TY - JOUR
T1 - Model-based analysis of biocatalytic processes and performance of microbioreactors with integrated optical sensors
AU - Semenova, Daria
AU - Fernandes, Ana C.
AU - Bolivar, Juan M
AU - Rosinha Grundtvig, Inês P.
AU - Vadot, Barbara
AU - Galvanin, Silvia
AU - Mayr, Torsten
AU - Nidetzky, Bernd
AU - Zubov, Alexandr
AU - Gernaey, Krist V.
PY - 2020
Y1 - 2020
N2 - Design and development of scale-down approaches, such as microbioreactor (µBR) technologies with integrated sensors, are an adequate solution for rapid, high-throughput and cost-effective screening of valuable reactions and/or production strains, with considerably reduced use of reagents and generation of waste. A significant challenge in the successful and widespread application of µBRs in biotechnology remains the lack of appropriate software and automated data interpretation of µBR experiments. Here, it is demonstrated how mathematical models can be usedas helpful tools, not only to exploit the capabilities of microfluidic platforms, but also to reveal the critical experimental conditions when monitoring cascade enzymatic reactions. A simplified mechanistic model was developed to describe the enzymatic reaction of glucose oxidase and glucose in the presence of catalase inside a commercial microfluidic platform with integrated oxygen sensor spots. The proposed model allowed an easy and rapid identification of the reaction mechanism, kinetics and limiting factors. The effect of fluid flow and enzyme adsorption inside the microfluidic chip on the optical sensor response and overall monitoring capabilities of the presented platform was evaluated via computational fluid dynamics (CFD) simulations. Remarkably, the model predictions were independently confirmed for µL- and mL- scale experiments. It is expected that the mechanistic models will significantly contribute to the further promotion of µBRs in biocatalysis research and that the overall study will create a framework for screening and evaluation of critical system parameters, including sensor response, operating conditions, experimental and microbioreactor designs.
AB - Design and development of scale-down approaches, such as microbioreactor (µBR) technologies with integrated sensors, are an adequate solution for rapid, high-throughput and cost-effective screening of valuable reactions and/or production strains, with considerably reduced use of reagents and generation of waste. A significant challenge in the successful and widespread application of µBRs in biotechnology remains the lack of appropriate software and automated data interpretation of µBR experiments. Here, it is demonstrated how mathematical models can be usedas helpful tools, not only to exploit the capabilities of microfluidic platforms, but also to reveal the critical experimental conditions when monitoring cascade enzymatic reactions. A simplified mechanistic model was developed to describe the enzymatic reaction of glucose oxidase and glucose in the presence of catalase inside a commercial microfluidic platform with integrated oxygen sensor spots. The proposed model allowed an easy and rapid identification of the reaction mechanism, kinetics and limiting factors. The effect of fluid flow and enzyme adsorption inside the microfluidic chip on the optical sensor response and overall monitoring capabilities of the presented platform was evaluated via computational fluid dynamics (CFD) simulations. Remarkably, the model predictions were independently confirmed for µL- and mL- scale experiments. It is expected that the mechanistic models will significantly contribute to the further promotion of µBRs in biocatalysis research and that the overall study will create a framework for screening and evaluation of critical system parameters, including sensor response, operating conditions, experimental and microbioreactor designs.
KW - Mechanistic modeling
KW - Computational Fluid Dynamics
KW - Microbioreactor
KW - Enzymatic biocatalysis
KW - Oxygen Monitoring
KW - Bioprocess Modeling
U2 - 10.1016/j.nbt.2019.11.001
DO - 10.1016/j.nbt.2019.11.001
M3 - Journal article
C2 - 31704414
SN - 1871-6784
VL - 56
SP - 27
EP - 37
JO - New Biotechnology
JF - New Biotechnology
ER -