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Title

Modeling bacterial population growth from stochastic single-cell dynamics

AuthorsAlonso, Antonio A. ; Molina, Ignacio; Theodoropoulos, Constantinos
Issue Date2014
PublisherAmerican Society for Microbiology
CitationApplied and Environmental Microbiology 80(17): 5241-5253 (2014)
AbstractFew bacterial cells may be sufficient to produce a food-borne illness outbreak, provided that they are capable of adapting and proliferating on a food matrix. This is why any quantitative health risk assessment policy must incorporate methods to accurately predict the growth of bacterial populations from a small number of pathogens. In this aim, mathematical models have become a powerful tool. Unfortunately, at low cell concentrations, standard deterministic models fail to predict the fate of the population, essentially because the heterogeneity between individuals becomes relevant. In this contribution a stochastic differential equation (SDE) model is proposed to describe variability within single cell growth and division and to simulate population growth from a given initial number of individuals. We provide evidence of the model ability to explain the observed distributions of times to division, including the lag-time produced by the adaptation to the environment, by comparing model predictions with experiments from the literature for Escherichia coli, Listeria innocua and Salmonella enterica. The model is shown to accurately predict experimental growth population dynamics both for small and large microbial populations. The use of stochastic models for the estimation of parameters to successfully fit experimental data is a particularly challenging problem. For instance if Monte Carlo methods are employed to model the required distributions of times to division, the parameter estimation problem can become numerically intractable. We overcome this limitation by converting the stochastic description to a partial differential equation (backward Kolmogorov) instead, which relates to the distribution of division times. Contrary to previous stochastic formulations based on random parameters, the present model is capable of explaining the variability observed in populations that result from the growth of a few number of initial cells as well as the lack of it when compared to populations initiated by a larger number of individuals, where the random effects become negligible.
Description12 páginas, 11 figuras, 2 tablas
Publisher version (URL)http://dx.doi.org/10.1128/AEM.01423-14
URIhttp://hdl.handle.net/10261/101313
DOI10.1128/AEM.01423-14
ISSN0099-2240
E-ISSN1098-5336
Appears in Collections:(IIM) Artículos
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