arXiv:1101.1893v2 [nlin.CG] 18 Mar 2011 1 Introduction Abstract $^{∗}$A preliminary version of this work was presented at the ALife XII conference in Odense, Denmark on August 20 $^{th}$, 2010. [36] 1 Random Boolean networks (RBN) have been a popular model of genetic regulatory networks (GRNs) [25, 26, 16]. Most studies have been made on RBNs with random topologies. Never- theless, it has been found that topologies affect considerably the properties of RBNs. For example, Aldana studied RBNs with a scale-free topology [1], discovering important differences with random Random Boolean networks (RBNs) have been a popular model of genetic regulatory net- works 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, modu- lar RBNs have more attractors and are closer to criticality when chaotic dynamics would be expected, compared to classical RBNs. Modular Random Boolean Networks ∗ Rodrigo Poblanno-Balp †‡ Carlos Gershenson ‡† † Centro de Ciencias de la Complejidad, UNAM, M´exico ‡ Instituto de Investigaciones en Matem´ aticas Aplicadas y en Sistemas, UNAM, M´exico October 25, 2018