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Phenotypic Variability in Synthetic Biology Applications: Dealing with Noise in Microbial Gene Expression

The stochasticity due to the infrequent collisions among low copy-number molecules within the crowded cellular compartment is a feature of living systems. Single cell variability in gene expression within an isogenic population (i.e., biological noise) is usually described as the sum of two independ...

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Autores principales: Bandiera, Lucia, Furini, Simone, Giordano, Emanuele
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4824758/
https://www.ncbi.nlm.nih.gov/pubmed/27092132
http://dx.doi.org/10.3389/fmicb.2016.00479
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author Bandiera, Lucia
Furini, Simone
Giordano, Emanuele
author_facet Bandiera, Lucia
Furini, Simone
Giordano, Emanuele
author_sort Bandiera, Lucia
collection PubMed
description The stochasticity due to the infrequent collisions among low copy-number molecules within the crowded cellular compartment is a feature of living systems. Single cell variability in gene expression within an isogenic population (i.e., biological noise) is usually described as the sum of two independent components: intrinsic and extrinsic stochasticity. Intrinsic stochasticity arises from the random occurrence of events inherent to the gene expression process (e.g., the burst-like synthesis of mRNA and protein molecules). Extrinsic fluctuations reflect the state of the biological system and its interaction with the intra and extracellular environments (e.g., concentration of available polymerases, ribosomes, metabolites, and micro-environmental conditions). A better understanding of cellular noise would help synthetic biologists design gene circuits with well-defined functional properties. In silico modeling has already revealed several aspects of the network topology’s impact on noise properties; this information could drive the selection of biological parts and the design of reliably engineered pathways. Importantly, while optimizing artificial gene circuitry for industrial applications, synthetic biology could also elucidate the natural mechanisms underlying natural phenotypic variability. In this review, we briefly summarize the functional roles of noise in unicellular organisms and address their relevance to synthetic network design. We will also consider how noise might influence the selection of network topologies supporting reliable functions, and how the variability of cellular events might be exploited when designing innovative biotechnology applications.
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spelling pubmed-48247582016-04-18 Phenotypic Variability in Synthetic Biology Applications: Dealing with Noise in Microbial Gene Expression Bandiera, Lucia Furini, Simone Giordano, Emanuele Front Microbiol Microbiology The stochasticity due to the infrequent collisions among low copy-number molecules within the crowded cellular compartment is a feature of living systems. Single cell variability in gene expression within an isogenic population (i.e., biological noise) is usually described as the sum of two independent components: intrinsic and extrinsic stochasticity. Intrinsic stochasticity arises from the random occurrence of events inherent to the gene expression process (e.g., the burst-like synthesis of mRNA and protein molecules). Extrinsic fluctuations reflect the state of the biological system and its interaction with the intra and extracellular environments (e.g., concentration of available polymerases, ribosomes, metabolites, and micro-environmental conditions). A better understanding of cellular noise would help synthetic biologists design gene circuits with well-defined functional properties. In silico modeling has already revealed several aspects of the network topology’s impact on noise properties; this information could drive the selection of biological parts and the design of reliably engineered pathways. Importantly, while optimizing artificial gene circuitry for industrial applications, synthetic biology could also elucidate the natural mechanisms underlying natural phenotypic variability. In this review, we briefly summarize the functional roles of noise in unicellular organisms and address their relevance to synthetic network design. We will also consider how noise might influence the selection of network topologies supporting reliable functions, and how the variability of cellular events might be exploited when designing innovative biotechnology applications. Frontiers Media S.A. 2016-04-08 /pmc/articles/PMC4824758/ /pubmed/27092132 http://dx.doi.org/10.3389/fmicb.2016.00479 Text en Copyright © 2016 Bandiera, Furini and Giordano. 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) or licensor 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 Microbiology
Bandiera, Lucia
Furini, Simone
Giordano, Emanuele
Phenotypic Variability in Synthetic Biology Applications: Dealing with Noise in Microbial Gene Expression
title Phenotypic Variability in Synthetic Biology Applications: Dealing with Noise in Microbial Gene Expression
title_full Phenotypic Variability in Synthetic Biology Applications: Dealing with Noise in Microbial Gene Expression
title_fullStr Phenotypic Variability in Synthetic Biology Applications: Dealing with Noise in Microbial Gene Expression
title_full_unstemmed Phenotypic Variability in Synthetic Biology Applications: Dealing with Noise in Microbial Gene Expression
title_short Phenotypic Variability in Synthetic Biology Applications: Dealing with Noise in Microbial Gene Expression
title_sort phenotypic variability in synthetic biology applications: dealing with noise in microbial gene expression
topic Microbiology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4824758/
https://www.ncbi.nlm.nih.gov/pubmed/27092132
http://dx.doi.org/10.3389/fmicb.2016.00479
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