University Grenoble-Alpes, Inria, Grenoble, France
University Côte d'Azur, Inria, INRA, CNRS, UPMC University Paris 06, BIOCORE team, Sophia-Antipolis, France.
J R Soc Interface. 2017 Nov;14(136). doi: 10.1098/rsif.2017.0502.
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.
微生物的生长包括将环境中的营养物质转化为生物量,主要是蛋白质和其他大分子。这种转化是通过跨越细胞功能的生化反应网络来实现的,例如代谢、基因表达、运输和信号传递。数学建模是理解这个庞大而复杂系统的功能以及个体成分和机制所起作用的有力工具。这需要微生物生长模型提供对反应网络的综合视图,并在从单个反应到种群生长的尺度上进行衔接。在这篇综述中,我们从关于基础反应系统的基本假设出发,推导出微生物生长的动力学建模的一般框架。此外,我们还表明,文献中提出的几种近似模型家族,特别是通量平衡模型和粗粒度全细胞模型,可以在其他简化假设的帮助下推导出来。这种观点清楚地表明,表面上截然不同的建模方法在更深层次上是相关的,并为进一步的研究提出了方向。