{"publication_status":"published","year":"2022","_id":"11377","intvolume":" 10","language":[{"iso":"eng"}],"title":"Designing Robust Biotechnological Processes Regarding Variabilities Using Multi-Objective Optimization Applied to a Biopharmaceutical Seed Train Design","date_updated":"2024-05-21T09:30:15Z","date_created":"2024-04-25T13:35:04Z","status":"public","keyword":["Gaussian processes","Bayes optimization","Pareto optimization","multi-objective","cell culture","seed train"],"type":"scientific_journal_article","citation":{"ama":"Hernández Rodriguez T, Sekulic A, Lange-Hegermann M, Frahm B. Designing Robust Biotechnological Processes Regarding Variabilities Using Multi-Objective Optimization Applied to a Biopharmaceutical Seed Train Design. Processes. 2022;10(5). doi:10.3390/pr10050883","din1505-2-1":"Hernández Rodriguez, Tanja ; Sekulic, Anton ; Lange-Hegermann, Markus ; Frahm, Björn: Designing Robust Biotechnological Processes Regarding Variabilities Using Multi-Objective Optimization Applied to a Biopharmaceutical Seed Train Design. In: Processes Bd. 10. Basel, MDPI AG (2022), Nr. 5","van":"Hernández Rodriguez T, Sekulic A, Lange-Hegermann M, Frahm B. Designing Robust Biotechnological Processes Regarding Variabilities Using Multi-Objective Optimization Applied to a Biopharmaceutical Seed Train Design. Processes. 2022;10(5).","chicago-de":"Hernández Rodriguez, Tanja, Anton Sekulic, Markus Lange-Hegermann und Björn Frahm. 2022. Designing Robust Biotechnological Processes Regarding Variabilities Using Multi-Objective Optimization Applied to a Biopharmaceutical Seed Train Design. Processes 10, Nr. 5. doi:10.3390/pr10050883, .","havard":"T. Hernández Rodriguez, A. Sekulic, M. Lange-Hegermann, B. Frahm, Designing Robust Biotechnological Processes Regarding Variabilities Using Multi-Objective Optimization Applied to a Biopharmaceutical Seed Train Design, Processes. 10 (2022).","bjps":"Hernández Rodriguez T et al. (2022) Designing Robust Biotechnological Processes Regarding Variabilities Using Multi-Objective Optimization Applied to a Biopharmaceutical Seed Train Design. Processes 10.","apa":"Hernández Rodriguez, T., Sekulic, A., Lange-Hegermann, M., & Frahm, B. (2022). Designing Robust Biotechnological Processes Regarding Variabilities Using Multi-Objective Optimization Applied to a Biopharmaceutical Seed Train Design. Processes, 10(5), Article 883. https://doi.org/10.3390/pr10050883","ufg":"Hernández Rodriguez, Tanja u. a.: Designing Robust Biotechnological Processes Regarding Variabilities Using Multi-Objective Optimization Applied to a Biopharmaceutical Seed Train Design, in: Processes 10 (2022), H. 5.","short":"T. Hernández Rodriguez, A. Sekulic, M. Lange-Hegermann, B. Frahm, Processes 10 (2022).","chicago":"Hernández Rodriguez, Tanja, Anton Sekulic, Markus Lange-Hegermann, and Björn Frahm. “Designing Robust Biotechnological Processes Regarding Variabilities Using Multi-Objective Optimization Applied to a Biopharmaceutical Seed Train Design.” Processes 10, no. 5 (2022). https://doi.org/10.3390/pr10050883.","mla":"Hernández Rodriguez, Tanja, et al. “Designing Robust Biotechnological Processes Regarding Variabilities Using Multi-Objective Optimization Applied to a Biopharmaceutical Seed Train Design.” Processes, vol. 10, no. 5, 883, 2022, https://doi.org/10.3390/pr10050883.","ieee":"T. Hernández Rodriguez, A. Sekulic, M. Lange-Hegermann, and B. Frahm, “Designing Robust Biotechnological Processes Regarding Variabilities Using Multi-Objective Optimization Applied to a Biopharmaceutical Seed Train Design,” Processes, vol. 10, no. 5, Art. no. 883, 2022, doi: 10.3390/pr10050883."},"place":"Basel","abstract":[{"text":"consuming and often performed rather empirically. Efficient optimization of multiple objectives such as process time, viable cell density, number of operating steps & cultivation scales, required medium, amount of product as well as product quality depicts a promising approach. This contribution presents a workflow which couples uncertainty-based upstream simulation and Bayes optimization using Gaussian processes. Its application is demonstrated in a simulation case study for a relevant industrial task in process development, the design of a robust cell culture expansion process (seed train), meaning that despite uncertainties and variabilities concerning cell growth, low variations of viable cell density during the seed train are obtained. Compared to a non-optimized reference seed train, the optimized process showed much lower deviation rates regarding viable cell densities (<10% instead of 41.7%) using five or four shake flask scales and seed train duration could be reduced by 56 h from 576 h to 520 h. Overall, it is shown that applying Bayes optimization allows for optimization of a multi-objective optimization function with several optimizable input variables and under a considerable amount of constraints with a low computational effort. This approach provides the potential to be used in the form of a decision tool, e.g., for the choice of an optimal and robust seed train design or for further optimization tasks within process development.","lang":"eng"}],"publication":"Processes","publication_identifier":{"eissn":["2227-9717"]},"volume":10,"article_number":"883","user_id":"83781","issue":"5","author":[{"first_name":"Tanja","last_name":"Hernández Rodriguez","full_name":"Hernández Rodriguez, Tanja","id":"52466"},{"full_name":"Sekulic, Anton","first_name":"Anton","last_name":"Sekulic"},{"id":"71761","full_name":"Lange-Hegermann, Markus","last_name":"Lange-Hegermann","first_name":"Markus"},{"last_name":"Frahm","first_name":"Björn","id":"45666","full_name":"Frahm, Björn"}],"publisher":"MDPI AG","doi":"10.3390/pr10050883","department":[{"_id":"DEP4000"}]}