IDEAS home Printed from https://ideas.repec.org/a/plo/pcbi00/1006717.html
   My bibliography  Save this article

Bayesian inference and comparison of stochastic transcription elongation models

Author

Listed:
  • Jordan Douglas
  • Richard Kingston
  • Alexei J Drummond

Abstract

Transcription elongation can be modelled as a three step process, involving polymerase translocation, NTP binding, and nucleotide incorporation into the nascent mRNA. This cycle of events can be simulated at the single-molecule level as a continuous-time Markov process using parameters derived from single-molecule experiments. Previously developed models differ in the way they are parameterised, and in their incorporation of partial equilibrium approximations. We have formulated a hierarchical network comprised of 12 sequence-dependent transcription elongation models. The simplest model has two parameters and assumes that both translocation and NTP binding can be modelled as equilibrium processes. The most complex model has six parameters makes no partial equilibrium assumptions. We systematically compared the ability of these models to explain published force-velocity data, using approximate Bayesian computation. This analysis was performed using data for the RNA polymerase complexes of E. coli, S. cerevisiae and Bacteriophage T7. Our analysis indicates that the polymerases differ significantly in their translocation rates, with the rates in T7 pol being fast compared to E. coli RNAP and S. cerevisiae pol II. Different models are applicable in different cases. We also show that all three RNA polymerases have an energetic preference for the posttranslocated state over the pretranslocated state. A Bayesian inference and model selection framework, like the one presented in this publication, should be routinely applicable to the interrogation of single-molecule datasets.Author summary: Transcription is a critical biological process which occurs in all living organisms. It involves copying the organism’s genetic material into messenger RNA (mRNA) which directs protein synthesis on the ribosome. Transcription is performed by RNA polymerases which have been extensively studied using both ensemble and single-molecule techniques. Single-molecule data provides unique insights into the molecular behaviour of RNA polymerases. Transcription at the single-molecule level can be computationally simulated as a continuous-time Markov process and the model outputs compared with experimental data. In this study we use Bayesian techniques to perform a systematic comparison of 12 stochastic models of transcriptional elongation. We demonstrate how equilibrium approximations can strengthen or weaken the model, and show how Bayesian techniques can identify necessary or unnecessary model parameters. We describe a framework to a) simulate, b) perform inference on, and c) compare models of transcription elongation.

Suggested Citation

  • Jordan Douglas & Richard Kingston & Alexei J Drummond, 2020. "Bayesian inference and comparison of stochastic transcription elongation models," PLOS Computational Biology, Public Library of Science, vol. 16(2), pages 1-21, February.
  • Handle: RePEc:plo:pcbi00:1006717
    DOI: 10.1371/journal.pcbi.1006717
    as

    Download full text from publisher

    File URL: https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1006717
    Download Restriction: no

    File URL: https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1006717&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pcbi.1006717?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. Joshua W. Shaevitz & Elio A. Abbondanzieri & Robert Landick & Steven M. Block, 2003. "Backtracking by single RNA polymerase molecules observed at near-base-pair resolution," Nature, Nature, vol. 426(6967), pages 684-687, December.
    2. Eric A. Galburt & Stephan W. Grill & Anna Wiedmann & Lucyna Lubkowska & Jason Choy & Eva Nogales & Mikhail Kashlev & Carlos Bustamante, 2007. "Backtracking determines the force sensitivity of RNAP II in a factor-dependent manner," Nature, Nature, vol. 446(7137), pages 820-823, April.
    3. Elio A. Abbondanzieri & William J. Greenleaf & Joshua W. Shaevitz & Robert Landick & Steven M. Block, 2005. "Direct observation of base-pair stepping by RNA polymerase," Nature, Nature, vol. 438(7067), pages 460-465, November.
    4. Unknown, 2005. "Forward," 2005 Conference: Slovenia in the EU - Challenges for Agriculture, Food Science and Rural Affairs, November 10-11, 2005, Moravske Toplice, Slovenia 183804, Slovenian Association of Agricultural Economists (DAES).
    Full references (including those not matched with items on IDEAS)

    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. Pilar Lopez-Llompart & G. Mathias Kondolf, 2016. "Encroachments in floodways of the Mississippi River and Tributaries Project," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 81(1), pages 513-542, March.
    2. Michelle Sheran Sylvester, 2007. "The Career and Family Choices of Women: A Dynamic Analysis of Labor Force Participation, Schooling, Marriage and Fertility Decisions," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 10(3), pages 367-399, July.
    3. DAVID M. BLAU & WILBERT van der KLAAUW, 2013. "What Determines Family Structure?," Economic Inquiry, Western Economic Association International, vol. 51(1), pages 579-604, January.
    4. Afanasyev, Dmitriy O. & Fedorova, Elena A. & Popov, Viktor U., 2015. "Fine structure of the price–demand relationship in the electricity market: Multi-scale correlation analysis," Energy Economics, Elsevier, vol. 51(C), pages 215-226.
    5. Peter Viggo Jakobsen, 2009. "Small States, Big Influence: The Overlooked Nordic Influence on the Civilian ESDP," Journal of Common Market Studies, Wiley Blackwell, vol. 47(1), pages 81-102, January.
    6. Billio, Monica & Casarin, Roberto & Osuntuyi, Anthony, 2016. "Efficient Gibbs sampling for Markov switching GARCH models," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 37-57.
    7. Jan Babecký & Fabrizio Coricelli & Roman Horváth, 2009. "Assessing Inflation Persistence: Micro Evidence on an Inflation Targeting Economy," Czech Journal of Economics and Finance (Finance a uver), Charles University Prague, Faculty of Social Sciences, vol. 59(2), pages 102-127, June.
    8. Lloyd, S. P., 2017. "Unconventional Monetary Policy and the Interest Rate Channel: Signalling and Portfolio Rebalancing," Cambridge Working Papers in Economics 1735, Faculty of Economics, University of Cambridge.
    9. Ichiro Fukunaga, 2007. "Imperfect Common Knowledge, Staggered Price Setting, and the Effects of Monetary Policy," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 39(7), pages 1711-1739, October.
    10. Albertazzi, Ugo & Gambacorta, Leonardo, 2009. "Bank profitability and the business cycle," Journal of Financial Stability, Elsevier, vol. 5(4), pages 393-409, December.
    11. Beck, Thorsten & Demirgüç-Kunt, Asli & Merrouche, Ouarda, 2013. "Islamic vs. conventional banking: Business model, efficiency and stability," Journal of Banking & Finance, Elsevier, vol. 37(2), pages 433-447.
    12. Jinho Bae & Chang-Jin Kim & Dong Kim, 2012. "The evolution of the monetary policy regimes in the U.S," Empirical Economics, Springer, vol. 43(2), pages 617-649, October.
    13. McMahon, Rob, 2020. "Co-developing digital inclusion policy and programming with indigenous partners: Interventions from Canada," Internet Policy Review: Journal on Internet Regulation, Alexander von Humboldt Institute for Internet and Society (HIIG), Berlin, vol. 9(2), pages 1-26.
    14. George W. Evans & Seppo Honkapohja, 2009. "Robust Learning Stability with Operational Monetary Policy Rules," Central Banking, Analysis, and Economic Policies Book Series, in: Klaus Schmidt-Hebbel & Carl E. Walsh & Norman Loayza (Series Editor) & Klaus Schmidt-Hebbel (Series (ed.),Monetary Policy under Uncertainty and Learning, edition 1, volume 13, chapter 5, pages 145-170, Central Bank of Chile.
    15. Lehtonen, Heikki & Kujala, Sanna, 2007. "Climate change impacts on crop risks and agricultural production in Finland," 101st Seminar, July 5-6, 2007, Berlin Germany 9259, European Association of Agricultural Economists.
    16. Michael Pomerleano, 2011. "Developing Regional Financial Markets – the Case of East Asia," Chapters, in: Ulrich Volz (ed.), Regional Integration, Economic Development and Global Governance, chapter 9, Edward Elgar Publishing.
    17. Gary Charness & Francesco Feri & Miguel A. Meléndez-Jiménez & Matthias Sutter, 2023. "An Experimental Study on the Effects of Communication, Credibility, and Clustering in Network Games," The Review of Economics and Statistics, MIT Press, vol. 105(6), pages 1530-1543, November.
    18. Kitsul, Yuriy & Wright, Jonathan H., 2013. "The economics of options-implied inflation probability density functions," Journal of Financial Economics, Elsevier, vol. 110(3), pages 696-711.
    19. Dieter Balkenborg & Rosemarie Nagel, 2016. "An Experiment on Forward vs. Backward Induction: How Fairness and Level k Reasoning Matter," German Economic Review, Verein für Socialpolitik, vol. 17(3), pages 378-408, August.
    20. J. Park & T. P. Seager & P. S. C. Rao & M. Convertino & I. Linkov, 2013. "Integrating Risk and Resilience Approaches to Catastrophe Management in Engineering Systems," Risk Analysis, John Wiley & Sons, vol. 33(3), pages 356-367, March.

    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:pcbi00:1006717. 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.