Data from: Long-term evolution on complex fitness landscapes when mutation is weak

Understanding evolution on complex fitness landscapes is difficult both because of the large dimensionality of sequence space and the stochasticity inherent to population-genetic processes. Here I present an integrated suite of mathematical tools for understanding evolution on time-invariant fitness landscapes when mutations occur sufficiently rarely that the population is typically monomorphic and evolution can be modeled as a sequence of well-separated fixation events. The basic intuition behind this suite of tools is that surrounding any particular genotype lies a region of the fitness landscape that is easy to evolve to, while other pieces of the fitness landscape are difficult to evolve to (due to distance, being across a fitness valley, etc.). I propose a rigorous definition for this ``dynamical neighborhood' of a genotype which captures several aspects of the distribution of waiting times to evolve from one genotype to another. The neighborhood structure of the landscape as a whole can be summarized as a matrix, and I show how this matrix can be used to approximate the expected waiting time for certain evolutionary events to occur and to provide an intuitive interpretation to existing formal results on the index of dispersion of the molecular clock.解析复杂适应度景观（fitness landscape）上的演化过程极具挑战，这一方面源于序列空间的高维特性，另一方面则源自群体遗传过程固有的随机性。本文提出一套集成化数学工具集，用于研究突变发生频率足够低、种群通常处于单态且演化可被建模为一系列间隔分明的固定事件的时不变适应度景观上的演化过程。该工具集的核心直觉在于：任意特定基因型周边均存在一块易于演化抵达的适应度景观区域，而景观的其他区域则因距离较远、需跨越适应谷等原因，难以通过演化抵达。本文针对基因型的这一"动态邻域"提出了严谨的定义，该定义可刻画从某一基因型演化至另一基因型的等待时间分布的多项特征。整个景观的邻域结构可通过矩阵进行总结，本文展示了如何利用该矩阵近似特定演化事件发生的期望等待时间，并为现有关于分子钟离散指数的形式化研究结果提供直观解释。