A community-driven global reconstruction of human metabolism

By Ines Thiele, Neil Swainston, Ronan M. T. Fleming, A. Hoppe, Swagatika Sahoo, Maike K. Aurich, Hulda S. Haraldsdóttir, Monica L. Mo, Ó. Rolfsson, Miranda D. Stobbe and 36 more. Nature Biotechnology, 2013

1,073 citations recorded by the source corpus.

Multiple models of human metabolism have been reconstructed, but each represents only a subset of our knowledge. Here we describe Recon 2, a community-driven, consensus 'metabolic reconstruction', which is the most comprehensive representation of human metabolism that is applicable to computational modeling. Compared with its predecessors, the reconstruction has improved topological and functional features, including ∼2× more reactions and ∼1.7× more unique metabolites. Using Recon 2 we predicted changes in metabolite biomarkers for 49 inborn errors of metabolism with 77% accuracy when compared to experimental data. Mapping metabolomic data and drug information onto Recon 2 demonstrates its potential for integrating and analyzing diverse data types. Using protein expression data, we automatically generated a compendium of 65 cell type–specific models, providing a basis for manual curation or investigation of cell-specific metabolic properties. Recon 2 will facilitate many future biomedical studies and is freely available at http://humanmetabolism.org/.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/eed6c57db3da255bfc46b4a3dad8912d366bc72d

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Citations in the corpus

Constraint-based models predict metabolic and associated cellular functions (2014, methodology): “With the availability of the second build (that is, Recon 2) 97 , we foresee greater applied uses in human disease.”
Using Genome-Scale Models to Predict Biological Capabilities (2015, positive): “Cell-Type-and Condition-Speciﬁc Models Starting from a large reconstructed reaction network (e.g., representing all metabolic reactions encoded in the human genome [Thiele et al., 2013]), the imposition of experimental data results in the generation of cell-type-and condition-speciﬁc models.”
An atlas of human metabolism (2020, methodology): “This effort began in earnest with the development of Recon1 (10) and the Edinburgh Human Metabolic Network (EHMN) (11), which served as the starting point for two parallel model series: the Recon series (Recon1, 2, and 3D) (10, 12, 13) and the Human Metabolic Reaction series (HMR1 and 2) (14, 15).”
Deep learning improves prediction of drug–drug and drug–food interactions (2018, methodology): “Also, to complement DeepDDI in understanding the mechanisms of action of multiple drugs, increasingly available complementary data and methods can be used, including transcriptome data under drug treatment conditions (27), and the use of drug target association-based methods (12–14), text mining-bas”
Systems Biology and Multi-Omics Integration: Viewpoints from the Metabolomics Research Community (2019, methodology): “These include the Human Metabolome Database or HMDB [102], Recon 2 [12], Recon3D [80] and the Virtual Metabolic Human database [82].”
The Virtual Metabolic Human database: integrating human and gut microbiome metabolism with nutrition and disease (2018, background): “It is also possible to search for specific reactions making it easier to investigate specific pathways of interest (13) and upload simulation or experimental data (14) through the interface or the COBRA Toolbox (10).”
Recon3D: A Resource Enabling A Three-Dimensional View of Gene Variation in Human Metabolism (2018, neutral): “%) internal, mass-balanced reactions of the Recon 3D derived model (Online Methods; Supplementary Note 3) .”
An Integrated Metabolic Atlas of Clear Cell Renal Cell Carcinoma (2016, neutral): “To identify a transcriptomic metabolic signature, we reasoned that we should restrict ourselves to comparison of 1,506 metabolic genes from the Recon2 human metabolic network (Thiele et al., 2013).”
ChEBI in 2016: Improved services and an expanding collection of metabolites (2015, background): “ChEBI is widely used for many different purposes including as a source of stable unique identifiers for chemicals in annotations in a wide range of bioinformatics databases, including as of recently UniProt for references to chemicals as cofactors, (4) and systems biology models (5,6).”