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BSim: An Agent-Based Tool for Modeling Bacterial Populations in Systems and Synthetic Biology

Overview of attention for article published in PLOS ONE, August 2012
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Title
BSim: An Agent-Based Tool for Modeling Bacterial Populations in Systems and Synthetic Biology
Published in
PLOS ONE, August 2012
DOI 10.1371/journal.pone.0042790
Pubmed ID
Authors

Thomas E. Gorochowski, Antoni Matyjaszkiewicz, Thomas Todd, Neeraj Oak, Kira Kowalska, Stephen Reid, Krasimira T. Tsaneva-Atanasova, Nigel J. Savery, Claire S. Grierson, Mario di Bernardo

Abstract

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.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 216 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 5 2%
Spain 5 2%
United Kingdom 2 <1%
Lithuania 1 <1%
South Africa 1 <1%
Netherlands 1 <1%
Germany 1 <1%
India 1 <1%
Belgium 1 <1%
Other 0 0%
Unknown 198 92%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 65 30%
Researcher 48 22%
Student > Master 20 9%
Student > Bachelor 20 9%
Student > Doctoral Student 13 6%
Other 35 16%
Unknown 15 7%
Readers by discipline Count As %
Agricultural and Biological Sciences 55 25%
Biochemistry, Genetics and Molecular Biology 33 15%
Computer Science 29 13%
Engineering 24 11%
Physics and Astronomy 13 6%
Other 36 17%
Unknown 26 12%