基因组尺度人类代谢网络的亚细胞及组织定位

发布时间：2018-05-04 08:09

本文选题：人类代谢网络 + 亚细胞定位　；参考：《天津大学》2010年博士论文

【摘要】：人体代谢系统的实验研究与人体内不同亚细胞结构和组织的功能是密切相关的,因而人体蛋白和代谢反应的亚细胞定位及组织分布是人体生物学研究和药物开发的重要研究对象。为了更好地理解人体代谢网络的复杂性,融合亚细胞和组织信息的人类代谢网络不可或缺。本文通过添加亚细胞定位信息、运输反应和组织定位信息对已构建的爱丁堡人类代谢网络模型(EHMN进行了扩展,并且在亚细胞定位的基础上对网络中错误的蛋白-反应关系进行了修正。首先蛋白亚细胞位置信息来自于不同的数据库,而EHMN中所有反应的位置信息则根据蛋白位置通过蛋白-反应关系获得,由此初步构建了亚细胞分室网络;之后,本文通过对每个途径中子网络的图论分析确定了网络中的空白和孤立反应并对其进行了修正;此外,本文基于文献及教科书中对途径位置的描述进一步校正了网络中反应的位置。最终初步分室网络中上百个反应的位置得到了修正,基于修正后反应的亚细胞位置,错误的蛋白-反应关系也得到了更正。亚细胞分室完成后,本文基于Recon 1模型、数据库以及代谢末端分析添加了1400多个运输反应,使网络中的各个亚细胞位置连通起来。为了验证分室网络的质量,本文通过途径分析检验了EHMN对近70种重要代谢物的合成和降解能力以及代谢位置,结果表明EHMN中这些代谢物的代谢过程与文献或教科书一致。在亚细胞分室网络的基础上,利用与亚细胞定位相同的方法,本文将组织信息也融合到EHMN中,从而构建了更加完整的包含亚细胞和组织定位的人类代谢网络。最后,本文从拓扑结构的角度对定位后的线粒体和脑两个子网络进行了功能验证,表明从拓扑结构出发的网络分析结果与子网络的功能特点相符。本文首次将图论分析的方法用于亚细胞及组织定位过程中并且利用途径分析工具对网络的可靠性进行分析,并开发了新的将分类树与模块化指标相结合的网络解耦方法对网络进行解耦以进行进一步的功能分析。亚细胞及组织定位后的网络可以从EHMN网站(www.ehmn.bioinformatics.ed.ac.uk)上下载并免费提供给学术研究。
[Abstract]:The experimental study of human metabolic system is closely related to the function of different subcellular structures and tissues in the human body. Therefore, subcellular localization and tissue distribution of human protein and metabolic reactions are important research objects in human biology and drug development. In order to better understand the complexity of human metabolic networks, it is indispensable to integrate subcellular and tissue information. In this paper, EHMN, an Edinburgh human metabolic network model, was extended by adding subcellular localization information, transport response and tissue localization information. On the basis of subcellular localization, the wrong protein-response relationship in the network was corrected. First of all, the location information of protein subcells comes from different databases, and the location information of all reactions in EHMN is obtained by protein-response relationship, and the subcellular compartment network is constructed. In this paper, the blank and isolated reactions in the network are determined and corrected by the graph theory analysis of each path neutron network. This paper further corrects the response position in the network based on the description of the path location in the literature and textbooks. Finally, the positions of hundreds of reactions in the primary compartment network were corrected, and the wrong protein-response relationship was corrected based on the subcellular position of the modified reaction. After subcell compartmentalization was completed, more than 1400 transport reactions were added to the network based on Recon 1 model, database and metabolic terminal analysis, which connected each subcell position in the network. In order to verify the quality of compartments network, the synthesis and degradation ability and metabolic location of nearly 70 important metabolites of EHMN were tested by means of path analysis. The results showed that the metabolic process of these metabolites in EHMN was consistent with that in literature or textbooks. On the basis of subcellular compartments network and using the same method as subcellular localization, this paper fuses tissue information into EHMN and constructs a more complete human metabolic network containing subcellular and tissue localization. Finally, from the point of view of topological structure, the functional verification of the mtDNA and brain subnetworks after localization is carried out, which shows that the network analysis results based on the topology structure are consistent with the functional characteristics of the subnetworks. In this paper, the method of graph theory analysis is used in subcellular and tissue localization for the first time, and the reliability of network is analyzed by means of path analysis tool. A new decoupling method combining classification tree and modularization index is developed to decouple the network for further functional analysis. The subcellular and tissue localization network can be downloaded from the EHMN website at www.ehmn.bioinformatics.ed.ac.ukand is available for free for academic research.
【学位授予单位】：天津大学
【学位级别】：博士
【学位授予年份】：2010
【分类号】：R341

【共引文献】