4.8 Article

A community-driven global reconstruction of human metabolism

期刊

NATURE BIOTECHNOLOGY
卷 31, 期 5, 页码 419-+

出版社

NATURE PORTFOLIO
DOI: 10.1038/nbt.2488

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资金

  1. Marie Curie International Reintegration Grant within the 7th European Community Framework Program [249261]
  2. European Research Council [232816]
  3. Rannis research grant [100406022]
  4. Biotechnology and Biological Sciences Research Council (BBSRC)
  5. Engineering and Physical Sciences Research Council [BB/C008219/1]
  6. European Union [201142]
  7. Knut and Alice Wallenberg Foundation
  8. BBSRC [BB/F005938, BB/F00561X]
  9. German Federal Ministry for Education and Research within the Virtual Liver Network [0315756, 0315741]
  10. US National Institutes of Health [GM088244]
  11. National Science Foundation [0643548]
  12. Cystic Fibrosis Research Foundation [1060]
  13. National Cancer Institute
  14. Science and Technology Facilities Council
  15. US National Institute of General Medical Sciences [R01GM070923, R01GM080219]
  16. BioRange programme of The Netherlands Bioinformatics Centre under a Besluit Subsidies Investeringen Kennisinfrastructuur grant through The Netherlands Genomics Initiative [SP1.2.4]
  17. BBSRC [BBS/E/B/000C0419, BB/E006248/1, BB/J019305/1] Funding Source: UKRI
  18. Biotechnology and Biological Sciences Research Council [BB/E006248/1, BB/J019305/1, BB/C008219/1, BBS/E/B/000C0419] Funding Source: researchfish

向作者/读者索取更多资源

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 similar to 2x more reactions and similar to 1.7x 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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