• Energy Research

The bacterium E. coli is one of the most important hosts for recombinant protein production. The benefits are high growth rates, inexpensive media, and high protein titers. However, complex proteins with high molecular weight and many disulfide bonds are expressed as inclusion bodies (IBs). In the last decade, the overall perception of these IBs being not functional proteins changed, as enzyme activity was found within IBs. Several applications for direct use of IBs are already reported in literature. While fluorescent proteins or protein tags are used for determination of IB activity to date, direct measurements of IB protein activity are scacre. The expression of recombinant hyaluronidase from Apis mellifera in E. coli BL21(DE3) was analyzed using a face centered design of experiment approach. Hyaluronidase is a hard to express protein and imposes a high metabolic burden to the host. Conditions giving a high specific IB titer were found at 25 °C at low specific substrate uptake rates and induction times of 2 to 4 h. The protein activity of hyaluronidase IBs was verified using (Fourier transform) FT-IR spectroscopy. Degradation of the substrate hyaluronan occurred at increased rates with higher IB concentrations. Active recombinant hyaluronidase IBs can be immediately used for direct degradation of hyaluronan without further down streaming steps. FT-IR spectroscopy was introduced as a method for tracking IB activity and showed differences in degradation behavior of hyaluronan dependent on the applied active IB concentration.

On-line monitoring devices for the precise determination of a multitude of components are a prerequisite for fast bioprocess quantification. On-line measured values have to be checked for quality and consistency, in order to extract quantitative information from these data. In the present study we characterized a novel on-line sampling and analysis device comprising an automatic photometric robot. We connected this on-line device to a bioreactor and concomitantly measured six components (i.e. glucose, glycerol, ethanol, acetate, phosphate and ammonium) during different batch cultivations of Pichia pastoris. The on-line measured data did not show significant deviations from off-line taken samples and were consequently used for incremental rate and yield calculations. In this respect we highlighted the importance of data quality and discussed the phenomenon of error propagation. On-line calculated rates and yields depicted the physiological responses of the P. pastoris cells in unlimited and limited cultures. A more detailed analysis of the physiological state was possible by considering the off-line determined biomass dry weight and the calculation of specific rates. Here we present a novel device for on-line monitoring of bioprocesses, which ensures high data quality in real-time and therefore refers to a valuable tool for Process Analytical Technology (PAT).

The real-time measurement of biomass has been addressed since many years. The quantification of biomass in the induction phase of a recombinant bioprocess is not straight forward, since biological burden, caused by protein expression, can have a significant impact on the cell morphology and physiology. This variability potentially leads to poor generalization of the biomass estimation, hence is a very important issue in the dynamic field of process development with frequently changing processes and producer lines. We want to present a method to quantify "biomass" in real-time which avoids off-line sampling and the need for representative training data sets. This generally applicable soft-sensor, based on first principles, was used for the quantification of biomass in induced recombinant fed-batch processes. Results were compared with "state of the art" methods to estimate the biomass concentration and the specific growth rate µ. Gross errors such as wrong stoichiometric assumptions or sensor failure were detected automatically. This method allows for variable model coefficients such as yields in contrast to other process models, hence does not require prior experiments. It can be easily adapted to a different growth stoichiometry; hence the method provides good generalization, also for induced culture mode. This approach estimates the biomass (or anabolic bioconversion) in induced fed-batch cultures in real-time and provides this key variable for process development for control purposes.

AbstractMixed substrate feeding strategies are frequently investigated to enhance the productivity of recombinant Pichia pastoris processes. For this purpose, numerous fed batch experiments or time‐consuming continuous cultivations are required to optimize control parameters such as the substrate mixing ratio and the applied methanol concentration. In this study, we decoupled the feeding of methanol and glycerol in a mixed substrate fed batch environment to gain process understanding for a recombinant P. pastoris Muts strain producing the model enzyme horseradish peroxidase. Specific substrate uptake rates (qs) were controlled separately, and a stepwise increased qGly‐control scheme was applied to investigate the effect of various substrate fluxes on the culture. The qs‐controlled strategy allowed a parallel characterization of the metabolism and the recombinant protein expression in a fed batch environment. A critical‐specific glycerol uptake rate was determined, where a decline of the specific productivity occurred, and a time‐dependent acceleration of protein expression was characterized with the dynamic fed batch approach. Based on the observations on recombinant protein expression, propositions for an optimal feeding design to target maximal productivities were stated. Thus, the dynamic fed batch strategy was found to be a valuable tool for both process understanding and optimization of product formation for P. pastoris in a mixed substrate environment. © 2012 American Institute of Chemical Engineers Biotechnol. Prog., 2012

Spore inoculum quality in filamentous bioprocesses is a critical parameter influencing pellet morphology and, consequently, process performance. It is essential to determine the concentration of viable spores before inoculation, to implement quality control and decrease batch-to-batch variability. The ability to assess the spore physiologic status with close-to-real time resolution would offer interesting perspectives enhanced process analytical technology (PAT) and quality by design (QbD) strategies. Up to now, the parameters contributing to spore inoculum quality are not clearly defined. The state-of-the-art method to investigate this variable is colony-forming unit (CFU) determination, which assesses the number of growing spores. This procedure is tedious, associated with significant inherent bias, and not applicable in real time.Here, a novel method is presented, based on the combination of viability staining (propidium iodide and fluorescein diacetate) and large-particle flow cytometry. It is compatible with the complex medium background often observed in filamentous bioprocesses and allows for a classification of the spores into different subpopulations. Next to viable spores with intact growth potential, dormant or inactive as well as physiologically compromised cells are accurately determined. Hence, a more holistic few on spore inoculum quality and early-phase biomass composition is provided, offering enhanced information content.In an industrially relevant model bioprocess, good correlation to CFU counts was found. Morphological parameters (e.g. spore swelling) that are not accessible via standard monitoring tools were followed over the initial process phase with close temporal resolution.

Biomass subpopulations in mammalian cell culture processes cause impurities and influence productivity, which requires this critical process parameter to be monitored in real-time.For this reason, a novel soft sensor concept for estimating viable, dead and lysed cell concentration was developed, based on the robust and cheap in situ measurements of permittivity and turbidity in combination with a simple model. It could be shown that the turbidity measurements contain information about all investigated biomass subpopulations. The novelty of the developed soft sensor is the real-time estimation of lysed cell concentration, which is directly correlated to process-related impurities such as DNA and host cell protein in the supernatant. Based on data generated by two fed-batch processes the developed soft sensor is described and discussed.The presented soft sensor concept provides a tool for viable, dead and lysed cell concentration estimation in real-time with adequate accuracy and enables further applications with respect to process optimization and control.

Capacitance probes have the potential to revolutionize bioprocess control due to their safe and robust use and ability to detect even the smallest capacitors in the form of biological cells. Several techniques have evolved to model biomass statistically, however, there are problems with model transfer between cell lines and process conditions. Errors of transferred models in the declining phase of the culture range for linear models around +100% or worse, causing unnecessary delays with test runs during bioprocess development. The goal of this work was to develop one single universal model which can be adapted by considering a potentially mechanistic factor to estimate biomass in yet untested clones and scales. The novelty of this work is a methodology to select sensitive frequencies to build a statistical model which can be shared among fermentations with an error between 9% and 38% (mean error around 20%) for the whole process, including the declining phase. A simple linear factor was found to be responsible for the transferability of biomass models between cell lines, indicating a link to their phenotype or physiology.

Microbial bioprocesses need to be designed to be transferable from lab scale to production scale as well as between setups. Although substantial effort is invested to control technological parameters, usually the only true constant parameter is the actual producer of the product: the cell. Hence, instead of solely controlling technological process parameters, the focus should be increasingly laid on physiological parameters. This contribution aims at illustrating a workflow of data life cycle management with special focus on physiology. Information processing condenses the data into physiological variables, while information mining condenses the variables further into physiological descriptors. This basis facilitates data analysis for a physiological explanation for observed phenomena in productivity. Targeting transferability, we demonstrate this workflow using an industrially relevant Escherichia coli process for recombinant protein production and substantiate the following three points: (1) The postinduction phase is independent in terms of productivity and physiology from the preinduction variables specific growth rate and biomass at induction. (2) The specific substrate uptake rate during induction phase was found to significantly impact the maximum specific product titer. (3) The time point of maximum specific titer can be predicted by an easy accessible physiological variable: while the maximum specific titers were reached at different time points (19.8 ± 7.6 h), those maxima were reached all within a very narrow window of cumulatively consumed substrate dSn (3.1 ± 0.3 g/g). Concluding, this contribution provides a workflow on how to gain a physiological view on the process and illustrates potential benefits. © 2016 American Institute of Chemical Engineers Biotechnol. Prog., 33:261–270, 2017