Online 2D Fluorescence Monitoring in Microtiter Plates Allows Prediction of Cultivation Parameters and Considerable Reduction in Sampling Efforts for Parallel Cultivations of Hansenula polymorpha

Berg, Christoph; Ihling, Nina; Finger, Maurice; Emire, Shimelis Admassu; Hitzmann, Bernd; Büchs, Jochen

doi:10.3390/bioengineering9090438

Cited by 5 publications

(29 citation statements)

References 71 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Similar to the study by Berg et al [ 41 ], the PLS models were generated based on online spectral data of two replicates and the linearly interpolated offline values of the sparse, 6 h sampling interval of the cultures shown in Fig. 1 D-F.…”

Section: Resultsmentioning

confidence: 99%

“…The appropriate number of LVs was identified by a decreasing RMSE Cal, sparse , accompanied by an increase of the RMSE Cal, full . According to Berg et al [ 41 ], at this point, the model starts to overfit the sparse data, instead of representing the inherent biology-based spectral dynamics. Following this methodology, the PLS models were generated using two LVs for the glycerol concentration and the CDW and three LVs for the pH value.…”

Section: Resultsmentioning

confidence: 99%

“…For this parameter, excluding the cultures of the highest phosphate supplementation resulted in a minimum RMSE Pred, full, − 100%, which was comparable to the RMSEs of the calibration datasets. In conclusion, the criterium for choosing the number LVs [ 41 ] did only apply for the CDW and led to five LVs. However, for the glycerol concentration and the pH value the criterium did not apply.…”

Section: Resultsmentioning

confidence: 99%

“…This study aims to evaluate the capabilities of the new MTP-based monitoring system under more practical-oriented conditions. The methodology for 2D fluorescence-based PLS modeling, presented by Berg et al [ 41 ], is applied to experimental data of higher complexity. Following the H. polymorpha screening experiments of Kottmeier et al [ 47 ], three separate experiments are conducted, with each varying the supplementation of one secondary substrate of magnesium (Mg 2+ ), potassium (K + ), or phosphate (PO 4 3− ).…”

Section: Introductionmentioning

confidence: 99%

“…Subsequently, the spectral data of cultures of two cultivation conditions in combination with a subset of the offline data is then used to generate PLS models, before internal and external validation is conducted using data not used for calibration. Based on the results and the comparison to the study by Berg et al [ 41 ], the capabilities and limitations of the 2D fluorescence monitoring technology in MTPs are assessed.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Assessing the capabilities of 2D fluorescence monitoring in microtiter plates with data-driven modeling for secondary substrate limitation experiments of Hansenula polymorpha

et al. 2023

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Assessing the capabilities of 2D fluorescence monitoring in microtiter plates with data-driven modeling for secondary substrate limitation experiments of Hansenula polymorpha

et al. 2023

Self Cite

View full text Add to dashboard Cite

show abstract

Advancing 2D fluorescence online monitoring in microtiter plates by separating scattered light and fluorescence measurement, using a tunable emission monochromator

Berg,

Busch,

Alawiyah

et al. 2023

Biotech & Bioengineering

Self Cite

View full text Add to dashboard Cite

Online fluorescence monitoring has become a key technology in modern bioprocess development, as it provides in‐depth process knowledge at comparably low costs. In particular, the technology is widely established for high‐throughput microbioreactor cultivation systems, due to its noninvasive character. For microtiter plates, previously also multi‐wavelength 2D fluorescence monitoring was developed. To overcome an observed limitation of fluorescence sensitivity, this study presents a modified spectroscopic setup, including a tunable emission monochromator. The new optical component enables the separation of the scattered and fluorescent light measurements, which allows for the adjustment of integration times of the charge‐coupled device detector. The resulting increased fluorescence sensitivity positively affected the performance of principal component analysis for spectral data of Escherichia coli batch cultivation experiments with varying sorbitol concentration supplementation. In direct comparison with spectral data recorded at short integration times, more biologically consistent signal dynamics were calculated. Furthermore, during partial least square regression for E. coli cultivation experiments with varying glucose concentrations, improved modeling performance was observed. Especially, for the growth‐uncoupled acetate concentration, a considerable improvement of the root‐mean‐square error from 0.25 to 0.17 g/L was achieved. In conclusion, the modified setup represents another important step in advancing 2D fluorescence monitoring in microtiter plates.

show abstract

Machine learning for optical chemical multi-analyte imaging

Zieger

Koren

2023

Anal Bioanal Chem

View full text Add to dashboard Cite

Simultaneous sensing of metabolic analytes such as pH and O2 is critical in complex and heterogeneous biological environments where analytes often are interrelated. However, measuring all target analytes at the same time and position is often challenging. A major challenge preventing further progress occurs when sensor signals cannot be directly correlated to analyte concentrations due to additional effects, overshadowing and complicating the actual correlations. In fields related to optical sensing, machine learning has already shown its potential to overcome these challenges by solving nested and multidimensional correlations. Hence, we want to apply machine learning models to fluorescence-based optical chemical sensors to facilitate simultaneous imaging of multiple analytes in 2D. We present a proof-of-concept approach for simultaneous imaging of pH and dissolved O2 using an optical chemical sensor, a hyperspectral camera for image acquisition, and a multi-layered machine learning model based on a decision tree algorithm (XGBoost) for data analysis. Our model predicts dissolved O2 and pH with a mean absolute error of < 4.50·10−2 and < 1.96·10−1, respectively, and a root mean square error of < 2.12·10−1 and < 4.42·10−1, respectively. Besides the model-building process, we discuss the potentials of machine learning for optical chemical sensing, especially regarding multi-analyte imaging, and highlight risks of bias that can arise in machine learning-based data analysis.

show abstract

Online 2D Fluorescence Monitoring in Microtiter Plates Allows Prediction of Cultivation Parameters and Considerable Reduction in Sampling Efforts for Parallel Cultivations of Hansenula polymorpha

Cited by 5 publications

References 71 publications

Assessing the capabilities of 2D fluorescence monitoring in microtiter plates with data-driven modeling for secondary substrate limitation experiments of Hansenula polymorpha

Assessing the capabilities of 2D fluorescence monitoring in microtiter plates with data-driven modeling for secondary substrate limitation experiments of Hansenula polymorpha

Advancing 2D fluorescence online monitoring in microtiter plates by separating scattered light and fluorescence measurement, using a tunable emission monochromator

Machine learning for optical chemical multi-analyte imaging

Contact Info

Product

Resources

About