Bioprocess scale-up/down as integrative enabling technology

from fluid mechanics to systems biology and beyond

Journal Article (2017)
Author(s)

Frank Delvigne (Sart Tilman B52)

Ralf Takors (University of Stuttgart)

RF Mudde (TU Delft - ImPhys/Imaging Physics, TU Delft - ChemE/Transport Phenomena)

W.M. van Gulik (TU Delft - OLD BT/Cell Systems Engineering)

Henk Noorman (DSM, TU Delft - BT/Bioprocess Engineering)

Research Group
ChemE/Transport Phenomena
Copyright
© 2017 Frank Delvigne, Ralf Takors, R.F. Mudde, W.M. van Gulik, H.J. Noorman
DOI related publication
https://doi.org/10.1111/1751-7915.12803
More Info
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Publication Year
2017
Language
English
Copyright
© 2017 Frank Delvigne, Ralf Takors, R.F. Mudde, W.M. van Gulik, H.J. Noorman
Research Group
ChemE/Transport Phenomena
Issue number
5
Volume number
10
Pages (from-to)
1267-1274
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Abstract

Efficient optimization of microbial processes is a critical issue for achieving a number of sustainable development goals, considering the impact of microbial biotechnology in agrofood, environment, biopharmaceutical and chemical industries. Many of these applications require scale-up after proof of concept. However, the behaviour of microbial systems remains unpredictable (at least partially) when shifting from laboratory-scale to industrial conditions. The need for robust microbial systems is thus highly needed in this context, as well as a better understanding of the interactions between fluid mechanics and cell physiology. For that purpose, a full scale-up/down computational framework is already available. This framework links computational fluid dynamics (CFD), metabolic flux analysis and agent-based modelling (ABM) for a better understanding of the cell lifelines in a heterogeneous environment. Ultimately, this framework can be used for the design of scale-down simulators and/or metabolically engineered cells able to cope with environmental fluctuations typically found in large-scale bioreactors. However, this framework still needs some refinements, such as a better integration of gas–liquid flows in CFD, and taking into account intrinsic biological noise in ABM.