Estimation and simulation of gene networks for developing in silico biological networks

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We developed a series of computational and informatics key technologies for analyzing and understanding biological systems through data analysis and simulation. By using Bayesian network, state space model and AR model as basic concepts, we devised innovative computational methods for extracting gene networks. These methods enabled us to emboss large-scale static/dynamic gene networks from gene expression data based on large-scale gene knockdowns and drug responses. We also designed Cell System Markup Language for describing knowledge of dynamic pathways and Cell System Ontology for pathway simulation with which we developed systematic methods for analyzing and understanding biological systems.