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A consensus yeast metabolic network reconstruction obtained from a community approach to systems biology

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Abstract

Genomic data allow the large-scale manual or semi-automated assembly of metabolic network reconstructions, which provide highly curated organism-specific knowledge bases. Although several genome-scale network reconstructions describe Saccharomyces cerevisiae metabolism, they differ in scope and content, and use different terminologies to describe the same chemical entities. This makes comparisons between them difficult and underscores the desirability of a consolidated metabolic network that collects and formalizes the 'community knowledge' of yeast metabolism. We describe how we have produced a consensus metabolic network reconstruction for S. cerevisiae. In drafting it, we placed special emphasis on referencing molecules to persistent databases or using database-independent forms, such as SMILES or InChI strings, as this permits their chemical structure to be represented unambiguously and in a manner that permits automated reasoning. The reconstruction is readily available via a publicly accessible database and in the Systems Biology Markup Language (http://www.comp-sys-bio.org/yeastnet). It can be maintained as a resource that serves as a common denominator for studying the systems biology of yeast. Similar strategies should benefit communities studying genome-scale metabolic networks of other organisms.

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Figure 1: An example of the SBML annotation of a metabolite species using the example of ATP, as used in the reconstruction of the consensus network, illustrating its use of the Systems Biology Ontology (http://www.ebi.ac.uk/sbo/) and its MIRIAM compliance.
Figure 2: Degree distribution of the metabolic network.

Change history

  • 07 May 2012

    In the HTML version of this article initially published, Nils Blüthgen’s name was spelled as Büthgen. The error has been corrected in the HTML version of the article.

References

  1. Kell, D.B. Metabolomics, modelling and machine learning in systems biology: towards an understanding of the languages of cells. The 2005 Theodor Bücher lecture. FEBS J. 273, 873–894 (2006).

    Article  CAS  Google Scholar 

  2. Arakawa, K., Yamada, Y., Shinoda, K., Nakayama, Y. & Tomita, M. GEM System: automatic prototyping of cell-wide metabolic pathway models from genomes. BMC Bioinformatics 7, 168 (2006).

    Article  Google Scholar 

  3. Palsson, B.Ø. Systems Biology: Properties of Reconstructed Networks. (Cambridge University Press, Cambridge; 2006).

    Book  Google Scholar 

  4. Duarte, N.C. et al. Global reconstruction of the human metabolic network based on genomic and bibliomic data. Proc. Natl. Acad. Sci. USA 104, 1777–1782 (2007).

    Article  CAS  Google Scholar 

  5. Mager, W.H. & Winderickx, J. Yeast as a model for medical and medicinal research. Trends Pharmacol. Sci. 26, 265–273 (2005).

    Article  CAS  Google Scholar 

  6. Goffeau, A. et al. Life With 6000 genes. Science 274, 546–567 (1996).

    Article  CAS  Google Scholar 

  7. Winzeler, E.A. et al. Functional characterization of the S. cerevisiae genome by gene deletion and parallel analysis. Science 285, 901–906 (1999).

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  9. Yen, K., Gitsham, P., Wishart, J., Oliver, S.G. & Zhang, N. An improved tetO promoter replacement system for regulating the expression of yeast genes. Yeast 20, 1255–1262 (2003).

    Article  CAS  Google Scholar 

  10. Hughes, T.R. et al. Functional discovery via a compendium of expression profiles. Cell 102, 109–126 (2000).

    Article  CAS  Google Scholar 

  11. Allen, J.K. et al. High-throughput characterisation of yeast mutants for functional genomics using metabolic footprinting. Nat. Biotechnol. 21, 692–696 (2003).

    Article  CAS  Google Scholar 

  12. Zhu, H. et al. Global analysis of protein activities using proteome chips. Science 293, 2101–2105 (2001).

    Article  CAS  Google Scholar 

  13. Castrillo, J.I. et al. Growth control of the eukaryote cell: a systems biology study in yeast. J. Biol. 6, 4 (2007).

    Article  Google Scholar 

  14. Delneri, D. et al. Identification and characterization of high-flux-control genes of yeast through competition analyses in continuous cultures. Nat. Genet. 40, 113–117 (2008).

    Article  CAS  Google Scholar 

  15. Wu, J., Zhang, N., Hayes, A., Panoutsopoulou, K. & Oliver, S.G. Global analysis of nutrient control of gene expression in Saccharomyces cerevisiae during growth and starvation. Proc. Natl. Acad. Sci. USA 101, 3148–3153 (2004).

    Article  CAS  Google Scholar 

  16. Oliver, S. A network approach to the systematic analysis of gene function. Trends Genet. 12, 241–242 (1996).

    Article  CAS  Google Scholar 

  17. Suter, B., Auerbach, D. & Stagljar, I. Yeast-based functional genomics and proteomics technologies: the first 15 years and beyond. Biotechniques 40, 625–644 (2006).

    Article  CAS  Google Scholar 

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

    Article  Google Scholar 

  19. Duarte, N.C., Herrgard, M.J. & Palsson, B.Ø. Reconstruction and validation of Saccharomyces cerevisiae iND750, a fully compartmentalized genome-scale metabolic model. Genome Res. 14, 1298–1309 (2004).

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  22. Hucka, M. et al. The systems biology markup language (SBML): a medium for representation and exchange of biochemical network models. Bioinformatics 19, 524–531 (2003).

    Article  CAS  Google Scholar 

  23. Le Novère, N. et al. Minimum information requested in the annotation of biochemical models (MIRIAM). Nat. Biotechnol. 23, 1509–1515 (2005).

    Article  Google Scholar 

  24. Çakir, T. et al. Integration of metabolome data with metabolic networks reveals reporter reactions. Mol. Syst. Biol. 2, 50 (2006).

    Article  Google Scholar 

  25. Kümmel, A., Panke, S. & Heinemann, M. Putative regulatory sites unraveled by network-embedded thermodynamic analysis of metabolome data. Mol. Syst. Biol. 2, 2006.0034 (2006).

  26. Reed, J.L., Vo, T.D., Schilling, C.H. & Palsson, B.Ø. An expanded genome-scale model of Escherichia coli K12 (iJR904 GSM/GPR). Genome Biol 4, R54 (2003).

    Article  Google Scholar 

  27. Förster, J., Famili, I., Palsson, B.Ø. & Nielsen, J. Large-scale evaluation of in silico deletions in Saccharomyces cerevisiae. OMICS 7, 193–202 (2003).

    Article  Google Scholar 

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

    Article  CAS  Google Scholar 

  29. Nash, R. et al. Expanded protein information at SGD: new pages and proteome browser. Nucleic Acids Res. 35, D468–D471 (2007).

    Article  CAS  Google Scholar 

  30. Caspi, R. et al. The MetaCyc Database of metabolic pathways and enzymes and the BioCyc collection of Pathway/Genome Databases. Nucleic Acids Res. 36, D623–D631 (2008).

    Article  CAS  Google Scholar 

  31. Li, X.J. et al. in Metabolic profiling: its role in biomarker discovery and gene function analysis. (eds. Harrigan, G.G. & Goodacre, R.) 293–309 (Kluwer Academic Publishers, Boston, 2003).

    Book  Google Scholar 

  32. Goble, C. & Wroe, C. The Montagues and the Capulets. Comp. Funct. Genomics 5, 623–632 (2004).

    Article  CAS  Google Scholar 

  33. Ananiadou, S., Kell, D.B. & Tsujii, J. Text mining and its potential applications in systems biology. Trends Biotechnol. 24, 571–579 (2006).

    Article  CAS  Google Scholar 

  34. Poolman, M.G., Bonde, B.K., Gevorgyan, A., Patel, H.H. & Fell, D.A. Challenges to be faced in the reconstruction of metabolic networks from public databases. Syst. Biol. (Stevenage) 153, 379–384 (2006).

    Article  CAS  Google Scholar 

  35. Spasić, I. et al. Facilitating the development of controlled vocabularies for metabolomics with text mining. BMC Bioinformatics 9, S5 (2008).

    Article  Google Scholar 

  36. Williams, A.J. Internet-based tools for communication and collaboration in chemistry. Drug Discov. Today 13, 502–506 (2008).

    Article  CAS  Google Scholar 

  37. Williams, A.J. A perspective of publicly accessible/open-access chemistry databases. Drug Discov. Today 13, 495–501 (2008).

    Article  CAS  Google Scholar 

  38. Ma, H. et al. The Edinburgh human metabolic network reconstruction and its functional analysis. Mol. Syst. Biol. 3, 135 (2007).

    Article  Google Scholar 

  39. Brooksbank, C., Cameron, G. & Thornton, J. The European Bioinformatics Institute's data resources: towards systems biology. Nucleic Acids Res. 33, D46–D53 (2005).

    Article  CAS  Google Scholar 

  40. Wheeler, D.L. et al. Database resources of the National Center for Biotechnology Information. Nucleic Acids Res. 35, D5–D12 (2007).

    Article  CAS  Google Scholar 

  41. Weininger, D. SMILES, a chemical language and information system. 1. Introduction to methodology and encoding rules. J. Chem. Inf. Comput. Sci. 28, 31–36 (1988).

    Article  CAS  Google Scholar 

  42. Coles, S.J., Day, N.E., Murray-Rust, P., Rzepa, H.S. & Zhang, Y. Enhancement of the chemical semantic web through the use of InChI identifiers. Org. Biomol. Chem. 3, 1832–1834 (2005).

    Article  CAS  Google Scholar 

  43. Wishart, D.S. et al. HMDB: the Human Metabolome Database. Nucleic Acids Res. 35, D521–D526 (2007).

    Article  CAS  Google Scholar 

  44. The UniProt Consortium. The universal protein resource (UniProt). Nucleic Acids Res. 36, D190–D195 (2008).

  45. Ashburner, M. et al. Gene Ontology: tool for the unification of biology. Nat. Genet. 25, 25–29 (2000).

    Article  CAS  Google Scholar 

  46. Sud, M. et al. LMSD: LIPID MAPS structure database. Nucleic Acids Res. 35, D527–D532 (2007).

    Article  CAS  Google Scholar 

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

    Article  Google Scholar 

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

    Article  CAS  Google Scholar 

  49. Hoops, S. et al. COPASI: a COmplex PAthway SImulator. Bioinformatics 22, 3067–3074 (2006).

    Article  CAS  Google Scholar 

  50. Vallabhajosyula, R.R., Chickarmane, V. & Sauro, H.M. Conservation analysis of large biochemical networks. Bioinformatics 22, 346–353 (2006).

    Article  CAS  Google Scholar 

  51. Shannon, P. et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 13, 2498–2504 (2003).

    Article  CAS  Google Scholar 

  52. Funahashi, A., Tanimura, N., Morohashi, M. & Kitano, H. CellDesigner: a process diagram editor for gene-regulatory and biochemical networks. BIOSILICO 1, 159–162 (2003).

    Article  Google Scholar 

  53. Li, P., Oinn, T., Soiland, S. & Kell, D.B. Automated manipulation of systems biology models using libSBML within Taverna workflows. Bioinformatics 24, 287–289 (2008).

    Article  Google Scholar 

  54. Bornstein, B.J., Keating, S.M., Jouraku, A. & Hucka, M. LibSBML: an API library for SBML. Bioinformatics 24, 880–881 (2008).

    Article  CAS  Google Scholar 

  55. Surowiecki, J. The Wisdom of Crowds: Why the Many Are Smarter Than the Few (Abacus, London, 2004).

    Google Scholar 

  56. Tapscott, D. & Williams, A. Wikinomics: How Mass Collaboration Changes Everything (New Paradigm, Toronto, 2007).

    Google Scholar 

  57. Palsson, B. Two-dimensional annotation of genomes. Nat. Biotechnol. 22, 1218–1219 (2004).

    Article  CAS  Google Scholar 

  58. Whelan, K.E. & King, R.D. Using a logical model to predict the growth of yeast. BMC Bioinformatics 9, 97 (2008).

    Article  CAS  Google Scholar 

  59. 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 

  60. Kell, D.B. Systems biology, metabolic modelling and metabolomics in drug discovery and development. Drug Discov. Today 11, 1085–1092 (2006).

    Article  CAS  Google Scholar 

  61. Nookaew, I. et al. The genome-scale metabolic model iIN800 of Saccharomyces cerevisiae and its validation: a scaffold to query lipid metabolism. BMC Syst. Biol. 2, 71 (2008).

    Article  Google Scholar 

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Acknowledgements

The Manchester groups thank the UK Biotechnology and Biological Sciences Research Council (BBSRC) and the Engineering and Physical Sciences Research Council (EPSRC) for financial support including for the Manchester Centre for Integrative Systems Biology (http://www.mcisb.org/). The UCSD participants thank the National Institutes of Health for financial support (NIH R01 GM071808). We thank Diane Kelly, Sarah Keating and Norman Paton for many useful discussions. The Jamboree was held under the auspices and with the sponsorship of the Yeast Systems Biology Network (EC Contract: LSHG-CT-2005-018942).

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All authors conceived the idea of the consensus reconstruction, the majority were present during the jamboree itself and all contributed to the writing of, and approved, the manuscript.

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Correspondence to Douglas B Kell.

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Herrgård, M., Swainston, N., Dobson, P. et al. A consensus yeast metabolic network reconstruction obtained from a community approach to systems biology. Nat Biotechnol 26, 1155–1160 (2008). https://doi.org/10.1038/nbt1492

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