Bayesian graphical models for computational network biology

Background: Computational network biology is an emerging interdisciplinary research area. Among many other network approaches, probabilistic graphical models provide a comprehensive probabilistic characterization of interaction patterns between molecules and the associated uncertainties. Results: In this article, we first review graphical models, including directed, undirected, and reciprocal graphs (RG), with an emphasis on the RG models that are curiously under-utilized in biostatistics and bioinformatics literature. RG's strictly contain chain graphs as a special case and are suitable to model reciprocal causality such as feedback mechanism in molecular networks. We then extend the RG approach to modeling molecular networks by integrating DNA-, RNA- and protein-level data. We apply the extended RG method to The Cancer Genome Atlas multi-platform ovarian cancer data and reveal several interesting findings. Conclusions: This study aims to review the basics of different probabilistic graphical models as well as recent development in RG approaches for network modeling. The extension presented in this paper provides a principled and efficient way of integrating DNA copy number, DNA methylation, mRNA gene expression and protein expression.