Background Microarray experiments can simultaneously identify thousands of genes that show significant perturbation in expression between two experimental conditions. Response networks, computed through the integration of gene interaction networks with expression perturbation data, may themselves contain tens of thousands of interactions. Gene set enrichment has become standard for summarizing the results of these analyses in terms functionally coherent collections of genes such as biological processes. However, even these methods can yield hundreds of enriched functions that may overlap considerably.

Results We describe a new technique called Markov chain Monte Carlo Biological Process Networks (MCMC-BPN) capable of reporting a highly non-redundant set of links between processes that describe the molecular interactions that are perturbed under a specific biological context. Each link in the BPN represents the perturbed interactions that serve as the interfaces between the two processes connected by the link. We apply MCMC-BPN to publicly available liver-related datasets to demonstrate that the networks formed by the most probable inter-process links reported by MCMC-BPN show high relevance to each biological condition. We show that MCMC-BPN’s ability to discern the few key links from in a very large solution space by comparing results from two other methods for detecting inter-process links.

Conclusions MCMC-BPN is successful in using few inter-process links to explain as many of the perturbed gene-gene interactions as possible. Thereby, BPNs summarize the important biological trends within a response network by reporting a digestible number of inter-process links that can be explored in greater detail.