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

• Published in: PLoS genetics Vol. 19; no. 1; p. e1010505
• Main Authors: Sanders, Sara, Joshi, Kunaal, Levin, Petra Anne, Iyer-Biswas, Srividya
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 05.01.2023; Public Library of Science (PLoS)
• Subjects: Bacteria; Bacteria - genetics; Biology and Life Sciences; Cell cycle; Cell Cycle - genetics; Cell division; Cell Division - genetics; Cell growth; Chromosomes; Chromosomes, Bacterial; DNA; DNA biosynthesis; DNA replication; DNA Replication - genetics; DNA, Bacterial - genetics; E coli; Genetic aspects; Growth; Medicine and Health Sciences; Microbiological research; Physical Sciences; Physiological aspects; Physiology; Proteins; Replication; Research and Analysis Methods; Social Sciences; Stochasticity; Viewpoints; United States
• ISSN: 1553-7404; 1553-7390; 1553-7404
• DOI: 10.1371/journal.pgen.1010505

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.