Research ArticleMetabolism

An atlas of human metabolism

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Science Signaling  24 Mar 2020:
Vol. 13, Issue 624, eaaz1482
DOI: 10.1126/scisignal.aaz1482

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Reconstructing human metabolism in silico

Genome-scale models enable a holistic understanding of the interconnected pathways that form the basis for human metabolism. Robinson et al. generated Human1, an extensively curated, genome-scale model of human metabolism that unified two parallel model lineages using an open source repository to enable rapid, trackable updates. The authors also developed Metabolic Atlas (https://www.metabolicatlas.org/), an online platform for exploring Human1. They demonstrated the utility of Human1 by highlighting potential metabolic vulnerabilities in acute myeloid leukemia, predicting genes that are essential for specific metabolic tasks, and estimating metabolic fluxes and growth rates. Thus, Human1 and Metabolic Atlas advance the ability to model metabolic pathways relevant to human health and disease and provide a means of consolidating efforts in refining human genome-scale metabolic models.

Abstract

Genome-scale metabolic models (GEMs) are valuable tools to study metabolism and provide a scaffold for the integrative analysis of omics data. Researchers have developed increasingly comprehensive human GEMs, but the disconnect among different model sources and versions impedes further progress. We therefore integrated and extensively curated the most recent human metabolic models to construct a consensus GEM, Human1. We demonstrated the versatility of Human1 through the generation and analysis of cell- and tissue-specific models using transcriptomic, proteomic, and kinetic data. We also present an accompanying web portal, Metabolic Atlas (https://www.metabolicatlas.org/), which facilitates further exploration and visualization of Human1 content. Human1 was created using a version-controlled, open-source model development framework to enable community-driven curation and refinement. This framework allows Human1 to be an evolving shared resource for future studies of human health and disease.

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