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Construction of Multiscale Genome-Scale Metabolic Models: Frameworks and Challenges
Genome-scale metabolic models (GEMs) are effective tools for metabolic engineering and have been widely used to guide cell metabolic regulation. However, the single gene–protein-reaction data type in GEMs limits the understanding of biological complexity. As a result, multiscale models that add cons...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9139095/ https://www.ncbi.nlm.nih.gov/pubmed/35625648 http://dx.doi.org/10.3390/biom12050721 |
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author | Bi, Xinyu Liu, Yanfeng Li, Jianghua Du, Guocheng Lv, Xueqin Liu, Long |
author_facet | Bi, Xinyu Liu, Yanfeng Li, Jianghua Du, Guocheng Lv, Xueqin Liu, Long |
author_sort | Bi, Xinyu |
collection | PubMed |
description | Genome-scale metabolic models (GEMs) are effective tools for metabolic engineering and have been widely used to guide cell metabolic regulation. However, the single gene–protein-reaction data type in GEMs limits the understanding of biological complexity. As a result, multiscale models that add constraints or integrate omics data based on GEMs have been developed to more accurately predict phenotype from genotype. This review summarized the recent advances in the development of multiscale GEMs, including multiconstraint, multiomic, and whole-cell models, and outlined machine learning applications in GEM construction. This review focused on the frameworks, toolkits, and algorithms for constructing multiscale GEMs. The challenges and perspectives of multiscale GEM development are also discussed. |
format | Online Article Text |
id | pubmed-9139095 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-91390952022-05-28 Construction of Multiscale Genome-Scale Metabolic Models: Frameworks and Challenges Bi, Xinyu Liu, Yanfeng Li, Jianghua Du, Guocheng Lv, Xueqin Liu, Long Biomolecules Review Genome-scale metabolic models (GEMs) are effective tools for metabolic engineering and have been widely used to guide cell metabolic regulation. However, the single gene–protein-reaction data type in GEMs limits the understanding of biological complexity. As a result, multiscale models that add constraints or integrate omics data based on GEMs have been developed to more accurately predict phenotype from genotype. This review summarized the recent advances in the development of multiscale GEMs, including multiconstraint, multiomic, and whole-cell models, and outlined machine learning applications in GEM construction. This review focused on the frameworks, toolkits, and algorithms for constructing multiscale GEMs. The challenges and perspectives of multiscale GEM development are also discussed. MDPI 2022-05-19 /pmc/articles/PMC9139095/ /pubmed/35625648 http://dx.doi.org/10.3390/biom12050721 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Review Bi, Xinyu Liu, Yanfeng Li, Jianghua Du, Guocheng Lv, Xueqin Liu, Long Construction of Multiscale Genome-Scale Metabolic Models: Frameworks and Challenges |
title | Construction of Multiscale Genome-Scale Metabolic Models: Frameworks and Challenges |
title_full | Construction of Multiscale Genome-Scale Metabolic Models: Frameworks and Challenges |
title_fullStr | Construction of Multiscale Genome-Scale Metabolic Models: Frameworks and Challenges |
title_full_unstemmed | Construction of Multiscale Genome-Scale Metabolic Models: Frameworks and Challenges |
title_short | Construction of Multiscale Genome-Scale Metabolic Models: Frameworks and Challenges |
title_sort | construction of multiscale genome-scale metabolic models: frameworks and challenges |
topic | Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9139095/ https://www.ncbi.nlm.nih.gov/pubmed/35625648 http://dx.doi.org/10.3390/biom12050721 |
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