Linear algebra of the permutation invariant Crow-Kimura model of prebiotic evolution

TL;DR

This paper analyzes the Crow-Kimura model with permutation invariant fitness landscapes using linear algebra, providing new analytical solutions, asymptotic results, and a mathematical definition of the error threshold.

Contribution

It introduces a basis change leveraging the mutation matrix's structure to derive analytical and asymptotic results, including explicit formulas for the quasispecies distribution.

Findings

Eigenvectors approach shows quasispecies distribution approaches a binomial distribution.

Provides explicit formulas for the limit quasispecies eigenvector in special cases.

Proposes a mathematical definition of the error threshold based on distribution closeness.

Abstract

A particular case of the famous quasispecies model - the Crow-Kimura model with a permutation invariant fitness landscape - is investigated. Using the fact that the mutation matrix in the case of a permutation invariant fitness landscape has a special tridiagonal form, a change of the basis is suggested such that in the new coordinates a number of analytical results can be obtained. In particular, using the eigenvectors of the mutation matrix as the new basis, we show that the quasispecies distribution approaches a binomial one and give simple estimates for the speed of convergence. Another consequence of the suggested approach is a parametric solution to the system of equations determining the quasispecies. Using this parametric solution we show that our approach leads to exact asymptotic results in some cases, which are not covered by the existing methods. In particular, we are able to present not only the limit behavior of the leading eigenvalue (mean population fitness), but also the exact formulas for the limit quasispecies eigenvector for special cases. For instance, this eigenvector has a geometric distribution in the case of the classical single peaked fitness landscape. On the biological side, we propose a mathematical definition, based on the closeness of the quasispecies to the binomial distribution, which can be used as an operational definition of the notorious error threshold. Using this definition, we suggest two approximate formulas to estimate the critical mutation rate after which the quasispecies delocalization occurs.