Reverse Engineering Yeast Gene Regulatory Networks using Graphical Models

We investigate in this paper reverse engineering of gene regulatory networks from time series microarray data. We propose a dynamic Bayesian networks (DBNs) modeling and a full Bayesian learning scheme. The proposed DBN models directly the continuous expression levels and also is associated with parameters that indicate the degree as well as the types of regulations. To learn the network from data, we proposed a reversible jump Markov chain Monte Carlo (RJMCMC) algorithm. The RJMCMC algorithm can provide not only more accurate inference results than the deterministic alternative algorithms but also an estimate on the a posteriori probabilities (APPs) of the network topology. The estimated APPs provide useful information on the confidence of the inferred results and can also be used for efficient Bayesian data integration. The proposed approach was tested on yeast cell cycle microarray data and the results were compared with the KEGG pathway map