Abstract：
Phenotypic plasticity provides an attractive strategy for adapting to various environments, but the evolutionary mechanism of the underlying genetic system is poorly understood. We use a simple gene regulatory network model to explore how a species acquires phenotypic plasticity, particularly focusing on discrete phenotypic plasticity, which has been difficult to explain by quantitative genetic models. Our approach employs a population genetic framework that integrates the developmental process, where each individual undergoes growth to develop its phenotype, which subsequently becomes subject to selection pressures. Our model considers two alternative types of environments, with the gene regulatory network including a sensor gene that turns on and off depending on the type of environment. With this assumption, we demonstrate that the system gradually adapts by acquiring the ability to produce two distinct optimum phenotypes under two types of environments without changing genotype, resulting in phenotypic plasticity. We find that the resulting plasticity is often discrete after a lengthy period of evolution. Our results suggest that gene regulatory networks have a notable capacity to flexibly produce various phenotypes in response to environmental changes. This study also shows that the evolutionary dynamics of phenotype may differ significantly between mechanistic-based developmental models and quantitative genetics models, suggesting the utility of incorporating gene regulatory networks into evolutionary models.
摘要：
表型可塑性为适应各种环境提供了有吸引力的策略，但是对潜在遗传系统的进化机制知之甚少。我们使用一个简单的基因调控网络模型来探索物种如何获得表型可塑性，特别关注离散表型可塑性，这很难用定量遗传模型来解释。我们的方法采用了群体遗传框架，整合了发育过程，每个个体都经历生长以发展其表型，随后受到选择压力。我们的模型考虑了两种替代类型的环境，基因调控网络包括一个传感器基因，根据环境的类型打开和关闭。有了这个假设,我们证明了系统通过获得在两种类型的环境下产生两种不同的最佳表型而不改变基因型的能力而逐渐适应。导致表型可塑性。我们发现，经过漫长的进化，所得的可塑性通常是离散的。我们的结果表明，基因调控网络具有显着的能力，可以灵活地产生各种表型以响应环境变化。这项研究还表明，表型的进化动力学在基于机制的发育模型和数量遗传学模型之间可能存在显着差异。表明将基因调控网络纳入进化模型的实用性。
