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توضیحات

MetEvolSim (Metabolome Evolution Simulator) Python Package

ویژگی	مقدار
سیستم عامل	OS Independent
نام فایل	MetEvolSim-0.6.2
نام	MetEvolSim
نسخه کتابخانه	0.6.2
نگهدارنده	[]
ایمیل نگهدارنده	[]
نویسنده	Charles Rocabert, Gábor Boross, Orsolya Liska, Balázs Papp
ایمیل نویسنده	-
آدرس صفحه اصلی	https://github.com/charlesrocabert/MetEvolSim
آدرس اینترنتی	https://pypi.org/project/MetEvolSim/
مجوز	GNU General Public License v3 (GPLv3)

<img src="https://github.com/charlesrocabert/MetEvolSim/raw/master/pic/metevolsim_logo.png" width=300> Metabolome Evolution Simulator A Python package to simulate the long-term evolution of metabolic levels. <a href="https://badge.fury.io/py/MetEvolSim"><img src="https://badge.fury.io/py/MetEvolSim.svg" alt="PyPI version" height="18"></a> <a href="https://github.com/charlesrocabert/MetEvolSim/actions"><img src="https://github.com/charlesrocabert/MetEvolSim/workflows/Upload Python Package/badge.svg" /></a>  <a href="https://github.com/charlesrocabert/MetEvolSim/LICENSE.html"><img src="https://img.shields.io/badge/License-GPLv3-blue.svg" /></a> ----------------- 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 <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. 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 <a href="mailto:charles[DOT]rocabert[AT]helsinki[DOT]fi">Charles Rocabert</a>. <img src="https://github.com/charlesrocabert/MetEvolSim/raw/master/pic/BRC_logo.png" height="100px"></a>   <img src="https://github.com/charlesrocabert/MetEvolSim/raw/master/pic/MTA_logo.png" height="100px"></a> ## Table of contents - [Publications](#publications) - [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) ## Publications <a name="publications"></a> • Project cited in O’Shea & Misra (2020) (https://doi.org/10.1007/s11306-020-01657-3). ## Dependencies <a name="dependencies"></a> - 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 <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: - 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 <code>output</code>. ### Evolution experiments: <img src="https://github.com/charlesrocabert/MetEvolSim/raw/master/pic/mcmc_algorithm.png"> 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: - <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 [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', 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. ```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. 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 <a name="examples"></a> 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 <a name="tested_models"></a> | **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 <a name="copyright"></a> Copyright © 2018-2022 Charles Rocabert, Gábor Boross, Orsolya Liska and Balázs Papp. All rights reserved. ## License <a name="license"></a> 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/.

نیازمندی

مقدار	نام
-	python-libsbml
-	numpy
-	networkx

زبان مورد نیاز

مقدار	نام
>=3	Python

نحوه نصب

نصب پکیج whl MetEvolSim-0.6.2:

pip install MetEvolSim-0.6.2.whl

نصب پکیج tar.gz MetEvolSim-0.6.2:

pip install MetEvolSim-0.6.2.tar.gz