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Bioprocess scale‐up/down as integrative enabling technology: from fluid mechanics to systems biology and beyond

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...

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Detalles Bibliográficos
Autores principales: Delvigne, Frank, Takors, Ralf, Mudde, Rob, van Gulik, Walter, Noorman, Henk
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5609235/
https://www.ncbi.nlm.nih.gov/pubmed/28805306
http://dx.doi.org/10.1111/1751-7915.12803
Descripción
Sumario: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.