可调崎岖适应度景观的适应性：粗糙富士山模型
Adaptation in tunably rugged fitness landscapes: The Rough Mount Fuji Model
摘要：
目前的适应理论大多基于吉莱斯皮的突变景观模型（MLM），该模型假设由单个突变步骤连接的基因型的适应值是独立的随机变量。另一方面，越来越多的经验证据表明，真正的适应度景观，虽然拥有相当数量的崎岖，比传销预测的更平滑。在这篇文章中，我们提出并分析了一个简单的适应度景观模型与可调的坚固性的基础上粗糙富士山（RMF）模型最初介绍的Aita等人。[生物聚合物54：64-79（2000）]的蛋白质进化的背景下。我们提供了一个全面的收集结果有关的地形结构的RMF景观，包括明确的公式预期数量的局部适应性最大值，全球峰值的位置，和健身相关函数。主要通过模拟来探索RMF景观上的单个和多个自适应步骤的统计，并将结果与MLM模型中的已知行为进行比较。最后，我们表明RMF模型可以解释在最近的微绿噬菌体进化实验中在高度拟合的第一步背景上观察到的大量第二步突变[Miller等人，Genetics 187：185-202（2011）]。
英文摘要：
Much of the current theory of adaptation is based on Gillespie's mutational landscape model (MLM), which assumes that the fitness values of genotypes linked by single mutational steps are independent random variables. On the other hand, a growing body of empirical evidence shows that real fitness landscapes, while possessing a considerable amount of ruggedness, are smoother than predicted by the MLM. In the present article we propose and analyse a simple fitness landscape model with tunable ruggedness based on the Rough Mount Fuji (RMF) model originally introduced by Aita et al. [Biopolymers 54:64-79 (2000)] in the context of protein evolution. We provide a comprehensive collection of results pertaining to the topographical structure of RMF landscapes, including explicit formulae for the expected number of local fitness maxima, the location of the global peak, and the fitness correlation function. The statistics of single and multiple adaptive steps on the RMF landscape are explored mainly through simulations, and the results are compared to the known behavior in the MLM model. Finally, we show that the RMF model can explain the large number of second-step mutations observed on a highly-fit first step backgound in a recent evolution experiment with a microvirid bacteriophage [Miller et al., Genetics 187:185-202 (2011)].
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