A Computational Investigation of the Glycolysis Reaction–Diffusion System via Radial Basis Function Neural Networks

The metabolic process known as glycolysis, which occurs within the cytosol, is considered a fundamental model of a widely applicable pathway for fulfilling cellular energy needs. Its extensive presence suggests that glycolysis is among the earliest recognized metabolic routes, representing a prevalent method for supplying limited energy to organisms in the natural living environment. However, its importance is underscored by its ability to swiftly provide energy, particularly in oxygen-deprived scenarios, such as intense physical activity and high-altitude hypoxia. Glycolysis has emerged as an illustrative and classic system for biochemical reactions. The objective of this study was to evaluate the efficacy of a radial basis function neural network, which employs radial basis functions as activation functions, in conducting an in-depth mathematical examination of the general glycolysis model, particularly its spatiotemporal pattern formation. The glycolysis model was designed to compute pattern formation in glucose concentrations. The main objective is to compute the spatial and wavy patterns from the chemical concentration. Our findings also indicate that the radial basis function neural network is an excellent tool for analyzing models of this nature.