用马尔可夫链近似推断基因表达延迟随机模型的动力学参数。

Henrik Mannerstrom, Olli Yli-Harja, Andre S Ribeiro
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引用次数: 6

摘要

我们提出了延迟随机模拟算法的马尔可夫链近似,以从RNA水平的动力学中推断原核生物转录机制的性质。我们使用延迟随机建模策略和转录起始率和RNA降解的实际参数值来模拟转录。从该模型中,我们在单分子水平上生成RNA水平的时间序列,从中我们使用该方法推断启动子开放复合物形成的持续时间。即使在RNA水平上加入外部高斯噪声,这也是可能的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Inference of kinetic parameters of delayed stochastic models of gene expression using a markov chain approximation.

Inference of kinetic parameters of delayed stochastic models of gene expression using a markov chain approximation.

Inference of kinetic parameters of delayed stochastic models of gene expression using a markov chain approximation.

Inference of kinetic parameters of delayed stochastic models of gene expression using a markov chain approximation.
We propose a Markov chain approximation of the delayed stochastic simulation algorithm to infer properties of the mechanisms in prokaryote transcription from the dynamics of RNA levels. We model transcription using the delayed stochastic modelling strategy and realistic parameter values for rate of transcription initiation and RNA degradation. From the model, we generate time series of RNA levels at the single molecule level, from which we use the method to infer the duration of the promoter open complex formation. This is found to be possible even when adding external Gaussian noise to the RNA levels.
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