Reconstruction of Genetic networks using information theory

L. DIAMBRA

Puerto Madryn

Workshop; Statistical Physics in Biological Networks; 2020

In the last years many efforts have been dedicated to systematically elucidate the complex web of interacting genes and proteins. These efforts include experimental and computational methods. Microarray and RNA-seq technologies offer opportunities 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 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 choose those genes that will be perturbed in subsequent experiments in order to refine our knowledge about the architecture of an underlying gene regulatory network. The performance of the proposed approach has been studied by experiments in silico. 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.