Recon 2.2: from reconstruction to model of human metabolism

• DOI: 10.1007/s11306-016-1051-4
• Publication Date: 2016-06-07T05:46:56Z
• 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 Kyriakop...; 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
• Keywords: Human genetics

The human genome-scale metabolic reconstruction details all known reactions occurring in humans, and thereby holds substantial promise for studying complex diseases phenotypes. Capturing the whole is an on-going task since last community effort generated a consensus reconstruction, several updates have been developed. We report new version, Recon 2.2, which integrates various alternative versions with significant additional updates. In addition to re-establishing further key objectives included providing more comprehensive annotation of metabolites genes, ensuring full mass charge balance reactions, developing model that correctly predicts ATP production on range carbon sources. 2.2 has developed through combination manual curation automated error checking. Specific include respecification fatty acid metabolism, oxidative phosphorylation coupling electron transport chain synthase activity. All definitive chemical formulae charges specified, these are used ensure reaction balancing linear programming approach. Additionally, improved integration transcriptomics proteomics data facilitated updated relationships between proteins reactions. now represents most predictive metabolism date as demonstrated here. Extensive increased size 5324 metabolites, 7785 1675 associated mapped single standard. focus upon along better representation energy generation, produced flux yield different Through we achieved complete best annotated available, increasing ability this resource provide novel insights into normal disease states human. freely available from Biomodels database (http://identifiers.org/biomodels.db/MODEL1603150001).