  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.
