cfaed Publications

@article{10.1371/journal.pcbi.1005865,
author = {Ostrenko, Oleksandr AND Incardona, Pietro AND Ramaswamy, Rajesh AND Brusch, Lutz AND Sbalzarini, Ivo F.},
journal = {PLOS Computational Biology},
publisher = {Public Library of Science},
title = {pSSAlib: The partial-propensity stochastic chemical network simulator},
year = {2017},
month = {12},
volume = {13},
url = {https://doi.org/10.1371/journal.pcbi.1005865},
pages = {1-15},
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.},
number = {12},
doi = {10.1371/journal.pcbi.1005865}
}