ArticleNature methods2014
Scalable inference of heterogeneous reaction kinetics from pooled single-cell recordings.
Article in Nature methods, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 63 papers.
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Who cites it
63 citing papers in PubMed.
- Scalable inference and identifiability of kinetic parameters for transcriptional bursting from single cell data.Bioinformatics (Oxford, England) · 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
- Reconstructing noisy gene regulation dynamics using extrinsic-noise-driven neural stochastic differential equations.PLoS computational biology · 2025Article
- PEtab.jl: advancing the efficiency and utility of dynamic modelling.Bioinformatics (Oxford, England) · 2025Article
- Basal association of a transcription factor favors early gene expression.PLoS genetics · 2025Article
- Incorporating spatial diffusion into models of bursty stochastic transcription.Journal of the Royal Society, Interface · 2025Article
- Decoding stimulus-specific regulation of promoter activity of p53 target genes.Frontiers in cell and developmental biology · 2025Article
- Energy Aware Technology Mapping of Genetic Logic Circuits.ACS synthetic biology · 2024Article
- Energy Aware Technology Mapping of Genetic Logic Circuits.bioRxiv : the preprint server for biology · 2024Article
- Advanced methods for gene network identification and noise decomposition from single-cell data.Nature communications · 2024Article
- Density physics-informed neural networks reveal sources of cell heterogeneity in signal transduction.Patterns (New York, N.Y.) · 2024Article
- Quantifying and correcting bias in transcriptional parameter inference from single-cell data.Biophysical journal · 2024Article
- Modelling capture efficiency of single-cell RNA-sequencing data improves inference of transcriptome-wide burst kinetics.Bioinformatics (Oxford, England) · 2023Article
- Transcriptional bursting: stochasticity in deterministic development.Development (Cambridge, England) · 2023Review
- Context-Aware Technology Mapping in Genetic Design Automation.ACS synthetic biology · 2023Article
- Gene expression model inference from snapshot RNA data using Bayesian non-parametrics.Nature computational science · 2023Article
- Modeling Cellular Signaling Variability Based on Single-Cell Data: The TGFβ-SMAD Signaling Pathway.Methods in molecular biology (Clifton, N.J.) · 2023Review
- Efficient inference and identifiability analysis for differential equation models with random parameters.PLoS computational biology · 2022Article
- Identifying cell-to-cell variability in internalization using flow cytometry.Journal of the Royal Society, Interface · 2022Article
- Scalable and flexible inference framework for stochastic dynamic single-cell models.PLoS computational biology · 2022Article
3 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Mathematical methods combined with measurements of single-cell dynamics provide a means to reconstruct intracellular processes that are only partly or indirectly accessible experimentally. To obtain reliable reconstructions, the pooling of measurements from several cells of a clonal population is mandatory. However, cell-to-cell variability originating from diverse sources poses computational challenges for such process reconstruction. We introduce a scalable Bayesian inference framework that properly accounts for population heterogeneity. The method allows inference of inaccessible molecular states and kinetic parameters; computation of Bayes factors for model selection; and dissection of intrinsic, extrinsic and technical noise. We show how additional single-cell readouts such as morphological features can be included in the analysis. We use the method to reconstruct the expression dynamics of a gene under an inducible promoter in yeast from time-lapse microscopy data.
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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.