Evolutionary accessibility of mutational pathways

TL;DR

This paper investigates the accessibility of optimal genotypes in fitness landscapes, showing that accessible mutational pathways are highly probable and numerous in high-dimensional landscapes, supported by empirical data from Aspergillus niger.

Contribution

The study provides a theoretical framework for understanding mutational pathway accessibility and validates it with empirical data, revealing high accessibility in complex fitness landscapes.

Findings

Accessible pathways are highly probable in high-dimensional landscapes.

The number of accessible pathways grows without bound as landscape complexity increases.

Empirical data from Aspergillus niger supports the theoretical predictions.

Abstract

Functional effects of different mutations are known to combine to the total effect in highly nontrivial ways. For the trait under evolutionary selection (`fitness'), measured values over all possible combinations of a set of mutations yield a fitness landscape that determines which mutational states can be reached from a given initial genotype. Understanding the accessibility properties of fitness landscapes is conceptually important in answering questions about the predictability and repeatability of evolutionary adaptation. Here we theoretically investigate accessibility of the globally optimal state on a wide variety of model landscapes, including landscapes with tunable ruggedness as well as neutral `holey' landscapes. We define a mutational pathway to be accessible if it contains the minimal number of mutations required to reach the target genotype, and if fitness increases in each mutational step. Under this definition accessibility is high, in the sense that at least one accessible pathwayexists with a substantial probability that approaches unity as the dimensionality of the fitness landscape (set by the number of mutational loci) becomes large. At the same time the number of alternative accessible pathways grows without bound. We test the model predictions against an empirical 8-locus fitness landscape obtained for the filamentous fungus \textit{Aspergillus niger}. By analyzing subgraphs of the full landscape containing different subsets of mutations, we are able to probe the mutational distance scale in the empirical data. The predicted effect of high accessibility is supported by the empirical data and very robust, which we argue to reflect the generic topology of sequence spaces.