Computational Analysis of S-type Biological Systems

S-systems identify the direct interaction of genes and proteins in biological systems. Therefore, mathematical and computational analysis of the S-type models is important to achieve a true understanding of biological systems. However, theoretical approaches for Ssystems are limited, and computational identification of S-systems’ parameters is challenging. How to make a trade-off between accuracy and computational cost is in development. In this study, we first propose a memetic differential evolution scheme to identify the parameters of S-systems. The proposed scheme ameliorates the disadvantages in traditional gradient-based optimization methods, and solves the slow-convergence problem of stochastic algorithms. This scheme not only improves the global-search power of differential evolution (DE) but also largely increases the convergence speed. We then discuss and analyze the dynamic behavior of S-type biological systems. Four biological systems are used to demonstrate our approaches.