AI Article Synopsis
- A biosensor consists of a bioreceptor, a recognition molecule, and a signal transducer that help detect target substances, with DNA-based biosensors specifically using DNA for hybridization with analytes.
- Current DNA biosensor research tends to focus on simple oligonucleotides and often overlooks the complexities found in real sample situations, making it crucial to find appropriate recognition molecules for actual target analytes.
- This study introduces a hybrid evolution-based algorithm that utilizes DNA computing and the Traveling Salesman Problem (TSP) to develop and evaluate DNA sequences that act as bioreceptors, enhancing the efficiency and stability of DNA biosensors for various recognition requirements.
Article 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.
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|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3270844 | PMC |
| http://dx.doi.org/10.3390/s100100330 | DOI Listing |
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