Reverse engineering gene regulatory networks using approximate Bayesian computation

TL;DR

This paper introduces a novel application of Approximate Bayesian Computing with MCMC to infer gene regulatory networks from high-dimensional, limited time-series gene expression data, demonstrating its effectiveness through simulations and bacterial data.

Contribution

It adapts Approximate Bayesian Computing for gene network inference from longitudinal data, addressing challenges of high dimensionality and limited samples.

Findings

Effective inference of gene networks demonstrated in simulations.

Successful application to E. coli gene expression data.

Method outperforms traditional approaches in complex biological systems.

Abstract

Gene regulatory networks are collections of genes that interact with one other and with other substances in the cell. By measuring gene expression over time using high-throughput technologies, it may be possible to reverse engineer, or infer, the structure of the gene network involved in a particular cellular process. These gene expression data typically have a high dimensionality and a limited number of biological replicates and time points. Due to these issues and the complexity of biological systems, the problem of reverse engineering networks from gene expression data demands a specialized suite of statistical tools and methodologies. We propose a non-standard adaptation of a simulation-based approach known as Approximate Bayesian Computing based on Markov chain Monte Carlo sampling. This approach is particularly well suited for the inference of gene regulatory networks from longitudinal data. The performance of this approach is investigated via simulations and using longitudinal expression data from a genetic repair system in Escherichia coli.