Parameter Estimation in Computational Systems Biology Models: A Comparative Study of Initialization Methods in Global Optimization

Abstract

This paper compares different initialization methods and investigates their performance and effects on estimating kinetic parameters’ value in models of biological systems. Estimating parameters values is difficult and time-consuming process due to their highly nonlinear and huge number of kinetic parameters involved. Global optimization method based on an enhanced scatter search (ESS) algorithm is a suitable choice to address this issue. However, despite its resounding success, the performance of ESS may decrease in solving high dimension problem. In this work, several choices of initialization methods are compared and experimental results indicated that the algorithm is sensitive to the initial value of kinetic parameters. Statistical results revealed that uniformly distributed random number generator (RNG) and controlled randomization (CR) that being used in ESS may lead to poor algorithm performance. In addition, the different initialization methods also influenced model accuracy. Our proposed methodology shows that initialization based on opposition-based learning scheme have shown 10% better accuracy in term of cost function.