Gene-network inference by message passing

TL;DR

This paper introduces a message-passing algorithm for inferring gene regulatory networks from expression data, capable of identifying sparse, directed, and combinatorial interactions with analytical performance characterization.

Contribution

It develops a novel message-passing approach from a statistical physics perspective for gene-network inference, including analytical performance analysis and real data application.

Findings

Identified clear cases of combinatorial gene regulation.

Found enrichment in functional annotations among regulated genes.

Demonstrated the algorithm's effectiveness on yeast expression data.

Abstract

The inference of gene-regulatory processes from gene-expression data belongs to the major challenges of computational systems biology. Here we address the problem from a statistical-physics perspective and develop a message-passing algorithm which is able to infer sparse, directed and combinatorial regulatory mechanisms. Using the replica technique, the algorithmic performance can be characterized analytically for artificially generated data. The algorithm is applied to genome-wide expression data of baker's yeast under various environmental conditions. We find clear cases of combinatorial control, and enrichment in common functional annotations of regulated genes and their regulators.