TY - JOUR
T1 - Model-based analysis of biocatalytic processes and performance of microbioreactors with integrated optical sensors
AU - Semenova, D.
AU - Fernandes, A.C.
AU - Bolivar, J.M.
AU - Rosinha Grundtvig, I.P.
AU - Vadot, B.
AU - Galvanin, S.
AU - Mayr, T.
AU - Nidetzky, B.
AU - Zubov, A.
AU - Gernaey, K.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 - Bioprocess modeling
KW - Computational fluid dynamics
KW - Enzymatic biocatalysis
KW - Mechanistic modeling
KW - Microbioreactor
KW - Oxygen monitoring
UR - http://www.scopus.com/inward/record.url?scp=85075072141&partnerID=8YFLogxK
U2 - 10.1016/j.nbt.2019.11.001
DO - 10.1016/j.nbt.2019.11.001
M3 - Article
SN - 1871-6784
VL - 56
SP - 27
EP - 37
JO - New Biotechnology
JF - New Biotechnology
ER -