Modeling large-scale bioreactors with diffusion equations. Part II: Characterizing substrate, oxygen, temperature, carbon dioxide, and pH profiles

Large-scale fermentation processes involve complex dynamic interactions between mixing, reaction, mass transfer, and the suspended biomass. Empirical correlations or case-specific computational simulations are usually used to predict and estimate the performance of large-scale bioreactors based on data acquired at bench scale. In this two-part-study, one-dimensional axial diffusion equations were studied as a general and predictive model of large-scale bioreactors. This second part focused on typical fed-batch operations where substrate gradients are known to occur, and characterized the profiles of substrate, pH, oxygen, carbon dioxide, and temperature. The physically grounded steady-state axial diffusion equations with first- and zeroth-order kinetics yielded analytical solutions to the relevant variables. The results were compared with large-scale Escherichia coli and Saccharomyces cerevisiae experiments and simulations from the literature, and good agreement was found in substrate profiles. The analytical profiles obtained for dissolved oxygen, temperature, pH, andCO 2 ${\text{CO}}_{2}$ were also consistent with the available data. Distribution functions for the substrate were defined, and efficiency factors for biomass growth and oxygen uptake rate were derived. In conclusion, this study demonstrated that axial diffusion equations can be used to model the effects of mixing and reaction on the relevant variables of typical large-scale fed-batch fermentations.

Challenges and progress towards industrial recombinant protein production in yeasts: A review

Recombinant proteins (RP) are widely used as biopharmaceuticals, industrial enzymes, or sustainable food source. Yeasts, with their ability to produce complex proteins through a broad variety of cheap carbon sources, have emerged as promising eukaryotic production hosts. As such, the prevalence of yeasts as favourable production organisms in commercial RP production is expected to increase. Yet, with the selection of a robust production host on the one hand, successful scale-up is dependent on a thorough understanding of the challenging environment and limitations of large-scale bioreactors on the other hand. In the present work, several prominent yeast species, including Saccharomyces cerevisiae, Pichia pastoris, Yarrowia lipolytica, Kluyveromyces lactis and Kluyveromyces marxianus are reviewed for their current state and performance in commercial RP production. Thereafter, the impact of principal process control parameters, including dissolved oxygen, pH, substrate concentration, and temperature, on large-scale RP production are discussed. Finally, technical challenges of process scale-up are identified. To that end, process intensification strategies to enhance industrial feasibility are summarized, specifically highlighting fermentation strategies to ensure sufficient cooling capacity, overcome oxygen limitation, and increase protein quality and productivity. As such, this review aims to contribute to the pursuit of sustainable yeast-based RP production.

Dynamic Interplay between O2 Availability, Growth Rates, and the Transcriptome of Yarrowia lipolytica

Industrial-sized fermenters differ from the laboratory environment in which bioprocess development initially took place. One of the issues that can lead to reduced productivity on a large scale or even early termination of the process is the presence of bioreactor heterogeneities. This work proposes and adopts a design–build–test–learn-type workflow that estimates the substrate, oxygen, and resulting growth heterogeneities through a compartmental modelling approach and maps Yarrowia lipolytica-specific behavior in this relevant range of conditions. The results indicate that at a growth rate of 0.1 h−1, the largest simulated volume (90 m3) reached partial oxygen limitation. Throughout the fed-batch, the cells experienced dissolved oxygen values from 0 to 75% and grew at rates of 0 to 0.2 h−1. These simulated large-scale conditions were tested in small-scale cultivations, which elucidated a transcriptome with a strong downregulation of various transporter and central carbon metabolism genes during oxygen limitation. The relation between oxygen availability and differential gene expression was dynamic and did not show a simple on–off behavior. This indicates that Y. lipolytica can differentiate between different available oxygen concentrations and adjust its transcription accordingly. The workflow presented can be used for Y. lipolytica-based strain engineering, thereby accelerating bioprocess development.

Single-Cell Technologies to Understand the Mechanisms of Cellular Adaptation in Chemostats

There is a growing interest in continuous manufacturing within the bioprocessing community. In this context, the chemostat process is an important unit operation. The current application of chemostat processes in industry is limited although many high yielding processes are reported in literature. In order to reach the full potential of the chemostat in continuous manufacture, the output should be constant. However, adaptation is often observed resulting in changed productivities over time. The observed adaptation can be coupled to the selective pressure of the nutrient-limited environment in the chemostat. We argue that population heterogeneity should be taken into account when studying adaptation in the chemostat. We propose to investigate adaptation at the single-cell level and discuss the potential of different single-cell technologies, which could be used to increase the understanding of the phenomena. Currently, none of the discussed single-cell technologies fulfill all our criteria but in combination they may reveal important information, which can be used to understand and potentially control the adaptation.