Gene duplication and subsequent diversification strongly affect phenotypic evolvability and robustness.

V Jouffrey,A S Leonard,S E Ahnert

doi:10.1098/rsos.201636

V Jouffrey, A S Leonard + Show 1 more

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https://doi.org/10.1098/rsos.201636

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Abstract

We study the effects of non-determinism and gene duplication on the structure of genotype–phenotype (GP) maps by introducing a non-deterministic version of the Polyomino self-assembly model. This model has previously been used in a variety of contexts to model the assembly and evolution of protein quaternary structure. Firstly, we show the limit of the current deterministic paradigm which leads to built-in anti-correlation between evolvability and robustness at the genotypic level. We develop a set of metrics to measure structural properties of GP maps in a non-deterministic setting and use them to evaluate the effects of gene duplication and subsequent diversification. Our generalized versions of evolvability and robustness exhibit positive correlation for a subset of genotypes. This positive correlation is only possible because non-deterministic phenotypes can contribute to both robustness and evolvability. Secondly, we show that duplication increases robustness and reduces evolvability initially, but that the subsequent diversification that duplication enables has a stronger, inverse effect, greatly increasing evolvability and reducing robustness relative to their original values.

Highlights

Over the past three decades, many computational models of genotype–phenotype (GP) maps and fitness landscapes have been put forward [1,2,3]
The first part provides a statistical analysis of the distribution of accessible phenotypes upon a single mutation of a genotype. This step is necessary in order to characterize the local properties of the full GP map, before focusing our analysis on gene duplication
It is key to understand the properties prior to duplication in order to understand the consequences of this altered local neighbourhood on the accessible phenotype distribution. These results directly inform future models of evolutionary dynamics by providing insights upon how evolvability and robustness are affected by duplication events

Summary

Introduction

Over the past three decades, many computational models of genotype–phenotype (GP) maps and fitness landscapes have been put forward [1,2,3]. They offer a perspective on the study of biological evolution that is complementary to experimental work. GP maps are an abstract description of the way in which biological sequences (genotypes) map to different biological outcomes (phenotypes). The analysis of their structure reveals important information about the local properties of genetic royalsocietypublishing.org/journal/rsos R.

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