A Control Perspective on the Evolution of Biological Modularity

Abstract

Abstract Biological networks can be nearly decomposed into sets of components or subunits commonly referred as modules. Despite the ubiquity of modularity in biological networks, the understanding of its evolutionary origins remains an open problem in biology. Our previous work on sparsity-promoting control of complex networks has shown that the increased cost of feedback channels selects organized topological structures such as modular ones in networks with Laplacian dynamics as the cheapest option to control. Here we test the hypothesis that the minimization of total control cost promotes modularity in biological networks such as gene regulatory systems. We employ genetic algorithms of network populations using the total control cost as the fitness function for natural selection. Our results suggest that blind random mutations do not create modular networks, even though they offer the optimal fitness from a control perspective. However, mutation schemes combining up to 80% of random mutations and 20% of biased mutations to maximize the diffusion of biological information can increase the average modularity and organized structures of the population. We conclude that control efficiency is an important driver of modularity in biological networks, when the evolutionary process is not entirely random and gradual.