Sensors 2010, 10(1), 330-341; doi:10.3390/s100100330
Article

An Evolution Based Biosensor Receptor DNA Sequence Generation Algorithm

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.
Received: 9 November 2009; in revised form: 30 November 2009 / Accepted: 21 December 2009 / Published: 31 December 2009
(This article belongs to the Special Issue Microbial Sensors and Biosensors)
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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.
Keywords: biosensor; DNA computing; DNA sequence; TSP (Traveling Salesman Problem); evolution programming

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MDPI and ACS Style

Kim, E.; Lee, M.; Gatton, T.M.; Lee, J.; Zang, Y. An Evolution Based Biosensor Receptor DNA Sequence Generation Algorithm. Sensors 2010, 10, 330-341.

AMA Style

Kim E, Lee M, Gatton TM, Lee J, Zang Y. An Evolution Based Biosensor Receptor DNA Sequence Generation Algorithm. Sensors. 2010; 10(1):330-341.

Chicago/Turabian Style

Kim, Eungyeong; Lee, Malrey; Gatton, Thomas M.; Lee, Jaewan; Zang, Yupeng. 2010. "An Evolution Based Biosensor Receptor DNA Sequence Generation Algorithm." Sensors 10, no. 1: 330-341.

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