Combining light microscopy, dielectric spectroscopy, MALDI intact cell mass spectrometry, FTIR spectromicroscopy and multivariate data mining for morphological and physiological bioprocess characterization of filamentous organisms

Posch, Andreas E.; Koch, Cosima; Helmel, Michaela; Marchetti‐Deschmann, Martina; Macfelda, Karin; Lendl, Bernhard; Allmaier, Günter; Herwig, Christoph

doi:10.1016/j.fgb.2012.11.008

Cited by 19 publications

(18 citation statements)

References 33 publications

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“… In a first step, the spore inoculum concentration, pO2 control and the glucose feeding regime were identified as key process parameters based on a risk assessment workshop with industry experts . Spore inoculum concentration was previously shown to influence penicillin production as well as pellet size for the investigated system . Penicillin is the value‐added product and maintaining pellet morphology can influence penicillin productivity as well as considerably lower process costs by reducing broth viscosity .…”

Section: Methodsmentioning

confidence: 93%

“…Process duration was 180 h. Fed‐batch processes were inoculated with 80 mL/L cultivation broth from batch processes after a pH increase of 0.5 indicating carbohydrate exhaustion. For further technical details on batch and fed‐batch cultivations the reader is referred to our previous publication …”

Section: Methodsmentioning

confidence: 99%

“…Consequently, p O 2 control was based on partial substitution of the inlet airflow with oxygen, rather than stirrer‐based p O 2 control. Moreover, the agitation speed was controlled at the average level from the initial experiments, with p O 2 control to >40% . This approach guaranteed stirrer agitation speeds with maximum deviation in the range of ±10% for all cultivations, and thus allowed quantitative analysis of morphological effects due to different p O 2 levels.…”

Section: Methodsmentioning

confidence: 99%

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Physiological description of multivariate interdependencies between process parameters, morphology and physiology during fed‐batch penicillin production

Posch¹,

Herwig²

2014

Biotechnology Progress

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Optimization of productivity and economics of industrial bioprocesses requires characterization of interdependencies between process parameters and process performance. In the case of penicillin production, as in other processes, process performance is often closely interlinked with the physiology and morphology of the organism used for production. This study presents a systematic approach to efficiently characterize the physiological effects of multivariate interdependencies between bioprocess design parameters (spore inoculum concentration, pO2 control level and substrate feed rate), morphology, and physiology. Method development and application was performed using the industrial model process of penicillin production. Applying traditional, statistical bioprocess analysis, multivariate correlations of raw bioprocess design parameters (high spore inoculum concentration, low pO2 control as well as reduced glucose feeding) and pellet morphology were identified. A major drawback of raw design parameter correlation models; however, is the lack of transferability across different process scales and regimes. In this context, morphological and physiological bioprocess modeling based on scalable physiological parameters is introduced. In this study, raw parameter effects on pellet morphology were efficiently summarized by the physiological parameter of the biomass yield per substrate. Finally, for the first time to our knowledge, the specific growth rate per spore was described as time-independent determinant for switching from pellet to disperse growth during penicillin production and thus introduced as a novel, scalable key process parameter for pellet morphology and process performance.

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Section: Methodsmentioning

confidence: 93%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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Physiological description of multivariate interdependencies between process parameters, morphology and physiology during fed‐batch penicillin production

Posch¹,

Herwig²

2014

Biotechnology Progress

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“…Four fed-batch fermentations of P. chrysogenum (strain: BCB1) were carried out similar to a recently described protocol except for POX addition [24]. Since accurate determination of the optimal harvest time was the goal, POX was not added continually, but in a pulsed way, so depletion could be observed more than once during a fermentation run.…”

Section: Materials and Methods/experimentalmentioning

confidence: 99%

“…Since accurate determination of the optimal harvest time was the goal, POX was not added continually, but in a pulsed way, so depletion could be observed more than once during a fermentation run. In order to allow applicability of the FT-IR probe with 25 mm Ingold ® design however, fed-batch cultivations were performed in a 15 L autoclavable, fully automated and controlled stirred bioreactor (Infors, Switzerland) instead of the previously described 7.5 L glass reactor system [24]. For all fed-batch cultivations, process duration was in the range of 140–160 h.…”

Section: Materials and Methods/experimentalmentioning

confidence: 99%

Multi-analyte quantification in bioprocesses by Fourier-transform-infrared spectroscopy by partial least squares regression and multivariate curve resolution

Koch

Posch

Goicoechea

et al. 2014

Analytica Chimica Acta

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Observability analysis of biochemical process models as a valuable tool for the development of mechanistic soft sensors

Golabgir

Hoch

Zhariy

et al. 2015

Biotechnology Progress

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By enabling the estimation of difficult-to-measure target variables using available indirect measurements, mechanistic soft sensors have become important tools for various bioprocess monitoring and control scenarios. Despite promising higher process efficiencies and increased process understanding, widespread application of soft sensors has been stalled by uncertainty about the feasibility and reliability of their estimations given present process analytical constraints. Observability analysis can provide an indication of the possibility and reliability of soft sensor estimations by analyzing the structural properties of first-principle (mechanistic) models. In addition, it can provide a criteria for selection of suitable measurement methods with respect to their information content; thereby leading to successful implementation of soft sensors in bioprocess development and manufacturing environments. We demonstrate the utility of observability analysis for two classes of upstream bioprocesses: the processes involving growth and ethanol formation by Saccharomyces cerevisiae and the process of penicillin production by Penicillium chrysogenum. Results obtained from laboratory-scale cultivations in addition to in-silico experiments enable a comparison of theoretical aspects of observability analysis and the real-life performance of soft sensors. By taking the expected error of measurements provided to the soft sensor into account, an innovative scaling approach facilitates a higher degree of comparability of observability results among various measurement configurations and process conditions.

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Combining light microscopy, dielectric spectroscopy, MALDI intact cell mass spectrometry, FTIR spectromicroscopy and multivariate data mining for morphological and physiological bioprocess characterization of filamentous organisms

Cited by 19 publications

References 33 publications

Physiological description of multivariate interdependencies between process parameters, morphology and physiology during fed‐batch penicillin production

Physiological description of multivariate interdependencies between process parameters, morphology and physiology during fed‐batch penicillin production

Multi-analyte quantification in bioprocesses by Fourier-transform-infrared spectroscopy by partial least squares regression and multivariate curve resolution

Observability analysis of biochemical process models as a valuable tool for the development of mechanistic soft sensors

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