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Cited by 132Gene Regulatory Network AnalysisMicrobial Metabolic Engineering and Bioproductionstochastic dynamics and bifurcation

Stochastic fluctuations in metabolic pathways

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Levine, Erel, Hwa, Terence·2007·Proceedings of the National Academy of Sciences
DOI 10.1073/pnas.0610987104PMC PMC1890476
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Abstract

Fluctuations in the abundance of molecules in the living cell may affect its growth and well being. For regulatory molecules (e.g., signaling proteins or transcription factors), fluctuations in their expression can affect the levels of downstream targets in a network. Here, we develop an analytic framework to investigate the phenomenon of noise correlation in molecular networks. Specifically, we focus on the metabolic network, which is highly interlinked, and noise properties may constrain its structure and function. Motivated by the analogy between the dynamics of a linear metabolic pathway and that of the exactly soluble linear queuing network or, alternatively, a mass transfer system, we derive a plethora of results concerning fluctuations in the abundance of intermediate metabolites in various common motifs of the metabolic network. For all but one case examined, we find the steady-state fluctuation in different nodes of the pathways to be effectively uncorrelated. Consequently, fluctuations in enzyme levels only affect local properties and do not propagate elsewhere into metabolic networks, and intermediate metabolites can be freely shared by different reactions. Our approach may be applicable to study metabolic networks with more complex topologies or protein signaling networks that are governed by similar biochemical reactions. Possible implications for bioinformatic analysis of metabolomic data are discussed.

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Crossref · 10.1073/pnas.0610987104

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  • Stochasticity rules all living cells through fluctuations in gene expression, molecular interactions, and metabolic fluxes, even systems governed by deterministic laws.

    “Actually, stochasticity rules all living cells through fluctuations in gene expression, molecular interactions, and metabolic fluxes, even systems governed by deterministic laws, such as monogenic genetics [131,132,133,134].”

    From Revisiting the Pathogenesis of X-Linked Adrenoleukodystrophy (2025) · cites this paper as [134]

  • A large part of phenotype variability across patients with the same ABCD1 genotype may be due to stochasticity.

    “Beyond identifiable causes, a large part of phenotype variability across patients with the same ABCD1 genotype may “simply” be due to stochasticity, a factor hitherto considered “noise” by Cartesian researchers.”

    From Revisiting the Pathogenesis of X-Linked Adrenoleukodystrophy (2025) · cites this paper as [134]

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Authors
Levine, E., & Hwa, T.
Affiliations
  • University of California San Diego (US)
  • Center for Theoretical Biological Physics and Department of Physics, University of California at San Diego, La Jolla, CA 92093-0374
ORCID
Levine, Erel
Venue
Proceedings of the National Academy of Sciences
Publisher
National Academy of Sciences
Published
2007-05-29
Volume
104
Issue
22
Pages
9224-9229
ISSN
  • 0027-8424 (ISSN-L)
  • 1091-6490
DOI
10.1073/pnas.0610987104
PMC
PMC1890476
OpenAlex
W1979682650
Cited by
132
References
51
Citations by year
2026: 2 · 2025: 5 · 2024: 7 · 2023: 7 · 2022: 6 · 2021: 7 · 2020: 9 · 2019: 5 · 2018: 6 · 2017: 8 · 2016: 9 · 2015: 4 · 2014: 10 · 2013: 4 · 2012: 10
FWCI
3.85
Type
journal article
Language
en
Open access
Locations
  • Proceedings of the National Academy of Sciences (publishedVersion)
  • PubMed Central (publishedVersion)
  • PubMed (publishedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • arXiv (Cornell University)
Funders
  • National Science Foundation (0211308, 0225630, 0216576)
  • Division of Materials Research
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Microbial Metabolic Engineering and Bioproduction
  • Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › stochastic dynamics and bifurcation
Keywords
stochastic fluctuations, metabolic networks, noise correlation, metabolic intermediates, enzyme levels, steady-state fluctuations, metabolomics, protein signaling networks
MeSH terms
Probability, Proteins, Substrate Specificity, Metabolic Networks and Pathways

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