Deep Neural Network-Based Simulation of Sel’kov Model in Glycolysis: A Comprehensive Analysis

Abstract

The Sel’kov model for glycolysis is a highly effective tool in capturing the complex feedback mechanisms that occur within a biochemical system. However, accurately predicting the behavior of this system is challenging due to its nonlinearity, stiffness, and parameter sensitivity. In this paper, we present a novel deep neural network-based method to simulate the Sel’kov glycolysis model of ADP and F6P, which overcomes the limitations of conventional numerical methods. Our comprehensive results demonstrate that the proposed approach outperforms traditional methods and offers greater reliability for nonlinear dynamics. By adopting this flexible and robust technique, researchers can gain deeper insights into the complex interactions that drive biochemical systems.