ArticleBiophysical journal2012
Transcription stochasticity of complex gene regulation models.
Article in Biophysical journal, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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23 citing papers in PubMed, 110 citations in OpenAlex.
- Article
- Two-State Stochastic Model of In Vivo Observations of Transcriptional Bursts.Brazilian journal of physics · 2025Article
- Leveraging gene correlations in single cell transcriptomic data.BMC bioinformatics · 2024Article
- Solving stochastic gene-expression models using queueing theory: A tutorial review.Biophysical journal · 2024Review
- GAiN: An integrative tool utilizing generative adversarial neural networks for augmented gene expression analysis.Patterns (New York, N.Y.) · 2024Article
- Transcriptional bursting dynamics in gene expression.Frontiers in genetics · 2024Review
- Leveraging gene correlations in single cell transcriptomic data.bioRxiv : the preprint server for biology · 2023Article
- Poisson representation: a bridge between discrete and continuous models of stochastic gene regulatory networks.Journal of the Royal Society, Interface · 2023Article
- Deciphering a global source of non-genetic heterogeneity in cancer cells.Nucleic acids research · 2023Article
- Inferring transcriptional bursting kinetics from single-cell snapshot data using a generalized telegraph model.Royal Society open science · 2023Article
- RNA cytometry of single-cells using semi-permeable microcapsules.Nucleic acids research · 2023Article
- Regulatory Mechanisms for Transcriptional Bursting Revealed by an Event-Based Model.Research (Washington, D.C.) · 2023Article
- Queuing Models of Gene Expression: Analytical Distributions and Beyond.Biophysical journal · 2020Article
- Phenotypic selection through cell death: stochastic modelling of O-6-methylguanine-DNA methyltransferase dynamics.Royal Society open science · 2020Article
- Limit theorems for generalized density-dependent Markov chains and bursty stochastic gene regulatory networks.Journal of mathematical biology · 2020Article
- Models of protein production along the cell cycle: An investigation of possible sources of noise.PloS one · 2020Article
- Markovian approaches to modeling intracellular reaction processes with molecular memory.Proceedings of the National Academy of Sciences of the United States of America · 2019Article
- Dissecting the stochastic transcription initiation process in live Escherichia coli.DNA research : an international journal for rapid publication of reports on genes and genomes · 2016Article
- Multiplex Eukaryotic Transcription (In)activation: Timing, Bursting and Cycling of a Ratchet Clock Mechanism.PLoS computational biology · 2015Article
- Effects of promoter leakage on dynamics of gene expression.BMC systems biology · 2015Article
Corrections and comments
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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
Transcription is regulated by a multitude of factors that concertedly induce genes to switch between activity states. Eukaryotic transcription involves a multitude of complexes that sequentially assemble on chromatin under the influence of transcription factors and the dynamic state of chromatin. Prokaryotic transcription depends on transcription factors, sigma-factors, and, in some cases, on DNA looping. We present a stochastic model of transcription that considers these complex regulatory mechanisms. We coarse-grain the molecular details in such a way that the model can describe a broad class of gene-regulation mechanisms. We solve this model analytically for various measures of stochastic transcription and compare alternative gene-regulation designs. We find that genes with complex multiprotein regulation can have peaked burst-size distributions in contrast to the geometric distributions found for simple models of transcription regulation. Burst-size distributions are, in addition, shaped by mRNA degradation during transcription bursts. We derive the stochastic properties of genes in the limit of deterministic switch times. These genes typically have reduced transcription noise. Severe timescale separation between gene regulation and transcription initiation enhances noise and leads to bimodal mRNA copy number distributions. In general, complex mechanisms for gene regulation lead to nonexponential waiting-time distributions for gene switching and transcription initiation, which typically reduce noise in mRNA copy numbers and burst size. Finally, we discuss that qualitatively different gene regulation models can often fit the same experimental data on single-cell mRNA abundance even though they have qualitatively different burst-size statistics and regulatory parameters.
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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.