%0 Journal Article %1 bioengineering4020027 %A Kuschel, Maike %A Siebler, Flora %A Takors, Ralf %D 2017 %J Bioengineering %K myown %N 2 %P 27 %R 10.3390/bioengineering4020027 %T Lagrangian Trajectories to Predict the Formation of Population Heterogeneity in Large-Scale Bioreactors %U http://www.mdpi.com/2306-5354/4/2/27 %V 4 %X 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.

@article{bioengineering4020027, abstract = {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.}, added-at = {2018-06-08T11:25:53.000+0200}, author = {Kuschel, Maike and Siebler, Flora and Takors, Ralf}, biburl = {https://puma.ub.uni-stuttgart.de/bibtex/219057f22a1283d5e129319f911e23a9e/ralftakors}, description = {Bioengineering | Free Full-Text | Lagrangian Trajectories to Predict the Formation of Population Heterogeneity in Large-Scale Bioreactors}, doi = {10.3390/bioengineering4020027}, interhash = {3ed97c8866d919d8ba0f19073381a239}, intrahash = {19057f22a1283d5e129319f911e23a9e}, issn = {2306-5354}, journal = {Bioengineering}, keywords = {myown}, number = 2, pages = 27, timestamp = {2018-06-08T09:25:53.000+0200}, title = {Lagrangian Trajectories to Predict the Formation of Population Heterogeneity in Large-Scale Bioreactors}, url = {http://www.mdpi.com/2306-5354/4/2/27}, volume = 4, year = 2017 }