[{"citation":{"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>Processes</i> 10 (2022), H. 5.","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).","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: <i>Processes</i> Bd. 10. Basel, MDPI AG (2022), Nr. 5","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. <i>Processes</i>, <i>10</i>(5), Article 883. <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>Processes</i>, vol. 10, no. 5, 883, 2022, <a href=\"https://doi.org/10.3390/pr10050883\">https://doi.org/10.3390/pr10050883</a>.","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.” <i>Processes</i> 10, no. 5 (2022). <a href=\"https://doi.org/10.3390/pr10050883\">https://doi.org/10.3390/pr10050883</a>.","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. <i>Processes</i>. 2022;10(5). doi:<a href=\"https://doi.org/10.3390/pr10050883\">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,” <i>Processes</i>, vol. 10, no. 5, Art. no. 883, 2022, doi: <a href=\"https://doi.org/10.3390/pr10050883\">10.3390/pr10050883</a>.","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).","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. <i>Processes</i> <b>10</b>.","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. <i>Processes</i> 10, Nr. 5. doi:<a href=\"https://doi.org/10.3390/pr10050883\">10.3390/pr10050883</a>, ."},"publisher":"MDPI AG","intvolume":"        10","publication":"Processes","title":"Designing Robust Biotechnological Processes Regarding Variabilities Using Multi-Objective Optimization Applied to a Biopharmaceutical Seed Train Design","department":[{"_id":"DEP4000"}],"publication_identifier":{"eissn":["2227-9717"]},"publication_status":"published","issue":"5","doi":"10.3390/pr10050883","keyword":["Gaussian processes","Bayes optimization","Pareto optimization","multi-objective","cell culture","seed train"],"volume":10,"type":"scientific_journal_article","_id":"11377","abstract":[{"text":"<jats:p>consuming and often performed rather empirically. Efficient optimization of multiple objectives such as process time, viable cell density, number of operating steps &amp; 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 (&lt;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"}],"place":"Basel","status":"public","author":[{"first_name":"Tanja","last_name":"Hernández Rodriguez","full_name":"Hernández Rodriguez, Tanja","id":"52466"},{"first_name":"Anton","last_name":"Sekulic","full_name":"Sekulic, Anton"},{"first_name":"Markus","last_name":"Lange-Hegermann","full_name":"Lange-Hegermann, Markus","id":"71761"},{"first_name":"Björn","full_name":"Frahm, Björn","id":"45666","last_name":"Frahm"}],"date_created":"2024-04-25T13:35:04Z","language":[{"iso":"eng"}],"year":"2022","date_updated":"2024-05-21T09:30:15Z","user_id":"83781","article_number":"883"},{"quality_controlled":"1","publisher":"MDPI","citation":{"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>, .","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":"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>.","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.","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>.","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","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).","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>.","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>","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>","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.","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."},"series_title":"Processes : open access journal","publication":"Bioprocess Systems Engineering Applications in Pharmaceutical Manufacturing","doi":"https://doi.org/10.3390/pr10050883","publication_identifier":{"eisbn":["978-3-0365-5209-5"],"eissn":["2227-9717"],"isbn":["978-3-0365-5210-1"]},"publication_status":"published","title":"Designing robust biotechnological processes regarding variabilities using multi-objective optimization applied to a biopharmaceutical seed train design","department":[{"_id":"DEP4000"}],"keyword":["Gaussian processes","Bayes optimization","Pareto optimization","multi-objective","cell culture","seed train"],"_id":"10193","page":"21-48","type":"book_chapter","volume":"special issue","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"}],"editor":[{"first_name":"Ralf","last_name":"Pörtner","full_name":"Pörtner, Ralf"},{"first_name":"Johannes","full_name":"Möller, Johannes","last_name":"Möller"}],"place":"Basel","author":[{"id":"52466","full_name":"Hernández Rodriguez, Tanja","last_name":"Hernández Rodriguez","first_name":"Tanja"},{"first_name":"Anton","full_name":"Sekulic, Anton","last_name":"Sekulic"},{"last_name":"Lange-Hegermann","id":"71761","full_name":"Lange-Hegermann, Markus","first_name":"Markus"},{"last_name":"Frahm","id":"45666","full_name":"Frahm, Björn","first_name":"Björn"}],"status":"public","date_created":"2023-08-08T12:58:36Z","language":[{"iso":"eng"}],"year":"2022","date_updated":"2023-08-16T09:24:16Z","user_id":"83781"},{"author":[{"full_name":"Berns, Fabian","last_name":"Berns","first_name":"Fabian"},{"first_name":"Markus","last_name":"Lange-Hegermann","id":"71761","full_name":"Lange-Hegermann, Markus"},{"last_name":"Beecks","full_name":"Beecks, Christian","first_name":"Christian"}],"status":"public","_id":"12812","page":"87-92","type":"conference_editor_article","editor":[{"last_name":"Panetto","full_name":"Panetto, H.","first_name":"H."},{"first_name":"K.","full_name":"Madani, K.","last_name":"Madani"},{"first_name":"A.","full_name":"Smirnov, A.","last_name":"Smirnov"}],"abstract":[{"text":"Discerning unexpected from expected data patterns is the key challenge of anomaly detection. Although a multitude of solutions has been applied to this modern Industry 4.0 problem, it remains an open research issue to identify the key characteristics subjacent to an anomaly, sc. generate hypothesis as to why they appear. In recent years, machine learning models have been regarded as universal solution for a wide range of problems. While most of them suffer from non-self-explanatory representations, Gaussian Processes (GPs) deliver interpretable and robust statistical data models, which are able to cope with unreliable, noisy, or partially missing data. Thus, we regard them as a suitable solution for detecting and appropriately representing anomalies and their respective characteristics. In this position paper, we discuss the problem of automatic and interpretable anomaly detection by means of GPs. That is, we elaborate on why GPs are well suited for anomaly detection and what the current challenges are when applying these probabilistic models to large-scale production data.","lang":"eng"}],"date_updated":"2025-06-26T13:31:38Z","year":"2020","user_id":"83781","date_created":"2025-04-17T06:20:07Z","language":[{"iso":"eng"}],"citation":{"chicago-de":"Berns, Fabian, Markus Lange-Hegermann und Christian Beecks. 2020. <i>Towards Gaussian Processes for Automatic and Interpretable Anomaly Detection in Industry 4.0</i>. Hg. von H. Panetto, K. Madani, und A. Smirnov. <i> Proceedings of the International Conference on Innovative Intelligent Industrial Production and Logistics IN4PL - Volume 1</i>. SCITEPRESS - Science and Technology Publications. doi:<a href=\"https://doi.org/10.5220/0010130300870092\">10.5220/0010130300870092</a>, .","bjps":"<b>Berns F, Lange-Hegermann M and Beecks C</b> (2020) <i>Towards Gaussian Processes for Automatic and Interpretable Anomaly Detection in Industry 4.0</i>, Panetto H, Madani K and Smirnov A (eds). SCITEPRESS - Science and Technology Publications.","din1505-2-1":"<span style=\"font-variant:small-caps;\">Berns, Fabian</span> ; <span style=\"font-variant:small-caps;\">Lange-Hegermann, Markus</span> ; <span style=\"font-variant:small-caps;\">Beecks, Christian</span> ; <span style=\"font-variant:small-caps;\">Panetto, H.</span> ; <span style=\"font-variant:small-caps;\">Madani, K.</span> ; <span style=\"font-variant:small-caps;\">Smirnov, A.</span> (Hrsg.): <i>Towards Gaussian Processes for Automatic and Interpretable Anomaly Detection in Industry 4.0</i> : SCITEPRESS - Science and Technology Publications, 2020","chicago":"Berns, Fabian, Markus Lange-Hegermann, and Christian Beecks. <i>Towards Gaussian Processes for Automatic and Interpretable Anomaly Detection in Industry 4.0</i>. Edited by H. Panetto, K. Madani, and A. Smirnov. <i> Proceedings of the International Conference on Innovative Intelligent Industrial Production and Logistics IN4PL - Volume 1</i>. SCITEPRESS - Science and Technology Publications, 2020. <a href=\"https://doi.org/10.5220/0010130300870092\">https://doi.org/10.5220/0010130300870092</a>.","short":"F. Berns, M. Lange-Hegermann, C. Beecks, Towards Gaussian Processes for Automatic and Interpretable Anomaly Detection in Industry 4.0, SCITEPRESS - Science and Technology Publications, 2020.","mla":"Berns, Fabian, et al. “Towards Gaussian Processes for Automatic and Interpretable Anomaly Detection in Industry 4.0.” <i> Proceedings of the International Conference on Innovative Intelligent Industrial Production and Logistics IN4PL - Volume 1</i>, edited by H. Panetto et al., SCITEPRESS - Science and Technology Publications, 2020, pp. 87–92, <a href=\"https://doi.org/10.5220/0010130300870092\">https://doi.org/10.5220/0010130300870092</a>.","van":"Berns F, Lange-Hegermann M, Beecks C. Towards Gaussian Processes for Automatic and Interpretable Anomaly Detection in Industry 4.0. Panetto H, Madani K, Smirnov A, editors.  Proceedings of the International Conference on Innovative Intelligent Industrial Production and Logistics IN4PL - Volume 1. SCITEPRESS - Science and Technology Publications; 2020.","ieee":"F. Berns, M. Lange-Hegermann, and C. Beecks, <i>Towards Gaussian Processes for Automatic and Interpretable Anomaly Detection in Industry 4.0</i>. SCITEPRESS - Science and Technology Publications, 2020, pp. 87–92. doi: <a href=\"https://doi.org/10.5220/0010130300870092\">10.5220/0010130300870092</a>.","ufg":"<b>Berns, Fabian/Lange-Hegermann, Markus/Beecks, Christian</b>: Towards Gaussian Processes for Automatic and Interpretable Anomaly Detection in Industry 4.0, hg. von Panetto, H./Madani, K./Smirnov, A., o. O. 2020.","havard":"F. Berns, M. Lange-Hegermann, C. Beecks, Towards Gaussian Processes for Automatic and Interpretable Anomaly Detection in Industry 4.0, SCITEPRESS - Science and Technology Publications, 2020.","apa":"Berns, F., Lange-Hegermann, M., &#38; Beecks, C. (2020). Towards Gaussian Processes for Automatic and Interpretable Anomaly Detection in Industry 4.0. In H. Panetto, K. Madani, &#38; A. Smirnov (Eds.), <i> Proceedings of the International Conference on Innovative Intelligent Industrial Production and Logistics IN4PL - Volume 1</i> (pp. 87–92). SCITEPRESS - Science and Technology Publications. <a href=\"https://doi.org/10.5220/0010130300870092\">https://doi.org/10.5220/0010130300870092</a>","ama":"Berns F, Lange-Hegermann M, Beecks C. <i>Towards Gaussian Processes for Automatic and Interpretable Anomaly Detection in Industry 4.0</i>. (Panetto H, Madani K, Smirnov A, eds.). SCITEPRESS - Science and Technology Publications; 2020:87-92. doi:<a href=\"https://doi.org/10.5220/0010130300870092\">10.5220/0010130300870092</a>"},"publisher":"SCITEPRESS - Science and Technology Publications","keyword":["Anomaly Detection","Gaussian Processes","Explainable Machine Learning","Industry 4.0"],"publication":" Proceedings of the International Conference on Innovative Intelligent Industrial Production and Logistics IN4PL - Volume 1","conference":{"name":"International Conference on Innovative Intelligent Industrial Production and Logistics (IN4PL)","end_date":"2020-11-04","start_date":"2020-11-02","location":"Budapest, HUNGARY"},"publication_status":"published","publication_identifier":{"isbn":["978-989-758-476-3"]},"title":"Towards Gaussian Processes for Automatic and Interpretable Anomaly Detection in Industry 4.0","department":[{"_id":"DEP5000"}],"doi":"10.5220/0010130300870092"}]