An enhanced scatter search with combined opposition-based learning for parameter estimation in large-scale kinetic models of biochemical systems

Muhammad Akmal Remli, Safaai Deris, Mohd Saberi Mohamad, Sigeru Omatu, Juan Manuel Corchado

Research output: Contribution to journalArticlepeer-review

25 Citations (Scopus)

Abstract

An enhanced scatter search (eSS) with combined opposition-based learning algorithm is proposed to solve large-scale parameter estimation in kinetic models of biochemical systems. The proposed algorithm is an extension of eSS with three important improvements in terms of: reference set (RefSet) formation, RefSet combination, and RefSet intensification. Due to the difficulty in estimating kinetic parameter values in the presence of noise and large number of parameters (high-dimension), the aforementioned eSS mechanisms have been improved using combination of quasi-opposition and quasi-reflection, which were under the family of opposition-based learning scheme. The proposed algorithm is tested using one set of benchmark function each from large-scale global optimization (LSGO) problem as well as parameter estimation problem. The LSGO problem consists of 11 functions with 1000 dimensions. For parameter estimation, around 116 kinetic parameters in Chinese hamster ovary (CHO) cells and central carbon metabolism of E. coli are estimated. The results revealed that the proposed algorithm is superior to eSS and other competitive algorithms in terms of its efficiency in minimizing objective function value and having faster convergence rate. The proposed algorithm also required lower computational resources, especially number of function evaluations performed and computation time. In addition, the estimated kinetic parameter values obtained from the proposed algorithm produced the best fit to a set of experimental data.

Original languageEnglish
Pages (from-to)164-180
Number of pages17
JournalEngineering Applications of Artificial Intelligence
Volume62
DOIs
Publication statusPublished - Jun 1 2017
Externally publishedYes

Keywords

  • Artificial intelligence
  • Bioinformatics
  • Evolutionary algorithm
  • Metabolic engineering
  • Opposition-based learning
  • Scatter search

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Artificial Intelligence
  • Electrical and Electronic Engineering

Fingerprint

Dive into the research topics of 'An enhanced scatter search with combined opposition-based learning for parameter estimation in large-scale kinetic models of biochemical systems'. Together they form a unique fingerprint.

Cite this