Integration of Epigenetic Data in Bayesian Network Modeling of Gene Regulatory Network

Abstract

The reverse engineering of gene regulatory network (GRN) is an important problem in systems biology. While gene expression data provide a main source of insights, other types of data are needed to elucidate the structure and dynamics of gene regulation. Epigenetic data (e.g., histone modification) show promise to provide more insights into gene regulation and on epigenetic implication in biological pathways. In this paper, we investigate how epigenetic data are incorporated into reconstruction of GRN. We encode the histone modification data as prior for Bayesian network inference of GRN. Bayesian framework provides a natural and mathematically tractable way of integrating various data and knowledge through its prior. Applying to the gene expression data of yeast cell cycle, we demonstrate that integration of epigenetic data improves the accuracy of GRN inference significantly. Furthermore, fusion of gene expression and epigenetic data shed light on the interactions between genetic and epigenetic regulations of gene expression.