Effect of Replication Fork Dynamics on Escherichia coli Cell Size

TL;DR

This study presents a mechanistic model linking DNA replication fork dynamics to cell size variability in E. coli, demonstrating how stochastic replication processes influence cell size distribution across growth rates.

Contribution

The paper introduces a population dynamics model that attributes cell size variability to stochastic DNA replication fork behavior, providing a mechanistic understanding of size regulation.

Findings

Model predicts increased cell size variability with growth rate.

Replication fork stalling affects cell size distribution.

Multi-fork replication introduces multiplicative noise.

Abstract

The variability in cell size of an isogenic population of Escherichia coli has been widely reported in experiment. The probability density function (PDF) of cell lengths has been variously described by exponential and lognormal functions. While temperature, population density and growth rate have all been shown to affect E. coli cell size distributions, and recent models have validated a link between growth rate and cell size through DNA replication, cell size variability is thought to emerge from growth rate variability. A mechanistic link that could distinguish the source of stochasticity, could improve our understanding of cell size regulation. Here, we have developed a population dynamics model of individual cell division based on the BCD, birth, chromosome replication and division model, with DNA replication based on the Cooper and Helmstetter (CH) multi-fork replication. In our model, stochasticity in the model arises solely from the dynamics of DNA replication forks. We model the forks as two-state systems: stalled and recovered. Our model predicts an increase in cell size variability with growth rate, consistent with previous experimental reports. We perturb the model to test the effect of increased replication fork (RF) stalling frequency, or uncoupling RF stalling from the cell-division machinery. Indeed, despite ignoring DNA and protein segregation asymmetry, the model can faithfully reproduce quantitative changes in cell size distributions. In our model, multi-fork replication produces multiplicative 'noise' and provides a mechanism linking growth rate and cell size variability.