Multilevel Cellular Automata as a Tool for Studying Bioinformatic Processes
The signature feature of Cellular Automata is the realization that “simple rules can give rise to complex behavior”. In particular how fixed “rock-bottom” simple rules can give rise to multiple levels of organization. Here we describe Multilevel Cellular Automata, in which the microscopic entities (states) and their transition rules themselves are adjusted by the mesoscale patterns that they themselves generate. Thus we study the feedback of higher levels of organization on the lower levels. Such an approach is preeminently important for studying bioinformatic systems. We will here focus on an evolutionary approach to formalize such Multilevel Cellular Automata, and review examples of studies that use them.
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