【摘要】：單個細胞中的具體蛋白質(zhì)水平由基因調(diào)控網(wǎng)絡調(diào)控,這是個細胞中復雜的隨機過程。但是,生物過程中的噪聲依舊是個謎。許多研究者發(fā)現(xiàn)在現(xiàn)有理論框架下很難解釋清楚噪聲。幾年前,Zhu等人[1]用二維朗之萬方程,并且提出新的勢函數(shù)解決的辦法,很好的解釋了基因開關的穩(wěn)定性,健壯性和轉(zhuǎn)換的有效性。我們基于這個成功應用的模型,對于Zhu等人發(fā)現(xiàn)的外源性噪聲和DNAlooping[1]的情況進行重新建模和計算分析。 對于最近Anderson和Yang[2]的一片文章中的數(shù)據(jù),我們采用隨機動力學模型去分析。隨機動力學分析方法用于建模生物系統(tǒng)的數(shù)學形式,朗之萬方程,得出的結(jié)果在CI平均水平上有很好的一致性,但是系統(tǒng)外的外源性噪聲的確是存在的,它區(qū)別于生物過程本身的內(nèi)源性噪聲。因此,我們發(fā)現(xiàn)外源性噪聲可以最終很顯著地擴大分布的方差,并且這種影響在低表達蛋白系統(tǒng)中更加的明顯,比如野生型噬菌體。 我們通過擴展最小的一維朗之萬模型成為二維逐步朗之萬模型,看到mRNA在CI分布方差的貢獻上有重要的作用,它能解釋40%到70%的實驗觀察到的總方差。而且我們發(fā)現(xiàn)隨機的細胞生長速率也能對CI分布的方差貢獻擴大10%左右,即80%左右的四種突變體噪聲可以很好的被解釋,但野生型的只能解釋最多50%。通過更多隨機因素的考慮,我們可以更好的解釋實驗數(shù)據(jù)并且未知的外源性噪聲會變得更小。
[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.
【學位授予單位】：上海交通大學
【學位級別】：碩士
【學位授予年份】：2012
【分類號】：R318.0

【共引文獻】

相關期刊論文 前3條

1 P.Ao;;Emerging of Stochastic Dynamical Equalities and Steady State Thermodynamics from Darwinian Dynamics[J];Communications in Theoretical Physics;2008年05期

2 徐岷涓;朱曉梅;林保宏;敖平;;酶反應速率方程的普適形式[J];生物化學與生物物理進展;2011年08期

3 Lik Wee Lee;Mary E. Lidstrom;;Towards Kinetic Modeling of Global Metabolic Networks:Methylobacterium extorquens AM1 Growth as Validation[J];生物工程學報;2008年06期

相關會議論文 前1條

1 ;Potential Function in Dynamical Systems and the Relation with Lyapunov Function[A];中國自動化學會控制理論專業(yè)委員會A卷[C];2011年

相關碩士學位論文 前4條

1 徐為;基于勢函數(shù)的耗散動力系統(tǒng)的穩(wěn)定性研究及其應用[D];上海交通大學;2011年

2 丁崇芳;演化算法的動力學分析[D];上海交通大學;2012年

3 李小青;勢函數(shù)在進化動力學中的應用[D];上海交通大學;2010年

4 喬長昭;隨機動力系統(tǒng)穩(wěn)定性分析框架[D];上海交通大學;2010年

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本文編號：2148609