ArticleGenetics2014
Adaptation in tunably rugged fitness landscapes: the rough Mount Fuji model.
Article in Genetics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers.
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35 citing papers in PubMed.
- Review
- Inference of fitness landscapes with heterogeneous patterns of epistasis across sites.bioRxiv : the preprint server for biology · 2026Article
- How far can you go? Extrapolating values of catalytic activity from known protein landscapes in natural and directed evolution.Chemical Society reviews · 2026Review
- Mount Fuji's stubby peak: the genotypic density of additive landscapes near maximal fitness.bioRxiv : the preprint server for biology · 2026Article
- The SLO Hierarchy of Pseudo-Boolean Functions and Runtime of Evolutionary Algorithms.Algorithmica · 2026Article
- Learning sequence-function relationships with scalable, interpretable Gaussian processes.bioRxiv : the preprint server for biology · 2025Article
- On the Probability of Reaching High Peaks in Fitness Landscapes by Adaptive Walks.Molecular biology and evolution · 2025Article
- Resolving discrepancies between chimeric and multiplicative measures of higher-order epistasis.Nature communications · 2025Article
- Immune pressure is key to understanding observed patterns of respiratory virus evolution in prolonged infections.Virus evolution · 2025Article
- The highly rugged yet navigable regulatory landscape of the bacterial transcription factor TetR.Nature communications · 2024Article
- Promoters Constrain Evolution of Expression Levels of Essential Genes in Escherichia coli.Molecular biology and evolution · 2024Article
- Effects of selection stringency on the outcomes of directed evolution.bioRxiv : the preprint server for biology · 2024Article
- The distribution of fitness effects during adaptive walks using a simple genetic network.PLoS genetics · 2024Article
- Synergistic epistasis among cancer drivers can rescue early tumors from the accumulation of deleterious passengers.PLoS computational biology · 2024Article
- Article
- Article
- Article
- A Family of Fitness Landscapes Modeled through Gene Regulatory Networks.Entropy (Basel, Switzerland) · 2022Article
- Conditional prediction of consecutive tumor evolution using cancer progression models: What genotype comes next?PLoS computational biology · 2021Article
- Unbiased inference of the fitness landscape ruggedness from imprecise fitness estimates.Evolution; international journal of organic evolution · 2021Article
Corrections and comments
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Authors and funding
3 authors.
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No grant is acknowledged in the PubMed record.
Abstract
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 analyze a simple fitness landscape model with tunable ruggedness based on the rough Mount Fuji (RMF) model originally introduced by Aita et al. in the context of protein evolution. We provide a comprehensive collection of results pertaining to the topographical structure of RMF landscapes, including explicit formulas 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 background in a recent evolution experiment with a microvirid bacteriophage.
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