论文标题
自动调节爆发基因表达的随机模型中的小蛋白质数效应
Small protein number effects in stochastic models of autoregulated bursty gene expression
论文作者
论文摘要
Kumar等人的自动调节爆发基因表达的随机模型。 [物理。莱特牧师。 113，268105（2014）]在稳态条件下已在隐含的假设中精确地解决了蛋白质数足够大，因此可以忽略由于可逆蛋白质促进剂结合而引起的蛋白质数量的波动。在这里，我们得出了一个替代模型，该模型考虑了这些波动，因此可以用于研究低蛋白质数效应。精确的稳态蛋白质数分布得出作为高斯超几何函数的总和。我们使用该理论来研究启动子切换速率以及反馈的类型如何影响蛋白质噪声的大小和噪声引起的双重性。此外，我们表明我们对蛋白质数分布的模型预测与Kumar等人的模型明显不同。当蛋白质平均值很小时，基因转换很快，并且蛋白质结合比解开速度快。
A stochastic model of autoregulated bursty gene expression by Kumar et al. [Phys. Rev. Lett. 113, 268105 (2014)] has been exactly solved in steady-state conditions under the implicit assumption that protein numbers are sufficiently large such that fluctuations in protein numbers due to reversible protein-promoter binding can be ignored. Here we derive an alternative model that takes into account these fluctuations and hence can be used to study low protein number effects. The exact steady-state protein number distributions is derived as a sum of Gaussian hypergeometric functions. We use the theory to study how promoter switching rates and the type of feedback influence the size of protein noise and noise-induced bistability. Furthermore we show that our model predictions for the protein number distribution are significantly different from those of Kumar et al. when the protein mean is small, gene switching is fast, and protein binding is faster than unbinding.
