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Metabolic network structure determines key aspects of functionality and regulation

Author

Listed:
  • Jörg Stelling

    (Max Planck Institute for Dynamics of Complex Technical Systems)

  • Steffen Klamt

    (Max Planck Institute for Dynamics of Complex Technical Systems)

  • Katja Bettenbrock

    (Max Planck Institute for Dynamics of Complex Technical Systems)

  • Stefan Schuster

    (Max Delbrück Center for Molecular Medicine)

  • Ernst Dieter Gilles

    (Max Planck Institute for Dynamics of Complex Technical Systems)

Abstract

The relationship between structure, function and regulation in complex cellular networks is a still largely open question1,2,3. Systems biology aims to explain this relationship by combining experimental and theoretical approaches4. Current theories have various strengths and shortcomings in providing an integrated, predictive description of cellular networks. Specifically, dynamic mathematical modelling of large-scale networks meets difficulties because the necessary mechanistic detail and kinetic parameters are rarely available. In contrast, structure-oriented analyses only require network topology, which is well known in many cases. Previous approaches of this type focus on network robustness5 or metabolic phenotype2,6, but do not give predictions on cellular regulation. Here, we devise a theoretical method for simultaneously predicting key aspects of network functionality, robustness and gene regulation from network structure alone. This is achieved by determining and analysing the non-decomposable pathways able to operate coherently at steady state (elementary flux modes). We use the example of Escherichia coli central metabolism to illustrate the method.

Suggested Citation

  • Jörg Stelling & Steffen Klamt & Katja Bettenbrock & Stefan Schuster & Ernst Dieter Gilles, 2002. "Metabolic network structure determines key aspects of functionality and regulation," Nature, Nature, vol. 420(6912), pages 190-193, November.
  • Handle: RePEc:nat:nature:v:420:y:2002:i:6912:d:10.1038_nature01166
    DOI: 10.1038/nature01166
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    Cited by:

    1. Gao, Qiang & Liang, Zhentao & Wang, Ping & Hou, Jingrui & Chen, Xiuxiu & Liu, Manman, 2021. "Potential index: Revealing the future impact of research topics based on current knowledge networks," Journal of Informetrics, Elsevier, vol. 15(3).
    2. João F Matias Rodrigues & Andreas Wagner, 2009. "Evolutionary Plasticity and Innovations in Complex Metabolic Reaction Networks," PLOS Computational Biology, Public Library of Science, vol. 5(12), pages 1-11, December.
    3. Mirja Meyer & Marc-Thorsten Hütt & Julia Bendul, 2016. "The elementary flux modes of a manufacturing system: a novel approach to explore the relationship of network structure and function," International Journal of Production Research, Taylor & Francis Journals, vol. 54(14), pages 4145-4160, July.
    4. Timo R Maarleveld & Meike T Wortel & Brett G Olivier & Bas Teusink & Frank J Bruggeman, 2015. "Interplay between Constraints, Objectives, and Optimality for Genome-Scale Stoichiometric Models," PLOS Computational Biology, Public Library of Science, vol. 11(4), pages 1-21, April.
    5. Irene Otero-Muras & Pencho Yordanov & Joerg Stelling, 2017. "Chemical Reaction Network Theory elucidates sources of multistability in interferon signaling," PLOS Computational Biology, Public Library of Science, vol. 13(4), pages 1-28, April.
    6. Hannesson, Erik & Sellers, Jordan & Walker, Ethan & Webb, Benjamin, 2022. "Network specialization: A topological mechanism for the emergence of cluster synchronization," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 600(C).
    7. Gheorghe Maria & Cristiana Luminiţa Gîjiu & Cristina Maria & Carmen Tociu, 2018. "Importance of Considering the Isotonic System Hypothesis When Modelling the Self-Control of Gene Expression Regulatory Modules in Living Cells," Current Trends in Biomedical Engineering & Biosciences, Juniper Publishers Inc., vol. 12(2), pages 29-48, February.

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