Coevolving Boolean and Multi-Valued Regulatory Networks

TL;DR

This paper investigates the evolution of Boolean and multi-valued gene regulatory networks within rugged fitness landscapes, revealing that core properties persist across different logic levels and update schemes, with topology influencing behavior.

Contribution

It introduces a coevolution framework for Boolean and multi-valued networks and examines how topology and logic levels affect network dynamics.

Findings

Boolean properties persist with higher logic levels

Topology asymmetry alters network behavior

Results are consistent across update schemes

Abstract

Random Boolean networks have been used widely to explore aspects of gene regulatory networks. A modified form of the model through which to systematically explore the effects of increasing the number of gene states has previously been introduced. In this paper, these discrete dynamical networks are coevolved within coupled, rugged fitness landscapes to explore their behaviour. Results suggest the general properties of the Boolean model remain with higher valued logic regardless of the update scheme or fitness sampling method. Introducing topological asymmetry in the coevolving networks is seen to alter behaviour.