Literature DB >> 22499939

Using gene expression noise to understand gene regulation.

Brian Munsky1, Gregor Neuert, Alexander van Oudenaarden.   

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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Year:  2012        PMID: 22499939      PMCID: PMC3358231          DOI: 10.1126/science.1216379

Source DB:  PubMed          Journal:  Science        ISSN: 0036-8075            Impact factor:   47.728


  31 in total

1.  Stochasticity in transcriptional regulation: origins, consequences, and mathematical representations.

Authors:  T B Kepler; T C Elston
Journal:  Biophys J       Date:  2001-12       Impact factor: 4.033

2.  Intrinsic noise in gene regulatory networks.

Authors:  M Thattai; A van Oudenaarden
Journal:  Proc Natl Acad Sci U S A       Date:  2001-07-03       Impact factor: 11.205

3.  Control of stochasticity in eukaryotic gene expression.

Authors:  Jonathan M Raser; Erin K O'Shea
Journal:  Science       Date:  2004-05-27       Impact factor: 47.728

4.  Real-time kinetics of gene activity in individual bacteria.

Authors:  Ido Golding; Johan Paulsson; Scott M Zawilski; Edward C Cox
Journal:  Cell       Date:  2005-12-16       Impact factor: 41.582

5.  Gene regulation at the single-cell level.

Authors:  Nitzan Rosenfeld; Jonathan W Young; Uri Alon; Peter S Swain; Michael B Elowitz
Journal:  Science       Date:  2005-03-25       Impact factor: 47.728

6.  Noise propagation in gene networks.

Authors:  Juan M Pedraza; Alexander van Oudenaarden
Journal:  Science       Date:  2005-03-25       Impact factor: 47.728

7.  The finite state projection algorithm for the solution of the chemical master equation.

Authors:  Brian Munsky; Mustafa Khammash
Journal:  J Chem Phys       Date:  2006-01-28       Impact factor: 3.488

8.  Analytical distributions for stochastic gene expression.

Authors:  Vahid Shahrezaei; Peter S Swain
Journal:  Proc Natl Acad Sci U S A       Date:  2008-11-06       Impact factor: 11.205

9.  Visualization of single RNA transcripts in situ.

Authors:  A M Femino; F S Fay; K Fogarty; R H Singer
Journal:  Science       Date:  1998-04-24       Impact factor: 47.728

10.  Single-RNA counting reveals alternative modes of gene expression in yeast.

Authors:  Daniel Zenklusen; Daniel R Larson; Robert H Singer
Journal:  Nat Struct Mol Biol       Date:  2008-11-16       Impact factor: 15.369

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  267 in total

1.  Maximum Caliber Can Build and Infer Models of Oscillation in a Three-Gene Feedback Network.

Authors:  Taylor Firman; Anar Amgalan; Kingshuk Ghosh
Journal:  J Phys Chem B       Date:  2019-01-09       Impact factor: 2.991

2.  Translational cross talk in gene networks.

Authors:  William H Mather; Jeff Hasty; Lev S Tsimring; Ruth J Williams
Journal:  Biophys J       Date:  2013-06-04       Impact factor: 4.033

3.  Method of conditional moments (MCM) for the Chemical Master Equation: a unified framework for the method of moments and hybrid stochastic-deterministic models.

Authors:  J Hasenauer; V Wolf; A Kazeroonian; F J Theis
Journal:  J Math Biol       Date:  2013-08-06       Impact factor: 2.259

4.  Heterogeneity in protein expression induces metabolic variability in a modeled Escherichia coli population.

Authors:  Piyush Labhsetwar; John Andrew Cole; Elijah Roberts; Nathan D Price; Zaida A Luthey-Schulten
Journal:  Proc Natl Acad Sci U S A       Date:  2013-08-01       Impact factor: 11.205

5.  Epigenetic memory emerging from integrated transcription bursts.

Authors:  Volker Kurz; Edward M Nelson; Nicolas Perry; Winston Timp; Gregory Timp
Journal:  Biophys J       Date:  2013-09-17       Impact factor: 4.033

6.  Ribozyme-based insulator parts buffer synthetic circuits from genetic context.

Authors:  Chunbo Lou; Brynne Stanton; Ying-Ja Chen; Brian Munsky; Christopher A Voigt
Journal:  Nat Biotechnol       Date:  2012-10-03       Impact factor: 54.908

7.  Stochastic Kinetics of Nascent RNA.

Authors:  Heng Xu; Samuel O Skinner; Anna Marie Sokac; Ido Golding
Journal:  Phys Rev Lett       Date:  2016-09-13       Impact factor: 9.161

8.  LlamaTags: A Versatile Tool to Image Transcription Factor Dynamics in Live Embryos.

Authors:  Jacques P Bothma; Matthew R Norstad; Simon Alamos; Hernan G Garcia
Journal:  Cell       Date:  2018-05-10       Impact factor: 41.582

Review 9.  Single cell protein analysis for systems biology.

Authors:  Ezra Levy; Nikolai Slavov
Journal:  Essays Biochem       Date:  2018-10-26       Impact factor: 8.000

10.  Quantifying the dynamics of IRES and cap translation with single-molecule resolution in live cells.

Authors:  Amanda Koch; Luis Aguilera; Tatsuya Morisaki; Brian Munsky; Timothy J Stasevich
Journal:  Nat Struct Mol Biol       Date:  2020-09-21       Impact factor: 15.369

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