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• W2892368703 abstract "Abstract Stochastic simulation can make the molecular processes of cellular control more vivid than the traditional differential-equation approach by generating typical system histories instead of just statistical measures such as the mean and variance of a population. Simple simulations are now easy for students to construct from scratch, that is, without recourse to black-box packages. In some cases, their results can also be compared directly to single-molecule experimental data. After introducing the stochastic simulation algorithm, this article gives two case studies, involving gene expression and error correction, respectively. Code samples and resulting animations showing results are given in the online supplement." @default.
• W2892368703 created "2018-09-27" @default.
• W2892368703 creator A5003276906 @default.
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• W2892368703 date "2018-09-15" @default.
• W2892368703 modified "2023-10-15" @default.
• W2892368703 title "Stochastic Simulation to Visualize Gene Expression and Error Correction in Living Cells" @default.
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• W2892368703 doi "https://doi.org/10.1101/418772" @default.
• W2892368703 hasPublicationYear "2018" @default.
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