Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Global organization of metabolic fluxes in the bacterium Escherichia coli

Nature volume 427, pages 839–843 (2004)Cite this article

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.

This is a preview of subscription content, access via your institution

Relevant articles

Open Access articles citing this article.

Access options

Rent or buy this article

Prices vary by article type

from$1.95

to$39.95

Learn more

Prices may be subject to local taxes which are calculated during checkout

Figure 1: Characterizing the overall flux organization of the E. coli metabolic network.
Figure 2: Characterizing the local inhomogeneity of the metabolic flux distribution.
Figure 3: High-flux backbone for FBA-optimized metabolic network of E. coli on a glutamate-rich substrate (see Supplementary Fig. S12b for succinate-rich substrate).
Figure 4: Effect of growth conditions on individual fluxes.

References

  1. Jeong, H., Tombor, B., Albert, R., Oltvai, Z. N. & Barabási, A.-L. The large-scale organization of metabolic networks. Nature 407, 651–654 (2000)

    Article  ADS  CAS  Google Scholar 

  2. Wagner, A. & Fell, D. A. The small world inside large metabolic networks. Proc. R. Soc. Lond. B 268, 1803–1810 (2001)

    Article  CAS  Google Scholar 

  3. Ravasz, E., Somera, A. L., Mongru, D. A., Oltvai, Z. N. & Barabási, A.-L. Hierarchical organization of modularity in metabolic networks. Science 297, 1551–1555 (2002)

    Article  ADS  CAS  Google Scholar 

  4. Holme, P., Huss, M. & Jeong, H. Subnetwork hierarchies of biochemical pathways. Bioinformatics 19, 532–538 (2003)

    Article  CAS  Google Scholar 

  5. Savageau, M. A. Biochemical Systems Analysis: a Study of Function and Design in Molecular Biology (Addison-Wesley, Reading, MA, 1976)

    MATH  Google Scholar 

  6. Heinrich, R. & Schuster, S. The Regulation of Cellular Systems (Chapman & Hall, New York, 1996)

    Book  Google Scholar 

  7. Goldbeter, A. Biochemical Oscillations and Cellular Rhythms: the Molecular Bases of Periodic and Chaotic Behavior (Cambridge Univ. Press, Cambridge, UK, 1996)

    Book  Google Scholar 

  8. Edwards, J. S. & Palsson, B. O. The Escherichia coli MG1655 in silico metabolic genotype: its definition, characteristics, and capabilities. Proc. Natl Acad. Sci. USA 97, 5528–5533 (2000)

    Article  ADS  CAS  Google Scholar 

  9. Edwards, J. S., Ibarra, R. U. & Palsson, B. O. In silico predictions of Escherichia coli metabolic capabilities are consistent with experimental data. Nature Biotechnol. 19, 125–130 (2001)

    Article  CAS  Google Scholar 

  10. Ibarra, R. U., Edwards, J. S. & Palsson, B. O. Escherichia coli K-12 undergoes adaptive evolution to achieve in silico predicted optimal growth. Nature 420, 186–189 (2002)

    Article  ADS  CAS  Google Scholar 

  11. Edwards, J. S., Ramakrishna, R. & Palsson, B. O. Characterizing the metabolic phenotype: a phenotype phase plane analysis. Biotechnol. Bioeng. 77, 27–36 (2002)

    Article  CAS  Google Scholar 

  12. Segre, D., Vitkup, D. & Church, G. M. Analysis of optimality in natural and perturbed metabolic networks. Proc. Natl Acad. Sci. USA 99, 15112–15117 (2002)

    Article  ADS  CAS  Google Scholar 

  13. Blattner, F. R. et al. The complete genome sequence of Escherichia coli K-12. Science 277, 1453–1474 (1997)

    Article  CAS  Google Scholar 

  14. Gerdes, S. Y. et al. Experimental determination and system level analysis of essential genes in Escherichia coli MG1655. J. Bacteriol. 185, 5673–5684 (2003)

    Article  CAS  Google Scholar 

  15. Emmerling, M. et al. Metabolic flux responses to pyruvate kinase knockout in Escherichia coli. J. Bacteriol. 184, 152–164 (2002)

    Article  CAS  Google Scholar 

  16. Smith, R. L. Efficient Monte-Carlo procedures for generating points uniformly distributed over bounded regions. Oper. Res. 32, 1296–1308 (1984)

    Article  ADS  MathSciNet  Google Scholar 

  17. Lovász, L. Hit-and-run mixes fast. Math. Program. 86, 443–461 (1999)

    Article  MathSciNet  Google Scholar 

  18. Goh, K. I., Kahng, B. & Kim, D. Universal behavior of load distribution in scale-free networks. Phys. Rev. Lett. 87, 278701 (2001)

    Article  CAS  Google Scholar 

  19. Barabási, A.-L. & Albert, R. Emergence of scaling in random networks. Science 286, 509–512 (1999)

    Article  ADS  MathSciNet  Google Scholar 

  20. Barthelemy, M., Gondran, B. & Guichard, E. Spatial structure of the Internet traffic. Physica A 319, 633–642 (2003)

    Article  ADS  Google Scholar 

  21. Ma, H. W. & Zeng, A. P. The connectivity structure, giant strong component and centrality of metabolic networks. Bioinformatics 19, 1423–1430 (2003)

    Article  CAS  Google Scholar 

  22. Dandekar, T., Schuster, S., Snel, B., Huynen, M. & Bork, P. Pathway alignment: application to the comparative analysis of glycolytic enzymes. Biochem. J. 343, 115–124 (1999)

    Article  CAS  Google Scholar 

  23. Schuster, S., Fell, D. A. & Dandekar, T. A general definition of metabolic pathways useful for systematic organization and analysis of complex metabolic networks. Nature Biotechnol. 18, 326–332 (2000)

    Article  CAS  Google Scholar 

  24. Stelling, J., Klamt, S., Bettenbrock, K., Schuster, S. & Gilles, E. D. Metabolic network structure determines key aspects of functionality and regulation. Nature 420, 190–193 (2002)

    Article  ADS  CAS  Google Scholar 

  25. Sauer, U. et al. Metabolic flux ratio analysis of genetic and environmental modulations of Escherichia coli central carbon metabolism. J. Bacteriol. 181, 6679–6688 (1999)

    CAS  PubMed  PubMed Central  Google Scholar 

  26. Canonaco, F. et al. Metabolic flux response to phosphoglucose isomerase knock-out in Escherichia coli and impact of overexpression of the soluble transhydrogenase UdhA. FEMS Microbiol. Lett. 204, 247–252 (2001)

    Article  CAS  Google Scholar 

  27. Fischer, E. & Sauer, U. Metabolic flux profiling of Escherichia coli mutants in central carbon metabolism using GC-MS. Eur. J. Biochem. 270, 880–891 (2003)

    Article  CAS  Google Scholar 

  28. Hartwell, L. H., Hopfield, J. J., Leibler, S. & Murray, A. W. From molecular to modular cell biology. Nature 402, C47–C52 (1999)

    Article  CAS  Google Scholar 

  29. Wolf, D. M. & Arkin, A. P. Motifs, modules and games in bacteria. Curr. Opin. Microbiol. 6, 125–134 (2003)

    Article  CAS  Google Scholar 

Download references

Acknowledgements

We thank M. Bárász, J. Becker, E. Ravasz, A. Vazquez and S. Wuchty for discussions; and B. Palsson and S. Schuster for comments on the manuscript. Research at Eötvös University was supported by the Hungarian National Research Grant Foundation (OTKA), and work at the University of Notre Dame and at Northwestern University was supported by the US Department of Energy, the NIH and the NSF.

Author information

Authors and Affiliations

  1. Department of Physics, University of Notre Dame, Indiana, 46556, Notre Dame, USA

    E. Almaas, B. Kovács & A.-L. Barabási

  2. Biological Physics Department and Research Group of HAS, Eötvös University, H-1117, Budapest, Hungary

    B. Kovács & T. Vicsek

  3. Department of Pathology, Northwestern University, Illinois, 60611, Chicago, USA

    Z. N. Oltvai

Authors
  1. E. Almaas
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. B. Kovács
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. T. Vicsek
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Z. N. Oltvai
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. A.-L. Barabási
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to A.-L. Barabási.

Ethics declarations

Competing interests

The authors declare that they have no competing financial interests.

Supplementary information

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Almaas, E., Kovács, B., Vicsek, T. et al. Global organization of metabolic fluxes in the bacterium Escherichia coli. Nature 427, 839–843 (2004). https://doi.org/10.1038/nature02289

Download citation

  • Received:

  • Accepted:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nature02289

This article is cited by

Comments

By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing