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Open AccessJournal ArticleDOI

A community-driven global reconstruction of human metabolism

Ines Thiele, +53 more
- 01 May 2013 - 
- Vol. 31, Iss: 5, pp 419-425
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TLDR
Recon 2, a community-driven, consensus 'metabolic reconstruction', is described, which is the most comprehensive representation of human metabolism that is applicable to computational modeling and has improved topological and functional features.
Abstract
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/.

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Reconstruction of genome-scale human metabolic models using omics data.

TL;DR: Recent trends in genome-scale human metabolic modeling are reviewed, including various generic and tissue/cell type-specific human metabolic models developed to date, and methods, databases and platforms used to construct them.
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A census of pathway maps in cancer systems biology.

TL;DR: It is found that many cancer networks have been provided only in journal figures and not for programmatic access, underscoring the need to deposit network maps in community databases to ensure they can be readily accessed.
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ReconMap: an interactive visualization of human metabolism.

TL;DR: A comprehensive map is drawn that is consistent with the content of Recon 2.0 and presented within a web interface that allows content query, visualization of custom datasets and submission of feedback to manual curators.
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Deep learning meets metabolomics: a methodological perspective.

TL;DR: The DL-based approaches discussed here may assist computational biologists with the integration, prediction and drawing of statistical inference about biological outcomes, based on metabolomics data.
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Network Medicine in Pathobiology.

TL;DR: The key principles of network Medicine and the human disease network are discussed and the latest applications of network medicine in this multi-omic era are explored.
References
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