v@9sHSq%HgZoW=m;1AIHTlF}W{En8CDQHHfpIUwaFBqyH)=CJx$_*kStxv>PoSKRusRD@iN_D4nTbW0aSd*Eif1Z&RPJ9zm8r zMeBEfM5wL4vf8Nyd;z*DM)_qWhowj+*3X|mdZb}yHs^u$GG=DxGjVae2teurtE*W* zfBqaEz9nTPAwytQblr7D?p+ffU%wV2j#1gFf#@7G4sBpu>^>Ca{`<0ERwdH{08i3S zK)Pt5nmu{)-X%L2c^RgeRSLC@K10E0nq;u}5yr9+amgoZl;CE+vKnvgjEQmshXOv$MBPI{xF6T|*KpPIJhT zvQdGwv@~VkaWx``(LgMak&y&#Bc&U)Z`tNSCcwuhacl3F+Yq(Z-*41oEyCDj{f)BMGYdC=)U#K-4=7DW=uyC@^a3WDEz;wz2mkQ`a1lp`b~Dz!7ZIiA z_4{UJ-r1gIN=iXs89zMy^XE_O`=-=r99QZ$f2i8coim61YPaa>)%Ui><)Z}y2g`g~ zT>bN5QG8*F3#VWsWQ#e=_k|jIq@Ok3u_GicO$OV!&#R+!D=I$vZVR7b>HW{+cu>I7 zZ}@uq2jWOY%~O%gew4xtA`nDY=c>}Y=IaW#&wf~T9^VXg{>fF9nNad~XvopdSMv)V zpD!n;AFYxua9Pz&4ybdf!)pbggf`$6z}M>h1J5Y~ASFs+F&RimjN&T|Vk_-tAn;ot z7osSbpdvOoKnJ!#lHwfpPItMq%qCbfD<90~;Fp)W5^w%q1Z~6OXWaW0w83b@iO%K& zmX>}_rNeB1eUV#dsgxbk(%7*45R8Il|LJ0Dc44YU>o?OD+haCaY1wZU=c_0 zPLjN&aXPrLc8G%L)BWp>@aLg*q%`r%HUjN5;HjP&Ps#+u@r2ION8n%qYmn7I$u4o7 zM{B;^hF*blH8@k?AS=j4|HQ);=pVFGj-Y402)&gw!1L#4{nG%$(k0oxH04SyU$G)W zEiE;b6|n_kvl2uW(v9FMz_e9}kfldjG=Oe2%+1G=pI=#K!-lrkv?Uk3C?w@pbo_ey z`e!@8H6%|D<`a(sY4Z5lQ>1O=Ax^c4{}O&xZtlLv>-Fb*%-A0wgx1bZ)9zwIEU+99 z$>Yy5JzNwm4IhY?3V=)sSn&3hj~=n3#npufS6||FyfNFN>`B+TQTAWEhJi{#>XUT1 zz#p}Pd1a0I_3iy^Bp4>fsky7O%71UzzJ0s0_t23H7p8JBoX$giR(lFvSi%1=7&bOG z_$cvfs&19B7CszYToJ~;&+0a$|M+qJp_q!x0+iu+$@s6WtxV$L@y50pi%`ca@n*S4 zySCu;Z96jwyE=^|Ry0DOLTkXrAjAiDH8ecD612;amU5=q=2#P!bs`OBE zsVF0d68zJ{;hvvDss~T2kB3@S1y_`pGobfDIGqNufigSbGCHVPL(;dQMc^~fMshKr zY>wECLyE0J6Uk_4Y4J->Plt}c0NKZ25W9Yjn^K5hS^R+ZI7>edafhR_O zSAdb0-+q%sm^!z0wWnuf$MC5S{`zJ6T*YTpnWPGI@{Drqrfu~~M(?{jTy!$2Fai%6 z&wfctYK|34Ft;!>co%IN8_($ZY~P?S@7;{7tD&?mpwcsyv-^;BJXV1pL;1b;;OFck5-!nUcUBh_2G9bAtzH~6Yv>U5=l9( zpbuJyp%eq=Lz!s7ITutt2CkEdfi_HU($MA|b!|{JY(8zAf z`nl0;!24E5#Hmx-5kiYO)n20vQ(WWJJuBx0whO&B*veCBaQ?!)snb_DrwQqK!fgnx+)rSwonh&4ALV-+>d=fCNRshGzxt7G(@IKsV7FsP4f~f4jOzBvLhLVh~lQ^<^1O|0MWU9Bq{- zwN_>0f4LCy1umU+x(D3!y?kts!SZ(7{+0gk{-J)GCl{Bom>0Q5ulW1K_Z-6xdx~gD zTbVZjssPjYz95j?+LHXF$R|h`AH25KYy0*Gu04$$Xl_7>SAe&KLYXaa`dqVZ0SZmL z+Ul~MFX3j1Q2P$@8QD+*y9LCUSHp*<9cGoJgD=zKi%HHv(+s6dgu$36DheqDQNW6m zHCy=}e#`8hqo{#_!3;>s%WNJd9C#jgam4{%=uwGSmKAg9uA+>lzdNbj}5 z78Gh!1iP1MJaD!Ik-D|!zM6&nuz0nbhfj04KT88*n5-latZ<0rjs}DAWk`En%P0yT zAHQJROKE0j-ZZo`e(~|*WJtszPnETXy&(ehQ46|)9_s^R5d&_)8VM|&=Cc`f;uM@qHYOl|hVDkR;tm9codIa-9V)T9}oLPQ}+b($i!|7nn?_a;FK_^-rJjek}WhGJCu}gFTbdU74tVg}f-{T>t3F84B zhnu_Z=ubgA>FuqozedU)g#-m9qJ3rO;;KR_kQ-Em#%foez*IW?dG6qkW{Xu^I|Xn) znED(y-Pl^IzHmZC$NBj2ki&|bvk71`uh4sN7rG!^xN^{VEU?W?#yQ}@beo0>@GcAzG@Yl z;po?1_!9}Q07%sdZLh8UIkMZxrfcGUvO<0(_7e6#*Rbu|$X_MPQm8nLR`?Xk77BfwflOIvkBs%qJSYe#-wiF5<~&~y~+EgK&nUq@%B6*5N?Sd(0@&A#2p zYEV{mkuEr&ygq^Ujo}xOjwKUm}<*DuVBq1F&_B7w@w z%a=d3`$|L;plz2Gk_vDZMy!PTe^ICX1!V`@*?*co#NQ?=;hn= zVx7Fayv%m(A{%wJW3!3auxLTV?zRyLGz#usUhRO5kOXw%&Vd_(YES_)2I3}u-Zl#h zapd#*H*c2o_YMM&g7G)&6)QuaEBPJY{1_G|red$3^Ho#|o4mw+9=Hy?ROjkrO5POc z^^&nM;{%VEAeS8XUt6xUawU1bM<^K3_BSe>9b6g}d%Db@Jx zm2h#dmzx_vXOU6)(1!w}sdNyZawcFGAn)Bk515Apm+zKF5}Z9d1b{*qayEF|hX|@o zquyv<2`6*b6Y3HTaR}^XCJBM3RB`rA3amJqUvkr*Kze+z=ogw%v%e z<8KedCt?}he}3lC)Y9UbN4*wu32EA3b3i|naklC1VR9rumGg7CmR*;IE^FJiZN$Dq zFQs3FJOtJt7s&#hN`8Gw11LZ21exHh4jfo`U%eoyw*Ah4EMNzzMUeB2?OuNbp@#FV zXJDYPEk7N+fepfVsJ}+MAJNhdN(6MJCX`q)HTOy0>i&;Xq$JOnrjT7&$kUN0x}J$D z?7qtlPmB)zn7Yp?Y&+RA=Bzq2XV*PPb1Rj)O$!9904|!)$mQObpUf*CC5fbakts2kyw)Q-PeH2+s+MbQMfK*hBf-V^2cbVR{w@ z6p~-%i-N$pcGdnTb4C$#c@yv^xg`eMgNF`M)q4XBdEnXA3RJBlz*l?jAL9Yz;Qp&e z5ad=pbPnq0DdRRYFfovvVtPhjT*S6PehI(Uxnv0>Lzql14EfZ7J`hqlx~K8E3$%@6 zy-ofI*7;wA)6IR4Woxg*(ZrH#^w7|g^3l#Pg7%ILYjF-tSXl%IcRmvf-tBdQ*40i|)D5f)60*I5C zfJ|*{<#mi)45llC6oz5DlZzz%K}AJ{b^FiBP?$T(RJfAE9H)}pZ>YY4Q%34&ZB6rn zbgu~Y4)n&LoHj5W46W0RQ-5CfdzaB&wHVQ|xauqGPN;(M<%GnDBgQew!luut;I#jg zyg5_*fB&q*hk|8nd}617$?{>*hV9`2%$nLMxA#B08V2}DyzD<@6*$Sn+(INL<7mSf zSu#b@a88m&`iBwFBtIfo1x0Cs88Tr^ays?^?SWvN;Gb8Rq#~1;CkY^960(WgLgEJW z`$raolSYv(fy+({rd(`?z?62$J^5MiC40_U>$wu_4KqagNybN#K|E9X*^4U=3Xs+X z(@SD9ts~RqEKv-7H%`#w&-?I9DJ$u_jZ&9Ej|b(?hEg*1c4Y9?j?rx{J<$LO< z)2XC4`Pl=8+qP9f&Z7<+>cP~CW-8M*nPiZ=8dDk|SaK%UBRza3Xh>ycGom5&_4NGE z8hpsEh}V$|m-HED5Jq+>+h8j-_~rp9ooCOUHQjNTjXzvyCq&WnE_`u1&RNEzc_tfi z=nb;kn~OcpsYjS7cub5km7|5Hrzo@t(LVrg57Y(?`H&faeR7{O7b#_!$O)=yn+~*8 zu-tZd!wdbQ{JHvsg6q6YOVA=@4Y9JdB?TirBZEUUOfDDTeU6Ao8;Hy9_xG~vozI7x z$mxJMBYWRu`lnF<6CIddSw~=RCO@Gy5(fO_*Xy0rnWbf9l2FGf{{tREdM>Q4u4ZCj zK48fafzIT#v|!Xzvi%4P3v1S(^-hutXT8H9(*a~H>M`Nsn61id>W2rxLre$Z5A!4B zx>>3M8Gb`U?vt*SXexPP2aLP7#gw3&FSGk7ZET6%g$uNNsJm1?0Q!Z!v^M!x9`-%T^m`1>=V zDGLTWhz`@VTiJxHgxC$38>gW&;NjxpN|cHY4?BIjYwTq_6d!I-`OsS?qt{6ALBK9c zxaj)8Or6Wq-yj!L`TqS}#1hU}AdG_b(R!>{=l=08Tj8ZT2~#B0g8zIym%TJ`l~1m% ziQQZ$7p-#iGkKALP`Y2=x>E0~r-R>g4lbGo1w0tqu$s@P*<`qzROcjY7r+>iKXe$l zCz?_WYHy)i29&D6xo>T0neRH!*lPHX;mev|*eQ77{zu$ft-rNk;L3Sn5s|!&mjs}O zihq6{e;UA_ntJTKU`;^LK5W7=m{OpBvrp2&nRA31^#eE8oqtd41x)J-yKb{1sbg>? zqW}Q`Cg*7FaSk4I2p||YXle#boib#dTl?^QDnY@))_~gNB*s`1o-dQRt-YPekJXLi z7lbWURaIq=ft3ILr9k{rA-Sh;^w1$z!^RXCrDdw^vxHS~SWy`L5k$wt-C&Wfma(n$ zg4GlzYOqd3emdD5|J%@DKotbA!rElrj>WHG6hV z@IG710|!FTZgPr}#FWV{)EL9SZ)`yo15ZGx3`qfwk{x(9o$8vJ`~=05c+)m$Y!FYT zBmz?=g>-q2RkG*G0K~X~qe(b~y$fX!E(=pLtVgVO?D|+mbI^WIcY@THG^Q__cIA9+ zthQuDD8CePvp;D2NxzPsNp-(iPhQ6MTOydY2m})N7ZnWr4m*A)#6&I#BRh}_`Jpg5 zI5^Pj#A2!pX#m?LwPwwlz@Q+GsW+luIu#Veick#!Lb5`t)2uP#ATtKILzqUWzXjzl z0goO-S<8IGv17;F{|?Fmq)nA3V`scwA{08S?;i?>K3{QaO1XJ;`%N={l2XtgPmYZ7 z@-rU&U2r70=nzD3J^&SQg&eN(`$3mhn&SUk0iPg;~t=|LYhSF%9$7Hoh9(>bes>r9Tx&{#sGr-8T>- zHxB_HFLUiSL=!^NJWiQhplIpiY>f4g_h5lQ*kt7E1?47Wb>Qb+!GceNEd$BY8cZtv zyfOCdN4(;7SU*~zFcf(G$@;I~+0KNwSx)pYOyvO0A?SCHs4SMfh5RSf*x3j#3M5RI zx2x%y1m!seD`afN|3I;zIZBv7kR6t4OGaZZAzTnKDBawdg25V)K&^mrQiL<%5C3uq#ro+!Rw*3hAbLJ1?LASmtZ- zETf|x-H?2P2k6;gSbjjOK|Qd-N$lFE9se6WQ7g#5#RFzI8#tSV9ru}O9!*lx!)E<0 zYyFog3drjCg!=(!O>d$_+!}W-I+_V4M0CeQvLS_;YAjFM`V}5qRO%39M$H;ZGA+p~ z8sY0avwM}PHEJrEft8e|kV8t6nc1h|Awwq}6uMjXpq*U{1{)3# z{?c}-3fL7?(wqW!CtN?*Q&JcVP|?ZVcD5HlUEmehZ`+pts2!~}>>o?ug+LligOO7D z`t|FW$e0ep3MUm~%;5MP-&ERc@g0nTgEvOA2$_Pp<^!3YD7ix)g*E2xuq2{ZR$Zu3 zPO)s`TWGIbO0tiaq-$wv28M2zK?Tr&a27qHmrWJ$O%}}qLTj3@p_=qUc=s(?HHcYGVQ;Km(3BH)CLp7lu9|Hx;qivm1V+GPVF#Rp@?gPj}63M57wB zzu6uJ1jZs0!wGaW^nb$X+qJ0Kp%pU0}_{r2f&yC7#*4ynf3m_HOveEJj1z= z7oe)FJO`Us1v!U&KK?TWVm^V@fJl#j&kz?sjpNl$#M$P}IEfo#oB?4Y+rz&sJic3D(+`)&?~7mFoV9op|B4iYZBHWgs#uR$7_QRU*L+Xd z$9KrqC)#f5lP8yi_dc(&5qo%5<&6iQ`2pT+l{bZ3+>~IiBC!qpp~R*}d*BsD`B(Hh zp?cDBac{$JUjX439v;3FY!ywHJ32bT8m$RR976&NhszLL>~MD>AMRUDR^21xqaNpD zVk*dXa?dC`Ab=UZIc}^1nkwR?F+#=Z>+1{VJOA0@;PI@RH@VR-1mJ)}M7w-L=I?Q^ zZ8XjWdf(90l$9|cToxG_*RtrHe&;^hkqJ+4WdF*P-%asHMcKeE4{ox4CzjuVeV7aSbvZCXYaY#{ETo*UU` zK&wF_gSnCgO$5}D_1m`#si~=vvB0_gA;&0hXxw6i4+ekJe_lm$0q=5(~m5P zQiTHmuN)hnIPP_kjRCq9<1XY%K}~oBK@G(~IBlatBk#xsy#!UIVPE|;1)yxR!&FO?TbP~T$?^hKiTp6|$8gQ#KWM_2Iek*YimobHESWfK)~xw!*32Cq z9$q9XyWZATnvsx`GXvi!C@47acs3|QlD?2I%P~|1JzZ`0$7vwr(upo-6y87kkufQ%)8VOK$C;{ z6^n5iKX!D?8a*2n42R0Vw&#~{_Xt=}lFf*I$}(RwTm~}vYH_cMat9S>lb~~FmA1j^ zUjxpHtZt}M5|EToWCuF&o^+uGhBrVuM*h1k1QeK?7)FjjbTjwulT3ZL;yFsfB00IU z_jVW?2Mi3j%n}l+6K7>*ef0c!4QTsloIISCRur6puuE_Rko_1plq6u1fn4L{*n$9{ zG@cgPBPT0c0ePXnE8dp^4*rr!|N4y^m`0oqpnlu&qX;iAFTD?-20RZ;_)VI6dGdsN z@vhrvEL><}zwFz4%ptdY{v3=+orHpdIjO0sc$ipq*q}Q{YU!Ec;uOUt+FDu|&{+X| zK@f`3pag2=w2`$3&!VG6=Q|s-r2jc`{zby+H*XRLuq93{3F_*b`qZd3Ybv9 z)y}%P{ly79YiL*)*>;Zn?%j#TK={?GS9JFRnuk`r5+Fod(Cod4kNkHb`_(-M*-5@a z#hV;UEIlz7!my5;n-avT5DZrluQEr~brIlD^6PgibzEInl0J#E1C-2VPFE{@nZaf+ z>NfV*ZJvtDG<-`Hsv8beIBFyLwIO{|GX~v2LjC?t9-^aB(H@Coa~3Ho27@O-QKTsm zZsFyDS{50#%a<>w8VS~d20d{oA@_!$0|U|ureW1*&$e_gkI%S$dlv04oWNwO0kfQ3 zTs-g7r%zNL=n>I3a+;^T167}hSAth#a;CWx04bW+D&e&R$y4C6@B;pDc~{|@9O!;J z7!IT%8-P_#^CaWieamTnnd~)_=KPu(EtzwP_)VNvP4F!=m8^K~w)W1Q!an1}2gs^} zNWlF-{*c!9f)Iu{DGuwFW^8&+csM8Z{Aj{R^>P03X)R8x4S+9k)WGwBibko99}4J_ z4hf9sVcGbL$WQh{g1d2-B618d!)PQxe>>8oD7hKc+?QfiWXe|Mt6(i3V&SQ5(NIEB zkqGqnN{EoltRJvHJ8ST($fc7LTM~%DcroNBvX0!yE)B;XSAwwY9x!Qx17ZkfE9Z_! z{D4Ub*m<)YOD3#k#<_T5%nRAUW>4-U2;Yg1!J)N2smikK3a?|N{7A7BaTCO~oSUs7Y2!l#ZW zzy8*jKL+`i7;>Dr05td8v-X7HFTOr_6qT2^9J!q?69TO^roW;40x-o;o^YKRg@*ng z@FH4>Q1}al#ZL_Kr{Kbh)ac2(muT!AA{joW4`j=719=L25_HKyPA>`^7QD)b&%xTd z5_fq3ZMbqbieNie&?Gz9fRhM}r~{3SbR?les(=bU1s#cef$+F##_DX&v8myu(LUs+ zA$THO$Jag1+z;U%KV<`$GSqUCk7#1VMdD-o^C&q>ArSCKzgTAIa0Vob71TJy&Rjeq zdvcczdYNK>X4p}(v;)Ko6q7h{JE(}Zz0pW)8g zx42%xy0vJ@l7HA^8D6E0q97<5MJpo5*mSe9c(xNZd7`Zi%p> zJ%ZQp^4P5$lUA_)4`4v^hi%;wGU~1sqr10mJso?Lvg`|II zgZW6?@B+BSVV{1$H}mIDdnEZe2%L{u464u<3F@&Ebg)QC^z?+qU;r$?V5p;PI z_!*4aIUH)K1w6px;gW?@IJj_}X?CCOwjf(6nsaKl&q{B){2^|}$Qk6~y1Kd^wR^by z$KcZP)b(F)yd<9oZdGeNw{=|wU~rIPmj!uN5f0_>Hd-WZ`j_7p=YVz~-yM!(Q-QP8 z1M5y+5$aWd>=5f7cYVH>cer_YK&AU34^!5E?)~vzvk#Y-*NDtv2QrD|+D!lC(IrYYH83bNwge&p zZ9)LRAYB8s`CV`kJh7JG@2G+J{r#i7tIFWKxvLJ910B%~9jU=zcJ+J+xm`;JW1tiW z0CuDGPB1g^lHw?tYLg&T526v%;E~WcZwy{O2(b`Zm4#6X?bp*{n~4c|WtC)mF#^+m z#i`UE6LQFWf1bQ~iVA{*&FQ3j4PFiMkkPz23LvszrY&EG!PrPXd4->J8tt{8?+OPh+^!`mmu|Q)G)(yYjqVxZ2*c{5VH$9VdUcp4BbAh@vA<*`OnrKHEy4ku1z^AS zmp>^=Xhm^D26fZM#z~EL=k`ECN2e7z|9#rux9{IeEnKMk?SPo+9X_R?`Vl=8-9LO- zFZ*M2(plRuY!%kYCtqjpCD zQHg%jH2q^RJc?R~lKlJ9XVZ@2>K@z-0WA5d$lUas)@JFBb!X8`ft`)HL$mWKVS_=6 z356oKLyyWFXsDQ{;bUq}#rrKiPL|#5XcpR2`fDNHcF3=Iug;>tu$ z9=@k2n^30+9jl@F1HVjn6@ui8xO@2hs-lu$5LZIfuY*NNKQuSxA{6x`^uWk%+dZlL zj2EtDkXZ>9ycanE5>M6%pjiWioda-fUzS#qh$KCoAy-mD_XH`ab2lyK(!q|# zF^Ern)UewatVL*J6cRpIV}j^-?O-e$df8X)1bNY4(O4vxIp^Aw;PG_DPXwa9f4xR{ zb_U(7wQi($1)kGLYQHM&lksZaVIfEm)#!VO%o_zvLg5fBO1a*6+`L=8Wcc$HxSOLk z+|5o$M7G20{t$pE(fQoghL^XP$?^XJQP2PD?p;>53* zhg+gjF`R~#q^9LRbmx(;YO>V^g zCT_An23m{mnj=CMJP6H(4F2-CaqMs#Jmr|=ltinGGFFa74^-TE9h4*tvus2q&-tz!RVCnuM@FH`U;$+!;5AR&guRs%n8$J_co{}L$@7BK;r-|1aok(T zEHHCskm9BTU6JvaNqoJ$v$JyuA3J%6+`fG@5J_eh82S&S zWa)w$rZL!2@D6l~BmR{306B%jT!bSfFHZm`NpRiYZYj(+Tca6;lo@pY{{8w?4jEA8 zIK+5M4WtP8If5bjh@)_7#-^<-=9@}eEO#!P1*w5Xz7PLt!zecn#s)Y}$^C}>v|hon z^dw(+M1(W$>>yzTak>GsKVvC%4S^ENiaw2O@aTc;gx;_b zHAs^<&`-!?3+^8#`u1OMEOad`E%S#&&samzp$n%;fdexFi}UcX1V)(QtPch=S1u@b z8mG+ISb#i8=(hw>AZQdBHra3-v1^{$6Av^vk za|MQ@B1gVWxG<8@R%pgH*^y-q^Vd|F&@&>TSSarR-)vA-ds_EU&l<3DhDdlp`LDHPe-Jv<&r$CpY|3xnvLwq8+e8G=-xqUpCelMxkPVb zV>%}WR;^3nMITjwd-UiMI{0#N*@_V6t|71!x4YN9m6dL$!Qmwm8Y32T7dIrYm`3XG z0Q%J2`xt<{1xLH0Mq!3zf<8Wc=@(uq0S0EYt%e&HzYCZW&-}rn-?(}60S@?)pPx4q zV!{X}VRPp{-_4;c)>_~qb5`6-0Hb^*IDLo>nAFGg2rF(w!cC$wzPI4T^jpZ30_FLc z?+z1_goFe}P*Bh!1qE#*BmRM(Kl`z<=VN0Zzt73drD3Xi-01?sp$d2ctuVUyYnOdi zXit1c!v$x7Ho_aGJ5oNNZY3iLF>pHki28o_P6Qf1eLVykv3AbQ7dN9|!gNaV zVD9VqZE9<4i}Im^h=nd?XlO|F9TIjD-hA)mC4zVWx`GS3OW)w2HmG#hwimp%)|2)T zQ_F-jPul|rq%eLBp9)>qj<*C~i2E-WPOL^ugX#qj#falxeQOLim{?e>+m;7k;I7+t zOgK8Y#-$1mirPP*3pCLRe-}(AFn4cH4z3qa!X3mq@MgirjyoCFLL(B#ReYEPjhVP| z?%YYpgs^q7F!c5HJ0w1!b7Rnrd7x+MRvpM)&;zy6d*RY87JMk8?2$O^dI2RChb#Yc z?l1G)BIVo*!%0-|9Am*F5{18!?3O4V>MRF z>nYycpyimSmgB|V$^Rp_WJ#A>J^k&ja72!th8nKK#*dcJ!I(CcbpHJxlf8SNO+HLY z>f~RV7#USK(D4bqJqzC-?Kjni>UmM-^iDQFS@$Z|aKFk-eU4oBvz~Wt&)T;4kF~9}o^5;I`@TiL@9+Gc=W!m#zVF9=Xm?BHb98n= z11LP*wOFp=fB&S&B)b+_G%MOB58Iv?r%-!>x1MexpJVdxBV4R~1h0P|q0RqOKZ+(E zkR>+5o3l1IwyaaThI*{wl0T1`_MZp;=es~Rc{6MOLVR4@)7olSyIbvFXz~Zx$y-$W z(`SFaV%h)oM+I|151PArXfL#*c&DvsPWQ!@5jnZJ<5xJ!3yaoKDJYl=E%0#puxp>G zZyFiwwa#qO)TI-iTa~i;KU52U61}P1chT zkrSTHF#xZkac^+aXuLllPkpKP;*MBr?~smHctb5JP@aJra9=d6tX7&2$L2)UPEvJ!zB453KDCg2$ zaItpKYEW36@_VLeluo(1pFfDm|wT^LTSx3(==3U>N@4XPU&w6WfuD z240(2uY%S}A9Q0wk_M5I!zM}72{O%I^9pp`LM)@3(pd2+t<7Qu1)RFJ#>MF>xSfdf zBVn69y*<(xG|Y;W+*jX9eIs5W}B0 zI9iX<^Zz8QE`T7XhduI?E5E|T13uGNtyf(n8`47qyzA9HMBcqR?>^KClSky_8^Go0 zcoWQB;R^9&DQQxMlL2Jv42)LljfkhiTwgvat%NY}E^&eg{L{Vb7y{D~1;HbR@iulPW z*uL(X#6y83=VS(B1_ovUk2JDcF^I`0xx09p9V?%#w#X1awXJdfZ23$&aeE?Ypx4VN z7o=boZ6bBYnn&-I7CR4*-$uFY?MuL+)ZtCy^d)ZD|HYh6|eVy4{D`P%-#R&F_xUqj4w~rqHYV zuo>KuT*{KqfBRV6!*zi2k>u2WN?VS)FgxJHi5}HI-}P9vYL)1}c#EbXH`dvxn?KyS zCE5`OzE620do)NaJ9nNn z^|N$Y%4?6fq{jRXYk0Q27?NhN7BS7fsbe|d)i9?`6v zczd?)OExF0h5zl@yOfX7V1xB=(+esJSGfHCVMQaerl)k?5u2SpEKenLNxDC0WWoKD zL*E!!X02YcMtMrzyPNLW2IjfU-_yyy->CZewF}b!&rQQCQmo~WBtqzMIOJusQ)&ad z{6#b6>9yH|yh{b{k=pntN zHU~Oj91D7?pu%rM%BYsfmeb^1Qq>t{O#ySH-_7(G!l~mKu_t7R*-7=rRH3PPZU}&5Oc|nMT<$oxV&uU!IY~j#~S|EV@~C*(&VCmmQs&tWMQ_!)C~DFEL32I~E)K>d)ad zkmcuc-t}8>U&ei0p`VXjU2M;#9)5)12~^N^L?`OM*J|CJK5gB)X-ex6ci!69uQ544 z&ex-(JR3(3R*Q4ngF1pq4TZ<2G$iuzBBCN9Ou9WhlaO$gFkfd-rEOK!-y?Vnf)NO= zNY1!p(c1qVTFPo|53`!+n;u8B7B0SjvJRs0Ye1dA(2aq;4+4|?AW#6hM+6ydwe)Z; zU**B=ARyPQG~cU;{pd~*>lJSNXUEPyIoxl3?G=KJtw`&{{)VSbKNP-BnNkdxP``Tk zh;H{oYCNIZHgJJ+oLJa{KiYKIk#6!>MFi+Lj?fYh5)o^4rYTi4CE>l*i@**rej7&0 zKkmfTZl3EP<)k%}Ndaw=!)UG*x{q*s1)lLh z$YB`qGb(Dn*4OzTJX$NrT7$G>NqFMUL!Gi~CXa9F4s;qFYS@6+oIAN1Aml2Y?xOCI zK_iTecuEf|lHlW}53wkL4O{za%lK}8xUeB#hmMTaKm4oi>yHZ=ituq)rqi1z$ro>) zu`s)q|LuiaS*vv*Jzu-SsdYwNogZ-qde)ZGk1vqZIMO?G{gpL9Oj0T9S z6$e@yGd;Q=HgZ2elGmgCiS>$ZGqSY2 za9NaMvJ2WQzEo%B%9Y6cL|x=ogfD<_*9d*z?JrRuwCZHuX6<)*d7tp`buMXCK#c;q}Xp3_~A^2#(R@oUngBdBH_BOt`ke*2rl5e5l$aL@)bZm>zD;m56ONj==+Le&K!XT$ zIBgOgIUh2-2uZ?X+krHo%1n%&8L_gL?YliGo}a|d7o0~>Gl0vf z9$)WvN}5t^y&V>B{_P9gHj1BKs(j`GJ-(z-&5E)yRN`TcA4~r8Y+=*iVQh)C(^DKB z8$8XlO%#a+c2{&atKl()=41od3V2w*_;%V;WUSI9G&5)vH-?0?`eSA|#Ba-I!{x-YpV^;J-;^wX5A*;aRKHcnc zqJ5`MO5m&;@4CV`NU4CEgwiLcfXx^Xdlw}pC9x3gCxw%T@EW91-KWLKFDi#0omI~# z%=S`v8WS>g&AfwwqK8rVgxtXJd?)RN_ox)sgzwz84Q^{Vkovnp#V9Q)pPqB0$fN=V zX`vLKF0O|$ug{&rfQ#axuS=rVlp#~^8NhTS^-oy+*;)MVNI}yeeElH`c*&VPW>p?S zPlBgm8?gW)h!!2)axbw*zXP4V&d&45{+=J-S_pR@Mg{*pF+TyE9s9GdFm$vc3|_w4 z#YL}Hlz^Eu7GS1iSK^XvV_az#StgZ*!YWUfW)3_se$Nz`s4 z3G zhLj_X@@g)HwQM?AC}W~5Pb4^~O{0#-JmB^+?yW8f9zUHf3sLt0L%@{))3yiYi7Y5` zEn6j<5p4JA-mLka>0iV82u)=-cB~lT+#9gmXuQ*=aepKN~aq+bHq$Z^Sw7m`04=x;- zaECIQme;37=}m|wY%*;a8sW7m;Cs%v_E&I0EO#8mAxOM&Z-sjY&bzK$dE)agks@JG zITO-KIrrwH$tQcvUokEA)h2L5v8F_xq44xnuCYVb-p2%l1E2}ljoa|8-rY`U-J8pJnB1iFOyrH!4P$65Y z9I2Sl;sE~K{b0E3A;fTD6k#X_DAsNv*s>}c==qXm8XcQ$LoN7IDcAY%2ahAY7IxOr znJ!;}ekOBsMZp^YO|19zZA9AAXpLu5=&%$`rVp;~9G(!Zp}uwy_3P2^S$gR;YzMXx?JYnt~#28piC5&1j`*0Z5)w=*-n_Sm0Qm6 z&DZx?Ma3a?p7QYs6_1?FZfyL+gd36VEMpKWd+ji+2f1jcV;fjuxMljC{eAk{_G^_L zS=`%pzGl0|BOFh6vu!@OVZ9{h=YNp2|Fx(ynf9ixr5fdK&Ck$&#qQ0~2H6ek6FR=J z|H1nq+d?(Re)t~xZSO&sue+8d^thLL*}BnHlgHLBMSWaC35EZ|-)Y{bT`k4@|7ywj zKUSe=GuFRdiMM*>$ho_@dcC@D7~@;BGd!)hfr~-A#_{pIqZv?js~Ww}GP{64Wf+{a z<(p<(UGosJZ6`e)_r_pr+=f+ybY@FB81c}?+S+Mar#!oZIE<@DZf~zWZ!{b;6(wQl2vU)PF~2^g<9=QYF3g*l*c};Q^u(_U#zGV^t&2*wC@#$JbN%@^yfvRLYk7 z1eA^6J4RpePt6=%OTH8w{ic${+o^?ln%X%?H6Gvk&jOb!NlD%3#h=&uSpHeGb6W3o55((QPaxjVr5V!N>c2=9%f9!&AZsH5S?8r`VU%HJc433y#@YYE#Qpw% z{9#8|Wc14w3bLt7zuHwea{TzK2Qqz2TjS3{$oKlMZv=|CWv%?}(pngz58`6^tN*Uq z|MFM=`v* zyJ5hQ_@sAsZkq})qeYYHxj+y<&TX4nw4$_8&}u#l)O*Lo9*EI^kf5B$*v&TE(VK~m zVuKDCwPWI9(ll|zoIGX9*fFi`@@wB>Enf!IEDj&Y0Nr+DLw~Q$9%xw$!GZ;|y>L@U zyW1z8Z5AcX*8>YzEL*l_-i?tQkbm1Mpu!Fxu|D|PfSFW~&0@Gfw}DX>-M-8t`chzo zE^QA1A67r&xWP8{9rCrz#W#W6r2}ndNGht`M~ujH@U&(v+VdxH&iIS&yD+k&cB4WB zxzhSgH9{|jeb{VhsJP?Dch^iFH*T{C3s_wht>~p98bCuNMhPNc0v?~T0}{^YL^2&5 z$Pg9bJFx+u^f;zjA2r$dKB7Ga_}fZRhM4eWZp~-)w_>%(r<;L;dxkA5Q#;9T@M1VL{ijxn$PhVS(_|k(0CNZ-S6Gu>-W1oTb*b$BKpr>=|DkAl} zlRMhIs9kp3vQK4Xsk6W|6y7Sm1-umzwun5$C=fW|>b#EH*reR$Ai4?>x|A@L zdrRpj!c(j{_%%&_2)9)>=zCn)!60bq8?>8IG8mNg+Y2U^?65Vmbp;@>I$b*cTq(y{ ze>XZk@cA)g$1+nJMao{NN>X-!Uy0Se8Ux2YwQ_@fE2VdG+;jxFHXa8Qm@6iuMuh|3 zn%dUOGW5Sg^{Vk@`KSp9SCBh8{`GT!(Pl(vEINSV!fD4B6#MLN!gNW4K4sna20Nl?p9^=fgNnyIG%_hitWMB*z8G zih@n2jz_{CjKN%s?#^HJ_H0$ZK`BB4XargEGWV|10=_O9bn}RtTh-?J=?5#&P> zTGF4t4*SF{{hwj{f>pH_MFsHz@o1)J;UD4zo>q7IShMJb%vb$H9eVG4crte$jr@v7 zed5YFvg2RRf%`(yaWyMzWQ5OF^_>zHMxm#vM-NYBt~6E*SRT8+MN|JX6EC>N|H`8Q zb0YvC2_=C6*_;*`(iUQFHc=Pd2=qEh zI)_qAx%`3t>-fwjDM;I>3{87U6BKDvfdtRa(6prgZ8`#7<7>`A+L~@ za+r2_CFu}Ab|L*qLx!Z}xaEzS;q0u$^MfY}WI^#D!|>2n5Ybqde`sNhSupZmxp6_X z9%n^5%W$*mz$Zzhpl+96%8We5^5KdWiLSsUykiojXGxuhNVt%F@cV*l-Z51P0M2iJ zq>>()H1Urv$LcXdM%&ng{QU-5Ys{kYU0Y_QxSu(7Y7>w+oC!v=T|VB!^%#CCzIPjA z|HBf^e#`K1(OcpaAZis*bWs=)(R!pf-$8A_CsOKr16hmeh{q>~zWzt8A+-W-9mOsG zTGcFo8?oUZjY8JLE^bf79yHWn{IqQsHD+@{Z^lNjWX3 zwM-wO&bWeU5fJj*npBsbO7rZ`vR_E}+aod(d{`PE@ymm~zxgqVCZD?OZbaf&*u5M& z;MEO?M_8+OgOKrp?rMi`weH#yeX^>|TR|`C3zS0}amHDmtv)@{EL7!dtEEeOA>?9f zt*0MQ3)qoTNSbzWbaHDUyPZlM;itGt$)YJMyQwnyx2Oik+^pPK9=;vKFu z>0y;GcOt&*B5POi;ze=Q(z1j*JOn|&EytjZy81N~RZl_?&X!E;M1~s0W<*C&Q9TKf z4VNR3T}IWSg+tb{9hf!D$+*Sj+y*@^eX1)89U(#3yVBB>;@c?(F~vdW6EvIR8*j=) zsGRU2J9(Kj=8Ec6{t?!%4Z&kY;3*^Sh`x$pdnduKup4HzJ|zQTHN zVe!O5+~f@_Mf3V~|JKp$GF0v4uGq;`5Bc_?ch|%7e^>noiS*IEzqZl0&G(yj`S!%t zZr03>E227`iQCp|c+}QVv#6_U-iPY8R88Gjw{ZQGgf2eH2UQhadgpE*lrzMvr_ZhT ztXk(5|3|v)d`KgBL zjc6@;N0Sz9OsZR8LA-<02Nce(^Ja&=KG*mkd)w_tRhu@wN`BLzetj^RaJpq8 z#vRccf4nW1Z8ZOI%;7QHtb+Ap9Q$3x8}_RY`P?!fcJy*dLr~wV_L#aKWg=pJYqDn? zl|u8Drv5ETHF$##?c1*hSpEzqjTkC^OuT*Q<;e*%Qx_zpuf~pytHR}{q|dY%#E`rc zU4BHnhH?x2XxGUzbfP0#n!c>R*yj!V;S_ksfkCTHBTc<&r|HYkMRA>e@O;z9l$hAq zX6ouU!EK};iWhGlb>}t?qU6+&8#aFT=UFJvD^Ea>cMWmKgXFdS*x=W0-08Co{5dil#hxj8=5uD0J|vMo54gbQCi^_Vwv%m3_QW2GC0MboGY_uQ9%gUb zP%ANsQ@})qRMK0dhLJ1BW+g?nT`sDrF;Qm{+FVnTojD$!mvc#5Qn>&R}Zrs z+RZp@Y5VJnvwvZHIqS{vHUYf+^JjX5_A;>>uE84kHIBEdy1fROZ>}sig$wB= zo&jFk<~~fOv`Sg`TO2|0>##~!RWehar)75LGnVYMX61rW=D@ckoIQJp)S5=nP}tEf z8oAS1%H_C>!66I-yyUOE2CTb|R+*O1m>0A4^x{&hF3@|2Tz?L^Ifi&U*&$*}zy#gV zWL&|1a}4SAq|FJaIs`E}o=D~d)JkXw7tAD7b&O)i9pTH&V%5Ny1CbBr$JuotzHI04 zD=Mqn8Xdd?=99|yhhkg)uCNrIP96@aMYIE)U{~h4C9OFZ7ZziXXw*B`rk@$-7Vo>Jm3L zd$TzkSktALYO{*h;k<->BC`@FWKz$V7Y)4GBwjt&&}Aj&d^BRrTC`|1V0!7j6X6TC z`#0dhNQ9&q;9Luw%UR}Ns9RZ7=@KKp2%F9RME8PZ^B4T+%fQFo2)O&~UbPeh;ZEES<0iwVMrgmasK>>PG8=~-9 zaC(-nJ!d%$d5d(15DTI_YpPkk>k((1vOdt3kxh(~F6NF_K~M4Cjp8>hOEvo2f8)gZ zGlGCkGf4m7T9qM(kSlHEQ0QuOd3a~8;rl1a`9J6IZn zWiKsBn#`fxu`Ule5~XYfY(<=*Roe#<0Tk6$%B*cPYvtWhnFzX!)h%tvf0G%q(55W; zsj_PUY3&CM+AoVibFgissUl%yI4>~Ruj3aJ4xyJF&(U@2r9B*4`{Xrlyf<^6HOMcx zOaeMgO#BQt}d>?BfX$C}Alj^L(U>j;3{!y#}*`{5n>rkQWd*(g{#; z_A$!%_Xbqm>n$2|z+x-P3^wuXr&l&4ZoE)O`qI(&+~92CK68?R59bxT?F*E3GlW_c zvmQj&&NsF;RnagS^I>_xDS~!yxH$S8K7IR!ZrsSqVoeWw+t&+dtrMww(!^|vNteeL zlJfk`l*ll1fdI1z9D8YirlTWhX?2amk-MUv*Yy15k3^eMX4aMV8S6C$t=c8tpY!J3 z6VfF4B?ryv5UjCW}>Q0W1&`?h;#(Uo95ht05yz43Ql2TsEvHmd~-j^-t(wrbvd80uZILfF%Hp)dmW z)@{zA+K}0=L{@Wz5)gv%wGYyQcXgH0A&MsDxrXX8LBJ|VH_|n_ZI6x}z3CKL##9^r ztr>4PJzBtLg1pe`HQexH8}O|K?O-MV-8;{|}c`h|py z-(BVi-)@5_FmKQqb_|dIHRs;yRjZ^6tE`x$cgMwxRcn(RQ`UKa;pPqMi<^S*8D+&& zCQlaN^z(k|EBbu)e4jUL8ZTJ<(Mh+TVFwgrwyhbX)-6jXm@0UBuK$gBfwW%B5FEyt zXC^N1(rd+);%QAF;WlxkmorbD&&UWLG9*}ktRw@5^9z01vh%|=+qB7IJ_!z{CFo@+ zM(m?4n;ss2B8QF%Qjom0XOF3(QKUuan)GiiUHS|jK(+hi38fR(d}V9^^@+DtjL=Dr zKdah|oK}5^$kfxb+MNdYa~S)qJsYos-dWC{w{i<<4T4Y>4+;J-CTd$(?eWAerIE+R z<;!Hy1B0_b-wfP0{ZM2U9&-lZl7x=N1@-r;X6H@APRY0|T!|8yal-424ea}6@Cxe#gZ2{4 z1>81T@qQ~+8(^w%tAtW26*M-#Fps$hjcZa{ ze0u+&x4c1S==Enz5sx_5C}1jx;BCgylj=^mxZ+sO8P(ojE~|Lvj;UgoLqc}u;fWAY zdT}+SKtM7JoX;souBWeI;nZ~R;`UxWC%~(g@InYn6FR4UX|sFxZpkE3ype_kJo_wT zKI~4D}a%Hn}yL zVww?VGp4|(IQ-%5stTDnLIOv4Mm}$pU)JRqkRRmsKGBh=>pa-1T3}j#lPcQ}zH7N&F}~$WzStWBHF2!q;^9jzJ4tFm*cnZBipO45&cpnB z_m{HE6>jeCw(mnZyW#TJ`zwn*(z(~3thr<#V+F|D492q8D0^11KatM~t3?@@Qrh1L zSwT83F~pqEsozHQdwE%VY>yB_!YSRklxzMj3OR`^{y{G$nUk;t=?# zSSBE1S~LINuBybCnAx!p4b4zkJcD6mU+Qa^JJpfgK*@zCaxKkMq?J1P^qd(pt}+dg zw19VLke$HpBTU|4e%+UVl394spW@yr8#k0+%q^n|>fEe(bGO1pB&^b`)2heR0>?I{ zPGmXWMDZ8&B!-2BJt?}IexHqE$yRHiqT<9AB@}kj#IX3~$$OoR+<$zsPOj!EklwK( zw|012-lSLPJl98T&i6>9to&Ca6ryWfY#IQRzGexFx}^P_xxk}xmfnMq&m~zF1o8>k zVbTg?wAkj&?%ECX8Rg$nI$dz6iMY1{>1)!%Q-E|9RX@1br)4)QiUTf!SoSuUr6Sr+D#zbbUvFbj3AYBj)p_3jZN9$IH~|1Y zS7i4q#$P6bD}?^98CTZVS>vd4n8;Xg)G-ju%`PTO8gmxznCyjHpo~A4_|GyqM3~fJ>6flpk#nv1=|XA|rbSOWZrjkDY}0|sf??gK z?`i7ctAXwdq3KS|bw4%`bJ>>}uz$jdP@p-J`+s!z!1rpJd$}URM1*JAdsDRPknfeC zwl27pVvqwoCtHhxn+h(wVD*qOXa@$Yc)w?Ak^MEa9j=!nrVjUw>@i1i*qbcUz#Ax` z=(r0z+tGETWM)?xeV3o5-LmiY zAI|@_y+xNa-jQq3<|d^q!34~`Z=QHbO-2y-V;UYQO<%Fk=`CfvqUUBHf$ zwb7%n5Q$36%}41e;3-k|@pCzeq3JX%WONR9zs4C@{qu`UY|d{jGQxB|DY0bC1JY`4 zK!eGrM{25^?=h#vu+l72$5FkTo2Yf}9aFEq){T_Z8#msRJt_4(b>@sSJ3-V=($I;P zr2F;X2OEt-HpT?A8+myFys0h$S|nYAJgX=BpE1vAN$)87hM8sW*1i`wfRc)-xa*0V z32VO2!zB1i;jIHaf~2AT+UBSb7sYNb?f0Ug6z~S_Pbl2Lc49@9Df>mb`C}?yE@yn16BlGaNm6P=%vhqBPA8Pc~k#ki+lw# za*QzS%gcgP5@dDXX7TG^=7vvuT~_~bfaJ?dz8)0Q-AML@ziol4g8>@=(I>Vfk zAI$8SywC#Nqmp~rwWyj`+ahd5mu1IE)I`f-e%2xH?)}I+b625N-mLhEzF~zpymLa+ zL%K~$17(pM5WHG5O#J^qM@g_M3KQ53`H1b3t*>7O(d8$1R}Zx6*{fG1k7E>G@oSpO zu^w~!u)o!JY-XF;QDKCg>ELQpyT4r+{=%PgucsQgqvjVU1(L5+1E(}%rJmivzwnW5 zF42*XVMJ39o-wpH$LFsl(yhy;Vxh)9pHtLCy8|kf7u@A+rqOrWUT8Z|I)=IPNp6VS zP20800qqlcW!%IheT>xZ7~dnoAyzI&FURPvPlb!>0@P?es+JEPdsvk`6lclxoGP}; zbwnhNm)!?|a#$^IF}an!gTnw!ZUFi2x(u{>3r32`Crt$AciL{w3vs*JI619U zwMGjq-W=FAJoCgd?8K#)+6U$#dn>rQ$!7y__r{hnDCkM;iTD+ozbfvlcsn!b&P9U|5RRHDe^kP`7g%hSf$^ICxw-FP7uYsJ0@?bS<8kE8axxXXM!^X zvqfqD=N&SG7HfG8eT#o1+zOz`_&aO0Q>z}3;BcShRSqsPy)+Vlgfg>_Y?NNT>`MD< zZ|_EI#Si!t7eP$an@4bbH;JTp$MLRuZ?x zxK#jeD04pW2s!B5w!8+pO-~F986YD33RaJ$maPNU#w{n;_$C7ZDdFX#13Nw(-r)D= z@^W$3gH6~4Lap{bq{9W`yVzAyO3wrUQ>sc$Ty+-oL@*(a^`YO_Y@9R=N+#8x@kxx@ zpkL4eq&|E&skDDxfwQEWLqAxVh27LW);y+7ia)AOe4)#vNxA(4_P&ZfrPq03L53D_I7;9@AmlcS#LESZ-27 zP-73~iHwSy-`}-2G*k*enk%w^HPnpVSH*s7+Uh4IfJL~m2g8A%Qz;o-Z@;hh7Ey&X z-ZOYDQ<#s6Y7j(aJ;bNJMi+|_mX_j^#K!Pr<)8Dw?cg%p_CY&~#=n05Udqr6;td2ET&;O6 zSl#i)+JvN}*^p7_+jjwh%Af`E9#QNVh5o3K6d8z2;yO?NlAJ?w!O{sz)L2GXwDo56 zt6*iU(x>m=#~bbbQG>y_BN|<~YX~E7EnB&l0{bnV8WYt(Bfvb2Zf?QmS*pu0 zeUR=Y{5Z@&toMSH*22xZ?SP|a)4Pbd=ysxCFK|k(un5EDN=^g< z+G0>aDr#lXd`)c$7dLp_uRW-QT2k-pk2{({%4o$GwvBhHo0EPHu$C7l3GSE^Q^N=m zYF~DDt1$J!&Md z!+6*6#9qJ2V<(1?r$2z28Mxv-!uicylCta6`!0xcJ-tAv62AFKwCn&IBF^T0N3AM< zZe@`*j$EE^h`uJ6(;v`dnlTx3>pt^>+_gf6jQ~Z@7zayx6QuZl;%u2D?#gfsj*ES~ zeaD!n)<>x&PlDfz?*g##C>BG$Fi(};SJ4|#HHCAM2n9^rZAzJ*-cOnTIm zL}k#RwshZfR^<*W$*O8QnkZy;=F7{><}BzI`Wqpbo=c(19b+uqDs@TQ3M5|9XwnWqFC z@?EJek(~+Vh$(}W#jU1f#pO&E;!VQR1_lM0q2_U667RpDf|Z5gTx#LT=a$r=N`k4% zp%|00m0U+ae75YABUXw(|Dy#c-FnSLGKY*p-Wm^2x8w$vx@n6R<}p^mKzot&#*Jb4 zQu_PFKbZ!>u@@^di8>U9GO!r&0}1n~ug~?MIc@8&-raIS5Ki#K-E6MjofAiF^}XzGtim6=Q}3)0Satwi$hckmPEj4 zT%*mQmn0p7JCGs?zK~%H;R}x&V?E}@p`q(1n}96>3CfCt#0$8LP4uD7nltB+t97!R zA8*71e9JAQ&|nk{*$}GJfn6ojje)rcL>Yx$HqSRyZ)fbDK>|YS500&S&?iLQnJWjK zR6#`5vN&`n#JuovMS{}2NfVhCM#OstB!Ew@G$^23A?b;QcnH$9)1K6dev@sFm@h`r z&$ysl;Ae=_h5BcSWK#n;&90eS9$rfUPheez{ls!kOu3pNu1`wpGxqS9NK!xvV$cxw ziB2On)l64|%BJJ~JnSAl_uo&ciuoAjQo{y<@@Ig1U_NV{N)k9hGQOve0+3%7*r1rfv@{EuJkWHKTM zs5SoxC_1S7lu*98!cdmRp5G9ild zzaq$T3P4-H&K*un9n80y6{NW?F_dk1k2Hw6GcxvAIBlB5%3AbTPX%dr!PSKdz?>d6 zJ;y{HnnPmYp;Yz9ND=bJV}&((5>ifH23`YKQ3S01^mN0w*SvQHfKUf!)R&E2MOnyv z;t|~JUqZ*fvB1raSZbXg(u2zpSWy9@HiE0BsCx3HRZEpyb+-nW(z<-FYy; zr+@c1FbIH6k{NVB74%AM=(`rMfCggA{1Ci8_Z&NMqv2yApR^2U-j(s-T(l*uE$t@D zD3J--$2^uRp;U^o^w)lwy)T47A3)h&7T&FESDMfYr#`EG9gba1(rboOAvM73_Q>yT z+qZY<+jlo%xhd9M6OSofxpm98Sw|~dE``J6jvY>3W=mCk-ZV(COM6PWo4gKMJXK&@ zqF5<83nXEbGEp7}XaXVd>v&fG;`3Wk<}Z7l!!BvWfxik$=ircU_+9(q{rhvbe}WE0 zRiMmn7(`k?$~*P4dnQg^@;p#CQrc>0jB;|)VM0;ifvlT1eW)j9E?#WI=L!=KOb3GH zz>>E;Rp@0ojcg99jb~gDDAcx#(gPflAd?D|Q2cfz?=>wa$B#UU+NE8^%giX|8@_r4 zp6+s@Zq2D_yoRz?H$Hj(HZnGz`p|P44@0iOI+gIIWk}4!@6SGde<_<2mJ*dhgT{?t z(|Z}q4K#d!gPEGKBm~;cd6yz#6AcT|ng+ekzbcncy|gOw_eYv+&>0cDhfh*TPfMFu zvtM4$mIC%NRzJh3khu*t`MAz}`uztF?2b<81&1~?GoYg$_XyccaO1O@LUA5)(uCkU3L7u%SAz#|-j(o*dK{OSgHlM4a_ z@@F&@7S0PAgb~;4YkfHiZ9y4{4B@zIP}^aAS1%AOOc#dIm=B2xduq|6jNA19Fd+oh zk&tg*KbpWG#!$=yvcHWlE^c1D|(OmdAKUbH8&`kWr?+>zh~x_U+$Tkp0G;@3_*F50He^&?hO!jM zUI!>&tcG?o>${tDfO@fO`Ir0m=_ojAdK2oxwH|@3rBdO%&32VI!V*X9L-5& zqT^aRT1lC8n|tV#VZ*rkT8TS34P2w;-vucS;f5?7JPfc;HUW>jX7Qx0D;p>)A0*fX zF@X2u!h!w!FTof^WL0G1Bdo0p)++fAt_`+pIyqeS&Nx8y27YQRY&9ZX?p>3Ft|UXd zH%?UB9}&@Bz2Bmv6RLnAz;Ll$(J@cTF!=KY1!_nPr~aWR%D_iak~H$Bv4%TYEgq(5 zilUco=Zu`@ToH=P(H5dT1q$SuX_P7a z2fg!6_-?h*jz8aR#wD^#K)0Dd@u$UHlP z0y09P#4<*2eDr)prapwY*keE$6U;X?_1;8M0gDj5%)1{;~q-hgM)jr062;rHDR^n%bJp{u{#ag*mGbL*Q#Jvv4bC#p? zQ05w$gG%NPUiLAYOoXD2vBq(H+hHV5&DyqIaPqs&p+itP0CwJ->Iy6lp?KB$OBgrJ zb-H=BNt#r#bSjFdf)3atuKOE5fB7=H+gvZ(+PV(}X~*FRCFzf+l6emG6?|~wr9kV- zmZtRw2X-kiRTDE{;aGq-MnXgd`F+011P;-+$(ItvK!5GOl~q)txvw@Pn zlOtPR`y|REE|vr^Qvy+p>;A5&`mh9sE6xba}Hn7SGx zbS+zUY|c&~u1f{9V|bLSs@7k^!(kXdQlvIOrcthK!*DiyPC`umY+9O>>fzK|)|+Q&1$}{P{LB&>fa`$g9p#roA`Xwb zvji%Ppn3?fxt`D$JB9}y`#waIc2rbzG(XH-xG-#C%C6=gJB7dW;@3V=y~91lx<|GL zfdM_J#13Pxk=xVCIif{5j+$2;rC+HDrQl4 zdNNNPq%ZOA!OhQ8{sf6-CgEpj;zPTSRH zFh`j7#dxmYV4JsQ^tFDl{=)?qJ6G@Xjg|OXv!A1I`-G^Dcxx~*8Qkwdeo^CzJ+DB_ zNYQ&;_t;SEJJNp1_LY?<)-8j5AUPcYYyY+N$fbzYy75)TX4*a;#PS?_x?P5T@qcc( zqs^&^!T)?T#if%w9_!Z?Z*Pn5eFdzIZ#BuS5@Nx0W zoa1}pUH`y?`K2$0Z%$`S$1RW^u!R|+=}gnb zpYqnHC-bzbj}iV*(`rmQI zg^BHX$VjtU5hS@mR5ZI%7W=He(WOxlKo*}tAGFO!{^^!IXRW$;v~JAq+r8Y z$=;K1Wh4K_K5I&DsoA*N>%>)u1cbT4K7584wsKw@TpC<55d4(G(7Z*9R5})&yr*bA zxemC*e^+9QE!OzVhq6#iy$S~~d6nVq4?VMq4jQbG7kgN)bak~p)$;+QD=O5|sdlQ* zr^w|&x`LdoD4qWKCl}nFVttZM(;VVQ{&CdDJwCmA^?HUhXa=pLxRE4MTAw`-2rSJN znJsf|Ep?5D=d9h*l*>UKr53cclonuT6w&!dE5axyz>P$K!>(c?hS{?lrfOUIHT?1G zVuUf6oFKj^Sbh<1}p-nw;zUmluYd84bpzHNa4v&aNl zL|Z6bZ-<7n9QaQnf~er-hXg$^k*RBITq(DqpJyQ31_B4_ebu%qCIJ>unnKY|9;9f; zLA+mfdB5>rW-m2(5!kzUHur{&kE-4^!p8cP@q^tEkBuHYYV>GlRJ1g!V*zuG%U91f zVtv3;@1Dx^T@)RgTkUA82|>Zx2nL8e1sc|Nl68vRL9+&wett+8dCy~DZUA?rJp*(I zdd-Q6Jw=|K9aZeyYi1M>9KM1ccQwc@j+;h7S?-``FWf5 z{BFJdW=`Hv9m|m06BH#%QmZf*q^7k+_xIyNIx(*sT{A(3hfOfU)+0UVmmsVOagC4aR9z-?jG$!jShzf<3? zU$lzR1~~@q$Uo}X8^3DOGpdfw&^8B#c#6#`)?mUH#Efi3%^p87>F~7_@9GwM_FjML zzq-3#DJRiT*446;!7)0d+YVge%fmmd`MH}o!k0wxX_mRl=R5)x@aZ{J@MuE(frh|i zNEL?gwb=nP=ghG{)j;{E)4%_Oi;bS{TbolduIG#Pq`e$KHpju=J*$|-qDRe85gX5K zGO!9wq7q>8(5O4&^Y;4$9n7Ss7`BylZ4zwdO*0Oi7EE*+aDDKRc&YEN8zO-!+b&?7=S$;gzi%A)I3cFwg>`JwUQ48!|)9cpXRs!?eFef zVNxFv(koz2*s8^$N#AUMWTVOA*I4Ts4Eqo%MOrAC?U_A&`}Lbc!eejUu>1h<;rGI^ z&d3MYbWBn>MDP|S70W}heAJ80C%+Z;3=S!;z?Xr#ATN1+q$>3u#1kJU&J<L}AhunEI1Dn7tl20|IteOEhaeCfet24KLHeRfJpr6xFgYtyx2U`TYP;S{Z zZo2yQL)7vR2Bb%?;29}kZO<>8txy)SnN~uU<=e3qmEO<%g%2LEInw(3LS!Pm!Yfy= zZU7aB(+TqX>sK*GOqzJv>FDZOmZ}ZMR>ADzu|TwqLJ<%kz(MQDFr@r%Bf-uA7+hDZ z>zLLVN=nijAm6LLJ6mU#z6gx0`3T}%@7Lo)P%&}eFI{-ANhH_l+SPi_>%VoWgn94J zxwC{JA^R7-BoHb>4J8h5p-a_+ZGnLfp_7_?87i;rrCQCS!eN)X4NTEcdp@{;!yW}7 zLJ9FPo~y~__*>^bt;3&m8@SWv~b}d9s3$1Fd{*$ia!)Ft1;V zK2!yim)5A=tQxC`v!V*>E~-U{>eBUf-1RRl zUmy2~f<_*#OtjY;6!dUPtk=3qCH!cS7B^{q`Tv|YN>2Ibf0}iwEpPw7`oSJO4U_s> zDq{h{kXTrL-$QOR`(}_Rn<%un+Yg2;U;OaQ4qD`@!(M~*I+M|923e^69rN>L%0jRe zuJ${WHzex8uaMlA$;3zns5cpwSTqYs-fWZ{AHzl*8+VHL1pJmw+~l*`Ln283rf^6^ zUU1{%9_!#@2;tHz2lx`WXAiiEdEC@wBa(VyyFNZX6WRMl`3MSMWP*TnFpWA)Vxd9W z7D@>h)=@!v>olQZ&`s?25T=Sd#9_5kEo<>Zo z#*!#tDr!?zrxC+tx1&0>i?wb|j#4#L^R|K%_Q(fJIMfdiFwwW^Omc@r-CNp!z`3GV zU*59iZ>qW&9su51C`ux*4eTAc$Sb7on!i5Ij8j?aE)LoVk6&{8N+ko!k?4}&iS3&P zW?ug3(~lq1CSPjB*H$1p5|=gu;B&2iU78YICotjX<5_*2PFxldhN*jd!m0lF{09F4dg4WNOV!c?d|C3RepPR_^%!W!Rgc`SBf?Bsw=PRLMBO(PcaD* zZ~W#GOimioZjmuyk4&bxkuXB4KNHBWbe%~(tTz8|yBj{I#QEEPP= z-8MD`WZ}~(fQ0vCuRwG6T?I#1Q!P=xGo;`kw!SY2kWl;P| zEA1(rO+AerA9p5qkiLT_YksAC$OV7t!emcr#sspoPnvhvdJg+0jg7{;=b)Lus|yt- zA_{tu0mLugOJ0Z4h%=uDH?DH1opa~S2FAz0t--$(Mav*aArS8=oh=!IhLvg#2==}E zAW))tdAq!|`O6rF3sO&{(p&QZT``r)aO#91`dfN-zwkZ~U$E)W&@S!TP^_;_J_Erc zPsizBii!?zME}yZ|4RQk=|UoL4up{*i^SB?k4`cGh$@w7ni?9}SPKpn1rI=3>~b}u zVQs@j48$ZCZgpuP{qpwib~eQG4s#j}zU0`&m1%G*_+Py7al z=|WeF5JyB#pYXL5>k!$gZ^mb;Y7P(aFgS$}i2mdk)J}D)r@D6P+xJ^kj?0W0GD3R- znrtv0w4;KHa7hX_&-NJk-3>v;xr-MaF1xQ5CmO3@k0BLmZ+|!;cR=(Zw?Av~lp0#R zcQnIBxT_5-(;VQfRo$j_-h?m>r{a~Z$SD0mk0s7XrUj2gJB-iF*O$q+K!RE?8mYBR zY09_EezafM@S(j2wWIf&OJ^_h;#j^L4<76#C+h`n&^2p1To3iFv^uJ>M8JF8P|05} zQPuo6(f*n4u{GJd(hhmfgSu2ev*Wni!o7d(lku7za#mB)Z|&;j;*tr#Kz+wP(O>Za zSkeB_Xbs07C4-&5mj$>!=vSR#x4^|WhRnk~<<=G0UF%ikWgXkGjEbbl{)vYh3%f<|I;2z!E> zbhvV-vP?R9+aTTUx^Un?T!J~CAdc9`t+EX}&)9-A_o2D`v9vTkHxAW(e9oy_*1cE2ZUnf~8s!2xU z`rzWq9{k(ixp%jCuhacq9h_hCCBf*T|5(Z`?RwDHopJv7kuuikVbXkgou_l9t%lQy zHq+sh^C+R8K|OQngBs#B&+zTF^Yf4T@ZhSH^@gR2&6h{CAQ1p4_-_9we`>!-fKP1To zCrUy+*#~`2Hy%COPdB6i@&fNa^oL{_8h$|t^k0JRsMsk~;M z_IBmYyC!GeHteKcS!4-8C(s*>qiqvfm2IIdRnp@JKlQ~POO#5)1P3FZLg4|SmpStG zMm|3plPwDz$K;MFa~5X&2)ew#r*U%QQcdLRHKg4s~{I&GAp0jPzq(xiB$@Senj-8Y=&-_TzxuT*ASJu64sTj2JrtRdd8_zYV zJF%&1i=Y;p7g$zQUDvEE^l2IN((C2ADdW3O3LNyh(SS~`bC&0<4&6-63%IMu2PT{u z>2SG$mY3yVmjS~@Nq(Jas@RABh(C1ns7&rC>?MDwJ$r5={?ssX^U>L%3^t@Su>EGk zXuq!Ek-JYD>$$9UYVi9*X1Z<7^pSb%Y65m!1lSGbw~g9bc-^W~$BrHMc8lwH%*JYB zJ4e%$wH3VzhTIQ0U7QkK;_qZwvagBh)`Gy2-UX8CH`UOOt9_h_f1buJVq>WMJnG*c zEB^aqoXs%oIq5f4o=$JrzmFUfsPfCi4y=ojw3qS2Ux7}sEtk?`w zyYe!heMLnR(?&RLIdGsIgCr)Tw~ro*e^*Z&7x-@C>dLL&>7AvSsmS*TgTqL`5CjN9 zQRu%11(@QN>sN#H9QZzU*!VoyNk4O^Ci0fAnM@g){i0m0v%dyjOk->Vou2^`zz(#> zF-WH4Q4J^hndyi^hXD`U#PJJ9B=GQ~9eUwcvl&oGhEjlf0kRII4tY1hEX`IGW!= zp>g%u5^=JY77viDX6GAp^dDWj*XkLTO!|fCU7htg_RY7IeEhhR`LfdyhhpQ{%+<}! z(L%c!V_cNU2yT${)()hg$)#D=@z2=;@rivq4KIk@0Nh*3KjO)i{yh2ZpvC2A-s_nt z_VFLYgi@IoOYXP@3_@n;VYi^jU#{zfpFT!gVu{(HfgqM;KYnWhDR~1Ai3F5YqPed- zhE{Oe^1N$p7a9+L z8yFJuHx5QhD^1xR zdeUs)PXET|DQJ3%h8>GCQrq7$wUb8q%* zLzq2jD!)PMZ(JqB+(7m)DaX+cBl-{_#D;|ILQpbv2`Am&jr%#MQ}}ai)(nHxOL4_`LAQM0-HIOUV9k7<>#kWgJhXxssLSdB>ZB~P?%sj802LKPb7Y$ zH0rKLYCJ__qr2*VJZF$B}cjj>q0#HKUjP7sGis8{rh8{ zsSqMj8A^u8aN?BGBpgbH%#|rqLO2wms8AyF&`3pwLdMddGK16WGRmltobjdb1}LDzVeCbJ9drCR5m@sGc?m0@ zN8Kja8Q8lkp2XR)4eQhdaoOn0Htq|QCM{~q7^iKsH}dtWyDBx5W4J|quqxT(RTfT4 zMi!*)g-e$}AK_FJGK2>-E>-E%4+H>KhDN$}YOgSrFU(80J(V zdEX?9wR0zbt7*Z94|!Y)DO~obg6h&nD#eSNp62f?M;?!JrcA^@^SdSca$vQ@qB1=6 zq`6agV^M{Y%b(^7^Q2J`%Jv?uWnvHr5M#k8Faq?|nqXv6(6s+x7%-)Vbwv_f*xhV> z&Hkjukp2|v`E>+QoEvws_$H84TaW=bN@OE05cvdpVFeo2MEfmcOm6!VZP-@akP|IC ztN6*fjXyW-Dbw_zZA`#M`HWkJs)0jE%4C!>fKtfuX)#Y8^5Vsd95-Npgo5?72vom2vzp#6RZvxYgYyu+XLWCCehGY^giM)Hfk!~2OIKRckQ<~W1QehgZ zh&X8S>#WppqNP^wI=NbvLlLLd7Y|<$nwOW5aJ67(6;(|A_us$7;~8f&Ch-oRqCZRJ z)AybU2>#V7CO&l^Ie<75nH+GOslXJ14ANN#4y;VOjwji7!z^WcywCi(4gW_V71_L@ z>Mt`nUOo*`H8qnCQ5jzjl(gb={*nvWSGTM(4#-I5Kav|rlbqk+qqp9Jf8llO{PbK9 z!Nr6UV?)Og`vb0Ttf+9OFXFICfJbp&J+WPptcmV~>=7*k#lwO@y5fEbupqtSgt+p+h$M`)HNMRm(3S0?8%qi%3*9=U6nI75hU0j+)O4NP=Y%uk2@l>iXK01 zE5uazwyxjt(G7*B*6?V$(5UNVnWRiMTJ1iCw1@Lxg9A5MyinGTR+ckgAU+XG7E}rj zU;vWJb3bngK1ngh68QG%{Hu4(qWB2?Em<-q+>)#T>L`j9S;x~tClU&UCp=-_o4WJS zcy2}v66|`4;W7xJZTt3Xzq|6KMVKYSyXD#i#vIwG zQ7sAig@v2>>bbNL;RJDfMfBrMVwel&*2=;3tA5S%w1tFIO?vh|iq3=#*2Alw8{!xl z6Rwb!II5UCeUFZe?h*C=YnS^n4d>O83mSS+&foN(MdsOh( zOhGGoooNTQZ#yR=diW*|^kJ3aV?n^F)oXWo@LeV$CzBe_UE0s0CPL|-I(KdpNDC!Y z(wimvv=?!wbYfNtDJQj}j*bHzI1IXSI52+s_fOB-5G1Tdj3AA1Kf+VUWKPA0o;45l z4ZXa~zGHIrm~rDgimEX0g|(LMUAYDT((4f?>JM-9%b1-k!^CJ%SoHF8BO>hqTBXD9 zLf)J6o{{$VIui6c`1kYKqk*nlyVkt*38-N;_J2-^y*`XpOtl3EJ=>VGI7!B8)37cc^sbwU~${`(DGDeA* ziHgE)DgNsTT@OQUpzLB9P*-_s@55O`)@1IdQbok<*|W-Yk&D^J6y|ROA_Kw+J7)(8Q%Y8{?Hdpy76AFr>I2#aTmn`~6NAeSY~2SQ`z`EzBEhAaTLLq4V+J1sj<{ zg|D{{J4JRHtd6vH6P4L;>uB(s)Vrq>?M8l3PEC4`Xp6@RI8H7cj~POAq*t%-!5Y-1 zbMRa8MDZk$3zS3J4PS3@=-k)N`;_r%fMtz_sZ{V>JWT4 z^7Ndh6uK?YykH(bgN?nO(9xOkv9;GJNZPr6NF~>wIMIuzuuMDClvdrAuisR=_)Nw< zS*kTgi_xv*=X^hg@m>?eN0>IH%yTZ&#qWUXM%3Yx&Mj)TZm1{pJ}a=v&2F#B9NAH~ z$f@gKTAuq>lT>^qTnAgJZMPkxm^1{Y0(Q}@VEZgo5gEg16cHKDie4TAFdIOS(2&K; z%+8*@XPeJ6Zn{i7{vFne!8d!7P66gU>4T#5{m05Gi}ZtgPa&5H+cyuTMk!_!3<+%9 zF`xm^1-Xq0r5mhcWDPKLZ69!@Bcj8{i5`PFeE7ZnlV&iCbN~#qo2)}Uw|-0Mpepfm zR=|p_nG@+k2LTTGm_(|IeJ4d@J-!#pgCz91%PV(jqRR8)m7N;><(E6a3OAAyZCvuR z2L1bcubt=YY%+H2w}V#-AIIhoo^!rYpxK`k285va;e_eQOy_3zu9_3P3y@QKpj~a> zQ@65;UgBHL%jmV6c5QLCR>%OY+)tmHz53*T8ljbnktGlpe1d0u`~Y6Xs7orFW%?nu zI5dUzI^};~&u+qmCgpD2&3qQ_*#v)$&Py@{CLQRap2jS8=%34*#zDx@@U>MU=yb7GC~uAX?0N*^E3nXmX>Cg9=UjN z(s_q{2M%;#nuXWh!0h5?KW3OV{AGq@tdxf-rtwBvx%pRQHub=tyf$pUn0z&ZDj`3- z1RrbD5hI$=2rS-^652>)8nMjlnS8*Z%lVb0xpfYcK}p4U(|dczVS8U(a8C<^oK|9> z1hC-kF@&QjQ|uX|w>_R)i#2VguC6X0h5pb4SXqj$wr$&nK+$M?e1@MyGTZ!1(YM-c z4EA{g4h27z`{6@l)|+NIlVQWchcCIgop>+R4OLgD?87+4uHX zT%1a~cI|k!HZP$~x1gD5kI#BakB#gw(@shar?=!F2{-{!InS>6sa^j5YR#Vc?F|JW zt`5Z`o$ib@^buGo*KWRP15O?C9~B|Ya%WHzS5Qa(=cW@UJ`c`n2yk8xQ5;jwhT?CZ z^sL1NrtM+=&s@-ggSBuUr@Ggbf!pk?++cq3%F42{Uv}--p;e*ui7g1lQjeQVyzXh; zOixcglRBr)i{EuHtp_{zCcE*u)EJ9#`U7R6iAqX3KBd3sxjOyibrxjSBJs|A_|P1@ z)4THGXZCA-H#awj>GLr#Jyk%zyz-&5z#J)wQy-lyT!csYwcEE_|NQj)`4op7D7${)LHbSxIyMj6~piC$uXi zs*+f?PRuyV8WC+%O3qz0?r&f~F|T{TBZNzvpsR4%{EfUu;d1vVc3z<|F-^G_iY`BT z_`{py0wt$LexZ2zb2ujdC=pZ0Tw&e4Irq$F!fm}a9egViXAld_NPP)KB6z@8JB3~3 zQT9EwBz+ZSD1>$`s494$S}fkSUAs0uz4P$=xBs*NPbbN!hY1tJ&BhY|7{v1)7FKA! z8Z&^Mz@d6)Xmt6Dd2{DBjJ&gZ*RI5Vn-5>U+=1Yc(th_%bFO);_lrr#1RG|z)=amQ z_2b$`$&pF{7ZMqn@piZNYyt`G)Mm`4>^NklCuwo+BW#%;dJBOw_@=8nKNg0 z>e;gyTR)}x1oMeEZmdt9qdy}_A;Lyf(kU&5d_BRtoa^kIOL19!w_Tmv<3NH?Xn1jT zbYjKmx{snN;&V?;Z{N0U!*VwY(!IyED(B=d@F4@CF4G_U$q-p#6Asm&<93`v=7!2p zS|Qm24kc?>Z@T+V3yA}Th?psJEWOD-C_=(XOH$Tk0^9KXp%CsVDHES_;|Z>|WlK|v zShLbOvuAfLbx&NJb(p`SaB1+J)s&`rO3*%hl@D!x5Y93vt``6LI!k5_@{53yGqbXW zah&C|N!#mC*;*2j(~SBLCeUySSK{+5X8r7g@86d_$j+vk5EDZgMu~)1uU~tYMs;h; z5@O=tp*cwNkfDHnWJYp-b7Htm!;c>QaIp7o(g)dvWT$F5J~f=q-U-x9RPUXz%tGQc z6jdSH5{#%ZVM#UxunG}lj`~>>GFf?ypdc>@R5{L(k>T9uVhBV+A>0E$AM3AcgNu(J zVjk4pbz;H|e0goCgI*z!m3ef)A)Tr1fp@44?N&0TaPENieYh+60$B7DDBH+Rbd$(RKOjR0TCMl6g{k%5Dl8_3)Q8B4*PAXhc0zaR}vJnuAQu2fna7u+GvdDI%{L-(#PLl4d|}Lxb0bVl!6}|MJzG8S z0#tJ@2_C^JCfq%4?axBgA+O)ONys|cplRDqZo8WuZ9gM}U&BdY4-g122$8AOyqcM1 zAGLSiOhdgZWda$^&`wGMm!TWR8%=>!>&b$|F~7F0{p|4tL;EQSt6I~ueI9h3vWj}4 z8Q|l{1A}6pv}!&onn1|zOeKzEL{bknlhz0gYUfmTV`zAIs$pI?nPkC=$#iIT)+<^Z z=>hFWdmB9_JL7k{~TKwgJ{o(&rcYC{W^ zm~agmH0biuU)}A$KxV1XFztvwf-SZ1=kyMhZPap3wa*dgJa83}p<(03GS)$`6cTgC6t6~D zoM|I1OET;@mimK`Cp#E1L-MPKT-eSpOcTvbAAp>OIpxrLrJ8=6vl1Mnz!hJ%yS z2Hr|)cJ}z$eg!R}qoR^%3_-CiT!IW-AuOixK@p3rOG%thq1J?n5q5*jEFFo$;YG`D zWIzGn2+>lYa|)|_Y;>`ghLa0AnqW59{hLExhu_MOX7Km+|4;LXh!O>Y5T5q zjwq9RQd6_GsE06uAsd$Q0pj=NW)Q1yU?Aui^3I8tzd{(&E9QAptgtAOG$TS5XO1gX zKAc|U82MCPQcuyKyA{#nh_qLRy?eKy@g*Faz$rlwPff{O!)8!JOJ{c+hF)i zC}L6Lcyl=Wmmdtr2XP%Mf&!{N#@{l3y$?;}d)H3CcI(zvfGQ;njR886A%gh63$n}g z$hKY0{!SG9T+@wG3R5pXRz-{Yk|9KU`p@5WYm`T22c)Eeh!EA;W-j^aP^Jmki;^YI|3~m#_pCGfmeiy?X1Y{|PUM!- zqb%$SGX;_!Ki=b}7oGI(n;Z{PeG}da!sNNjb3P1wz!fjdIh)V7z?dT@u!iZdVc0SI zfC^i@eU#S!EKIP#XGEyBbbx^#cUiUgAjt+2zFba(K=ItDhs#Fva%G@TI+kb~G2x*X ztS%il(pzMoar6@?BxXq&ArW|7h9 z0|kTJmq1oS&{qbS6nnxAw5ZzV-PJR&B;%E%1nhHa!IQplZUb&Q$7ukg)PeDok&+@k z$kOA%#T$$lc#6(Sj$6OmTAwhW%gxWP1JnVCl8hkHEuuss52wKlF8d2E>i0t?nUTIr zhRNR*vy8Tbm+KW;6$wFrKrs~hh(!JR>%*b@tObNEdoSahn@iU^t-o3}`3=ZzGT!;S zw4%}Al*zJUBp_!9xBA3?N&(3+5cfs2Xs;|#HMyA#gsz~OM8YzrVm-z^I&C**OcD$e zsDTdwuiwpBzJi!W0!m9jc9=W&n%?FZoXoX2>YFIC9Jm1mT!Bd~oTzyDPjMB_)TdCB zp;i->V&LAJuTIHW7f~%pkxQrIm}%4e#r^CmcDrk<(X(+u5f&vhJM|?bU zZM2Lpeg!F*M8%u-asWs_NQ_8PX3#yxR%)}ler=)RQM(F(wx+sr#gW4IL|lD_OVGzU zgFw-iM(NXfaozRwni?4y$-5PQ_cNJ>(ESnvp}d^X_1R77A1fF<@DOe5DO@Cb{|SvO zZZTDWb8vz8aG-O-o47z zMyR6!QWz#_r+)mjYtbdNW#dmw@nu`8u>P}b?Y^xXBKws;rN7<<>l1im=X7#eQLhRc zJ+T-(VCd#Id^#73Cn1F1>8<3hz6l=oGF?vC66ALL&l!E=LyDWN-l|@62RSbn%>@_> zLQS!!8@1#XX=h1xRL8-~nhoZfw-55UM`e11 zutQWl1D?1tnv*#(TTof$=6s?xJp3;3f+_cmLL@riqKh7E8f9v_{!{|FC8L=xyxhNW zV|up=v%FA16Y$t(3^8-;X=FZ}P%1jm<)8LFemZ2;_wIz``WhO+U3(md` z=Uxy{j&y_yO3fHSuvAj8ID98B|_N5Q3{z;ZmOCg0l!w9cuC`YDmaS<-Vpa;O$ri&L1ncblo zcewoF5;SZZm7a$)zn?x-LH6f~;Y%lm)=D#odgO3QWWVDfcW&QK<4|KCT95g# zfoD_Wrr)G&CCGDR21zrk11}48e;UmQ%=h?;d${ z@P@B0$U>9h-bJYv_$G>>65RqZ9l4If2M1LA{)R2z_VF) zysO64Bo$v$TWGo*V@dGWOyb;8)@W@XpnX`wN{w7l?0q{lw_JH-d{D%{?n`M!2b)VF zrBf{a`O*H%%FA8|^9S?ltiOuC6RvOXk_id9uKjOt&XtFi(6wOrs%A3FFmJIeW_q*M6*OG+_d0IMQu3b?`&L=9#UIA%w#zDJx=W~V2a2^@_ zl>4A4-SJ)=a`i=CLTsy_~l{;PRu$e0@*z6CNlq+<)= z$nD$bInJNI85j?#rSI!fC^!(tQSlp=4)Z-deRJ(&iOnwmee4jnYWGL^4eD@bPUSNf zJ-#}(0!XIJ!%VCyo6nf(%lG=Vp7^sKt!g}Ipn5f13?59T+1j_S)_z~qBxc%tf&sA5 z*oa`~`$H0GNKrntv;NCopF}%zb#Tkr)_BtpEV2dcAn* zk|krNPQ5+z-IU=4kK6{&?xCwYVBRM^`I0Y+DeJ9g_UnV%Df!Kkw5n+tVa5Us18Sn6 zOCqILQdfUbFk(mS$0l55mL?|24Nk^ZUSN?0)6Txx)jFj-Wz#A5<8I5AnU(GBdOUwW z;V7SmCM+dazAGwh{BiV?Py1&Xmp=aU72yJE1J1{uI|fRRWCaRD7+Wfcs+jru1Jdah z@+$OK3%__#4IzNY0=^E;%FG;UV-xuJ*}{2lW4{2~m@lcgn4`S8aLehBkK7;&8z>4e zi~v+_|Gh)D$0fqIqEp<6t~-N*6iRD@XS~n=39h984{x@{U;BGKY6s3=V25f9L{h4{ z_KGP_-gIisagchmpN4hr50>`?yxR`V<4gNH@mPl|l1RfyJ)XbHkNw;rX;a4U0;e8) z2z*&T=hd_(;u9Hx2bJ?@i@UGuJ5Oh5LBaV(0(z(FOS zmr^Z~CGFFGb|nFl0)50py#XuWt%QrA+S*_77=srxnoP~(Ey}N)`uK>4GhMQzFSrU- z>Eoz(aUY%~J##*fao&137AyagAt9c?WBvX;>GVVcQEC$fYLEC~~0w%pyhLyq8#;FPr3M3q!w^q^RI$=Tj%`CIvd z27kVw)U%v#%`2bZZ4%KjX_Q9A;%x>z=>{g8^PR+JL9igOau^4TY10l}%Wg-(-Hb2f z#Y$WG_g(}CV-7ECKH$^j4ugHyZaH7Z`)U9rN(AmRKU=SYfjNdm8|BQTL{t?cINAa>{%gDAKMme4O*aW;(^HUSydN^7j z8fMw)o2>HQsULfE5k8i0AhQvz{99PPD}5{KLL}qHE7wBWa{+7wRk!d+jH?(}9b3Hh zz|f${<5WzoT1GQa-rv7D%_W}XLw*rkxh1ZgP8RbeCdXH`FiK2(G2u~+eby_TnCeAE zPLc)Bne(px-|_#@njxO@ung#ShQe)g4~k$N*Yo>PPW`z8+lcIvrq93*!Sz7HNF;n; z$LTQelpG|5%D4Sl{RvK1rdJ^QHjp|=(n-L|LtEhm%~@yd`jXg!XYjKK%2ScQ3HBIx zrj~--L~L|u2dWpiz)rhSWv!LZx;=RCK&)#RtnV5)-p3Bl!ltl2Efg{|E^v|0 zSZEW$WU+-1UCp(por4!qBx46FLu~d}{GA*8&!HiL)^sLQXFs;KADlN6ES)@V_US%q z8}7byqEkxj+3AlYi`(j={cP=47v$-kBc6bQX`%hl8aK!Ia6Li`?^Z0&qIHj~J69Nm z)$L-ob64YdcruYNk%{C|evew?e&3*P-@ZypN}lX~ANLjZrbWZ|eA5ZTP%`I%g9tWv?O9my z*4Oi{{kaiHjWu>eEsH4Cs|-WzoRQ! z9(mh|4if|jl6vzR6qhT*hTd?}@0Y_3r zo#}IUs(r^CD@S96+MkT;BaI;`aNsCY9@Cph);7^{yFJ(8MEvR~&0;ZA&2}~G3K50= z-b5m0*<_}Gk$3B(J9h^#Rk63bslpA54~Ax+K1#S+bHH1!-bnVE!US=iMs!p^8d>l) z*5L7vRZUX>m!bt1wVSimM6}GgT#RBZaG}5X%cHB0KCQ% zU`h`mmWK~-L|t&_qixH$&)^Z_{D4J{_t;+%<6bBx*<~dbKE>3fEwV0ebgQ71<7X}m z3?O5YZeHZsUhI5*9XS*wEz}LY7(YCNAc0(98~5Y70HH#;jtpF_(E z%3vArfOg&Q?+r1fkVdWyDq+gTn2%`&L-X4YzxVi06%)-1BA(S31s};8=)h@z?S6vO z)0snHm_bP`Qvvw@6EjqFXki2B)WSa~;*(%e5)J`BWNZeR#^y7Be@(o6d5GL&;09?V zT6<5Ha2;NSAybALAoU4Fvf$j&-WK8uiJeS3piWl4dGUP<8W?1BSzvJR+<-Cr|I-2l zz^HNcZL(K&zM5ZLBg{tng`13H*`%z}jR!~n!Lj1OwOz4I5>djK}JhK2FJl zfo0n(6z;pS6`D3oC{)Q5j~!4P{|v_xvdGDrl=4iuW3Le`V`2Q+hjbxE&6 zE8BH9-^g$qK_|$fa^JtdZm6==rETef;;kuwGXs1EbVM@f50ebk85$P0p717TnU1GO zH%m>W)~&k?8K&B?qrX^j`fHEpe_w@9Mf~H3p_k8@p5%Hkw{-$EjHzIILj&Erw8Dh| zU3to6Y1HKl3h%fS@LzMxu^Ue#*oA3 zN*JWsxersTsofeNzKwB8*pxmn?4ezL`=^9r&2iLhq|O`3GU_M@OZqfGGOS{~Yuwvz zBv-;-0BIu>lXxYAklgf+%)WEL7nC;_wB2#xIm9feg*Naaxjr_L96^BAL5k;iyK_$% zYo?|78&{Rq5ttpuTzEm>o)LwqZF@u)244>b`(DLM$c z4Mfa=$s|%fkdwp3?R)6CcPH8nLW~mRp(Xq4)S?%LBXMjz(yDgD=FQVZc)w#u>g!wM zR;>~Ma6PZl6p68nzab(DO7>IlwhaPAcp^#Mh$pEWWab=kx7RA18>|QM&&?@CF3yqw z^7Z(Nd!IaeH5PVDjxdUJZnC!h=Ls*O0QSnhr#7mS5>w(76qBftYzI89`BF(gN50dCg`z}5a zEeIsp_l(+aby2hEkl(&Wut3bNi_$QaOeHCIF^}35|8Ep0!_yiRMsgQnt8Yg8EvHGe zbkdSaYDbA3(r=1J9~AZ2tF>d-JE+$~`@%o0gKbAXQ5)oWt>>yGs z>NST23$`HVa0FQ*pYC6{Qd69$m~N8`VBfuGPY|-!a*&~=u74c4ks?-h01ebYa!5kE za_Rc>_rs`UV)FZ6K+GU|41r((n6l0t^*%H%nsrX!LI z>WNUh#NPKy-+JVA{7u;Z#rTXQTIhuGWbs5jgmMEs$%4N=F+(aEUG1)2#mX2}umwUi zWIKH*9YzKlNw0B?mi1*a4@VaB+V$&!QN7g#1S74)T&yl@Lk|}^%M89EB}WV28@i2o zGL%!MVB{hMgHIJ>Em#syDjZrIZW)F>ruq=dPZ(@c6G1QtuToZu#+?R{h_Vs&UVYj< zipo+RijP9)1S|OJ{rg5#Mtdi&&{b+kr%V@S4~sF9!V{z@6)_PcV$jjv6NzRttjfZ` z#CIAvunlvV1vKUo(-~)6m;pR=`}aAybu>CUnR`$~IH=d*?&-Q?0%v_b$&9YtkYmSg z8or&ATfCB7J(Y!^UuhE>#V|ynr0DBG5^)#IJT{?sw*LOs%{SFSp@ouStmcZrC&`GY zWu+IGeqqpb`s>>x!P&%Q4PIxL&)dfYBOmPqx`joK3DZX?fX1MSR##WwqG8;T%UrD7 zNA|P_1Cw|}U=``AyqNX_7I?COi!~qA^+n)DS5mQ^7h+ z`VZJh2p2?s0fA-vS9wV2Tz9RPzoYH?b1t`t-l?bxq~t_pMWC#YpcEKy15pswk4$Mo z8R_Tte3gjg=FE}j5)BdH2E2-Ju)5a141RDYa3Yy1eY_7{#L*&=<3o~gMZ=g+q#DVHgJ zqGp4K=4f1h{m&yfauH-e#up?yC)D=SyScuiAKNYPBfX>glL z!Ppdw$K`E)A^8x|9~Ld8({>|}LC{UuKu|9xBVd#}eRb|^-3hgddg6fb+Ky!N-Fx-2 zAlS2wW%3>udTk8Jz0IbU976WU=AbcFWM4cGKU_-=3Pn2+Go2=d_f7IvUAs}5{#%Sy z@F04}<8L*Mhd&57A~{y30fT(GGyECpqQVokL~>@Pyj1)QYJ{razq7}Cd{rOz%iZYf zw{JBXRNyEsYBR1qD%USes0W=GM#v@zUuB1R4I~Wa2IU}868aPrn0pKtcyu!O%gA!R_sr$D3nGtltYMiKGmvQtZB3|vP@-<&nGKhJ^x?aPv)n0n4T0ii zrrgZwlW0t=FJI)&opjN*GY~EJIL*jMs{;}S2OQI>u2xiC{IosDBd4#`{w|aCQ?40q z34OiUQLkP4Bg5>7nEqS0#J0BB<)a=3bLbOIeWzKZ$F=ywEmw(m840t}<}`%@2|GsE8o-ibfPjw!m|A^=)&W&{H?LhwA`Okj z=MEzqH2|k~JqKFn@}FutRbD0>`S)2cVKAT$C=k1>f_45Kyy#>zh~Pjl2q*1nZDyR#I(HR$9+3$Eln8dm)4 zc?pRljYEEL4kF?Qll!_xBkVWmID#9$gGh8vvkS<_hzRE^`j_uW6K2Lcdh zlLSo4Xr5f6UtI7(@Q)9VP6pt-czw-hRBiM*tVdY)an8xe$OhaAR)=jx%$`)eCgaWP z*PXPq>d?g^YtJ#|w!?9u%T4LXpt47^B6SYZSyumKrbdX)jq;Z{gyQRK7I~IkzM@>a z$vn-lyWinhx%)MBbcX-Li@pcqVJ+SvA4_#;Nxv7Tztc|Z&Vg1lU8L`1W|j&}gPd8i z1?d3g+YG~jjr3{@XE2P+W-e2Qc`aUa0C@b|e;x4Efu5txM~^INbfm+p_(`=!YZFpe zoR+pSbZQ2ej%kKX!`O*iM8U_6B+6=0%0L1!&|Nxp;vTIe5vFLHsdWu0KX4-ypOS9J z>%5@8ym#8sy02b{HHAD9z?TJzpqVF&Q1qS}Q|1ZeH3OB%V~iSUc4w065_ej@tltl2 zwDNNQ)%-RVZ`Ql>dB*+v_T~EZ0V-E$toa)|$9h~pyoK!t-}zmU`4&_<;qo2v0Au?_ z2NeYv#egIui7`h7h*PQ$^-$;j{ad34*nSM18JXZkIwwL@8WhKs*YgWFk%Z>kwRMbbOJTdsN*k}X#>Tfw>%J-P zsAgvN-G+CcLW%B6JanYEI(6oZ47c+3Km3*N;RS!AGIs3EW89jK*S2-mJ5o>N@}en1 zKFX@-~oSO<|80^&&-1JY9@-32p~e%UDn}jGJ(^_dYkoO`4ys6 z;5w>IVd3ll&mCXsUn2SR}VHq9a$ z7gC~$!;V1VEJ`DFCi-%lAo!K@K~8qZQ>WfPzq)FMshDXhOU?#TA;*H&trU7`XZE;! zzgqGERP5bj%Uu6m0w+wng(I!irj|M-ba9EKQqTeH$%PsQ326wXtD0pOdY1nRXOl|A zgOPFz%@3LkP?riZ2Uwltcrj>@=_oTZ4Z9(e{sxvOL^@F6Q$n^j+;u~;HI#4Sa0quk zy%dfm-#A4G4t)Cb=}gxL%`KyReF`@CSsn`y3kw0!mdu6u#>>~{`JLbpiV+4| zPiI-H3q*=k3Ma7zSQfOT2clXyb#!i~^j9lsXtZF8rD~R0bx3IFI`sMEAv5S=G|kYN z0PETZ;6`lQ^p4#0sn=gw*&_(wd|ryE-_KvFxy(tv4)KOl(091fmFtt{7#e)Da3DaS^@ze@GBdt1>PW5w3A95a7AFqf3J>|4R`kHAjGcfsU&>-o>p^?DUGH^f5H8Q>v2st*> z=EhEpm$um3%W1KfB)W>ot%P*ftQ@ZywX=6 zKQ;xokZXj62a74lKE6Nmp3ej8;s-_K<><&q77Uu#lWl`|nj|rqAthe^;n@?%v{bf< zcAh_9dIbdFW2zlfDo&9WggP~vjX1xQksvqI=6cZc1GHP4({ti`*j{ZuqaCLsPyA4-{{Zb8hL0)J84q_tn~H| z{~BSdQ&v(G0E{7;Xvz#FL)!~U5MF9@<4njhs&$tev3LYq(`_Cb6q&aw12vhGKf{F+<^InCpOj>7deGtllS zmM9`u<4uMk4&<~CEG_P*tbF|->Rm@CCw+SS+`ru(h^&bG>0)MVtE1EeoY-Vm-P_-` zE2b24nUsLfZRcgJc6QrotxQr#5-F8Aee~iF53W3MWJ{euEA14Hx3sSiF*s6(yAWas zz+J42o7kV8djL2Bc-&;Ga(s*)+Y6 zF^LiJT7aYplyCIrn_5}l*t5kYV@$w$O#Evf+ZHFy{CIJxDj7 z&_!UhZ1@u_x@7sW6ZMt_js8lYNu?_0LThR0XH5`p4*$mBbZt00l_0XPOecs?k=lnNf`ww1I&fQpG#jX&{?l#dKZ}9 zW=bnEG^WKw7wVtq21Rz#=%}(f`LBjS9rPV5OOMHViXlaN z35^O87d!W!rYHxX_f!qXM@#R3if>(_w5p+XBczAYm?J)$)GEUY23h0J-;~M~a6J@x zG)n#SpSx5>^iG+syV5W_3tC|?ModQ+f7!f~@%a1US z#436D|G3WgU|4wo;8jEQ!*#i^sB@j@CpERSIgh#k5ANn{EDMSmNJC!;e=w}G2UDmc@O=rNMS1X|;Np~WRujDvv_PbSV%Koz>nVn#E0 z(u?b-bI+cmbShz9tEX%9#zqpB3{bLk} z2!? zrcgIxwM3EvXe!!9pi&^$-9!f~b7>qP?d2}5dQ=JZ85Lm`+!b;Qw!8FIewtPhq4)CG zbwg|3)TDcl9?8zeIe#6>Bhs$jWz9gNODGdghIn-WA|8l0-Nzat$ z&GINH)2TWhvIfHJPj5~*Ns{$H7HPj9?gNrbVy@T^2p0lita0$hgj?K^;D(#*bjK~W z4x#6vkAcBel9bjjC(yPmzIH^lpvTkf?CKKiu0rVJj*%i1)}o-q63M*f1}OHiY9{MUpCnr-eQ*FAe+vaHOytLj zMFOO=gb6E>hjSA#>7x-EApCa^Dc(o5m5b1JwPZdVGR~_vPpHO6YD|rO$dQ-!HVcc5 zDc>%fKi>&=S-|zipCVW8%gc%EXMNa~Nk?t@Va#q0o0d-qBK_ntt0PG%36`iPs3Xy7 zZFtmF#U@%%WqhopZG|bL);OqEG%q=>i#W+v)@e74Eo>480w}=c5*{J zGDO9|S4cy{B<+W={O*gZrGbjqf%Ilw%IPPMABRv8fP!2yE!XK4g{VgR*>WmTvQ7Gf zK5e)wAQtFTROW(No&OJsDiEFCvLTwbW8t-~LLA7Ehk3P*PdFdq@8_qYJ4wnM#mhtC zn|s^natL`IXw5OAH)zF*9FGRE6Js(*($WIy zP#Q7+@=aapL|Q{=$Vdl95zL7#v#agFHAu)YSn>yZn}HXZ#5XFNH~YfLJ;}r^(D#n0 zeExsBTdwujQr_u8$Sb1Ncm*Yl6bir_T`rBiN^Pf504P>IdZ#;L=FEr%Gs~!w#3ht5 zDyImrEe^E=jYeL#kz%iVzj@xjw^C0+^K&|QfIl~G-Fka)k5F!^mWsvi?x;({WvIQZ z8=XNq%btphB3J%ZM}1bo3s53_EjG_o7p%NKt`2h8K^m2O&CZkMkt%d6=>T}t{fEzr z|A*U3joV_$T}!h!!PsrVM}7&eu4uqthp=2TC;mRHi3AYop#XM|dwAxsZS?&`$4g!O zEN>tW7xzbqdBk}MS2u-_70|z(j2fT&x&?qP2`MR#fs*cWZ>{aR`G-0~=hi00l;$z9 z0pY+&F$>6@0mvxC=c`YZ$BQ7GIPW@o83a60ivmONdMrPG8qH$>;)|6T6*G&#-78kA zKvY)o!SJ%OB1G|Pf2T4UsX3K9xW=0M?Jxe{KxD_X#th;Z@%e&Jl;f&zZ<|TFM?y*W z&N-JBt5>ha*(emJBD7eZFrLhVaYg8$a4D)Ai%Tc*!{XF)x;nO19-^z%8}fp8AzV4n zDFz6r%S@=X$O~Zo@bSU?PiJ9HV!}uFiS93p?jPRF z-#Bq~IegxGZsvNo(e3NlAIkf7k!C(S%$=?dP~5Lor@ocO->bCezVzENHqqB7Duuya z?XaV1NV@`GFPw~#F}JmNC6WsxRYeJ9?NHgH?9c3f;j&XHG zKUCpIx82)JKeV8qoF&rH=M75!k5!Cxq={6|obJBX*l>4}+S>5{hA)NH1{u z6Q7pPIahkG&I$u+L_E(l=FxnOuQgvI&h+9QR2~3Biy34KZ#wp~M{SqQC3(&-0fR*7 z{PKxttti0-h5-7YlwJofC*6`5A=p{<(hR#o=SC9HpY+F)QICv29ug9Abq%4Fn#a^E z$k*S$m#b0lX@kb1HQ@23aai1`xs@8X4y@96G^v|r*pg#}DSB_13?_|5;<_YY4Zs6w zU(kz`qoucl%ruipsgTmk2nO$&W#xZI6X&}nsmgs_6Oq z&SbTehZJu|JGBGev!1Cttqs-|s#HL76L-2$Fm+1M7Ou{n(s74-g$RY9Ii?>eH==qF zmv}7W#tbzTh*ykn`Mu(}ac((s$`Nw7sEO8}UHD8@ByEI_H`y7+H0i0Z9d>Mu5LF#z z0=<+XX_B-Fak>M79`W9DARM34Gc!B96L%X@Ee7A4F5m6tD7Hk1$AS(GXNS`0gD{DE z^zPOzTf{e4rXcOkh-zE2Sr$Ru2?_p3)IuNgI`Di^KqWm(vDa2f==VYfJ8*lZA?#_r zy^v%`7&TxQ8KiRK_zOgOng~!e18Woj6gaOZovO}a=`#`83q44U#nPtnZj_Ot(u%}n%I4PWWN?J96K z)R<;T(};`Y;$QF79&MK9cUQwtb{D9Pn42&(B99bf{m5U5%nDG1Aok~a zHW#jfb98rJR(krzk|X1l{HffOb3gsa5-$aCLvaQMXN-Yb5LX}~W139IyVIaH8Iu<^ z1G<&E)>fu6*&Yzx+uua@XlX?#*IaWz+F;YCy;}VI{Gtic+)$^W5jAi`%Cnu*M)6gE zTNG$=aqqP5%?78=w4GQE4pUpfB(!$iVZCE(A15>*ty}!|Ry&bYh{RA_5HKe=kvFwg zbbj$Lzj5wWW3;(kF0ARK2CG3dzi_rB+As3J&CP7N=TE~{2#=tt+pU%T$TlO8_cBW2 z<%Pc|kZF0IYf?dFsU|lI|H^xm;eLYa{k`ft17;3?yf{=-b6i(5{brt<{iEB-W;Odh zoM`OI8b7&lI_cTKHH4EmO?%D2p?az}8-J8WSWs=6(*SCE!4hnQ#mwFBk|;J8^?n%< z4_|vr6$qtbrfC9(uSmkMnQSL&{Bbn~{x-*sO;iTLbTfReGNJTt-jxYQ?S{W9TJPgy zl_(jf@;yuExpPA(XefH@9*3FoSRXdF(dSd;&(2zs2H#&4;erm064b^k6FN9wK-W_#9N$dTy7Fnzg?B0^~+0 zQ|Vt}$7mTFYe=d`c9B^Ac`*E5oZVkm#WgVh_q~!GLPRQtf_cht5Rn;JSU7^ugT^OG zA_?@D{L~12r^L<=Rld50o&Af}u7R7rc4#S`0wPV~#V8a|h4%W*6rioiFvPM7JnZg^ zdv;lAi%Lq2nIXKS_#q6Yg9XVX3e-E&vLT%Z>}l)DzmKVm8?xdgJ=!=U$U`kgEDKKK zh}X3!P$df^UAyo1-2HKOtpL>c48oiCSj#lnIV(;jg1u#2Yc z$KpzTXsZk)#kDT?jLN!ye`WwZ4`NFzO%YtS)-Qg7b=2HMs830x<~}{6!wVggu*yxE zIjCX_o}NASJh9C^WH?>3Nv4egbD>ubM9j@5blzlBbsm5sM$M>$1@oW+^P=3 zBA(y>{;Up%^oBBM=Eeiq zukuciv0#d8U)-fn1vrCTLvO6{2dgLVKCiuT`?fjd0uxZDeJH#zJobF|cFM|u>;GO` z)j@oJNL`85LD@ekdS2z{Z{SsgQ==^TwA-p9!!kGX6|Yx$(8nR(*<^V;M{Q`v2$tAb zeMVd4a_ew}vV4kQDmp$m`H!hnmMW##kJj8;4T&m$WD5mi$3i)B+>W?$cfko zi8=u(2P}yLw>ZhM&e+wQUWF(3|FwQco~nF!UMZ6xYAXPQZBG1E@XghWlH1CJR9mDx z+!kVpN>gt4*z?=Mournz7-!cr?-A}<;K^_Q46@r>&?rawD( zRKEO<9W6PHqDK|rLCT8Rv%AWHAeR>L2usP%>OuRb`1(GX_mfmwq`t_2*}`vZN8T(i zWh!L{NKb3`7j(3Q-)+wnJ9g_vmMRq8v$W;y?I)5Y1!{taeb3o+Z0Pa1DiRJ-26o_Z zQd#h^maS3EU3T53cz@oNLZpZkG2fq@+oLn;84@@AsT}u?kL;pygWte@XrLh8cLA1A zD5%m_B6#Cr1msf;6##~p$fO_1p5h+HWI*j8iWT4kTq5YK2bwpm{M^Tzgk8(O1Ogy! zIwKY=UB0|3;vH;aTR|Oh2q$`VEB6eG(aj5T9QW6X>ajRfiA;qDOM3C5^7N8zj*DyF zhJV$X)jTvAf~ixpNEB>qo0r*>M-Nih%5pfTA{Ffe#u2G83J{ zpJelru#+nE)b}`3I$$s=uN?~kV`888RrWYh;-SE@$P-bCm9`3@=FLIpd%zhAPe`RF zOQG2CChFGyEW^lUYLisn7iNunT|9LG_fA`yNg8O}1@s4wjHO?C(BQ%1c8P|eR&y;BaR)Db7-Qw^M*&+ zk4;ZI>N|QZFnC=w9bcKcUzUd+$y*4}x_y3E&407*-7`V(7ZM#UV`&mg&Z={Hka6BP zl!XTbC3FMI5sTkqmW@QK>mIH~<{fi_#b^Wec-MF|ZOp?6aSAG1mpKV@Di z+FjA7&&wJRc(nAxv6HkZe1BNHJHoiXR2}fdD9lA3cQ?+X0;QsMhSP2F&Up z99)J-ofljpBZSkm&D*t-39`x5BZ|uh%SinTZGU8OwYgVUmWZB|YU$;ryCl`p&Bk$% zB=T^@KMyvKdH0zsS&U5qJLm76u%O!;2jN~KW{}x}^Vd_zxwMV8>-n^+ZELbpODw2_ z2;s8RWUElyG{ioTbWju<@>VzFNH#PYyPmPfIhEk~I^Dr0|3p$6i_)Ee<-^_znYB zM?i7$L3GbJ$jUvR&A3@pO-QW6&=Q?n$N0A z9wz6k=)FiGfgg0Sa(HDcQEGzjNLvx5F%}P4I@J|%J%IC>vu8&knB|Fw8DCsFIH)PB|fh*^s5YyGHs8Rw3^v6)kGC1P!!)K~K7|Ia-3e>~&= zhvc|tXdNY}n=n*hjnz%wfZf83k11#yQGaDcrm;?b1uX8ZM;&$4CG`#~k&Gi#+0GWD zA}{};^E@+JBm}VXpb_q$0`L2xhp{VAl3d&DyD?dZTz>RRNT;U=3#nTQ?jLT#3zNQh zpyE)iK)?L{Qhh*Z9-;U03pyJ3R4%dq1c8K5k)v#OF4L~5vHG0A{zQ8J+vrgg4Gznh0G0Q1O&hWm)7oW-_dfUS{!G0r>UyA2!cNFf)0M zDb$RoBZrUd|CBb8W5&o05BRV{|(wew7T+aqTYp?$PA3?P;*1D zIo`_s#>VSJ7zVADPqt5V|4VeZqEQuP?xq{AwMY zZKSqa%y0Ou<*@t5^~OL6b2fj)I;7S|r76=-kV!m%ERf?wntysvSMR)rQr`1ZaZt=6 zI|5!n1$i*jHRv-6B+<3*8?aE^c878DBexJJVcGiVUTryQh(`mCTRu= zUYF?_b)W`xlJA&KbqO=OXQ0cWOq-w&Y?{Z+^I*Wr#F0B}|7?h;4rPdl#P3>AiQihg zlv%h}v#AA6ppVU7ze@wk( zot&CL^yUESR;Uy|EhwvOFtq$U%RUb=rUL;vCqrDD|_Tozj+5A z%8Smrx<^xv&;2Lme=W=+Zb@Vi#NeM-KDVLMhxANW=iwFU{UnH0y9I-sqz=G!p6*dGIQ<{PSr-SRu*6Pw#tL%6A zi||~g4oc6|A2mSH3(K92Kkzn5TVEvvhzbpJLsBK2VI#NiP_At8XWbh+$|L)l36;BA z;|jGul}ONm50xxCn07*m(OdQxX^#V?3r>4um-G#*{%^%h{&$-7<-bFWjHb6eh|A8Q|vRhP;HpgVn9&} z$Xi#zn&J?#4h)=g$}F~?b1gJ$YAj{taahyDm`GMf+wzlm?f?LI1^7E|jH-RqrB{jw zUTdgtVHB)MEya!R>YWsdjsu39nb=W1I_i)5t&QN=)N`V30^J?sA4d)NacSkMv(yRh z4sk4_{jR+AhQPc~k$cdu(n78?bBN||SkBDs4ek2p>3xoPv}n+<|@@7=3YT3|R%?@ye( z5eht=Pc#uNKjfWM^+YE$RM&sn_~(pJ22*3ekp49+SUs4z2Pc+1@NW4%+&ugi7ib0d~~*9n+nBICaa72uQ7 zdlyC-EM7K*VHpb>P7Nl54vF(*vEuBN-1pqvd#1~8#=!PoV1#8o0&2}E@M=)g% z^wQsc0(CZslvuj(t-AMe5P`m2ge2$IcZb(q{3m333ua5$5QVTeZb%3sYRQNu_EctO z>2zlhcURguOqRTa&o|SbdH|$hPEEg*5J~hTh_ZCEvr4P~9@Lb|hSDO9S0{xg)gdPYFq1=um4wd=N^ys{r>S=sx6Dwx9F&?B`a!e zVy)=bWWqN@D0kC=7%E|9m5i;^w>4Hq2Q7zCBsyTVq^tuSR#BvoTCs}6Djlf&p4Uyk z$Nu^K`+Yoq{TKJ`zCWMO`+dEy>vg?eugk%}^doy4bp$2M776}|%78V_*gt)m!A&=4 z4~V{JSxdbxk}V?aJGT}MeGM-j$mOd+0|uN;V2ktwhvS>2aPtWWVT-E-@1yKVN>0{* zsx3}Bu!c82{^s8Xj9XLZKW`USHN?>um84Dj0o44~0EgIn%KpUE8h{|>)XsT6ZuD0P zD#@S#ox-KAP%^~&oYV6Jx(Fe3Ucc>cy+Dx>IR z4jVP9C;AgB009xn;c?eR01b2v(pTB*F;-A_9x$v1cU2L@78V}kAP2yhDufCpQR4bd z?(Dp|a~1T|gg+|DH6}hIjqk0Gjp|$z;|1J+rSm84$j_G6_r+ifA?Z83kyJ+1XRH%$ zM0AO>r5#^|19%;gjSNwmbYPV{%vFR|cz9wb6w)oo#pq3XV9o1gJ}($H&JcrGDZtKOLTLA*+TINbK9;&52yoA>QrYVmd}@hlA%yU>Y8ZRq+RAYSIYb;xquOI*mp`Lt-e?S38st0Sy2y zZ)g^jPZ|KxfXIC(=w-Y~C5`rQ@_`|WF{bbg3Hz){E&a?dxoYXl63jG#Dx}Fp4^7_s z<^(gk$3f8_3tR~8AR!AWM`9~%jVA%1!e^KMxa9m$@P+lj`+9UfN29;sJIeczEy&bR zQ~%4>_iXEG5Kziq7aAJpsPE5or-R zE7O27+k3XDw@p{;Oj3ue#f=cNup@{BP=MWExNb$Hq&_Y}fK+CxjWgQ!?A zufs@+Ewq71myCgnKt41SO3=%=9Ln?~J_zMjYGqmrlDF`EcXqDJ9;2wQP(8tV6>_Pm zX|4IDC!ncN6BUXjjg`7A{+G9o(Ss1mKC?`u1-oqd^0*_G>#e^hqKc_9B=#Z}!L;Mw zhD#?|hE|abV1m?Lf!CHHC)xR@Q6o!8Q^mF&j*A$oP|oU}?o5=sY{!tpXh!yrxG)U67PR9G4tE~$#T5p~}tch0} z;|G$+Kg9bHK1JKbJ0x>fL|J9Sk-reRe*VFxBv@PRM-l-z04rQWjy${h3V&xB@MEMc zot&~!%S)g_Yiomzgm-aHXa1G_eJ2cM0NjLuMlo-1A?ALBTW{o?iRDuO(-lbwvJ%TL zu9I0j>|he;$$Yq2PKXu-)dA$UW7Tb$vC@7DG#;g(&pg{Yu-3Zyx;_N?zyu&_!v_8ft61 za?Fb78ANj%7$qE#btVq^0_@i~Uf6ty_JX1?Y{OkrBQN@JAx(W0lv~2Af;%uWHQZ&+ zn>!W5%zC1palstujLeMD0#@RvFJ>R%y<)WGSAYhMTLl{u!etIKP0q=D$&Kw}N%YsO zw4GGnc`|OAM08pQD*1Wq5GKO_?@Ip%H>EDVzP=&;7)0rl$nMQ_kivCufD*E&pqSv)}C+KrGKqOy7m zb|h(Ix-_HaeEXssWK@Z&m)jDNM$pPJcqWuDsfmG^ZxMc_?iJqvk&v_9!Nn#63GSbN zHpV5-oN>3T`&(kAK}IYhB$^_!AZVkxUNb8b@fZmg^F}i^KD7(~Cu*qcO#6`z^BU2e zK?c#fpV9$O$GxiR;03)`ee=YL+dCGCzwUo}53|1^EXB~KnkdQ^QavkXvKbm0N-vr= z^WPDTH^Qd6w`dms-D}(@)^BSsdEfHrhwuAt7;I88zvzp-fAl>%YG|H|>6Yc5b~)Qj z;(B|Q$ER(Id;5Hu*_y5)weK#@m^=G&$->ziw=YZXl5qFglnp+r*1^A@tb0|Npnb8f z?UhADbMd;bkKIMbaNWY!%`HyZdT$tQrFs+*kD8mO1qJQ*{bTD;OH0e`#rb`RiUU8M z8%dO{Fu0Raw{x1NLZL9kVW5N~Cl~lLthIGi^)^lwKd>&8!B+rIE<~^LU2CPyE4QZS z=DL!yxpQcyWcw7|$eB6nho~qwR06qhmvZhT4Y-{=pBF~1N2`{LRxyk5v#bd-bDh_| zNV<$hj&!!S|KYlG$<*LA19{=c9^v5y2`eAjF$`c14?b;VuMzn>W@Tl)-i?KaL!z?4 zBWjUrU|=@eD~)5L0lCo8uO9F8NmhUL3zK|&(uRaTafVavhYi$`_SbC}qRV_6ujfo* zAG)qwc@&Y4f9POS@9IZ#1*V$FFe|Bq7_%!7P~7It@%kAKw4KX=d0NV*JKf*$P0c6f zI`QMR%y@mtRPwf2gg;j7&**W|ckcWkXIS0<_e?i3<@m8*MPpdCmjOZ5B?}5lew~)C z!NvRH12P3nuKwon<7BB%SoA-*#XB$&%U-@zK)@a_xIHA~=V}u6tgiCbR)11Yk1Y2r zC|(fdv~+Z?(uXSt1 zz+dLcA7d}wgSzGJ;1G4aRJDeQ69vsD-l3tMfV7ZFQ##JO=8etnxO=~x_9~?r)pE{w zpKT1Oks0Y2lMicZ=a0Tb!y0eIT@W5$w6!hZ-fC%Uo0;pWMs(a4D}EV3-;dlO?qPnD z@^o?W0?hU5uN_FaUK(FO0qIW_+zQJO0o-mnkPfuPi2%M+Cck5o+?{W(+R9v^wA9pl zt9mInp7tUh!~)8c!&JygF*A;M`S&^m8p(V<0s$uiinEv3P7Lwhhw+qiCs{X1eF>vo zmoCji*1wN}C*$zpnXoGcDpww+-l}VCToh_CedZ`VeLNkZu*|?Rgs0wes*e+t41cX& zz3$$>;pf_y39gDHUF;gRT+Q~(1i;ETc+iCmju%vros{7bm~+Q0gZt|Vq1zQJ4*6aj zK}H{cJdKL}3bt*vckXzHTIafS{;`a#EH_8TXgbvCl>3=GcUqBJ4zyUdAAL*9nPESr z1338>A2n`iX=!C~xXzrJPJov!05xN~=ok9*NdedH*0t+a{XUBn>aF6JR^QMdP$1R= z#^&a;a2mlH#tlRdtC>_dudsh@xlfz&^YgCXP4zbe z0J`sxA=!j0+^Wu0R8%}T?B_kAjN342kT8ZCZ{*Ja*mGyV17R(TE47coaj_h|eG5kD zFt|3-tfq@!&QKAUz-Im*Z8(iTmB|jvh}neJ5_AuHSOE9QmE7&@)=R|>jf|eN6Jl}g zLhY@@9a>|#<Sd--^z7IJin$#Nqbo=;)biRB$gVmTLpACr&TgP4V!aO~9&^ik{dBE~z^D3L@!5Q@B zjZ96S%%0LMzNdN*MqZ#?&UQ|*@u%B0aN>$w2=Rf-zhseujA!I zBXn!2;QUeMj%yraYq?;(FdnfeRM)nTfZsj_2LogAenrBWIP(!Hkg6Zi`fjHuHt6Ekq#pYLU#9 zX03Q|pg8vDpS?p&lI{no?`Wat<42D!=Uh@&1;rhqoc(iR`J@G>btQ%?qHe>_h6NsD zF!A}VRjn|n2MuxQo@*2B`dp=tR&tJzqqDKHON(Cbra>}- zLyLuSne=w?wSKy~Y1D>xm#KL7VFb4}97NmR$?4F@Ee1TR1+*P1^S{|g9$3eSS*V#f z;dv6nNd@AV0dwI_mv6pT@6TNE#b6e9v3wwX*@Cu*`7x=Ll`4R9d;H`Wcy$?=%Zyjq z3}{P4+F9NFnDUd;G@C44MR29(u#F6*bbkc3;kiqfDzJi%+qNx(VyG(itk;1NSllIs zKix(`l}mUapx>i*nh<<@wem%6pV1x%tz&37h;hj^Q)SSHhwBFM5d2R6^2;yyf>}yp z#WF_veubW5)7fh1LszlfHnNk?N>YOC2zfYCAk8 z6|nSE->HSZ9p6PalWoi5Ax0PwUkjdAE(pPM&w>Oe0SQB3rII_ag&{H^ehijFF-y?nPQI%dwWebOgpdfVcu}{-Cr=d@AHU+#T_-Yj1TQDU zlKWv9LE*mW#t}Dw)7<4p$?RCBBh1a)EPo1X$U0;SHpczze@?qvr{lh8a-7sQHL)kH zbo=%-Jv*2fUJe;5@Y=6TcjHHXD1fx90Il|*6t-=Be6PKzRGoWtK6X=l%n_VR#5kH+u&^0%GiYL-o2eC)+8 zXcj>ykI^(8VPIfQR93+xzToY|Bq&_tOQ|in$UU4L>A11;>sCl|rr5`~G*--qf6#sI6#k=?PWZCcwO|R0Sv*QPzk)o>`|gm?Vw+&9U6FQF{zN-XHr(J zEqXl;;&urU+6-%4YP3%mwzpmRUA`bq!0i`FG>=h?Et~TAWg1Dp*jJ*3$mC!hZ*ERf zFqT6>7}2IDBIud&`OEc|Hz>RU(k^s6b_~a zI5g34=+Mt0sT_aHKYq@TTcMq4vKzj|p{oa(9G?Ss{k{8e3>sX(%WtwrFXp z8B@?UPMXcIZSTdH*1Jiq_D@FP8I z%+yq(IBf4OEcZ1pXkX*q>bAi{`B7NzJ*j(t*WW82Rvfe0=y zTe-=<@o);NCzAgU(Z~7C=tL7162haP zaBpP9v43D71iw&HvYehsPv2>L`0(5J?>n8J4-E|+Gc&V3eL5^4U~S3ik9F16{^ljl zeIp~9zq(3tjy~p;Io;Xe{9N_MZ!tl^;2SrnU%h@E5*F4zb6P?|qNcVs>ej9Mqurd$ zwp}((PC3F|_wL<0@9y3)bGpR&!>hM%LpE$-J?Y|-|KU=Q(3TC7&z{T-e*aF#%BpQ> zNO$y+MPFSwo5k}>;cR=)-inP~f3>hsVs36uPF~)|!66t=gkO-yb8<54&Bfx}xpT)b z$CzJ_Eq3?Ox;JmAuP!ZlOG-*gcVkW8G&S+>*%Ll9Gec=5EG)cJK;ZVRTPij-yi-$C zJr!Q@FJ356>^kyL?e+C7GFJG)%pjGBPr;c&4`Yb}bzpJ{g%3m2b>kTvqq}baZt)yw71x99!v6{nu^S;JWbJ4m&QfcW;(ug{R!UeSP1* zt0l=_%s%m4R$pJ=B-d1LbYy)`xmz$cnU{|*1P?7QpO~F3gnhym*VWVnX6oJT9~%qJ z&E3r*?|%Q{kIuyRN2rY|cM1v)3=h{QNI6i`(C~_j-@;#_IOF0XXY{83t3TG^H9%*=BLsskCu)-Eo3xu*H&FJ2tXh_`(8 zKHigH-;^lKanyv)I6YRy+gl-cD@T8`^6K}N)Pw068Akc$GUlac^?r2bUvP7KpcO5! zW5*65jw22>Ha3&<^MY8gt5+$`_LTVt1+B+f8JPWO<-@bh-CnwkP z#KFMQZ~3C<+|Q8M*w{8B^?m&6<`U}a>QjwkRRunOFCj<^3kvSLbU$_f`HBAW<;#{H zed{y$#$rExI_B-;gOKn0`T2>hy*)O*;RZe54%stz7|*DVtSK>XQeItpJ@>17VR`;r zx!ds854lI{v3s<%v|UeKj-QmaQ_nKaq297(up{R%*4K5kjbB$!Z}e-$em;$ebgL@g z_2W#;{a3W2=GNupPNrq`oUQ zbf7+R`d8_d2Gl=?r9V@@<&6z5U6S2@>F3GzOug^l zzl)#l6j56Fb1LDevBHrck+u$JnQuPx94Wqw!m_fm9-f|U89LiDPK9XPle=(@UnlNX zVqyb+>&kG_;#h}~eB7T^Nhzs?(X5nD#ZC-* ziPF~iI@eki1*4$dZTzKYzZ%!l$e3 zKA0$Tx()|92c>{x|0P>I8~IeRY?EDIuMBf2C@LmMJ8i>L#8Kwo6c-mi`KiGAOjmJ; zl*9WLL=4hcOa%XoZjfB`Q@>ST1nkS+>VO1^Q`bB7cRSeIE6KK;MD8ng{xBBut@O-i zN#CU@jixF={d;fzObvv6d4!i4exF3rTS0+13UIc0$u3kj4-b!)oWp|1w5eBoot<}4 z(Q=ZEYEkYQ^z-K#b`B1wuP^qUs*k9xtYN8FlnvUm7aw|vdP z!Ai~j2>%Mt*))R`rFRKZSte(ds$2(Y&Q&f5P$2Q=SyffO85R%}Y)6(wn2dIpif!61 z>OC}97x4i2EAb^SAGop4ys z#B)r#TIMUaaN|X4!?rW9vFYG|B_t%+JeR|n)F zoc64P!CT)YD?T^Tph`?gFnVxk3ljduEnD{dzJkmyg>)`w4h7NYa`_uQ z8(U+sQ+rGV-;7$hXj`U!lJ?oNqHe?Q_sm8~(K^I#_%qjiZj+Bbl4I5K92a4)Kbw`6Rr2z7!Gj=ri6;0dQ5nf8s?zRFqLjcjtB)=Z=$zFB1RQlU*Dd zBmS<()7P(GUwCEO-v0f)kdl%T{Vwl;@V)2HpYQ$tHX5k(SK%+(tlQ^ZU3Xnzqjh+w zlX{@JQ3bmzoAW~WMFjunrs8FeT7c!^;>gJW%4@WO{W&+Hkj6Jn4 z>d~;Xw=Y7uM*z-SxRjNbTc0~eupxthZZEbc+vMT4Uq2@MUQ@i7|LVoRxB_Hfxw3c~ zxIqg?cWQF7_d%pme?#nU{;qFN##$dx&5nIIHBc8W?_@xo0jN3knpSyo=Pw0 zxzuR>ZSFulA?s&&FSdgwwQ0h>{MM{ot~cFQtsxERO(`H zUxz$ylw;z(x;+0fAYmH$%(!35bailOs1B*$tl;FD6VKdv@S6BODQxt!%Mg^mbUh7 zo#ZstKYu5xoI0|NqS_5(GK*SDodPOnTsS#7?lueTK0>9EQ;rI%0lrv8U823{Uv}{d+Za^{A8-t1&#JU7q{Zw)3kiK6nPEiSv=15(isbweF`bPTf!Uh`pGkR9>1~3*>a=%#k28MMwjo z4<7JYT3Yh2ETj0}N>1LYf>RdiZE0(}Ga@2_-}ylq9#%p|hF?nRiAguTDi;v!u4kPE zm2)m#X$C%b0sa8ie*XL^saSHuLuv50Zen=&midWqg8TO$FWd}(TGQ6H?cBL@STMCG z_D#h_->zQ0D)>IEBU6v!?RP?)`W05d?h3E@P?3k6`m&#ah$tf;A1{qS z-NZ)+<>ZXI4UYhGE-WmpTenW(>91fe-}_$kE!{i;Up_p7PCDm7jR-RvXGhDkPG>QI2tj@#!qRyRx=+ zc1|l_QV&wep7~_>efoH@hCct3dHVjT9Owc04nJ*>w9t zZ^y*ctgg%}qo05G&2Ke4KR=&$U!Y+Zx`L7RELz|*o3Agf$Xii9E}O|PXpLuAUqG6_ z1H_6CAs8vtmTobtXS)-{g$ozTo^LD4A`)xzvQFkK$uu%OT&!3>O4wMf})q~1C7X`?sHWiy00A{u?&*au&nUB zsGqEW!Y{*j=;q7jW`j}9ewKyBMM*S8;41cA=nrIcRHZ=sCI9^L#1Lr&P|pAfGx$Or z?i=SEdwPaN%Klw1YN#!C+xUJ6sy`PcGGcr_p93Bcm68$Dc0&DxLXU6om3N zYI|18=&8va-YxfyGqjhMrkhF2^3C^eI8f_uCE;|TuFp>#Sfm~4(4^TSG|2XZJ~}QG zSn1qTCgISWoB>Fzo2-zKb2!U&_&ogHTO|7@W%>e^JlOX>naQ13d3B49K0E+H@|sI|(^@2lUc zPr}24Jf-Kp+2bWhgK>QeTmRr-EgGv-cM+b3d!T`s_n6n9Zw4_ojFUZxfRFKCPqXm< z=tXykUk%!{y{*9dAl_5*{;Taylf6Stq>XA;`g;?M*0W2$8qo4wELsZoD9~I468T(u zjthmzdu3r5d(C|6)TxXI8;vsg1q6z0-)=*$R!3lx8VpbzPn#xx>+an~1oWq87k95& zv*yL(Sgwdg*{yg>Pc3xKo1=Jt(eK;0@65N#Byf%NbaW~>$SmkRk*%T|*PF*BB)n~X zps@h>v~lCc>ZYb}M6|f~pY!{c#RB8L;DCY>`;9DxqC>jD9g?=xKw)vMf#%FW*ytKR zm%9mq>)gyPcPmn9QB!FqY+Dp>J~et_@ueLt-K!(AP)EyGM^6 z6?JqR20FPg)*+08B`>>vu$9qz7q>ImdzTKIByd60iz5R8xByC1I zM<5e`N(3;VLj-ARQk$8X{SQaDIQL5jpKlwk$J1~l_vqsyAZ&ah4)FjEEPlvwQ3e2j zRrbuWM;f6IKRBc3r{20%09)th=lAN>D|D%#z;B|nX~{C+k_T_qr+g=@jUPlb`t|i7 z^{~8?Q&SKT*slUpwSbbdu&{7MHX)E3q?6sai3fVS(z9O(14o*CJcsM-*pai}Dz~U0 z7oWd;ISVx&P3Ia=zCjF|c#@BT0~&5lsjsi6!lEuMEotiO*M0hQ2h5t&q6~ftJZa;1 zSC`SzQME&dT#;rdeU5v2$_oex5b7kcG9MZ9U)eD;;Rbhm`_CT_N=i!V&6{`r-=r+~ z;5Tn>;F)_tnByZZxVom{3qY7}p`xM!$%l@(-RUb@I8^h4s;ZQiC%*CnQ-DSX(|sc% zLO@nF4yYBl9F)EP5JL&tT%119=%gof{h!e)gSa%kZ?$Rj=8ilTbD~P5m_EPc;{4%Z z5QK{l505;6jspBJx@1|}(P4sbc`*77tDxsIwpE5P zj85_~QBnVx@CyUihko=`F>Kprm}8PSY?@;n1g7pV)FF{QdkTw-(_IG}Omd9Z@7}!| zd}cv$afiDHy5z)HqJY9rM02z=coh{nO3d{@!8kcNrDtVDM@1dLD&iMC&n|``h3a)f zynxoycK^Uy#E2$#OWG=&Rp$0z(LlQO=n>q0b;&%ks(LiqjeqK^sjWkmV_sG!2Q@?H zuO##gq|?O8pRG#oKYupse9!2q5h|@16(6sGy=ZN1CFBJ1FA{<(4gkIw-JIi!^1gj6 zcqdoaqM^T15Pp)PLPE6s_21XkZ2-THzNz5p)6S)jU0tkve0-!e0n>a3TqZ=Un(FEU z<4@>R9o0=unMu6jq#?Vq*)+tRY8~IW@b|#j#`mMSkqsiVduW+ zObrNkPB?C;ye}IYbsT;Teg956!ow?M9a>AER4pL9Rr?82$3RRKJ^9HRjwzBJ;Mk! z=x(FME?it(@)yTy5bGq1xpzIKYu3R&O`*=VgPWa0-vIdkNb~xEfDQEQ?C-#3T~vBz zLT#j!1cDkwC7~~nMjvWk2iiQ}U$YT}z|P#78-lV+vEBEfWN-iUvs+O2MGZh&h!pKQhf9i-3p+ySS47hkvg_JJF>Z`FRDdHE`6 z6XY+Oh2MjrLtt(C@d>9^mKJ(mTshn-qI#p~9MA-CG#V+^S{i8&aXDxFDWMmk`qjEp zPvw2rO+-?BWYlU+>U5797VPaZh73aMStipnhb zXz3OH=8fjk^75>Gju49)cc6!*_Eu&gZKP_k%0IkpvS&{I{8R``-=CANdAJ2!CgGpK zquxK9rLSEttg?ZgnnzokYr`hSL9jyhNCqU2B9RDlXxtEll+1%OEA7-4h@>$M)n+%x zu!Q6d6>6hxi;~Cg3z0u`>5752P~g-q0Pbl@P4c`74_>n$RlplKR9izhW-~9HYD{3| zW;|Hzt|P|^ZPuee4WaOp2MfWQQ=;CUcXJzfK2bRU%wa08A7xADPuFiY(tXlj@XD`} zkC6Wa0CPOulndxqQ6nN0*{yADZEJugXMRTyJs>}xRyK11O9ds;sjFBRl;2>qfszqu zx*)tqJ=(gL=~~fpek%(3<|W4R`h{<}DS2wzN2X8ur+I_@BJioD#gHh2=twyE)DyRU z0h~Z-i4W0P6L}k(aYL|mzM4`Uv`qDf4@{^QU}*FI{qR~UcSjJbNUP#aaA~@NZ|f`EZFCG9EsB80~8_mJ@(4rR_qX$IW|M?NQi{R`5mmJiXPG<=BYncmLi< z+|CJa1SlJVP6J>n4Vf2b7a;0Td%5yHoOe7XM4I91K`o) z(stgEP%ZrMf`e%Z}H}L zs2@7imS-jfp|W;nv>lJsL%xHuVH{t95`>C<6-oo-#mfn+SJW1`*HUSJhAv84V!X`} z`A}1`!0TeZV8tY)rEQxMWzaS#qoGho+ezUnkZ*z{5r+Q34NTE9_t7+W_IwiZCT9k% z@zw$c$?pK3%E6yQipNti-D7mh*eN9yjYlGj1?eRZeW!A0ycw#m z`5cbdbTt*%`SG587AH=`MjRd^3(j=#3a5x+4CK-x?*&(6Fg1iPdf{E%_+B9ii6{i9 zl;?~ibj(b5_9U`^F@A$ruz*0*P9Gk|dQ&rn3V-|9Uo0dfG?LQ{>Kaug5UtP;?x+=_ zny#hVYWw^9TU!SQ<*9N=|MJ@WfkKb6Dny$hD4{7ggBBhn+(H2snILn&r70>uoNR^^ zFVtwL7#Cx7O&FnrMwgcEMbDvrc7Aqt5R#J(b{wXKAL0qV$I4y#Nl%6RXyE-dS<%Zq zMw8lz9SZ-?Z&g)qet}s7^-2ec7DYGhwe2DSshc_EW6(O}Tb05^S5bq!GPl zl&S8=M<)i*f(4yzY-~IQW@KihH2|FHm|TLd(0lE5z^Uv?-p5L-1L+JH85mxJB@FAa z{A$W96aplSPU003c9^$5oFEWSJQrw7*&6%`f#`rTtV3n(u| zmwuT86{Gp_@^=9<$0sXm8Y-Q75!-tZkFqOvtWf_Q7+`cz(SYG^iZmlhJ}0~$D5dqB z$}kd8L`6ku*KAW|gi^-ZTd0!^rs6(H$CpO4ZhMSo#1ozv%-T*dF@xzpuX9DPnTK~J z3T5G}UJssUNzcsO#>Tcu<!S?Q-AB>nRV$`*O~68;b={rl`@8hhMdO- zZ_yj7jzk7K_;K!OLyA{Hll-~ZDcZPjrt{kRO*^FQH$zMQsKCt5zFG5|K(Rq+vCWMe zH(Ep{W+oZhUtF1{rluxzr@C*7d44D3#X&tlO|U-dXD7Zq-wSs4qe#{^SccGNY}l}& zEox(`1}C!y%nG*XzUD42GvM~C2kf35HL7d zf3$d!dJRb+4bb)zaTXoApp+DYj*bpLZ~QKROVmvkSQAL?2003Shq9UcI%)r9`&WS* z!PS6!+}9+_(kEQ3y(ifjimvm)g9l5n*`9B1@r(gmJnSxZdhouv`DpG3=ZnXV-SI>x zG6wkeP)|?q19JEnT63X$=6h8S9{d2h(OCs7r^_r<#t(u9$tJ^tgE=3ZOFUcd9~i@` z=G2F?WywPQTk8yeNYLR;Jg~jVOGp@PajXOZlP)VQZL$hV-MPd(Ute3>nEd+~&%x{2 zyN^D~lNA>?tistCo0)MBijJ>D$1$tH<@4_^9nCjCqGLYzEEh9Yb>|kU3 z2edF_sDDn2vcU7RTz*Tb=Ut7G-*hF_PWFe1Igfu7mz5PzR!+gsLNjed7K6p-*-$yk zjU7b($Rz9C%{YAt`!9<^)a~%@FD@!dgJ}iY*%a8%G}GL-&COJtJx>Gzr~(ZwPoCsP zPq*P^P0hMkZFS(o1@M=jD!lfBmS}r%_Ses!pNi}@`7F*DNfy0dMsavC+j%^34fCNx zhrmg;gRKGxugCu&Dy-+!Jvaj{N9y932|r($s~X~qXyV{WBNflu+h-=&6X=k4;&~jP z@Q8f!*FRu9cPM$64Y_8bieI^MC9+A_*4eoM8r1HeKL=Gu!th#z66>v|Y6<3J4i689 zhLuQrg7_&QNeqp)I{twv$)s8p0wXI&WI| z#iXY4Kn*4=$kK%0YP*lPB2l>?S(Lv555CcGWMaIv+X}GTI9*ZQn(rbGpVOCTG6xPE z(7hvmK(w2Q1b{__XDo=UHH?2a-b%Bpju3vvc|7YH6_--@&6{^*PBSYhDH+_m_8_gO zq{I%S6N+LA1Rtfj1Jd&H&su2b0H6Q!7O7Y0S=rb?tzAP?2dt3^DU2qo{Txo(DG1Oc z3E#PU_q1;$GK+Eg##E|TuU<*VkKGIpzi{c&5OmvjN&B}WNd&czmV$aY4~JEgN@y^b znwzUDv?oK!L?MlIO|MWq;cc*WQBm|L;(VYsK*8!z&22su$=QU)2crFJP#kcH3bzHe ztVP2KgW5^Z6oSEt9s*IJieM6VQ zN4-8K*jEw^8kWHN)T!41_mF}TI_FNrE%LjRU!LHE4$Zf7XCRCRL|4e*8{IN0s54&3 z>rx-VQFQ+-i=@ZoN#xUcDU$%YAvb}_7=T5cweKT>| z8N1xqMJnBqbxG9kGJEDO44**9n9}URyg55E@2>3*l(o~sjmx%96&o5|tJ3$_Rrs|t zj|!%wu&W#Jh!wZ-R2}7s;eTA+##ePbb&u=Ljl`il!h_W#rrjCDDDm#nd1{7AlRu$W z-1+~q^J+y2kUvQcPDggf54Rk|2LGQv#x^fJ30&iK801t-6A}}Rv-CN>dQa_Oquv8aqPI z%zOxB1}F%aY1Sh{qg7V}{~+CLpr~H6&RvF)`D;W2q%R ztMiR*ZN}4zL{UMUe){x@qTqa=YEw0B!G(cCpky>a7R!MTmEu^UNb#9t*~Bbz9~}Vn zkzl};$#w(fR`gZGq(^{6)c zCWG#Iyg!Sgr!-uKw!pwpM-#z`PruqEHu>2hr4T*Q@M?9C2Mu1A0%j8_r?#D@k1Vuo zZ2G5{Z+U8eeUQAwxt?i2PAE)!)-?;s5DC|Qs=t;XHoHbm#`cN9FyeFPPuC`ThH?9+TI0zta(`2#kz0 zD4c_7Rvjes7KpUB=GC139`oq*$;!w;5rZL5kH#XDPSxp|w@ZxuaXTY*pUB$-D$1kj zn$Lay$|CY0+TVs2{v%B*92^Wm4U`3B9pZCxoI>V+c(21q0f`00#UF?|)i_7H_%Q^b zwUh9K#*X}LOlwnM4t+iW*aKFYu#Z?~k1JQ4WP<7VQ)wTEha#2&E87}I-lu&Z9K0}4 zx8=o)7r-_4(7>86)}fPP03X<2SWmTub*5(G2c!e!H{zs*vJ^rTf4Km7juH8%7H3A` zjqU&OLkGpj>Ej~~7)#vI(I1iyS!zhv7x~RU*C~?G!iM!+VPn0FoS5*slF?7E~xQOo=5?Xr{O>l zwG~v$e7V#{Ba6kwu59X=srd&66Gp04G0s6SlDvfJ0Sjg`J=7F<{kn5S_@3iWTdMq4 zc}wA^{0)5@8H^p(JN)_c{b(rd`d$T@H=g{DXU6P>hXiyI^$g<+-`_;lC&$Aw0aSj$ z-MyxhLED zO>vi^xzRPO6kkmvnEn@L+Lqv4gWlzNs5Ffj8`7a7qp9-m@0HQxK3* z&FTQSv=yAQ6oi^irDpqxop=@liJfs#kv2FDY1<;#EYQPL`-N=H+TWb~GqL)i(&!Qd z^b*&-gr&?~^1^9zb5_#iB6tbZUjK9pr@|eqK@e1X^eZN5%LaF4w*aHUH2UW4TOLR} zXfdMDL=&POhd%4dID+1+%2yHov0|6*IQ%WfKcNv3zK>E$M1&C(T7Pkxi7t&|>AYfz zez6x0fiyIT1{^;8YEFm8N_f62YLAGmo%M^T1AY?6l7Lzbt%!cbe8ujOnJs)C4LjH1tQI(gM zw+u4@NYt=m#$$8E;p25kelrCJFH9~CcTcQ8!o2vle;lT^ReNNrpmAi(^5&&AhZZx^ zMt^uQ-k%+yNKfh5kPkG)viOO`l(B8W3Zv)&^A)UBE5=Ewb;#TW^85<2*QfvAjU zA|26;Pu=VM^pI3(mHwzuIn9U}sPk5N@z4l9-ZD7SJz@FnH}x08Jk9n}uYEIn{y+Zg zR%6otKltr`0|Re{b)AQddt!)=pKkm1;MmwBIi(DYj3#3)F*3jR({nG|TBloWqka2r zWP!;n{fejOm}Ia}Dk^uZCmV6=;(u;ArF?2;e9N|d_(|3Mo6%vLkJ70!*5BeeNP*lI z{7NgZB?8SXY0n7mfsgLtu?RHD5^h7fIC${i=GyFoVh3>AuvxfG?F!hsYElcKL!waD zqa7?10fq-rO{|lkOtVdLZrhjz(yeN2*ovK?R9YP2gQ&596vecsg#r!KXSBVbV7%u( zJA~TPt1fZX_Oy##yV5F%ZU6?iM54+rX6|lTpftgOdxOS+0PPlf0GvA#rwY(^;afrTb^sWYa!5#D4f?cK(PWs; z-4>obgmozVX=Fw&V}P*%4sDpm3LTm`6Z%!DgZ_bYd9Z857elCDm#$(X`S%RSE#buY z3_y4G>x&#{1-nENt{seFkz5}vlsn_}EWE$r{re#xnYym7L^2i<@=i5oud4vQRy#pS zuhhr_v;?tnz#jGlgby?pBc6ZXmZ8a&)h+Z01g#WI$%Li{M+{6Ezj^`>{$b|! zqjL|W;99VY3**lxVj>Rn7P0!%HAG3F-!P(Sc;(Jk%acn_ok~yU*puJ!zb8TfPdE6n)VL8)++g*TVGv4Xqxyctj zgH!|AQ_C#*?1Qp@KM182KX?tZAY_sSEqYdhXyyy^Vn=#x>JT;xb8lAI$(ZBh0czZK ziM6{h#^)X44m_(#8ldJJ1+pl>)5DlYiupi|A)5dEq);UJ$*e3v2xYL1q2&=Fi#7i5 zuko8CW3jfHg{#~k76a%}hFAXkOxls8`!b?rvc_(B{>U%gA@80|zA^6KIyYzYl5hO+ z@o_x9twoZoR6mPJ@qZWQFIkuy4h6PvZ`17ka_GWa=XI)NG6x^23wf0ocUATOXFa;0 zD$%9A3JVYS8FSmUh}ECX%cTe6|G^zgXBUmXbdC%U-o9xOy7O&n&ELt8ZSTP-*#iR5~IClskRE~tcRl72m&a0Q%EtJsz z=n%27T)TG7{f_R9BB)NOM>o)N>VmpL?V38({U1Mo_s=Ob{=$!p9`yI1Bux(%FuP_kC^sBH4LXj8s7Z{GC6g#?{jC`O-pSwKCpWj~HQ8ot|b z+x&*DjB()w&MQe&A|fIl7~65bV;Ca{^9L0tmz_|%i=%-Nmj2Ko%JXwS&G>CLrjiKv z%z{KoSal4;AUNwm@^N}aK-hqz_CUVrG%R#**5s=g8*xI;UCVgKTZWHsEdYn-%t$rr z9HX|WFB(hIP{JLJS-4h(l>*J@7zO1lcUeHNuDKwc%*lTjtKvDJ8I3F(-##%yqj>l1?ylo**>Gq{++ zPm_jfs$V^bfhN^2eV*PELS+byix@Ly05LiG`H5g(Umtw)?Vzym8xpdzLolz^ynU-R zT9$URp|P=@k&0HZ7_#SH_$C#BJAKA{A|xNItb!y3c{Ci-yfE@0GW$p_Gc1jVfEN)R za4LvnYpd6)GOCUjGkg931nz?|Z#}1l@3ZrLloY7wiF@lYY@}A`o%ZuNwh}7Q%K(32 zOh;NH(UPl;`RF4K zn*2CuZ zeQ4gN`TbGB$Md+L=Vx(t*jnGd-uk}jMLQg z^Khmv4=?XE^xGOV#oJurkN{spW)?BOpyK9dj(K8#$|gM9xDG?Va{5Pl&vR(^FsACf z;)2#2rWYpNoecpRR9h5g3h1UWUD970CSp}-G1S|B`>2&qEN0D$99wyD)WszvgbTjF zICIE@W{QhsNpwJUU5DwjG5z5QPv-_XJuhAf^+uL&rj75M__p=yf)e( zh(m*Z7lE;tm>+Sv64xAnV0mx64|;8WFt5OViOv-0@2#3@E9Z- zyP{{&GbhjgdI1;E$o>C7vSSN=*GO%I`FcgL3?^0w0G^RSCn9n$h3j| z$4hbkYgGlwYe9NP@_}4T$G|}3KX{{HE{KkafoDC_wCf?>A-_L}eX8~rywG{L^1wvjz=Zg&UD|HCXkc6?zACo0wH3E@zTP_mubd&YpnDkL zM-PiLW>Mvv1Vbf3JY12hQILrxq=*dgL*QPn zVeU2ot=}87GUM}rZ|{>88v&a!e<2A5Zzt5}>C%GI(k!UV@Xr~;NeyF^H68jsavSC2ho8vVfQ& zNtAZF2HOMDS!`NHjB zB^E-hzK_i)d-m)%G`BX;_7#8b>O=`7>)DM4CWO9vQQQ%1 z7cx);m-2h49hm>y467a)e?~ju907+St6`O|kMqYz`ELf*A=!cD!-2pzxPeA%@&k_u zg^ZY%N!O1>Yb`l_6h;m^wJE7xZ>PYuq`%3?L1;o~9rS1^f+h*gw2q9FV7dho3k=a% zM1PDNf;@Z)QyqE{W1Nbl)n5Yx10>j67O-UIVHh@JD7F4g^KfZlU{JpawQPPh#m zZbRe+HkWh#PEAJN#l`86mO*vsf*X>t0x4=|-$04yhm{zz7Wne>zzj$;*Ua-X(XY5q zd`-gE4D|IG*-xAn(t|%7-PKjnrGI}zkB&P493)!%bnFv}S5!J&l9A)yVS~Ft1f`{8 zaB@I8fW{eGExbHzEipd9_41ocdu7+In4S03cowRl7^=dzeX)Sgep!>|6paL2>q#k3B3s{Z4oO$FpaZR@3hkQZ8rxO(-8WQ!>Ev zqitBTHF)jT-~dEHDxODIgGtAPb|>7YFpJ@8302^LhK2^LeIPneVk#kqm*SF=gb1_T zv^3tGJ9mQaqw<*S^*=d$5Zg_r(uw^O`VONOvvb=Uzkb*98{y%kx9a`%D;!P5$mnP_ z(j}Pc1E5V(A3b{S{%*sO@~FDHI$op*NUkj%9W|&}64tNQp@PYdO`D?N5vdJ)DX+9N zqqh~L1lcUFgNlR<1wLI^GY|d=X z(6F!z_#|NYZ5Vooi7^0dJ$&>xFjB9gqZ2lI<6C}lF(WOff(nLu!Kq9x&YlJ^-GJV* z4oXHVI{DOaJw4W(57^NQ;9X!K74@F$UPfUHmp%RlQX#)qNBg%_U+ zS1G7KiR!~GD9{lOn3@svEnK57 z!D9ISQLuMzV=XT#k_31@iFXhZ6|KWFVkU2A>oBc2>NBkzICwnh{f7@j@nx@SYt?x# z&%?<S3rpkEJpV|v8Pftb zT|;U{!$~~dR({JRt@p-^xX>>U*iVWvA@Zb9U5w2jdX2g}k7 z&CNr>;P&C@-inVW&I$>;2F8%j11hsE1v^VPJrp=s7JrCgX*Pg9t%LB^0{EVJDx?BM z9!tcHCn3a|f`UQ|oJF3GgYkGm*f6)DcLy+>A;-QIaQ@KnT^T&hS8G%=>P?$SGL2y0 z_xl7}b~fz+PeSiDLWq_(K=ZhKS>ino|g4%tRlAtU(4(h@v}s?3>ywx58}` zT6i!pIR27Myur9bh4PrWvFe8~%vX4ERdx0C@GfuMzyG;sMKT^p04f@A{ra$4Qt5%0 z??mkUfZ2d>Fws|=haqTgLM+0-MNWAZUAr_Na7YgeIA5!EqcwFzkS; z*&gMLM`7)^q(#~y7a~CSD1`SqRbPtd7tN+kg^(z5Rl!Ewv{VCOaO?%u5l?|fJgYFS zYyo~G;nw`z`>~Sv?Mzg7FGj(_ZMKvMc+tV<+m6oQQ%~sO+HMhUuj3ILBZc zYhk}!ot~7CaWammAFxJZC20n50QXfjxu+l6gP|8<2P?ZU&^tQ%R`WsKple@m?^Tr6 zpIK@?bJh#U9-P`s{(0MEj)t}qYs_wH9s+nGCBZ< zi>&QEJ?s$tk8on5!~fyKT^Mo=K`uH4Z6EE`qi(V4`xpQ`33EGWe&L=kU(}UWa0dWr z2Y-0}U%q?yGx(#R=&YgEfdgw67Z*wN07cg>UQ)xM4!~kVjkpPLA%6P+>sa46;8jwQ z;gRcy4E(KRtAMx}Eh?<|-o5n*zCxSYP%g`q>>u|trzq}2_mlHo^7x_})`C7q0;DLp z9S9^w{_K^GbS(+o)QNby$L9KisB&{|0Cc11{LirNoZi zUml+#_n{$oOnNAw`Q_!|A)a2q_t$7$3*nV4OnEH~+a0>D05Ff4x(U2R7cl4;jx>aO zsg{F>#&b5d&r*0N#4Lc$!a!0zf+o{tal{Ij$GlkdoyBS(%90d9+DS!{^>4!QH(XcMP!3&VtGMK*@&D2bI3 zjT*Gs!vk(L^ScEWc(|0j$#{YUzBEJz&ac>Gb5%9x-mKQRKzz$ z(TjJNQ5uHF6Qg3)NPj_zwXo(RfxzXda{hdQVZAdoak!Y6m=s{YqHfM30R%MJZz>U} z$>7AbQ=57ln;t9sEbc76z}kS3sFyZ{guROj7tWt0{a3-~GvG+qP>@#IO9erMMay8A+ZyG&J-)oDF1z8I$j+5wQsg zAvo->VYqqt@S!S)4*OUih;WLlkRdf|PDtY&CqaszM3q2x3TUa19`Yo41+hx}^5nWM zu#iAxXK!5$3l;j&2W1VFZu_Fs_WsFA?g)r;;&@kc+F!%HDqpz6O`0A0cQ4S88mAX0Zb^e95!AVC% z&=QyoTtA==Xo3LhGQX_8D#RQX1lSvvuJz>UQ|8Y;+ttlz!IOY^B6lyLq!P#j*aRtL zJ*+@E^NV*L;<}gh7;D<-vTPOI=}DLsxOhW>e%4YPy18SG?A>sU z)CBy-W~$q%$g!Amz2MxA++<$a#!QWBkSbOU$3AOD% z71vg>k3TIcqKt3MpISRvC2qFq>g10;bh!v0tPp+I4l9QF=8c&vUqzTVf$H|a#Rw&K zIN%9rh+|@7C0v8vfvmfa7lnO{8yB}>|8Pl$I?AJZTqE+ zrbq3k(UTe;J4TI|K&GUYvJ3}KrciN=@7~VHNI~bLCw_t!9q78rL4o;;{98)^fp8m& zSg`~^JmyHI7N#9=xth-TKix1715r?11vSJiAyN4L$Ej!JXH~43d(p1jqm_hz4WV?{gZk7?FWP^z7Ar=f`Hda0=Iwdk~FW zJ_EPmcEY(0Z;wR-j-y>wIdS5I&<}^gW9Xi%(ca;^&RG|60AZ_Sa{nLYPVv>l~IFa%aq_~bXDC!z(7&B)9QVm$Bt54(S%9FUN} zf>5SoW7}OU;mp@CAN0&-Y$6GEUpS;-hc-QqyXIn4F{Tf{D=VhwgJh$ItInNwadHX) zVfJBhDe)1f`4fA+BwC%$O92S|LtuZqc};v``Y@kF@H zbt5o$e!F~Co!TCh$(Nm-%&@Pjrc~*gkMZEfo(AdNGG7drf%`rT-8_RFj|y8=KCZ3X zLCO8hbBByGBl?i_^HnaIX}(t3xb7n8+BHfAkIDO%kJsK=@S<9`?j(2tRLz4WZtHST zG|-+pLQ*25ci@;gjA%ov^`Jx!qO)&*bEZt3c7wyg5q z0z-Xjg!(M^irNuc;L=)TDBP_oR+f~Q{-mJbDjHn19;a)7{&;-==j*uZsASN+>@W>l z72@GSlq}iX>E;J*o{!Yx&d1r?cY~eU_Uq4Q<>gk(e+kyW!*AWKo8Hx! zcS+X!dKJKIkT(s$-9(ih!;bgIpQTT$Qd~vR1g`?W z396M;NTo}c>Ulp^S18%EdcU=(F55@63&OJ!EnNibbR~Z`go_Mc? z>Y#?yB9it}KNNd(8<(|OS6BBu3QH)emi(*T`b!k^otz{A&13!L+;F>ir}u>(_AQ58 zzjo~%^^|}&PA$~NlxwuS9nqDvK&4YBA5dt4=ve<+k1}nmk>!Y`C!l!E!^SO+jdI>I z9@Hz%U%eTftXKc~D+*55%513(3HkYrDu-j96rgHPVjj3wA)qI5~1hdcx-$v{9UDa@=Rms2dwf>eJLO{n!8~CY}K^ohf=7nN7u%2C^l+ z*1g3Si)B3kQHZjEWWuBr;%i!DzGpmCCEcz8VCzUJ{7knuc0bsyh^Mll>bnkm!3ThE zkh;aEQ5(J~co?^NNo#&rtxFpnm^8jzW8-^UF({G#Zx9~Hl!oj7(HM>CTa*9bHYAF3 zPH^wN&RQ4YV@GZ&UO5lKM|VRdZTwg4u*wV8D=87sN!`ZpRa(2H!Re=mOCibK(a zYFhRY8UKki3&*Ca=GW$JxxRIdxMft z-70aO^7T!`;K<~mDWJGK(XK&5*92#K5EOBWk**0#X5(t33G$dS~V5ALU$ zybFrnNcQ&a+qav9x??9!G#NJR&yBgw{v1g6WeSL#VB50#*-@u|s!us-vFtXvyqE<` z1x`M26Rv!~n)fC_R>yVruu4}K+-olt?yB6xR0N>gSRfO!r%wgvqYXTT{@VFVmkL~Z z3sP(OXp`G7ctP>2a*e?2Z$5r3MzCH~QBUGOd4Gy{XYMlU2HRcU7Ca9CcHXB?${ZXZ zb7#Svk%-KvH3q0Wg-UB6QRM6FjzgRq>e(lHA%-P8iHM2uhVc>ip(!(FEHVf)(bm;9 z2C-x5Z4Kx)+L6rK_Tr)z)JLKIKi**Dg|$Otp;xIhLuvh_yTW8p(YYS^B^#hPlM#vs z?%&@KR;e0J|07+-eow2`LrWutAtLIMn%|oQ?Ev&0Lw#}3|JkvVT|bQv zE0)t2L@N~#jz`ykn}Kc=Uz4IGy8-UJX-Fr!1rf6W?j3>aTMW_}03sg-C5~23SwM9- z2XR3Jcuh`AJO23V`$_-d6ll;O*FZAS6jTZC+@%SRh6MKD`%?gw#VU=I#qmFM!tmAe zqu%6Kk@Hdj(vZ^-(rwDjndxY}@;-m=oAsCZ0PyYWX){`}Zige7si`iTOcfr%xhn_1 zFdC;$-E%dJ;Pr^YR30;og|?2)trsr@Pgs)q$O5Wo*qf_s8j!GiA&`Zzkt=WB)vyz{ zmNI$t?;hnlM-m^78oF%*cY}gcr}mO+l33`61UG_O%F3)=?B2a?ZY{V0;5Gl^3vu2N zTOnZ03G1R3E&A)fT7W2J_saS-5&A(LfW&OMU*JgF<7x!g?CFS;C-1nL1VwH&FU1 zx6+0@PxvmZ!y90K6O-6Fw$G_1r*?AIR8&^pdh(<#NmWC?RAa75dJeyTdDW3l0w)8< z1Q=`$ST)``iNjdhaM?LcsIORz${u<;EV#D-`f%#PQ_-xWNhS?IDQP@@37PnWpn*F^ z+PdV$g`Q8=Bo&d0NRda=b`a$p%od_4!Ig0K-a^YRud6;O^YzFtoyi!f>NSVAt0qr6 zZ`&7|B{@609eZ9fHqy@=yHRsz%-BJSrinKUJgL+s94@yCTuNvbxJ3^b}G;m(32Sd{Y-4LdaW@oz_8#F6)`t|chz%swpF**+rDUxk!?(bXc9j33< z4j|Ip(lVJdwB*Cxwvw=b1B*Kmm!;V9a8VRIve>X>KYe<-`Xo3TsnqI8J1Ii*I?r6Q zCVu{pV)i&lMa22@Dpc@5FJs7S*a2SDZSK_-)~lzvb?|G%3DikdH3AFMgqD707u#Gh{=gm`JK6K9N46_yG zQ*3O!e0`P2s=Lo?8w`AIVbK%=CDvhwYKfxYD6Yfj+&S4*Eso#>%W4&Fqz+rcf;1sU zbTxAKk+P-c4{G`)^rxaR)2;Yx+0v!P)26j$Z*EIU0QHuGr?9M~=t*yd{qbbjzVX)9 zt@#V0fgkvr0N`+r-10U*>!4FGCA`LkdFo06cF#NH;rFN;v@(BqJuBU(|tET|wB()KS zEB#i$Rr}XtC#pGrhx&*0R1!!IKtcNQ4W>r5$Jl_gnFtX@^s`FWoXWY@lo%!O0Bwr* zTn~@OU%v&E5nGh#b3SL7+52r_`o`Z1UgZ!c4W{76+&z;Bs7Y=V4w1~As za4>~Ae`2lX&AR^nHMbj=N_x}gbBR{%#OuAUX+`_<4n>V1ZO1~`5&I7MXMdDE1(9t_ zThSA1pZ#A_Z^+I!&PSiURYQdN)~xNTJ@(te{1FE1H)Ut{hsL z4=TJ9TtUnrIF;rb_?txa+7mc?iL>)NPCpPs#m*G`!iK`eW|6lB(PGP*8K~~9KZtB* z3?Kp6{Xu`NA+$qn!E~~LEwpwM*<6Zfjj2(gA$n1OOt)PMJ2%$zZRI z$YRM;x*BfSLeLwizEj_r4*riXFZW>`45WMec)s}=LZK7eHFWUemcp>|qZUiElwzY$ zXd}T;=FCdr%!`oJt;a5;7dwf%h^+>5n~pq1HccAl0o-N~eN%o2VI5G6O07CC;>{Ma z=IE|RjXadOHrzuRA>!S3$Y5X9CMrT{hKE>#W7u0XJi0Y5dbfd|zI}Y${rW5OQKL53 zbRp#%2i`CA+jUis2J-Yb;;D7>*x2oIYEe;Sj{2Cm54utWHpK9%zbAG|EiA)epgCU{ zwl}IPT3l37O*som6_1?D=um&-;vUgQ(az9owgWiQk>I7>uirw0z38v+-r_wX5wUEc z=5QE^8cJgyNcwjiQda3fO0YIC7=W>ms<6Do}Nl#lWOGDHnG3U?-ve#^yuVievvh=*(Z0Bnd0d0c1v> z0&cno{hw%O9@RHiW82(<5f>ded*7#MxMLr9cDRiStTPVsF?)y(+fWYE6~Gmp!PC7d zh%>GO(Tyj1NtkN!5v&cd#TG<*(vRfYC0?vU;Z(_oeYvOXrBp4FeDc*#tZz#x$g^t> zpi|!^$BsZ(Sf3(K(s3lQgp+U=h5L!{1U}6-i zrmyt_#IyvnJ?Rg=laaB=pk;Sb)A>j*uoIE$+A7`o5_`_X9O9UHGOg&}P!lI$gO*Wb zkqtZeneKbuW*5KQT);#KNh`C~*N~4TlOqEh3ljCFz1P_9_D5ATPu-?v=fZcs$>QnOlR7=leWh8v$@N;Iw8-ju|_4D`1c;KcyF_!{5;@ z4QH)?Z&d*d68$2=g3DV@kiah$FowehzUJir5{F2xrOU)-`gA$M9(yY^Xd$-9d6MeKQ}6iwCRkQp_K$z9leC zP~AM2YwXMVeyPbYTr6)_DQgfDkOOmJch=9s))`%|H$s_Xn`>*V@Z#qHEU_(ifneZeY2xtY6|3ZI%ekNV>{%Mi zeyK;s<`vB%(_ltjlQ$Rq9-fep&{|Cm3D|_z(cRaX7Dl($a)dERXM>A$aV`zYlj0a9 z(8lG|*Rr#%&Qce2*Vgvw71y)PDpo4aNjLe{WgZ!SUTM?|MfbBJHx{v2@-a%mtVLL- z$X#W8|t42GtchM{5gXnUMRw~@8)(wO&~SwdYef7zXTad9gWo`Lk-w?3g2 z?eOB&t0?lT%U!P&QUY%%iS(KF^xQc&IVc37f?T-8IyMR1oeh-ZH9)+ggb3u2qfz5d z?Tv$j!yoNTN=jOi{4|AoT4oq={n#-D#rX!2pb`?d zqy~4F{()LNIwr=WYy|(pmfkes`ianfy*kAX3Kbkr%WC{5KT@<#w;khGvkL`=kO2b#=F8lVQU^$7bo6Nd_g=YRq-!AJbpxi` z*vLi{I7(k%zmw&7^Aw286u|=!-*6)6-Y5K0nyOmPctgMjT%MMHfoCIXs0v`-cwBH= z_wHUU*-CR_o9HrU$qg?h;opGjvZRXR?+u_iI z=s!4xv>H=`tifjrSkwU?U;{xZxB#|Pq4CH1z9$W%OUpGMF-T$@1pyryPmeyzFTt94 z3w1NPl3jg!Yp%Nmg^8aa0U_HqoZpevPnc~KF#TEL)z`Tft{9@1JAd)wI6&EV`T64t zeJzzrPU;#f2_@F4lM*s+#OpTKUMF3!*i=(Jg4*eh&F!VSNxNpgotQ^zCJ+*`?$d^G zP`9{IZ^0x7q+=jnj}Xqg4D~c~wHQ|jdP$GBxHS!5ou>c}J!P0qW+)~PFck{-5to$4 zEdOCSeWjm$;oW=plIi63CHwD=nuuSvO6^{5!i#VO^J*kATcjF7rrdxIxmi2R8MxhX zUA?gdf0pwvF|k(N^yBkZD%<&qF{BS-+%9Y-e#zo+0th>OxlyK4#V`2>Dk>-Xms6_MUekS?N0DiD&>%y&nppWq~vAjo>yB= z)pIxY$w42I|C3=~aVJisZw5rD<*m9a+!fWdF?=KA2=%|iE(R_1Fj6D}4{ipfa_``m z`V;Gy-aUGJ!D*JZu8_oG~%Z1N5o?rU> z?40zFef#n+zF1zvaaw`JkbBFWDbKUA-jCk&V|USp4IL@2eMMnx&YW+TsPA4>0S zDesgHc<4evekjM z8vCW9ZZH)sCZLLLM>V{UlzzAmjscTFZ1oei6o==Mt6VZx+IHnagdj6`1PZok$c4!D0Axg`E=u5I^wG-Av_i@;D>k)AfFveNT3A7BVw_Js%DEm6vF~XSXdx{ zo^`nZ>|$HllTZ;_;moPZrE`trfWSF^fj@o7N0!b!t*fKs0Q8OqsY>*5bIE`h?~0!6VK- zAe5haIP%V-i?Rsvy`9=TqZ%6EEu%Oz$$eY$YLoCl?Ly5A;MYle3LU7B#x$;WZdNeffc)b-(x z^Bo+?*HUt)-UR5V$7)6C=9pK7IOHr5!1=P zM2yMX8E|DK=EYPwj_;Da=^;8dv!!vUhX1P>sB+KQB?u2Fpgkd#({nsEoG_IF@|52j z+oOS048~--GUHL9_@5#%HdH!E5*52LzAt2vJLA_iY1~+A(4Y%%^V=r(So8V{c(i!J z;0i0}9Hm|k!(AxTePJxMNDb3BuhIHL7XCes(p#K510sPy1SLh>0wdYDYrsyz?lAL~T6XezmPH>w9^*I2z#)ZW@1Vy* z21=U<>#>EwP)#c*$5)g*c&l_iHL8|oR`Bck0EsBCI_o!c=*pYYuTRX@n)fG2u3e)0 zF8WKFu9$0ld+W(R&u;DmR*FuFHeLI#W%)i8QO%kt1x-cLYDOC%F^$w*`@%Zw42(&6o%%QcnR+ydtxUiJ?Tf{jngZND&y;*%D-A52RMzz9|LR#1vDX2`lCYE*Q`e3g;ZqSQ>$kqW{k_ew0tikH!0L`ydk zzM%-oW5@1u`To48iLwq-M@}FjbPLaq@mJP?-p@KeGp{%tOj@A;Inmy523n;>*e(@o z2gg@cVYE3And8?MPJ+a_l~3~#nAVa^LoZa}t+iqRs&K6FqFU9!?AGzwM4zipOJ5m~ zw8?8GV}$@nBqF@u#V(EBPb46%QYc8coHKXz_rA8$__li6S2HNmMMzIiCpDtYh#0}d z334qCZ1riyh7V#?-`~<3v86W2aycGw(~pXUCwZ=DBhfdeLfV>K`G+F^*@j6k=^sff z{@K&S=JuLbrw+H;qu*b@Q4`HJ47ofv>Cs5H(jMK5kr|De*kVkNy;oPwi7r8z@xRBC zogrhj%%h8xyv%#Pyy7z;^WCKXe?!p! zK_pv#yn%{wjlF=(tvziy3IKhMojv22?AAS9n9utc{;#6uBhDANB+19cDSE9fjS18`$HWdlm<8}5};m*lrG z_PG>M_so0sY7CnnI($uJX4;Y`$6MI;oe`X_`|}irVeh37dYQu~Pptc7fGN=v8?67Q z@1=o~`o+Xd|H70%7jiBKOe1JE(*`-{eW^Kr(n9_ui15Z~ww-@6(=lN^^COhy3Uv!b zN|1TXY*bR^@gHY#vP|Vk^nEG1JI#)H!g8{JCZ|66C=HC;{6Ey;YioE@WkS& zj#r|{)r=pRDrxTZ;*F|FTOb|nejldvR!BB3xgg~wfkNt5)HCyOJeVJE`;l3xgaM%| z7-=GMamEz*iYLE(J5uwsRrLngJvXGZY+k&64t7I=ZSJ!mEvMSdf zee4Xka{!izT$sD(!#3Aq3URMrJ-k$iz~)hS%*pEm!tq;h)Zyw#lXjYykyFT9Q_=Z> zj)D;nrqMeM`Y=r;62$On*XPv8jQ!jJ`H_zlN2~7b2uBp1Lz9N}j2XSM#vKXfVIs?! zoqqv>G=d0g3qa&EjrH(B5-gp+d17?p(nPY${WAwa)Bb7|!zjLF5V`cOEm?BXXCJ@4 zpzgPWk()AYVSLMV_3gthu`IH$zh;t(pd5R9PMBhCt(m-lcdE)eHL(OOsYg;SSXcLa z3;`Q``t&K+Jp*}5NpR-F2sjNFGmv98&7{CF=< z{I@^9`Ku$|_=^o%lFbQe5oC;kuUs9+dZ_=@-avihIOyY-KGi)Q#iP%uyX7(tPsASP zw<9tvNuCKMDZp|nOcHSILSolUPNRj=l>jwX*Q*#~hfDVbdc*F;48m$ zho4F>002{Hk#_Dw8uBEaU=-OCYtl|@xumf$+ut7+$Q|O_p0oUlPn#h(WTSHk%Y>z- zE1PQ8p|0kEVAfBseUosOrLE$cK~IYDE0vd z{f19ZK~9)L%Z4;tffgaS4K&#FyJ|>OT<>hs~9IY(xhf&7{ISeAaoK82C57ML9;n}k+Aj+sw zgfc=aAb|N15wTH3jt9z1uv?E39>OVwWomxcr-z`KO(V~q-2=0LE&i2z`8*N{;WT_K z^*GzZb6mKg+aL%Y8BNOFPVLPqg|-1}I{nAHW``c`IC|9X@^k1Qb@ZgPF}I&?rBzqu zZKJeMC{R5%41fe-6$0-yV7))JODQY^quLIF7F4iY8-=>`voQr=ppBQyMZ9f0gavX9wW1q0qo;Thwdlfbf+hklZcHM zJxTY_IasLSBS+4hQv(4Fq`{ZL%Uv_!J*w_0SE6^K`LkS^$!3YNG zRg|WEegD2b%IAiS8abdfX4gCDuH0UP3=7+;-K-Nx(m(?-LW1oQ+)H}qkiMz(?bEk! zL+BxnD}{p2%fPVH7c5fta=5!6AE#pcX-9hgSe6SJDK9}NOTgXTAkHF#f_FFN$tkdG zh)L@+w&zm{53rA|+(z9t-Wgcy3X_PKs#(nn`U|P9>AhA-`|Dugw|( zU)vm~P=RHU_HoSoPq0z3TiV za1W>>u#_q%bH4=o5epV3xo6K71f*{z2U&karoUOZ2C@Q$giEf7g) zwi$0`)&$Yaw^g8MGWd-nbq8$G_PZu2IXRu+Q+HAhe3{t2&AZ&0wqS>xohjMb`y)(D zrpmhVdMu)?p+TTgV0+@8g#2W{VJlaeISW%D1PC-=b#K&hB0&R*p_}T%<>*QOJ_)MK z8#lTvAKF<&uUvP-j&1cj;0z&+t{9k)>3FiOgH= zf1Hv+10%ijE2#6E{qttlj2Vpyu!imxDvTgc9e4`|)KCF>`F%h~fuW&3tlxLU9Pkarw}KDzq28A) zTNXu~P6Nu(U*aw~c(+aMO?GqRZn! zvj9m(B}3*x=drJODQ(W2hMK2?s2M_n$2h4WI~ccg(Xb?V0pZX=XX}pB;U<=-Sl_d` zuWtI;pVkTy8v?7CtRB{i#Dm4I%&D|(#awQelVbXnRb*?oQVrR-D8qbq@Ajgjh_lyb z;f?q29ZAEhyKVZZ+TrQt8jtD;Gfh7G9NU&)e7=vyA5%WZ&pfkv-0daVCoK#gcN&t~ z$!SvL<96*wF4?WNCud|%i&Qn!zDhny+8Xs!%92wIH6!Z}y0|#>*V=h&O&9%9Ind$j zwZ&f&o@wdmSp3dKCxA3E3h(2apD@bFWP~Z^OkPMGXcvz}Ws&+vh59(LP^{b^7IqIP zR(xi#A`x#Oq&6VTvGe{KiVIXEnPB$0d54fSF|+S<|0|dJQE(DqU7$wk*}BE{kOAdx zoK~2R=r7L+gk8!1`)>i9zHdC!i=J3S@@bsr!Tws`vq{kh9XbA-?BLt1oK=>!YSE0oGG-BKc%F|LT~5@}o_=b7)<8ciB)uHrV$DY}&4?bqsHmu_oIlpDpJ@LYGGIx|^rN)u+02eQ*OJF`R5%l02R|9f-}r^U|L`4*_@6RsR}8 zSuZyWZz0kYCSw`&z%>p){G#XYjU;IsaOaer2bPuksoC?>bZ)j~IuBASlCnZFN$ljd zKr@Tc!FDw#>s&1Nn!g17uHejkal>se3r%`6SXb{MTLyT)xKGCj(x$HB{LMRbrsqor z5t0o(8j`>DoQM!{W15d5hzF&7|G@*9^GQq*k$M+(bvMQ$&}q_V`Y9W0gQJnC46kh{ z(GukYa#^ONk#;-)RCZ=CMOMk}B)OcaD1eb^&F2?LhPJaP0D@}gCiG2ROYSW;vjADE z^KRBdDeZ>kw;R@#VA@j*j=rRVkt%~X8N=J*@hB7Q=9B1%q6X1`2xHOwS;7G|ubD8@ z4*P@(RyKoW^RV$3heFb28SxquwW~HrYhtJUw8r~!C|_iHD_YCs+TXdgcN0__Nh1~3 z&%NQjEpXSZ{Cx2dg9Ae+xhnuu#FL6Z_mCdSVE^uyw3#lAPf+RI-r>|T6VXjc8{n;$ z-{*EBt}jN(AwnQFGmEsxTE9;U7Rp!@xbMX$l44(BVlT0ASwQoXu${`FRUV!n^ zB*m-&tKJ*^J$TCQc#uGbTE{z@V(Z$k0yPRK3XTk#sKrA_4~okJ2U#}5b&8l$$hA>t zi-G>V_Kn4z*f&A^uxTr!AyJ-c-Q3Ab)$N)PZ0aWCU3V3Re=xRk<@z%$C5gi54JW;T z$zojD#kfpj5uo<03kziW!rxH=MiLG!({(#wHAXS#gh?>aU)`7pL}D>csy#47jt@S1 zGy@X8g6T06;4wmtJg&QU&sJbOOth5?SUty!xiFQbF-@yj6nMmkFPIx5J-Zlhpbt-{bdbx{8u8Yx zP+jc#xuWt#LzPN4f{xH~a+uBqSu`JQNZR|OZjs(U%skn}v2}yun{OM`wwZE-CSAES znmJ+ONVJO?^JEaX9(t%T^!RnsUlQo(bYS8UgJ2$1DQ1Y;7dBgem+7lmSWDjmBw$GW ziT9~DY_T{Y6Qkh~+O{7z(a_nVZE6}RA)!XfUYTmXzyG|dJu)bz%2+RxdZFamoK*=f z37{YXQkOsaclLs&{V#g^dy>6W&Sr~T^SwMHAp^6p*||y7w*+EzR%42P zbucX^4B)^J#Bo+eFzR@BXz=hWwK~8L{I=xFBGSf15So;wki{lNWDv_X-}>iiXBbr( z3XaH`7K(zz$u01uZr6-!mbFhy8$@cFR`@@}-98fI6~Ii)iml07NGk(~q1KQg!GI-Y z40mm}akILCYaWv}@2@>bw)BLhdZb?#OBjXIJEDa6TNbBi34KHTC`!M(8zyi3OpT?# z>TQ?ay?e{(hTVJoe$)@V60m}03|vEk-w)&_nqreGrwn~G?=}rZgA5{7(2S6M_kKC& z=VDB_FRjWwN`TW=KViG?{*g#qsEPT+dxU1TPGu#tY;BQ+q_dqlDK`|kP+JWN9Z}Cd z72{4!!n*M4#e=;3_a-+t>m*MP&x&_V&VHTK?g)0G;mkP0$tPJ$FVkg7S&o!Rvv}u3 z_eHoW5o;S@_n>QYatsG2L+iXVTAiPVo40HL7^5_{BQqDzh(GUJ{6r3pSlbKW2(hNpX(a2ja+p8f3i_VwnPvTm%j%UKtq_c(0={^Hak=dio> znw4+L6t_uspQ=wtAk4|JrHd%of9mqOXC(VOC%!w5rdbtEL)xg(2$3|g_C&M@@{VNNLxs$tG}hR zqUPnny?-dl96AIX7^8!KJLl&ZF?C(R?(~TIa%1z~5}KPN5x(xP-jCpbvGd!t zOPK1o--~ANZ`{1o7;okFl0DJ|VKX`jr4z`L1$4aMa(f}M@7~pU?!+~DzntOjZb*r= zh|~E2{~AEGHKWgI`W0anrh=vie&y2S;zwTz+~o5dBfB&#DM3>%V0|Jh@h$5JU&`B0 zpH5%)EBkx=@4Qf!VttG!5!U>|?H3{%JcWBBmnmqz{i z_U$ynr3Y4&;ht4ZH+b3~hHtF>xw&>`^si5u5e)zqu64y!L?`=7;_n}G*wyLiYnL#7 zg%ucS?5!+w$MQq=0ZJ{&Ij>0{n%bMmb}|{9a3mdGmlIc6Z{WuSbo0~s5zfUq&u+Jy zVX2;rf(zl`n#$6Jd%mOu$}4_J!J{+lLiG5=gNZU0MTdC~-09y$cjQ&*TF=Da?_{dE z_{RAglX5t<7Q?=)z1d6@y5{lg%J`opgT5yG?8@rLbIdB}x8F9Ia)W~TS(WDyx8;Se z_XdTAR;0bE8W!XKm5~|N7Zz9$OrwInbT*JzNJW-R|7Y)8X_Sarx z#w{jSoM@FV&+ScL&(R5ROUzuhE@_7`_Ug~E;I)J^h8mxp`==CuMn>4iIG=6*Wui3t zXgn&p$8x@j<#Ej8-?>Sf?c=pt%zqF%5ar5#S_D%E`Ba~tefiY0O$`b&v*W+u8AhWf z*czr2{T?kOqMK4VAT1&scF%n^<5PwIu3ehk^kb*LhSx1d@D4E{<7jCAqITrh8{?#5 zt$%;t&q*3^T-&g#llj!t+H_lb6B6jcf5e8--#Gat|8o_;4?@us_QeUzxx}TUzuTu> z#8(%@RN&F=UL^5QBt_?uAwfy!_z+uQTHUW+9WKq`h{MJKR399+%5LkG)7d2+xQ<$e z^zSz<&e`X|8=dcO>9OW(T+Uep_+=&aHeAo(Xbyh&qEf01J;R@m&AJu*HFJ?&+cPsy zPT5LqEY$nf2LbH`%$ZhZWYkYf33WgcoMEpZp)X#EyJcwKkEHACac*S0yWMx>3 zsgZI75M=&xMSgY8}*C465b-SgyOTTeTUTL zR`~59G3twt;}9w~%XGJ~c0VsxA7VvPW1X9{{O80`?iGPNnkQG+IHCB7nzqmMXYum@ zLPm$X9-hV*sfoq;f&4(CGrCM|^`b^$hK<*iR2U%-mz0H9-?wzu43m{u-#VsJN=hvP z8*u-(voelpt(Or?bgBOs&w5(SNL z5yV>=A6%rZ)s1hJDFWbBi#XMMTikc}$SS-;0ycqC375RstToNIwR4WN9XNQf69N>z z@EfViMF^?rc-y+En2cfVX}Ld-3=(8wLXo)}_J@nerAmKBnBhnKkjxpimJ{K3P^;4& z(6{9?hy*=>jK8JLMlV}MGm=TzZByCoP08ZBbGGQ@x|g+p>p0+6px>7gPOPq=8jyvu zw^K7RCKcJneLx=zEl(LY)4=ymZ-h2f9OHQtsvJ?62Lx%&NdYZ7h+7uSqyq!SrC{5b zxh&}QzFxkU$&?1u>;qa!f6b0be`sf}C-R}W-a=M>5n(Fm;SPS16<>;+$eYD0do(7> zr#|TS-waX`6&P9PVTg)OlqxY!?MH%Gh2BkpbdNi%X08bZAO%=J73e6gnVg9T0>=Y};>GYmhgrJ<4Mt=iRV-lSi9IutNwk9lURfw-{?%(Rkdnc>iTYsWv*4|#t(>QnVZl2z81-1&AA8pK2 zr-L#{0|hlRi>{3F^L=6AiD~1Of0#+w;Sn$7;gRPt;UrRpGarmOpS9ZLsC`;j*nD2{ zl;HSoNt7Wx^yB=U_e&bR1Cr z%$CmqemXPzvak3*GFpsUfJwk!cAh1=D_KR2b(p~pge(*KNU~S4b#85`*8qxt-}l*7 zA>f|?NlzPO(MT3VX;tL0=719cog#P#9%s5U&YX1ZQnG`yKpF%`SAT0j7@}Snv^sAL zF4bpT^^cU&a81QHnWKs~76XXI6qQ57K1b~FT@6b{@4Lq2CSMwXB1Z3plJZM~9JqOS`YiDJwJ{|I?< ztTs4VN2Fi7yN*(0XbpoGcXPpl9Z@Dl{meCByzFo? zYyc3xoY_)lB+rm`0aaOX5p#ct*bHwd)Pt6b_d53F;HFoRkGx4*$k2+LoRxeW9vqg; zu4CB2g{*NeCBaS=vuOTYT-+_w+f@p@8MlUefa{xxY=k0^ zf#~%RVz4p(__n`-_Tp;mApC{q)!S zi*#TQk|9sTlTUWrA~!I0Pppz5@cxBUO&AJ0eLSNhq(VGl#^dBe^v~(f^R8;EFWYRzx&d&Z=%>4EMqRWE`J(ATM#RJ$tqv@L>e$lA4|;-LB|PrSVd}ms6PVbB$hjR)fOk+}wjy z7pJ?yjvQm^vD(8JJ$H9^2-zJE#>_u<)X*&%J|6+hJMKVUolH@uEXYp(gT(+3jqKT< z^JrOh*NOPQQ|Sn%jfW0Ny$jYdb?T4}3g2WpPS-v0~ zjIo>=aQ#RKAe78(`_FU)fDF2SL?^|etq?)mvu6jC+v}1{p7q53We17gR5JW^aZ`Dn z1d1n}uuk}Q(8HxmeJ}h> znZoG;%`K+@(wb=o{+SM_%04+66b7xGiydbmXSifF@_4PRdN6LAAuqr|xav?-C^~fL zfUDZnQ#P?3{l75qNakUHkTE&I0Ci;Jwc#f>FyBzKrZ>C*=UrHBb>V2p)VlU0s2uYS z2sk-@AKcv9p#6)a2sPR(cxd!Ln-^(Ec2Wk>!Odil&W$_6uFdb)P{3r`5byB1X3hGmBj5BXn31bHoMrrI+-T@IsDT>p*RzD z)J>HfB%f+5Q#?rS24U{Ro=!zkxZtfpNqscYL}m_)fGle-mBy3$uUFoF9*AAs{yt7TP~3B_eIrhYtQI*ojjr-0lE+9PWJk$MVV=s=3a~_JpDrz(YH%QPEpdul zo!8$@S+gBe(lVFP1#ler#l5m1M7mJHwX7qI8!@)WF_Ozb;Q{{_IdVkXD1u%%bgVo^)PKSLJ$)lUmZo6j zNMe5S-%nI+;$=5t{Xbn`?hu!5g*(W+xOj3*_g#+XxDY}3iaN*u_PM{eXetv$iW?dyWZ8f@jO0V%9XBWJCcZ&@C>gspf z>ek74*2xhmJqhYCK6%uT3_{UWx9Akg1=8u7!pZNqIH2E3Sx!ns88@Qblb4Oys)08 z*9@+yUF-JkO@ZeYIJ!m*GBd7E3wdzL(;7&(ainq*_a%`Np<6l$zLwn>m4W&-fka~5@FQsEv}^i6V;*?H?w z;!Plfk!}Od8|g?UW-iq@;t4fxXKW;c^PMm+ls z>B1}-)5GQ;2UF&6b%(G51u9tff}KYf=hL=PU(p}}GqvcQ-HMqxdAw>wv>lg_&HN+I znpHo)#zic6KOLhNOL9S$JQZA~HNc?*uMZxr{gMRX9lqs_<#36KSYX_Z2VUBbwN5z7 zVud{KPgYqz!RjM>v++NV~CA-SrL40CEWEGqZf+MTE~${)eS;B3!0@#!Oi z`jB=RF+tLbAGk_13x{q4y=n9s} z`hA3$=hERRqsxn)?BpiOIMRi2TfY2rv-|RNDCQar;P6>t)~;5Ij|n&&Sqx3GfUYLJ z8(q6~n+$TYDgM)YSL>izoC=Tgx*Laig#eBd0)m(F*%}X&%BD$pE4+qUB zv!=_J(U^g?*FJ)Ey+?DhnG@K~QrGpMH5ta0EKM)L)lPs%%Iqgn!%|42^1C0RNgZ^; zQv3V{MB&-b{Jl0~;2XT$n6hC^mQP6PWneISWsBBsdk^r2AY}THK{PnJd9eP#fnvEy zcn;J{tk?!*E7KHpGT&|?X@dEqS%dD$yVRkI>_%Y(S$Vr4oOeUtZ|0!YwvN0#;j3ml zEi>^B!ApkzrHb10@BBVAJ+lv*Lv!n7-k!iZv(sh%GoepB*~kbaT64p~jd6YNKYhB# zMT{Y2@Ap6Tli3(|oU=3T-K#$(JZI0IJu;nqfl^y_+(xXRFa+%5`0!^O)}gglg4ov=NG@{rqBn%>plSqMrX*EecN1nxm3Hek%g9dJFjbOovrryv)hfasz*LexP zjlf{P@m&);@W?*0Fv-4S=iIAtThFblavTQNOHAz}JH-`wEXB@K)7hj;Adrjr9E!VX zuyWpQJB%i>Amr>z7P>`y2mOa0FyLM60 z1Ni|J8p<)$|51qt*F)JBL+USz6jrg+ODqLuBH@B)o#^h?Xm4>6pp**@ndQG$!V$A1 zsk^5SKUhH*SJf_}20m}miC#T#-^X^;np3_!WPoVl3=J2wv@4l?JkH*vQ<%g;P7u2p zNu%9Ly^?Bc4)LFn&&!BKQXi8sJ;95KNCwoKIsSvA6IjtF{|3OlGzA+g>#N)RxpH3D z(VkWeEUm6>9k-Cfn&!}1f38&L*;7v3%e$;)AKi1}hrJB>Bb<=!jg6UFu<;=)8uXW% z33K!a0xD~uE3zL{Q{$oHKm7e;*#09&CQ?dXBMvK|e}uSsXY)QcGAfGcbA==c%%0I! zH~T0ZU*H^ylQZMGQk?FcVOuggYj0!TNz#Duun6Vm&3j;u$HB-^%;`dNn9MWdIf&aC zD%=wk;d)jIyG}&K0=P5Nz#A33(*}6VV3c@}PbXd5?;kxFSoe6_wYOgK2AJiI#Hzn( z*ny(5%9pgiBaecL170|mwMLmy48i=eu6oMq1%98YX%{yr-Eh|lF^$@$uTXJkD!%q^ z_1jGVF{jObjh^C)@tF#i-_N^KIr~LAk3G1i?Evkpy+1|+APYxa)XCS<$|~AqR~x`A zKDKudcL}l8~h{?!utw2_S{UfHip6xxC2Tk$3BWqyuy++}2<&`-Z*XH)Q zWVTYH%~CWx52qh%TJW*h;&mWIPB6!vuKKu-sw1wLjwQM9Qt`&$B<|=SCNb-em8r$r z4Z%_V$xVto`rb@Q(>F{KW@{z%t-Rhg9Xp;N*Dx7aH(a=#amHakm%G!HB6O&oHBB_B z+*QYruc@fX0AS;GV#5#Er(tAdM{4ThXj%99(Ui;Iu%M1{;^TElw^Sknfy=bzPn^gu zjD3tx*~UlC{imJrlHGORZcaq*P4g4U=zl+?2aV7iF8lqt;N$=P5sORovm`3U?WP^K zY?3(>0=@?d)<@5!mwEN-`D8?B7CTYaon5hs@y@8nG#{;_PRXE$xY>TwjymOU=h)ij z<>igRW>1xN2nyD?xvS7;GA&K_`C4I^USAC&@T+v|7MJlV(PI;A?06(GPw#W)XR!+A z7R#{rKi_UF4o*rzECDxvxoR;Xkyf2=$u7vD0Wuq4^dxPFSzlspbgwLbk<8u}D?pBS z(YaxcLOMDYLOZ*`UmX^?c`RDEFqPwpyR{n^yEZKeAGcis6wrt%{Qumzbzfcvqh+Z# zfN9f|>oJ~ZS>^PcUmJ5xfsXG5{ilmF4!x6K!o%Wo4YEL&!%nN!sQ%x&r&a?*8?H7ydI#y|?M?*632AJbzcYmp`3}D<$Lu>CN65egLhApL5m3eTBQ9$4-f;FVFJZ4f9`xQ~D#d!T&md;uy?fSN&DL zz-t#%SPKG;wawJ1@h#Xi@<*&S2dVF-~5(P##QC=*;C$h>s!aRQk*0Hd7ct(A4O8wRt3GnnF2hlI_Wsw)P{!OBCA zd*}*pA%`iw2MWNY2W6etO=>nLTo&m`bJ);*{LFa&~VG4SX`rJnx{*$(ah|%h%C|@GH6a|3#DX@cF1-th^}B7Lk&X zM3xfiFt)8qc4@C`qd2D6P+A-Y*`Br+@v+CzapK%r4xG$oK+8X-HyCcx|;z zB@uZU+1UsvK(vqxk=b~^%!NLh5^EDTD)&bDvpEb!c5rl@dh)l@7ZToaL~S9dv7n9Q zh@JiB2XZFq3K1Zc8xW3z&RAfHfvY&9u~@FT{7b}*9Lr?foj2H3-zrP~k}Gu>Yh?te z0EDF2e=(UiYeh#E;$j-LOlJ=~cDJm&Sl}jBtU^YHm4js8nDXlWh};i{CwO`8(>m+3 zaM2=k676L;6O(zmd3?7%*LnQq_%C3yM!!A}=4sx8xgjf9OnTyGH@?p#->L+VI~71} zVGMxyffmH>f*n7CYAxgEAAlwb?Pif_0xbM4{J4T0?+-{oAZp2AS(+`4UxJ^(BvY0lagwion5APk+OopJP|e5SntB% z5U9`7LeBGZ>6c4^3t*87^p}&gd9*OaP*60+`w>-35gOaL`hqhwH zk-;ul4i2&}HW)r!9E!jfgw7|!O6j~%=i?MD<6#yPX1A_b{AaNo3OK7$Bj03IyDoYK zr(93qpjC%0LY9;!)XH#bkq7^Lv!2p=5mq#DUFLiiTBM<2x32l?Tn^lw)mx7he;hJf zUKnwwEj~#XFRo4OziRGZf9&B0r)#^qe3)D6K1**hbSg({DjJ7$cqVa!0uk&?jw%`` z7`CLxrB(NGa<1CiXY}*Gv@S#2M;KhHFr$jR1`63JG#W)*nI2fSC(~nUbo+rQvJi$$ zrVRkn>S>>y6Egq4vbN>x_lA#-^~(KKS1u|51RBjY8l8~lV$cw`)%P*?r7*zR7-Cdt zK?r8IBHJakh)0H!(O{rydTzoX*oXRgbV{Qs`W*a9l4fXCIeFk0*;InT`Yw0Vyky$B zusK%M<-fz-kmVHQ2aIOhjF7=n0A*-4Dw!{=d^zcG*+dW;uNu_Q4LD{)Y2l-OGBwIu z?(pZ4we~i^Wq$er=N^C96y6;eT_gAB%obgSGY`8hIs!y;oD;i1_E?_~G6li+TVicW z28KD{L^x0Tk1xYRz$av}0H>98G1Dt-^KtZ?Cm?lW$YSiSi{VNAlS$=70?qx$_pD7j zE!Kp>@}&1WM64GX((7v%r_3~R$rwhA@+fc-;vVeH&Hui(>&|x0YVV7REGQM|W6Ykg zv%B`E6|3G{4@TFKc;1~-`}&v-Pn#@R6LI$^Q|}+iERe!wZcUmrfy}ukDw-D!YKp*G z1ZD-z8*0D3B?GcW2@o7SJ#o%sm+`|lzsQ+D{xbC24JWZl{#Lz;lyfWHM~2VuI=h_& zM2|So(YWK1s>AgL06k&9{%G!_BWN8t6NG8^%zz0Z6`(%f)WLOS4v!oX^6025?<%Jl zqXpi8lrw7lp87Stj1NVdKT#nIE$6E+X1K^iz8$md_bIJLe*mVT zNAZ&FI<*a|6L0*aFG+;_2sL=})W{PPS1C0Xy9M}mC#Dg~LzSuyI$7*{!2*)mPCz4~ zPbd2yV*kn=Ne5Q189%p#NbScP^(ZPP!$_m5A>{$TubCT1B-}$r*0y6u8RSQw`yx^U zN!X-)1vyE=<5FX7i+zQ~A#NProD&5X9~nilG_KhdbKVdZ7$fe_$b$ka3Z#U8rNO_w)ULPoA(&3Bx`_tP8!fH|MZ6;vHy(j zedi(XDuq>84?C&5%@XwdTwVdg4<>gP4Wn#zFhepH=gedEIOEe_y&B0_n3jN_FgS-7 zH=AcI3S3gBxP(@p!Y}=Pn^Ly#D!C}TDwTHNZr#G?hWIxde5;CRB4ZZ`z2xMB2<;uO z*Vbm4O^;h@C|qvc-JCYQA1etFkCBu^(a&TQt~ z-0#LuW1nrAm)Gt%SJ3(;oE18ynfHQz+9_G9T>m2@^Rl7w$PX?j{__D^)Q)^+6W|j0 z=!or7kMX0-u~HWDqmLR=P=A%qujzH}f!_;^E6#=Dg*V%#(|Nr^IYSSH&jjo711h5V1jld)8K)WTHJ za@)#0+$IM@Lvh@QacwU5q0RlM3uVH;CM)~zW6{ZS4V0RepSE*0Fqbl!|8E-Bk`Is% zni^fCaqYE^{8WDR(!Y=HfBUEjbuW$ZAr}FO$8UEUF2Bwp_U~UbhSL0Bzy0p0|8XJz zdH3c)$G89I<;e&A`zdwbiJa-bU;n>cDRhn8*8j(kMMd`ia997!$NrB8AO=LRh3AQx#lV=}toUkk%|N;gd@th)*oYQCeDQ(xmXGbS9D9 zq9n3ER!%9Ej`cCqERuHYW!+}BFSUpZBrR**x$}LbU8LRD?9l2(l5FUmDSNRvJf3zf z%J7(&{l@#y>)(S8DqG$ja9fSHbM5E^WFjEL?ES*ZpFG0I4IIB?2QPXspH&E7-}AY^ z^r*f+Y|MbKgxwv0#tsqa*@vOV<4~(py+lw+2u$&O8J3E7pN55D`3~VnJ>c=~EAApD zC2ZtvBIFW<+~(R}_C~+IPb)9$)vMRFe=LNj;%I0vAIy99BOU$4#4ePLnS>V@hXaL! zI8bQP_M+2Mumn22E_a^FYt!O^2cnQ5$TXY}BO*@2cRVJ)1ho~RB99&xV%q5${ZdPP zBcC%+{M5|23?8H?MUZ^!^&Jdp4aL{;Cfzuf)x~51tA=SgXj)I&#I{j@nhHGjFVbv* zR~$XBQ-9VEzCSlLfR%jx+jS^xDChzZgip^49~?}fWuu<7-@*Y;?ID5Z3oF`$~@`l-MCTeE`_K~NvzvV>Z7k&XfzHr2(#Ka@*3sQ@F6GCC;y%! z&Yoi*Hs8*soDZQO{fN@GM1m!f1`=Kl>IVGhKPtXt3-4Z#RPPYHtsFawqCVc|Sh3Pk?Vg-L5ICLnqf%&K%VSr-Lw=;*wWcC0S;$PLvJ~H0L z*M*`~@=VpvlewJThs)R9X01N0oe^vEsnv2mgj5E_y^U^IW_>6zsB2%GFsrT|PO3*H z_U~U2UJW9to#=D#|9q#uptuPWBr8Bn#_^5Y=Qaop zJ9sd5=Mt6FaAez*5|4&F5x-BQbv6TndDt=Rq;X5LPmU!8N59$N=;Y&qJM}qix#S7Z z*;DW9pysqdGq7&PI~yM7e%K!WgF}`bxNdd=Jrk!VN`%=TNa1+x0|yP#LR$yS0O>1B z!^%F%Cv!i1JPE4>LJlO?WH6FQ)%je!8%yUS3tA!SH>xjbZXsc{58X27V*ojec$&j> z%ghbPlg3Bacc3l1`+x9U@(s%3R$DmhSuK3Rq~F|-wY%!VLRZbUrXnZ<@&(tZkI?T8rct8MUqJ7U5d zeaFy3PPx^K9#lywK%38H$fKx$=pQm8wbll>68R4?iF@-WUz~QzMrWPhK~@>< zxl~>)r5StF`)cq*k>m^T?_=slAP7KM?UEWf_ED_8M*+fD4f zhZ10KVp)DB-3HIULZE%4R~y=6k|~n%l7D!%)pE=B?Jfi`)Nn;kN*NmJn*zxhvUjF~> z#6|{P1y!HwsC2<$hoV{Yo<{DvP*kMJ65>uS)DN(FGOD3i^AvIoYEK7l=MVU^a0M0i zRr=)O9DG-61dDx``Dm?;D@ zM8YVPPNzMJ2D>A-M~@DqZd!@iuPf&u+_; z$p~ZcVVA$|)Jf6i8WubR>n!3j@Q;VX_G$-vp6lRm$R7$UT`8Ad;udCLQX%f4e;fAtu#`C_R;;S zl+S@ch+D!ic9c4Lf-(=OrxvCE1hhr3p%!WG02`ZEbXLd!30Ud3`KYu{2%(aSxR9ec znEEGHT(NpiZC1oKg^SL0rE62-l61cwgqp_dy6L~%5jEK^-Qy5CBGmiD=zNS#)bn4~ z6O$)MHeq5Khy#W6IN!73j7P}{%v-sP4KC#Zex>7(UKhs}A1*5x`w=r~ucuGk> z(-;TdZVzifh`Fqliz8L1Y3Cn!my0bY2oz#4nb%Czr(O-8WDD{q8E#j+7R3kJQL+Qm z5+VDwBcTUgcpBL?`~Y)Qx5lxY-?U{G#lkUTbg24rb! z_kKJx=nw(}lB3x#|^D@%ggx$de$xK;n;&ft9!pn0>D>~1b$iGWNDc6(ejPq$G z&i~E$Lzd;=&->#7<*#=1AHY9&6`HL9Vm%cD3m_e#+ttBr$+PcJAfjH3H7KEGj2K3= za$vJbnI7rypX`{=FmC}eX3&*Nm|@qbFztSW$%PKYdICJahKDFnSo;qyBLMo^!KdI( zrL=v%z4TVfKe&jS!#>eQUw8Y&>zb{rV;lNonU|OAan^&bYla zJ;OWeR9;qf^GhQq_R4SLtkSnkPdntOOffukv-j|ShQ!Q$@-Cw1>o{-ozB6}kyYi*r zpwHHyo(x{Pv*6K^gr_iBK4g~!{`AubN*S5AIFxId6mjzAC+lu=w;JWiz$ODuC+@SK_oq?d&-K)E0lcQ1O|v2-TNL?Sy=;1i7#5%I6NcTaI$Y9<k=p2h*F&I`4rsd!t1=ga0>P6{a3XX?5Uvf!rO^d57tFEc3 z>BivX{CQKZe>yRJy^tDZ5Gu%s1DTJzn_GxzN(~s$4mk@Q1+5&#XiBZTzg62zuL)4B zq!2XAtpQu9a+-Di#Gptg7yW+%pYoz)mKhYK{rO2-;9iH(rU$;ffsUzL2an=M30X2% zt(tFhBzWi8vd#VPgR5s~$33=49XdG$6?h4D!&NlHw8ht7g z1Qj80F`jMt3)63GX67_=byC*aQQgjB=%h{tLt*65$*@SUb`czzB;06hRBGmzRd@q= z!yFCf+%9{*oIab7%*=p-(lQgOgcd%>T9MZj!HTpmHCC3l*FT*01)B;ee1?R|#AqfN zuf&%mKJ@%&Cniw5?m$MBwP0V+SHWk?g!#U!y61jrwgvZ{Rq&+dzW=mfYPhz^Zb`)2 z^L?%&3y&rAUX}IQo~R@Op4+MS51W>`tJQ%HVHN?v*s$+(fcj00N)dgPXZOgI$t)v# zT}CU3A}(s0W&zjn=yBnwb9+9qd=0Q9Z}~3W-MMT>7vYXGWT&_LCf1g zp8`!bW7Ni{&z}ovrh+&yy@@mRYs6gKawZRndK9#anvsm*5~Uh!ppapC4XH#sUU~j8 z)AyOMg`1K-38_2wd4nRJyq2**q8Aw(RqN{U@@4GP*!;e}{6iAWXk>7>JbvFTH%P7NV;bpF0%m~-Ljy5~P`Aeb+v z-}>!a;;6)cPbouN7QMczqG@{pyUUrak!8lxY}m9ZfT5CU-?c7K%V>?b95Wg3}}C1vG#i z9YtyyAsb}K^hm@EJ5TAINLD@K@bj6|r~Bt^Ox(OE7?PWTVgVUd{+yTgDZ_ZY4xIpp z)@>ZEN}J82G1yziMd~#Pb0{?;-g&fWAQ1N&^0I*Qla`VjaZb^)bR07#x}?;V$pFNc zCU;L^C5TUWupLhMrz4nr$*Cqd@O=LIO61qU3aJ^Bkp1^p(14}63LPFRn}a^ROWF3u ziI?*qnt3Ei+1$o{8ce*=pZn2A{rB;wd#zNj0L9In190R5kW!6N=kR5bOK|;beCuT0cZqa zuqvO_L1Sc}a5*QYk{jaKb#7!>bgCCd!LPo2kd|?dB_Qex=T5LpSXP z;SVs$M#h!lKf_TN|4cXDZ8#xAzrGjS75|~p?u@Gq6rh?)xKo^tEpwVFQ`V>14Lw2m z>nm)vghvAt>sCHk205aZBGaiQUncxH<2bexiIbB)l^QdGiXF|43G3C z)wgn5;`d=V9`)H5XTP#j$kYBq@Bm$*16AINatXWo!bI6)+=W;JN-LqeQF|*}cq802N}c9eF=nZz zkah`eYgwla(J6^CnU=`80;!4kl?UK}B=f)CqiSQl`sYT4h1^`%9z7ZpLFpTXshwte zUWomX(+im2g!NkSSgYJ*7pz?9#-jXs}mx@}|~C&da#A_0+B&X%^;aC zMAR?6@{fLLs7VQKDoigK+lW3^P=vLv`@nJUW3?5?T@_IfOm;J4WWoksU1YSUF%4Ow zW+M9(BLT+O-KO>Qe(lHSOpdDi`tCHd<-gldpp9^?FO<8j^aH~iUJtO6)v2AUN;P9& zThr!2t5v<2TZx4~!z2QT&Dxl|_0j1+Qw!pT_@q6)ZB9DHdct!uw9~dlpZp zl}H$|NetQ2y88O*`EeJH_?Kyt@E7A7Wn{L@z@{P6z-Yf6Bd>~ab9o5}6=ERxKQ5)c$LPSubYR3g}o=kx&~U!PM4uh#~6(Y!f#>Yy^ZQ*EAjNu z+>$-bd3~anTEY769GcurqzOs>aBiI>^H1nKX*~td4rFkxjiS^U#hKS$rxj-7q+}kR zh(C}nlmTs%I|w3^lG->9?44pS0fdYi<;@7cFMpoLgI6Chi8V+Nh!%?o^>IHNS)AfK zcf13wvd1#7K}4lkCy_#S11gXyc4SeGKrgD&HtS!f>Bhu+l~1mrBN!e-2RUj$C^8ro z)@Wytq6id8ja(O*#zoUX-3a1|n0h)vHf|dFk#cT}luBn&$-6Ci_TRTdnKvjp?(|LD zlI6agw&!%TU9v4edvxbv(a2aC!88;AiH6;E03D3qaSK#s7sWcLJf=CX&`S3ZhN~tXkbYJBQhYQhFmX>?qA_Sn-M%FQS1timzVHv0IfhKNj6~(}cWW z2`7m?pa-Uv~PEndPV{bome($cOjUy2MA^@bcjLZ<#9w(x;6ny zTMyJRmDrfrdD!MXdp3az2%|y7hDio`o(m|$H4=XrVbkbV(j>rK!yF?(JWYN|S|Hzk zjIX;p)w!#($Dy<>!|9|JB@bwZ?N}XqSoa)(^*Fc`wy;0SSwu(;$)?NP$LTfE>cp5( zz}%)BH0f1aNYD>hBeSrp(0nJqY6>Z|%stijy-j82s-^~326@Wl2TdSEWt_{+j^w;Kn$1+ z5NA2hV&xaWa3`P;=}|V4kmTU)g@7Iu-@Dfd-(pAY-#G4~$;CKW`~%vVP!4txewrAXLauY^*f0OI>S5P9H4p)Ex|Hz6{-mXrN>XI8%JWzM{F}a zl|6elEqdM67#VkweIJ&v(oVMH*Rl2N>06+E9#Pa+#co8>sJ5z1`r7pcH6uL?U zTS>wz^gcs5yA=QoNb9+0OYwjLrTi=t3Z+ZD`a8tsF`rFuo_`PcNXNEjh{q>uZh&V6 zob+5JYVG9Rgg{OsM~=;&&CGOIu1PRU$m|;vl>94Y8^5Q4^|WtW(fI%a%4waJf)&+= z2$Aj{IIum65Lr{kdU&9u9Rm4Yan<#ZzMh^!A&bEr>DSzCUe zs-l|MKLzQXKs_#5uK4$lh~;t+z5e@0_kPK}T$dr5`b>w9xP!kyQvSLALXG(Yvw74U zyTkwU55X7~?;ZXJ{$^lih@~xbIaC-N((x=fD|HE@z^}t$>wh_aAds z_>%1m4QKVy-6Cgx-KjllbzbL@+Y3izE^P;^1=6QA%a&b*7`6l619TsxHo@l&ZN)FNgyY zQo9uX1JF(Dydm9mWz$D3cZ6$ii-GG4EaG!&WxJY0|uagTS$|y+mIJ zgzF`zGHAc;k}tp$lV0ucw`g%jqs5T;n+OWR=u{})gY8=z#c+nmzE@Jy#& zf;FQX;*kS$!xuOhhFjbC^=h?>SEDP56IcMKO0_i{1mT0U^vvl(mK+Vbkg&Ww1Ps%$ z^D2`*`ntN2n6we>yoT`Yu`dmRz780u9pYu3TIVj!0Xj=C)DcxS^G(Zly4xC#H)--Ki#D#)2Pno=oO^Mv}sq@o6;i zc>bcw84bH%fon=`k2YvnbX{+997-b1MXpou;;1O{DWANGpKMiWmj}3d4}_u8)x<;< z*en~T*O2)!$?XT2Mh4xf8MxW-sW_TiC+~r8>BXV*)!_Tp6fI%g_)=rHIJ+M-Z+&#q zVFsH}T^bv>anV3#QIYGJ+c*>iTz?v(cA9V~?U@{*`t}`8YE=R3No+g3TUDke)EChnS$OJ)bKmoaG`PX-@%y7BeHKBSiD z0~Qt}u+);w8Jx0W9JYo6z!~4OZq_#FA5<1D#Wz(*D3g@bg_^T2p@>2;apFWG8HtxA zl;`!OwB3V`n|rovfFW%wGC@pJA$cbu7^wj?RX+ky06DKyD+kiMpISEeDKprB#3Z|B zyKI7%hjdXRq98V~Hifs>^X?yuZgnX5>j%@s%i~`DPL@XE-xzpSQAEAgQ>(dRH51@P zGBX45bp|bYJz2BY%}grO6i17he1K;WE8EJ@kS~mvLqs?h2AIQ69{=Pe-F^3rMsA-< zTbBe=^mU5e0F~$h66trBqM?#`L~*BR=o3ut^rmcoeOxRkO9Q@gSg zERMVU`H8->W{7a7pF(~5bI!{Sl!V2#qJKm)2`!-of3ydD&$F{AIxJ%f z1Du5;La^xWcel5hnWEUdQ$ zo+y2C2jmPU79PcC3_#R{Wou<%pzZ2@<{R(0f;D_0Z>v`2SmC2grf3r6vPO4G}o&bOQjf-m(X#Q1k~n1%v)%bq^8 zf+I5+v!p=V^MAwhSNcY(oqBndH~g6r0IeMlh&c(glw?VMcaI!Bs#8;aeKJI8Erkdc z_1e@sc2QkCAe{~poi%qJ6BgDN(x4THza;39X%(EmOV*QldX13Rd$4;06RdiX`mE=4 z+GTns%szbM)w~mr4*nWDJASgK$FE-pT1O1!x+kM39Tg*+!eta8#0j>&6D!HQVT5>Y zJK7(eJ@9JLfv8(v&HF!x18XfP2aH1OXDFnWs5vK~GfF*-HnEsJd!|$mfg{$i=Cij& z?2@{8^wG`(agMLfrx?yytd7*gGOMj&)B&mL6urK_8&2d5!+qES@UX$~@uOIaQ%ec~ zDErM#WXo+Z9X;~G^nOs+8z{u|UrYAHvveBcm?=))EIh2a`^qUV+=b6aT2x0ws?TX27H$QLNdFv!m8HT~Ht&mY1 zM8KO@-W6&bb)N+^Sv`9C^z4~48d&cVD)5&EFWgU*h6}hiAg-_*PbuNel_WzTm9$t;a-iPW>9W6+?AA(6N_z%e6zB7?S~75#bQ1PTgEi)6T8;M{>i5lFw)=dbU=o&ML`)&}k~`1JI^yPSS( z90U|cHwdWOlQ*}{e_|)4a(RD?0Pj(gZW+Z`{r69U|8)L;aLE5(-u!}7pkmTn41)xn zfz6f+R-ul@^Loy+2zdMk^HG6%wZ75Xd!iM7yWppNRqeQ^$HlSZTYxg?8x4Oh`+>DK z#%AHR^o*YF>aoS8OqU4bUgI3+u8Jadu{aJXdSdueDkYH}l3=B&;AKJ*)Dj6U8k?y` z**i*@GTR~`jegxIeErTLD-7tmiIOt6YI&4tl#6ty;v|JJgsQh)U`M`Dcs|y_@&lW% zZ&1xt@HH9i`R!*+6Reh}6Rs5jcR&Vx@KzE?&D^xIondRi5EPGhLq?>A&_tgjvQKBQz?3UTl>KYRvsO|NMx)cnGa#i zhWhPb!*;llMPRYD|NKrvKUQ@mSN)-WiFdIdW*sF)LiS%|XaTXly_D{U(qir~d~q5p zOM&BI{dRav+#8GJmNpy_Ap+9MGW^?R&hmfMzIjvMzQ{0g9|G4Yw_IIZ%5BLclz;Lf zW4+=3^XD~`j=ES|zm57!uyB`jj}aGn33(kx8aiJB#Do{7a(CAy&3RqYdI>3lL{)2_ z$w%7q5!$p2;M=}`e)N7W?)TQgM20G1vIF|GCjkG4=sl{6;gV7xppJC9V{@=iNnsJhBfCR;g)*!_!@s1eo#DvS(0mvq} z9W+opGh+>mjIwRA;Z(-4Giz@R~L3PnAJ2LuFYK{QSQq!KejzIw2_ zkRmGH#bE1hZh7#w*sB%Bk(4W1?pgJZBx)mvEGz;=hM0H^qNy-8sQFXlJ43844Spo1 zUHe#asycv}IQ)J#FV3Q6z4<^$N6 zI{;}`d#MHrZxk-=M#>s8(w42U_{$F$%hZI{G7)yVCp)Qvr>w-gt|jk#h;I(3krx@K z$=>LlYr)^v-7Kp=h_!9Sm^tymLLw#n8QwdQ;cCZjEE-x$S(j$5lkiV3=tMhcc#B{JBd?Dpsw{DOMvQe2(^LQFp3Gui<_%8C+wq zr5jQoL{IjRui=>Ma7&Pp3vNuHR7Ib>2J!99PmG3D4BoReJMEeba_in*2Y)|;05da( zBBVt9f?gBLP#X1YQ|s;Jl|-uU&yXR3uVIeL1YE!-&9A>XO{_;sf{w>o?FGc9I5L1# zo3eo}IVIhFnhKso$UKtg!?SfHXB9Y)9@?z+6PD3X_&F8?UcU@RtJ|J}!8HWsf ze4Y*ibX|26k}bjsdO!kA9+<+re7TcIu957t+mtaD(x<#GJUnZ+l^gn#B>%;~|K6U= zPh_G&k5!Jn;&?6}h^ya_2K!N?nvoLt0Wfwqg(;{A6gVdI;iIp&SW=t*ebP zvpwj5b0ij+K|mT}|0yy7=k;QyR`KP_P2m&5xXiw{OVfglN4U5UT0hzVXng9D7)fL& zokgM*7O3)Q@U3uSxMc@C0$Plt`N%^;Y(ODLoDO8M`LfvB+B?Hr27TSSUkWPpYMurD z6J!wfgYTYR_6sTbDtiC;Y+c>}^ot1sg|J3uGT zG>*vtbQOSnq(V)$rNM#FqY$jlQ*Z%>0UIFL5&~tirJ|Ld-a2|&W527}?+)%Fgk-$3 z4v>qWyRf^+C41R`-!#U#<>>Pg5k(RH#(ByqxpWeutm+kbpvitB*4xJyGk4P~l!|8F zoZms~b%l}{(}l>!1%u>3+(&UlbU_K*M+BM_%Io<1?$MT%@M>ymipT@%<2H?;LG8Ds zxS91qgA6)mi#;n}HKw9=zK61*WYp*dgpjsO0|yO)Dn}LU{Ho#g^#><=Xfa%yqH#E7 z_2vKk-NPn!a@@t~jI-(p5|IlNg=(PiDs~1(tDRgigqJ9x!%f)8X6ZzhGVAvrsZoN) zPN~iZNliw0X8y|iR9uBdb8`>nifE#@(>J$b_9^uCe0vAu+vmqC?QZTVJb98ChF-IF z@hSv}oNpc84b?XtigH_FD{DDd-HFj$7e##=atq@O0J0xtpIi5k~93nJWp+w|V z=aU`OM!kfc=1AD55UE^JgJplM(N>_go>lt|j2fGjq+tu$4U&|$!XK?0%7l4`Nti*g z5-fgL157j(|0&T=j5f9ib*qgc#4hE1oz5$}{60XKp+}FFl1x;9kP|VO$)O1jiyn3m<>hikROzt*l0fxCPL%}3aFv`CLEL^DzP@f;FxlY z`;;DW!Mst;9M`oHOF5O}0J;E2z%O$KS-Sv+lrpSiA5Th0Xct^YvODji^P0nR zgKMJ!>(qAj>QmDzPt|?>Bqe2A+mPvgEL70R6JxJF=M0cH{#NyQ;=OzKL`^{?YyK!) z(UyX7gL+yo86W>XA}}z|yM$WGT9QleBX3in@{l+f4;$8b%F0?TDd%H9jZI7%!ex{> zTL{Qz{DZruU)xBqR=q#Rr2Sg*{mK}tAqn*Qf#Rfc>J5!Ae931ttb z`kPYVZmi(c#iYre8ekEy9QE)93Ympedx+W7@KSW(Psm{+)3vDm_8vR(Ac?3?4nl>GQlicKz z-t9mYo=E`b5!1eR>=bB(+^%8(ovavZ@|E5FHtBq>>oH*RO0PjRpPjsT;Tj4!P0~Lh znp(g4K?jGX+Dg!E+D7RO3dMENyU~W_o-paQW!TQ09nq0$g7Zn9?sx5OecAb9Jkerc zqtt=-Eedh$eaJRzX566$C>m42&DY0nBU_Ow!* z5-k&YEE<$8tePUETYj{ZIX36l?A4MoG~EOaf_EE!KSzz>bpAM?>+=4vDBd%C@ERv` z)GnKGfgLr);cu8=)JAeN(j=T&C8Uv3QdsMcTQiQn{eIKZ?KX?e+46gD`o%}!$*Gh{ zBP7kjq5YfrQ}TuDQJ3CbVT&abvKIcja>x8rLW4%OZjz2S`uFDN+RaFJGm}v$BD+;e zP8s+lv)RXxu?t$o#OM9BuTSI00G&calPqLd@KD~J%CUR}H!S0!WeR}M_pe_c8)&jz zgR-^Im@?vp+Xq0%{wT%3h!jOx%o~|GMPtb(09;Y>YBxA2x60{F%Wpg%tL4t5V1+Zx z|HB2?weaibWuQ}{+sN!yb(PcdrvxeOm>&V8M1$5Q>X4Dqs+@mGOQ{nUEm`6KrwFgI zR;#JOR7CKLJ9SQX6=LB}Pe!f$`r)Z4We{3Q>{WKC%9G zGqfV=C2MJDZ$UmtASieZ^ciED?PF+6wI$FYvyyr~Cr#$bWQ@<=UghtpEno^2Z;4N! z?PqB6OpDNIFQQl&0{4QHnb@Qg>)wYXT9ZJ&tTxYsOj%f1Q&xY_BK8;o-=`1RH;7qt zA|?Z)O_$CvxKe8A-S@0i;-bdc5~_@08`J=$2;L!VDmSD>7&vM~);0p9C2yXc&*-sc zdO(21lvx+x7C??g&syF9sBzJt28)H7A*;lmTVH>MMjB@RLI5v81dBt1ssVxed34D( z(ai}H!&%2HnEsFC(-$t_U z0ijGi6^c3y-L5KTlWAe{AHBngl?h+?qke-cUxk%BFcpa@zsKQiwZcGES6Rny^C^TJ0N z+i;{|Kt2ap$YlRgnCQKafwO^krLi@0^&VUmM|<2EBH{MX5Wgec5LIl-;x_w;LfHYGv94*(42YEBsp*2I8oxv*8HZ1?J;{V!4h-91x@QZ^$OW6mn=H;ZPe}T5ghe zQX1=!<{FGvD~%f z8b4ANA)y1rYsecW56S$UBwApTUt_-ANUSX-s9G@3X%vPBEctsYtL7kT1({=%<&6jy zB8AT{Xgh*Y*dfgvJl{bVyc3M$?-cwo3JQdTR zbEzMFCW9wEcQ4!>AKy@MviImDiqedA)fV!4YO9z|MB(L15p0AnNQZS46TlRrgQ0*y zK5DvE;S$6r^5)(hAJ|M;{vBq(5W(muSPi#&_wU!Q7|RW^D&2IV4w|*2F^6yydFE*Q z^tLQ>W55stdH)+@suZe^Dv~9fyAaT5CBcrTf1HJI7_A;8g<*ne5Vt|(ACviR&#>T5 zxX89`CcDxs^MHtj-%eZD&-CSA-OC4nRW#fCFy&o%&MUy*6mrXJXHN|LTDs!;-TfL>@k_z>R_BM8cEnN1Y4 zg0O!0^}cUqHoC~!c0nS=wL=$W)I;k4m8O)7hy#xW*A0bO(~>U43)2rzFrbG+>u>OR z!!eon;Wo(ltK1~rm=a!gfM~`X@H(U!T=%6v22Ok1C~ZH#%EaG#nwi;>u7|AqxS@aZ zRxxHgtasXUkGlevV=|#RL|<)9r`^sQ<3*Yw9FH%Cs+D~10;s9@26_xya4LZ}snsWC zWHcga(>HRRkHn-F{TuM}qN>bPX%P^nVzjY_TRuf<5b3hP&%$2DIXC&%mArpovKPH4 zK~Nl2r9l_DO%V*0iF4brD+7Lw!*+1uB64zaypL>w&v9gKfY3U{Gp6{Rd_;GK9D#MR zvy$5Gx%k6mh}=>|r(yc$W~Y+~E~%qyytzavQ*%_MU6ey)Yx|i`IJl8SYeTeq1%6t{ z+}_I!6Vz`S%zN3gkc8i2sCo%h!Y*+atqwC{b*jiu&i~^`M1&6TqTAlV zcZeYxfX2x%Pi{a(ppcf$-wuJ;Dr8W`zl?kW5ou}xwSc)Q<=(tmcPSHFyk3!a{@)g? ziV9s5lV94U_N;HvZVwAw+}Udv}kzupAG3R&gaNzU+H`1ecgz)7u~94B5KI% z5X033-%b-~Zx*Y<*`YCLufq{kW7o0qPre!gjwB4k#m67iENB08V`cPdv(Dr*1PrvH zoz8<#-a^1)Xm!pBBW4kZN~=a0V-=(iT3qt81Ni7Jm}8y>gWLA_b7)ukfU`M6F70h9 zeH(-d;`R)=yv<%VfB$)Z6j45upbm)Nu6JV7yGKaZIXrE zsIrl~Mi@zxUo#j98Ww~cv%tHYB71T}O#Ztu167r8{Z(mmK@^;HP6j#70E43ARGMk9 zIF0WA!$B%L9UP27I{lM@J8~FB#JU<(qo}@Gv)ESU|djgZsU@E*r&~}S+kku^3nlcdSi9vn!QHk!~jpy zFd|;tLk^z%|H00g4|LjruA*}UUpH-zG=iff%C>}5O7k#YpyS2=g<3;nb=15SKo8U= zBA8Klr$k@MUsTamNHwYWjr#A51k4oVsHpCAgP$N0rvD~GXaO>#;G&2@mT3i722#}M{rcFQ zDv~)V3`55I_QMj=&WC+Lp{3Rn*G*+jSw2y?H82~O$uTwE{lX;6_Ung7ArrR!3)X~M z-FZpHH}^yE^FNol#Xr^1u8H{RtZ^f=hZ>%v{2LCpb?@89YPhXQ6ZcF*`-Y?9t48^a zC?B=2^2XjgqYiUR&TaL-WUO3j;iX|eyw-MNRP&>e9a9!1?%1M#`+4VGeezQ(Za=J9XmDLv&uz6Wh)mL?~tUAz0ua~0iQ)#ZpIx*%QH`Njl;Pca)#O} zd{j4KyIU@99pfS`9Z|JcgTJ<+TS?{Swpl*5S`2c)M%rP>s;5E~v||%}bU$vV)1}-* z{FKd_@_<>h)G{?l>NshV5p_qY zOov^ySDCc*?ze4Jx4=e9kD*lja7>o4qf7$z3f@18bFI01|EmGx2(BT8L8YK<)59SF zDl0lW`cQ94K7D#hmDwU_>QM;87+5Ji+G9YhQpm)U_wAFE*v@fVF1%`-Xqc-!9 zO(gs5CQjUO&Ami?xc}_gQXbqwuAmw=3t^I0kt%2x1~!+r(y)%p)RH(K_ffW$EmpRP z__3I`{*#cbs?1WmZE<1z%=w=&mxtL}{+=9fcW-a$=Wc8?!xK0zyrjf|8rm4SiAs6g z)Q1%n8f4kT@J;v#ChlOS?%IgAHNPuY9l)!JqEl&qWWi{v%LP zbffMNI2apjScr^Ra_Zn)R9W37OdwlRnG=(%9ex1=VTF#D=WI(LTS}a=x;-|({;l-q ztr)^FXhhJUd-LW^7Bhy12Qm;m8QV#j(pcVp-^LVhD6KJz!ZI*kSKq)OB{^Axh)how zV;$zYY-?3j)l?vQm_%bQjlLBZ7bl(O5V@OT?)>d!J+7eB7#$w2%`6&Lz?G1#h`M=@ zL6rK2pwLm<7(@dQ6+iEnJhnw(U|<(m^|DlL_)#+&-%OM zy`?Nf3m_CC3_4ZDfqIG%FN+eK{s$~(W^;MYws?7A0R{Xv>N4z>;NW5Js!f#noHbRY z0i=~zuNI-gG@{Ckj=D40E9xj>ag+s|@05iT4^MAw(A&rCeS?O(cI_eyt0d}58!r!L zC!$K^FeyimPRMPBo3_r&%WKuKqlK5YQn^fSt#RY#&CkOV;RWwm;WKmQzT$qUV@{aS(C|qtqts*} zUHvbgJaXiFcsIP@O=x#?zxUU(XTu#F!tGqVsY5{Ft6xR&JFzl;mB;I1Y56kpCDQ&W zIXSMzrVKjM02+Yvy5-Ytl?rBC*A(}jmX;Bo=0>%Jz#vMpAb2>xw7EYFkv_$R!Zh43 zR6k8f#@Em;{flp1l7_qb?TTPv32C0A#cV0~Ri$pacKdcCg(w%vAa%tn%K)A`Na-d^NzM zvv{k6m92GqG_5}eRL!x|P5F+j4=NfphW>bhAar>IGL?n-e#)MHGuHpn@6Fq{jJ9e3 zoqbVbc5X~Be{=Y4WD(K=HE!HEX|KO?=Z-?*?d`p2NE{<6 zH6m5T`)neevI8!Dedn#ETh@+UUj!%s{1eU_N%QCdgTsB?xUhToCd&a^u%MOY$r?>* zTRFOH%8KXKgk7R-GS82P=&)P%DQ&mxA*-)Kin_M717p)|T8eLsqW}cs;@!{Sk1NH(dB38ft8QY7^TSo@?kTv2NjTZjW zZ`q%ly2V|j(0(DmOzqT_RfzGCW>`oV*n$;Vw&XzgqY#Vwp;G)_bzsj9jf8!aWvHk}e@r|QP+Ez`GA z=w^22!G%c5K0X#ytE8i=?Ao=EfBspbK0hsR>C%>+@0OMI{hWX3P;E{70TdL~DkZCR zma$;{xuhlC~_jM5?U;>A2rb~-2YlrdG2`-ULBo}B&V{vgY9(`sv4 zclz?>OV!JljuR%dJ$~XuMBVeh|5lA1+h+TY9c4FePzKOepMO|!NLpT~OBKPk21J+G ziP~QG4@d1)Rtx2}l!v@F;w>xKs>je>lTyX0Q$tDq9+K~$LrxAhRs^JRU6KvpK*Qb> zHw=qN6O@7kM%)&r%_Q_^TMK_~FC-oJ?BN3|b7J zDWr_wuddc!wQ5yzYHQsg61$(>l9<>P0z7RU0%5XvA`2i%2FN!Q4?D;B4n!AJL{mvC zNQ`U=58;vti=MQl2odD4D0RHHV06`0RkonsqP&uoWiF~<@Mc5^g3?10rK7-e&Slg^ z2t(vCOifKO+2<*}lILiNr>@kE3CG-(cOg+Ip1EgLI8_hMsuYGX<>5#PmFWFEGr-^9 zmeK!UL;#g2oti|k{4vhUh19WHHI&xjKqsoww$0=$R9dK|YOQ4fhR9CNuOVxpt%C6L- zy|lYiv5?jj={}&RMwfP-1GO`aBK;R}Y%`M&9_)?EWLt8Oa9Dk!`j3rhVl;AwwUw2$ zK}cpVm1uNeI+DHGl`56i^@uQS*|on-obQWm;+{l7_v+^h&a5{{xtqItC`tL}7fVmC znAUX}-jFcdqfP6`hMiZ|Xb6`ChC^2yo9*v*_6Q;HfRZs3r6Ag2jNwQQ`*6{9U9`Pw zmgL*bt*ot$SyP~7=~+5N%Up8T%tC}O=m1MCC}@ue4DLtYrnPQnd*6Q+7%K}h_wQeu znArBt-Mc_Nm)3mm{N&eN8}QS@WaHI1{P^SEYcXo4U*!37xez@w*uR$aw?j+Rw~2Mo zjZ}doo)PpGWSZc)u~DS@MkC9mtur%r@9u<{D;?Ms`*The!y)K4c(0em>gpcXz%(V^ zzxt(sz8yxAt$tre?utIrAP4KG--_p{?nXOeV>KNeOWlnI{VuaRK@gE|0pS@z4KYod zu)=dQ-H;vTp9~cUhgsS$KFuCKZk&);6f%q*eV@UvZxg4(r5Ish(3PbL{&;;)$&mZp z&w3E_sGyAiD5pMF_e}2<;E}0EnYXuj&z=im^RdZCRynEC9tIV}IcbsCRM0p?w_Hf^ z_qJ^#RU+Uhk`$D!XLf?LoH2e!?xsZi(bxB-!|Y62S0tYPoPKSg+Nrs$+}5vOSAVXa zaM})T6kbQ~`%Qe3&{so20rauDRDyR3Ow>1o4!(6-VA$c zo`yxIEW4ITH)+4{0Zl{~;1i_o-TUMFPgp2WtHxAHAALW;*Vh=MvO9?R>MQ=JBwO)? zGI4=YJrx%Z@*3rsU+-Mml{VYRKG-aiNFT_2Ws1&eE{R z0|Q|xXzevmsVGmdU<*R}?~;P$JYKM!*J);&2aR z{H1Eeia}hc+o~6$C<(!vyMi<};p5{z#AS@}Y=-TpLBM&>>GX81pP+ufSr+NEN;d0e zpxCMY{*6YD9vwIIC^a{N-l=ou3=WuC_VpIH@8%sl2JPL01<(l5y=6hu;hC>rK0S_| zYl0&d-zi!J=I3tS)CZY1GdFKN{i|?D@iCBV*IXGkUJluhS9eDdL8Jo7;j{eb%`15s zT(<&mY5w=_Jao%F$B$=>#zZJrF+V=Iw@YQ-&B{s-8CKUh{=Hr6PFH;BnR9Xt15+Pv zD;+d&;E=b4Q|dn6q5ymS*pn5zxp3DmU8Z`LeoewBiy92U9svNP?r_eD9~%+lWpv#u zbqn1FkV#uZ1S5}avTd#UMrZup`W|LxB02_JB{!&cT5{#Jx@Rw6*7wGgbeR7Du*|O1 zSgB9Sl_rYPVc10|6?PMMEbV0$C&r(`@_Se{1+{~9M>ncF-X=4Na65&~9<=ZWj~0lE z2+IHq{lhi2e{oQ^C?|U$Qe(n@Sd@M1p`F+9F)zk_3PuDnh#4{Qv;MCA2;U`&Flid3 zA&Cp4qkm2NWwfdqXvZmxP+$1PLiB&>+QyBLBPAuJON>*^rr^&qG1QLsBI}~dEaczt zb{aKnb`HO|5Y5u$O8>McPkvk%5s{jc(-O9qg+HOEyuneJdEix*msdN=8EcV=t9kVP z5odKc@aE*83N4{v*C+dU6J0SU`%+Wa@=i2`(80l|-7w|zniUS_TY-U~v9XIF$gP2C zUvFByRB1m3xf;e8%8+m}>2;x@8Zth><%4Z#*8c2lp>?17@T9}%ik87eD4pp5)+GDs z#uz@_W~H=d{!nD_m%paKnxsNvB`QnDkJ}{sWoi+)o&NmT((A;MUcw%QQIERLcC9G2 zUieJvw>LNrFruw}?(m)D+_{@?{4RnDH0nXzzIDTh9V48>Ln3Qfd>%c=lbhs!0+Bpf@t(yq$pQa(xUgwPSgc*vxa5QZb{ z!~ch&cz|t81`!KXg~@aWDM%5#aWlnmdMRI3XJoFIX@&JYkTpSq+K4C)KZg02 ziNOBz=SPsp(8Z%bc4fezL6tOamhiw^mVAiBCQ1{BS+8E_3Dh=}mwS*_+X9P1ovL~L zdM&v-?$qv9(vgR_G3M+=6Xtz4+#C*nkj~^$&U(4eLE?!KwuQBaV(0eZ?qJ94e*%SJO4{NrptMs?6`>YD!n#t)$a literal 0 HcmV?d00001 diff --git a/tests/network/graph/toy_stoichiometry_cycle_import_rxn_visu.png b/tests/network/graph/toy_stoichiometry_cycle_import_rxn_visu.png new file mode 100644 index 0000000000000000000000000000000000000000..03041ccb569a85dedd2b5ce6eef2ba77ad71a008 GIT binary patch literal 73133 zcmdSB2{_enyEeQuic~}-Dw0eQr6fbKjFDMLhBT^V4w))4WXO~;GG$6gDwTPzC?Xon zgs2E*itoI&pKtHyJ>GZk_uYHH-*J5Z=Q)o4|14|$)_q_1b)DCFp4aWCc|?hUj-8G| zp)jZ@Q?)6SB`Ork(plOS_#2H$_IdoV;>2MkDrJHE?`~0A7=^NxqC!2S>l8QA?rdB? z-6K1C^iEt@oUYtS?&gz62iT)GswUM2thP8O%(eUWuGeS14EG3ss21;KpA~;RR{Gjn zK5?DS@yl;dM+@z^cS=qr>sQ0{pY8!yahXQP8Qpi<@m4UGhmld7QD0x5cGW7z11pNk zr^b4#f)46co)0CTw2b_mk#Uuo3i&)H@>WzTkLPwj{7jj0m|NgQMA2!~;ef!!cMJm;-K1N(wMdgsIt7}9}N_8NyTi9bT_ z+zD*SI%?5Z8xk{l;PUt8@Q4JD`HeSj+-Q0E=wuyJoTLNY_`sW*naN>uOH2QW345{^ zS^C-KU+QDAfLi9U;o;2pk3ZjV?%X-N=l%Ci%~NCcn;04O-rhIdZ9n{;;e6`@@8V*a zU*Fqr#>K_CZ2a{3GsofELNt{7CPmNWMCl& z;I%?RLR-V?%ub#14wju~ThFVu?z7dqd+NDQp44<#_?VwOsXM+UfPFjJL#E~j#}7QR zYG?27?|Wg8z~defw-`}B6B z#~1u^b9Z^&xY6^u&<>xs;mhl2o#e6C_4PDTJ5R}Bxqg>t?7jQl!^+Z_1eNkz#7js> z2u-1J(1YX8-w(H4{P?IWN#Q1iB6##cvuld>xxim(ntX4cIh>~|D472m`S8?rT1;0! z@3`VUUSC$L^^97}Pn|lYDG))sdbQWpt2DQTkJA+zoAmzi_VG!+{G&reXv+b|k;6~M zCnhR~-lbu0uDbu3NmL5nEZ$&t@}$?~aBD?p=N3GJZ$JPQJ2;A1D6gwqC4Xae#xI8> z%@cVzE+J+iHVzI7nwsnG-@k8Ja4GTmrx!&*(E2?T47IUBAxA!LeFLM@Q)RGv;lI0qJA&8AIJed%3x<-MmSUFMb2j z&^_`YpH0S9q9I=TCW6iIk(#KesD#~@)iSdqyD1tXNe%V&*)1}u+Wd-v?A{1yZUF&0 z#Ac;0<2JI1RX16;ad9OIC+Vy&BWIfB(Zx?21oiKGMntfqK{CvCA;o09m)=~;&v=!9pN&Kv`wX>7|{k?tc)1?wUmtoh7>QTFBc4)-k&lSS= zVihs*N=QgJZTQ^rTdsAt97RLpIYLT6mA!i3ScQ$em z%i{|luHCwIOW@MB38s$&rhK!n!})dAtzW;CVr^|*+EH}n{G%T)$?3;lduC+`;<3bE zC6ovXi-@efR5baX{*$oLBigVX8t(mU0ugj$V`CDRzjK^8abmQqZ0X6j2LIxWZ3kQ zF#OlLv4M(>l@~`C8={261B*2?>!yODf*2*jPcwWtpT(Ch_((CM%7OkFP(lLMtpWO+TyUgwIxP z?is;U?N!H)9iu8K$tP<1a{AZ@hqnITsCEB%%Kzgc`QP%3O!Y-qepB1y2Tv_#&^f)@*x1;N{`@KD z?7ZJIEv^0DkvsmQqt+D9$Vk?YPcDfV={Iq@d>zpEg%sr1hohRCmv>b)^%|A?DxO+S zUcUSL_aM};bX@@~0GEu+W~}yx1H$5Ns${z@cXfCyxN#hCT%DSl`u5$sEbGR{s5)_X z?$~^Jy^(Q!=o+n`q|A%6xx2gb?cBMXGBfu8%A)`mi0R}0_PCUR_?BO&^S9HCM z5R5q=N;zn1y2a7a5kRGy{*5|!6Vqm_SJI6tR3(83RqOrqOf5SW|7wLos&*pqp7N0+ z@8fLGzq`kvt)s&gp{j61NzzLR)im9_v9-14OoH1Aw?Anf3LDwjy54EXo;sA|QnL-a z*4!oK{Bs$nr2WAoN7k%nURvwb_)&YcXue{{Ca4WOX;p}d^)*E)VhKMHuj{M@;W=|`73Hv`2DHfOBA zaNz>VvF?C{PFSUn3KOH&q_peQI%G!Q;9wu^jLL7Ho+RoDjNy$eg&*(TyZ45-_j0^V z<306FkIubYar*S>U3>Q`qL^_-z7pR8@VQ~95ki(i;bB^Zz`BYUc>46|-Me=wfD4p( zleRkv3BmY|ef|Ahva(yIr>AS{>lNJHWgb3!cq=62Djt?=+qR_}H*RE6HS}4-X=Y)u z6uG7CnoG(1{zM(q|Q{} zOQa0HeD#WvmDQ)WCb$><0=|bgj>MfioFm`9;c>bK2Wi)@U!Rx!J{G&n!^5*xSa?ld zUfxsNe!8FIr`Fv|&~$jWwHZ9JY@q`(_5`%y z;N+|dQgXPCz92nAke~k$VBF)2pKj!zd6OjXv&Q=J<(eo_i?*K4aZe zxeADB>6$fbqQuW{;MIr^z<@VMRnk&u^f*-^wH7LY+@&AU%Yrhb}cVi z+>lgsd}E{&^V6qSn3|fp{;b;}=^%Y{eK{7pxtW=T#eDory#cU<|IM4C=H<)OfPTJx zELeXarlGdBwiGzt2E7teP#N+7UfO$IXcH?be)sxt3{m_luv286wrtt5xoVx3iOFVE z{p(nkvbHuhg4~`wdD51bZb=p|=2~Ch$=C7Hn}Dw>(N1*_58vE-@uM&2&H0l1_k}Ke zc(@!PUXHw+d-2m=`=KU<5rYX^vVVc?#cs2EQqO*PcxviX=RB_?Pi7?=BgS>>Je!+! zFMNK=h{i3kEhODm)h%y5YIc2iM1(o&OO)6ty6Pa#n+fhSe#v!{9?;}tmayqrhVFtN zpYNOY2^lmtG?eMZ?DyxS9Uz0(%LmXxzrhH^~C*;%ScDjvGLA< zhC4U0t@_T!5vty#m#Yclls|ETn~t8oF;h>l?ZbyMfc}PthFpZ?XF#GQ=ilEyq^e5S z)7zURbz1(eu7IfB7d6tk0rU6RMX(b#cx_+C!J~^Yckvxpr+y0LOkNl;8)yoaX1}B^qBV7Kn|24?7MnY3Y11YWB;DQJQ)rJWgXG_kqHf7E zKh?Wd$nfE^-KSrB$|b&?ahjQ#iIQ=Xq?mnreDPXm9S%vNybnG@r`LvQ{dyV-Q0=wh z_V)JWks_wW9PiRJe5l+RV{HryZ{np%RC;=PMsCo~G+m6n%FykH&CZ=O%y|+Ryjhz4 z;-|+|)8hj+v%kKvzF(N?4QAVORx{fq=)9HH8VcIlq^=pXc16~i3{Ea7S^Uw6K;*`* zA3sDKnP(QrIbgr~`n4MA)X|(NbkytW57v3 zMWQ&(Ftr2ZoWQws5F_8&+IpA%1LIY!tgOtetlK0c*bu60doQpZiI+0m(fi`{;rYS+xf(DhbRkD`X^TdJvS;+D*Ntv>svr}K?`E?kcm>L5_b z1`(40FE202O^0i-7l}DJYj^BOqkDDiiB%x_=N@!*HQUi0?$))$kqID6I?6)-#KkX{7=Re_!v8T^1u)cVoiB@3Lk zg&gQ5o&HR=Hln;peznIOn%FNRi8S3i|HCL z+;H(_L&FVJlJ_-1b7jNe$}zZ7p|gff4{{TU&L=={&$Jdf?u=wS-{5Kxu@&d^Lp2jbKI+?t-Zusj__4DItMj0G)9zlePpKl)&ubxKX+uXEi@jH zSjOk9P7Xc5`B=qT*{zb2Mp;ifb^d(Hvsj6K#z*?fuiUNTTV0Qk7yYc>bg9c{i+d?J z1hWelR->TjZPUvXxO}!A6}z~&STZD8Y~|X`l_m3&BFMu1-YX45pDw1eXq7NtEMfPfh7$JZMOp^b2$oSBFk{QPPQDSrKhiuU6|H*ogiyEqRit-$Tsm4Aj;SdI0^x$ zEm^XptE+3PBae$W=*ix!oZ@mo5}U!dTu+}p^XK&Q8Y{nuigW?_)Hf*TMt1g2(u;w7 ztv^tkZqAR^{$s&;(Q{2aFMfSIPjDo?_R_^ZMZFUBI}bs|#>Qp_j)qOzWzXu>PI}&L z2nsCwjh)9H6CjUItpWCD?{he*OOp@=7!fYzGC@m2Lj#Dj5^wHCB9jjbHYJ&vj%Tv4w&Hhi1!Du zi-hw}o;4g2o|To<0L}v&K>K-RXJ@xHrn@Y{tcSqaUC)0MSHw$SxvQTw)^Om589-!i ze*XFcMq$b5UvOr+u&9JnBajm1Ecwds? z{kA-k_Jdm;UnmY^X~;v!$;nv{zu#$a?3hJE+K5K{w z1WkIVbp+f1q7YUSad;PXm>`=D*Ku_|W;(Ep`Xfli<2Ohjf5B~Z2<+Vq1OcVhAkq!{79%$+gCTg02nI9b;E$y8DwQsYihP3frbl$rd$1 zxep)80a1>9JfD;3F#Mjhekky0P=}?*aVkQP4t>^cA-om%5(QgZF>P(_TXAs#pFbN@ z4x(!{zjSFWmSE!BCsq*fgDqLCG2-X-e?(^-B5{!vbNI-S8`>GouHQcwUME2Y;)0Kl z@2zW&VgBiuhbNIXKzet-d*N({LsN?HpRbO06C`gTX zsYB>=LBNJYM0f`TtPa0@+w$?bju-nSFMnT6y4oSY2a za$JraP133+F&fy}|FT;3-$KOwD``ri?>{Z9DH8^7y~CBH$VH;8k%WB7+|R>-j~Ujk zRl-5%+`&B~yW+%3P@L`JCu`Ten0$Zi?5D>&S1_>a_J4a|QsnPV&)S1zW~8R{EQk<6 z%DMoz*T9rneSWHCUKk(086?Erw6vq<&d6nG&xV3aQeDDdFH13m$PawOYI-@}q#L@K0ik5DB!TFPmZ0;!}7LB71? zsHP@m?b@|CTIL8;(CrG+JGLJuDT$gCyWfsCX5XbCL~m(*Jp*DeBexj6R(yOs%7Y>v zc-NT*j*!q$;lIle9TU@&94xbhQdw11_UaWq*ndiVyko4Sg8*bI8j6XD$=S~0qO*o7 zmj^QvWIr56ScD>%mQ`1;0H+Rcg{Ma5A}j%b_Fe4^EP&6{l+(ob_G2SYnr zxn~=>MNfnT^$`BlQgg*>HcwQ8vkz33P~6*gKK^X${{4n_FSFHS_aZlVqcZI099s961XKVwnBewnE_Lw zPtL=7a_xt-^DOol05(?RKP!U{O(xGMg~sm(md87oM4G1ueFJt!Mg&LC zC@u!v$M7ZKkdu=H6vxUS8VZ}N`-?Wl^mu|1xGi(pRS$8uSCTmSZ$H~BCPkHjG_?#2 z_?{jSh)6==XWM7RK>}V{x=8ZVZ&OYwr*%MNl~NVqjejvPS*-2g@WWfm2k zG=l_sfRsym`jlIR{8D43a6zU=4nEE{EN@O}2C_YaQK<9Lj;KW9bEPKrH7k-RNrxTNcx zip#ud5~`sPXaOQxqE9XOb>+{D1G*8j*HNOQV`EoA`9gX`DjNk5pr3!WnnHX8P{_O> z0#-mXqw^-L4e0!GV5~PW3wNnt7jEt^zGwIKo_eg=rAxu+5i4qI(}mqRnhr7a#H0Y~ zk$TN1B2oi9^hm54Dk|yR=w1Q85aYne$c6jHh1&U2yLPR^5rW2^^Gm|d&yPX@k6AYP z{rh(jq1loM_Y9U3!e=me(mFsT$&Z*wuZ z&ZqJwtnQ@4Guca*E-hQWT<_UD>8DV%88>XW@%F7|+=0t$o>;Y)$L+VX+!K#=@=Qxr zQ!a7;1JI_G>c+BR1BoRt;o_3P8r0^ux|f4;hT@D`JtA*eS+3hY1fU2a(bHI12_4U{ z!w6bmGbCh+ClUK)v@;$l&g$?npKX684#2+)QlD|L>qdcyOvPC`28QJnEP1lxCm{Z1 z1<}33wnw&SK5!{~o-GDA_)lWHQTLMJuXHF>0GOr*GL9o{kyZmxl0fZj1Br(4kq38U z8AobA0J~2~i7ePCMg$VhN_YLfPOq@A^$0=j)ZsWWh!N%$D}byYH&xV8sSJk4UynJ6 zr!loqiz0avxqH8U^}l;}D|*is%T8$RZBK11yiav>bm)D2jAM-wT#7G;ZCb<18p%#* zL)tZKsxx%btf9>q!w^zdR))X1fsP$E0e{AAin`C-z%7stcwdLpQd74{y+FHlj4k^v zz8)*MqvL0p*`e`7+T+R~8VOO?DeE}tD;s1N=Iq>*8qq$Np+by)IFkU%q0AJWefBT@ z2vt$bWh+~*`ntNrayRdsdwhYy8Klkjb< zq?B#bL;D~OU(FbHQupb_7Ia&H>)DPQxViIM9D{P|<5YEzB}MX3pyBcA^B2q*`uSGsvf8;ie9=vMru{?Yshi_K#kffL za!*LDhY~fnB1gN;Ok4#=2<75fvRjG&Uo!v=1;if9cmG%5M~_`9N}aX>WsCeuK!sYo z)O5t`V|aOh@d^Ji2d2}Y#9bPv!~E=7TBIF_@+X>7R9J#GNMG3u3c(_!et$8qod5-n z3mW9)?A(zLoomqt=eX`!&8VrdeF(IQ-b@F&(npVf4BU~v^0>*Ny1E+FlTpF%=>XV} zI5;@a%xm^vEMAzOWe--O^6b6Xu$Z_?vEBN1`x)OJJhG_xi|--)0UT;nx1Dvbbo)Oh zkuJNn^~vU@Jujp{h;1CuwOq}9LB;ayK;%MDNVUVN_1|9I|K(X$TR!VX z<|bNNq+xncd!e1u2rZEnHf$g?>4}=N*64O%f}6Zwz5QIp4F-qk>yJ{PrJhg%B>M9I z+6h7iCt%Gp8d=A1RW7jH2={+uocJ&G z0RQRc;lJ+}@9y5t4_*YAMZ7}>x@}cel|vL#D)_%0Uw=_Wl6P;7kuL_>j zgKq$r#T8cf=vIKQ@@3$||4G{-sS}!_%@5Cv_x8iLbRgs$VoPZBl zuuv+Jj6NMNa$%uo74G}2W|X>n`zi{_%Y?1%bo<>-Ka6JDR6<)a!!G!Gg)VQhF+F_6q*Ra z*>OnPrP`|X4#ZmVS9lFtcaS^c zui)zGMnytj4SrEQ;Ax7yeI^f0DTww&bQ(krLi@4kjbL#HK@kZm z>T+}60R#8H*+eDVZ67im;s;KFJ&|$!`tkD1MD_x2NukXB`2#}S&O?n#owP)CIg3sn z{nt`Rs8IY<%4eW+qjqphO0vT$Sph^rRaJdP-Ldd%VI=%eC^AjJ`jC9 zLUp+xb;;4AM`0i=Z*S)W6%WA$E(>``>Mp;(?{qY_oC%ja7V17Tw1WtyaM9umy^}ht z%^gDYZTyEk04V&C)FZ0Ylmq{vSL18n&|V7L?$fJ5uTi>&3a#+@cslh%KY#r?eF%Q1 zGq7>Qm>Mn29B?%>+z3wrkD^eM79Ih%g1Nc5J#X3o9hLAS&$I<@QX*eFA!yE_enM<< z?VeVGAU4=hbO7auS6i$}0l!HxgWR!8Oe{0^b!jODxB)+ZuABjhJGP{xq)RXb+iB{Q z%M-G9$X+G%RX@>6(H-wW)A=&jY3u}}4kP2xUNF!0BW-zdTw?q7`9wxW`erDz;`K{B zVF@*`>o|tyqZ0`8UAuNs zMklO=VT?Hs2@7?f5`Q}dAjdz>*FFS>qObCY4$=IJJp@YfR$1AS^7i$paAg3Sh!MIB zU8C+_+S|5mgVKfWqa4-{US3|7BX0m%>rb9JbN?vIP_8ryn{i+Iq{2yErEG5L^0yPg76F)-Z7POjN z2mAc-(K(&8UVtBaC^^8e%BrfFyF{SNZ1I?%-T~Kf3csSmH$!u>prmsfzu^A;uQA2s zsku)&6T^ae=6+3%D~2c&$-0NLf^sVFdKIRh^ zCM)~b!-M{J?WRq!GXXsCm3cqE{DbTXJjSGzK}WF)Ozdh-NoLTnRVYxeI#2s5p9hEg zKXV1$Ic?<#F&Xu>O>0;k&U{FWs0}_?c;iCb<>2A*OuLcvZd~qgUWpvX@b&D*mcki+ z6?8$w8AC)AJORlN#GVK$)9Xp6`zE++n;g9q`vE$z&cqW0uU{hO0N^_4xxQAW6cPo_ zmarq0+p>>58+v7I(pEk`TJS80qxT%*T71l|!i{!lN*Fh7%8HJS_#a$F9n+sD&gSqC zdl?~6a5{XRK9zWMzI6@A0Ur<@SS{y2>MYVQ%fW_Ri3Es)5QM}9@!lM`+X<}0)Unl! zdWZk3mZaqGhqdt@k1urwAz3GZBw>?s+68`g7dF?#Lgz3*81d^LiIcbrQlk9z>(yu+ zNI?Lz22T@}K^HnTX&hkNA$9 zf1ccPsl#~0N!QLnClqoKc@xZ!kS-ZWt_I?asBNF}=ApM}sq8sqT-iWL(cZeO`zo-tNDki$-l;sWQJ;(5 zHi7Dehx={D%}&2`qD~Sb3k;PU40OC|QJ#QavxC!cb{pfgf+AA;i!wRc?0OMMI#?MF^F%_KJ4SMW19z z!T^#jBPA9+Yr=-Z&0fgM#H2R{i}&R#eNm%g_!6ud_e+^D9~dY4>Rbrs5T)j zbPWuw1cD)fB(5~}QZbBP+?lDwFNJOa=@I+y>Eq)Aqgf1@ zk06f#vJWrr5>H`xlXbWWA1iwKyRi_={5tp4GV((ftzcd6flU4@R ztaq`e*iwhx0v{%`aw4NOcz-RH)XoUUBy zC7vwwumz!A_>P3SrVxR)4dI#ESr5+$&O1cTT{x{|a_D34^z3YJbB0cotcQ$Z5XUMQ zbyL#PdeJoyK1xkZ4g6u^^gPBi)-f|Lla-ZSy+wut`asFT{DIpdra_QUKqfQ5(5O7= zVa*sG9&Uy+^we%(4UQgkR6(*cyeJ@Jy*1nR>|rJ@j{?V|Hu4FvG+Y0--D>=TMPDU> z>0lfy#knugzZD-+cM{$^;wT}i)zEJQ?$N9PSK|_QPFQLPNGFWBxj9X=+6qu(u5)8G zQM=5qbvgdKBry9x#wG0xW-#|Vi6el7G$_myO5917GxM`Q7@3*9AiSLWlPAdFX4UZ@ zx!xFsRM2xELr{@`N$2*>=WS~%a~>MwRqi_=twO>?TxbN=sHmweqd-EXe{lKoWe@WY zur~#sdUd-f_cQbX07dM6y3gfhtyPq9eq(G#vW z^^{!KXKsuEvsib<#KtbAz#G8#wAi%7U4)|{TMgwK@YNg*7z7daGfdZES|+(Dr(~Wz zc#G_Y18W%d8voUbx390TFr&S0A1q14HQCu|I>I-xu&7D}zjNH$6RF zb+!(?2r}F*FyX3emzJn0Dz1RmplN-$RQI03#G-K))bGK$jUL|Iv>m zQ*EXtK|u5_8-sl>1YpvLI@GwCZU2AXLb$B#8y!9UqL+85X*uMgj4?q&)D)si0Z;20 zbYRxd{@X_uk`U2I86>+M^}&e%NB}BLo6Pe~cl-`Z@sM}@WFk(cLqDdhC|eDD$xs;J z{&|PFGcc(Ma^!6rdtt5TsHGK>&RPLrLZD+C5)2sDD+DBC)~5NR6Lf0uSRf0i=ved4 zw+a)=-db88<0~?MCUgu847Tptm0P_$BPC@SU^Qj5X@}{K?c2-B%aar>bX)Ql?x4^; zl3c^svU%WicVXcJso2<4V$p*?3im(}#w2d~#=#8M`Fs1s#TAYm31TzcGmnHVFC0nc zNpZp@$t>uO*}#80rPnp2^LXLwa88NhxpVx*(_dogp2WI1Idwr}&}#r?We&TEAN3kg zGaPT~qvJw1_e#%p7J^ta+H%v^(#I};^t16%@O}^{ky*x|QfCM1uzdLN0lmQ)*3D8*o-pK!{-jXw%6n?jOJHKjuK8HvZz1H>3mb9Z6r#^4aC@C4*ByT&1XBW7w7+gqhRSC>KPc zs{sK4E`_}OSXc0k9tHFH+AXxj&o2V<|BGwu(ac@qr#mX~W9DC-mg$Bfm_!Ax)??R5 z)h1I;@W;!msRexTjs7Q-Wkz}XHoGsc%Md9uO@GjZ2M;2XMTkj-{kulX_r;d*k+5u2 z3Zeofb~q1kW*x4f@c)Erz(U8>UKHbj> zN8%aU$({f30-T@Z40yx`A2bAmDokOTxWEi1E(C)tBt|ehcy6WSE8CfhKJQ3yU6QKp z2b1X`;mDhuK9p0bzw<a|J&>Tc}fB{`vP%jrci#cIo%z1M`Pe0;01$CMMP0gKJ zt!R8_H4w6}k6~DX8g^wrPsoF|5)avXbF#m_A9(O`W?^CK`nQA2Z9IMVYrds=2Op@2 zd#Y`>wSig3W`M4sshKA`Zhw>JtE;MC1Z?_LwQ94^c2SsXuX4-(k{I~yBinG4>F_N( z;p$=c=QcVGg$)kU`(HRGv`!X37H)xFDMUoK=wqFA{W#$%#FO{<5ZzVahv^qfOsS^ zrVchELjID%Eu-wym~+2xkhcVkGvzZN{pUDwVS`9VdH(4DU144Bh8gHan|i*-LE$GjWncw*ZF&R#6XoSb;7`3i^{ zAP&}GR_qiuGe$*7A#uDD?+tF6bf^igKUd-OTVQ|^5baoOcjYE{O4fk|_yX%P`m|s& z(n-g|<(~*1k+>?qzR!LIL2B&h&(NC}=`wJ=K=J&{dFUr{@GTcwcVA;x_Wpc6n5SqE zjhyR5qY@DV!KGtZTp?98;!%i3Un3rB8p#D`&8#_Hi-^+Td{Qk|aoL-}flUxK8#{YX zclRMA#8aCe$sJ79&coaSGUZ`JAZ{lxha1&)vtjq|&K*`I{_t6R`V_Q(=F%JesKdfFTy7TZIL|!vTq6w3FqJKmm`9@j?d8h z?AQPv!GqcQc2lF~P{!qflaK+<%+F057#Rt0s=tVzV^#S8$z>Ye53;i0o=~MD;75F= zuH(=Lg%d-n)&RnwZ=x|x3I!renwd-WDe%$;VdGk?O5VeVNdUdrt)q@79k%lG`(f|` zT&_HzCx#_XVQi^6``FQ;v)q?DUMRoBChx`|LM_G}lne~mz)h9c*9Q_G1qKz@#jaa0 zH8$peA@(FHx0jF4J6MSkuWFcNNtIxd>$8TX6uGAi#vL!C?0euTCw_EpfeSS?nFXu! zxjWL--5d)InI7K#m*|2rOiPNJ8maTl#v$SAsoG=!409NDC^7t|#ZpKiNodX>LBG6p zNhbB)J=?h{8xRu`SAGcrgst8v=7p({rd)IR)ZSHGbcJ4C%kZqku0@)+$B!SAT9WPWMootkKmqNx4cV2$8_?k$l3G>E{b!fsqJaZ5PYXPpd z@$toDad+;(C|dY>YHkaTiGdwvtR7Oe1ky=3Iq4wub)Q*dI)$T4ZQinAk zlobX!!lI(sfaZy_4tIB4xpHM=$oyJ}`F-eZN!JVa96_)^>zFA?LQf{frs;b2iLy&0 zt~x-7tStHSYZHoVRb{0YT7hwRtKWjrhd=bNYlW)1dOy0Sx#@ufkUryRwt!Ir^7D72 zPbZT#x(|#Ehoa&#fK~5cJsdg3>k!KzY#iyNqqPNCGq-L?JbIhJ|*HHX{=-NJg z(s_Kr@VL^u3~0vaXvt3HC70JvU`)(KeZd;e zp;qGnlX)e~*6jIX3w9@6FKYw5iK;a%>=^ommkum&FW?C{oCu(i6VP>lUI3EQz!c9x zYZM$i@N2uLm_pM{47Jk2#Q>HDE<{+->d|C0zIzAui5)$hImSneiliOBHkZA8nOQ6T z8pAzMwn^Ba^G0W54dMg4jaD}e^;qJ-0kZl8rf4TCGNf*!$0>s)1In5BA6qy~V57%i zu;vr1WR3-FZA1LOU8 z$9C;v<(KMI>N2t(VB^SlM0aZkR7C;gNjE8y6t`@?i|5zPG2uLX_%QNnx>11uymgW# zF~Zxod*i%8Capj(OrR1dB-qknKrh3VYh|^tZQQ6;;_gHum=6b(_7{d7Ge_ zM1dlc+53xodU_~)M;~zDb4shL72oYUnyIVuPUM(@!JyPQK#=&KpQw4h{{C1hxp(_^ z@bFOjBtjeENkSCJfkNqjc|l9!X3A$6SczH$#8ZJLj}ZMB!{Zea3V`rDV6b;~?L*h; zpW!=?Z9oaZC#Oxb#xH1{6x6vFa9aN(@;r1O&w)E~$iRk%hCIByp|@_8f(^!*SFf{xL1`A3Xs{q*=%54l!kGVp_&oukKG z&r$e7MEC4@ZL{<{>+I0TwH5O@2}jh`w~LB~fMN&<4^J6Nv#_vmo}0?MC$f_7__I9) zmMtzBrjH8>QX!OobPJDl|Ic!b5rH(;T$7Cq2hFbWN6OC8A(W3bR+&d(>@x^)ms zi_G}$#XRgHQ0{0nGj;h@BLtRv!kvui?ZM$;okTe@(k;4scQUGoZn`E7suNnNyho3k z=Z+Ov>J^GnX}O%-YlWYig1RYj#=e4|;DrZHsf1(877a^F0njRNsV{k>QRS4Euvc8X zW#n}XLh+v3MtoL8vTVcmG(o-0@MKfu!v-zuRbw&!N+G)I;P)q%EC6CvB}O^gvhli& zD2T71SKHXyYD)(3Ie%x)-hJ15og6KfJ=LFw%WIvSud#9n5B{VLq2~`Je{$J!DTIZ1<6xq}b?LXbF z5G(J>~yDogl96|^G+^vI;NK!m`XoIprQpFVBE6zfi&^>V(Uf>Kg(-7yRXudy!f>gw5$ACHp;k40Faskx9yL= zGBd%C92N5e#t-;E=Ky4Yvh`ZEyw@dFl8Y%&jxdwN#KMy54A=a6@Z-1zLuvyK0JuQ# z>ZbsiR4ZAwEBgtAQs6?pfp`b(_=0vMrl!TxA*d)~zugz`Eh{WrGOG~QIaFOPFFr;H z#M5ZENRVSbWdKW1C>$9Vr-n-O^zx6*m~Fp{c2n|*fNBiWQJzz*(nt=lw#TAJDSGr8 zbj;MxS0Xf+wxgPsR2Her(I)C(oGlT<-2j}i&=GOxKzQ#oS|PGJh;&ggu@iGsV|3eD zwyXHfO(cj~tf1hVgBLn0%%zvrf;s9~OcB$dN>}4?nP2gHt=sM$9ZfWhB_w6Sibw>D zF6T}ucqO4((Ubxd0(5B$W%2EwXcpN#vhz^P-it3`K*mKCY4}{sw`-w(1{@lt7(&80EYPQi60X8XORK^D+gMv`NTa*kc9; zhI4RTqGssrO5{8AJ-oK!B83hgG$4&&xGI7ec8@p4m$>?ehNhb;L*>ru z_z8!Crq~D?1BWuE2HwEv{dV`RWq7SiIynq#1x2e?Yn`Ei0lAcExsG3GI+az00ua%} z@%ue42`MYP`n%(b#s^I-$2C04Zj0*G^&AfPT+&10P=;GCSCrVtF5 zrK0Ss)~u5Y-vvlPXrm!E3Pu;M04|UribH+7?}>39nD~4U#sYam z+==iCgBRoHl&94yC$ovT%i!1$oA-P4(8O339>ite8*~Egm8+W@eO1IsR$*g|l9}v& zk;Ma#?5`i4Irgi$sH;v{Sm>NpoY9DrI0PagJctJ5a8?teR}jAm56cE%PCdzM$Z8&Z zLd!$>qE+YoKK;$!A^&%?H*?0O&wDR*#Nj|1C*ZoKFBm%`s1EZ$26cH22-<&imioO?^FdQv6enA_HLxIiBbaAY-v2YEXRQ-`ijc z0DTU@-|!9M6tkeN02O1PyXo+9?Z?n`Jh|j=Xl7_{N0Iji2F6^9B3f7Ex@%KoI1aRp1U zAL-+KwjeeOdx0)x+`%dwJ8;zbTP*#nX9{(?_x)4QJQ7(MiwpVUNW95#Umq2{I=S=> zlt(|b#B|+E*M2giLjk^OXdT9)NjQg*FY^aPU=ywz0fD6mv1AI}^w{{g;M>(nQvw?l zw2_s_AUc?~vuA6;P>YI-*T0hl@lgRK2Rv9MdI2a?;9+WTi13?`z9$RbV`VEK4Z=@! z>4DE6mM(>Fap~>=b6#6cPbOLLqZ*_|!kf%|&012&<^P0Re0k|jVp6z%T|TIWWNOGx zPzCB*CGW(=5x)m&VGr6Q3^fG7uVLL)N`pzIDCsME!RWd55BK);V4CwTvOgYXhv`Oq zFyA}H&bUqobmIGKev6-fU-|0QO>{Y=UB|rKx#sl7O{bWuj)iclxQuDP#d0;mP=rcc zjjpQWY!U3uxM(sA^8mIm|Kqa0AzYOM1Jdib{b7*fjZGs%L(8G|q#u92mxPOHNo{cf zu-v7OUt{TQ+tF+<>4KHcsaGw@NpM3#pI)Xe8FF z7iWZO72K-xO}41S$f6PGynFilw}X6=yIVxI?0BIqt{#$cGct3gj_F>IsaIE1i;{8{ zvn-G_4f8)<>>3SAXYtQz9FwJQf-fy#oO2v?u?sikRbg&=+=_XP8~`>Tg6dyA`hzP9GPfum>o<>6^lg>3zCwAe>Ch<~`k4{~t-X4CxB z42g+}7;9m&u;`sz&WgTxM1AJV+=E$A5@jtdld1hcJIEv}rdl~Ixb8-=r~omk#Yr%F zLc;7n&s~EnGr9)`l;B;#oh6@3JQB8e%!S~~$3ta&n}0^5{vkLK3(RyP(+5J^!HVDN z6%-SDSn9qP;3ktU=~Ba+XV*BrlJadC~`+pU&_U?1pnriiy|F^ zyJiJ^VCx#ijKSI4ghm1=?*ZYn$FiALNvS6Cfpx7qN?Y3t=iO+j&OrKvz0?bQ1JZ#4 zAvRDZF{4-~Y7{QL1-lV`5@~S!xfMzSV2e*e!WJMqGCGdBN0tsfH3+F=G$r&&5(3%^ zBrBSnFT@20{BMRy@x;}L_}EtWdWaa~FF5gqtR8*XS5o&3w2m53#Kz-(nii^`@Z$22>?RFe703nrjeGgu8aOSku9_CqYem*|sAzJ*!{2}}eb8R=4 z>i^3&Be97FKn%VsO-aQ>Td-HD+WkuBWhFK&O9+G?ZUUC^I zOR7p(QBBNqn#0#hO1zB=EcDs-zU8HLb$-~luKxa1duFUs6Vf@kCs}+S`Xt;SF{g zCPQ+SU2QaPkHcb92OatI(-3lZ5e~an{%Li*HWSfAD8|36Tm~L?*}uFN7KJ?4xJq_#{z`f0IYlj!5R@1CW0hZiuC$M zW;OuCh9IXBv)teJ4THvqU6luHoyKEC1dS`=5{H)nYr!Du;JDO9Z+vpG+a3D|_G*jU z1S1yn!l%a!z`?28iZJjm4GvWqm+=s58#1G+5|g*@Vq0wT2^i!9?jyy4t$6uTbx2}4 z9Bu*NNJz>CXPb^S_WirY1*CF-_I(J?1?tOP`?w`7VE>B${5pngS%cK`054Yl`(jy4 zDT$etQt(1t0hrcg^XLSo8A*Erm_U4M{vIZadDt(3N9j2j>N5t#6h2cq+|@R9g1K%;D2~fvJP6R z6K%QX0c@hzQ1b*hmH7UW4kB6^x{Dr7d3oZ2$Fx&;P>LO{f~jt5VuIZt{CGsrHs-C1 z0Tc;|A_3Nq>*%}*V#M1pB7dI(4Kp{{YPyJ_CFE%S?sPZg5u#8+MhhoOqH2wnF33|d zcmxPYen+*2QF1XdX#;V1MV&^?W`2Ep1*=4OkPaW9D)9?`Zf{>jPp=~x^p zRe{drKlWHic5d`~lMP+OkK)-NAXZUq5O{>}pl$y`s8GmNUzklG*QLFLk;OhvE>$-} z8^Z`B7m=RIo+EMF;?s8FEy-4Zv+W)nOvfwh-oL+%o5-%>ZwOq)K5#CcdDW+YY_MZg zd(te$H5w^+fC4P_Zrr#gB_)MBu&^<2#mKWJEqnF)^+`CZAi0yfj8c12_t+@v!U$usx3GF7~sBzih0Q_3|cvJ4hH)V5yi^4;A93>nR zqGCkZ0KIL0D{~4r%PtP^c64+Q^t@0w2gTdrmMZfjCLJD3WT0UU`Oym3iK*}`vmEm( zWCA!mf0;%WqWdS8u6@~jiu;Gl#9$euQ9|8=xSVP|h4J8Os7UtVYRutyBfSi5Z=fnz zXn)RqL`4J;!W%WpiQ#J)faL3kxRB$Dd7nTawi6dG*5gPzMBSeC4S_RQ6#!cIep`4y7xq8fL6?AsrEzj{%5|BvglaO@WCt`Y63Be& z^l5UGF}{Gl+55o>IXp2r@@&R`QAam>R-Kh=!UvocK2kd+Yha;apQK8kM zo=W)r6Y3#Hun3S5fziXmf~<*W{V@86sq`5&){~3yZpWncf{=3wToO?f(aE2A!isBWR|OVuj`ovx1g1F zgtPuPW;+Q{0q(-)XP*iVwTX+moZLf;`49i_0xZGRVO$ub!sTd%;&tb77wZ>zW20hY zdj|)7qoY3@eg!=blJ-k<$E)CJc=^(QB={4=t+x;cj@N8(zMMu4;-SI8T8T-{uIZMo zkaVEBTGFM!cByie2dfGwMJdQd)IEag)m%pfaWFq^-e)&&ZfE#10FAj~H#R)A(lFrKRs+NaHLW4;Ym>GRZrk0H#kBUSt?0-$_}Sk=qKFwmrTW%VeX_@tMC- zK^r%bfLwYd8u0>-6S!N*O+|FPV2fX5nU=_)d5!roiD}-wdwHd$kM9lz6QYzL<7R-w z1nyo{VW%Us%$l?WcYpXTgMQB>{E$5edBP+n=@7H(_TSc<$i-}E(_Y!c$$%CHb`XdH zmG)CuMkbzwypvkCK77abqL)5E(Lmdt3-cBKExI<$YE%Ie7UJS|?BUAzVD|rqv^NjS zdH>qKuQF#&g^VR6BtwSGO)Ao$WU5rAQZl4%o>D1_BB4!5(SS@v5s_qPBMm|unZ=Hf zp)x$LW#9MjIiBCY&wcFUKK9pkUDs!Lzt?-M^E}tN=mz?5`{WkV)@39QJ>5e`>D@6? z&0&QOX*rEXE7T`ISfl5ffLV8K)VT4*8n+g=>(fQ!;QM$CZ?mt{InU`-DB=`BS#=GU zBcztmFCP>a7w`NO8M;N)Rfmgc$q9}x@5WaJxw+4}S#ZvQb4{@A;~Z$8^Wg60?FR4QZ#-yEo)*({a2^ zx;OT{ki)t18~@)1?$lt|uzZAj0D@+xwqEJO@}dWX8rO>7r`+CW7YjjkGp`5h@-U~l zVwav{C^zB()r+SA?Tg>be1!gmKIYa1gXWBs>KPQm>uYekG{nmj5PPn~rbV<1(@xA* z0+r@Zm(jAeeytBz#Wk}2pv87PTL$1_wk*vK{7BCsDenG(y?dvJU$l5xw|cPIm#b9( zJ#2hHw9?Lx*H%|-eEEys`Jy4m+o#p&HkC_+cw#SmFj@i0AFd@1ycJ>@2g_tAwHKig z#=308ii+7Rz6sEi9MW_(BdH!gX;KTWW5jXWzXniSfNs!kFZ?7zivXz;@6PlUsGFU& zJuz{v(sxs1Pk7RXO`5dp+I2EwO+by}GpVc&)lQuz(qg*Nn|x#&H3w~M!p`&M2SLn{ z6hC|U6419XXui1Y@K6)SW%NamHNmPHEOhk3IROqFJUX`Jt8RfMDV5n8`5(+ zJ_(*jZf)s{0x~6AI6L4g(4|B&A$tD2%!6Dm{9#9WB#kTJ4KTqwA|vBh>i6kmIe1ew znhLy93|(29qt<=jL%l90He4#o^lkIfNRU6G!H5R zA-gbv*h$vClwbc@_jUk_A=oO>*wrf5Jj2LrP;z*Bh`o0D~ zXvsS`9bFgyum8@6F0?&fH<7ip+72d`-P5;=vq_@;hu3#{+&N(Dnf(nKky+dzIG!kYmB3ULKTjPfluc=~34<|(Ll=y06^9V46P zd_-K(h0Pgx3LYB(gZ0*)v)KCNp@oz=w4!e+EG+Eo)0Z(63R*0&MI2B4TS+e{HtnI$ z`@N$W7y%F}KEOzrpCb)L{(*JlBtirUP%nfOcg~0?fv?=m3U8;OVUAn_%ng~)GKv{i zY$%6bOU)s1Ak5gG?L*LNOLWLPrDu9yyAK4sIOM&5e_LojOszrLkU_a)V6%UJ7p_9o zCE2T%-<*5Y%+cIDXr4~LdO*mZ+I{*oW8Kcc6czk~E+=u&HjnukXq6}fS!j3P!pmMp zUx0%2azRN_TLySsg$p7ZRS)q$G&Pl_RKY7H2+$c}iV>`Wr3VFw6^=2aPU4=sqCo=t z%ZH1Tlf^{U|+iU)T2`7gkTt}E&39ZBs$b0&gO81^>MXoA2%v?vIJxY|5! zNn#<1722*?kY|nL@UtgEw{4q4IHz(|ZPR8P@p(Z}LNjvY1gh^L3kt_oZfOKPft=VF zu?$^e(z+oGr~kiOKvmc-yEj!D&iQI0`5GNI?0U(iggS&*I5{%EH_Rd_%u-TRF2anr z#L}l5Jc7!${VV@NG1`3)tKt9y-iC{(s3~NPp<|1t9dkmf-rr5ov;B{53xI;w-UX#8 z>J&FuN=wtP?su)G5P4Qr6-GN3J~oUH626%F&OTn{4f{LvE7U>n8dAfiosZSf}lrL78UM<--4;s z5wPSOTJfO4yyg&YG3wmRX`|QH?V>`KuG(d|qL3_9!gKP<4?`iCx!3$O^h~p$9B#{- z^59wVx*M?tv-~QJ;re(3A)N=Uum+qW)e}?^?@43!vJI&ZMsT6QdWTJLOhB==mKhT}{tCKwsPE8#FL-Kh2uhFn1@c>UXZ%Vfk z(=5l<{GtI=M(JQ5bAw=xq-QS1On8W&vfV%6Pn$-rI?n7OzE943$`xr%mCjo*mg9eV zdm*TYAC?h-6GiPsUeHGc#w;5YA-MfuM5q@32nd{rZ{x;C{p_2?R4tvbH-Dy78P^ z-*;PWO*Sj~ezp}48s?}mb!Y#KA6bZ!sc}Tj&DP*+u1x5*)*-&t+xDALWaH)j7oaR& z1DF;Da9Ui@4eyt|GQy9jC;c6CVp;Ik#a@I!+eqE^doYJD98|7#3M-h8MPOjmbP2uGju_})mU3K-cSp}=r?20XDlYOd)zfyFI#9nE`!1#`NJ z*07`VjU!|m^s30DG9Ixe+~JkHJhRGajoxs>xN1nJaSAbr>4YyQ~tOXAEP>P0D*YiIXu2m;KU5Sbx`25cbe06^SnuhWLhV&TfAP=Gch zoTmCExIZHe!9sisDeuQpCs&++6I7R6EF!%Sq?oNHR{4MY@zs6g$Ab$cFQP3}q#yaf z=PcBVy5X&!@7TTjny}H>*m080p>(+L<4d_!zTQ;S^U{@j|Ki$_90t;9!I{~&%iHD8 zvn{M_Z06Gw%bnPvQT%NL)1^zgy6tnM`0jM{;z9Q(H3~fNKI4@hH`u&9i#%4CbfiHQ zeH?$d5e+>8<6xF0yd%Au`R=QiC$JW{dD$%j@FQfQa5(OF)V-4BAw9aJ(5T|a^UQaJ z?;5~j1UHo)cC0#y7J6+*s#-xdwc?a~0VV=N7m4kPMWw~Bp1x*9$o?zwdEhAHEsdYt zs{d7zM!Iu+1-R-sWI-GJ)dmb{w?1(5=0XSs3OLy@xNrs*MTBOfo_vNc8!$kVR-NN` zX;u^TY-uq8PTqwGgIBGy1Bz)&#}J?@vFY67o` zfLui8@I5yOEK%V#Ggtw+1fGuU8Wvo1-@9sU$o)V_C4nczNR?tWq2T8JvInKiljsPd zntcV~t|VSWjGfAs`4pBpY!%N3XZnzh zJcYxuny|`)lSPhTL~7z;*Jp>{BpaLU#12~8I&1WsmP2buykEjE(s*r})}Q95Dl)%ocYhV(S9dd;vjL!Z_ivVg7OCO4TEI14p$Z<3!8DuZPUV+F@$n0IaAchQsA9(TdN(Dd<=pe@ei+f#Ev?RU zJkNG_H>M;^BJY)`&MU(TUfq?IIpCc_-~d!o{!Z=w)*xP+63P}vno zJ=yl4GU?Raeq-T)*tq!=+GuN=Q)B*STHs+g8#o8diQS)m2K2>WUl>CnHUw2a-O&R8 zh3u{+Wif|LV$S^crOP|v(8FWj#$%QKwlUu-8lZ+<`Q_h^=m6X)iyJm>tb9A|jNY8} zH94P_t>9e-vAszs17EI5zaS0ZqPc`%Xf&p3%klD(tPSKXW!ot(%rMVD*=Om1cA91=fXT z)A^sEd9cMY( zlS6zxI?O3(e1QQZ9MCJ-qJu&Ava+=#X42XK5Mqa77@_X83o8b)&{?N1M{Ymfu~Vnh zkZPFrO9Z6GG`jze*bPwShALvl`Rt9yAGFB<6!=Eph~zus{l%5Y00t6k7EB%?Z9(zj zo1J`2%K7za+%>2UheMw(y>C-pPX}=#Iu<^-;GN|?CBdT8E>Vx-iSe{LKGREBP(nUZFfC%g>YHMCaJI^zhuN`yy38i@F1Z*wo(h ziXA&);5nbmp*YzfBUA=$4Gx}g@$A9A=%a*_VdCk{$L4l2g@QJu`hqSoo8nT3evapU zkQXIYE|aNLP^c<%9%kXPoX?R=-BDi_q2^o<$-&EbLc3$>8^`TgUV}Nm_N}{)$|UN> zTH2HXYYR?xrU})OL$L!(2#lg~xMpU?ZbCCH$d5U7TXPa zHIO}ZY*7=x;vm8<6>{C}`$5#2lNhpda8$_`ZNONT&9z~V*BtWORmRgEA$1Ro{+V@q zFX=xh*-C$q34H$wl4q$*g`k5y8h7_tUjW$_T}SPqYh5k$P68YA&pv z)E(sKm(Xowyf&%IjEj23a*&P>s|k0mrw30bTapBR^87*om3fp`=vf&G6Z%Lyh8s9_UcLQHzPjeq&0*5Y%)ud|EE3bJoV`38 z0_-Pu(sp3u6Abq_=EG@vnpg_Jn7A{$qw|@A9JUHu?rzwO+o`X!oM9+FG8#bQEgW<6 zH9sC8H!wlq!@0Wm?%BkxL!MFn=5xV{FMfraR~yBQy{ zq`snR;((5uoZ-~DPRsUil>mO^T2G!mI~4W#RsKXXXs(c6s>7(U3=Dd1U&{7 z5l|sLBdqMj+qV!i&2v#y)tKCW>y%Q3STRsE$zY}FKF{+enniL;a6=d9 zwJ~b#K|#fdg&p2^lpi^t`W_O&==-)Y>**zaip(0OXNRN@B*eI4A^T3CJfXj<8h!#} zCYmV0kY!dxZGngo1Ob7;wB#rhZXT|cg>8PM_xB8*AbDgvtdpWXH)+(!4uE&R%l<=K zhc;aBqD`uO)s=z*73yl*`;VvlsOFK-aGH!gUzSx-+Q>#du9PhpQ%a_9>U^LuFe?PH% z*;;4(07duCvq3R1{P8Oj8Q$2v>{8lBug(P``I4zX>WMwGm9XgW{c40;3lri}3-#PsR zHYOLD3NIQrmwj?*Jl=UKyA|*dIOJt<5upa*_yaEZk@$j`E9V*9QQnOkeIGZnU$x4J zY#Gd5cg!&BFYhtgp5FKE_U+rt9`yY6%RI&>i6ldjd0x0wtTa!1kFZ$&-l^k{Bt6`0A-bmV3RMkwyCdSIb!uI*-LbI_oT!{MhI+p~$ z@;;6!YRF~-})FpBj3meW6Q?lvJ2BiWO+ikvO}oDxs2KjdUNhdTy^{=zVX8a5C-M9Ww1I zo|v_4Kd!KUVYVTt1hLz7ddVD{pFoze&pv?vw9(WoAeuLSZZL%+$NS9v;%3rk2@a+DxCJECKYu>N6?)wb zlV4`6R0o(lW_^7a^%#-~W9kroMjY)rk60>GwEjP0&*Dj;UvAfbdR|7V@;8~Jbo}<2 za=3-C{B){4$3DLT#KLG~?@FSTj2K+_Dt5%kk@R4U=XGiiwq2OoGOXj^AmoN}T2hjN zeYXZ*>@jqx23?D!SZ377WVP!K+)Z`(J@}7odHBeYY}!W2&|nhHc_EkleunEWIAmS* z@O(7V@QSG073#SezMl&yZBoeyAu%3wyUQ{Q0^ z-UL&oN!J>M^DB~G4i3BX{yrL?*UtH0EdcT>)RGlc1R{7_yY|PZ?X&yb_h(F6NLZNl z0e^$^K4q)VhIJcz;D1_9k4@f%%Is6;=-O@U5e+zcFuh8UV6blT!Ei5Ne zt~+mzz7dd$)~MO^B?Qj<=I}k1Z+^tYUa7!A0T3|Gfax2@4I<}>zh&-h_K1To2%{R~ zW!;WbHy*+n-)O*}E0#&Sv5ejT0q)=L^yXLV0?H|{27xAu=rAw*g-Vscwmh*pWX>}# zRc#|M1OD{2AEvBtDYYJJw|SmhL>Ycld2|4MBNI*^njd}r5~yUGHf?N^7A^mTEgCjW zwX@P@X#|=;nNt>dkU|6|j#GA7a;~Kqjc~K5pFVu~GWEIrBD-o%wT{e#g2$Op{5HD( z5+q)F7ioBlhcGa@fB2g(ufzkXza%IT9ltz={@132m+YRZNh7nqw}unhDl7T}x`mU7 z8IiXdpp3#dkKAUv_NTKU>xVHCfZpd!dUQFz?2RU8{L}jETn@W97MEz`*8NyT!gTD$ z;^(VcMR%;(huwpBGdU^CYMTYc1ASIBak7dPpm zAkir9>|ZZ)AYQ!Mo3Z|EV0slGP6X%mDM012iw5H-{LHr4^eQ@!j{7NWTygxzBl7r_ z5M@$gLy2gF6Y*l0Td)hLuJ?fogtM1zkB`BDw03_t-^xBi zs(cxp;_I@s*rFv?M*)jOw@&&2Sl-Vec%q9Md>`Ayu*Z%8E(tw$iH)IHLvRJs<#V6mkgmY_udyDEd^FBdP*r&P3^K{X9*KTTO z$v9w$ryQGMNC7e>p}CIa`ywWmut{+p6+T59cYr}yJd*C^!U;9^+1T**tM-%tZGarb zt>#0>&WTwdrUu{@z|h54rgc}!oYZS?CpbJAHBUD<5<@N46EmkhN5-r$A=HZe`q%dC z`c@0ytf^Q0w7$}KdaE{#&Tg_g*8CrpM(Y2lWav%qTxv9N=K0P(UDEH~HCp177dXSN z@9Ly!Z5>jyHx<~9Yq)Fl;M?`V18ObrTP+>*?)LH}$#~qFANX_4r$0yDz6=5Vfw&~v ze&*TCIo1`S7F47gm5QJl zn1*^0@5?omwS#89w%-2`hh$7s#=$4BGlMymPdi6WN=!(oK+tDyY^+R&?k4_pio(|& zbGCaR?sRL(L{y+uXRVmHPR9K-JcyCsfPZBM3nMifOYhkEeo$cL z+^R0rbvnCnN~S?s>#^TAV7x2S8`(|)5fOG%+FNSk+tG|v+8E9Vs}Gr|HyiD~ceG;~ zP@@N^sCY%fqslmD?jz%d{VLx}J4P58VOBD8UfJs@LzFivDD>=9b7WfFDPWlWbC#jnc%z-zk5U{gHy|9&zZtG0WjZ+qzFi~4V<=5&5MBRF^k1d z71Try4Gzp}>;o~t$(Xp%9+xpOj{W#?)3koqKzTP2ty2EE-NyzjMf{YOpljneaHAY7 zFj!ra@wK&O6wY0Y|8$UiLk-`$Pcr(J6Pq7t zC^IeLV(#lteA29;GNi5O0|nRN2$2y<;$~7+G)E76PMWSp%FyIi!2!~kw9{tH!Jp-g zRk!8=LdpDLP;Ui_+N03(9E0A`U-b9s>o`VQ=!oeQubG@(g_@mI(u22Dq<+EjnWKY4 zcSNsv`-lQ+h@l~BfQ=vt2T|X7N0%jt7dtS!5Q2atQku)=l8%9Ew`X<;8V?{%D=NJv zN`QS}iN8t7#SQ^Lr2^W^mm=WXM%$k|8Gp_C&)opzh1_K8*y2phev&qX37`%4^sSHc zuzyc*!u(0h`mtUnDPr|k4tEFuUJ?NTM)fOx)EUY&B*3Fqq(t-1pZR*HMyt|C=1QYi z0XY(nW&y{s8k703h(MlaJv}|B;O0#;AQz&Wh(DV)R2E}tqMqbP1>OE>q^u-68rqw7 zu9jd0GLx9pbK0d-!STjPb^WbySpO@G#METw%uc*}VHP=L zf{C4}YmL&9lEeWIs%MIx#W^PMl=9 z6t>9fl<7DUKQ35{4RChC>#L3*%y4XO)(lrE48@to*$hC1R%CfqR5^NQ-#wUlubHK9 zyRCVQ_RM8DNREJG41PiFI=5;9S|9}xOhIFUHma0nQ1er>rZ*T9yPfu41|Di*W()GN z;`_IOOq#j;Vkv&A0lX*iXBT##3ge#sbWfdLy=3k+eoE)yd+{iDn=_{&A~R0$cBG@m zz>jwrN1rkASScvn2bzNcv$XJOhX5bPap1RnxeTHrhf@)GmxoSoa$3Ti34#V$fd>w> z!plHLxN#6%ZPIPc2HvGKSGl=1WPk+EN4v`ukl_`uNatqhgU^26`-9*qr(AGUxT6>m`N?ik~9_#WwOaxKoh6!1XARW)naGr7ibs zDCUhN@nF9&Oz5{bk{$RA#43;Xr?@cvWG1z%3~9ld9-Oc_a3N;oo!P?+u$KDg$SCF_ zMc_NiI5>tz;G@u-2xehpGhxOIO|q&SgBAp2dA`zh0ySz%2$liYfzJQh-ESIb41y!G zPEhKPN20sgSWAJmL>tmWQzU!hWLO4 zP?*WP+GcQ{yZ#Nf%*b8#I>3YQ(av2*Se{PnCkZJ8!)Y(IX>6$ta5DOe9j!r_@tz!+$;Ze z=evnhrnKWK%-NtKl)%+udR-~%gmj-4QAb$f9$;P_FkIo7kaZ>dgUC%fY)s^Jxwxj! zLl;6f-w=dGowy}eMO6SFVa%Tc+*N#7F}p)!&dE(++{7j>X2Y;1d0YwS7?qe@409-$ zgkX;ICPo>lvupslT0pHpG>+$&unG-8E;4m*l@V5IG&KZ}GsxH|wFGqwGqXk#gGv5y zGU}nFr6gk}SceUjl@uFpybyuc0tkF0L6U3HrzwN(xU51N(r#5;`0*7V^euVrm5AVYNl}AIFCK3V4ehQP z!kg@uy?)7(lQlUBhYmHM#ay8X&fR$OkQ;rKbZD4^Ct{ea|DzZc z@hY=vY9s{YxE`c7%xz3wemD5`Z*zA9;8jEsa>>~PTbUP8Ub*1T;%ybU&dsIFd6w6^R8L1Gde5FPo6UZY0MTC{!Q zzH78vb@cJ^>D9Y;aFzWQl?T+O9#|>*cTD-W&&}MSMIdx{SWM!(S7m>Frc57|7Xo5{ zXm{GquK_Y%67#Ia3RJ4v0|um7es%3cJQ25bF}VX_@_+xRe{_CNYY%=KqoXP1i6ggo zV-+)T{@D<3TMI8898OUHium^W&6{)2cUI2H&u_wEew98ulEp{8-e2Bfp+g60cFhp4 z7lsF(n-NNpL+4YN2nU7utEqN`#iH#tk8W}VwQ1Kb0O0tPvx=RaTI8JV#ziCd4D;>E zGnGs`W1`(^lni1SAr%9p<=&bA&>{^EU4BLJsNc+!kdp>Wyp zleBuKZ#Rlh(;Iz__|3(A! zXIXT6-hI%N`zL;U>3!6)IDSV>xF|5l+%6-NtgQPUj!HA;V`Se$`l%13I}h*No|gBF#J6*r7w9&izCD1dUt) zAnz4?=lt_(=~oO|2>7&y?kzFmfGO*9I-lBS+Ua>=e!VKH<}E&G+mx2u%BKrInzwuy z8m5^MGRh%H=gq{(E-IGqW%M$wC!!rFPfThQ>T0EMI* zy|RPWk0Qh31P;i5hJ?Er3Iyl_@{va9d|F3XY=$S&Gj(lqtnk=y0UjXVMy;yZQWvph z(%d<7a-(uXlpYiDO?ByqW%M2ZuD^Lxum z+LRRDyvSp)%N-igNyYNi1g*-^A6AT_BU=qBDY9Je?>F&BkG6oYFai4@+oKfFiOxqD zJ%l36fG$~?G(pa$=-$#YCnj}HeSN*uJ^0$&?Rh-f(C_qHo(q%Sj_^j>bF37WlqBqs z^}jC%h1S93TmmYKSnaQ>lhFPx3BJgitmcR5F{sCO)r%@Cc11o9_t@gpv|^QqA1Czn zZa1_r(Q?jKzIXb?H6uuVWa0tu*o&l2A{ZGR^GceLUT2QZV5I>LFMd&=4o`ISK^5s8 z-KVKvesEK9tO%7mIX_%W3j22}IbnUzSj#d)SH~~{*4T|UGXd?&6Ln>&f^Ds}0(<+o|b0fRyJ;@9eM}FP`%LsaPVHhrHjcnKgI2W=6A_x;lSp1Sa%4 zgJco8;smYvYHH4;60p!J8^>*+QJuoV@|_{sd-ko*fnG6|%8&kB`nkF~$-h?BY<$ay zr_u?8UiW$(jXR)oKYmBaW*M7AtyGw3rCEtG4(F{6hY$CHQ!dQ#nF-iw$AdVg)l71L z#MC8ywY^^N4xnFw%22mPH+BBn{j}?ys1g`UdOCv1N_l)LgjBKVKnx{~7eW|S zMS3}wO^Q4f7xeD1)BO{p;i57oYI$ujj`X&(@S-_!5jpP_w&p#3vnNn@t%3VkOI^gl z4NB*n`8|-I$B7FOIx=brubr=R8lVvdW0R97lp zM-6WW2Vf|LLth=GlENPsliXrXg#EtWNF~4?q=ikud*iO%`z4jwENAC|*g%L{&uUqn zmHPZhJv)=H=MM(?#5eu@+H^WjM`X;3TKwcnBOX!*stajqkulihGwidNsz}XAQ6{S6 zT0VTgi6E*S_uu6u@E}Ck)W1pfFREsA&N?!%l7lcfD5!CHPs7lZ-~_9Z!iNT|Jhetd z@pv{R`JOS=Ds2FH{(X)bA$ywl_nwXHs2ottgJr5I)^jcFcNYh|575kdJE3K{shurP z^Tq0Pdu@I%kjj^-Qn&c%{jw%6b)ON z;K}P8h@}+x&1|6g<%^DZ_4V@wAcND&1r|B6=%G=@jg`K#@$TGF+Me%mUM$7~K|}%Zj)uiw3a;uWVnwst(XsJwnvY|uL# zux^|`F%Kj;jVOAaM1zo+?oe)EPlic^TwJ7`82WPrc4U z_&%=qRNK$tu7TvMBUQ&Kl1~4Uez$!j?f{#ijs7}1)Nz8AS$>z{kzP1FspswH z1B<1O?#;V7F&AbMPm{@EKb9^JOaNkj%CUh}SlZEv4%5{lpUXg9=JZTT&+7@+P(k~e zz2oPJ4Dh2{59!3h3(lb8kOtR?RZD=oag?&VrC!h^Fd;>=yzn9UDs>i6ka@V{#ZJID z^nyGCt#iC+)3SVXe-5l~&v)8XFfD|!Yz~<>0n@-`ICC5?B5`U0qe4b$aUe0JGeITk z@DxwqdRFYgW5>eCRje+xrznBw3G`7OO&-KR*svq*zpWX=nN6$PW-#PVAS(SFbi2KI zbj~>@XsF-YH*c=cu|Sa;?Yr@P+{aBf$l0UTrJXsWM#7Hh-bVe&;s4bFxGr3K{N>A+ zPkDz+lOvM40c1KjI0!RADUw)I&nK|*8nanrTLQB)lHN6RqQV}G|@iw7*Kn_aDIbmE98-9-nA+T-o0Bx zOz?pu+-Zg?io5z`F@;RuE~&-`+pvMI@vfzh=FNmvHlbe&j$Xxqs2Vm$glEv8-GDcz z`>6QAM~n3af7#Z~POHk!{^|}tqUD8kKQt97e=Y(Gy$X2xfo(&hSIM_42fcK4N{?x^ zd`K-myMy_Y)jJ`&FF#om?4U)Q;+vl3QHd z4kXD8i5F*RD5tZ+chlOrHbty^0kVlBKGqu!+_9cCabk1$55XCTqB2+=8Lc{~fMNrL z!9j*z-b-KDo{TCnR&&bLVxOo1eyp~l4Lhh(VY_9tw2b`(Ez6r1veZN?dEIA2zdsls zqd=wZ^kUcP_~coa_vAjQ;P%9vmG-BEmgUfo(wGABC)6Iz9H(v-FWx*v-P+n3fo$WP zn00n=`;?Rd3*+i21~T+WDHN~opYF}N&!3l>vQcZ$CptR^b#4``l=J${n?dx(&Y=}@ zuXt0TLz*k{2XNy-iakK%r<@n5W5gA4_Yo;}kXwgdGzv=3n20}l3;SNEyjVz{`!^PV z_w-FXrSE<(t|o(#dmTP;VuxFYpiG*!8*I2!SU4Ls?X{(W>7TxSov$!ZbkL8!!FG)} znUOJ%x_XaTMctv#_|Ziu&Wsy9y@GDPqH*Jl+wjh<4kai5Maew~b*KmLrC@R9tj3Dm zXCL;!Nt|*{(OY3fXs7+|@`42mESm7ebD4jnHTwG0bzum`tL#(ZsUjPnwsL??rC-c8>)0_} zn1)w0#Sp^m7mXk3Rcgu68*jgQhM=uzogU=}g?9iinonoE8*dEi3SZZXs@9mbi|1hZK!xj zAsnYFuUp-JAeSRDIcj_FU9PETN!xf3i-&i{s7)MA`Z<_cI0~O4R5GWAg;VgW#lyk< zHj*ISC{1pqV!5;gK$tOK?SVa*Cv+_G=gLuCw?_T2f!W3cVfx!DUKZR$i`*Q1|Kz*y z{YYqDovb%w$$2CF=;i?5{2;{&;?AZ}S;tj3>*bnRM?Y+<5-9Q#R0aW@&(}}a82uQg z!{t@47$r7W#MODpdd4X!e_GEtF?(ap>RI|uiTeGHH&vToXKam?f;I#sS+L`#i1@#C zfPGsNlS(h99sJPC2BkAqA0_sPRUiqSpS<|QxfsT$H< zO(nT^vXYoYz>p%dY|0!c0npjg!U%ePJKotTC+0pzz3=s+r_YXo69#4^jzDG!!wkT8 z+F3)Zl*cA)c}~nLbwlrAqm3R;-43yL{?MDnpRDWZwPv)(G9~4?h2*bZ3pMYQ{kkaav|rLJULxb}pqNBkc6p*fi(CT$ zRreoXC%e9>|4d18!`r*~!|sdszNb9-48Eshh^<|3L2`=oAul#T0Zr<5NWVqyxbAJn z&766DeEE^YL^lY)LC38II=C2_+s%tq2sA=n*oR}C$ zL4O&}PsZ;I?;QTm*Dm=yy}!S#wQNf zQ%%b(D#wfM|I^q!p^k=gPe1dBSwk|q5O`y+UpXO{UQ?&cOAx=L434jE%lR)h$wljB z>=xO3O2$KqM)9|l#&gg@(_{5SI}rc~R7D*QWt3Y|qK4WlSHuNtzE$hBzvJ`om$1Zw z+bcE8-wewDs~6%OMFB;~rrWOsN)pnK)V@#{m#Q@VAv<*%KqAS67)1_7b})9(nWqaUsjv7AKPgwc5a ze96Z1X${l2NLvFB9B|n=>bQ|@;0}jQ)59&PLxo2EjoyKgbNCpd;QUf0f|6?LxM!(p z7mZ=Hs4v7FU8bn=vlBQpPtqStCO!51CD1-OTuDVCkIL$XOU<8|xib4RMIYm3nvwS^ zetwPr=nAQ{87;DmyFyxanK$|1!2@w=CX}XT>kjd+zh?W2oAxQ~T8^qxs4m(8lwf8^ z5t_`*@ZJbKO<&WOTlGH%s95&Hl}To-ch}e|6mn)&J%AhHh~9+%onsECOnHGqX}8BI zh8`&%eIKjQzWuP*W5}=WVaAEEb_MFoXG6&p1V#t06dP=>v&%APVJEG;p~m^wdiD10 zpMju&&%0zBK16>&&LU0TiV7|mtz2nF>YDa0wab2$ArpDDu>M+{T)boFa`d?YXYQV* zOSC0-2&W5p@czvEU-3Jx4ua3fteUxddtyO}mQMDu`?U&UhBC1w7Hx*!p(# zmM`2hGi3xH0gy>~IEMWLsHOt2p2w-0@^Bp<6JnDIc>MXUlpr)rD=|iM9Q3*{Pn2{{ z<;S@z#Rg)F=2tP4qt3qg=}Xx7+qEeRyvrta%=fou!1(1?pYEV5F#j4`8f2{696~Zg z;Cm8wLc0CFwW)oVH?=&~VHg<+$wMCU!=}8H%H)tBkz*)^Bq^O{1u10K`PB2)lp!FE zjdBg{(bJS(C#nz-os#+vlp<1Kpg31l+-$B^{W9;xC45%{#5$S$<}yjt62MpsoyZwq zAKt67d{%hxc#K|H6-HEw902qf?^x<@R+Y#QI9wnI8;eXC@>ciz#nSzH!3l@E4nvN_ zKWQbI~>m9LRp_TECn48-?1mjAY1b5dmPiO>qO=;50sfw_DgVKCvvXlg4Ba5e^f*k#06(w`y zOV2JaC#|N-(CU{p1pqs&CSZscUgcj|P~drjdOWG9P$24{%$kjun|@ex#_X>4vGFqZ zm^nJ%9|?xoHsJPh$?o*3mgA{p9<6id^|^tKh}?|DqPFVs^Ysq>b^coT52qw@wZ?Q^ zih_ow?6?^OG+fX;UhPu8^9|4S@jZP21)4ky8BQoI z#RaPztv4D+cBc1LMMiZasDkCNe>Gv4y659JlS_?@%AXr9B5!P>Ky)zmP=@dIBN2Kr zQ;}}Cz8wmLk3steq70lXA^FaXvaPjgXd&Z3Ly&Gp-?FFVllcIO4G=8^{nE_VUAH&E znDaYxNQB4(gQBf}pZ99}E@4%5ed_N)bbh0hmnn}F*@i2}DTB1`CuRa)DFwMb@?0`F z0V2$%k@m<%BÚW+|@;k zhh!`~qT*2x(8_bLl^#@xNlQ4i8eSF!&~t*ct(G?}%{k<{JDDC_QabG@e*OPP6t zpz!m#U47O0epyC&Rix!E$qQ;x4~G z!(LOtMN3X1!$)HfZYzBExz?^3vtihPeIy;}T@(7wAA(BjvFGZd;auS1e?3<7Gm=FE z8O0?gmcFhZrWUB9K_OD<_mUfXG-s>JU#lL!vFc(R+3#8yB8_?TdI?Rzo|RBZTI^N* zqkaDwn^DwzfYyt;85XH~o2H`UAZCd=S_Fln`31clx*%4X(dc~hTI=?00H%^MzxZxB zmgg-El53z=X7;7@t-0h?)rx1A{d@}Wc?FI-(@Ab0S$M?KqG4jj&Y z#p>1ddPh{)wFsCVCH*K3%q>h^uzw6=n;F&8n$754z1A!1+!GjqlbjuTk7MA0$+ndg zU<%jpHkp(4UapzuX~TDaP?J*QT4ir{?SlqMTwnP0JE#F0Kep*ek5-HIj8+erdORu^ zsf!1-J6G&mz4rO&UK@uEn9NdNz$}=?t-EfMH&jw`=2m^_A8iYX`{N1V6k@@cmy!Iq zRCzr7_3%B=z}pMgqK+N&Nq^7xwFYe5{qy|ct?SS;O|4I2X#7oN>f!989u#!4V9^^< zFhFbjyRM zE%%$KhFRvBUW)!Y;a6~|W!v>|xoW0soBOx*Pg-UXKQMXE>#rYwzW!x9u+P)?fxmxb zc`Hl&l;7#<@PjJyuNVLCf897vp%~=+D9k5tWa0n^;hH!U(iC~%FG5zW9=|2|&NNW`w9 z`lWKs@$X}L`Qf=G4LE*fa26YFLhKeDM=F9-B)JF3s~X)|3H3BtUzZx`{$dsG^LC+DkRSANWOSQ&)~uJ@i;pYk6F;&vR$A z_06HhseghYD;Y1{N&fay3=Q#%&}2@-WuDLLu-&SMWMRDa>$I{PNmJl|N&y0YHD*C4 zomC^SIr-5Z@n#2nALv<85Iy*!_ZwNvzkg`$&DgQs&|*LhsdFTVi2?t8kTfT;=MI9? zsqJuRD5dJSBjpxTM80mS-IgXDm9E=^frR-DPj`yKU{E)l&bI<<8&8-JToq~2E>5nD z#mU#+(+@~ez^#cG3KEkMiPCFb-ZP@tM{sWWArKLMsKx*Jp*obeh^!1?dPom=#9>vD z*H>FH7l2%OHbn}j>NqE-X!)Vv8zN~*%#d`rC`u4F)M=-0oQTYU-1uhE7$nU$~(vLkux0t*a zxZHC{lpsX!>u()W2JULF0DpSQ%)9Tq6)W z7W7^xc*z6Vp$V=0vcj%wZqLsJ3X+-`$BkB$wcgRDUKl_%ruf6mwg*mm_^t4V&o{^t z0H;xl-14_$<3v&6KEECz2{{k#EUZ#4wJ<5N+K!U{eJxuqEb#Pv0+cEW@maHGRpYYF z^HNY>khdx*l{W_k#ZxiJ_Y3f_oHg*=lJkHPFo2ADOx^1qB1EZ*ESZ6r^w0hKMNdIQ z5kH!$$Sr@`S^Uq9#2i1a|C5$^+S+qL6d~KaqZ4+cUckz}X^InLhA`WOA8)f99`bCZ6mOy9W$s{ z6hJ=lZIE!b0r^B-$sR+iyqymkySZt}2O)wWnBu^xd0T1%OnKRj*vZz=4JDJ|#+AvJ zCAH^=%6s-nFW6A?@x?^>2uo5vBCF!}M~;+l(Pn_1F@{OplmTumBswRYBkf5_udrRK zk>3M89Af#)=i12UNH-PSWats17VW{~V5eA6wliiF_*+^fk}d;p4BFt|vUX%^qPt|Cr<{g2Plo%MFc27Mm|1}F1Z7^~U_$1~Jb z1qTPk|EsIhN7x;4m0;##@|WYbhy*F_Q~YD4??H}Bl-JK456|H6Fr6-Nhv>R_A!K0s z`x_m*3BQooZIZ1&32`pj;G2J-qU1y()1o1BGp+W2rt~NX=MhtwO4k^8UU!n9$FzTp zV)5qYzTr9@wb%Tg!!>+r@r-Xk` z@D__|_{1IwP0q>Ww;G)}x=_3F zQ7<)>4@kj!^y}Ax=oW+qzIka|$8lPg<2@HFU?5F%$P8!~SYDR5Xw7jXJ~J#Cl>sd& zZkaPmP04Qd;#AfQJR+A>M8D-Vy|a$DF2GIf2`k!Xil~8lTFIfcB^%BhzMK|P@zNSv z&_iz%D`Fe-oArDnt(Jz*aZZkr`^rjD(q z0o@JE5-D5*B;HiKuf(Nc8x&`6JG<%09y-tW$`iteedz(ENP>RcmfBcpy1L-?Om`r zx8%&0#&*LcWd(2tY^5%!#`c<%{j~FLtMjB$Ai6EcsmF)thS6Uk>=$)lKKNteWAyDi zZiR;CJ=KfWABcvYk{Oixg^RBTBhDS_EyixoZ@@pi6;thG)i_xSi=P;aizFpz^s;o* zzk1r)L`DimOF&sMsw)J9iIQb2&Q&{enq97v;(y>=17N?*F%0a@Kai(u#$0-k{!rRC z)zq%q=Y`1>fXaT;dY+utg8GSnLQIw61S#*E!G*8*@U+poZ%>-2SUlffSnknCTVrcP zZ0vR(3K{Rggap&N@GWi~XhYFLq=DT#+R#P{whbAX36cCi@OC39Pt4e$0#<9q(#$hg zmF=uplESS}#%!pm@ngSsfFTev>+s=uQTH{t0P*SJJoYK^_)m4;jYAt@YLv^6v+?o9 zbsyFMFPWI+>a8y;E)HOs@wQVM9ms<1hWvFnKiv1l?P6x(;ri17nNxy)!y=v6eldUU zii#4D6nUGBoF(95^R$smxBxj1)F+=x@boNY}wk1AZn-@;=LpOH4AQ=AsL!}q%)hDG3 zmE-@Xlco?Mh5$CW@FssYj(GDLNeg6YL`|20S}D(I7LZAnpilu&@d^bXH1K)^v@0i- zVguJiQU{E;u<}E})vFCj(2Jh!=1<{)6Jk3Qmz3 zls-FfL^09EXRRU2=C0S=awn@{O&+|_1oG;IS^Hdy0Ia~C3Ka_+*?(m)7rk& zX1RT)clHw15L4N8xXedHMsC7=7Q=CMpltqOJDoUtHkgx=^2$K^C>MY*#Z;10x`aVx_ zh!Ei;FB@Rv`Ti(SJ-QY7MwYfQKEUX+?xZ>m0P2lLwfU_gp!IAouTX-)?{aNvn3a`P z934YwE3thCF;&1gp2M~nT3!i44LOKAl^7U;87KCA{k^(lRV-Vo!=`dPU*RBN{sb+6 zBS4{pl^Qgj4c4Jo_N>Q$ES5Y4v}+U4Wk{PV7Y#ii62b@Dl2b$-CsnwH2K^&iVfG|y zv@!lkHnS>hFj+Q)tOqUoR9uUz^+-N*l>dl4wY#m(+7Ojd}*yAXVqKY z{SijyQ`20+ct^a_gsCigvF*oDd@EuHX$TWJ5I5%druLgYSA*;-kPJ(U_fCExDAbtx ze+L^K*clWDX{a3`lfH%Olin-d5%STN492i3ovGu?hMX;D9;@hZkp-=1zApqZ{b+-~KYl~qGwq;Qky3}0)u zWXV8D-$nnYY~KzgwG5eDjGFe`xpU$POnjI?>tuPNp=Pdu-u)euciGVKja^bpa1w$3 zWi~b)TDsk{(cWzZp?e0&aRn0bBvL+hwlD8H$% zO(#$L%07Pk$q#7@mqURi{*)Xr;LxE% zg(piUvkQblVT;9OWoaVufh|0F)bcQAFdXJ%1>(6`U@!pEwda5}wsS=nf^rde$RJvpPwILaq+iOaf{&5L1c5x^bM|_lrf%Vc zygmAh=o`X@J^Gga6>_&3fnwLs_ww6tLM=6mZ{Dm5Jb!1Qr{`tlhIpLwV9KJ+ZAv_)0n4v39DQzGxs$9VfH0SS1hy&O3z#6~k^BAmD!s&qM z_1z(qR`;!<2I6>zvTbX0YvK|c)jkivUJWq8BO|t)*QlB8ff1<}!O>P3KIv`Wg0n@; z{m?01qt@FTZlY{k9;pMo%Lk;XpA>p&r+oARXgy?f5p~z6qO!Gg;|;ph?D&5I5rhAy zv@;K@d2iePB1L5?^AO6aNandMLl(74hLnXs(c~p@(^F7aNRBN68IQJNObG>CmXj*h2D z+h_)hvk4MKA-jWM*w2~S!vF2VX3}J2qkR5Xg)48^+69ohNFe}hT#XCc-`o_u~j9f;yW5};LVe571^Hnzl5 zMe)|)cYF{8L%Qm9&KV9J<)*P41+hS) ze(+O{PHmZ#169EjgfRc3M{x1rF8Ath2I1_24Aqdes#1#5V`_@iTL_?xFsSCY2l|8d2C zN5*8e$q@ChVJf0sBdfh8FZX{xIA>e(AJVMqcP}z^Ua>JMs(nh= z?JG?~8=7Ja0mDbHwQI8%`!fs|98CIgR>Q&ny-W*Ogw%HFnX}0#*)3|pYb1{=1Um_H zcU3ipNnK5`kZrE{nZFkL`}dSFV&MFPemlvBj>+⪚HAZl=t$EmH*XF< zJWfq9T}T!b1f-e%#bh>`s(&SVz=+Mu9vcr{tx$AgG-J9is|?NZ*~4$p=B@eUwU;N$ zBV#LIr#Vjy6r=6QP!YrWZ=H&5wY0~@sfjc(8bRu}Z{GxjXw`plyL*M@yyTeM7@^hZ zoYFO9^VrIt6*5om&Ye58x}A%8VL6sH`JIL$-Hs3{(yOy;J9Y92SoMIqipJDLK8__# zibuxRA2(Y3D0}r~!xu&wE(k0*f^}i9uZQnnn=@}Fo+*fgCE%7e96X}9ail(DYADm`_*(cibn>O1oqq42$mPPh z)N9_gy|*0|)*kZj)!IL6@@li?f)QPY!z7}YtKPMptP*+lit`sQYVmKRmyN9SDnv{* z5x^behLNXyjd?F4kh+d?b`B!!d`Pl46?-n&2W^;8IO)~nh$l{YALg}Ch;=AsO(q*d zaJWt9QB*F3w8Gd8{hdy&RK-mPZC6q9kZ_p$HT?c3EQZN!9YUzl*Q_4(K=Ta0A8Gxf zw&1#(Sk=w*g1D5Bn0SGHw6R}9lM7{pAsQ5ysj5VK6J40oor<7Ns4D&9tmX-m^otV` zgsH{Fv|d);RjMAOWV%Mh+GR#xDo>LgPb6sBr&jF!hgyQRz&u6+kx1G{do^gE%(;p| zD>zCf^Q8b}21mC&JJ8d2ZMSR}0lvvmS{xtPIkDz(G-Nl~NRjlcCIA;epLDvaw!JNC z9swsM%!xT&QJ>D2eKl1BDsUYbjQT@d^w_Iv8L7QQ@h&k@Rv&n*s;E!g!jeV9op$29 z@`MUr7%}8nXdg`clwmy5{`=jIREO?9x@4tFxtXNnDcwpbj|D*FUg{aC+ZtI1Hsm9N z=|$>mbVmQs)KFl>BXjNq_<(~|NUdXIo7)F!yqxKYIgi$I!`}B(#w^R(H=?yLIVr6r z*npY_g~xS;JS;;%#BdC3^&$mCaJZ^^ZU{#v3%pEOBw9At3ev;8r zMNqufw~G7+Pzz2{Yovl&L`r}qFS28sOcT>RqfB)bV$FbBRIVR1AsM^*m6 zL%Ni9z9iPP0OC?w8OLOF?b78uue_M-;g}?ev*;tZS;66Z8?z>~$wD#lG~nVPQx1Ww z85};u-lovr$p~@ZLujT_$;liZLOjr*(ZUbULUV38yF+Le-!nnzDXJK@B!ksByP)u) zak+?#RzOLklQR+evbgn&_a5QUug8{8G)zoLIVcyz!{p!I%16EFf#DgEN(Ck^8Qf=q zVW{4plC_A0Om+$~Cw-$4-guVC%resDOhBI?=`Qjz5U!s*Zfnwvu?)>%>4);`G-tfX zTNb|D`G;66B_&xz7ba5L)Q}>RjMtLgGla7cuA@iU?vnV=uarH9QtNTZ)xw&D$guF$ zfs`)v@lT#Ujodi)$CR^_&Q)uGbCkiAkC%KIFLspZ7;S7f=^osqy4ne1JMfK^B2>Vu z(br%-(d5wC&)aiuZMGjfwi)lU)Iu=Mh04jmsPkN9!0vwE%}W-odf+;0?ATB+E7@2@ z@XW@JQBT41=h_sL(S^E6=yAlFDrrJy5<5~Ro9dB8U*<@{ zMCC<>x(XsP8*EUxdB{KixRct7&;_~b8aK<;II>WxhT$hr%V>kMmP2l(2GopVNN_0Z zV<4Z(*Tp2P_v3{!kIKe&^J!$Vf({YwzrI^mDgG-W(}yUX&czVQcm0tvNp0c+kR(hj zEZ>B@Os*wKRRK5!GcPXOy*s8TWPx<}C>0p((iYeT9F#riBJZWBw&^1{6@|pPyJ?z> zEES}C2puKCLEyz)2L=Dn5A@Fd+@bwed zwP-I*$z-)p67L8xT3<*UA_HR!V>^$=JwzJBB!<$Dg0UChe|I7vs_Z+$zNLmN$Dmk3 zC-tg0?W3B3?;XRN?Q=S_$eVaJo?Z|sf(|@ldE%5s^gIxi*2A&C7)L)N%*>R24+BG0 zCir$060)%F5c6T6%u(aV*P&Lz3Q-(Iz^0}@I(0l}zS{b*@(QMuLutFwz*;TvHJ6Ap z6)6#98e16m@Bf*32nJPMdG^e9!~;*@ShRN6tn=ByHvVcyh*KDg9j7o+V2U%3g3>hC zdNLKQnB38YdcMms>WDJp6t~-kErZ`Pt2jzuM-t!#CnMhzAfUulnWwk`f=}nCaALQXW>`il1KM}!Ab!qJDmUT^ zNOB|iGEpLXe9{BX9>E`&Q$z6F+|Ky=a436oQ(=yqpW(>PW5!fh`1trBgk6Es3a~5& z-T=zZK{IBwr!6I6oM+*XiD_{<^3b}xjm56iOF+jU=1hmQWxt?935|wzzjXRp>;Pqqr-cXH=b&+FCbg|HokeOos9iWL%;a&ADRJ?%0^Bp zIt{)kc~h>mcF^b*6!hXBE>Gf9{T>cgsp9%rsx5-nJm58IxW-f+Tsp6XfkDL6!^*_^ zt>!R+)~O|jNWVCn;Ij6jCeDT)H9C_U8vZ*@!B>YuMEmva<)-S(teyoU3^_n`4$w4G zDL*w)?bP+xPc`N9s~2hHr&jUd!*WvXbsRKn|2--B1aG6L4FtDDBTcD+>jgv5~{L~PlT_v{PZAHqmh7lVk`N0jZGVLu~4J3C=n z|H=_-9*9&0>N)`V)$S$+2GHKbT$t`*&*BPrX!y^jt{-i1rLluXLzj02CRa=7Z*T~1POh(cC1i`+HLZuIHXM~A)nAYkvwI|O_*B1 zYykz!6qAH%n5CmY&eGV{EDWw|d3tg_UXkWwisK0Wl}k}i=52@W}Z z=EvQeLmJ9dL$S$(`m_5t$31`jWylMheHL)_sPF&@6ccC8OfE`Lg=OR?*!z&yQjb8iWNa>IDDX~V~z z4^la1YU1MP!9u;S2V?_)^K{?trPWKz^Ja6irOBX}2z=P@+z8c5csVr`;=AkyqfI}1 z-`>4K5huNFiis#c)5s_yLG7m5d>>j4^Q3B0xr6G9okec)0~DE3%ne2??cngL^?(b! zW1EoLQHobnP?BCIR>WmL$95a5qTEfVDM&5thQ{n=olhnr$eBRgq4r)!Fm6L1kNzQw za~mK4QpPTjcKlx7A60Zx6&KA45|9mw%s`WfiR|#=xt5>5Ja9CeZ9 zm`ZPkd=Rrgw(qBONhA+t^JDdn;>VU%yqaK>sWhq6o6$7Id9x`T$yc(jT^meajlG^7 zC>SaeXqdy3I8`0)<}+~!21l+(mIF6WT7}SE{c?+G+dU3^)iK)3Fk*FgEJl#{%3Y#w zPXw9HPX4aVL_`%>ThQVW9 z;bhR$h#iIb5P$o2nEeXRbk>%rA(tnHgzUn36YLzP`F-CqLj_NI^?Nrhb`3b|@9$lV2aUfxAP#5gi-5N@&F(BSm&9RcE)qrs%Q? z<KNEPCJBb&d;eZ63_*kq1$$Hx`2NMozj z*X1v{+Ph34c;Jcm2Bi839Sg5{3)BLCMuXD)pQ_E7>u%ZlXwtP0-DJm=X(F^ zS92O!Ek8y6I+*tO*=!sDMHq{(qfmlaOb!2f0}E3ugJ_iiHxB~ik{k^#fbhrp>}=<{ zCQcJ$Y|3WvQ+%1WF0!>Y;CtXdZBLl0QwE^MhwA+eIo(Wrr_1 zfr4P9i%T#q{+{0-RyQ#jn>~r%F}NFASjdSv9L=|tvwyHbO0yC{kpA4cbEBTgmtOU? z1jGYF=-dyD; z#$EsV=co(fNhr-=2oe(Gm_^X=+-%%5q+$oc#fz|xzM(Zql zooic7VCoAiwc)Zjx`(`RNvS|c9lLk04!~FtFzcuHY~$i+*6qe%Rf+8`-=-1WCaDcW zXq@qa3x^E@>UOgpVD>eTras_}9`#{Vc=88#xR0vsVWy!)rVED~8bnMjEa+wGl%HF^ ztT#I{G%?p|GeSttny>L~n%IJss9`9WUyVSWY_*9`%bSzp<}5rJR%Ck8JT%1a=nhvgTs!}QIM=E2s!kAjk=9JTwys?dZ zdz6RAbve1_>rF!wTCrzRxD-eRStmoIa^3(=i%|8g{MbZ3UdH!z6#xC72_4juV8*4g zMU{}~)L1SVW{+kLhNG+*Ne~?$uT8-e9b4ZZVxV7e-94P31yt%zezoIAySrQX)kwuE zY$ayFRrrsMvH=yL&ww@7OUvDgYQ;^Zc!yeRi(I0%qje_ms zr>kk-_dwB_{;AKLZI3>4P5slK#Wik^o)WN5}g8ubB=73 zw{YK!_TmJW%QYZ47XR%3`!A?1|NAeh9_1yC>QireR8ZW!<=)KW-YkRgszsvBy$t6; z&*j+FWP6`s#f?w9a^nUGK_{qP9f5gG4D0iF^2JUFF7-eElxMFBvD@@MMN##4??+N# z@RTcz?%5FsmUA?1v@N!5U_s%yWLNi6n|%M13Q-Deo6I&QCM)=~bu1lW;C(p*IluiR zraU`|7Ib_9?@|p(+QQ4HZxkF7QiJFFX*~y#GXM!}Vx?zbpvx*KYDT+qFaSZY{Fc4^ zdb2Zsg%ND9gn{vupjaowUuI+UvJ?e2V`u@DCM7Z zU=LpG=zaUXY-rH%>_gVCD3hUO$gW_jt)o+2A(DDP{m;QK*@E!q4Z~PpCRzZvt_`=l zqqJ1pLt(5?7)6 zVK34w0_6(|Pu7U3B^*IaKGeJi7Bk@&{Euvze*uLTuL*ct31k%nI!SO!wR98LI#Zep z)+}vidPyrHBk%f}&&Cl?rpB@du3WC`;_B#Fhl1}gSr ziXF^Hz%KAr?t~r9U@2@OuU?#OA#Hx{xng7mWKRxK-N0YG9;BQ^q$AyyBcMPi^}25THG_ zWV`1}coB2G6)ezCizHj}I9Z>1+9so|PY*8e$doIZmv`F)e4gJbf9@$bU7{DK%p!x2 zxOy}tMOF_P!k33q8Do4HJ}@z4d$_53h;%k6;V3~~^-}Ah4(xJCkdzom2pjT|% z>&YQsjZn)-QgyejO_)Im76jJ=T2AzVihBd1*tTVu4j88Xj$!@;9J(=onugk~=8@gP zjH0->8rzG|%g*zYt3il{MRn6A(Np}iP@~;8>=WU`?#7IFT?fO6_132w3T*!#3jlqi z&;<2DlhuaUrQ&g7ENx~mZCYK`zrIps@rpj7EKlcM!GOiQ;@H&5ldJQ}gTY-Ss=Mzk zdM=F^>PY+*#>=WnoU_XUCNQmnq~zrI?6L0dCXj%Ex+6M`!kz`++XmtgA*^E1yDzIZ zITc!|^<+B$uI5jhUxN>_u3uM8ooXf+4;u29X507XB9)AaYDV6}P^fws1qiE@YC#CD zh{yn8r<68GJO~A1$1j_2*>x>QC)kcfy0DQ}e}xe8AmElFgaoQdJFHZXzSGEzTT|@e z(tCnG`6!FyjJc_9@H+KX2UeqSf1uyDg#ykde}POiu6I(;E?t&`22r)hSOsFU+>U?Y z*#UKG*WUG@!xTL4Wik=L{sp52nxx&P5CtT^ctK#( z(|_1XyW#cg+EfgeD}!~#O1TKfM&OyY(x(-EHIqK57iHG543hyiu7ZL=9^-w58!p-# zU*C@}U$hs{7%%qD?}h!&(h@d9@7^gd-~PrES_{YE<%>khGN_^bdXK>R1gG^{_=gEQ z5S+M*s0}p*u_vuA|>CfaDki6_P%kxYrkZ&GSH})R(|I2C&jG}bV4jhC8#g|txToVR_x(N#8nu+R9%`Da%j`LT`Brmo|lNY zLPnP|)ZAQ40lyH&Z5@g!4r+Jk@Mv$kE@I8dT$>w<9!Iz@g8YpsxeOy_bg(oZ;Q9-p zL{NoCzr|X#Ud<~*^U!2xGFZSyayg>bnX`EKRyc5;=(*& zC<%l-XNWTrTj+fSu|ek;c<+sgsm>`tc)9==0{Q}-0DWn6{j#<^d9_peNj6$cQ2^eH zg11}`bsi6aU*`GI!YgMs94)Wqyjt8VCSp%u{!LPz=T-=(v|Q}d;BHH62!AHra#pN) zk&sBPY=VM;gzhr#p)!$$HoNGukZKg&N!*n4zM&acP1z5YaGt7ImpoSn^R!;_$pChL zaQMA;#GJu|Y$`=kW+iA<*ZZly<`9-fjUKH9=T~fc5YVh3;&C`6&hiS73re0%x?h05 zKBUY7UI>{<5*%)qw92%2H)cJ;f)Ec=A)pJ1l`gpq83_(IJ4uEJ!crhzQOvoSoG7Ex z2$IQ{3K&6PE4w$3xN@AKJOCVQBppE!K%^l=LUj zA_yQ#cH!G<-|C6#q-<~+nPebiu_#3a5Pcl*(kLvoB)Kb8YSBy4ppurby#4SCpdlq3 zd)Y}?MJO=_RD5b6*_vO=7&H1s7rDuz_UgB^yeaLb45E+F3!-D&$MYsjBo_V}WSB1E z8}{?tOPDY9t(v#*92qx_>k@soSnVvYrX{ubt~(EEk$s9C02?k#G6)20(*47eY%&8c zaNg42!*c69G7}LC`_^Z{N1}dd5Yx*+SI!8qzL^Gz1KPIJbEm!%;)u^(@iKnC=N!& zifI?Bz#$x1BoJ^dW|kWt-^=JWSy9v#qBVlp*tSiZ+b9m2$B~=}Pem|uk#=fpS2caze4;@U zgs6a40(^X(j89`@gy!&kW6}=uuy)dRLPpq?VVQ`j3g8S(9i-_BB|%2HiA)cZa#>dLq_WTGg$p;33#soOFH z4=FuOA(cp%ZrxTMJ$kfFVS}D`;3Z2tLsAjy#>s=66sU%ztPo5G_c5t5h{F0t>uSV} zG6kldDU^jAru{%UUFW*0-8Q;t!k&OUI|?(D~v1>CDl6fqm6_Cj>q%Cfndu+>SIp-RRAHpdvudV{sn58Kh8a3g5gEDrC%Iv{v z*c|Dgh4Gb(VEIB@9J4CJ?-zu*%5%}{`(683S@kM9zLrv7j3H#~3FU%ok#(e)3`4?w z3Nd-~{cWnS`FvRH!(Q{lw|=TR_>nYzeed+)i@WC!iEYyE@%Mdf?z%a4^hoo171tPP`TGp{iOk;$}B7L3oK_fo|~F1`1ULs`IwXSz(+4abQ2N=MtBn8wEU zyYtx+hLRZ4@)&L0-M2r?JiYi^Hg06snV=yr3Xz$q@LU(PmVcgpdv6_4n6jOnD=kN!m6Nt?nT(z2c#(K9 zA8K3b>j8Y60X#Q7`0U-0?U(2V z5xlXLUi*#iTa|pC0#L>|>!tQRfX_Y^fgXR!{TlE^rZt7PvX)VOSFJOX0l{Qmrwm~} z39S*~6Bda&;Kji-0irhS12=$P*96*;t-t^A>nRiV!MtI6hdr`cBqcClzJC91F{Pi& z?qpJa**ZvEz#F}Y2U1JZ3whRSFV+v#hN7s+f_K^9XS{|ykKU>_9}W&HhUP^|A=4yD z=r;9V%F<^l=#@|Q2T6VLS+M?6W|4WlHS@?|Y7t_MalA#jodJ+8^VcjVobw1Y+_#_G zRvWNn!FWSamIK@(Yu@;vsmiet)F+_S4pTZi8AZQL+8Zg(?CIIr^++My&Moe{^v9dt zGVqZvGkVg5q-jdzk9RT}?DmKOxbt7BB8!gK7&d4yiLg&LYB6gV%TwMzA!2@hU9I$9 zo<9f8fR3fSeWc{{9go=#`cj=2i=U(md?W1Tqy}g4OQ8#{$ zmuyHxb3Rga(p$(0*@7(mhH=X5puJ~|!*Ee7U&ST~c2g|M2-2Yjy=L7^Qlh?iR zZ_Kk(*KS^SFsItHMCy}`Wh-R^;z5a6<^qxReSYQ7Ca{8BvhH)g=H&h$SAGAfcrb#SwK6JmrmX3JTPg}vpy`=aR@YgdZt5-k6 zPF7EOG4hpW3NAJrRIl4-5!H~(m>{kv((TQi5Kfz`9=l>cyn@l9>2jyG|XERag_%xX<_P4x19pYF8DLq?puPtx_Ot0 z!qg#6?2z?Unkd7Dtxq43H7kh}OGLx|P0!3wD2%GBRfC*iW?M^6h(RpF$b_cL{KV*o zH`S*wk0AZ_?2|cg49_*5W=FHKcloVxKpEob7}lmRIoIPj;&1k;0^WqnY}2JYDluK7 zW}tLX4IH@q;pZ+DtaB`3p53_BGvuHACwHOrLM*s|i3-w%wERm)Fpa2OID+zKUz1kj zPWjDPb?DRd>yB*+eH+M%dKVq{C+8Nno`_T_^OM|W;>w`(tytgG&+E4lGj;&f9>bEPf^hq_5Q`f9n zQz``TgMjC6?%6UKnnXa@%wnsUx_*$oxyu`eT;8`X&>BzyIvq^Tbh=F$`()=Ik1s$a zExlsCJ8SU|$=8H(iGp*`a65CKc3_ee&;`6DfMAAJbt@bp=Sk0yB0B1z@euotE(hwR zR67s2nFHpzxGc}r#x^>ToW}!VQ^6jU7o>!NGj8I%UtQpIgW8LSQ4D|P)*(sz>N0`|NGEmqmx#rI#x+o*)Zp_iD} zJMSI(E*Uz0WNAfU>W=Pe`wLdhwtB8ED1Zdb6YdzjI7~1&{Z~;jikwJZ>f%n|q zGyUSl_jkw5J<=w^zWDx;wl{ZKuVXtB;y`Yrz3L+p9Dxjs$^5m|t7Z&SS66^}P-w@_ z%|9o>k?#kG$BmL;PQDgHl}aC28nw@Qje*MnHYL;3&>%dE=LVhhD>g)EA|B*`Qo<#_ zbNB9Kl42+*9clfpAwAJE3YElF_vV?U` zlL3|A>anPe?{!^9{_J<3M*4TMUlx13IG_~tOwxWQ7(CQRhl7@VRa`B!dm2z!l zk)5(jSO>L&glY}wgIHJ*2}MI&_Uc}6czh%gP&8D+k;i=a-4RAG2k-C_5-VZ!2m1=2U+p9%8nu1+WZy8itCTBz91dTU?DEe<|~tK|kWy@*)%~ zm3JJ?@*KemMd&NG4{TeP0a6=T6?412C>ixyY_Rhb4>hL5i=RALZPT7U4RIZ@%(8Bu zcZ()5gD}?6<)r#+jY4 zRlk4z`oolPgz~Y`%|QMYU$59YR&R*_ z05oA0lteO8$~6!sXx3a4b%k6dAAK;dYBOq~9ltSY(xVigNFR@cZeQCuyEvEtJ^ zeE3k*i9*a@-?Fdf%azz=>6%X?i0<<#IYs)+5Ty>dJWH-AoddKuO{{iJ1OSkM&Qj{b zvzDnYG6@x?kM2+P7|X^YzM?RbzG)Brh!}}ql|L))pIy|O;4d?$CtX~90jC~BVm6r# zR7@Ntvy_F(=?~`Xu+Akq?(V-NDBLz_g=u0m8L8M8V$0f+_VL0u$L~FP{ydoHcb*x- z2flQnx)9E+_?|Irjm?L6qfZDQw!8oM#PC8wnUQ$aiHpqRrK|0B0l2Uw$jEwGqGE3V z>ldb$;ORiidPbuT!sanx0y@bo@eT8m8XQ0rup%Bnn4o7A;jHiAoln@<$lX!ejQ%$MBzH+LMN#i_OuE`kNN&u=xQg|~1K#{$S~(I(>5fd9I@Q9v zzsV5z*5@hTWnu&OLT35VM8J}uGgNPxX$`2MZ}f4Cu&kk_@fwM?k#-JOM(I;Pvw+Jd z#11do7I(@`xlBo10kVPB>YSAW^)u4l&9ye zPd%lqK5=3-jS-d*^wH{sFsw4pqX(u4$@L#ua<0j`%egHk++8vOg8#qclUbO;^`@gh z!+-bbvbB(tg{%fT3w$Z|jRZUUvnk2OBAds#Oh(r)*nK_p4p%V7Nh<$H1Ff`&p$uC8=9Z(h_y)T zT?{8sCRVbte_dq=S4kdJKs);q>XuG^o6QpU?}zGG15i$yZ6;2mE!E9^L_X^0=lu6s zHTb?QJx+n|tGGqmxsP}@x?{DF4vsyK^fkJ$hdVU|7?zgsd4z!^pPGo08Ka}NPz*c! zZ62DKl$1_BTFRpNb+ejsvuya!-1T%7`ce zcdWjZ{i>a01%eOb2-TtLoV_^b;V>Er#P=EN$YTi#|2V(}x-ohtF#BIe>Kmy{=dsJ| z`CqO7%F(E-WyCuWl55oydf}$2#oi=?Yy?mz2Z}B%kEKflJhIVMp20)tKUIWnVTc3d z1&4od5(PaFCn;ssU2YDKMqX)twtmz=}% zJ9fSB&-pzOsoJ+{M~5|PGEQqpv~SU{=JO5as^%#}i=GZ^(L%qAK~Jp)I+x-Tc4nRI zbGPxwVI%cFrfVE(WfheFEXo{f?)hn6pLYSN{dV8DaW?;7_}p4xTm+8f!vV<|3#J%41@-`x^fBDbo;vE2kPgEp!LEwXP*ChWU4-BQD_*7_6` z<<9V&#x`^?bbN}-E_t-f50~+^jWnhh^QB%_4sULn878cUXUE8dIK~!r8LtPe3I?qy|FtSZ` zA@F_UfK@G;HXTU8#5MQ0)~=zRBxC>ry=PRiuToH4x}F$veVu9OURvF&DU0`U z6-uJvZebR2#Z6Om?%$I1^XK0yPJlba9ys7w+HFT)nC5$q95En+ifOebWSAIleg7T^ z4|L+<#U5!(*miQ8Ox+F4NgNwOnGRmp(P+!`f7~>U`y3dv4%9M=t0^FClzCNJU!F4@_!Ag;H68EinQdaC9EK0q zQAmw~P+?O>&*pVJmwxlEU9VnYa1&2qjmNh&j0!RhExqrxL2}lj*RLttYN9R~Id*K~ z00$U>!DMO>wgxBX$;8g@OD2j*>su)n30&i>vw^G>?CkDc9TM$|xhrihoitB`WFf|iAFi0+=-=BQ z^yPt+$GQ2}67qb?mbr3=n0C&3-~2vgFXZ06&)|z3cMo&*4A34kCcV=6`VzBZBAR$t z&Ao57eHDcPnsMEDrx}OGciIY0J1E9kwbx4>^TVAU8Vy0ME%w?`!*rsCP3e(IQ{Q%1 z6QL=Gp0}{L)w1k*_v|NRH}}#;uxC#~sBO5K!;ZkXpy<|&W{(-LbIwi89q1_x zqw(}+D)TdpLPxpM|Au*u0===^R~h zYc?xQYSn)I)`OZJz23JG(p9Xu*9O*(iAp+f;4-_UssH*Jw~PH2VyPxmKWWdeJGbld zUqDhk3J0TA%1bYmH%2!P(?NCWl)3FV#hLhJC3@9=w3S*70I8k-dhKM0#jpY*taQ}2 z7rU!JdXNpk4^%}hTD-XB*|TS53UIrR>*4`1s4bl`+jO&M2}?Z{-gP#2bo%VsmwmIX z&HXpeIE~$Pn%kd>(p}2!_(vx9b=??})r}s*%PE6QzV{YhsfkH7D5(6Y{!58bLukE_ zWq-*u4AF1$M@dj*542NMi8dmt2&w8l^VS%Q92K85?B1}{l|}ge$KY%K1LX9wl@`Z z2uO(#k*RhGm`CqFT#(ulgHLgA;8DQCS`T2&Q485K*-W;?7Dw>8^WmI8q1g2 zJI$SYXzBj6wAND3Ky>B39P*)lGlJpz7x)iSj7>l8?)Ure?o;Lf8*_6JINW?v#!Ex7 z$T1;uZ`bL1?|lv4V7jp_X6b_`FeeR+a~1V$#qaE%LRbCzHAJJWfX)+;7&a_jqcUU6 zYNX#=ENnee7ifbi>g@X*nzitSXC|8=Bx_>B`? zC3+GvjQh-G!I3_CWIJ&@k@?w;Zw#7(t~rpn+I4!5=KKb!0s-UCH?ME_z39(=3L02j z!6cPTcFFaPI*i$7TJzT8X>a`>=Hx7gau}HVx#sx0diCnj!0d_46Tq-0D``{rrkl*o z%rY!L-cE;NzB1_XJZSZ}pwuNXM+F z?_NClaK}!aZYC^w^zb2*Q-g%EgNH2H`}R?hi62Ubb-&T@9obXm&pxxqk2iuOJJQv4 z4R%-#Rw+)25--6X)_|b4L?Hw(=l1&U1~+UcAM_f1{KSc;Ah|<8HWb2Ttf;8aJU2eo zTJDA5)0hzb8b2wsf~-2cs9U9N#NTV`wZT0mgr+)z7r(2_rpG;w7$!5!1ej(V zrb74~z*`cp%DvZ2HH}nu`v*xG9u2`SUQ8vbth-*pJSmZa&)dCsubJZf=n>Z5cjay0 z_G)1|tbtK3F5ZXV@YI6b4`lrI?o4bxi#v$CMN#STJIP^v(|Qqb!Lp_kMd>xwQ1BiP z-gkTKl(!Vil9#ZTu-J!Cn0)y7Q4)OAu7B1GZCkbWovAZKuq{Hyde)ydUU1_XLYSHN z@r7gm+aXhpqHY>?2(la=drOSIfgT{ob@OQ*-FaR4mnNfzlYKRCA2+U=f=moyhvDqE z0K$79>;T1!0H2h3kF$l)#DL6+yHo$Ecc}w9KOw$NIM?q$MTq4M3>T=;2&_Ex}CG4 zhib~&zfXiu8`sMHq?=~?P}-hx;R99Odpx-Ne%7_Y`b|2E==%R74*ahHB=}A>I}bL0 z`ifhuGWIXPXfv^G{bmPn7DtTel<77yaM7ap@PS8bIOO)*x_ftL|DdtOWIbE0s#hfi zckbPbJ$KHtq@7A%Gi=}O;<&oVIaEEaO2ApM$ZsE)h5hmL UL}*E@j)FhxA;VP%Z6>b#KUkX%M*si- literal 0 HcmV?d00001 diff --git a/tests/network/graph/toy_stoichiometry_cycle_rm_rxn_visu.png b/tests/network/graph/toy_stoichiometry_cycle_rm_rxn_visu.png new file mode 100644 index 0000000000000000000000000000000000000000..cd42db8aa3e5c71e409c8b6dce47434f29a76331 GIT binary patch literal 68879 zcmd432{hJi`!;%~G?>aPh0H_7CNj%Blp#|Rp$R1vnU%~!NYY?drjnA(k*V1bB}pV1 z%Unq86N7@^(%s z+qFZ9dj&VFiPK!g_~G2O`9FQlW${haN9AfNQwNi^ZzXU4%>JTK>i9f2SE61v6(jyp z+Lyl7lZuazFJmEGFd{rWT!*61gx`;&C>5>Tz!>NG;`M7y8yoJasVO64;|n)$Zk}~M zdi1D>q@?-shiA{$hK7cYsN6_SRufBn+TPA``}S?=?c4L;j%9IK#)pUJyt{JcifX#i zmfgEI4i68P`u@7N@Lu8EkKUy}zens%KXS7Q(bCd(c6R3QynFZVeo>Ly=)1dpLqlsF z9Qv(zZg2{7*?iK{(Ab%noE&lO8b^J7J^3(R-W4<w~H-59F*m3`}Bim-x#S$}g6NE172v8^y8bLyPuje#vm6X&vdQ_02nroVI z|NaJ6R#p)a5mh@oUK(23xYX3xr%(5M{rYvw)~&R^#y@MDo3lN5@E|X7+rfhe<>cj` zK7YP$-@biA?YYeI=YG&RJCDjQGMKDPJFJUEJpSUP$UfBro2seI?y_wRPckFQy|a%Eh?vf+UP8{692F2~24e2=?%^HRmR zaVm;c@res>ZYiisCS~d;Ne=Z?DMTfBI*>0JiBCvyTUzv9T>3q}cKv$oJ$tAGnAUYY za$a-%_;EP}g}R0Y>NjuRM1QePTU_+3Z))OKR_0!}Ze6Kc|7w?>%80x?v0~@<3`#yT z=kMGR_L&_C*>$QboGWRB0b3telAo8i=j_=X0e@yK78gbf!y_YuH25w&e7L>4yL(I0 z%f7z875n$^FUowE93RhA-_XD-FVAUgY+UL-K!f#by}ftS%;@{|<5j=7E32wTJ9@U| zd5G(3XmsV|<Pck5}j|L+Z(H($b|VM%g+U4J&2^%qL#cfCnwFB zGiO$VJHyu(P)hGRrr&8|y5xZ^~e=BVWjom6dh4tJqO4U{NlVepBfA^AsGNJ!6(L z%kJ*(8(3I^SFT}5J2&3bS?tK%RdQ;pr?}6qs{(u(p)OeJ5tEq)cH7=)*`5swOiB$M z`4&Y-o-<`-XWM-$_m~^XjIEE+Fuc>ks<48QvFhfF7kaO5$gaoh)!nzNn|N!eQnaPx z%q@A3rx#YPk;XC_q$qA&v0_C|PR<4eHYMs`0*s71RT~-_IJO;Gi=85$O~)avI;LZ) ze*DeN+B>NMG-cK?ncA@#j&E<q>D;PHT@oc68kbqitMGUhsWJV&61Lm z0roQWO1?kS&J4b3?sl0S>F6wS2wQ00e(Y7v718}RUe?(r5vhSo8?|FLtJ&N0>FMdU z6dc%2&d}Sp%=p3J=;-L)5<5FP%QAOCll}V{nVF}bI=owh{ImbI!G)+O+I=_WYCRH1 z*#9n&My{!SeK_MS4FZ$w>}nKRHU8@2?+gX=r>#d=UgB$i95Br`s&I z!8Y)ZM|2Y{9bJ6{v#_-9&y(agj~zQ!dTyLMV19u8esKPgQu%P__bqH4Gj&)bY?a*lkm`~US9Gg;awL>&$(46 zmV<+%rm=A~PH=d1wExI&&+(o=dd|{^o&<35^73-XpA}jB^T!U^6ltmR!w0)}1qV2G zyJz)!ZrXM7{EZu%7`PQX)86yApFOK#WV8XvDJ<~MsI7fddeeQfZyO$;{@j?M871vK zbp(exI`&R}eiYt{ih}$iFqaY(M73GYm9n(7B&DOzHjZ=%yl<~> z^gj)uy|(KVr=)E)UAD=*Uh4b<9Gm<04K#Ceb2XnnaiyfBT-$z};Y)x2PUVt&_aqx* zd19G`_iYgr)a>tXoh~9vMe+SPcHzQ>6(mFYjrG-q)jfSmMUvy)gOA&r$C1?zRe1Uj zBxWN1ihO3BEDs#uRaD%JrJI!B zQy+JI9T=!>ZZ6!nkLK6ZR1k_HcKhyR(a`+g#CQI4z;AkPZf@xvI|`5znte72i3}zt%bHKn^giT z%^SV1K5~A~r=-M1%Orrk2%yWsTd1hKJJ+K6a4{T&iqr z${75&{4Sr$XWN-?j6TgV&OChVSm@=;8<3lNduuO}>e}`Aw5_uPN&_9|jx)p^Ul>ykIRj_;{Z~zpen2mzP&fUEN9` zlFpJ-oA%w3&n`N;{>96e#+H_ADPF(7KTch!{`&QZrInSFKpj62kcg;g>k!9`eW`E+ zz5oD7-Nq&*XQT4sggPKdaBy&%|L>n(e`eo151h%&$Y^VRfl0Q3?Hd(i~nICo0rCaXVkq4Zg z+`oF8im`GldY#X=D5R-gI`L@>JA0DVru{mftNo45%tDY_g>63KMHXg9!%+I8k5SH- zmdc$S>oo4~Z~0F+jz3ve=Hus&OG%M*ipkx7=L^n{>zOn8v&|{8r@8m++2cCiqjV)H zi66VH;5k;aUS;w8dmCT+i>vALW@UI}kH6WBgwdC}yeN#UjfD!YtW;c_d25K2_jGZ7 zdS|ukQBTiZOLP6}qXX~G*n5rd;>%c__TT}Nr>7@}q8CcRwe;4aa?kHp-)~++T=a$U z1bB`74*BULZd1uoU0p4;BgG_F!@z*O!;&yV|`u@{q41!?@w`5^TYP`R2CK%2G^uloUWQRk5(zX zU2^JUDBfy|lG33oTQ}x%_VivwNtqkTFPxZtZ`(i7R|jOusKC(iprAmUj!T{e2cz*t z;?{;3bpmYYwY|M}k8*2S!k&59jaKJY#`*hnTkb`M8}28 zdfA=T{tCb^JACGqI;tcNmh_H&c-UEXMwr$_>8S@nC6{GC`!Rofe7Xrai#%^&U|U7>AkT3VVg+XDf&zj;L(@$~HZ^ICkOv6WRMYGlVt z_0#X~9b#6XI=_(6KaN-_IsG{X2iCqJlJ!w}0aXVBDP5~i09WAIuI+SYV`5^ufAYh& z9gb}p00lvM`udaegQ@)hK65`mo|Zw4D&mi$q?GQ}Xg?nk7Us}--|q7BBbE=2tn2>t zsUb$A+3nAQhu_ltpw~q23Wm*c=au~z!nq=XC+klEL=>P9qR%*=kibkb_)P8~EB_pI&I@&^B&sJPCD$JgWWRw^ke8QhX*Me@Ip!rh#=;f6_W zeH_0wfh?$|1vXWj%t8h^NrQ35JFUrL%ajE!D_<2q5QIgpdGUe<$e`~0ij`^kFITSd z*qd$A_w{)~XQ>-M5~*~+qL=uga+bfaEAu}* zY!eboq+yq`sRssi?X6jHDWGnURZ&e*5lfw4!+c78sRs(bxqp&69M*$i^0`vb@L)SSc+nO#<)cty>+j z03|z~O`k^bzKotLICvH5WzbuqQ$mdhiHl=II-&udIy?G~e!Oy;0bON%jDbfK>623m zk-N|lgvZ3(`}yqkYii21Yu9Redbp9#?mvF4oo{}(1(gUj;wdUS8ylNP$~-_i=?;z^ z6Z-M_No;ZP4ivjB2qsZc(Pz!gYbZNTb+O=`(PU_oe&*Y^PzEk}Ra@I9Z?^BRBE5o! zl$4+#HBHE#Kc$E)(#mCJ39zuU=YPIaSXd~jFC-*{gV@2XrKQ#K_U%PHp3jZjdvD|g zF6XpR)}J0nBXpsy&4h)GEdworrlzKdxVT0@fHGNv#Ke%uNSfJQ_*t^(H*enT9U8Ly z-q+ik_Te6(s+h?w~KGZf9D0F&+^EkOy=7HVX0?sQ_IqoZp9>_R)2n`)73sXs$ax1s zdUZ`^<=*Q%a{IHgPLZ|OIZK)*94v(Hy3?mmv#_$7`Br6QP+eTJ9yHIYnb}zx|An)0 z@$tJz=HhDwcVgF;rAs+KG;{(iKn55DATt-F-KMfLgA@vI!TpjFHi@l{>$y7m*Z7jP zi@$PZ70@TJEV2W6==AjTiOI=YpqeyOvGmN$i(rxn`WY*2pA#jImD4lVdvU$%*Jq5T zrlzW7iF7~u`1lNccqqItRawG*Pa7W%w+laChL6M2;(Sg{4u!J#=lC3YTXD<#s+TOY zvPqc=A4z=N<3o$WWt^I@ic(%)?&|5Ob@JpG^QaJ`<66?$8vkM9;8=?uV@{ElsYCDw z-Z8^R6C~8Bk0m;~y2d>2&aD~Ls76~7^)&ZsTLqJi%n4}i=x7j5^`koUDQsB-G+JMO ztjaO94EZJ{gth57R2r%$eOu#EsLn4*rnJMw5lGt_U$RKEZQ^1VJiFg z{qi0@e5kIY6W%F9p&%?TCM6}2U-D~R+MqzyAw%$h#;m!&s7}_c|0xGe)o_dg`r^LF z_bygJLBXIx-E5O$mrtB{pC&XAX_=Y(`-n=I={ciyKNg2 zAWMjWP#w-!(XrRplNJ^=quA_#42EzNm!?yfd!H=-k@Eb}y$;9{Yet1e+|T|-d%NkY zo4c7m`~TuXqiH)l%Kx2^R~nq@g@x97Iv+W+gI;x-+4#QDR$E7BEz&*> zARnn6H8m6z6o9g;ht;Ug({I{U^ZGUYn)TelMMZ9>{B~!B{5@8g1qB79+L)S|^)*Is z+S#>BMuw5n+uOTkt~@q2ws#r?VgvXE-Z&kn9o((`e}n!F*nsw-KPXL<^Vo8l>Yi+U z_TZ3^swp{*J^w!TM^9B0ATN3tbX51WKE4=m-*LKUGrOd9O|j!!*ZHZVIA!#Lf_X6{ zYwgHDNd(=omX0nsC54+!%7!l4z&x{Ade^STk&b*M_mGJR*SX)n>(EEivP){#c zUP%epr)*#}mH?f)SsrcZ_c8#+Epl=kcy+1SWcRYoO)*?`U2riEX+qye&__s5SI&v#_fOFQ~v)0U<9rMUm8Ca_F*Ht!L--J zgb9dr#^hs)!Z`*vH@8PO!-oQA>KRnQ?&kv|cn#h<*Ye<~{>)Hof5_tspII>ytl(sw zrMVmx$noSe-iro~l>3t>Pd*y-ID7UmR1hw%TTacwLPCV)2gL=g#$#h*F&)A25l-zn z#z5_)!;iaq^>%+pL&`_>zAxV{h^{YT#Z1szJyZ0|k7a1sJB|R^&jW zf*EWAj?mQ8>k9sh&WVk%KG&c(L5^LsEaEpM6c-TvMMgfQaWoI z%QGg3wbRAgZRX-a;y@B64;>sDO4=bD!yuE~;FuT&v@|V64%$WiCjEE*YDqr*7uPG- zpudIiwt_5?@TpO-XvdpVlZcTN9&Oq8LKk*6O%Y(z>kEV!YO#oL{LdNiwl`0$o1dfC0x;yR{D z|F8@zT2k;Ld>}3TpWQ{d1MI&D@?bZYBjevZyP#=d!Jg_jweGA3&k}R?ojYM*ysuG5 zjBNgkbgK57nyvztCoE)3%LbqpV0?B3Hb?f5l+JY{djJwq^DZYPg=J*$p&>D^@Zpn{ zwfJKLjgGV|%ZLv;(9qYClWYI!iiDt@E(C*D^!)W}v*yq%Lrlt*#Kx1%S-W%|*XXa8ldbw{E|F9GV?RyBn8~ za1n}>2XCGPA^|Nvn77?%`t6*ZX`P*&0UK(%x;XDzJqkiI0%^`Tzn_V*;~a1ZI&+2zge**+c-3965i zw6gt;^dc-YqzMOwt_3xaQiIA5F_ZMy=*us#Nm$jOk3_441`q#A%g9_X5ZXV!ahtTX zQ7lxRjB5J~gaDprQ5ak-)+fM2YGR9diW_ z7o(S-&70Heh#^HiwmME`c(?*HhP_o{Qh>mY$mi21X`;=ydlymST*tl z*yEv=^p*eWQw)T%eiYCBsaZN2rm07c9s2@?y%bmwOS*xX`6AYUavr^pk8%lGdte8< z)2G)fC{#G9T#CF~P(bJC=-Bb?+m$OU0>Z-m_%I5$joN!WI=G3m!)?@`D$X5uYjgQ> zH4DP>O_e}y2r$rd>ImUsW5a+G3nixp#}cBgYafr`?f>!uxVfo;N8nss;6SjCDA~nw z6IL7Bd)UP#=3I~W8b}S`aq3aqpI7bG5Qs$2MLz$@@{$60kGRX1iRA71z02%I(yd#! zHZ_!~&d<+>$Hr>=`6+_sBXQSV>CJ3rW_IHJJuaV_p%tJzW=1+_PjwYDDoA$RDwooI z?DFa9@Y_2CQQuVd=Y#-o|KY=nP=i2uLJ=k`K~kJXHW1P1I|&VKZGPb8xVSh32?H9D zR&6vvpy|JUbTvQi@sPpk@o}b@8hCyK<*@BQYc$o{}k<$-6Ja#>%C9S*M zgBAyg$mM7>ujmQ(1pGdAiCc*i?aO(5cqd~-&IKsnPthjcKmA!5su{nE3J)l?kt!kg zvYVq!O1E*oz@Utj9Y{A)6-uxJY9pHK+Ek%T3VT+rCj2`X?oCh~FJNVMIKQJquTc*&4l%{+shG$0$Tl8j_P+P@u`x(E z(9B6vL{)=2wm!vSIua7I>*NE@ z$X~#5^z-wRK09QDUdsx~x(&!5w8haN2l;mGV*Byq$2EBmvBpaqSMu}ogLHhEe=9!A zB$r71tXs`{+p_h+X*QrilLm45W4PrqUaSwigxuMo4JZNRll^{vIF93Zv9mQiCk0lF znsSfP`vHG^2vrGKfmPOoK3E^~?nFa<{b3*h2&^PO11n0O`(b@oJ~f}wdp4d0)TE)x z9~La)(Azuf$s?6n+ZV19&!jaIHw}wRvXwM2#zQXpPGC))j-a{oXb(8bo;=tS8Xe&R0}a+@><2 z7fsYdnD!&cE`n3p<?4Imzk~!Ha4aH#Moz5)E6hlx@5FvpH zTXSRAsXfqzKovBbKa4+%TtK&J7ack7mo{$i9UN@RZy(8p;fLsWbEpk6J~Jmy_Ec^J zqr<4c#^_jsOxKY;6eU$nm{(p;0iQ@nQ1<8wfEDVng%hpXJP}+GDL)$Y+t98_08=&|PxWu4ZsfcMef(95(k3it9m0MR zqD2%}M7WJHKu0Lu#vpW|5SCYgGKPhrqXl0vF?=U=k7QE+P-~`QSn-l~fNmFL0ccVe zK&#oIECQfYQaq>1H|iuWOl@ znT<_Mg1{EkBC#V+6E+E$rhUA*xw(n-vQuAmVa-A7PIvX{RZ@&_4j~OJK6Qx7EiPsu zTI!J_0&h}P*2~Ju7V$H->XS(LKbRaI`Lr6SC*R;<|39%AQi{j?(~LxJoWVpmpHhPkV;=EBNJ z?D>1IN(kn}L<7x|{H7hSFFC+TwgI(hrtO@ZBJbaqp8fq~?h7rpkPu{EzdoJgO?W4uU@^HSjxP-7D&nUMo&OQEb;v!?dFzn@=<*ALhh>e+4mChMnx8c2hLr5fqwu6@)9R~}lf zrrFW=#5qvZzrmQkyV`$K6r03T=v|{nT6-20Wmnc9G0V7psn>FQnU9^~f(Zvn@%eUI z(GH2)_!)eBFY=US`B}$X-+yj`8^@~TWW@AfGe0kHU&MPHttf6KpUO6R4heB_3I+NS zi(eIJCd08e{?`flpO{$N+R7*|FE4dVmyRe?=M4* zm78Bjf7h#a>u)571*hL$JAGN$G9C&|FutXIn5N4lmj&A1dA;nXXesln%&o02K~pE> zaN&+nYe}LWxb;1yHn_2iK1s?p)Fjt0x0!~Xo|*!K(R_AEGc?gi)~&t${VUI%JLl!& zWB=ynZUCXc+~#~yTU%RD#gI(z>}%xDut>OdE7~?-o*FE3;oFxDo4UFe^FS0$zLx!* z2C51C38^m13q$+}c${B@Oo!2t5qo$@9ghe5!p*UoW2g@DIJ>f)mPWAt}5x>rv zq_eTHLDSZRG`~}sP3beUpeQUT;K5+@%Ac0T=W!@_iUUd!)w{PZdlY1s&#=<2K;fj9 zZeyJ?;s;B}M~H0QO31nm930^Re-`@YCi|Pam62$OJOSVu(F|!Wdt1Sw3N|!}miG^@ zATSi}4APO{&o5xh!MoayoN-1KrzM9MIpfwPZ4IgM$Dac(hJ~%6qo;?7CRAGXedu`g zZwAtX0lyQrm-Gf`!me#QN`oYKZ*p=4{88)i{B>xVM~^7_lnCp!p*1_~?7X!rC@xbg zIw(1LBkT^aVg{k2WZ)rB0B_ul=1tU-InlBO9uWpYeXW97P$|+NU9cmiOQ0epHFLO*zD9S3*r=2s}6pzkXNr>B`u6yhj9S`y~o5P zzOe~x-5E3%cTzg7*+-_ktq&qTS5ksPfQHtBaBCf!iv<^F`q4rAi}#m;F{pUPhN0Vc z?FvmzT|x5S^{Fd6 z;N@~p0+|5cLp;c0Vr`vrQlJgkzkU0baV#w?MMRfN{;VnhD){z>XDcb_lsRNwHXqq` zAo}W~KyIX3aOaAT+7+#CJZ)&m?wZ~t>!JZC8n)A7YZx!AUWClS^C?Zj`1r8}7AJ7( zV8olJkt1u-lgGrwaPmZj+w2Wv;Mx&TI_y32vD^A1WEE`IOL{sU<<02W+OS*Tl+z)z zdu(G<`p7K!9Cktw60lBPYp5hB?7(fA_NEx6ggsE2A`4|u&_=|P3@XW zV8AW$7*8C;%HKaKD(z&9S5Cm`MA|bPw2W^#QfD3tp%SktDJdbecEIwVbC8mFw{K^* z^`B*h<$2`m%`9Y|#mOcf&Sk6NhP6Evr|_o4-H2?qv)srY#*%!lbKl?H-4uXlzEf?R zb3p39ZP%IQ-u{nHPCNa%dSZGy-*fgEAoC>fjLP&GQcmErsxk`IMoU1N7eYmE+_;g1 zM1KBOc=FCeT+7!=o!v&v8t5bl@`a(|?VOj!j1znba8rjtV8>b6$jOz@`|5Q~-PWa) zpH;+`0+;L0J^d<@AefY7!QMRuTXCrTY%Lz36$geh&}J_V2|bKWsy;sDA{N_Bc0QFY ztqS-f4}ze`Ytnv}o*~yQS;==7=tD*xp5_~(OglG_RjOWEaNex!$FXbIF4!Qgu0lme zx+fq6hSBU!w;@NtbzXD(YhccH8*1T&vnB7|#kkdQ8_^*E8b3Tdp6dNTfU!dgR6Ys| z;BR~`CZ&(vWoO`lcKzP969Np%c$(LU#~3O;wbf%6Liy!O9k>%JB`L`Oj;Ic= za^P|}Cr7SnBpf5)%lBj^ecu1+a(V1QLzzaybZ;fO)w+6PP={o~tZjV}A<6eV(+jy0#y6Gu|NqM3g(^Qitm zE7DadTLL1js+=y{e0aP5u@cj!O~`zP#iu^9!D}e--sMCZ(W#-^W}tG*c~1#|41-%p zqwp|n)7P-L4{=Yp*RREUJK72T>GcKq64v&lPSD?}+ynTo460`3u zhP*P!e-2=Oz0jqQs>3Fbp+yuEVmw2x3&;0GR#W&o3>+Or;aV(0Tj>WGLtJOJ{md@` zrFay3Mt&{g7bVswtyu0qZI8dDm6MZnx_W*!jKQ_|v!b)ZCQGvXPDenikd6vFLRttk zzwjUrmLgJ=mlw|f+fddpY&Mdz*PVScjZe1+bqBMUeQgl+--hPW#)~&(PdjbfTonXI z=;3FVc?ruDbn)V3hlOp?7RH@>fKdrS0sF;YN0M#zP4D@sERG*5N9bAeLYwGy%t8~24;IGfgKUalSkUj8HVi3+mrL`jQPU zRy!Ea2@n5EAPREU|Fr>YdP;CO4jB=~^_4Ja?Eug%?}v>Cs%3I2ei(T?@*V$8TZ<&VM7%kx^A+V@6_^Liq7QAO)JLdvNq+ z@7J#ao;&iAEu?f~!IvIJ{kD7(pakwH1o&bTFu@+ACrGxoo77vDUqV|)S3Nl%xV%YM zSC_bPv6uuJT;=D>`v!1oS>Y)`q+k>q!c-wV!yue@wsLZTuY{?iPi1+2Jx~)8)kU2D zx{eOBu@f`yPK5$W86eRX=iCU$xMP^Ag$|yWY>B-If!00nJ05bW+t0BBaFq5Uw$E2r zD=#iCN}u^=aCWFgZOlu< zHG%xkkNFYQM$-P__(h`>o;j}CpZ+0!rnIxul1z9&wbX?3lbBOrnk9BDWbX@^nVIjF zW6fYT8hu~bPZW3@&Pmj1GK+*SBa8qr9s+r^Ax5+u6b&hoXlYPYOBa6cI9x*^u1|Ev04Zby#p0acup5f#$fpFdPjFCO z-*t)x-E-+b;~KydNN?Rk+WGqtI_f7+io;tA#yT1gO4w&0PA%f(!v}+isb~%43D@z1 zOAqWK8On&^R*J-F{f|2q3I;TP5VF8Vj0*P}A-M|(3id&xh5D$9WpVjjO&_5_1$R0Y z+a7?*t3O({cxjEV#Fl-!KGtStti;Ix(jz!5j9*-w4iDba+Ipd?N(nYNFb;IEA`r(k z{Lb`00$)9NunoR6GG~RUA;|e;rvRtY0c0Utqjkyv%Zj3~25fsnjDd#Ly?;)-J3cl` z)6FWz*49L~fe$iw@EGQi!1&x&O9@PXz5;d{X8kRwSkMu4baYgak}qAlMD}#Hd-09J zcujr1#@I8{$MsUWK*c5)%nE_=hsY0NVl+@jR#S#XM>8?c0*74~p5N+`6E)a#OH0d| zwl*eWvJ2RG2-Go%SQ4b5?TJemY9Y~lbamCR<&d!8G6GYVw-iwTdKG57LHk&^Xw1yb z37rYzwu2j{YYGMOM(4+mbr>g7k_C9TR+2hsY1!`m4G1!4aDW7fgakcm=}6gb5XF={ zuIEGDWn@MQrp`4s$$MuX3ao=trHSb*oJ9Of{Cdd#Uywwfy?BxLvqw9S7aUw9M$$kh z*W%s(Y@L{pYT$2dY#cFj)YsD^!e4J+mJym=z)}EpK7123H8rvAA(u@}PlIT`0PE}Q z@!`?YI%GS*Q&)HQARLVaBI9sB7Rt~XXtkMo()=3P%J5Zm?6+^))06${!PUZ&Z&tD5 zSO4@(=AM;{r&A;)Bs9qk91V?;scBZqoil%3f!Qf9SJZ$~KmdB%+jyQm8+`HN#bZx& z+l_YKjGqz5@qhN}l?HfDsI8Z;UM1uZFAzB~1ipN^e_0t%oS2x1Hpb|*Eoe@nm7Y&e z=S9!9f&z8`X8DUS3K;C&Yv4oTCzQD?pg!36{037Brl!Llk^vTw$-_6}c0CFlOrw~*t6=r+r1^M~fY#`MW6A-Mp z=9&NHS9Eg`if+3ezluVrfM1^jV^MCDC$EQuG%)){0@BN16pjeib#|Fya4LlY_<+f?%<5oMKrfWWwQJW>C{WnRcwokW z0ACv$jvaxJlr5;qKzJD0q?&Mb;Z_MyBnmEt=v|Kz7`p!+glxm34)5v0tkB^632m|Cg$dbh4wPOdVs&FE*01%x$ zdzOf;fC`aZNt`Mn@2+N%UKaXRuwHf$p2jC#7e9xUK^5RfRtJs2Cyd4ACKxYf^X?$> zokK&W3XTWC@(@u5E1+qhW2KRv81|8J7$iG#3iZT<aqPY~VQp2hr^Ey?< z_K*7L=fKdyf`a-WYTEj6O)EWT{df(&TJWG9&L=?xr2o~fpM;}nXWq`u7-#M?diQZ} zU*BZ?f;RDcdU$9)3HYO-r`4t7tj|O-xGBeoT;7rL7G`Y7gw}@+Y3gZeWPL&(LVcQ- zm4joh9)^|cSsjA9LJ7R^SwXHF8ARk0QySxbMn=cSXld(udaA8>AKbrx9wzVbT%Aph z9tb|r__?vZARaTo7(nAoDoqa$FTRFcsyzRdd6v@y4ptJ~Ag(m`X?1Ch>c>Y%(_>DI z8dDUM_2&jL>RXBdjP=O|t9$Kq5lxZLB6KzlN9=S(5dZ~N&slvPdqKLs|0HR!f+C&* zXnp=pWO8U^9sM72mWG$?i~nolPT)|NC3U10_@_QbO~dLKMo=s(y^1H)QZ#ZSx#*Z^nz-|$L2;wjB)X1j_`>t}2t=MKhPddIf6Lj~Onbz3Uwq5cH&@ z)Pytg*T<-O7|g_*+Cp~M99s*cmh5cymN?J01?I74mT)%n4cHkjUY!uF3?WsSDi=bC6GsUy~ zIongRK*RSzRim1ic+&!grVeR!1E!Ucg7-kSo}U^(FZx>Fe+ikIjI7N3_!Btx>71o$ z)9<-IvO4>7?N_j5bW+qBL_9CCHeBV_)fJkmK7|#{Jj!E+%JzKEex$z|Eib+D>ERid3D2KByU!deCyhBkXd_-3e(i5f zJb_1W7(-%gq4I4tFG#~0k|_`n@#x$d(K12P@58a$=wB?G&i?JJtpT=pQJ`uIh zY^ylo&ur+KK?D^4vWc|a#&*6Z?lap+&(zq7IslwA9;@FJXDEHpU9C4Kk8U)cU-beu_u_td6l|)x4lqT<|Yr-pDK} zSX)p|60lL+%6^sw?=f{oWrU;7mwL|K-Dk8&Q2*+EugNcuxleZ$i*|K&VfckF>17ma z*p{_>G^cs}A6_{{yC*~}z9;MM-9rWaApBQ=1KT<^4=i{F8}iF(>-Oyl;M(B9H_a$B z4%)Jo)y(bpw+C${M!U^&_=bT`Dt%^Y4jnqgx$7hzpv0*o5-$RtnN`Rz#Xwb+vMsql z!_CXvdxf;!(_p45n+>bd`Q|TI&#Qk_2GxRI2(GGK#iu`~NL9~Kfsr7v#MtlL0qu;-0M{9#wm=~4a)3BQeY~4Cq)fEvNdmKbM zsdkz+8O*EHa>VsAPcOBU>^Cv7EIGLmg8dh${OxxSCgF@uLDajdw|P&4`Ms+YueW4G z_2I*yhkK4?SB9M8v5046b86}p0>1H#)H8%mwK*RSyVIv z<~vedp&*%NaI8|>@M8PXnb(4+d=UyE)rGcClo3vEs6KOHoP%WDr7zO|jKfFq9r*;j>UbAnNkS@^jruze5x=G|d``B3F63o=3nKyAaSEAM3kPkemj)Io)tF4Eh- zFpAx#nRdB%_wH+$l0&Q|rCKxW(G1%Wnf60~BWKxB*gd9jb@OWk7@*!Ay z(3*{lJJBd-gREio@k37xFT(@3ey!r-Vm-D}1T^(eUjH+?A4#)^W9mKXRc8GLi@joL z_F~Z%RqCzr?d|Pk4vhlTxV3dp43Z7n@dWhpuz3L&d;!!ujz5E^P8uC>Ls3&K&mh|N z0nBg{wh+duJ+=To**_ny!y;Q`x5_4EeTO9l9P=7L8_ZCB!}me-PeqN8_M3B+aXdI@ zI56^~L{?T_y!2%{JR@ZGG|~Z%aswRnQjbwK%yT#O^eo*~*c{mgc>D9mrYM{@}BxN z(h-I0Dp-Ht3^GdFbI1iJXu~Qs5s3^PrhmWMQ&!$>&a_-1?`(`C{tC}!x*>YY+8|2C zX|@+(G`0fNt5@rBAyWiRsAzl^zq00w`wjdDHi%IpPJsL($Y_I6&{riix znj19Z|EK)59a#)D&CRjl;cIj2MD_zRihJOTP3>`ZKnt3WNWZuVhg`1u22+D6K1ZYu zlQ1Kp16Edl-yzY|+IllA23O_j)Gpt=ZY`Ol&!usIkexWGuc6##F=%4#tk0Y~cl_(K zIF!pEh>5yf3}9)p5a#fuBqJ;6a>W=0C^DXj#}WpcOb@m4lchs!9TK7uIB%#tAB@a1 z1qS&HP;+h_;|s=r3zW?V)q_lA6ciBNzP*p-0a$;qXB%-R!t>|PO~UxEF#p>by$0+A zpak)oo^W*3<=V$PGBN^|Diw4C8N@GDPaSe7?{kt_!*N|b=w|5MWW3mo}ozaNvk{OaHnNCNBV1WFSD8D)V67%2f zGqQ*05X@%_$gQiAhr~SO#*czz#;2|UNdxO#7K64(-M=`v(h1og>3R^9p(@7iMqGQM zu3zVbALF)OqL|-g1G{}d{KUThyxtLyF{(JIeQ5l&y$U1Epbp7_`7S=>h2D77tRR&!i5g8ee zHKF@=t|kicXnshi40r!XA$yAQUNp+|YLmT(4#E2}UF|g85CtGRg3Kf$ftN zp1{R8=m|PpFkdSu`L)9)kBQ><7W`LL6H%By2yP- z$f|bGFbJeDNZzJ7;`(A0f|+=h;5LPM6Q?levIsfbt0>s~jUbG1{mdX(`ULr#+m513 z-f^OZ%o#(}kszNN{`Ye!yX2!Ph3hW)~+tp?&Qf=5SFtT?pXiT2|PVb-mS;sk17tO)jjN91D#$kqkZz^e5_C3YSx`hmwTTn9@|)}o`J zHa6P~GL&2oMZgRi;l;s8RC*IN(>;#)J2cy1;{h`%F*%B0CNW86+cc@|)K1*9Df%W$z zd;$?`PMta>s3C;fQx3IH&%of7LTWP#HUj1jBo#!fO-P)XBIxbl+)Zr{|GBrY8E- z6%`e8tRCgj@aKE&*s+7$mjo1p8y@;@H8nIgE)5Q)82vQ8&bRI`NO@8zFh^H8d$Xu$ z@*uw^h)P24dw6(2@z2kb#;H_(hK$j(drf0q-9^lTk;_z27)&=GXfQ3bQ6i}@ENl(L zVv(t9xJbj#-`{L=@l&sg+QB|#kg9wOKc>IoP(uevHLq`wxsx);RR^1!8XGY?s^jkd zU{&inL9^UQ0m1;J-@e_5wihatiK1lEOJihrzbCjQ2l|sPuI7LT=m|?YkSfbZz@O*X z28_#va5uv~)X)%uhF}U8GU>7$YJh`ho4m(Ja8%UGgAGP7U?3wPL+Z2KGGa=+rz0lf zgY!l=VE4g=tPLZ2V-ysG8jfawn6PASR5m@-t$v}|A9Yn3a_T;%o4ebBUqmi zKcJJ%rAtM0NZ@}T=b`<3_eQ6sZNW&5V59-oe2yS9kczaYYTGd?0a50&6cQ?RDdjL11p*cgBuRZC7_J!P^hw_T+mPEJr9WuZavJ_FtW zIhN(K^NACOj~uB-|F@2T;TW_-CM}D3K)EPjYPY!wJ3v5vgb_=#BaFV9f-l$6=dLsg z!MHy_f$qM2LcnLZ$D*NL3vUco^aJjs0XG6%-1q(aU9VFJotAg+8ZiI{Nf7dFWM#B@ zjvVH$baiw3aU`}dgNUjv1Th=Uy+i2|PKCto~&4$VFe zw{$>ff~DBx(gIXU5f|=Ut4CtcktiU3jUG9fL06L5wk-oid)X%j9Wt5g3JqO-{rYv% zFCq|3fK>*TT%Rgnfn<(6+pzF!ycwC0P!^a!!nKdCF!TlbpMt0h%PnTeL0X#(`GcR? zU-Ukwyj&i>$H?i8oSbYUQT0}VJh!{|bz_Q2c-yvkoVAA8Pe^cLOWe%NnL77m$7e+7 z!D=+XIEL4O$d!BWu_T7L2Uj)Q&Q4cU!TPiz^CQ}(-f>x(=)^Sp*f?N zTYNP`KF6!TTbA^q4C@1&31BDGUTDjMYhu8nX`>l)^Y9Rql}&;XfOI}i=v%}FS@aAc zUJ~0qvB^m$J;ZPeT2@VX&6-nH$Q$Eb<1b$}8Vb!=kBb#DpKU_s(0s!(qmQAy3@M+- z4+75}gyaF)0<)S)Ve5HdjAAAk7NLqZezoesgEt`J8drFpf*B~+ET0X0IYz0$K}7)u zlS>#zM#Qv2d%7zy=o}06lmvK5$u2aNk%NdMR{pivM_gAST8)VGo*iKY&f$}kj6u5u zqgLUZX-;9GAvy`i?ot_~jtmq{Ty{vV0cGLzVb!yBaUoa>-1~X>+%X&h^9vaf@w&Ag z+l1sDhO&4Vv5x@@Y&?XAtxN6i-bwBd%K{lc=Sy_YWIC@~cMv;>M_eRRFt;6XiiJVq zQinPZO+T`7C(?LhV<@HpFi)rt(H83eRZtVoWS7^}x?$KuIVe{ocKO zm^GvsFhjdYJCFdQM@ODnCXAAi%4t}z$d`XUeE1N#h&xvVK^&sELN3Rtz_?}zv@rC4 zUP#O+$C$^$T_US0CEs99jl~-uvK25V3dw)Rllyx6gc2svBj2-qa&IUME+kOoP@(}U zZa+)Lw_vPr&)=tr!uMk@FxgF#27e6HRhcgO%Rp}Cwx*Y5sprYNo;_R4_a5O@R8n#q zs| z6SAkcz7*O`8GZn71y&1=fkwEqycT9eacdnP?x>ZJkboyd z7Z#vlTtJ7*Rpy#siU+ylYx~B>o6*a_@-zrf6)|_>pJy-!apJy2T?80T_PvZw<44#F zoM3n)C9WLADOr<-Zw0G$6;pV`kqo1VnC?y1LpT+LA%zK_>_5&IaU7!};g`3;Z0tDM zX%we`QyOWnO+!LjT>KK~PbN;z7oZ5d7w2S9#!eu4J2*g&UkTSMxnB}lL5D40BTFJ4 zS+&$_k`MV5Gn2{W%6HnUn)EWxX}@?h?FJ5m~ry5_M}=%4gw#8@9rBIAlIJaIAF~-V34RG38sM~)Ms^J z;}vxm5)lc9Fp!X#sF@(Nmki-xSJQwS6Ya8eXEjX#$}mevhH6W5^T&uBU#AK;E295zjiR}-lzqYfJ10ESD<$F?<{a=_h9W>X}(wYKc1q5;d z)^1cbT=Nr8g6G~L1T4}sDjs=r@6%`7w zoWbH{clfX>tj@&R0{G36#Jr@9B|~~^eE&WP`Is-^Iq`dwJ6K?-ZhI{S8H5_W(P8kV zih)a;L79^ahT+u67Rw>;h*OM>{{jykiXbr)Zi8O&LXK`p4UP^nIrU^|VJq&NK&pLv z_n<6(SQB#vVtXf(Z{B=`6G*P{!yN&D%~KfK+UD3M0GckFq0j)yi+m}jFK-MsrxLmi zmrm%S6WRKS%^ar%gL)}YImq2~K&TvYu3IorLX@Wg`E)7u9aIfhOHx98&C zy*}H#9oUy=qmazTmcp4HZx=lZGnuV5Vr@2 z*pU{!yt9!4XuA<;^KfI7!J-R;8|bVyC_4cnw&SHCSX}`9nCb_c_>b(w14u*A|vMU;GuVb*P9?^>VVv;qEVl!BsvL^j8(((XK zUfi0v2O)C}u?E@M{_NdgAPh2Qjc>&QUm^oiFiPdtiJ1`+7*ztzNaBi>t6^K7)BCl2 zyBHH1lUUp-aA$<30EdtLIqAI+XgS3v3aE=%B7?}2jpnN{Ctq1v*$KuG74tZvbP@O2 z99@dJ2r`*GI`9B?AL;6BoO~8dKOk#?by+;DfdPw?+UVkqMZazRgai#EH*VQ}foW(r z++BK~pC|9<)PO#@&gxJSFKLY*w zCKzaR%VuCxOoU`h4EF6i23Cjgr0~(pU>_&7Zm&YE9qTSjz?-$=ZW0V#{G#zamt`$^ z9PJZ^R&O96?&H#HyrZy(C2JeL5Dp2Mt}ee>g{}W}FF+SM_izN2K9@-ZnS&S^(L+*3 z=pXH^xsdqfVz6n7qW4pf3j`>j_#eRpt4Y|5ZKQ>OlHc5AI1+BczzYx{Boubl4!r?U z%Wf)q$Af)(2)b$8!wCy$!u|_0ylC(Uy9~}!kIN+F9PV5M10IDo2)Zo%18gQ?rg_USK+uSy8rO==_HRDzT#!lIZtY_V+!0$A5T_eH`x>_1yP! z-`BOSwa)Wg=St6FD@2W4k@P^TLkP$jEgCVj`r&?MrS`yp6@879{3%`UWCtFEK#y;Y zKYj0L$pzeMD+>kErG&CQvv`I_v)Zx4TooP$J0K5*?B`NB zou?awv!&^nYu=*8WvE9WI}1&_MB0+wc|bgjjRYc`B6_T_h(P!&2y!uquWMCN4uNLc zyX`L_L7arcWLLODa1?F*y#;NUgHc+u(xsgZf5jam<6=jxIkD|exJ-9S=j9(??n5FS z)F`p5WRE0MI#g6J{2&0mr}v?xI>`Ey^oy4t)W|Z2f>_o(Xuu&75){+6@3tFHWA~F< zyyp9`PVChz3`obX`n6~;&_B0T-0<^bLtN|fF~`^fXlunVkLTB0MJEUm?^c+m_TYqE zjC`2gqWz=HB454??Uw*CML@fHr`yk+J^R9nC=WEq4Qo2NpxP6xIQZk?Nqer+F#PKK z1@J#kwxvZ^TA*yZar362;9^fnG$fg^n>Q&C#46{f3+-O)CGcTQa&k7E;jk{|bJ+Mk z4PSkTo*^{32-_O5zX7rpDR2ObE)^WuZu;5DNnH)ZjYNQL3IpI{^cwb5g5Z0TIs3rV z8lwxySv>VA;A0f~pWYN$5yUb2D=c);S9GE)RU$RVv{wzi=Fbm37@>UP>o$f?dIFR+e>mFYB)Z-tq|mtTmuIWKFCW# zRj*`dcw~^#<9FOXZV|ghuR#r)nl|d%rCE!OV2_0W0P__BK))Tr`~hGJuRkYT1HG@i z?Y<){jJ%ghd?TSG6}g7nfh7Qal187%s0-#V-R8}+0QMu3^dNho_7z*ToGKn%DF}~5 z1XjYqBS-RtTsgN~pUl#OdL2CTSnZgzO*r;soo?$srWE|jz-i!6Lqln$!t3NFpKj&- zd8T98_KCgR`5nn@gaLXw_ke6Cg#K%4-nQdWV?)q=wfAAiPTwcDxv)QbFpc^T9zLx4 z`nCVy>W9JZ5I%iqL?QnRuS@NjbUSg_q9@7g`J-Yfd>ew4T9b-IyUWfy=p+qK&r7GV zfZ`z(O3TpWigob1dF&!tVBqZGApVpJjk#^<{Rd#@`88U~yB_3yZ6-AX{-;>{MnJ?( zho`Lhkhh3D04RSwWt9H0u?Eq{j%}eulftHnvhu}hGMg{ncU97*$%Gxrr(>Hsp7{a- zJ3W8Cx6|M`7);#Np7oV4Eqcju#Y@+5V4^1ka@n;Y?5OmtdWpTj@@QtVCwt@ z3r=!~^4`HN{h^bcNO9P_?j#jfPCF+>jBupTru(`@j;8^y-7sRMqacd%F@(m%qdsf8 zgK%)U-op7!@Fx{`yA8!D$GbUN8si{HID3lyO>wR~GL(c8nTWNw7gJ%O{ zm&gw0>Xy`Gz&f-dT_&wMR^Q{n!w&vf8W#kQ>h=5Y5oGQ@cgOrgdaB_2_y6=QR(*m~ z>TJu<9qmM%4%PySo#3+VwVfPoVEE#1#A&%D|4zsD?Xy{Qos3o;hLv$ZSlhdO#CeiyW`;Z3H3l62ne(X$r(1tz^*2nvMG9Xnu!N$eToh8=6;wo8iT~e}fPLY!4<+Yco=OiW9&C zX&rVYt`P#m&$@m7^r?{xEp@v8c;i|&=0{C?GJzNlVL9Qrr1U6p=376Qvkb?O&F~$` zJ`L;Q_c5yGZ!lwOmxQp%gOjeuka{TYvIg4xRW`$6U&qd!JBtVp_F)%zfZdW=ous|; z)Tua>X;x!)8h9p&7P_Inx(!uKlbM&EHVR&&tE=TWMiLzKA(VAfpN)9A6X6z+h#OJd zV8kr*w}E4KKNomUl%+3T%+RmcdBw@x4sMphp3wmoC6`4>O-DHoh~jluQI&LIy?$MJ zc~zmqktthnR;1Y505eSRSzhrh0;vu@&HMKP-M@X3axiz}y+@Dc zuu_S4G8E_a=Y6*1@qMc{-D*Hg(ScN}2)}(PhB?ipqGT~yQ#y~}+g$qpmE2)Fe}@BV z7o!2l$zv#ZG`e&dysXG9;*VNRPeY<{?|}nt^(!zPaHDrK|NP$$q2qk*`lVP!bQJh| z*Dg8v58Fl^khe%?IKyUyWMNJfk0BK%(dT`8TNc ztVVSg)4u5Iv%f=BlJq(RJ?nd%oU`Z7yft+-iFVI?e%7N}d|+!;&@et~1k0=M>lG=^ z&*E;jq@Rcd5%Fl<{5AZHZ*H~K(;xYqerSmVkMi%1Tc(Y(X_1x(iy@Ha_Vq}_4b!O$*giU3zpfMvT9|g&T6Me)zM7~@LfW2*>Cx^FuZ*! z8rFW)0Goy_(uGmu*4)fy)v5g47jb&@CI|wz*4vWw4Fk~`@hF1bihiD_vpABp`O;Ut zeGk@%MRvcmE(W#X(+Bh(7ZDy|k19Z6r^+8Rn%_ zQgV|!*h(pcO_u&=-Fb1h_fhlEzn*LMVJtc(Doef}5#7378?l2Hr4@8Wgo@{}q`H zCf1gV8&8?i+MCo@I8&OhSG`<;-NIm*vO$0iJ{^cEoPRob` zPSZ!M;1C3_f0gcNb9vGbwnfglBNp%0^)fI>n)oK1I<;ufF`nAoK7IRg#P<=A0uTD* zsN-=97FtFbDK|{<@UT97M%{#YKv$6HQUxT=UbJUb)R6gM`RZ@>&`$lztNJ(A3Tl*gdhl|66?>+eQ?kqDwI_Vo!Hm-D$9Y&rK@!|<@PFFX$$-FUS9Ji5gCCOweIC>CZz)lz zg?vtEsL(Zd$o9{KKQBAw(ie68XNBex+lCJ=q^e z&$udY+b++>o10%e?H7RbW^Mn+Jsih~4wLF>+|s3$PCcuW7(KG#^y%LzN$fCCwfi!T z7~FN#>g^;Flx86~UOspH_w{QrgSB2>ZWa8Tq(+9#k!1mc#fSf`W`D2C^Ou)=y5nZ2 z-e+3$ljO+>(=zJb8PY^1#Q(dbcVeH%Tap159&>6PYJEso1&x&S9Nz%D!0MSZGrTOR zS)z=+&*hGv;a?Ore3>?BsmGk|$9XPHr3*jTZ*OIxV55SAQgi3!8Wfr@S&ASykJ?Tg z$aT+08tUEQ@d^(Ebm>r+HkkZEjAxw}HF1uj$$Kl@Vt7!V_Q$*j55h^QMJ5=3tV%ch zxiC%i2iQBkz=ki#*VkA3qqBN$0CYL;`x;z^jFI+Daxk<~ z0US1w=YO;}Oai55^?rM^V={WGXi6BG)HKA-2=h6e_h(u(EJkn2UibvqNKvJ~Vz_c} z#`7sX;H`y}L+1uS1v#H&uyO4Y^BJ#x%z|p0Qg^r{RrT6=1XPS*(-4S_a_U(A=VYFI zHrT)u4)?V`_B%|9YFOzpus_+qw1q>nx$$}ojF!wlhp8|bdiJeb$0ly|(|`Z!VxQ9X z7iQ+l2rpPUj0|leU%pi^xWLhQ<#*EIyf@#YQX3ZK{q;elqtI)f{N&e+fhOiZFIsGP zd?v?e80(Vf8QyycB1e`VQH@Dv zQ_Bb&=tcHV|9efUQ{Cu!$7~@8WNaXl3lp;wPeBw!jqx!SbDmePE=`=#Z;d@*9gbMR z?XC}7lHV>)&}*>{qY}+S;jg=5c*BvmBP^HjR68MWc38Oj>+!xueyQM+KZ{TyO1jM9 zWL2W9dST|%ddgK6l^?Fg;UV5x2C*OsGM|vDV>oqudcW-o+*8EuYdYuwJ1cDDUMqb- z0>K~9{DAQU4RY#n`Tb{qic>b?0PF#FdKJFOU5pp3;Oyl|e%VgY~(ON%|H7{)jm>Ff__Y`vab!??8{;LA&%5@!48h>nWag)~S!n^2y816~D!~By#MS#3u`K6YV0nm!IFij!1@sCaKs!_l`ip zWbPBeS_cjtI>mF7sii;NzXp;l4fs%XY>>;*MUAuM_CWKCcLWn|)np~)-fp2$Hs&Fit&zjY}fBzGMMm(;Z zApis)O~xmJRcVo=AGufE_23VD8Cg1rq)2L~J=$4iQF~{ErFjb#6*nMD{%edvo%J@$ z)i@$@6*=~=zR+*q#HfY)ah&t2&(Hem$>?RRfj2~mo3H-Qk()R^4evI2U4{Q zjB|j}6gY;i3akqS@-SDLnTsW?SYH50xU)&nYGzFQW5YT@A_V_|7QL6Y$f@+sfyorZ z@vV=T=H9+Nhm=4>rdYLMCqJ{>qubv<1{Hj?OCIo;G|gh-#MzW-B%SbxHqT5{J{FSE zi(F6GI53D*9O@`j`>dGOfVtRS;RI0lp^l3pdms@2diVTzYnVWWzoKKsy{V7T=!o5Y zGQZ04Ii2t6_IP>B8+cm5_IR~C)D+58pBI0`k4h(A5O{h)IFLFGbj@m#H1$M3UQGaU z_Aw3hgJs?Rd1@1+jQTUZj8=wgLLGVl^b5+vCdP7nJgg-_$Sl5md;Ci1UuxE>FJG?a z<_5$03}(cY=dgudII+P)m;mHqpBCljXQ{Uz-iIHR1`{%vxh1&~VIMB*#iJ3DW?v>R z5k=;bedN3}wpcx*>zcJ|Pm%+Y&y=xT#bO%g(bNBBdtY0Fs6dnHGM>yVq z`{iKAI%x)s82`Q?H~a0qJ;oO-3=n4I(rx#I@twS8Fr{vskDN6QA3jFwFQB>+l{4`4 z8`izlyAlatZo5E2(04r9?`B3!X!)*lC^!dtgNCc}h z`ZUUW|7{JAYqS8|EcVS^hHXU;Iqh9??gcb~92vJLyoH@*B}k_L(aP-I-EaeAEL{X&CFamWUnN zeyeT@S;*;ESy?Hu23w?o3m)~s9mgQ}p!VL;Ya`(`J(;3HhaNK7mVjU#FCA&XYW6bX zJ{gHfnE^VpmoS$9i}JNkWCgG-u23F=#mn?V4CQwT@U||YIkF%jhJ+6$GdBMI?_gmy zu+)feT`?|vU1-fAX50}6t`lM@e6NrpP!iKUy$QXC)l{#3+mME(dwm=9KPsLljpNQ` z0Ha#C97C_N{X2FDPa(#wT*rqN-h)8L^aEICoID#Y{18iCX5OMSU>Z&+`;x`%Qswh* zv=AgDod#*Q#3u^ng)AYYF+m4+jK?gE6j7}bzv za!$(Kk@nn&-nN>xZ?1>OGOz=&Gy$>UWEe{$7PJJY6YAA*1{I_BF&zWFgLss&>Z0M` zp|#~lunRC){()p2p~hJr9p;3nfb7uGA;8EYAIz&M^7fbx28Cke8Ux6{aF(xFArb&d zIWAw0$?|DVyGIe^++L$qTgA7l(s?J4y1A}0eINhv4L@Dv>LJR>A z%<>T36XqdVuV#=CTQh|fD!hpO_5700feRN#i6D^GE(344KAg zC{6K}qRE^S>MkcT$Cga&&Henm&*~TLl$$gW*I!B(ODn5bBCae~R3Z481bQ|Mj?{&m zE#uG={7$|)8a0CLeA;=ye6p`zJ@kBAJ{``>;0RH;kiQml+B?8bi#vZ&ktv0R*)023 zusGzMe(cM(>gwbFSM15B@T!OKNKDmsL(N0!wMTxyYaUP61J(0zBcs7kD;y1m&(AGb zvi*?7KlW%e(HN107QKI;M-|7rF`YE&H*{pktgJX*Q&Lk!vy7)m@lE0cVps+r z$C)j?5888C-RM9Mp^);Z`(cO%kwj2-3T6Y+Swb@$ShvD*fP69${W8emg92 zca(ILUBc;VFH!M4JiJWLa-FV@j(j>AR#|?WSOEWcc_%#?lbe;bfpQE7kZgc1VZp#J z`<`5KCBmHS3yl!UI&oJgk{r1XEYKNgd}F{2UN3U)R^V~ST#wZZ3q>zNqDco@DQk$* zr2YR?b|{%V(=XiBTR`5#0^N7k+WH1rhGe5u3$m#Q5P(@@XU^<~h)S4hVm;p?h)s`^ zhR8?x!6UQ(0C?)k3*brV)x~c3sVC!^!0&Q4O_Psiq55B{uW#*f(!#(B#GNv}h;Te= z%_l#^{?)Zr?!58UoINq_bdHm_Y927K`9Ced_*t_K5RYI8Lx6CKCP&}xHU|{Ye)&An z$PRl%P(U#)3hTaow*bMx`hl!SV;z5>5&p*Y>o%w231c)Ah(MOai538hs)xee7!7oY z;Mg%^>%Ps87x_d9D~GMq_J3+sm{Xova53>+fNFe#yuH0i{0Cx4OwtQOMY{C|Ff>s} znl?ErA#S;W#xqw1qi80VLo+)O%Qo^b#1~<^c3EJPAM@i1XvjvO05m&da?eVFU~ai( zEqGl-g27X-s3~6fF^EM)mNSrzOZut)%#>2|U-gI|Ac>Nfvn#l6d zkX(ix@8f#rFV$)$uz&pP0(j5v3l|=J>+|y8ZOF^icAl0l?6X65@7N)eH!ADKInSBX z3+g4<%W~2r!lZeX$*~?2l$a@r!o;p-f=nBJ^2DL;M%@yh{1r~;4c_55?gzc z!rblnCO_&tnG;2K&=?LnpR$C>pa|?;^m?M+1S+9h6aYUI5H07`$<$P;XMdpVNp z^^enSlh0j1RF5QBD0`Zo5a@6^2NbzUbL1r* z6MY&a`_IZi;2vwUxB_=K;Cuc`6zT}5_w74YyEGyyDslBe=DD8Jp4;-J#+J>SMSn&E zd8iZHD)`>=|1`on-_!lWcI1@=e?7Xv3s$!m>4n_#0Y2+ZSsC1WqYFP3vnsVF@vv+M zFiJ~01Xz&AuSaaC7a2VIf?&e|z~b$9{@GB_{*7r0a}gYllQ#NPzFz+zrg=j#?M0rz zYv0JM-VmqapRHWCF5@|~|5{&%znPx2s9_I9`gI(;d!FDC)B}4N7@e@;D0!mw_|c)8gKd<4kd^@j{GFJcaJ^f|vL|kxu^a$Ho6YXG3)H?)$T16BwF!HM4V)m6bza*! zBISru(B6hyn%=`NuS9tTmU$17f~+bB0Nc@XX3TPUy?wClQ=i2fH7QBrvAHdGvT1Fr z(_sfI0-(i(kS{p2=2nF?DpLrF-((elLS$W6hO~)N?8Mp4e zo}B-f(CgF1-(F9)Xzr-CFnYtqXg3q(_Xa9@UmB^K^?8z)wBW^x882q6oj${GWeTKr zbpA0HcSi>slkh{EtU}f8N`GYUbZ)mPT4%?nAA7UYAI~0CojkGEjG4{k#SvNLb> z08T)n>R9biMXlGX&S6j=v)`SIe|4hlx_v(*TTR{FKq8afug(1A|(Fx~khR;k0{ggAH6_}w& zfz$igt5cLqvO;q0&s2k&wmoe7(N+ZZq9@KAy*=Be>vv85QPSyAoEw+Sv~AaJJ!JbqG7`k) z9=gr+UjTe_|F26+<=b3M(7Dk)BJQIDsIAO#;8)y?g2%gRNEhWV1J1d3x*6T82OnI0E@U=bdF06CPW2!zi;d_B-&}jJ>NyKdJbW4 zCim^v>&77t{a9A6hKcIATrB- z)~}&sG*#1lQBaPXxPp-!`7;{vc}vyGOBXF#Ws8(s+^uZ%{NMa z=Z?N}=C1GJlCh1`g0%1L*ZFoZtY#x+_h!C}E6C)+3AWbME-0|2X-{O0(!u{BPc0-z z6yXYmlmJMGWcnzT8fTICkdvYa_pWfEa`wFApgtsek2eSYVWk2y*ZiS5H2qab|$iLhEH?qaQ&sSDCj%enfHsXK}_@U%`N=NCy$FPczAKMVR5cb*4 z|M`VapMj_6dWzTIp7c_=Tvi=(?ARn-%dH7Ij=n4Z{aP@?C6tLwC~uhQWmD&jXu%epb@?S0ehLzR=KiMsGuMB2nm2ZskxH|LlR>j8 z^vpJr+Syx%^)#^T5N#K4C7TRju4RnP^_5*Cc7}#3HEY(4T&D>Ixj*?$R-`wl1-WK1 zs8#;O@%~~Zaj|-(79fVG^U9BB{4w?Ilu_rhhPpO>vD++z``?aUs1pUa02>9i4$pXs zQrlC2OO{5{gNjqyv_9gMPGw?~XczBxr>wNpl+nshit&mW3&0%iruh1J-qR>{yC3a@*~ zuH*dY&U<+w{pS0Y5ox_!)OuZi2-N+g!a7`;k2yhMd7t}J>AXW{mP43<4b{p$hQ^(9 zv>ZV*hYB4Okc8t&e}&Xt8K;UpW5l0ficC_5e|}%=Vb=IuNiknR*rOS)dy+SN(Ydkn z)8c!M52)y+b%u?Xb}+$DCgkGSj<884lu_nC+3H|0eQlTnH*tKmf8#W8gV8XZ4Y@NE9zPBzco7MpFqboM7N+VxnZGZ5!ks=FpB$(HP>gCwYri zn3t_(k_$5wsdukY0&j=OTiG>!1%(US{~%FZS|!LYo@~{zr&&vpP`^K}|2^Li-6u3o zYfZe$C+}Y=;JVF`r`s^8!woBKQyF@U}sGH)mfu-WNw?A$F-%ExymiEFf z!&)P|k6E8Gt3}I};~ZbV&5~@2rQ|R{y&Wo4Ymm*-}cvw1aw5 zTmY!JqA8VyhkqT9Y0f+6Fh%FPKSoO=ZA#z^u?pKOy>=vHad}GhV-U}a9tAFKxL56f z)uXxFfM>Q%<<1UAlCUUiS*UqoS=9w_&PGS9Z9;_hU{o znl0WL&~$m;uV0htAv{`*09T{nKV!pm=5j_!X~uwIb~U=) z=3d-rOaFOeSKW{=*^86YMPq!O`BsNM29}IEH+UJ zedcM!^~t1&FyR~WN{GlYLza#Gim}LS<4b>+{lPv74zUxvt0 z3^)hK9h(}sHRvBcl8vM0g4l^MlTceOL3d!qFxa<9iODt5QN4WU&c{9X2e#VCE_DO7 zeL_p?oZEX7eU}HvwEnv2yX}}k(cfus@|-v&1qm5jWtZxKgGvQ{zooX0_dcS-Zz4Kb zb0xnymmw->2s%chrllSdGTMiep?Q-=>R}PLI1NH+BmcWI$0a3(JT7S*;UOK~wN%Aa zxBTQs&AZfSek3}1BWB3t`b?Imwb${bnJcMj)b<9?OJz!+-L=!}j_3gRt59WyISpMg zLD$j*#o!j4h>A%@bM{wPRjoJEcGYrx3Ik?cdTVZu+dwhYC%t^l>C2SrDJ~;(aBJmI zYsi>8b`>NF6D+Khb?XO8QuOJQ!I%AUe8aw|fi7bQY}K`Y?;|?kv%g%b2?cL7Z5P>E z+HL3N58@e-)30CDo;@uojw9{73&675si`$--+qVcf&PR!5^2V;Y?LXyczLS)k)Yua z7Hsg*_XEY1_J9Fxk*3(X63E5+DM zdsBrAo^T8(D1$dAt&|uW#>*&2)4g7tJ~%+U|K&8H3ZDARBw9{EGj08<$_-HGKb&m(52Ey?d8&^d3n}#(-@)!D3}7QWcTVw zcVn+iepM|jbY3?QHmVNMc(1N5-BPm*5er2m3i=s!G|hKLk>(QgMC$y+ty2jT?S^Q$8G#188 z4=R0K{Y@q#0Ds-XetEua(bC}}X+M6%G^Q`9? zA%x)Pm*RGQtPG&wa`Y@QD{t1w@s^#?Akp9T^T%3+p6A)Vg9o=qtg#xAF=?80{cD?H z4q>wm^L!W66*i#N!5a4+q~Mu_OHq3Dzi9Fq6zBE7|2og!hR=@yG}AV+D+X0|A?VoV zejRAr9r2rh#?bRYz>_itHnYra*6?2QVUNY8T5&ffO=axIw<=f5swJsJuc!$$EB%8= z*0zE3C^D&2ZI1o;ibgptP$nAk2nXFtn#Z ztBANI-yivYV0AZEASwyz>N#bo|I`7cONS4K-2njd1w6B8wvbjI=^9D~U*cy6k|{?8 z7B)}RBZP@^+xJefInOT$tu~H!ZRuEK)|6;{*M@*AWsIUeoP7zDL^upr3PwcWr$TXFAN{214d|NNGj) z|3HNXb|lj0QjzfPwQ_{c2qWsD?Bc38!{Z>{_c>wW z7yZlg@5L(LkF#tq)qKTjmAQzCrFFkT!$6mgbCmh+`1#$2>mSH(_q8)7MZdlBis}r! z5O$xeytn|JbLHv_+REkRgsw&g_iT9Pq3W?&D^68M?b@^F~Z?g_TgYA z`#nf>ydHYF*chxS2`U9%V93VU1;0*x_#@^s1NOt zBm$v|XJO@aN{#s?eCic%$ zr%$KgYKYdXQ>$#XPTza0sg!Rx++mYd*aorfBW|03GthY8d{j^0KIRE&1Cl&qUVL{| zfUo?)y2HC}ty%WE`uC-jPOV7iWeOo*{OFs=-ks0R*o)OJC_-p>xK7`jfR}B9%&->s z8{32AWO1mWlFypH17V?L5Eg~}!YlnB?pzzDvTS(QrNg_zC^Qk3h*k$J^YruQi)nE( z!z-ueT_w*M^(S-STu1Dyiw=t$%hS3Bd&k^cKSY+=6$i*dcKz!E6iP*Z%x0tyW*5FDo}TqAQ2|_bifi5>G9+yA`JQSjQU&#y zaZZh`F=h7hsEmG=T%V;uulp4Mp6OQR7PkIt=|tYG<2{imcx-W>%nQ;^bbdi+b03p9}!M&4kGWTj(_-J)*F)As~=sLYTuV>Q87qB%cL`0=eif zLS7kqD3$CU+YVMbswR4B`McrM#di}eX6PH zb%{N`=mIL)w|=Hl-GWIWnmuOt$o z|B%H21qfyve@4$Wk4Iive!sV3Y`j&(T{?$FE%MJ1e`r+PSk>2TVIK)3TX{4X%hP7B zs{;=Y+7uLYne*YvR@*s_Ne+_Q4EXDT9-axsR4`{DO{mhEwGM2|&XN&*G{9CQ*!dT~ zla$k|wsQUFuZISq@AHH9bS~|Cr{dVTd*>@LlMb0w3*kB-ombeZ_diNQK~i5-Yez4IgR_rjOydMi!i#RNz$ST7B5>&dH9a zdMCC51{Cxqy7M5jyXyH(%A! zZ!r{fWX(i}=`F(!*~hC&!IgpBJ!2|nSs1vwB^EMJHXNt zaA<)mYBFrt8SS~vBWun{)dVAM-fb0&8d)U3cA8ShDZ+~3#=b7J^yts{b7mUj?415Il?9n!w6I#*-$>^!lA{VSn z<>`!&@yqmWD-=KixC@y=q6&_O)J&suXK8&x9kRo;Y~QY3n@}FQLgXS0%HH-Pth1S> zm1>vwvmuHh(Ulb<2%=B6_SR$XA6lsljG~1)3!aBT+Shn}#f9@jM46 z6B%F?)`&i=gM2-aq;eF|W~L?m5er{RBmy9n@yp)cKLEFS*#w3;bY63O2L+E9REmUJ zAvXa00h*)WK>=s$fqeE3x+8I(hzUG@Ktr6 z?!Ngt>R7?Mi;LR;8gLLMFc6&j!aA&Sw=V)0`Kc%UV+C zf9dOEwAw$$-QU^lV%hu`Q?9M}&fyh4=Z76Qw_uDYEQgY0%tWGI)lZpq(FtCd;2i@er9*ot(rWm zTh0Fd!H+7!5{u5w2xA+PMnQunJTJauHBL83naLq0)Ns_C0?|c8ojH{67pAlAb1z1n zz?{|cobfC&96iKV9h#)E@1Wl@7orbPyrwS8*6BS8az#JO4i{gdamk`v5EPUEm4nR1mN_i! zE14^Bvw97OkRqDS)K`1ca9>mSvQbnItYY*$&xz)TO$dyvx{m>xK&t?QzHv!EnX|#x zA5T`Hq#zPD+jdPn<}1!MTKD#vLNe!ecIu&VRMC;#d{a6Kvo(w)giURDI-)7QetMT0H@dEN!nQ}goVdcjW zSeWQq-p=rHS7!bzMF>AGa9Q|2FW>k)IHsfj*F{dp1iUe2v=*4AHsDg}=cySESdS?V z3|sfgZuq3a1y+M-N0k(w2mrk&!G_naJ9@8H9*j-s_U%mj4b?gP1TWj}!S^%88NM0K5*2E3}@36?r%f_u(bPx2qq+S1&Z-LnEgWXf=KnV@nhFF{FRa_itfmr#ecQP7m6_qfJmM3=;+cmKlw#cCfq zgYJYMKr>0~iOyCRXWo1)vjrhEWc=8x?m)y);;bWi z?J#t-{?ct0 z)5g{o?cPr+EI;d^xlug7;fHA1GA~`u_FK&wmZ`Bs;i&tV%66uil;t*oJR5`LkR_#* z67rro?Bmz!Wz^FZ+{!oh)!7D4D=>nK57s<;@&p3$;p>l4`@=Pl6~x;I{t8)s)JEiS z!Oo?z$h!bC?yb62y}%)Cnv5|aeMdNR{(*hZh-EK#TkZR*(;@8ZKlZCv{z1~3H^iGr zBsRgkk&fwEl{;x-m!#uY(Wc(Fu5=XgpMeyEoX3&h_N+t`PI|fl!C-i8J*9$@SGRhD zw#`|?chTayCw3{%8r_)(_5FCc<+r=%W(;zib!OGSPu5ch3i|0~eChd#{)PsYeMoWg zUqo@bB_)TQR@JX`4}eQ1&$z-iakZZIf4DM`G)O1biAEi-M6rbM&hhSe;9EMUI=t!izQPyEwumR@x$i4c4Z7^6 zYT0=TRanq}_vG~t-@e_KpD!d5qmSxW{ocWr3ZW8lcw~9*>0pr&15~u#efhV<=A2pGjDDTgIPklI1R)ED*)Yr547a4qc zLUV?6!#<&n%SN`drqyk z)(P{RG^<;OZ~L1WcQ>HB+$L!1`s2-$k}oRHdogG23ntBv59)ZnKA%$U9g(;f8UQ_Z@icipC@Ec2D>e$Y97CWp}kL}0}`q^*H)}LJr zdVAELx3RQSeRa;IqibNVojQN^j%fd5#=5RQhB^F^Te~VIdy(Ju=o`OE0!=PIJ35RU zes>5t{2&`kZNIj+%=x!YQb&$GzVl*l3WL2|UHiXs!I|08 z<0&M9N-`E)CwP|2#(-1Q=W0h2XNw8Bs8fYII?X{pwa?#d5& z(G`7dhfF^(*C8xpeRg3FMkw++7f#UXWAi9`kzlMOAy2oOSop{CgG0LpxN4-0+nzf8 z;y=HmhYe(BL7{KP7NRBejuodr+d!0h#Hqqn>d)!uPb7bNL z%PgK=$*10#ZS!cfrP(*(p-=M<93HHRPQT8TwqiZ_$WbGI(Zg1BO1uG4iIrvq$&YtZDPqeYZ!t645 zga}oj@%VAqnyEd_nvQ%vK4T|NNLOGW{@B|b36`|aEu5gOqKdo$ov5@R%Lqj8dv>nB z)XhZ&H&chIirs!5QgX$HsY{mu_T;H}E=?X-_ zfK?xyrQ|^s%XK_IF$7GAMKErM?GKv#TA|sC;=@p*=lC+1QHjE zpKTOE($^ujuSTkg3b`>r4jD^Qp<(tL<|1v-potm*IQy3zL&wepZm0=OXkz`R61F=g z%xqID%2S6pdJfuiAyTBqT!=W#h1S`iq?C~))N;~s1QPGgg~tAR0!xfxMFTF=lys1f|R3dKlaS=j_-%UdWW2n5-S z%=SwahPM)LQ+)FT>%o!X%E~@=!O&GGtd&Uc$D(^hWBPQf*_+K@Bs6F)?)m9ziKqC{ z%5TxIoe8zUbFz!XZeuAHOByf*Kgb7*9y}O z(o-cTBkhx;k04$M#=dYu{yg#oe;gSBOC_3!zkuw8Um=d~5qt;@BIu6zkP5y?-u`53 zT&M^O;H?L=+Q>P9S~->7<}1;P_>|QYNhRs35}<;7GI8qZ);L?BsX%gE(?04l%N1E1 ztGmyC%WP#A*ARf@MvWSYb14h)HRmGt)*Qj5T-480=GBS8a$3)J>a2n|wN}|c)x4&x z-Q>c>fZ_=5Fa&KZre9m*9$_bW_sG=#m;>}2FkriiD%Y`?&&Oy4@V}-xQgUKL1%ku0 z%+@>r52LU@ttL+nloWOFU}pXWzzgu5^ptpZ;ih_OjTAhKqenm>RFEzrIrEG|GV% ze2id?h%i1mS)q@<2J)h2GrM=uSW9BSIbvvnUBB++pu6zA0mie zC%MPirIS^;#oQ}dS&g{T>hE9rLcax);-vLVj5~U?k3iP||Ko8kV*hddSU)lW$x=D-%@j1)&b6F!l)BwjtHv*&hU@VgXofwhB`% ztXeJVGqSMi87F1|46~UGhewneJl{cl&|_e#A$gkQLmC(;HECoLHS_S|j)xH88r)y* zuUwAwn2KgRb@Q9On-fMFSXv3^^p<=ez1eKIQq@N56>J|*t3zv4zUox19u>Kxt=%~i z*B-F93?{ltP>bu`^}1*2%}&ZX_Ip$7jX~l>wuH*MEiFALs|ZAw zEN}X-*591pq>&O00__-f1}B=0NYkWzQ_{h(I6nyZK(-bnv59u5MR%GxB|QzE_#P*9 z*!RmhIm+VNu-xJF_`rfq2IAjEDDWe!US|5_^|ayzd6dQ>CpB`syipl%4wH-Mo39(4(_BU$(SJP)Z%p4|xNXX%Z z!J|7#c)YqUq%lq4`om}BxN9_}K?T}huj(5sf4GqmXGRxBHjagW?XY+IuQ&4-O}GjL z5@uMDLrHJLx*H6=dyyT8KsguECQ;GR|R|P@kj6 zN*2fDh-qOYX7p-oK#x&GBoGg-mMwzh5yk=3xy+frs0regB|Xejc3O2p|F z6CNl<=;-DYR}H{Vo#JjltHz*CEbX2^EVMZ1r&xj~1Q1g4=09vNUumJtH(CC+Ezdas zn>_V3$Cuv8V^u%hG(`&+1_Ae?S6gejI^uk4?~^sW2;e+8>D- zvbdX3Lv28T&(;WFH8)mZVUbx@9p5q8(VW-4W1Y#60Rw!&@JXI;IC zT7`vu2QQ4Do4~PJ-cVMxdKWS!Fv6#s4?iL(t&sew9{N}ae`>rooF{xszALFlV8!Z^ zNK_`gaI04bkvwP&Tig|JRuq$B_fz6$HskKHCc1NxTA-6b9Che=#PW3Kejqnj-3EQ0 z;2yRqN#F|$oee-Xl(2S1LwGFEc9~4k41T_~m{5$~?6}pwA1mPGd9~#`uQFukHE~AJ zTS$K0>eIxOR@rwuPPFUg5auAiDKKOCCFv{R?&TQGad@}ws!lscjMIZzTdrh3gsA`y zI#GfwT(}toL9A#5H{?!`n+KEgaws*fy)YUGHrXm7j(oAhxBS&mR^l2!tdjW65fvI0 z6-0|XS`AJ1d2LO3dr%PZN^@t8^*Pi#$uB#6dAr|YmqU;QBa!Dp^8|4pm5I0RMv;FS zA!rR~t*H`YV__bYuo=wE4c?S)_F+&#{k`{IE4KU^9Gp|6eWsthdEu2ve*Me(o}l_w zGm3|VL=CkeyAUO_n!b&AH#kU&1d4}H45QgW!>imqRFBE21NHV>!XUa zO=Wr{X7hr+u!Jsi;)B48&VL3g?Z+_IpG&)$X0)|NnQEz{D)!1kxI z&ba|QRG@s;kGv1r2ZINX1F4YhBK<)iyySFYKOK@BTJWLyA(o%Md9jEgM-d6BqejX1 zSXWomf@W`~lbh14A``YFBK&z3@}fxV%#r21%xtTt=L24yKlLU35p+%v<5}1sGRHp}X!4)>?o*uH8$X@d+B4qJk z{9bU3gNJQveMjRy{SzPVsfXl{>4o+ihM_xmm%qrdupwjZ*VZgS8dJ>abCEr!xp6-4 zigRAN4#oXmkqHGzznVX+Mv9cFCJ8z}5>QA;TNvkVHY}5Zoghd*08_uf#=`{)wJ&*f2ceaLpQRPGQXDXnbr+H8LIr4vXdTpHtE_hc8tC zgBsXbw6I;liQK$ZD>JxNI_tKe?DuumK%#9jb*c!9$J6c>U(h!#V|b96w#cZs0=+R; z=vb#YvT2Z6BmScSNCd6CB=|0jxKCQO8r9pt-^y+cT0?aBJsBK~lnKbz7p(-JD4v1k zleqR@Dd;CiB z+wzIBys0*5-S?&PC!JEk3EK=bOYG8E7HG6jYNng9bY$o?uA&5R(Q4y}N3fo8Uxzax#Sa4}8Ug&+p@|P? z?nDfZl-9zBaF1nw5Lj^b!-pO4_;5ASww;79jhQ5weB?6LD={L5nhvd@R0{MAKHYlQ zru&$64E1VGKjpHr+nVK3gLz8<_(K1=Unz~=3UL&LCg2&yO$7Q#LClIvhYET+439*$ z$O;Kx*~ZqK#9r80$}_s?{P1+)?Ybdd#ALv)rZmGZTqz>%#K2aK;UZA_79lhdDFt}X zU8-_AWOr4pw$h?X0(_!lA;+iTN1&J$@d+TaHb;I#1adwFG6?hNlROtYEGq;aeUkon zZ+SbcS$8_|xg(n+UyHwzk+O26IRUb#Ig%!vOx5Wbimn);j}qBmN3{i-d6^STlbwPW z|oni6fr%2)1MBwp%#iyDNyVWE{p`wV5l`OoLetmwCK` zfeRPMS_W)5bpN$=C_zTL!eORFc!yp|Ou7GhZmVS1&mj!bjOK(&x~Y3`F;Sq>?{F`{ zwrASGL|lHmirfX?hw;cVY1R*>OFyY**i&|-8+M82iz~)9FCYQMJ~EiRN@P)>C)ZGV zu!gf%4vFXyY9WTJIBs=eYm3X2#IwN0OOzw3v8P!DMV}q__S%;HbCAkAnJ~FA@Glkg>?2Z)vID!39zi5 zM4@b2sKL-h(kKZz8!uVYdJ$->6gI3=bIu}>n&L^6V!Alp)ZxJR2pz4CIAC7^ZWHb}LB22Oj-nG+yy8wscw zWy3Fs3NFpPUF7_Umd{e$o3S_iIg_&RM$ZV+ZwpsR+M5ES?|fl(I}*ZU$$P%#qF%{5 zM13I^`H}$>Gt5_=o~l4-*P?x|*HCZ5#{wgjkDTB6kDgZf9Nq(H%)xh~C2-Cm`?~4* z^kG1!DR6e+!Q0mrcmqi<6xn8q%jHH=QxK8uZnW?2FdcG6P%xPy0{H3>K4jOk120yR z@ZtlYBK}n4=`7T$#@&RLvPGfS#9EXHoagm|3UC>}S$+x6xge;U@cB_hmz)pd?-VY@ zbEY{Am}v1t!Hc9L?~8RZv&mb~YcP)?4Z_hQ%cLy?+sYQ$_?Xdb3<%wH1ne#~HLbNi znNOdlo0cZ_DL9&s=Sx>k6T|zbg2QR0T(wFHN_BB0dbX%TXqV5%=PTIHLTppW7#!`P z3#G5ef!b{WIIxZDMT8=zvNy>QTnBo-ARHID8~dLg_WGi28)0Ey%xJB zH1Q3Zmm;KaZU(~JAD~KNSz2*;Fj$~@2;>P2%K$u7R17VAKP^0IRX6`t#*bC?8t z7zbPTRoLfr!`;1m!Lm&o5*Jw_b~EFA#8+SYaq%~5N#cb<0kd}G%6x!tKkmg1&sEPn zB?SzY38$=JQUDM)R!r|8EhcZlB>37&L1tMi_qtKnnHzLH@fCOd`8S8QtgT;bSR9kq z|2OO~A<%O`io<`l)JvT9*Yq&t0YB^B3nygo#;#hj4uXOlte94HT>BFRlMEwcv6bwG zIdNg8r^0w!j-9gf4f4~{&6ajh4HbkQDm4j1g+)cN+nu!avxhDq?oC6X|Gae8J?z$e zQD&ev;563T{%2$y9ny3{S+?AGq3WBLG~Knzc0nCszCRDL0B@23^s@NRW?oR!%5RZ| z+Mx-~CvIK8o=N&8H4Gs}d^>o+S7^m}vvScv7Qf*3U>LicOB;I1BVZn(`piq~ws4vo z&R&yy>Q-I1g$s``v=85IL*&lR{i=KCTK2UL`;T4sC!iWGjOy2~AD#&Yoa9wxcICyr z|JduhWy^2m;L+Q=0`n@PdYeKm95w4J>?cFxRf%j8U7%T76SGA1ix%koa>e0MGICek zzpA4K1WPQ;1rolC0gV64X-=U9M}fp;koLE4FH5=8E}=NE3ii+Nx+xrQ%oSLqIfeOpt_U)h!>9-8Z|4sY&;{F}(g>_O}Th$Zj zqvzw2wk0uhx+O_-8l@A2NIU!aT=-MN`(Kq#rY=cg8OwykO8KPusk( z@Q4Uyat;6fQ~$qZ1-UC`ga)_m)TxDnF@GzfPV|{coeJ(M{LZIWlgOJ9s~`W%dI)+J zbt?Z38O}q`OebhMrJvYw^-Ee(8$>DZ&>_9eUtSApZ<_2w(K3!wCnRl~D|+XpS4dX%K?*9UqjZ4B;XrIsYFYj|t~# zCrja$gd;|BQ#&(`WUM0x0EWLE2{d`1pOHl>2|G9Q-w`rjnjul9gCgT9m*{@9)@d7i zlwB7Z7M7Kp`t0lvI7*c?c9_NE9&B6z5lMbiR<6%tQEAgn_@VJYUDXy+AhWJy zB0UH1HC%6!Ij=mo#Y=k{o)%kR3JO{2R81z7Y)C!UGo&xQTN!p9Xro5FV}RxvkG8Ql zNpk`~k>IjYQ@zX=oiu2)ryvO=NCrCy>HXi!>hhf7a+&%0s$6UWT97xXn@=ox`Q#Vr zZTcVSjYZs)(rmZ*7q1^N>W*?wCeo3}0dY^i`YQS?Nv>pAWUN$df)WV4f(qk$MeJUB zZ+#km;#(P)KV#ZrvIGgg^jAOKYWlz#ut5BKDb=K}#&deWpoJ%spwsEGNL7wpzhzN` znf7{8bUL(xNd0m>I+>MUN%F94E7-@85hw6u$2;fr$}*t6z<&IPs>`ZVy1UzKdjaurpr z;51C$rPKlMLBspa3Lgdf{@3#!>`d|p{N)rT2^`LRGGc*m?qSqg|1wRqkW*swnF2}u z==A*iO+ier@h&Rq-bgFn-XAB;Mu1_iLw|EfYgwLvx0tXP#BJPoQUFL9weB6|eGe#B<3%E-r z&k!yTz+d$rF`|?7cG3_kX`6COb)n8Dx?8DMep_cm$C@~DNf9LNRFs5nMv>1{n>VSf zsqy2IaHcYmPmCZcruijo2HNW}YE)MmpXB(#2FQ=qn4)Q20cK8q{k zeq--o$QLOX34seI*tZnD9j;F_yfp<^YfSnx@7XE+f|N~Rch817B*?-ZF+G_ZS_^+ZL= zbuIhPypYLT7i9#vUJv||h)jnDM_ab*>=dRD*DoHkDEK*nMb|8yxU4Aw*$S#00uZ!= z?T+89u^ycHAn{S~5wd`a(b{p12=K1oK9$JiKVd9b#;%#ZO2b#YQ{xcex3ee`tC%G$ zg)&U!M%oMznZ8YGh%@`kXJ6bWD8x(>@)YFI)LJnZ&{4|A$I%XQkV_cQbY}lbK9V ziAbnKVjF{adijA-+97o>Uw@f^U_KioayTrKqG2wJ7jGpM&t_sDy)Fx{xjzKiLon;jddgy!Rk?;FP#64MEWnPVQIo7xhrI95v{2FFHIn-ZIE!$kVgwA zgdd@*LxUj%E>nF>z$ek8O1p-LB6{`ewQzz?l3-%bA=*Sy&xt9p9o_>65!T>>-&6Ff zBxa{IqU&js!7!~mB4Cl4Lvm9)2|~qKY-LJ9e+rp`fhAi>WIW&lh)NqV@&pDEPa*o; zF{5t5j1jDgPv^c0IZB5YPNA%?vA}$sFwJS{Wx=_!6oIQu2!~I%S~}1t|M4?-1ox_i zngH%gvM+d^yBF5{&5%D;5$IwT%?F47GfIZdN!Z~<_9TBpkT9O3oNjTx1twk!q$T*` z*)IC35fQ*`c7%$Q(V;AP!Iem#01mlRa!*SiFiin=(uyN3Pzz8GV&yHUvCy%2bwL71 zK?Lc(bop{W2z4BCZm)EX?M5GM6TS*7xd;K1U{=_tRkDYXqEvqKlBRZ%!U5{b0z|wT z15_@`!dkD4=hYW|8oK=tqNU6PN5qt98%GS@Ntz0n#9l9Y&RMV^1P8B7l9>-r!#lAO zj$<%HgjtV3&bnb3AeX|1s!pzi2FVQ412|iSBA3~LH@w@VvdRC$q(f-7W1w(527Bf& znu5~+Pl|T&yNUL_LD!^13fwcu`R{gH%ylhilIF=5=Zk3rEaERBo^Tkc1@BFA!p%s^ zKnO*srO=wg;O4KZR_AX~-I*7$F56H~Z|u*8)2qss***;Uo_WAk!^GUBiE_B^Dy6F6 z?{+HvUfbEG%$xE2_rI+_nm@Ri@4WO*YQA&sl_6(5CzvSRSJ@d7{HC>bj~;rddPak* zzaQ*h-Cnch(mA*L-1+{wNcUFMh%%EsZSL%?9#Iw^*+^kvU|?fsx1p%WO-)@rHaWSi zECW|pErIb!#tvS%;G8~ML`FcTm}lnpA2w_lZdnsUe2nVW>jrK1H;tiJI@R+6v{h&E z!z71tJil@;)hwzW7g`7?1@*5TOR7qGw=n{$;?wuT*N!B84EcXrI}@;+^S19_vR9U} zhN!Enq_U1IjkPY-WiJ(qEH#4+gTdH~w4ta}W3OaXBq4j2GKGecvPU$H=#eZ#65h|5 zx#xMF_qmVvc#rpZ=eY0V7~@+0*Z=?foxk(^e!u7U+^}K%q)EXnjIxsqXZI&}ut@!= z3U{PER~H-r8ogVN1z)yzz=Lr%YfUxxTWdG0d~|*@#mQp=5g{{+BS}s&MyGcB@_TNs z|7bHiuH5hZ^}@u{9hYHNvDJV5*IZOOrED#t#CdGX$bI||VoB7^F!=z-P0)1kw41&= zTG26DrNrf4LG%{jAAk)kSH7fdV$n%cT)9{7-Q77Aw+bu4_&;3 zTIKIQEjx(^yyMDA5pSEcIKdL1(_&Oa=f`NMl*$(~GzBJu9@z6KgZY&8C=h~!j|G`( zHdig^sHYeH?y%|^lW<$n8gy7CZ>={fVmI{D*S9p}E!o(Eu8fd(`I&0+zP_k2FZ%oQ zr)vtnLHxnz8}i~nQ*1FEzvW7Fzg|h*D|charFbYK7T^Zv^>f4v8MSZ}zx#Kgp9 zk6-)RJctetU&{gYu#`wUf+%J_2 zJXm0y%8hwSW9(H*;nO7>Cl1(TtO3YjY59yDk>F{iv9Y&mD{3<|ExwNzxtb=$$7?}& zV0BQiz*ZT5{_LCU-?pbw8X?8pzfP0W#veSW%hB3lqdRH^XyHNTUBZbhVz$Vcp;XsQZC**!uDquxvu@xYh| z56RZ#5`;uX-)^fmRhs-+R@M-HR$87Yi&j5=Bbhz91lFnexy{OhldS(s*C`qh# zfR=zXJ(>n&88>e1}T~$Mc49BEN^}|oO*s_V!3|A{0JS4%r zFh`fJJd+>rD51Ror@&>n1TVw~y6o;e0ElIu{<_V?10(&A&+l4w(hE0h$PeS;DMTqk zAODzBRu~PCNnO2C3}w_-W#TlICD;pkOL7B}7y2`$$4W%9RHD_@)v;&Kc7jJ2m=vIG zeb^|qJySheQ=gK2jyG>Dpe7B$jQiO{GHmhGOsX`rS9&n<*ZAotIkC7IEWEAJ(NCCC zA^k5~Vq$`j9J#o;JsJAt!{+?nA(ts_ewT%-khy?8BTF9c% zL9`uU>hywNy>|1E8nnMIF9vH7(bls-*7Pw^?y7nm)WxD_{qgCIJ5^UKcmUoy1O1ry zpm=vLO+kI|bFsr7J?6l)k4GtVYQ62%Hd%M?8n7pLB1N6sAzK=>{K&AR;OAkcnsKXY zU*!}XI@UehQJ7v%=V=#Q)as&uz(BV%niELaC;@OuwVZAdf123V3dQCy)CIJH*Q>Se z9kJ*4V8`1?WE#TeIEA_LbXf3#7x?f%VxDl3i%!YoVc0Ii^Ym@>^=<%(ZmYj-bR&p$ z%EUNwG+PH{a;t|W#~X=~nLM?LVSN@NNY!wdAff$WW||n%f%W{kHbSB}ua-A-aG-p0 zYUL202<{&ORc2V-piwwwvnxy_h{dhv{jT&1X{{Y#Ti){Y{JTfK6RB2y@5Q&sJEc87 z&MUE)vZ|{1=SRlfx@pSI)jjvIKPWF-+63N$AQpDQHpl2w3~}F@y(qPJoR=&%K<;4> zFYCj!6nJ>(+i2+(Nl7Z+UMcfZ2`<3uYO29w$FEUB(77;r?tPQ2oPnAZV(4l!VM}nZ z=Oz3r4m1pLY&A(DeXz@P39(9N4toCN*pS1Kf7<-N|K;d_=F=0IeGnEAp&yltW}q>M z6+vVs{g>2&?vsBezDffuE!9$gGvHMiCNlM2jJt)$Ipj7l`0|3L)`5fP5~^qcNiC`g zDGXQ{(f1DuxjH+O3z*ijZ1IJ^1egg1+?<)^Mpjn3yu0Y$>Dvb((cF z;Y=vn!AI$YcAx@HV|XjOcGc$(PkpWoR|k%Y^lBe|Xof~Vn7kJq8gXU;BS}&Yeq^X~ z7S;Rim1{7#AzclG@HTI)o&LSCGArRq?}Jx4X_hC3HuxxU&+JX<{4es2j zQ*EE#A%*KqH99WMv_vKirv{X+x^w!?n>Vj6Htr&tG4ThVX3<#BpvRc784n*mq&aqK zdJg5Sr_V0+^USPy?p1HQ9ipI-1CSsn4g42xO(hRSC>cj3Dy>4JM^~TM(o6dFO7BgM zF|YdXjkabuC^*ep(km(DDb;;e7Zb_8e0u3iZ(R^iwXI5-+?(%o1vE$93FE&0NGxhzfJ*xwWl2RbgOq=wf8Zbh{)aie6@AuYy8W-ToSdLbl#6whyLe=q94GQNwMt*;ScM-EJNYkV zSWox&+$BLbXeVn(OSfN8kVASZPdoj)UJcr`X=4?k?ipM6?yCRj*KB!BqcGQ5fmma= z=j3o6@-+Rnr=7GQz>-jq>2aR1{BGY7W1fUR_Y@qCiitU@3cpeCuKo93UjBrOIe*MD zPPU9zH5_@fB?xl=-wV>k-9Z9Fc7kX)wE9yx))o1>o z2fy06Ox^xvGRHtR$N8}>fhW6$BrY8g7#>HQHD+hoI>hc@Fjn8`#R6RuJ4wBIUXUC# zJJd9!yy1YZA;FhVrm6HY6q3&w^p94>w}T*D3nZ7{n;>FD?$?uZtZ|Zy1$RxU-(-~j z4IKd+7{^oIUaeNOl(*oc^i)znlk5m}0z(h805DIEpDw$chqQlSNFE+-+y9x6YuGhB7DjrgMP7|8=c?Vanv$nO&=BQ z7Swoz4?P?0a8JE%MEHE;#6Qu4<LI`u}j^LpUHEyU%0Th zwvNtZ0{Es*+*+)@`zZVf;FmA+#=$1!F^W+<^fY~)=+!<6z^}xmCJ+bDqn{YI&xQKl3EJRD!Dx`xQUfU+<5c46NKe52XWb2Ja7<#uMfjGUP~_OD!GCHGo*`63Tax?9kkA67FIg^)kMuUZ+N@5G{QOuh4I4+qG_YuCoe zB26&i13tIlM(Pb_-8T_`ZmQF~2#X|Ot@x75fe{z{0K z^6dCH-ibJ1lj{8Y2Vx@(_Ru5ZGZec#dP z8m${MF>*sdmwdB=GrviaOY)yC>gOC1{vy@~h$|uYN?%b7zR*%%U#3|%GyP_Y>;5mT zoUUd3pQ;WL*Xms zrV3$?>RftZStRNIv+w|Yooi|4w2PcJQ7~*MGa$W77*A~7Y}r~H36UKuqD$^qL(uW= z?i)C>we=>ZwrSf|2{RUXJmk3%{U_`%5#vtjF>#0|FQUTs$s?AU)8!@oYy)g;e7?1C zN|CQYnm9xJx-dbJTLS-?w91oxfqYhANwHA?>nU91{OLXS**0!GMX-}0qIf2Gw-V9- z7;;+mS(NKW2;Vqj$lBdUJG%TVB?L7`BHmYM?V8~X+`3I0R}W7P0i;xHbNHaz7m5)* zmp&^no#f$>_1kYF2s10X(JrE>BG;G+B%IeB<7}A)I&##g8>!Pof#rifY?l-iFw(-g zDMT=I_G9IlEmyo1pKbo5AJdlTc-B%pT>XA3lNFjoyY69LVN;HRK}SOq!=Ra?O7rN( zI83N7J33cT<_Y-jyEG7Qf zu=pTUNUV$(Psu2ReB(oAz+u=r7#DcpkaiJiHj~N3Vy>(#Kz0Ly9OjNBQaf`bE+ng2 zV`RKKoE6^~h)}#RFj}YU$92cCd1yqXMHV+eEwjRD9C`qempG>NI(4#I+)0_?I%!f9 zB3kQq?Iw2I4_FH@B^p2x%%EA&>S~&trs_QZW_=Vcwj)L~RFvEu>oIxqmEzkyg@-in z-o30-)A$%~WOsyD9>cJ>@x!RVA&Cbr#qZ#af0Jj1Kn3~qKv9F1o~r1w?U!FJAkPC^ z)HmM-)Ba2!s#I`l;=r?v7DH>tFE>zZ8~tnTLoy`Mn}Z!}c@^vvN_oN#+kem6arc^? zIqxefX=W1imq;2AW}EO{`a`hJ4N*J4Sjc_mdUUpKNUQ`6S&uy|e|w;f66;KPzFNN) z_`1$It{1)nQG-Q`w0geR>e>m&6B~{m;nE*0CG*my1|ZVCdVi?c^E+1}>5dcKa%&@> z^$f6;x`g1A@e%qC}sF!4c5mE9ApsetX!fxNJ=}yPyb#8%geQ+`!*XcK zWcNSJB8&f}1#ricy_0iNYv&}F$-kEW1{Ws^Wsp}wxMNS(&W;8{uJl=cKJGvo!l(*=XFwln~~C$k>LiGRJY_9Dw6!6Wg+5$41B{ zpZa5_qPJ^7afy z6W2)J455qm(h+OceEum$m9#NAlQV07rvu5^2RO|#i;AM&C#d48W=3pxc5+%PW{Ax1 zkqUzgScr35S*Xvo-+vd-oL)Z6C_vupaWlTc?Jl3^eE+@MywoiWaf5Vz{NhC@UR~yS z%Pf2JuxHXdr!_+P*xGMU4$5_0zito73-0LT=GI67qJ;gq97Z@p_|XR>F$#$1-WQ!# zestSPRS!d`!w>7lBOBoRc@RMOE8^M{R4!s!NSLXo;8L#kzKY))~h zUY$@2-;df|L^$G9aBp>2mWvbep?NzktgO_~ZRrc2lkgCWsL_OlLS!fx1NRvZ91H1` zV&pr`+htNjp(&bc@_H$&L=z|-o%{Cfn_ZZw2S&A#dl}Cej_Sf1{mfeWJG7(!;*&*_ z$)PXE1zKXEN8uVoDlJ8JU31~YX}>{W-GE!Uc*JMJXtTPx4GgohR75&4x*!w%rp%0_ zIDQO|0K7u}G>}l2(&+%CK>+AeljH67A2}i}aaYrx3)g@@vFN2K0vz`_6B6o6Vh%(h zwlWX)H4gUFALTstO4%Zz9HB@Y7(OEEHidZiV7(tKtU^{+eGt0*jTP<5_z)sJMpw?V zP@^dj9ghMj^*7Ql0T=~lZJBrY<}9($LS`ks5-T5u4;RO$u{#H>MX5#xs)W_!WV&?z z^Its~_-y|EdUlqQC{vA(4KL|+4IxADZpuW7apU0RZ$u;v z>pO&99S}Crl8dr~h@o1P7~2mPMq~lf_0AJy-5L_dGyUq-H;Nh=5VpS!hc=Qa2Pmkq zy9b3(9>^00NtHQt5^Lg2B0f*o)O^cg75gb3W&`d|^@k6S;m#dUPp%I$I|*h=J}1&l z={-jT(~Ajb)ipKGe|tOvj)GSkTUm{+18Gj4Y!B5Zm&B9Rg0}#S^}-d!e!?bCwOORJ zTuURFJZiXznOu#Sz2q!RZW^s}l}kh&T}qt7T1FVQhs#Qv?HyQgpdZJwXn@%rPxuIF zmV*EbK=!t4*BKX%%p{i$Vo?K*Wt(H197*gJs}3<72YkbIpM%Jc9$sz3FoJ!B08Lh3Dt9@_Afwr7ep_axlvzR4b3;y38K|hROtXd;9&9^n8AY;5xWI81tls z>ILS4B2g~xTL2@WptUxH3g_|uJ!@o&licVvrOrNQc(_7`pSo}CwaM;i zFOUClcOBd^0X@+T`ZfTis#q5id1+?ZdZUZ;0n1ZU!q`@;m_kZS%?2Op&A2tsdOLS# zM;yc6OC&1RQ=JQLd_Vs_bO!nL*CJRudxR`*Hpop;% z^8wN~0TUrMZFBlGA*&H<1m%V@b&KAiA}*iMS|fjsTr-J)N+!`ciZI ze1j{{V$I0spRoR{Mkc(GNui?M<}0(g4Y;OQE>`*fI^O&+lQ&lJJp$Wc3vq2!qxStv z??<+Vb-nGmvKcp%XWoR-{jSML6e`on>;%Zi z{O$6(goa-|3$pl$uV4?P;Fjn9Aq7` z2=@J@i+E-@AgAtgpDgcXZF_o(VSwP^#JbyebLX^A=4CwAHISBlh;Ng&Zu;d$r}-?T z&Z1P4PX`b8`CvME%9Pp%yAz@3UUg|p=1wy88@5 z?~`ZF+%7+LPZfDZ{1c^>p3*G;`QT_d=cs3%pjjxo*Du7!i^_7@%Z&F`nD0T-4(nb~ z_*a_G-&p32pVGTgpG%`Awj5}CGCkTGbSWM1z>R`O)<3Ww49BT^@03h}Qh>#XjqK|* zv~ll#oF?)l5NU;i022sj)1ifpVgWstW$%asQT_L>ewUk>8Q*+p_)>Zc#X*$x2;G65 zlT*0c@s+eGiLN0vJ;!+%P6l z4*6W|6<4}Nu_<1)8oX1ojdQiHM}gTwjO~R%F5m&K6Kq@omutt2B%S&8B-m z8aG2d^YclO!H4MQPLFX}UR&K;tlU}DbF3Vqkdf^gRQRqh_5G@&wRuGq~Rx3VJTLP}paej4A-N zmfjF_^dn2}ui)=R(j{#;#HtnZKadnv-J6?w@yZpAmw_|}AU5;);nVYe?0C5&Kzaq$ z$7Mc}-MoZINNri9(%L=0*uC`r&44s=E?wex{+*hl?v#xjbeLVVvC*CrK0HQ6!q2+P z%F7#P*?)fu84{ydb@-3ORaXx;xFq(hG#GTqd(}tWpz!;YzDwE3oQ-0YCPqoX>OPNl z!(BdH^KqW2CIJS0rujxuC_g6JT4@p@WzUMw0ls3E_iw&WvmiO;{1%xKhKpj}@jH%_ zDBSb!^eZkd=ImR$XHAX9>%LL3SH9^p&MI>2pw!gVTgMG(`k~x`8PNjnaT;=t{lbAg zHD%T!#(fe+_ujjm+vywM>MCQ=Qi+(5kSZ!T4lc1-#hZEBz>o>WG7Sa3Hq=vJ5Dt<& z8qrT=A0O^Ak+36Xi^q<%OWKg{BRE6yG44aND72)6>SxsUUA-TF)R1tCDjBg{?B2m}KYr_V|0K5c*$?Z-8E_4che zS_nvv{HBM z_k&37tdGxQLx8=^{o#|G?C;*)x>FP4X^$i*C%FSkap`ZaK`i=WA-zB1B}&|~Te|eP z{qdHnn7$y^ZbB~ugw}zc_E;wuVNDu9V)C=MQy64!p!s`#ujm-FiXEFiO#=u1TwC;@}9|l5OTT%iCMw zmz`}U6I~ZrgNaU#3O;ROR+Z&pC`Vkctq;<(D%z~zz@eQO`oZ)0eze1gT3guXK-c~ zs8r#KX59+rXd10P*-Syb6eLz0$T{2DXeS|+cNa%W+9f+o_WkO+bZwp5=0~J#A{3^* zn;Irh6k+0r$L&lrGBEJ(sezCNW)npwdw6}A-8i0IHd2c;4CF9}pX=R`y}EO=dWA^I z+v7Unc4$fnDlHwupxs9hIYS!n!^ioV6#?WrSJe42mBaq=O(lp~xd@{1QN$UogmWKmJ1q+khJw`ok>b zb-#T3a}PxB*2kMEmy8-a)}D49k$l3$xm)&sYF32T?IxaF1Awi<%R4m%%L9jYP1-=O zE_I^}2ZzS9-}}Dl-gakb%#oQh6%))1T7RFgMoK#YLPIs+l(~R;Wa52}>P{|D^U6X(nl~!`}PG1+7sS)Z%?C z*mGcuF4tR)J0>R&xnd@V%O(<4Jy1d<3u_IgYP0|!*Zp|zLnApD0WRcR1-v zP?GWfVHQg0SIiBY$)WDP%}e}jcyJ3U4W6Y_n~RmYbPP}s$czd03U4ie)fRt~Cc3)o zIUNLLqQGxzxN)(cQ*?p_a$*+uFigIp{I`tnzpZayyjwD<=Qc2rGoHx6;)rl-ybEd{ zYI73egM!v%iK{-8#Yl@b*<~O{M=LUkbq^v7T22Kz;y}AVy49OvkQmsO%)AxFRFk~} z&%D?pb}3!fR2w6-tb&7=Y4W1K%ujnemcH4gl@CJD^~a**AYmRz9=5MYm$J?Q_;>*= zry_qiaNn#T0p?G7AxOBSJxl5-q&6O2?=8OZ7M~$OLfW(m5Ec23AJ45lbLLEb8ks_# zslq2dA)x{AWDbzeE8H_h#eho$MvLuiwqF-EgXNY+s>8+=`z#8OAR^tZqb|i8RMdA{ zUbLFy4jwN-8^i;r+|E(~cKq?|#oUI4!#EVreXRUe@=73_Nr$J`d${V&m^iQS`7<@n z`#>9rsF@Vw_!HM>GI={9x7hNG9=)li$#>RLI%43*B4vcL4j2LHG!fS|f6;3m*<;vf zApiB#4T6tJHO>;qB7BLY@$82`cSwn9nR=GWe3!F}iwI1lmcu;FfgC^@O}TlS-e1{! zJ}ctw&sTGErJEgt-Fny#TGF!`h!*|A?c446QSEGYPnTI%wbcuylK`nM7EdjhS-1(M z*Ttb%u9hu=%rGL$<+14Z)d@ci>{?0)${~C+Vtxb4NSWZ%e!v1FM9J&Jx(0I|%Ti#c z|4mv5&Lpw}1oE~yCZ8@uxJ;0~OF9oWlPQ~Er8wKmPX(%KHD*8Xj0Gk^iq15ZdI z#kZVFki%LwAAz}|aA7SwB^F#0Z-NbDAFc^>j0C*E*<4UHabD-Sqqi18y?76DWN8~Z z?PWgm-xC0pW@ciTFSgyn)<|m~$3xP%Qh`NfvW=lCNBlSQz&z>1QUZn0*zvFZ@~35Kr@aGpX1gxNA(Ff z=!mGEP~Aye1ZsC%Cnr5B?h>rNiA179pt)}ZX4?T_HMY{h4hT&Rl~~J)up`V1Gy+sM z6Aa8IJ*Rwxl-KO>ndE=MCmB$3@aycpYQzP5Cb|IITjp5s@se6Cw+s zJ*u>jv&zZSQ!3DlpaV#%WM$-yHd``7B441~NBDM8%w;rQA4p{)?1Kj0=`JL*XYj1fVklbaHC}bB~t%w8_T=lMsIF z)1)uzFJjHat#l2Wz}MOlcoeaqShH`$2P`@$QegVVaKaFG0FRrN9=EHIRpRze55$ z56OX0#Y|d6%@|U^G4PZ`yC4-flbDtpZcTn!w&>LB>=@TaF)=Yx0uhH~8Xyrq96=%F zxX%gCw4+D;$)xDfrX8j^J@FLY!}q^mS|lVL4K}(op^Y!~9~f@zWZqf;d&Dh*4T{x? zz|M%7s|bT-Z{+K-sDjCqA)O*^XNmWP=%Aempx1*CY1#Z;C@RJfkMJ4F<4aUd4G1)T*>@1fD@A*=&o;fj>c8zvIQg?F9u^QOc>idCMNi*POX}n#^=4@LXbCP@ zg>R}CO+~g2H-bsVN<>s48W5k~BD_`7iVZB&@sBan*!;R|C+QOwNsafgNfDdW(2>1n z=Nkbtxs;px{LB#nT5j<6?nA9X+i;&7B0zC>(Csp0O5H8Lz{UPTiVViul2fV<7vG;PfV*Unj~-3?=T#3V z{%=s`GTQF31o9ja@x_AGLEDkdn5w0lv zEU?n7=djGeFo=Rh&*th-EX%~}KvFTNNUj>~(M2K?%85HC7n+cztq*&1LTXD?0Iv?x zlTa`qTBW6sZcRZo>G02Y@=PSLVdVAj>C5a9`@jPLyApdD_FPD;}k zUR|@&^`5n7bh$Xn5U7`|_(ax0iC(mGgJZ^|i1GlL5K3!2T*jwU-*NdL@7pbWeuS{Y zam|jNv&uv5^6ksBZ|dju_ERWyy*29LF3kbzrc{gy6pUFEyvL3ej_;%VYq}=B9qS;I z6(~|?MC@)JOIgK8KIRUxqpwZmImG9=b)QLg5y1INLp?OO+q`XvFRmVC%uewJI>$v` zd2fTpWfr&b1ed4-=MUFB`GeRgF9arz3VK{9P`b3~3EXwsP?>z@pH_zUi%w1Xux!qp zXFv7-eaOF)5E%^IHaZYcZ}5rzi6fD=JO%=$4!3N4f$D`{F4x|%b|E7y5oJT?aj@Ci+8+C5i>N?0?L=6Qh?SrA3Kk%FkjHlvFLJc6 z`B2tQF-)yqzv%AKGk1!f@$qWxrZ}McQZ27oPJyTJ1MF{$0)Zz<&l}`2OuZ;;kOfS* zuNU%sy=jQhaZ;$A#7{=}2i7tn)%H}(WsXzy_A)7uE5d0mc~#10HcW#Tj~P1$#6?MYfvtWGc>?-7?Qlwe4mlGmj3 z76eKcU=MJr5!06jV@JApY3O6hGZ?aJz?zRQ`qSEn|FInmF2phUA$<{vOWk&uy;JQ1 z&qhrkWKQ9^`N{%a=~W>z&|X3L1(d_((Q2enpaVl8KB;7@Iz1~wZ2_FyjMH%)cKoj+ zV=cD-5_t@ZCK*daeLLaf=@7@ChSXNsl})k#x7wo+&&%}S!U{;w%8Z%uHNI_@ywsqU z%A~3rIAe!ni-F&ME0uzNNW#5RE`uo5*1V0h)7*F@ZSreAf0%{r`>RX8tH+^Aossx4 z=1G*xxEK7^W%lgZ>4Wq572*!h)NQ}IriqZx;n!s%BlPS? z=Q8rcgP&h=DOV&I07xb+{qBb)tYJMec#+2g!6d)1KS3Z8mkHKY8V6YV!8aq2RrX?X zO6Yw5^6%uykPq7CBw7d9)dtiWR(F<2X=r*=#Z%RdQMg~+IJ1tnwHe=k|7%P`#IPb4 zRjV!Pdh7I((75tXf|h7Z^}@CP-~SSv-l$m2dbe)fnmC2zaxjz1WTOjT;HWCNdb*8v zLorXF(#J2#n6Sx-EL&B~;|Lx_Hag+Pt)8#0JxD{u#o}T^LQp>v(&X+PA%a2L8Y+ag z1Mk>Bg)BOdy8FQ3JPs1t2&V0Qs_%+9wzG{?y_Ll}3b+P)^fCIaT8W@t4j#TuKviUX z>BlSCz%yZ8M4mI`OYKiw&tQ-}G8Db;mNK~`9|JsJ?IL~`cewu!3sHGnF)f!8-f7i^>z3oP!&_&XU z!fL_d#g`&XM@bJW)K*jB$gbFWU+(*1$mgzTW~$5gZdTN~Mu25T<)Yyd4=Jn3M;dSk z+^^WVOt8|pctLndrz!GvlJ)c>mFSRUsv{@j!FQ`@pC)Z)QXt~!qIG7Ea$IRLD5E9EptCWn@zXz2@N zzDXZf529}!uCFQ}cj$*dS}`3MRe9`)Szi#r#hikKn5%Z)i99}@N;Ysr^5A}q-|CQYi;jF@8^3&hKU5&z`}aTY zJ95^%#rwWDbD!)~IX`=LbI#tqdwU~$VbG3H0P7BUD8pmPL#qFX9EbZ@sAQ(V0IQ#k zRs$EkEIoaiJuj*r(rCp6yl&`qHlNfv9>`4nCGjhN(iDbC1Qk=5D%iL0GnJEaadFtL##RXKqqB#gXxMNfO zQTj!}+qM~qhgX2D)BcV5b7mI1Mh;G2lG0AOdjFO83&+SzJD9;s2U5>zD+E^LNedvl z=8o~MOYDq4OH1oh{O@FF4rh$}H8U4)wuLg46%tJ+Jfl z-mrLkp|2^EMUQ<#y{g+YPOvWLs#-WrO;b6A(j_x`&k#d}upB*HnJ47Y3_S(C!U`C$Ibrn2m7peJ6m8Oh0yqzhlQrB1f7zN0Q7_|UjS6j zv$L*l3s=XauQh1zd6cjNk_j~W5_R<@_56xgECoX>dGz^ z?w$M4Yy8%kXc`bUOx;s+`(e)M=%7vQ>YAVbKOQ3g{rH(~u~<=X5NQRP$EJpM`-u=9 z^9fM|GcRnrV< zyts*U78*ug8u;(dz;)gHGHiqBeNh(le*dM*qhZ&#zOuQzRKY)*5o6VdY^SgLUk_Cf AUH||9 literal 0 HcmV?d00001 diff --git a/tests/network/sbml/toy_paper.sbml b/tests/network/sbml/toy_paper.sbml new file mode 100644 index 0000000..58a1680 --- /dev/null +++ b/tests/network/sbml/toy_paper.sbml @@ -0,0 +1,135 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/tests/network/sbml/toy_paper_no_rev_rm_R2.sbml b/tests/network/sbml/toy_paper_no_rev_rm_R2.sbml new file mode 100644 index 0000000..8f750e9 --- /dev/null +++ b/tests/network/sbml/toy_paper_no_rev_rm_R2.sbml @@ -0,0 +1,139 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/tests/network/sbml/toy_paper_no_reversible.sbml b/tests/network/sbml/toy_paper_no_reversible.sbml new file mode 100644 index 0000000..6d647be --- /dev/null +++ b/tests/network/sbml/toy_paper_no_reversible.sbml @@ -0,0 +1,139 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/tests/normalization.py b/tests/normalization.py new file mode 100644 index 0000000..7e1432b --- /dev/null +++ b/tests/normalization.py @@ -0,0 +1,201 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- + +""" +Description: +Test seed2lp +""" +from os import path +from tests.utils import get_network +import re + +##### ###### ##### DIRECTORIES AND FILES ################### +TEST_DIR = path.dirname(path.abspath(__file__)) + +# All reaction are not reversible +# except import/export reaction for exchange metabolite S1, S2 C and G +INFILE = path.join(TEST_DIR,'network/sbml/toy_paper.sbml') +######################################################## + + +################### FIXED VARIABLES #################### + +######################################################## + + + +################# EXPECTED SOLUTIONS ################### +EXCH = {"R_EX_S1", "R_EX_S2", "R_EX_C", "R_EX_G"} + +DEL = {"R_R2"} + +# For exchange reaction when import reaction deleted, +# the source reversible parameter is not changed. +# The deletion is on ASP facts with atom reaction and bounds prefixed with "rm_" +# and product or reactant prefixed with "rm_" +REV = {"R_BIOMASS"} + +META_EXCH = {"R_R7"} + +FORBID_TAF = {"M_F_c"} + +RM_RXN = ['rm_reaction("rev_R_EX_S1").', + 'rm_reaction("rev_R_EX_S2").', + 'rm_reaction("rev_R_EX_C").', + 'rm_reaction("rev_R_EX_G").', + 'rm_bounds("rev_R_EX_S1","0.0000000000","1000.0000000000").', + 'rm_bounds("rev_R_EX_S2","0.0000000000","1000.0000000000").', + 'rm_bounds("rev_R_EX_C","0.0000000000","1000.0000000000").', + 'rm_bounds("rev_R_EX_G","0.0000000000","1000.0000000000").', + 'rm_product("M_S1_e","1.0000000000","rev_R_EX_S1","exchange").', + 'rm_product("M_S2_e","1.0000000000","rev_R_EX_S2","exchange").', + 'rm_product("M_C_c","1.0000000000","rev_R_EX_C","exchange").', + 'rm_product("M_G_c","1.0000000000","rev_R_EX_G","exchange").' + ] +SIZE_RM_RXN=len(RM_RXN) + +SEED_TI = ['seed("M_S1_e","exchange").', + 'seed("M_S2_e","exchange").', + 'seed("M_C_c","exchange").', + 'seed("M_G_c","exchange").' + ] +SIZE_SEED_TI=len(SEED_TI) + + +MATCH_RM = 'rm_' +MATCH_SEED = 'seed\(' + +######################################################## + +def test_exchange(): + run_mode = "full" + targets_as_seeds = False + topological_injection = False + keep_import_reactions = False + + network = get_network(INFILE, run_mode, targets_as_seeds, + topological_injection, keep_import_reactions) + assert set(network.exchanged_reactions) == EXCH + + +def test_delete(): + run_mode = "full" + targets_as_seeds = False + topological_injection = False + keep_import_reactions = False + + network = get_network(INFILE, run_mode, targets_as_seeds, + topological_injection, keep_import_reactions) + assert set(network.deleted_reactions) == DEL + + +def test_rev_modified(): + run_mode = "full" + targets_as_seeds = False + topological_injection = False + keep_import_reactions = False + + network = get_network(INFILE, run_mode, targets_as_seeds, + topological_injection, keep_import_reactions) + assert set(network.reversible_modified_reactions) == REV + + +def test_meta_modified(): + run_mode = "full" + targets_as_seeds = False + topological_injection = False + keep_import_reactions = False + + network = get_network(INFILE, run_mode, targets_as_seeds, + topological_injection, keep_import_reactions) + assert set(network.meta_modified_reactions) == META_EXCH + + +# import reaction removed, prefixed by "rm_" on atom +# reaction reaction and bounds +# and +def test_rm_rxn(): + run_mode = "full" + targets_as_seeds = False + topological_injection = False + keep_import_reactions = False + + network = get_network(INFILE, run_mode, targets_as_seeds, + topological_injection, keep_import_reactions) + network.convert_to_facts() + + rm_list_found=re.findall(MATCH_RM, network.facts) + size_rm_found=len(rm_list_found) + assert size_rm_found == SIZE_RM_RXN + + seed_list_found=re.findall(MATCH_SEED, network.facts) + size_seed_found=len(seed_list_found) + assert size_seed_found == 0 + + for rm in RM_RXN: + assert rm in network.facts + +# import reaction kept, no prefix on atom reaction +def test_kir(): + run_mode = "full" + targets_as_seeds = False + topological_injection = False + keep_import_reactions = True + + network = get_network(INFILE, run_mode, targets_as_seeds, + topological_injection, keep_import_reactions) + network.convert_to_facts() + + rm_list_found=re.findall(MATCH_RM, network.facts) + size_rm_found=len(rm_list_found) + assert size_rm_found == 0 + + seed_list_found=re.findall(MATCH_SEED, network.facts) + size_seed_found=len(seed_list_found) + assert size_seed_found == 0 + + +# import reaction kept, no prefix on atom reaction +# atom seed added product or reactant (dependeing way it is written on sbml) +def test_ti(): + run_mode = "full" + targets_as_seeds = False + topological_injection = True + keep_import_reactions = True + + network = get_network(INFILE, run_mode, targets_as_seeds, + topological_injection, keep_import_reactions) + network.convert_to_facts() + + rm_list_found=re.findall(MATCH_RM, network.facts) + size_rm_found=len(rm_list_found) + assert size_rm_found == 0 + + seed_list_found=re.findall(MATCH_SEED, network.facts) + size_seed_found=len(seed_list_found) + assert size_seed_found == SIZE_SEED_TI + + for seed in SEED_TI: + assert seed in network.facts + + + +def test_taf(): + run_mode = "target" + targets_as_seeds = False + topological_injection = False + keep_import_reactions = False + network = get_network(INFILE, run_mode, targets_as_seeds, + topological_injection, keep_import_reactions) + assert set(network.forbidden_seeds) == FORBID_TAF + + +def test_tas(): + run_mode = "target" + targets_as_seeds = True + topological_injection = False + keep_import_reactions = False + network = get_network(INFILE, run_mode, targets_as_seeds, + topological_injection, keep_import_reactions) + # check if list is empty + assert not network.forbidden_seeds \ No newline at end of file diff --git a/tests/pytest.ini b/tests/pytest.ini new file mode 100644 index 0000000..6b8a3b1 --- /dev/null +++ b/tests/pytest.ini @@ -0,0 +1,3 @@ +[pytest] +filterwarnings = + ignore::DeprecationWarning \ No newline at end of file diff --git a/tests/target.py b/tests/target.py new file mode 100644 index 0000000..2368ecf --- /dev/null +++ b/tests/target.py @@ -0,0 +1,286 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- + +""" +Description: +Test seed2lp +""" +from os import path +from tests.utils import search_seed + +##### ###### ##### DIRECTORIES AND FILES ################### +TEST_DIR = path.dirname(path.abspath(__file__)) + +# All reaction are not reversible +# except import/export reaction for exchange metabolite S1, S2 C and G +INFILE = path.join(TEST_DIR,'network/sbml/toy_paper_no_reversible.sbml') +######################################################## + + +################### FIXED VARIABLES #################### +RUN_MODE="target" +######################################################## + + +################# EXPECTED SOLUTIONS ################### + +# Seeds for Reasoning, filter, Gues-Check, Guess-Check Diversty and Hybrid +SEEDS_SUBMIN_TAF= [{"M_B_c", "M_D_c", "M_E_c"}, + {"M_B_c", "M_D_c", "M_I_c"}, + {"M_B_c", "M_D_c", "M_H_c"}, + {"M_B_c", "M_I_c", "M_S2_e"}, + {"M_B_c", "M_E_c", "M_S2_e"}, + {"M_B_c", "M_H_c", "M_S2_e"}, + {"M_I_c", "M_J_c", "M_S1_e", "M_S2_e"}, + {"M_D_c", "M_I_c", "M_J_c", "M_S1_e"}, + {"M_H_c", "M_J_c", "M_S1_e", "M_S2_e"}, + {"M_E_c", "M_J_c", "M_S1_e", "M_S2_e"}, + {"M_D_c", "M_H_c", "M_J_c", "M_S1_e"}, + {"M_D_c", "M_E_c", "M_J_c", "M_S1_e"} + ] +SIZE_SUBMIN_TAF=len(SEEDS_SUBMIN_TAF) + +SEEDS_MIN_TAF = [{"M_B_c", "M_D_c", "M_E_c"} , + {"M_B_c", "M_D_c", "M_I_c"} , + {"M_B_c", "M_D_c", "M_H_c"} , + {"M_B_c", "M_I_c", "M_S2_e"} , + {"M_B_c", "M_E_c", "M_S2_e"} , + {"M_B_c", "M_H_c", "M_S2_e"} + ] +SIZE_MIN_TAF=len(SEEDS_MIN_TAF) + + +SEEDS_SUBMIN_TAS= [{"M_B_c", "M_D_c", "M_E_c"}, + {"M_B_c", "M_D_c", "M_I_c"}, + {"M_B_c", "M_D_c", "M_H_c"}, + {"M_B_c", "M_D_c", "M_F_c"}, + {"M_B_c", "M_I_c", "M_S2_e"}, + {"M_B_c", "M_E_c", "M_S2_e"}, + {"M_B_c", "M_H_c", "M_S2_e"}, + {"M_B_c", "M_F_c", "M_S2_e"}, + {"M_I_c", "M_J_c", "M_S1_e", "M_S2_e"}, + {"M_F_c", "M_J_c", "M_S1_e", "M_S2_e"}, + {"M_D_c", "M_I_c", "M_J_c", "M_S1_e"}, + {"M_D_c", "M_F_c", "M_J_c", "M_S1_e"}, + {"M_H_c", "M_J_c", "M_S1_e", "M_S2_e"}, + {"M_E_c", "M_J_c", "M_S1_e", "M_S2_e"}, + {"M_D_c", "M_H_c", "M_J_c", "M_S1_e"}, + {"M_D_c", "M_E_c", "M_J_c", "M_S1_e"} + ] +SIZE_SUBMIN_TAS=len(SEEDS_SUBMIN_TAS) + +SEEDS_MIN_TAS = [{"M_B_c", "M_D_c", "M_E_c"} , + {"M_B_c", "M_D_c", "M_I_c"} , + {"M_B_c", "M_D_c", "M_H_c"} , + {"M_B_c", "M_D_c", "M_F_c"} , + {"M_B_c", "M_I_c", "M_S2_e"} , + {"M_B_c", "M_E_c", "M_S2_e"} , + {"M_B_c", "M_H_c", "M_S2_e"}, + {"M_B_c", "M_F_c", "M_S2_e"} , + ] +SIZE_MIN_TAS=len(SEEDS_MIN_TAS) + +######################################################## + + + + + + +######################### TESTS ######################## +#TODO: +# - test with accumulation +# - test keep import reaction and topological injection +# - find Network to have different value in Reasoning / Filter / Gues_check / Guess_Check_Div +# - find Network to have different value with accumulation authorized +# - test without maximization for hybrid +# - test unsat +# - test possible_seeds (subset of seed that maximize number of producible targets) +# - test changing objective +# - test target file + +# solve values: 'reasoning', 'filter', 'guess_check', 'guess_check_div', 'hybrid' +# optim values: 'submin', 'min' + +# ---------------------- TARGET ARE FORBIDDEN ---------------------- +# ----------- SUBSETMIN ----------- +def test_reasoning_submin_taf(): + solve="reasoning" + optim="submin" + list_solution=search_seed(INFILE, RUN_MODE, solve, optim) + for solution in list_solution: + assert set(solution) in SEEDS_SUBMIN_TAF + assert len(list_solution) == SIZE_SUBMIN_TAF + +def test_filter_submin_taf(): + solve="filter" + optim="submin" + list_solution=search_seed(INFILE, RUN_MODE, solve, optim) + for solution in list_solution: + assert set(solution) in SEEDS_SUBMIN_TAF + assert len(list_solution) == SIZE_SUBMIN_TAF + +def test_gc_submin_taf(): + solve="guess_check" + optim="submin" + list_solution=search_seed(INFILE, RUN_MODE, solve, optim) + for solution in list_solution: + assert set(solution) in SEEDS_SUBMIN_TAF + assert len(list_solution) == SIZE_SUBMIN_TAF + +def test_gcd_submin_taf(): + solve="guess_check_div" + optim="submin" + list_solution=search_seed(INFILE, RUN_MODE, solve, optim) + for solution in list_solution: + assert set(solution) in SEEDS_SUBMIN_TAF + assert len(list_solution) == SIZE_SUBMIN_TAF + + +def test_hybrid_submin_taf(): + solve="hybrid" + optim="submin" + list_solution=search_seed(INFILE, RUN_MODE, solve, optim) + for solution in list_solution: + assert set(solution) in SEEDS_SUBMIN_TAF + assert len(list_solution) == SIZE_SUBMIN_TAF + + +# ----------- MINIMIZE ----------- +def test_reasoning_min_taf(): + solve="reasoning" + optim="min" + list_solution=search_seed(INFILE, RUN_MODE, solve, optim) + for solution in list_solution: + assert set(solution) in SEEDS_MIN_TAF + assert len(list_solution) == SIZE_MIN_TAF + +def test_filter_min_taf(): + solve="filter" + optim="min" + list_solution=search_seed(INFILE, RUN_MODE, solve, optim) + for solution in list_solution: + assert set(solution) in SEEDS_MIN_TAF + assert len(list_solution) == SIZE_MIN_TAF + +def test_gc_min_taf(): + solve="guess_check" + optim="min" + list_solution=search_seed(INFILE, RUN_MODE, solve, optim) + for solution in list_solution: + assert set(solution) in SEEDS_MIN_TAF + assert len(list_solution) == SIZE_MIN_TAF + +def test_gcd_min_taf(): + solve="guess_check_div" + optim="min" + list_solution=search_seed(INFILE, RUN_MODE, solve, optim) + for solution in list_solution: + assert set(solution) in SEEDS_MIN_TAF + assert len(list_solution) == SIZE_MIN_TAF + + +def test_hybrid_min_taf(): + solve="hybrid" + optim="min" + list_solution=search_seed(INFILE, RUN_MODE, solve, optim) + for solution in list_solution: + assert set(solution) in SEEDS_MIN_TAF + assert len(list_solution) == SIZE_MIN_TAF + + +# ---------------------- TARGET AS SEEDS ---------------------- +# ----------- SUBSETMIN ----------- +def test_reasoning_submin_tas(): + solve="reasoning" + optim="submin" + targets_as_seeds=True + list_solution=search_seed(INFILE, RUN_MODE, solve, optim, targets_as_seeds) + for solution in list_solution: + assert set(solution) in SEEDS_SUBMIN_TAS + assert len(list_solution) == SIZE_SUBMIN_TAS + +def test_filter_submin_tas(): + solve="filter" + optim="submin" + targets_as_seeds=True + list_solution=search_seed(INFILE, RUN_MODE, solve, optim, targets_as_seeds) + for solution in list_solution: + assert set(solution) in SEEDS_SUBMIN_TAS + assert len(list_solution) == SIZE_SUBMIN_TAS + +def test_gc_submin_tas(): + solve="guess_check" + optim="submin" + targets_as_seeds=True + list_solution=search_seed(INFILE, RUN_MODE, solve, optim, targets_as_seeds) + for solution in list_solution: + assert set(solution) in SEEDS_SUBMIN_TAS + assert len(list_solution) == SIZE_SUBMIN_TAS + +def test_gcd_submin_tas(): + solve="guess_check_div" + optim="submin" + targets_as_seeds=True + list_solution=search_seed(INFILE, RUN_MODE, solve, optim, targets_as_seeds) + for solution in list_solution: + assert set(solution) in SEEDS_SUBMIN_TAS + assert len(list_solution) == SIZE_SUBMIN_TAS + + +def test_hybrid_submin_tas(): + solve="hybrid" + optim="submin" + targets_as_seeds=True + list_solution=search_seed(INFILE, RUN_MODE, solve, optim, targets_as_seeds) + for solution in list_solution: + assert set(solution) in SEEDS_SUBMIN_TAS + assert len(list_solution) == SIZE_SUBMIN_TAS + + +# ----------- MINIMIZE ----------- +def test_reasoning_min_tas(): + solve="reasoning" + optim="min" + targets_as_seeds=True + list_solution=search_seed(INFILE, RUN_MODE, solve, optim, targets_as_seeds) + for solution in list_solution: + assert set(solution) in SEEDS_MIN_TAS + assert len(list_solution) == SIZE_MIN_TAS + +def test_filter_min_tas(): + solve="filter" + optim="min" + targets_as_seeds=True + list_solution=search_seed(INFILE, RUN_MODE, solve, optim, targets_as_seeds) + for solution in list_solution: + assert set(solution) in SEEDS_MIN_TAS + assert len(list_solution) == SIZE_MIN_TAS + +def test_gc_min_tas(): + solve="guess_check" + optim="min" + targets_as_seeds=True + list_solution=search_seed(INFILE, RUN_MODE, solve, optim, targets_as_seeds) + for solution in list_solution: + assert set(solution) in SEEDS_MIN_TAS + assert len(list_solution) == SIZE_MIN_TAS + +def test_gcd_min_tas(): + solve="guess_check_div" + optim="min" + targets_as_seeds=True + list_solution=search_seed(INFILE, RUN_MODE, solve, optim, targets_as_seeds) + for solution in list_solution: + assert set(solution) in SEEDS_MIN_TAS + assert len(list_solution) == SIZE_MIN_TAS + + +def test_hybrid_min_tas(): + solve="hybrid" + optim="min" + targets_as_seeds=True + list_solution=search_seed(INFILE, RUN_MODE, solve, optim, targets_as_seeds) + for solution in list_solution: + assert set(solution) in SEEDS_MIN_TAS + assert len(list_solution) == SIZE_MIN_TAS \ No newline at end of file diff --git a/tests/utils.py b/tests/utils.py new file mode 100644 index 0000000..e0bf50e --- /dev/null +++ b/tests/utils.py @@ -0,0 +1,181 @@ +from os import path +from seed2lp.network import Network +from seed2lp.reasoning import Reasoning +from seed2lp.linear import Hybrid, FBA +from seed2lp.__main__ import get_reaction_options, get_input_datas +from seed2lp import logger +from seed2lp.file import is_valid_dir + +################ DIRECTORIES AND FILES ################### +TEST_DIR = path.dirname(path.abspath(__file__)) +RESULT_DIR=path.join(TEST_DIR,"results") +TMP_DIR=path.join(TEST_DIR,"tmp") +is_valid_dir(RESULT_DIR) +is_valid_dir(TMP_DIR) +logger.set_log_dir(path.join(TEST_DIR, RESULT_DIR,"logs")) +is_valid_dir(logger.LOG_DIR) +######################################################## + + +################### FIXED VARIABLES #################### +TIME_LIMIT = 0 +NB_SOLUTION = 0 +CLINGO_CONF='jumpy' +CLINGO_STRAT="none" +INTERSECTION=False +UNION=False +VERBOSE=False +######################################################## + +####################### METHODS ######################## + +# solve values: 'reasoning', 'filter', 'guess_check', 'guess_check_div', 'hybrid' +# optim values: 'submin', 'min' +def search_seed(infile:str, run_mode:str, solve:str, optim:str, targets_as_seeds:bool=False): + """ + Test FN without accumulation, remove import reaction, for: + - Remove import reaction + - No accumulation + - Subset minimal / Minimize + - Reasoning / Filter / Guess_Check / Guess_Check_div / Hybrid + - Maximization + - No file given, no objectif modification + """ + reasoning_submin = list() + filter_submin = list() + gc_submin = list() + gcd_submin = list() + reasoning_min = list() + filter_min = list() + gc_min = list() + gcd_min = list() + hybrid_submin = list() + hybrid_min = list() + fba_submin = list() + fba_min = list() + + topological_injection= False + keep_import_reactions= False + accumulation = False + maximization = True + + + + match optim: + case "submin": + subset_minimal = True + minimize = False + case "min": + subset_minimal = False + minimize = True + case _: + subset_minimal = True + minimize = True + + + options=get_reaction_options(keep_import_reactions,topological_injection, + targets_as_seeds,maximization,run_mode,accumulation,solve) + + + + + network= get_network(infile, run_mode, targets_as_seeds, topological_injection, + keep_import_reactions, accumulation, opt_short=options['short']) + network.convert_to_facts() + network.simplify() + + if run_mode != "fba": + model = Reasoning(run_mode, solve, network, TIME_LIMIT, NB_SOLUTION, + CLINGO_CONF,CLINGO_STRAT, INTERSECTION, UNION, minimize, subset_minimal, + TMP_DIR, options['short'], VERBOSE) + model.search_seed() + + model = Hybrid(run_mode, solve, network, TIME_LIMIT, NB_SOLUTION, + CLINGO_CONF,CLINGO_STRAT, INTERSECTION, UNION, minimize, subset_minimal, + maximization, TMP_DIR, options['short'], VERBOSE) + model.search_seed() + else: + model = FBA(run_mode, network, TIME_LIMIT, NB_SOLUTION, + CLINGO_CONF,CLINGO_STRAT, INTERSECTION, UNION, minimize, subset_minimal, + maximization, TMP_DIR, options['short'], VERBOSE) + model.search_seed() + + for result in network.result_seeds: + match result.solver_type, result.search_mode,result.search_type: + case "REASONING","Subset Minimal","Enumeration": + reasoning_submin.append(result.seeds_list) + case "REASONING FILTER","Subset Minimal","Enumeration": + filter_submin.append(result.seeds_list) + case "REASONING GUESS-CHECK","Subset Minimal","Enumeration": + gc_submin.append(result.seeds_list) + case "REASONING GUESS-CHECK DIVERSITY","Subset Minimal","Enumeration": + gcd_submin.append(result.seeds_list) + + case "REASONING","Minimize","Enumeration": + reasoning_min.append(result.seeds_list) + case "REASONING FILTER","Minimize","Enumeration": + filter_min.append(result.seeds_list) + case "REASONING GUESS-CHECK","Minimize","Enumeration": + gc_min.append(result.seeds_list) + case "REASONING GUESS-CHECK DIVERSITY","Minimize","Enumeration": + gcd_min.append(result.seeds_list) + + case "HYBRID","Subset Minimal","Enumeration": + hybrid_submin.append(result.seeds_list) + + case "HYBRID","Minimize","Enumeration": + hybrid_min.append(result.seeds_list) + + case "FBA","Subset Minimal","Enumeration": + fba_submin.append(result.seeds_list) + + case "FBA","Minimize","Enumeration": + fba_min.append(result.seeds_list) + + # solve value : 'reasoning', 'filter', 'guess_check', 'guess_check_div', 'hybrid', 'all' (-for FBA) + match solve, optim: + case 'reasoning','submin': + return reasoning_submin + case 'filter','submin': + return filter_submin + case 'guess_check','submin': + return gc_submin + case 'guess_check_div','submin': + return gcd_submin + case 'hybrid','submin': + return hybrid_submin + case 'all','submin': + return fba_submin + + + case 'reasoning','min': + return reasoning_min + case 'filter','min': + return filter_min + case 'guess_check','min': + return gc_min + case 'guess_check_div','min': + return gcd_min + case 'hybrid','min': + return hybrid_min + case 'all','min': + return fba_min + + +# TODO: Possible seeds, forbidden seeds, seeds, target and obejctive modification +def get_network(infile:str, run_mode:str, targets_as_seeds:bool, + topological_injection:bool, keep_import_reactions:bool, accumulation:bool=False, + seeds_file:str=None, forbidden_seeds_file:str=None, possible_seeds_file:str=None, + opt_short:str="test"): + + logger.get_logger(infile, opt_short, VERBOSE) + input_dict = get_input_datas(seeds_file, forbidden_seeds_file, possible_seeds_file) + network = Network(infile, run_mode, targets_as_seeds, + topological_injection, keep_import_reactions, + input_dict, accumulation) + + if not targets_as_seeds: + network.forbidden_seeds += network.targets + return network + +########################################################