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A community-driven global reconstruction of human metabolism
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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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“A community-driven reconstruction, Recon 2, was released in 2013 and included 65 cell type–specific models (309).”
From Metabolic Flexibility as an Adaptation to Energy Resources and Requirements in Health and Disease (2018) · cites this paper as [309]
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Orth, J. D., Thiele, I., & Palsson, B. Ø. (2010) · Nature Biotechnology · doi:10.1038/nbt.1614 · Cited by 3,903
Self-citationHucka, M., Finney, A., Sauro, H. M., Bolouri, H., Doyle, J. C., Kitano, H., Arkin, A. P., Bornstein, B. J., Bray, D., Cornish-Bowden, A., et al. (2003) · Bioinformatics · doi:10.1093/bioinformatics/btg015 · Cited by 3,060
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Wishart, D. S., Knox, C., Guo, A. C., Cheng, D., Shrivastava, S., Tzur, D., Gautam, B., & Hassanali, M. (2007) · Nucleic Acids Research · doi:10.1093/nar/gkm958 · Cited by 2,896
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Wikoff, W. R., Anfora, A. T., Liu, J., Schultz, P. G., Lesley, S. A., Peters, E. C., & Siuzdak, G. (2009) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0812874106 · Cited by 2,677
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Uhlen, M., Oksvold, P., Fagerberg, L., Lundberg, E., Jonasson, K., Forsberg, M., Zwahlen, M., Kampf, C., Wester, K., Hober, S., et al. (2010) · Nature Biotechnology · doi:10.1038/nbt1210-1248 · Cited by 2,440
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Wishart, D. S., Knox, C., Guo, A. C., Eisner, R., Young, N., Gautam, B., Hau, D., Psychogios, N., Dong, E., Bouatra, S., et al. (2008) · Nucleic Acids Research · doi:10.1093/nar/gkn810 · Cited by 1,874
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Schellenberger, J., Que, R., Fleming, R. M. T., Thiele, I., Orth, J. D., Feist, A. M., Zielinski, D. C., Bordbar, A., Lewis, N. E., Rahmanian, S., et al. (2011) · Nature Protocols · doi:10.1038/nprot.2011.308 · Cited by 1,835
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Duarte, N. C., Becker, S. A., Jamshidi, N., Thiele, I., Mo, M. L., Vo, T. D., Srivas, R., & Palsson, B. Ø. (2007) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0610772104 · Cited by 1,374
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Dai, Z., Xu, L., Xu, L., Liao, K., Deng, S., Chen, Q., Wang, Q., Wang, Q., Gao, S., Li, B., et al. (2019) · Cell Communication and Signaling · doi:10.1186/s12964-019-0439-y · Cited by 26
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Sier, J. H., THUMSER, A. E. A., & Plant, N. J. (2017) · BMC Systems Biology · doi:10.1186/s12918-017-0520-3 · Cited by 29
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Duran-Pinedo, A. E., Solbiati, J., Teles, F., & Frias‐Lopez, J. (2022) · Journal Of Clinical Periodontology · doi:10.1111/jcpe.13737 · Cited by 22
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Wu, H., von Kamp, A., Leoncikas, V., Mori, W., Sahin, N., Gevorgyan, A., Linley, C., Grabowski, M., Mannan, A. A., Stoy, N. S., et al. (2016) · npj Systems Biology and Applications · doi:10.1038/npjsba.2016.32 · Cited by 20
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Li, J., Lu, Y., Li, N., Li, P., Su, J., Wang, Z., Ting, W., Yang, Z., Yang, Y., Chen, H., et al. (2020) · International Journal of Molecular Medicine · doi:10.3892/ijmm.2020.4537 · Cited by 19
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Carbonell, P. (2016) · ALTEX · doi:10.14573/altex.1602071 · Cited by 18
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Sertbaş, M., & Ülgen, K. Ö. (2018) · OMICS A Journal of Integrative Biology · doi:10.1089/omi.2018.0088 · Cited by 18
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Feizi, A., & Bordel, S. (2013) · Scientific Reports · doi:10.1038/srep03041 · Cited by 17
Self-citation
Metadata
- Authors
- Thiele, I., Swainston, N., Fleming, R. M. T., Hoppe, A., Sahoo, S., Aurich, M. K., Haraldsdottir, H., Mo, M. L., Rolfsson, O., Stobbe, M. D., Thorleifsson, S. G., Agren, R., Bölling, C., Bordel, S., Chavali, A. K., Dobson, P., Dunn, W. B., Endler, L., Hala, D., Hucka, M., Hull, D., Jameson, D., Jamshidi, N., Jonsson, J. J., Juty, N., Keating, S., Nookaew, I., Le Novère, N., Malys, N., Mazein, A., Papin, J. A., Price, N. D., Selkov, E., Sigurdsson, M. I., Simeonidis, E., Sonnenschein, N., Smallbone, K., Sorokin, A., van Beek, J. H. G. M., Weichart, D., Goryanin, I., Nielsen, J., Westerhoff, H. V., Kell, D. B., Mendes, P., & Palsson, B. Ø.
- Affiliations
- University of Iceland (IS)
- University of Manchester (GB)
- Charité – Universitätsmedizin Berlin (DE)
- University of California San Diego (US)
- Netherlands Bioinformatics Centre (NL)
- Chalmers University of Technology (SE)
- University of Virginia (US)
- University of Sheffield (GB)
- Manchester Academic Health Science Centre (GB)
- University of Vienna (AT)
- University of North Texas (US)
- California Institute of Technology (US)
- European Bioinformatics Institute (GB)
- University of Edinburgh (GB)
- Institute for Systems Biology (US)
- University of Luxembourg (LU)
- Constructor University (DE)
- Russian Academy of Sciences (RU)
- Okinawa Institute of Science and Technology Graduate University (JP)
- Corresponding author
- Thiele, Ines
- ORCID
- Thiele, Ines
- Swainston, Neil
- Fleming, Ronan M T
- Hoppe, Andreas
- Sahoo, Swagatika
- Stobbe, Miranda D
- Bordel, Sergio
- Dobson, Paul
- Dunn, Warwick B
- Endler, Lukas
- Hala, David
- Hucka, Michael
- Hull, Duncan
- Jameson, Daniel
- Jamshidi, Neema
- Juty, Nick
- Keating, Sarah
- Nookaew, Intawat
- Malys, Naglis
- Mazein, Alexander
- Papin, Jason A
- Price, Nathan D
- Sigurdsson, Martin I
- Sonnenschein, Nikolaus
- Sorokin, Anatoly
- Goryanin, Igor
- Nielsen, Jens
- Westerhoff, Hans V
- Kell, Douglas B
- Mendes, Pedro
- Palsson, Bernhard Ø
- Venue
- Nature Biotechnology
- Published
- 2013-03-03
- Volume
- 31
- Issue
- 5
- Pages
- 419-425
- ISSN
- 0733-222X (ISSN-L)
- 1087-0156
- 1546-1696
- PMC
- PMC3856361
- OpenAlex
- W2132218677
- Cited by
- 1,019
- References
- 52
- Citations by year
- 2026: 28 · 2025: 35 · 2024: 45 · 2023: 45 · 2022: 45 · 2021: 60 · 2020: 82 · 2019: 93 · 2018: 101 · 2017: 125 · 2016: 98 · 2015: 116 · 2014: 95 · 2013: 51
- FWCI
- 53.59
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature Biotechnology (publishedVersion)
- PubMed (publishedVersion)
- UvA-DARE (University of Amsterdam) (publishedVersion)
- Amsterdam UMC (VU Amsterdam) - Institutional Repository (publishedVersion)
- VU Research Portal (publishedVersion, other-oa)
- MIMS EPrints (University of Southampton) (acceptedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Chalmers Publication Library (Chalmers University of Technology) (acceptedVersion)
- Open Repository and Bibliography (University of Luxembourg) (submittedVersion, other-oa)
- CaltechAUTHORS (California Institute of Technology) (submittedVersion)
- Data Archiving and Networked Services (DANS) (submittedVersion)
- UvA-DARE (University of Amsterdam) (submittedVersion)
- Research Explorer (The University of Manchester) (submittedVersion, other-oa)
- University of Birmingham Research Portal (University of Birmingham) (submittedVersion)
- Chalmers Research (Chalmers University of Technology) (submittedVersion)
- Hirsla (Landspítali University Hospital) (submittedVersion)
- Data Archiving and Networked Services (DANS) (submittedVersion, other-oa)
- Funders
- National Science Foundation (0643548, 0315756)
- Bioprocessing Research Industry Club
- European Commission (249261, 201142)
- Knut och Alice Wallenbergs Stiftelse
- National Institutes of Health
- Directorate for Biological Sciences
- Engineering and Physical Sciences Research Council (BBC0082191)
- Biotechnology and Biological Sciences Research Council (BB/C008219/1, BB/E006248/1, BBS/E/B/000C0419, BB/C008219, BB/J019305/1, BB/F00561X, 201142, BB/F005938)
- Science and Technology Facilities Council
- National Cancer Institute
- National Institute of General Medical Sciences (R01GM080219, R01GM070923, R01GM088244, GM088244, U01GM102098)
- FP7 Ideas: European Research Council (232816)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Microbial Metabolic Engineering and Bioproduction
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
- Keywords
- metabolic network topology, inborn errors of metabolism, metabolic biomarkers, metabolomics data integration, protein expression data
- MeSH terms
- Computer Simulation, Humans, Models, Biological, Proteome, Databases, Protein, Metabolome
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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