Year: 2024
Type: article
Abstract: The Michaelis-Menten kinetics model plays a fundamental role in enzyme kinetics studies due to its ability to describe the rate of enzymatic reactions. However, traditional approaches to determine the kinetic parameters face limitations in accurately capturing the underlying complex biochemical dynamics. This paper addresses the current challenges in Michaelis-Menten kinetics research by leveraging Adaptive Gaussian Process Regression, a machine learning technique capable of adaptively learning complex nonlinear patterns. Our innovative approach offers a more flexible and data-driven method for estimating kinetic parameters, enhancing the accuracy and robustness of the model. By presenting a comprehensive analysis of the proposed method's performance, this study contributes to advancing the understanding and application of Michaelis-Menten kinetics in biochemical research.
Authors: Ji-hoon Kim, Soo-young Park, Min-seo Lee
Institution: Sunchon National University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W4410470734