Chemostat cultivation mode imposes selective pressure on the cells, which may result in slow adaptation in the physiological state over time. We applied a two‐compartment scale‐down chemostat system imposing feast–famine conditions to characterize the long‐term (100 s of hours) response of Saccharomyces cerevisiae to fluctuating glucose availability. A wild‐type strain and a recombinant strain, expressing an insulin precursor, were cultured in the scale‐down system, and analyzed at the physiological and proteomic level. Phenotypes of both strains were compared with those observed in a well‐mixed chemostat. Our results show that S. cerevisiae subjected to long‐term chemostat conditions undergoes a global reproducible shift in its cellular state and that this transition occurs faster and is larger in magnitude for the recombinant strain including a significant decrease in the expression of the insulin product. We find that the transition can be completely avoided in the presence of fluctuations in glucose availability as the strains subjected to feast–famine conditions under otherwise constant culture conditions exhibited constant levels of the measured proteome for over 250 hr. We hypothesize possible mechanisms responsible for the observed phenotypes and suggest experiments that could be used to test these mechanisms.

Morphology is important in industrial processes involving filamentous organisms because it affects the mixing and mass transfer and can be linked to productivity. Image analysis provides detailed information about the morphology but, in practice, it is often laborious including both collection of high quality images and image processing. Laser diffraction is rapid and fully automatic and provides a volume-weighted distribution of the particle sizes. However, it is based on a number of assumptions that do not always apply to samples. We have evaluated laser diffraction to measure cell clumps and pellets of Streptomyces coelicolor compare to image analysis. Samples, taken five times during fed-batch cultivation, were analyzed by image analysis and laser diffraction. The volume-weighted size distribution was calculated for each sample. Laser diffraction and image analysis yielded similar size distributions, i.e. unimodal or bimodal distributions. Both techniques produced similar estimations of the population means, whereas the estimates of the standard deviations were generally higher using laser diffraction compared to image analysis. Therefore, laser diffraction measurements are high quality and the technique may be useful when rapid measurements of filamentous cell clumps and pellets are required.

Trichoderma reesei expresses a large number of enzymes involved in lignocellulose hydrolysis and the mechanism of how these enzymes work together is too complex to study by traditional methods, for example, by spiking with single enzymes and monitoring hydrolysis performance. In this study, a multivariate approach, partial least squares regression, was used to see whether it could help explain the correlation between enzyme profile and hydrolysis performance. Diverse enzyme mixtures were produced by T. reesei Rut-C30 by exploiting various fermentation conditions and used for hydrolysis of washed pretreated corn stover as a measure of enzyme performance. In addition, the enzyme mixtures were analyzed by liquid chromatography-tandem mass spectrometry to identify and quantify the different proteins. A multivariate model was applied for the prediction of enzyme performance based on the combination of different proteins present in an enzyme mixture. The multivariate model was used for identification of candidate proteins that are correlated to enzyme performance on pretreated corn stover. A very large variation in hydrolysis performance was observed and this was clearly caused by the difference in fermentation conditions. Besides β-glucosidase, the multivariate model identified several xylanases, Cip1 and Cip2, as relevant proteins to study further.

The recent process analytical technology (PAT) initiative has put an increased focus on online sensors to generate process-relevant information in real time. Specifically for fermentation, however, introduction of online sensors is often far from straightforward, and online measurement of biomass is one of the best examples. The purpose of this study was therefore to compare the performance of various online biomass sensors, and secondly to demonstrate their use in early development of a filamentous cultivation process. Eight Streptomyces coelicolor fed-batch cultivations were run as part of process development in which the pH, the feeding strategy, and the medium composition were varied. The cultivations were monitored in situ using multi-wavelength fluorescence (MWF) spectroscopy, scanning dielectric (DE) spectroscopy, and turbidity measurements. In addition, we logged all of the classical cultivation data, such as the carbon dioxide evolution rate (CER) and the concentration of dissolved oxygen. Prediction models for the biomass concentrations were estimated on the basis of the individual sensors and on combinations of the sensors. The results showed that the more advanced sensors based on MWF and scanning DE spectroscopy did not offer any advantages over the simpler sensors based on dual frequency DE spectroscopy, turbidity, and CER measurements for prediction of biomass concentration. By combining CER, DE spectroscopy, and turbidity measurements, the prediction error was reduced to 1.5 g/l, corresponding to 6% of the covered biomass range. Moreover, by using multiple sensors it was possible to check the quality of the individual predictions and switch between the sensors in real time.

In the biotechnological industry, economic decisions in investment are typically based on laboratory-scale experiments. Scale-down as a tool is therefore of high industrial importance to transfer the processes into larger production scale without loss in performance. In this study, large-scale prolonged continuous cultivations with a heterologous protein producing Saccharomyces cerevisiae strain have been scaled-down to a two-compartment scale-down reactor system. The effects of glucose, pH, and oxygen concentration gradients have been investigated by comparison with corresponding 300 mL standard continuous cultivations. It was found that substrate gradients within a limited range result in increased productivity of the heterologous protein under regulation of glycolytic TPI promoter and delay the decrease of protein and trehalose production during continuous cultivation. Based on these results, it is argued that introduction of variations in substrate concentration can be beneficial for industrial continuous cultivations.