Combinatorial algorithms in bioinformatics

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One of the great scientific feats of the 20th century is the understanding of the quantitative nature of biological systems. The resulting explosion of information has challenged scientists to develop novel in-silico tools to model, analyze, and infer the underlying processes. Dubbed bioinformatics, the result is a multi-disciplinary effort bringing together mathematics and computer scientists with biologists and chemists. My long-term research interest on algorithms for analyzing combinatorial structures has, over the past ten years, increasingly centered on cellular processes; problems my lab is considering include: 1. Haplotypes give the structure of individual chromosomes and as such are the basis for genetic disease susceptibility. In-vitro techniques are prohibitively expensive for determining individual haplotypes and our focus is on the development of efficient computational tools. 2. Drug synthesis, an expensive endevour, is now turning to computational tools. Our long-term project on Inverse Protein Folding attempts to construct a desired protein given information about the protein's properties. Such proteins are the basis for novel drug design as they will exhibit pre-determined behaviour. 3. We have developed a novel technique to cluster extremely large high-throughput data and used it for flow cytometry data. The result is a general purpose tool that can, for example, identify populations of cells that express similar intercellular or surface proteins. We are interested in the efficiency and efficacy of algorithms in real-life settings. Students graduating from my lab have a mix of life-sciences and computational skills, a combination sought out by industry and academia.