An intelligent framework for modeling nonlinear irreversible biochemical reactions using artificial neural networks

Area of Science:

• Biochemistry
• Computational Biology
• Biophysics

Background:

• Nonlinear irreversible biochemical reactions (NIBR) are fundamental to biological processes.
• Modeling NIBR accurately is crucial for understanding cellular mechanisms.
• Existing models may face challenges with complex reaction kinetics.

Purpose of the Study:

• To develop an intelligent computational framework for modeling NIBR.
• To utilize artificial neural networks (ANNs) for simulating biochemical reaction dynamics.
• To compare the performance of different ANN training algorithms for NIBR modeling.

Main Methods:

• Biochemical reactions modeled using an extended Michaelis-Menten kinetic scheme and nonlinear ordinary differential equations (ODEs).
• Datasets generated via the Runge-Kutta 4th order (RK4) method.
• Multilayer feedforward ANNs trained using the Backpropagation Levenberg-Marquardt (BLM) algorithm, compared with Bayesian Regularization (BR) and Scaled Conjugate Gradient (SCG).
• Model validation across six kinetic scenarios with varying rate constants.

Main Results:

• The BLM-ANN model significantly outperformed BR and SCG in accuracy, convergence speed, and robustness.
• Mean Squared Error (MSE) as low as [Formula: see text] achieved by the BLM-ANN model.
• High correlation between BLM-ANN predictions and RK4 solutions confirmed by regression plots.
• Error distributions validated the model's predictive capability.

Conclusions:

• The proposed BLM-ANN framework offers a highly accurate and reliable method for modeling NIBR.
• The framework demonstrates excellent generalization capability across diverse kinetic profiles.
• This approach provides a powerful computational tool for biochemical systems analysis.