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Research within the group is in the area of bioinformatics and computational biology, and encompasses all aspects of biomolecular networks, their operation and evolution. For example, we have been involved in the reconstruction of the metabolic network of the malaria parasite and are presently working on modelling parasite growth. We have also considered network evolution by horizontal gene transfer in other important parasites, with applications in drug target discovery. In plants, we have worked on gene functional networks and transcriptional regulatory networks. More details of these aspects of our work can be found in the publications below. Recently, collaborative work with experimental groups (Prof. C. Bonifer and Dr. R. Tooze) at the Leeds Institute of Molecular Medicine has generated genome-wide data sets related to cellular differentiation and cancer in heamatopoetic (blood) cells, using DNA microarrays and high-throughput sequencing. These data sets map gene expression, DNA accessibility, chromatin marks (H3K4Me3), the binding of relevant transcription factors and RNA polymerase II. Future wet lab work will extend them substantially, to include full cellular differentiation series and aberrant differentiation in cancers. These data represent an unprecedented opportunity to understand the normal process of cellular differentiation, and how it goes wrong, for instance in cancer. This project will develop methods to analyse this data, and will consider, for example, (1) how best to quantify the measured parameters (e.g. transcription factor (TF) binding in a promoter) so that changes can be followed effectively as cells change in identity, (2) how expression, sequence and TF binding data can be integrated to define the connectivity of transcriptional networks, (3) mathematical models of network operation and how these can be used to answer relevant questions, and (4) the effect of genetic variation between individuals on these molecular networks and disease susceptibility. Funding Notes Students applying for these projects have access to a number of funding sources, in competition. UK/EU students can apply for BBSRC DTG funding (the main deadline for this is 6 January 2010, but applications are accepted all year round when funds are available). There is also MRC DTG funding, and more details will be available for this early in 2010. For UK/EU students there are some scholarship funds available from the University of Leeds, and for International Students the University awards FIRS (Fully funded International Research Scholarships). All of these funds are limited and awarded in competition. Webb, E.C., Westhead, D.R. 2009. The transcriptional regulation of protein complexes: a cross-species perspective. Genomics In press. Needham, C.J., Manfield, I.W., Bulpitt, A.J., Gilmartin, P.M., Westhead, D.R. 2009. From gene expression to gene regulatory networks in Arabidopsis. BMC Systems Biology 3:85. Tedder, P.M.R., Bradford, J.R., Needham, C.J., McConkey, G.A., Bulpitt, A.J., Westhead, D.R. Bayesian data integration and enrichment analysis for predicting gene function in malaria. In. Lecture Notes in Computer Science 5635. Mathematical Theory and Computational Practice. Ambos-Spies, Lowe and Merkle (Eds.). pp 457-466. Springer-Verlag Berlin Heidelberg 2009. Whitaker, J.W., Westhead, D.R., McConkey, G.A. 2009. Alio intuitu: the automated reconstruction of the metabolic networks of parasites. Trends in Parasitology 25: 396-397. Whitaker, J.W., McConkey, G.A. and Westhead, D.R. 2009. Prediction of horizontal gene transfers in eukaryotes: approaches and challenges. Biochemical Society Transactions 37: 792-795. Whitaker, J.W., McConkey, G.A. and Westhead, D.R. 2009. The transferome of metabolic genes explored: analysis of the horizontal transfer of enzyme encoding genes in unicellular eukaryotes. Genome Biology 10: R36. Tedder, P., Zubko, E., Westhead, D.R., Meyer, P. 2009. Small RNA analysis in Petunia hybrida identifies unusual tissue-specific expression patterns of conserved miRNAs and of a 24mer RNA. RNA 15: 1012-1020. Care, M.A., Bradford, J.R., Needham, C.J., Bulpitt, A.J. and Westhead, D.R. 2009. Combining the interactome and deleterious SNP predictions to improve disease gene identification. Human Mutation 30(3):485-492. Whitaker, J.W., Letunic, I., McConkey, G.A. and Westhead, D.R. 2009. metaTIGER: a metabolic evolution resource. Nucleic Acids Research 37: D531-8. |
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Research Assessment Exercise (RAE) 2008 Results
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