【作者】 蒋达；

【导师】 张述伟；

【作者基本信息】 大连理工大学 ， 化学工艺， 2006， 硕士

【摘要】 生物体功能不仅涉及某个代谢物的单独作用,而是通过网状的交互作用完成。因此,不仅要对单个代谢物深入研究,同时随着数据海量增长,也必然需要将大量数据整合起来进行分析,需要以系统的观点研究生物网络的组织结构。本文围绕代谢网络作了一些研究工作,主要结果如下: 1.当前可以清楚的知道一些模式生物的代谢网络拓扑结构,然而其代谢通量分布并不十分清晰,特别是对于基因敲除后的代谢通量分布还需要深入研究。本文针对不同的目标函数,利用约束优化方法来计算代谢网络的代谢流分布,同时考察了不同的约束优化方法对代谢流分布的影响。首先构建大肠杆菌中心碳代谢网络,然后以代谢平衡分析(Flux Balance Analysis,FBA)和最小代谢调节分析(Minimization Of Metabolic Adjustment,MOMA)这两种方法为基础,构建多个代谢流数学模型,以碳摩尔流率计算基因敲除后的代谢流分布。结果表明标准化后的MOMA是比较合理的计算方法。 2.基元通量模式(elementary flux mode,EFM)是以计量学为基础,通过枚举输入物到达目的产物过程中的路径来表示代谢过程,路径数相对多少表示生长能力的强弱.而同属于计量学范畴的通量平衡分析(FBA)设定输入物的量,通过实现最大化目标函数来计算最多能够生成的生物质量,在预测生物体的生长现象上,EFM和FBA分别能够做到定性预测和定量预测。通过EFM预测了基因突变后的酵母细胞生长现象,模拟预测结果和实验结果很好吻合;通过与FBA方法得到的模拟结果比较,EFM方法能更好的把基因突变和其表型(生长)联系起来。 3.由于代谢通量并不和转录水平存在联系,在基元通量模式的基础上计算路径重要性(control—effective fluxes,CEF),然后与转录表达数据比较。得到代谢路径与转录水平存在一定联系,通过使用路径重要性把酶能力与路径效率联系起来。 网络的快速发展显示了细胞中也存在通用规则,它提供了一种全新的概念来使我们重新认识生物学。更多还原

【Abstract】 It has been recognized that the biologic function is not only involved in a single metabolite but also the multiple interaction of many metabolites. Therefore, a system viewpoint should be taken account to analyze the biological network. Nowadays, though the topological structures of the central metabolic networks of certain organism have been clarified, their metabolic flux distributions remain unclear. Moreover, some researches should be taken into deep investigation especially on the gene-knockout mutant. That the flux distributions of large scale metabolic network have been analyzed by various target functions is also our concern, since which may cause inconsistent results. Another purpose of this work is to illustrate the relationship between genotype and phenotype in the complex cellular network of saccharomyces cerevisiae. The main results are summarized as follows:1. The E.coil central carbon metabolic network is studied firstly. Based on Flux Balance Analysis (FBA) and Minimization of Metabolic Adjustment (MOMA), some mathematical models were constructed to compute the carbon distributions in the gene-knockout mutant metabolic network. Compared with the experimental results, the analysis shows that the normalized MOMA analysis is reasonable to solve the carbon distribution of mutant metabolic network.2. As a structure-oriented method, Elementary flux mode (EFM) analysis has obtained its popularity in analysis of the robustness of the central metabolism, as well as network function of some organisms. However, this method has not been widely used in modeling of gene deletion phenotype. By enumerating all the metabolic pathways, the EFM analysis presented herein has identified the functional features and predicted the growth phenotype of the S.cerevisiae. As compared with the FBA, the performance of EFM analysis was superior to FBA in prediction of gene deletion phenotype. EFM analysis has been demonstrated as an effective tool to bridge the gap between metabolic network and growth phenotype.3. Control-effective fluxes (CEF) are determined directly from the set of EFM, representing the importance of each reaction for the efficient and flexible operations of the entire network. To our knowledge, there may be no relationships between the metabolic flux distribution and the gene transcription level, however the CEF analysis presented here may exhibit a reasonable correction function between the pathway structure and the experimental transcription data.更多还原