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arXiv 0907.3504q-bio.MNq-bio.QM
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具有突发和调控的基因表达随机模型的谱解

Spectral solutions to stochastic models of gene expression with bursts and regulation

Andrew Mugler, Aleksandra M. Walczak, Chris H. Wiggins

摘要:

细胞内的信号处理分子通常以低拷贝数存在,这需要概率模型来解释固有噪声。传统上,概率分布是使用基于模拟的方法找到的,然后需要从许多样本中估计分布。在这里,我们详细介绍了另一种方法,直接计算概率分布的控制方程,这是线性的自然本征函数的扩展。我们将所得的谱方法应用于随机基因表达的三种一般模型:具有多个表达状态的单个基因(通常用作两个状态极限的突发模型),基因调控级联,以及突发和调控的组合模型。在所有情况下,我们发现无论是分析结果或数值处方,大大优于模拟的效率和准确性。在最后一种情况下,我们表明,在慢开关的限制双峰响应不仅是可能的,但最佳的信息传输。

英文摘要:

Signal-processing molecules inside cells are often present at low copy number, which necessitates probabilistic models to account for intrinsic noise. Probability distributions have traditionally been found using simulation-based approaches which then require estimating the distributions from many samples. Here we present in detail an alternative method for directly calculating a probability distribution by expanding in the natural eigenfunctions of the governing equation, which is linear. We apply the resulting spectral method to three general models of stochastic gene expression: a single gene with multiple expression states (often used as a model of bursting in the limit of two states), a gene regulatory cascade, and a combined model of bursting and regulation. In all cases we find either analytic results or numerical prescriptions that greatly outperform simulations in efficiency and accuracy. In the last case, we show that bimodal response in the limit of slow switching is not only possible but optimal in terms of information transmission.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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