Fourier-enhanced Neural Networks For Systems Biology Applications

TL;DR

This paper introduces SB-FNN, a Fourier-enhanced neural network that improves accuracy and efficiency in modeling complex biological systems with oscillatory behavior, outperforming physics-informed neural networks.

Contribution

The paper proposes SB-FNN, a novel Fourier-enhanced neural network with adaptive activation and cyclic penalty functions for better biological system modeling.

Findings

SB-FNN outperforms PINN in accuracy on six biological models.

SB-FNN demonstrates higher efficiency than PINN.

SB-FNN effectively models oscillatory biological dynamics.

Abstract

In the field of systems biology, differential equations are commonly used to model biological systems, but solving them for large-scale and complex systems can be computationally expensive. Recently, the integration of machine learning and mathematical modeling has offered new opportunities for scientific discoveries in biology and health. The emerging physics-informed neural network (PINN) has been proposed as a solution to this problem. However, PINN can be computationally expensive and unreliable for complex biological systems. To address these issues, we propose the Fourier-enhanced Neural Networks for systems biology (SB-FNN). SB-FNN uses an embedded Fourier neural network with an adaptive activation function and a cyclic penalty function to optimize the prediction of biological dynamics, particularly for biological systems that exhibit oscillatory patterns. Experimental results demonstrate that SB-FNN achieves better performance and is more efficient than PINN for handling complex biological models. Experimental results on cellular and population models demonstrate that SB-FNN outperforms PINN in both accuracy and efficiency, making it a promising alternative approach for handling complex biological models. The proposed method achieved better performance on six biological models and is expected to replace PINN as the most advanced method in systems biology.