IDEAS home Printed from https://ideas.repec.org/a/plo/pcbi00/1008572.html
   My bibliography  Save this article

Reconciling kinetic and thermodynamic models of bacterial transcription

Author

Listed:
  • Muir Morrison
  • Manuel Razo-Mejia
  • Rob Phillips

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.Author summary: With the advent of new technologies allowing us to query biological activity with ever increasing precision, the deluge of quantitative biological data demands quantitative models. Transcriptional regulation—a feature that lies at the core of our understanding of cellular control in myriad context ranging from development to disease—is no exception, with single-cell and single-molecule techniques being routinely deployed to study cellular decision making. These data have served as a fertile proving ground to test models of transcription that mainly come in two flavors: thermodynamic models (based on equilibrium statistical mechanics) and kinetic models (based on chemical kinetics). In this paper we study the correspondence between these theoretical frameworks in the context of the simple repression motif, a common regulatory architecture in prokaryotes in which a repressor with a single binding site regulates expression. We explore the consequences of different levels of coarse-graining of the molecular steps involved in transcription, finding that, at the level of mean gene expression, the different models cannot be distinguished. We then study higher moments of the gene expression distribution which allows us to discard several of the models that disagree with experimental data and supporting a minimal kinetic model.

Suggested Citation

  • Muir Morrison & Manuel Razo-Mejia & Rob Phillips, 2021. "Reconciling kinetic and thermodynamic models of bacterial transcription," PLOS Computational Biology, Public Library of Science, vol. 17(1), pages 1-30, January.
  • Handle: RePEc:plo:pcbi00:1008572
    DOI: 10.1371/journal.pcbi.1008572
    as

    Download full text from publisher

    File URL: https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1008572
    Download Restriction: no

    File URL: https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1008572&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pcbi.1008572?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Thomas B. Kepler & Timothy C. Elston, 2001. "Stochasticity in Transcriptional Regulation: Origins, Consequences and Mathematical Representations," Working Papers 01-06-033, Santa Fe Institute.
    2. Jason Gertz & Eric D. Siggia & Barak A. Cohen, 2009. "Analysis of combinatorial cis-regulation in synthetic and genomic promoters," Nature, Nature, vol. 457(7226), pages 215-218, January.
    3. Mattias Rydenfelt & Hernan G Garcia & Robert Sidney Cox III & Rob Phillips, 2014. "The Influence of Promoter Architectures and Regulatory Motifs on Gene Expression in Escherichia coli," PLOS ONE, Public Library of Science, vol. 9(12), pages 1-31, December.
    4. Arjun Raj & Charles S Peskin & Daniel Tranchina & Diana Y Vargas & Sanjay Tyagi, 2006. "Stochastic mRNA Synthesis in Mammalian Cells," PLOS Biology, Public Library of Science, vol. 4(10), pages 1-13, September.
    5. Niraj Kumar & Abhyudai Singh & Rahul V Kulkarni, 2015. "Transcriptional Bursting in Gene Expression: Analytical Results for General Stochastic Models," PLOS Computational Biology, Public Library of Science, vol. 11(10), pages 1-22, October.
    6. Bruno MC Martins & Peter S Swain, 2011. "Trade-Offs and Constraints in Allosteric Sensing," PLOS Computational Biology, Public Library of Science, vol. 7(11), pages 1-13, November.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Tatiana N. Lakhova & Fedor V. Kazantsev & Aleksey M. Mukhin & Nikolay A. Kolchanov & Yury G. Matushkin & Sergey A. Lashin, 2022. "Algorithm for the Reconstruction of Mathematical Frame Models of Bacterial Transcription Regulation," Mathematics, MDPI, vol. 10(23), pages 1-9, November.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Mohammad Soltani & Cesar A Vargas-Garcia & Duarte Antunes & Abhyudai Singh, 2016. "Intercellular Variability in Protein Levels from Stochastic Expression and Noisy Cell Cycle Processes," PLOS Computational Biology, Public Library of Science, vol. 12(8), pages 1-23, August.
    2. Singh, Abhyudai & Vahdat, Zahra & Xu, Zikai, 2019. "Time-triggered stochastic hybrid systems with two timer-dependent resets," OSF Preprints u8fzg, Center for Open Science.
    3. Marc S Sherman & Barak A Cohen, 2014. "A Computational Framework for Analyzing Stochasticity in Gene Expression," PLOS Computational Biology, Public Library of Science, vol. 10(5), pages 1-13, May.
    4. Zachary R Fox & Brian Munsky, 2019. "The finite state projection based Fisher information matrix approach to estimate information and optimize single-cell experiments," PLOS Computational Biology, Public Library of Science, vol. 15(1), pages 1-23, January.
    5. Amy L. Hughes & Aleksander T. Szczurek & Jessica R. Kelley & Anna Lastuvkova & Anne H. Turberfield & Emilia Dimitrova & Neil P. Blackledge & Robert J. Klose, 2023. "A CpG island-encoded mechanism protects genes from premature transcription termination," Nature Communications, Nature, vol. 14(1), pages 1-19, December.
    6. Alistair N Boettiger & Peter L Ralph & Steven N Evans, 2011. "Transcriptional Regulation: Effects of Promoter Proximal Pausing on Speed, Synchrony and Reliability," PLOS Computational Biology, Public Library of Science, vol. 7(5), pages 1-14, May.
    7. Matthieu Wyart & David Botstein & Ned S Wingreen, 2010. "Evaluating Gene Expression Dynamics Using Pairwise RNA FISH Data," PLOS Computational Biology, Public Library of Science, vol. 6(11), pages 1-14, November.
    8. Qiwen Sun & Zhaohang Cai & Chunjuan Zhu, 2022. "A Novel Dynamical Regulation of mRNA Distribution by Cross-Talking Pathways," Mathematics, MDPI, vol. 10(9), pages 1-14, May.
    9. Stuart Aitken & Marie-Cécile Robert & Ross D Alexander & Igor Goryanin & Edouard Bertrand & Jean D Beggs, 2010. "Processivity and Coupling in Messenger RNA Transcription," PLOS ONE, Public Library of Science, vol. 5(1), pages 1-12, January.
    10. Najme Khorasani & Mehdi Sadeghi & Abbas Nowzari-Dalini, 2020. "A computational model of stem cell molecular mechanism to maintain tissue homeostasis," PLOS ONE, Public Library of Science, vol. 15(7), pages 1-25, July.
    11. Elijah Roberts & Andrew Magis & Julio O Ortiz & Wolfgang Baumeister & Zaida Luthey-Schulten, 2011. "Noise Contributions in an Inducible Genetic Switch: A Whole-Cell Simulation Study," PLOS Computational Biology, Public Library of Science, vol. 7(3), pages 1-21, March.
    12. Ross D. Jones & Yili Qian & Katherine Ilia & Benjamin Wang & Michael T. Laub & Domitilla Del Vecchio & Ron Weiss, 2022. "Robust and tunable signal processing in mammalian cells via engineered covalent modification cycles," Nature Communications, Nature, vol. 13(1), pages 1-17, December.
    13. Keun-Young Kim & Jin Wang, 2007. "Potential Energy Landscape and Robustness of a Gene Regulatory Network: Toggle Switch," PLOS Computational Biology, Public Library of Science, vol. 3(3), pages 1-13, March.
    14. Jingyao Wang & Shihe Zhang & Hongfang Lu & Heng Xu, 2022. "Differential regulation of alternative promoters emerges from unified kinetics of enhancer-promoter interaction," Nature Communications, Nature, vol. 13(1), pages 1-14, December.
    15. Anissa Guillemin & Ronan Duchesne & Fabien Crauste & Sandrine Gonin-Giraud & Olivier Gandrillon, 2019. "Drugs modulating stochastic gene expression affect the erythroid differentiation process," PLOS ONE, Public Library of Science, vol. 14(11), pages 1-19, November.
    16. Rajesh Ramaswamy & Ivo F Sbalzarini & Nélido González-Segredo, 2011. "Noise-Induced Modulation of the Relaxation Kinetics around a Non-Equilibrium Steady State of Non-Linear Chemical Reaction Networks," PLOS ONE, Public Library of Science, vol. 6(1), pages 1-10, January.
    17. Anton J M Larsson & Christoph Ziegenhain & Michael Hagemann-Jensen & Björn Reinius & Tina Jacob & Tim Dalessandri & Gert-Jan Hendriks & Maria Kasper & Rickard Sandberg, 2021. "Transcriptional bursts explain autosomal random monoallelic expression and affect allelic imbalance," PLOS Computational Biology, Public Library of Science, vol. 17(3), pages 1-16, March.
    18. Chen, Aimin & Tian, Tianhai & Chen, Yiren & Zhou, Tianshou, 2022. "Stochastic analysis of a complex gene-expression model," Chaos, Solitons & Fractals, Elsevier, vol. 160(C).
    19. Cappelletti, Daniele & Pal Majumder, Abhishek & Wiuf, Carsten, 2021. "The dynamics of stochastic mono-molecular reaction systems in stochastic environments," Stochastic Processes and their Applications, Elsevier, vol. 137(C), pages 106-148.
    20. Manuel Cambón & Óscar Sánchez, 2022. "Thermodynamic Modelling of Transcriptional Control: A Sensitivity Analysis," Mathematics, MDPI, vol. 10(13), pages 1-18, June.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pcbi00:1008572. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: ploscompbiol (email available below). General contact details of provider: https://journals.plos.org/ploscompbiol/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.