How Good Are Statistical Models at Approximating Complex Fitness Landscapes?

ABSTRACT: Fitness landscapes determine the course of adaptation by constraining and shaping evolutionary trajectories. Knowledge of the structure of a fitness landscape can thus predict evolutionary outcomes. Empirical fitness landscapes, however, have so far only offered limited insight into real-world questions, as the high dimensionality of sequence spaces makes it impossible to exhaustively measure the fitness of all variants of biologically meaningful sequences. We must therefore revert to statistical descriptions of fitness landscapes that are based on a sparse sample of fitness measurements. It remains unclear, however, how much data are required for such statistical descriptions to be useful. Here, we assess the ability of regression models accounting for single and pairwise mutations to cor

SUBMITTER: du Plessis L 

PROVIDER: S-EPMC4989103 | biostudies-literature | 2016 Sep

REPOSITORIES: biostudies-literature