从图论和控制论的视角研究生物复杂网络的结构与功能
发布时间：2018-07-25 07:09
【摘要】：寻找复杂生物分子网络的结构与功能之间的联系并提出相应的设计原则至今仍是系统生物学领域的巨大挑战。而这是系统性地理解细胞的工作原理并有效地获取细胞状态调控方法的重要途径。网络结构分析和动力学模拟对生物复杂系统的研究已经取得了很大进步。但是统计角度的结构研究无法测定生物网络中某些具体的相互作用的变化对网络整体性质的改变。动力学的研究对参数的苛刻要求和对计算资源的巨大需求同样限制了它的应用。设计一套完备的复杂系统分析方法对我们理解,分析,并改造生物系统有着重要的生物学和临床意义。借助图论和控制论的方法,我们设计了一种能对生物分子反应网络的信号前导通路和反馈调控回路进行模块化的算法并成功地得到了三个重要的信号转导网络的前导模块和层次化的反馈模块。虽然反馈模块对系统动力学的影响各异,但是它们之间的协同调控却是重要的生理现象。结合这三个网络的结构和动力学特征,我们提出了一个抽象的信号转导模型。它不仅可以有效地进化,而且可以合理地解释细胞如何高效并且可控地转导刺激信号以及同源的信号体系为什么在不同的组织细胞中呈现差异化的功能。此外,通过路径和模块扰动分析,我们发现信号前导模块的多通路并行介导以及全局反馈模块对同一个核心节点的重复调控增强了系统的鲁棒性。另一方面,输出信号对核心节点的参数依赖以及局域反馈缺失引起系统动力学的巨大改变体现了系统的脆弱性。这两者的对立性在进化的大逻辑框架下得以统一。我们的研究对理解生物网络的设计原则以及构建具备特定功能的生物系统都有重要的意义。神经网络的连接结构是理解神经系统功能的基础。然而在最基本的神经元层面通过研究信息的传递来实现功能的分化和整合仍然是个很具挑战的问题。本文对目前已被完整探测的C.elegans神经系统的全连接网络进行结构分解。我们对承载它两个重要生理功能的神经元回路进行探索,挖掘其核心神经元基团和辅助神经元基团的功能实现。此外,通过神经网络的反馈调控和不同功能间神经元基团的分化和整合可以理解神经系统运行的潜在机制。为进一步实验探索C.elegans神经系统的功能实现提供了较好的理论基础。
[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.
【学位授予单位】：清华大学
【学位级别】：博士
【学位授予年份】：2016
【分类号】：O157.5;O231
本文编号：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.
【学位授予单位】：清华大学
【学位级别】：博士
【学位授予年份】：2016
【分类号】：O157.5;O231
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1 徐建峰;从图论和控制论的视角研究生物复杂网络的结构与功能[D];清华大学;2016年
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