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

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.

@article{Munirah2020,
  title     = {The Development of Parameter Estimation Method for Chinese Hamster Ovary Model using Black Widow Optimization Algorithm},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {11},
  number    = {11},
  year      = {2020},
  publisher = {The Science and Information Organization},
  author    = {Nurul Aimi Munirah and Muhammad Akmal Remli and Noorlin Mohd Ali and Hui Wen Nies and Mohd Saberi Mohamad and Khairul Nizar Syazwan Wan Salihin Wong},
  doi       = {10.14569/IJACSA.2020.0111126},
  url       = {https://doi.org/10.14569/IJACSA.2020.0111126}
}