Literature DB >> 33926131

Stochastic Gene Expression Revisited.

Andrzej Tomski1, Maciej Zakarczemny2.   

Abstract

We investigate the model of gene expression in the form of Iterated Function System (IFS), where the probability of choice of any iterated map depends on the state of the phase space. Random jump times of the process mark activation periods of the gene when pre-mRNA molecules are produced before mRNA and protein processing phases occur. The main idea is inspired by the continuous-time piecewise deterministic Markov process describing stochastic gene expression. We show that for our system there exists a unique invariant limit measure. We provide full probabilistic description of the process with a comparison of our results to those obtained for the model with continuous time.

Entities:  

Keywords:  gene expression process simulation; iterated function system; limit measure; pre-mRNA; stochastic gene expression

Year:  2021        PMID: 33926131     DOI: 10.3390/genes12050648

Source DB:  PubMed          Journal:  Genes (Basel)        ISSN: 2073-4425            Impact factor:   4.096


  7 in total

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3.  On a stochastic gene expression with pre-mRNA, mRNA and protein contribution.

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Journal:  J Theor Biol       Date:  2015-10-03       Impact factor: 2.691

4.  Transcriptional stochasticity in gene expression.

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Journal:  J Theor Biol       Date:  2005-07-21       Impact factor: 2.691

5.  Steady-state fluctuations of a genetic feedback loop: an exact solution.

Authors:  R Grima; D R Schmidt; T J Newman
Journal:  J Chem Phys       Date:  2012-07-21       Impact factor: 3.488

6.  Discrete dynamical system modelling for gene regulatory networks of 5-hydroxymethylfurfural tolerance for ethanologenic yeast.

Authors:  M Song; Z Ouyang; Z L Liu
Journal:  IET Syst Biol       Date:  2009-05       Impact factor: 1.615

7.  60 years ago, Francis Crick changed the logic of biology.

Authors:  Matthew Cobb
Journal:  PLoS Biol       Date:  2017-09-18       Impact factor: 8.029

  7 in total

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