path: root/python-metevolsim.spec

%global _empty_manifest_terminate_build 0
Name:		python-MetEvolSim
Version:	0.6.3
Release:	1
Summary:	MetEvolSim (Metabolome Evolution Simulator) Python Package
License:	GNU General Public License v3 (GPLv3)
URL:		https://github.com/charlesrocabert/MetEvolSim
Source0:	https://mirrors.aliyun.com/pypi/web/packages/e8/bf/a4902d47bca920fed13dd30d77d7b8491854dbbe0a7ede2c1cf1543a46a1/MetEvolSim-0.6.3.tar.gz
BuildArch:	noarch

Requires:	python3-libsbml
Requires:	python3-numpy
Requires:	python3-networkx

%description
<p align="justify">
MetEvolSim (<em>Metabolome Evolution Simulator</em>) 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 <a href="http://sbml.org/Main_Page" target="_blank">SBML-formatted</a> 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 <a href="http://copasi.org/" target="_blank">Copasi</a> software.
</p>
<p align="justify">
MetEvolSim is being developed by Charles Rocabert, Gábor Boross, Orsolya Liska and Balázs Papp.
</p>
<p align="justify">
Do you plan to use MetEvolSim for research purpose? Do you encounter issues with the software? Do not hesitate to contact <a href="mailto:charles[DOT]rocabert[AT]helsinki[DOT]fi">Charles Rocabert</a>.
</p>
<p align="center">
<img src="https://github.com/charlesrocabert/MetEvolSim/raw/master/pic/BRC_logo.png" height="100px"></a>&nbsp;&nbsp;&nbsp;<img src="https://github.com/charlesrocabert/MetEvolSim/raw/master/pic/MTA_logo.png" height="100px"></a>
</p>
## Table of contents
- [Citing MetEvolSim](#citing)
- [Dependencies](#dependencies)
- [Installation](#installation)
- [First usage](#first_usage)
- [Help](#help)
- [Ready-to-use examples](#examples)
- [List of tested metabolic models](#tested_models)
- [Copyright](#copyright)
- [License](#license)
## Citing MetEvolSim <a name="citing"></a>
- O. Liska, G. Boross, C. Rocabert, B. Szappanos, R. Tengölics, B. Papp. Principles of metabolome conservation in animals. <em>BioRXiv preprint</em> (2022) (https://doi.org/10.1101/2022.08.15.503737).
## Dependencies <a name="dependencies"></a>
- Python &ge; 3,
- Numpy &ge; 1.21 (automatically installed when using pip),
- Python-libsbml &ge; 5.19 (automatically installed when using pip),
- NetworkX &ge; 2.6 (automatically installed when using pip),
- CopasiSE &ge; 4.27 (to be installed separately),
- pip &ge; 21.3.1 (optional).
## Installation <a name="installation"></a>
&bullet; To install Copasi software, visit http://copasi.org/. You will need the command line version named CopasiSE.
&bullet; To install the latest release of MetEvolSim:
```shell
pip install MetEvolSim
```
Alternatively, download the <a href="https://github.com/charlesrocabert/MetEvolSim/releases/latest">latest release</a> 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 <a name="first_usage"></a>
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](#tested_models)).
The package provides a class to manipulate SBML models: the class <code>Model</code>. It is also necessary to define an objective function (a list of target reactions and their coefficients), and to provide the path of <a href="http://copasi.org/">CopasiSE</a> software. Please note that coefficients are not functional in the current version of MetEvolSim.
```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_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:
- <strong>Evolution experiments</strong>, based on a Markov Chain Monte Carlo (MCMC) algorithm,
- <strong>Sensitivity analysis</strong>, 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 <code>output</code>.
### Evolution experiments:
<p align="center">
<img src="https://github.com/charlesrocabert/MetEvolSim/raw/master/pic/mcmc_algorithm.png">
</p>
<p align="justify">
<strong>Algorithm overview:</strong> <strong>A.</strong> 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). <strong>B.</strong> At each iteration <em>t</em>, a single kinetic parameter is selected at random and mutated through a log10-normal distribution of standard deviation &sigma;. <strong>C.</strong> The new steady-state is computed using Copasi software, and the MOMA distance <em>z</em> between the mutant and the wild-type target fluxes is computed. <strong>D.</strong> If <em>z</em> is under a given selection threshold &omega;, the mutation is accepted. Else, the mutation is discarded. <strong>E.</strong> A new iteration <em>t+1</em> is computed.
</p>
<br/>
Six types of selection are available:
- <code>MUTATION_ACCUMULATION</code>: Run a mutation accumulation experiment by accepting all new mutations without any selection threshold,
- <code>ABSOLUTE_METABOLIC_SUM_SELECTION</code>: Run an evolution experiment by applying a stabilizing selection on the sum of absolute metabolic abundances,
- <code>ABSOLUTE_TARGET_FLUXES_SELECTION</code>: Run an evolution experiment by applying a stabilizing selection on the MOMA distance of absolute target fluxes,
- <code>RELATIVE_TARGET_FLUXES_SELECTION</code>: 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()
```
### One-At-a-Time (OAT) sensitivity analysis:
For each kinetic parameter p, each metabolic abundance [X<sub>i</sub>] and each flux &nu;<sub>j</sub>, the algorithm numerically computes relative derivatives and control coefficients.
```python
# 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 &sigma;, and relative derivatives and control coefficients are computed.
```python
# 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 <a name="help"></a>
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.

%package -n python3-MetEvolSim
Summary:	MetEvolSim (Metabolome Evolution Simulator) Python Package
Provides:	python-MetEvolSim
BuildRequires:	python3-devel
BuildRequires:	python3-setuptools
BuildRequires:	python3-pip
%description -n python3-MetEvolSim
<p align="justify">
MetEvolSim (<em>Metabolome Evolution Simulator</em>) 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 <a href="http://sbml.org/Main_Page" target="_blank">SBML-formatted</a> 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 <a href="http://copasi.org/" target="_blank">Copasi</a> software.
</p>
<p align="justify">
MetEvolSim is being developed by Charles Rocabert, Gábor Boross, Orsolya Liska and Balázs Papp.
</p>
<p align="justify">
Do you plan to use MetEvolSim for research purpose? Do you encounter issues with the software? Do not hesitate to contact <a href="mailto:charles[DOT]rocabert[AT]helsinki[DOT]fi">Charles Rocabert</a>.
</p>
<p align="center">
<img src="https://github.com/charlesrocabert/MetEvolSim/raw/master/pic/BRC_logo.png" height="100px"></a>&nbsp;&nbsp;&nbsp;<img src="https://github.com/charlesrocabert/MetEvolSim/raw/master/pic/MTA_logo.png" height="100px"></a>
</p>
## Table of contents
- [Citing MetEvolSim](#citing)
- [Dependencies](#dependencies)
- [Installation](#installation)
- [First usage](#first_usage)
- [Help](#help)
- [Ready-to-use examples](#examples)
- [List of tested metabolic models](#tested_models)
- [Copyright](#copyright)
- [License](#license)
## Citing MetEvolSim <a name="citing"></a>
- O. Liska, G. Boross, C. Rocabert, B. Szappanos, R. Tengölics, B. Papp. Principles of metabolome conservation in animals. <em>BioRXiv preprint</em> (2022) (https://doi.org/10.1101/2022.08.15.503737).
## Dependencies <a name="dependencies"></a>
- Python &ge; 3,
- Numpy &ge; 1.21 (automatically installed when using pip),
- Python-libsbml &ge; 5.19 (automatically installed when using pip),
- NetworkX &ge; 2.6 (automatically installed when using pip),
- CopasiSE &ge; 4.27 (to be installed separately),
- pip &ge; 21.3.1 (optional).
## Installation <a name="installation"></a>
&bullet; To install Copasi software, visit http://copasi.org/. You will need the command line version named CopasiSE.
&bullet; To install the latest release of MetEvolSim:
```shell
pip install MetEvolSim
```
Alternatively, download the <a href="https://github.com/charlesrocabert/MetEvolSim/releases/latest">latest release</a> 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 <a name="first_usage"></a>
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](#tested_models)).
The package provides a class to manipulate SBML models: the class <code>Model</code>. It is also necessary to define an objective function (a list of target reactions and their coefficients), and to provide the path of <a href="http://copasi.org/">CopasiSE</a> software. Please note that coefficients are not functional in the current version of MetEvolSim.
```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_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:
- <strong>Evolution experiments</strong>, based on a Markov Chain Monte Carlo (MCMC) algorithm,
- <strong>Sensitivity analysis</strong>, 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 <code>output</code>.
### Evolution experiments:
<p align="center">
<img src="https://github.com/charlesrocabert/MetEvolSim/raw/master/pic/mcmc_algorithm.png">
</p>
<p align="justify">
<strong>Algorithm overview:</strong> <strong>A.</strong> 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). <strong>B.</strong> At each iteration <em>t</em>, a single kinetic parameter is selected at random and mutated through a log10-normal distribution of standard deviation &sigma;. <strong>C.</strong> The new steady-state is computed using Copasi software, and the MOMA distance <em>z</em> between the mutant and the wild-type target fluxes is computed. <strong>D.</strong> If <em>z</em> is under a given selection threshold &omega;, the mutation is accepted. Else, the mutation is discarded. <strong>E.</strong> A new iteration <em>t+1</em> is computed.
</p>
<br/>
Six types of selection are available:
- <code>MUTATION_ACCUMULATION</code>: Run a mutation accumulation experiment by accepting all new mutations without any selection threshold,
- <code>ABSOLUTE_METABOLIC_SUM_SELECTION</code>: Run an evolution experiment by applying a stabilizing selection on the sum of absolute metabolic abundances,
- <code>ABSOLUTE_TARGET_FLUXES_SELECTION</code>: Run an evolution experiment by applying a stabilizing selection on the MOMA distance of absolute target fluxes,
- <code>RELATIVE_TARGET_FLUXES_SELECTION</code>: 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()
```
### One-At-a-Time (OAT) sensitivity analysis:
For each kinetic parameter p, each metabolic abundance [X<sub>i</sub>] and each flux &nu;<sub>j</sub>, the algorithm numerically computes relative derivatives and control coefficients.
```python
# 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 &sigma;, and relative derivatives and control coefficients are computed.
```python
# 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 <a name="help"></a>
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.

%package help
Summary:	Development documents and examples for MetEvolSim
Provides:	python3-MetEvolSim-doc
%description help
<p align="justify">
MetEvolSim (<em>Metabolome Evolution Simulator</em>) 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 <a href="http://sbml.org/Main_Page" target="_blank">SBML-formatted</a> 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 <a href="http://copasi.org/" target="_blank">Copasi</a> software.
</p>
<p align="justify">
MetEvolSim is being developed by Charles Rocabert, Gábor Boross, Orsolya Liska and Balázs Papp.
</p>
<p align="justify">
Do you plan to use MetEvolSim for research purpose? Do you encounter issues with the software? Do not hesitate to contact <a href="mailto:charles[DOT]rocabert[AT]helsinki[DOT]fi">Charles Rocabert</a>.
</p>
<p align="center">
<img src="https://github.com/charlesrocabert/MetEvolSim/raw/master/pic/BRC_logo.png" height="100px"></a>&nbsp;&nbsp;&nbsp;<img src="https://github.com/charlesrocabert/MetEvolSim/raw/master/pic/MTA_logo.png" height="100px"></a>
</p>
## Table of contents
- [Citing MetEvolSim](#citing)
- [Dependencies](#dependencies)
- [Installation](#installation)
- [First usage](#first_usage)
- [Help](#help)
- [Ready-to-use examples](#examples)
- [List of tested metabolic models](#tested_models)
- [Copyright](#copyright)
- [License](#license)
## Citing MetEvolSim <a name="citing"></a>
- O. Liska, G. Boross, C. Rocabert, B. Szappanos, R. Tengölics, B. Papp. Principles of metabolome conservation in animals. <em>BioRXiv preprint</em> (2022) (https://doi.org/10.1101/2022.08.15.503737).
## Dependencies <a name="dependencies"></a>
- Python &ge; 3,
- Numpy &ge; 1.21 (automatically installed when using pip),
- Python-libsbml &ge; 5.19 (automatically installed when using pip),
- NetworkX &ge; 2.6 (automatically installed when using pip),
- CopasiSE &ge; 4.27 (to be installed separately),
- pip &ge; 21.3.1 (optional).
## Installation <a name="installation"></a>
&bullet; To install Copasi software, visit http://copasi.org/. You will need the command line version named CopasiSE.
&bullet; To install the latest release of MetEvolSim:
```shell
pip install MetEvolSim
```
Alternatively, download the <a href="https://github.com/charlesrocabert/MetEvolSim/releases/latest">latest release</a> 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 <a name="first_usage"></a>
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](#tested_models)).
The package provides a class to manipulate SBML models: the class <code>Model</code>. It is also necessary to define an objective function (a list of target reactions and their coefficients), and to provide the path of <a href="http://copasi.org/">CopasiSE</a> software. Please note that coefficients are not functional in the current version of MetEvolSim.
```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_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:
- <strong>Evolution experiments</strong>, based on a Markov Chain Monte Carlo (MCMC) algorithm,
- <strong>Sensitivity analysis</strong>, 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 <code>output</code>.
### Evolution experiments:
<p align="center">
<img src="https://github.com/charlesrocabert/MetEvolSim/raw/master/pic/mcmc_algorithm.png">
</p>
<p align="justify">
<strong>Algorithm overview:</strong> <strong>A.</strong> 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). <strong>B.</strong> At each iteration <em>t</em>, a single kinetic parameter is selected at random and mutated through a log10-normal distribution of standard deviation &sigma;. <strong>C.</strong> The new steady-state is computed using Copasi software, and the MOMA distance <em>z</em> between the mutant and the wild-type target fluxes is computed. <strong>D.</strong> If <em>z</em> is under a given selection threshold &omega;, the mutation is accepted. Else, the mutation is discarded. <strong>E.</strong> A new iteration <em>t+1</em> is computed.
</p>
<br/>
Six types of selection are available:
- <code>MUTATION_ACCUMULATION</code>: Run a mutation accumulation experiment by accepting all new mutations without any selection threshold,
- <code>ABSOLUTE_METABOLIC_SUM_SELECTION</code>: Run an evolution experiment by applying a stabilizing selection on the sum of absolute metabolic abundances,
- <code>ABSOLUTE_TARGET_FLUXES_SELECTION</code>: Run an evolution experiment by applying a stabilizing selection on the MOMA distance of absolute target fluxes,
- <code>RELATIVE_TARGET_FLUXES_SELECTION</code>: 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()
```
### One-At-a-Time (OAT) sensitivity analysis:
For each kinetic parameter p, each metabolic abundance [X<sub>i</sub>] and each flux &nu;<sub>j</sub>, the algorithm numerically computes relative derivatives and control coefficients.
```python
# 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 &sigma;, and relative derivatives and control coefficients are computed.
```python
# 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 <a name="help"></a>
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.

%prep
%autosetup -n MetEvolSim-0.6.3

%build
%py3_build

%install
%py3_install
install -d -m755 %{buildroot}/%{_pkgdocdir}
if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi
if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi
if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi
if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi
pushd %{buildroot}
if [ -d usr/lib ]; then
	find usr/lib -type f -printf "\"/%h/%f\"\n" >> filelist.lst
fi
if [ -d usr/lib64 ]; then
	find usr/lib64 -type f -printf "\"/%h/%f\"\n" >> filelist.lst
fi
if [ -d usr/bin ]; then
	find usr/bin -type f -printf "\"/%h/%f\"\n" >> filelist.lst
fi
if [ -d usr/sbin ]; then
	find usr/sbin -type f -printf "\"/%h/%f\"\n" >> filelist.lst
fi
touch doclist.lst
if [ -d usr/share/man ]; then
	find usr/share/man -type f -printf "\"/%h/%f.gz\"\n" >> doclist.lst
fi
popd
mv %{buildroot}/filelist.lst .
mv %{buildroot}/doclist.lst .

%files -n python3-MetEvolSim -f filelist.lst
%dir %{python3_sitelib}/*

%files help -f doclist.lst
%{_docdir}/*

%changelog
* Thu Jun 08 2023 Python_Bot <Python_Bot@openeuler.org> - 0.6.3-1
- Package Spec generated