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질병기전의 이해와 주요인자 동정을 위한 시스템 생물학적 기법 개발

질병기전의 이해와 주요인자 동정을 위한 시스템 생물학적 기법 개발
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The development of high-throughput technologies enables the measurements and catalogs of genes, proteins, interactions, and behavior. A systems biology effort will be necessary to convert the information contained in multidimensional data sets into following three major goals: 1) disease-relevant information that can identify the drivers of pathogenesis, 2) useful biomarkers that can classify disease by prognosis and response to therapeutic modalities, and 3) potential association of specific networks with disease that can discover novel therapeutic targets. To achieve these goals, we present systems frameworks which take heterogeneous data integration and network modeling and analysis. First, to extract disease relevant information for identification of the pathogenic drivers, we introduced network based data integration approach to rheumatoid arthritis (RA). Using this approach, we identified the RA associated genes (RAGs) showing differential expression patterns. Functional enrichment analyses of the RAGs showed that RA-dominant RAGs play key roles in activating immune-related processes and also promoting ‘pannus formation’ related processes. Furthermore, the RA-perturbed network revealed that RA FLS act as a major player in ‘pannus formation’
and anti-TNF- therapy moves many RA-perturbed processes toward normality. Finally, we identify 19 key transcription factors (TFs) and 108 putative modulators that can act as metrics or modulators of disease-perturbed networks. Among the 19 key TFs, NFAT5 is critical for development of inflammatory arthritis. Second, we developed systems frameworks for identification of useful biomarkers. Early diagnosis and monitoring disease activity is emphasized to patients and doctors because decision making of treatment with appropriate drugs and timing may help prevent disease progression. Subtractive analysis and network model based marker selection procedure were applied for identification of biomarker in urine samples. Subsets of biomarker candidates were validated with clinically well defined samples. This systems analysis provides a comprehensive basis for identifying potential biomarker candidates. Finally, to identify the hidden association between network comprised of specific protein family and disease, interaction map with multiple cancer genomic data was used. Identification of cancer-associated factors has been a subject of primary interest not only to understand basic mechanism of tumorigenesis but also to discover therapeutic targets. The mammalian aminoacyl-tRNA synthetases (ARSs) have essential roles in translation and a range of diverse functions in other processes. Our network model describes the roles of tRNA syntheses outside of translation, and examines their cancer-associated profile that may reflect many more functions in tumorigenesis awaiting discovery. This Analysis addresses potential pathophysiological implications of ARSs in tumorigenesis. Our systems biology approaches identifying molecular drivers and biomarkers will lead to the implementation of smaller, shorter, cheaper, and individualized clinical trials that will increase the success rate. Also our network modeling and analysis framework can serve as a basis for various studies where we evaluate how a particular therapeutic agent affects disease-associated cellular processes at the system level.
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