Quantitative analyses of empirical fitness landscapes

@article{Szendro2012QuantitativeAO,
  title={Quantitative analyses of empirical fitness landscapes},
  author={Ivan G. Szendro and Martijn F. Schenk and Jasper Franke and Joachim H A Krug and J. Arjan G.M. de Visser},
  journal={Journal of Statistical Mechanics: Theory and Experiment},
  year={2012},
  volume={2013}
}
The concept of a fitness landscape is a powerful metaphor that offers insight into various aspects of evolutionary processes and guidance for the study of evolution. Until recently, empirical evidence on the ruggedness of these landscapes was lacking, but since it became feasible to construct all possible genotypes containing combinations of a limited set of mutations, the number of studies has grown to a point where a classification of landscapes becomes possible. The aim of this review is to… 
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180 Citations

Epistasis and constraints in fitness landscapes
TLDR
This work proposes two new sets of measures that explicitly capture two relevant features of fitness landscapes: epistasis and constraints, and shows how these measures can help uncovering the amount and the nature of epistatic interactions in two experimental landscapes.
Epistasis and the Structure of Fitness Landscapes: Are Experimental Fitness Landscapes Compatible with Fisher’s Geometric Model?
TLDR
A rigorous statistical framework based on Approximate Bayesian Computation is developed and used to fit a broad class of phenotypic fitness models (including Fisher’s model) to 26 empirical landscapes representing nine diverse biological systems.
Measuring epistasis in fitness landscapes: the correlation of fitness effects of mutations
TLDR
A new natural measure of the amount of epistasis based on the correlation of fitness effects of mutations, which has a natural interpretation, captures well the interaction between mutations and can be obtained analytically for most landscape models.
Measuring epistasis in fitness landscapes: The correlation of fitness effects of mutations.
A Family of Fitness Landscapes Modeled through Gene Regulatory Networks
TLDR
This work investigates fitness landscape models through the lens of Gene Regulatory Networks (GRNs), where the regulatory products are computed from multiple genes and collectively treated as the phenotypes, and deduces a mesoscopic structure underlying GRN Fitness landscape models that retains necessary information for evolutionary dynamics with minimal complexity.
Measuring ruggedness in fitness landscapes
TLDR
An elegant combination of simulations and experiments with Escherichia coli is used to show that even in adaptation over the course of a few weeks involving only a handful of mutations, interactions among those mutations can have very large effects.
Evolutionary Accessibility in Tunably Rugged Fitness Landscapes
TLDR
Some measures of accessibility behave non-monotonically as a function of K, indicating a special role of the most sparsely connected, non-trivial cases K=1 and 2, and the relation between models for fitness landscapes and spin glasses is addressed.
Inferring fitness landscapes by regression produces biased estimates of epistasis
TLDR
This work critically examine regression techniques used to approximate fitness landscapes from data, finding that such regressions are subject to two inherent biases that distort the biological quantities of greatest interest, often making evolution seem less predictable than it actually is.
Epistasis and Adaptation on Fitness Landscapes
Epistasis occurs when the effect of a mutation depends on its carrier’s genetic background. Despite increasing evidence that epistasis for fitness is common, its role during evolution is contentious.…
Evolutionary constraints in fitness landscapes
TLDR
This paper presents some measures of evolutionary constraints based on the similarity between accessible paths and the abundance and characteristics of “chains” of obligatory mutations, that are paths going through genotypes with a single fitter neighbor, and shows how they shed light on evolutionary constraints and predictability in experimentally resolved landscapes.
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