Using an improved bee memory differential evolution algorithm for parameter estimation to simulate biochemical pathways

Chuii Khim Chong, Mohd Saberi Mohamad, Safaai Deris, Mohd Shahir Shamsir, Lian En Chai, Yee Wen Choon

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)


When analyzing a metabolic pathway in a mathematical model, it is important that the essential parameters are estimated correctly. However, this process often faces few problems like when the number of unknown parameters increase, trapping of data in the local minima, repeated exposure to bad results during the search process and occurrence of noisy data. Thus, this paper intends to present an improved bee memory differential evolution (IBMDE) algorithm to solve the mentioned problems. This is a hybrid algorithm that combines the differential evolution (DE) algorithm, the Kalman filter, artificial bee colony (ABC) algorithm, and a memory feature. The aspartate and threonine biosynthesis pathway, and cell cycle pathway are the metabolic pathways used in this paper. For three production simulation pathways, the IBMDE managed to robustly produce the estimated optimal kinetic parameter values with significantly reduced errors. Besides, it also demonstrated faster convergence time compared to the Nelder-Mead (NM), simulated annealing (SA), the genetic algorithm (GA) and DE, respectively. Most importantly, the kinetic parameters that were generated by the IBMDE have improved the production rates of desired metabolites better than other estimation algorithms. Meanwhile, the results proved that the IBMDE is a reliable estimation algorithm.

Original languageEnglish
Pages (from-to)101-121
Number of pages21
JournalJournal of Biological Systems
Issue number1
Publication statusPublished - Mar 2014
Externally publishedYes


  • Artificial Bee Colony Algorithm
  • Differential Evolution Algorithm
  • Kalman Filter
  • Memory Feature
  • Parameter Estimation

ASJC Scopus subject areas

  • Ecology
  • Agricultural and Biological Sciences (miscellaneous)
  • Applied Mathematics


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