@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{12402,
  abstract     = {{In anaerobic technology, pH values are crucial for targeted volatile fatty acid production. While pH dynamics can be modeled using the Anaerobic Digestion Model No. 1 (ADM1), simulation results may be biased. To address this issue, the pH prediction routine of Visual Water, a specialized water chemistry simulator, was validated. Unlike ADM1, it accounts for ionic strength and activities while also providing an automated uncertainty analysis. The analysis revealed Visual Water simulations to better fit measured pH data from acidic solutions in a miniaturized stirred-tank reactor.}},
  author       = {{Kosse, Pascal and Hernández Rodriguez, Tanja and Frahm, Björn and Lübken, Manfred and Wichern, Marc}},
  booktitle    = {{Chemie Ingenieur Technik}},
  issn         = {{1522-2640}},
  keywords     = {{Anaerobic Digestion Model No. 1 (ADM1), Anaerobic technology, pH simulation, Uncertainty assessment, Visual Water}},
  number       = {{4}},
  pages        = {{528--534}},
  publisher    = {{Wiley}},
  title        = {{{Comparative Analysis of pH Prediction Routines in ADM1 and a Specialized Water Chemistry Simulator}}},
  doi          = {{10.1002/cite.202300188}},
  volume       = {{96}},
  year         = {{2024}},
}

@misc{11381,
  author       = {{Hernández Rodriguez, Tanja and Ramm, Selina and Lange-Hegermann, Markus and Frahm, Björn}},
  location     = {{Berlin, Germany}},
  publisher    = {{DECHEMA e.V.}},
  title        = {{{A systematic, model-based workflow for risk-based decision making in upstream development}}},
  year         = {{2023}},
}

@misc{11382,
  author       = {{Gassenmeier, Veronika and Kuhfuß, Fabian and Deppe, Sahar and Ifrim, George and Hernández Rodriguez, Tanja and Frahm, Björn}},
  location     = {{Recklinghausen, Germany}},
  title        = {{{View on a mechanistic model of Chlorella vulgaris in shake flasks}}},
  year         = {{2023}},
}

@misc{11383,
  author       = {{Hernández Rodriguez, Tanja and Posch, Christoph and Pörtner, Ralf and Lange-Hegermann, Markus and Wurm, Florian M. and Frahm, Björn}},
  location     = {{Berlin, Germany}},
  publisher    = {{DECHEMA e.V.}},
  title        = {{{Model-assisted design strategies for bioprocesses – Advanced statistical methods in industrial upstream cell culture}}},
  year         = {{2023}},
}

@misc{11385,
  abstract     = {{Accurate pH calculations are essential for scientists across different disciplines to design optimal reactor solutions, but they can be arduous to calculate for complex acid-base solutions. Visual Water is a powerful software tool that provides accurate pH calculations with automated mathematical uncertainty analysis. Its workflow is presented and validated using acids and bases, showing a deviation of < 0.2 pH units between measured and calculated pH values. This highlights the software's reliability, which can help to simplify the work of non-experts in water chemistry.}},
  author       = {{Kosse, Pascal and Hernández Rodriguez, Tanja and Frahm, Björn and Lübken, Manfred and Wichern, Marc}},
  booktitle    = {{Chemie Ingenieur Technik}},
  issn         = {{1522-2640}},
  keywords     = {{Acid-base equilibria, Carboxylic acids, Dissociation constants, pH calculation software, Uncertainty assessment}},
  number       = {{12}},
  pages        = {{1960--1969}},
  publisher    = {{Wiley}},
  title        = {{{Validation and Uncertainty Assessment of a Software‐Integrated Workflow for pH Calculations}}},
  doi          = {{10.1002/cite.202300082}},
  volume       = {{95}},
  year         = {{2023}},
}

@misc{11487,
  author       = {{Ramm, Selina and Hernández Rodriguez, Tanja and Frahm, Björn and Pein-Hackelbusch, Miriam}},
  location     = {{Berlin, Germany}},
  publisher    = {{DECHEMA e.V.}},
  title        = {{{Comparison of Preprocessing Methods of Dielectric Spectroscopy  Data and the Effects on Linear Regression }}},
  year         = {{2023}},
}

@misc{13650,
  author       = {{Uhlendorff, Selina and Hernández Rodriguez, Tanja and Frahm, Björn and Pein-Hackelbusch, Miriam}},
  location     = {{Lemgo, Germany}},
  title        = {{{Systematic Preprocessing of Dielectric Spectroscopy Data and Estimating Viable Cell Densities}}},
  year         = {{2023}},
}

@misc{10201,
  author       = {{Hernández Rodriguez, Tanja and Ramm, Selina and Lange-Hegermann, Markus and Frahm, Björn}},
  location     = {{Recklinghausen, Germany}},
  title        = {{{A systematic, model-based workflow for risk-based decision making in upstream development}}},
  year         = {{2023}},
}

@misc{10788,
  abstract     = {{For process monitoring, an adequate data preprocessing is crucial to link accessible inline process data with offline measured target variables. Literature, however, does not provide systematic preprocessing strategies. The effects of five different preprocessing strategies on data from a Dielectric Spectroscopy system applied to the Viable Cell Density (VCD) of a mammalian cell cultivation were thus evaluated. Single-frequency measurements are typically used to model the VCD over the growth phase using linear regression or the Cole-Cole model and served as a reference. As multi-frequency measurement is promising to model the VCD beyond the growth phase using Partial Least Squares Regression (PLSR), we further aimed to determine, whether replacing linear regression by PLSR shows comparable modeling performance. All five preprocessing strategies led to comparable results. Exemplary, when using capacitance values at a frequency of 3347 kHz, linear regression resulted in a R2 of 0.90 and a standard deviation of 0.4 % on average. Both normalization techniques had the same positive effect on the results of PLSR. The order of smoothing and normalization was irrelevant for both regression methods. Comparing the results of linear regression and PLSR, the latter obtained on average 9 % better results. Therefore, we concluded that PLSR is preferable over linear regression and is potentially suitable to model the VCD beyond the growth phase, which is suggested to be investigated based on more data sets.}},
  author       = {{Ramm, Selina and Hernández Rodriguez, Tanja and Frahm, Björn and Pein-Hackelbusch, Miriam}},
  booktitle    = {{2023 IEEE 21st International Conference on Industrial Informatics (INDIN)}},
  editor       = {{Jasperneite, Jürgen and Wisniewski, Lukasz and Fung Man, Kim}},
  isbn         = {{978-1-6654-9314-7}},
  issn         = {{1935-4576}},
  keywords     = {{Spectroscopy, Smoothing methods, Systematics, Phase measurement, Linear regression, Data models, Dielectric measurement}},
  location     = {{Lemgo}},
  pages        = {{1--6}},
  publisher    = {{IEEE}},
  title        = {{{Systematic Preprocessing of Dielectric Spectroscopy Data and Estimating Viable Cell Densities}}},
  doi          = {{10.1109/INDIN51400.2023.10218012}},
  year         = {{2023}},
}

@misc{11377,
  abstract     = {{<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.}},
  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}},
}

@misc{7932,
  author       = {{Hernández Rodriguez, Tanja and Ramm, Selina and Lange-Hegermann, Markus and Frahm, Björn}},
  location     = {{Barcelona, Spain}},
  title        = {{{A systematic, model-based workflow for risk-based decision making in upstream development}}},
  year         = {{2022}},
}

@misc{7976,
  author       = {{Gassenmeier, Veronika and Deppe, Sahar and Hernández Rodriguez, Tanja and Kuhfuß, Fabian and Moser, André and Hass, Volker C. and Kuchemüller, Kim B. and Pörtner, Ralf and Möller, Johannes and Ifrim, George Adrian and Frahm, Björn}},
  booktitle    = {{Current Research in Biotechnology}},
  issn         = {{2590-2628 }},
  pages        = {{102--119}},
  publisher    = {{Elsevier}},
  title        = {{{Model-assisted DoE applied to microalgae processes, Current Research in Biotechnology}}},
  doi          = {{10.1016/j.crbiot.2022.01.005}},
  volume       = {{4}},
  year         = {{2022}},
}

@misc{7977,
  abstract     = {{Kinetic growth models are a useful tool for a better understanding of microalgal cultivation and for optimizing cultivation conditions. The evaluation of such models requires experimental data that is laborious to generate in bioreactor settings. The experimental shake flask setting used in this study allows to run 12 experiments at the same time, with 6 individual light intensities and light durations. This way, 54 biomass data sets were generated for the cultivation of the microalgae Chlorella vulgaris. To identify the model parameters, a stepwise parameter estimation procedure was applied. First, light-associated model parameters were estimated using additional measurements of local light intensities at differ heights within medium at different biomass concentrations. Next, substrate related model parameters were estimated, using experiments for which biomass and nitrate data were provided. Afterwards, growth-related model parameters were estimated by application of an extensive cross validation procedure.}},
  author       = {{Kuhfuß, Fabian and Gassenmeier, Veronika and Deppe, Sahar and Ifrim, George Adrian and Hernández Rodriguez, Tanja and Frahm, Björn}},
  booktitle    = {{Bioprocess and Biosystems Engineering}},
  issn         = {{1615-7605}},
  pages        = {{15--30}},
  publisher    = {{Springer}},
  title        = {{{View on a mechanistic model of Chlorella vulgaris in incubated shake flasks}}},
  doi          = {{10.1007/s00449-021-02627-2}},
  volume       = {{45}},
  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}},
}

@misc{10197,
  author       = {{Hernández Rodriguez, Tanja and Posch, Christoph and Pörtner, Ralf and Frahm, Björn}},
  booktitle    = {{ACHEMA 2022}},
  location     = {{Frankfurt am Main, Germany}},
  title        = {{{Model assisted Design Strategies for Bioprocesses – Advanced statistical methods in industrial upstream cell culture}}},
  year         = {{2022}},
}

@misc{10198,
  author       = {{Hernández Rodriguez, Tanja and Pörtner, Ralf and Lange-Hegermann, Markus and Wurm, Florian M. and Frahm, Björn}},
  location     = {{Lisbon, Portugal }},
  title        = {{{A systematic, model-based approach for decision making in upstream development – Considerations regarding clone selection and cell expansion}}},
  year         = {{2022}},
}

@misc{11376,
  abstract     = {{<jats:p>concentration is an important objective. The phenotype of the cells in a reactor plays an important role. Are clonal cell populations showing high cell-specific growth rates more favorable than cell lines with higher cell-specific productivities or vice versa? Five clonal Chinese hamster ovary cell populations were analyzed based on the data of a 3-month-stability study. We adapted a mechanistic cell culture model to the experimental data of one such clonally derived cell population. Uncertainties and prior knowledge concerning model parameters were considered using Bayesian parameter estimations. This model was used then to define an inoculum train protocol. Based on this, we subsequently simulated the impacts of differences in growth rates (±10%) and production rates (±10% and ±50%) on the overall cultivation time, including making the inoculum train cultures; the final production phase, the volumetric titer in that bioreactor and the ratio of both, defined as overall process productivity. We showed thus unequivocally that growth rates have a higher impact (up to three times) on overall process productivity and for product output per year, whereas cells with higher productivity can potentially generate higher product concentrations in the production vessel.}},
  author       = {{Hernández Rodriguez, Tanja and Morerod, Sophie and Pörtner, Ralf and Wurm, Florian M. and Frahm, Björn}},
  booktitle    = {{Processes}},
  issn         = {{2227-9717}},
  keywords     = {{clonal cell population, phenotypic diversity, inoculum train, uncertainty-based, cell culture model, biopharmaceutical manufacturing}},
  number       = {{6}},
  publisher    = {{MDPI AG}},
  title        = {{{Considerations of the Impacts of Cell-Specific Growth and Production Rate on Clone Selection—A Simulation Study}}},
  doi          = {{10.3390/pr9060964}},
  volume       = {{9}},
  year         = {{2021}},
}

@misc{7927,
  author       = {{Hernández Rodriguez, Tanja and Frahm, Björn}},
  location     = {{SeAMK, Seinäjoki, Finnland}},
  title        = {{{Model-based methods in bioprocess technology - Bioprocess modeling, simulation and prediction}}},
  year         = {{2021}},
}

@misc{7931,
  author       = {{Moser, André and Möller, Johannes and Kuchemüller, Kim B. and Deppe, Sahar and Hernández Rodriguez, Tanja and Gassenmeier, Veronika and Ifrim, George Adrian and Frahm, Björn and Pörtner, Ralf and Hass, Volker C.}},
  location     = {{as a web conference, Prague, Czech Republic}},
  title        = {{{Model-assisted Design of Experiments - Concept, Software and Applications}}},
  year         = {{2021}},
}

@inbook{7983,
  abstract     = {{For the manufacturing of complex biopharmaceuticals using bioreactors with cultivated mammalian cells, high product concentration is an important objective. The phenotype of the cells in a reactor plays an important role. Are clonal cell populations showing high cell-specific growth rates more favorable than cell lines with higher cell-specific productivities or vice versa? Five clonal Chinese hamster ovary cell populations were analyzed based on the data of a 3-month-stability study. We adapted a mechanistic cell culture model to the experimental data of one such clonally derived cell population. Uncertainties and prior knowledge concerning model parameters were considered using Bayesian parameter estimations. This model was used then to define an inoculum train protocol. Based on this, we subsequently simulated the impacts of differences in growth rates (±10%) and production rates (±10% and ±50%) on the overall cultivation time, including making the inoculum train cultures; the final production phase, the volumetric titer in that bioreactor and the ratio of both, defined as overall process productivity. We showed thus unequivocally that growth rates have a higher impact (up to three times) on overall process productivity and for product output per year, whereas cells with higher productivity can potentially generate higher product concentrations in the production vessel.}},
  author       = {{Hernández Rodriguez, Tanja and Morerod, Sophie and Pörtner, Ralf and Wurm, Florian M. and Frahm, Björn}},
  booktitle    = {{Bioprocess Systems Engineering Applications in Pharmaceutical Manufacturing}},
  isbn         = {{978-3-0365-5210-1}},
  issn         = {{2227-9717 }},
  keywords     = {{clonal cell population, phenotypic diversity, inoculum train, uncertainty-based, cell culture model, biopharmaceutical manufacturing}},
  pages        = {{49--74}},
  publisher    = {{MDPI}},
  title        = {{{Considerations of the impacts of cell-specific growth and production rate on clone selection – a simulation study}}},
  doi          = {{10.3390/pr9060964}},
  volume       = {{special issue}},
  year         = {{2021}},
}

@misc{7985,
  abstract     = {{Bioprocess modeling has become a useful tool for prediction of the process future with the aim to deduce operating decisions (e.g. transfer or feeds). Due to variabilities, which often occur between and within batches, updating (re-estimation) of model parameters is required at certain time intervals (dynamic parameter estimation) to obtain reliable predictions. This can be challenging in the presence of low sampling frequencies (e.g. every 24 h), different consecutive scales and large measurement errors, as in the case of cell culture seed trains. This contribution presents an iterative learning workflow which generates and incorporates knowledge concerning cell growth during the process by using a moving horizon estimation (MHE) approach for updating of model parameters. This estimation technique is compared to a classical weighted least squares estimation (WLSE) approach in the context of model updating over three consecutive cultivation scales (40–2160 L) of an industrial cell culture seed train. Both techniques were investigated regarding robustness concerning the aforementioned challenges and the required amount of experimental data (estimation horizon). It is shown how the proposed MHE can deal with the aforementioned difficulties by the integration of prior knowledge, even if only data at two sampling points are available, outperforming the classical WLSE approach. This workflow allows to adequately integrate current process behavior into the model and can therefore be a suitable component of a digital twin.}},
  author       = {{Hernández Rodriguez, Tanja and Posch, Christoph and Pörtner, Ralf and Frahm, Björn}},
  booktitle    = {{Bioprocess and Biosystems Engineering}},
  issn         = {{1615-7605}},
  keywords     = {{Dynamic parameter estimation, Bioprocess, Cell cultures, Moving horizon estimation, Prior knowledge}},
  number       = {{4}},
  pages        = {{793--808}},
  publisher    = {{Springer}},
  title        = {{{Dynamic parameter estimation and prediction over consecutive scales, based on moving horizon estimation - applied to an industrial cell culture seed train}}},
  doi          = {{10.1007/s00449-020-02488-1 }},
  volume       = {{44}},
  year         = {{2021}},
}

@misc{7986,
  abstract     = {{Bioprocess development and optimization are still cost- and time-intensive due to the enormous number of experiments involved. In this study, the recently introduced model-assisted Design of Experiments (mDoE) concept (Möller et al. in Bioproc Biosyst Eng 42(5):867, https://doi.org/10.1007/s00449-019-02089-7, 2019) was extended and implemented into a software (“mDoE-toolbox”) to significantly reduce the number of required cultivations. The application of the toolbox is exemplary shown in two case studies with Saccharomyces cerevisiae. In the first case study, a fed-batch process was optimized with respect to the pH value and linearly rising feeding rates of glucose and nitrogen source. Using the mDoE-toolbox, the biomass concentration was increased by 30% compared to previously performed experiments. The second case study was the whole-cell biocatalysis of ethyl acetoacetate (EAA) to (S)-ethyl-3-hydroxybutyrate (E3HB), for which the feeding rates of glucose, nitrogen source, and EAA were optimized. An increase of 80% compared to a previously performed experiment with similar initial conditions was achieved for the E3HB concentration.}},
  author       = {{Moser, André and Kuchemüller, Kim B. and Deppe, Sahar and Hernández Rodriguez, Tanja and Frahm, Björn and Pörtner, Ralf and Hass, Volker C. and Möller, Johannes}},
  booktitle    = {{Bioprocess and Biosystems Engineering}},
  isbn         = {{1615-7591}},
  issn         = {{1615-7605}},
  keywords     = {{Biocatalysis, Monte Carlo methods, Fed-batch strategy, Model-assisted design of experiments, Quality by design}},
  number       = {{4}},
  pages        = {{683--700}},
  publisher    = {{Springer}},
  title        = {{{Model-assisted DoE software: Optimization of growth and biocatalysis in Saccharomyces cerevisiae bioprocesses}}},
  doi          = {{10.1007/s00449-020-02478-3}},
  volume       = {{44}},
  year         = {{2021}},
}

@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}},
}

@misc{7929,
  author       = {{Hernández Rodriguez, Tanja and Deppe, Sahar and Gassenmeier, Veronika and Kuhfuß, Fabian and Tölle, Marius and Ifrim, George Adrian and Frahm, Björn}},
  location     = {{als Web-Konferenz, Aachen, Germany}},
  title        = {{{Modeling of Chlorella vulgaris in shake flasks with respect to light intensity and light duration}}},
  year         = {{2020}},
}

@misc{7930,
  author       = {{Hernández Rodriguez, Tanja and Posch, Christoph and Pörtner, Ralf and Frahm, Björn}},
  location     = {{als Web-Konferenz, Aachen, Germany}},
  title        = {{{Towards integration of prior knowledge and propagation of uncertainty for digital twins of biopharmaceutical production processes}}},
  year         = {{2020}},
}

@misc{7936,
  author       = {{Hernández Rodriguez, Tanja and Frahm, Björn and Posch, Christoph}},
  location     = {{Sandoz, Austria}},
  title        = {{{Integration of prior knowledge and digital tools for uncertainty-based decision support}}},
  year         = {{2020}},
}

@misc{7937,
  author       = {{Hernández Rodriguez, Tanja and Deppe, Sahar and Gassenmeier, Veronika and Kuhfuß, Fabian and Tölle, Marius and Ifrim, George Adrian and Frahm, Björn}},
  booktitle    = {{Chemie Ingenieur Technik, Tagungsband zur 10. ProcessNet-Jahrestagung und 34. DECHEMA-Jahrestagung der Biotechnologen 2020}},
  pages        = {{1225 – 1226}},
  publisher    = {{Wiley}},
  title        = {{{Modeling of Chlorella vulgaris algae in shake flasks with respect to light intensity and light duration}}},
  doi          = {{10.1002/cite.202055235}},
  volume       = {{No. 9}},
  year         = {{2020}},
}

@misc{7938,
  author       = {{Hernández Rodriguez, Tanja and Posch, Christoph and Pörtner, Ralf and Frahm, Björn}},
  booktitle    = {{Chemie Ingenieur Technik, Tagungsband zur 10. ProcessNet-Jahrestagung und 34. DECHEMA-Jahrestagung der Biotechnologen 2020}},
  pages        = {{1241 – 1242}},
  publisher    = {{Wiley}},
  title        = {{{Towards integration of prior knowledge and propagation of uncertainty for digital twins of biopharmaceutical production processes}}},
  doi          = {{10.1002/cite.202055234}},
  volume       = {{No. 9}},
  year         = {{2020}},
}

@misc{2822,
  author       = {{Hernández Rodriguez, Tanja and Posch, Christoph and Schmutzhard, Julia and Stettner, Josef  and Weihs,  Claus  and Pörtner, Ralf and Frahm, Björn}},
  booktitle    = {{BMC Proceedings}},
  issn         = {{1753-6561}},
  location     = {{Copenhagen, Denmark.}},
  pages        = {{P408}},
  publisher    = {{BioMed Central}},
  title        = {{{Predicting Industrial Cell Culture Seed Trains - A Bayesian Approach}}},
  doi          = {{10.1186/s12919-020-00188-y}},
  volume       = {{14}},
  year         = {{2020}},
}

@misc{3007,
  author       = {{Kuhfuß, F. and Tölle, M. and Hernández Rodriguez, Tanja and Gassenmeier, Veronika and Deppe, S. and Ifrim, George Adrian and Frahm, Björn}},
  location     = {{Recklinghausen, Germany}},
  title        = {{{Modeling of Chlorella vulgaris in shake flasks with respect to light intensity and light duration}}},
  year         = {{2020}},
}

@misc{3008,
  author       = {{Deppe, S. and Kuhfuß, F. and Tölle, M. and Hernández Rodriguez, Tanja and Gassenmeier, Veronika and Ifrim, George Adrian and Möller, J. and Pörtner, R.  and Moser, A. and Hass, V. C. and Frahm, Björn}},
  location     = {{Recklinghausen, Germany}},
  title        = {{{Model-assisted Design of Experiments for algae cultivation}}},
  year         = {{2020}},
}

@misc{3009,
  author       = {{Ifrim, George Adrian and Deppe, S. and Hernández Rodriguez, Tanja and Gassenmeier, Veronika and Frahm, Björn}},
  location     = {{Recklinghausen, Germany}},
  title        = {{{Modeling of microalgae in an air-lift photobioreactor for biomass production}}},
  year         = {{2020}},
}

@misc{2391,
  author       = {{Hernández Rodriguez, Tanja and Posch, Christoph and Schmutzhard, Julia and Stettner, Josef and Weihs, Claus and Pörtner, Ralf and Frahm, Björn}},
  booktitle    = {{Biotechnology and Bioengineering}},
  issn         = {{1097-0290}},
  number       = {{11}},
  pages        = {{2944--2959}},
  publisher    = {{Wiley}},
  title        = {{{Predicting industrial‐scale cell culture seed trains–A Bayesian framework for model fitting and parameter estimation, dealing with uncertainty in measurements and model parameters, applied to a nonlinear kinetic cell culture model, using an MCMC method}}},
  doi          = {{10.1002/bit.27125}},
  volume       = {{116}},
  year         = {{2019}},
}

@misc{2956,
  author       = {{Möller, J. and Müller, J. and Hernández Rodriguez, Tanja and Kuchemüller, K. B. and Arndt, L. and Frahm, Björn and Eibl, R. and Eibl, D. and Pörtner, R.}},
  location     = {{Wädenswil, Switzerland}},
  title        = {{{Workflow for model-assisted design and scale-up of biopharmaceutical production processes}}},
  year         = {{2019}},
}

@misc{2957,
  author       = {{Möller, J. and Kuchemüller, K. B. and Moser, A. and Hass, V. C. and Hernández Rodriguez, Tanja and Deppe, S. and Frahm, Björn and Pörtner, R.}},
  location     = {{Florence, Italy}},
  title        = {{{Model-Assisted Design of Experiments}}},
  year         = {{2019}},
}

@misc{3000,
  author       = {{Hernández Rodriguez, Tanja and Posch, C. and Schmutzhard, J. and Stettner, J. and Weihs, J. and Pörtner, R. and Frahm, Björn}},
  location     = {{Recklinghausen, Germany}},
  title        = {{{Predicting industrial scale cell culture seed trains – considerations of input uncertainty, new process data and prognostic intervals using a Bayesian approach and sequential Bayesian updating}}},
  year         = {{2019}},
}

@misc{3001,
  author       = {{Hernández Rodriguez, Tanja and Posch, C. and Schmutzhard, J. and Stettner, J. and Weihs, J. and Pörtner, R. and Frahm, Björn}},
  location     = {{Copenhagen, Denmark}},
  title        = {{{Predicting industrial scale cell culture seed trains – considerations of input uncertainty, new process data and prognostic intervals using a Bayesian approach and sequential Bayesian updating}}},
  year         = {{2019}},
}

@misc{3002,
  author       = {{Möller, J. and Kuchemüller, K. B. and Hernández Rodriguez, Tanja and Deppe, S. and Ifrim, George Adrian and Moser, A. and Hass, V. C. and Frahm, Björn and Pörtner, R.}},
  location     = {{Hamburg, Germany}},
  title        = {{{Model-Assisted Design of Experiments – Software Toolbox}}},
  year         = {{2019}},
}

@misc{3003,
  author       = {{Deppe, S. and Hernández Rodriguez, Tanja and Gassenmeier, Veronika and Tölle, M. and Kuhfuß, F. and Möller, J. and Kuchemüller, K. B.  and Pörtner, R. and Moser, A. and Hass, V. C. and Ifrim, George Adrian and Frahm, Björn}},
  location     = {{Hamburg, Germany}},
  title        = {{{Model-assisted Design of Experiments for algae cultivation}}},
  year         = {{2019}},
}

@misc{3004,
  author       = {{Ifrim, George Adrian and Deppe, S. and Hernández Rodriguez, Tanja and Gassenmeier, Veronika and Frahm, Björn}},
  location     = {{Hamburg, Germany}},
  title        = {{{Modeling of microalgae in an air-lift photobioreactor for biomass production}}},
  year         = {{2019}},
}

@misc{3005,
  author       = {{Hernández Rodriguez, Tanja and Posch, C. and Schmutzhard, J. and Stettner, J. and Weihs, J. and Pörtner, R. and Frahm, Björn}},
  location     = {{Hamburg, Germany}},
  title        = {{{Predicting industrial scale cell culture seed trains – considerations of input uncertainty, new process data and prognostic intervals using a Bayesian approach and sequential Bayesian updating}}},
  year         = {{2019}},
}

@misc{7935,
  author       = {{Hernández Rodriguez, Tanja and Frahm, Björn and Posch, Christoph and Schmutzhard, Julia and Stettner, Josef}},
  location     = {{Kundl, Austria}},
  title        = {{{A model-assisted approach for design, optimization and control of bioprocesses – a software tool for seed train simulation and optimization}}},
  year         = {{2018}},
}

@misc{2390,
  author       = {{Möller, J. and Kuchemüller, K. B.  and Hernández Rodriguez, Tanja and Frahm, Björn and Hass, V. C. and Pörtner, R. }},
  booktitle    = {{American Pharmaceutical Review}},
  issn         = {{1099-8012}},
  pages        = {{39--41}},
  publisher    = {{Russel}},
  title        = {{{Model-Assisted Design of Process Strategies for Cell Culture Processes}}},
  volume       = {{3}},
  year         = {{2018}},
}

@misc{2821,
  author       = {{Hernández Rodriguez, Tanja and Krull, Susan and Hass, Volker C. and Möller, Johannes and Pörtner, Ralf and Frahm, Björn}},
  booktitle    = {{BMC Proceedings}},
  issn         = {{1753-6561}},
  location     = {{Lausanne, Switzerland}},
  pages        = {{P175}},
  publisher    = {{BioMed Central}},
  title        = {{{Model-assisted cell culture control – unstructured, unsegregated models as a key element for adaptive seed train and fed-batch optimization}}},
  doi          = {{10.1186/s12919-018-0097-x}},
  volume       = {{112}},
  year         = {{2018}},
}

@misc{2997,
  author       = {{Möller, J. and Kuchemüller, K. B. and Freiberger, F. and Levermann, P. and Hernández Rodriguez, Tanja and Frahm, Björn and Hass, V. C. and Pörtner, R.}},
  location     = {{Recklinghausen, Germany}},
  title        = {{{Model-Assisted Design and Optimization of Process Strategies}}},
  year         = {{2018}},
}

@misc{2999,
  author       = {{Hernández Rodriguez, Tanja and Möller, J. and Kuchemüller, K. B. and Freiberger, F. and Levermann, P. and Hass, V. C. and Frahm, Björn and Pörtner, R.}},
  location     = {{Berlin, Germany}},
  title        = {{{Model-Assisted Strategies for Design, Optimization and Control of Bioprocesses}}},
  year         = {{2018}},
}

@misc{2920,
  author       = {{Hernández Rodriguez, Tanja and Kern, S. and Platas Barradas, O. and Pörtner, R. and Frahm, Björn}},
  location     = {{Lemgo, Germany}},
  title        = {{{Produktion von Biopharmazeutika – Ein MATLAB©-Tool zur Simulation und Optimierung von Zellvermehrungsverfahren}}},
  year         = {{2017}},
}

@misc{2921,
  author       = {{Pörtner, R. and Möller, J. and Hernández Rodriguez, Tanja and Frahm, Björn and Hass, V. C.}},
  location     = {{Neu-Ulm, Germany}},
  title        = {{{Model-assisted design and optimization of biotechnological processes}}},
  year         = {{2017}},
}

@misc{2950,
  author       = {{Hernández Rodriguez, Tanja and Koopmann, K. S. and Klaes, J. and Kern, S. and Platas Barradas, O. and Pörtner, R. and Frahm, Björn}},
  location     = {{Munic, Germany}},
  title        = {{{Produktion von Biopharmazeutika – Ein MATLAB©-Tool zur Simulation und Optimierung von Zellvermehrungsverfahren}}},
  year         = {{2017}},
}

@misc{2994,
  author       = {{Hernández Rodriguez, Tanja and Koopmann, K. S. and Pörtner, R. and Frahm, Björn}},
  location     = {{Recklinghausen, Germany}},
  title        = {{{Influence of parameter accuracies in cell culture seed train simulations}}},
  year         = {{2017}},
}

@misc{2995,
  author       = {{Hernández Rodriguez, Tanja and Krull, S. and Hass, V. C. and Möller, J. and Pörtner, R. and Frahm, Björn}},
  location     = {{Lausanne, Switzerland}},
  title        = {{{Model-based cell culture control – unstructured, unsegregated models as a key element for adaptive seed train and fed-batch optimization}}},
  year         = {{2017}},
}

@misc{2996,
  author       = {{Hernández Rodriguez, Tanja and Krull, S. and Hass, V. C. and Pörtner, R. and Frahm, Björn}},
  location     = {{Neu-Ulm, Germany}},
  title        = {{{Model-based cell culture control – unstructured, unsegregated models as a key element for adaptive seed train and fed-batch optimization}}},
  year         = {{2017}},
}

@misc{2992,
  author       = {{Hernández Rodriguez, Tanja and Vörtler, S. and Kern, S. and Platas Barradas, O. and Pörtner, R. and Frahm, Björn}},
  location     = {{Recklinghausen, Germany}},
  title        = {{{A software tool for biopharmaceutical seed train design and optimization}}},
  year         = {{2016}},
}

@misc{2993,
  author       = {{Koopmann, K. S. and Hernández Rodriguez, Tanja and Vörtler, S. and Beshay, U. and Möller, J. and Pörtner, R. and Frahm, Björn}},
  location     = {{Koblenz, Germany}},
  title        = {{{Simulation of cell culture seed trains – investigation of the influence of parameter inaccuracies}}},
  year         = {{2016}},
}

@misc{2819,
  author       = {{Hernández Rodriguez, Tanja and Pörtner, Ralf and Frahm, Björn}},
  booktitle    = {{BMC proceedings / BioMed Central}},
  issn         = {{1753-6561}},
  publisher    = {{BioMed Central}},
  title        = {{{Considerations for cell passaging in cell culture seed trains}}},
  doi          = {{10.1186/1753-6561-9-s9-p43}},
  volume       = {{9}},
  year         = {{2015}},
}

@misc{2917,
  author       = {{Frahm, Björn and Pörtner, R. and Kern, S. and Hernández Rodriguez, Tanja}},
  location     = {{Frankfurt am Main, Germany}},
  title        = {{{Seed train design and optimization for animal cell suspension culture}}},
  year         = {{2015}},
}

@misc{2919,
  author       = {{Frahm, Björn and Hernández Rodriguez, Tanja and Kern, S. and Koopmann, K. S. and Beshay, U. and Platas-Barradas, O. and Pörtner, R.}},
  location     = {{Magdeburg, Germany}},
  title        = {{{Seed train design and optimization for animal cell suspension culture}}},
  year         = {{2015}},
}

@misc{2985,
  author       = {{Hernández Rodriguez, Tanja and Pörtner, R. and Frahm, Björn}},
  location     = {{Hamburg, Germany}},
  title        = {{{Considerations for cell passaging in cell culture seed trains}}},
  year         = {{2015}},
}

@misc{2987,
  author       = {{Hernández Rodriguez, Tanja and Pörtner, R. and Frahm, Björn}},
  location     = {{Munic, Germany}},
  title        = {{{A software tool for biopharmaceutical seed train design and optimization}}},
  year         = {{2015}},
}

@misc{2988,
  author       = {{Hernández Rodriguez, Tanja and Pörtner, R. and Frahm, Björn}},
  location     = {{Barcelona, Spain}},
  title        = {{{Considerations for cell passaging in cell culture seed trains}}},
  year         = {{2015}},
}

@misc{2817,
  author       = {{Hernández Rodriguez, Tanja and Pörtner, Ralf and Frahm, Björn}},
  booktitle    = {{BMC proceedings / BioMed Central }},
  issn         = {{1753-6561}},
  publisher    = {{BioMed Central }},
  title        = {{{Seed train optimization for suspension cell culture}}},
  doi          = {{10.1186/1753-6561-7-s6-p9}},
  volume       = {{7}},
  year         = {{2013}},
}

@misc{2983,
  author       = {{Hernández Rodriguez, Tanja and Pörtner, R. and Frahm, Björn}},
  location     = {{Lille, France}},
  title        = {{{Seed train optimization for suspension cell culture}}},
  year         = {{2013}},
}