Stochastic modelling and inference of temperature-dependent transcription supercoiling dynamics

    Research output: Other conference contributionAbstractScientific

    Abstract

    Based on a stochastic biophysical model of multi-step transcription based on empirical parameter values of the rate constants of a fully induced LacO3O1 promoter at optimal temperature, we hypothesize that temperature downshifts increase promoter escape times from supercoiled states as they enhance the energy barrier of this process. Next, from simulations of the model and statistical inference, we predict the consequences of this hypothesis on the mean and cell-to-cell variability in RNA numbers at low temperatures and on the rate-limiting steps of the multi-step transcription process. To validate our results, we produced empirical in vivo single RNA-level data from a single-copy LacO3O1 promoter, when chromosome-integrated and when plasmid-borne (impervious to supercoiling due to lack of topological barriers). We show that the original hypothesis and the predicted transcription kinetics alterations are highly accurate, in a statistical sense. The data further supports that this phenomenon is solely biophysical, as its kinetics rapidly changes following the temperature shifts. Thus, we conclude that our temperature-dependent transcription model is accurate within the range of 37 o C and 10 o C. In the future, we expect this validated temperature-dependent stochastic model of transcription for chromosome-integrated promoters to be of use in predicting the plasticity of temperature-sensitive synthetic genetic circuits.
    Original languageEnglish
    Publication statusPublished - 2018
    Publication typeNot Eligible

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