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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...

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Detalles Bibliográficos
Autores principales: Bi, Xinyu, Liu, Yanfeng, Li, Jianghua, Du, Guocheng, Lv, Xueqin, Liu, Long
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
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.
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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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