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dc.contributor.authorGorochowski, TE
dc.contributor.authorMatyjaszkiewicz, Antoni
dc.contributor.authorTodd, T
dc.contributor.authorOak, N
dc.contributor.authorKowalska, K
dc.contributor.authorReid, S
dc.contributor.authorTsaneva-Atanasova, Krasimira
dc.contributor.authorSavery, NJ
dc.contributor.authorGrierson, CS
dc.contributor.authorDi Bernardo, Mario
dc.date.accessioned2016-02-03T13:23:34Z
dc.date.issued2012
dc.description.abstractLarge-scale collective behaviors such as synchronization and coordination spontaneously arise in many bacterial populations. With systems biology attempting to understand these phenomena, and synthetic biology opening up the possibility of engineering them for our own benefit, there is growing interest in how bacterial populations are best modeled. Here we introduce BSim, a highly flexible agent-based computational tool for analyzing the relationships between single-cell dynamics and population level features. BSim includes reference implementations of many bacterial traits to enable the quick development of new models partially built from existing ones. Unlike existing modeling tools, BSim fully considers spatial aspects of a model allowing for the description of intricate micro-scale structures, enabling the modeling of bacterial behavior in more realistic three-dimensional, complex environments. The new opportunities that BSim opens are illustrated through several diverse examples covering: spatial multicellular computing, modeling complex environments, population dynamics of the lac operon, and the synchronization of genetic oscillators. BSim is open source software that is freely available from http://bsim-bccs.sf.net and distributed under the Open Source Initiative (OSI) recognized MIT license. Developer documentation and a wide range of example simulations are also available from the website. BSim requires Java version 1.6 or higher.en_GB
dc.description.sponsorshipEngineering and Physical Sciences Research Council (EPSRC)en_GB
dc.description.sponsorshipBiotechnology and Biological Sciences Research Council (BBSRC)en_GB
dc.identifier.citationVol. 7, e42790en_GB
dc.identifier.doi10.1371/journal.pone.0042790
dc.identifier.grantnumberEP/E501214/1en_GB
dc.identifier.grantnumberEP/ I018638/1en_GB
dc.identifier.otherPONE-D-12-12594
dc.identifier.urihttp://hdl.handle.net/10871/19579
dc.language.isoenen_GB
dc.publisherPublic Library of Science (PLoS)en_GB
dc.relation.urlhttp://www.ncbi.nlm.nih.gov/pubmed/22936991en_GB
dc.rightsCopyright: 2012 Gorochowski et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.en_GB
dc.subjectComputational Biologyen_GB
dc.subjectOperonen_GB
dc.subjectSynthetic Biologyen_GB
dc.subjectSystems Biologyen_GB
dc.titleBSim: an agent-based tool for modeling bacterial populations in systems and synthetic biology.en_GB
dc.typeArticleen_GB
dc.date.available2016-02-03T13:23:34Z
dc.identifier.issn1932-6203
exeter.place-of-publicationUnited States
dc.descriptionOpen Access Articleen_GB
dc.identifier.journalPLoS Oneen_GB
dc.identifier.pmcidPMC3427305
dc.identifier.pmid22936991


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