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Quantitative and Predictive Genetic Parts for Plant Synthetic Biology

Plant synthetic biology aims to harness the natural abilities of plants and to turn them to new purposes. A primary goal of plant synthetic biology is to produce predictable and programmable genetic circuits from simple regulatory elements and well-characterized genetic components. The number of ava...

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Autores principales: McCarthy, Diane M., Medford, June I.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7573182/
https://www.ncbi.nlm.nih.gov/pubmed/33123175
http://dx.doi.org/10.3389/fpls.2020.512526
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author McCarthy, Diane M.
Medford, June I.
author_facet McCarthy, Diane M.
Medford, June I.
author_sort McCarthy, Diane M.
collection PubMed
description Plant synthetic biology aims to harness the natural abilities of plants and to turn them to new purposes. A primary goal of plant synthetic biology is to produce predictable and programmable genetic circuits from simple regulatory elements and well-characterized genetic components. The number of available DNA parts for plants is increasing, and the methods for rapid quantitative characterization are being developed, but the field of plant synthetic biology is still in its early stages. We here describe methods used to describe the quantitative properties of genetic components needed for plant synthetic biology. Once the quantitative properties and transfer function of a variety of genetic parts are known, computers can select the optimal components to assemble into functional devices, such as toggle switches and positive feedback circuits. However, while the variety of circuits and traits that can be put into plants are limitless, doing synthetic biology in plants poses unique challenges. Plants are composed of differentiated cells and tissues, each representing potentially unique regulatory or developmental contexts to introduced synthetic genetic circuits. Further, plants have evolved to be highly sensitive to environmental influences, such as light or temperature, any of which can affect the quantitative function of individual parts or whole circuits. Measuring the function of plant components within the context of a plant cell and, ideally, in a living plant, will be essential to using these components in gene circuits with predictable function. Mathematical modeling will be needed to account for the variety of contexts a genetic part will experience in different plant tissues or environments. With such understanding in hand, it may be possible to redesign plant traits to serve human and environmental needs.
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spelling pubmed-75731822020-10-28 Quantitative and Predictive Genetic Parts for Plant Synthetic Biology McCarthy, Diane M. Medford, June I. Front Plant Sci Plant Science Plant synthetic biology aims to harness the natural abilities of plants and to turn them to new purposes. A primary goal of plant synthetic biology is to produce predictable and programmable genetic circuits from simple regulatory elements and well-characterized genetic components. The number of available DNA parts for plants is increasing, and the methods for rapid quantitative characterization are being developed, but the field of plant synthetic biology is still in its early stages. We here describe methods used to describe the quantitative properties of genetic components needed for plant synthetic biology. Once the quantitative properties and transfer function of a variety of genetic parts are known, computers can select the optimal components to assemble into functional devices, such as toggle switches and positive feedback circuits. However, while the variety of circuits and traits that can be put into plants are limitless, doing synthetic biology in plants poses unique challenges. Plants are composed of differentiated cells and tissues, each representing potentially unique regulatory or developmental contexts to introduced synthetic genetic circuits. Further, plants have evolved to be highly sensitive to environmental influences, such as light or temperature, any of which can affect the quantitative function of individual parts or whole circuits. Measuring the function of plant components within the context of a plant cell and, ideally, in a living plant, will be essential to using these components in gene circuits with predictable function. Mathematical modeling will be needed to account for the variety of contexts a genetic part will experience in different plant tissues or environments. With such understanding in hand, it may be possible to redesign plant traits to serve human and environmental needs. Frontiers Media S.A. 2020-10-06 /pmc/articles/PMC7573182/ /pubmed/33123175 http://dx.doi.org/10.3389/fpls.2020.512526 Text en Copyright © 2020 McCarthy and Medford. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Plant Science
McCarthy, Diane M.
Medford, June I.
Quantitative and Predictive Genetic Parts for Plant Synthetic Biology
title Quantitative and Predictive Genetic Parts for Plant Synthetic Biology
title_full Quantitative and Predictive Genetic Parts for Plant Synthetic Biology
title_fullStr Quantitative and Predictive Genetic Parts for Plant Synthetic Biology
title_full_unstemmed Quantitative and Predictive Genetic Parts for Plant Synthetic Biology
title_short Quantitative and Predictive Genetic Parts for Plant Synthetic Biology
title_sort quantitative and predictive genetic parts for plant synthetic biology
topic Plant Science
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7573182/
https://www.ncbi.nlm.nih.gov/pubmed/33123175
http://dx.doi.org/10.3389/fpls.2020.512526
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