Complexity in genetic networks: topology vs. strength of interactions

TL;DR

This paper investigates how the complexity of genetic regulatory networks, characterized by multiple stable states, is primarily influenced by adjustable interaction parameters rather than network topology.

Contribution

It introduces simple models to clarify the relative importance of network topology versus interaction strengths in determining complexity.

Findings

Complexity is mainly governed by adjustable parameters.

Topology plays a lesser role in the number of stable states.

Implications for evolution of real genetic networks.

Abstract

Genetic regulatory networks are defined by their topology and by a multitude of continuously adjustable parameters. Here we present a class of simple models within which the relative importance of topology vs. interaction strengths becomes a well-posed problem. We find that complexity - the ability of the network to adopt multiple stable states - is dominated by the adjustable parameters. We comment on the implications for real networks and their evolution.