Functional dissection of regulatory models using gene expression data of deletion mutants.

Genome-wide gene expression profiles accumulate at an alarming rate, how to integrate these expression profiles generated by different laboratories to reverse engineer the cellular regulatory network has been a major challenge. To automatically infer gene regulatory pathways from genome-wide mRNA expression profiles before and after genetic perturbations, we introduced a new Bayesian network algorithm: Deletion Mutant Bayesian Network (DM_BN). We applied DM_BN to the expression profiles of 544 yeast single or double deletion mutants of transcription factors, chromatin remodeling machinery components, protein kinases and phosphatases in S. cerevisiae. The network inferred by this method identified causal regulatory and non-causal concurrent interactions among these regulators (genetically perturbed genes) that are strongly supported by the experimental evidence, and generated many new testable hypotheses. Compared to networks reconstructed by routine similarity measures or by alternative Bayesian network algorithms, the network inferred by DM_BN excels in both precision and recall. To facilitate its application in other systems, we packaged the algorithm into a user-friendly analysis tool that can be downloaded at http://www.picb.ac.cn/hanlab/DM_BN.html.