Cell population heterogeneity driven by stochastic partition and growth optimality

TL;DR

This paper presents a model explaining how cell population heterogeneity, including bimodality, can arise from stochastic partitioning and growth optimality, even with homogeneous single-cell processes.

Contribution

It introduces a theoretical framework linking stochastic partition errors and growth rate tolerance to population bimodality, explaining phenomena like aneuploid state maintenance.

Findings

Population bimodality can emerge from homogeneous processes.

Variance in partition errors influences distribution shape.

The model explains maintenance of aneuploid states.

Abstract

A fundamental question in biology is how cell populations evolve into different subtypes based on homogeneous processes at the single cell level. Here we show that population bimodality can emerge even when biological processes are homogenous at the cell level and the environment is kept constant. Our model is based on the stochastic partitioning of a cell component with an optimal copy number. We show that the existence of unimodal or bimodal distributions depends on the variance of partition errors and the growth rate tolerance around the optimal copy number. In particular, our theory provides a consistent explanation for the maintenance of aneuploid states in a population. The proposed model can also be relevant for other cell components such as mitochondria and plasmids, whose abundances affect the growth rate and are subject to stochastic partition at cell division.