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Neural network execution using nicked DNA and microfluidics (2307.00686v1)

Published 2 Jul 2023 in cs.ET

Abstract: DNA has been discussed as a potential medium for data storage. Potentially it could be denser, could consume less energy, and could be more durable than conventional storage media such as hard drives, solid-state storage, and optical media. However, computing on data stored in DNA is a largely unexplored challenge. This paper proposes an integrated circuit (IC) based on microfluidics that can perform complex operations such as artificial neural network (ANN) computation on data stored in DNA. It computes entirely in the molecular domain without converting data to electrical form, making it a form of in-memory computing on DNA. The computation is achieved by topologically modifying DNA strands through the use of enzymes called nickases. A novel scheme is proposed for representing data stochastically through the concentration of the DNA molecules that are nicked at specific sites. The paper provides details of the biochemical design, as well as the design, layout, and operation of the microfluidics device. Benchmarks are reported on the performance of neural network computation.

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References (42)
  1. Next-Generation Digital Information Storage in DNA. Science (New York, NY). 2012;337:1628. doi:10.1126/science.1226355.
  2. Molecular digital data storage using DNA. Nature Reviews Genetics. 2019;20(8):456–466. doi:10.1038/s41576-019-0125-3.
  3. Nanopore-Based DNA Hard Drives for Rewritable and Secure Data Storage. Nano Letters. 2020;20(5):3754–3760. doi:10.1021/acs.nanolett.0c00755.
  4. An alternative approach to nucleic acid memory. Nature Communications. 2021;12(1):2371. doi:10.1038/s41467-021-22277-y.
  5. Adleman LM. Molecular computation of solutions to combinatorial problems. Science. 1994;266(5187):1021–1024.
  6. Yurke B. A DNA-fuelled molecular machine made of DNA. Nature. 2000;406(6796: 605).
  7. DNA as a universal substrate for chemical kinetics. Proceedings of the National Academy of Sciences. 2010;107(12):5393–5398. doi:10.1073/pnas.0909380107.
  8. Qian L, Winfree E. A Simple DNA Gate Motif for Synthesizing Large-Scale Circuits. Journal of the Royal Society Interface. 2011;.
  9. SIMD||||| |DNA: single instruction, multiple data computation with DNA strand displacement cascades. In: DNA25: International Conference on DNA Computing and Molecular Programming. vol. 11648. Springer. LNCS; 2019. p. 219–235.
  10. Parallel Pairwise Operations on Data Stored in DNA: Sorting, Shifting, and Searching. In: Lakin MR, Šulc P, editors. 27th International Conference on DNA Computing and Molecular Programming (DNA 27). vol. 205 of Leibniz International Proceedings in Informatics (LIPIcs). Dagstuhl, Germany: Schloss Dagstuhl – Leibniz-Zentrum für Informatik; 2021. p. 11:1–11:21. Available from: https://drops.dagstuhl.de/opus/volltexte/2021/14678.
  11. Computing mathematical functions with chemical reactions via stochastic logic. PLOS ONE. 2023;18(5):1–26. doi:10.1371/journal.pone.0281574.
  12. Jiang F, Doudna JA. CRISPR–Cas9 structures and mechanisms. Annual review of biophysics. 2017;46:505–529.
  13. DNA punch cards for storing data on native DNA sequences via enzymatic nicking. Nature Communications. 2020;11. doi:10.1038/s41467-020-15588-z.
  14. Ielmini D, Wong HSP. In-memory computing with resistive switching devices. Nature electronics. 2018;1(6):333–343.
  15. Flynn MJ. Some Computer Organizations and Their Effectiveness. IEEE Trans Comput. 1972;21(9):948–960. doi:10.1109/TC.1972.5009071.
  16. Gaines B. Stochastic Computing Systems. In: Advances in Information Systems Science. vol. 2. Plenum Press; 1969. p. 37–172.
  17. An Architecture for Fault-Tolerant Computation with Stochastic Logic. IEEE Transactions on Computers. 2011;60(1):93–105.
  18. Effect of bit-level correlation in stochastic computing. In: 2015 IEEE International Conference on Digital Signal Processing (DSP). IEEE; 2015. p. 463–467.
  19. Time-Encoded Values for Highly Efficient Stochastic Circuits. IEEE Transactions on Very Large Scale Integration (VLSI) Systems. 2017;25(5):1644–1657. doi:10.1109/TVLSI.2016.2645902.
  20. Uniform Approximation and Bernstein Polynomials with Coefficients in the Unit Interval. European Journal of Combinatorics. 2011;32(3):448–463.
  21. Transforming Probabilities with Combinational Logic. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems (to appear). 2011;.
  22. Jenson D, Riedel MD. A Deterministic Approach to Stochastic Computation. In: International Conferences on Computer-Aided Design; 2016.
  23. Performing Stochastic Computation Deterministically. IEEE Transactions on Very Large Scale Integration (VLSI) Systems. 2019;27(12):2925–2938. doi:10.1109/TVLSI.2019.2929354.
  24. Enzyme-Free Nucleic Acid Logic Circuits. In: Science. vol. 314; 2006. p. 1585–1588.
  25. Zhang DY, Seelig G. Dynamic DNA nanotechnology using strand-displacement reactions. Nature chemistry. 2011;3(2):103.
  26. DNA Punch Cards: Encoding Data on Native DNA Sequences via Nicking. bioRxiv. 2019;doi:10.1101/672394.
  27. Cherry KM, Qian L. Scaling up molecular pattern recognition with DNA-based winner-take-all neural networks. Nature. 2018;559(7714):370–376. doi:10.1038/s41586-018-0289-6.
  28. Computing Mathematical Functions using DNA via Fractional Coding. Scientific Reports. 2018;8(1):8312. doi:10.1038/s41598-018-26709-6.
  29. Enzyme-Free Nucleic Acid Logic Circuits. Science. 2006;314(5805):1585–1588. doi:10.1126/science.1132493.
  30. Probabilistic switching circuits in DNA. Proceedings of the National Academy of Sciences. 2018;115(5):903–908. doi:10.1073/pnas.1715926115.
  31. Chen T, Riedel M. Concentration-Based Polynomial Calculations on Nicked DNA. In: ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP); 2020. p. 8836–8840.
  32. Gaines BR. In: Stochastic Computing Systems. Boston, MA: Springer US; 1969. p. 37–172. Available from: https://doi.org/10.1007/978-1-4899-5841-9_2.
  33. Qian W, Riedel MD. The Synthesis of Robust Polynomial Arithmetic with Stochastic Logic. In: Design Automation Conference; 2008. p. 648–653.
  34. Convery N, Gadegaard N. 30 years of microfluidics. Micro and Nano Engineering. 2019;2:76–91. doi:https://doi.org/10.1016/j.mne.2019.01.003.
  35. Design and Development of a Disposable Lab-on-a-Chip for Prostate Cancer Detection. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC); 2019. p. 1579–1583.
  36. Hayward GS. Unique Double-Stranded Fragments of Bacteriophage T5 DNA Resulting from Preferential Shear-Induced Breakage at Nicks. Proceedings of the National Academy of Sciences. 1974;71(5):2108–2112. doi:10.1073/pnas.71.5.2108.
  37. Qian L, Winfree E. Scaling up digital circuit computation with DNA strand displacement cascades. Science. 2011;332(6034):1196–1201. doi:10.1126/science.1200520.
  38. Droplet incubation and splitting in open microfluidic channels. Analytical Methods. 2019;11:4528–4536. doi:10.1039/C9AY00758J.
  39. Design of microfluidic channel geometries for the control of droplet volume, chemical concentration, and sorting. Lab on a Chip. 2004;4(4):292–298.
  40. Plug-and-play reservoirs for microfluidics. Chips and Tips (Lab on a Chip). 2009;.
  41. Interconnects for DNA, Quantum, In-Memory, and Optical Computing: Insights From a Panel Discussion. IEEE Micro. 2022;42(3):40–49. doi:10.1109/MM.2022.3150684.
  42. Effective design principles for leakless strand displacement systems. Proceedings of the National Academy of Sciences. 2018;115(52):E12182–E12191. doi:10.1073/pnas.1806859115.

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