Analytical distributions for detailed models of stochastic gene expression in eukaryotic cells
Zhixing Cao,Ramon Grima +1 more
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TLDR
The classical two-state model of stochastic mRNA dynamics in eukaryotic cells is extended 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, and derive expressions for the approximate time-dependent protein-number distributions.Abstract:
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.read more
Citations
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Effects of cell cycle variability on lineage and population measurements of messenger RNA abundance.
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Neural network aided approximation and parameter inference of non-Markovian models of gene expression.
Qingchao Jiang,Xiaoming Fu,Xiaoming Fu,Shifu Yan,Runlai Li,Wenli Du,Zhixing Cao,Feng Qian,Ramon Grima +8 more
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Dynamical phase diagram of an auto-regulating gene in fast switching conditions.
Chen Jia,Ramon Grima +1 more
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Frequency domain analysis of fluctuations of mRNA and protein copy numbers within a cell lineage: theory and experimental validation
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TL;DR: This work constructed strains of Escherichia coli that enable detection of noise and discrimination between the two mechanisms by which it is generated and reveals how low intracellular copy numbers of molecules can fundamentally limit the precision of gene regulation.
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Journal ArticleDOI
RNA velocity of single cells
Gioele La Manno,Gioele La Manno,Ruslan A. Soldatov,Amit Zeisel,Amit Zeisel,Emelie Braun,Emelie Braun,Hannah Hochgerner,Hannah Hochgerner,Viktor Petukhov,Viktor Petukhov,Katja Lidschreiber,Maria Eleni Kastriti,Peter Lönnerberg,Peter Lönnerberg,Alessandro Furlan,Jean Fan,Lars E. Borm,Lars E. Borm,Zehua Liu,David van Bruggen,Jimin Guo,Xiaoling He,Roger A. Barker,Erik Sundström,Gonçalo Castelo-Branco,Patrick Cramer,Patrick Cramer,Igor Adameyko,Sten Linnarsson,Sten Linnarsson,Peter V. Kharchenko +31 more
TL;DR: It is shown that RNA velocity—the time derivative of the gene expression state—can be directly estimated by distinguishing between unspliced and spliced mRNAs in common single-cell RNA sequencing protocols, and expected to greatly aid the analysis of developmental lineages and cellular dynamics, particularly in humans.