Analysis of a Fractional Order Enzymatic Reaction Model with Artificial Neural Network Validation

Authors

  • Asma . Department of Mathematics, COMSATS University of Islamabad, Sahiwal Campus, Punjab, Pakistan,
  • Israr Ahmad Governoment Post Graduate Jhanzeb College
  • Zeeshan Ali Department of Information Management, National Yunlin University of Science and Technology, Douliu, Yunlin, Taiwan, R.O.C
  • Saowaluck Chasreechai Department of Mathematics, Faculty of Applied Science, King Mongkuts University of Technology North Bangkok, Bangkok 10800, Thailand
  • Thanin Sitthiwirattham Mathematics Department, Faculty of Science and Technology, Suan Dusit University, Bangkok 10300, Thailand,

DOI:

https://doi.org/10.29020/nybg.ejpam.v18i4.6923

Keywords:

Enzymatic model; Qualitative study; Approximate solution; Neural network, validation; Sensitivity analysis

Abstract

This paper investigates an enzymatic reaction model formulated with the Atangana–Baleanu–Caputo (ABC) fractional derivative, aiming to enhance the classical description of enzyme kinetics. Existence and uniqueness of the solution are established through nonlinear functional analysis, while approximate solutions are obtained via the Laplace Adomian decomposition method (LADM). To validate and complement the numerical scheme, we employ a neural network framework that demonstrates the capability of intelligent computing to approximate fractional biochemical dynamics. Sensitivity analysis is carried out, and numerical simulations are illustrated through 2D and 3D plots. The study highlights how the combination of fractional calculus and neu-
ral networks offers new perspectives for modelling enzyme kinetics. The results indicate potential applications in drug development, metabolic engineering, and biochemical process optimization, providing a pathway for more precise control strategies in biochemical systems.

Author Biographies

  • Asma ., Department of Mathematics, COMSATS University of Islamabad, Sahiwal Campus, Punjab, Pakistan,

    Professor in Department of Mathematics, COMSATS University of Islamabad, Sahiwal Campus, Punjab, Pakistan,

  • Zeeshan Ali, Department of Information Management, National Yunlin University of Science and Technology, Douliu, Yunlin, Taiwan, R.O.C

    Associate professor in  Department of Information Management, National Yunlin University of Science and Technology, Douliu,
    Yunlin, Taiwan, R.O.C

  • Saowaluck Chasreechai, Department of Mathematics, Faculty of Applied Science, King Mongkuts University of Technology North Bangkok, Bangkok 10800, Thailand

    Professor in Department of Mathematics, Faculty of Applied Science, King Mongkuts University of Technology North
    Bangkok, Bangkok 10800, Thailand

  • Thanin Sitthiwirattham, Mathematics Department, Faculty of Science and Technology, Suan Dusit University, Bangkok 10300, Thailand,

    Professor in Mathematics Department, Faculty of Science and Technology, Suan Dusit University, Bangkok 10300,
    Thailand,

Downloads

Published

2025-11-05

Issue

Vol. 18 No. 4: (October 2025)

Section

Differential Equations

License

Copyright (c) 2025 Asma ., Israr Ahmad, Zeeshan Ali, Saowaluck Chasreechai, Thanin Sitthiwirattham

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

Upon acceptance of an article by the European Journal of Pure and Applied Mathematics, the author(s) retain the copyright to the article. However, by submitting your work, you agree that the article will be published under the Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0). This license allows others to copy, distribute, and adapt your work, provided proper attribution is given to the original author(s) and source. However, the work cannot be used for commercial purposes.

By agreeing to this statement, you acknowledge that:

  • You retain full copyright over your work.
  • The European Journal of Pure and Applied Mathematics will publish your work under the Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0).
  • This license allows others to use and share your work for non-commercial purposes, provided they give appropriate credit to the original author(s) and source.

How to Cite

Analysis of a Fractional Order Enzymatic Reaction Model with Artificial Neural Network Validation. (2025). European Journal of Pure and Applied Mathematics, 18(4), 6923. https://doi.org/10.29020/nybg.ejpam.v18i4.6923