University of Cambridge > > Optimization and Incentives Seminar > Probabilistic refinement of cellular pathway models.

Probabilistic refinement of cellular pathway models.

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Building better models of cellular pathways is one of the major challenges of systems biology and functional genomics. There is a need for methods to build on established expert knowledge and reconcile it with results of high-throughput studies. Available data sources are very heterogeneous and need to be combined in a way specific for the part of the pathway they are most informative for. Also, some data sources like protein domains or phosphorylation states have so far been under-utilized for inferring pathway structure. Here we present a compartment-specific strategy to refine a given pathway hypothesis from a compendium of heterogeneous data sources, which includes novel methodology to integrate protein attributes. We demonstrate the power of our approach in a case study in S. cerevisiae and show that it can assist experimentalists in planning future studies by proposing testable extensions and refinements of a given pathway hypotheses.

This talk is part of the Optimization and Incentives Seminar series.

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