File Entry: Aspartate Biosynthesis Pathway Simulation Using an Improved Differential Evolution Algorithm through Parameter Estimation

 An improved Differential Evolution algorithm (IDE) is proposed in this paper. It is aimed at enhancing its efficiency in estimating the relevant kinetic parameters for metabolic pathway data to simulate aspartate biosynthesis pathway for plant model Arabidopsis. Metabolic pathway data are anticipated to be of vital help in the development of effectual tools in parameter estimation and kinetic modeling platforms. However, a number of computation algorithms face difficulty, produces low accuracy results and longer computational time needed to estimate the relevant kinetic parameters, due to the complexity of the system and noisy data. A hybrid of a Differential Evolution algorithm (DE) and a Kalman Filter (KF), IDE, is proposed in this paper. The results of IDE are proven to be superior than DE and a Genetic Algorithm (GA). The outcome from this experiments show estimated optimal kinetic parameters values, better accuracy of simulated results (91.36% and 57.48% improved accuracy), and shorter computation time (6% and 4% time reduced) compared with DE and GA respectively. Beside, IDE shows it is a reliable algorithm which it passed the statistical test and constant standard deviation value close to 0 after 50 runs. We foresee the applicability of IDE into other metabolic pathway simulations.