Universality Classes of Interaction Structures for NK Fitness Landscapes

@article{Hwang2017UniversalityCO,
  title={Universality Classes of Interaction Structures for NK Fitness Landscapes},
  author={Sungmin Hwang and Benjamin Schmiegelt and Luca Ferretti and Joachim H A Krug},
  journal={Journal of Statistical Physics},
  year={2017},
  volume={172},
  pages={226-278}
}
Kauffman’s NK-model is a paradigmatic example of a class of stochastic models of genotypic fitness landscapes that aim to capture generic features of epistatic interactions in multilocus systems. Genotypes are represented as sequences of L binary loci. The fitness assigned to a genotype is a sum of contributions, each of which is a random function defined on a subset of $$k \le L$$k≤L loci. These subsets or neighborhoods determine the genetic interactions of the model. Whereas earlier work on… 
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23 Citations
Background Citations
11
Methods Citations
4

23 Citations

Adaptive walks on high-dimensional fitness landscapes and seascapes with distance-dependent statistics.
Adaptive walks on high-dimensional fitness landscapes and seascapes with distance-dependent statistics
TLDR
This work introduces a broad class of high-dimensional random fitness landscapes for which the correlations between fitnesses of genomes are a general function of genetic distance, and studies the properties of these landscapes focusing on the simplest evolutionary process: random adaptive walks.
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.
Distribution of the number of fitness maxima in Fisher’s geometric model
TLDR
The distribution of the number of maxima when the limit $L \to \infty$ is taken at finite $n$ is identified and this analysis is expected to help to elucidate the fluctuation structure of metastable states in various spin glass problems.
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.…
Computational Complexity as an Ultimate Constraint on Evolution
TLDR
A distinction between easy landscapes of traditional theory where local fitness peaks can be found in a moderate number of steps, and hard landscapes where finding local optima requires an infeasible amount of time is introduced.
Accessibility percolation in random fitness landscapes 3
TLDR
This chapter reviews studies of accessibility percolation that use probabilistic fitness landscape models to explore the emergence of such paths as a function of the initial fitness, the parameters of the landscape or the structure of the genotype graph and discusses their implications for the predictability of evolutionary processes.
Predictable Properties of Fitness Landscapes Induced by Adaptational Tradeoffs
TLDR
A mathematical model for fitness landscapes generated by tradeoff between adaptation to environmental extremes is developed, based on experiments that determine the antibiotic dose-response curves of Escherichia coli strains, and previous observations on antibiotic resistance mutations.
A Driven Disordered Systems Approach to Biological Evolution in Changing Environments
TLDR
The model can be described as a system with interacting hysteretic elements, and it exhibits effects such as hysteresis loops and memory formation under antibiotic concentration cycling, which provides a general framework for studying motifs of evolutionary dynamics in biological systems in a changing environment.
On the sparsity of fitness functions and implications for learning
TLDR
It is shown that GNK fitness functions with parameters set according to protein structural contacts can be used to accurately approximate the number of samples required to estimate two empirical protein fitness functions, and are able to identify important higher-order epistatic interactions in these functions using only structural information.
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References

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TLDR
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