MetEvolSim 0.6.3

MetEvolSim (Metabolome Evolution Simulator) Python Package

Metabolome Evolution Simulator

A Python package to simulate the long-term evolution of metabolic levels.

 

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MetEvolSim (Metabolome Evolution Simulator) is a Python package providing numerical tools to simulate the long-term evolution of metabolic abundances in kinetic models of metabolic network. MetEvolSim takes as an input a SBML-formatted metabolic network model. Kinetic parameters and initial metabolic concentrations must be specified, and the model must reach a stable steady-state. Steady-state concentrations are computed thanks to Copasi software.

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

Do you plan to use MetEvolSim for research purpose? Do you encounter issues with the software? Do not hesitate to contact Charles Rocabert.

   

Table of contents

• Citing MetEvolSim
• Dependencies
• Installation
• First usage
• Help
• Ready-to-use examples
• List of tested metabolic models
• Copyright
• License

Citing MetEvolSim

• O. Liska, G. Boross, C. Rocabert, B. Szappanos, R. Tengölics, B. Papp. Principles of metabolome conservation in animals. BioRXiv preprint (2022) (https://doi.org/10.1101/2022.08.15.503737).

Dependencies

• Python ≥ 3,
• Numpy ≥ 1.21 (automatically installed when using pip),
• Python-libsbml ≥ 5.19 (automatically installed when using pip),
• NetworkX ≥ 2.6 (automatically installed when using pip),
• CopasiSE ≥ 4.27 (to be installed separately),
• pip ≥ 21.3.1 (optional).

Installation

• To install Copasi software, visit http://copasi.org/. You will need the command line version named CopasiSE.

• To install the latest release of MetEvolSim:

pip install MetEvolSim

Alternatively, download the latest release in the folder of your choice and unzip it. Then follow the instructions below:

# Navigate to the MetEvolSim folder
cd /path/to/MetEvolSim

# Install MetEvolSim Python package
python3 setup.py install

First usage

MetEvolSim has been tested with tens of publicly available metabolic networks, but we cannot guarantee it will work with any model (see the list of tested metabolic models). The package provides a class to manipulate SBML models: the class Model. It is also necessary to define an objective function (a list of target reactions and their coefficients), and to provide the path of CopasiSE software. Please note that coefficients are not functional in the current version of MetEvolSim.

# 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_wild_type_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_wild_type_steady_state()
model.compute_mutant_steady_state()

# Run a metabolic control analysis on the wild-type
model.compute_wild_type_metabolic_control_analysis()
# This function will output two datasets:
# - output/wild_type_MCA_unscaled.txt containing unscaled control coefficients,
# - output/wild_type_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")

# Compute a flux drop analysis to measure the contribution of each flux to the fitness
# (in this example, each flux is dropped at 1% of its original value)
model.flux_drop_analysis(drop_coefficient=0.01,
                         filename="flux_drop_analysis.txt",
                         owerwrite=True)

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, either by exploring every kinetic parameters in a given range and recording associated fluxes and metabolic abundances changes (One-At-a-Time sensitivity analysis), or by exploring the kinetic parameters space at random, by mutating a single kinetic parameter at random many times (random sensitivity analysis).

All numerical analyses output files are saved in a subfolder output.

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,
• 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.

# 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()

One-At-a-Time (OAT) sensitivity analysis:

For each kinetic parameter p, each metabolic abundance [X_i] and each flux ν_j, the algorithm numerically computes relative derivatives and control coefficients.

# Load a sensitivity analysis instance
sa = metevolsim.SensitivityAnalysis(sbml_filename='glycolysis.xml',
                                    copasi_path='/Applications/COPASI/CopasiSE')

# Run the full OAT sensitivity analysis
sa.run_OAT_analysis(factor_range=1.0, factor_step=0.01)

Random sensitivity analysis:

At each iteration, a single kinetic parameter p is mutated at random in a log10-normal distribution of size σ, and relative derivatives and control coefficients are computed.

# Load a sensitivity analysis instance
sa = metevolsim.SensitivityAnalysis(sbml_filename='glycolysis.xml',
                                    copasi_path='/Applications/COPASI/CopasiSE')

# Run the full OAT sensitivity analysis
sa.run_random_analysis(sigma=0.01, nb_iterations=1000)

Help

To get some help on a MetEvolSim class or method, use the Python help function:

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)

Ready-to-use examples

Ready-to-use examples are included in the Python package. They can also be downloaded here: https://github.com/charlesrocabert/MetEvolSim/raw/master/example/example.zip.

List of tested metabolic models

Reference	Model	Running with MetEvolSim
Bakker et al. (1997)	Trypanosoma brucei glycolysis	:x:
Curto et al. (1998)	Human purine metabolism	:x:
Mulquiney et al. (1999)	Human erythrocyte	:white_check_mark:
Jamshidi et al. (2001)	Red blood cell	:x:
Bali et al. (2001)	Red blood cell glycolysis	:white_check_mark:
Lambeth et al. (2002)	Skeletal muscle glycogenolysis	:white_check_mark:
Holzhutter et al. (2004)	Human erythrocyte	:white_check_mark:
Beard et al. (2005)	Mitochondrial respiration	:x:
Banaji et al. (2005)	Cerebral blood flood control	:white_check_mark:
Bertram et al. (2006)	Mitochondrial ATP production	:x:
Bruck et al. (2008)	Yeast glycolysis	:white_check_mark:
Reed et al. (2008)	Glutathione metabolism	:x:
Curien et al. (2009)	Aspartame metabolism	:x:
Jerby et al. (2010)	Human liver metabolism	:x:
Li et al. (2010)	Yeast glycolysis	:x:
Bekaert et al. (2010)	Mouse metabolism reconstruction	:x:
Bordbar et al. (2011)	Human multi-tissues	:x:
Koenig et al. (2012)	Hepatocyte glucose metabolism	:white_check_mark:
Messiha et al. (2013)	Yeast glycolysis + pentose phosphate	:white_check_mark:
Mitchell et al. (2013)	Liver iron metabolism	:x:
Stanford et al. (2013)	Yeast whole cell model	:x:
Bordbar et al. (2015)	Red blood cell	:x:
Costa et al. (2016)	E. coli core metabolism	:white_check_mark:
Millard et al. (2016)	E. coli core metabolism	:white_check_mark:
Bulik et al. (2016)	Hepatic glucose metabolism	:white_check_mark:

Copyright

Copyright © 2018-2023 Charles Rocabert, Gábor Boross, Orsolya Liska 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/.