Analysis, Predictive Modelling and In Vitro Validation of Gene Expression During 2-aminoethylphosphonate Metabolism in Sinorhizobium meliloti 1021.

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This research project is a collaborative effort between a microbiologist and a control systems engineer who have a shared interest in examining the ways in which mathematical models can be used to predict gene expression in bacterial systems. This proposal will enable the applicants to develop expertise at the interface of their disciplines leading to new insight in the mechanisms of complex biological systems. This is expected to initiate a long-term collaborative research program beyond this project, focused on developments in the treatment and manipulation of biological systems for industrial and medical applications. For this investigation, we are using a relatively simple system, namely the metabolic route for biodegradation of an analogue of the amino acid beta-alanine, called 2-aminoethylphosphonate. This compound has a covalent carbon to phosphorus (C-P) bond that makes it very resistant to enzymatic attack. However, in the soil bacterium Sinorhizobium meliloti 1021 there is a novel pathway that allows this microorganism to use 2AEP as a source of carbon, nitrogen and phosphorus for growth. The pathway is unusual in that it generates an antiviral compound, phosphonoacetate, as an intermediate in metabolism: this is a new phenomenon, which has not been observed in bacteria until now and we wish to know more about the way in which the genes of the pathway are expressed in response to different environmental conditions. We wish to find out whether or not the five genes involved in the metabolism of 2AEP are differentially expressed in bacterial cultures that are actively growing on 2AEP. In order to do this, we will use a technique called reverse transcriptase PCR (RT-PCR) which will allow us to measure, with reference to standards of known amounts of DNA, the levels of gene expression in several samples of cells removed from cultures during growth on 2AEP. The data we will produce during this work will be of the highest quality and will therefore serve as a benchmark study in terms of both reliability and rigour of data, and should be of significant interest to systems biology researchers.A principal aim of his work is to develop modelling techniques which enable the true biochemical pathway of cellular processes to be uncovered. A major aspect of this project is therefore devoted to investigating methods for bringing together knowledge and analysis of the biochemistry with mathematical analysis. This will enable the development of a comprehensive system model, which we will then use to make predictions of the dynamics of gene expression in response to different 2AEP substrate concentrations and nutrient limitations. Validation of the feasibility of the proposed model is extremely important and following rigorous simulation tests, further targeted gene expression analysis will be carried out on bacterial cultures fed with small amounts of 2AEP in order to test the in silico predictions. The biological implications of the model will be studied in terms of understanding of the responses of soil microbes to nutrient influx; determining the cellular control mechanisms; and robustness of the system to environmental noise. The research will be of interest to scientists interested in metabolism of organophosphonate compounds, which are widely used in medicine, agriculture and industry. The techniques developed will also be of wider interest to others in the field of modelling biochemical pathways dynamics. The techniques could be of use in predicting gene expression behaviour in similar systems, including perhaps those related to pathogenesis of certain bacteria and this could provide a starting point for the development of new antimicrobials. The investigators will benefit significantly from the work, with the reciprocal scientific interchange broadening their understanding of the ways by which systems biology experiments may be fed into and used to design predictive models of metabolism.