Volume 20, Issue 9 (12-2017)
J Arak Uni Med Sci 2017, 20(9): 96-109 |
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Yahya T, Zare Karizi S, Jahanian A. Profiling the miRNAs for Early Cancer Detection using DNA-based Logic Gates . J Arak Uni Med Sci 2017; 20 (9) :96-109
URL: http://jams.arakmu.ac.ir/article-1-5191-en.html
URL: http://jams.arakmu.ac.ir/article-1-5191-en.html
1- Department of Biology, Faculty of Basic Sciences, Varamin-Pishva Branch, Islamic Azad University, Varamin, Iran
2- Department of Biology, Faculty of Basic Sciences, Varamin-Pishva Branch, Islamic Azad University, Varamin, Iran , shohrehzare@yahoo.com
3- Department of Computer Architecture, Faculty of Computer Science and Engineering, Shahid Beheshti University, Tehran, Iran
2- Department of Biology, Faculty of Basic Sciences, Varamin-Pishva Branch, Islamic Azad University, Varamin, Iran , shohrehzare@yahoo.com
3- Department of Computer Architecture, Faculty of Computer Science and Engineering, Shahid Beheshti University, Tehran, Iran
Abstract: (2725 Views)
Abstract
Background: DNA-based computing is an emerging research aspect that enables the in-vivo computation and decision making with significant correctness. Recent papers show that the expression level of miRNAs are related to the progress status of some diseases such as cancers and DNA computing is introduced as a low cost and concise technique for detection of these biomarkers. In this paper, DNA-based logic gates are implemented in the laboratory to detect the level of miR-21 as the biomarker of cancer.
Materials and Methods: At the first, required strands for designing DNA gates are synthesized. Then, double stranded gate is generated in laboratory using a temperature gradient that followed by electrophoresis process. This double strand is the computation engine for detecting the miR-21 biomarker. miR-21 is as input in designed gate. At the end, the expression level of miR-21 is identified by measuring the generated fluorescent.
Results: at the first stage, the proposed DNA-based logic gate is evaluated by using the synthesized input strands and then it is experimented on a tumor tissue. Experimental results on synthesized strands show that its detection quality/correctness is 2.5x better than conventional methods.
Conclusion: Experimental results on the tumor tissues are successful and are matched with those are extracted from real time PCR results. Also, the results show that this method is significantly more suitable than real time PCR in view of time and cost.
Background: DNA-based computing is an emerging research aspect that enables the in-vivo computation and decision making with significant correctness. Recent papers show that the expression level of miRNAs are related to the progress status of some diseases such as cancers and DNA computing is introduced as a low cost and concise technique for detection of these biomarkers. In this paper, DNA-based logic gates are implemented in the laboratory to detect the level of miR-21 as the biomarker of cancer.
Materials and Methods: At the first, required strands for designing DNA gates are synthesized. Then, double stranded gate is generated in laboratory using a temperature gradient that followed by electrophoresis process. This double strand is the computation engine for detecting the miR-21 biomarker. miR-21 is as input in designed gate. At the end, the expression level of miR-21 is identified by measuring the generated fluorescent.
Results: at the first stage, the proposed DNA-based logic gate is evaluated by using the synthesized input strands and then it is experimented on a tumor tissue. Experimental results on synthesized strands show that its detection quality/correctness is 2.5x better than conventional methods.
Conclusion: Experimental results on the tumor tissues are successful and are matched with those are extracted from real time PCR results. Also, the results show that this method is significantly more suitable than real time PCR in view of time and cost.
Type of Study: Original Atricle |
Subject:
Basic Sciences
Received: 2017/06/21 | Accepted: 2017/11/21
Received: 2017/06/21 | Accepted: 2017/11/21
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