噬菌体λ溶源态下CI表达的外源性噪声研究

发布时间：2018-07-27 17:28

【摘要】：单个细胞中的具体蛋白质水平由基因调控网络调控,这是个细胞中复杂的随机过程。但是,生物过程中的噪声依旧是个谜。许多研究者发现在现有理论框架下很难解释清楚噪声。几年前,Zhu等人[1]用二维朗之万方程,并且提出新的势函数解决的办法,很好的解释了基因开关的稳定性,健壮性和转换的有效性。我们基于这个成功应用的模型,对于Zhu等人发现的外源性噪声和DNAlooping[1]的情况进行重新建模和计算分析。对于最近Anderson和Yang[2]的一片文章中的数据,我们采用随机动力学模型去分析。随机动力学分析方法用于建模生物系统的数学形式,朗之万方程,得出的结果在CI平均水平上有很好的一致性,但是系统外的外源性噪声的确是存在的,它区别于生物过程本身的内源性噪声。因此,我们发现外源性噪声可以最终很显著地扩大分布的方差,并且这种影响在低表达蛋白系统中更加的明显,比如野生型噬菌体。我们通过扩展最小的一维朗之万模型成为二维逐步朗之万模型,看到mRNA在CI分布方差的贡献上有重要的作用,它能解释40%到70%的实验观察到的总方差。而且我们发现随机的细胞生长速率也能对CI分布的方差贡献扩大10%左右,即80%左右的四种突变体噪声可以很好的被解释,但野生型的只能解释最多50%。通过更多随机因素的考虑,我们可以更好的解释实验数据并且未知的外源性噪声会变得更小。
[Abstract]:The specific protein level in a single cell is regulated by a gene regulatory network, which is a complex random process in a cell. But the noise in biological processes remains a mystery. Many researchers have found it difficult to explain noise clearly under the existing theoretical framework. A few years ago, Zhu et al. [1] used the two-dimensional Langevin equation and put forward a new method to solve the potential function, which explained the stability, robustness and conversion efficiency of the gene switch. Based on this successfully applied model, we remodel and calculate the exogenous noise and DNAlooping [1] found by Zhu et al. For the data in a recent paper by Anderson and Yang [2], we use the stochastic dynamics model to analyze the data. The stochastic dynamics analysis method is used to model the mathematical form of biological system, Langevin equation. The results are consistent with the CI average level, but the extraneous noise does exist. It is distinguished from the endogenous noise of the biological process itself. Therefore, we found that exogenous noise can eventually significantly expand the variance of distribution, and this effect is more obvious in low expression protein systems, such as wild type phage. By extending the smallest one-dimensional Langevin model into a two-dimensional step-by-step Langevan model, we see that mRNA plays an important role in the contribution of CI variance, which can explain 40% to 70% of the observed total variance. Moreover, we found that the random cell growth rate can increase the variance of CI distribution by about 10%, that is, 80% of the four mutants noise can be well explained, but the wild type can only explain 50%. By considering more random factors, we can better interpret the experimental data and the unknown exogenous noise will become smaller.
【学位授予单位】：上海交通大学
【学位级别】：硕士
【学位授予年份】：2012
【分类号】：R318.0

【共引文献】