發(fā)布時(shí)間：2018-11-28 20:46

【摘要】：生物網(wǎng)絡(luò)中時(shí)常會(huì)出現(xiàn)多個(gè)表型或多條動(dòng)力學(xué)路徑共存的現(xiàn)象,但其表型和動(dòng)力學(xué)路徑在隨機(jī)漲落影響下的穩(wěn)定性問題迄今尚未得到完整的認(rèn)識(shí).本文通過分析隨機(jī)布爾網(wǎng)絡(luò)模型來嘗試回答這個(gè)問題.表型和動(dòng)力學(xué)路徑的穩(wěn)定性對(duì)應(yīng)于隨機(jī)布爾網(wǎng)絡(luò)吸引子的魯棒性和相對(duì)穩(wěn)定性,而后者可以用指數(shù)擾動(dòng)馬氏鏈理論來加以刻畫和分析.已有的指數(shù)擾動(dòng)馬氏鏈理論已經(jīng)告訴我們,從某個(gè)吸引子的吸引域溢出的時(shí)間的對(duì)數(shù)正比于該吸引域的"非平衡態(tài)活化能勢(shì)壘",于是本文首先推廣了這個(gè)理論,證明了指數(shù)擾動(dòng)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(shì)壘"相等的情形下,吸引子的相對(duì)穩(wěn)定性是由最佳轉(zhuǎn)移路徑的個(gè)數(shù)來決定的.該理論還預(yù)示著,當(dāng)模型中的隨機(jī)漲落很小時(shí),將會(huì)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(dòng)力學(xué)路徑將以某一比例共存在;而在另一些區(qū)域內(nèi),某個(gè)表型或某條動(dòng)力學(xué)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(dòng)力學(xué)模型中應(yīng)用和驗(yàn)證了該理論,并且通過計(jì)算吸引子穩(wěn)定性對(duì)于具體動(dòng)力學(xué)參數(shù)的敏感性,提供了一種辨別網(wǎng)絡(luò)中重要節(jié)點(diǎn)和節(jié)點(diǎn)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(xué)基金(批準(zhǔn)號(hào):10901040,21373021和11622101) 中國優(yōu)秀博士論文作者基金會(huì)(批準(zhǔn)號(hào):201119)資助項(xiàng)目
【分類號(hào)】：O211
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本文編號(hào)：2364214