Literature DB >> 33351158

Statistics of Nascent and Mature RNA Fluctuations in a Stochastic Model of Transcriptional Initiation, Elongation, Pausing, and Termination.

Tatiana Filatova1,2, Nikola Popovic2, Ramon Grima3.   

Abstract

Recent advances in fluorescence microscopy have made it possible to measure the fluctuations of nascent (actively transcribed) RNA. These closely reflect transcription kinetics, as opposed to conventional measurements of mature (cellular) RNA, whose kinetics is affected by additional processes downstream of transcription. Here, we formulate a stochastic model which describes promoter switching, initiation, elongation, premature detachment, pausing, and termination while being analytically tractable. We derive exact closed-form expressions for the mean and variance of nascent RNA fluctuations on gene segments, as well as of total nascent RNA on a gene. We also obtain exact expressions for the first two moments of mature RNA fluctuations and approximate distributions for total numbers of nascent and mature RNA. Our results, which are verified by stochastic simulation, uncover the explicit dependence of the statistics of both types of RNA on transcriptional parameters and potentially provide a means to estimate parameter values from experimental data.

Entities:  

Keywords:  Distributions of RNA molecules; Master equation; RNA fluctuations; Singular perturbation theory; Stochastic gene expression; Stochastic simulations

Mesh:

Substances:

Year:  2020        PMID: 33351158      PMCID: PMC7755674          DOI: 10.1007/s11538-020-00827-7

Source DB:  PubMed          Journal:  Bull Math Biol        ISSN: 0092-8240            Impact factor:   1.758


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9.  Bursty gene expression in the intact mammalian liver.

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10.  Deciphering Transcriptional Dynamics In Vivo by Counting Nascent RNA Molecules.

Authors:  Sandeep Choubey; Jane Kondev; Alvaro Sanchez
Journal:  PLoS Comput Biol       Date:  2015-11-06       Impact factor: 4.475

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1.  Neural network aided approximation and parameter inference of non-Markovian models of gene expression.

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