Abstract
We assess the effect of substrate heterogeneity on the metabolic response of P. chrysogenum in industrial bioreactors via the coupling of a 9-pool metabolic model with Euler-Lagrange CFD simulations. In this work, we outline how this coupled hydrodynamic-metabolic modeling can be utilized in 5 steps. (1) A model response study with a fixed spatial extra-cellular glucose concentration gradient, which reveals a drop in penicillin production rate qp of 18–50% for the simulated reactor, depending on model setup. (2) CFD-based scale-down design, where we design a 1-vessel scale down simulator based on the organism lifelines. (3) Scale-down verification, numerically comparing the model response in the proposed scale-down simulator with large-scale CFD response. (4) Reactor design optimization, reducing the drop in penicillin production by a change of feed location. (5) Long-term fed-batch simulation, where we verify model predictions against experimental data, and discuss population heterogeneity. Overall, these steps present a coupled hydrodynamic-metabolic approach towards bioreactor evaluation, scale-down and optimization.
Highlights
We used Euler-Lagrange computational fluid dynamics (CFD) to study the environmental fluctuations experienced by micro-organisms (Haringa et al, 2016), and showed how fluctuation statistics can be acquired from such simulations to guide scale-down (SD) simulator design (Haringa et al, 2017a)
We reported on the use of coupled hydrodynamic-metabolic simulations to assess large-scale fermentation processes in five parts: (I) industrial-scale metabolic response analysis, (II) scaledown design, (III) scale-down verification, (IV) design optimization and (V) industrial-scale fed-batch analysis
Numerical evaluation showed that the proposed scale-down design is predicted to accurately reflect the metabolic response recorded in the industrial reactor
Summary
We used Euler-Lagrange computational fluid dynamics (CFD) to study the environmental fluctuations experienced by micro-organisms (called lifelines) (Haringa et al, 2016), and showed how fluctuation statistics can be acquired from such simulations to guide scale-down (SD) simulator design (Haringa et al, 2017a). These works focused on simulation and fluctuation quantification using the substrate uptake (qs) lifeline, and did not quantitatively consider the metabolic response. We explore various aspects of the use of coupled hydrodynamic-metabolic modeling for process evaluation and optimization
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