On the role of sparseness in the evolution of modularity in gene regulatory networks

Abstract

Modularity is a widespread property in biological systems. It implies that interactions occur mainly within groups of system elements. A modular arrangement facilitates adjustment of one module without perturbing the rest of the system. Therefore, modularity of developmental mechanisms is a major factor for evolvability, the potential to produce beneficial variation from random genetic change. Understanding how modularity evolves in gene regulatory networks, that create the distinct gene activity patterns that characterize different parts of an organism, is key to developmental and evolutionary biology. One hypothesis for the evolution of modules suggests that interactions between some sets of genes become maladaptive when selection favours additional gene activity patterns. The removal of such interactions by selection would result in the formation of modules. A second hypothesis suggests that modularity evolves in response to sparseness, the scarcity of interactions within a system. Here I simulate the evolution of gene regulatory networks and analyse diverse experimentally sustained networks to study the relationship between sparseness and modularity. My results suggest that sparseness alone is neither sufficient nor necessary to explain modularity in gene regulatory networks. However, sparseness amplifies the effects of forms of selection that, like selection for additional gene activity patterns, already produce an increase in modularity. That evolution of new gene activity patterns is frequent across evolution also supports that it is a major factor in the evolution of modularity. That sparseness is widespread across gene regulatory networks indicates that it may have facilitated the evolution of modules in a wide variety of cases.

Using digital organisms to investigate the effect of whole genome duplication in (artificial) evolution

“…Furthermore, the modular arrangement of duplicated GRNs may also result in functionally redundant modules, contributing to the genetic or mutational robustness at the GRN level. This facilitates the rewiring of novel functional modules without disturbing the ancestral one, a feature that is expected to be especially advantageous under unstable challenging environments (23,36). In this respect, our observations are compatible with previous claims that increased modularity results in increased evolvability (32,(39)(40)(41)(42)(43)(44).…”

“…A duplicated genome also results in an increase in the modularity of its encoded GRNs, i.e., the occurrence of multiple functional subnetworks or modules formed by highly connected genes that are co-expressed and/or co-regulated by the same set of key regulators, while establishing sparser connections with nodes from different modules (1,(33)(34)(35)(36)(37)(38). As a consequence, single substitutions in duplicated genomes have the potential to affect many more key regulators, in turn having a greater impact on the underlying GRN and the resulting phenotypes.…”

The evolutionary conundrum of whole‐genome duplication

“…Duplication of entire genomes and their GRNs immediately creates redundant “modules” in the genome. Modules are represented by subnetworks of the global GRN formed by highly connected genes that are co‐expressed and/or co‐regulated by the same set of key regulators, while establishing sparser connections with genes from different modules (Espinosa‐Soto, 2018). The role of gene duplication and cis ‐regulatory evolution in rapid GRN rewiring has been reviewed (see e.g., Das Gupta and Tsiantis, 2018).…”

Network architectures supporting learnability

“…Hierarchical modularity has been described as the generic architecture of complexity [54], and is observed beyond the neurosciences, in metabolic, ecological and gene regulatory networks, and in human-made systems, such as large organizations and the Internet [93]. hierarchical modularity [93][94][95][96][97][98]. In the human brain, hierarchical modularity has been noted in the structural networks linking large-scale areas [65] and in functional networks linking these same areas by shared information [99].…”