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.

Notes

1

A preliminary version of this work was presented at the ALife XII conference in Odense, Denmark on August 20th, 2010 [36].

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Centro de Ciencias de la Complejidad, Universidad Nacional Autónoma de México, México. Instituto de Investigaciones en Matemáticas Aplicadas y en Sistemas, Universidad Nacional Autónoma de México, México.

Instituto de Investigaciones en Matemáticas Aplicadas y en Sistemas, Universidad Nacional Autónoma de México, México. Centro de Ciencias de la Complejidad, Universidad Nacional Autónoma de México, México. E-mail: cgg@unam.mx