Exact solution of a three-stage model of stochastic gene expression including cell-cycle dynamics DOI
Yiling Wang, Zhenhua Yu, Ramon Grima

et al.

The Journal of Chemical Physics, Journal Year: 2023, Volume and Issue: 159(22)

Published: Dec. 8, 2023

The classical three-stage model of stochastic gene expression predicts the statistics single cell mRNA and protein number fluctuations as a function rates promoter switching, transcription, translation, degradation dilution. While this is easily simulated, its analytical solution remains an unsolved problem. Here we modify to explicitly include cell-cycle dynamics then derive exact for time-dependent joint distribution numbers. We show large differences between which captures effects implicitly via effective first-order dilution reactions. In particular find that Fano factor numbers calculated from population snapshot measurement are underestimated by whereas correlation can be either over- or underestimated, depending on timescales switching relative mean duration time.

Language: Английский

Transcription factor exchange enables prolonged transcriptional bursts DOI Creative Commons
Wim Pomp, Joseph V.W. Meeussen, Tineke L. Lenstra

et al.

Molecular Cell, Journal Year: 2024, Volume and Issue: 84(6), P. 1036 - 1048.e9

Published: Feb. 19, 2024

Language: Английский

Citations

28
DNA supercoiling restricts the transcriptional bursting of neighboring eukaryotic genes DOI Creative Commons
Heta Patel, Stefano Coppola, Wim Pomp

et al.

Molecular Cell, Journal Year: 2023, Volume and Issue: 83(10), P. 1573 - 1587.e8

Published: May 1, 2023

DNA supercoiling has emerged as a major contributor to gene regulation in bacteria, but how impacts transcription dynamics eukaryotes is unclear. Here, using single-molecule dual-color nascent imaging budding yeast, we show that transcriptional bursting of divergent and tandem GAL genes coupled. Temporal coupling neighboring requires rapid release supercoils by topoisomerases. When accumulate, one inhibits at its adjacent genes. Transcription inhibition the results from destabilized binding factor Gal4. Moreover, wild-type yeast minimizes supercoiling-mediated maintaining sufficient levels Overall, discover fundamental differences control between bacteria ensures proper expression

Language: Английский

Citations

34
The minimal intrinsic stochasticity of constitutively expressed eukaryotic genes is sub-Poissonian DOI Creative Commons
Douglas E. Weidemann, James Holehouse, Abhyudai Singh

et al.

Science Advances, Journal Year: 2023, Volume and Issue: 9(32)

Published: Aug. 9, 2023

Gene expression inherently gives rise to stochastic variation ("noise") in the production of gene products. Minimizing noise is crucial for ensuring reliable cellular functions. However, cannot be suppressed below a certain intrinsic limit. For constitutively expressed genes, this limit typically assumed Poissonian noise, wherein variance mRNA numbers equal their mean. Here, we demonstrate that several cell division genes fission yeast exhibit variances significantly The reduced can explained by model incorporating multiple transcription and degradation steps. Notably, sub-Poissonian regime, distinct from or super-Poissonian regimes, cytoplasmic effectively through higher export rate. Our findings redefine lower eukaryotic uncover molecular requirements achieving ultralow which expected important vital

Language: Английский

Citations

29
Transient power-law behaviour following induction distinguishes between competing models of stochastic gene expression DOI Creative Commons
Andrew G. Nicoll, Juraj Szavits-Nossan, M. R. Evans

et al.

Nature Communications, Journal Year: 2025, Volume and Issue: 16(1)

Published: March 22, 2025

Abstract What features of transcription can be learnt by fitting mathematical models gene expression to mRNA count data? Given a suite models, data selects an optimal one, thus identifying probable transcriptional mechanism. Whilst attractive, the utility this methodology remains unclear. Here, we sample steady-state, single-cell distributions from parameters in physiological range, and show they cannot used confidently estimate number inactive states, i.e. rate-limiting steps initiation. Distributions over 99% parameter space generated using with 2, 3, or 4 states well fit one single state. However, that for many minutes following induction, eukaryotic cells increase mean obeys power law whose exponent equals sum visited initial active state post-transcriptional processing steps. Our study shows estimation sufficient determine lower bound on total regulatory initiation, splicing, nuclear export.

Language: Английский

Citations

1
Approximating solutions of the Chemical Master equation using neural networks DOI

Augustinas Sukys,

Kaan Öcal, Ramon Grima

et al.

iScience, Journal Year: 2022, Volume and Issue: 25(9), P. 105010 - 105010

Published: Aug. 28, 2022

Language: Английский

Citations

38
Modelling capture efficiency of single-cell RNA-sequencing data improves inference of transcriptome-wide burst kinetics DOI Creative Commons
Wenhao Tang, Andreas Christ Sølvsten Jørgensen, Samuel Marguerat

et al.

Bioinformatics, Journal Year: 2023, Volume and Issue: 39(7)

Published: June 24, 2023

Gene expression is characterized by stochastic bursts of transcription that occur at brief and random periods promoter activity. The kinetics gene burstiness differs across the genome dependent on sequence, among other factors. Single-cell RNA sequencing (scRNA-seq) has made it possible to quantify cell-to-cell variability in a global genome-wide level. However, scRNA-seq data are prone technical variability, including low variable capture efficiency transcripts from individual cells.

Language: Английский

Citations

21
Inferring transcriptional bursting kinetics from single-cell snapshot data using a generalized telegraph model DOI Creative Commons
Songhao Luo, Zhenquan Zhang, Zihao Wang

et al.

Royal Society Open Science, Journal Year: 2023, Volume and Issue: 10(4)

Published: April 1, 2023

Gene expression has inherent stochasticity resulting from transcription's burst manners. Single-cell snapshot data can be exploited to rigorously infer transcriptional kinetics, using mathematical models as blueprints. The classical telegraph model (CTM) been widely used explain bursting with Markovian assumptions. However, growing evidence suggests that the gene-state dwell times are generally non-exponential, switching is a multi-step process in organisms. Therefore, interpretable non-Markovian and efficient statistical inference methods urgently required investigating kinetics. We develop an tractable model, generalized (GTM), characterize allows arbitrary dwell-time distributions, rather than exponential incorporated into ON OFF process. Based on GTM, we propose method for kinetics approximate Bayesian computation framework. This demonstrates scalable estimation of frequency size synthetic data. Further, application genome-wide mouse embryonic fibroblasts reveals GTM would estimate lower higher those estimated by CTM. In conclusion, corresponding effective tools dynamic static single-cell

Language: Английский

Citations

20
Studying stochastic systems biology of the cell with single-cell genomics data DOI Creative Commons
Gennady Gorin, John J. Vastola, Lior Pachter

et al.

Cell Systems, Journal Year: 2023, Volume and Issue: 14(10), P. 822 - 843.e22

Published: Sept. 25, 2023

Language: Английский

Citations

19
Spectral neural approximations for models of transcriptional dynamics DOI
Gennady Gorin, Maria Carilli, Tara Chari

et al.

Biophysical Journal, Journal Year: 2024, Volume and Issue: 123(17), P. 2892 - 2901

Published: May 6, 2024

Language: Английский

Citations

8
What can we learn when fitting a simple telegraph model to a complex gene expression model? DOI Creative Commons
Feng Jiao, Jing Li, Ting Liu

et al.

PLoS Computational Biology, Journal Year: 2024, Volume and Issue: 20(5), P. e1012118 - e1012118

Published: May 14, 2024

In experiments, the distributions of mRNA or protein numbers in single cells are often fitted to random telegraph model which includes synthesis and decay protein, switching gene between active inactive states. While commonly used, this does not describe how fluctuations influenced by crucial biological mechanisms such as feedback regulation, non-exponential inactivation durations, multiple activation pathways. Here we investigate dynamical properties four relatively complex expression models fitting their steady-state number simple model. We show that despite underlying mechanisms, with three effective parameters can accurately capture product distributions, well conditional state, models. Some reliable reflect realistic dynamic behaviors models, while others may deviate significantly from real values The also be applied characterize capability for a exhibit multimodality. Using additional information single-cell data at time points, provide an method distinguishing Furthermore, using measurements under varying experimental conditions, even reveal regulation effectiveness these methods is confirmed analysis E. coli mammalian cells. All results robust respect cooperative transcriptional extrinsic noise. particular, find faster relaxation speed steady state more precise parameter inference large

Language: Английский

Citations

8