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Dynamical model fitting to a synthetic positive feedback circuit in E. coli

Applying the principles of engineering to Synthetic Biology relies on the development of robust and modular genetic components, as well as underlying quantitative dynamical models that closely predict their behaviour. This study looks at a simple positive feedback circuit built by placing filamentou...

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Detalles Bibliográficos
Autores principales: Tica, Jure, Zhu, Tong, Isalan, Mark
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The Institution of Engineering and Technology 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9996705/
https://www.ncbi.nlm.nih.gov/pubmed/36970395
http://dx.doi.org/10.1049/enb.2020.0009
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author Tica, Jure
Zhu, Tong
Isalan, Mark
author_facet Tica, Jure
Zhu, Tong
Isalan, Mark
author_sort Tica, Jure
collection PubMed
description Applying the principles of engineering to Synthetic Biology relies on the development of robust and modular genetic components, as well as underlying quantitative dynamical models that closely predict their behaviour. This study looks at a simple positive feedback circuit built by placing filamentous phage secretin pIV under a phage shock promoter. A single‐equation ordinary differential equation model is developed to closely replicate the behaviour of the circuit, and its response to inhibition by TetR. A stepwise approach is employed to fit the model's parameters to time‐series data for the circuit. This approach allows the dissection of the role of different parameters and leads to the identification of dependencies and redundancies between parameters. The developed genetic circuit and associated model may be used as a building block for larger circuits with more complex dynamics, which require tight quantitative control or tuning.
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spelling pubmed-99967052023-03-24 Dynamical model fitting to a synthetic positive feedback circuit in E. coli Tica, Jure Zhu, Tong Isalan, Mark Eng Biol Research Article Applying the principles of engineering to Synthetic Biology relies on the development of robust and modular genetic components, as well as underlying quantitative dynamical models that closely predict their behaviour. This study looks at a simple positive feedback circuit built by placing filamentous phage secretin pIV under a phage shock promoter. A single‐equation ordinary differential equation model is developed to closely replicate the behaviour of the circuit, and its response to inhibition by TetR. A stepwise approach is employed to fit the model's parameters to time‐series data for the circuit. This approach allows the dissection of the role of different parameters and leads to the identification of dependencies and redundancies between parameters. The developed genetic circuit and associated model may be used as a building block for larger circuits with more complex dynamics, which require tight quantitative control or tuning. The Institution of Engineering and Technology 2020-06-23 /pmc/articles/PMC9996705/ /pubmed/36970395 http://dx.doi.org/10.1049/enb.2020.0009 Text en © 2020 The Institution of Engineering and Technology https://creativecommons.org/licenses/by/3.0/This is an open access article published by the IET under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0/ (https://creativecommons.org/licenses/by/3.0/) )
spellingShingle Research Article
Tica, Jure
Zhu, Tong
Isalan, Mark
Dynamical model fitting to a synthetic positive feedback circuit in E. coli
title Dynamical model fitting to a synthetic positive feedback circuit in E. coli
title_full Dynamical model fitting to a synthetic positive feedback circuit in E. coli
title_fullStr Dynamical model fitting to a synthetic positive feedback circuit in E. coli
title_full_unstemmed Dynamical model fitting to a synthetic positive feedback circuit in E. coli
title_short Dynamical model fitting to a synthetic positive feedback circuit in E. coli
title_sort dynamical model fitting to a synthetic positive feedback circuit in e. coli
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9996705/
https://www.ncbi.nlm.nih.gov/pubmed/36970395
http://dx.doi.org/10.1049/enb.2020.0009
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