ArticleJournal of the Royal Society, Interface2022
Inference and uncertainty quantification of stochastic gene expression via synthetic models.
Article in Journal of the Royal Society, Interface, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Article
- Reconstructing noisy gene regulation dynamics using extrinsic-noise-driven neural stochastic differential equations.PLoS computational biology · 2025Article
- Methods in quantitative biology-from analysis of single-cell microscopy images to inference of predictive models for stochastic gene expression.Physical biology · 2025Review
- Artificial intelligence in traditional Chinese medicine: advances in multi-metabolite multi-target interaction modeling.Frontiers in pharmacology · 2025Review
- 3D chromatin architecture, BRD4, and Mediator have distinct roles in regulating genome-wide transcriptional bursting and gene network.Science advances · 2024Article
- Review
- Single-cell CaJournal of the Royal Society, Interface · 2023Article
- Gene expression model inference from snapshot RNA data using Bayesian non-parametrics.Nature computational science · 2023Article
- Genome-wide inference reveals that feedback regulations constrain promoter-dependent transcriptional burst kinetics.Nucleic acids research · 2023Article
- Analysis and design of single-cell experiments to harvest fluctuation information while rejecting measurement noise.Frontiers in cell and developmental biology · 2023Article
- Article
- Inference and uncertainty quantification of stochastic gene expression via synthetic models.Journal of the Royal Society, Interface · 2022Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Estimating uncertainty in model predictions is a central task in quantitative biology. Biological models at the single-cell level are intrinsically stochastic and nonlinear, creating formidable challenges for their statistical estimation which inevitably has to rely on approximations that trade accuracy for tractability. Despite intensive interest, a sweet spot in this trade-off has not been found yet. We propose a flexible procedure for uncertainty quantification in a wide class of reaction networks describing stochastic gene expression including those with feedback. The method is based on creating a tractable coarse-graining of the model that is learned from simulations, a
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Registered trials
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