An atlas of human metabolism
Journal article, 2020

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.

Author

Jonathan Robinson

Chalmers, Biology and Biological Engineering, Systems and Synthetic Biology

Pinar Kocabas

Chalmers, Biology and Biological Engineering, Systems and Synthetic Biology

Hao Wang

Chalmers, Biology and Biological Engineering, Systems and Synthetic Biology

University of Gothenburg

Pierre-Etienne Cholley

Chalmers, Biology and Biological Engineering, Systems and Synthetic Biology

Daniel John Cook

Chalmers, Biology and Biological Engineering, Systems and Synthetic Biology

Avlant Nilsson

Chalmers, Biology and Biological Engineering, Systems and Synthetic Biology

Petre Mihail Anton

Chalmers, Biology and Biological Engineering, Systems and Synthetic Biology

Raphael Ferreira

Chalmers, Biology and Biological Engineering, Systems and Synthetic Biology

Iván Domenzain Del Castillo Cerecer

Chalmers, Biology and Biological Engineering, Systems and Synthetic Biology

Virinchi Billa

Chalmers, Biology and Biological Engineering, Systems and Synthetic Biology, Data management

Angelo Limeta

Chalmers, Biology and Biological Engineering, Systems and Synthetic Biology

Alex Hedin

Chalmers, Biology and Biological Engineering, Systems and Synthetic Biology

Johan Gustafsson

Chalmers, Biology and Biological Engineering, Systems and Synthetic Biology

Eduard Kerkhoven

Chalmers, Biology and Biological Engineering, Systems and Synthetic Biology

Thomas Svensson

Chalmers, Biology and Biological Engineering, Systems and Synthetic Biology

Bernhard O. Palsson

University of California

Technical University of Denmark (DTU)

Adil Mardinoglu

King's College London

Royal Institute of Technology (KTH)

Lena Hansson

Chalmers, Biology and Biological Engineering, Systems and Synthetic Biology

Mathias Uhlen

Royal Institute of Technology (KTH)

Technical University of Denmark (DTU)

Jens B Nielsen

BioInnovation Institute

Technical University of Denmark (DTU)

Chalmers, Biology and Biological Engineering, Systems and Synthetic Biology

Science signaling

1937-9145 (ISSN)

Vol. 13 624 eaaz1482

Subject Categories

Other Computer and Information Science

Bioinformatics (Computational Biology)

Bioinformatics and Systems Biology

DOI

10.1126/scisignal.aaz1482

PubMed

32209698

More information

Latest update

5/25/2020