Literature DB >> 21889454

Fitting experimental transcription data with a comprehensive template-dependent modular kinetic model.

Sandra J Greive1, Brandon A Dyer, Steven E Weitzel, Jim P Goodarzi, Lisa J Main, Peter H von Hippel.   

Abstract

In the companion article, we developed a modular scheme for representing the kinetics of transcription elongation by RNA polymerase. As an example of how to use these approaches, in this article we use a comprehensive modular model of this sort to fit experimental transcript elongation results obtained on the canonical tR2 template of phage λ by means of complementary bulk gel electrophoresis and surface plasmon resonance assays. The gel electrophoresis results, obtained in experiments quenched at various times after initiation of transcription, provide distributions of RNA lengths as a function of time. The surface plasmon resonance methods were used to monitor increases and decreases in the total mass of transcription elongation complexes in the same experiments. The different measures of transcription dynamics that these methods provide allow us to use them in combination to obtain a set of largely robust and well-defined kinetic parameters. The results show that our modular approach can be used to develop and test predictive kinetic schemes that can be fit to real transcription elongation data. They also suggest that these approaches can be extended to simulate the kinetics of other processes that involve the processive extension or shortening of nucleic acid chains and related systems of sequential branching reaction events.
Copyright © 2011 Biophysical Society. Published by Elsevier Inc. All rights reserved.

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Year:  2011        PMID: 21889454      PMCID: PMC3164171          DOI: 10.1016/j.bpj.2011.07.043

Source DB:  PubMed          Journal:  Biophys J        ISSN: 0006-3495            Impact factor:   4.033


  40 in total

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Authors:  Wenbing Zhang; Shi-Jie Chen
Journal:  Proc Natl Acad Sci U S A       Date:  2002-02-12       Impact factor: 11.205

2.  Shortening of RNA:DNA hybrid in the elongation complex of RNA polymerase is a prerequisite for transcription termination.

Authors:  Natalia Komissarova; Jodi Becker; Stephanie Solter; Maria Kireeva; Mikhail Kashlev
Journal:  Mol Cell       Date:  2002-11       Impact factor: 17.970

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Review 4.  The regulatory roles and mechanism of transcriptional pausing.

Authors:  R Landick
Journal:  Biochem Soc Trans       Date:  2006-12       Impact factor: 5.407

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Authors:  Sergei Borukhov; Evgeny Nudler
Journal:  Trends Microbiol       Date:  2008-02-14       Impact factor: 17.079

6.  RNA polymerase switches between inactivated and activated states By translocating back and forth along the DNA and the RNA.

Authors:  N Komissarova; M Kashlev
Journal:  J Biol Chem       Date:  1997-06-13       Impact factor: 5.157

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Authors:  K S Wilson; P H von Hippel
Journal:  Proc Natl Acad Sci U S A       Date:  1995-09-12       Impact factor: 11.205

8.  A model for the mechanism of polymerase translocation.

Authors:  R Guajardo; R Sousa
Journal:  J Mol Biol       Date:  1997-01-10       Impact factor: 5.469

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Authors:  J A Monforte; J D Kahn; J E Hearst
Journal:  Biochemistry       Date:  1990-08-28       Impact factor: 3.162

Review 10.  RNA polymerase active center: the molecular engine of transcription.

Authors:  Evgeny Nudler
Journal:  Annu Rev Biochem       Date:  2009       Impact factor: 23.643

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  4 in total

1.  Development of a "modular" scheme to describe the kinetics of transcript elongation by RNA polymerase.

Authors:  Sandra J Greive; Jim P Goodarzi; Steven E Weitzel; Peter H von Hippel
Journal:  Biophys J       Date:  2011-09-07       Impact factor: 4.033

Review 2.  The Mechanisms of Substrate Selection, Catalysis, and Translocation by the Elongating RNA Polymerase.

Authors:  Georgiy A Belogurov; Irina Artsimovitch
Journal:  J Mol Biol       Date:  2019-05-31       Impact factor: 5.469

3.  Nucleosomal elements that control the topography of the barrier to transcription.

Authors:  Lacramioara Bintu; Toyotaka Ishibashi; Manchuta Dangkulwanich; Yueh-Yi Wu; Lucyna Lubkowska; Mikhail Kashlev; Carlos Bustamante
Journal:  Cell       Date:  2012-11-09       Impact factor: 41.582

4.  A model for aryl hydrocarbon receptor-activated gene expression shows potency and efficacy changes and predicts squelching due to competition for transcription co-activators.

Authors:  Ted W Simon; Robert A Budinsky; J Craig Rowlands
Journal:  PLoS One       Date:  2015-06-03       Impact factor: 3.240

  4 in total

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