Beyond the average: An updated framework for understanding the relationship between cell growth, DNA replication, and division in a bacterial system

Abstract

Illuminating the molecular mechanisms underlying biological phenomena requires an accurate understanding of the context in which these phenomena occur. Currently, population based experiments frame our understanding of the bacterial cell cycle. Most famously, in 1968 Stephen Cooper and Charles Helmstetter took advantage of their ability to synchronize E. coli cells with regard to birth and division using then new technology, the baby machine. Based on baby- machine data, they proposed the existence of two nutrient-dependent growth regimes -- slow in which all steps in cell cycle progression are proportional to growth rate and fast in which progression of DNA polymerase plateaus, while new rounds of replication and division remain pegged to growth rate. In the fast regime, the relationship between initiation and division become determinant with the latter following the former by a specific amount of time. Still the prevailing model, recent work in single cells reveals a disconnect between the behavior of an idealized average cells (the outcome of population-based analysis) and cell cycle progression in individual cells. To resolve this disconnect, we take an agnostic view of the forces modulating bacterial cell cycle progression and, leveraging extant single cell data, propose a new framework from which to understand this essential process. Significantly, our framework accounts for the stochasticity inherent in biological processes. It is also flexible, providing the ability to assess cell cycle progression independent of medium or genetic background. Using this framework, we test potential explanations for the divergence between single cell growth rate and replication fork elongation rate, identifying limiting reagents as the primary cause of this widespread phenomenon.

Emergent Simplicities in Stochastic Intergenerational Homeostasis

“…Finally, we have shown how the appropriate deterministic reduction of our fully stochastic description can recapitulate the known heuristics of the popular sizer-timer-adder framework. The key insight is that the mythical "average cell" described by the deterministic limit fundamentally fails to capture the true behaviors of the typical individual cell that experiences stochastic homeostasis [34].…”

Intergenerational scaling law determines the precision kinematics of stochastic individual-cell-size homeostasis

“…In summary, we have developed a stochastic first-principles-based theoretical framework to characterize cell size homeostasis of bacterial cells in balanced growth conditions. Prior works typically describe homeostasis building on a quasi-deterministic scheme (the sizer–timer–adder framework), with noise “added on top” in adhoc ways motivated by analytical or computational tractability [19, 23–26]. The deterministic condition for homeostasis obtained from the sizer-timer-adder framework, α < 1, applies literally only to the mythical “average” cell.…”

Single-cell mass distributions reveal simple rules for achieving steady-state growth

“…Verifying steady-state or balanced growth is laborious, but essential for examining bacterial cell size regulation (10,11), cell cycle progression (12)(13)(14), and cell organization (15). Many independent aspects of growth must be measured for each culture while repeatedly performing dilutions to guarantee nutrients are available in excess of biosynthetic demand.…”