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Open AccessArticle

Lagrangian Trajectories to Predict the Formation of Population Heterogeneity in Large-Scale Bioreactors

Institute of Biochemical Engineering, University of Stuttgart, 70569 Stuttgart, Germany
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Academic Editor: Christoph Herwig
Bioengineering 2017, 4(2), 27; https://doi.org/10.3390/bioengineering4020027
Received: 23 February 2017 / Revised: 23 March 2017 / Accepted: 24 March 2017 / Published: 29 March 2017
(This article belongs to the Special Issue Hybrid Modelling and Multi-Parametric Control of Bioprocesses)
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. View Full-Text
Keywords: computational fluid dynamics; cell cycle model; Lagrange trajectory; scale-up; stirred tank reactor; population dynamics; energy level computational fluid dynamics; cell cycle model; Lagrange trajectory; scale-up; stirred tank reactor; population dynamics; energy level
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MDPI and ACS Style

Kuschel, M.; Siebler, F.; Takors, R. Lagrangian Trajectories to Predict the Formation of Population Heterogeneity in Large-Scale Bioreactors. Bioengineering 2017, 4, 27. https://doi.org/10.3390/bioengineering4020027

AMA Style

Kuschel M, Siebler F, Takors R. Lagrangian Trajectories to Predict the Formation of Population Heterogeneity in Large-Scale Bioreactors. Bioengineering. 2017; 4(2):27. https://doi.org/10.3390/bioengineering4020027

Chicago/Turabian Style

Kuschel, Maike; Siebler, Flora; Takors, Ralf. 2017. "Lagrangian Trajectories to Predict the Formation of Population Heterogeneity in Large-Scale Bioreactors" Bioengineering 4, no. 2: 27. https://doi.org/10.3390/bioengineering4020027

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