Literature DB >> 11062240

The effect of transcription and translation initiation frequencies on the stochastic fluctuations in prokaryotic gene expression.

A M Kierzek1, J Zaim, P Zielenkiewicz.   

Abstract

The kinetics of prokaryotic gene expression has been modelled by the Monte Carlo computer simulation algorithm of Gillespie, which allowed the study of random fluctuations in the number of protein molecules during gene expression. The model, when applied to the simulation of LacZ gene expression, is in good agreement with experimental data. The influence of the frequencies of transcription and translation initiation on random fluctuations in gene expression has been studied in a number of simulations in which promoter and ribosome binding site effectiveness has been changed in the range of values reported for various prokaryotic genes. We show that the genes expressed from strong promoters produce the protein evenly, with a rate that does not vary significantly among cells. The genes with very weak promoters express the protein in "bursts" occurring at random time intervals. Therefore, if the low level of gene expression results from the low frequency of transcription initiation, huge fluctuations arise. In contrast, the protein can be produced with a low and uniform rate if the gene has a strong promoter and a slow rate of ribosome binding (a weak ribosome binding site). The implications of these findings for the expression of regulatory proteins are discussed.

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Year:  2000        PMID: 11062240     DOI: 10.1074/jbc.M006264200

Source DB:  PubMed          Journal:  J Biol Chem        ISSN: 0021-9258            Impact factor:   5.157


  44 in total

1.  Intrinsic and extrinsic contributions to stochasticity in gene expression.

Authors:  Peter S Swain; Michael B Elowitz; Eric D Siggia
Journal:  Proc Natl Acad Sci U S A       Date:  2002-09-17       Impact factor: 11.205

2.  Signaling in small subcellular volumes. I. Stochastic and diffusion effects on individual pathways.

Authors:  Upinder S Bhalla
Journal:  Biophys J       Date:  2004-08       Impact factor: 4.033

3.  Bridging the gap between stochastic and deterministic regimes in the kinetic simulations of the biochemical reaction networks.

Authors:  Jacek Puchałka; Andrzej M Kierzek
Journal:  Biophys J       Date:  2004-03       Impact factor: 4.033

4.  Reconciling molecular regulatory mechanisms with noise patterns of bacterial metabolic promoters in induced and repressed states.

Authors:  Matthew L Ferguson; Dominique Le Coq; Matthieu Jules; Stéphane Aymerich; Ovidiu Radulescu; Nathalie Declerck; Catherine A Royer
Journal:  Proc Natl Acad Sci U S A       Date:  2011-12-21       Impact factor: 11.205

5.  The effects of combinatorial treatments with stress inducing molecules on growth of E. coli colonies.

Authors:  Steven L Middler; Salvador Gomez; Christapher D Parker; Peter M Palenchar
Journal:  Curr Microbiol       Date:  2011-10-04       Impact factor: 2.188

6.  Stochastic simulations of the origins and implications of long-tailed distributions in gene expression.

Authors:  Sandeep Krishna; Bidisha Banerjee; T V Ramakrishnan; G V Shivashankar
Journal:  Proc Natl Acad Sci U S A       Date:  2005-03-16       Impact factor: 11.205

Review 7.  Noise in gene expression: origins, consequences, and control.

Authors:  Jonathan M Raser; Erin K O'Shea
Journal:  Science       Date:  2005-09-23       Impact factor: 47.728

8.  Translational repression contributes greater noise to gene expression than transcriptional repression.

Authors:  Michał Komorowski; Jacek Miekisz; Andrzej M Kierzek
Journal:  Biophys J       Date:  2009-01       Impact factor: 4.033

9.  In silico evolved lac operons exhibit bistability for artificial inducers, but not for lactose.

Authors:  M J A van Hoek; P Hogeweg
Journal:  Biophys J       Date:  2006-07-28       Impact factor: 4.033

10.  Type of noise defines global attractors in bistable molecular regulatory systems.

Authors:  Joanna Jaruszewicz; Pawel J Zuk; Tomasz Lipniacki
Journal:  J Theor Biol       Date:  2012-10-11       Impact factor: 2.691

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