@inbook{13652, abstract = {{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. Zu 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. Dieses 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.}}, author = {{Hernández Rodriguez, Tanja and Frahm, Björn}}, booktitle = {{Digitale Zwillinge - Werkzeuge und Konzepte für intelligente Bioproduktion}}, editor = {{Herwig, Christoph and Pörtner, Ralf and Möller, Johannes}}, isbn = {{978-3-031-75697-9}}, keywords = {{Bayes, Digitaler Zwilling, Parameterabschätzung, Seed-Train, Unsicherheit}}, pages = {{107--145}}, publisher = {{Springer }}, title = {{{Digitale Seed Train Zwillinge und statistische Methoden}}}, doi = {{https://doi.org/10.1007/978-3-031-75698-6_5}}, year = {{2025}}, } @misc{11377, abstract = {{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.}}, author = {{Hernández Rodriguez, Tanja and Sekulic, Anton and Lange-Hegermann, Markus and Frahm, Björn}}, booktitle = {{Processes}}, issn = {{2227-9717}}, keywords = {{Gaussian processes, Bayes optimization, Pareto optimization, multi-objective, cell culture, seed train}}, number = {{5}}, publisher = {{MDPI AG}}, title = {{{Designing Robust Biotechnological Processes Regarding Variabilities Using Multi-Objective Optimization Applied to a Biopharmaceutical Seed Train Design}}}, doi = {{10.3390/pr10050883}}, volume = {{10}}, year = {{2022}}, } @inbook{10193, abstract = {{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.}}, author = {{Hernández Rodriguez, Tanja and Sekulic, Anton and Lange-Hegermann, Markus and Frahm, Björn}}, booktitle = {{Bioprocess Systems Engineering Applications in Pharmaceutical Manufacturing}}, editor = {{Pörtner, Ralf and Möller, Johannes}}, isbn = {{978-3-0365-5210-1}}, issn = {{2227-9717}}, keywords = {{Gaussian processes, Bayes optimization, Pareto optimization, multi-objective, cell culture, seed train}}, pages = {{21--48}}, publisher = {{MDPI}}, title = {{{Designing robust biotechnological processes regarding variabilities using multi-objective optimization applied to a biopharmaceutical seed train design}}}, doi = {{https://doi.org/10.3390/pr10050883}}, volume = {{special issue}}, year = {{2022}}, } @inbook{3349, abstract = {{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. For 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. This 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.}}, author = {{Hernández Rodriguez, Tanja and Frahm, Björn}}, booktitle = {{Digital Twins Tools and Concepts for Smart Biomanufacturing}}, editor = {{Herwig, Christoph and Pörtner, Ralf and Möller, Johannes }}, isbn = {{978-3-030-71659-2}}, issn = {{1616-8542}}, keywords = {{Bayes, Digital twin, Parameter estimation, Seed train, Uncertainty}}, pages = {{97–131}}, publisher = {{Springer}}, title = {{{Digital Seed Train Twins and Statistical Methods}}}, doi = {{https://doi.org/10.1007/10_2020_137}}, volume = {{176}}, year = {{2021}}, } @inbook{2394, abstract = {{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.}}, author = {{Frahm, Björn}}, booktitle = {{Animal Cell Biotechnology}}, isbn = {{9781627037327}}, issn = {{1064-3745}}, keywords = {{Seed train Optimization Modeling Prediction Space-Time-Yield (STY) Systems approach Bioinformatics Computational biotechnology Suspension Production}}, pages = {{355--367}}, publisher = {{Humana Press}}, title = {{{Seed Train Optimization for Cell Culture}}}, doi = {{10.1007/978-1-62703-733-4_22}}, volume = {{1104}}, year = {{2013}}, } @inbook{10214, abstract = {{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.}}, author = {{Frahm, Björn}}, booktitle = {{Animal Cell Biotechnology - Methods and Protocols}}, editor = {{Pörtner, Ralf}}, isbn = {{978-1-62703-732-7}}, issn = {{1940-6029}}, keywords = {{Seed train, Optimization, Modeling, Prediction, Space-Time-Yield (STY), Systems approach, Bioinformatics, Computational biotechnology, Suspension, Production}}, pages = {{355–367}}, publisher = {{Humana Press}}, title = {{{Seed Train Optimization for Cell Culture}}}, doi = {{10.1007/978-1-62703-733-4_22}}, volume = {{1104}}, year = {{2013}}, }