[{"series_title":"Advances in Biochemical Engineering/Biotechnology","publication_status":"published","department":[{"_id":"DEP4000"}],"abstract":[{"text":"Modellbasierte Konzepte und Simulationstechniken in Kombination mit digitalen Werkzeugen erweisen sich als Schlüssel, um das volle Potenzial biopharmazeutischer Produktionsprozesse zu erschließen, die mehrere herausfordernde Entwicklungs- und Prozessschritte enthalten. Einer dieser Schritte ist der zeit- und kostenintensive Zellproliferationsprozess (auch als Seed Train bezeichnet), um die Zellzahl vom Auftauen der Zellen bis zum Produktionsmaßstab zu erhöhen. Herausforderungen wie komplexer Zellstoffwechsel, Chargen-zu-Chargen-Variationen, Variabilitäten im Zellverhalten und Einflüsse von Änderungen der Kultivierungsbedingungen erfordern adäquate digitale Lösungen, um Informationen über den aktuellen und zukünftigen Prozesszustand bereitzustellen und korrekte Prozessentscheidungen abzuleiten.\r\n\r\nZu diesem Zweck haben sich digitale Seed Train Zwillinge als effizient erwiesen, die das zeitabhängige Verhalten wichtiger Prozessvariablen basierend auf mathematischen Modellen, Strategien und Anpassungsverfahren digital darstellen.\r\n\r\nDieses Kapitel skizziert die Notwendigkeit der Digitalisierung von Seed Trains, den Aufbau eines digitalen Seed Train Zwillings, die Rolle der Parameterschätzung und verschiedene statistische Methoden in diesem Zusammenhang, die auf mehrere Probleme im Bereich der Bioprozessierung anwendbar sind. Die Ergebnisse einer Fallstudie werden vorgestellt, um einen Bayes’schen Ansatz zur Parameterschätzung und Vorhersage eines industriellen Zellkultur-Seed Trains für die Seed Train Digitalisierung zu veranschaulichen.","lang":"eng"}],"quality_controlled":"1","publisher":"Springer ","type":"book_chapter","user_id":"83781","place":"Berlin, Heidelberg","doi":"https://doi.org/10.1007/978-3-031-75698-6_5","_id":"13652","title":"Digitale Seed Train Zwillinge und statistische Methoden","citation":{"chicago-de":"Hernández Rodriguez, Tanja und Björn Frahm. 2025. Digitale Seed Train Zwillinge und statistische Methoden. In: <i>Digitale Zwillinge - Werkzeuge und Konzepte für intelligente Bioproduktion</i>, hg. von Christoph Herwig, Ralf Pörtner, und Johannes Möller, 107–145. Advances in Biochemical Engineering/Biotechnology. Berlin, Heidelberg: Springer . doi:<a href=\"https://doi.org/10.1007/978-3-031-75698-6_5\">https://doi.org/10.1007/978-3-031-75698-6_5</a>, .","din1505-2-1":"<span style=\"font-variant:small-caps;\">Hernández Rodriguez, Tanja</span> ; <span style=\"font-variant:small-caps;\">Frahm, Björn</span>: Digitale Seed Train Zwillinge und statistische Methoden. In: <span style=\"font-variant:small-caps;\">Herwig, C.</span> ; <span style=\"font-variant:small-caps;\">Pörtner, R.</span> ; <span style=\"font-variant:small-caps;\">Möller, J.</span> (Hrsg.): <i>Digitale Zwillinge - Werkzeuge und Konzepte für intelligente Bioproduktion</i>, <i>Advances in Biochemical Engineering/Biotechnology</i>. Berlin, Heidelberg : Springer , 2025, S. 107–145","short":"T. Hernández Rodriguez, B. Frahm, in: C. Herwig, R. Pörtner, J. Möller (Eds.), Digitale Zwillinge - Werkzeuge und Konzepte für intelligente Bioproduktion, Springer , Berlin, Heidelberg, 2025, pp. 107–145.","chicago":"Hernández Rodriguez, Tanja, and Björn Frahm. “Digitale Seed Train Zwillinge und statistische Methoden.” In <i>Digitale Zwillinge - Werkzeuge und Konzepte für intelligente Bioproduktion</i>, edited by Christoph Herwig, Ralf Pörtner, and Johannes Möller, 107–45. Advances in Biochemical Engineering/Biotechnology. Berlin, Heidelberg: Springer , 2025. <a href=\"https://doi.org/10.1007/978-3-031-75698-6_5\">https://doi.org/10.1007/978-3-031-75698-6_5</a>.","apa":"Hernández Rodriguez, T., &#38; Frahm, B. (2025). Digitale Seed Train Zwillinge und statistische Methoden. In C. Herwig, R. Pörtner, &#38; J. Möller (Eds.), <i>Digitale Zwillinge - Werkzeuge und Konzepte für intelligente Bioproduktion</i> (pp. 107–145). Springer . <a href=\"https://doi.org/10.1007/978-3-031-75698-6_5\">https://doi.org/10.1007/978-3-031-75698-6_5</a>","ieee":"T. Hernández Rodriguez and B. Frahm, “Digitale Seed Train Zwillinge und statistische Methoden,” in <i>Digitale Zwillinge - Werkzeuge und Konzepte für intelligente Bioproduktion</i>, C. Herwig, R. Pörtner, and J. Möller, Eds. Berlin, Heidelberg: Springer , 2025, pp. 107–145. doi: <a href=\"https://doi.org/10.1007/978-3-031-75698-6_5\">https://doi.org/10.1007/978-3-031-75698-6_5</a>.","ufg":"<b>Hernández Rodriguez, Tanja/Frahm, Björn</b>: Digitale Seed Train Zwillinge und statistische Methoden, in: <i>Herwig, Christoph/Pörtner, Ralf/Möller, Johannes (Hgg.)</i>: Digitale Zwillinge - Werkzeuge und Konzepte für intelligente Bioproduktion, Berlin, Heidelberg 2025 (Advances in Biochemical Engineering/Biotechnology),  S. 107–145.","havard":"T. Hernández Rodriguez, B. Frahm, Digitale Seed Train Zwillinge und statistische Methoden, in: C. Herwig, R. Pörtner, J. Möller (Eds.), Digitale Zwillinge - Werkzeuge und Konzepte für intelligente Bioproduktion, Springer , Berlin, Heidelberg, 2025: pp. 107–145.","bjps":"<b>Hernández Rodriguez T and Frahm B</b> (2025) Digitale Seed Train Zwillinge und statistische Methoden. In Herwig C, Pörtner R and Möller J (eds), <i>Digitale Zwillinge - Werkzeuge und Konzepte für intelligente Bioproduktion</i>. Berlin, Heidelberg: Springer , pp. 107–145.","mla":"Hernández Rodriguez, Tanja, and Björn Frahm. “Digitale Seed Train Zwillinge und statistische Methoden.” <i>Digitale Zwillinge - Werkzeuge und Konzepte für intelligente Bioproduktion</i>, edited by Christoph Herwig et al., Springer , 2025, pp. 107–45, <a href=\"https://doi.org/10.1007/978-3-031-75698-6_5\">https://doi.org/10.1007/978-3-031-75698-6_5</a>.","van":"Hernández Rodriguez T, Frahm B. Digitale Seed Train Zwillinge und statistische Methoden. In: Herwig C, Pörtner R, Möller J, editors. Digitale Zwillinge - Werkzeuge und Konzepte für intelligente Bioproduktion. Berlin, Heidelberg: Springer ; 2025. p. 107–45. (Advances in Biochemical Engineering/Biotechnology).","ama":"Hernández Rodriguez T, Frahm B. Digitale Seed Train Zwillinge und statistische Methoden. In: Herwig C, Pörtner R, Möller J, eds. <i>Digitale Zwillinge - Werkzeuge und Konzepte für intelligente Bioproduktion</i>. Advances in Biochemical Engineering/Biotechnology. Springer ; 2025:107-145. doi:<a href=\"https://doi.org/10.1007/978-3-031-75698-6_5\">https://doi.org/10.1007/978-3-031-75698-6_5</a>"},"author":[{"first_name":"Tanja","last_name":"Hernández Rodriguez","id":"52466","full_name":"Hernández Rodriguez, Tanja"},{"first_name":"Björn","id":"45666","last_name":"Frahm","full_name":"Frahm, Björn"}],"year":"2025","page":"107-145","status":"public","date_created":"2026-03-27T17:45:02Z","publication_identifier":{"isbn":["978-3-031-75697-9"],"eisbn":["978-3-031-75698-6"]},"publication":"Digitale Zwillinge - Werkzeuge und Konzepte für intelligente Bioproduktion","date_updated":"2026-04-08T12:46:45Z","editor":[{"full_name":"Herwig, Christoph","first_name":"Christoph","last_name":"Herwig"},{"full_name":"Pörtner, Ralf","last_name":"Pörtner","first_name":"Ralf"},{"first_name":"Johannes","full_name":"Möller, Johannes","last_name":"Möller"}],"language":[{"iso":"ger"}],"keyword":["Bayes","Digitaler Zwilling","Parameterabschätzung","Seed-Train","Unsicherheit"]},{"user_id":"83781","intvolume":"        10","type":"scientific_journal_article","place":"Basel","_id":"11377","article_number":"883","doi":"10.3390/pr10050883","publication_status":"published","publisher":"MDPI AG","department":[{"_id":"DEP4000"}],"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"}],"issue":"5","date_updated":"2024-05-21T09:30:15Z","publication_identifier":{"eissn":["2227-9717"]},"publication":"Processes","date_created":"2024-04-25T13:35:04Z","keyword":["Gaussian processes","Bayes optimization","Pareto optimization","multi-objective","cell culture","seed train"],"language":[{"iso":"eng"}],"volume":10,"citation":{"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","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>, .","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>.","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>.","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>","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).","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>.","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>.","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.","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>","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)."},"title":"Designing Robust Biotechnological Processes Regarding Variabilities Using Multi-Objective Optimization Applied to a Biopharmaceutical Seed Train Design","status":"public","year":"2022","author":[{"last_name":"Hernández Rodriguez","id":"52466","full_name":"Hernández Rodriguez, Tanja","first_name":"Tanja"},{"last_name":"Sekulic","first_name":"Anton","full_name":"Sekulic, Anton"},{"full_name":"Lange-Hegermann, Markus","id":"71761","last_name":"Lange-Hegermann","first_name":"Markus"},{"last_name":"Frahm","first_name":"Björn","full_name":"Frahm, Björn","id":"45666"}]},{"volume":"special issue","citation":{"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.","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","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>.","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>","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>.","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>","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).","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.","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.","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>.","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."},"publication_status":"published","title":"Designing robust biotechnological processes regarding variabilities using multi-objective optimization applied to a biopharmaceutical seed train design","series_title":"Processes : open access journal","publisher":"MDPI","quality_controlled":"1","status":"public","page":"21-48","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"}],"author":[{"id":"52466","first_name":"Tanja","last_name":"Hernández Rodriguez","full_name":"Hernández Rodriguez, Tanja"},{"first_name":"Anton","full_name":"Sekulic, Anton","last_name":"Sekulic"},{"last_name":"Lange-Hegermann","full_name":"Lange-Hegermann, Markus","id":"71761","first_name":"Markus"},{"last_name":"Frahm","first_name":"Björn","full_name":"Frahm, Björn","id":"45666"}],"year":"2022","department":[{"_id":"DEP4000"}],"editor":[{"last_name":"Pörtner","full_name":"Pörtner, Ralf","first_name":"Ralf"},{"first_name":"Johannes","full_name":"Möller, Johannes","last_name":"Möller"}],"user_id":"83781","date_updated":"2023-08-16T09:24:16Z","publication_identifier":{"isbn":["978-3-0365-5210-1"],"eissn":["2227-9717"],"eisbn":["978-3-0365-5209-5"]},"publication":"Bioprocess Systems Engineering Applications in Pharmaceutical Manufacturing","type":"book_chapter","date_created":"2023-08-08T12:58:36Z","_id":"10193","doi":"https://doi.org/10.3390/pr10050883","keyword":["Gaussian processes","Bayes optimization","Pareto optimization","multi-objective","cell culture","seed train"],"place":"Basel","language":[{"iso":"eng"}]},{"author":[{"id":"52466","last_name":"Hernández Rodriguez","first_name":"Tanja","full_name":"Hernández Rodriguez, Tanja"},{"id":"45666","last_name":"Frahm","full_name":"Frahm, Björn","first_name":"Björn"}],"year":"2021","status":"public","page":"97–131","title":"Digital Seed Train Twins and Statistical Methods","volume":176,"citation":{"ama":"Hernández Rodriguez T, Frahm B. Digital Seed Train Twins and Statistical Methods. In: Herwig C, Pörtner R, Möller J, eds. <i>Digital Twins Tools and Concepts for Smart Biomanufacturing</i>. Vol 176. Advances in Biochemical Engineering/Biotechnology. Springer; 2021:97-131. doi:<a href=\"https://doi.org/10.1007/10_2020_137\">https://doi.org/10.1007/10_2020_137</a>","ieee":"T. Hernández Rodriguez and B. Frahm, “Digital Seed Train Twins and Statistical Methods,” in <i>Digital Twins Tools and Concepts for Smart Biomanufacturing</i>, vol. 176, C. Herwig, R. Pörtner, and J. Möller, Eds. Berlin, Heidelberg: Springer, 2021, pp. 97–131. doi: <a href=\"https://doi.org/10.1007/10_2020_137\">https://doi.org/10.1007/10_2020_137</a>.","chicago-de":"Hernández Rodriguez, Tanja und Björn Frahm. 2021. Digital Seed Train Twins and Statistical Methods. In: <i>Digital Twins Tools and Concepts for Smart Biomanufacturing</i>, hg. von Christoph  Herwig, Ralf  Pörtner, und Johannes  Möller, 176:97–131. Advances in Biochemical Engineering/Biotechnology. Berlin, Heidelberg: Springer. doi:<a href=\"https://doi.org/10.1007/10_2020_137\">https://doi.org/10.1007/10_2020_137</a>, .","din1505-2-1":"<span style=\"font-variant:small-caps;\">Hernández Rodriguez, Tanja</span> ; <span style=\"font-variant:small-caps;\">Frahm, Björn</span>: Digital Seed Train Twins and Statistical Methods. In: <span style=\"font-variant:small-caps;\">Herwig, C.</span> ; <span style=\"font-variant:small-caps;\">Pörtner, R.</span> ; <span style=\"font-variant:small-caps;\">Möller, J.</span> (Hrsg.): <i>Digital Twins Tools and Concepts for Smart Biomanufacturing</i>, <i>Advances in Biochemical Engineering/Biotechnology</i>. Bd. 176. Berlin, Heidelberg : Springer, 2021, S. 97–131","short":"T. Hernández Rodriguez, B. Frahm, in: C. Herwig, R. Pörtner, J. Möller (Eds.), Digital Twins Tools and Concepts for Smart Biomanufacturing, Springer, Berlin, Heidelberg, 2021, pp. 97–131.","bjps":"<b>Hernández Rodriguez T and Frahm B</b> (2021) Digital Seed Train Twins and Statistical Methods. In Herwig C, Pörtner R and Möller J (eds), <i>Digital Twins Tools and Concepts for Smart Biomanufacturing</i>, vol. 176. Berlin, Heidelberg: Springer, pp. 97–131.","havard":"T. Hernández Rodriguez, B. Frahm, Digital Seed Train Twins and Statistical Methods, in: C. Herwig, R. Pörtner, J. Möller (Eds.), Digital Twins Tools and Concepts for Smart Biomanufacturing, Springer, Berlin, Heidelberg, 2021: pp. 97–131.","mla":"Hernández Rodriguez, Tanja, and Björn Frahm. “Digital Seed Train Twins and Statistical Methods.” <i>Digital Twins Tools and Concepts for Smart Biomanufacturing</i>, edited by Christoph  Herwig et al., vol. 176, Springer, 2021, pp. 97–131, <a href=\"https://doi.org/10.1007/10_2020_137\">https://doi.org/10.1007/10_2020_137</a>.","ufg":"<b>Hernández Rodriguez, Tanja/Frahm, Björn</b>: Digital Seed Train Twins and Statistical Methods, in: <i>Herwig, Christoph/Pörtner, Ralf/Möller, Johannes (Hgg.)</i>: Digital Twins Tools and Concepts for Smart Biomanufacturing, Bd. 176, Berlin, Heidelberg 2021 (Advances in Biochemical Engineering/Biotechnology),  S. 97–131.","van":"Hernández Rodriguez T, Frahm B. Digital Seed Train Twins and Statistical Methods. In: Herwig C, Pörtner R, Möller J, editors. Digital Twins Tools and Concepts for Smart Biomanufacturing. Berlin, Heidelberg: Springer; 2021. p. 97–131. (Advances in Biochemical Engineering/Biotechnology; vol. 176).","chicago":"Hernández Rodriguez, Tanja, and Björn Frahm. “Digital Seed Train Twins and Statistical Methods.” In <i>Digital Twins Tools and Concepts for Smart Biomanufacturing</i>, edited by Christoph  Herwig, Ralf  Pörtner, and Johannes  Möller, 176:97–131. Advances in Biochemical Engineering/Biotechnology. Berlin, Heidelberg: Springer, 2021. <a href=\"https://doi.org/10.1007/10_2020_137\">https://doi.org/10.1007/10_2020_137</a>.","apa":"Hernández Rodriguez, T., &#38; Frahm, B. (2021). Digital Seed Train Twins and Statistical Methods. In C. Herwig, R. Pörtner, &#38; J. Möller (Eds.), <i>Digital Twins Tools and Concepts for Smart Biomanufacturing</i> (Vol. 176, pp. 97–131). Springer. <a href=\"https://doi.org/10.1007/10_2020_137\">https://doi.org/10.1007/10_2020_137</a>"},"keyword":["Bayes","Digital twin","Parameter estimation","Seed train","Uncertainty"],"language":[{"iso":"eng"}],"publication_identifier":{"issn":["0724-6145"],"eisbn":["978-3-030-71660-8"],"eissn":["1616-8542"],"isbn":["978-3-030-71659-2"]},"publication":"Digital Twins Tools and Concepts for Smart Biomanufacturing","date_created":"2020-08-19T07:14:11Z","editor":[{"full_name":"Herwig, Christoph ","last_name":"Herwig","first_name":"Christoph "},{"full_name":"Pörtner, Ralf ","first_name":"Ralf ","last_name":"Pörtner"},{"full_name":"Möller, Johannes ","first_name":"Johannes ","last_name":"Möller"}],"date_updated":"2023-08-16T06:48:35Z","abstract":[{"text":"Model-based concepts and simulation techniques in combination with digital tools emerge as a key to explore the full potential of biopharmaceutical production processes, which contain several challenging development and process steps. One of these steps is the time- and cost-intensive cell proliferation process (also called seed train) to increase cell number from cell thawing up to production scale. Challenges like complex cell metabolism, batch-to-batch variation, variabilities in cell behavior, and influences of changes in cultivation conditions necessitate adequate digital solutions to provide information about the current and near future process state to derive correct process decisions.\r\nFor this purpose digital seed train twins have proved to be efficient, which digitally display the time-dependent behavior of important process variables based on mathematical models, strategies, and adaption procedures.\r\nThis chapter will outline the needs for digitalization of seed trains, the construction of a digital seed train twin, the role of parameter estimation, and different statistical methods within this context, which are applicable to several problems in the field of bioprocessing. The results of a case study are presented to illustrate a Bayesian approach for parameter estimation and prediction of an industrial cell culture seed train for seed train digitalization.","lang":"eng"}],"department":[{"_id":"DEP4021"}],"publisher":"Springer","quality_controlled":"1","series_title":"Advances in Biochemical Engineering/Biotechnology","publication_status":"published","_id":"3349","doi":"https://doi.org/10.1007/10_2020_137","place":"Berlin, Heidelberg","type":"book_chapter","intvolume":"       176","user_id":"83781"},{"place":"Totowa, NJ","_id":"2394","doi":"10.1007/978-1-62703-733-4_22","user_id":"74004","intvolume":"      1104","type":"book_chapter","publisher":"Humana Press","department":[{"_id":"DEP4021"}],"abstract":[{"text":"For the production of biopharmaceuticals a seed train is required to generate an adequate number of cells for inoculation of the production bioreactor. This seed train is time- and cost-intensive but offers potential for optimization. A method and a protocol are described for the seed train mapping, directed modeling without major effort, and its optimization regarding selected optimization criteria such as optimal points in time for cell passaging. Furthermore, the method can also be applied for the set-up of a new seed train, for example for a new cell line. Although the chapter is directed towards suspension cell lines, the method is also generally applicable, e.g. for adherent cell lines.","lang":"eng"}],"publication_status":"published","series_title":"Methods in Molecular Biology ","keyword":["Seed train Optimization Modeling Prediction Space-Time-Yield (STY) Systems approach Bioinformatics Computational biotechnology Suspension Production"],"language":[{"iso":"eng"}],"date_updated":"2023-03-15T13:49:39Z","publication":"Animal Cell Biotechnology","publication_identifier":{"isbn":["9781627037327"],"eisbn":["9781627037334"],"issn":["1064-3745","1940-6029"]},"date_created":"2020-05-19T14:45:43Z","status":"public","page":"355-367","year":2013,"author":[{"last_name":"Frahm","full_name":"Frahm, Björn","first_name":"Björn"}],"volume":1104,"citation":{"apa":"Frahm, B. (2013). Seed Train Optimization for Cell Culture. In <i>Animal Cell Biotechnology</i> (Vol. 1104, pp. 355–367). Totowa, NJ: Humana Press. <a href=\"https://doi.org/10.1007/978-1-62703-733-4_22\">https://doi.org/10.1007/978-1-62703-733-4_22</a>","chicago":"Frahm, Björn. “Seed Train Optimization for Cell Culture.” In <i>Animal Cell Biotechnology</i>, 1104:355–67. Methods in Molecular Biology . Totowa, NJ: Humana Press, 2013. <a href=\"https://doi.org/10.1007/978-1-62703-733-4_22\">https://doi.org/10.1007/978-1-62703-733-4_22</a>.","van":"Frahm B. Seed Train Optimization for Cell Culture. In: Animal Cell Biotechnology. Totowa, NJ: Humana Press; 2013. p. 355–67. (Methods in Molecular Biology ; vol. 1104).","havard":"B. Frahm, Seed Train Optimization for Cell Culture, in: Animal Cell Biotechnology, Humana Press, Totowa, NJ, 2013: pp. 355–367.","mla":"Frahm, Björn. “Seed Train Optimization for Cell Culture.” <i>Animal Cell Biotechnology</i>, vol. 1104, Humana Press, 2013, pp. 355–67, doi:<a href=\"https://doi.org/10.1007/978-1-62703-733-4_22\">10.1007/978-1-62703-733-4_22</a>.","bjps":"<b>Frahm B</b> (2013) Seed Train Optimization for Cell Culture. <i>Animal Cell Biotechnology</i>, vol. 1104. Totowa, NJ: Humana Press, pp. 355–367.","ufg":"<b>Frahm, Björn (2013)</b>: Seed Train Optimization for Cell Culture, in: <i>Animal Cell Biotechnology</i> (=<i>Methods in Molecular Biology  1104</i>), Totowa, NJ, S. 355–367.","short":"B. Frahm, in: Animal Cell Biotechnology, Humana Press, Totowa, NJ, 2013, pp. 355–367.","din1505-2-1":"<span style=\"font-variant:small-caps;\">Frahm, Björn</span>: Seed Train Optimization for Cell Culture. In: <i>Animal Cell Biotechnology</i>, <i>Methods in Molecular Biology </i>. Bd. 1104. Totowa, NJ : Humana Press, 2013, S. 355–367","chicago-de":"Frahm, Björn. 2013. Seed Train Optimization for Cell Culture. In: <i>Animal Cell Biotechnology</i>, 1104:355–367. Methods in Molecular Biology . Totowa, NJ: Humana Press. doi:<a href=\"https://doi.org/10.1007/978-1-62703-733-4_22,\">10.1007/978-1-62703-733-4_22,</a> .","ieee":"B. Frahm, “Seed Train Optimization for Cell Culture,” in <i>Animal Cell Biotechnology</i>, vol. 1104, Totowa, NJ: Humana Press, 2013, pp. 355–367.","ama":"Frahm B. Seed Train Optimization for Cell Culture. In: <i>Animal Cell Biotechnology</i>. Vol 1104. Methods in Molecular Biology . Totowa, NJ: Humana Press; 2013:355-367. doi:<a href=\"https://doi.org/10.1007/978-1-62703-733-4_22\">10.1007/978-1-62703-733-4_22</a>"},"title":"Seed Train Optimization for Cell Culture"},{"abstract":[{"lang":"eng","text":"For the production of biopharmaceuticals a seed train is required to generate an adequate number of cells for inoculation of the production bioreactor. This seed train is time- and cost-intensive but offers potential for optimization. A method and a protocol are described for the seed train mapping, directed modeling without major effort, and its optimization regarding selected optimization criteria such as optimal points in time for cell passaging. Furthermore, the method can also be applied for the set-up of a new seed train, for example for a new cell line. Although the chapter is directed towards suspension cell lines, the method is also generally applicable, e.g. for adherent cell lines."}],"department":[{"_id":"DEP4000"}],"publisher":"Humana Press","series_title":"Methods in Molecular Biology","publication_status":"published","_id":"10214","doi":"10.1007/978-1-62703-733-4_22","place":"Totowa, NJ","type":"book_chapter","intvolume":"      1104","user_id":"83781","year":"2013","author":[{"last_name":"Frahm","full_name":"Frahm, Björn","id":"45666","first_name":"Björn"}],"status":"public","page":"355–367","title":"Seed Train Optimization for Cell Culture","citation":{"short":"B. Frahm, in: R. Pörtner (Ed.), Animal Cell Biotechnology - Methods and Protocols, 3rd ed., Humana Press, Totowa, NJ, 2013, pp. 355–367.","din1505-2-1":"<span style=\"font-variant:small-caps;\">Frahm, Björn</span>: Seed Train Optimization for Cell Culture. In: <span style=\"font-variant:small-caps;\">Pörtner, R.</span> (Hrsg.): <i>Animal Cell Biotechnology - Methods and Protocols</i>, <i>Methods in Molecular Biology</i>. Bd. 1104. 3. Aufl. Totowa, NJ : Humana Press, 2013, S. 355–367","chicago-de":"Frahm, Björn. 2013. Seed Train Optimization for Cell Culture. In: <i>Animal Cell Biotechnology - Methods and Protocols</i>, hg. von Ralf Pörtner, 1104:355–367. 3. Aufl. Methods in Molecular Biology. Totowa, NJ: Humana Press. doi:<a href=\"https://doi.org/10.1007/978-1-62703-733-4_22\">10.1007/978-1-62703-733-4_22</a>, .","van":"Frahm B. Seed Train Optimization for Cell Culture. In: Pörtner R, editor. Animal Cell Biotechnology - Methods and Protocols. 3rd ed. Totowa, NJ: Humana Press; 2013. p. 355–67. (Methods in Molecular Biology; vol. 1104).","ama":"Frahm B. Seed Train Optimization for Cell Culture. In: Pörtner R, ed. <i>Animal Cell Biotechnology - Methods and Protocols</i>. Vol 1104. 3rd ed. Methods in Molecular Biology. Humana Press; 2013:355-367. doi:<a href=\"https://doi.org/10.1007/978-1-62703-733-4_22\">10.1007/978-1-62703-733-4_22</a>","ufg":"<b>Frahm, Björn</b>: Seed Train Optimization for Cell Culture, in: <i>Pörtner, Ralf (Hg.)</i>: Animal Cell Biotechnology - Methods and Protocols, Bd. 1104, Totowa, NJ <sup>3</sup>2013 (Methods in Molecular Biology),  S. 355–367.","havard":"B. Frahm, Seed Train Optimization for Cell Culture, in: R. Pörtner (Ed.), Animal Cell Biotechnology - Methods and Protocols, 3rd ed., Humana Press, Totowa, NJ, 2013: pp. 355–367.","bjps":"<b>Frahm B</b> (2013) Seed Train Optimization for Cell Culture. In Pörtner R (ed.), <i>Animal Cell Biotechnology - Methods and Protocols</i>, 3rd ed., vol. 1104. Totowa, NJ: Humana Press, pp. 355–367.","mla":"Frahm, Björn. “Seed Train Optimization for Cell Culture.” <i>Animal Cell Biotechnology - Methods and Protocols</i>, edited by Ralf Pörtner, 3rd ed., vol. 1104, Humana Press, 2013, pp. 355–67, <a href=\"https://doi.org/10.1007/978-1-62703-733-4_22\">https://doi.org/10.1007/978-1-62703-733-4_22</a>.","apa":"Frahm, B. (2013). Seed Train Optimization for Cell Culture. In R. Pörtner (Ed.), <i>Animal Cell Biotechnology - Methods and Protocols</i> (3rd ed., Vol. 1104, pp. 355–367). Humana Press. <a href=\"https://doi.org/10.1007/978-1-62703-733-4_22\">https://doi.org/10.1007/978-1-62703-733-4_22</a>","ieee":"B. Frahm, “Seed Train Optimization for Cell Culture,” in <i>Animal Cell Biotechnology - Methods and Protocols</i>, 3rd ed., vol. 1104, R. Pörtner, Ed. Totowa, NJ: Humana Press, 2013, pp. 355–367. doi: <a href=\"https://doi.org/10.1007/978-1-62703-733-4_22\">10.1007/978-1-62703-733-4_22</a>.","chicago":"Frahm, Björn. “Seed Train Optimization for Cell Culture.” In <i>Animal Cell Biotechnology - Methods and Protocols</i>, edited by Ralf Pörtner, 3rd ed., 1104:355–67. Methods in Molecular Biology. Totowa, NJ: Humana Press, 2013. <a href=\"https://doi.org/10.1007/978-1-62703-733-4_22\">https://doi.org/10.1007/978-1-62703-733-4_22</a>."},"volume":1104,"keyword":["Seed train","Optimization","Modeling","Prediction","Space-Time-Yield (STY)","Systems approach","Bioinformatics","Computational biotechnology","Suspension","Production"],"language":[{"iso":"eng"}],"edition":"3","publication_identifier":{"eissn":["1940-6029"],"isbn":["978-1-62703-732-7"],"eisbn":["978-1-62703-733-4"],"issn":["1064-3745"]},"publication":"Animal Cell Biotechnology - Methods and Protocols","date_created":"2023-08-14T17:50:56Z","editor":[{"last_name":"Pörtner","first_name":"Ralf","full_name":"Pörtner, Ralf"}],"date_updated":"2023-08-16T06:41:21Z"}]