  Constructing gene regulatory networks is a fundamental task in systems
biology. We introduce a Gaussian reciprocal graphical model for inference about
gene regulatory relationships by integrating mRNA gene expression and DNA level
information including copy number and methylation. Data integration allows for
inference on the directionality of certain regulatory relationships, which
would be otherwise indistinguishable due to Markov equivalence. Efficient
inference is developed based on simultaneous equation models. Bayesian model
selection techniques are adopted to estimate the graph structure. We illustrate
our approach by simulations and two applications in ZODIAC pairwise gene
interaction analysis and colon adenocarcinoma pathway analysis.
