Abstract
The objective of the work is to offer workflow enabling us to execute both empirical and analytical studies of enzyme kinetics. For this purpose, on the one hand, we are based on a series of experimental research involving the traditional methods and techniques used when studying biochemical reactions and designing electrochemical biosensors: conductance research, spectroscopy, and electromagnetic field study.
On the other hand, when studying enzyme kinetics analytically we employ the Michaelis-Menten approach while modelling enzyme-substrate-inhibitor interactions and extend it to multi-substrate multi-inhibitor complexes.
Enforcing traditional Big Data workflow is offered with the help of meta-analysis facilities of existing repositories of biochemical studies located on the BRENDA platform.
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This work was supported by the European Union's Erasmus+ Program for Education under Key Action 2: Partnerships for Cooperation Grant (The Future is in Applied Artificial Intelligence) under Project 2022-1-PL01-KA220-HED-000088359. It was fulfilled within the framework of studies of the work package 2 "Good practices in the use of Artificial Intelligence and Machine Learning".
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Vasyl, M., Aleksandra, KW., Andrii, S. (2023). Electrochemical Biosensor Design Through Data-Driven Modeling Incorporating Meta-Analysis and Big Data Workflow. In: Rutkowski, L., Scherer, R., Korytkowski, M., Pedrycz, W., Tadeusiewicz, R., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing. ICAISC 2023. Lecture Notes in Computer Science(), vol 14126. Springer, Cham. https://doi.org/10.1007/978-3-031-42508-0_22
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