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General Analyses of Gene Expression Dependencies on Genetic Burden

Over the last decade, the combining of newly developed molecular tools for DNA editing with engineering principles has allowed the creation of complex cellular devices, usually based on complex genetic circuits, for many different purposes. However, when the technological evolution of genetic circui...

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Autores principales: González-Colell, Marc, Macía, Javier
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7481379/
https://www.ncbi.nlm.nih.gov/pubmed/32984285
http://dx.doi.org/10.3389/fbioe.2020.01017
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author González-Colell, Marc
Macía, Javier
author_facet González-Colell, Marc
Macía, Javier
author_sort González-Colell, Marc
collection PubMed
description Over the last decade, the combining of newly developed molecular tools for DNA editing with engineering principles has allowed the creation of complex cellular devices, usually based on complex genetic circuits, for many different purposes. However, when the technological evolution of genetic circuitry is compared with previous technologies such as electronic circuitry, clear limitations regarding the technological scalability of genetic circuitry are observed due to the lack of predictability. To overcome this problem, it is necessary to create new theoretical frameworks for designing genetic circuits in a feasible and reliable manner, taking into account those limitations. Among a number of such limitations, the so-called genetic burden is one of the main constraints. Surprisingly, despite its relevance, little attention has been paid to genetic burden, and it is often not considered when designing genetic circuits. In this study, a new general mathematical formalism is presented, describing the effects of genetic burden on gene expression. The mathematical analysis shows that alterations in gene expression due to genetic burden can be qualitatively described independently of the specific genetic features of the system under consideration. The mathematical model was experimentally tested in different genetic circuits. The experimental evidence coincides with the expected behaviors described by the model in complex scenarios. For instance, observed modulations in the expression levels of constitutive genes in response to changes in the levels of external inducers of gene expression that do not directly modulate them, or the emergence of limitations in gene overexpression, can be understood in terms of genetic burden. The present mathematical formalism provides a useful general framework for gene circuit design that will help to advance synthetic biological systems.
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spelling pubmed-74813792020-09-24 General Analyses of Gene Expression Dependencies on Genetic Burden González-Colell, Marc Macía, Javier Front Bioeng Biotechnol Bioengineering and Biotechnology Over the last decade, the combining of newly developed molecular tools for DNA editing with engineering principles has allowed the creation of complex cellular devices, usually based on complex genetic circuits, for many different purposes. However, when the technological evolution of genetic circuitry is compared with previous technologies such as electronic circuitry, clear limitations regarding the technological scalability of genetic circuitry are observed due to the lack of predictability. To overcome this problem, it is necessary to create new theoretical frameworks for designing genetic circuits in a feasible and reliable manner, taking into account those limitations. Among a number of such limitations, the so-called genetic burden is one of the main constraints. Surprisingly, despite its relevance, little attention has been paid to genetic burden, and it is often not considered when designing genetic circuits. In this study, a new general mathematical formalism is presented, describing the effects of genetic burden on gene expression. The mathematical analysis shows that alterations in gene expression due to genetic burden can be qualitatively described independently of the specific genetic features of the system under consideration. The mathematical model was experimentally tested in different genetic circuits. The experimental evidence coincides with the expected behaviors described by the model in complex scenarios. For instance, observed modulations in the expression levels of constitutive genes in response to changes in the levels of external inducers of gene expression that do not directly modulate them, or the emergence of limitations in gene overexpression, can be understood in terms of genetic burden. The present mathematical formalism provides a useful general framework for gene circuit design that will help to advance synthetic biological systems. Frontiers Media S.A. 2020-08-27 /pmc/articles/PMC7481379/ /pubmed/32984285 http://dx.doi.org/10.3389/fbioe.2020.01017 Text en Copyright © 2020 González-Colell and Macía. 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 Bioengineering and Biotechnology
González-Colell, Marc
Macía, Javier
General Analyses of Gene Expression Dependencies on Genetic Burden
title General Analyses of Gene Expression Dependencies on Genetic Burden
title_full General Analyses of Gene Expression Dependencies on Genetic Burden
title_fullStr General Analyses of Gene Expression Dependencies on Genetic Burden
title_full_unstemmed General Analyses of Gene Expression Dependencies on Genetic Burden
title_short General Analyses of Gene Expression Dependencies on Genetic Burden
title_sort general analyses of gene expression dependencies on genetic burden
topic Bioengineering and Biotechnology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7481379/
https://www.ncbi.nlm.nih.gov/pubmed/32984285
http://dx.doi.org/10.3389/fbioe.2020.01017
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