%0 Journal Article %J Artificial Life %D 2011 %T Modular Random {Boolean} Networks %A Rodrigo {Poblanno-Balp} %A Carlos Gershenson %X 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, compared to classical RBNs. %B Artificial Life %I MIT Press %V 17 %P 331–351 %G eng %U http://arxiv.org/abs/1101.1893 %R 10.1162/artl_a_00042 %0 Book Section %B {Artificial Life XII} Proceedings of the Twelfth International Conference on the Synthesis and Simulation of Living Systems %D 2010 %T Modular Random {Boolean} Networks %A Rodrigo {Poblanno-Balp} %A Carlos Gershenson %E Harold Fellermann %E Mark Dörr %E Martin M. Hanczyc %E Lone Ladegaard Laursen %E Sarah Maurer %E Daniel Merkle %E Pierre-Alain Monnard %E Kasper St$ø$y %E Steen Rasmussen %B {Artificial Life XII} Proceedings of the Twelfth International Conference on the Synthesis and Simulation of Living Systems %I MIT Press %C Odense, Denmark %P 303-304 %G eng %U http://mitpress.mit.edu/books/chapters/0262290758chap56.pdf