Context Microbial metabolism consists of networks of reactions that bacteria use to convert nutrients into molecules that make up the cell and release energy for cell processes. Such networks have an enormous potential to produce a huge range of molecules that might be useful, for example as biofuels or biopharmaceuticals, but these are rarely produced at high levels. Genome sequencing has revealed metabolic pathways that produce these molecules, and genetic modifications of these pathways allow us to manipulate microbes for biotechnology purposes. However, the high complexity of bacterial metabolism has considerably limited such efforts. This calls for new approaches that can incorporate the complexity of metabolic systems and predict appropriate modifications. Aims We will develop novel computational approaches to modelling microbial metabolism, and use the results to optimise growth in the laboratory. When developed in close collaboration with experimentalists, models can effectively guide experiment design, and models are also safe and inexpensive to work with. Computational models are increasingly important tools to analyse complex interaction networks, as they can incorporate many interactions in large complex systems. We will develop models representing metabolic network,s and algorithms to analyse these and direct experiments towards optimal modifications to improve growth. The underlying model will use genome sequence information and gene expression data to determine the enzymes that are present in the bacterium and thus which reactions are operating under different conditions. Based on this, together with data we collect in the laboratory, we will predict and test ways to optimise the metabolic network, for example to find nutrient conditions that permit the most growth on the least expensive medium. We will use the bacterium Bordetella pertussis as a model system. The relevant genome sequence and gene expression data are available, and B. pertussis has an intriguing metabolism and will provide a different perspective from previous method development work, which has focussed largely on E coli, although the methods themselves have been applied in other organisms. We will develop optimisation algorithms to predict ways to improve the growth of B. pertussis either through altered growth media or by genetic alterations to enhance growth. We will test these predictions in laboratory experiments to validate and refine the novel methods we develop, and to develop its applications. The combination of theoretical modelling with experimental testing is a powerful approach that is superior to purely theoretical systems. Applications and benefits This proposal will develop new methods to use genome sequence information and gene expression data in computational models of microbial metabolism. The cost of whole genome sequencing is dropping rapidly while the capacity of genome sequencing centers to generate data is rapidly increasing. Thus new approaches to interpret and exploit genomic data are needed urgently. The move away from single-gene studies towards genome level studies facilitates a more holistic view of an organism than before and motivates genome-scale, systems-based research approaches. The concepts and approaches developed in this proposal will thus be widely applicable to other studies using genome sequence data. The data generated will also be widely usable. Metabolism is fundamental to the physiology of all bacteria, so the wider perspective of metabolism gained by our studies is of interest to a broad audience. Improved growth methods for B. pertussis will be valuable to sectors of the biotechnology industry that grow this bacterium on a large scale, such as vaccine manufacturers. Thus, although we are using B. pertussis as a model organism for the development of novel systems biology methods, this will generate outputs that have immediate impact outside of academia.