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• W2891638346 abstract "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." @default.
• W2891638346 created "2018-09-27" @default.
• W2891638346 creator A5001650268 @default.
• W2891638346 date "2018-09-19" @default.
• W2891638346 modified "2023-10-09" @default.
• W2891638346 title "Long-term evolution on complex fitness landscapes when mutation is weak" @default.
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• W2891638346 doi "https://doi.org/10.1038/s41437-018-0142-6" @default.
• W2891638346 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/6180110" @default.
• W2891638346 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/30232363" @default.
• W2891638346 hasPublicationYear "2018" @default.
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