Predicting synchronized gene coexpression patterns from fibration symmetries in gene regulatory networks in bacteria

TL;DR

This paper investigates whether the structure of gene regulatory networks can predict gene synchronization patterns, using the concept of symmetry fibrations to connect network topology with gene coexpression in bacteria.

Contribution

It introduces the application of symmetry fibrations to predict gene coexpression from network structure and validates this approach with bacterial gene expression data.

Findings

Gene coexpression patterns are consistent with symmetry fibrations.

Network structure alone provides useful predictions of gene synchronization.

Evolution may streamline gene input functions within fibers for coexpression.

Abstract

Background: Gene regulatory networks coordinate the expression of genes across physiological states and ensure a synchronized expression of genes in cellular subsystems, critical for the coherent functioning of cells. Here we address the questions whether it is possible to predict gene synchronization from network structure alone. We have recently shown that synchronized gene expression may be predicted from symmetries in the gene regulatory networks (GRN) and described by the concept of symmetry fibrations. We showed that symmetry fibrations partition the genes into groups called fibers based on the symmetries of their 'input trees', the set of paths in the network through which signals can reach a gene. In idealized dynamic gene expression models, all genes in a fiber are perfectly synchronized, while less idealized models - with gene input functions differencing between genes - predict symmetry breaking and desynchronization. Results: To study the functional role of gene fibers and to test whether some of the fiber-induced coexpression remains in reality, we analyze gene fibrations for the gene regulatory networks of E. coli and B. subtilis and confront them with expression data. We find approximate gene coexpression patterns consistent with symmetry fibrations with idealized gene expression dynamics. This shows that network structure alone provides useful information about gene synchronization, and suggest that gene input functions within fibers may be further streamlined by evolutionary pressures to realize a coexpression of genes. Conclusions: Thus, gene fibrations provides a sound conceptual tool to describe tunable coexpression induced by network topology and shaped by mechanistic details of gene expression.