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.

  • Innovation
  • Published:

New surveyor tools for charting microbial metabolic maps

Nature Reviews Microbiology volume 6, pages 156–161 (2008)Cite this article

Abstract

The computational reconstruction and analysis of cellular models of microbial metabolism is one of the great success stories of systems biology. The extent and quality of metabolic network reconstructions is, however, limited by the current state of biochemical knowledge. Can experimental high-throughput data be used to improve and expand network reconstructions to include unexplored areas of metabolism? Recent advances in experimental technology and analytical methods bring this aim an important step closer to realization. Data integration will play a particularly important part in exploiting the new experimental opportunities.

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

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

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

Figure 1: Methods for correcting and expanding the metabolic map.
Figure 2: Network reconstruction using ultra-high-accuracy mass spectrometry.
Figure 3: Metabolite module identification using genetical genomics.

Similar content being viewed by others

References

  1. Fleischmann, R. D. et al. Whole-genome random sequencing and assembly of Haemophilus influenzae Rd. Science 269, 496–512 (1995).

    Article  CAS  PubMed  Google Scholar 

  2. Price, N. D., Reed, J. L. & Palsson, B. Ø. Genome-scale models of microbial cells: evaluating the consequences of constraints. Nature Rev. Microbiol. 2, 886–897 (2004).

    Article  CAS  Google Scholar 

  3. Notebaart, R. A., van Enckevort, F. H., Francke, C., Siezen, R. J. & Teusink, B. Accelerating the reconstruction of genome-scale metabolic networks. BMC Bioinformatics 7, 296 (2006).

    Article  PubMed  PubMed Central  Google Scholar 

  4. Reed, J. L., Famili, I., Thiele, I. & Palsson, B. O. Towards multidimensional genome annotation. Nature Rev. Genet. 7, 130–141 (2006).

    Article  CAS  PubMed  Google Scholar 

  5. Ma, H. & Zeng, A. P. Reconstruction of metabolic networks from genome data and analysis of their global structure for various organisms. Bioinformatics 19, 270–277 (2003).

    Article  CAS  PubMed  Google Scholar 

  6. Kanehisa, M. et al. From genomics to chemical genomics: new developments in KEGG. Nucleic Acids Res. 34, D354–D357 (2006).

    Article  CAS  PubMed  Google Scholar 

  7. Caspi, R. et al. MetaCyc: a multiorganism database of metabolic pathways and enzymes. Nucleic Acids Res. 34, D511–D516 (2006).

    Article  CAS  PubMed  Google Scholar 

  8. Feist, A. M., Scholten, J. C., Palsson, B. O., Brockman, F. J. & Ideker, T. Modeling methanogenesis with a genome-scale metabolic reconstruction of Methanosarcina barkeri. Mol. Syst. Biol. [online], (2006).

  9. Mahadevan, R. et al. Characterization of metabolism in the Fe(III)-reducing organism Geobacter sulfurreducens by constraint-based modeling. Appl. Environ. Microbiol. 72, 1558–1568 (2006).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Kharchenko, P., Chen, L., Freund, Y., Vitkup, D. & Church, G. M. Identifying metabolic enzymes with multiple types of association evidence. BMC Bioinformatics 7, 177 (2006).

    Article  PubMed  PubMed Central  Google Scholar 

  11. Kharchenko, P., Vitkup, D. & Church, G. M. Filling gaps in a metabolic network using expression information. Bioinformatics 20, (Suppl. 1), I178–I185 (2004).

    Article  CAS  PubMed  Google Scholar 

  12. Green, M. L. & Karp, P. D. A Bayesian method for identifying missing enzymes in predicted metabolic pathway databases. BMC Bioinformatics 5, 76 (2004).

    Article  PubMed  PubMed Central  Google Scholar 

  13. Chen, L. & Vitkup, D. Distribution of orphan metabolic activities. Trends Biotechnol. 25, 343–348 (2007).

    Article  PubMed  Google Scholar 

  14. Lespinet, O. & Labedan, B. Orphan enzymes? Science 307, 42 (2005).

    Article  CAS  PubMed  Google Scholar 

  15. Fiehn, O. & Weckwerth, W. Deciphering metabolic networks. Eur. J. Biochem. 270, 579–588 (2003).

    Article  CAS  PubMed  Google Scholar 

  16. 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  PubMed  Google Scholar 

  17. Loh, K. D. et al. A previously undescribed pathway for pyrimidine catabolism. Proc. Natl Acad. Sci. USA 103, 5114–5119 (2006).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Breitling, R., Pitt, A. R. & Barrett, M. P. Precision mapping of the metabolome. Trends Biotechnol. 24, 543–548 (2006).

    Article  CAS  PubMed  Google Scholar 

  19. Dettmer, K., Aronov, P. A. & Hammock, B. D. Mass spectrometry-based metabolomics. Mass Spectrom. Rev. 26, 51–78 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Dunn, W. B., Bailey, N. J. & Johnson, H. E. Measuring the metabolome: current analytical technologies. Analyst 130, 606–625 (2005).

    Article  CAS  PubMed  Google Scholar 

  21. Hollywood, K., Brison, D. R. & Goodacre, R. Metabolomics: current technologies and future trends. Proteomics 6, 4716–4723 (2006).

    Article  CAS  PubMed  Google Scholar 

  22. Want, E. J., Nordstrom, A., Morita, H. & Siuzdak, G. From exogenous to endogenous: the inevitable imprint of mass spectrometry in metabolomics. J. Proteome Res. 6, 459–468 (2007).

    Article  CAS  PubMed  Google Scholar 

  23. Hu, Q. et al. The Orbitrap: a new mass spectrometer. J. Mass Spectrom. 40, 430–443 (2005).

    Article  CAS  PubMed  Google Scholar 

  24. Makarov, A., Denisov, E., Lange, O. & Horning, S. Dynamic range of mass accuracy in LTQ Orbitrap hybrid mass spectrometer. J. Am. Soc. Mass Spectrom. 17, 977–982 (2006).

    Article  CAS  PubMed  Google Scholar 

  25. Brown, S. C., Kruppa, G. & Dasseux, J. L. Metabolomics applications of FT-ICR mass spectrometry. Mass Spectrom. Rev. 24, 223–231 (2005).

    Article  CAS  PubMed  Google Scholar 

  26. Koulman, A. et al. High-throughput direct-infusion ion trap mass spectrometry: a new method for metabolomics. Rapid Commun. Mass Spectrom. 21, 421–428 (2007).

    Article  CAS  PubMed  Google Scholar 

  27. Aharoni, A. et al. Nontargeted metabolome analysis by use of Fourier Transform Ion Cyclotron Mass Spectrometry. OMICS 6, 217–234 (2002).

    Article  CAS  PubMed  Google Scholar 

  28. Breitling, R., Ritchie, S., Goodenowe, D., Stewart, M. L. & Barrett, M. P. Ab initio prediction of metabolic networks using Fourier transform mass spectrometry data. Metabolomics 2, 155–164 (2006).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Nobeli, I., Ponstingl, H., Krissinel, E. B. & Thornton, J. M. A structure-based anatomy of the E.coli metabolome. J. Mol. Biol. 334, 697–719 (2003).

    Article  CAS  PubMed  Google Scholar 

  30. Arkin, A., Shen, P. & Ross, J. A test case of correlation metric construction of a reaction pathway from measurements. Science 277, 1275–1279 (1997).

    Article  CAS  Google Scholar 

  31. Vance, W., Arkin, A. & Ross, J. Determination of causal connectivities of species in reaction networks. Proc. Natl Acad. Sci. USA 99, 5816–5821 (2002).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Torralba, A. S., Yu, K., Shen, P., Oefner, P. J. & Ross, J. Experimental test of a method for determining causal connectivities of species in reactions. Proc. Natl Acad. Sci. USA 100, 1494–1498 (2003).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Fiehn, O. et al. Metabolite profiling for plant functional genomics. Nature Biotechnol. 18, 1157–1161 (2000).

    Article  CAS  Google Scholar 

  34. Camacho, D., de la Fuente, A. & Mendes, P. The origin of correlations in metabolomics data. Metabolomics 1, 53–63 (2005).

    Article  CAS  Google Scholar 

  35. Steuer, R. On the analysis and interpretation of correlations in metabolomic data. Brief. Bioinform. 7, 151–158 (2006).

    Article  CAS  PubMed  Google Scholar 

  36. Steuer, R., Kurths, J., Fiehn, O. & Weckwerth, W. Interpreting correlations in metabolomic networks. Biochem. Soc. Trans. 31, 1476–1478 (2003).

    Article  CAS  PubMed  Google Scholar 

  37. Steuer, R., Kurths, J., Fiehn, O. & Weckwerth, W. Observing and interpreting correlations in metabolomic networks. Bioinformatics 19, 1019–1026 (2003).

    Article  CAS  PubMed  Google Scholar 

  38. Voit, E. O., Marino, S. & Lall, R. Challenges for the identification of biological systems from in vivo time series data. In Silico Biol. 5, 83–92 (2005).

    CAS  PubMed  Google Scholar 

  39. Jansen, R. C. & Nap, J. P. Genetical genomics: the added value from segregation. Trends Genet. 17, 388–391 (2001).

    Article  CAS  PubMed  Google Scholar 

  40. Keurentjes, J. J. et al. The genetics of plant metabolism. Nature Genet. 38, 842–849 (2006).

    Article  CAS  PubMed  Google Scholar 

  41. Fu, J., Swertz, M., Keurentjes, J. & Jansen, R. MetaNetwork: a computational protocol for the genetic study of metabolic networks. Nature Protoc. 2, 685–694 (2007).

    Article  CAS  Google Scholar 

  42. Brem, R. B., Yvert, G., Clinton, R. & Kruglyak, L. Genetic dissection of transcriptional regulation in budding yeast. Science 296, 752–755 (2002).

    Article  CAS  PubMed  Google Scholar 

  43. Cheung, V. G. et al. Mapping determinants of human gene expression by regional and genome-wide association. Nature 437, 1365–1369 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  45. Sauer, U. Metabolic networks in motion: 13C-based flux analysis. Mol. Syst. Biol. [online], (2006).

  46. Herrgard, M. J., Fong, S. S. & Palsson, B. O. Identification of genome-scale metabolic network models using experimentally measured flux profiles. PLoS Comput. Biol. 2, e72 (2006).

    Article  PubMed  PubMed Central  Google Scholar 

  47. Dudley, A. M., Janse, D. M., Tanay, A., Shamir, R. & Church, G. M. A global view of pleiotropy and phenotypically derived gene function in yeast. Mol. Syst. Biol. [online], (2005).

  48. Forster, J., Famili, I., Fu, P., Palsson, B. O. & Nielsen, J. Genome-scale reconstruction of the Saccharomyces cerevisiae metabolic network. Genome Res. 13, 244–253 (2003).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Reed, J. L., Vo, T. D., Schilling, C. H. & Palsson, B. O. An expanded genome-scale model of Escherichia coli K-12 (iJR904 GSM/GPR). Genome Biol. 4, R54 (2003).

    Article  PubMed  PubMed Central  Google Scholar 

  50. Kuepfer, L., Sauer, U. & Blank, L. M. Metabolic functions of duplicate genes in Saccharomyces cerevisiae. Genome Res. 15, 1421–1430 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. 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  PubMed  Google Scholar 

  52. Reed, J. L. et al. Systems approach to refining genome annotation. Proc. Natl Acad. Sci. USA 103, 17480–17484 (2006).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Kumar, V. S., Dasika, M. S. & Maranas, C. D. Optimization based automated curation of metabolic reconstructions. BMC Bioinformatics 8, 212 (2007).

    Article  Google Scholar 

  54. Kümmel, A., Panke, S. & Heinemann, M. Putative regulatory sites unraveled by network-embedded thermodynamic analysis of metabolome data. Mol. Syst. Biol. [online], (2006).

  55. Guimera, R. & Nunes Amaral, L. A. Functional cartography of complex metabolic networks. Nature 433, 895–900 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Pal, C., Papp, B. & Lercher, M. J. Adaptive evolution of bacterial metabolic networks by horizontal gene transfer. Nature Genet. 37, 1372–1375 (2005).

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  58. Clardy, J., Fischbach, M. A. & Walsh, C. T. New antibiotics from bacterial natural products. Nature Biotechnol. 24, 1541–1550 (2006).

    Article  CAS  Google Scholar 

  59. Fischbach, M. A. & Walsh, C. T. Biochemistry: directing biosynthesis. Science 314, 603–605 (2006).

    Article  CAS  PubMed  Google Scholar 

  60. Jansen, R. et al. A Bayesian networks approach for predicting protein–protein interactions from genomic data. Science 302, 449–453 (2003).

    Article  CAS  PubMed  Google Scholar 

  61. Marcotte, E. M., Pellegrini, M., Thompson, M. J., Yeates, T. O. & Eisenberg, D. A combined algorithm for genome-wide prediction of protein function. Nature 402, 83–86 (1999).

    Article  CAS  PubMed  Google Scholar 

  62. Fong, S. S. & Palsson, B. O. Metabolic gene-deletion strains of Escherichia coli evolve to computationally predicted growth phenotypes. Nature Genet. 36, 1056–1058 (2004).

    Article  CAS  PubMed  Google Scholar 

  63. 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  PubMed  Google Scholar 

  64. 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  PubMed  Google Scholar 

  65. 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  PubMed  PubMed Central  Google Scholar 

  66. Burgard, A. P., Pharkya, P. & Maranas, C. D. Optknock: a bilevel programming framework for identifying gene knockout strategies for microbial strain optimization. Biotechnol. Bioeng. 84, 647–657 (2003).

    Article  CAS  PubMed  Google Scholar 

  67. Fong, S. S. et al. In silico design and adaptive evolution of Escherichia coli for production of lactic acid. Biotechnol. Bioeng. 91, 643–648 (2005).

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

The authors thank M. Spitzer, J. Wildenhain, M. Swertz, R. Jansen, J. Fu, Y. Li, E. Takano, D. Höller and R. Steuer for their constructive comments on the manuscript. We apologize to those authors whose relevant work we could not cite owing to space constraints.

Author information

Authors and Affiliations

  1. Rainer Breitling is at the Groningen Bioinformatics Centre, Groningen Biomolecular Sciences and Biotechnology Institute, University of Groningen, Haren, 9751, NN, The Netherlands

    Rainer Breitling

  2. Department of Biomedical Informatics, Dennis Vitkup is at the Center for Computational Biology and Bioinformatics, Columbia University, New York, 10032, New York, United States

    Dennis Vitkup

  3. Michael P. Barrett is at the Division of Infection and Immunity, Institute of Biomedical and Life Sciences, University of Glasgow, Glasgow G12 8QQ, United Kingdom.,

    Michael P. Barrett

Authors
  1. Rainer Breitling
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Dennis Vitkup
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Michael P. Barrett
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Rainer Breitling or Dennis Vitkup.

Related links

Related links

DATABASES

Entrez Genome Project

Arabidopsis thaliana

Escherichia coli

Geobacter sulphurreducens

Haemophilus influenzae

Methanosarcina barkeri

Trypanosoma brucei

FURTHER INFORMATION

Rainer Breitling's homepage

Enzyme Commission

Glossary

De novo pathway reconstruction

The inference of metabolic pathways directly from experimental measurements, without any prior information.

Emergent property

A property that emerges only in the context of an integrated system, not in its components; also called systems property.

Flux-balance analysis

A computational method that is used to obtain feasible flux distributions in metabolic networks. Linear constraints on nutrient uptake, reaction irreversibility and steady-state conservation of metabolite concentrations are applied using a stoichiometric model. The fluxes that are optimal for a given objective function (for example, biomass production or ATP synthesis) are then obtained using linear optimization.

Genetical genomics

The combination of high-throughput measurements of gene expression, protein levels or metabolite concentrations with classical genetic strategies.

Metabolomics

The analysis of the concentration and dynamics of small cellular molecules (the metabolome).

Optimal metabolic network identification

(OMNI). A computational method for correcting stoichiometric models based on a small number of pathway flux measurements.

Stable-isotope flux analysis

An analysis that traces the metabolic fate of non-radioactive atoms from labelled precursors to biomass components. The steady-state labelling pattern can be used to infer the activity of metabolic pathways (fluxes) with the help of stoichiometric models.

Stoichiometric model

A detailed description of metabolism without information on the kinetic or thermodynamic parameters. The model specifies how many molecules of each substrate are used and how many product molecules are generated (the reaction stoichiometry) for every reaction.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Breitling, R., Vitkup, D. & Barrett, M. New surveyor tools for charting microbial metabolic maps. Nat Rev Microbiol 6, 156–161 (2008). https://doi.org/10.1038/nrmicro1797

Download citation

  • Issue Date:

  • DOI: https://doi.org/10.1038/nrmicro1797

This article is cited by

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