Minimum complexity drives regulatory logic in Boolean models of living systems

Boolean modeling is an established framework for studying gene regulatory networks. In a Boolean network, different molecular components correspond to the nodes that receive inputs and relay outputs according to node-specific input-output rules, determining how their states change with time. In this talk, I will present our work where we have examined the rules arising in curated Boolean models of diverse regulatory networks. We have studied the complexity of these logic rules using two definitions in computer science: Boolean complexity based on string lengths in formal logic which is yet unexplored in the biological context, and the average sensitivity. We find that an overwhelming majority of the rules in these biological models minimize the two complexities, pointing to complexity as a major force in selecting regulatory logic in living systems. We provide quantitative support for the long-standing hypothesis that logic rules in gene regulatory networks are likely to be ‘simple’, or in other words, possess 'minimum complexity’. These observations have implications for ongoing efforts to build predictive models of biological systems.