Our understanding of the bacterial cell cycle is framed largely by population-based experiments that focus on the behavior of idealized average cells. Most famously, the contributions of Cooper and Helmstetter help to contextualize the phenomenon of overlapping replication cycles observed in rapidly growing bacteria. Despite the undeniable value of these approaches, their necessary reliance on the behavior of idealized average cells masks the stochasticity inherent in single-cell growth and physiology and limits their mechanistic value. To bridge this gap, we propose an updated and agnostic framework, informed by extant single-cell data, that quantitatively accounts for stochastic variations in single-cell dynamics and the impact of medium composition on cell growth and cell cycle progression. In this framework, stochastic timers sensitive to medium composition impact the relationship between cell cycle events, accounting for observed differences in the relationship between cell cycle events in slow- and fast-growing cells. We conclude with a roadmap for potential application of this framework to longstanding open questions in the bacterial cell cycle field.
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.
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