Kinetics of Muller's Ratchet from Adaptive Landscape Viewpoint

TL;DR

This paper introduces a new analytical approach using adaptive landscape to study Muller's ratchet, enabling estimation of the ratchet's progression and population decay dynamics.

Contribution

It develops a novel method linking adaptive landscape with Muller's ratchet, providing analytical tools to quantify population decline and evolutionary processes.

Findings

Derived the decaying time of the fittest class as a mean first passage time.

Constructed a dynamical adaptive landscape independent of stationary distribution.

Described the absorption phenomenon in populations with non-normalizable stationary distribution.

Abstract

Background: The accumulation of deleterious mutations of a population directly contributes to the fate as to how long the population would exist. Muller's ratchet provides a quantitative framework to study the effect of accumulation. Adaptive landscape as a powerful concept in system biology provides a handle to describe complex and rare biological events. In this article we study the evolutionary process of a population exposed to Muller's ratchet from the new viewpoint of adaptive landscape which allows us estimate the single click of the ratchet starting with an intuitive understanding. Methods: We describe how Wright-Fisher process maps to Muller's ratchet. We analytically construct adaptive landscape from general diffusion equation. It shows that the construction is dynamical and the adaptive landscape is independent of the existence and normalization of the stationary distribution. We generalize the application of diffusion model from adaptive landscape viewpoint. Results: We develop a novel method to describe the dynamical behavior of the population exposed to Muller's ratchet, and analytically derive the decaying time of the fittest class of populations as a mean first passage time. Most importantly, we describe the absorption phenomenon by adaptive landscape, where the stationary distribution is non-normalizable. These results suggest the method may be used to understand the mechanism of populations evolution and describe the biological processes quantitatively.