Center for Computational and Evolutionary Biology, Key Laboratory of Animal Ecology and Conservation Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, China.
Phys Chem Chem Phys. 2010 Mar 14;12(10):2418-26. doi: 10.1039/b912111k. Epub 2010 Jan 20.
To investigate the effects of bidirectional regulation on the noise in protein concentration, a theoretical and simple three-gene network model is considered. The basic idea behind this model is from Paulsson's proposition (J. Paulsson, Phys. Life Rev. 2005, 2, 157-175), where the synthesis and degradation of a mRNA species corresponding to a target protein are regulated directly and indirectly by a certain sigma-factor, and a random increase in the concentration of the sigma-factor should increase both the synthesis and degradation rates of the mRNA species (bidirectional regulation). Using the standard Omega-expansion technique (linear noise approximation) and Monte Carlo simulation, our main results show clearly that for the steady-state statistics the effects of the noise of the sigma-factor on the stochastic fluctuation of the target protein could partially cancel out.
为了研究双向调节对蛋白质浓度噪声的影响，考虑了一个理论上简单的三基因网络模型。该模型的基本思想来自于 Paulsson 的提议（J. Paulsson，Phys. Life Rev. 2005, 2, 157-175），其中对应于靶蛋白的 mRNA 种类的合成和降解直接和间接受到特定 sigma 因子的调节，而 sigma 因子浓度的随机增加应该会同时增加 mRNA 种类的合成和降解速率（双向调节）。使用标准的 Omega 展开技术（线性噪声逼近）和蒙特卡罗模拟，我们的主要结果清楚地表明，对于稳态统计，sigma 因子噪声对靶蛋白随机波动的影响部分可以相互抵消。
