The Development of Parameter Estimation Method for Chinese Hamster Ovary Model using Black Widow Optimization Algorithm

Nurul Aimi Munirah, Muhammad Akmal Remli, Noorlin Mohd Ali, Hui Wen Nies, Mohd Saberi Mohamad, Khairul Nizar Syazwan Wan Salihin Wong

Research output: Contribution to journalArticlepeer-review

11 Citations (Scopus)

Abstract

Chinese Hamster Ovary (CHO) cells are very famous in biological and medical research, especially in the protein production industry. It is because the characteristic of the cells with low chromosome numbers make it suitable for genetic study. However, all the data tends to be noisy and not fit. That is why many parameter estimation methods have been developed since their first introduction to determine the best value for a particular parameter. Metaheuristic parameter estimation is an algorithm framework that is processed using some technique to generate a pattern or graph. It will help the researcher get the fitted graph model, correct data, and estimate the value based on the data’s behaviour. This process started with implementing the parameter estimation that can be generated by using the combination of mathematical models and all the data obtained from the researcher’s experiments. This way, biomedical research’s cell culture can benefit from all this metaheuristic parameter estimation used. A kinetic model can estimate the data obtained from the Chinese Hamster Ovary (CHO) cells. Therefore, this paper proposed a Black Widow Optimisation (BWO) algorithm inspired by the bizarre mating behaviour of a spider as the method to use to solve the problem. The proposed algorithm has been compared with the other three famous algorithms, which are Particle Swarm Optimization (PSO), Differential Evolutionary (DE), and Bees Optimization Algorithm (BOA). The results showed that the proposed algorithm could get better value in terms of the best cost despite taking a long time to use.

Original languageEnglish
Pages (from-to)200-207
Number of pages8
JournalInternational Journal of Advanced Computer Science and Applications
Volume11
Issue number11
DOIs
Publication statusPublished - 2020
Externally publishedYes

Keywords

  • Black Widow optimization
  • Chinese Hamster Ovary
  • genetic study
  • metaheuristic
  • parameter estimation

ASJC Scopus subject areas

  • General Computer Science

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