Agent-based modeling of competence phenotype switching in Bacillus subtilis

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Abstract

Background: It is a fascinating phenomenon that in genetically identical bacteria populations of Bacillus subtilis, a distinct DNA uptake phenotype called the competence phenotype may emerge in 10-20% of the population. Many aspects of the phenomenon are believed to be due to the variable expression of critical genes: a stochastic occurrence termed "noise" which has made the phenomenon difficult to examine directly by lab experimentation. Methods. To capture and model noise in this system and further understand the emergence of competence both at the intracellular and culture levels in B. subtilis, we developed a novel multi-scale, agent-based model. At the intracellular level, our model recreates the regulatory network involved in the competence phenotype. At the culture level, we simulated growth conditions, with our multi-scale model providing feedback between the two levels. Results: Our model predicted three potential sources of genetic "noise". First, the random spatial arrangement of molecules may influence the manifestation of the competence phenotype. In addition, the evidence suggests that there may be a type of epigenetic heritability to the emergence of competence, influenced by the molecular concentrations of key competence molecules inherited through cell division. Finally, the emergence of competence during the stationary phase may in part be due to the dilution effect of cell division upon protein concentrations. Conclusions: The competence phenotype was easily translated into an agent-based model - one with the ability to illuminate complex cell behavior. Models such as the one described in this paper can simulate cell behavior that is otherwise unobservable in vivo, highlighting their potential usefulness as research tools. © 2013 Stiegelmeyer and Giddings; licensee BioMed Central Ltd.

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Stiegelmeyer, S. M., & Giddings, M. C. (2013). Agent-based modeling of competence phenotype switching in Bacillus subtilis. Theoretical Biology and Medical Modelling, 10(1). https://doi.org/10.1186/1742-4682-10-23

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