Motifs emerge from function in model gene regulatory networks

TL;DR

This paper investigates how specific network motifs in gene regulatory networks emerge as a consequence of their functions, using Monte Carlo sampling to analyze motif statistics in functionally constrained networks.

Contribution

It introduces a framework linking network function to motif emergence, revealing how different functions favor distinct motif patterns in gene regulatory networks.

Findings

Mutual inhibition and self-activation motifs are common in multi-stable networks.

Bifan-like motifs are prevalent in networks with periodic gene expression.

Function constrains motif statistics in gene regulatory networks.

Abstract

Gene regulatory networks arise in all living cells, allowing the control of gene expression patterns. The study of their topology has revealed that certain subgraphs of interactions or "motifs" appear at anomalously high frequencies. We ask here whether this phenomenon may emerge because of the functions carried out by these networks. Given a framework for describing regulatory interactions and dynamics, we consider in the space of all regulatory networks those that have a prescribed function. Monte Carlo sampling is then used to determine how these functional networks lead to specific motif statistics in the interactions. In the case where the regulatory networks are constrained to exhibit multi-stability, we find a high frequency of gene pairs that are mutually inhibitory and self-activating. In contrast, networks constrained to have periodic gene expression patterns (mimicking for instance the cell cycle) have a high frequency of bifan-like motifs involving four genes with at least one activating and one inhibitory interaction.