ArticlePLoS computational biology2015
Transcriptional Bursting in Gene Expression: Analytical Results for General Stochastic Models.
Article in PLoS computational biology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 56 papers.
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Who cites it
56 citing papers in PubMed, 238 citations in OpenAlex.
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- iNOME-seq: in vivo simultaneous genome-wide mapping of chromatin accessibility, nucleosome positioning, DNA-binding protein sites, and DNA methylation in Arabidopsis.Genome biology · 2025Article
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- Regulation of Transcriptional Bursting and Spatial Patterning in EarlybioRxiv : the preprint server for biology · 2025Article
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- Incorporating spatial diffusion into models of bursty stochastic transcription.Journal of the Royal Society, Interface · 2025Article
- Identification of molecular determinants of gene-specific bursting patterns by high-throughput imaging screens.Molecular cell · 2025Article
- Gene function revealed at the moment of stochastic gene silencingCommunications biology · 2025Article
- Deciphering HIV-1 Transcription Initiation and Elongation from Single-Molecule Imaging Data.Research (Washington, D.C.) · 2025Article
- Sequestration of gene products by decoys enhances precision in the timing of intracellular events.Scientific reports · 2024Article
- How Transcription Factor Clusters Shape the Transcriptional Landscape.Biomolecules · 2024Review
- Analysis of a detailed multi-stage model of stochastic gene expression using queueing theory and model reduction.Mathematical biosciences · 2024Article
- Identification of molecular determinants of gene-specific bursting patterns by high-throughput imaging screens.bioRxiv : the preprint server for biology · 2024Article
- Solving stochastic gene-expression models using queueing theory: A tutorial review.Biophysical journal · 2024Review
- Mitigating transcription noise via protein sharing in syncytial cells.Biophysical journal · 2024Article
- CHEX-seq detects single-cell genomic single-stranded DNA with catalytical potential.Nature communications · 2023Article
- Modelling capture efficiency of single-cell RNA-sequencing data improves inference of transcriptome-wide burst kinetics.Bioinformatics (Oxford, England) · 2023Article
- Inferring transcriptional bursting kinetics from single-cell snapshot data using a generalized telegraph model.Royal Society open science · 2023Article
- Regulatory Mechanisms for Transcriptional Bursting Revealed by an Event-Based Model.Research (Washington, D.C.) · 2023Article
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Gene expression in individual cells is highly variable and sporadic, often resulting in the synthesis of mRNAs and proteins in bursts. Such bursting has important consequences for cell-fate decisions in diverse processes ranging from HIV-1 viral infections to stem-cell differentiation. It is generally assumed that bursts are geometrically distributed and that they arrive according to a Poisson process. On the other hand, recent single-cell experiments provide evidence for complex burst arrival processes, highlighting the need for analysis of more general stochastic models. To address this issue, we invoke a mapping between general stochastic models of gene expression and systems studied in queueing theory to derive exact analytical expressions for the moments associated with mRNA/protein steady-state distributions. These results are then used to derive noise signatures, i.e. explicit conditions based entirely on experimentally measurable quantities, that determine if the burst distributions deviate from the geometric distribution or if burst arrival deviates from a Poisson process. For non-Poisson arrivals, we develop approaches for accurate estimation of burst parameters. The proposed approaches can lead to new insights into transcriptional bursting based on measurements of steady-state mRNA/protein distributions.
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