Network-based Metabolic Flux and Structure Analysis
|School||Dalian University of Technology|
|Keywords||Metabolic networks Metabolic pathway analysis Metabolic balance analysis|
Biometric feature not only relates to a metabolite alone, but by the interaction of the mesh. Not only in-depth study of a single metabolite With the massive growth of data, but also the inevitable need to integrate large amounts of data for analysis, the organizational structure of the system point of view the study of biological networks is required. This article made some research around the metabolic network, the main results are as follows: 1. Clear know some model organisms metabolic network topology, however, their metabolic flux distribution is not very clear, especially after the knockout metabolism flux distribution can also be studied in depth. For a different objective function using the constrained optimization method to calculate metabolic flux distribution of metabolic networks, also investigated the impact of different constrained optimization of metabolic flux distribution. First build Escherichia coli central carbon metabolism network, then (Flux Balance Analysis, FBA) and minimum metabolic regulation of these two methods (Minimization Of Metabolic Adjustment, MOMA) was constructed based on the mathematical model of multiple metabolic flux analysis in metabolic balance to carbon molar flow rate calculated the metabolic flux distribution after the knockout. The results show that The MOMA standardized reasonable calculation method. 2. The primitives flux mode (elementary flux mode, EFM) in metrology based on the path to reach the desired product in a metabolic process by enumerating input was number of paths relative said the ability to grow the strength of how many. belong to the same areas of metrology flux balance analysis (FBA) to set the amount of input material, the biological quality calculated to maximize the objective function to be able to generate up to predict the growth of the organism phenomenon, EFM and FBA, respectively, can be done forecast to qualitative and quantitative forecast. Predict the growth of yeast cells after the gene mutation by EFM, and experimental results of the simulation forecast is good agreement; Comparative simulation results obtained with FBA method EFM method can better gene mutation and its phenotype (growth) Contact up. Metabolic flux does not link and transcription level, calculated on the basis of the primitives flux mode path importance (control-effective Fluxes, CEF), and then compared with the transcriptional expression data. Metabolic pathways and transcriptional level there is a certain contact, through the use of path importance enzyme capacity with path efficiency linked. The rapid development of network cells also exists a general rule, it provides a new concept to make our new understanding of biology.