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pSSAlib: The partial-propensity stochastic chemical network simulator.

Authors :
Oleksandr Ostrenko
Pietro Incardona
Rajesh Ramaswamy
Lutz Brusch
Ivo F Sbalzarini
Source :
PLoS Computational Biology, Vol 13, Iss 12, p e1005865 (2017)
Publication Year :
2017
Publisher :
Public Library of Science (PLoS), 2017.

Abstract

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.

Subjects

Subjects :
Biology (General)
QH301-705.5

Details

Language :
English
ISSN :
1553734X and 15537358
Volume :
13
Issue :
12
Database :
Directory of Open Access Journals
Journal :
PLoS Computational Biology
Publication Type :
Academic Journal
Accession number :
edsdoj.4366b30fb90440aa98bd33bf62ea10ea
Document Type :
article
Full Text :
https://doi.org/10.1371/journal.pcbi.1005865