{"citation":{"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. In: Pörtner R, Möller J, editors. Bioprocess Systems Engineering Applications in Pharmaceutical Manufacturing. Basel: MDPI; 2022. p. 21–48. (Processes : open access journal; vol. special issue).","short":"T. Hernández Rodriguez, A. Sekulic, M. Lange-Hegermann, B. Frahm, in: R. Pörtner, J. Möller (Eds.), Bioprocess Systems Engineering Applications in Pharmaceutical Manufacturing, MDPI, Basel, 2022, pp. 21–48.","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.” In <i>Bioprocess Systems Engineering Applications in Pharmaceutical Manufacturing</i>, edited by Ralf Pörtner and Johannes Möller, special issue:21–48. Processes : Open Access Journal. Basel: MDPI, 2022. <a href=\"https://doi.org/10.3390/pr10050883\">https://doi.org/10.3390/pr10050883</a>.","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, in: R. Pörtner, J. Möller (Eds.), Bioprocess Systems Engineering Applications in Pharmaceutical Manufacturing, MDPI, Basel, 2022: pp. 21–48.","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. In: Pörtner R, Möller J, eds. <i>Bioprocess Systems Engineering Applications in Pharmaceutical Manufacturing</i>. Vol special issue. Processes : open access journal. MDPI; 2022:21-48. doi:<a href=\"https://doi.org/10.3390/pr10050883\">https://doi.org/10.3390/pr10050883</a>","bjps":"<b>Hernández Rodriguez T <i>et al.</i></b> (2022) Designing Robust Biotechnological Processes Regarding Variabilities Using Multi-Objective Optimization Applied to a Biopharmaceutical Seed Train Design. In Pörtner R and Möller J (eds), <i>Bioprocess Systems Engineering Applications in Pharmaceutical Manufacturing</i>, vol. special issue. Basel: MDPI, pp. 21–48.","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. In: <i>Bioprocess Systems Engineering Applications in Pharmaceutical Manufacturing</i>, hg. von Ralf Pörtner und Johannes Möller, special issue:21–48. Processes : open access journal. Basel: MDPI. doi:<a href=\"https://doi.org/10.3390/pr10050883\">https://doi.org/10.3390/pr10050883</a>, .","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,” in <i>Bioprocess Systems Engineering Applications in Pharmaceutical Manufacturing</i>, vol. special issue, R. Pörtner and J. Möller, Eds. Basel: MDPI, 2022, pp. 21–48. doi: <a href=\"https://doi.org/10.3390/pr10050883\">https://doi.org/10.3390/pr10050883</a>.","mla":"Hernández Rodriguez, Tanja, et al. “Designing Robust Biotechnological Processes Regarding Variabilities Using Multi-Objective Optimization Applied to a Biopharmaceutical Seed Train Design.” <i>Bioprocess Systems Engineering Applications in Pharmaceutical Manufacturing</i>, edited by Ralf Pörtner and Johannes Möller, vol. special issue, MDPI, 2022, pp. 21–48, <a href=\"https://doi.org/10.3390/pr10050883\">https://doi.org/10.3390/pr10050883</a>.","apa":"Hernández Rodriguez, T., Sekulic, A., Lange-Hegermann, M., &#38; Frahm, B. (2022). Designing robust biotechnological processes regarding variabilities using multi-objective optimization applied to a biopharmaceutical seed train design. In R. Pörtner &#38; J. Möller (Eds.), <i>Bioprocess Systems Engineering Applications in Pharmaceutical Manufacturing: Vol. special issue</i> (pp. 21–48). MDPI. <a href=\"https://doi.org/10.3390/pr10050883\">https://doi.org/10.3390/pr10050883</a>","ufg":"<b>Hernández Rodriguez, Tanja u. a.</b>: Designing robust biotechnological processes regarding variabilities using multi-objective optimization applied to a biopharmaceutical seed train design, in: <i>Pörtner, Ralf/Möller, Johannes (Hgg.)</i>: Bioprocess Systems Engineering Applications in Pharmaceutical Manufacturing, Band <i>special issue</i>, Basel 2022 (Processes : open access journal),  S. 21–48.","din1505-2-1":"<span style=\"font-variant:small-caps;\">Hernández Rodriguez, Tanja</span> ; <span style=\"font-variant:small-caps;\">Sekulic, Anton</span> ; <span style=\"font-variant:small-caps;\">Lange-Hegermann, Markus</span> ; <span style=\"font-variant:small-caps;\">Frahm, Björn</span>: Designing robust biotechnological processes regarding variabilities using multi-objective optimization applied to a biopharmaceutical seed train design. In: <span style=\"font-variant:small-caps;\">Pörtner, R.</span> ; <span style=\"font-variant:small-caps;\">Möller, J.</span> (Hrsg.): <i>Bioprocess Systems Engineering Applications in Pharmaceutical Manufacturing</i>, <i>Processes : open access journal</i>. Bd. special issue. Basel : MDPI, 2022, S. 21–48"},"department":[{"_id":"DEP4000"}],"year":"2022","title":"Designing robust biotechnological processes regarding variabilities using multi-objective optimization applied to a biopharmaceutical seed train design","quality_controlled":"1","publication_status":"published","publisher":"MDPI","series_title":"Processes : open access journal","volume":"special issue","publication":"Bioprocess Systems Engineering Applications in Pharmaceutical Manufacturing","date_updated":"2023-08-16T09:24:16Z","publication_identifier":{"eissn":["2227-9717"],"eisbn":["978-3-0365-5209-5"],"isbn":["978-3-0365-5210-1"]},"status":"public","type":"book_chapter","_id":"10193","doi":"https://doi.org/10.3390/pr10050883","keyword":["Gaussian processes","Bayes optimization","Pareto optimization","multi-objective","cell culture","seed train"],"author":[{"id":"52466","first_name":"Tanja","last_name":"Hernández Rodriguez","full_name":"Hernández Rodriguez, Tanja"},{"full_name":"Sekulic, Anton","first_name":"Anton","last_name":"Sekulic"},{"last_name":"Lange-Hegermann","first_name":"Markus","id":"71761","full_name":"Lange-Hegermann, Markus"},{"id":"45666","first_name":"Björn","last_name":"Frahm","full_name":"Frahm, Björn"}],"page":"21-48","date_created":"2023-08-08T12:58:36Z","editor":[{"full_name":"Pörtner, Ralf","first_name":"Ralf","last_name":"Pörtner"},{"full_name":"Möller, Johannes","first_name":"Johannes","last_name":"Möller"}],"user_id":"83781","language":[{"iso":"eng"}],"place":"Basel","abstract":[{"text":"Development and optimization of biopharmaceutical production processes with cell cultures is cost- and time-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"}]}