Genetic Composition of Supercritical Branching Populations under Power Law Mutation Rates

TL;DR

This paper models the evolution of genetic composition in cancer cell populations with power law mutation rates, providing asymptotic descriptions of subpopulation sizes and evolutionary pathways.

Contribution

It introduces a detailed individual-based model under power law mutation rates and characterizes the asymptotic behavior of subpopulations over time.

Findings

Asymptotic descriptions of subpopulation sizes in log-time scale.

Characterization of evolutionary pathways in cancer cell populations.

Analysis of subpopulation growth without growth rate restrictions.

Abstract

We aim to understand the evolution of the genetic composition of cancer cell populations. To achieve this, we consider an individual-based model representing a cell population where cells divide, die and mutate along the edges of a finite directed graph $(V,E)$. The process starts with only one cell of trait $0$. Following typical parameter values in cancer cell populations we study the model under power law mutation rates, in the sense that the mutation probabilities are parametrized by negative powers of a scaling parameter $n$ and the typical sizes of the population of interest are positive powers of $n$. Under a non-increasing growth rate condition, we describe the time evolution of the first-order asymptotics of the size of each subpopulation in the $\log(n)$ time scale, as well as in the random time scale at which the wild-type population, resp. the total population, reaches the size $n^{t}$. In particular, such results allow for the perfect characterization of evolutionary pathways. Without imposing any conditions on the growth rates, we describe the time evolution of the order of magnitude of each subpopulation, whose asymptotic limits are positive non-decreasing piecewise linear continuous functions.