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A community-driven global reconstruction of human metabolism

Nature Biotechnology volume 31, pages 419–425 (2013)Cite this article

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Abstract

Multiple models of human metabolism have been reconstructed, but each represents only a subset of our knowledge. Here we describe Recon 2, a community-driven, consensus 'metabolic reconstruction', which is the most comprehensive representation of human metabolism that is applicable to computational modeling. Compared with its predecessors, the reconstruction has improved topological and functional features, including ∼2× more reactions and ∼1.7× more unique metabolites. Using Recon 2 we predicted changes in metabolite biomarkers for 49 inborn errors of metabolism with 77% accuracy when compared to experimental data. Mapping metabolomic data and drug information onto Recon 2 demonstrates its potential for integrating and analyzing diverse data types. Using protein expression data, we automatically generated a compendium of 65 cell type–specific models, providing a basis for manual curation or investigation of cell-specific metabolic properties. Recon 2 will facilitate many future biomedical studies and is freely available at http://humanmetabolism.org/.

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Figure 1: Overview of the community-driven reconstruction approach to assemble Recon 2.
Figure 2: Pathway coverage in Recon 1 and Recon 2.
Figure 3: Predicted biomarkers for IEMs.
Figure 4: Comparison of metabolomic data with the extracellular metabolites present in Recon 2 metabolites.
Figure 5: Summary properties of the 65 draft cell type–specific models.

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Acknowledgements

I.T. was supported, in part, by a Marie Curie International Reintegration Grant (249261) within the 7th European Community Framework Program. I.T., O.R., S.G.T. and M.A. were supported by the European Research Council grant proposal number 232816. S.S. and H.H. were supported by a Rannis research grant (100406022). Authors from Manchester thank the Biotechnology and Biological Sciences Research Council (BBSRC), and Engineering and Physical Sciences Research Council for their funding of the Manchester Centre for Integrative Systems Biology (grant BB/C008219/1), H.W. for Bioprocessing Research Industry Club grants, and P.M. and N. Swainston for support from the European Union FP7 project UNICELLSYS (grant agreement 201142). The Knut and Alice Wallenberg Foundation supported R.A., S.B. and I.N.; D.J. thanks the BBSRC for funding of Systems Approaches to Biological Research grants BB/F005938 and BB/F00561X. A.H. and C.B. were supported by the German Federal Ministry for Education and Research within the Virtual Liver Network (grant numbers 0315756 and 0315741). A.K.C. and J.A.P. acknowledge funding from the US National Institutes of Health (grant GM088244), National Science Foundation (grant 0643548) and Cystic Fibrosis Research Foundation (grant 1060). N.D.P. was supported by a National Cancer Institute to Independence Award in Cancer Research. I.G. thanks the Science and Technology Facilities Council for Scottish Bioinformatics Research Network funding. M.H. and P.M. thank the US National Institute of General Medical Sciences for support under grants R01GM070923 and R01GM080219. M.D.S. thanks the BioRange programme (project SP1.2.4) of The Netherlands Bioinformatics Centre for support under a Besluit Subsidies Investeringen Kennisinfrastructuur grant through The Netherlands Genomics Initiative.

Author information

Author notes

  1. Ines Thiele and Neil Swainston: These authors contributed equally to this work.

Authors and Affiliations

  1. Center for Systems Biology, University of Iceland, Reykjavik, Iceland

    Ines Thiele, Ronan M T Fleming, Swagatika Sahoo, Maike K Aurich, Hulda Haraldsdottir, Ottar Rolfsson, Stefan G Thorleifsson, Martin I Sigurdsson & Bernhard Ø Palsson

  2. Faculty of Industrial Engineering, Mechanical Engineering and Computer Science, University of Iceland, Reykjavik, Iceland

    Ines Thiele

  3. Manchester Centre for Integrative Systems Biology, University of Manchester, Manchester Institute of Biotechnology, Manchester, UK

    Neil Swainston, Warwick B Dunn, Daniel Jameson, Naglis Malys, Kieran Smallbone, Dieter Weichart, Hans V Westerhoff, Douglas B Kell & Pedro Mendes

  4. School of Computer Science, University of Manchester, Manchester, UK

    Neil Swainston, Duncan Hull, Daniel Jameson & Pedro Mendes

  5. Department of Biochemistry and Molecular Biology, University of Iceland, Reykjavik, Iceland

    Ronan M T Fleming & Jon J Jonsson

  6. Computational Systems Biochemistry Group, Charité– Universitätsmedizin Berlin, Berlin, Germany

    Andreas Hoppe & Christian Bölling

  7. Department of Bioengineering, University of California, San Diego, La Jolla, California, USA

    Monica L Mo, Neema Jamshidi & Bernhard Ø Palsson

  8. Department of Clinical Epidemiology, Biostatistics and Bioinformatics, Academic Medical Center, University of Amsterdam, Amsterdam, the Netherlands

    Miranda D Stobbe

  9. Netherlands Bioinformatics Centre, Nijmegen, the Netherlands

    Miranda D Stobbe

  10. Department of Chemical and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden

    Rasmus Agren, Sergio Bordel, Intawat Nookaew & Jens Nielsen

  11. Department of Biomedical Engineering, University of Virginia, Charlottesville, Virginia, USA

    Arvind K Chavali & Jason A Papin

  12. Department of Chemical and Biological Engineering, University of Sheffield, Sheffield, UK

    Paul Dobson

  13. Centre for Advanced Discovery and Experimental Therapeutics (CADET), Central Manchester University Hospitals NHS Foundation Trust, Manchester Academic Health Sciences Centre, Manchester, UK

    Warwick B Dunn

  14. Institute for Theoretical Chemistry, University of Vienna, Vienna, Austria

    Lukas Endler

  15. Department of Biology, University of North Texas, Denton, Texas, USA

    David Hala

  16. Computing and Mathematical Sciences Department, California Institute of Technology, Pasadena, California, USA

    Michael Hucka

  17. European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, UK

    Nick Juty, Sarah Keating & Nicolas Le Novère

  18. Babraham Institute, Babraham Research Campus, Cambridge, UK

    Nicolas Le Novère

  19. Faculty of Life Sciences, University of Manchester, Manchester, UK

    Naglis Malys

  20. School of Life Sciences, Gibbet Hill Campus, University of Warwick, Coventry, UK

    Naglis Malys

  21. School of Informatics, University of Edinburgh, Edinburgh, UK

    Alexander Mazein, Anatoly Sorokin & Igor Goryanin

  22. Institute for Systems Biology, Seattle, Washington, USA

    Nathan D Price & Evangelos Simeonidis

  23. Genome Designs, Inc., Walnut Creek, California, USA

    Evgeni Selkov Sr

  24. Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Campus Belval, Esch-sur-Alzette, Luxembourg

    Evangelos Simeonidis

  25. School of Engineering and Science, Jacobs University Bremen, Bremen, Germany

    Nikolaus Sonnenschein

  26. School of Mathematics, University of Manchester, Manchester, UK

    Kieran Smallbone

  27. Institute of Cell Biophysics, Russian Academy of Sciences, Moscow region, Pushchino, Russia

    Anatoly Sorokin

  28. Department of Molecular Cell Physiology, Vrije Universiteit, Amsterdam, the Netherlands

    Johannes H G M van Beek & Hans V Westerhoff

  29. Department of Clinical Genetics, Section Medical Genomics, Vrije Universiteit University Medical Centre, Amsterdam, the Netherlands

    Johannes H G M van Beek

  30. Netherlands Consortium for Systems Biology, Amsterdam, The Netherlands

    Johannes H G M van Beek

  31. School of Dentistry, The University of Manchester, Manchester, UK

    Dieter Weichart

  32. Okinawa Institute Science and Technology, Okinawa, Japan

    Igor Goryanin

  33. School of Chemical Engineering and Analytical Science, University of Manchester, Manchester, UK

    Hans V Westerhoff

  34. Swammerdam Institute for Life Sciences, Faculty of Science, University of Amsterdam, Amsterdam, The Netherlands

    Hans V Westerhoff

  35. School of Chemistry, The University of Manchester, Manchester, UK

    Douglas B Kell

  36. Virginia Bioinformatics Institute, Virginia Tech, Blacksburg, Virginia, USA

    Pedro Mendes

Authors
  1. Ines Thiele
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  2. Neil Swainston
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  3. Ronan M T Fleming
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  4. Andreas Hoppe
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Contributions

I.T. and N. Swainston led the project and developed the reconstruction. I.T., N. Swainston, B.Ø.P., P.M. and D.K. wrote the manuscript. B.Ø.P., D.B.K. and P.M. conceived the project. R.M.T.F. and A.H. performed validation of the reconstruction. S.S. and M.L.M. performed substantial manual curation of the reconstruction. M.K.A., H.H., O.R., M.D.S. and S.G.T. contributed to the analysis of the reconstruction and its models. I.T., N. Swainston, R.M.T.F., A.H., S.S., M.K.A., H.H., M.L.M., O.R., M.D.S., S.G.T., R.A., C.B., S.B., A.K.C., P.D., W.B.D., L.E., D. Hala, M.H., D. Hull, D.J., N. Jamshidi, J.J.J., N. Juty, S.K., I.N., N.L.N., N.M., A.M., J.A.P., N.D.P., E. Selkov, M.I., E. Simeonidis, N. Sonnenschein, K.S., A.S., J.H.G.M.v.B., D.W., I.G., J.N., H.V.W., D.B.K., P.M. and B.Ø.P. attended one or more jamboree meetings and provided manual curation of the reconstruction.

Corresponding author

Correspondence to Ines Thiele.

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Competing interests

The authors declare no competing financial interests.

Supplementary information

Supplementary Text and Figures

Supplementary Notes 1–4, Supplementary Figures 1–6 (PDF 2031 kb)

Supplementary Data

Recon 2 and cell type–specific models in SBML format. (ZIP 31200 kb)

Supplementary Table 1

Unbalanced reactions and missing chemical formulae. (XLS 116 kb)

Supplementary Table 2

Metabolic task results. (XLS 380 kb)

Supplementary Table 3

Cancer exometabolome results. (XLS 357 kb)

Supplementary Table 4

Cell-type reactions. (XLS 338 kb)

Supplementary Table 5

IEM information (XLS 22 kb)

Supplementary Table 6

Evidence code (ECO) terms associated with Recon 2 reactions. (XLS 21 kb)

Supplementary Table 7

Modeling constraints. (XLS 793 kb)

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Thiele, I., Swainston, N., Fleming, R. et al. A community-driven global reconstruction of human metabolism. Nat Biotechnol 31, 419–425 (2013). https://doi.org/10.1038/nbt.2488

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