Modelling osmotic stress by Flux Balance Analysis at the genomic scale

Predictive microbiology for food safety is still primarily based on empirical models describing the effect of the environmental conditions of the food on the kinetics of the growth of foodborne pathogens. One way to make these models more mechanistic is to use systems biology methods such as Flux Balance Analysis (FBA). FBA consists of evaluating the possible fluxes through the metabolic reactions taking place in a cell. Using this method, the specific growth rate of Escherichia coli can be predicted by assuming, as an objective function, that the cells maximise their biomass production during balanced growth. Whilst this works under favourable environmental conditions, our simulations show that this objective function is not sufficient to explain the decrease of the growth rate due to osmotic stress. One feature of the FBA models is that the parameters and objective function in general refer to chemostat experiments where the carbon source is the main limiting factor. This may be relevant to some foods where the carbon to nitrogen balance is limiting but, in general, it is the physico-chemical conditions which are the most stringent. We therefore need to examine the effect of such constraints on the fluxes and/or modify the objective function, or to elaborate the metabolic model by taking into account other functional levels of the cell in order to develop mechanistic predictive models for osmotic stress conditions.

► Systems biology can bring mechanistic insights in empirical predictive models.
► Flux Balance Analysis evaluates the metabolic fluxes in a cell like E. coli.
► Growth rate in favourable conditions can be predicted by maximising the biomass.
► Simulations show that this objective function is not enough under osmotic stress.
► We need other constraints, objective functions or functional levels of the cell.