Reverse Engineering Regulatory Networks in Cells Using a Dynamic Bayesian Network and Mutual Information Scoring Function

Abstract

In systems biology, two important regulatory networks are gene regulatory networks (GRNs) and regulatory networks of microRNAs (RNMs). A GRN is modeled as a directed graph in which a node represents a gene or transcription factor (TF), and an edge from a TF to a gene indicates that the TF regulates the expression of the gene. An RNM is modeled as a bipartite directed graph with two disjoint sets of nodes: a set of nodes that represent microRNAs (miRNAs) and a set of nodes that represent genes or TFs. Directed edges between these two sets of nodes represent miRNA-target interactions or TF-miRNA regulatory relations. In this paper, we present an approach to reverse engineering GRNs and RNMs using a dynamic Bayesian network and mutual information scoring function. Our approach is able to automatically infer both GRNs and RNMs from time series of expression data. Experimental results on different datasets show that our approach is more accurate than other time-series based network inference methods.