Successful scale-up of bioprocesses requires that laboratory-scale performance is equally achieved during large-scale production to meet economic constraints. In industry, heuristic approaches are often applied, making use of physical scale-up criteria that do not consider cellular needs or properties. As a consequence, large-scale productivities, conversion yields, or product purities are often deteriorated, which may prevent economic success. The occurrence of population heterogeneity in large-scale production may be the reason for underperformance. In this study, an in silico method to predict the formation of population heterogeneity by combining computational fluid dynamics (CFD) with a cell cycle model of Pseudomonas putida KT2440 was developed. The glucose gradient and flow field of a 54,000 L stirred tank reactor were generated with the Euler approach, and bacterial movement was simulated as Lagrange particles. The latter were statistically evaluated using a cell cycle model. Accordingly, 72% of all cells were found to switch between standard and multifork replication, and 10% were likely to undergo massive, transcriptional adaptations to respond to extracellular starving conditions. At the same time, 56% of all cells replicated very fast, with µ ≥ 0.3 h−1 performing multifork replication. The population showed very strong heterogeneity, as indicated by the observation that 52.9% showed higher than average adenosine triphosphate (ATP) maintenance demands (12.2%, up to 1.5 fold). These results underline the potential of CFD linked to structured cell cycle models for predicting large-scale heterogeneity in silico and ab initio.
Predictability of k L a in stirred tank reactors under multiple operating conditions using an Euler-Lagrange approachIn industrial cell culture engineering, the production process consists of a multiscale seed train from lab scale to large scale. The oxygen demand of the cells has to be satisfied in all scales. Computational fluid dynamics (CFD) simulations can provide a tool to predict the mass transfer between the gas phase and the liquid phase. In this work, CFD was applied using an Euler-Lagrange approach for the prediction of the mass transfer coefficient (k L a) in stirred tank bioreactors for a wide range of operating conditions. The turbulent dissipation was studied for two different scales that show similar flow behavior. Breakup and coalescence of bubbles was not considered. A standard k − ε model was used for the simulation of turbulence and the mass transfer was assumed to be isotropic and turbulence driven. A minimalistic model was found, which was able to successfully predict the mass transfer behavior with high accuracy for the lab-scale bioreactor (2.3 L) covering a wide range of typical operating conditions. In the given setup, bubbles remained close to the sparger, almost not interfering with the impellers. This supports the assumption of monodisperse bubbles for stirrer speeds between 140 and 260 rpm. Simulation results of an 80 L stirred tank reactor (STR) revealed the need to integrate physical phenomena like breakup and coalescence and a more sophisticated prediction of the initial bubble size distribution. Two-phase Euler-Lagrange CFD simulations were performed for two differently scaled STRs and the mass transfer coefficient k L a was calculated and compared to experiments in order to evaluate the applied models.
The reduction of greenhouse gas emissions and future perspectives of circular economy ask for new solutions to produce commodities and fine chemicals. Large‐scale bubble columns operated by gaseous substrates such as CO, CO2, and H2 to feed acetogens for product formations could be promising approaches. Valid in silico predictions of large‐scale performance are needed to dimension bioreactors properly taking into account biological constraints, too. This contribution deals with the trade‐off between sophisticated spatiotemporally resolved large‐scale simulations using computationally intensive Euler–Euler and Euler–Lagrange approaches and coarse‐grained 1‐D models enabling fast performance evaluations. It is shown that proper consideration of gas hold‐up is key to predict biological performance. Intrinsic bias of 1‐D models can be compensated by reconsideration of Sauter diameters derived from uniquely performed Euler–Lagrange computational fluid dynamics.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.