Most functions and structures in living organisms seem to depend on subsets of elements organized as modules. In a modular system a certain process, performed by a module, does not depend heavily on the elements outside the module, and hence it is semi-autonomous. Modularity promotes evolvability, an organism’s capacity to generate heritable phenotypic variation, because it permits adjustment of a module without perturbing other functions and allows the combination of previously evolved functions. Understanding modularity is critical for the study of evolution and development of phenotypic traits. As several biological regulatory systems can be represented as directed networks, it would be useful to study these representations of biological systems to search for network traits that could underlie modules, and to test under which evolutionary scenarios these traits may appear. Modularity in networks has been addressed by looking for densely connected groups of nodes with sparser connections between groups. This is appropriate for undirected networks, but with directed networks a dense connectivity in a group of nodes does not suffice for semi-autonomous behavior. Feedback loops and strongly connected components (SCC) might be related to the behavior expected for a module, and thus, be considered structural signatures of modules. If this is the case, we would find more of these structures in regulatory (modular) networks than expected by chance. The first objective of this project is to test if the perceived abundance of loops and SCCs in regulatory networks is statistically significant. No study has addressed the appearance of loops and SCCs in directed networks and its relationship to modularity under realistic evolutionary scenarios. In this project several evolutionary scenarios for the evolution of modularity in directed networks will be tested, avoiding most limitations in previous attempts.
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