Literature DB >> 33465069

Reconciling kinetic and thermodynamic models of bacterial transcription.

Muir Morrison1, Manuel Razo-Mejia2, Rob Phillips1,2.   

Abstract

The study of transcription remains one of the centerpieces of modern biology with implications in settings from development to metabolism to evolution to disease. Precision measurements using a host of different techniques including fluorescence and sequencing readouts have raised the bar for what it means to quantitatively understand transcriptional regulation. In particular our understanding of the simplest genetic circuit is sufficiently refined both experimentally and theoretically that it has become possible to carefully discriminate between different conceptual pictures of how this regulatory system works. This regulatory motif, originally posited by Jacob and Monod in the 1960s, consists of a single transcriptional repressor binding to a promoter site and inhibiting transcription. In this paper, we show how seven distinct models of this so-called simple-repression motif, based both on thermodynamic and kinetic thinking, can be used to derive the predicted levels of gene expression and shed light on the often surprising past success of the thermodynamic models. These different models are then invoked to confront a variety of different data on mean, variance and full gene expression distributions, illustrating the extent to which such models can and cannot be distinguished, and suggesting a two-state model with a distribution of burst sizes as the most potent of the seven for describing the simple-repression motif.

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Year:  2021        PMID: 33465069      PMCID: PMC7845990          DOI: 10.1371/journal.pcbi.1008572

Source DB:  PubMed          Journal:  PLoS Comput Biol        ISSN: 1553-734X            Impact factor:   4.475


  68 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.  Dynamic competition between transcription initiation and repression: Role of nonequilibrium steps in cell-to-cell heterogeneity.

Authors:  Namiko Mitarai; Szabolcs Semsey; Kim Sneppen
Journal:  Phys Rev E Stat Nonlin Soft Matter Phys       Date:  2015-08-13

Review 3.  How transcription factors can adjust the gene expression floodgates.

Authors:  Denis Michel
Journal:  Prog Biophys Mol Biol       Date:  2009-12-16       Impact factor: 3.667

Review 4.  Philosophy and the practice of Bayesian statistics.

Authors:  Andrew Gelman; Cosma Rohilla Shalizi
Journal:  Br J Math Stat Psychol       Date:  2012-02-24       Impact factor: 3.380

5.  Stochastic models of transcription: from single molecules to single cells.

Authors:  Alvaro Sanchez; Sandeep Choubey; Jane Kondev
Journal:  Methods       Date:  2013-04-01       Impact factor: 3.608

6.  The OR control system of bacteriophage lambda. A physical-chemical model for gene regulation.

Authors:  M A Shea; G K Ackers
Journal:  J Mol Biol       Date:  1985-01-20       Impact factor: 5.469

Review 7.  Figure 1 Theory Meets Figure 2 Experiments in the Study of Gene Expression.

Authors:  Rob Phillips; Nathan M Belliveau; Griffin Chure; Hernan G Garcia; Manuel Razo-Mejia; Clarissa Scholes
Journal:  Annu Rev Biophys       Date:  2019-05-06       Impact factor: 12.981

Review 8.  Modeling network dynamics: the lac operon, a case study.

Authors:  José M G Vilar; Călin C Guet; Stanislas Leibler
Journal:  J Cell Biol       Date:  2003-05-12       Impact factor: 10.539

9.  Quality and position of the three lac operators of E. coli define efficiency of repression.

Authors:  S Oehler; M Amouyal; P Kolkhof; B von Wilcken-Bergmann; B Müller-Hill
Journal:  EMBO J       Date:  1994-07-15       Impact factor: 11.598

10.  The quantitative and condition-dependent Escherichia coli proteome.

Authors:  Alexander Schmidt; Karl Kochanowski; Silke Vedelaar; Erik Ahrné; Benjamin Volkmer; Luciano Callipo; Kèvin Knoops; Manuel Bauer; Ruedi Aebersold; Matthias Heinemann
Journal:  Nat Biotechnol       Date:  2015-12-07       Impact factor: 54.908

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