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From molecular noise to behavioural variability in a single bacterium

Author

Listed:
  • Ekaterina Korobkova

    (The University of Chicago)

  • Thierry Emonet

    (The University of Chicago)

  • Jose M. G. Vilar

    (The Rockefeller University
    Memorial Sloan-Kettering Cancer Center)

  • Thomas S. Shimizu

    (Institute for Advanced Biosciences, Keio University Fujisawa
    Harvard University)

  • Philippe Cluzel

    (The University of Chicago)

Abstract

The chemotaxis network that governs the motion of Escherichia coli has long been studied to gain a general understanding of signal transduction. Although this pathway is composed of just a few components, it exhibits some essential characteristics of biological complexity, such as adaptation and response to environmental signals1. In studying intracellular networks, most experiments and mathematical models2,3,4,5 have assumed that network properties can be inferred from population measurements. However, this approach masks underlying temporal fluctuations of intracellular signalling events. We have inferred fundamental properties of the chemotaxis network from a noise analysis of behavioural variations in individual bacteria. Here we show that certain properties established by population measurements, such as adapted states, are not conserved at the single-cell level: for timescales ranging from seconds to several minutes, the behaviour of non-stimulated cells exhibit temporal variations much larger than the expected statistical fluctuations. We find that the signalling network itself causes this noise and identify the molecular events that produce it. Small changes in the concentration of one key network component suppress temporal behavioural variability, suggesting that such variability is a selected property of this adaptive system.

Suggested Citation

  • Ekaterina Korobkova & Thierry Emonet & Jose M. G. Vilar & Thomas S. Shimizu & Philippe Cluzel, 2004. "From molecular noise to behavioural variability in a single bacterium," Nature, Nature, vol. 428(6982), pages 574-578, April.
  • Handle: RePEc:nat:nature:v:428:y:2004:i:6982:d:10.1038_nature02404
    DOI: 10.1038/nature02404
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    Cited by:

    1. Zhou Fang & Ankit Gupta & Sant Kumar & Mustafa Khammash, 2024. "Advanced methods for gene network identification and noise decomposition from single-cell data," Nature Communications, Nature, vol. 15(1), pages 1-19, December.
    2. Oliver Pohl & Marius Hintsche & Zahra Alirezaeizanjani & Maximilian Seyrich & Carsten Beta & Holger Stark, 2017. "Inferring the Chemotactic Strategy of P. putida and E. coli Using Modified Kramers-Moyal Coefficients," PLOS Computational Biology, Public Library of Science, vol. 13(1), pages 1-24, January.
    3. YĆ¼ksel, Yusuf, 2021. "Dynamic phase transition properties and metamagnetic anomalies of kinetic Ising model in the presence of additive white noise," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 580(C).

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