Browsing by Subject "Hansenula polymorpha"

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Assessing the capabilities of 2D fluorescence monitoring in microtiter plates with data-driven modeling for secondary substrate limitation experiments of Hansenula polymorpha
(2023) Berg, Christoph; Herbst, Laura; Gremm, Lisa; Ihling, Nina; Paquet-Durand, Olivier; Hitzmann, Bernd; Büchs, Jochen; Berg, Christoph; AVT - Aachener Verfahrenstechnik, Biochemical Engineering, RWTH Aachen University, Aachen, Germany; Herbst, Laura; AVT - Aachener Verfahrenstechnik, Biochemical Engineering, RWTH Aachen University, Aachen, Germany; Gremm, Lisa; AVT - Aachener Verfahrenstechnik, Biochemical Engineering, RWTH Aachen University, Aachen, Germany; Ihling, Nina; AVT - Aachener Verfahrenstechnik, Biochemical Engineering, RWTH Aachen University, Aachen, Germany; Paquet-Durand, Olivier; Department of Process Analytics & Cereal Science, Institute for Food Science and Biotechnology, University of Hohenheim, Stuttgart, Germany; Hitzmann, Bernd; Department of Process Analytics & Cereal Science, Institute for Food Science and Biotechnology, University of Hohenheim, Stuttgart, Germany; Büchs, Jochen; AVT - Aachener Verfahrenstechnik, Biochemical Engineering, RWTH Aachen University, Aachen, Germany
Background: Non-invasive online fluorescence monitoring in high-throughput microbioreactors is a well-established method to accelerate early-stage bioprocess development. Recently, single-wavelength fluorescence monitoring in microtiter plates was extended to measurements of highly resolved 2D fluorescence spectra, by introducing charge-coupled device (CCD) detectors. Although introductory experiments demonstrated a high potential of the new monitoring technology, an assessment of the capabilities and limits for practical applications is yet to be provided. Results: In this study, three experimental sets introducing secondary substrate limitations of magnesium, potassium, and phosphate to cultivations of a GFP-expressing H. polymorpha strain were conducted. This increased the complexity of the spectral dynamics, which were determined by 2D fluorescence measurements. The metabolic responses upon growth limiting conditions were assessed by monitoring of the oxygen transfer rate and extensive offline sampling. Using only the spectral data, subsequently, partial least-square (PLS) regression models for the key parameters of glycerol, cell dry weight, and pH value were generated. For model calibration, spectral data of only two cultivation conditions were combined with sparse offline sampling data. Applying the models to spectral data of six cultures not used for calibration, resulted in an average relative root-mean-square error (RMSE) of prediction between 6.8 and 6.0%. Thus, while demanding only sparse offline data, the models allowed the estimation of biomass accumulation and glycerol consumption, even in the presence of more or less pronounced secondary substrate limitation. Conclusion: For the secondary substrate limitation experiments of this study, the generation of data-driven models allowed a considerable reduction in sampling efforts while also providing process information for unsampled cultures. Therefore, the practical experiments of this study strongly affirm the previously claimed advantages of 2D fluorescence spectroscopy in microtiter plates.
Online process state estimation for Hansenula polymorpha cultivation with 2D fluorescence spectra-based chemometric model calibrated from a theoretical model in place of offline measurements
(2023) Babor, Majharulislam; Paquet-Durand, Olivier; Berg, Christoph; Büchs, Jochen; Hitzmann, Bernd
The use of 2D fluorescence spectra is a powerful, instantaneous, and highly accurate method to estimate the state of bioprocesses. The conventional approach for calibrating a chemometric model from raw spectra needs a large number of offline measurements from numerous runs, which is tedious, time-consuming, and error-prone. In addition, many process variables lack direct signal responses, which forces chemometric models to make predictions based on indirect responses. In order to predict glycerol and biomass concentrations online in batch cultivation of Hansenula polymorpha, this study substituted offline measurements with simulated values. The only data from cultivations needed to generate the chemometric model were the 2D fluorescence spectra, with the presumption that they contain sufficient information to characterize the process state at a measurement point. The remainder of the evaluation was carried out with the aid of a mathematical process model that describes the theoretical interferences between process variables in the system. It is shown that the process model parameters, including microbial growth rate, the yield of biomass from glycerol, and lag time can be determined from only the spectra by employing a model-based calibration (MBC) approach. The prediction errors for glycerol and biomass concentrations were 8.6% and 5.7%, respectively. An improved model-based calibration (IMBC) approach is presented that calibrates a chemometric model for only biomass. Biomass was predicted from a 2D fluorescence spectrum in new cultivations, and glycerol concentration was estimated from the process model utilizing predicted biomass as an input. By using this method, the prediction errors for glycerol and biomass were reduced to 5.2% and 4.7%, respectively. The findings indicate that model-based calibration, which can be carried out with only 2D fluorescence spectra gathered from prior runs, is an effective method for estimating the process state online.