Two-State Stochastic Model of In Vivo Observations of Transcriptional Bursts

In vivo measurements of gene expression in single cells show behavior that has been interpreted as stochastic bursts of transcription. In one case, these data have been interpreted as random ON-OFF transitions of the gene, but there is no experimental measurements or theoretical treatment of the number of transcripts produced at each burst event. In another case, such data have been interpreted to indicate multiple underlying transcriptional states. Here, we place both of these experiments in a common theoretical framework. In it, we couple two stochastic processes, one for synthesis of transcripts and one for their removal. Analysis of the resulting model is greatly aided by the existence of exact solutions of the master equation. We find the bursting limit of the exact solutions for our two-state gene expression model and show the occurrence of bursts of multiple sizes and durations by exact stochastic simulations. We also demonstrate that data from <i>Drosophila melanogaster</i> interpreted in terms of multiple underlying transcription states is fully compatible with underlying two-state ON or OFF transcriptional behavior. We discuss what experimental data is required to unambiguously determine the number of underlying promoter states.

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Publication Details

Journal
Brazilian Journal of Physics
Published
2025-05-08
DOI
https://doi.org/10.1007/s13538-025-01785-y
Citations
1
Primary Topic
Gene Regulatory Network Analysis
Type
article
Field-Weighted Citation Impact
0.81

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article

Two-State Stochastic Model of In Vivo Observations of Transcriptional Bursts

Romain Yvinec, Luiz Guilherme S. da Silva, John Reinitz, Alexandre F. Ramos et al.
1 citations
Brazilian Journal of Physics
Gene Regulatory Network Analysis
0.81
article

Two-State Stochastic Model of In Vivo Observations of Transcriptional Bursts

Romain Yvinec, Luiz Guilherme S. da Silva, John Reinitz, Alexandre F. Ramos, Guilherme Nery Prata, V. K. Dhar
article en
1 citations

Abstract

In vivo measurements of gene expression in single cells show behavior that has been interpreted as stochastic bursts of transcription. In one case, these data have been interpreted as random ON-OFF transitions of the gene, but there is no experimental measurements or theoretical treatment of the number of transcripts produced at each burst event. In another case, such data have been interpreted to indicate multiple underlying transcriptional states. Here, we place both of these experiments in a common theoretical framework. In it, we couple two stochastic processes, one for synthesis of transcripts and one for their removal. Analysis of the resulting model is greatly aided by the existence of exact solutions of the master equation. We find the bursting limit of the exact solutions for our two-state gene expression model and show the occurrence of bursts of multiple sizes and durations by exact stochastic simulations. We also demonstrate that data from <i>Drosophila melanogaster</i> interpreted in terms of multiple underlying transcription states is fully compatible with underlying two-state ON or OFF transcriptional behavior. We discuss what experimental data is required to unambiguously determine the number of underlying promoter states.

Brazilian Journal of PhysicsVol. 55(4)
Federal Institute of São Paulo (BR), Université de Tours (FR), Centre National de la Recherche Scientifique (FR), Université Paris-Saclay (FR), University of Chicago (US), Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement (FR), Instituto do Câncer do Estado de São Paulo (BR), Centro Universitário Lusíada (BR), Physiologie de la Reproduction et des Comportements (FR), Centro Universitário Belas Artes de São Paulo (BR), Biologie des oiseaux et aviculture (FR), Centre Inria de Saclay (FR), Services déconcentrés d'appui à la recherche Val de Loire (FR), Ripple (United States) (US)
McGill University, Coordenação de Aperfeiçoamento de Pessoal de Nível Superior, National Institutes of Health
Openalex Percentile: Top 28%
Gene Regulatory Network Analysis
0.81
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