Information Scientific Foundations of Knowledge Discovery from Proteome Data

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This research developed information scientific foundations of computational knowledge discovery from proteome data which came out from the genome-wide analysis of proteins and their related information. Our contributions are classified in two subjects :1.Inferrining protein networksWe developed a statistical and computational method for inferring and estimating gene networks by using microarray gene expression data and protein-protein interaction data. This method uses a Bayesian network method combined with nonparametric regression which employs protein-protein interaction information obtained by Y2H and MS as its prior. We also developed a computational method for estimating protein complexes by applying the principal component analysis to microarray gene expression data. Further, we constructed a model which combines Bayesian networks for gene regulatory networks and Markov networks for protein networks. With this model, more accurate protein networks can be estimated from protein-protein interaction information and microarray gene expression data.2.Computational method for pathway modeling and simulationWe defined a new basic architecture called Hybrid Functional Petri Net with extension on which a new software tool for pathway modeling and simulation was developed. This architecture allows us to use protein subcellular localization information and protein modification information in pathway modeling. By using the literature and attached data, we constructed models of pathways including protein networks with the software tool. We interpreted and evaluated these models and demonstrated the effectiveness of this method through modeling of a apoptosis signaling pathway, a cell cycle pathway model of fission yeast, and a network around p53.