The emergence of complexity and restricted pleiotropy in adapting networks

We have developed a computational approach to study the emergence of complexity by subjecting neural networks to adaptive evolution in environments exacting different levels of demands. We monitored complexity by a variety of metrics. Top down metrics derived from Fisher's geometric model correlated better with the environmental demands than bottom up ones such as network size. Phenotypic complexity was found to increase towards an environment-dependent level through the emergence of restricted pleiotropy. Such pleiotropy, which confined the action of mutations to only a subset of traits, better tuned phenotypes in challenging environments. However, restricted pleiotropy also came at a cost in the form of a higher genetic load, as it required the maintenance by natural selection of more independent traits. Consequently, networks of different sizes converged in complexity when facing similar environment.Phenotypic complexity evolved as a function of the demands of the selective pressures, rather than the physical properties of the network architecture, such as functional size. Our results show that complexity may be more predictable, and understandable, if analyzed from the perspective of the integrated task the organism performs, rather than the physical architecture used to accomplish such tasks. Thus, top down metrics emphasizing selection may be better for describing biological complexity than bottom up ones representing size and other physical attributes.The evolution of the complexity of organisms has been a challenge for Darwinian theories of evolution [1]. How does evolution produce complex organs, when the functioning of such organs requires the successful interaction of many components? Despite the recent proliferation of large nucleotide, proteomic, and metabolic databases, it remains difficult to define the complexity of organisms [2,3], and even more to understand the determinants underlying the emergence of complexity.Any attempt to understand the evolu