ReviewNature reviews. Genetics2014
Empirical fitness landscapes and the predictability of evolution.
Review in Nature reviews. Genetics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 374 papers, 1 of them a synthesis that pooled it.
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Who cites it
374 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Detecting fitness epistasis in recently admixed populations with genome-wide data.BMC genomics · 2020Pooled it
- Graph-based representations in modern protein science.The Biochemical journal · 2026Review
- Review
- Deconstructing empirical fitness seascapes across scales of granularity.G3 (Bethesda, Md.) · 2026Article
- Eco-Evolutionary Dynamics of Proliferation Heterogeneity: A Phenotype-Structured Model for Tumor Growth and Treatment Response.Bulletin of mathematical biology · 2026Article
- A case study of evolutionary variability and determinism among type III polyketide synthases.Nature communications · 2026Article
- Evolutionary strategies of late-stage diversification in bacterial biosynthetic gene clusters.Essays in biochemistry · 2026Review
- Evolutionary adaptation proceeds through a small number of phenotypic modules.Molecular biology and evolution · 2026Article
- Function-specific epistasis shapes evolutionary trajectories towards antibiotic resistance.Nature communications · 2026Article
- The adaptive landscapes of three globaleLife · 2026Article
- Direct estimation of genotype fitness from time series.bioRxiv : the preprint server for biology · 2026Article
- Resolving translational challenges in cancer biology through yeast experimental evolution.Cancer metastasis reviews · 2026Review
- 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
- Accessible and robust machine learning approaches to improve the opsin genotype-phenotype map.Molecular biology and evolution · 2026Article
- Predictions From Evolutionary Theory for Urban Environments.Evolutionary applications · 2026Article
- Maynard Smith's analogy, realized: Common ancestry constrains evolutionary percolation through protein space.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Selecting synthetic data for successful simulation-based transfer learning in dynamical biological systems.BMC bioinformatics · 2026Article
- Global epistasis in ecosystems arises from resource constraints.bioRxiv : the preprint server for biology · 2026Article
- Environment heterogeneity creates fast amplifiers of natural selection in graph-structured populations.Nature communications · 2026Article
314 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The genotype-fitness map (that is, the fitness landscape) is a key determinant of evolution, yet it has mostly been used as a superficial metaphor because we know little about its structure. This is now changing, as real fitness landscapes are being analysed by constructing genotypes with all possible combinations of small sets of mutations observed in phylogenies or in evolution experiments. In turn, these first glimpses of empirical fitness landscapes inspire theoretical analyses of the predictability of evolution. Here, we review these recent empirical and theoretical developments, identify methodological issues and organizing principles, and discuss possibilities to develop more realistic fitness landscape models.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.