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BSim: An Agent-Based Tool for Modeling Bacterial Populations in Systems and Synthetic Biology
Large-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 inter...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2012
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3427305/ https://www.ncbi.nlm.nih.gov/pubmed/22936991 http://dx.doi.org/10.1371/journal.pone.0042790 |
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author | Gorochowski, Thomas E. Matyjaszkiewicz, Antoni Todd, Thomas Oak, Neeraj Kowalska, Kira Reid, Stephen Tsaneva-Atanasova, Krasimira T. Savery, Nigel J. Grierson, Claire S. di Bernardo, Mario |
author_facet | Gorochowski, Thomas E. Matyjaszkiewicz, Antoni Todd, Thomas Oak, Neeraj Kowalska, Kira Reid, Stephen Tsaneva-Atanasova, Krasimira T. Savery, Nigel J. Grierson, Claire S. di Bernardo, Mario |
author_sort | Gorochowski, Thomas E. |
collection | PubMed |
description | Large-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. |
format | Online Article Text |
id | pubmed-3427305 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2012 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-34273052012-08-30 BSim: An Agent-Based Tool for Modeling Bacterial Populations in Systems and Synthetic Biology Gorochowski, Thomas E. Matyjaszkiewicz, Antoni Todd, Thomas Oak, Neeraj Kowalska, Kira Reid, Stephen Tsaneva-Atanasova, Krasimira T. Savery, Nigel J. Grierson, Claire S. di Bernardo, Mario PLoS One Research Article Large-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. Public Library of Science 2012-08-24 /pmc/articles/PMC3427305/ /pubmed/22936991 http://dx.doi.org/10.1371/journal.pone.0042790 Text en © 2012 Gorochowski 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, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. |
spellingShingle | Research Article Gorochowski, Thomas E. Matyjaszkiewicz, Antoni Todd, Thomas Oak, Neeraj Kowalska, Kira Reid, Stephen Tsaneva-Atanasova, Krasimira T. Savery, Nigel J. Grierson, Claire S. di Bernardo, Mario BSim: An Agent-Based Tool for Modeling Bacterial Populations in Systems and Synthetic Biology |
title | BSim: An Agent-Based Tool for Modeling Bacterial Populations in Systems and Synthetic Biology |
title_full | BSim: An Agent-Based Tool for Modeling Bacterial Populations in Systems and Synthetic Biology |
title_fullStr | BSim: An Agent-Based Tool for Modeling Bacterial Populations in Systems and Synthetic Biology |
title_full_unstemmed | BSim: An Agent-Based Tool for Modeling Bacterial Populations in Systems and Synthetic Biology |
title_short | BSim: An Agent-Based Tool for Modeling Bacterial Populations in Systems and Synthetic Biology |
title_sort | bsim: an agent-based tool for modeling bacterial populations in systems and synthetic biology |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3427305/ https://www.ncbi.nlm.nih.gov/pubmed/22936991 http://dx.doi.org/10.1371/journal.pone.0042790 |
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