AitanaRBN: Representational Redescription of Boolean Networks.

Manuel Marques Pita, Informatics, Indiana University, The Gulbenkian Institute of Science, and Portland State University

While there have been many advances toward understanding the structure of natural networks as well as on modeling specific biological systems as networks of automata, it is still largely an open question how their dynamics can lead to emergent, collective computation and how to harness them to perform specific tasks. Indeed, the need for a better understanding of collective computation in complex natural networks has been identified in many problems, from models of gene regulation dynamics, to networks of stomatal apertures on leaf surfaces and biochemical intracellular signal transduction networks. We propose a novel cognitively-inspired method to characterize conceptual properties of discrete complex networks such as cellular automata and Boolean networks. One way to think about the conceptual redescriptions produced by our method is as “dynamical motifs ”. In contrast to identifying structural motifs, the goal of our proposed redescriptions is to uncover higher-level patterns in the dynamics of automata networks.