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Analysis of human metabolism by reducing the complexity of the genome-scale models using redHUMAN
Altered metabolism is associated with many human diseases. Human genome-scale metabolic models (GEMs) were reconstructed within systems biology to study the biochemistry occurring in human cells. However, the complexity of these networks hinders a consistent and concise physiological representation....
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7272419/ https://www.ncbi.nlm.nih.gov/pubmed/32499584 http://dx.doi.org/10.1038/s41467-020-16549-2 |
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author | Masid, Maria Ataman, Meric Hatzimanikatis, Vassily |
author_facet | Masid, Maria Ataman, Meric Hatzimanikatis, Vassily |
author_sort | Masid, Maria |
collection | PubMed |
description | Altered metabolism is associated with many human diseases. Human genome-scale metabolic models (GEMs) were reconstructed within systems biology to study the biochemistry occurring in human cells. However, the complexity of these networks hinders a consistent and concise physiological representation. We present here redHUMAN, a workflow for reconstructing reduced models that focus on parts of the metabolism relevant to a specific physiology using the recently established methods redGEM and lumpGEM. The reductions include the thermodynamic properties of compounds and reactions guaranteeing the consistency of predictions with the bioenergetics of the cell. We introduce a method (redGEMX) to incorporate the pathways used by cells to adapt to the medium. We provide the thermodynamic curation of the human GEMs Recon2 and Recon3D and we apply the redHUMAN workflow to derive leukemia-specific reduced models. The reduced models are powerful platforms for studying metabolic differences between phenotypes, such as diseased and healthy cells. |
format | Online Article Text |
id | pubmed-7272419 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-72724192020-06-15 Analysis of human metabolism by reducing the complexity of the genome-scale models using redHUMAN Masid, Maria Ataman, Meric Hatzimanikatis, Vassily Nat Commun Article Altered metabolism is associated with many human diseases. Human genome-scale metabolic models (GEMs) were reconstructed within systems biology to study the biochemistry occurring in human cells. However, the complexity of these networks hinders a consistent and concise physiological representation. We present here redHUMAN, a workflow for reconstructing reduced models that focus on parts of the metabolism relevant to a specific physiology using the recently established methods redGEM and lumpGEM. The reductions include the thermodynamic properties of compounds and reactions guaranteeing the consistency of predictions with the bioenergetics of the cell. We introduce a method (redGEMX) to incorporate the pathways used by cells to adapt to the medium. We provide the thermodynamic curation of the human GEMs Recon2 and Recon3D and we apply the redHUMAN workflow to derive leukemia-specific reduced models. The reduced models are powerful platforms for studying metabolic differences between phenotypes, such as diseased and healthy cells. Nature Publishing Group UK 2020-06-04 /pmc/articles/PMC7272419/ /pubmed/32499584 http://dx.doi.org/10.1038/s41467-020-16549-2 Text en © The Author(s) 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Article Masid, Maria Ataman, Meric Hatzimanikatis, Vassily Analysis of human metabolism by reducing the complexity of the genome-scale models using redHUMAN |
title | 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_short | 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 |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7272419/ https://www.ncbi.nlm.nih.gov/pubmed/32499584 http://dx.doi.org/10.1038/s41467-020-16549-2 |
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