Vol. 4 No. 1 (2024): Issue 4
Articles

Michaelis-Menten Kinetics using Adaptive Gaussian Process Regression

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  • Ji-hoon Kim,
  • Soo-young Park ,
  • Min-seo Lee
Ji-hoon Kim
Department of Biotechnology, Sunchon National University
Soo-young Park
Center for Mathematical Modeling and Simulation, Chungbuk Institute of Science and Technology
Min-seo Lee
Applied Sciences Research Center, Kyungnam Institute for Advanced Studies
DOI: https://doi.org/10.71070/jcbm.v4i1.97

Published 2024-08-15

Keywords

  • Enzyme Kinetics,
  • Michaelis-Menten Model,
  • Kinetic Parameters,
  • Adaptive Gaussian Process Regression,
  • Nonlinear Patterns

How to Cite

Kim, J.- hoon, Park , S.- young, & Lee , M.- seo. (2024). Michaelis-Menten Kinetics using Adaptive Gaussian Process Regression. Journal of Computational Biology and Medicine, 4(1). https://doi.org/10.71070/jcbm.v4i1.97

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.

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