Literature DB >> 32384856

Dynamical phase diagram of an auto-regulating gene in fast switching conditions.

Chen Jia1, Ramon Grima2.   

Abstract

While the steady-state behavior of stochastic gene expression with auto-regulation has been extensively studied, its time-dependent behavior has received much less attention. Here, under the assumption of fast promoter switching, we derive and solve a reduced chemical master equation for an auto-regulatory gene circuit with translational bursting and cooperative protein-gene interactions. The analytical expression for the time-dependent probability distribution of protein numbers enables a fast exploration of large swaths of the parameter space. For a unimodal initial distribution, we identify three distinct types of stochastic dynamics: (i) the protein distribution remains unimodal at all times; (ii) the protein distribution becomes bimodal at intermediate times and then reverts back to being unimodal at long times (transient bimodality); and (iii) the protein distribution switches to being bimodal at long times. For each of these, the deterministic model predicts either monostable or bistable behavior, and hence, there exist six dynamical phases in total. We investigate the relationship of the six phases to the transcription rates, the protein binding and unbinding rates, the mean protein burst size, the degree of cooperativity, the relaxation time to the steady state, the protein mean, and the type of feedback loop (positive or negative). We show that transient bimodality is a noise-induced phenomenon that occurs when the protein expression is sufficiently bursty, and we use a theory to estimate the observation time window when it is manifested.

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Year:  2020        PMID: 32384856     DOI: 10.1063/5.0007221

Source DB:  PubMed          Journal:  J Chem Phys        ISSN: 0021-9606            Impact factor:   3.488


  4 in total

1.  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

2.  Inference and uncertainty quantification of stochastic gene expression via synthetic models.

Authors:  Kaan Öcal; Michael U Gutmann; Guido Sanguinetti; Ramon Grima
Journal:  J R Soc Interface       Date:  2022-07-13       Impact factor: 4.293

3.  Using average transcription level to understand the regulation of stochastic gene activation.

Authors:  Liang Chen; Genghong Lin; Feng Jiao
Journal:  R Soc Open Sci       Date:  2022-02-16       Impact factor: 2.963

4.  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

  4 in total

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