Toward a next generation of predictive models: A systems biology primer

Abstract

In the area of predictive microbiology, most models focus on simplicity and general applicability, and can be classified as black box models with the main emphasis on the description of the macroscopic (population level) microbial behavior as a response to the environment. Their validity to describe pure cultures in simple, liquid media under moderate environmental conditions is widely illustrated and accepted. However, experiments have shown that extrapolation of these models outside the range of experimental validation is not allowed as such. In general, the applicability and robustness of existing models under a wider range of conditions and in more realistic situations can definitely be improved by unraveling the underlying mechanisms and incorporating intracellular (microscopic) information. Following a systems biology approach, the link between the intracellular fluxes and the extracellular measurements is established by techniques of metabolic flux analysis. The modeling approach presented in this paper will lead to more accurate predictive models for more complex systems, such as co-cultures and structured environments, based on a top–down systems biology approach. A theoretical case study in predictive microbiology is presented in which the potentials of metabolic network-based models are illustrated. This tutorial paper is directed toward food scientists, who want to get familiar with the mathematical framework used in metabolic flux analysis and adopt these tools in predictive microbiology; the paper is also oriented toward researchers in systems biology, who want to explore the potential and limitations of systems biology tools when applied to challenging (non-steady state) conditions as encountered with bacterial populations in food products.