• 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.
  • 3 Soft Computing and Intelligent System Research Group, Faculty of Computer Systems and Software Engineering, Universiti Malaysia Pahang, 26300, Kuantan, Pahang, Malaysia
  • 4 Department of System Design Engineering, Faculty of Robotics & Design Engineering, Osaka Institute of Technology, Osaka, 535-8585, Japan
  • 5 School of Computing and Information Systems, University of Melbourne, Victoria, 3010, Australia
  • 6 Biomedical Research Institute of Salamanca/BISITE Research Group, University of Salamanca, Salamanca, 37007, Spain
Interdiscip Sci, 2019 Mar;11(1):33-44.
PMID: 30758766 DOI: 10.1007/s12539-019-00324-z


In recent years, metabolic engineering has gained central attention in numerous fields of science because of its capability to manipulate metabolic pathways in enhancing the expression of target phenotypes. Due to this, many computational approaches that perform genetic manipulation have been developed in the computational biology field. In metabolic engineering, conventional methods have been utilized to upgrade the generation of lactate and succinate in E. coli, although the yields produced are usually way below their theoretical maxima. To overcome the drawbacks  of such conventional methods, development of hybrid algorithm is introduced to obtain an optimal solution by proposing a gene knockout strategy in E. coli which is able to improve the production of lactate and succinate. The objective function of the hybrid algorithm is optimized using a swarm intelligence optimization algorithm and a Simple Constrained Artificial Bee Colony (SCABC) algorithm. The results maximize the production of lactate and succinate by resembling the gene knockout in E. coli. The Flux Balance Analysis (FBA) is integrated in a hybrid algorithm to evaluate the growth rate of E. coli as well as the productions of lactate and succinate. This results in the identification of a gene knockout list that contributes to maximizing the production of lactate and succinate in E. coli.

* Title and MeSH Headings from MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine.