從圖論和控制論的視角研究生物復雜網(wǎng)絡的結構與功能
發(fā)布時間：2018-07-25 07:09
【摘要】：尋找復雜生物分子網(wǎng)絡的結構與功能之間的聯(lián)系并提出相應的設計原則至今仍是系統(tǒng)生物學領域的巨大挑戰(zhàn)。而這是系統(tǒng)性地理解細胞的工作原理并有效地獲取細胞狀態(tài)調(diào)控方法的重要途徑。網(wǎng)絡結構分析和動力學模擬對生物復雜系統(tǒng)的研究已經(jīng)取得了很大進步。但是統(tǒng)計角度的結構研究無法測定生物網(wǎng)絡中某些具體的相互作用的變化對網(wǎng)絡整體性質(zhì)的改變。動力學的研究對參數(shù)的苛刻要求和對計算資源的巨大需求同樣限制了它的應用。設計一套完備的復雜系統(tǒng)分析方法對我們理解,分析,并改造生物系統(tǒng)有著重要的生物學和臨床意義。借助圖論和控制論的方法,我們設計了一種能對生物分子反應網(wǎng)絡的信號前導通路和反饋調(diào)控回路進行模塊化的算法并成功地得到了三個重要的信號轉導網(wǎng)絡的前導模塊和層次化的反饋模塊。雖然反饋模塊對系統(tǒng)動力學的影響各異,但是它們之間的協(xié)同調(diào)控卻是重要的生理現(xiàn)象。結合這三個網(wǎng)絡的結構和動力學特征,我們提出了一個抽象的信號轉導模型。它不僅可以有效地進化,而且可以合理地解釋細胞如何高效并且可控地轉導刺激信號以及同源的信號體系為什么在不同的組織細胞中呈現(xiàn)差異化的功能。此外,通過路徑和模塊擾動分析,我們發(fā)現(xiàn)信號前導模塊的多通路并行介導以及全局反饋模塊對同一個核心節(jié)點的重復調(diào)控增強了系統(tǒng)的魯棒性。另一方面,輸出信號對核心節(jié)點的參數(shù)依賴以及局域反饋缺失引起系統(tǒng)動力學的巨大改變體現(xiàn)了系統(tǒng)的脆弱性。這兩者的對立性在進化的大邏輯框架下得以統(tǒng)一。我們的研究對理解生物網(wǎng)絡的設計原則以及構建具備特定功能的生物系統(tǒng)都有重要的意義。神經(jīng)網(wǎng)絡的連接結構是理解神經(jīng)系統(tǒng)功能的基礎。然而在最基本的神經(jīng)元層面通過研究信息的傳遞來實現(xiàn)功能的分化和整合仍然是個很具挑戰(zhàn)的問題。本文對目前已被完整探測的C.elegans神經(jīng)系統(tǒng)的全連接網(wǎng)絡進行結構分解。我們對承載它兩個重要生理功能的神經(jīng)元回路進行探索,挖掘其核心神經(jīng)元基團和輔助神經(jīng)元基團的功能實現(xiàn)。此外,通過神經(jīng)網(wǎng)絡的反饋調(diào)控和不同功能間神經(jīng)元基團的分化和整合可以理解神經(jīng)系統(tǒng)運行的潛在機制。為進一步實驗探索C.elegans神經(jīng)系統(tǒng)的功能實現(xiàn)提供了較好的理論基礎。
[Abstract]:It is still a great challenge in the field of systems biology to find out the relationship between the structure and function of complex biological molecular networks and to put forward the corresponding design principles. This is an important way to systematically understand the working principle of cells and to obtain effective methods of cell state regulation. Great progress has been made in the study of complex biological systems by network structure analysis and dynamic simulation. However, the statistical study of the structure of the network can not determine the changes of some specific interactions in the biological network to the overall properties of the network. The rigorous requirements for parameters and the huge demand for computational resources in dynamics research also limit its application. Designing a complete method of complex system analysis has important biological and clinical significance for us to understand, analyze, and transform biological systems. By means of graph theory and cybernetics, We designed a modularization algorithm for the signal leading pathway and feedback control circuit of the biomolecular reaction network and successfully obtained three important leading modules of the signal transduction network and the hierarchical feedback module. Although feedback modules have different effects on system dynamics, cooperative regulation between them is an important physiological phenomenon. Considering the structural and dynamic characteristics of these three networks, we propose an abstract signal transduction model. It can not only effectively evolve, but also reasonably explain how cells can efficiently and controllably transduce stimulatory signals and why homologous signal systems present different functions in different tissues and cells. In addition, through the path and module perturbation analysis, we find that the multi-channel parallel mediation of the signal lead module and the repeated regulation of the global feedback module to the same core node enhance the robustness of the system. On the other hand, the system fragility is reflected by the parameter dependence of the output signal on the core node and the huge change of the system dynamics caused by the absence of local feedback. The opposites of these two are unified under the big logical frame of evolution. Our research is of great significance for understanding the design principles of biological networks and building biological systems with specific functions. The connection structure of neural network is the basis of understanding the function of nervous system. However, functional differentiation and integration at the most basic neuron level through the study of information transmission is still a challenging issue. In this paper, the fully connected network of C.elegans nervous system which has been completely detected has been structurally decomposed. We explore the neuronal circuits carrying two important physiological functions, and excavate the functional realization of its core neuronal groups and auxiliary neuronal groups. In addition, the neural network feedback regulation and the differentiation and integration of different functional neuronal groups can understand the underlying mechanism of nervous system operation. It provides a good theoretical basis for further experimental exploration of the functional realization of C.elegans nervous system.
【學位授予單位】：清華大學
【學位級別】：博士
【學位授予年份】：2016
【分類號】：O157.5;O231
本文編號：2143032
[Abstract]:It is still a great challenge in the field of systems biology to find out the relationship between the structure and function of complex biological molecular networks and to put forward the corresponding design principles. This is an important way to systematically understand the working principle of cells and to obtain effective methods of cell state regulation. Great progress has been made in the study of complex biological systems by network structure analysis and dynamic simulation. However, the statistical study of the structure of the network can not determine the changes of some specific interactions in the biological network to the overall properties of the network. The rigorous requirements for parameters and the huge demand for computational resources in dynamics research also limit its application. Designing a complete method of complex system analysis has important biological and clinical significance for us to understand, analyze, and transform biological systems. By means of graph theory and cybernetics, We designed a modularization algorithm for the signal leading pathway and feedback control circuit of the biomolecular reaction network and successfully obtained three important leading modules of the signal transduction network and the hierarchical feedback module. Although feedback modules have different effects on system dynamics, cooperative regulation between them is an important physiological phenomenon. Considering the structural and dynamic characteristics of these three networks, we propose an abstract signal transduction model. It can not only effectively evolve, but also reasonably explain how cells can efficiently and controllably transduce stimulatory signals and why homologous signal systems present different functions in different tissues and cells. In addition, through the path and module perturbation analysis, we find that the multi-channel parallel mediation of the signal lead module and the repeated regulation of the global feedback module to the same core node enhance the robustness of the system. On the other hand, the system fragility is reflected by the parameter dependence of the output signal on the core node and the huge change of the system dynamics caused by the absence of local feedback. The opposites of these two are unified under the big logical frame of evolution. Our research is of great significance for understanding the design principles of biological networks and building biological systems with specific functions. The connection structure of neural network is the basis of understanding the function of nervous system. However, functional differentiation and integration at the most basic neuron level through the study of information transmission is still a challenging issue. In this paper, the fully connected network of C.elegans nervous system which has been completely detected has been structurally decomposed. We explore the neuronal circuits carrying two important physiological functions, and excavate the functional realization of its core neuronal groups and auxiliary neuronal groups. In addition, the neural network feedback regulation and the differentiation and integration of different functional neuronal groups can understand the underlying mechanism of nervous system operation. It provides a good theoretical basis for further experimental exploration of the functional realization of C.elegans nervous system.
【學位授予單位】：清華大學
【學位級別】：博士
【學位授予年份】：2016
【分類號】：O157.5;O231
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1 徐建峰;從圖論和控制論的視角研究生物復雜網(wǎng)絡的結構與功能[D];清華大學;2016年
，本文編號：2143032
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