ArticleJournal of the Royal Society, Interface2017
Stochastic models of gene transcription with upstream drives: exact solution and sample path characterization.
Article in Journal of the Royal Society, Interface, 2017. 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.
- Aging by the clock and yet without a program.Nature aging · 2025Review
- Incorporating spatial diffusion into models of bursty stochastic transcription.Journal of the Royal Society, Interface · 2025Article
- Quantifying and correcting bias in transcriptional parameter inference from single-cell data.Biophysical journal · 2024Article
- Poisson representation: a bridge between discrete and continuous models of stochastic gene regulatory networks.Journal of the Royal Society, Interface · 2023Article
- Review
- Studying stochastic systems biology of the cell with single-cell genomics data.bioRxiv : the preprint server for biology · 2023Article
- Gene expression model inference from snapshot RNA data using Bayesian non-parametrics.Nature computational science · 2023Article
- "Bet hedging" against climate change in developing and adult animals: roles for stochastic gene expression, phenotypic plasticity, epigenetic inheritance and adaptation.Frontiers in physiology · 2023Review
- Interpretable and tractable models of transcriptional noise for the rational design of single-molecule quantification experiments.Nature communications · 2022Article
- Mother-Fetus Immune Cross-Talk Coordinates "Extrinsic"/"Intrinsic" Embryo Gene Expression Noise and Growth Stability.International journal of molecular sciences · 2022Article
- Article
- RNA velocity unraveled.PLoS computational biology · 2022Article
- Modeling bursty transcription and splicing with the chemical master equation.Biophysical journal · 2022Article
- A Hill type equation can predict target gene expression driven by p53 pulsing.FEBS open bio · 2021Article
- Mutational pressure by host APOBEC3s more strongly affects genes expressed early in the lytic phase of herpes simplex virus-1 (HSV-1) and human polyomavirus (HPyV) infection.PLoS pathogens · 2021Article
- The circadian oscillator analysed at the single-transcript level.Molecular systems biology · 2021Article
- Transcriptional bursts explain autosomal random monoallelic expression and affect allelic imbalance.PLoS computational biology · 2021Article
- Order Through Disorder: The Characteristic Variability of Systems.Frontiers in cell and developmental biology · 2020Review
- Computation of Single-Cell Metabolite Distributions Using Mixture Models.Frontiers in cell and developmental biology · 2020Article
- A multiscale model of epigenetic heterogeneity-driven cell fate decision-making.PLoS computational biology · 2019Article
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2 authors.
Funding
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Abstract
Gene transcription is a highly stochastic and dynamic process. As a result, the mRNA copy number of a given gene is heterogeneous both between cells and across time. We present a framework to model gene transcription in populations of cells with time-varying (stochastic or deterministic) transcription and degradation rates. Such rates can be understood as upstream cellular drives representing the effect of different aspects of the cellular environment. We show that the full solution of the master equation contains two components: a model-specific, upstream effective drive, which encapsulates the effect of cellular drives (e.g. entrainment, periodicity or promoter randomness) and a downstream transcriptional Poissonian part, which is common to all models. Our analytical framework treats cell-to-cell and dynamic variability consistently, unifying several approaches in the literature. We apply the obtained solution to characterize different models of experimental relevance, and to explain the influence on gene transcription of synchrony, stationarity, ergodicity, as well as the effect of time scales and other dynamic characteristics of drives. We also show how the solution can be applied to the analysis of noise sources in single-cell data, and to reduce the computational cost of stochastic simulations.
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