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Related Experiment Videos

Relevant parameters in models of cell division control.

Jacopo Grilli1, Matteo Osella2, Andrew S Kennard3,4

  • 1Department of Ecology and Evolution, University of Chicago, 1101 E 57th Street, Chicago, Illinois 60637, USA.

Physical Review. E
|April 19, 2017
PubMed
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This study unifies bacterial cell division models using a flexible framework, revealing a single parameter governs division control against cell variability. Current data supports a linear response, but distinguishing mechanisms requires larger sample sizes.

Area of Science:

  • Microbiology and Systems Biology
  • Quantitative Biology
  • Cell Biology

Background:

  • Single-cell data reveals complex stochastic dynamics in bacterial cell division control.
  • Existing models for cell division mechanisms lack comparative analysis, hindering understanding of data support.
  • A unified framework is needed to link different formalisms and analyze division control mechanisms.

Purpose of the Study:

  • To develop a generic framework enabling interchangeable use of continuous-time (hazard function) and discrete-time (cell size across generations) formalisms for bacterial cell division.
  • To analyze how this framework describes various division control mechanisms, including time/size control and constant added size.
  • To clarify the key parameters governing cell division control and variability.

Main Methods:

Related Experiment Videos

  • Developed a generic framework integrating continuous-time hazard functions and discrete-time Langevin equations for cell division.
  • Employed perturbative expansion around mean initial size/interdivision time to analyze division control mechanisms.
  • Utilized analytical estimates and numerical simulations to validate the framework and assess data requirements.

Main Results:

  • The framework precisely describes available data using a first-order approximation (linear response) for size fluctuation correction.
  • A single dimensionless parameter quantifies division control strength against cell-to-cell variability (noise).
  • Distinguishing between mechanisms requires higher-order terms, necessitating sample sizes slightly larger than current datasets.

Conclusions:

  • The developed framework provides a unified approach for studying bacterial cell division control.
  • A linear response regime accurately captures current data, simplifying the analysis of division variability.
  • Future studies need larger sample sizes to differentiate between underlying division control mechanisms based on higher-order effects.