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In a biotechnological process, standard monitored process variables are pH, partial oxygen pressure (pO2), and temperature. These process variables are important, but they do not give any information about the metabolic activity of the cell. The ISICOM is an in situ combi-sensor that is measuring the cell-specific oxygen uptake rate (qOUR) online. This variable allows a qualitative judgement of metabolic cell activity. The measuring principle of the ISICOM is based on a volume element enclosed into a small measuring chamber. Inside the measuring chamber, the pO2 and the scattered light is measured. Within a defined measuring interval, the chamber closes, and the oxygen supply for the cells is interrupted. The decreasing oxygen concentration is recorded by the pO2 optode. This measuring principle, known as the dynamic method, determines the oxygen uptake rate (OUR). Together with the scattered light signal, the cell concentration is estimated and the qOUR is available online. The design of the ISICOM is focused on functionality, sterility, long-term stability, and response time behavior so the sensor can be used in bioprocesses. With the ISICOM, measurement of online and in situ measurement of the OUR is possible. The OUR and qOUR online measurement of an animal cell batch cultivation is demonstrated, with maximum values of OUR = 2.5 mmol L−1 h−1 and a qOUR = 9.5 pmol cell−1 day−1. Information about limitation of the primary and secondary substrate is derived by the monitoring of the metabolic cell activity of bacteria and yeast cultivation processes. This sensor contributes to a higher process understanding by offering an online view on to the cell behavior. In the sense of process analytical technology (PAT), this important information is needed for bioprocesses to realize a knowledge base process control. [ABSTRACT FROM AUTHOR]

Mechanistic models require a significant investment of time and resources, but their application to multiple stages of fermentation process development and operation can make this investment highly valuable. This Opinion article discusses how an established fermentation model may be adapted for application to different stages of fermentation process development: planning, process design, monitoring, and control. Although a longer development time is required for such modeling methods in comparison to purely data-based model techniques, the wide range of applications makes them a highly valuable tool for fermentation research and development. In addition, in a research environment, where collaboration is important, developing mechanistic models provides a platform for knowledge sharing and consolidation of existing process understanding. [ABSTRACT FROM AUTHOR]

The monitoring of biopharmaceutical products using Fourier transform infrared (FT-IR) spectroscopy relies on calibration techniques involving the acquisition of spectra of bioprocess samples along the process. The most commonly used method for that purpose is partial least squares (PLS) regression, under the assumption that a linear model is valid. Despite being successful in the presence of small nonlinearities, linear methods may fail in the presence of strong nonlinearities. This paper studies the potential usefulness of nonlinear regression methods for predicting, from in situ near-infrared (NIR) and mid-infrared (MIR) spectra acquired in high-throughput mode, biomass and plasmid concentrations in Escherichia coli DH5- *#945; cultures producing the plasmid model pVAX-LacZ. The linear methods PLS and ridge regression (RR) are compared with their kernel (nonlinear) versions, kPLS and kRR, as well as with the (also nonlinear) relevance vector machine (RVM) and Gaussian process regression (GPR). For the systems studied, RR provided better predictive performances compared to the remaining methods. Moreover, the results point to further investigation based on larger data sets whenever differences in predictive accuracy between a linear method and its kernelized version could not be found. The use of nonlinear methods, however, shall be judged regarding the additional computational cost required to tune their additional parameters, especially when the less computationally demanding linear methods herein studied are able to successfully monitor the variables under study. [ABSTRACT FROM AUTHOR]

The on-line estimation of non-measurable substances and key process variables is relevant for the real-time monitoring of the bioprocesses. This paper deals with the on-line estimation of microbial growth rate and biomass concentration based on measures of the product concentration. The algorithm does not require a model of the growth rate and it can be applied to batch, fed-batch and continuous processes. The proposed observer, which is based on second-order sliding mode ideas, provides smooth estimates with reduced-order error dynamics. It is also shown that in case of error in the product formation parameters, convergence to an error bound can be achieved. Numerical simulations under different operating conditions are provided and the estimates obtained for lactic acid production are contrasted with documented experimental data. [ABSTRACT FROM AUTHOR]

Monoclonal antibody production in glycoengineered Pichia pastoris was optimized for high cell density fed-batch fermentations. Results show that antibody productivity was 1.72-fold higher when maintaining oxygen-limited conditions compared to methanol-limited conditions during the protein production phase of the bioprocess. Under oxygen-limited conditions with a 30 mmol L −1 h −1 of oxygen uptake rate, an optimum methanol dosage level of 10 gL −1 was established by comparing antibody titers under different methanol dosages during protein production. Real-time on-line NIR monitoring of glycerol concentration, methanol concentration, and relative cell density provided valuable data for process characterization and optimization. Real-time profiles of glycerol concentration and relative cell density documented run-to-run consistency during the batch and fed-batch phases by analyses of the glycerol consumption and cell growth kinetics. The volumetric methanol consumption rate was characterized for the induction process following the end of the glycerol fed-batch phase. Under oxygen-limited conditions, the volumetric methanol consumption rate increased over a 60-h period and this increase was independent of the methanol concentration over a range of 2.5–30 gL −1 when the oxygen utilization rate was maintained at 30 mmol L −1 h −1 . Utility of the on-line NIR monitor is demonstrated over the one-year period of this investigation by accurately tracking biomass, glycerol concentration, and methanol concentration without user required recalibration as well as by documenting abnormal events during high cell density bioprocesses. [ABSTRACT FROM AUTHOR]

A rapid, single-step method for the analysis of volatile flavours based on the extraction using the medium-chain triglyceride (MCT) Tegosoft CT and subsequent thermal desorption gas chromatography was developed. The MCT was spiked with a mixture of volatile compounds to validate the protocol. Thermal desorption was complete over a dynamic range of more than three orders of magnitude (except γ-dodecalactone) and linear for all constituents of the volatile mixture. The respective limits of detection and quantification were in the range between 12 and 28 mg L MCT for γ-dodecalactone and 39 and 95 mg L for isoamyl acetate. The applicability of the method was evaluated with aqueous and lipid-containing matrices which were spiked with a known amount of the volatile mixture and extracted subsequently using MCT. A complete recovery of non-polar, medium recovery of semi-polar and zero recovery of the most polar compound (maltol) showed the potential and limits of the protocol. The kinetics of volatile formation during the cultivation of the basidiomycete Fomitopsis rosea grown in a lipid-enriched medium were monitored. [ABSTRACT FROM AUTHOR]

Cultivations of Streptomyces peucetius in two types of medium were monitored on-line using a Fourier transform infrared (FTIR) spectrometer combined with an attenuated total reflection probe. The quantitative measurements of the glucose, starch and acetate concentrations were implemented using partial least squares calibration models. These were regressed on spectral and concentration information obtained by adding together single constituent spectra of the main constituents in the medium according to a full factorial design. The accuracy achieved was considered to be satisfactory, with an average root mean square error of prediction of 1.5 g/l for glucose and 0.25 g/l for acetate. The methodology used is considered to be a rapid technique for generation of calibration data, and a step towards the use of library type data for calibration purposes in quantitative FTIR spectroscopy applications in bioprocesses. [ABSTRACT FROM AUTHOR]

The glycerol and methanol concentrations in Pichia pastoris fermentations were measured on-line using Fourier transform infrared spectroscopy and an attenuated total reflection probe. Partial least squares regression was used to obtain calibration models. The models were regressed on synthetic multi-component spectra and semi-synthetic fermentation broth spectra. These were obtained by spectral addition. The accuracy for the on-line measurement of glycerol, given as standard error of prediction (SEP), was determined to 0.68 g/l, and the SEP of methanol was 0.13 g/l. We show how reliable calibration models are obtained relatively effortlessly by replacing extensive sampling from the reactor with simple mathematical manipulations of the model regression spectra. [ABSTRACT FROM AUTHOR]

Background: The Respiration Activity Monitoring System (RAMOS) is an established device to measure on-line the oxygen transfer rate (OTR), thereby, yielding relevant information about metabolic activities of microorganisms and cells during shake flask fermentations. For very fast-growing microbes, however, the RAMOS technique provides too few data points for the OTR. Thus, this current study presents a new model based evaluation method for generating much more data points to enhance the information content and the precision of OTR measurements. Results: In cultivations with E.coli BL21 pRSET eYFP-IL6, short diauxic and even triauxic metabolic activities were detected with much more detail compared to the conventional evaluation method. The decline of the OTR during the stop phases during oxygen limitations, which occur when the inlet and outlet valves of the RAMOS flask were closed for calibrating the oxygen sensor, were also detected. These declines reflected a reduced oxygen transfe rdue to the stop phases. In contrast to the conventional calculation method the new method was almost independent from the number of stop phases chosen in the experiments. Conclusions: This new model based evaluation method unveils new peaks of metabolic activity which otherwise would not have been resolved by the conventional RAMOS evaluation method. The new method yields substantially more OTR data points, thereby, enhancing the information content and the precision of the OTR measurements. Furthermore, oxygen limitations can be detected by a decrease of the OTR during the stop phases. [ABSTRACT FROM AUTHOR]

Abstract: As the understanding of variation is the key to a good process and product quality one should pay attention to dynamics on the single-cell level. The basic idea of this approach was to qualify and quantify variations on the single-cell level during bioreactor cultivations by monitoring the expression of an eGFP tagged target protein (human membrane protein) using fully automated real-time, flow injection flow cytometry (FI-FCM). The FI-FCM system consists of a sampling- and defoaming- as well as of a dilution-section. It allows a very short monitoring interval (5min) and is able to dilute the reactor sample by a factor ranging up to more than 10,000. In bioreactor cultivations of recombinant Pichia pastoris expressing the eGFP tagged target protein, high correlations (R 2 ≥0.97) between the FI-FCM fluorescent signal and other, however, population-averaged fluorescence signals (off-line fluorescence, in situ fluorescence probe) were obtained. FI-FCM is the only method able to distinguish between few cells with high fluorescence and many cells with low fluorescence intensity and proved that cells differ significantly from each other within the population during bioreactor cultivations. Single-cell fluorescence was distributed over a broad range within the cell population. These distributions strongly suggest that (a) the AOX-I promoter is leaky and (b) a fraction of the population is able to express more protein of interest within shorter time and (c) a fraction of the population does not express the fusion protein at all. These findings can help in the selection of high producing, stable strains. To show the platform-independency of the system, it has successfully been tested during bioreactor cultivations of three different strains (P. pastoris, Saccharomyces cerevisiae, Escherichia coli). Along with its applications in PAT, the FI-FCM could be used as a platform-independent (prokaryotes and eukaryotes) method in various other applications; for example in the closed-loop-control of bioprocesses using different kinds of fluorescent reporters, (waste- and drinking-) water analysis, clone selection in combination with FACS or even for surgery applications. [Copyright &y& Elsevier]

Fed-batch cultivations of Streptomyces coelicolor, producing the antibiotic actinorhodin, were monitored online by multiwavelength fluorescence spectroscopy and off-gas analysis. Partial least squares (PLS), locally weighted regression, and multilinear PLS (N-PLS) models were built for prediction of biomass and substrate (casamino acids) concentrations, respectively. The effect of combination of fluorescence and gas analyzer data as well as of different variable selection methods was investigated. Improved prediction models were obtained by combination of data from the two sensors and by variable selection using a genetic algorithm, interval PLS, and the principal variables method, respectively. A stepwise variable elimination method was applied to the three-way fluorescence data, resulting in simpler and more accurate N-PLS models. The prediction models were validated using leave-one-batch-out cross-validation, and the best models had root mean square error of cross-validation values of 1.02 g l−1 biomass and 0.8 g l−1 total amino acids, respectively. The fluorescence data were also explored by parallel factor analysis. The analysis revealed four spectral profiles present in the fluorescence data, three of which were identified as pyridoxine, NAD(P)H, and flavin nucleotides, respectively. [ABSTRACT FROM AUTHOR]

Biotechnology process development involves strain testing and improvement steps aimed at increasing yields and productivity. This necessitates the high-throughput screening of many potential strain candidates, a task currently mainly performed in shake flasks or microtiter plates. However, these methods have some drawbacks, such as the low data density (usually only end-point measurements) and the lack of control over cultivation conditions in standard shake flasks. Microbioreactors can offer the flexibility and controllability of bench-scale reactors and thus deliver results that are more comparable to large-scale fermentations, but with the additional advantages of small size, availability of online cultivation data and the potential for automation. Current microbioreactor technology is analyzed in this review paper, focusing on its industrial applicability, and directions for future research are presented. [ABSTRACT FROM AUTHOR]

Abstract: The need for effective bioprocess (fermentation) monitoring is growing in importance due to the rapid pace of change in the fermentation industry, and attendant financial pressures. Vibrational spectroscopy has shown great promise in bioprocess monitoring. In particular, recently attention has been focused on the capability of mid infrared spectroscopy (MIRS) to monitor multiple analytes in highly complex fermentation fluids. The potential of this powerful analytical technique is critically evaluated by discussion of relevant studies. The advantages and limitations of MIR are discussed in the context of “rival” technologies, such as near infrared, focusing especially on employing such techniques in bioprocesses for real time (either in situ or ex situ) measurements. The potential barriers to the development of MIRS for real time monitoring are identified and further research directions highlighted. [Copyright &y& Elsevier]

Abstract: A rapid and simple technique (microthermometry) was developed for the measurement of microbe''s metabolic activities and its inactivation process by an inhibitor. To analyze the results and to determine the parameters for the estimation of the activity and inactivation degree, a simple model was proposed. Yeast cells (Saccharomyces cerevisiae) were used as a microbe. A differential method using two bead-type thermistors as reference and measuring probes was employed for the detection of a temperature change caused by the heat of metabolism of added carbon source (glucose) by the yeast. Experiments were conducted in a 30°C water bath under non-growth conditions (without nitrogen source). The simple thermal response model was applied to obtain two characteristic parameters for the estimation of the yeast activities, i.e., a metabolic heat production rate, ΔQ, and a metabolic heat inhibition rate, ΔK. The proposed model was well in agreement with the experimental results, and the curve fitting gave ΔQ or ΔK. In the case of the addition of glucose to yeast as a carbon source, ΔQ was proportional to the number of live cell (CFU). The slope of CFU versus ΔQ for yeast in exponential phase was larger by about two-fold than that in the stationary phase. In the case of the addition of gultaraldehyde as an inhibitor to the above system, the logarithmic value of ΔK increased with increasing the concentration of gultaraldehyde within the range of 0.02–6.2wt.%. It was suggested that the two model parameters, ΔQ and ΔK, can be regarded as characteristics to estimate the activities of bacteria and the degree of their inactivation by the inhibitor. [Copyright &y& Elsevier]

Formation of excessive foam can cause a number of problems during fermentation processing, for example, it can lead to ‘foam out’ and subsequent contamination. Antifoam is commonly added to control foaming, but this can affect mass transfer and can add significantly to process costs. Furthermore, there is a tendency to run fermentations at below maximal working volume to ensure foam containment; this obviously has cost implications. Methods for measuring and controlling foam formation are currently relatively simplistic and are not based on a thorough understanding of foam formation and stability behaviour. Foams are complex gas–liquid dispersions whose characteristics depend on solution properties and process conditions, principally, for an impeller driven fermenter, gas flow rate and impeller speed. In this paper, dynamic multi-point measurements of conductance across a foam phase formed during a continuous Pseudomonas sp. fermentation are reported for a range of process regimes in which the only variables were gas flow rate, gas composition and impeller speed. Foam behaviour is characterised in terms of foaming frequency, a steady state conductance, liquid distribution through the foam phase and foam formation rates. The dependence of these parameters on process variables is described. Two different types of foam behaviour are identified. On the basis of this analysis suggestions are made for developing strategies to minimise foam formation or to minimise antifoam additions. [Copyright &y& Elsevier]

Near infrared spectroscopy (NIRS) was used to monitor an industrial bioprocess for the production of the antibiotic, tylosin, using a segmented modelling approach. Models were built over the entire time course of the fermentation from 0 to 150 h, and also in two distinct phases or segments of the bioprocess from 50 to 100 h (synthetic phase) and from 100 to 150 h (stationary phase). All models were validated externally and the performance of the full range and segmented models compared. The standard error of prediction (SEP) of the segmented models was less in both 50–100 h and 100–150 h and the correlation highest in the 50–100 h range. This would suggest that data segmentation is potentially a useful method of accommodating the impact of the pronounced matrix changes which occur in some bioprocesses in NIRS models for key analytes. While there are many reports on bioprocess monitoring using NIRS, there have been no previous studies on the use of segmented NIR models within a bioprocess as a means of accommodating matrix change. [ABSTRACT FROM AUTHOR]