Literature DB >> 19416069

Single-molecule approaches to stochastic gene expression.

Arjun Raj1, Alexander van Oudenaarden.   

Abstract

Both the transcription of mRNAs from genes and their subsequent translation into proteins are inherently stochastic biochemical events, and this randomness can lead to substantial cell-to-cell variability in mRNA and protein numbers in otherwise identical cells. Recently, a number of studies have greatly enhanced our understanding of stochastic processes in gene expression by utilizing new methods capable of counting individual mRNAs and proteins in cells. In this review, we examine the insights that these studies have yielded in the field of stochastic gene expression. In particular, we discuss how these studies have played in understanding the properties of bursts in gene expression. We also compare the array of different methods that have arisen for single mRNA and protein detection, highlighting their relative strengths and weaknesses. In conclusion, we point out further areas where single-molecule techniques applied to gene expression may lead to new discoveries.

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Year:  2009        PMID: 19416069      PMCID: PMC3126657          DOI: 10.1146/annurev.biophys.37.032807.125928

Source DB:  PubMed          Journal:  Annu Rev Biophys        ISSN: 1936-122X            Impact factor:   12.981


  52 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.  Regulation of noise in the expression of a single gene.

Authors:  Ertugrul M Ozbudak; Mukund Thattai; Iren Kurtser; Alan D Grossman; Alexander van Oudenaarden
Journal:  Nat Genet       Date:  2002-04-22       Impact factor: 38.330

3.  Phenotypic diversity, population growth, and information in fluctuating environments.

Authors:  Edo Kussell; Stanislas Leibler
Journal:  Science       Date:  2005-08-25       Impact factor: 47.728

4.  Gene regulation through nuclear organization.

Authors:  Tom Sexton; Heiko Schober; Peter Fraser; Susan M Gasser
Journal:  Nat Struct Mol Biol       Date:  2007-11-05       Impact factor: 15.369

5.  Probing transcription factor dynamics at the single-molecule level in a living cell.

Authors:  Johan Elf; Gene-Wei Li; X Sunney Xie
Journal:  Science       Date:  2007-05-25       Impact factor: 47.728

6.  Stochastic protein expression in individual cells at the single molecule level.

Authors:  Long Cai; Nir Friedman; X Sunney Xie
Journal:  Nature       Date:  2006-03-16       Impact factor: 49.962

7.  Noise in gene expression determines cell fate in Bacillus subtilis.

Authors:  Hédia Maamar; Arjun Raj; David Dubnau
Journal:  Science       Date:  2007-06-14       Impact factor: 47.728

8.  Transcriptome-wide noise controls lineage choice in mammalian progenitor cells.

Authors:  Hannah H Chang; Martin Hemberg; Mauricio Barahona; Donald E Ingber; Sui Huang
Journal:  Nature       Date:  2008-05-22       Impact factor: 49.962

9.  Transcriptional pulsing of a developmental gene.

Authors:  Jonathan R Chubb; Tatjana Trcek; Shailesh M Shenoy; Robert H Singer
Journal:  Curr Biol       Date:  2006-05-23       Impact factor: 10.834

10.  Real-time analysis of the transcriptional regulation of HIV and hCMV promoters in single mammalian cells.

Authors:  M R White; M Masuko; L Amet; G Elliott; M Braddock; A J Kingsman; S M Kingsman
Journal:  J Cell Sci       Date:  1995-02       Impact factor: 5.285

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  141 in total

1.  Stochastic steady state gain in a gene expression process with mRNA degradation control.

Authors:  Hiroyuki Kuwahara; Russell Schwartz
Journal:  J R Soc Interface       Date:  2012-01-11       Impact factor: 4.118

2.  Multiscale stochastic modelling of gene expression.

Authors:  Pavol Bokes; John R King; Andrew T A Wood; Matthew Loose
Journal:  J Math Biol       Date:  2011-10-07       Impact factor: 2.259

3.  Grand challenge commentary: Exploiting single-cell variation for new antibiotics.

Authors:  Erick Strauss
Journal:  Nat Chem Biol       Date:  2010-12       Impact factor: 15.040

4.  Quantifying negative feedback regulation by micro-RNAs.

Authors:  Shangying Wang; Sridhar Raghavachari
Journal:  Phys Biol       Date:  2011-08-10       Impact factor: 2.583

Review 5.  The developmental genetics of biological robustness.

Authors:  Lamia Mestek Boukhibar; Michalis Barkoulas
Journal:  Ann Bot       Date:  2015-08-20       Impact factor: 4.357

6.  Quantitative spatial analysis of transcripts in multinucleate cells using single-molecule FISH.

Authors:  ChangHwan Lee; Samantha E Roberts; Amy S Gladfelter
Journal:  Methods       Date:  2015-12-12       Impact factor: 3.608

7.  Design of a bistable switch to control cellular uptake.

Authors:  Diego A Oyarzún; Madalena Chaves
Journal:  J R Soc Interface       Date:  2015-12-06       Impact factor: 4.118

8.  Piecewise parameter estimation for stochastic models in COPASI.

Authors:  Frank T Bergmann; Sven Sahle; Christoph Zimmer
Journal:  Bioinformatics       Date:  2016-01-18       Impact factor: 6.937

Review 9.  Using variability in gene expression as a tool for studying gene regulation.

Authors:  Olivia Padovan-Merhar; Arjun Raj
Journal:  Wiley Interdiscip Rev Syst Biol Med       Date:  2013-08-29

10.  Spatial reconstruction of immune niches by combining photoactivatable reporters and scRNA-seq.

Authors:  Chiara Medaglia; Amir Giladi; Liat Stoler-Barak; Marco De Giovanni; Tomer Meir Salame; Adi Biram; Eyal David; Hanjie Li; Matteo Iannacone; Ziv Shulman; Ido Amit
Journal:  Science       Date:  2017-12-07       Impact factor: 47.728

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