ArticlePLoS computational biology2016
Beyond the Hypercube: Evolutionary Accessibility of Fitness Landscapes with Realistic Mutational Networks.
Article in PLoS computational biology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
- Predicting the topography of fitness landscapes from the structure of genotype-phenotype maps.Genetics · 2026Article
- Neutral transcriptome rewiring promotes quantitative disease resistance evolvability at the species level.The Plant cell · 2025Article
- Not Quite Lost in Translation: Mistranslation Alters Adaptive Landscape Topography and the Dynamics of Evolution.Molecular biology and evolution · 2023Article
- Impact of population size on early adaptation in rugged fitness landscapes.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2023Article
- Accessibility percolation on Cartesian power graphs.Journal of mathematical biology · 2023Article
- The structure of genotype-phenotype maps makes fitness landscapes navigable.Nature ecology & evolution · 2022Article
- Getting higher on rugged landscapes: Inversion mutations open access to fitter adaptive peaks in NK fitness landscapes.PLoS computational biology · 2022Article
- On the incongruence of genotype-phenotype and fitness landscapes.PLoS computational biology · 2022Article
- A Family of Fitness Landscapes Modeled through Gene Regulatory Networks.Entropy (Basel, Switzerland) · 2022Article
- Evolutionary Pathways and Trajectories in Antibiotic Resistance.Clinical microbiology reviews · 2021Article
- Both Binding Strength and Evolutionary Accessibility Affect the Population Frequency of Transcription Factor Binding Sequences in Arabidopsis thaliana.Genome biology and evolution · 2021Article
- Intelligent host engineering for metabolic flux optimisation in biotechnology.The Biochemical journal · 2021Review
- A mutational hotspot that determines highly repeatable evolution can be built and broken by silent genetic changes.Nature communications · 2021Article
- Article
- Epistasis between cultural traits causes paradigm shifts in cultural evolution.Royal Society open science · 2020Article
- Competing evolutionary paths in growing populations with applications to multidrug resistance.PLoS computational biology · 2019Article
- Article
- Article
- On the networked architecture of genotype spaces and its critical effects on molecular evolution.Open biology · 2018Review
- Genotypic Complexity of Fisher's Geometric Model.Genetics · 2017Article
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3 authors.
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Abstract
Evolutionary pathways describe trajectories of biological evolution in the space of different variants of organisms (genotypes). The probability of existence and the number of evolutionary pathways that lead from a given genotype to a better-adapted genotype are important measures of accessibility of local fitness optima and the reproducibility of evolution. Both quantities have been studied in simple mathematical models where genotypes are represented as binary sequences of two types of basic units, and the network of permitted mutations between the genotypes is a hypercube graph. However, it is unclear how these results translate to the biologically relevant case in which genotypes are represented by sequences of more than two units, for example four nucleotides (DNA) or 20 amino acids (proteins), and the mutational graph is not the hypercube. Here we investigate accessibility of the best-adapted genotype in the general case of K > 2 units. Using computer generated and experimental fitness landscapes we show that accessibility of the global fitness maximum increases with K and can be much higher than for binary sequences. The increase in accessibility comes from the increase in the number of indirect trajectories exploited by evolution for higher K. As one of the consequences, the fraction of genotypes that are accessible increases by three orders of magnitude when the number of units K increases from 2 to 16 for landscapes of size N ∼ 106 genotypes. This suggests that evolution can follow many different trajectories on such landscapes and the reconstruction of evolutionary pathways from experimental data might be an extremely difficult task.
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