Network-based Metabolic Flux And Structure Analysis

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.