PDSE @Sungkyunkwan Univ.

RESEARCH

PDSE’s main research focus is placed on elucidating complex biological parts/systems and improving/designing their characteristics and properties towards biotechnological and healthcare applications. The central task of this bigdata- and hypothesis-driven research is to comprehensively collect, manage and analyze the global cellular information, e.g., high-throughput omics data, and to generate predictive computational models of the intricate biological processes, e.g., metabolic, signaling and regulatory networks of cells and bioreactors.

Thus, better understanding the cellular physiology, regulation and metabolism, multi-cellular interactions and bioprocesses at the systems level and subsequently designing strategies for achieving desirable states are a prime target of this research. To this end, we can exploit various systems engineering techniques (statistical analysis, machine learning/artificial intelligence (ML/AI), mathematical modeling, process control and systems optimization).

Current research topics of interest, applications and specific ongoing projects include, but not limited to:

Bioprocess digital twins of mammalian cell culture for
self-driving biomanufacturing

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.

Real time monitoring and adaptive control of
cell cultures by PAT

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.

Data-driven model for
forecasting cell culture profiles

We have suggested integratgive framework for data‐driven predictive modeling to forecast multistep ahead profiles of mammalian cell culture towards bioprocess digital twin.

Mechansitic model for characterizing
mammalian cells and cultures

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.

Microbiome Engineering for designing customized live biotherapeutic products

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.

Integrated computational synthetic biology platform enabling multimodal next-generation biotherapeutics design and biomanufacturing

Our group investigates genes and cellular systems at a holistic level, with a focus on codon usage and sequence architecture for applications in biotechnology and healthcare. By integrating multi-omics datasets with advanced computational methods and AI/ML-driven analytics, we construct environment- and species/tissues-specific codon usage landscapes. These insights enable the development of customizable codon optimization strategies that support precise, tailored gene design for bioproduction, gene therapy (AAV- and LV-based), and vaccine development. In parallel, we are also advancing peptide linker and fusion construct design to further enhance molecular performance and functionality.

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