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Related Experiment Videos

Stochastic fluctuations in metabolic pathways.

Erel Levine1, Terence Hwa

  • 1Center for Theoretical Biological Physics and Department of Physics, University of California at San Diego, La Jolla, CA 92093-0374, USA. elevine@ucsd.edu

Proceedings of the National Academy of Sciences of the United States of America
|May 23, 2007
PubMed
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Cellular molecule fluctuations impact growth. This study shows metabolic network noise is mostly uncorrelated, meaning fluctuations don't spread, allowing metabolite sharing.

Area of Science:

  • Systems Biology
  • Biochemistry
  • Computational Biology

Background:

  • Cellular molecule abundance fluctuations, or noise, can impact cell growth and function.
  • Regulatory molecule noise can alter downstream network targets.
  • Metabolic networks are highly interconnected, and noise properties may influence their structure and function.

Purpose of the Study:

  • To develop an analytic framework for investigating noise correlation in molecular networks.
  • To analyze noise properties within the metabolic network, focusing on intermediate metabolite fluctuations.

Main Methods:

  • Developed an analytic framework for noise correlation analysis.
  • Utilized analogies between linear metabolic pathways, queuing networks, and mass transfer systems.

Related Experiment Videos

  • Derived results for fluctuations in intermediate metabolites across common metabolic network motifs.

    • Main Results:

    • Steady-state fluctuations in different nodes of metabolic pathways were found to be effectively uncorrelated in most cases.
    • Enzyme level fluctuations were shown to impact only local properties, not propagating throughout the network.
    • Intermediate metabolites can be freely shared among different reactions within the metabolic network.

    Conclusions:

    • Noise in metabolic networks is largely localized, with limited propagation.
    • The findings suggest intermediate metabolites are flexibly shared, impacting network dynamics.
    • The framework can be applied to more complex metabolic or protein signaling networks and inform metabolomic data analysis.