A Bayesian Network Approach to Building Gene Regulatory Networks

Continuous abiotic stresses (such as extreme temperatures, high winds and edaphic conditions) can have adverse effects on plant life. This can become a major constraint in crop production. In order to alleviate these problems, it is important to understand the cold stress mechanisms at the molecular and cellular levels, regarding the signaling pathways from cold perception to activation of gene expression. We took a system biology approach, aiming to build the gene regulatory network using the cold stress microarray data that have been obtained, to gain a better understanding of plant responses to cold stress at the molecular level. The results generated from this approach will be utilized to generate hypothesis, which can be tested by other experimental approaches. To discover the gene regulatory network in response to cold stress on plants, we have employed a new proposed reverse engineering method implemented in the "GeneNet " R package (Opgen-Rhein and Strimmer 2007). This method is a heuristic algorithm applying an extended graphical Gaussian model and dynamical correlation shrinkage estimators to the inference of partial directed causal networks from high-dimensional time series expression data