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+%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.nju.edu.cn/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
+* Mon May 15 2023 Python_Bot <Python_Bot@openeuler.org> - 0.6.3-1
+- Package Spec generated