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The framework couples ordinary differential equation (ODE) models with constraint-based metabolic modeling and machine learning components trained on Bayesian-estimated metabolic rates. Applied to 23 CHO fed-batch cultures, viable cell density, product titer, and key metabolite concentrations are accurately predicted under varying feeding and media conditions within a unified simulation engine, where empirical variability is incorporated through multivariate statistical constraints derived from experimental data. Cross-validation analyses demonstrated strong generalization across process variations, highlighting the framework\u2019s capacity to capture both biochemical constraints and adaptive cellular behavior. This hybrid modeling approach provides a mechanistically interpretable yet data-adaptive foundation for constructing bioprocess digital twins. By bridging statistical, mechanistic, and machine learning methodologies, it advances the computational representation of CHO cell culture systems and offers a generalizable strategy for predictive modeling in complex biological production processes.<\/jats:p>","DOI":"10.34133\/csbj.0078","type":"journal-article","created":{"date-parts":[[2026,4,10]],"date-time":"2026-04-10T08:17:58Z","timestamp":1775809078000},"update-policy":"https:\/\/doi.org\/10.34133\/aaas_crossmark_01","source":"Crossref","is-referenced-by-count":3,"title":["A Hybrid Modeling Framework for Predictive Digital Twins of CHO Cell Culture"],"prefix":"10.34133","volume":"35","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1491-114X","authenticated-orcid":true,"given":"Anne","family":"Richelle","sequence":"first","affiliation":[{"name":"Sartorius Corporate Research","place":["Belgium"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1094-5297","authenticated-orcid":false,"given":"David","family":"Andersson","sequence":"additional","affiliation":[{"name":"Sartorius Corporate Research","place":["Sweden"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5422-0055","authenticated-orcid":false,"given":"Athanasios","family":"Antonakoudis","sequence":"additional","affiliation":[{"name":"Sartorius Corporate Research","place":["UK"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jesper","family":"Jakobsson","sequence":"additional","affiliation":[{"name":"Sartorius Corporate Research","place":["Sweden"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shanti","family":"Pijeaud","sequence":"additional","affiliation":[{"name":"Sartorius Corporate Research","place":["Germany"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anton","family":"Vernersson","sequence":"additional","affiliation":[{"name":"Sartorius Corporate Research","place":["UK"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3799-6094","authenticated-orcid":false,"given":"Johan","family":"Trygg","sequence":"additional","affiliation":[{"name":"Sartorius Corporate Research","place":["UK"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"221","published-online":{"date-parts":[[2026,5,4]]},"reference":[{"issue":"12","key":"e_1_3_4_2_2","doi-asserted-by":"crossref","first-page":"1722","DOI":"10.1038\/s41587-022-01582-x","article-title":"Biopharmaceutical benchmarks 2022","volume":"40","author":"Walsh G","year":"2022","unstructured":"Walsh G, Walsh E. 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