Literature DB >> 26986364

Gene expression dynamics with stochastic bursts: Construction and exact results for a coarse-grained model.

Yen Ting Lin1, Charles R Doering2.   

Abstract

We present a theoretical framework to analyze the dynamics of gene expression with stochastic bursts. Beginning with an individual-based model which fully accounts for the messenger RNA (mRNA) and protein populations, we propose an expansion of the master equation for the joint process. The resulting coarse-grained model reduces the dimensionality of the system, describing only the protein population while fully accounting for the effects of discrete and fluctuating mRNA population. Closed form expressions for the stationary distribution of the protein population and mean first-passage times of the coarse-grained model are derived and large-scale Monte Carlo simulations show that the analysis accurately describes the individual-based process accounting for mRNA population, in contrast to the failure of commonly proposed diffusion-type models.

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Year:  2016        PMID: 26986364     DOI: 10.1103/PhysRevE.93.022409

Source DB:  PubMed          Journal:  Phys Rev E        ISSN: 2470-0045            Impact factor:   2.529


  11 in total

1.  Scaling methods for accelerating kinetic Monte Carlo simulations of chemical reaction networks.

Authors:  Yen Ting Lin; Song Feng; William S Hlavacek
Journal:  J Chem Phys       Date:  2019-06-28       Impact factor: 3.488

2.  Gene expression noise is affected differentially by feedback in burst frequency and burst size.

Authors:  Pavol Bokes; Abhyudai Singh
Journal:  J Math Biol       Date:  2016-09-24       Impact factor: 2.259

Review 3.  Stochastic Modeling of Autoregulatory Genetic Feedback Loops: A Review and Comparative Study.

Authors:  James Holehouse; Zhixing Cao; Ramon Grima
Journal:  Biophys J       Date:  2020-02-25       Impact factor: 4.033

4.  Generalizing Gillespie's Direct Method to Enable Network-Free Simulations.

Authors:  Ryan Suderman; Eshan D Mitra; Yen Ting Lin; Keesha E Erickson; Song Feng; William S Hlavacek
Journal:  Bull Math Biol       Date:  2018-03-28       Impact factor: 1.758

5.  Optogenetic Control Reveals Differential Promoter Interpretation of Transcription Factor Nuclear Translocation Dynamics.

Authors:  Susan Y Chen; Lindsey C Osimiri; Michael Chevalier; Lukasz J Bugaj; Taylor H Nguyen; R A Greenstein; Andrew H Ng; Jacob Stewart-Ornstein; Lauren T Neves; Hana El-Samad
Journal:  Cell Syst       Date:  2020-09-07       Impact factor: 10.304

6.  The Switch in a Genetic Toggle System with Lévy Noise.

Authors:  Yong Xu; Yongge Li; Hao Zhang; Xiaofan Li; Jürgen Kurths
Journal:  Sci Rep       Date:  2016-08-19       Impact factor: 4.379

7.  Efficient and flexible implementation of Langevin simulation for gene burst production.

Authors:  Ching-Cher Sanders Yan; Surendhar Reddy Chepyala; Chao-Ming Yen; Chao-Ping Hsu
Journal:  Sci Rep       Date:  2017-12-04       Impact factor: 4.379

8.  A stochastic and dynamical view of pluripotency in mouse embryonic stem cells.

Authors:  Yen Ting Lin; Peter G Hufton; Esther J Lee; Davit A Potoyan
Journal:  PLoS Comput Biol       Date:  2018-02-16       Impact factor: 4.475

9.  Stochastic fluctuations can reveal the feedback signs of gene regulatory networks at the single-molecule level.

Authors:  Chen Jia; Peng Xie; Min Chen; Michael Q Zhang
Journal:  Sci Rep       Date:  2017-11-22       Impact factor: 4.379

10.  Efficient analysis of stochastic gene dynamics in the non-adiabatic regime using piecewise deterministic Markov processes.

Authors:  Yen Ting Lin; Nicolas E Buchler
Journal:  J R Soc Interface       Date:  2018-01       Impact factor: 4.118

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