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

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.

Figure 1

Computational framework used for scaling‐up/down studies. A concentration gradient flow field is simulated based on the numerical resolution of the Navier–Stokes equations based on a Eulerian reference. Cell trajectories can be simulated with the same model translated in a Lagrangian reference. Superposition leads to a cell lifelines distribution. An agent‐based model incorporating a relevant metabolic network is finally needed for the precise simulation of the environmental impact on the state of individual cells. The panel showing the Lagrangian reference has been adapted from Kuschel et al. (2017).

Figure 2

Scale‐down devices using the stirred tank reactor (STR) as an ideally mixed compartment connected to other STRs or plug‐flow reactors (PFR). Built‐in components (BIC) to reduce homogeneity also have been applied. Furthermore, large‐scale stimuli can be reflected in STR and monitored in rapid sampling (RS) approaches.

Figure 3

Experimental and modelling approaches are needed for incorporating microbial individuality in the scale‐up/down computational framework.