A team led by Dr. Moo Sun Hong at Seoul National University is optimizing digital twin models for end-to-end biomanufacturing by leveraging eight years of data sets collected originally at MIT. Their approach combines batch and time-series process data to better trace variability in monoclonal antibody impurity profiles from bioreactor through downstream steps. They developed automated analytics to recommend optimal modeling techniques, improving root cause analysis and enabling more precise process control strategies in biomanufacturing pipelines.