Hindawi Publishing Corporation BioMed Research International Volume 2015, Article ID 124537, 10 pages http://dx.doi.org/10.1155/2015/124537 Research Article Gene Knockout Identification Using an Extension of Bees Hill Flux Balance Analysis Yee Wen Choon,1 Mohd Saberi Mohamad,1 Safaai Deris,1 Chuii Khim Chong,1 Sigeru Omatu,2 and Juan Manuel Corchado3 1 Artificial Intelligence and Bioinformatics Research Group, Faculty of Computing, Universiti Teknologi Malaysia, 81310 Skudai, Johor, Malaysia 2 Department of Electronics, Information and Communication Engineering, Osaka Institute of Technology, Osaka 535-8585, Japan 3 Biomedical Research Institute of Salamanca/BISITE Research Group, University of Salamanca, 37008 Salamanca, Spain Correspondence should be addressed to Mohd Saberi Mohamad;
[email protected] Received 21 August 2014; Revised 22 October 2014; Accepted 31 October 2014 Academic Editor: Juan F. De Paz Copyright © 2015 Yee Wen Choon et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Microbial strain optimisation for the overproduction of a desired phenotype has been a popular topic in recent years. Gene knockout is a genetic engineering technique that can modify the metabolism of microbial cells to obtain desirable phenotypes. Optimisation algorithms have been developed to identify the effects of gene knockout. However, the complexities of metabolic networks have made the process of identifying the effects of genetic modification on desirable phenotypes challenging. Furthermore, a vast number of reactions in cellular metabolism often lead to a combinatorial problem in obtaining optimal gene knockout.