Topic: CCSB Seminar Series Start Time: Feb 9, 2022 11:55 AM
Abstract
Although computational modeling of biological networks has shown a great promise in helping biologists understand complex biological processes, the application of computational modeling for biological networks has been significantly limited due to the large amount of data required for the inference of computational models. However, the recent emergence of large scale transcriptomic data has made the use of such computational tools to model complex biological networks more plausible. In addition to large scale transcriptomic data, curated biological knowledge available in public repository also plays increasingly important role in computational modeling of biological networks. In this talk, I will briefly overview several computational models for biological networks, and demonstrate how differential analysis of computationally derived biological networks can lead to novel biological discovery. The first study uses a publicly available large scale transcriptomic data of cancer cells combined with the cancer cell’s response to chemotherapies to discover novel use of chemotherapy in pulmonary hypertension disease. Single cell RNA sequencing captures transcriptional profiles of tens of thousands of individual cells in a sample, which makes it an ideal data sets for computational network modeling. The second example utilizes single cell RNA sequencing data of pulmonary hypertension patients to discover rewiring of biological pathways associated with drug treatments.
Speaker Bio
Dr. Seungchan Kim is a Chief Scientist and Executive Professor at the Department of Electrical and Computer Engineering and Director of the CRI Center for Computational Systems Biology at the Prairie View A&M University (PVAMU), initiated by funding from Texas A&M University Systems’ Chancellor’s Research Initiative (CRI) and Prairie View A&M University. Prior to this appointment, He was the Head of Biocomputing Unit and an Associate Professor at Integrated Cancer Genomics Division of Translational Genomics Research Institute (TGen). He was one of the founding faculty members of TGen, founded in 2002, by Dr. Trent, then-Scientific Director of the National Human Genome Research Institute at the National Institutes of Health. He had led computational systems biology research at the institute since 2003. He was also an Assistant Professor in the School of Computing, Informatics, Decision Systems Engineering (CIDSE) at the Arizona State University from 2004 till 2011. Dr. Kim received B.S. and M.S. degrees in Agriculture Engineering from the Seoul National University, and Ph.D. in Electrical Engineering from the Texas A&M University. He also got his post-doctoral training at the Cancer Genetics Branch of National Human Genome Research Institute.
Dr. Kim’s research interests include: 1) mathematical modeling of genetic regulatory networks, 2) development of computational methods to analyze multitude of high throughput multi-omics data to identify disease biomarkers, and 3) computational models to diagnose patients or predict patient outcomes, for example, disease subtypes or drug response. His studies have had a large influence on the development of computational tools to study underlying mechanisms for cancer development and better understand the molecular mechanisms behind cancer biology and biological systems.