Analysis of a Fractional Order Enzymatic Reaction Model with Artificial Neural Network Validation (European Journal of Pure and Applied Mathematics, 2025)
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.
Keywords: Enzymatic model; Qualitative study; Approximate solution; Neural network; validation; Sensitivity analysis