Research On The Method Of Analyzing The Altered Metabolism For Multicellular Organisms

Altered metabolism is closed linked to the phenotype changes, including different stage of cellular growth and development, permutation of the environment as well as the appearance of various diseases. Modelling of the altered metabolism between these changes help us understanding the underlying mechanisms of these phenotype changes, lead to the identification of novel prognostic biomarkers and the development of new therapies. In the past decade, Genome-scale metabolic models (GEMs) have been employed for studying metabolism in a systematic manner. Such metabolic models have started contributing to the understanding of the mechanistic relationship between genotype and phenotype. However, some difficulties are encountered when model for multicelluar organisms beacause of their inherent complexity. On the basis of previous research, we presented some novel methods on this tissue, expecting to advance the relevant work.We focused on the altered metabolism under various conditions for multicellular organisms. Integrating with the high-throughput gene expression data, genome-scale model of the altered metabolism was constructed. Modelling the altered metabolism directly revealed more accurate prediction of the changes in the metabolic network compare to the model of the metabolic network in each condition independently. Our works include:(1) proposed a method for predicting the altered metabolism in two different conditions. The method eliminates the need of required metabolic objectives in most of the previous constraint-based models. This assumption greatly expands the application of the proposed method. The method was validated on the data of different tissues as well as normal tissue and tumor tissue in human body.(2) Developed an integrative tool for analyzing the altered metabolism based on the proposed genome-scale metabolic model. The tool gives user more relevant analysis result compared with previous tools. The altered metabolism database is constructed to provide researchers around the world for statistics analysis of altered metabolism between different conditions.(3) For the analysis of time-course metabolic changes, two new methods are presented. Firstly, we inferred the time-course metabolic changes based on the result of enrichment analysis method on each two time points. Then, a new genome-wide metabolic model based on temporal gene expression data is provided to model the state changes. The altered metabolism between two previous adjacent time points is incorporated into the prediction of metabolic changes of current two points. Both the new methods were validated on the data of cell differentiation for mouse.