UMP Institutional Repository

A non-dominated sorting differential search algorithm flux balance analysis (ndsDSAFBA) for in silico multiobjective optimization in identifying reactions knockout

Kauthar, Mohd Daud and Mohd Saberi, Mohamad and Zalmiyah, Zakaria and Rohayanti, Hassan and Zuraini, Ali Shah and Safaai, Deris and Zuwairie, Ibrahim and Suhaimi, Napis and Sinnott, Richard O. (2019) A non-dominated sorting differential search algorithm flux balance analysis (ndsDSAFBA) for in silico multiobjective optimization in identifying reactions knockout. Computers in Biology and Medicine, 113 (103390). pp. 1-2. ISSN 0010-4825

[img]
Preview
Pdf
A non-dominated sorting differential search algorithm flux balance.pdf

Download (353kB) | Preview

Abstract

Metabolic engineering is defined as improving the cellular activities of an organism by manipulating the metabolic, signal or regulatory network. In silico reaction knockout simulation is one of the techniques applied to analyse the effects of genetic perturbations on metabolite production. Many methods consider growth coupling as the objective function, whereby it searches for mutants that maximise the growth and production rate. However, the final goal is to increase the production rate. Furthermore, they produce one single solution, though in reality, cells do not focus on one objective and they need to consider various different competing objectives. In this work, a method, termed ndsDSAFBA (non-dominated sorting Differential Search Algorithm and Flux Balance Analysis), has been developed to find the reaction knockouts involved in maximising the production rate and growth rate of the mutant, by incorporating Pareto dominance concepts. The proposed ndsDSAFBA method was validated using three genome-scale metabolic models. We obtained a set of non-dominated solutions, with each solution representing a different mutant strain. The results obtained were compared with the single objective optimisation (SOO) and multi-objective optimisation (MOO) methods. The results demonstrate that ndsDSAFBA is better than the other methods in terms of production rate and growth rate.

Item Type: Article
Additional Information: Indexed by Scopus
Uncontrolled Keywords: Multi-objective evolutionary algorithms; Metabolic engineering; Flux balance analysis; Reaction knockout; Pareto dominance; Artificial intelligence; Bioinformatics
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
T Technology > TK Electrical engineering. Electronics Nuclear engineering
Faculty/Division: Faculty of Electrical & Electronic Engineering
Depositing User: Mrs Norsaini Abdul Samat
Date Deposited: 10 Dec 2019 02:07
Last Modified: 10 Dec 2019 02:07
URI: http://umpir.ump.edu.my/id/eprint/26602
Download Statistic: View Download Statistics

Actions (login required)

View Item View Item