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pSSAlib: The partial-propensity stochastic chemical network simulator

Chemical reaction networks are ubiquitous in biology, and their dynamics is fundamentally stochastic. Here, we present the software library pSSAlib, which provides a complete and concise implementation of the most efficient partial-propensity methods for simulating exact stochastic chemical kinetics...

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Detalles Bibliográficos
Autores principales: Ostrenko, Oleksandr, Incardona, Pietro, Ramaswamy, Rajesh, Brusch, Lutz, Sbalzarini, Ivo F.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5730222/
https://www.ncbi.nlm.nih.gov/pubmed/29206229
http://dx.doi.org/10.1371/journal.pcbi.1005865
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author Ostrenko, Oleksandr
Incardona, Pietro
Ramaswamy, Rajesh
Brusch, Lutz
Sbalzarini, Ivo F.
author_facet Ostrenko, Oleksandr
Incardona, Pietro
Ramaswamy, Rajesh
Brusch, Lutz
Sbalzarini, Ivo F.
author_sort Ostrenko, Oleksandr
collection PubMed
description Chemical reaction networks are ubiquitous in biology, and their dynamics is fundamentally stochastic. Here, we present the software library pSSAlib, which provides a complete and concise implementation of the most efficient partial-propensity methods for simulating exact stochastic chemical kinetics. pSSAlib can import models encoded in Systems Biology Markup Language, supports time delays in chemical reactions, and stochastic spatiotemporal reaction-diffusion systems. It also provides tools for statistical analysis of simulation results and supports multiple output formats. It has previously been used for studies of biochemical reaction pathways and to benchmark other stochastic simulation methods. Here, we describe pSSAlib in detail and apply it to a new model of the endocytic pathway in eukaryotic cells, leading to the discovery of a stochastic counterpart of the cut-out switch motif underlying early-to-late endosome conversion. pSSAlib is provided as a stand-alone command-line tool and as a developer API. We also provide a plug-in for the SBMLToolbox. The open-source code and pre-packaged installers are freely available from http://mosaic.mpi-cbg.de.
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spelling pubmed-57302222017-12-22 pSSAlib: The partial-propensity stochastic chemical network simulator Ostrenko, Oleksandr Incardona, Pietro Ramaswamy, Rajesh Brusch, Lutz Sbalzarini, Ivo F. PLoS Comput Biol Research Article Chemical reaction networks are ubiquitous in biology, and their dynamics is fundamentally stochastic. Here, we present the software library pSSAlib, which provides a complete and concise implementation of the most efficient partial-propensity methods for simulating exact stochastic chemical kinetics. pSSAlib can import models encoded in Systems Biology Markup Language, supports time delays in chemical reactions, and stochastic spatiotemporal reaction-diffusion systems. It also provides tools for statistical analysis of simulation results and supports multiple output formats. It has previously been used for studies of biochemical reaction pathways and to benchmark other stochastic simulation methods. Here, we describe pSSAlib in detail and apply it to a new model of the endocytic pathway in eukaryotic cells, leading to the discovery of a stochastic counterpart of the cut-out switch motif underlying early-to-late endosome conversion. pSSAlib is provided as a stand-alone command-line tool and as a developer API. We also provide a plug-in for the SBMLToolbox. The open-source code and pre-packaged installers are freely available from http://mosaic.mpi-cbg.de. Public Library of Science 2017-12-04 /pmc/articles/PMC5730222/ /pubmed/29206229 http://dx.doi.org/10.1371/journal.pcbi.1005865 Text en © 2017 Ostrenko et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Ostrenko, Oleksandr
Incardona, Pietro
Ramaswamy, Rajesh
Brusch, Lutz
Sbalzarini, Ivo F.
pSSAlib: The partial-propensity stochastic chemical network simulator
title pSSAlib: The partial-propensity stochastic chemical network simulator
title_full pSSAlib: The partial-propensity stochastic chemical network simulator
title_fullStr pSSAlib: The partial-propensity stochastic chemical network simulator
title_full_unstemmed pSSAlib: The partial-propensity stochastic chemical network simulator
title_short pSSAlib: The partial-propensity stochastic chemical network simulator
title_sort pssalib: the partial-propensity stochastic chemical network simulator
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5730222/
https://www.ncbi.nlm.nih.gov/pubmed/29206229
http://dx.doi.org/10.1371/journal.pcbi.1005865
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