A non-dominated sorting Differential Search Algorithm Flux Balance Analysis (ndsDSAFBA) for in silico multiobjective optimization in identifying reactions knockout

Kauthar Mohd Daud; Mohd Saberi Mohamad; Zalmiyah Zakaria; Rohayanti Hassan; Zuraini Ali Shah; Safaai Deris; Zuwairie Ibrahim; Suhaimi Napis; Richard O Sinnott

doi:10.1016/j.compbiomed.2019.103390

A non-dominated sorting Differential Search Algorithm Flux Balance Analysis (ndsDSAFBA) for in silico multiobjective optimization in identifying reactions knockout

Comput Biol Med. 2019 Oct:113:103390. doi: 10.1016/j.compbiomed.2019.103390. Epub 2019 Aug 16.

Authors

Kauthar Mohd Daud¹, Mohd Saberi Mohamad², Zalmiyah Zakaria¹, Rohayanti Hassan³, Zuraini Ali Shah¹, Safaai Deris⁴, Zuwairie Ibrahim⁵, Suhaimi Napis⁶, Richard O Sinnott⁷

Affiliations

¹ Artificial Intelligence and Bioinformatics Research Group, School of Computing, Faculty of Engineering, Universiti Teknologi Malaysia, 81310, Skudai, Johor, Malaysia.
² Institute for Artificial Intelligence and Big Data, Universiti Malaysia Kelantan, City Campus, Pengkalan Chepa, 16100, Kota Bharu, Kelantan, Malaysia; Faculty of Bioengineering and Technology, Universiti Malaysia Kelantan, Jeli Campus, Lock Bag 100, 17600, Jeli, Kelantan, Malaysia. Electronic address: saberi@umk.edu.my.
³ Software Engineering Research Group, School of Computing, Faculty of Engineering, Universiti Teknologi Malaysia, 81310, Johor Bahru, Malaysia.
⁴ Institute for Artificial Intelligence and Big Data, Universiti Malaysia Kelantan, City Campus, Pengkalan Chepa, 16100, Kota Bharu, Kelantan, Malaysia; Faculty of Bioengineering and Technology, Universiti Malaysia Kelantan, Jeli Campus, Lock Bag 100, 17600, Jeli, Kelantan, Malaysia.
⁵ Faculty of Electrical and Electronic Engineering, Universiti Malaysia Pahang, Pekan, Pahang, Malaysia.
⁶ Department of Cell and Molecular Biology, Faculty of Biotechnology and Biomolecular Sciences, Universiti Putra Malaysia, Universiti Putra Malaysia, 43400, Serdang, Selangor, Malaysia.
⁷ School of Computing and Information Systems, Melbourne School of Engineering, University of Melbourne, Victoria, 3010, Australia.

PMID: 31450056
DOI: 10.1016/j.compbiomed.2019.103390

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.

Keywords: Artificial intelligence; Bioinformatics; Flux balance analysis; Metabolic engineering; Multi-objective evolutionary algorithms; Pareto dominance; Reaction knockout.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Algorithms*
Computer Simulation*
Metabolic Engineering*
Models, Biological*