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 thelac 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.
Citation: Gorochowski TE, Matyjaszkiewicz A, Todd T, Oak N, Kowalska K, et al. (2012) BSim: An Agent-Based Tool for Modeling Bacterial Populations in Systems and Synthetic Biology. PLoS ONE 7(8): e42790. doi:10.1371/journal.pone.0042790
Editor: Avi Ma'ayan, Mount Sinai School of Medicine, United States of America
Received: May 1, 2012; Accepted: July 10, 2012; Published: August 24, 2012
Copyright: © 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.
Funding: TEG, SR, TT and NO were supported by Engineering and Physical Sciences Research Council (EPSRC) United Kingdom Grant No. EP/E501214/1. AM was supported by the Biotechnology and Biological Sciences Research Council United Kingdom, Cell Signalling Network SIGNET. KK was supported by an EPSRC Summer Vacation Bursary from the Faculty of Engineering, University of Bristol, United Kingdom. KTT-A was supported by EPSRC United Kingdom Grant No. EP/I018638/1. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The authors have declared that no competing interests exist.
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