Model-Enabled Knowledge Transfer across cell lines, culture scales and conditions

Abstract

Mechanistic models are central to quantitative understanding and optimisation of Chinese hamster ovary (CHO) cell culture processes, but their utility is often restricted by parameter sets calibrated for specific cell lines, scales, or operating conditions. In this study, we present the application of the Ensemble Kalman Filter (EnKF) to bioprocessing, introducing an ensemble-based framework for dual state and parameter estimation that enables mechanistic model adaptation across distinct systems. The EnKF recursively assimilates process measurements to update uncertain kinetic parameters and predict system states, allowing a model developed for one system to be transferred to a new one without reparametrisation and using only a single experimental dataset. The evolving parameter ensembles provide a time-resolved sensitivity analysis that identifies which parameters have dominant influence under new process conditions and when their effects become significant. The framework was evaluated across six CHO cell experimental datasets differing in scale, cell line, temperature, and feeding strategy, demonstrating accurate reconstruction of system dynamics and progressive improvement in long-term predictions as new data became available. By maintaining full mechanistic transparency while flexibly adapting to new data, the EnKF offers a practical route for knowledge transfer across systems, strengthening the role of mechanistic modelling in data-informed bioprocess understanding and control.