@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}},
}

@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}},
}