Noninvasive online monitoring of Corynebacterium glutamicum fed-batch bioprocesses subject to spent sulfite liquor raw material uncertainty

Sinner, Peter; Stiegler, Marlene; Herwig, Christoph; Kager, Julian

doi:10.1016/j.biortech.2020.124395

Cited by 17 publications

(22 citation statements)

References 42 publications

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“…The growth rate is very imprecise, and therefore, it is hard to observe significant changes over time, which can hinder subsequent correlation and regression analysis. Overall, the uncertainty lies within the reported uncertainties of 20% [ 13 , 24 ] and can therefore be regarded as reliable.…”

Section: Resultssupporting

confidence: 79%

“…The underlying reaction kinetics are hereby described by a mass balance model with different reaction kinetics, e.g., first-order or Monod terms [ 11 ]. By fitting the model to the data, realistic rate trajectories can be deduced by the model dynamics [ 12 ] or by employing model-based state estimation techniques [ 13 , 14 ]. Although the approximation of reaction rates based on an underlying model combined with offline and online measurements leads to good results, an appropriate model and knowledge of the internal reaction dynamics are needed, which is often not the case in biotechnological processes.…”

Section: Introductionmentioning

confidence: 99%

“…According to [ 21 ], error propagation based on Monte Carlo sampling is the most reliable approach to assign realistic errors to calculated results. For biotechnological processes, Monte Carlo methods have already been successfully used to determine rate calculation errors [ 13 ], confidence bounds of model parameters [ 22 ], and simulations outputs [ 23 ]. The determination of the realistic uncertainties of the target variable is of central relevance for further correlation and regression analysis, where visual inspection model identification and process design can be significantly facilitated by the inclusion of measurement and calculation uncertainties.…”

Section: Introductionmentioning

confidence: 99%

“…The determination of the realistic uncertainties of the target variable is of central relevance for further correlation and regression analysis, where visual inspection model identification and process design can be significantly facilitated by the inclusion of measurement and calculation uncertainties. For specific reaction rates, uncertainties in the range of 20% have been reported to be suitable for conclusive interpretation [ 13 ] and process control [ 24 ].…”

Section: Introductionmentioning

confidence: 99%

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Monte Carlo-Based Error Propagation for a More Reliable Regression Analysis across Specific Rates in Bioprocesses

Kager

Herwig

2021

Bioengineering

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During process development, bioprocess data need to be converted into applicable knowledge. Therefore, it is crucial to evaluate the obtained data under the usage of transparent and reliable data reduction and correlation techniques. Within this contribution, we show a generic Monte Carlo error propagation and regression approach applied to two different, industrially relevant cultivation processes. Based on measurement uncertainties, errors for cell-specific growth, uptake, and production rates were determined across an evaluation chain, with interlinked inputs and outputs. These uncertainties were subsequently included in regression analysis to derive the covariance of the regression coefficients and the confidence bounds for prediction. The usefulness of the approach is shown within two case studies, based on the relations across biomass-specific rate control limits to guarantee high productivities in E. coli, and low lactate formation in a CHO cell fed-batch could be established. Besides the possibility to determine realistic errors on the evaluated process data, the presented approach helps to differentiate between reliable and unreliable correlations and prevents the wrong interpretations of relations based on uncertain data.

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Section: Resultssupporting

confidence: 79%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Monte Carlo-Based Error Propagation for a More Reliable Regression Analysis across Specific Rates in Bioprocesses

Kager

Herwig

2021

Bioengineering

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“…SSL contains mannose (up to more than 40% of the total sugars), glucose (up to more than 20%), and xylose (up to 20%) as the main carbohydrates, depending on the wood type used in fluctuating proportions ( Nigam, 2001 ; He and Chen, 2020 ; Hoheneder et al, 2021 ). However, although SSL in general contains potential inhibitors for bacterial growth ( Al-Rudainy et al, 2017 ), C. glutamicum carrying our plasmid pVWEx1-manA has recently been shown to grow in minimal medium containing 20% of ultrafiltrated SSL from softwood pulping, consuming completely both glucose and mannose ( Sinner et al, 2021 ). Moreover, Perez-Gracia et al (2021) used synthetic SSL, containing glucose, mannose, and xylose, as a substrate for a riboflavin-producing C. glutamicum strain with the plasmid-bound expression of manA and xylAB genes, the latter enabling xylose utilization.…”

Section: Discussionmentioning

confidence: 99%

Exploring the Potential of Corynebacterium glutamicum to Produce the Compatible Solute Mannosylglycerate

Schwentner

Neugebauer

Weinmann

et al. 2021

Front. Bioeng. Biotechnol.

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The compatible solute mannosylglycerate (MG) has exceptional properties in terms of protein stabilization and protection under salt, heat, and freeze-drying stresses as well as against protein aggregation. Due to these characteristics, MG possesses large potential for clinical and biotechnological applications. To achieve efficient MG production, Corynebacterium glutamicum was equipped with a bifunctional MG synthase (encoded by mgsD and catalyzing the condensation of 3-phosphoglycerate and GDP-mannose to MG) from Dehalococcoides mccartyi. The resulting strain C. glutamicum (pEKEx3 mgsD) intracellularly accumulated about 111 mM MG (60 ± 9 mg gCDW−1) with 2% glucose as a carbon source. To enable efficient mannose metabolization, the native manA gene, encoding mannose 6-phosphate isomerase, was overexpressed. Combined overexpression of manA and mgsD from two plasmids in C. glutamicum resulted in intracellular MG accumulation of up to ca. 329 mM [corresponding to 177 mg g cell dry weight (CDW)−1] with glucose, 314 mM (168 mg gCDW−1) with glucose plus mannose, and 328 mM (176 mg gCDW−1) with mannose as carbon source(s), respectively. The product was successfully extracted from cells by using a cold water shock, resulting in up to 5.5 mM MG (1.48 g L−1) in supernatants. The two-plasmid system was improved by integrating the mgsD gene into the manA-bearing plasmid and the resulting strain showed comparable production but faster growth. Repeated cycles of growth/production and extraction of MG in a bacterial milking-like experiment showed that cells could be recycled, which led to a cumulative MG production of 19.9 mM (5.34 g L−1). The results show that the newly constructed C. glutamicum strain produces MG from glucose and mannose and that a cold water shock enables extraction of MG from the cytosol into the medium.

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Online estimation of changing metabolic capacities in continuous Corynebacterium glutamicum cultivations growing on a complex sugar mixture

Sinner

Stiegler

Goldbeck

et al. 2021

Biotech & Bioengineering

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Model-based state estimators enable online monitoring of bioprocesses and, thereby, quantitative process understanding during running operations. During prolonged continuous bioprocesses strain physiology is affected by selection pressure. This can cause time-variable metabolic capacities that lead to a considerable model-plant mismatch reducing monitoring performance if model parameters are not adapted accordingly. Variability of metabolic capacities therefore needs to be integrated in the in silico representation of a process using model-based monitoring approaches. To enable online monitoring of multiple concentrations as well as metabolic capacities during continuous bioprocessing of spent sulfite liquor with Corynebacterium glutamicum, this study presents a particle filtering framework that takes account of parametric variability. Physiological parameters are continuously adapted by Bayesian inference, using noninvasive off-gas measurements. Additional information on current parameter importance is derived from time-resolved sensitivity analysis. Experimental results show that the presented framework enables accurate online monitoring of long-term culture dynamics, whereas state estimation without parameter adaption failed to quantify substrate metabolization and growth capacities under conditions of high selection pressure. Online estimated metabolic capacities are further deployed for multiobjective optimization to identify timevariable optimal operating points. Thereby, the presented monitoring system forms a basis for adaptive control during continuous bioprocessing of lignocellulosic byproduct streams.

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Noninvasive online monitoring of Corynebacterium glutamicum fed-batch bioprocesses subject to spent sulfite liquor raw material uncertainty

Cited by 17 publications

References 42 publications

Monte Carlo-Based Error Propagation for a More Reliable Regression Analysis across Specific Rates in Bioprocesses

Monte Carlo-Based Error Propagation for a More Reliable Regression Analysis across Specific Rates in Bioprocesses

Exploring the Potential of Corynebacterium glutamicum to Produce the Compatible Solute Mannosylglycerate

Online estimation of changing metabolic capacities in continuous Corynebacterium glutamicum cultivations growing on a complex sugar mixture

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