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

Abstract

SummaryEfficient 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.

Modelling concentration gradients in fed‐batch cultivations of E. coli – towards the flexible design of scale‐down experiments

“…the strength of concentration gradients; and (iii) the number of times a particular cell goes through a cycle in the PFR, which is the frequency of exposure to the stress. When the exposure time is of the same or a few orders of magnitude as the characteristic growth time (1/µ), there is a marked influence of the stress on both physiological and metabolic, as well as transcriptional processes in the cell . Therefore, it is important to determine, in the simulations of the 2CR, how the recycling rate affects both the frequency and magnitude of glucose gradients that the cells are exposed to.…”

“…These operation mechanisms produce zones similar to feeding and starvation zones in large‐scale bioreactors and result in periodic exposure of the culture to varying stresses . Scale‐down techniques have been applied for the successful study of the impact of large‐scale gradients for most industrially relevant organisms, with significant differences in process behaviour compared with standard small‐scale cultivations …”

Using gas mixtures of CO, CO2 and H2 as microbial substrates: the do's and don'ts of successful technology transfer from laboratory to production scale

“…The successful solution of this challenge asks for improvements in strain and process engineering. Intrinsic problems of mass transfer and bioreactor design need to be solved which demands for the application of novel knowledge-based scale-up approach as already outlined in Takors (2012) and Delvigne et al (2017). Besides, the economic access to electron-donating substrates (CO, H 2 ) is a prerequisite of every industrial process.…”

Coupled metabolic‐hydrodynamic modeling enabling rational scale‐up of industrial bioprocesses

“…Thus, new avenues via manipulating metabolite dynamics and cell heterogeneity can be implemented to boost ensemble strain performance (A. C. Schmitz et al, ). Bioreactor scale‐down holds great promise for the generation of actionable knowledge to address the effects of scale‐related factors (Delvigne, Takors, Mudde, van Gulik, & Noorman, ; Wang et al, ). Scale‐down studies relating to diverse environmental gradients such as for substrate, dissolved oxygen, dissolved carbon dioxide, and pH, have been performed on bacteria, yeasts, and filamentous fungi as well as mammalian cells (Lara et al, ).…”

“…On the other side, it will be highly valuable for such an industrial process if a proper model can describe such a scenario and provide explicit and quantitative suggestions on the strain and process improvement. The integration of microbial kinetics and fluid dynamics offered great potential in describing details in a hundred‐ton fermentor (Delvigne et al, ; Haringa et al, ). For instance, Lapin, Muller, and Reuss () and Lapin, Schmid, and Reuss ()) first successfully combined the CFD framework with an E. coli kinetic model.…”