Evolution of regulatory networks towards adaptability and stability in a changing environment

TL;DR

This paper presents a minimal evolution model for Boolean regulatory networks that explains how biological networks develop sparse, heterogeneous connectivity patterns balancing adaptability and stability in changing environments.

Contribution

It introduces a novel evolutionary model demonstrating how biological networks evolve connectivity patterns that optimize both robustness and flexibility.

Findings

Emergence of sparse, heterogeneous connectivity patterns.

Scaling behavior reflecting robustness and flexibility.

Dynamic crossover in perturbation spread analysis.

Abstract

Diverse biological networks exhibit universal features distinguished from those of random networks, calling much attention to their origins and implications. Here we propose a minimal evolution model of Boolean regulatory networks, which evolve by selectively rewiring links towards enhancing adaptability to a changing environment and stability against dynamical perturbations. We find that sparse and heterogeneous connectivity patterns emerge, which show qualitative agreement with real transcriptional regulatory networks and metabolic networks. The characteristic scaling behavior of stability reflects the balance between robustness and flexibility. The scaling of fluctuation in the perturbation spread shows a dynamic crossover, which is analyzed by investigating separately the stochasticity of internal dynamics and the network structures different depending on the evolution pathways. Our study delineates how the ambivalent pressure of evolution shapes biological networks, which can be helpful for studying general complex systems interacting with environments.