Literature DB >> 31511967

Path integral approach to generating functions for multistep post-transcription and post-translation processes and arbitrary initial conditions.

Jaroslav Albert1.   

Abstract

Stochastic fluctuations in the copy number of gene products have perceivable effects on the functioning of gene regulatory networks (GRNs). The master equation (ME) provides a theoretical basis for studying such effects. However, solving the ME can be a task that ranges from simple to difficult to impossible using conventional methods. Therefore, discovering new techniques for solving the ME is an important part of research on stochastic GRNs. In this paper, we present a novel approach to obtaining the generating function (GF), which contains the same information as the ME, for a one gene system that includes multi-step post-transcription and post-translation processes. The novelty of the approach lies in solving the GF for proteins in terms of a particular path taken by the partially and fully processed mRNAs in the time-copy number plane, after which the GF is summed over all possible paths. We prove a theorem that shows the summation of all paths to be equivalent to an equation similar to the ME for the mRNAs. On a system with six gene products in total and randomly selected initial conditions, we confirm the validity of our results by comparing them with Gillespie simulations.

Keywords:  Generating function; Master equation; Path integral; Stochastic gene expression

Year:  2019        PMID: 31511967     DOI: 10.1007/s00285-019-01426-4

Source DB:  PubMed          Journal:  J Math Biol        ISSN: 0303-6812            Impact factor:   2.259


  17 in total

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2.  Efficient formulation of the stochastic simulation algorithm for chemically reacting systems.

Authors:  Yang Cao; Hong Li; Linda Petzold
Journal:  J Chem Phys       Date:  2004-09-01       Impact factor: 3.488

3.  Multiscale stochastic modelling of gene expression.

Authors:  Pavol Bokes; John R King; Andrew T A Wood; Matthew Loose
Journal:  J Math Biol       Date:  2011-10-07       Impact factor: 2.259

4.  Avoiding negative populations in explicit Poisson tau-leaping.

Authors:  Yang Cao; Daniel T Gillespie; Linda R Petzold
Journal:  J Chem Phys       Date:  2005-08-01       Impact factor: 3.488

5.  Solving the chemical master equation for monomolecular reaction systems analytically.

Authors:  Tobias Jahnke; Wilhelm Huisinga
Journal:  J Math Biol       Date:  2006-09-05       Impact factor: 2.259

6.  Efficient step size selection for the tau-leaping simulation method.

Authors:  Yang Cao; Daniel T Gillespie; Linda R Petzold
Journal:  J Chem Phys       Date:  2006-01-28       Impact factor: 3.488

7.  Exact and approximate distributions of protein and mRNA levels in the low-copy regime of gene expression.

Authors:  Pavol Bokes; John R King; Andrew T A Wood; Matthew Loose
Journal:  J Math Biol       Date:  2011-06-08       Impact factor: 2.259

8.  A geometric analysis of fast-slow models for stochastic gene expression.

Authors:  Nikola Popović; Carsten Marr; Peter S Swain
Journal:  J Math Biol       Date:  2015-04-02       Impact factor: 2.259

9.  Stochastic hybrid models of gene regulatory networks - A PDE approach.

Authors:  Pavel Kurasov; Alexander Lück; Delio Mugnolo; Verena Wolf
Journal:  Math Biosci       Date:  2018-09-20       Impact factor: 2.144

10.  Selected-node stochastic simulation algorithm.

Authors:  Lorenzo Duso; Christoph Zechner
Journal:  J Chem Phys       Date:  2018-04-28       Impact factor: 3.488

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  1 in total

1.  Mixture distributions in a stochastic gene expression model with delayed feedback: a WKB approximation approach.

Authors:  Pavol Bokes; Alessandro Borri; Pasquale Palumbo; Abhyudai Singh
Journal:  J Math Biol       Date:  2020-06-24       Impact factor: 2.259

  1 in total

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