dynamics of RBNs. Most RBN studies have been made on homogeneous or normal topologies. However, it is known that real genetic regulatory networks have a modular structure. A general model of modular RBNs is presented. Statistical and analytical studies show that modularity can considerably change the properties of RBNs. In particular, modularity produces critical dynamics in networks where their average connectivity would suggest chaotic dynamics. Also, more attractors are observed on average in modular RBNs. Droop and Hickinbotham [9] use an artificial chemistry to study the prop- erties of networks constructed from mutation patterns observed in nature. The resulting small-world networks offer a balance between random and regular topologies, resonating with the results of Lizier et al. [13]. This balance is advantageous for the exlporation of evolutionary space, i.e. evolvability. A second contribution by Brede [5] studies the popular prisoner’s dilemma [19] on regular and heterogenous networks with heterogeneous payoff landscapes. Results illustrate the non-trivial relations between network topology and the facilitation of the evolution of cooperation. The final paper of the issue [1] presents a study of complex networks that utilizes colored motifs as the building blocks of the networks. Again, the ap- proach turns to information theory as a generic method of choice: the motifs are used to define the information content of the network. Importantly, the colored motif information is related to aspects of selection, investigated via the inter- action between instructions in genomes of digital life organisms, as well as the C. elegans brain. The central observation (that the colored motif information content changes during evolution, depending on how the genomes are organized) offers an interesting tool to dissect genomic rearrangements, and provides yet another element of a potentially unifying information-theoretic framework for complex networks. 4