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REVIEW article
Front. Genet.
Sec. Computational Genomics
Volume 15 - 2024 |
doi: 10.3389/fgene.2024.1451461
This article is part of the Research Topic Advancements in Sequencing Technologies for Epigenomic and Transcriptomic Analysis: From Bulk to Single-Cell Resolution View all articles
Transcriptional Bursting Dynamics in Gene Expression
Provisionally accepted- 1 Wuhan Textile University, Wuhan, China
- 2 School of Mathematics, Sun Yat-sen University, Guangzhou, Guangdong Province, China
Gene transcription is a stochastic process that occurs in all organisms. Transcriptional bursting, a critical molecular dynamics mechanism, creates significant heterogeneity in mRNA and protein levels. This heterogeneity drives cellular phenotypic diversity. Currently, the lack of a comprehensive quantitative model limits the research on transcriptional bursting. This review examines various gene expression models and compares their strengths and weaknesses to guide researchers in selecting the most suitable model for their research context. We also provide a detailed summary of the key metrics related to transcriptional bursting. We compared the temporal dynamics of transcriptional bursting across species and the molecular mechanisms influencing these bursts, and highlighted the spatiotemporal patterns of gene expression differences by utilizing metrics such as burst size and burst frequency. We summarized the strategies for modeling gene expression from both biostatistical and biochemical reaction network perspectives. Single-cell sequencing data and integrated multiomics approaches drive our exploration of cutting-edge trends in transcriptional bursting mechanisms. Moreover, we examined classical methods for parameter estimation that help capture dynamic parameters in gene expression data, assessing their merits and limitations to facilitate optimal parameter estimation. Our comprehensive summary and review of the current transcriptional burst dynamics theories provide deeper insights for promoting research on the nature of cell processes, cell fate determination, and cancer diagnosis.
Keywords: Transcriptional bursting, Single-cell sequencing data, data integration, Gene expression model, parameter inference
Received: 19 Jun 2024; Accepted: 30 Aug 2024.
Copyright: © 2024 Zhang, Cao, Wang, Yin, Sun, Tian, Hu, Tan and Zhang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Qiuyu Zhang, Wuhan Textile University, Wuhan, China
Jiaqi Wang, Wuhan Textile University, Wuhan, China
Yihao Yin, Wuhan Textile University, Wuhan, China
Rui Sun, Wuhan Textile University, Wuhan, China
Zunyi Tian, Wuhan Textile University, Wuhan, China
Yuhan Hu, Wuhan Textile University, Wuhan, China
Yalan Tan, Wuhan Textile University, Wuhan, China
Ben-gong Zhang, Wuhan Textile University, Wuhan, China
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