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Global organization of metabolic fluxes in the bacterium Escherichia coli

Author

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  • E. Almaas

    (University of Notre Dame)

  • B. Kovács

    (University of Notre Dame
    Eötvös University)

  • T. Vicsek

    (Eötvös University)

  • Z. N. Oltvai

    (Northwestern University)

  • A.-L. Barabási

    (University of Notre Dame)

Abstract

Cellular metabolism, the integrated interconversion of thousands of metabolic substrates through enzyme-catalysed biochemical reactions, is the most investigated complex intracellular web of molecular interactions. Although the topological organization of individual reactions into metabolic networks is well understood1,2,3,4, the principles that govern their global functional use under different growth conditions raise many unanswered questions5,6,7. By implementing a flux balance analysis8,9,10,11,12 of the metabolism of Escherichia coli strain MG1655, here we show that network use is highly uneven. Whereas most metabolic reactions have low fluxes, the overall activity of the metabolism is dominated by several reactions with very high fluxes. E. coli responds to changes in growth conditions by reorganizing the rates of selected fluxes predominantly within this high-flux backbone. This behaviour probably represents a universal feature of metabolic activity in all cells, with potential implications for metabolic engineering.

Suggested Citation

  • E. Almaas & B. Kovács & T. Vicsek & Z. N. Oltvai & A.-L. Barabási, 2004. "Global organization of metabolic fluxes in the bacterium Escherichia coli," Nature, Nature, vol. 427(6977), pages 839-843, February.
    • Handle: RePEc:nat:nature:v:427:y:2004:i:6977:d:10.1038_nature02289
    DOI: 10.1038/nature02289
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    Cited by:

    1. Giorgio Fagiolo & Javier Reyes & Stefano Schiavo, 2010. "The evolution of the world trade web: a weighted-network analysis," Journal of Evolutionary Economics, Springer, vol. 20(4), pages 479-514, August.
    2. Huang, He & Yan, Zhijun & Pan, Yaohui, 2014. "Measuring edge importance to improve immunization performance," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 416(C), pages 532-540.
    3. repec:hal:spmain:info:hdl:2441/9795 is not listed on IDEAS
    4. Hütt, M.-Th. & Lüttge, U., 2005. "The interplay of synchronization and fluctuations reveals connectivity levels in networks of nonlinear oscillators," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 350(2), pages 207-226.
    5. Zhang, Jiang & Feng, Yuanjing, 2014. "Common patterns of energy flow and biomass distribution on weighted food webs," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 405(C), pages 278-288.
    6. Shirin Fallahi & Hans J Skaug & Guttorm Alendal, 2020. "A comparison of Monte Carlo sampling methods for metabolic network models," PLOS ONE, Public Library of Science, vol. 15(7), pages 1-24, July.
    7. Jaideep Ghosh & Avinash Kshitij & Sandeep Kadyan, 2015. "Functional information characteristics of large-scale research collaboration: network measures and implications," Scientometrics, Springer;Akadémiai Kiadó, vol. 102(2), pages 1207-1239, February.
    8. Marcelo Rivas-Astroza & Raúl Conejeros, 2020. "Metabolic flux configuration determination using information entropy," PLOS ONE, Public Library of Science, vol. 15(12), pages 1-19, December.
    9. Wang, Fan & Tian, Lixin & Du, Ruijin & Dong, Gaogao, 2018. "The robustness of interdependent weighted networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 508(C), pages 675-680.
    10. Onur Şeref & J. Paul Brooks & Bernice Huang & Stephen S. Fong, 2017. "Enumeration and Cartesian Product Decomposition of Alternate Optimal Fluxes in Cellular Metabolism," INFORMS Journal on Computing, INFORMS, vol. 29(2), pages 197-210, May.
    11. Scholz, Jan & Dejori, Mathäus & Stetter, Martin & Greiner, Martin, 2005. "Noisy scale-free networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 350(2), pages 622-642.
    12. Aur'elien Hazan, 2016. "Volume of the steady-state space of financial flows in a monetary stock-flow-consistent model," Papers 1601.00822, arXiv.org, revised Jan 2017.
    13. M. Serrano & Marián Boguñá & Alessandro Vespignani, 2007. "Patterns of dominant flows in the world trade web," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 2(2), pages 111-124, December.

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