공정설계 및 시스템공학 연구실

We develop bioprocess digital twins (DT) of cell cultures by incorporating in-line monitoring, advanced data analytics with machine and deep learning, and mechanistic models representing the mammalian cells and bioreactor for virtually mirroring their behaviors under adjustable process conditions. Bioprocess DT integrate sensors, models, and control to transform reactive biomanufacturing into predictive, adaptive, and autonomous systems. We have successfully established an in-house experimental setup for the Raman monitoring system, currently developing a multi-sensor integrated control system. In addition, we have developed AI/ML based soft sensor model for real-time estimation of mammalian cell culture. We have suggested integratgive framework for data‐driven predictive modeling to forecast multistep ahead profiles of mammalian cell culture towards bioprocess digital twin. We are currently enhancing genome-scale metabolic models (GEMs) of Chinese hamster ovary (CHO) cells at multiple biological layers and scales to accurately represent condition-specific regulatory mechanisms that shape culture performance and metabolic phenotypes. We have proposed genome-scale metabolic model-guided systematic framework for designing customized live biotherapeutic products. Also, Systems biology approaches have been successfully applied for deciphering dynamic antibiotics-microbiome-metabolome interactions in preterm infants. Sungkyunkwan University (SKKU) and the Institute of Experimental Biology and Technology (iBET) co-hosted the SKKU-iBET Joint Symposium on Advanced Biotherapeutics and Intelligent Biomanufacturing at the SKKU Natural Sciences Campus.