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In this study, we present the application of the ensemble Kalman filter (EnKF) to bioprocessing, introducing an ensemble\u2010based 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. This allows a model calibrated for one system to be transferred to another without reparameterization, using only a single experimental dataset. The evolving parameter ensembles provide a time\u2010resolved 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 datasets spanning different scales, cell lines, temperatures, and feeding strategies. It accurately reconstructed system dynamics and showed progressive improvement in long\u2010term predictions as data accumulated. 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 modeling in data\u2010informed bioprocess understanding and control.<\/jats:p>","DOI":"10.1002\/bit.70269","type":"journal-article","created":{"date-parts":[[2026,6,17]],"date-time":"2026-06-17T06:41:52Z","timestamp":1781678512000},"page":"2613-2628","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Model\u2010Enabled Knowledge Transfer Across Cell Lines, Culture Scales and Conditions"],"prefix":"10.1002","volume":"123","author":[{"given":"Luxi","family":"Yu","sequence":"first","affiliation":[{"name":"Department of Chemical Engineering, South Kensington Campus Imperial College London London UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Antonio del 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