Cited by 1,019Microbial Metabolic Engineering and BioproductionBioinformatics and Genomic NetworksGene Regulatory Network Analysis

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A community-driven global reconstruction of human metabolism

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Thiele, Ines, Swainston, Neil, Fleming, Ronan M T, Hoppe, Andreas, Sahoo, Swagatika, Aurich, Maike K, Haraldsdottir, Hulda, Mo, Monica L, Rolfsson, Ottar, Stobbe, Miranda D, Thorleifsson, Stefan G, Agren, Rasmus, Bölling, Christian, Bordel, Sergio, Chavali, Arvind K, Dobson, Paul, Dunn, Warwick B, Endler, Lukas, Hala, David, Hucka, Michael, Hull, Duncan, Jameson, Daniel, Jamshidi, Neema, Jonsson, Jon J, Juty, Nick, Keating, Sarah, Nookaew, Intawat, Le Novère, Nicolas, Malys, Naglis, Mazein, Alexander, Papin, Jason A, Price, Nathan D, Selkov, Evgeni, Sigurdsson, Martin I, Simeonidis, Evangelos, Sonnenschein, Nikolaus, Smallbone, Kieran, Sorokin, Anatoly, van Beek, Johannes H G M, Weichart, Dieter, Goryanin, Igor, Nielsen, Jens, Westerhoff, Hans V, Kell, Douglas B, Mendes, Pedro, Palsson, Bernhard Ø·2013·Nature Biotechnology

DOI 10.1038/nbt.2488PMC PMC3856361

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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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Additional file 6 of Computational models applied to metabolomics data hints at the relevance of glutamine metabolism in breast cancer
Trilla‐Fuertes, L., Gámez‐Pozo, A., López‐Camacho, E., Prado-Vázquez, G., Zapater‐Moros, A., López-Vacas, R., Arevalillo, J. M., Díaz-Almirón, M., Navarro, H., Maı́n, P., et al. (2020) · Figshare · doi:10.6084/m9.figshare.12134208
Additional file 7 of Computational models applied to metabolomics data hints at the relevance of glutamine metabolism in breast cancer
Trilla‐Fuertes, L., Gámez‐Pozo, A., López‐Camacho, E., Prado-Vázquez, G., Zapater‐Moros, A., López-Vacas, R., Arevalillo, J. M., Díaz-Almirón, M., Navarro, H., Maı́n, P., et al. (2020) · Figshare · doi:10.6084/m9.figshare.12134217
Additional file 4 of A strategy to detect metabolic changes induced by exposure to chemicals from large sets of condition-specific metabolic models computed with enumeration techniques
Fresnais, L., Périn, O., Riu, A., Grall, R., Ott, A., Fromenty, B., Gallardo, J.-C., Stingl, M., Frainay, C., Jourdan, F., et al. (2024) · Figshare · doi:10.6084/m9.figshare.26748047
Additional file 5 of A strategy to detect metabolic changes induced by exposure to chemicals from large sets of condition-specific metabolic models computed with enumeration techniques
Fresnais, L., Périn, O., Riu, A., Grall, R., Ott, A., Fromenty, B., Gallardo, J.-C., Stingl, M., Frainay, C., Jourdan, F., et al. (2024) · Figshare · doi:10.6084/m9.figshare.26748050
Additional file 3 of Exploring the associations between transcript levels and fluxes in constraint-based models of metabolism
Sinha, N., van Schothorst, E. M., Hooiveld, G. J. E. J., Keijer, J., dos Santos, V. A. P. M., & Diez, M. S. (2021) · Figshare · doi:10.6084/m9.figshare.17089596
Additional file 6 of A strategy to detect metabolic changes induced by exposure to chemicals from large sets of condition-specific metabolic models computed with enumeration techniques
Fresnais, L., Périn, O., Riu, A., Grall, R., Ott, A., Fromenty, B., Gallardo, J.-C., Stingl, M., Frainay, C., Jourdan, F., et al. (2024) · Figshare · doi:10.6084/m9.figshare.26748053
Additional file 2 of A strategy to detect metabolic changes induced by exposure to chemicals from large sets of condition-specific metabolic models computed with enumeration techniques
Fresnais, L., Périn, O., Riu, A., Grall, R., Ott, A., Fromenty, B., Gallardo, J.-C., Stingl, M., Frainay, C., Jourdan, F., et al. (2024) · Figshare · doi:10.6084/m9.figshare.26748041
Additional file 4 of Computational models applied to metabolomics data hints at the relevance of glutamine metabolism in breast cancer
Trilla‐Fuertes, L., Gámez‐Pozo, A., López‐Camacho, E., Prado-Vázquez, G., Zapater‐Moros, A., López-Vacas, R., Arevalillo, J. M., Díaz-Almirón, M., Navarro, H., Maı́n, P., et al. (2020) · Figshare · doi:10.6084/m9.figshare.12134193
Additional file 7 of A strategy to detect metabolic changes induced by exposure to chemicals from large sets of condition-specific metabolic models computed with enumeration techniques
Fresnais, L., Périn, O., Riu, A., Grall, R., Ott, A., Fromenty, B., Gallardo, J.-C., Stingl, M., Frainay, C., Jourdan, F., et al. (2024) · Figshare · doi:10.6084/m9.figshare.26748056
Additional file 12 of A strategy to detect metabolic changes induced by exposure to chemicals from large sets of condition-specific metabolic models computed with enumeration techniques
Fresnais, L., Périn, O., Riu, A., Grall, R., Ott, A., Fromenty, B., Gallardo, J.-C., Stingl, M., Frainay, C., Jourdan, F., et al. (2024) · Figshare · doi:10.6084/m9.figshare.26748071
Additional file 5 of Computational models applied to metabolomics data hints at the relevance of glutamine metabolism in breast cancer
Trilla‐Fuertes, L., Gámez‐Pozo, A., López‐Camacho, E., Prado-Vázquez, G., Zapater‐Moros, A., López-Vacas, R., Arevalillo, J. M., Díaz-Almirón, M., Navarro, H., Maı́n, P., et al. (2020) · Figshare · doi:10.6084/m9.figshare.12134202
Additional file 8 of Computational models applied to metabolomics data hints at the relevance of glutamine metabolism in breast cancer
Trilla‐Fuertes, L., Gámez‐Pozo, A., López‐Camacho, E., Prado-Vázquez, G., Zapater‐Moros, A., López-Vacas, R., Arevalillo, J. M., Díaz-Almirón, M., Navarro, H., Maı́n, P., et al. (2020) · Figshare · doi:10.6084/m9.figshare.12134220
Additional file 9 of Computational models applied to metabolomics data hints at the relevance of glutamine metabolism in breast cancer
Trilla‐Fuertes, L., Gámez‐Pozo, A., López‐Camacho, E., Prado-Vázquez, G., Zapater‐Moros, A., López-Vacas, R., Arevalillo, J. M., Díaz-Almirón, M., Navarro, H., Maı́n, P., et al. (2020) · Figshare · doi:10.6084/m9.figshare.12134226
Additional file 1 of A strategy to detect metabolic changes induced by exposure to chemicals from large sets of condition-specific metabolic models computed with enumeration techniques
Fresnais, L., Périn, O., Riu, A., Grall, R., Ott, A., Fromenty, B., Gallardo, J.-C., Stingl, M., Frainay, C., Jourdan, F., et al. (2024) · Figshare · doi:10.6084/m9.figshare.26748038
Additional file 8 of A strategy to detect metabolic changes induced by exposure to chemicals from large sets of condition-specific metabolic models computed with enumeration techniques
Fresnais, L., Périn, O., Riu, A., Grall, R., Ott, A., Fromenty, B., Gallardo, J.-C., Stingl, M., Frainay, C., Jourdan, F., et al. (2024) · Figshare · doi:10.6084/m9.figshare.26748059
Additional file 11 of A strategy to detect metabolic changes induced by exposure to chemicals from large sets of condition-specific metabolic models computed with enumeration techniques
Fresnais, L., Périn, O., Riu, A., Grall, R., Ott, A., Fromenty, B., Gallardo, J.-C., Stingl, M., Frainay, C., Jourdan, F., et al. (2024) · Figshare · doi:10.6084/m9.figshare.26748068
Additional file 4 of Exploring the associations between transcript levels and fluxes in constraint-based models of metabolism
Sinha, N., van Schothorst, E. M., Hooiveld, G. J. E. J., Keijer, J., dos Santos, V. A. P. M., & Diez, M. S. (2021) · Figshare · doi:10.6084/m9.figshare.17089599
Additional file 3 of A strategy to detect metabolic changes induced by exposure to chemicals from large sets of condition-specific metabolic models computed with enumeration techniques
Fresnais, L., Périn, O., Riu, A., Grall, R., Ott, A., Fromenty, B., Gallardo, J.-C., Stingl, M., Frainay, C., Jourdan, F., et al. (2024) · Figshare · doi:10.6084/m9.figshare.26748044
Additional file 10 of A strategy to detect metabolic changes induced by exposure to chemicals from large sets of condition-specific metabolic models computed with enumeration techniques
Fresnais, L., Périn, O., Riu, A., Grall, R., Ott, A., Fromenty, B., Gallardo, J.-C., Stingl, M., Frainay, C., Jourdan, F., et al. (2024) · Figshare · doi:10.6084/m9.figshare.26748065
Additional file 1 of Exploring the associations between transcript levels and fluxes in constraint-based models of metabolism
Sinha, N., van Schothorst, E. M., Hooiveld, G. J. E. J., Keijer, J., dos Santos, V. A. P. M., & Diez, M. S. (2021) · Figshare · doi:10.6084/m9.figshare.17089590
Additional file 9 of A strategy to detect metabolic changes induced by exposure to chemicals from large sets of condition-specific metabolic models computed with enumeration techniques
Fresnais, L., Périn, O., Riu, A., Grall, R., Ott, A., Fromenty, B., Gallardo, J.-C., Stingl, M., Frainay, C., Jourdan, F., et al. (2024) · Figshare · doi:10.6084/m9.figshare.26748062
Thermo-Dynamic Flux Balance Analysis, a novel approach to simulate circadian metabolismPreprint
Messa, G. M., Liu, P., Napolitano, F., Tegner, J. N., Gao, X., & Orlando, V. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2025.12.17.694837
Die Zelle – ein wunderbarer Mikrokosmos
Capel, P. J. A. (2026) · Springer eBooks · doi:10.1007/978-3-662-71831-5_2
Emergent Threshold Dynamics in Unified Metabolic-Regulatory Models: Achieving Spontaneous Homeostasis Through Bidirectional ATP-Pathway CouplingPreprint
Simão, E. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.02.06.704333

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
Publisher: Springer Science and Business Media LLC
Published: 2013-03-03
Volume: 31
Issue: 5
Pages: 419-425
ISSN: 0733-222X (ISSN-L)
1087-0156
1546-1696
DOI: 10.1038/nbt.2488
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

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