文献类型：期刊文献

英文题名：Analytical time-dependent dynamics of stochastic gene expression with sRNA-mRNA interactions

作者：Yu, Zhenhua[1];Wang, Yiling[1];Wang, Zhenyu[1];Shu, Zhanpeng[2];Cao, Zhixing[1,3]

机构：[1]East China Univ Sci & Technol, State Key Lab Bioreactor Engn, Shanghai, Peoples R China;[2]Shanghai Dianji Univ, Sch Elect Engn, Shanghai, Peoples R China;[3]Queens Univ, Dept Chem Engn, Kingston, ON, Canada

年份：2026

卷号：125

期号：7

起止页码：1645

外文期刊名：BIOPHYSICAL JOURNAL

收录：;WOS:【SCI-EXPANDED(收录号:WOS:001741346500001)】；

基金：This work is supported by an NSFC grant (62573195) , a Shanghai Action Plan for Technological Innovation grant (23S41900500) , and a Natural Science and Engineering Research Council of Canada's (NSERC's) Discovery grant (RGPIN-2024-06015) . We thank Dr. Ramon Grima at the University of Edinburgh for stimulating discussions.

语种：英文

摘要：The antagonistic interaction between small RNAs (sRNAs) and messenger RNAs (mRNAs) constitutes a fundamental regulatory mechanism of gene expression in both prokaryotic and eukaryotic cells. However, the stochastic nature of transcription renders mean-field approximations inadequate for quantitative analysis of such systems. In the regime of strong sRNA-mRNA antagonism, we generalize the conventional probability-generating function (PGF) framework and derive a novel approximate solution in the form of a generalized PGF, which can be analytically transformed into the time-dependent joint distribution of sRNA and mRNA via Laurent series expansion. The proposed approximation accurately captures the full stochastic dynamics across diverse systems exhibiting strong antagonism, while incorporating key biological features such as transcriptional burstiness, translation, and sRNA recycling over the entire temporal range. Building on this analytical foundation, we further develop a generalized-PGF-based parameter inference method that enables efficient and precise estimation of kinetic parameters, achieving inference speeds up to three orders of magnitude faster than traditional maximum-likelihood estimation approaches.