In this project, researchers from Imperial College London and the University of Bath will work together with the company TMO Renewables Ltd to (a) understand fundamental aspects of the physiology and biochemistry of the thermophilic bacterium Geobacillus thermoglucosidasius, which the company uses in its current bio-ethanol process, and (b) develop computer based metabolic models, using a combination of genome sequence information and experimental measurements, which will be useful for predicting how to make changes to the organism so that it can produce a specific end-product from a variety of different substrates. While the company has been successful in creating a strain of Geobacillus thermoglucosidasius that can produce ethanol from renewable lignocellulose and fermentable components of waste, this was done with little understanding of how the organism behaves under complex fermentation conditions. During this process, many observations have been made that are not easy to explain from our limited current knowledge of the organism. As well as a financial contribution to the project, the company will provide the genome sequence for their parent strain. This is the first (available) complete genome sequence for this species of thermophile and provides the academic researchers with a significant platform from which to make new discoveries. Building on this platform, the research team will apply recently-developed methods for model building, model validation and physiological investigation. The latter will involve the newly-developed approach of 'transcriptomics' by 'RNA -sequencing' to understand how the organism regulates its metabolism and behaviour under different physiological conditions. Direct analysis of RNA (strictly speaking, it has to be converted to DNA before sequencing) using modern methods of high-throughput sequencing is an advance on the previous approach using microarrays, because it does not rely on initial deduction of which are bona-fide gene sequences in a genome. Because the analysis is essentially blind to prior assumptions, it has revealed many unexpected features of regulation in different bacteria. Papers on the application of this method to bacteria only started appearing in 2009, and most of these either focus on methods development or pathogenic organisms. This project will give us the opportunity to look at an industrially relevant organism, addressing questions that are pertinent to industrial fuel and chemical production from biomass and ultimately testing hypotheses and strains in an industrial context. Therefore, there is a strong chance for discovering new and fundamental processes underlying the regulation of microbial growth and metabolism. One of the outputs from this project will be a set of metabolic models. In silico metabolic models can be useful for predicting how metabolic flux should be altered to achieve a specific outcome (eg enhanced growth or metabolite overproduction). So, as part of this exercise, we will use the models in a metabolic engineering programme to make a new metabolite, not normally produced by this strain. Using the model, we should be able to predict how flux through different pathways should be changed to accomplish the dual requirements of rapid growth and product formation. In addition to this, we hope to link the transcriptomic analysis to the models. Metabolic models are essentially static pictures that do not adequately incorporate the dynamic aspects of physiological regulation. By studying cells under different growth conditions, we can generate a set of 'condition-specific models' which can be linked through comparative analysis of the transcriptomic data. The team involves a systems biologist who is expert at integrating different types of data, who will explore the possibility of linking the two types of analysis in a meaningful manner.