Michaelis-Menten Kinetics using Adaptive Gaussian Process Regression

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.