Analysis And Application Of Metabolic Network Model Integrating Amino Acid And Transcriptional Data

With the implementation of the completion of the human genome project and subsequent DNA sequencing projects, the second generation of automated sequencing technology mature and widely used makes these biological data into a geometric multiples of growth, how to effectively integrate and use these data becomes an important problem in modern biological research. The research of metabolic network is an important part of the research of system biology. This paper studies to build microbial metabolic networks, and constructed Penicillium genome-scale metabolic networks based on RAVEN. Through the data integration of the flux balance analysis method is extended, and puts forward some new methods of analysis.’The current commonly used flux balance analysis can only analysis of the transient metabolic process, can not effectively integrate external environmental variables (such as glucose, nitrogen source and substrate concentration), this paper proposes a kind of integration of external environment variable dynamic flux balance analysis method. The experimental results show that the algorithm not only has the versatility and practicality and equilibrium with the static flow analysis method compared to the algorithm can intuitively reflect the variation of rate of biological growth rate changes and metabolic reactions.Gene mutations will lead to amino acid change, resulting in changes in protein activity, and the metabolic network simulation analysis does not consider the influence of. The amino acid polymorphism information added to the metabolic network simulation, was used for the study of protein in a site mutations influence on the activity of the protein, at the same time, the influence of added to the metabolic network to verify the reaction of the protein and its control in the biological importance of long. Experimental results show that based on consider single amino acid variation of biological growth rate method can not only influence degree of the study of amino acid variation on the activity of protein amino acid polymorphisms, also can through the growth rate of change of the protein on the biological growth of the degree of importance.In the metabolic network not only need to know the number of genes involved in, also want to consider to gene expression. In this paper, based on the integration of transcriptome data and metabolic network predicted genes, proposed ITD-MEPE (integrated transcriptional group data and metabolic network to predict gene) method, the transcriptome sequencing data added to the metabolic network according to the metabolic network can directly reflect the growth status of biological characteristics to predict genes related with biological traits. The results of the experiments show that the accuracy of the integration of the sequencing data of the transcriptional group and metabolic network prediction is better than the classical BR algorithm based on the rate of change of the DESeq algorithm based on the analysis of gene expression difference.