Global organization of metabolic fluxes in the bacterium Escherichia coli


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.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.

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.


  1. 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)

  2. 2

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

  3. 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)

  4. 4

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

  5. 5

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

  6. 6

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

  7. 7

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

  8. 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)

  9. 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)

  10. 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)

  11. 11

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

  12. 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)

  13. 13

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

  14. 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)

  15. 15

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

  16. 16

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

  17. 17

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

  18. 18

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

  19. 19

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

  20. 20

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

  21. 21

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

  22. 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)

  23. 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)

  24. 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)

  25. 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)

  26. 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)

  27. 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)

  28. 28

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

  29. 29

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

Download references


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

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) doi:10.1038/nature02289

Download citation

Further reading


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.