【摘要】：生物網(wǎng)絡(luò)中時常會出現(xiàn)多個表型或多條動力學路徑共存的現(xiàn)象,但其表型和動力學路徑在隨機漲落影響下的穩(wěn)定性問題迄今尚未得到完整的認識.本文通過分析隨機布爾網(wǎng)絡(luò)模型來嘗試回答這個問題.表型和動力學路徑的穩(wěn)定性對應(yīng)于隨機布爾網(wǎng)絡(luò)吸引子的魯棒性和相對穩(wěn)定性,而后者可以用指數(shù)擾動馬氏鏈理論來加以刻畫和分析.已有的指數(shù)擾動馬氏鏈理論已經(jīng)告訴我們,從某個吸引子的吸引域溢出的時間的對數(shù)正比于該吸引域的"非平衡態(tài)活化能勢壘",于是本文首先推廣了這個理論,證明了指數(shù)擾動馬氏鏈模型中吸引子之間的最佳轉(zhuǎn)移路徑都是等概率的,因此在吸引子間"非平衡態(tài)活化能勢壘"相等的情形下,吸引子的相對穩(wěn)定性是由最佳轉(zhuǎn)移路徑的個數(shù)來決定的.該理論還預(yù)示著,當模型中的隨機漲落很小時,將會出現(xiàn)如下的相變現(xiàn)象:在參數(shù)空間的某一區(qū)域內(nèi),這些表型和動力學路徑將以某一比例共存在;而在另一些區(qū)域內(nèi),某個表型或某條動力學路徑將占據(jù)主導(dǎo)地位,從而成為全局吸引子.最后,在人造模型以及蛋白質(zhì)p53動力學模型中應(yīng)用和驗證了該理論,并且通過計算吸引子穩(wěn)定性對于具體動力學參數(shù)的敏感性,提供了一種辨別網(wǎng)絡(luò)中重要節(jié)點和節(jié)點間重要相互作用的新方法.
[Abstract]:There are many phenotypes or multiple dynamic paths coexisting in biological networks, but the stability of their phenotypes and kinetic pathways under the influence of random fluctuations has not been fully understood. This paper attempts to answer this question by analyzing the stochastic Boolean network model. The stability of phenotypic and dynamic paths corresponds to the robustness and relative stability of attractors in stochastic Boolean networks, which can be characterized and analyzed by exponential perturbation Markov chain theory. The existing exponential perturbed Markov chain theory has told us that the logarithm of the spillover time from an attractor is proportional to the "non-equilibrium activation energy barrier" of the attraction domain. It is proved that the optimal transfer paths between attractors in the exponential perturbed Markov chain model are equiprobability, so in the case of equal "non-equilibrium activation energy barrier" among attractors, The relative stability of attractors is determined by the number of optimal transition paths. The theory also indicates that when the random fluctuations in the model are very small, the following phenomena will occur: in a certain region of the parameter space, these phenotypic and kinetic pathways will co-exist in a certain proportion; In other regions, a phenotype or a dynamic path will dominate and become a global attractor. Finally, the theory is applied and validated in artificial model and protein p53 kinetic model, and the sensitivity of attractor stability to specific kinetic parameters is calculated. A new method for identifying important interactions between important nodes and nodes in the network is presented.
【作者單位】： Department
【基金】：國家自然科學基金(批準號:10901040,21373021和11622101) 中國優(yōu)秀博士論文作者基金會(批準號:201119)資助項目
【分類號】：O211
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本文編號：2364214