ArticleiScience2023
Coupling gene expression dynamics to cell size dynamics and cell cycle events: Exact and approximate solutions of the extended telegraph model.
Article in iScience, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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15 citing papers in PubMed, 47 citations in OpenAlex.
- Article
- Quantifying and comparing causal patterns in stochastic chemical reaction networks.NPJ systems biology and applications · 2026Article
- Stochastic gene expression in proliferating cells: Differing noise intensity in single-cell and population perspectives.PLoS computational 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
- Optimisation of gene expression noise for cellular persistence against lethal events.Journal of theoretical biology · 2025Article
- 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
- Stochastic Gene Expression in Proliferating Cells: Differing Noise Intensity in Single-Cell and Population Perspectives.bioRxiv : the preprint server for biology · 2024Article
- What can we learn when fitting a simple telegraph model to a complex gene expression model?PLoS computational biology · 2024Article
- Quantifying and correcting bias in transcriptional parameter inference from single-cell data.Biophysical journal · 2024Article
- Transcriptional bursting dynamics in gene expression.Frontiers in genetics · 2024Review
- Poisson representation: a bridge between discrete and continuous models of stochastic gene regulatory networks.Journal of the Royal Society, Interface · 2023Article
- Biophysically Interpretable Inference of Cell Types from Multimodal Sequencing Data.bioRxiv : the preprint server for biology · 2023Article
- The minimal intrinsic stochasticity of constitutively expressed eukaryotic genes is sub-Poissonian.bioRxiv : the preprint server for biology · 2023Article
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Authors and funding
2 authors at 2 institutions in 2 countries.
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No grant is acknowledged in the PubMed record.
Abstract
The standard model describing the fluctuations of mRNA numbers in single cells is the telegraph model which includes synthesis and degradation of mRNA, and switching of the gene between active and inactive states. While commonly used, this model does not describe how fluctuations are influenced by the cell cycle phase, cellular growth and division, and other crucial aspects of cellular biology. Here, we derive the analytical time-dependent solution of an extended telegraph model that explicitly considers the doubling of gene copy numbers upon DNA replication, dependence of the mRNA synthesis rate on cellular volume, gene dosage compensation, partitioning of molecules during cell division, cell-cycle duration variability, and cell-size control strategies. Based on the time-dependent solution, we obtain the analytical distributions of transcript numbers for lineage and population measurements in steady-state growth and also find a linear relation between the Fano factor of mRNA fluctuations and cell volume fluctuations. We show that generally the lineage and population distributions in steady-state growth cannot be accurately approximated by the steady-state solution of extrinsic noise models, i.e. a telegraph model with parameters drawn from probability distributions. This is because the mRNA lifetime is often not small enough compared to the cell cycle duration to erase the memory of division and replication. Accurate approximations are possible when this memory is weak, e.g. for genes with bursty expression and for which there is sufficient gene dosage compensation when replication occurs.
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