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  Center for Genomics and Proteomics (CGP)
Modern biology becomes more and more an information-driven science. Personal genome information will become a routine part of a person셲 medical folder in the near future. CGP scientists will be engaged in solving complex informatics and modeling problems utilizing vast amounts of biological and medical information. Solutions to biological problems at the levels of genes (genomics) and proteins (proteomics) will be sought with the help of modern computational tools and will ultimately lead to the new ways of health-care treatment and discovery of new therapeutics (drugs). The CGP systemically generate and utilize enormous amounts of information about biological system, which constitutes an entry point of the pipeline leading to the discovery of novel drugs as the exit point to improving human health.

To achieve these goals, CGP is composed of four major divisions: 1) Genomics, 2) Proteomics, 3) Bioinformatics, 4) Molecular Imaging divisio, through which scientists are collaborate to carry out various scientific projects. By bridging together the world셲 best scientists and providing them with the state-of-the-art instrumentation, CGP is going to create a uniquely creative environment conducive to most profound scientific discoveries that will turn 쁯ersonalized medicine into reality.

Recently, we found evidence that microglial cells in brain may synthesize albumin. This report describes experiments designed to confirm de novo synthesis of albumin in microglial cells and its potential role in relation to AD. In this study, the presence of albumin was demonstrated in immortalized human microglial cells, human primary microglial cells, and human fetal and adult brain tissues using immunocytochemistry (ICC),immunohistochemistry (IHC), tandem mass spectrometry (MS/MS), and immunoblot. In addition, we demonstrate that the synthesis and secretion of albumin from microglial cells is enhanced upon microgial activation by A棺1-42- or lipopolysaccharide (LPS)-treatment.
  Research Initiatives
A. Analysis of Normal and Diseased Human Genomes

DNA array analysis to profile gene expression in cancer and diseased states
This project will define the human gene expression profiling of cells in normal and diseased states to deduce function by linking variation of genetic to phenotypic expressions. The information obtained for cells or tissues provide crucial clues to molecular bases thus potential functional roles of the genes. The abundance and rate changes of transcript for the genes will be compared for sample tissues collected and stored at BioBanks of Validation Core.

The analysis of expression patterns and relating these to biological functions to group into a pattern that is relevant to particular physiological functions require significant reworking and consolidating of existing programs. A new user-interface will be developed to facilitate these tasks of managing databases and knowledge bases. Ultimately, these studies will identify improved markers for diagnosis and cure for the disease of interest. Detailed information about the gene expression profiles in relation to normal tissues will be used as a basis to derive molecular mechanisms of disease development and to design preventive and therapeutic measures of the disease.

B. Proteomic analysis for diagnosis and discovery of disease markers

Proteomic Profiling Platform
This project will define the human proteome profiling to deduce function. The bioinformatics interface supplemented by the computational biology of each of the engine modules used to define the proteome will create the resource, which will be accessed by basic biologists and the clinical community. Fundamental discoveries will depend on each assay uniquely associated with the relevant proteins. For clinical research, the resource of the proteome data will immediately be utilized by all clinicians involved in studying diseases linked to any particular organs under characterization. Furthermore, information about other organs can be expedited by the proteomics characterization of the sample organ and by interfacing with bioinformatics knowledge base which enables the ready acquisition of any new data identified for the proteins with respect to the new organs under study.

HUPO stem cell initiative
The objective is to effectuate the implementation of cutting edge proteomic technology applied to stem cell research to further our understanding of stem cell lineage. This initiative has been primarily prompted by major breakthroughs in stem cell biology and the potential of stem cells for biomedical application, and the awareness that proteomics has a means to accelerate this progress further, or to open yet unexplored areas. With other researchers from HUPO (Human Proteomics Organization) initiatives, this project will share technologies and knowledge bases, but will focus to profile membrane, secreted and cytosolic proteins from embryonic and adult stem cells for the purpose of identifying their biomarkers. This project will be carried out as a collaboration with Korean Stem cell biomarker discovery project (KOSEF).

MS data analysis pipeline and database
We will establish and maintain a protein identification pipeline server for MS data. The server will become a versatile standard platform for proteomic data management and peptide identification using mass spectrometry method. The protein identification engine will be adapted and developed for multi-processor systems to achieve appropriate turn-around time. The analysis pipeline and database will provide user-friendly web-based access for wide range of users.

C. Molecular medicinal validation of genome, proteome, and metabolome

Establishment of functional validation pipeline
This research initiative provides the most versatile conduit for a variety of collaborative projects focused on different disease targets. Using the Validation Core where computational tools and algorithms are developed to analyze data and consolidate information obtained from genomic, proteomic, and functional studies, we will establish unique functional assay systems tailored for individual target genes, proteins, or physiological states. Most often, one of the key methods employed for this study will involve high-resolution time-resolved microscopic imaging systems. A great deal of cellular imaging and processing such data requires high-end computing capabilities, and software to manage such data will be established. Computational programs to automate the image analysis through pattern recognition algorithms will be developed.

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