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High-throughput and reliable acquisition of in vivo turnover number fuels precise metabolic engineering
As synthetic biology enters the era of quantitative biology, mathematical information such as kinetic parameters of enzymes can offer us an accurate knowledge of metabolism and growth of cells, and further guidance on precision metabolic engineering. k(cat), termed the turnover number, is a basic pa...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
KeAi Publishing
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8749077/ https://www.ncbi.nlm.nih.gov/pubmed/35059513 http://dx.doi.org/10.1016/j.synbio.2021.12.006 |
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author | Li, Zhenghong Zhang, Chengyu Wang, Zhengduo Li, Chuan Yang, Zhiheng Li, Zilong Zhang, Lixin Wang, Weishan |
author_facet | Li, Zhenghong Zhang, Chengyu Wang, Zhengduo Li, Chuan Yang, Zhiheng Li, Zilong Zhang, Lixin Wang, Weishan |
author_sort | Li, Zhenghong |
collection | PubMed |
description | As synthetic biology enters the era of quantitative biology, mathematical information such as kinetic parameters of enzymes can offer us an accurate knowledge of metabolism and growth of cells, and further guidance on precision metabolic engineering. k(cat), termed the turnover number, is a basic parameter of enzymes that describes the maximum number of substrates converted to products each active site per unit time. It reflects enzyme activity and is essential for quantitative understanding of biosystems. Usually, the k(cat) values are measured in vitro, thus may not be able to reflect the enzyme activity in vivo. In this case, Davidi et al. defined a surrogate [Formula: see text] (k(app)) for k(cat) and developed a high throughput method to acquire [Formula: see text] from omics data. Heckmann et al. and Chen et al. proved that the surrogate parameter can be a good embodiment of the physiological state of enzymes and exhibit superior performance for enzyme-constrained metabolic model to the default one. These breakthroughs will fuel the development of system and synthetic biology. |
format | Online Article Text |
id | pubmed-8749077 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | KeAi Publishing |
record_format | MEDLINE/PubMed |
spelling | pubmed-87490772022-01-19 High-throughput and reliable acquisition of in vivo turnover number fuels precise metabolic engineering Li, Zhenghong Zhang, Chengyu Wang, Zhengduo Li, Chuan Yang, Zhiheng Li, Zilong Zhang, Lixin Wang, Weishan Synth Syst Biotechnol Article As synthetic biology enters the era of quantitative biology, mathematical information such as kinetic parameters of enzymes can offer us an accurate knowledge of metabolism and growth of cells, and further guidance on precision metabolic engineering. k(cat), termed the turnover number, is a basic parameter of enzymes that describes the maximum number of substrates converted to products each active site per unit time. It reflects enzyme activity and is essential for quantitative understanding of biosystems. Usually, the k(cat) values are measured in vitro, thus may not be able to reflect the enzyme activity in vivo. In this case, Davidi et al. defined a surrogate [Formula: see text] (k(app)) for k(cat) and developed a high throughput method to acquire [Formula: see text] from omics data. Heckmann et al. and Chen et al. proved that the surrogate parameter can be a good embodiment of the physiological state of enzymes and exhibit superior performance for enzyme-constrained metabolic model to the default one. These breakthroughs will fuel the development of system and synthetic biology. KeAi Publishing 2022-01-05 /pmc/articles/PMC8749077/ /pubmed/35059513 http://dx.doi.org/10.1016/j.synbio.2021.12.006 Text en © 2021 The Authors https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). |
spellingShingle | Article Li, Zhenghong Zhang, Chengyu Wang, Zhengduo Li, Chuan Yang, Zhiheng Li, Zilong Zhang, Lixin Wang, Weishan High-throughput and reliable acquisition of in vivo turnover number fuels precise metabolic engineering |
title | High-throughput and reliable acquisition of in vivo turnover number fuels precise metabolic engineering |
title_full | High-throughput and reliable acquisition of in vivo turnover number fuels precise metabolic engineering |
title_fullStr | High-throughput and reliable acquisition of in vivo turnover number fuels precise metabolic engineering |
title_full_unstemmed | High-throughput and reliable acquisition of in vivo turnover number fuels precise metabolic engineering |
title_short | High-throughput and reliable acquisition of in vivo turnover number fuels precise metabolic engineering |
title_sort | high-throughput and reliable acquisition of in vivo turnover number fuels precise metabolic engineering |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8749077/ https://www.ncbi.nlm.nih.gov/pubmed/35059513 http://dx.doi.org/10.1016/j.synbio.2021.12.006 |
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