Literature DB >> 23685065

Exact results for amplitude spectra of fitness landscapes.

Johannes Neidhart1, Ivan G Szendro, Joachim Krug.   

Abstract

Starting from fitness correlation functions, we calculate exact expressions for the amplitude spectra of fitness landscapes as defined by Stadler [1996. Landscapes and their correlation functions. J. Math. Chem. 20, 1] for common landscape models, including Kauffman's NK-model, rough Mount Fuji landscapes and general linear superpositions of such landscapes. We further show that correlations decaying exponentially with the Hamming distance yield exponentially decaying spectra similar to those reported recently for a model of molecular signal transduction. Finally, we compare our results for the model systems to the spectra of various experimentally measured fitness landscapes. We claim that our analytical results should be helpful when trying to interpret empirical data and guide the search for improved fitness landscape models.
Copyright © 2013 Elsevier Ltd. All rights reserved.

Keywords:  Epistasis; Experimental evolution; Fitness landscapes; Fourier decomposition; Sequence space

Mesh:

Year:  2013        PMID: 23685065     DOI: 10.1016/j.jtbi.2013.05.002

Source DB:  PubMed          Journal:  J Theor Biol        ISSN: 0022-5193            Impact factor:   2.691


  20 in total

1.  Average Fitness Differences on NK Landscapes.

Authors:  Wim Hordijk; Stuart A Kauffman; Peter F Stadler
Journal:  Theory Biosci       Date:  2019-06-18       Impact factor: 1.919

2.  Long-term evolution on complex fitness landscapes when mutation is weak.

Authors:  David M McCandlish
Journal:  Heredity (Edinb)       Date:  2018-09-19       Impact factor: 3.821

3.  Adaptation in tunably rugged fitness landscapes: the rough Mount Fuji model.

Authors:  Johannes Neidhart; Ivan G Szendro; Joachim Krug
Journal:  Genetics       Date:  2014-08-13       Impact factor: 4.562

4.  Inferring fitness landscapes by regression produces biased estimates of epistasis.

Authors:  Jakub Otwinowski; Joshua B Plotkin
Journal:  Proc Natl Acad Sci U S A       Date:  2014-05-19       Impact factor: 11.205

Review 5.  Empirical fitness landscapes and the predictability of evolution.

Authors:  J Arjan G M de Visser; Joachim Krug
Journal:  Nat Rev Genet       Date:  2014-06-10       Impact factor: 53.242

6.  On the sparsity of fitness functions and implications for learning.

Authors:  David H Brookes; Amirali Aghazadeh; Jennifer Listgarten
Journal:  Proc Natl Acad Sci U S A       Date:  2022-01-04       Impact factor: 12.779

7.  Minimum epistasis interpolation for sequence-function relationships.

Authors:  Juannan Zhou; David M McCandlish
Journal:  Nat Commun       Date:  2020-04-14       Impact factor: 14.919

Review 8.  Should evolutionary geneticists worry about higher-order epistasis?

Authors:  Daniel M Weinreich; Yinghong Lan; C Scott Wylie; Robert B Heckendorn
Journal:  Curr Opin Genet Dev       Date:  2013-11-27       Impact factor: 5.578

9.  Multidimensional epistasis and the transitory advantage of sex.

Authors:  Stefan Nowak; Johannes Neidhart; Ivan G Szendro; Joachim Krug
Journal:  PLoS Comput Biol       Date:  2014-09-18       Impact factor: 4.475

10.  The Context-Dependence of Mutations: A Linkage of Formalisms.

Authors:  Frank J Poelwijk; Vinod Krishna; Rama Ranganathan
Journal:  PLoS Comput Biol       Date:  2016-06-23       Impact factor: 4.475

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