Stabilizing Gene Regulatory Networks Through Feedforward Loops

TL;DR

This paper investigates how feedforward loops in gene regulatory networks, modeled with stochastic Boolean networks, contribute to the robustness of these networks against noise and mutations, introducing a new stability measure.

Contribution

It introduces a novel stability measure for stochastic networks and demonstrates that specific feedforward loops enhance network robustness.

Findings

Feedforward loops buffer networks against stochastic effects

Certain network motifs increase stability in gene regulatory networks

New stability measure quantifies robustness

Abstract

The global dynamics of gene regulatory networks are known to show robustness to perturbations in the form of intrinsic and extrinsic noise, as well as mutations of individual genes. One molecular mechanism underlying this robustness has been identified as the action of so-called microRNAs that operate via feedforward loops. We present results of a computational study, using the modeling framework of stochastic Boolean networks, which explores the role that such network motifs play in stabilizing global dynamics. The paper introduces a new measure for the stability of stochastic networks. The results show that certain types of feedforward loops do indeed buffer the network against stochastic effects.