Swainston2016 - Reconstruction of human metabolic network (Recon 2.2)

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  • Recon 2.2: from reconstruction to model of human metabolism Click here to expand
  • Neil Swainston, Kieran Smallbone, Hooman Hefzi, Paul D. Dobson, Judy Brewer, Michael Hanscho, Daniel C. Zielinski, Kok Siong Ang, Natalie J. Gardiner, Jahir M. Gutierrez, Sarantos Kyriakopoulos, Meiyappan Lakshmanan, Shangzhong Li, Joanne K. Liu, Veronica S. Martínez, Camila A. Orellana, Lake-Ee Quek, Alex Thomas, Juergen Zanghellini, Nicole Borth, Dong-Yup Lee, Lars K. Nielsen, Douglas B. Kell, Nathan E. Lewis, Pedro Mendes
  • Metabolomics , DOI: 10.1007/s11306-016-1051-4
  • Manchester Centre for Synthetic Biology of Fine and Speciality Chemicals (SYNBIOCHEM), Manchester Institute of Biotechnology, The University of Manchester, Manchester M1 7DN, UK
  • Abstract Introduction The human genome-scale metabolic reconstruction details all known metabolic reactions occurring in humans, and thereby holds substantial promise for studying complex diseases and phenotypes. Capturing the whole human metabolic reconstruction is an on-going task and since the last community effort generated a consensus reconstruction, several updates have been developed. Objectives We report a new consensus version, Recon 2.2, which integrates various alternative versions with significant additional updates. In addition to re-establishing a consensus reconstruction, further key objectives included providing more comprehensive annotation of metabolites and genes, ensuring full mass and charge balance in all reactions, and developing a model that correctly predicts ATP production on a range of carbon sources. Methods Recon 2.2 has been developed through a combination of manual curation and automated error checking. Specific and significant manual updates include a respecification of fatty acid metabolism, oxidative phosphorylation and a coupling of the electron transport chain to ATP synthase activity. All metabolites have definitive chemical formulae and charges specified, and these are used to ensure full mass and charge reaction balancing through an automated linear programming approach. Additionally, improved integration with transcriptomics and proteomics data has been facilitated with the updated curation of relationships between genes, proteins and reactions. Results Recon 2.2 now represents the most predictive model of human metabolism to date as demonstrated here. Extensive manual curation has increased the reconstruction size to 5324 metabolites, 7785 reactions and 1675 associated genes, which now are mapped to a single standard. The focus upon mass and charge balancing of all reactions, along with better representation of energy generation, has produced a flux model that correctly predicts ATP yield on different carbon sources. Conclusion Through these updates we have achieved the most complete and best annotated consensus human metabolic reconstruction available, thereby increasing the ability of this resource to provide novel insights into normal and disease states in human. The model is freely available from the Biomodels database (http://identifiers.org/biomodels.db/MODEL1603150001).
Submitter of the first revision: Neil Swainston
Submitter of this revision: Neil Swainston
Modeller: Neil Swainston

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Mathematical Modelling Ontology Ordinary differential equation model

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  • Model originally submitted by : Neil Swainston
  • Submitted: Mar 15, 2016 4:27:13 PM
  • Last Modified: Jun 9, 2016 10:12:05 PM
  • Version: 2 public model Download this version
    • Submitted on: Jun 9, 2016 10:12:05 PM
    • Submitted by: Neil Swainston
    • With comment: Current version of Swainston2016 - Reconstruction of human metabolic network (Recon 2.2)
  • Version: 1 public model Download this version
    • Submitted on: Mar 15, 2016 4:27:13 PM
    • Submitted by: Neil Swainston
    • With comment: Original import of Recon 2.2

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