Inferring Gene Regulatory Networks in the Arabidopsis Root Using a Dynamic Bayesian Network Approach

Abstract

Gene regulatory network (GRN) models have been shown to predict and represent interactions among sets of genes. Here, we first show the basic steps to implement a simple but computationally efficient algorithm to infer GRNs based on dynamic Bayesian networks (DBNs), and we then explain how to approximate DBN-based GRN models with continuous models. In addition, we show a MATLAB implementation of the key steps of this method, which we use to infer an Arabidopsis root GRN.

Keywords: Arabidopsis root; Dynamic Bayesian network; Gene regulatory network; Ordinary differential equation.