A statistical mechanics approach to reverse engineering: sparsity and biological priors on gene regulatory networks

TL;DR

This paper introduces a reverse engineering method for gene networks that leverages statistical mechanics principles, incorporating biological priors and sparsity to improve accuracy using time-series data.

Contribution

It presents a novel algorithm that integrates biological prior knowledge and sparsity constraints for more reliable gene network inference from limited data.

Findings

Algorithm successfully deduces network topology in simulated data.

Performance improves with more data and biological priors.

Method applied to E. coli gene network data.

Abstract

The important task of determining the connectivity of gene networks, and at a more detailed level even the kind of interaction existing between genes, can nowadays be tackled by microarraylike technologies. Yet, there is still a large amount of unknowns with respect to the amount of data provided by a single microarray experiment, and therefore reliable gene network retrieval procedures must integrate all of the available biological knowledge, even if coming from different sources and of different nature. In this paper we present a reverse engineering algorithm able to reveal the underlying gene network by using time-series dataset on gene expressions considering the system response to different perturbations. The approach is able to determine the sparsity of the gene network, and to take into account possible {\it a priori} biological knowledge on it. The validity of the reverse engineering approach is highlighted through the deduction of the topology of several {\it simulated} gene networks, where we also discuss how the performance of the algorithm improves enlarging the amount of data or if any a priori knowledge is considered. We also apply the algorithm to experimental data on a nine gene network in {\it Escherichia coli