On-line estimation of biomass, glucose and ethanol in Saccharomyces cerevisiae cultivations using in-situ multi-wavelength fluorescence and software sensors

Ödman, Peter; Johansen, Claus Lindvald; Olsson, Lisbeth; Gernaey, Krist V.; Lantz, Anna Eliasson

doi:10.1016/j.jbiotec.2009.08.018

Cited by 85 publications

(72 citation statements)

References 57 publications

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“…Biomass growth, for example, has been estimated by soft sensors based on base titration [15,16], absorbance measurement [11], and exhaust gas analysis [12] using mechanistic models. Other soft sensors have used regression models, multivariate models, and neural networks [17] in combination with on-line fluorimetry [18] and multisensor arrays [19].…”

Section: Introductionmentioning

confidence: 99%

Soft sensor control of metabolic fluxes in a recombinant Escherichia coli fed-batch cultivation producing green fluorescence protein

Gustavsson

Mandenius

2012

Bioprocess Biosyst Eng

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A soft sensor approach is described for controlling metabolic overflow from mixed acid fermentation and glucose overflow metabolism in a fed-batch cultivation for production of recombinant green fluorescence protein (GFP) in Escherichia coli. The hardware part of the sensor consisted of a near-infrared in situ probe that monitored the E. coli biomass and an HPLC analyzer equipped with a filtration unit that measured the overflow metabolites. The computational part of the soft sensor used basic kinetic equations and summations for estimation of specific rates and total metabolite concentrations. Two control strategies for media feeding of the fed-batch cultivation were evaluated: (1) controlling the specific rates of overflow metabolism and mixed acid fermentation metabolites at a fixed pre-set target values, and (2) controlling the concentration of the sum of these metabolites at a set level. The results indicate that the latter strategy was more efficient for maintaining a high titer and low variability of the produced recombinant GFP protein.

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Section: Introductionmentioning

confidence: 99%

Soft sensor control of metabolic fluxes in a recombinant Escherichia coli fed-batch cultivation producing green fluorescence protein

Gustavsson

Mandenius

2012

Bioprocess Biosyst Eng

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“…Essentially, there are two types of estimators: data-driven estimators represented by classical multivariable correlations or ANNs, and process model-driven estimators represented by mass and energy balance models, adaptive observers, and statistical filters based on the Kalman-Bucy filters [248]. In nonalgal bioprocesses, gas analyzers, fluorescence sensors and DO sensors are often used as hardware sensor inputs [249]. In a wider sense, practically all advanced monitoring methods used for bioprocesses could be called software sensors because all measurement methods producing complex signals such as Raman spectroscopy must be accompanied by complex multistep evaluation algorithms to provide meaningful results [227].…”

Section: Software Sensors and Other Computer-aided Monitoring Methodsmentioning

confidence: 99%

Monitoring of Microalgal Processes

Havlík

Scheper

Reardon

2015

Microalgae Biotechnology

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Process monitoring, which can be defined as the measurement of process variables with the smallest possible delay, is combined with process models to form the basis for successful process control. Minimizing the measurement delay leads inevitably to employing online, in situ sensors where possible, preferably using noninvasive measurement methods with stable, low-cost sensors. Microalgal processes have similarities to traditional bioprocesses but also have unique monitoring requirements. In general, variables to be monitored in microalgal processes can be categorized as physical, chemical, and biological, and they are measured in gaseous, liquid, and solid (biological) phases. Physical and chemical process variables can be usually monitored online using standard industrial sensors. The monitoring of biological process variables, however, relies mostly on sensors developed and validated using laboratory-scale systems or uses offline methods because of difficulties in developing suitable online sensors. Here, we review current technologies for online, in situ monitoring of all types of process parameters of microalgal cultivations, with a focus on monitoring of biological parameters. We discuss newly introduced methods for measuring biological parameters that could be possibly adapted for routine online use, should be preferably noninvasive, and are based on approaches that have been proven in other bioprocesses. New sensor types for measuring physicochemical parameters using optical methods or ion-specific field effect transistor (ISFET) sensors are also discussed. Reviewed methods with online implementation or online potential include measurement of irradiance, biomass concentration by optical density and image analysis, cell count, chlorophyll fluorescence, growth rate, lipid concentration by infrared spectrophotometry, dielectric scattering, and nuclear magnetic resonance. Future perspectives are discussed, especially in the field of image analysis using in situ microscopy, infrared spectrophotometry, and software sensor systems.

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“…However, most importantly, the PAT methodology, as defined by the FDA [1], requires effective control of CPPs affecting the CQAs. This task is aided by the MVDA techniques enabling on-line estimation of such critical parameters as biomass, glucose and various other metabolites [10][11][12].…”

Section: Potential Of Mvda In Implementing Pat On Biopharmaceutical Pmentioning

confidence: 99%

Process analytical technology (PAT) for biopharmaceuticals

Glassey

Gernaey

Clemens

et al. 2011

Biotechnology Journal

Self Cite

177

101

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Process analytical technology (PAT), the regulatory initiative for building in quality to pharmaceutical manufacturing, has a great potential for improving biopharmaceutical production.The recommended analytical tools for building in quality, multivariate data analysis, mechanistic modeling, novel models for interpretation of systems biology data and new sensor technologies for cellular states, are instrumental in exploiting this potential. Industrial biopharmaceutical production has gradually become dependent on large-scale processes using sensitive mammalian cell cultures. This further emphasizes the need for improved PAT solutions. We summarize recent progress in this area based on an expert workshop held at the 8 th European Symposium on Biochemical Engineering Sciences (Bologna, 2010), and highlight new opportunities for exploiting PAT when applied in biopharmaceutical production. We conclude with recommendations for advancing PAT applications in the biopharmaceutical industry.

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On-line estimation of biomass, glucose and ethanol in Saccharomyces cerevisiae cultivations using in-situ multi-wavelength fluorescence and software sensors

Cited by 85 publications

References 57 publications

Soft sensor control of metabolic fluxes in a recombinant Escherichia coli fed-batch cultivation producing green fluorescence protein

Soft sensor control of metabolic fluxes in a recombinant Escherichia coli fed-batch cultivation producing green fluorescence protein

Monitoring of Microalgal Processes

Process analytical technology (PAT) for biopharmaceuticals

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