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1

Advanced Graduate Education Center for Electronics of Jeonbuk and Information Technology-BK21, Jeonju, Jeonbuk, 561-756, Korea

2

The Research Center of Industrial Technology, School of Electronics & Information Engineering, ChonBuk National University, 664-14, 1Ga, DeokJin-Dong, JeonJu, ChonBuk, 561-756, Korea

3

The School of Engineering and Technology, National University, 11255 North Torrey Pines Road, La Jolla, CA 92037, USA

4

School of Electronics and Information Engineering, Kunsan National University, San 68, Miryoung-dong, Gunsan, Jeollabuk-do, 573-701, Korea





*

Author to whom correspondence should be addressed.



Abstract A biosensor is composed of a bioreceptor, an associated recognition molecule, and a signal transducer that can selectively detect target substances for analysis. DNA based biosensors utilize receptor molecules that allow hybridization with the target analyte. However, most DNA biosensor research uses oligonucleotides as the target analytes and does not address the potential problems of real samples. The identification of recognition molecules suitable for real target analyte samples is an important step towards further development of DNA biosensors. This study examines the characteristics of DNA used as bioreceptors and proposes a hybrid evolution-based DNA sequence generating algorithm, based on DNA computing, to identify suitable DNA bioreceptor recognition molecules for stable hybridization with real target substances. The Traveling Salesman Problem TSP approach is applied in the proposed algorithm to evaluate the safety and fitness of the generated DNA sequences. This approach improves efficiency and stability for enhanced and variable-length DNA sequence generation and allows extension to generation of variable-length DNA sequences with diverse receptor recognition requirements. View Full-Text

Keywords: biosensor; DNA computing; DNA sequence; TSP Traveling Salesman Problem; evolution programming biosensor; DNA computing; DNA sequence; TSP Traveling Salesman Problem; evolution programming





Author: Eungyeong Kim 1, Malrey Lee 2,* , Thomas M. Gatton 3, Jaewan Lee 4 and Yupeng Zang 2

Source: http://mdpi.com/



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