Emergence of robustness against noise: A structural phase transition in evolved models of gene regulatory networks

TL;DR

This paper models the evolution of gene regulatory networks under noise robustness pressure, revealing a phase transition to a segregated core structure similar to real biological networks, supported by simulations and empirical data.

Contribution

It introduces a statistical physics framework to understand the structural emergence in evolved gene regulatory networks under selective pressure.

Findings

Identification of a phase transition from random to segregated core topology.

Theoretical phase diagram of network evolution under different pressures.

Empirical validation with yeast and E. coli data.

Abstract

We investigate the evolution of Boolean networks subject to a selective pressure which favors robustness against noise, as a model of evolved genetic regulatory systems. By mapping the evolutionary process into a statistical ensemble and minimizing its associated free energy, we find the structural properties which emerge as the selective pressure is increased and identify a phase transition from a random topology to a "segregated core" structure, where a smaller and more densely connected subset of the nodes is responsible for most of the regulation in the network. This segregated structure is very similar qualitatively to what is found in gene regulatory networks, where only a much smaller subset of genes --- those responsible for transcription factors --- is responsible for global regulation. We obtain the full phase diagram of the evolutionary process as a function of selective pressure and the average number of inputs per node. We compare the theoretical predictions with Monte Carlo simulations of evolved networks and with empirical data for Saccharomyces cerevisiae and Escherichia coli.