Robustness to noise in gene expression evolves despite epistatic constraints in a model of gene networks

Stochastic noise in gene expression causes variation in the development of phenotypes, making such noise a potential target of stabilizing selection. Here we develop a new simulation model of gene networks to study the adaptive landscape underlying the evolution of robustness to noise. We find that epistatic interactions between the determinants of the expression of a gene and its downstream effect impose significant constraints on evolution, but these interactions do allow the gradual evolution of increased robustness. Despite strong sign epistasis, adaptation rarely proceeds via deleterious intermediate steps, but instead occurs primarily through small beneficial mutations. A simple mathematical model captures the relevant features of the single-gene fitness landscape and explains counterintuitive patterns, such as a correlation between the mean and standard deviation of phenotypes. In more complex networks, mutations in regulatory regions provide evolutionary pathways to increased ro...基因表达中的随机噪声（stochastic noise）会引发表型发育过程中的变异，使得这类噪声成为稳定选择（stabilizing selection）的潜在作用靶标。本研究构建了一种全新的基因网络仿真模型，用以探究噪声鲁棒性（robustness to noise）演化背后的自适应景观（adaptive landscape）。研究发现，基因表达调控因子与其下游效应因子之间的上位性互作（epistatic interactions）会对演化过程施加显著约束，但这类互作仍可推动鲁棒性实现逐步演化。尽管存在强烈的符号上位性（sign epistasis），适应性演化极少通过有害的中间步骤推进，而是主要依靠微小的有益突变完成。一个简易的数学模型可捕捉单基因适合度景观（fitness landscape）的核心特征，并解释诸如表型均值与标准差之间的相关性这类反直觉现象。在更为复杂的网络中，调控区域的突变可为提升鲁棒性的演化过程提供可行路径……