Analysis of human metabolism by reducing the complexity of the genome-scale models using redHUMAN

Analysis of human metabolism by reducing the complexity of the genome-scale models using redHUMAN

The complexity of genome-scale metabolic networks (GEMs) hinders their application in specific physiological contexts. Here, the authors introduce a framework to reduce thermodynamically curated GEMs to the subnetworks of interest and demonstrate its application by deriving leukemia-specific models.

Bibliographic Details
Main Authors: Maria Masid, Meric Ataman, Vassily Hatzimanikatis
Format: Article
Language:English
Published: Nature Publishing Group 2020-06-01
Series:Nature Communications
Online Access:https://doi.org/10.1038/s41467-020-16549-2
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spelling doaj-3d41aec2fe0240f39f36e6ac9ec3d22c2021-06-06T11:15:25ZengNature Publishing GroupNature Communications2041-17232020-06-0111111210.1038/s41467-020-16549-2Analysis of human metabolism by reducing the complexity of the genome-scale models using redHUMANMaria Masid0Meric Ataman1Vassily Hatzimanikatis2Laboratory of Computational Systems Biotechnology, École Polytechnique Fédérale de Lausanne (EPFL)Computational and Systems Biology, Biozentrum, University of BaselLaboratory of Computational Systems Biotechnology, École Polytechnique Fédérale de Lausanne (EPFL)The complexity of genome-scale metabolic networks (GEMs) hinders their application in specific physiological contexts. Here, the authors introduce a framework to reduce thermodynamically curated GEMs to the subnetworks of interest and demonstrate its application by deriving leukemia-specific models.https://doi.org/10.1038/s41467-020-16549-2
collection DOAJ
language English
format Article
sources DOAJ
author Maria Masid
Meric Ataman
Vassily Hatzimanikatis
spellingShingle Maria Masid
Meric Ataman
Vassily Hatzimanikatis
Analysis of human metabolism by reducing the complexity of the genome-scale models using redHUMAN
Nature Communications
author_facet Maria Masid
Meric Ataman
Vassily Hatzimanikatis
author_sort Maria Masid
title Analysis of human metabolism by reducing the complexity of the genome-scale models using redHUMAN
title_short Analysis of human metabolism by reducing the complexity of the genome-scale models using redHUMAN
title_full Analysis of human metabolism by reducing the complexity of the genome-scale models using redHUMAN
title_fullStr Analysis of human metabolism by reducing the complexity of the genome-scale models using redHUMAN
title_full_unstemmed Analysis of human metabolism by reducing the complexity of the genome-scale models using redHUMAN
title_sort analysis of human metabolism by reducing the complexity of the genome-scale models using redhuman
publisher Nature Publishing Group
series Nature Communications
issn 2041-1723
publishDate 2020-06-01
description The complexity of genome-scale metabolic networks (GEMs) hinders their application in specific physiological contexts. Here, the authors introduce a framework to reduce thermodynamically curated GEMs to the subnetworks of interest and demonstrate its application by deriving leukemia-specific models.
url https://doi.org/10.1038/s41467-020-16549-2
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AT mericataman analysisofhumanmetabolismbyreducingthecomplexityofthegenomescalemodelsusingredhuman
AT vassilyhatzimanikatis analysisofhumanmetabolismbyreducingthecomplexityofthegenomescalemodelsusingredhuman
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