ReviewMethods (San Diego, Calif.)2015
Integrating single-molecule experiments and discrete stochastic models to understand heterogeneous gene transcription dynamics.
Review in Methods (San Diego, Calif.), 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers.
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Who cites it
37 citing papers in PubMed.
- Paradigms, innovations, and biological applications of RNA velocity: a comprehensive review.Briefings in bioinformatics · 2025Review
- Incorporating spatial diffusion into models of bursty stochastic transcription.Journal of the Royal Society, Interface · 2025Article
- An integrative phenotype-structured partial differential equation model for the population dynamics of epithelial-mesenchymal transition.NPJ systems biology and applications · 2025Article
- Quantifying cell fate change under different stochastic gene activation frameworks.Quantitative biology (Beijing, China) · 2025Article
- Decoding stimulus-specific regulation of promoter activity of p53 target genes.Frontiers in cell and developmental biology · 2025Article
- Spectral neural approximations for models of transcriptional dynamics.Biophysical journal · 2024Article
- Stochastic Modeling of Biophysical Responses to Perturbation.bioRxiv : the preprint server for biology · 2024Article
- Inferring Stochastic Rates from Heterogeneous Snapshots of Particle Positions.Bulletin of mathematical biology · 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
- Article
- Biophysically Interpretable Inference of Cell Types from Multimodal Sequencing Data.bioRxiv : the preprint server for biology · 2023Article
- Single-cell analysis of multiple invertible promoters reveals differential inversion rates as a strong determinant of bacterial population heterogeneity.Science advances · 2023Article
- Monte Carlo samplers for efficient network inference.PLoS computational biology · 2023Article
- Gene expression model inference from snapshot RNA data using Bayesian non-parametrics.Nature computational science · 2023Article
- Choosing the Probe for Single-Molecule Fluorescence Microscopy.International journal of molecular sciences · 2022Review
- Molecular Origins of Transcriptional Heterogeneity in Diazotrophic Klebsiella oxytoca.mSystems · 2022Article
- RNA velocity unraveled.PLoS computational biology · 2022Article
- Using average transcription level to understand the regulation of stochastic gene activation.Royal Society open science · 2022Article
- Tenuous transcriptional threshold of human sex determination. II. SRY exploits water-mediated clamp at the edge of ambiguity.Frontiers in endocrinology · 2022Article
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Authors and funding
3 authors.
Funding
Abstract
The production and degradation of RNA transcripts is inherently subject to biological noise that arises from small gene copy numbers in individual cells. As a result, cellular RNA levels can exhibit large fluctuations over time and from one cell to the next. This article presents a range of precise single-molecule experimental techniques, based upon RNA fluorescence in situ hybridization, which can be used to measure the fluctuations of RNA at the single-cell level. A class of models for gene activation and deactivation is postulated in order to capture complex stochastic effects of chromatin modifications or transcription factor interactions. A computational tool, known as the finite state projection approach, is introduced to accurately and efficiently analyze these models in order to predict how probability distributions of RNA change over time in response to changing environmental conditions. These single-molecule experiments, discrete stochastic models, and computational analyses are systematically integrated to identify models of gene regulation dynamics. To illustrate the power and generality of our integrated experimental and computational approach, we explore cases that include different models for three different RNA types (sRNA, mRNA and nascent RNA), three different experimental techniques and three different biological species (bacteria, yeast and human cells).
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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.