Toward robust network based complex systems: from evolutionary cellular automata to biological models

In this work we demonstrate in two different contexts how we can introduce recent discoveries and technological advances into existing computational models. In the first case, we worked on improving the performance of a simple paradigm for distributed computation: cellular automata. This was achieved by applying principles inspired by Darwinian evolution to alter the connections between the cells of the system, hence changing its topological structure. We have studied the performance of these evolved structures on prototypical problems, and analyzed their response to probabilistic transient faults and permanent failures. In the second case, we consider the context of biological genetic regulatory networks and in particular a model thereof proposed by Kaufmann in the late 60’s: random Boolean networks. Since the model was developed, biology has made tremendous progress and these new discoveries can be used to improve the original model. From the structure of the network, to timing of the event taking place on it, to the specifics of the genes’ activation, we have added a great deal of modern knowledge into the original model, studying , analyzing, and validating it on biological case studies.