Using gene expression noise to understand gene regulation

B. Munsky; G. Neuert; A. van Oudenaarden

doi:10.1126/science.1216379

Using gene expression noise to understand gene regulation

B. Munsky, G. Neuert, A. van Oudenaarden

Hubrecht Institute

Onderzoeksoutput: Bijdrage aan wetenschappelijk tijdschrift/periodieke uitgave › Artikel › Wetenschappelijk › peer review

563 Citaten (Scopus)

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Phenotypic variation is ubiquitous in biology and is often traceable to underlying genetic and environmental variation. However, even genetically identical cells in identical environments display variable phenotypes. Stochastic gene expression, or gene expression "noise," has been suggested as a major source of this variability, and its physiological consequences have been topics of intense research for the last decade. Several recent studies have measured variability in protein and messenger RNA levels, and they have discovered strong connections between noise and gene regulation mechanisms. When integrated with discrete stochastic models, measurements of cell-to-cell variability provide a sensitive "fingerprint" with which to explore fundamental questions of gene regulation. In this review, we highlight several studies that used gene expression variability to develop a quantitative understanding of the mechanisms and dynamics of gene regulation.

Originele taal-2	Engels
Pagina's (van-tot)	183-187
Tijdschrift	Science Magazine
Volume	336
Nummer van het tijdschrift	6078
DOI's	https://doi.org/10.1126/science.1216379
Status	Gepubliceerd - 2012

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abstract = "Phenotypic variation is ubiquitous in biology and is often traceable to underlying genetic and environmental variation. However, even genetically identical cells in identical environments display variable phenotypes. Stochastic gene expression, or gene expression {"}noise,{"} has been suggested as a major source of this variability, and its physiological consequences have been topics of intense research for the last decade. Several recent studies have measured variability in protein and messenger RNA levels, and they have discovered strong connections between noise and gene regulation mechanisms. When integrated with discrete stochastic models, measurements of cell-to-cell variability provide a sensitive {"}fingerprint{"} with which to explore fundamental questions of gene regulation. In this review, we highlight several studies that used gene expression variability to develop a quantitative understanding of the mechanisms and dynamics of gene regulation.",

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Using gene expression noise to understand gene regulation

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