What can we learn from fitness landscapes?

Abstract

A combinatorially complete data set consists of studies of all possible combinations of a set of mutant sites in a gene or mutant alleles in a genome. Among the most robust conclusions from these studies is that epistasis between beneficial mutations often shows a pattern of diminishing returns, in which favorable mutations are less fit when combined than would be expected. Another robust inference is that the number of adaptive evolutionary paths is often limited to a relatively small fraction of the theoretical possibilities, owing largely to sign epistasis requiring evolutionary steps that would entail a decrease in fitness. Here we summarize these and other results while also examining issues that remain unresolved and future directions that seem promising.

Fig. 1

Mapping of the activity of a hypothetical enzyme onto fitness. The model assumes that fitness is proportion to flux through a metabolic pathway when enzyme activity is limiting to flux according to simple Michaelis-Menten enzyme kinetics, and the curve is normalized to a fitness of 1 when activity equals 25. The colored dots correspond to fitness for wildtype (red), any of three single mutants (blue), any pair of double mutants (green), and thr triple mutant (orange).

Fig. 2

Qualitative analysis of a fitness landscape with three mutant sites or genes (circles). Wildtype is symbolized by blue, mutant by red. Red arrows indicate pathways accessible from the nonmutant genotype (all blue) and blue arrows represent pathways that are inaccessible from this state. The fitness maximum is realized by the triple mutant, and there are two accessible pathways to this state from the double mutant.