Modeling Analysis Of Biological Metabolism By Integrating Gene Expression

Intracellular metabolism of the organism is all ordered chemical reactions to sustain life activity and is the exchange process of material and energy with the outside cell. The analysis of biological metabolism includes studies of metabolite concentration, metabolic flux and metabolic function. People can understand the metabolic mechanisms more accurately by modeling biological metabolic systems which is very helpful to observation the pathogenesis of the disease, create drugs and analyze efficacy. Research on biological metabolism by way of computing helps reduce experimental expenses, as well as predicting some metabolic data which can’t be measured in the laboratory.Compared with proteomics, metabolomics data, the measurement of genomics can be spent less but more accurate. Moreover, there is the correlation between gene expression and biological metabolism, so more and more research and studies try to analyze biological metabolic system by using gene expression. The prediction of cellular function from a genotype is a fundamental goal in biology. For metabolism, constraint-based modelling methods systematize biochemical, genetic and genomic knowledge into a mathematical framework that enables a mechanistic description of metabolic physiology. We utilize constraint-based modeling approach that integrates transcriptomic data to metabolic networks to qualitatively analyze metabolic flux distribution of multicellular organisms and create context-specific models. Our main works include:(1) For microbial metabolic flux analysis, proposed a metabolic computing model to predict significantly changed fluxes based on the differential gene expression. Our method assumed "If the genes encoding the enzyme significantly changes, the metabolic fluxes which are catalyzed by the enzyme should also significantly change". The model integrates gene expression differences to metabolic network to predict significantly altered fluxes. Compared to the result of "flux balance analysis", our method predicted the changes in microbial metabolic fluxes more accurately than "flux balance analysis". (2) Gave a method to analyze the extent of metabolic pathway’s change according to altered metabolic fluxes, based on the "fisher’s exact test" and analyzed the changes of cancer cell’s pathways with our method. Compared the result of our method to traditional gene set enrichment method (GSEA) with "receiver operating characteristic curve (ROC)", the performance of our method is better than GSEA method. (3) Gave a method of analyzing the common metabolic feature of cancer cell from the perspective of metabolic "work" network. Constructed the 17 kinds of human normal and cancer tissues’"context-specific" models with iMAT method and partitioned the metabolic network to study the metabolic feature of different cancer tissue.