From 16f1d5423b85a3e55444c693eb5116b0842c63eb Mon Sep 17 00:00:00 2001 From: Charles Rocabert Date: Sat, 21 Dec 2019 10:55:52 -0800 Subject: [PATCH] Python package case sensitivity debugging --- MetEvolSim.egg-info/PKG-INFO | 213 -- MetEvolSim.egg-info/SOURCES.txt | 21 - MetEvolSim.egg-info/dependency_links.txt | 1 - MetEvolSim.egg-info/requires.txt | 3 - MetEvolSim.egg-info/top_level.txt | 1 - build/lib/metevolsim/__init__.py | 27 - build/lib/metevolsim/metevolsim.py | 2440 ---------------------- dist/MetEvolSim-0.2.1-py3-none-any.whl | Bin 26745 -> 0 bytes dist/MetEvolSim-0.2.1.tar.gz | Bin 39721 -> 0 bytes dist/MetEvolSim-0.2.2-py3-none-any.whl | Bin 26736 -> 0 bytes dist/MetEvolSim-0.2.2.tar.gz | Bin 39690 -> 0 bytes dist/MetEvolSim-0.2.3-py3-none-any.whl | Bin 26734 -> 0 bytes dist/MetEvolSim-0.2.3.tar.gz | Bin 39793 -> 0 bytes dist/MetEvolSim-0.3.0-py3-none-any.whl | Bin 27596 -> 0 bytes dist/MetEvolSim-0.3.0.tar.gz | Bin 40708 -> 0 bytes dist/MetEvolSim-0.4.0-py3-none-any.whl | Bin 30332 -> 0 bytes dist/MetEvolSim-0.4.0.tar.gz | Bin 41996 -> 0 bytes dist/MetEvolSim-0.4.1-py3-none-any.whl | Bin 30334 -> 0 bytes dist/MetEvolSim-0.4.1.tar.gz | Bin 41989 -> 0 bytes dist/MetEvolSim-0.4.4-py3-none-any.whl | Bin 30464 -> 0 bytes dist/MetEvolSim-0.4.4.tar.gz | Bin 42120 -> 0 bytes metevolsim/__init__.py | 2 +- setup.py | 2 +- 23 files changed, 2 insertions(+), 2708 deletions(-) delete mode 100644 MetEvolSim.egg-info/PKG-INFO delete mode 100644 MetEvolSim.egg-info/SOURCES.txt delete mode 100644 MetEvolSim.egg-info/dependency_links.txt delete mode 100644 MetEvolSim.egg-info/requires.txt delete mode 100644 MetEvolSim.egg-info/top_level.txt delete mode 100644 build/lib/metevolsim/__init__.py delete mode 100644 build/lib/metevolsim/metevolsim.py delete mode 100644 dist/MetEvolSim-0.2.1-py3-none-any.whl delete mode 100644 dist/MetEvolSim-0.2.1.tar.gz delete mode 100644 dist/MetEvolSim-0.2.2-py3-none-any.whl delete mode 100644 dist/MetEvolSim-0.2.2.tar.gz delete mode 100644 dist/MetEvolSim-0.2.3-py3-none-any.whl delete mode 100644 dist/MetEvolSim-0.2.3.tar.gz delete mode 100644 dist/MetEvolSim-0.3.0-py3-none-any.whl delete mode 100644 dist/MetEvolSim-0.3.0.tar.gz delete mode 100644 dist/MetEvolSim-0.4.0-py3-none-any.whl delete mode 100644 dist/MetEvolSim-0.4.0.tar.gz delete mode 100644 dist/MetEvolSim-0.4.1-py3-none-any.whl delete mode 100644 dist/MetEvolSim-0.4.1.tar.gz delete mode 100644 dist/MetEvolSim-0.4.4-py3-none-any.whl delete mode 100644 dist/MetEvolSim-0.4.4.tar.gz diff --git a/MetEvolSim.egg-info/PKG-INFO b/MetEvolSim.egg-info/PKG-INFO deleted file mode 100644 index e167afe..0000000 --- a/MetEvolSim.egg-info/PKG-INFO +++ /dev/null @@ -1,213 +0,0 @@ -Metadata-Version: 2.1 -Name: MetEvolSim -Version: 0.4.4 -Summary: MetEvolSim (Metabolome Evolution Simulator) Python Package -Home-page: https://github.com/charlesrocabert/MetEvolSim -Author: Charles Rocabert, Gábor Boross, Balázs Papp -Author-email: -License: GNU General Public License v3 (GPLv3) -Project-URL: Source, https://github.com/charlesrocabert/MetEvolSim -Description:

MetEvolSim

-

- Python package dedicated to the evolution of metabolic concentrations -

- - - -
- -

- - ----------------- - -

- MetEvolSim is a Python package providing numerical tools to study the long-term evolution of metabolic concentrations. - MetEvolSim 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. -

- -

- MetEvolSim is being developed by Charles Rocabert, Gábor Boross and Balázs Papp. -

- -

-     -

- - ## Table of contents - - [Dependencies](#dependencies) - - [Installation](#installation) - - [First usage](#first_usage) - - [Help](#help) - - [Copyright](#copyright) - - [License](#license) - - ## Dependencies - - Python ≥ 3, - - Numpy ≥ 1.15 (automatically installed when using pip), - - Python-libsbml ≥ 5.17 (automatically installed when using pip), - - NetworkX ≥ 2.2 (automatically installed when using pip), - - pip ≥ 19.1 (optional). - - ## Installation - • To install Copasi software, visit http://copasi.org/. - - • To install the current release of MetEvolSim: - ```shell - pip install MetEvolSim - ``` - - Alternatively, download the latest release in the folder of your choice and unzip it. Then follow the instructions below: - ```shell - # Navigate to the MetEvolSim folder - cd /path/to/MetEvolSim - - # Install metevolsim Python package - python3 setup.py install - ``` - - ## First usage - MetEvolSim 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. - - ```python - # Import metevolsim package - import MetEvolSim - - # Create an objective function - target_fluxes = [['ATPase', 1.0], ['PDC', 1.0]] - - # Load the SBML metabolic model - model = MetEvolSim.Model(sbml_filename='glycolysis.xml', objective_function=target_fluxes, copasi_path='/Applications/COPASI/CopasiSE') - - # Print some informations on the metabolic model - print(model.get_number_of_species()) - print(model.get_WT_species_value('Glc')) - - # Get a kinetic parameter at random - param = model.get_random_parameter() - print(param) - - # Mutate this kinetic parameter with a log-scale mutation size 0.01 - model.random_parameter_mutation(param, sigma=0.01) - - # Compute wild-type and mutant steady-states - model.compute_WT_steady_state() - model.compute_mutant_steady_state() - - # Run a metabolic control analysis on the wild-type - model.compute_WT_metabolic_control_analysis() - # This function will output two datasets: - # - output/WT_MCA_unscaled.txt containing unscaled control coefficients, - # - output/WT_MCA_scaled.txt containing scaled control coefficients. - - # Compute all pairwise metabolite shortest paths - model.build_species_graph() - model.save_shortest_paths(filename="glycolysis_shortest_paths.txt") - ``` - - MetEvolSim offers two specific numerical approaches to analyze the evolution of metabolic abundances: - - Evolution experiments, based on a Markov Chain Monte Carlo (MCMC) algorithm, - - Sensitivity analysis, by exploring every kinetic parameters in a given range and recording associated fluxes and metabolic abundances changes. - - All numerical analyses output files are saved in a subfolder output, automatically created by MetEvolSim. - - ### Evolution experiments: -

- -

-

- Algorithm overview: A. The model of interest is loaded as a wild-type from a SBML file (kinetic equations, kinetic parameter values and initial metabolic concentrations must be specified). B. At each iteration t, a single kinetic parameter is selected at random and mutated through a log10-normal distribution of standard deviation σ. C. The new steady-state is computed using Copasi software, and the MOMA distance z between the mutant and the wild-type target fluxes is computed. D. If z is under a given selection threshold ω, the mutation is accepted. Else, the mutation is discarded. E. A new iteration t+1 is computed. -

- -
- Six types of selection are available: - - - MUTATION_ACCUMULATION: Run a mutation accumulation experiment by accepting all new mutations without any selection threshold, - - ABSOLUTE_METABOLIC_SUM_SELECTION: Run an evolution experiment by applying a stabilizing selection on the sum of absolute metabolic abundances, - - RELATIVE_METABOLIC_SUM_SELECTION: Run an evolution experiment by applying a stabilizing selection on the sum of relative metabolic abundances, - - ABSOLUTE_TARGET_FLUXES_SELECTION: Run an evolution experiment by applying a stabilizing selection on the MOMA distance of absolute target fluxes, - - RELATIVE_TARGET_FLUXES_SELECTION: Run an evolution experiment by applying a stabilizing selection on the MOMA distance of relative target fluxes. - - ```python - # Load a Markov Chain Monte Carlo (MCMC) instance - mcmc = 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') - - # Initialize the MCMC instance - mcmc.initialize() - - # Compute the successive iterations and write output files - stop_MCMC = False - while not stop_MCMC: - stop_mcmc = mcmc.iterate() - mcmc.write_output_file() - mcmc.write_statistics() - ``` - - ### Sensitivity analysis: - For each kinetic parameter p, each metabolic abundance [Xi] and each flux νj, the algorithm numerically computes relative derivatives and control coefficients. - - ```python - # Load a sensitivity analysis instance - sa = MetEvolSim.SensitivityAnalysis(sbml_filename='glycolysis.xml', factor_range=1.0, factor_step=0.01, copasi_path='/Applications/COPASI/CopasiSE') - - # Initialize the sensitivity analysis instance - sa.initialize() - - # Perform the sensitivity analysis for each kinetic parameter - stop_SA = False - while not stop_SA: - stop_SA = sa.explore_next_parameter() - ``` - - ## Help - To get some help on a MetEvolSim class or method, use the Python help function: - ```python - help(MetEvolSim.Model.set_species_initial_value) - ``` - to obtain a quick description and the list of parameters and outputs: - ``` - Help on function set_species_initial_value in module MetEvolSim: - - set_species_initial_value(self, species_id, value) - Set the initial concentration of the species 'species_id' in the - mutant model. - - Parameters - ---------- - species_id: str - Species identifier (as defined in the SBML model). - value: float >= 0.0 - Species abundance. - - Returns - ------- - None - (END) - ``` - - ## Copyright - Copyright © 2018-2019 Charles Rocabert, Gábor Boross and Balázs Papp. - All rights reserved. - - ## License -

- This 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. -

- -

- This 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. -

- -

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

- -Keywords: metabolism abundances evolution metabolic-network kinetic-model evolution-rate -Platform: UNKNOWN -Classifier: Development Status :: 4 - Beta -Classifier: Programming Language :: Python :: 3 -Classifier: License :: OSI Approved :: GNU General Public License v3 (GPLv3) -Classifier: Operating System :: OS Independent -Classifier: Intended Audience :: Science/Research -Classifier: Topic :: Scientific/Engineering :: Bio-Informatics -Requires-Python: >=3 -Description-Content-Type: text/markdown diff --git a/MetEvolSim.egg-info/SOURCES.txt b/MetEvolSim.egg-info/SOURCES.txt deleted file mode 100644 index 888c8c2..0000000 --- a/MetEvolSim.egg-info/SOURCES.txt +++ /dev/null @@ -1,21 +0,0 @@ -LICENSE.txt -MANIFEST.in -README.md -setup.cfg -setup.py -MetEvolSim/MetEvolSim.py -MetEvolSim/__init__.py -MetEvolSim.egg-info/PKG-INFO -MetEvolSim.egg-info/SOURCES.txt -MetEvolSim.egg-info/dependency_links.txt -MetEvolSim.egg-info/requires.txt -MetEvolSim.egg-info/top_level.txt -example/.DS_Store -example/models/.DS_Store -example/models/holzhutter2004.xml -example/models/holzhutter2004_objective_function.txt -metevolsim/__init__.py -metevolsim/metevolsim.py -scripts/.DS_Store -scripts/run_mcmc.py -scripts/run_sensitivity_analysis.py \ No newline at end of file diff --git a/MetEvolSim.egg-info/dependency_links.txt b/MetEvolSim.egg-info/dependency_links.txt deleted file mode 100644 index 8b13789..0000000 --- a/MetEvolSim.egg-info/dependency_links.txt +++ /dev/null @@ -1 +0,0 @@ - diff --git a/MetEvolSim.egg-info/requires.txt b/MetEvolSim.egg-info/requires.txt deleted file mode 100644 index 3301ad4..0000000 --- a/MetEvolSim.egg-info/requires.txt +++ /dev/null @@ -1,3 +0,0 @@ -python-libsbml -numpy -networkx diff --git a/MetEvolSim.egg-info/top_level.txt b/MetEvolSim.egg-info/top_level.txt deleted file mode 100644 index 333cf60..0000000 --- a/MetEvolSim.egg-info/top_level.txt +++ /dev/null @@ -1 +0,0 @@ -MetEvolSim diff --git a/build/lib/metevolsim/__init__.py b/build/lib/metevolsim/__init__.py deleted file mode 100644 index 33d14b0..0000000 --- a/build/lib/metevolsim/__init__.py +++ /dev/null @@ -1,27 +0,0 @@ -#!/usr/bin/env python3 -# coding: utf-8 - -#*************************************************************************** -# MetEvolSim (Metabolome Evolution Simulator) -# ------------------------------------------- -# MetEvolSim is a numerical framework dedicated to the study of metabolic -# abundances evolution. -# -# Copyright (c) 2018-2019 Charles Rocabert, Gábor Boross, Balázs Papp -# Web: https://github.com/charlesrocabert/MetEvolSim -# -# This 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. -# -# This 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. -# -# You should have received a copy of the GNU General Public License -# along with this program. If not, see . -#*************************************************************************** - -from .MetEvolSim import * diff --git a/build/lib/metevolsim/metevolsim.py b/build/lib/metevolsim/metevolsim.py deleted file mode 100644 index a8c341d..0000000 --- a/build/lib/metevolsim/metevolsim.py +++ /dev/null @@ -1,2440 +0,0 @@ -#!/usr/bin/env python3 -# coding: utf-8 - -#*************************************************************************** -# MetEvolSim (Metabolome Evolution Simulator) -# ------------------------------------------- -# MetEvolSim is a numerical framework dedicated to the study of metabolic -# abundances evolution. -# -# Copyright (c) 2018-2019 Charles Rocabert, Gábor Boross, Balázs Papp -# Web: https://github.com/charlesrocabert/MetEvolSim -# -# This 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. -# -# This 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. -# -# You should have received a copy of the GNU General Public License -# along with this program. If not, see . -#*************************************************************************** - -import os -import sys -import subprocess -import libsbml -import numpy as np -import networkx as nx - -#******************************************************************** -# Model class -# ----------- -# This class loads, saves and manipulates SBML models with specified -# kinetic parameters and initial metabolic concentrations. -# Manipulations include kinetic parameter mutations and steady-state -# computing with Copasi software. -#******************************************************************** -class Model: - """ - The Model class loads, saves and manipulates SBML models. - Manipulations include kinetic parameter mutations, steady-state computing, - metabolic control analysis (MCA) and shortest paths calculation. - Some constraints exist on the SBML model format: - - Species identifiers and names must be unique. All names must be specied, - or none of them (in this case, identifiers will be used as names). - - Reaction identifiers and names must be unique, - - Kinetic parameters must be specified (globally, and/or for each reaction). - All kinetic parameters will be mutable. - - If a species is a boundary condition, it is considered constant and its - statistics will not be tracked. - - The Model class rebuilds the list of metaids, even if metaids are not - defined in the original model. - - Attributes - ---------- - > sbml_filename : str - Name of the SBML model file. - > reader : libsbml.SBMLReader - SBML model reader. - > WT_document : libsbml.SBMLDocument - SBML document of the wild-type model. - > mutant_document : libsbml.SBMLDocument - SBML document of the mutant model. - > WT_model : libsbml.Model - Wild-type model. - > mutant_model : libsbml.Model - Mutant model. - > copasi_path: str - Location of Copasi executable. - > species : dict - List of model species. - > parameters : dict - List of model kinetic parameters. - > reactions : dict - List of model reactions. - > species_name_to_id : dict - Map of species identifiers from species names. - > reaction_name_to_id : dict - Map of reaction identifiers from reaction names. - > species_graph: networkx.Graph - A networkx Graph describing the metabolite-to-metabolite network. - > objective_function : list of [str, float] - Objective function (list of reaction identifiers and coefficients). - > WT_ABSOLUTE_SUM : float - Sum of evolvable metabolic absolute abundances in wild-type. - > mutant_ABSOLUTE_SUM : float - Sum of evolvable metabolic abundances in mutant. - > WT_RELATIVE_SUM : float - Sum of evolvable metabolic relative abundances in wild-type. - > mutant_RELATIVE_SUM : float - Sum of evolvable metabolic relative in mutant. - > ABSOLUTE_SUM_distance : float - Distance between wild-type and mutant absolute metabolic sums. - > RELATIVE_SUM_distance : float - Distance between wild-type and mutant relative metabolic sums. - > ABSOLUTE_MOMA_distance : float - Distance between the wild-type and the mutant, based on the - Minimization Of Metabolic Adjustment on absolute target fluxes. - > RELATIVE_MOMA_distance : float - Distance between the wild-type and the mutant, based on the - Minimization Of Metabolic Adjustment on relative target fluxes. - > ABSOLUTE_MOMA_ALL_distance : float - Distance between the wild-type and the mutant, based on the - Minimization Of Metabolic Adjustment on ALL absolute fluxes. - > RELATIVE_MOMA_ALL_distance : float - Distance between the wild-type and the mutant, based on the - Minimization Of Metabolic Adjustment on ALL relative fluxes. - - Methods - ------- - > __init__(sbml_filename, objective_function, copasi_path) - Constructor. - > rebuild_metaids() - Rebuild unique metaids for all model variables. - > build_species_list() - Build the list of metabolites. - > build_parameter_list() - Build the list of parameters. - > build_reaction_list() - Build the list of reactions. - > get_number_of_species() - Get the number of species. - > get_number_of_variable_species() - Get the number of variable species. - > get_number_of_parameters() - Get the number of parameters. - > get_number_of_reactions() - Get the number of reactions. - > get_WT_species_value(species_id) - Get WT species value. - > get_mutant_species_value(species_id) - Get mutant species value. - > get_WT_parameter_value(param_metaid) - Get WT parameter value. - > get_mutant_parameter_value(param_metaid) - Get mutant parameter value. - > set_species_initial_value(species_id, value) - Set a species initial value. - > set_WT_parameter_value(param_metaid, value) - Set WT parameter value. - > set_mutant_parameter_value(param_metaid, value) - Set mutant parameter value. - > get_random_parameter() - Get a random parameter. - > deterministic_parameter_mutation(param_metaid, factor) - Make a deterministic parameter mutation by a given log-scale factor, - centered on the WT value. - > random_parameter_mutation(param_metaid, sigma) - Make a random parameter mutation by a given log-scale mutation size - sigma, centered on the previous mutant value. - > write_WT_SBML_file() - Write WT SBML file. - > write_mutant_SBML_file() - Write mutant SBML file. - > create_WT_cps_file() - Create ancestor CPS file. - > create_mutant_cps_file() - Create mutant CPS file. - > edit_WT_cps_file(task) - Edit ancestor CPS file to schedule steady-state calculation. - > edit_mutant_cps_file(task) - Edit mutant CPS file to schedule steady-state calculation. - > run_copasi_for_WT() - Run Copasi for the WT model. - > run_copasi_for_mutant() - Run Copasi for the mutant model. - > parse_copasi_output(filename, task) - Parse Copasi output file. - > compute_WT_steady_state() - Compute WT steady-state. - > compute_mutant_steady_state() - Compute mutant steady-state. - > update_initial_concentrations() - Update species initial concentrations. - > compute_SUM_distance() - Compute the metabolic sum distance between the WT and the mutant. - > compute_MOMA_distance() - Compute the MOMA distance between the WT and the mutant, based on target - fluxes (MOMA: Minimization Of Metabolic Adjustment). - > compute_WT_metabolic_control_analysis() - Compute and save the wild-type metabolic control analysis (MCA). - > compute_mutant_metabolic_control_analysis() - Compute and save the mutant metabolic control analysis (MCA). - > build_species_graph(): - Build the metabolite-to-metabolite graph (mainly to compute shortest - paths afterward). - > save_shortest_paths(filename): - Save the matrix of all pairwise metabolites shortest paths (assuming an - undirected graph). - """ - - ### Constructor ### - def __init__( self, sbml_filename, objective_function, copasi_path ): - """ - Model class constructor. - - Parameters - ---------- - sbml_filename : str - Path of the SBML model file. The SBML model is automatically loaded. - objective_function : list of [str, float] - Objective function (list of reaction identifiers and coefficients). - copasi_path : str - Location of Copasi executable. - - Returns - ------- - None - """ - assert os.path.isfile(sbml_filename), "The SBML file \""+sbml_filename+"\" does not exist. Exit." - assert os.path.isfile(copasi_path), "The executable \""+copasi_path+"\" does not exist. Exit." - - #~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~# - # 1) Main SBML data # - #~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~# - self.sbml_filename = sbml_filename - self.reader = libsbml.SBMLReader() - self.WT_document = self.reader.readSBMLFromFile(self.sbml_filename) - self.mutant_document = self.reader.readSBMLFromFile(self.sbml_filename) - self.WT_model = self.WT_document.getModel() - self.mutant_model = self.mutant_document.getModel() - self.copasi_path = copasi_path - - #~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~# - # 2) List of model variables (species, reactions, parameters) # - #~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~# - self.species = {} - self.parameters = {} - self.reactions = {} - self.species_name_to_id = {} - self.reaction_name_to_id = {} - self.rebuild_metaids() - self.build_species_list() - self.build_parameter_list() - self.build_reaction_list() - - #~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~# - # 3) Graph analysis # - #~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~# - self.species_graph = nx.Graph() - - #~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~# - # 4) Model evaluation # - #~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~# - self.objective_function = objective_function - self.WT_ABSOLUTE_SUM = 0.0 - self.mutant_ABSOLUTE_SUM = 0.0 - self.WT_RELATIVE_SUM = 0.0 - self.mutant_RELATIVE_SUM = 0.0 - self.ABSOLUTE_SUM_distance = 0.0 - self.RELATIVE_SUM_distance = 0.0 - self.ABSOLUTE_MOMA_distance = 0.0 - self.RELATIVE_MOMA_distance = 0.0 - self.ABSOLUTE_MOMA_ALL_distance = 0.0 - self.RELATIVE_MOMA_ALL_distance = 0.0 - for target_flux in objective_function: - assert target_flux[0] in self.reactions - - ### Rebuilt the unique metaids for all the model variables ### - def rebuild_metaids( self ): - """ - Rebuild the unique metaids for all model variables. - - Parameters - ---------- - None - - Returns - ------- - None - """ - #~~~~~~~~~~~~~~~~~~~~~~~~~~~# - # 1) Rebuild WT metaids # - #~~~~~~~~~~~~~~~~~~~~~~~~~~~# - metaid = 0 - for species in self.WT_model.getListOfSpecies(): - species.setMetaId("_"+str(metaid)) - metaid += 1 - for parameter in self.WT_model.getListOfParameters(): - parameter.setMetaId("_"+str(metaid)) - metaid += 1 - for reaction in self.WT_model.getListOfReactions(): - for elmt in reaction.getListOfAllElements(): - if elmt.getElementName() == "parameter" or elmt.getElementName() == "localParameter": - parameter = elmt - parameter.setMetaId("_"+str(metaid)) - metaid += 1 - for reaction in self.WT_model.getListOfReactions(): - reaction.setMetaId("_"+str(metaid)) - metaid += 1 - #~~~~~~~~~~~~~~~~~~~~~~~~~~~# - # 2) Rebuild mutant metaids # - #~~~~~~~~~~~~~~~~~~~~~~~~~~~# - metaid = 0 - for species in self.mutant_model.getListOfSpecies(): - species.setMetaId("_"+str(metaid)) - metaid += 1 - for parameter in self.mutant_model.getListOfParameters(): - parameter.setMetaId("_"+str(metaid)) - metaid += 1 - for reaction in self.mutant_model.getListOfReactions(): - for elmt in reaction.getListOfAllElements(): - if elmt.getElementName() == "parameter" or elmt.getElementName() == "localParameter": - parameter = elmt - parameter.setMetaId("_"+str(metaid)) - metaid += 1 - for reaction in self.mutant_model.getListOfReactions(): - reaction.setMetaId("_"+str(metaid)) - metaid += 1 - - ### Build the list of metabolites ### - def build_species_list( self ): - """ - Build the species list. - - Parameters - ---------- - None - - Returns - ------- - None - """ - self.species = {} - self.species_name_to_id = {} - for species in self.WT_model.getListOfSpecies(): - species_metaid = species.getMetaId() - species_id = species.getId() - species_name = species.getName() - isConstant = species.getBoundaryCondition() - if species_name == "": - species_name = species_id - assert species_id not in list(self.species) - self.species[species_id] = {"metaid":"", "id":"", "name":"", "constant":False, "initial_value":0.0, "WT_value":0.0, "mutant_value":0.0} - self.species[species_id]["metaid"] = species_metaid - self.species[species_id]["id"] = species_id - self.species[species_id]["name"] = species_name - self.species[species_id]["constant"] = isConstant - self.species[species_id]["initial_value"] = species.getInitialConcentration() - self.species[species_id]["WT_value"] = species.getInitialConcentration() - self.species[species_id]["mutant_value"] = species.getInitialConcentration() - assert species_name not in self.species_name_to_id - self.species_name_to_id[species_name] = species_id - - ### Build the list of parameters ### - def build_parameter_list( self ): - """ - Build the parameters list. - - Parameters - ---------- - None - - Returns - ------- - None - """ - self.parameters = {} - #~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~# - # 1) Parse main parameters # - #~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~# - for parameter in self.WT_model.getListOfParameters(): - parameter_metaid = parameter.getMetaId() - parameter_id = parameter.getId() - self.parameters[parameter_metaid] = {"metaid":"", "id":"", "WT_value":0.0, "mutant_value":0.0} - self.parameters[parameter_metaid]["metaid"] = parameter_metaid - self.parameters[parameter_metaid]["id"] = parameter_id - self.parameters[parameter_metaid]["WT_value"] = parameter.getValue() - self.parameters[parameter_metaid]["mutant_value"] = parameter.getValue() - #~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~# - # 2) Parse parameters for each reaction # - #~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~# - for reaction in self.WT_model.getListOfReactions(): - for elmt in reaction.getListOfAllElements(): - if elmt.getElementName() == "parameter" or elmt.getElementName() == "localParameter": - parameter = elmt - parameter_metaid = parameter.getMetaId() - parameter_id = parameter.getId() - self.parameters[parameter_metaid] = {"metaid":"", "id":"", "WT_value":0.0, "mutant_value":0.0} - self.parameters[parameter_metaid]["metaid"] = parameter_metaid - self.parameters[parameter_metaid]["id"] = parameter_id - self.parameters[parameter_metaid]["WT_value"] = parameter.getValue() - self.parameters[parameter_metaid]["mutant_value"] = parameter.getValue() - - ### Build the list of reactions ### - def build_reaction_list( self ): - """ - Build the reaction list. - - Parameters - ---------- - None - - Returns - ------- - None - """ - self.reactions = {} - self.reaction_name_to_id = {} - for reaction in self.WT_model.getListOfReactions(): - reaction_metaid = reaction.getMetaId() - reaction_id = reaction.getId() - reaction_name = reaction.getName() - if reaction_name == "": - reaction_name = reaction_id - assert reaction_id not in list(self.reactions) - self.reactions[reaction_id] = {"metaid":"", "id":"", "name":"", "WT_value":0.0, "mutant_value":0.0} - self.reactions[reaction_id]["metaid"] = reaction_metaid - self.reactions[reaction_id]["id"] = reaction_id - assert reaction_name not in self.reaction_name_to_id - self.reaction_name_to_id[reaction_name] = reaction_id - - ### Get the number of species ### - def get_number_of_species( self ): - """ - Get the number of species. - - Parameters - ---------- - None - - Returns - ------- - int - """ - return len(self.species) - - ### Get the number of variable species ### - def get_number_of_variable_species( self ): - """ - Get the number of variable species. - - Parameters - ---------- - None - - Returns - ------- - int - """ - count = 0 - for species_id in self.species: - if not self.species[species_id]["constant"]: - count += 1 - return count - - ### Get the number of parameters ### - def get_number_of_parameters( self ): - """ - Get the number of kinetic parameters. - - Parameters - ---------- - None - - Returns - ------- - int - """ - return len(self.parameters) - - ### Get the number of reactions ### - def get_number_of_reactions( self ): - """ - Get the number of reactions. - - Parameters - ---------- - None - - Returns - ------- - int - """ - return len(self.reactions) - - ### Get wild-type species value ### - def get_WT_species_value( self, species_id ): - """ - Get the wild-type value of the species 'species_id'. - - Parameters - ---------- - species_id: str - The identifier of the species (as defined in the SBML model). - - Returns - ------- - float - """ - assert species_id in list(self.species) - return self.species[species_id]["WT_value"] - - ### Get mutant species value ### - def get_mutant_species_value( self, species_id ): - """ - Get the wild-type value of the species 'species_id'. - - Parameters - ---------- - species_id: str - The identifier of the species (as defined in the SBML model). - - Returns - ------- - float - """ - assert species_id in list(self.species) - return self.species[species_id]["mutant_value"] - - ### Get wild-type parameter value ### - def get_WT_parameter_value( self, param_metaid ): - """ - Get the wild-type value of the parameter 'param_metaid' - - Parameters - ---------- - param_metaid: str - The meta identifier of the parameter (as defined in the SBML model). - - Returns - ------- - float - """ - assert param_metaid in list(self.parameters) - assert self.parameters[param_metaid]["WT_value"] == self.WT_model.getElementByMetaId(param_metaid).getValue() - return self.parameters[param_metaid]["WT_value"] - - ### Get mutant parameter value ### - def get_mutant_parameter_value( self, param_metaid ): - """ - Get the mutant value of the parameter 'param_metaid' - - Parameters - ---------- - param_metaid: str - The meta identifier of the parameter (as defined in the SBML model). - - Returns - ------- - float - """ - assert param_metaid in list(self.parameters) - assert self.parameters[param_metaid]["mutant_value"] == self.mutant_model.getElementByMetaId(param_metaid).getValue() - return self.parameters[param_metaid]["mutant_value"] - - ### Get wild-type species values array ### - def get_WT_species_array( self ): - """ - Get a numpy array of wild-type species abundances. - - Parameters - ---------- - None - - Returns - ------- - numpy array - """ - vec = [] - for species_id in self.species: - if not self.species[species_id]["constant"]: - vec.append(self.species[species_id]["WT_value"]) - return np.array(vec) - - ### Get mutant species values array ### - def get_mutant_species_array( self ): - """ - Get a numpy array of mutant species abundances. - - Parameters - ---------- - None - - Returns - ------- - numpy array - """ - vec = [] - for species_id in self.species: - if not self.species[species_id]["constant"]: - vec.append(self.species[species_id]["mutant_value"]) - return np.array(vec) - - ### Get wild-type reaction values array ### - def get_WT_reaction_array( self ): - """ - Get a numpy array of wild-type reaction fluxes. - - Parameters - ---------- - None - - Returns - ------- - numpy array - """ - vec = [] - for reaction_id in self.reactions: - vec.append(self.reactions[reaction_id]["WT_value"]) - return np.array(vec) - - ### Get mutant reaction values array ### - def get_mutant_reaction_array( self ): - """ - Get a numpy array of mutant reaction fluxes. - - Parameters - ---------- - None - - Returns - ------- - numpy array - """ - vec = [] - for reaction_id in self.reactions: - vec.append(self.reactions[reaction_id]["mutant_value"]) - return np.array(vec) - - ### Set a species initial value ### - def set_species_initial_value( self, species_id, value ): - """ - Set the initial concentration of the species 'species_id' in the - mutant model. - - Parameters - ---------- - species_id: str - Species identifier (as defined in the SBML model). - value: float >= 0.0 - Species abundance. - - Returns - ------- - None - """ - assert species_id in list(self.species) - if value < 0.0: - value = 0.0 - self.species[species_id]["WT_value"] = value - self.mutant_model.getListOfSpecies().get(species_id).setInitialConcentration(value) - - ### Set wild-type parameter value ### - def set_WT_parameter_value( self, param_metaid, value ): - """ - Set the wild-type value of the parameter 'param_metaid'. - - Parameters - ---------- - param_metaid: str - Parameter meta identifier (as defined in the SBML model). - value: float - Parameter value. - - Returns - ------- - None - """ - assert param_metaid in list(self.parameters) - self.parameters[param_metaid]["WT_value"] = value - self.WT_model.getElementByMetaId(param_metaid).setValue(value) - - ### Set mutant parameter value ### - def set_mutant_parameter_value( self, param_metaid, value ): - """ - Set the mutant value of the parameter 'param_metaid'. - - Parameters - ---------- - param_metaid: str - Parameter meta identifier (as defined in the SBML model). - value: float - Parameter value. - - Returns - ------- - None - """ - assert param_metaid in list(self.parameters) - self.parameters[param_metaid]["mutant_value"] = value - self.mutant_model.getElementByMetaId(param_metaid).setValue(value) - - ### Get a random parameter ### - def get_random_parameter( self ): - """ - Get a kinetic parameter at random. - - Parameters - ---------- - None - - Returns - ------- - str - """ - param_index = np.random.randint(0, len(self.parameters)) - param_metaid = list(self.parameters)[param_index] - return param_metaid - - ### Make a deterministic parameter mutation by a given log-scale factor, ### - ### centered on the wild-type value ### - def deterministic_parameter_mutation( self, param_metaid, factor ): - """ - Mutate a given parameter 'param_metaid' by a given log10-scale factor. - The mutation is centered on the wild-type value. - x' = x*10^(factor) - - Parameters - ---------- - param_metaid: str - Parameter meta identifier (as defined in the SBML model). - factor: float - Log10-scale factor. - - Returns - ------- - int, int - """ - assert param_metaid in list(self.parameters) - wt_value = self.get_WT_parameter_value(param_metaid) - mutant_value = wt_value*10.0**factor - self.set_mutant_parameter_value(param_metaid, mutant_value) - return wt_value, mutant_value - - ### Make a random parameter mutation by a given log-scale mutation size ### - ### sigma, centered on the previous mutant value ### - def random_parameter_mutation( self, param_metaid, sigma ): - """ - Mutate a given parameter 'param_metaid' through a log10-normal law of - variance of sigma^2. - x' = x*10^(N(0,sigma)) - - Parameters - ---------- - param_metaid: str - Parameter meta identifier (as defined in the SBML model). - sigma: float > 0.0 - Log10-scale standard deviation. - - Returns - ------- - int, int - """ - assert param_metaid in list(self.parameters) - assert sigma > 0.0 - factor = np.random.normal(0.0, sigma, 1) - wt_value = self.get_mutant_parameter_value(param_metaid) - mutant_value = wt_value*10**factor[0] - self.set_mutant_parameter_value(param_metaid, mutant_value) - return wt_value, mutant_value - - ### Write wild-type SBML file ### - def write_WT_SBML_file( self ): - """ - Write the wild-type model in a SBML file. - - Parameters - ---------- - None - - Returns - ------- - None - """ - libsbml.writeSBMLToFile(self.WT_document, "output/WT.xml") - - ### Write mutant SBML file ### - def write_mutant_SBML_file( self ): - """ - Write the mutant model in a SBML file. - - Parameters - ---------- - None - - Returns - ------- - None - """ - libsbml.writeSBMLToFile(self.mutant_document, "output/mutant.xml") - - ### Create wild-type CPS file ### - def create_WT_cps_file( self ): - """ - Create a CPS file from the wild-type SBML file. - - Parameters - ---------- - None - - Returns - ------- - None - """ - cmd_line = self.copasi_path+" -i ./output/WT.xml" - process = subprocess.call([cmd_line], stdout=subprocess.PIPE, shell=True) - - ### Create mutant CPS file ### - def create_mutant_cps_file( self ): - """ - Create a CPS file from the mutant SBML file. - - Parameters - ---------- - None - - Returns - ------- - None - """ - cmd_line = self.copasi_path+" -i ./output/mutant.xml" - process = subprocess.call([cmd_line], stdout=subprocess.PIPE, shell=True) - - ### Edit wild-type CPS file to schedule steady-state calculation ### - def edit_WT_cps_file( self, task ): - """ - Edit wild-type CPS file to schedule steady-state calculation. - - Parameters - ---------- - task: str - Define Copasi task (STEADY_STATE/MCA). - Returns - ------- - None - """ - assert task in ["STEADY_STATE", "MCA"] - f = open("./output/WT.cps", "r") - cps = f.read() - f.close() - upper_text = cps.split("")[0] - lower_text = cps.split("")[1] - edited_file = "" - edited_file += upper_text - if task == "STEADY_STATE": - edited_file += ' \n' - edited_file += ' \n' - edited_file += ' \n' - edited_file += ' \n' - edited_file += ' \n' - edited_file += ' \n' - edited_file += ' \n' - edited_file += ' \n' - edited_file += ' \n' - edited_file += ' \n' - edited_file += ' \n' - edited_file += ' \n' - edited_file += ' \n' - edited_file += ' \n' - edited_file += ' \n' - edited_file += ' \n' - edited_file += ' \n' - edited_file += ' \n' - edited_file += ' \n' - edited_file += ' \n' - elif task == "MCA": - edited_file += ' \n' - edited_file += ' \n' - edited_file += ' \n' - edited_file += ' \n' - edited_file += ' \n' - edited_file += ' \n' - edited_file += ' \n' - edited_file += ' \n' - edited_file += ' \n' - edited_file += ' \n' - edited_file += ' \n' - edited_file += ' \n' - edited_file += ' \n' - edited_file += ' \n' - edited_file += ' \n' - edited_file += ' \n' - edited_file += ' \n' - edited_file += ' \n' - edited_file += ' \n' - edited_file += ' \n' - edited_file += ' \n' - edited_file += ' \n' - edited_file += ' \n' - edited_file += ' \n' - edited_file += ' \n' - edited_file += ' \n' - edited_file += ' \n' - edited_file += ' \n' - edited_file += ' \n' - edited_file += ' \n' - edited_file += ' \n' - edited_file += lower_text - f = open("./output/WT.cps", "w") - f.write(edited_file) - f.close() - - ### Edit mutant CPS file to schedule steady-state calculation ### - def edit_mutant_cps_file( self, task ): - """ - Edit mutant CPS file to schedule steady-state calculation. - - Parameters - ---------- - task: str - Define Copasi task (STEADY_STATE/MCA). - - Returns - ------- - None - """ - assert task in ["STEADY_STATE", "MCA"] - f = open("./output/mutant.cps", "r") - cps = f.read() - f.close() - upper_text = cps.split("")[0] - lower_text = cps.split("")[1] - edited_file = "" - edited_file += upper_text - if task == "STEADY_STATE": - edited_file += ' \n' - edited_file += ' \n' - edited_file += ' \n' - edited_file += ' \n' - edited_file += ' \n' - edited_file += ' \n' - edited_file += ' \n' - edited_file += ' \n' - edited_file += ' \n' - edited_file += ' \n' - edited_file += ' \n' - edited_file += ' \n' - edited_file += ' \n' - edited_file += ' \n' - edited_file += ' \n' - edited_file += ' \n' - edited_file += ' \n' - edited_file += ' \n' - edited_file += ' \n' - edited_file += ' \n' - elif task == "MCA": - edited_file += ' \n' - edited_file += ' \n' - edited_file += ' \n' - edited_file += ' \n' - edited_file += ' \n' - edited_file += ' \n' - edited_file += ' \n' - edited_file += ' \n' - edited_file += ' \n' - edited_file += ' \n' - edited_file += ' \n' - edited_file += ' \n' - edited_file += ' \n' - edited_file += ' \n' - edited_file += ' \n' - edited_file += ' \n' - edited_file += ' \n' - edited_file += ' \n' - edited_file += ' \n' - edited_file += ' \n' - edited_file += ' \n' - edited_file += ' \n' - edited_file += ' \n' - edited_file += ' \n' - edited_file += ' \n' - edited_file += ' \n' - edited_file += ' \n' - edited_file += ' \n' - edited_file += ' \n' - edited_file += ' \n' - edited_file += ' \n' - edited_file += lower_text - f = open("./output/mutant.cps", "w") - f.write(edited_file) - f.close() - - ### Run Copasi for the wild-type model ### - def run_copasi_for_WT( self ): - """ - Run Copasi to compute the wild-type steady-state and update model state. - - Parameters - ---------- - None - - Returns - ------- - None - """ - if os.path.isfile("./output/WT_output.txt"): - os.system("rm ./output/WT_output.txt") - cmd_line = self.copasi_path+" ./output/WT.cps" - process = subprocess.call([cmd_line], stdout=subprocess.PIPE, shell=True) - - ### Run Copasi for the mutant model ### - def run_copasi_for_mutant( self ): - """ - Run Copasi to compute the mutant steady-state and update model state. - - Parameters - ---------- - None - - Returns - ------- - None - """ - if os.path.isfile("./output/mutant_output.txt"): - os.system("rm ./output/mutant_output.txt") - cmd_line = self.copasi_path+" ./output/mutant.cps" - process = subprocess.call([cmd_line], stdout=subprocess.PIPE, shell=True) - - ### Parse Copasi output file ### - def parse_copasi_output( self, filename, task ): - """ - Parse the Copasi output 'filename'. - - Parameters - ---------- - filename: str - Name of the Copasi output (txt file). - task: str - Define Copasi task (STEADY_STATE/MCA). - - Returns - ------- - - list of [str, float] lists, list of [str, float] lists if task == "STEADY_STATE" - - list of [str], list of [str], list of list of [str] if task == "MCA" - """ - assert os.path.isfile(filename), "The Copasi output \""+filename+"\" does not exist. Exit." - assert task in ["STEADY_STATE", "MCA"] - #~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~# - # 1) Parse to extract the steady-state # - #~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~# - if task == "STEADY_STATE": - concentrations = [] - fluxes = [] - parse_concentrations = False - parse_fluxes = False - f = open(filename, "r") - l = f.readline() - while l: - if l == "\n" and parse_concentrations: - parse_concentrations = False - if l == "\n" and parse_fluxes: - parse_fluxes = False - if parse_concentrations: - name = l.split("\t")[0] - val = l.split("\t")[1] - concentrations.append([name, val]) - if parse_fluxes: - name = l.split("\t")[0] - val = l.split("\t")[1] - fluxes.append([name, val]) - if l.startswith("Species"): - parse_concentrations = True - if l.startswith("Reaction"): - parse_fluxes = True - l = f.readline() - f.close() - return concentrations, fluxes - #~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~# - # 2) Parse to extract the MCA result # - #~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~# - elif task == "MCA": - colnames = [] - rownames = [] - unscaled = [] - scaled = [] - N = 0 - f = open(filename, "r") - l = f.readline() - while l: - if l.startswith("Unscaled concentration control coefficients"): - l = f.readline() - l = f.readline() - l = f.readline() - l = f.readline() - colnames = l.strip("\n\t").split("\t") - N = len(colnames) - l = f.readline() - for i in range(len(colnames)): - colnames[i] = colnames[i].strip("()") - while l != "\n": - l = l.strip("\n\t").split("\t") - rownames.append(l[0]) - unscaled.append(l[1:(N+1)]) - l = f.readline() - if l.startswith("Scaled concentration control coefficients"): - l = f.readline() - l = f.readline() - l = f.readline() - l = f.readline() - l = f.readline() - while l != "\n": - l = l.strip("\n\t").split("\t") - scaled.append(l[1:(N+1)]) - l = f.readline() - break - l = f.readline() - f.close() - return rownames, colnames, unscaled, scaled - - ### Compute wild-type steady-state ### - def compute_WT_steady_state( self ): - """ - Compute and save the wild-type steady-state. - - Parameters - ---------- - None - - Returns - ------- - None - """ - #~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~# - # 1) Compute the steady-state with Copasi # - #~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~# - self.write_WT_SBML_file() - self.create_WT_cps_file() - self.edit_WT_cps_file("STEADY_STATE") - self.run_copasi_for_WT() - - #~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~# - # 2) Extract steady-state # - #~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~# - concentrations, fluxes = self.parse_copasi_output("./output/WT_output.txt", "STEADY_STATE") - - #~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~# - # 3) Update model and lists # - #~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~# - self.WT_ABSOLUTE_SUM = 0.0 - self.WT_RELATIVE_SUM = 0.0 - for elmt in concentrations: - species_name = elmt[0] - species_value = float(elmt[1]) - species_id = self.species_name_to_id[species_name] - assert species_id in self.species - if not self.species[species_id]["constant"]: - self.species[species_id]["WT_value"] = species_value - self.species[species_id]["mutant_value"] = species_value - self.WT_ABSOLUTE_SUM += species_value - self.WT_RELATIVE_SUM += 1.0 - for elmt in fluxes: - reaction_name = elmt[0] - reaction_value = float(elmt[1]) - reaction_id = self.reaction_name_to_id[reaction_name] - assert reaction_id in self.reactions - self.reactions[reaction_id]["WT_value"] = reaction_value - self.reactions[reaction_id]["mutant_value"] = reaction_value - - ### Compute mutant steady-state ### - def compute_mutant_steady_state( self ): - """ - Compute and save the mutant steady-state. - - Parameters - ---------- - None - - Returns - ------- - None - """ - #~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~# - # 1) Compute the steady-state with Copasi # - #~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~# - self.write_mutant_SBML_file() - self.create_mutant_cps_file() - self.edit_mutant_cps_file("STEADY_STATE") - self.run_copasi_for_mutant() - - #~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~# - # 2) Extract steady-state # - #~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~# - concentrations, fluxes = self.parse_copasi_output("./output/mutant_output.txt", "STEADY_STATE") - - #~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~# - # 3) Update model and lists # - #~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~# - self.mutant_ABSOLUTE_SUM = 0.0 - self.mutant_RELATIVE_SUM = 0.0 - for elmt in concentrations: - species_name = elmt[0] - species_value = float(elmt[1]) - species_id = self.species_name_to_id[species_name] - assert species_id in self.species - if not self.species[species_id]["constant"]: - self.species[species_id]["mutant_value"] = species_value - self.mutant_ABSOLUTE_SUM += species_value - self.mutant_RELATIVE_SUM += species_value/self.species[species_id]["WT_value"] - for elmt in fluxes: - reaction_name = elmt[0] - reaction_value = float(elmt[1]) - reaction_id = self.reaction_name_to_id[reaction_name] - assert reaction_id in self.reactions - self.reactions[reaction_id]["mutant_value"] = reaction_value - - ### Update species initial concentrations ### - def update_initial_concentrations( self ): - """ - Update initial concentrations in the mutant model. - - Parameters - ---------- - None - - Returns - ------- - None - """ - for species_item in self.species.items(): - species_id = species_item[0] - if not self.species[species_id]["constant"]: - self.set_species_initial_value(species_id, self.species[species_id]["mutant_value"]) - - ### Compute the metabolic sum distance between the wild-type and the mutant ### - def compute_SUM_distance( self ): - """ - Compute the metabolic sum distance between the WT and the mutant. - - Parameters - ---------- - None - - Returns - ------- - None - """ - self.ABSOLUTE_SUM_distance = abs(self.WT_ABSOLUTE_SUM-self.mutant_ABSOLUTE_SUM) - self.RELATIVE_SUM_distance = abs(self.WT_RELATIVE_SUM-self.mutant_RELATIVE_SUM) - - ### Compute the MOMA distance between the wild-type and the mutant ### - def compute_MOMA_distance( self ): - """ - Compute the MOMA distance between the WT and the mutant, based on - target fluxes. - (MOMA: Minimization Of Metabolic Adjustment) - - Parameters - ---------- - None - - Returns - ------- - None - """ - #~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~# - # 1) Compute absolute and relative MOMA distance on target fluxes # - #~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~# - self.ABSOLUTE_MOMA_distance = 0.0 - self.RELATIVE_MOMA_distance = 0.0 - for target_flux in self.objective_function: - reaction_id = target_flux[0] - coefficient = target_flux[1] - wt_value = self.reactions[reaction_id]["WT_value"] - mutant_value = self.reactions[reaction_id]["mutant_value"] - self.ABSOLUTE_MOMA_distance += (wt_value-mutant_value)*(wt_value-mutant_value) - self.RELATIVE_MOMA_distance += (wt_value-mutant_value)/wt_value*(wt_value-mutant_value)/wt_value - self.ABSOLUTE_MOMA_distance = np.sqrt(self.ABSOLUTE_MOMA_distance) - self.RELATIVE_MOMA_distance = np.sqrt(self.RELATIVE_MOMA_distance) - #~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~# - # 2) Compute absolute and relative MOMA distance on all the fluxes # - #~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~# - self.ABSOLUTE_MOMA_ALL_distance = 0.0 - self.RELATIVE_MOMA_ALL_distance = 0.0 - WT_flux_array = self.get_WT_reaction_array() - mutant_flux_array = self.get_mutant_reaction_array() - for i in range(self.get_number_of_reactions()): - wt_value = WT_flux_array[i] - mutant_value = mutant_flux_array[i] - if wt_value > 1e-10: - self.ABSOLUTE_MOMA_ALL_distance += (wt_value-mutant_value)*(wt_value-mutant_value) - self.RELATIVE_MOMA_ALL_distance += (wt_value-mutant_value)/wt_value*(wt_value-mutant_value)/wt_value - self.ABSOLUTE_MOMA_ALL_distance = np.sqrt(self.ABSOLUTE_MOMA_ALL_distance) - self.RELATIVE_MOMA_ALL_distance = np.sqrt(self.RELATIVE_MOMA_ALL_distance) - - ### Compute wild-type metabolic control analysis (MCA) ### - def compute_WT_metabolic_control_analysis( self ): - """ - Compute and save the wild-type metabolic control analysis (MCA). - - Parameters - ---------- - None - - Returns - ------- - None - """ - #~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~# - # 1) Compute the steady-state with Copasi # - #~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~# - self.write_WT_SBML_file() - self.create_WT_cps_file() - self.edit_WT_cps_file("MCA") - self.run_copasi_for_WT() - - #~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~# - # 2) Extract the MCA result # - #~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~# - rownames, colnames, unscaled, scaled = self.parse_copasi_output("./output/WT_output.txt", "MCA") - - #~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~# - # 3) Write control coefficients data # - #~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~# - f = open("./output/WT_MCA_unscaled.txt", "w") - f.write("species_id species_value flux_id flux_value control_coef\n") - for i in range(len(rownames)): - for j in range(len(colnames)): - species_id = rownames[i] - flux_id = colnames[j] - print(species_id, flux_id) - species_value = self.species[species_id]["WT_value"] - flux_value = self.reactions[flux_id]["WT_value"] - control_coef = unscaled[i][j] - f.write(species_id+" "+str(species_value)+" "+flux_id+" "+str(flux_value)+" "+control_coef+"\n") - f.close() - f = open("./output/WT_MCA_scaled.txt", "w") - f.write("species_id species_value flux_id flux_value control_coef\n") - for i in range(len(rownames)): - for j in range(len(colnames)): - species_id = rownames[i] - flux_id = colnames[j] - species_value = self.species[species_id]["WT_value"] - flux_value = self.reactions[flux_id]["WT_value"] - control_coef = scaled[i][j] - f.write(species_id+" "+str(species_value)+" "+flux_id+" "+str(flux_value)+" "+control_coef+"\n") - f.close() - - ### Compute mutant metabolic control analysis (MCA) ### - def compute_mutant_metabolic_control_analysis( self ): - """ - Compute and save the mutant metabolic control analysis (MCA). - - Parameters - ---------- - None - - Returns - ------- - None - """ - #~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~# - # 1) Compute the steady-state with Copasi # - #~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~# - self.write_mutant_SBML_file() - self.create_mutant_cps_file() - self.edit_mutant_cps_file("MCA") - self.run_copasi_for_mutant() - - #~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~# - # 2) Extract the MCA result # - #~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~# - rownames, colnames, unscaled, scaled = self.parse_copasi_output("./output/mutant_output.txt", "MCA") - - #~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~# - # 3) Write control coefficients matrices # - #~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~# - f = open("./output/mutant_MCA_unscaled.txt", "w") - for colname in colnames: - f.write(" "+colname) - f.write("\n") - for i in range(len(rownames)): - f.write(rowname) - for elmt in unscaled[i]: - f.write(" "+elmt) - f.write("\n") - f.close() - f = open("./output/mutant_MCA_scaled.txt", "w") - for colname in colnames: - f.write(" "+colname) - f.write("\n") - for i in range(len(rownames)): - f.write(rowname) - for elmt in scaled[i]: - f.write(" "+elmt) - f.write("\n") - f.close() - - ### Build the graph of species ### - def build_species_graph( self ): - """ - Build the metabolite-to-metabolite graph (mainly to compute shortest - paths afterward). - - Parameters - ---------- - None - - Returns - ------- - None - """ - self.species_graph.clear() - species_metaid = {} - #~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~# - # 1) Create nodes from species names # - #~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~# - for species_id in self.species.keys(): - self.species_graph.add_node(self.species[species_id]["name"]) - species_metaid[self.species[species_id]["metaid"]] = self.species[species_id]["name"] - #~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~# - # 2) Create edges from reactions # - # (All the species involved in the # - # reaction are connected) # - #~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~# - for reaction in self.WT_model.getListOfReactions(): - list_of_metabolites = [] - for reactant in reaction.getListOfReactants(): - list_of_metabolites.append(reactant.getSpecies()) - for product in reaction.getListOfProducts(): - list_of_metabolites.append(product.getSpecies()) - #for modifier in reaction.getListOfModifiers(): - # list_of_metabolites.append(modifier.getSpecies()) - for i in range(len(list_of_metabolites)): - for j in range(i+1, len(list_of_metabolites)): - self.species_graph.add_edge(list_of_metabolites[i], list_of_metabolites[j]) - del species_metaid - - ### Save shortest path lengths matrix ### - def save_shortest_paths( self, filename ): - """ - Save the matrix of all pairwise metabolites shortest paths (assuming an - undirected graph). - - Parameters - ---------- - filename: str - Name of the Copasi output (txt file). - - Returns - ------- - None - """ - #~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~# - # 1) Extract shortest path lengths # - #~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~# - sp = nx.shortest_path_length(self.species_graph) - - #~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~# - # 2) Build the matrix of shortest paths # - #~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~# - sp_dict = {} - list_of_metabolites = [] - header = "" - for line in sp: - sp_dict[line[0]] = {} - list_of_metabolites.append(line[0]) - header += line[0]+" " - for species in line[1]: - sp_dict[line[0]][species] = line[1][species] - #~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~# - # 3) Write the file # - #~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~# - f = open(filename, "w") - f.write(header.strip(" ")+"\n") - for sp1 in list_of_metabolites: - line = sp1 - for sp2 in list_of_metabolites: - line += " "+str(sp_dict[sp1][sp2]) - f.write(line+"\n") - f.close() - -#******************************************************************** -# MCMC class -# ---------- -# This class runs a Monte-Carlo Markov Chain (MCMC) evolution -# experiment on a SBML model, with cycles of mutations and fixations -# depending on a given selection threshold. -#******************************************************************** -class MCMC: - """ - The MCMC class runs a Monte-Carlo Markov Chain (MCMC) evolution experiment - on a SBML model, with cycles of mutations and fixations depending on a given - selection threshold. - - Attributes - ---------- - > sbml_filename : str - Name of the SBML model file. - > total_iterations : int > 0 - Total number of MCMC iterations. - > sigma : float > 0.0 - Standard deviation of the Log10-normal mutational distribution. - > selection_scheme : str - Selection scheme ('MUTATION_ACCUMULATION'/'ABSOLUTE_METABOLIC_SUM_SELECTION'/ - 'RELATIVE_METABOLIC_SUM_SELECTION'/'ABSOLUTE_TARGET_FLUXES_SELECTION'/ - 'RELATIVE_TARGET_FLUXES_SELECTION'). - > selection_threshold : str - Selection threshold applied on the MOMA distance. - > copasi_path : str - Location of Copasi executable. - > model : Model - SBML Model (automatically loaded from the SBML file). - > nb_iterations : int - Current number of iterations. - > nb_accepted : int - Current number of accepted mutations. - > nb_rejected : int - Current number of rejected mutations. - > param_metaid : str - Last mutated parameter meta identifier. - > param_id : str - Last mutated parameter identifier. - > param_value : float - Last mutated parameter value. - > param_previous : float - Parameter value before mutation. - > N_abund : float - Number of variable species. - > WT_abund : numpy array - Wild-type abundances tracker. - > WT_flux : numpy array - Wild-type fluxes tracker. - > mutant_abund : numpy array - Mutant abundances tracker. - > mutant_flux : numpy array - Mutant fluxes tracker. - > sum_abund : numpy array - Sum of abundances tracker. - > relsum_abund : numpy array - Sum of abundances relatively to the wild-type tracker. - > sqsum_abund : numpy array - Square sum of abundances tracker. - > relsqsum_abund : numpy array - Square sum of abundances relatively to the wild-type tracker. - > sum_flux : numpy array - Sum of fluxes tracker. - > relsum_flux : numpy array - Sum of fluxes relatively to the wild-type tracker. - > sqsum_flux : numpy array - Square sum of fluxes tracker. - > relsqsum_flux : numpy array - Square sum of fluxes relatively to the wild-type tracker. - > mean_abund : numpy array - Mean abundances tracker. - > var_abund : numpy array - Variance of abundances tracker. - > CV_abund : numpy array - Coefficient of variation of abundances tracker. - > ER_abund : numpy array - Evolution rate of abundances tracker. - > mean_flux : numpy array - Mean fluxes tracker. - > var_flux : numpy array - Variance of fluxes tracker. - > CV_flux : numpy array - Coefficient of variation of fluxes tracker. - > ER_flux : numpy array - Evolution rate of fluxes tracker. - > previous_abund : numpy array - Previous abundances tracker. - > previous_flux : numpy array - Previous fluxes tracker. - > output_file : file - Output file tracking each MCMC algorithm iteration. - - Methods - ------- - > __init__(sbml_filename, target_fluxes, total_iterations, sigma, selection_scheme, selection_threshold, copasi_path) - MCMC class constructor. - > initialize_output_file() - Initialize the output file (write the header). - > write_output_file() - Write the current MCMC state in the output file. - > update_statistics() - Update statistics trackers for the current state. - > compute_statistics() - Compute final statistics. - > write_statistics() - Write final statistics in an output file (output/statistics.txt). - > initialize() - Initialize the MCMC algorithm. - > reload_previous_state() - Reload the previous MCMC state. - > iterate() - Iterate the MCMC algorithm. - """ - - ### Constructor ### - def __init__( self, sbml_filename, objective_function, total_iterations, sigma, selection_scheme, selection_threshold, copasi_path ): - """ - MCMC class constructor. - - Parameters - ---------- - sbml_filename : str - Path of the SBML model file. The SBML model is automatically loaded. - objective_function : list of [str, float] - Objective function (list of reaction identifiers and coefficients). - total_iterations : int > 0 - Total number of MCMC iterations. - sigma : float > 0.0 - Standard deviation of the Log10-normal mutational distribution. - selection_scheme : str - Selection scheme ('MUTATION_ACCUMULATION'/'ABSOLUTE_METABOLIC_SUM_SELECTION'/ - 'RELATIVE_METABOLIC_SUM_SELECTION'/'ABSOLUTE_TARGET_FLUXES_SELECTION'/ - 'RELATIVE_TARGET_FLUXES_SELECTION'). - selection_threshold : float > 0.0 - Selection threshold applied on the MOMA distance. - copasi_path : str - Location of Copasi executable. - - Returns - ------- - None - """ - assert os.path.isfile(sbml_filename), "The SBML file \""+sbml_filename+"\" does not exist. Exit." - assert len(objective_function) > 0, "You must provide at least one reaction in the objective function. Exit." - assert total_iterations > 0, "The total number of iterations must be a positive nonzero value. Exit." - assert sigma > 0.0, "The mutation size 'sigma' must be a positive nonzero value. Exit." - assert selection_scheme in ["MUTATION_ACCUMULATION", "ABSOLUTE_METABOLIC_SUM_SELECTION", "RELATIVE_METABOLIC_SUM_SELECTION", "ABSOLUTE_TARGET_FLUXES_SELECTION", "RELATIVE_TARGET_FLUXES_SELECTION", "ABSOLUTE_ALL_FLUXES_SELECTION", "RELATIVE_ALL_FLUXES_SELECTION"], "The selection scheme takes two values only (MUTATION_ACCUMULATION / ABSOLUTE_METABOLIC_SUM_SELECTION / RELATIVE_METABOLIC_SUM_SELECTION / ABSOLUTE_TARGET_FLUXES_SELECTION / RELATIVE_TARGET_FLUXES_SELECTION / ABSOLUTE_ALL_FLUXES_SELECTION / RELATIVE_ALL_FLUXES_SELECTION). Exit." - assert os.path.isfile(copasi_path), "The executable \""+copasi_path+"\" does not exist. Exit." - - #~~~~~~~~~~~~~~~~~~~~~~~~~~~# - # 1) Main MCMC parameters # - #~~~~~~~~~~~~~~~~~~~~~~~~~~~# - self.sbml_filename = sbml_filename - self.total_iterations = total_iterations - self.sigma = sigma - self.selection_scheme = selection_scheme - self.selection_threshold = selection_threshold - self.copasi_path = copasi_path - - #~~~~~~~~~~~~~~~~~~~~~~~~~~~# - # 2) SBML model # - #~~~~~~~~~~~~~~~~~~~~~~~~~~~# - self.model = Model(sbml_filename, objective_function, copasi_path) - - #~~~~~~~~~~~~~~~~~~~~~~~~~~~# - # 3) Current state tracking # - #~~~~~~~~~~~~~~~~~~~~~~~~~~~# - self.nb_iterations = 0 - self.nb_accepted = 0 - self.nb_rejected = 0 - self.param_metaid = "_" - self.param_id = "WT" - self.param_value = 0.0 - self.param_previous = 0.0 - - #~~~~~~~~~~~~~~~~~~~~~~~~~~~# - # 4) Statistics # - #~~~~~~~~~~~~~~~~~~~~~~~~~~~# - ### Array lengths ### - self.N_abund = self.model.get_number_of_variable_species() - self.N_flux = self.model.get_number_of_reactions() - - ### Arrays ### - self.WT_abund = np.zeros(self.N_abund) - self.mutant_abund = np.zeros(self.N_abund) - self.WT_flux = np.zeros(self.N_flux) - self.mutant_flux = np.zeros(self.N_flux) - - ### Sum vectors ### - self.sum_abund = np.zeros(self.N_abund) - self.relsum_abund = np.zeros(self.N_abund) - self.sqsum_abund = np.zeros(self.N_abund) - self.relsqsum_abund = np.zeros(self.N_abund) - self.sum_flux = np.zeros(self.N_flux) - self.relsum_flux = np.zeros(self.N_flux) - self.sqsum_flux = np.zeros(self.N_flux) - self.relsqsum_flux = np.zeros(self.N_flux) - - ### Final statistics ### - self.mean_abund = np.zeros(self.N_abund) - self.var_abund = np.zeros(self.N_abund) - self.CV_abund = np.zeros(self.N_abund) - self.ER_abund = np.zeros(self.N_abund) - self.mean_flux = np.zeros(self.N_flux) - self.var_flux = np.zeros(self.N_flux) - self.CV_flux = np.zeros(self.N_flux) - self.ER_flux = np.zeros(self.N_flux) - - #~~~~~~~~~~~~~~~~~~~~~~~~~~~# - # 5) Previous state # - #~~~~~~~~~~~~~~~~~~~~~~~~~~~# - self.previous_abund = np.copy(self.mutant_abund) - self.previous_flux = np.copy(self.mutant_flux) - - #~~~~~~~~~~~~~~~~~~~~~~~~~~~# - # 6) Output file # - #~~~~~~~~~~~~~~~~~~~~~~~~~~~# - self.output_file = open("output/iterations.txt", "w") - self.output_file.close() - - ### Initialize the output file ### - def initialize_output_file( self ): - """ - Initialize the output file (write the header). - - Parameters - ---------- - None - - Returns - ------- - None - """ - header = "nb_iterations nb_accepted nb_rejected param_metaid param_id param_previous param_val" - for species_id in self.model.species: - if not self.model.species[species_id]["constant"]: - header += " "+species_id - for reaction_id in self.model.reactions: - header += " "+reaction_id - header += " wt_absolute_sum mutant_absolute_sum wt_relative_sum mutant_relative_sum absolute_sum_dist relative_sum_dist absolute_moma_dist relative_moma_dist absolute_moma_all_dist relative_moma_all_dist\n" - self.output_file = open("output/iterations.txt", "a") - self.output_file.write(header) - self.output_file.close() - - ### Write the current MCMC state in the output file ### - def write_output_file( self ): - """ - Write the current MCMC state in the output file. - - Parameters - ---------- - None - - Returns - ------- - None - """ - line = "" - - #~~~~~~~~~~~~~~~~~~~~~~~~~~~~~# - # 1) Write current MCMC state # - #~~~~~~~~~~~~~~~~~~~~~~~~~~~~~# - line += str(self.nb_iterations)+" " - line += str(self.nb_accepted)+" " - line += str(self.nb_rejected)+" " - line += str(self.param_metaid)+" " - line += str(self.param_id)+" " - line += str(self.param_previous)+" " - line += str(self.param_value) - - #~~~~~~~~~~~~~~~~~~~~~~~~~~~~~# - # 2) Write steady-state # - #~~~~~~~~~~~~~~~~~~~~~~~~~~~~~# - for species_id in self.model.species: - if not self.model.species[species_id]["constant"]: - line += " "+str(self.model.species[species_id]["mutant_value"]) - for reaction_id in self.model.reactions: - line += " "+str(self.model.reactions[reaction_id]["mutant_value"]) - - #~~~~~~~~~~~~~~~~~~~~~~~~~~~~~# - # 3) Write scores # - #~~~~~~~~~~~~~~~~~~~~~~~~~~~~~# - line += " "+str(self.model.WT_ABSOLUTE_SUM)+" "+str(self.model.mutant_ABSOLUTE_SUM)+" "+str(self.model.WT_RELATIVE_SUM)+" "+str(self.model.mutant_RELATIVE_SUM)+" "+str(self.model.ABSOLUTE_SUM_distance)+" "+str(self.model.RELATIVE_SUM_distance)+" "+str(self.model.ABSOLUTE_MOMA_distance)+" "+str(self.model.RELATIVE_MOMA_distance)+" "+str(self.model.ABSOLUTE_MOMA_ALL_distance)+" "+str(self.model.RELATIVE_MOMA_ALL_distance) - - #~~~~~~~~~~~~~~~~~~~~~~~~~~~~~# - # 4) Write in file # - #~~~~~~~~~~~~~~~~~~~~~~~~~~~~~# - self.output_file = open("output/iterations.txt", "a") - self.output_file.write(line+"\n") - self.output_file.close() - - ### Update statistics ### - def update_statistics( self ): - """ - Update statistics trackers for the current state. - - Parameters - ---------- - None - - Returns - ------- - None - """ - ########### - self.sum_abund += self.mutant_abund - self.sqsum_abund += self.mutant_abund*self.mutant_abund - #self.relsum_abund += (self.mutant_abund/self.WT_abund) - #self.relsqsum_abund += (self.mutant_abund/self.WT_abund)*(self.mutant_abund/self.WT_abund) - for i in range(self.N_abund): - if self.WT_abund[i] > 0.0: - self.relsum_abund[i] += (self.mutant_abund[i]/self.WT_abund[i]) - self.relsqsum_abund[i] += (self.mutant_abund[i]/self.WT_abund[i])*(self.mutant_abund[i]/self.WT_abund[i]) - ########### - self.sum_flux += self.mutant_flux - self.sqsum_flux += self.mutant_flux*self.mutant_flux - #self.relsum_flux += (self.mutant_flux/self.WT_flux) - #self.relsqsum_flux += (self.mutant_flux/self.WT_flux)*(self.mutant_flux/self.WT_flux) - for i in range(self.N_flux): - if self.WT_flux[i] > 0.0: - self.relsum_flux[i] += (self.mutant_flux[i]/self.WT_flux[i]) - self.relsqsum_flux[i] += (self.mutant_flux[i]/self.WT_flux[i])*(self.mutant_flux[i]/self.WT_flux[i]) - - ### Compute statistics for the current iteration ### - def compute_statistics( self ): - """ - Compute final statistics. - - Parameters - ---------- - None - - Returns - ------- - None - """ - #~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~# - # 1) Compute mean # - #~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~# - self.mean_abund = np.copy(self.sum_abund) - self.mean_abund /= float(self.nb_iterations) - ########### - self.mean_flux = np.copy(self.sum_flux) - self.mean_flux /= float(self.nb_iterations) - - #~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~# - # 2) Compute variance # - #~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~# - self.var_abund = np.copy(self.sqsum_abund) - self.var_abund /= float(self.nb_iterations) - self.var_abund -= self.mean_abund*self.mean_abund - ########### - self.var_flux = np.copy(self.sqsum_flux) - self.var_flux /= float(self.nb_iterations) - self.var_flux -= self.mean_flux*self.mean_flux - - #~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~# - # 3) Compute coefficient of variation # - #~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~# - self.CV_abund = np.copy(self.sqsum_abund) - self.CV_abund /= float(self.nb_iterations) - self.CV_abund -= self.mean_abund*self.mean_abund - #self.CV_abund = np.sqrt(self.CV_abund)/self.mean_abund - for i in range(self.N_abund): - if self.mean_abund[i] > 0.0: - self.CV_abund[i] = np.sqrt(self.CV_abund[i])/self.mean_abund[i] - else: - self.CV_abund[i] = 0.0 - - ########### - self.CV_flux = np.copy(self.sqsum_flux) - self.CV_flux /= float(self.nb_iterations) - self.CV_flux -= self.mean_flux*self.mean_flux - #self.CV_flux = np.sqrt(self.CV_flux)/self.mean_flux - for i in range(self.N_flux): - if self.mean_flux[i] > 0.0: - self.CV_flux[i] = np.sqrt(self.CV_flux[i])/self.mean_flux[i] - else: - self.CV_flux[i] = 0.0 - - #~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~# - # 4) Compute evolution rate # - #~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~# - self.ER_abund = np.copy(self.relsqsum_abund) - self.ER_abund /= float(self.nb_iterations) - self.ER_abund -= (self.relsum_abund/float(self.nb_iterations))*(self.relsum_abund/float(self.nb_iterations)) - self.ER_abund /= float(self.nb_iterations) - ########### - self.ER_flux = np.copy(self.relsqsum_flux) - self.ER_flux /= float(self.nb_iterations) - self.ER_flux -= (self.relsum_flux/float(self.nb_iterations))*(self.relsum_flux/float(self.nb_iterations)) - self.ER_flux /= float(self.nb_iterations) - - ### Write statistics in a file ### - def write_statistics( self ): - """ - Write final statistics in an output file (output/statistics.txt). - - Parameters - ---------- - None - - Returns - ------- - None - """ - f = open("output/statistics.txt", "w") - f.write("species_id WT mean var CV ER\n") - - #~~~~~~~~~~~~~~~~~~~~~~~~~~~~~# - # 1) Write species statistics # - #~~~~~~~~~~~~~~~~~~~~~~~~~~~~~# - index = 0 - for species_id in self.model.species: - if not self.model.species[species_id]["constant"]: - line = species_id+" " - line += str(self.WT_abund[index])+" " - line += str(self.mean_abund[index])+" " - line += str(self.var_abund[index])+" " - line += str(self.CV_abund[index])+" " - line += str(self.ER_abund[index])+"\n" - index += 1 - f.write(line) - - #~~~~~~~~~~~~~~~~~~~~~~~~~~~~~# - # 2) Write fluxes statistics # - #~~~~~~~~~~~~~~~~~~~~~~~~~~~~~# - index = 0 - for reaction_id in self.model.reactions: - line = reaction_id+" " - line += str(self.WT_flux[index])+" " - line += str(self.mean_flux[index])+" " - line += str(self.var_flux[index])+" " - line += str(self.CV_flux[index])+" " - line += str(self.ER_flux[index])+"\n" - index += 1 - f.write(line) - f.close() - - ### Initialize MCMC algorithm ### - def initialize( self ): - """ - Initialize the MCMC algorithm. - - Parameters - ---------- - None - - Returns - ------- - None - """ - self.model.compute_WT_steady_state() - self.model.compute_mutant_steady_state() - self.model.compute_SUM_distance() - self.model.compute_MOMA_distance() - self.initialize_output_file() - self.WT_abund = self.model.get_WT_species_array() - self.mutant_abund = self.model.get_mutant_species_array() - self.WT_flux = self.model.get_WT_reaction_array() - self.mutant_flux = self.model.get_mutant_reaction_array() - - ### Reload the previous MCMC state ### - def reload_previous_state( self ): - """ - Reload the previous MCMC state. - - Parameters - ---------- - None - - Returns - ------- - None - """ - #~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~# - # 1) Restore the mutated parameter # - #~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~# - self.model.set_mutant_parameter_value(self.param_metaid, self.param_previous) - - #~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~# - # 2) Copy back previous values # - #~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~# - self.mutant_abund = np.copy(self.previous_abund) - self.mutant_flux = np.copy(self.previous_flux) - index = 0 - for species_id in self.model.species: - if not self.model.species[species_id]["constant"]: - self.model.species[species_id]["mutant_value"] = self.mutant_abund[index] - index += 1 - index = 0 - for reaction_id in self.model.reactions: - self.model.reactions[reaction_id]["mutant_value"] = self.mutant_flux[index] - index += 1 - - ### Iterate MCMC algorithm ### - def iterate( self ): - """ - Iterate the MCMC algorithm. - - Parameters - ---------- - None - - Returns - ------- - None - """ - - #~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~# - # 1) Save previous state # - #~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~# - self.previous_abund = np.copy(self.mutant_abund) - self.previous_flux = np.copy(self.mutant_flux) - - #~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~# - # 2) Introduce a new random mutation # - #~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~# - self.param_metaid = self.model.get_random_parameter() - self.param_id = self.model.parameters[self.param_metaid]["id"] - self.param_previous, self.param_value = self.model.random_parameter_mutation(self.param_metaid, self.sigma) - - #~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~# - # 3) Compute the new steady-state # - #~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~# - self.model.compute_mutant_steady_state() - self.model.compute_SUM_distance() - self.model.compute_MOMA_distance() - self.mutant_abund = self.model.get_mutant_species_array() - self.mutant_flux = self.model.get_mutant_reaction_array() - - #~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~# - # 4) Select the new iteration event # - #~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~# - - #-----------------------------------------------------------------# - # 4.1) If the simulation is a mutation accumulation experiment, # - # keep all the mutational events. # - #-----------------------------------------------------------------# - if self.selection_scheme == "MUTATION_ACCUMULATION": - self.nb_iterations += 1 - self.nb_accepted += 1 - self.update_statistics() - self.compute_statistics() - #-----------------------------------------------------------------# - # 4.2) If the simulation is a absolute metabolic sum selection # - # experiment, keep only mutations for which the SUM distance # - # is lower than a given selection threshold. # - #-----------------------------------------------------------------# - elif self.selection_scheme == "ABSOLUTE_METABOLIC_SUM_SELECTION" and self.model.ABSOLUTE_SUM_distance < self.selection_threshold: - self.nb_iterations += 1 - self.nb_accepted += 1 - self.update_statistics() - self.compute_statistics() - elif self.selection_scheme == "ABSOLUTE_METABOLIC_SUM_SELECTION" and self.model.ABSOLUTE_SUM_distance >= self.selection_threshold: - self.nb_iterations += 1 - self.nb_rejected += 1 - self.reload_previous_state() - self.update_statistics() - self.compute_statistics() - self.param_metaid = "_" - self.param_id = "_" - self.param_value = 0.0 - self.param_previous = 0.0 - #-----------------------------------------------------------------# - # 4.3) If the simulation is a relative metabolic sum selection # - # experiment, keep only mutations for which the SUM distance # - # is lower than a given selection threshold. # - #-----------------------------------------------------------------# - elif self.selection_scheme == "RELATIVE_METABOLIC_SUM_SELECTION" and self.model.RELATIVE_SUM_distance < self.selection_threshold: - self.nb_iterations += 1 - self.nb_accepted += 1 - self.update_statistics() - self.compute_statistics() - elif self.selection_scheme == "RELATIVE_METABOLIC_SUM_SELECTION" and self.model.RELATIVE_SUM_distance >= self.selection_threshold: - self.nb_iterations += 1 - self.nb_rejected += 1 - self.reload_previous_state() - self.update_statistics() - self.compute_statistics() - self.param_metaid = "_" - self.param_id = "_" - self.param_value = 0.0 - self.param_previous = 0.0 - #-----------------------------------------------------------------# - # 4.4) If the simulation is a absolute target fluxes selection # - # experiment, keep only mutations for which the MOMA # - # distance is lower than a given selection threshold. # - #-----------------------------------------------------------------# - elif self.selection_scheme == "ABSOLUTE_TARGET_FLUXES_SELECTION" and self.model.ABSOLUTE_MOMA_distance < self.selection_threshold: - self.nb_iterations += 1 - self.nb_accepted += 1 - self.update_statistics() - self.compute_statistics() - elif self.selection_scheme == "ABSOLUTE_TARGET_FLUXES_SELECTION" and self.model.ABSOLUTE_MOMA_distance >= self.selection_threshold: - self.nb_iterations += 1 - self.nb_rejected += 1 - self.reload_previous_state() - self.update_statistics() - self.compute_statistics() - self.param_metaid = "_" - self.param_id = "_" - self.param_value = 0.0 - self.param_previous = 0.0 - #-----------------------------------------------------------------# - # 4.5) If the simulation is a relative target fluxes selection # - # experiment, keep only mutations for which the MOMA # - # distance is lower than a given selection threshold. # - #-----------------------------------------------------------------# - elif self.selection_scheme == "RELATIVE_TARGET_FLUXES_SELECTION" and self.model.RELATIVE_MOMA_distance < self.selection_threshold: - self.nb_iterations += 1 - self.nb_accepted += 1 - self.update_statistics() - self.compute_statistics() - elif self.selection_scheme == "RELATIVE_TARGET_FLUXES_SELECTION" and self.model.RELATIVE_MOMA_distance >= self.selection_threshold: - self.nb_iterations += 1 - self.nb_rejected += 1 - self.reload_previous_state() - self.update_statistics() - self.compute_statistics() - self.param_metaid = "_" - self.param_id = "_" - self.param_value = 0.0 - self.param_previous = 0.0 - #-----------------------------------------------------------------# - # 4.6) If the simulation is a absolute ALL fluxes selection # - # experiment, keep only mutations for which the MOMA # - # distance is lower than a given selection threshold. # - #-----------------------------------------------------------------# - elif self.selection_scheme == "ABSOLUTE_ALL_FLUXES_SELECTION" and self.model.ABSOLUTE_MOMA_ALL_distance < self.selection_threshold: - self.nb_iterations += 1 - self.nb_accepted += 1 - self.update_statistics() - self.compute_statistics() - elif self.selection_scheme == "ABSOLUTE_ALL_FLUXES_SELECTION" and self.model.ABSOLUTE_MOMA_ALL_distance >= self.selection_threshold: - self.nb_iterations += 1 - self.nb_rejected += 1 - self.reload_previous_state() - self.update_statistics() - self.compute_statistics() - self.param_metaid = "_" - self.param_id = "_" - self.param_value = 0.0 - self.param_previous = 0.0 - #-----------------------------------------------------------------# - # 4.7) If the simulation is a relative ALL fluxes selection # - # experiment, keep only mutations for which the MOMA # - # distance is lower than a given selection threshold. # - #-----------------------------------------------------------------# - elif self.selection_scheme == "RELATIVE_ALL_FLUXES_SELECTION" and self.model.RELATIVE_MOMA_ALL_distance < self.selection_threshold: - self.nb_iterations += 1 - self.nb_accepted += 1 - self.update_statistics() - self.compute_statistics() - elif self.selection_scheme == "RELATIVE_ALL_FLUXES_SELECTION" and self.model.RELATIVE_MOMA_ALL_distance >= self.selection_threshold: - self.nb_iterations += 1 - self.nb_rejected += 1 - self.reload_previous_state() - self.update_statistics() - self.compute_statistics() - self.param_metaid = "_" - self.param_id = "_" - self.param_value = 0.0 - self.param_previous = 0.0 - - #~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~# - # 5) Check the number of iterations # - #~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~# - print("> Current iteration = "+str(self.nb_iterations)+" (accepted = "+str(self.nb_accepted)+", rejected = "+str(self.nb_rejected)+")") - if self.nb_iterations == self.total_iterations: - return True - return False - -#******************************************************************** -# SensitivityAnalysis class -# ------------------------- -# This class runs a sensitivity analysis by exploring a range of -# values for each kinetic parameter and tracking the change for all -# fluxes and species. -#******************************************************************** -class SensitivityAnalysis: - """ - The SensitivityAnalysis class runs a sensitivity analysis by exploring a - range of values for each kinetic parameter and tracking the change for all - fluxes and species. - - Attributes - ---------- - > sbml_filename : str - Path of the SBML model file. The SBML model is automatically loaded. - > factor_range : float > 0.0 - Half-range of the log10-scaling factor (total range=2*factor_range) - > factor_step : float > 0.0 - Exploration step of the log10-scaling factor. - x' = x*10^(factor) - > copasi_path : str - Location of Copasi executable. - > model : Model - SBML model (automatically loaded). - > param_index : int - Current parameter index. - > param_metaid : str - Current parameter meta identifier. - > param_id : str - Current parameter identifier. - > param_wt : 0.0 - Current parameter wild-type value. - > param_val : 0.0 - Current parameter mutant value. - > output_file : file - Output file. - - Methods - ------- - > __init__(sbml_filename, factor_range, factor_step, copasi_path) - SensitivityAnalysis class constructor. - > initialize_output_file() - Initialize the output file (write the header). - > write_output_file() - Write the current sensitivity analysis state in the output file. - > initialize() - Initialize the sensitivity analysis algorithm. - > reload_WT_state() - Reload the wild-type state into the mutant model. - > explore_next_parameter() - Run a full parameter exploration for the next kinetic parameter. - """ - - ### Constructor ### - def __init__( self, sbml_filename, factor_range, factor_step, copasi_path ): - """ - SensitivityAnalysis class constructor. - - Parameters - ---------- - sbml_filename : str - Path of the SBML model file. The SBML model is automatically loaded. - factor_range : float > 0.0 - Half-range of the log10-scaling factor (total range=2*factor_range) - factor_step : float > 0.0 - Exploration step of the log10-scaling factor. - x' = x*10^(factor) - copasi_path : str - Location of Copasi executable. - - Returns - ------- - None - """ - assert os.path.isfile(sbml_filename), "The SBML file \""+sbml_filename+"\" does not exist. Exit." - assert factor_range > 0.0, "The factor range 'factor_range' must be a positive nonzero value. Exit." - assert factor_step > 0.0, "The factor step 'factor_step' must be a positive nonzero value. Exit." - assert os.path.isfile(copasi_path), "The executable \""+copasi_path+"\" does not exist. Exit." - - #~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~# - # 1) Main sensitivity analysis parameters # - #~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~# - self.sbml_filename = sbml_filename - self.factor_range = factor_range - self.factor_step = factor_step - self.copasi_path = copasi_path - - #~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~# - # 2) SBML model # - #~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~# - self.model = Model(sbml_filename, [], copasi_path) - - #~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~# - # 3) Parameter tracking # - #~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~# - self.param_index = 0 - self.param_metaid = "WT" - self.param_id = "WT" - self.param_wt = 0.0 - self.param_val = 0.0 - - #~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~# - # 4) Output file # - #~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~# - self.output_file = open("output/sensitivity_analysis.txt", "w") - self.output_file.close() - - ### Initialize the output file ### - def initialize_output_file( self ): - """ - Initialize the output file (write the header). - - Parameters - ---------- - None - - Returns - ------- - None - """ - - #~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~# - # 1) Write the header # - #~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~# - header = "param_metaid param_id param_wt param_val param_dln" - for species_id in self.model.species: - if not self.model.species[species_id]["constant"]: - header += " "+species_id - for reaction_id in self.model.reactions: - header += " "+reaction_id - - #~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~# - # 2) Write the wild-type steady-state # - #~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~# - first_line = "WT WT 0.0 0.0 0.0" - for species_id in self.model.species: - if not self.model.species[species_id]["constant"]: - first_line += " "+str(self.model.species[species_id]["WT_value"]) - for reaction_id in self.model.reactions: - first_line += " "+str(self.model.reactions[reaction_id]["WT_value"]) - - #~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~# - # 3) Save in output file # - #~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~# - self.output_file = open("output/sensitivity_analysis.txt", "a") - self.output_file.write(header+"\n"+first_line+"\n") - self.output_file.close() - - ### Write the sensitivity analysis state in the output file ### - def write_output_file( self ): - """ - Write the current sensitivity analysis state in the output file. - - Parameters - ---------- - None - - Returns - ------- - None - """ - line = "" - - #~~~~~~~~~~~~~~~~~~~~~~~~~~~~~# - # 1) Write current MCMC state # - #~~~~~~~~~~~~~~~~~~~~~~~~~~~~~# - line += str(self.param_metaid)+" " - line += str(self.param_id)+" " - line += str(self.param_wt)+" " - line += str(self.param_val)+" " - line += str((self.param_val-self.param_wt)/self.param_wt) - - #~~~~~~~~~~~~~~~~~~~~~~~~~~~~~# - # 2) Write steady-state # - #~~~~~~~~~~~~~~~~~~~~~~~~~~~~~# - for species_id in self.model.species: - if not self.model.species[species_id]["constant"]: - wt_val = self.model.species[species_id]["WT_value"] - mut_val = self.model.species[species_id]["mutant_value"] - dln_val = "NA" - if wt_val != 0.0: - dln_val = (mut_val-wt_val)/wt_val - line += " "+str(dln_val) - for reaction_id in self.model.reactions: - wt_val = self.model.reactions[reaction_id]["WT_value"] - mut_val = self.model.reactions[reaction_id]["mutant_value"] - dln_val = "NA" - if wt_val != 0.0: - dln_val = (mut_val-wt_val)/wt_val - line += " "+str(dln_val) - - #~~~~~~~~~~~~~~~~~~~~~~~~~~~~~# - # 3) Write in file # - #~~~~~~~~~~~~~~~~~~~~~~~~~~~~~# - self.output_file = open("output/sensitivity_analysis.txt", "a") - self.output_file.write(line+"\n") - self.output_file.close() - - ### Initialize sensitivity analysis algorithm ### - def initialize( self ): - """ - Initialize the sensitivity analysis algorithm. - - Parameters - ---------- - None - - Returns - ------- - None - """ - self.model.compute_WT_steady_state() - self.model.compute_mutant_steady_state() - self.initialize_output_file() - self.param_index = 0 - - ### Reload the wild-type state into the mutant model ### - def reload_WT_state( self ): - """ - Reload the wild-type state into the mutant model. - - Parameters - ---------- - None - - Returns - ------- - None - """ - for species_id in self.model.species: - self.model.species[species_id]["initial_value"] = self.model.species[species_id]["WT_value"] - self.model.species[species_id]["mutant_value"] = self.model.species[species_id]["WT_value"] - #if not self.model.species[species_id]["constant"]: - # self.model.set_species_initial_value(species_id, self.model.species[species_id]["mutant_value"]) - for parameter_metaid in self.model.parameters: - self.model.set_mutant_parameter_value(parameter_metaid, self.model.get_WT_parameter_value(parameter_metaid)) - for reaction_id in self.model.reactions: - self.model.reactions[reaction_id]["mutant_value"] = self.model.reactions[reaction_id]["WT_value"] - - ### Explore the next parameter ### - def explore_next_parameter( self ): - """ - Run a full parameter exploration for the next kinetic parameter. - - Parameters - ---------- - None - - Returns - ------- - bool - Returns True if the last parameter has been explored. Returns False - else. - """ - - #~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~# - # 1) Get the next parameter # - #~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~# - self.param_metaid = list(self.model.parameters)[self.param_index] - self.param_id = self.model.parameters[self.param_metaid]["id"] - self.param_wt = self.model.parameters[self.param_metaid]["WT_value"] - self.param_val = 0.0 - print("> Current parameter: "+str(self.param_id)) - - #~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~# - # 2) Explore the upper range # - #~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~# - factor = 0.0 - while factor <= self.factor_range+self.factor_step/2.0: # +step/2 for precision errors - self.param_val = self.param_wt*10**factor - self.model.set_mutant_parameter_value(self.param_metaid, self.param_val) - self.model.compute_mutant_steady_state() - self.write_output_file() - #self.model.update_initial_concentrations() - factor += self.factor_step - self.reload_WT_state() - - #~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~# - # 3) Explore the lower range # - 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z9QM-_`6FsfF?ZP~*r-nz;y}Q0JWlBL;#v@bvN4dnq*n(?wU+f&%2D9##AiyFKX?pV z=jECHlwqy*4z?64B5*3%jrFyy!dSwype^Bk>K`Eu. #*************************************************************************** -from .MetEvolSim import * +from .metevolsim import * diff --git a/setup.py b/setup.py index ace0d8e..c73f1cc 100644 --- a/setup.py +++ b/setup.py @@ -42,7 +42,7 @@ setup( name = "MetEvolSim", - version = "0.4.4", + version = "0.4.5", license = "GNU General Public License v3 (GPLv3)", description = "MetEvolSim (Metabolome Evolution Simulator) Python Package", long_description = long_description,