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Next-Generation Genome-Scale Metabolic Modeling through Integration of Regulatory Mechanisms
Genome-scale metabolic models (GEMs) are powerful tools for understanding metabolism from a systems-level perspective. However, GEMs in their most basic form fail to account for cellular regulation. A diverse set of mechanisms regulate cellular metabolism, enabling organisms to respond to a wide ran...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8470976/ https://www.ncbi.nlm.nih.gov/pubmed/34564422 http://dx.doi.org/10.3390/metabo11090606 |
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author | Chung, Carolina H. Lin, Da-Wei Eames, Alec Chandrasekaran, Sriram |
author_facet | Chung, Carolina H. Lin, Da-Wei Eames, Alec Chandrasekaran, Sriram |
author_sort | Chung, Carolina H. |
collection | PubMed |
description | Genome-scale metabolic models (GEMs) are powerful tools for understanding metabolism from a systems-level perspective. However, GEMs in their most basic form fail to account for cellular regulation. A diverse set of mechanisms regulate cellular metabolism, enabling organisms to respond to a wide range of conditions. This limitation of GEMs has prompted the development of new methods to integrate regulatory mechanisms, thereby enhancing the predictive capabilities and broadening the scope of GEMs. Here, we cover integrative models encompassing six types of regulatory mechanisms: transcriptional regulatory networks (TRNs), post-translational modifications (PTMs), epigenetics, protein–protein interactions and protein stability (PPIs/PS), allostery, and signaling networks. We discuss 22 integrative GEM modeling methods and how these have been used to simulate metabolic regulation during normal and pathological conditions. While these advances have been remarkable, there remains a need for comprehensive and widespread integration of regulatory constraints into GEMs. We conclude by discussing challenges in constructing GEMs with regulation and highlight areas that need to be addressed for the successful modeling of metabolic regulation. Next-generation integrative GEMs that incorporate multiple regulatory mechanisms and their crosstalk will be invaluable for discovering cell-type and disease-specific metabolic control mechanisms. |
format | Online Article Text |
id | pubmed-8470976 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-84709762021-09-27 Next-Generation Genome-Scale Metabolic Modeling through Integration of Regulatory Mechanisms Chung, Carolina H. Lin, Da-Wei Eames, Alec Chandrasekaran, Sriram Metabolites Review Genome-scale metabolic models (GEMs) are powerful tools for understanding metabolism from a systems-level perspective. However, GEMs in their most basic form fail to account for cellular regulation. A diverse set of mechanisms regulate cellular metabolism, enabling organisms to respond to a wide range of conditions. This limitation of GEMs has prompted the development of new methods to integrate regulatory mechanisms, thereby enhancing the predictive capabilities and broadening the scope of GEMs. Here, we cover integrative models encompassing six types of regulatory mechanisms: transcriptional regulatory networks (TRNs), post-translational modifications (PTMs), epigenetics, protein–protein interactions and protein stability (PPIs/PS), allostery, and signaling networks. We discuss 22 integrative GEM modeling methods and how these have been used to simulate metabolic regulation during normal and pathological conditions. While these advances have been remarkable, there remains a need for comprehensive and widespread integration of regulatory constraints into GEMs. We conclude by discussing challenges in constructing GEMs with regulation and highlight areas that need to be addressed for the successful modeling of metabolic regulation. Next-generation integrative GEMs that incorporate multiple regulatory mechanisms and their crosstalk will be invaluable for discovering cell-type and disease-specific metabolic control mechanisms. MDPI 2021-09-07 /pmc/articles/PMC8470976/ /pubmed/34564422 http://dx.doi.org/10.3390/metabo11090606 Text en © 2021 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 Chung, Carolina H. Lin, Da-Wei Eames, Alec Chandrasekaran, Sriram Next-Generation Genome-Scale Metabolic Modeling through Integration of Regulatory Mechanisms |
title | Next-Generation Genome-Scale Metabolic Modeling through Integration of Regulatory Mechanisms |
title_full | Next-Generation Genome-Scale Metabolic Modeling through Integration of Regulatory Mechanisms |
title_fullStr | Next-Generation Genome-Scale Metabolic Modeling through Integration of Regulatory Mechanisms |
title_full_unstemmed | Next-Generation Genome-Scale Metabolic Modeling through Integration of Regulatory Mechanisms |
title_short | Next-Generation Genome-Scale Metabolic Modeling through Integration of Regulatory Mechanisms |
title_sort | next-generation genome-scale metabolic modeling through integration of regulatory mechanisms |
topic | Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8470976/ https://www.ncbi.nlm.nih.gov/pubmed/34564422 http://dx.doi.org/10.3390/metabo11090606 |
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