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Metabolomics. 2016;12:109. Epub 2016 Jun 7.

Recon 2.2: from reconstruction to model of human metabolism.

Author information

1
Manchester Centre for Synthetic Biology of Fine and Speciality Chemicals (SYNBIOCHEM), Manchester Institute of Biotechnology, The University of Manchester, Manchester, M1 7DN UK ; Faculty of Life Sciences, The University of Manchester, Manchester, M13 9PL UK ; School of Computer Science, The University of Manchester, Manchester, M13 9PL UK.
2
School of Computer Science, The University of Manchester, Manchester, M13 9PL UK.
3
Department of Bioengineering, University of California, San Diego, La Jolla, CA USA ; Novo Nordisk Foundation Center for Biosustainability, University of California, San Diego School of Medicine, La Jolla, CA USA.
4
Harvard Extension School, 51 Brattle St., Cambridge, MA 02138 USA ; Computer Science and Artificial Intelligence Lab, Massachusetts Institute of Technology, 32 Vassar St., Cambridge, MA 02139 USA.
5
Department of Biotechnology, University of Natural Resources and Life Sciences, Vienna, Austria ; Austrian Centre of Industrial Biotechnology, Vienna, Austria.
6
Department of Bioengineering, University of California, San Diego, La Jolla, CA USA.
7
Department of Chemical and Biomolecular Engineering, National University of Singapore, 4 Engineering Drive 4, Singapore, 117585 Singapore ; Bioprocessing Technology Institute, Agency for Science, Technology and Research (ASTAR), 20 Biopolis Way, #06-01, Centros, Singapore, 138668 Singapore.
8
Faculty of Life Sciences, The University of Manchester, Manchester, M13 9PL UK.
9
Bioprocessing Technology Institute, Agency for Science, Technology and Research (ASTAR), 20 Biopolis Way, #06-01, Centros, Singapore, 138668 Singapore.
10
Bioinformatics and Systems Biology Program, University of California, San Diego, La Jolla, CA USA.
11
Australian Institute for Bioengineering and Nanotechnology (AIBN), The University of Queensland, Corner College and Cooper Roads (Bldg 75), Brisbane, QLD 4072 Australia.
12
Novo Nordisk Foundation Center for Biosustainability, University of California, San Diego School of Medicine, La Jolla, CA USA ; Bioinformatics and Systems Biology Program, University of California, San Diego, La Jolla, CA USA.
13
Austrian Centre of Industrial Biotechnology, Vienna, Austria.
14
Manchester Centre for Synthetic Biology of Fine and Speciality Chemicals (SYNBIOCHEM), Manchester Institute of Biotechnology, The University of Manchester, Manchester, M1 7DN UK ; School of Chemistry, The University of Manchester, Manchester, M13 9PL UK.
15
Novo Nordisk Foundation Center for Biosustainability, University of California, San Diego School of Medicine, La Jolla, CA USA ; Department of Pediatrics, University of California, San Diego, La Jolla, CA USA.
16
Manchester Centre for Synthetic Biology of Fine and Speciality Chemicals (SYNBIOCHEM), Manchester Institute of Biotechnology, The University of Manchester, Manchester, M1 7DN UK ; School of Computer Science, The University of Manchester, Manchester, M13 9PL UK ; Center for Quantitative Medicine, UConn Health, 263 Farmington Avenue, Farmington, CT 06030-6033 USA.

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).

KEYWORDS:

Human; Metabolism; Model; Modelling; Reconstruction; Systems biology

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