Inferring Gene Regulatory Networks in the Arabidopsis Root Using a Dynamic Bayesian Network Approach
Maria Angels de Luis Balaguer1, Rosangela Sozzani2
1Department of Plant and Microbial Biology, North Carolina State University, 2552A Thomas Hall, Raleigh, NC, 27695, USA.
Methods in Molecular Biology (Clifton, N.J.)
|June 18, 2017
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.


