Recon 2.2: from reconstruction to model of human metabolism
Open accessAbstract
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).
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Recon is used to predict biomarkers for inborn errors of metabolism, to identify drug targets and possible side effects, and to study cancer metabolism and the interactions between microorganisms and their host.
“Among other topics, Recon is used to predict biomarkers for inborn errors of metabolism, to identify drug targets and possible side effects, and to study cancer metabolism and the interactions between microorganisms and their host (310).”
From Metabolic Flexibility as an Adaptation to Energy Resources and Requirements in Health and Disease (2018) · cites this paper as [310]
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References 27 · Cited by 285
- Authors
- Swainston, N., Smallbone, K., Hefzi, H., Dobson, P. D., Brewer, J., Hanscho, M., Zielinski, D. C., Ang, K. S., Gardiner, N. J., Gutierrez, J. M., Kyriakopoulos, S., Lakshmanan, M., Li, S., Liu, J. K., Martínez, V. S., Orellana, C. A., Quek, L.-E., Thomas, A., Zanghellini, J., Borth, N., Lee, D.-Y., Nielsen, L. K., Kell, D. B., Lewis, N. E., & Mendes, P.
- Affiliations
- University of Manchester (GB)
- University of California San Diego (US)
- Harvard University (US)
- Austrian Centre of Industrial Biotechnology (Austria) (AT)
- Agency for Science, Technology and Research (SG)
- The University of Queensland (AU)
- Corresponding author
- Swainston, Neil
- ORCID
- Swainston, Neil
- Hefzi, Hooman
- Dobson, Paul D.
- Hanscho, Michael
- Zielinski, Daniel C.
- Ang, Kok Siong
- Gardiner, Natalie J.
- Gutierrez, Jahir M.
- Lakshmanan, Meiyappan
- Li, Shangzhong
- Liu, Joanne K.
- Orellana, Camila A.
- Quek, Lake-Ee
- Thomas, Alex
- Borth, Nicole
- Lee, Dong-Yup
- Nielsen, Lars K.
- Kell, Douglas B.
- Lewis, Nathan E.
- Mendes, Pedro
- Venue
- Metabolomics
- Published
- 2016-06-07
- Volume
- 12
- Issue
- 7
- Pages
- 109-109
- ISSN
- 1573-3882 (ISSN-L)
- 1573-3890
- PMC
- PMC4896983
- OpenAlex
- W2418056440
- Cited by
- 285
- References
- 27
- Citations by year
- 2026: 5 · 2025: 20 · 2024: 26 · 2023: 21 · 2022: 19 · 2021: 39 · 2020: 42 · 2019: 37 · 2018: 43 · 2017: 24 · 2016: 9
- FWCI
- 19.58
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Metabolomics (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- National University of Singapore (submittedVersion)
- DSpace@MIT (Massachusetts Institute of Technology) (submittedVersion, cc-by-nc)
- eScholarship (California Digital Library) (submittedVersion)
- eScholarship (California Digital Library) (submittedVersion, cc-by)
- Research Explorer (The University of Manchester) (submittedVersion, cc-by)
- Technical University of Denmark, DTU Orbit (Technical University of Denmark, DTU) (submittedVersion, other-oa)
- Funders
- Biotechnology and Biological Sciences Research Council (BB/M006891/1, BB/K019783/1, BB/M017702/1)
- National Institute of General Medical Sciences (GM080219)
- 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
- fatty acid metabolism, oxidative phosphorylation, electron transport chain, ATP synthase, metabolite annotation, biomodels
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