Probabilistic refinement of cellular pathway models.
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If you have a question about this talk, please contact Neil Walton.
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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