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dc.contributor.authorLopez, Carlos Francisco
dc.contributor.authorMuhlich, Jeremy
dc.contributor.authorBachman, John Ata
dc.contributor.authorSorger, Peter Karl
dc.date.accessioned2013-05-07T18:26:32Z
dc.date.issued2013
dc.identifier.citationLopez, Carlos F, Jeremy L Muhlich, John A Bachman, and Peter K Sorger. 2013. Programming biological models in python using PySB. Molecular Systems Biology 9: 646.en_US
dc.identifier.issn1744-4292en_US
dc.identifier.urihttp://nrs.harvard.edu/urn-3:HUL.InstRepos:10611794
dc.description.abstractMathematical equations are fundamental to modeling biological networks, but as networks get large and revisions frequent, it becomes difficult to manage equations directly or to combine previously developed models. Multiple simultaneous efforts to create graphical standards, rule-based languages, and integrated software workbenches aim to simplify biological modeling but none fully meets the need for transparent, extensible, and reusable models. In this paper we describe PySB, an approach in which models are not only created using programs, they are programs. PySB draws on programmatic modeling concepts from little b and ProMot, the rule-based languages BioNetGen and Kappa and the growing library of Python numerical tools. Central to PySB is a library of macros encoding familiar biochemical actions such as binding, catalysis, and polymerization, making it possible to use a high-level, action-oriented vocabulary to construct detailed models. As Python programs, PySB models leverage tools and practices from the open-source software community, substantially advancing our ability to distribute and manage the work of testing biochemical hypotheses. We illustrate these ideas using new and previously published models of apoptosis.en_US
dc.language.isoen_USen_US
dc.publisherNature Publishing Groupen_US
dc.relation.isversionofdoi:10.1038/msb.2013.1en_US
dc.relation.hasversionhttp://www.ncbi.nlm.nih.gov/pmc/articles/PMC3588907/pdf/en_US
dash.licenseLAA
dc.subjectapoptosisen_US
dc.subjectmodelingen_US
dc.subjectrule-baseden_US
dc.subjectsoftware engineeringen_US
dc.titleProgramming biological models in Python using PySBen_US
dc.typeJournal Articleen_US
dc.description.versionVersion of Recorden_US
dc.relation.journalMolecular Systems Biologyen_US
dash.depositing.authorSorger, Peter Karl
dc.date.available2013-05-07T18:26:32Z
dc.identifier.doi10.1038/msb.2013.1*
dash.contributor.affiliatedBachman, John
dash.contributor.affiliatedLopez Castro, Carlos
dash.contributor.affiliatedSorger, Peter
dash.contributor.affiliatedMuhlich, Jeremy
dc.identifier.orcid0000-0001-6095-2466


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