Evolving phenotypic networks in silico

Abstract

Evolved gene networks are constrained by natural selection. Their structures and functions are consequently far from being random, as exemplified by the multiple instances of parallel/convergent evolution. One can thus ask if features of actual gene networks can be recovered from evolutionary first principles. I review a method for in silico evolution of small models of gene networks aiming at performing predefined biological functions. I summarize the current implementation of the algorithm, insisting on the construction of a proper "fitness" function. I illustrate the approach on three examples: biochemical adaptation, ligand discrimination and vertebrate segmentation (somitogenesis). While the structure of the evolved networks is variable, dynamics of our evolved networks are usually constrained and present many similar features to actual gene networks, including properties that were not explicitly selected for. In silico evolution can thus be used to predict biological behaviours without a detailed knowledge of the mapping between genotype and phenotype.

Synthetic circuits reveal how mechanisms of gene regulatory networks constrain evolution

“…These questions require us to study the relationship between genotypic and phenotypic changes in GRNs. 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, ).…”

Phenotypic spandrel: absolute discrimination and ligand antagonism

“…Trade-off between different functionalities are major forces shaping evolution of complex phenotypes moving on evolutionary Pareto fronts [1,2]. In silico evolution of phenotypic models of gene networks [3] have further shown that selection of complex phenotypes leads to apparition of other traits that have not been explicitly selected for. For instance, a clock naturally appears when selecting for stripe formation in a model of segmentation evolution [4], or ligand antagonism when selecting for a model of immune recognition [5].…”

The Case for Absolute Ligand Discrimination: Modeling Information Processing and Decision by Immune T Cells

“…To answer this question, we turned to in silico evolution [40] (a review of this method can be found in [21]). The idea is to simulate a Darwinian process on a space of possible models to select for absolute discrimination.…”