Skip to main content

Thank you for visiting 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.

Large-scale in vivo flux analysis shows rigidity and suboptimal performance of Bacillus subtilis metabolism


Qualitative theoretical approaches such as graph theory1,2 and stoichiometric analyses3,4,5,6 are beginning to uncover the architecture and systemic functions of complex metabolic reaction networks. At present, however, only a few, largely unproven quantitative concepts propose functional design principles of the global flux distribution7,8. As operational units of function, molecular fluxes determine the systemic cell phenotype by linking genes, proteins and metabolites to higher-level biological functions9. In sharp contrast to other 'omics' analyses, 'fluxome' analysis remained tedious10. By large-scale quantification of in vivo flux responses, we identified a robust flux distribution in 137 null mutants of Bacillus subtilis. On its preferred substrate, B. subtilis has suboptimal metabolism because regulators of developmental programs maintain a 'standby' mode that invests substantial resources in anticipation of changing environmental conditions at the expense of optimal growth. Network rigidity and robustness are probably universal functional design principles, whereas the standby mode may be more specific.

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



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

Figure 1: Distribution of metabolic fluxes in 137 B. subtilis mutants.
Figure 2: Effect of knockouts on relative fluxes.
Figure 3: Effect of knockouts on absolute fluxes and optimality.


  1. Arita, M. The metabolic world of Escherichia coli is not small. Proc. Natl. Acad. Sci. USA 101, 1543–1547 (2004).

    Article  CAS  Google Scholar 

  2. Barabasi, A.L. & Oltvai, Z.N. Network biology: understanding the cell's functional organization. Nat. Rev. Genet. 5, 101–113 (2004).

    Article  CAS  Google Scholar 

  3. 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  CAS  Google Scholar 

  4. Almaas, E., Kovacs, B., Vicsek, T., Oltvai, Z.N. & Barabasi, A.L. Global organization of metabolic fluxes in the bacterium Escherichia coli . Nature 427, 839–843 (2004).

    Article  CAS  Google Scholar 

  5. Burgard, A.P., Nikolaev, E.V., Schilling, C.H. & Maranas, C.D. Flux coupling analysis of genome-scale metabolic network reconstructions. Genome Res. 14, 301–312 (2004).

    Article  CAS  Google Scholar 

  6. Papin, J.A. et al. Comparison of network-based pathway analysis methods. Trends Biotechnol. 22, 400–405 (2004).

    Article  CAS  Google Scholar 

  7. 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  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  9. Hellerstein, M.K. In vivo measurement of fluxes through metabolic pathways: the missing link in functional genomics and pharmaceutical research. Annu. Rev. Nutr. 23, 379–402 (2003).

    Article  CAS  Google Scholar 

  10. Sauer, U. High-throughput phenomics: experimental methods for mapping fluxomes. Curr. Opin. Biotechnol. 15, 58–63 (2004).

    Article  CAS  Google Scholar 

  11. Csete, M.E. & Doyle, J. Bow ties, metabolism and disease. Trends Biotechnol. 22, 446–450 (2004).

    Article  CAS  Google Scholar 

  12. Fischer, E. & Sauer, U. A novel metabolic cycle catalyzes glucose oxidation and anaplerosis in hungry Escherichia coli . J. Biol. Chem. 278, 46446–46451 (2003).

    Article  CAS  Google Scholar 

  13. 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 

  14. Fischer, E., Zamboni, N. & Sauer, U. High-throughput metabolic flux analysis based on GC-MS derived 13C-constraints. Anal. Biochem. 325, 308–316 (2004).

    Article  CAS  Google Scholar 

  15. Duetz, W.A. et al. Methods for intense aeration, growth, storage, and replication of bacterial strains in microtiter plates. Appl. Environ. Microbiol. 66, 2641–2646 (2000).

    Article  CAS  Google Scholar 

  16. Zamboni, N. & Sauer, U. Knockout of the high-coupling cytochrome aa3 oxidase reduces TCA cycle fluxes in Bacillus subtilis . FEMS Microbiol. Lett. 226, 121–126 (2003).

    Article  CAS  Google Scholar 

  17. Zamboni, N. et al. The Bacillus subtilis yqjI gene encodes the NADP+-dependent 6-P-gluconate dehydrogenase in the pentose phosphate pathway. J. Bacteriol. 186, 4528–4534 (2004).

    Article  CAS  Google Scholar 

  18. Msadek, T. When going gets tough: survival strategies and environmental signaling networks in Bacillus subtilis . Trends Microbiol. 7, 201–207 (1999).

    Article  CAS  Google Scholar 

  19. Servant, P., Le Coq, D. & Aymerich, S. CcpN (YqzB), a regulator for CcpA-independent catabolite repression of Bacillus subtilis gluconeogenic genes. Mol. Microbiol. 55, 1435–1451 (2005).

    Article  CAS  Google Scholar 

  20. Sauer, U. et al. Metabolic fluxes in riboflavin-producing Bacillus subtilis . Nat. Biotechnol. 15, 448–452 (1997).

    Article  CAS  Google Scholar 

  21. Moritz, B., Striegel, K., De Graaf, A.A. & Sahm, H. Kinetic properties of the glucose-6-phosphate and 6-phosphogluconate dehydrogenases from Corynebacterium glutamicum and their application for predicting pentose phosphate pathway flux in vivo . Eur. J. Biochem. 267, 3442–3452 (2000).

    Article  CAS  Google Scholar 

  22. Zamboni, N. et al. Transient expression and flux changes during a shift from high to low riboflavin production in continuous cultures of Bacillus subtilis . Biotechnol. Bioeng. 89, 219–232 (2005).

    Article  CAS  Google Scholar 

  23. Dauner, M., Storni, T. & Sauer, U. Bacillus subtilis metabolism and energetics in carbon-limited and carbon-excess chemostat culture. J. Bacteriol. 183, 7308–7317 (2001).

    Article  CAS  Google Scholar 

  24. Sonenshein, A.L., Hoch, J.A. & Losick, R. Bacillus subtilis and its closest relatives. From genes to cells. (ASM Press, Washington, DC, 2002).

  25. Pfeiffer, T., Schuster, S. & Bonhoeffer, S. Cooperation and competition in the evolution of ATP-producing pathways. Science 292, 504–507 (2001).

    Article  CAS  Google Scholar 

  26. Giaever, G. et al. Functional profiling of the Saccharomyces cerevisiae genome. Nature 418, 387–391 (2002).

    Article  CAS  Google Scholar 

  27. 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  CAS  Google Scholar 

  28. Nudler, E. & Mironov, A.S. The riboswitch control of bacterial metabolism. Trends Biochem. Sci. 29, 11–17 (2004).

    Article  CAS  Google Scholar 

  29. Stelling, J., Sauer, U., Szallasi, Z., Doyle III, F.J. & Doyle, J. Robustness of cellular functions. Cell 118, 675–685 (2004).

    Article  CAS  Google Scholar 

  30. Dauner, M. & Sauer, U. Stoichiometric growth model for riboflavin-producing Bacillus subtilis . Biotechnol. Bioeng. 76, 132–143 (2001).

    Article  CAS  Google Scholar 

Download references


We thank S. Aymerich, S. Bonhoeffer, H Hennecke, T. Pfeiffer and J. Stelling for critical comments on the manuscript and S. Aymerich, K. Kobayashi and N. Ogasawara for providing B. subtilis mutants. This work was supported in part by the Roche Research Foundation (E.F.).

Author information

Authors and Affiliations


Corresponding author

Correspondence to Uwe Sauer.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Supplementary information

Supplementary Table 1

Mutants used in this study. (XLS 37 kb)

Supplementary Table 2

Relative and absolute fluxes during aerobic batch growth on glucose in wild-type B. subtilis and the 137 viable mutants. (XLS 424 kb)

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Fischer, E., Sauer, U. Large-scale in vivo flux analysis shows rigidity and suboptimal performance of Bacillus subtilis metabolism. Nat Genet 37, 636–640 (2005).

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI:

This article is cited by


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