طراحی و شبیه‌سازی پردازنده‌ی مولکولی با کاشی‌های خودسامانی مبتنی بر دی‌ان‌ای جهت تشخیص ریزرِناها
محورهای موضوعی : مهندسی برق و کامپیوتر
1 - دانشكده مهندسی كامپيوتر، دانشگاه اصفهان، اصفهان، ایران
کلید واژه: پردازنده‌های مولکولی, تشخیص زودهنگام بیماری, خودسامانی رشته‌های دِنا, محاسبات مبتنی بر دِنا,
چکیده مقاله :
امروزه طراحی پردازشگرهای مولکولی جهت تشخیص بیماری‌ها از جمله تشخیص زودهنگام سرطان به یکی از حوزه‌ی داغ تحقیقاتی و فناوری تبدیل شده است. در این مقاله پردازنده‌های مولکولی مبتنی بر دنا معرفی شده است. ابتدا کاشی‌های خودسامانی کنترل پذیر و قابل گسترش طراحی شده. در این کاشی‌ها که با استفاده از رشته‌های سنجاق‌سر پیاده‌سازی شده است امکان دریافت تک رشته‌های ورودی و تولید خروجی قابل استفاده در مراحل بعد و اتصال آبشاری بین سطوح مختلف فراهم شده است. در گام بعدی با استفاده از کاشی‌های طراحی شده دروازه‌های منطقی پایه (AND ,OR ,NOT) طراحی شده و پردازشگری جهت تشخیص ۱۲ ریزرِنای با هدف تشخیص زودهنگام سرطان پیاده‌سازی شد؛ شبیه‌سازی‌ها با ابزار VisualDSD و با مدل قطعی انجام شده‌اند. نتایج شبیه‌سازی برای مجموعه‌ی آزمایشی مدنظر دارای 91/0 ≈PPV و 99/0≈NPV است که نشان از عملکرد مناسب پردازشگر طراحی شده دارد.
Nowdays, the design of molecular processors for disease diagnosis, particularly early cancer detection, has become one of the most active research and technology areas. In this paper, DNA-based molecular processors are introduced. First, controllable and scalable self-assembly tiles were designed. These tiles, implemented using hairpin DNA strands, are capable of receiving single-stranded inputs, generating outputs that can be utilized in subsequent steps, and enabling cascading connections across different levels. In the next step, basic logic gates (AND, OR, NOT) were constructed using the designed tiles, and a processor for detecting 12 microRNAs, aimed at early cancer diagnosis, was implemented. Simulations were performed using the VisualDSD tool under a deterministic model. The simulation results for the considered test set achieved PPV ≈ 0.91 and NPV ≈ 0.99, demonstrating the promising performance of the designed processor.
[1] Jia, Sisi, Hui Lv, Qian Li, Chunhai Fan, and Fei Wang. "DNA-based biocomputing circuits and their biomedical applications." Nature Reviews Bioengineering (2025): 1-14.
[2] World Health Organization. "International Agency for Research on Cancer". GLOBAL CAN-CER OBSERVATORY, GLOBOCAN 2022. https://gco.iarc.who.int/media/globocan/factsheets/populations/900-world-fact-sheet.pdf
[3] Nelson, D. L., Cox, M. M. (2021). Lehninger Principles of Biochemistry (8th Edition). W.H. Freeman and Company. ISBN: 978-1319108243 https://
www.whfreeman.com/lehninger8e [4] Hu, Yangyang, Guangyu Jiang, Yalun Wen, Yuchen Shao, Ge Yang, and Feng Qu. "Selection of aptamers targeting small molecules by capillary electrophoresis: advances, challenges, and prospects." Biotechnology Advances 78 (2025): 108491.
[5] Fang, Hong, Sa A. Wang, L. Jeffrey Medeiros, and Wei Wang. "Application of flow cytometry immunophenotypic analysis for the diagnosis of mature B-cell lymphomas/leukemias." Human Pathology 156 (2025): 105711.
[6] Unnikrishnan, Greeshma, Abhinav Singh, and Abhishek Purohit. "Diagnostic accuracy of polymerase chain reaction for detection of mpox in humans." Revista Panamericana de Salud Pública 48 (2025): e131.
[7] Alberts, Bruce, Alexander Johnson, Julian Lewis, Martin Raff, Keith Roberts, and Peter Walter. Molecular Biology of the Cell. 6th ed. New York: Garland Science, 2014. Chapter 7, “RNA and the Flow of Genetic Information.” https://www.ncbi.nlm.nih.gov/books/NBK21054/
[8] Bennett, Charles H. "Logical Reversibility of Computation." IBM Journal of Research and Development 17, no. 6 (1973): 525–532. https://doi.org/10.1147/
rd.176.052 [9] Macía, Javier, Francesc Posas, and Ricard Solé. "Distributed Computation: The New Wave of Synthetic Biology Devices." Trends in Biotechnology 30, no. 6 (2012): 342–349.
[10] J. Hemphill and A. Deiters, “DNA computation in mammalian cells: microRNA logic operations,” J. Am. Chem. Soc., vol. 135, no. 28, pp. 10512–10518, 2013.
[11] M. Yazdani, Z. Beiki, and A. Jahanian, “RNA secondary structured logic gates for profiling the microRNA cancer biomarkers,” IET nanobiotechnology, vol. 14, no. 3, pp. 181–190, 2020.
[12] M. Sanjabi and A. Jahanian, “Analytical design of multi-threshold and high fan-in DNA-based logical sensors to profile the pattern of MS microRNAs,” Biomed. Eng. Lett., vol. 11, no. 2, pp. 131–145, 2021.
[13] M. Sanjabi and A. Jahanian, “Multi-threshold and multi-input DNA logic design style for profiling the microRNA biomarkers of real cancers,” IET nanobiotechnology, vol. 13, no. 7, pp. 665–673, 2019.
[14] Y. Jiang and P. Miao, “DNA dumbbell and chameleon silver nanoclusters for mirna logic operations,” Research, vol. 2020, 2020.
[15] Emanuelson, Cole, Anirban Bardhan, and Alexander Deiters. "DNA computing: NOT logic gates see the light." ACS Synthetic Biology 10, no. 7 (2021): 1682-1689.
[16] Q. Yang et al., “DNA logic circuits for multiple tumor cells identification using intracellular MicroRNA molecular bispecific recognition,” Adv. Healthc. Mater., vol. 10, no. 21, p. 2101130, 2021.
[17] W. T. Huang, H. Q. Luo, and N. B. Li, “Boolean logic tree of graphene-based chemical system for molecular computation and intelligent molecular search query,” Anal. Chem., vol. 86, no. 9, pp. 4494–4500, 2014.
[18] R. M. Zadegan, M. D. E. Jepsen, L. L. Hildebrandt, V. Birkedal, and J. Kjems, “Construction of a fuzzy and Boolean logic gates based on DNA,” Small, vol. 11, no. 15, pp. 1811–1817, 2015.
[19] Sun, Gege, Congzhou Chen, Xin Li, Shangwei Hong, Chuanqi Gu, and Xiaolong Shi. "Rapid microRNA detection method based on DNA strand displacement for ovarian cancer cells." Journal of Cancer 14, no. 5 (2023): 707.
[20] Chen, Congzhou, Xin Chen, Xin Li, and Xiaolong Shi. "DNA computing for gastric cancer analysis and functional classification." Frontiers in Genetics 13 (2022): 1064715.
[21] Zhang, Chao, Yumeng Zhao, Xuemei Xu, Rui Xu, Haowen Li, Xiaoyan Teng, Yuzhen Du, Yanyan Miao, Hsiao-chu Lin, and Da Han. "Cancer diagnosis with DNA molecular computation." Nature nanotechnology 15, no. 8 (2020): 709-715.
[22] Winfree, Erik, Furong Liu, Lisa A. Wenzler, and Nadrian C. Seeman. “Design and Self-Assembly of Two-Dimensional DNA Crystals.” Nature 394, no. 6693
(1998): 539–44. https://doi.org/10.1038/28879 [23] Graveel, Carrie R., Heather M. Calderone, Jennifer J. Westerhuis, Mary E. Winn, and Lorenzo F. Sempere. "Critical analysis of the potential for microRNA biomarkers in breast cancer management." Breast cancer: targets and therapy (2015): 59-79.
[24] Fu, Sidney W., Liang Chen, and Yan-gao Man. "miRNA biomarkers in breast cancer detection and management." Journal of Cancer 2 (2011): 116.
[25] Fakeri, Mahsa, Shabnam Koulaeizadeh, Seyed M. Armandzadeh, Elmira Aboutalebi Vand Beilankouhi, Mohammad Valilo, and Mohammad Reza Alivand. "Involvement of miRNA-337 in various cancers." Current Cancer Therapy Reviews 20, no. 2 (2024): 188-199.
[26] Yuan, Naijun, Guijuan Zhang, Fengjie Bie, Min Ma, Yi Ma, Xuefeng Jiang, Yurong Wang, and Xiaoqian Hao. "Integrative analysis of lncRNAs and miRNAs with coding RNAs associated with ceRNA crosstalk network in triple negative breast cancer." OncoTargets and therapy (2017): 5883-5897.
[27] Mansoori, Behzad, Souzan Najafi, Ali Mohammadi, Haleh AsadollahSeraj, Pouria Savadi, Behnaz Mansoori, Afsaneh Nazari et al. "The synergy between miR-486–5p and tamoxifen causes profound cell death of tamoxifenresistant breast cancer cells." Biomedicine & Pharmacotherapy 141 (2021): 111925.
[28] Xia, M., H. Li, J-J. Wang, H-J. Zeng, and S-H. Wang. "MiR-99a suppress proliferation, migration and invasion through regulating insulin-like growth factor 1 receptor in breast cancer." European Review for Medical & Pharmacological Sciences 20, no. 9 (2016).
[29] Zhang, Ying, Haili Wang, Yiliang Wang, and Bo Ma. "FOXO1 mediates miR-99a-5p/E2F7 to restrain breast cancer cell proliferation and induce apoptosis." BMC cancer 25, no. 1 (2025): 747.