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.

13C-based metabolic flux analysis

Abstract

Stable isotope, and in particular 13C-based flux analysis, is the exclusive approach to experimentally quantify the integrated responses of metabolic networks. Here we describe a protocol that is based on growing microbes on 13C-labeled glucose and subsequent gas chromatography mass spectrometric detection of 13C-patterns in protein-bound amino acids. Relying on publicly available software packages, we then describe two complementary mathematical approaches to estimate either local ratios of converging fluxes or absolute fluxes through different pathways. As amino acids in cell protein are abundant and stable, this protocol requires a minimum of equipment and analytical expertise. Most other flux methods are variants of the principles presented here. A true alternative is the analytically more demanding dynamic flux analysis that relies on 13C-pattern in free intracellular metabolites. The presented protocols take 5–10 d, have been used extensively in the past decade and are exemplified here for the central metabolism of Escherichia coli.

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

Relevant articles

Open Access articles citing this article.

Access options

Buy article

Get time limited or full article access on ReadCube.

$32.00

All prices are NET prices.

Figure 1: Workflow of a 13C metabolic flux analysis experiment.
Figure 2: Detection of 13C labeling patterns by GC-MS analysis of derivatized amino acids.
Figure 3: Metabolic and isotopic dynamics during 13C labeling experiments in batch and continuous cultivations.
Figure 4: Metabolic flux ratios and absolute fluxes calculated for E. coli grown in glucose-limited continuous cultures at 0.12 h−1.
Figure 5: Difference in flux estimates obtained by iterative fitting with ill-defined networks.

References

  1. Ishii, N. et al. Multiple high-throughput analyses monitor the response of E. coli to perturbations. Science 316, 593–597 (2007).

    Article  CAS  Google Scholar 

  2. Sauer, U. Metabolic networks in motion: 13C-based flux analysis. Mol. Syst. Biol. 2, 62 (2006).

    Article  Google Scholar 

  3. 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 

  4. Stephanopoulos, G. Metabolic fluxes and metabolic engineering. Metab. Eng. 1, 1–11 (1999).

    Article  CAS  Google Scholar 

  5. Varma, A. & Palsson, B.O. Metabolic flux balancing: Basic concepts, scientific, and practical use. Bio/Technol. 12, 994–998 (1994).

    Article  CAS  Google Scholar 

  6. Wiechert, W. 13C metabolic flux analysis. Metab. Eng. 3, 195–206 (2001).

    Article  CAS  Google Scholar 

  7. Szyperski, T. 13C-NMR, MS and metabolic flux balancing in biotechnology research. Q. Rev. Biophys. 31, 41–106 (1998).

    Article  CAS  Google Scholar 

  8. Nanchen, A., Fuhrer, T. & Sauer, U. Determination of metabolic flux ratios from 13C-experiments and gas chromatography-mass spectrometry data: protocol and principles. Methods Mol. Biol. 358, 177–197 (2007).

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  10. Wiechert, W., Möllney, M., Petersen, S. & de Graaf, A.A. A universal framework for 13C metabolic flux analysis. Metab. Eng. 3, 265–283 (2001).

    Article  CAS  Google Scholar 

  11. 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 

  12. Zamboni, N., Fischer, E. & Sauer, U. FiatFlux - a software for metabolic flux analysis from 13C-glucose experiments. BMC Bioinformatics 6, 209 (2005).

    Article  Google Scholar 

  13. Marx, A., de Graaf, A.A., Wiechert, W., Eggeling, L. & Sahm, H. Determination of the fluxes in the central metabolism of Corynebacterium glutamicum by nuclear magnetic resonance spectroscopy combined with metabolite balancing. Biotech. Bioeng. 49, 111–129 (1996).

    Article  CAS  Google Scholar 

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

    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. Dauner, M. & Sauer, U. GC-MS analysis of amino acids rapidly provides rich information for isotopomer balancing. Biotechnol. Prog. 16, 642–649 (2000).

    Article  CAS  Google Scholar 

  17. Nöh, K. et al. Metabolic flux analysis at ultra short time scale: isotopically non-stationary 13C labeling experiments. J. Biotechnol. 129, 249–267 (2007).

    Article  Google Scholar 

  18. van Winden, W.A. et al. Metabolic-flux analysis of Saccharomyces cerevisiae CEN.PK113-7D based on mass isotopomer measurements of 13C-labeled primary metabolites. FEMS Yeast Res. 5, 559–568 (2005).

    Article  CAS  Google Scholar 

  19. Yuan, J., Fowler, W.U., Kimball, E., Lu, W. & Rabinowitz, J.D. Kinetic flux profiling of nitrogen assimilation in Escherichia coli . Nat. Chem. Biol. 2, 529–530 (2006).

    Article  CAS  Google Scholar 

  20. Schaub, J., Mauch, K. & Reuss, M. Metabolic flux analysis in Escherichia coli by integrating isotopic dynamic and isotopic stationary 13C labeling data. Biotechnol. Bioeng. 99, 1170–1185 (2008).

    Article  CAS  Google Scholar 

  21. Hua, Q., Yang, C., Baba, T., Mori, H. & Shimizu, K. Responses of the central metabolism in Escherichia coli to phosphoglucose isomerase and glucose-6-phosphate dehydrogenase knockouts. J. Bacteriol. 185, 7053–7067 (2003).

    Article  CAS  Google Scholar 

  22. 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).

    Article  CAS  Google Scholar 

  23. Christensen, B., Gombert, A.K. & Nielsen, J. Analysis of flux estimates based on 13C-labeling experiments. Eur. J. Biochem. 269, 2795–2800 (2002).

    Article  CAS  Google Scholar 

  24. Wittmann, C., Kiefer, P. & Zelder, O. Metabolic fluxes in Corynebacterium glutamicum during lysine production with sucrose as carbon source. Appl. Environ. Microbiol. 70, 7277–7287 (2004).

    Article  CAS  Google Scholar 

  25. Hellerstein, M.K. New stable isotope-mass spectrometric techniques for measuring fluxes through intact metabolic pathways in mammalian systems: introduction of moving pictures into functional genomics and biochemical phenotyping. Metab. Eng. 6, 85–100 (2004).

    Article  CAS  Google Scholar 

  26. Schwender, J. Metabolic flux analysis as a tool in metabolic engineering of plants. Curr. Opin. Biotechnol. 19, 131–137 (2008).

    Article  CAS  Google Scholar 

  27. Wittmann, C. Metabolic flux analysis using mass spectrometry. Adv. Biochem. Eng. Biotechnol. 74, 39–64 (2002).

    CAS  PubMed  Google Scholar 

  28. Gunnarsson, N., Mortensen, U.H., Sosio, M. & Nielsen, J. Identification of the Entner-Doudoroff pathway in an antibiotic-producing actinomycete species. Mol. Microbiol. 52, 895–902 (2004).

    Article  CAS  Google Scholar 

  29. 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 

  30. Blank, L.M., Kuepfer, L. & Sauer, U. Large-scale 13C-flux analysis reveals mechanistic principles of metabolic network robustness to null mutations in yeast. Genome. Biol. 6, R49 (2005).

    Article  Google Scholar 

  31. Schütz, R., Küpfer, L. & Sauer, U. Systematic evaluation of objective functions for predicting intracellular fluxes in Escherichia coli . Mol. Syst. Biol. 3, 119 (2007).

    Google Scholar 

  32. Feist, A.M. & Palsson, B.O. The growing scope of applications of genome-scale metabolic reconstructions using Escherichia coli . Nat. Biotechnol. 26, 659–667 (2008).

    Article  CAS  Google Scholar 

  33. Szyperski, T. Biosynthetically directed fractional 13C-labeling of proteinogenic amino acids. An efficient analytical tool to investigate intermediary metabolism. Eur. J. Biochem. 232, 433–448 (1995).

    Article  CAS  Google Scholar 

  34. Möllney, M., Wiechert, W., Kownatzki, D. & de Graaf, A.A. Bidirectional reaction steps in metabolic networks: IV. Optimal design of isotopomer labeling experiments. Biotechnol. Bioeng. 66, 86–103 (1999).

    Article  Google Scholar 

  35. Wiechert, W., Siefke, C., de Graaf, A.A. & Marx, A. Bidirectional reaction steps in metabolic networks: II. Flux estimation and statistical analysis. Biotechnol. Bioeng. 55, 118–135 (1997).

    Article  CAS  Google Scholar 

  36. Antoniewicz, M.R., Kelleher, J.K. & Stephanopoulos, G. Elementary metabolite units (EMU): a novel framework for modeling isotopic distributions. Metab. Eng. 9, 68–86 (2007).

    Article  CAS  Google Scholar 

  37. van Winden, W.A., Heijnen, J.J. & Verheijen, P.J. Cumulative bondomers: a new concept in flux analysis from 2D [13C,1H] COSY NMR data. Biotechnol. Bioeng. 80, 731–745 (2002).

    Article  CAS  Google Scholar 

  38. Rantanen, A. et al. An analytic and systematic framework for estimating metabolic flux ratios from 13C tracer experiments. BMC Bioinformatics 9, 266 (2008).

    Article  Google Scholar 

  39. 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 

  40. Schaub, J., Schiesling, C., Reuss, M. & Dauner, M. Integrated sampling procedure for metabolome analysis. Biotechnol. Prog. 22, 1434–1442 (2006).

    Article  CAS  Google Scholar 

  41. Antoniewicz, M.R. et al. Metabolic flux analysis in a nonstationary system: fed-batch fermentation of a high yielding strain of E. coli producing 1,3-propanediol. Metab. Eng. 9, 277–292 (2007).

    Article  CAS  Google Scholar 

  42. Monod, J. Récherches sur la croissance des cultures bactériennnes (Hermann et Compagnie Ed, Paris, 1942).

    Google Scholar 

  43. Kleijn, R.J. et al. 13C-labeled gluconate tracing as a direct and accurate method for determining the pentose phosphate pathway split ratio in Penicillium chrysogenum . Appl. Environ. Microbiol. 72, 4743–4754 (2006).

    Article  CAS  Google Scholar 

  44. Petersen, S. et al. In vivo quantification of parallel and bidirectional fluxes in the anaplerosis of Corynebacterium glutamicum . J. Biol. Chem. 275, 35932–35941 (2000).

    Article  CAS  Google Scholar 

  45. Stryer, L. Biochemistry 4th edn. 483–491 (Freeman and Company, New York, 1995).

  46. Dauner, M. et al. Intracellular carbon fluxes in riboflavin-producing Bacillus subtilis during growth on two-carbon substrate mixtures. Appl. Environ. Microbiol. 68, 1760–1771 (2002).

    Article  CAS  Google Scholar 

  47. Blank, L.M., Lehmbeck, F. & Sauer, U. Metabolic-flux and network analysis in fourteen hemiascomycetous yeasts. FEMS Yeast Res. 5, 545–558 (2005).

    Article  CAS  Google Scholar 

  48. Fischer, E., Zamboni, N. & Sauer, U. High-throughput metabolic flux analysis based on gas chromatography-mass spectrometry derived 13C constraints. Anal. Biochem. 325, 308–316 (2004).

    Article  CAS  Google Scholar 

  49. Kleijn, R.J. et al. Metabolic flux analysis of a glycerol-overproducing Saccharomyces cerevisiae strain based on GC-MS, LC-MS and NMR-derived C-13-labelling data. FEMS Yeast Res. 7, 216–231 (2007).

    Article  CAS  Google Scholar 

  50. Kummel, A., Panke, S. & Heinemann, M. Systematic assignment of thermodynamic constraints in metabolic network models. BMC Bioinformatics 7, 512 (2006).

    Article  Google Scholar 

  51. Herrgard, M.J. et al. A consensus yeast metabolic network reconstruction obtained from a community approach to systems biology. Nat. Biotechnol. 26, 1155–1160 (2008).

    Article  CAS  Google Scholar 

  52. Oh, Y.K., Palsson, B.O., Park, S.M., Schilling, C.H. & Mahadevan, R. Genome-scale reconstruction of metabolic network in Bacillus subtilis based on high-throughput phenotyping and gene essentiality data. J. Biol. Chem. 282, 28791–28799 (2007).

    Article  CAS  Google Scholar 

  53. Feist, A.M. et al. A genome-scale metabolic reconstruction for Escherichia coli K-12 MG1655 that accounts for 1260 ORFs and thermodynamic information. Mol. Syst. Biol. 3, 121 (2007).

    Article  Google Scholar 

  54. Schilling, C.H. et al. Genome-scale metabolic model of Helicobacter pylori 26695. J. Bacteriol. 184, 4582–4593 (2002).

    Article  CAS  Google Scholar 

  55. Oliveira, A.P., Nielsen, J. & Forster, J. Modeling Lactococcus lactis using a genome-scale flux model. BMC Microbiol. 5, 39 (2005).

    Article  Google Scholar 

  56. Cannizzaro, C., Christensen, B., Nielsen, J. & von Stockar, U. Metabolic network analysis on Phaffia rhodozyma yeast using 13C-labeled glucose and gas chromatography-mass spectrometry. Metab. Eng. 6, 340–351 (2004).

    Article  CAS  Google Scholar 

  57. Fuhrer, T., Fischer, E. & Sauer, U. Experimental identification and quantification of glucose metabolism in seven bacterial species. J. Bacteriol. 187, 1581–1590 (2005).

    Article  CAS  Google Scholar 

  58. Schmidt, K., Carlsen, M., Nielsen, J. & Villadsen, J. Modeling isotopomer distributions in biochemical networks using isotopomer mapping matrices. Biotechnol. Bioeng. 55, 831–840 (1997).

    Article  CAS  Google Scholar 

  59. Arita, M. In silico atomic tracing by substrate-product relationships in Escherichia coli intermediary metabolism. Genome Res. 13, 2455–2466 (2003).

    Article  CAS  Google Scholar 

  60. Pitkänen, E., Åkerlund, A., Rantanen, A., Jouhten, P. & Ukkonen, E. ReMatch: a web-based tool to construct, store and share stoichiometric metabolic models with carbon maps for metabolic flux analysis. J. Integr. Bioinform. 5, 102 (2008).

    Article  Google Scholar 

  61. Dauner, M., Bailey, J.E. & Sauer, U. Metabolic flux analysis with a comprehensive isotopomer model in Bacillus subtilis . Biotechnol. Bioeng. 76, 144–156 (2001).

    Article  CAS  Google Scholar 

  62. Pramanik, J. & Keasling, J.D. Effect of Escherichia coli biomass composition on central metabolic fluxes predicted by a stoichiometric model. Biotechnol. Bioeng. 60, 230–238 (1998).

    Article  CAS  Google Scholar 

  63. Guy, R.D., Fogel, M.L. & Berry, J.A. Photosynthetic fractionation of the stable isotopes of oxygen and carbon. Plant Physiol. 101, 37–47 (1993).

    Article  CAS  Google Scholar 

  64. Srere, P.A. Citric acid cycle redux. Trends Biochem. Sci. 15, 411–412 (1990).

    Article  CAS  Google Scholar 

  65. Antoniewicz, M.R., Kelleher, J.K. & Stephanopoulos, G. Accurate assessment of amino acid mass isotopomer distributions for metabolic flux analysis. Anal. Chem. 79, 7554–7559 (2007).

    Article  CAS  Google Scholar 

  66. Zamboni, N. in Topics in Current Genetics (eds. J. Nielsen & M. Jewett) (Springer, Berlin, 2007).

    Google Scholar 

  67. Isermann, N. & Wiechert, W. Metabolic isotopomer labeling systems. Part II: structural flux identifiability analysis. Math. Biosci. 183, 175–214 (2003).

    Article  CAS  Google Scholar 

  68. Rantanen, A., Mielikainen, T., Rousu, J., Maaheimo, H. & Ukkonen, E. Planning optimal measurements of isotopomer distributions for estimation of metabolic fluxes. Bioinformatics 22, 1198–1206 (2006).

    Article  CAS  Google Scholar 

  69. Sambrook, T. & Russell, D.W. in Molecular Cloning: A Laboratory Manual Vol 3, A2.2 (Cold Spring Harbor Press, Cold Spring Harbor, NY, 2001).

    Google Scholar 

  70. Bailey, J.E. & Ollis, D.F. in Biochemical Engineering Fundamentals 2nd edn. 373–456 (McGraw-Hill, Singapore, 1986).

    Google Scholar 

  71. Wahl, S.A., Dauner, M. & Wiechert, W. New tools for mass isotopomer data evaluation in (13)C flux analysis: mass isotope correction, data consistency checking, and precursor relationships. Biotechnol. Bioeng. 85, 259–268 (2004).

    Article  CAS  Google Scholar 

  72. Pázman, A. Nonlinear Statistical Models (Kluwer Academic Publishing, New York, 1993).

    Book  Google Scholar 

  73. Gottschalk, G. Bacterial Metabolism 2nd edn. 185 (Springer-Verlag, New York, 1986).

    Book  Google Scholar 

  74. Fong, S.S., Nanchen, A., Palsson, B.O. & Sauer, U. Latent pathway activation and increased pathway capacity enable Escherichia coli adaptation to loss of key metabolic enzymes. J. Biol. Chem. 281, 8024–8033 (2006).

    Article  CAS  Google Scholar 

  75. Wiechert, W. & de Graaf, A.A. Bidirectional reaction steps in metabolic networks: I. Modeling and simulation of carbon isotope labeling experiments. Biotechnol. Bioeng. 55, 101–117 (1997).

    Article  CAS  Google Scholar 

  76. Wiechert, W., Mollney, M., Isermann, N., Wurzel, M. & de Graaf, A.A. Bidirectional reaction steps in metabolic networks: III. Explicit solution and analysis of isotopomer labeling systems. Biotechnol. Bioeng. 66, 69–85 (1999).

    Article  CAS  Google Scholar 

Download references

Acknowledgements

We thank Katharina Nöh and Wolfgang Wiechert for support with 13CFLUX and comments on the respective protocol parts presented in this protocol, as well as Roelco J. Kleijn, Dominik Heer, Julian Schnidder, and Daniel Heine for constructive comments on the script.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Sarah-Maria Fendt.

Supplementary information

Supplementary Data 1

Raw GC-MS data and physiological parameters for E. coli example (ZIP 4350 kb)

Supplementary Data 2

FiatFlux files for E. coli example (ZIP 1521 kb)

Supplementary Data 3

13CFLUX files for E. coli example (ZIP 11113 kb)

Supplementary Method 1

Step-by-step tutorial for 13CFLUX software (PDF 111 kb)

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Zamboni, N., Fendt, SM., Rühl, M. et al. 13C-based metabolic flux analysis. Nat Protoc 4, 878–892 (2009). https://doi.org/10.1038/nprot.2009.58

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nprot.2009.58

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

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