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.

Geobacillus thermoglucosidasius is a metabolically versatile thermophilic facultative anaerobe, able to grow on a wide range of monomeric, dimeric and oligomeric carbohydrates derived from lignocellulose. It naturally carries out a mixed acid fermentation producing lactate, formate, acetate and ethanol as fermentation end products. In recent years this fermentation pathway has been redirected by metabolic engineering to produce ethanol almost exclusively, which has enabled TMO Renewables to scale-up and commercialise their cellulosic bioethanol process. Nevertheless, there are a number of potential areas for further improvement and the metabolic engineering has generated some unanswered physiological questions. Through a previous CASE studentship the group at Imperial College have developed a small scale (45ml) continuous culture system providing pH and temperature control as well as redox measurement for the economic study of metabolic flux using 13C labelling. Maintenance of a fixed redox potential and metabolic profiling of cultures at different redox potentials in continuous culture have proved to be valuable tools for reproducible physiological studies of G. thermoglucosidasius under fermentative conditions. In this project we propose to extend physiological studies of mutant and wild type strains through combined metabolic flux and transcript analysis. Selective transcriptome analysis will be done either using microarrays based on genome sequence information which is currently being assembled, or through transcriptome sequence analysis on high throughput platforms available at Imperial College or the BBSRC funded advanced genome centre. Current metabolic flux analysis uses the programme Fiatflux to generate information on flux ratios, but with the availability of genome sequence information (the TMO production strain has been sequenced and a similar strain,SB2, which is being worked on at Imperial College, is being sequenced) it is envisaged that the CASE student would build full metabolic models necessary for determining absolute fluxes. Using these approaches the initial focus would be to explore a range of issues (eg additional nutrient requirements) associated with approaching true anaerobic growth in wild type and engineered strains. In particular, we find that the knockout of pyruvate formate lyase, with or without upregulation of pyruvate dehydrogenase produces some undefined nutritional requirements. Additionally the student will investigate the regulation of the utilisation of multiple carbohydrates in G. thermoglucosidasius, which may require developing new interpretation methods based on the Fiatflux platform. Information arising from these analyses will then guide metabolic engineering strategies for strain improvement as part of an iterative programme.

TMO Renewables Ltd, the CASE collaborating company, has developed an ethanol fermentation process capable of using sugars derived from lignocellulosic substrates, based on engineering the fermentation pathways of Geobacillus thermoglucosidasius. As part of the work they have contracted to obtain the genome sequence of this organism, which is almost complete. In the initial stages, the annotation of a genome sequence is usually done automatically, based on the search for open reading frames (orfs) and bio-informatic homology searches. This means that initial annotations often contain errors and unclassified orfs. In this project the student will start to build a genomic scale metabolic model of this organism, using the initial genome sequence as a starting point. The ultimate goal is to build an in silico model of the metabolic capabilities of this organism based on Palsson's flux balance analysis approach (Palsson BO (2006) Systems Biology. Properties of Reconstructed Networks. Cambridge University Press, New York, USA). However, because Geobacillus spp are not extensively described at a biochemical and physiological level, this will require a considerable amount of experimental validation to eg confirm the gene assignments and fill in missing metabolic links. Once constructed, an in silico metabolic model can have predictive capabilities. Initially, these can be used to authenticate the quality of the model, which invariably includes an number of assumptions/estimates. However, in the long term, these may be used to predict the optimal route for metabolic engineering for a defined objective, which is one of the future aims of the company. In the interim, however, the logical iterative experimental and in silico construction of the model should generate new insights into the physiology and biochemistry of this increasingly important group of thermophiles. The combined experimental and modelling aspects of this programme will form an excellent training programme for a postgraduate student with a biological background, exposing them to one of the more accessible avenues of systems biology.

TMO Renewables Ltd, the CASE collaborating company, has developed an ethanol fermentation process capable of using sugars derived from lignocellulosic substrates, based on engineering the fermentation pathways of Geobacillus thermoglucosidasius. As part of the work they have contracted to obtain the genome sequence of this organism, which is almost complete. In the initial stages, the annotation of a genome sequence is usually done automatically, based on the search for open reading frames (orfs) and bio-informatic homology searches. This means that initial annotations often contain errors and unclassified orfs. In this project the student will start to build a genomic scale metabolic model of this organism, using the initial genome sequence as a starting point. The ultimate goal is to build an in silico model of the metabolic capabilities of this organism based on Palsson's flux balance analysis approach (Palsson BO (2006) Systems Biology. Properties of Reconstructed Networks. Cambridge University Press, New York, USA). However, because Geobacillus spp are not extensively described at a biochemical and physiological level, this will require a considerable amount of experimental validation to eg confirm the gene assignments and fill in missing metabolic links. Once constructed, an in silico metabolic model can have predictive capabilities. Initially, these can be used to authenticate the quality of the model, which invariably includes an number of assumptions/estimates. However, in the long term, these may be used to predict the optimal route for metabolic engineering for a defined objective, which is one of the future aims of the company. In the interim, however, the logical iterative experimental and in silico construction of the model should generate new insights into the physiology and biochemistry of this increasingly important group of thermophiles. The combined experimental and modelling aspects of this programme will form an excellent training programme for a postgraduate student with a biological background, exposing them to one of the more accessible avenues of systems biology.

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.