Inference of functional networks of condition-specific response--a case study of quiescence in yeast

Analysis of condition-specific behavior under stressful environmental conditions can provide insight into mechanisms causing different healthy and diseased cellular states. Functional networks (edges representing statistical dependencies) inferred from condition-specific expression data can provide fine-grained, network level information about conserved and specific behavior across different conditions. In this paper, we examine novel microarray compendia measuring gene expression from two unique stationary phase yeast cell populations, quiescent and non-quiescent. We make the following contributions: (a) develop a new algorithm to infer functional networks modeled as undirected probabilistic graphical models, Markov random fields, (b) infer functional networks for quiescent, non-quiescent cells and exponential cells, and (c) compare the inferred networks to identify processes common and different across these cells. We found that both non-quiescent and exponential cells have more gene ontology enrichment than quiescent cells. The exponential cells share more processes with non-quiescent than with quiescent, highlighting the novel and relatively under-studied characteristics of quiescent cells. Analysis of inferred subgraphs identified processes enriched in both quiescent and non-quiescent cells as well processes specific to each cell type. Finally, SNF1, which is crucial for quiescence, occurs exclusively among quiescent network hubs, while non-quiescent network hubs are enriched in human disease causing homologs.