Approximate Solutions of the Michaelis–Menten Nonlinear Biochemical Reaction Model Using Sigmoid-Weighted Neural Networks

Abstract

The Michaelis–Menten nonlinear biochemical reaction model is a simple and powerful framework to illustrate enzyme processes. In this research, we are interested in deriving an approximate solutions of this well known model. We perform a simple technique based on sigmoid-weighted neural network with back propagation algorithm. Differences between approximate solutions and the classical numerical methods are compared. The results ensure that the proposed method has high accuracy in comparison with the classical methods. Our results can be considered as a basis for investigating other biochemical reaction models within the future.