Professor Simon Hubbard
Michael Smith Building|Oxford Road|Manchester|M13 9PT
Our group is interested in using computers to understand biological phenomena, via data analysis, modelling and prediction. Our main focus at present is on the area of quantitative proteomics: how can we measure the levels of all the individual proteins in cells and tissues, and understand how these levels change in different conditions, as well as under stress. We are working on methods to do this in collaboration with mass spectrometrists and protein chemists, developing software for experimental design and downstream analysis of results, using yeast as a model system. In parallel, we are analysing experimental RNA sequencing data in yeast to understand how cells regulate the translation of mRNA in to proteins, a fundamental process of molecular biology which is now know to play a significant role in the regulation of gene expression, particularly under stress conditions.
- Rowe, William; Kershaw, Christopher; Castelli, Lydia; Costello, Joseph; Ashe, Mark; Grant, Chris; Sims, Paul; Pavitt, Graham; Hubbard, Simon. (2013). Puf3p induces translational repression of genes linked to oxidative stress. Nucleic Acids Research, n/a, n/a. eScholarID:209597 | DOI:10.1093/nar/gkt948
- Brownridge, P., Lawless, C., Payapilly, A., Lanthaler, K., Holman, S., Harman, V., Grant, C., Beynon, R. & Hubbard, S (2013). Quantitative analysis of chaperone network throughput in budding yeast. Proteomics, eScholarID:188540 | PMID:23420633 | DOI:10.1002/pmic.201200412
- Blakeley, P., Overton, I. & Hubbard, S (2012). Addressing statistical biases in nucleotide-derived protein databases for proteogenomic search strategies. J Proteome Res, 11(11), 5221-34. eScholarID:188541 | PMID:23025403 | DOI:10.1021/pr300411q
- Eyers, C., Lawless, C., Wedge, D., Lau, K., Gaskell, S. & Hubbard, S (2011). CONSeQuence: Prediction of Reference Peptides for Absolute Quantitative Proteomics Using Consensus Machine Learning Approaches. Mol Cell Proteomics, 10(11), M110.003384. eScholarID:130064 | PMID:21813416 | DOI:10.1074/mcp.M110.003384
PhD projects available
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