Mathematical modelling of microbes: metabolism, gene expression and growth

Hidde de Jong 1 Stefano Casagranda 2 Nils Giordano 3 Eugenio Cinquemani 1 Delphine Ropers 1 Johannes Geiselmann 3 Jean-Luc Gouzé 2
1 IBIS - Modeling, simulation, measurement, and control of bacterial regulatory networks
LAPM - Laboratoire Adaptation et pathogénie des micro-organismes [Grenoble], Inria Grenoble - Rhône-Alpes, Institut Jean Roget
2 BIOCORE - Biological control of artificial ecosystems
INRA - Institut National de la Recherche Agronomique, CRISAM - Inria Sophia Antipolis - Méditerranée , LOV - Laboratoire d'océanographie de Villefranche
Abstract : The growth of microorganisms involves the conversion of nutrients in the environment into biomass, mostly proteins and other macromolecules. This conversion is accomplished by networks of biochemical reactions cutting across cellular functions, such as metabolism, gene expression, transport and signalling. Mathematical modelling is a powerful tool for gaining an understanding of the functioning of this large and complex system and the role played by individual constituents and mechanisms. This requires models of microbial growth that provide an integrated view of the reaction networks and bridge the scale from individual reactions to the growth of a population. In this review, we derive a general framework for the kinetic modelling of microbial growth from basic hypotheses about the underlying reaction systems. Moreover, we show that several families of approximate models presented in the literature, notably flux balance models and coarse-grained whole-cell models, can be derived with the help of additional simplifying hypotheses. This perspective clearly brings out how apparently quite different modelling approaches are related on a deeper level, and suggests directions for further research.
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Hidde de Jong, Stefano Casagranda, Nils Giordano, Eugenio Cinquemani, Delphine Ropers, et al.. Mathematical modelling of microbes: metabolism, gene expression and growth. Journal of the Royal Society Interface, the Royal Society, 2017, 14 (136), pp.1-14. ⟨http://rsif.royalsocietypublishing.org/content/14/136/20170502⟩. ⟨10.1098/rsif.2017.0502⟩. ⟨hal-01666954⟩

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