Inverse problems of biological systems using multi-objective optimization

Mathematical modeling for dynamic biological systems is a central theme in systems biology. There are still many challenges in using time-course data to obtain an inverse problem of nonlinear dynamic biological systems. In this study, a multi-objective optimization technique is introduced to determine kinetic parameter values of biochemical reaction systems. The multi-objective parameter estimation was converted into the minimax problem through the satisfying trade-off method. The aspiration value was assigned as the minimum solution to the corresponding single objective estimation. The aim of this trade-off estimation was to obtain a compromised result by simultaneously minimizing both concentration and slope error criteria. Hybrid differential evolution was applied to solve the minimax problem and to yield a global estimation.