| Title |
Recon 2.2: from reconstruction to model of human metabolism
|
|---|---|
| Published in |
Metabolomics, June 2016
|
| DOI | 10.1007/s11306-016-1051-4 |
| Pubmed ID | |
| Authors |
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 |
| Abstract |
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. 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. 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. 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. 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). |
X Demographics
The data shown below were collected from the profiles of 25 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United Kingdom | 7 | 28% |
| United States | 3 | 12% |
| India | 2 | 8% |
| United Arab Emirates | 1 | 4% |
| Myanmar | 1 | 4% |
| Comoros | 1 | 4% |
| Unknown | 10 | 40% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 15 | 60% |
| Scientists | 8 | 32% |
| Practitioners (doctors, other healthcare professionals) | 1 | 4% |
| Science communicators (journalists, bloggers, editors) | 1 | 4% |
Mendeley readers
The data shown below were compiled from readership statistics for 389 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 3 | <1% |
| United Kingdom | 2 | <1% |
| Germany | 1 | <1% |
| France | 1 | <1% |
| Colombia | 1 | <1% |
| Israel | 1 | <1% |
| Iran, Islamic Republic of | 1 | <1% |
| Luxembourg | 1 | <1% |
| Unknown | 378 | 97% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 87 | 22% |
| Student > Master | 67 | 17% |
| Researcher | 66 | 17% |
| Student > Bachelor | 29 | 7% |
| Student > Doctoral Student | 19 | 5% |
| Other | 49 | 13% |
| Unknown | 72 | 19% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 108 | 28% |
| Agricultural and Biological Sciences | 85 | 22% |
| Engineering | 28 | 7% |
| Computer Science | 23 | 6% |
| Medicine and Dentistry | 12 | 3% |
| Other | 40 | 10% |
| Unknown | 93 | 24% |
Attention Score in Context
This research output has an Altmetric Attention Score of 14. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 15 January 2017.
All research outputs
#2,516,122
of 24,742,536 outputs
Outputs from Metabolomics
#115
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Outputs of similar age
#43,814
of 347,694 outputs
Outputs of similar age from Metabolomics
#4
of 38 outputs
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