Year: 2011
Type: article
Abstract: Random Boolean networks (RBNs) have been a popular model of genetic regulatory networks for more than four decades. However, most RBN studies have been made with random topologies, while real regulatory networks have been found to be modular. In this work, we extend classical RBNs to define modular RBNs. Statistical experiments and analytical results show that modularity has a strong effect on the properties of RBNs. In particular, modular RBNs have more attractors, and are closer to criticality when chaotic dynamics would be expected, than classical RBNs.
Source: Artificial Life
DOI: 10.1162/artl_a_00042
Authors: Rodrigo Poblanno-Balp, Carlos Gershenson
Institution: Universidad Nacional Autónoma de México
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W2089755546