Faculty of Biology, University of Freiburg, Schänzlestr. 1, 79104 Freiburg, Germany.
Prog Biophys Mol Biol. 2009 Sep-Oct;100(1-3):57-66. doi: 10.1016/j.pbiomolbio.2009.06.003. Epub 2009 Jun 11.
Noise in gene expression, either due to inherent stochasticity or to varying inter- and intracellular environment, can generate significant cell-to-cell variability of protein levels in clonal populations. To quantify the different sources of gene expression noise, several theoretical studies have been performed using either a quasi-stationary approximation for the emerging master equation or employing a time-dependent description, when cell division is taken explicitly into account. Here, we give an overview of the different origins of gene expression noise which were found experimentally and introduce the basic stochastic modeling approaches. We extend, and apply a time-dependent description of gene expression noise to experimental data. The analysis shows that the induction level of the transcription factor can be employed to discriminate the noise profiles and their characteristic signatures. On the basis of experimentally measured cell distributions, our simulations suggest that transcription factor binding and promoter activation can be modeled independently of each other with sufficient accuracy.
基因表达中的噪声，无论是由于固有随机性还是由于细胞内外环境的变化，都会导致克隆群体中蛋白质水平的显著细胞间变异性。为了量化基因表达噪声的不同来源，已经使用出现的主方程的准静态近似值或在明确考虑细胞分裂时使用时变描述进行了几项理论研究。在这里，我们概述了实验中发现的基因表达噪声的不同来源，并介绍了基本的随机建模方法。我们扩展并将基因表达噪声的时变描述应用于实验数据。分析表明，可以利用转录因子的诱导水平来区分噪声分布及其特征特征。基于实验测量的细胞分布，我们的模拟表明，转录因子结合和启动子激活可以彼此独立地以足够的精度进行建模。
