Quantitative analyses of empirical fitness landscapes

被引:163
作者
Szendro, Ivan G. [1 ]
Schenk, Martijn F. [2 ,3 ]
Franke, Jasper [1 ]
Krug, Joachim [1 ,4 ]
de Visser, J. Arjan G. M. [3 ]
机构
[1] Univ Cologne, Inst Theoret Phys, Cologne, Germany
[2] Univ Cologne, Inst Genet, Cologne, Germany
[3] Wageningen Univ, Genet Lab, NL-6700 AP Wageningen, Netherlands
[4] Univ Cologne, Cologne, Germany
来源
JOURNAL OF STATISTICAL MECHANICS-THEORY AND EXPERIMENT | 2013年
关键词
models for evolution (theory); models for evolution (experiment); mutational and evolutionary processes (theory); mutational and evolutionary processes (experiment); ADAPTIVE PROTEIN EVOLUTION; BENEFICIAL MUTATIONS; SIGN EPISTASIS; MEAN NUMBER; MODEL; EVOLVABILITY; ADAPTATION; SELECTION; ARCHITECTURE; POPULATIONS;
D O I
10.1088/1742-5468/2013/01/P01005
中图分类号
O3 [力学];
学科分类号
08 ; 0801 ;
摘要
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 identify measures of epistasis that allow a meaningful comparison of fitness landscapes and then apply them to the empirical landscapes in order to discern factors that affect ruggedness. The various measures of epistasis that have been proposed in the literature appear to be equivalent. Our comparison shows that the ruggedness of the empirical landscape is affected by whether the included mutations are beneficial or deleterious and by whether intragenic or intergenic epistasis is involved. Finally, the empirical landscapes are compared to landscapes generated with the rough Mt Fuji model. Despite the simplicity of this model, it captures the features of the experimental landscapes remarkably well.
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页数:22
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