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Exact distributions for stochastic models of gene expression with arbitrary regulation

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Abstract

Stochasticity in gene expression can result in uctuations in gene product levels. Recent experiments indicated that feedback regulation plays an important role in controlling the noise in gene expression. A quantitative understanding of the feedback effect on gene expression requires analysis of the corresponding stochastic model. However, for stochastic models of gene expression with general regulation functions, exact analytical results for gene product distributions have not been given so far. Here, we propose a technique to solve a generalized ON-OFF model of stochastic gene expression with arbitrary (positive or negative, linear or nonlinear) feedbacks including posttranscriptional or posttranslational regulation. The obtained results, which generalize results obtained previously, provide new insights into the role of feedback in regulating gene expression. The proposed analytical framework can easily be extended to analysis of more complex models of stochastic gene expression.

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Acknowledgements

This work was supported by National Natural Science Foundation of China (Grant Nos. 11931019, 11775314 and 91530320).

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Authors and Affiliations

  1. School of Mathematics, Sun Yat-sen University, Guangzhou, 510275, China

    Zihao Wang, Zhenquan Zhang & Tianshou Zhou

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  1. Zihao Wang
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  2. Zhenquan Zhang
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  3. Tianshou Zhou
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Correspondence to Tianshou Zhou.

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Wang, Z., Zhang, Z. & Zhou, T. Exact distributions for stochastic models of gene expression with arbitrary regulation. Sci. China Math. 63, 485–500 (2020). https://doi.org/10.1007/s11425-019-1622-8

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  • DOI: https://doi.org/10.1007/s11425-019-1622-8

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