A hybrid of Cuckoo Search and Minimization of Metabolic Adjustment to optimize metabolites production in genome-scale models

Muhammad Azharuddin Arif, Mohd Saberi Mohamad, Muhammad Shafie Abd Latif, Safaai Deris, Muhammad Akmal Remli, Kauthar Mohd Daud, Zuwairie Ibrahim, Sigeru Omatu, Juan Manuel Corchado

Research output: Contribution to journalArticlepeer-review

8 Citations (Scopus)

Abstract

Metabolic engineering involves the modification and alteration of metabolic pathways to improve the production of desired substance. The modification can be made using in silico gene knockout simulation that is able to predict and analyse the disrupted genes which may enhance the metabolites production. Global optimization algorithms have been widely used for identifying gene knockout strategies. However, their productions were less than theoretical maximum and the algorithms are easily trapped into local optima. These algorithms also require a very large computation time to obtain acceptable results. This is due to the complexity of the metabolic models which are high dimensional and contain thousands of reactions. In this paper, a hybrid algorithm of Cuckoo Search and Minimization of Metabolic Adjustment is proposed to overcome the aforementioned problems. The hybrid algorithm searches for the near-optimal set of gene knockouts that leads to the overproduction of metabolites. Computational experiments on two sets of genome-scale metabolic models demonstrate that the proposed algorithm is better than the previous works in terms of growth rate, Biomass Product Couple Yield, and computation time.

Original languageEnglish
Pages (from-to)112-119
Number of pages8
JournalComputers in Biology and Medicine
Volume102
DOIs
Publication statusPublished - Nov 1 2018
Externally publishedYes

Keywords

  • Artificial intelligence
  • Bioinformatics
  • Cuckoo Search
  • Gene knockout
  • Metabolic engineering
  • Minimization of Metabolic Adjustment

ASJC Scopus subject areas

  • Computer Science Applications
  • Health Informatics

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