ArticleProceedings of the National Academy of Sciences of the United States of America2020
Analytical distributions for detailed models of stochastic gene expression in eukaryotic cells.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 72 papers.
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72 citing papers in PubMed, 188 citations in OpenAlex.
- Integrating zero-inflation correction and transcriptional kinetics for single-cell transcriptomic analysis.PLoS computational biology · 2026Article
- Gillespie-based simulation and inference for non-Markovian stochastic reaction networks.Briefings in bioinformatics · 2026Article
- Deciphering global transcriptional dynamics coordinated by gene-gene regulatory interactions using single-cell data.Molecular systems biology · 2026Article
- A control theoretical approach to gene regulation reveals quantitative constraints for dynamic homeostasis in stochastic gene expression.Biochimica et biophysica acta. Molecular basis of disease · 2026Article
- HySimODE: a hybrid stochastic-deterministic simulation framework for multiscale models of biological systems.Bioinformatics (Oxford, England) · 2026Article
- Impact of variability in cell generation times on cell-to-cell variability of protein concentrations.bioRxiv : the preprint server for biology · 2026Article
- Efficiency, accuracy and robustness of probability generating function based parameter inference method for stochastic biochemical reactions.PLoS computational biology · 2026Article
- Unifying non-Markovian dynamics and agent heterogeneity in scalable stochastic networks.Nature communications · 2026Article
- From noise to models to numbers: Evaluating negative binomial models and parameter estimations in single-cell RNA-seq.PLoS computational biology · 2026Article
- Intrinsic OASL expression governs heterogeneity in interferon induction during influenza A virus infection.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Cyclo-stationary distributions of mRNA and Protein counts for random cell division times.bioRxiv : the preprint server for biology · 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
- Transient power-law behaviour following induction distinguishes between competing models of stochastic gene expression.Nature communications · 2025Article
- Quantifying cell fate change under different stochastic gene activation frameworks.Quantitative biology (Beijing, China) · 2025Article
- A generative benchmark for evaluating the performance of fluorescent cell image segmentation.Synthetic and systems biotechnology · 2024Article
- Sequestration of gene products by decoys enhances precision in the timing of intracellular events.Scientific reports · 2024Article
- Homeostasis of mRNA concentrations through coupling transcription, export, and degradation.iScience · 2024Article
- Analysis of a detailed multi-stage model of stochastic gene expression using queueing theory and model reduction.Mathematical biosciences · 2024Article
- A stochastic vs deterministic perspective on the timing of cellular events.Nature communications · 2024Article
12 more citing papers are in PubMed but not listed here.
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Authors and funding
2 authors at 2 institutions in 2 countries.
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Abstract
The stochasticity of gene expression presents significant challenges to the modeling of genetic networks. A two-state model describing promoter switching, transcription, and messenger RNA (mRNA) decay is the standard model of stochastic mRNA dynamics in eukaryotic cells. Here, we extend this model to include mRNA maturation, cell division, gene replication, dosage compensation, and growth-dependent transcription. We derive expressions for the time-dependent distributions of nascent mRNA and mature mRNA numbers, provided two assumptions hold: 1) nascent mRNA dynamics are much faster than those of mature mRNA; and 2) gene-inactivation events occur far more frequently than gene-activation events. We confirm that thousands of eukaryotic genes satisfy these assumptions by using data from yeast, mouse, and human cells. We use the expressions to perform a sensitivity analysis of the coefficient of variation of mRNA fluctuations averaged over the cell cycle, for a large number of genes in mouse embryonic stem cells, identifying degradation and gene-activation rates as the most sensitive parameters. Furthermore, it is shown that, despite the model's complexity, the time-dependent distributions predicted by our model are generally well approximated by the negative binomial distribution. Finally, we extend our model to include translation, protein decay, and auto-regulatory feedback, and derive expressions for the approximate time-dependent protein-number distributions, assuming slow protein decay. Our expressions enable us to study how complex biological processes contribute to the fluctuations of gene products in eukaryotic cells, as well as allowing a detailed quantitative comparison with experimental data via maximum-likelihood methods.
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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.