@misc{13475, abstract = {{In the context of data-driven bioprocess modeling, selecting appropriate regression models remains a critical challenge. This is especially the case when dealing with time-dependent process dynamics and complex measurement data. The practical relevance of this study lies in its critical assessment of the application constraints associated with multivariate linear regression models in bioprocess monitoring of cell culture processes. The applicability of Partial Least Squares and Ridge regression was evaluated for different cultivation phases. The results emphasize that no single linear modeling approach is universally suitable for capturing the complex behavior of mammalian cell cultures. This is why we present an enhanced segmented modeling approach by learning the optimal transition point from data and introducing a gradual model switch, allowing for smoother and more robust adaptation to process dynamics. This segmented model led to improved predictive performance compared to single-model regression across the entire process duration. Nevertheless, the heterogeneity of the 11 mammalian cell culture datasets used in this study posed significant challenges, with the best-performing models achieving prediction error of around 0.31 of the average offline viable cell density. These results underline the potential of phase-adaptive modeling, while also emphasizing the need for further optimization to robustly handle diverse bioprocess conditions.}}, author = {{Uhlendorff, Selina and Burankova, Tatsiana and Dahlmann, Katharina and Frahm, Björn and Pein-Hackelbusch, Miriam}}, booktitle = {{2025 International Workshop on Impedance Spectroscopy (IWIS)}}, isbn = {{979-8-3315-9323-0}}, keywords = {{cell culture, impedance spectroscopy, partial least squares, ridge regression}}, location = {{Chemnitz}}, pages = {{34--39}}, publisher = {{IEEE}}, title = {{{Application Constraints of Linear Multivariate Regression Models for Dielectric Spectroscopy in Inline Bioreactor Viable Cell Analysis}}}, doi = {{10.1109/iwis69004.2025.11339388}}, year = {{2026}}, } @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}}, } @misc{11376, abstract = {{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}}, } @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}}, }