Localization of signal transduction components in the bacterial cell

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The objective of this project is to develop an integrated understanding of the metabolic and genetic network that controls the transition from growth to glucose starvation in the model bacterium, Bacillus subtilis. In addition to serving as a model system, B. subtilis is an industrial workhorse for 'white biotechnology' since it serves is a primary producer of technical enzymes and other products (e.g. vitamins, antibiotics, flavour enhancers and biochemicals). The transition from growth to growth limitation is a fundamental ecophysiological response and is studied by academic researchers as a model for environmental signal processing and integration. Understanding this transition is also pivotal for industrial fermentations of Bacillus that occur predominantly under nutrient limitation. Our approach is to integrate biological data with mathematical models of the networks that regulate the transition from growth to starvation. The approach starts with quantitative monitoring of defined genetic and environmental perturbations under standardized growth conditions. These data are used for mathematical modelling of regulatory processes. As the programme develops, gaps in our understanding will be revealed by the failure of structural, genome-wide network analyses to describe the biological data. Our concept is to continuously probe model and data consistency in clearly defined (sub)projects, each involving an experimental and a modelling partner. The pivotal element is a model-driven experimental design, where model-based hypotheses are tested through targeted measurements of critical variables. Facilitated through standardized nomenclature, model formats, and defined input/output signals, modular mathematical models are then integrated into a consistent systems representation. In summary, the project will provide convincing evidence that close interactions between experimental and computational scientists on a well advanced model organism can significantly advance our quantitative understanding of, and eventually our ability to control, the highly dynamic and complex regulatory processes in microbes.