Literature DB >> 29347590

Emergent Lévy behavior in single-cell stochastic gene expression.

Chen Jia1, Michael Q Zhang2,3, Hong Qian4.   

Abstract

Single-cell gene expression is inherently stochastic; its emergent behavior can be defined in terms of the chemical master equation describing the evolution of the mRNA and protein copy numbers as the latter tends to infinity. We establish two types of "macroscopic limits": the Kurtz limit is consistent with the classical chemical kinetics, while the Lévy limit provides a theoretical foundation for an empirical equation proposed in N. Friedman et al., Phys. Rev. Lett. 97, 168302 (2006)PRLTAO0031-900710.1103/PhysRevLett.97.168302. Furthermore, we clarify the biochemical implications and ranges of applicability for various macroscopic limits and calculate a comprehensive analytic expression for the protein concentration distribution in autoregulatory gene networks. The relationship between our work and modern population genetics is discussed.

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Year:  2017        PMID: 29347590     DOI: 10.1103/PhysRevE.96.040402

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


  8 in total

Review 1.  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

2.  Limit theorems for generalized density-dependent Markov chains and bursty stochastic gene regulatory networks.

Authors:  Xian Chen; Chen Jia
Journal:  J Math Biol       Date:  2019-11-21       Impact factor: 2.259

3.  Modeling bursty transcription and splicing with the chemical master equation.

Authors:  Gennady Gorin; Lior Pachter
Journal:  Biophys J       Date:  2022-02-07       Impact factor: 4.033

4.  Spatially and Temporally Distributed Complexity-A Refreshed Framework for the Study of GRN Evolution.

Authors:  Alessandro Minelli; Alberto Valero-Gracia
Journal:  Cells       Date:  2022-05-30       Impact factor: 7.666

5.  Dissecting transition cells from single-cell transcriptome data through multiscale stochastic dynamics.

Authors:  Peijie Zhou; Shuxiong Wang; Tiejun Li; Qing Nie
Journal:  Nat Commun       Date:  2021-09-23       Impact factor: 17.694

6.  Concentration fluctuations in growing and dividing cells: Insights into the emergence of concentration homeostasis.

Authors:  Chen Jia; Abhyudai Singh; Ramon Grima
Journal:  PLoS Comput Biol       Date:  2022-10-04       Impact factor: 4.779

7.  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

8.  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

  8 in total

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