Estimating cellular pathways from an ensemble of heterogeneous data sources

TL;DR

This paper introduces a compartment-specific method using a local-move Gibbs sampler to integrate diverse data sources for refining cellular pathway models, demonstrated on yeast pheromone response pathways.

Contribution

It presents a novel approach combining heterogeneous data and protein attributes for pathway refinement using a local-move Gibbs sampler.

Findings

Effective integration of heterogeneous data sources.

Improved pathway hypothesis refinement.

Successful application to yeast MAPK pathway.

Abstract

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. Moreover, the available data sources are heterogeneous and need to be combined in a way specific for the part of the pathway in which they are most informative. Here, we present a compartment specific strategy to integrate edge, node and path data for the refinement of a network hypothesis. Specifically, we use a local-move Gibbs sampler for refining pathway hypotheses from a compendium of heterogeneous data sources, including novel methodology for integrating protein attributes. We demonstrate the utility of this approach in a case study of the pheromone response MAPK pathway in the yeast S. cerevisiae.