Abstract

Robustness, defined as tolerance to perturbations such as mutations and environmental fluctuations, is pervasive in biological systems. However, robustness often coexists with its counterpart, evolvability—the ability of perturbations to generate new phenotypes. Previous models of gene regulatory network evolution have shown that robustness evolves under stabilizing selection, but it is unclear how robustness and evolvability will emerge in common coevolutionary scenarios. We consider a two-species model of coevolution involving one host and one parasite population. By using two interacting species, key model parameters that determine the fitness landscapes become emergent properties of the model, avoiding the need to impose these parameters externally. In our study, parasites are modeled on species such as cuckoos where mimicry of the host phenotype confers high fitness to the parasite but lower fitness to the host. Here, frequent phenotype changes are favored as each population continually adapts to the other population. Sensitivity evolves at the network level such that point mutations can induce large phenotype changes. Crucially, the sensitive points of the network are broadly distributed throughout the network and continually relocate. Each time sensitive points in the network are mutated, new ones appear to take their place. We have therefore named this phenomenon “whack-a-mole” sensitivity, after a popular fun park game. We predict that this type of sensitivity will evolve under conditions of strong directional selection, an observation that helps interpret existing experimental evidence, for example, during the emergence of bacterial antibiotic resistance.

Highlights

  • Robustness, defined as tolerance to perturbations such as mutations and environmental fluctuations, is pervasive in biological systems [1, 2]

  • Previous models of gene regulatory networks have shown that robustness can evolve when the phenotype is under evolutionary pressure to remain constant

  • We developed a two-population model to investigate how robustness and sensitivity become distributed within a network under antagonistic coevolution

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Summary

Introduction

Robustness, defined as tolerance to perturbations such as mutations and environmental fluctuations, is pervasive in biological systems [1, 2]. Computational models of evolution aimed at understanding the relationship between gene-network evolution and behavior (gene expression dynamics) [3,4,5] These studies found that, a large number of different networks (genotypes) have the same gene expression dynamics (phenotype), they can usually be connected to one another via minimal changes (e.g. creation or deletion of single cis-regulatory interactions) that might occur during evolution via mutation. This capacity for neutral evolution can facilitate the evolution of robustness since it allows a population to migrate towards more robust genotypes without altering the phenotype [6]. Similar experiments on metabolic networks, in E. coli, have shown network robustness with respect to both gene knockouts and network rewiring [8,9,10]

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