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Bioengineering 2017, 4(2), 27; doi:10.3390/bioengineering4020027

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
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)
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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. 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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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Kuschel, M.; Siebler, F.; Takors, R. Lagrangian Trajectories to Predict the Formation of Population Heterogeneity in Large-Scale Bioreactors. Bioengineering 2017, 4, 27.

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