Dynamics of gene circuits shapes evolvability

Abstract

To what extent does the dynamical mechanism producing a specific biological phenotype bias the ability to evolve into novel phenotypes? We use the interpretation of a morphogen gradient into a single stripe of gene expression as a model phenotype. Although there are thousands of three-gene circuit topologies that can robustly develop a stripe of gene expression, the vast majority of these circuits use one of just six fundamentally different dynamical mechanisms. Here we explore the potential for gene circuits that use each of these six mechanisms to evolve novel phenotypes such as multiple stripes, inverted stripes, and gradients of gene expression. Through a comprehensive and systematic analysis, we find that circuits that use alternative mechanisms differ in the likelihood of reaching novel phenotypes through mutation. We characterize the phenotypic transitions and identify key ingredients of the evolutionary potential, such as sensitive interactions and phenotypic hubs. Finally, we provide an intuitive understanding on how the modular design of a particular mechanism favors the access to novel phenotypes. Our work illustrates how the dynamical mechanism by which an organism develops constrains how it can evolve. It is striking that these dynamical mechanisms and their impact on evolvability can be observed even for such an apparently simple patterning task, performed by just three-node circuits.

Synthetic circuits reveal how mechanisms of gene regulatory networks constrain evolution

“…We visualise the results in phenotype diagrams (Fig A and Appendix Fig S2), which are projections of the higher‐dimensional parameter space onto two dimensions (Jimenez et al , ). These diagrams are built as pixelated images in which for each combination of parameter values (for each “pixel”) the model predicts the resulting phenotype and assigns the corresponding colour (see legend, Fig A).…”

“…Computational models of gene regulation provide one avenue to understand such genotype–phenotype maps (MacCarthy et al , ; Wagner, ; Ma et al , ; Ciliberti et al , ,b; Francois et al , ; Cotterell & Sharpe, ; Francois, ; Payne & Wagner, ). Such models predict that GRNs with different topologies—qualitatively different patterns of interaction between a GRN's genes—can achieve the same gene expression phenotypes, while they differ in their ability to bring forth novel phenotypes through DNA mutations (MacCarthy et al , ; Ciliberti et al , ,b; Francois et al , ; Jimenez et al , ; Payne & Wagner, ). However, experimental validation of the latter prediction is still lacking.…”

The Role of Genotype in the Predictability of Dynamical Behavior in Complex Model Gene Regulatory Networks

“…In contrast, topological mapping of the network, which defines the precise set of rules governing gene/gene interactions, requires the use of a lookup table, as is done for simple Boolean networks. In small networks involving three genes, it is possible to assign interactions to one or more "modules" (e.g., positive feedback loops), and quantitative assessments of the effects of topological genotype are possible [13,39]. This is extremely difficult when N=4, and not possible for N=5 or higher.…”

“…Research into the relationship between innovation (necessary for evolution) and network robustness has found important clues to the mechanisms of evolution in both biological GRNs [4][5][6][7][8] and model GRNs [9][10][11][12][13][14].…”

“…This kind of approach has been used to define critical modular topologies within networks [23][24][25], for the theoretical determination of dynamical network properties [13,26,27], as well as to investigate the evolution of evolvability [28] and other properties of complex networks.…”