Abstract

The mapping between biological genotypes and phenotypes is central to the study of biological evolution. Here, we introduce a rich, intuitive and biologically realistic genotype–phenotype (GP) map that serves as a model of self-assembling biological structures, such as protein complexes, and remains computationally and analytically tractable. Our GP map arises naturally from the self-assembly of polyomino structures on a two-dimensional lattice and exhibits a number of properties: redundancy (genotypes vastly outnumber phenotypes), phenotype bias (genotypic redundancy varies greatly between phenotypes), genotype component disconnectivity (phenotypes consist of disconnected mutational networks) and shape space covering (most phenotypes can be reached in a small number of mutations). We also show that the mutational robustness of phenotypes scales very roughly logarithmically with phenotype redundancy and is positively correlated with phenotypic evolvability. Although our GP map describes the assembly of disconnected objects, it shares many properties with other popular GP maps for connected units, such as models for RNA secondary structure or the hydrophobic-polar (HP) lattice model for protein tertiary structure. The remarkable fact that these important properties similarly emerge from such different models suggests the possibility that universal features underlie a much wider class of biologically realistic GP maps.

Highlights

  • IntroductionWhile organismal genotypes are becoming accessible owing to rapid advances in sequencing technology, further understanding of the complicated mapping from sequence to phenotype is necessary for a richer understanding of evolutionary dynamics [1,2,3,4]

  • Evolution is one of the most fundamental principles in biology

  • The genotype is modelled as a character string representation of a set of Nt tiles which make up an assembly kit

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Summary

Introduction

While organismal genotypes are becoming accessible owing to rapid advances in sequencing technology, further understanding of the complicated mapping from sequence to phenotype is necessary for a richer understanding of evolutionary dynamics [1,2,3,4]. The mapping from genotype to phenotype—the GP map—links mutations to potentially selectable variation and is of critical importance in understanding evolutionary systems. GP maps provide a basis for understanding important biological concepts such as mutational robustness and evolvability, which may profoundly affect evolutionary dynamics, and help determine the fundamental topologies of the landscapes upon which evolutionary processes occur [5]. Genetic regulatory networks have been approximated using a variety of abstract models, including Boolean networks [6,7] Despite their simplicity, Boolean networks have demonstrated a remarkable ability to produce biologically realistic results.

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