Computational Modelling of Plasticity-Led Evolution

TL;DR

This paper reviews computational models of gene regulatory networks to understand plasticity-led evolution, emphasizing their role in phenotypic plasticity, genetic accommodation, and their relation to neural networks.

Contribution

It highlights how gene regulatory network models can simulate plasticity-led evolution and discusses their analogy to neural networks for deeper mechanistic insights.

Findings

Gene regulatory network models incorporate developmental and environmental interactions.

These models support the criteria for plasticity-led evolution.

Analogies between gene networks and neural networks offer new understanding.

Abstract

Plasticity-led evolution is a form of evolution where a change in the environment induces novel traits via phenotypic plasticity, after which the novel traits are genetically accommodated over generations under the novel environment. This mode of evolution is expected to resolve the problem of gradualism (i.e., evolution by the slow accumulation of mutations that induce phenotypic variation) implied by the Modern Evolutionary Synthesis, in the face of a large environmental change. While experimental works are essential for validating that plasticity-led evolution indeed happened, we need computational models to gain insight into its underlying mechanisms and make qualitative predictions. Such computational models should include the developmental process and gene-environment interactions in addition to genetics and natural selection. We point out that gene regulatory network models can incorporate all the above notions. In this review, we highlight results from computational modelling of gene regulatory networks that consolidate the criteria of plasticity-led evolution. Since gene regulatory networks are mathematically equivalent to artificial recurrent neural networks, we also discuss their analogies and discrepancies, which may help further understand the mechanisms underlying plasticity-led evolution.