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

Maria Angels de Luis Balaguer; Rosangela Sozzani

doi:10.1007/978-1-4939-7125-1_21

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

Methods Mol Biol. 2017:1629:331-348. doi: 10.1007/978-1-4939-7125-1_21.

Authors

Maria Angels de Luis Balaguer¹, Rosangela Sozzani²

Affiliations

¹ Department of Plant and Microbial Biology, North Carolina State University, 2552A Thomas Hall, Raleigh, NC, 27695, USA.
² Department of Plant and Microbial Biology, North Carolina State University, 2552A Thomas Hall, Raleigh, NC, 27695, USA. ross_sozzani@ncsu.edu.

PMID: 28623595
DOI: 10.1007/978-1-4939-7125-1_21

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.

Publication types

Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

Algorithms
Arabidopsis / genetics*
Bayes Theorem*
Computational Biology / methods*
Databases, Genetic
Gene Expression Profiling
Gene Expression Regulation, Plant
Gene Regulatory Networks*
Plant Roots / genetics*
Reproducibility of Results