Fault Tolerant DNA Computing Based on ‎Digital Microfluidic Biochips

Document Type : Research Paper

Author

Department of Computer Engineering, Lorestan University, Khorramabad, Iran. ‎

Abstract

   Historically, DNA molecules have been known as the building blocks of life, later on in 1994, Leonard Adelman introduced a technique to utilize DNA molecules for a new kind of computation. According to the massive parallelism, huge storage capacity and the ability of using the DNA molecules inside the living tissue, this type of computation is applied in many application areas such as medical and engineering. Despite these advantages, DNA computing fault is prone to error. These errors may affect the entire computation and lead error in final result. Design of tolerant systems is one of the hot topics in the field of circuit design. The error in DNA computing will appear by a change in the concentration in compare to a threshold. In this paper, a buffer to modify the level of concentration is introduced and the number of required buffers in order to reduce the overhead caused by additional buffers in system is investigated using normal distribution. Designed system will modify any error with 15% changes in output concentration level in compare to a threshold level using the proposed method, which will increase the reliability.

Keywords

References

  1. Currin, A., Korovin, K., Ababi, M., Roper, K., Kell, D. B., Day, P. J., King, R. D., (2017). “Computing Exponentially Faster: Implementing a Non-Deterministic Universal Turing Machine Using DNA”, Journal of the Royal Society Interface, 14(128).
  2. Adleman, L. M., (1994). “Molecular Computation of Solutions to Combinatorial Problems”, Science, 266(5187): 1021-1024.
  3. Sanches, C. A. A., Soma, N.Y., (2016). “A General Resolution of Intractable Problems in Polynomial Time Through DNA Computing”, BioSystems, 150: 119-131.
  4. Zhao, K., Wang, Z., Qin, J., Lu, Y., (2015).  “A New Biological DNA Computational Algorithm to Solve the k-Vertex Cover Problem”, Journal of Computational and Theoretical  Nanoscience, 12: 524-526.
  5. Seeman, N. C., (2010).  “Nanomaterials Based on DNA”, Annual Review of Biochemistry, 79: 65-87.
  6. Livstone, M. S., Landweber, L. F., (2004). "Mathematical Consideration in the Design of Micro Reactor-Based DNA Computers", In 9th International Workshop on DNA Computing (DNA9), 180-189.
  7. Sakamoto, k., Gouzu, H., Komiya, k., Kiga, D., Yokoyama, S., Yokomori, T., Hagiya, M., (2000). “Molecular Computation by DNA Hairpin Formation”, Science, 288: 1223-1226.
  8. Qian, L., Winfree, E., (2014). “Parallel and Scalable Computation and Spatial Dynamics with DNA-Based Chemical Reaction Networks on a Surface”, DNA Computing and Molecular Programming, 8727: 114-131.
  9. Qian, L., Winfree, E., (2009). "A simple DNA Gate Motif for Synthesizing Largescale Circuits", 14th International Workshop on DNA Computing, 70-89.
  10. Qian, L., Winfree, E., Bruck, J., (2011). “Neural Network Computation with DNA Strand Displacement Cascades”, Nature, 475: 368–372.
  11. Fan, D., Wang, K., Zhu, J., Xia, Y., Han, Y., Liu, Y., Wang, E., (2015). “DNA Based Visual Majority Logic Gate with One-Vote Veto Function”, Journal of Royal Society of Chemistry, .6(3): 1973-1978.
  12.  Hemphill, J., Deiters, A., (2013). “DNA Computation in Mammalian Cells: MicroRNA Logic Operations”, Journal of American Chemical Society, 135(28): 10512–10518.
  13. Wuab, L., Qu, X., (2015). “Cancer Biomarker Detection: Recent Achievements and Challenges”, Chemical Society Reviews, 44(10): 2963-2997.
  14. Grissom, D., Brisk P., (2012). "Fast Online Synthesis of Generally Programmable Digital Micro-fluidic Biochips", Proceedings of the 8thIEEE/ACM/IFIP International Conference on Hardware/Software Codesign and System Synthesis, 413-422.
  15.  Noort, D. V., (2005). "A Programmable Molecular Computer in Microreactors", 10th International Workshop on DNA Computing (DNA10), 365-374.
  16. Beiki, Z., Jahanian, A., (2017). “DENA: A Configurable Micro-architecture and Design Flow for Bio-medical DNA-based Logic Design”, IEEE Transactions on Biomedical Circuits and Systems, 11(5): 1077-1086.
  17. Amos, M., (1997). "DNA Computation", PhD Thesis, University of Warwick, UK.
  18. Cannon, B. L., Kellis, D. L., Davis, P. H., Lee, J., Kuang,  W., Hughes, W. L., Graugnard , E., Yurke, B., Knowlton, W. B.,  (2015). “Excitonic AND Logic Gates on DNA Brick Nanobreadboards”, ACS Photonic Journal, 2(3): 398–404.
  19. Yurke, B., Turbereld, A., Mills, A, Simmel, A., Neumann, J., (2000). “A DNA fuelled Molecular Machine Made of DNA”, Nature, 406: 605–608..
  20. Zhang, D.Y., Seelig, G., (2011). “Dynamic DNA Nanothechnology Using Strand-Displacement Reactions”, Nature Chemistry, 3: 100-113.
  21. Abdoli, A., Jahanian, A., (2015). "Fault-Tolerant Architecture and CAD Algorithm for Field-Programmable Pin-Constrained Digital Microfluidic Biochips", CSI Symposium on Real-Time and Embedded Systems and Technologies (RTEST), 1-8.
  22. Grissom, D., Curtis, D., Windh, S., Phung, C., Kumar, N., Zimmerman, Z., O’Neal, K., McDaniel, J., Liao, N., Brisk, P., (2015). “An Open-source Compiler and PCB Synthesis Tool for Digital Microfluidic Biochips”, Integration The VLSI Journal, 51:169-193
  23. VisualDSD, (2014). [Online]. Available on: http://dsd.azurewebsites. net/beta.
  24. Taajobian, M., Jahanian, A., (2016). “Higher flexibility of reconfigurable digital micro/nano fluidic biochips using an FPGA-inspired architecture”, Scientia Iranica, 23( 3): 1554-1562.
International Journal of Nanoscience and Nanotechnology
Volume 16, Issue 3
August 2020
Pages 167-179

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