{ "info": { "author": "Charles Rocabert, G\u00e1bor Boross, Bal\u00e1zs Papp", "author_email": "", "bugtrack_url": null, "classifiers": [ "Development Status :: 4 - Beta", "Intended Audience :: Science/Research", "License :: OSI Approved :: GNU General Public License v3 (GPLv3)", "Operating System :: OS Independent", "Programming Language :: Python :: 3", "Topic :: Scientific/Engineering :: Bio-Informatics" ], "description": "

MetEvolSim

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\n Python package dedicated to the evolution of metabolic concentrations\n

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\nMetEvolSim is a Python package providing numerical tools to study the long-term evolution of metabolic concentrations.\nMetEvolSim takes as an input any SBML metabolic network model, as soon as kinetic parameters and initial metabolic concentrations are specified, and a stable steady-state exists. Steady-state concentrations are computed thanks to Copasi software.\n

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\nMetEvolSim is being developed by Charles Rocabert, G\u00e1bor Boross and Bal\u00e1zs Papp.\n

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\n\n## Table of contents\n- [Dependencies](#dependencies)\n- [Installation](#installation)\n- [First usage](#first_usage)\n- [Help](#help)\n- [Copyright](#copyright)\n- [License](#license)\n\n## Dependencies \n- Python ≥ 3,\n- Numpy ≥ 1.15 (automatically installed when using pip),\n- Python-libsbml ≥ 5.17 (automatically installed when using pip),\n- pip ≥ 19.1 (optional).\n\n## Installation \n• To install Copasi software, visit http://copasi.org/.\n\n• To install Python dependencies:\n```shell\npip install numpy python-libsbml\n```\n\n• To install the current release of MetEvolSim:\n```shell\npip install MetEvolSim\n```\n\nAlternatively, download the latest release in the folder of your choice and unzip it. Then follow the instructions below:\n```shell\n# Navigate to the MetEvolSim folder\ncd /path/to/MetEvolSim\n\n# Install metevolsim Python package\npython3 setup.py install\n```\n\n## First usage \nMetEvolSim takes as an input any SBML metabolic network model, as soon as kinetic parameters and initial metabolic concentrations are specified, and a stable steady-state exists. MetEvolSim provides a class to manipulate SBML models: the class Model. It is also necessary to define an objective function (a list of reaction names and coefficients), and to provide the path of CopasiSE software.\n\n```python\n# Import metevolsim package\nimport MetEvolSim\n\n# Create an objective function\ntarget_fluxes = [['ATPase', 1.0], ['PDC', 1.0]]\n\n# Load the SBML metabolic model\nmodel = MetEvolSim.Model(sbml_filename='glycolysis.xml', objective_function=target_fluxes, copasi_path='/Applications/COPASI/CopasiSE')\n\n# Print some informations on the metabolic model\nprint(model.get_number_of_species())\nprint(model.get_WT_species_value('Glc'))\n\n# Get a kinetic parameter at random\nparam = model.get_random_parameter()\nprint(param)\n\n# Mutate this kinetic parameter with a log-scale mutation size 0.01\nmodel.random_parameter_mutation(param, sigma=0.01)\n\n# Compute wild-type and mutant steady-states\nmodel.compute_WT_steady_state()\nmodel.compute_mutant_steady_state()\n```\n\nMetEvolSim allows two types of numerical analyses on a SBML metabolic model:\n- Evolution experiments, based on a Markov Chain Monte Carlo (MCMC) algorithm,\n- Sensitivity analysis, by exploring every kinetic parameters in a given range and recording associated fluxes and metabolic abundances changes.\n\nAll numerical analyses output files are saved in a subfolder output, automatically created by MetEvolSim.\n\n### Evolution experiments:\nThree types of evolution experiments are available:\n- MUTATION_ACCUMULATION: Run a mutation accumulation experiment by accepting all new mutations without any selection threshold,\n- ABSOLUTE_METABOLIC_SUM_SELECTION: Run an evolution experiment by applying a stabilizing selection on the sum of absolute metabolic abundances,\n- RELATIVE_METABOLIC_SUM_SELECTION: Run an evolution experiment by applying a stabilizing selection on the sum of relative metabolic abundances,\n- ABSOLUTE_TARGET_FLUXES_SELECTION: Run an evolution experiment by applying a stabilizing selection on the MOMA distance of absolute target fluxes,\n- RELATIVE_TARGET_FLUXES_SELECTION: Run an evolution experiment by applying a stabilizing selection on the MOMA distance of relative target fluxes.\n\n```python\n# Load a Markov Chain Monte Carlo (MCMC) instance\nmcmc = MetEvolSim.MCMC(sbml_filename='glycolysis.xml', objective_function=target_fluxes, total_iterations=10000, sigma=0.01, selection_scheme=\"MUTATION_ACCUMULATION\", selection_threshold=1e-4, copasi_path='/Applications/COPASI/CopasiSE')\n\n# Initialize the MCMC instance \nmcmc.initialize()\n\n# Compute the successive iterations and write output files\nstop_MCMC = False\nwhile not stop_MCMC:\n stop_mcmc = mcmc.iterate()\n mcmc.write_output_file()\n mcmc.write_statistics()\n```\n\n### Sensitivity analysis:\n```python\n# Load a sensitivity analysis instance\nsa = MetEvolSim.SensitivityAnalysis(sbml_filename='glycolysis.xml', factor_range=1.0, factor_step=0.01, copasi_path='/Applications/COPASI/CopasiSE')\n\n# Initialize the sensitivity analysis instance \nsa.initialize()\n\n# Perform the sensitivity analysis for each kinetic parameter\nstop_SA = False\nwhile not stop_SA:\n stop_SA = sa.explore_next_parameter()\n```\n\n## Help \nTo get some help on a MetEvolSim class or method, use the Python help function:\n```python\nhelp(MetEvolSim.Model.set_species_initial_value)\n```\nto obtain a quick description and the list of parameters and outputs:\n```\nHelp on function set_species_initial_value in module MetEvolSim:\n\nset_species_initial_value(self, species_id, value)\n Set the initial concentration of the species 'species_id' in the\n mutant model.\n\n Parameters\n ----------\n species_id: str\n Species identifier (as defined in the SBML model).\n value: float >= 0.0\n Species abundance.\n\n Returns\n -------\n None\n(END)\n```\n\n## Copyright \nCopyright © 2018-2019 Charles Rocabert, G\u00e1bor Boross and Bal\u00e1zs Papp.\nAll rights reserved.\n\n## License \n

\nThis program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.\n

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\nThis program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.\n

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\nYou should have received a copy of the GNU General Public License along with this program. If not, see http://www.gnu.org/licenses/.\n

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