Faculty Research Profile
Kyemyung PARK
학력
· 2020: Ph.D., Biophysics, University of Maryland, College Park
· 2014: M.D., Yonsei University
· 2008: B.S., Physics, Seoul National University
주요 경력
· 2022~present: Assistant Professor, Biomedical Engineering Department, UNIST
· 2022: Senior researcher, Korea Virus Research Institute, Institute for Basic Science
· 2020~2022: Fellow, Department of Pharmacology, Yonsei University College of Medicine
· 2015~2020: Predoctoral Visiting Fellow, Laboratory of Immune System Biology, NIAID, NIH
수상/학회/외부활동
· Awards
- Woongbi Next Generation Research Award, Korean Association of Immunologist International Meeting 2021
· Membership
- Korea Genome Organization
- Korea Society for Bioinformatics
- Korean BioChip Society
- The Korean Association of Immunologist
- The Korean Society of Medical Informatics
- Korean Society for Industrial and Applied Mathematics
- Korean Society for Mathematical Biology
시스템 면역 다이내믹스 연구실
Systems ImmunoDynamics Lab
우리 연구실은 시스템 생물학적 방법론을 통해 면역계의 다이내믹스에 대한 예측 모델을 구축하는 것을 주된 목표로 하고 있다. 이렇게 구축한 모델을 이용하여 비선형 복잡계적 특성을 지닌 면역계를 총체적으로 이해하고 원하는 방향으로 면역계의 반응을 조절할 할 수 있는 치료방침을 제시하고자 한다. 궁극적으로는 면역계의 오작동으로 기인하는 여러 감염질환, 암, 자가면역질환들을 근원적으로 치료하는데 도움을 주고자 한다. 유전자, 세포, 조직, 개체 수준을 아우르는 다계층적(multiscale)이면서도 수많은 분자, 세포들의 상호작용(high-throughput)으로 작동하는 면역계를 현실적으로 기술하는 수리모델(scalable modeling)을 구축하기 위해 AI를 활용한 다중오믹스 데이터, 임상데이터, 이미지 데이터 등을 포괄하는 방법론을 개발하고, 기계학습, 베이지안통계, 데이터사이언스의 방법론을 전통적 수리모델에 접목하여 궁극적으로 여러 면역질환에 대한 환자맞춤형 치료에 우리가 구축한 모델을 이용하고자 한다.
We seek to construct predictive models describing the dynamical behavior of the immune system via systems biological approaches. Using these models, we wish to better understand complex and nonlinear immune behavior in its entirety. Ultimately, we design therapies to predictably modulate such behavior to the direction we desire to help cure infectious, malignant, and autoimmune diseases occurring due to dysregulations of the immune system. To realize this, we need to incorporate sufficient biological realities of the immune system into the models in two aspects: 1) the multiscale nature spanning across genes, cells, tissues, organs, and organisms/populations and 2) the high-throughput nature consisting of numerous molecular and cellular players with intricate interactions among each other. Therefore, we will develop computational frameworks to encompass data from various sources – multi-omics, clinics, or images using methods from AI/machine learning, Bayesian statistics, and data science within traditional mathematical modeling to be deployable to individualized therapies of immune diseases in the era of ‘precision’ medicine.
We seek to construct predictive models describing the dynamical behavior of the immune system via systems biological approaches. Using these models, we wish to better understand complex and nonlinear immune behavior in its entirety. Ultimately, we design therapies to predictably modulate such behavior to the direction we desire to help cure infectious, malignant, and autoimmune diseases occurring due to dysregulations of the immune system. To realize this, we need to incorporate sufficient biological realities of the immune system into the models in two aspects: 1) the multiscale nature spanning across genes, cells, tissues, organs, and organisms/populations and 2) the high-throughput nature consisting of numerous molecular and cellular players with intricate interactions among each other. Therefore, we will develop computational frameworks to encompass data from various sources – multi-omics, clinics, or images using methods from AI/machine learning, Bayesian statistics, and data science within traditional mathematical modeling to be deployable to individualized therapies of immune diseases in the era of ‘precision’ medicine.
연구분야
시스템/전산/수리 생물학, 면역학, 시스템 약리학, 생물물리학 / Systems/Computational/Mathematical Biology, Immunology, Systems Pharmacology, Biophysics
Systems/Computational/Mathematical Biology, Immunology, Systems Pharmacology, Biophysics
연구 희망분야
멀티스케일 모델링, 의료 데이터 사이언스, 면역 파운데이션 모델링, 면역 디지털 트윈 모델링 / Multiscale Modeling, Medical Data Science, Immune Foundation Modeling, Immune Digital Twin Modeling
Multiscale Modeling, Medical Data Science, Immune Foundation Modeling, Immune Digital Twin Modeling
연구주제
• Predictive quantitative exploration of the immune system with dynamic systems modeling for predictably modulating immune behavior to treat autoimmune, malignant, and infectious diseases
• Development of computational frameworks enabling scalable (high-dimensional and multiscale) dynamic modeling and AI-based integration of data and knowledge from various biological layers to construct an in-silico immune system (immune digital twin models).
• Predictive quantitative exploration of the immune system with dynamic systems modeling for predictably modulating immune behavior to treat autoimmune, malignant, and infectious diseases
• Development of computational frameworks enabling scalable (high-dimensional and multiscale) dynamic modeling and AI-based integration of data and knowledge from various biological layers to construct an in-silico immune system (immune digital twin models).
국가연구개발사업 기술 분류체계
국가과학기술표준분류
LA. 생명과학 > LA07. 융합바이오 > LA0705. 시스템생물학
논문
· Front. Immunol., Multi-physiology modeling of the immune system in the era of precision immunotherapy., Hong, S., and Park, K. (2025).
· Trends Immunol., Systems immunology of regulatory T cells: can one circuit explain it all?, Tripathi, S., Tsang, J.S. , and Park, K., (2023)
· Cell, A local regulatory T cell feedback circuit maintains immunological homeostasis by pruning self-activated T cells. Wong, H. S., Park, K., Gola, A., Baptista, A. P., Miller, C. H., Deep, D., Lou, M., Boyd, L. F., Rudensky, A. Y., Savage, P. A., Altan-Bonnet, G., Tsang, J. S., Germain, R. N.,(2021)