Analytical distributions for detailed models of stochastic gene expression in eukaryotic cells
| Autor: | Zhixing Cao, Ramon Grima |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Mature messenger RNA
Cell division stochastic gene expression RNA Stability master equation Computational biology Biology 01 natural sciences Mice 03 medical and health sciences Transcription (biology) Yeasts 0103 physical sciences Gene expression Animals Humans RNA Messenger 010306 general physics Gene Cells Cultured perturbation theory 030304 developmental biology Stochastic Processes 0303 health sciences Messenger RNA Models Statistical Multidisciplinary Dosage compensation Models Genetic Applied Mathematics Gene Expression Profiling Cell Cycle Genetic Variation Biological Sciences Cell cycle Biophysics and Computational Biology PNAS Plus Physical Sciences Single-Cell Analysis Transcriptome |
| Zdroj: | Cao, Z & Grima, R 2020, ' Analytical distributions for detailed models of stochastic gene expression in eukaryotic cells ', Proceedings of the National Academy of Sciences, vol. 117, no. 9, pp. 4682-4692 . https://doi.org/10.1073/pnas.1910888117 Proceedings of the National Academy of Sciences of the United States of America |
| ISSN: | 1091-6490 0027-8424 |
| DOI: | 10.1073/pnas.1910888117 |
| Popis: | Significance The random nature of gene expression is well established experimentally. Mathematical modeling provides a means of understanding the factors leading to the observed stochasticity. In this article, we extend the classical two-state model of stochastic mRNA dynamics to include a considerable number of salient features of single-cell biology, such as cell division, replication, mRNA maturation, dosage compensation, and growth-dependent transcription. By means of biologically relevant approximations, we obtain expressions for the time-dependent distributions of mRNA and protein numbers. These provide insight into how fluctuations are modified and controlled by complex intracellular processes. 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. |
| Databáze: | OpenAIRE |
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