Multilevel cellular automata as a tool for studying bioinformatic processes

Abstract

We have defined evolutionary multilevel CA and we have shown how microscale and mesoscale entities emerge in consort. In doing so we deviate from the usual CA approach in which the microscale is the rock-bottom on which only higher level entities emerge. We have seen that through this approach we can zoom on " rare but likely" cases with, in some sense, superior properties (e.g. robustness). Moreover it allows us to go beyond the "simple rules give complex behavior" to begin studying in a meaningful and relatively simple way "how complex rules give rise to complex behavior" (and vice versa) (see also [13]) . Doing so is necessary for studying bioinformatic processes keeping in mind Einsteins famous dictum "Everything should be made as simple as possible, but no simpler." © 2010 Springer-Verlag Berlin Heidelberg.