High Throughput Systems Biology Analysis Modelling and Stimulation of Large Biological Data Sets

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The volume of data generated by `omics¿ technologies is growing rapidly, following substantial investment in DNA microarray, high-throughput NMR and mass spectrometry facilities at the University of Birmingham. There is a corresponding growth of the number of researchers in bioinformatics and systems biology, both in the School of Biosciences, and in our collaborator schools: Computer Science, Engineering, Mathemetics and the Medical School. The aim of this initiative is to provide a high-performance computing facility that can act as a departmental resource for researchers and collaborators involved in the analysis of large biological data sets and modelling of biological systems. It will be used by a wide range of researchers in a wide range of fields; however, this diversity of research is unified by its foundation in real data and reliance on stochastic techniques. The majority of use will be in the following areas: Reverse engineering of gene networks from functional genomics data. We have successfully applied probabilistic graph models to reverse-engineer gene networks for T-cell activation from transcriptomic data. We are extending these methodologies to include metabolomic data, and realistic nonlinear interactions between the elements of the model. The new models will be applied to biological systems including acid stress response in E. coli and environmental toxicology. Statistical models for cell cross-talk. We use a number of different statistical modelling techniques to develop predictors of cell physiology based on the molecular state of nearby cells. We are applying these techniques to the interaction between normal and tumour cells and the interaction between human epithelial cells and bacterial pathogens during infection. Integrating biological knowledge with expression profiling data. We are using statecharts to develop models for T-cell activation that integrate functional genomics data with biological knowledge from public domain databases. We propose to build virtual lymph-nodes that will include many thousands of interacting statechart models. Metabolomic data analysis. Having invested in high-throughput NMR and mass spectrometry facilities, we are generating large metabolomic data sets in a number of fields, including toxicology and microbiology. These data sets require large-scale computation for spectral processing, decomposition and comparison using multivariate modelling. Microbiological systems biology. The exploiting genomics initiative has enabled us to develop a functional genomics laboratory that is generating 1000 DNA microarrays per year for E. coli (K12 and O157). These large data sets require significant analysis, and we are applying a number of methodologies, including reverse-engineering, non-linear data-mining and whole-genome regulon prediction to these data. Theoretical and experimental quantitative genetics. We are developing analytical and experimental strategies for studying the genetic basis of complex, polygenic triats using large `omics¿ data-sets in plants, animals, man and fungi. Physiological systems biology. We measure physiological traits in order to understand how the physiology of animals is adapted to functions such as flight or diving using a miniature implantable data logger that can record data on heart rate, body temperature, barometric pressure, tilt and acceleration. These give large year-long time-series which we mine to gain and understanding to the physiology of flight and diving. Stochastic pi-calculus models of signalling pathways. We are proposing the probabilistic model checker (PRISM) to apply the stochastic pi-calculus to develop and analyse formal models for cell-cell signalling in a number of applications, including FGF-mediated signalling and T-cell activation.