08 Fakultät Mathematik und Physik

Permanent URI for this collectionhttps://elib.uni-stuttgart.de/handle/11682/9

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Author: search.filters.author.Bluhmki, Erich

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    Solubilization of inclusion bodies : insights from explainable machine learning approaches
    (2023) Walther, Cornelia; Martinetz, Michael C.; Friedrich, Anja; Tscheließnig, Anne-Luise; Voigtmann, Martin; Jung, Alexander; Brocard, Cécile; Bluhmki, Erich; Smiatek, Jens
    We present explainable machine learning approaches for gaining deeper insights into the solubilization processes of inclusion bodies. The machine learning model with the highest prediction accuracy for the protein yield is further evaluated with regard to Shapley additive explanation (SHAP) values in terms of feature importance studies. Our results highlight an inverse fractional relationship between the protein yield and total protein concentration. Further correlations can also be observed for the dominant influences of the urea concentration and the underlying pH values. All findings are used to develop an analytical expression that is in reasonable agreement with experimental data. The resulting master curve highlights the benefits of explainable machine learning approaches for the detailed understanding of certain biopharmaceutical manufacturing steps.
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    Holistic process models : a Bayesian predictive ensemble method for single and coupled unit operation models
    (2022) Montano Herrera, Liliana; Eilert, Tobias; Ho, I-Ting; Matysik, Milena; Laussegger, Michael; Guderlei, Ralph; Schrantz, Bernhard; Jung, Alexander; Bluhmki, Erich; Smiatek, Jens
    The coupling of individual models in terms of end-to-end calculations for unit operations in manufacturing processes is a challenging task. We present a probability distribution-based approach for the combined outcomes of parametric and non-parametric models. With this so-called Bayesian predictive ensemble, the statistical moments such as mean value and standard deviation can be accurately computed without any further approximation. It is shown that the ensemble of different model predictions leads to an uninformed prior distribution, which can be transformed into a predictive posterior distribution using Bayesian inference and numerical Markov Chain Monte Carlo calculations. We demonstrate the advantages of our method using several numerical examples. Our approach is not restricted to certain unit operations, and can also be used for the more robust interpretation and assessment of model predictions in general.