Guiding the Self-organization of Random Boolean Networks

TL;DR

This paper reviews methods for guiding Random Boolean Networks towards the critical regime, which is important for understanding biological systems and designing adaptable, robust computational models.

Contribution

It provides a comprehensive review of eight methods for self-organizing RBNs to reach the critical regime, highlighting their relevance for biology and engineering.

Findings

Guidance methods effectively steer RBNs to the critical regime.

Critical RBNs exhibit properties beneficial for life and computation.

Review of how natural systems may evolve towards criticality.

Abstract

Random Boolean networks (RBNs) are models of genetic regulatory networks. It is useful to describe RBNs as self-organizing systems to study how changes in the nodes and connections affect the global network dynamics. This article reviews eight different methods for guiding the self-organization of RBNs. In particular, the article is focussed on guiding RBNs towards the critical dynamical regime, which is near the phase transition between the ordered and dynamical phases. The properties and advantages of the critical regime for life, computation, adaptability, evolvability, and robustness are reviewed. The guidance methods of RBNs can be used for engineering systems with the features of the critical regime, as well as for studying how natural selection evolved living systems, which are also critical.