ArticleJournal of the Royal Society, Interface2021
Distinguishing between models of mammalian gene expression: telegraph-like models versus mechanistic models.
Article in Journal of the Royal Society, Interface, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- Invariant nonequilibrium dynamics in gene regulation optimize information flow.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Transient power-law behaviour following induction distinguishes between competing models of stochastic gene expression.Nature communications · 2025Article
- 3D chromatin architecture, BRD4, and Mediator have distinct roles in regulating genome-wide transcriptional bursting and gene network.Science advances · 2024Article
- Solving stochastic gene-expression models using queueing theory: A tutorial review.Biophysical journal · 2024Review
- What can we learn when fitting a simple telegraph model to a complex gene expression model?PLoS computational biology · 2024Article
- Power-law behavior of transcriptional bursting regulated by enhancer-promoter communication.Genome research · 2024Article
- Quantifying and correcting bias in transcriptional parameter inference from single-cell data.Biophysical journal · 2024Article
- Inferring delays in partially observed gene regulation processes.Bioinformatics (Oxford, England) · 2023Article
- The minimal intrinsic stochasticity of constitutively expressed eukaryotic genes is sub-Poissonian.Science advances · 2023Article
- Inference on autoregulation in gene expression with variance-to-mean ratio.Journal of mathematical biology · 2023Article
- Inferring transcriptional bursting kinetics from single-cell snapshot data using a generalized telegraph model.Royal Society open science · 2023Article
- The minimal intrinsic stochasticity of constitutively expressed eukaryotic genes is sub-Poissonian.bioRxiv : the preprint server for biology · 2023Article
- Gene expression model inference from snapshot RNA data using Bayesian non-parametrics.Nature computational science · 2023Article
- Regulatory Mechanisms for Transcriptional Bursting Revealed by an Event-Based Model.Research (Washington, D.C.) · 2023Article
- Dynamics and consequences of the HTLV-1 proviral plus-strand burst.PLoS pathogens · 2022Article
- Article
- Inference and uncertainty quantification of stochastic gene expression via synthetic models.Journal of the Royal Society, Interface · 2022Article
- Article
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3 authors.
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Abstract
Two-state models (telegraph-like models) have a successful history of predicting distributions of cellular and nascent mRNA numbers that can well fit experimental data. These models exclude key rate limiting steps, and hence it is unclear why they are able to accurately predict the number distributions. To answer this question, here we compare these models to a novel stochastic mechanistic model of transcription in mammalian cells that presents a unified description of transcriptional factor, polymerase and mature mRNA dynamics. We show that there is a large region of parameter space where the first, second and third moments of the distributions of the waiting times between two consecutively produced transcripts (nascent or mature) of two-state and mechanistic models exactly match. In this region: (i) one can uniquely express the two-state model parameters in terms of those of the mechanistic model, (ii) the models are practically indistinguishable by comparison of their transcript numbers distributions, and (iii) they are distinguishable from the shape of their waiting time distributions. Our results clarify the relationship between different gene expression models and identify a means to select between them from experimental data.
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