Open Access

Downloads

Download data is not yet available.

Abstract

In recombinant protein production, transferring a wild type gene of one organism into another expression host sometime resulted in a low gene expression due to incompatibility between the gene and the expression system. In that case, the target gene needed to be optimized to be more compatible with the expression system through gene optimization process in which nucleotide composition of original gene would be replaced by synonym codons while retaining the protein sequence. In existing gene optimization programs, many optimization algorithms have been applied, such as Genetic Algorithm or Sliding Window, to search for the optimized gene sequence. In this research, we applied the Ant Colony Optimization (ACO) algorithm to construct a gene optimization program. The results showed that the gene after optimization has been improved in codon usage, GC content and reduced the occurrence of factors reducing transcription and translation efficiencies such as polycodon, polynucleotide, repeated sequence, and Shine - Dalgarno sequence. Comparing with some current programs using a gene encoding for human insulin also proved the efficiency in the gene optimization this program. These results have demonstrated the capabilities of applying ACO algorithm in the gene optimization problem.



Author's Affiliation
Article Details

Issue: Vol 2 No 2 (2018)
Page No.: 22-30
Published: May 16, 2019
Section: Original Research
DOI: https://doi.org/10.32508/stdjns.v2i2.730

 Copyright Info

Creative Commons License

Copyright: The Authors. This is an open access article distributed under the terms of the Creative Commons Attribution License CC-BY 4.0., which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

 How to Cite
Vo, N., Le, L., Huynh, V., Tran, T., & Nguyen, H. (2019). Constructing a gene optimization program based on the ant colony optimization algorithm for Escherichia coli. Science & Technology Development Journal: Natural Sciences, 2(2), 22-30. https://doi.org/https://doi.org/10.32508/stdjns.v2i2.730

 Cited by



Article level Metrics by Paperbuzz/Impactstory
Article level Metrics by Altmetrics

 Article Statistics
HTML = 476 times
Download PDF   = 585 times
Total   = 585 times