title: Step by Step, Cell by Cell: Quantification of the Bacterial Cell Cycle.
abstract: The Escherichia coli cell cycle is a classic, but we are still missing some of its essential aspects. The reason is that our knowledge is mostly based on population data, and our grasp of the behavior of single cells is still very limited. Today, new dynamic single-cell data promise to overcome this barrier. Existing data from single cells have already led to findings and hypotheses that challenge standard views, and have raised new questions. Here, we review these recent developments and propose that a systematic exploration of the correlation patterns between 'cell-cycle intervals' defined by key molecular events measured in many single cells could lead to a quantitative characterization of the cell cycle in terms of inherent stochasticity and homeostatic controls.
authors.affiliation: Dipartimento di Fisica and INFN, University of Torino, Torino, I-10125, Italy.
authors.forename: Matteo
authors.initials: M
authors.lastname: Osella
authors.affiliation: AMOLF, Science Park 104, 1098 XG Amsterdam, The Netherlands.
authors.forename: Sander J
authors.initials: SJ
authors.lastname: Tans
authors.affiliation: Sorbonne Universités, Université Pierre et Marie Curie, UMR 7238, Institut de Biologie Paris Seine, 75005 Paris, France; CNRS, UMR 7238 Computational and Quantitative Biology, Paris, France; FIRC Institute of Molecular Oncology (IFOM), 20139 Milan, Italy. Electronic address: marco.cosentino-lagomarsino@upmc.fr.
authors.forename: Marco
authors.initials: M
authors.lastname: Cosentino Lagomarsino
mesh_terms: D002455:Cell Division / Q000235:genetics* / Q000502:physiology; D004261:DNA Replication / Q000235:genetics*; D004269:DNA, Bacterial / Q000235:genetics; D004926:Escherichia coli / Q000235:genetics* / Q000254:growth & development* / Q000378:metabolism; D059010:Single-Cell Analysis / Q000379:methods*
chemical_list: D004269:DNA, Bacterial
keywords: E. coli; cell cycle; division homeostasis; key intervals; replication initiation; stochasticity