Analysis of Heterogeneities in a 20 L Bioreactor

Abstract

Biological systems are utilized in various industries to produce valuable products, including biopharmaceuticals. This is done in bioreactors, which are specialized vessels that are able to precisely control key parameters, including agitation, air flow, temperature, pH, dissolved oxygen, and nutrient supply. With the high demands for biopharmaceuticals caused by advancements in medicine, the need for efficient production and optimization of bioreactors has been evident. This has been especially seen during the COVID-19 pandemic, and the high costs of some products, which are inaccessible to many individuals. To optimize production, simulation models have been developed to predict effective control schemes for high growth and product yield. However, this is challenging to translate between lab-scale and industrial-scale due to the formation of gradients in industrial-scale systems, which have poor mixing. Gradients lower the efficiency of bioreactors as cells must constantly adapt to changing extracellular conditions, which cause stress and lower yields. Thus, it is necessary to validate simulation models using the gradients formed in large-scale bioreactors; however, this data is not readily available, and it is difficult to obtain such gradients in smaller-scale bioreactors.

In this work, fed-batch experiments are studied to investigate the formation of gradients in dissolved oxygen, kLa, pH, cell density, glucose, and acetate concentrations. This was done through the movement of sensors, turning the air on and off, and the usage of different sampling locations. The objectives of this work were first to characterize the culture with flask and batch experiments and then to use this information to carry out the fed-batch experiments to explore the potential of measuring these gradients. Dynamic metabolic responses were observed and measured depending on the control of the glucose feeding, and consistent gradients were observed for the dissolved oxygen, pH, and kLa, while gradients for cell density, glucose, and acetate were not observed, which may be due to limitations in sampling times.

Finally, the metabolic responses have been modeled using modified Monod kinetics, where the modifications include self-growth inhibition, an acetate metabolic switch, and a cell density-dependent lag function. This work was done using a genetic algorithm on Python to optimize parameters, and the model was able to adapt to the different extracellular conditions presented in the fed-batch experiments.