Real-time data-driven and multi-scale model-guided framework towards bioprocess digital twins
Dong-Yup Lee, School of Chemical Engineering, Sungkyunkwan University, Republic of Korea
Hosts: Rui Oliveira, LAQV-NOVA/UCIBIO-NOVA
Abstract:
Digital twin (DT) has become a rapidly expanding approach in various manufacturing fields including biologics industry. Basically, bioprocess DT combines physical system and its complementing digital counterpart via real-time monitoring and data collection, thus enabling their interactive communications for enhancing operational efficiency and reliable product supply of biomanufacturing processes. DT can be realized through developments and applications of emerging technologies in advanced soft-sensor, data management, advanced data analytics with artificial intelligence (AI) and mechanistic models representing the cells and bioreactor for virtually mirroring their behaviors under adjustable process conditions. In this talk, I will present our recent achievements and on-going efforts for in line monitoring as well as key components of virtual part within the bioprocess DT platform [1-7]. They include machine/deep learning algorithms for forecasting multi-step ahead profiles of cell culture performance, and genome-based mechanistic model of industrially relevant Chinese hamster ovary (CHO) cells for real-time simulations. Both components can be hybridized to describe their dynamic cellular behaviors and metabolic states given culture conditions, thus allowing us to effectively identify process bottlenecks and key engineering targets, suggest various control strategies to improve reliability, efficiency and efficacy of intricate bioprocess operations.
References:
[1] Park, S.-Y., D.-H. Choi, J. Song, M. Lakshmanan, A. Richelle, S. Yoon, C. Kontoravdi, N. E. Lewis and D.-Y. Lee*. 2024. Driving towards digital biomanufacturing by CHO genome-scale models. Trends Biotechnol., in press.
[2] Park, S.-Y., S.-J. Kim, C.-H. Park, J. Kim and D.-Y. Lee*. 2023. Data-driven prediction models for forecasting multi-step ahead profiles of mammalian cell culture towards bioprocess digital twins. Biotechnol. Bioeng., 120(9): 2494-2508.
[3] Hong, J. K., D.-H. Choi, S.-Y. Park, Y. R. Silberbergc, F. Shozui, E. Nakamurad, T. Kayahara and D.-Y. Lee*. 2022. Data-driven and model-guided systematic framework for media development in CHO cell culture. Metab. Eng., 63: 114-123.
[4] Yeo, H. C., S.-Y. Park, T. Tan, S. K. Ng, M. Lakshmanan and D.-Y. Lee*. 2022. Combined multivariate statistical and flux balance analyses uncover media bottlenecks to the growth and productivity of CHO cell cultures. Biotechnol. Bioeng., 119(7): 1740-1754.
[5] Lee, A. P., Y. J. Kok, M. Lakshmanan, D. Leong, L. Zheng, H. L. Lim, S. Chen, S. Y. Mak, K. S. Ang, N. Templeton, T. Salim, X. Wei, E. Gifford, A. H.-M. Tan, X. Bi, S. K. Ng, D.-Y. Lee*, W. L. Ling* and Y. S. Ho*. 2021. Multi-omics profiling of a CHO cell culture system unravels the effect of culture pH on cell growth, antibody titer and product quality. Biotechnol. Bioeng., 118(11): 4305-4316.
[6] Park, S.-Y., C.-H. Park, D.-H. Choi, J. K. Hong and D.-Y. Lee*. 2021. Bioprocess digital twins of mammalian cell culture for advanced biomanufacturing. Curr. Opin. Chem. Eng., 33: 100702.
[7] Yeo, H. C., J. Hong, M. Lakshmanan* and D.-Y. Lee*. 2020. Enzyme capacity-based genome scale modelling of CHO cells. Metab. Eng., 60: 138-147.
Short bio:
Dr. Dong-Yup Lee is an Associate Professor of the School of Chemical Engineering at the Sungkyunkwan University (SKKU), and leads Process Design and Systems Engineering (PDSE) Lab. He has stellar track record and expertise in Multi-omics integration, Model-guided metabolic engineering, and Mammalian systems biotechnology. He has coauthored more than 160 research articles on these and other topics. His current research interests include Bioprocess digital twins, Computational synthetic biology for gene therapy and vaccine design, and Microbiome engineering. Through a focus on application-oriented research, he has successfully managed a multitude of consultancy and industrial projects for many biopharma and biotech companies.