发表机构
The University of Tokyo; Ochanomizu University; RIKEN Center for AIP（东京大学; 御茶水女子大学; 理化学研究所人工智能研究中心）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
重新审视基因调控网络中噪声驱动的稳健性进化，通过明确计算细节重现主要定性结果，分析进化种群对初始条件变化的稳健性，提供可重复实现并开源代码，支持对动态GRN模型稳健性的进一步研究。
AI 中文摘要
基因调控网络（GRN）模型被广泛用于研究生物系统中的基因表达动态。Kaneko（2007）提出的进化GRN模型研究了对非遗传表型噪声的稳健性与对突变的稳健性之间的关系。我们通过明确原始研究中省略的计算细节来重建模拟协议，并重现其主要定性结果，包括低适应性个体的噪声依赖性抑制、同基因表型方差与遗传方差之间的关系以及在一定表达噪声下进化网络的稳健性增加。我们进一步分析了进化种群对初始条件变化的稳健性。虽然代表性网络分析显示与原始研究存在定量差异，但种群水平分析支持定性结论，即较高表型噪声下的进化会导致更广泛的吸引域。我们提供了一个可重复使用的实现并公开发布源代码，以支持可重复性并促进对动态GRN模型中稳健性的进一步研究。
英文摘要
Gene regulatory network (GRN) models are widely used to investigate gene-expression dynamics in biological systems. In this study, we re-examine an evolutionary GRN model proposed by Kaneko, which was used to investigate the relationship between robustness to nongenetic phenotypic noise and robustness to mutation. We explicitly reconstruct the simulation protocol by specifying computational details omitted from the original study and reproduce its main qualitative findings, including the noise-dependent suppression of low-fitness individuals, the relationship between isogenic phenotypic variance and genetic variance, and the increased robustness of evolved networks under certain expression noise. We further analyze the robustness of evolved populations to variations in initial conditions. While the representative-network analysis shows quantitative differences from the original study, the population-level analysis supports the qualitative conclusion that evolution under higher phenotypic noise leads to broader basins of attraction. We provide a reusable implementation and publicly release the source code to support reproducibility and facilitate further studies on robustness in dynamical GRN models.
Comments23 pages, 6 figures
