Bacterial growth rate reflects a bottleneck in resource allocation.

BACKGROUND: Growth rate management in fast-growing bacteria is currently an active research area. In spite of the huge progress made in our understanding of the molecular mechanisms controlling the growth rate, fundamental questions concerning its intrinsic limitations are still relevant today. In parallel, systems biology claims that mathematical models could shed light on these questions. METHODS: This review explores some possible reasons for the limitation of the growth rate in fast-growing bacteria, using a systems biology approach based on constraint-based modeling methods. RESULTS: Recent experimental results and a new constraint-based modelling method named Resource Balance Analysis (RBA) reveal the existence of constraints on resource allocation between biological processes in bacterial cells. In this context, the distribution of a finite amount of resources between the metabolic network and the ribosomes limits the growth rate, which implies the existence of a bottleneck between these two processes. Any mechanism for saving resources increases the growth rate. GENERAL SIGNIFICANCE: Consequently, the emergence of genetic regulation of metabolic pathways, e.g. catabolite repression, could then arise as a means to minimise the protein cost, i.e. maximising growth performance while minimising the resource allocation. This article is part of a Special Issue entitled Systems Biology of Microorganisms.

Modeling the effects of assimilable nitrogen and temperature on fermentation kinetics in enological conditions.

We propose a dynamic model of alcoholic fermentation in wine-making conditions. In this model, the speed at which CO(2) is released is related to the effects of the main factors involved in fermentation in wine-making conditions: temperature (which can vary within a predefined range) and nitrogen additions (which must not exceed the maximal authorized level). The resulting model consists of ordinary differential equations including numerous parameters that need to be identified and important interactions between explicative variables. These parameters were identified by uncoupling the effects of variables during specific experiments. The results were validated on another series of experiments in different conditions.