[BK21 Plus Seminar]
            
              
           ▶Subject: Systems Biology: a scaffold in the era of “Big Data” biology
                      
           ▶Speaker: Donghyuk Kim PH.D.
                          (Department of Bioengineering, University of California )
                                   
           ▶Date: 4:00PM/July/24(Fri.)/2015
                   
           ▶Place: Conference Room(#179), Postech Biotech Center

                     
                  *Abctract
             With the advent of high-throughput genomics, biologists are starting to wrestle with a massive amount of datasets, encountering challenges with handling, processing, interpreting and transferring information. In addition to the unprecedented size of the datasets, heterogeneity of the datasets, including genomic sequences, transcriptome, translatome, proteome, fluxome and many more, adds another layer of complexity in the “Big Data” biology area. Systems biology approaches, particularly with constraints-based reconstruction and analysis of metabolic and expression models, has been proven successful in contributing their predictive and explanatory capabilities to better understanding of cellular states and processes. As examples of predictive and explanatory power of in silico models, I present a couple of studies on carbon and nitrogen metabolism of bacteria. They were investigated at the genome-scale with integration of model-based computation and heterogeneous genome-wide measurements with cutting-edge experimental technologies, such as ChIP-exo (Chromatin Immunoprecipitation with exonuclease treatment), RNA-seq, and TSS-seq (Transcription Start Site profiling with deep-sequencing). In the carbon metabolism study, the integrative approach was applied to conclude the regulatory dominance of Cra over CRP is embedded in transcriptional regulation of carbon metabolism in bacteria. In the nitrogen metabolism study, a systems biology-driven workflow to investigate transcriptional regulation of bacteria was proposed, and this workflow exploited the predictive capability of in silico models to predict the optimal conditions for activation of NtrC and Nac, two major transcription factor in nitrogen metabolism. In conclusion, integrative approaches based on systems biology can work as a scaffold to integrate heterogonous datasets, contributing to biological findings.

           ▶Inquiry: Prof. Yoontae Lee (279-2354)
                  
           * This seminar will be given in English.
   please refrain from taking photos during seminars. *