Quasispecies dynamics with network constraints

TL;DR

This paper extends quasispecies theory by incorporating network constraints on mutations, analyzing how mutation connectivity affects adaptation and degeneracy in genotype populations.

Contribution

It introduces a network-structured mutation model with adjustable density and accounts for locus susceptibility, providing analytical and simulation insights into quasispecies dynamics.

Findings

Higher mutation network density leads to decreased adaptation.

Locus susceptibility variations influence quasispecies stability.

Transition from adaptation to degeneracy depends on network parameters.

Abstract

A quasispecies is a set of interrelated genotypes that have reached a situation of equilibrium while evolving according to the usual Darwinian principles of selection and mutation. Quasispecies studies invariably assume that it is possible for any genotype to mutate into any other, but recent finds indicate that this assumption is not necessarily true. Here we revisit the traditional quasispecies theory by adopting a network structure to constrain the occurrence of mutations. Such structure is governed by a random-graph model, whose single parameter (a probability p) controls both the graph's density and the dynamics of mutation. We contribute two further modifications to the theory, one to account for the fact that different loci in a genotype may be differently susceptible to the occurrence of mutations, the other to allow for a more plausible description of the transition from adaptation to degeneracy of the quasispecies as p is increased. We give analytical and simulation results for the usual case of binary genotypes, assuming the fitness landscape in which a genotype's fitness decays exponentially with its Hamming distance to the wild type. These results support the theory's assertions regarding the adaptation of the quasispecies to the fitness landscape and also its possible demise as a function of p.