Inference of Gene Regulatory Network Through Adaptive Dynamic Bayesian Network Modeling

Abstract

Background: The reconstruction of gene regulatory networks (GRN) using gene expression data can gain new insights into the causality of transcriptional and cellular processes that make a complex living system. Dynamic Bayesian network (DBN) modeling has been increasingly used to reconstruct GRN for the temporal pattern of transcriptional interactions in a time course, but this approach requires expression data measured at even time intervals. In practice, time points at which gene expression is recorded are usually uneven-spaced, determined on the basis of distinct phases of biological processes. We reform DBN modeling to accommodate to any possible irregularity and sparsity of time course microarray data.