Systems ImmunoDynamics Lab

시스템 면역 다이내믹스 연구실

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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 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.

관심분야

Systems/Computational/Mathematical Biology, Immunology, Systems Pharmacology, Scalable/Multiscale Modeling, Medical Data Science, Biophysics

희망분야

Systems/Computational/Mathematical Biology, Immunology, Systems Pharmacology, Scalable/Multiscale Modeling, Medical Data Science, Biophysics

Research Keywords and Topics

1) Dissect immune homeostasis, balance, and adaptation to treat autoimmunity, cancer, and
infectious diseases.

2) Develop computational frameworks of scalable (high-dimensional and multiscale) and patient-deployable dynamical modeling and integration of data from various biological layers aided by machine learning/AI to construct the in-silico immune system.

Research Publications

A pharmacometric model to predict chemotherapy-induced myelosuppression and associated risk factors in non-small cell lung cancer, Park, K., Kim, Y., Son, M., Chae, D., Park, K., Pharmaceutics (2022).
A simple risk scoring system for predicting the occurrence of aspiration pneumonia after gastric endoscopic submucosal dissection, Park, K., Kim, N. Y., Kim, K. J., Oh, C., Chae, D., Kim, S. Y., Anesthesia & Analgesia (2022).
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., Cell (2021).

국가과학기술표준분류

  • LA. 생명과학
  • LA07. 융합바이오
  • LA0705. 시스템생물학

국가기술지도분류

  • 건강한 생명사회 지향
  • 021900. 생체정보분석/활용 기술

녹색기술분류

  • 녹색기술관련 과제 아님
  • 녹색기술관련 과제 아님
  • 999. 녹색기술 관련과제 아님

6T분류

  • BT 분야
  • 기초/기반기술
  • 020114. 생명현상 및 기능연구