ArticleMolecular biology and evolution2016
How Good Are Statistical Models at Approximating Complex Fitness Landscapes?
Article in Molecular biology and evolution, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- How far can you go? Extrapolating values of catalytic activity from known protein landscapes in natural and directed evolution.Chemical Society reviews · 2026Review
- Hierarchical genotype networks and incipient ecological speciation in Q[Formula: see text] phage quasispecies.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Genotype sampling for deep-learning assisted experimental mapping of a combinatorially complete fitness landscape.Bioinformatics (Oxford, England) · 2024Article
- Reinforcement learning informs optimal treatment strategies to limit antibiotic resistance.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Article
- Reinforcement Learning informs optimal treatment strategies to limit antibiotic resistance.bioRxiv : the preprint server for biology · 2023Article
- On the sparsity of fitness functions and implications for learning.Proceedings of the National Academy of Sciences of the United States of America · 2022Article
- Evolutionary Pathways and Trajectories in Antibiotic Resistance.Clinical microbiology reviews · 2021Article
- Towards an engineering theory of evolution.Nature communications · 2021Review
- Inferring a complete genotype-phenotype map from a small number of measured phenotypes.PLoS computational biology · 2020Article
- Deconvolving mutational patterns of poliovirus outbreaks reveals its intrinsic fitness landscape.Nature communications · 2020Article
- An experimental assay of the interactions of amino acids from orthologous sequences shaping a complex fitness landscape.PLoS genetics · 2019Article
- The causes of evolvability and their evolution.Nature reviews. Genetics · 2019Review
- Biophysical Inference of Epistasis and the Effects of Mutations on Protein Stability and Function.Molecular biology and evolution · 2018Article
- Inferring the shape of global epistasis.Proceedings of the National Academy of Sciences of the United States of America · 2018Article
- Within-Epitope Interactions Can Bias CTL Escape Estimation in Early HIV Infection.Frontiers in immunology · 2017Article
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3 authors.
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Abstract
Fitness landscapes determine the course of adaptation by constraining and shaping evolutionary trajectories. Knowledge of the structure of a fitness landscape can thus predict evolutionary outcomes. Empirical fitness landscapes, however, have so far only offered limited insight into real-world questions, as the high dimensionality of sequence spaces makes it impossible to exhaustively measure the fitness of all variants of biologically meaningful sequences. We must therefore revert to statistical descriptions of fitness landscapes that are based on a sparse sample of fitness measurements. It remains unclear, however, how much data are required for such statistical descriptions to be useful. Here, we assess the ability of regression models accounting for single and pairwise mutations to correctly approximate a complex quasi-empirical fitness landscape. We compare approximations based on various sampling regimes of an RNA landscape and find that the sampling regime strongly influences the quality of the regression. On the one hand it is generally impossible to generate sufficient samples to achieve a good approximation of the complete fitness landscape, and on the other hand systematic sampling schemes can only provide a good description of the immediate neighborhood of a sequence of interest. Nevertheless, we obtain a remarkably good and unbiased fit to the local landscape when using sequences from a population that has evolved under strong selection. Thus, current statistical methods can provide a good approximation to the landscape of naturally evolving populations.
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