Evidence map›Paper›PMID 42547851›Full record

ArticleMolecular systems biology2026

Deciphering global transcriptional dynamics coordinated by gene-gene regulatory interactions using single-cell data.

Liying Zhou, Songhao Luo, Zhiwei Huang, Zhenquan Zhang, Zihao Wang, Jiajun Zhang

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Article in Molecular systems biology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Liying Zhou *School of Mathematics, Sun Yat-sen University, Guangzhou, 510275, China.ORCID http://orcid.org/0009-0004-9648-2696
Songhao Luo *Department of Mathematics, University of California Irvine, Irvine, CA, 92627, USA.ORCID http://orcid.org/0000-0003-1162-9608
Zhiwei Huang *School of Mathematics, Sun Yat-sen University, Guangzhou, 510275, China.
Zhenquan ZhangSchool of Mathematics and Statistics, Guangdong University of Technology, Guangzhou, 510520, China.ORCID http://orcid.org/0000-0002-2913-4905
Zihao WangSchool of Mathematics, Sun Yat-sen University, Guangzhou, 510275, China.ORCID http://orcid.org/0000-0001-5440-843X
Jiajun ZhangSchool of Mathematics, Sun Yat-sen University, Guangzhou, 510275, China. zhjiajun@mail.sysu.edu.cn.ORCID http://orcid.org/0000-0001-7107-4814

Funding

Guangdong Basic and Applied Basic Research Foundation 2022A1515011540Guangdong Basic and Applied Basic Research Foundation 2023A1515110273Guangdong Basic and Applied Basic Research Foundation 2024A1515012786Guangdong Basic and Applied Basic Research Foundation 2026A1515011801Guangdong Provincial Key Laboratory of Mathematical and Neural Dynamical Systems 2024B1212010004Key-area research and development program of Guangzhou 2019B110233002Key-area research and development program of Guangzhou 202007030004MOST | National Key Research and Development Program of China (NKPs) 2021YFA1302500MOST | National Natural Science Foundation of China (NSFC) 12171494MOST | National Natural Science Foundation of China (NSFC) 12301646MOST | National Natural Science Foundation of China (NSFC) 12501700
6 · The paper itself

Abstract

Transcription is an inherently dynamic and stochastic process that often occurs in bursts, governed by gene-gene regulatory interactions and thereby driving cell-to-cell heterogeneity. However, a genome-wide, mechanistic understanding of how regulatory networks globally shape transcriptional bursting dynamics remains lacking. Here, we present BurstLink, an interpretable and tractable statistical-mechanistic framework that simultaneously infers coupled regulatory interactions and transcriptional bursting kinetics at the genome-wide scale from single-cell data. BurstLink introduces reweighted mutual information to quantify regulatory strength as network edge weights, while jointly inferring regulatory directionality and interaction type for each gene pair within a unified mechanistic model of transcriptional bursting. Applied to mouse embryonic fibroblasts data, BurstLink reveals several genome-wide regulatory mechanisms on transcriptional bursting: downstream target genes exhibit higher burst frequency and gene-expression variability than upstream transcription factor genes; stronger transcription factor binding affinity is associated with lower burst frequency and higher burst size of target genes. Notably, positive regulation primarily enhances the burst frequency and gene-expression variability in target genes, in contrast to negative regulation. In summary, BurstLink deciphers multiple general principles of global transcriptional dynamics, providing novel biological insights into cell fate decisions.

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.