The Study of Constructing Sub-networks as the Guideline for Systematic Understanding of Neuronal Cell Differentiation

초록

In multicellular organisms, a cell can generally make the correct decision for the phenotypic fate in itself despite environmental and genetic variations. Information for a specific phenotype in a cell is mapped into a scenario-based system configured by the interconnection of genes in a gene module, a group of genes that implement a unique physiological function together. In other words, a particular cell phenotype and the cellular behavior are determined by the change of the quantity and route of information within a system with high stability. Therefore, to understand the cell phenomena, it is important to search gene modules as minimal functional mediators of the system and predict the information flow that occurs from both inside and outside the modules. In our study, the RnE algorithm proposed by [H Y Kim, 2009] was applied to the gene expression data obtained from hES cell-derived neural precursors, as the method for searching the gene module that is influenced by the common regulatory process. As a result of performing the algorithm, each module was concentrated into an eigenvector space that represents a unique function containing the essential information for biological function. And we assigned a generalized probability to each eigenvector for a gene within a given module by obtaining a density matrix of each gene at the eigenvector space. To construct sub-networks from the essential information of the gene, we induced the information loss of the module by removing one gene from the module. The significant change in contribution of each gene to the module after the information loss was evaluated by Kullback-Leibler divergence and this value was used for exploring the edges in a sub-network. The interconnection of genes in a sub-network was compared with the information on literature. The proposed method to construct sub-networks is believed to be the guideline for examination of genes with significant information and their functional mechanism related to neuronal cell differentiation.