ArticleNature communications2022
Interpretable and tractable models of transcriptional noise for the rational design of single-molecule quantification experiments.
Article in Nature communications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
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Who cites it
27 citing papers in PubMed.
- R-package agentBayes: Likelihood-based statistical methods for agent-based models.PLoS computational biology · 2026Article
- Biophysical constraints on mRNA decay rates shape macroevolutionary divergence in steady-state abundances.bioRxiv : the preprint server for biology · 2025Article
- Monod: model-based discovery and integration through fitting stochastic transcriptional dynamics to single-cell sequencing data.Nature methods · 2025Article
- Learning stochastic processes with intrinsic noise from cross-sectional biological data.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Cell-cycle dependence of bursty gene expression: insights from fitting mechanistic models to single-cell RNA-seq data.Nucleic acids research · 2025Article
- Incorporating spatial diffusion into models of bursty stochastic transcription.Journal of the Royal Society, Interface · 2025Article
- kallisto, bustools and kb-python for quantifying bulk, single-cell and single-nucleus RNA-seq.Nature protocols · 2025Review
- Spectral neural approximations for models of transcriptional dynamics.Biophysical journal · 2024Article
- Transcriptional bursting: from fundamentals to novel insights.Biochemical Society transactions · 2024Review
- Biophysical modeling with variational autoencoders for bimodal, single-cell RNA sequencing data.Nature methods · 2024Article
- Inferring Stochastic Rates from Heterogeneous Snapshots of Particle Positions.Bulletin of mathematical biology · 2024Article
- Dissection and integration of bursty transcriptional dynamics for complex systems.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- kallisto, bustools, and kb-python for quantifying bulk, single-cell, and single-nucleus RNA-seq.bioRxiv : the preprint server for biology · 2024Article
- Quantifying and correcting bias in transcriptional parameter inference from single-cell data.Biophysical journal · 2024Article
- Transcriptional bursting dynamics in gene expression.Frontiers in genetics · 2024Review
- Article
- Review
- Biophysically Interpretable Inference of Cell Types from Multimodal Sequencing Data.bioRxiv : the preprint server for biology · 2023Article
- The specious art of single-cell genomics.PLoS computational biology · 2023Article
- Dissection and Integration of Bursty Transcriptional Dynamics for Complex Systems.bioRxiv : the preprint server for biology · 2023Article
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Authors and funding
4 authors.
Funding
Abstract
The question of how cell-to-cell differences in transcription rate affect RNA count distributions is fundamental for understanding biological processes underlying transcription. Answering this question requires quantitative models that are both interpretable (describing concrete biophysical phenomena) and tractable (amenable to mathematical analysis). This enables the identification of experiments which best discriminate between competing hypotheses. As a proof of principle, we introduce a simple but flexible class of models involving a continuous stochastic transcription rate driving a discrete RNA transcription and splicing process, and compare and contrast two biologically plausible hypotheses about transcription rate variation. One assumes variation is due to DNA experiencing mechanical strain, while the other assumes it is due to regulator number fluctuations. We introduce a framework for numerically and analytically studying such models, and apply Bayesian model selection to identify candidate genes that show signatures of each model in single-cell transcriptomic data from mouse glutamatergic neurons.
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Registered trials
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.