Inferring genetic networks: An information theoretic approach

TL;DR

This paper introduces an information theoretic method to reconstruct gene regulatory networks from high-throughput gene expression data, enabling probabilistic inference of gene regulation and guiding experimental perturbations.

Contribution

It presents a novel mathematical approach that incorporates prior knowledge and quantifies information gain to improve gene network inference from microarray data.

Findings

Method effectively infers regulatory relationships with probabilistic confidence.

Approach is suitable for small subnetworks and guides experimental perturbations.

Performance demonstrated through extensive experiments.

Abstract

In the postgenome era many efforts have been dedicated to systematically elucidate the complex web of interacting genes and proteins. These efforts include experimental and computational methods. Microarray technology offers an opportunity for monitoring gene expression level at the genome scale. By recourse to information theory, this study proposes a mathematical approach to reconstruct gene regulatory networks at coarse-grain level from high throughput gene expression data. The method provides the {\it a posteriori} probability that a given gene regulates positively, negatively or does not regulate each one of the network genes. This approach also allows the introduction of prior knowledge and the quantification of the information gain from experimental data used in the inference procedure. This information gain can be used to chose genes to be perturbed in subsequent experiments in order to refine the knowledge about the architecture of an underlying gene regulatory network. The performance of the proposed approach has been studied by {\it in numero} experiments. Our results suggest that the approach is suitable for focusing on size-limited problems, such as, recovering a small subnetwork of interest by performing perturbation over selected genes.