Hunting for correctors of cystic fibrosis using existing data and systems biology thinking

Joanna Betts, Senior Scientific Investigator, Glaxo Smith Kline

Date: September 30^th , 2010
Time: 11am PDT/2pm EDT

The advent of platform -omics technologies has resulted in a drastic increase in biological data covering genetics, RNA/protein expression, metabolomics and protein interactions. It has also driven more open approaches to understanding biological mechanisms, as evidenced by the rise of systems biology. Such open, non-reductionist systems based approaches to scientific discovery require significant experimental input and generate significant amounts of complex data. To run such ambitious experiments on a regular basis is not feasible because of the costs involved in generating the data, analysing the results, constructing hypotheses, and testing these experimentally. In some cases, in silico methods, coupled to extensive data mining, can be used to construct testable hypotheses that emulate the output of systems approaches. Here we will discuss how we have used the GeneGo cystic fibrosis disease pathway maps together with publicly available platform data and literature searching to identify subsets of targets and compounds that would potentially elicit desired cellular responses in a CFTR corrector assay. This approach gave an improved .hit. rate and could potentially identify new indications for existing assets.