DNA computing for combinational logic

Abstract

With the progressive scale-down of semiconductor’s feature size, people are looking forward to More Moore and More than Moore. In order to offer a possible alternative implementation process, researchers are trying to figure out a feasible transfer from silicon to molecular computing. Such transfer lies on bio-based modules programming with computer-like logic, aiming at realizing the Turing machine. To accomplish this, the DNA-based combinational logic is inevitably the first step we have taken care of. This timely overview study introduces combinational logic synthesized in DNA computing from both analog and digital perspectives separately. State-of-the-art research progress is summarized for interested readers to quick understand DNA computing, initiate discussion on existing techniques and inspire innovation solutions. We hope this study can pave the way for the future DNA computing synthesis.

This is a preview of subscription content, access via your institution.

References

  1. 1

    Kish L B. End of Moore’s law: thermal (noise) death of integration in micro and nano electronics. Phys Lett A, 2002, 305: 144–149

    Article  Google Scholar 

  2. 2

    Desai S B, Madhvapathy S R, Sachid A B, et al. MoS2 transistors with 1-nanometer gate lengths. Science, 2016, 354: 99–102

    Article  Google Scholar 

  3. 3

    Yahiro W, Hagiya M, Implementation of Turing machine using DNA strand displacement. In: Proceedings of International Conference on Theory and Practice of Natural Computing. Berlin: Springer, 2016. 161–172

    Google Scholar 

  4. 4

    Wikipedia. Combinational logic. 2018. https://en.wikipedia.org/wiki/Combinational logic

    Google Scholar 

  5. 5

    Khalil A S, Collins J J. Synthetic biology: applications come of age. Nat Rev Genet, 2010, 11: 367

    Article  Google Scholar 

  6. 6

    Siuti P, Yazbek J, Lu T K. Synthetic circuits integrating logic and memory in living cells. Nat Biotechnol, 2013, 31: 448–452

    Article  Google Scholar 

  7. 7

    Andrianantoandro E, Basu S, Karig D K, et al. Synthetic biology: new engineering rules for an emerging discipline. Molecular Syst Biol, 2006, 2: 28

    Article  Google Scholar 

  8. 8

    Green A A, Kim J, Ma D, et al. Complex cellular logic computation using ribocomputing devices. Nature, 2017, 548: 117–121

    Article  Google Scholar 

  9. 9

    Feynman R P. There’s plenty of room at the bottom. Eng Sci, 1960, 23: 22–36

    Google Scholar 

  10. 10

    Trautman J K, Macklin J J, Brus L E, et al. Near-field spectroscopy of single molecules at room temperature. Nature, 1994, 369: 40–42

    Article  Google Scholar 

  11. 11

    Paun G, Rozenberg G, Salomaa A. DNA Computing: New Computing Paradigms. Berlin: Springer, 2005

    Google Scholar 

  12. 12

    Amos M. Theoretical and experimental DNA computation. Bull European Assoc Theor Comput Sci, 1999, 67: 125–138

    MathSciNet  MATH  Google Scholar 

  13. 13

    von Neumann J. First draft of a report on the EDVAC. IEEE Ann Hist Comput, 1993, 15: 27–75

    MathSciNet  Article  MATH  Google Scholar 

  14. 14

    Backus J. Can programming be liberated from the von Neumann style: a functional style and its algebra of programs. Commun ACM, 1978, 21: 613–641

    MathSciNet  Article  MATH  Google Scholar 

  15. 15

    Deaton R, Murphy R C, Rose J A, et al. A DNA based implementation of an evolutionary search for good encodings for DNA computation. In: Proceedings of IEEE International Conference on Evolutionary Computation, Indianapolis, 1997. 267–271

    Google Scholar 

  16. 16

    Tagore S, Bhattacharya S, Islam M, et al. DNA computation: application and perspectives. J Proteom Bioinform, 2010, 3: 234–343

    Article  Google Scholar 

  17. 17

    Extance A. How DNA could store all the world’s data. Nature, 2016, 537: 22–24

    Article  Google Scholar 

  18. 18

    Hameed K. DNA computation based approach for enhanced computing power. Int J Emerg Sci, 2011, 1: 23–30

    Google Scholar 

  19. 19

    Saxena S. Introduction to DNA computing. Int Acadmey Eng Medical Res, 2016, 1: 1–3

    Google Scholar 

  20. 20

    Kumar S N. A proper approach on DNA based computer. American Nanomater, 2015, 3: 1–14

    Google Scholar 

  21. 21

    Ma S, Tang N, Tian J. DNA synthesis, assembly and applications in synthetic biology. Curr Opin Chem Biol, 2012, 16: 260–267

    Article  Google Scholar 

  22. 22

    Bornholt J, Lopez R, Carmean D M, et al. A DNA-based archival storage system. SIGOPS Oper Syst Rev, 2016, 50: 637–649

    Article  Google Scholar 

  23. 23

    Hughes R A, Ellington A D. Synthetic DNA synthesis and assembly: putting the synthetic in synthetic biology. Cold Spring Harb Perspect Biol, 2017, 9: a023812

    Article  Google Scholar 

  24. 24

    Benenson Y, Gil B, Ben-Dor U, et al. An autonomous molecular computer for logical control of gene expression. Nature, 2004, 429: 423–429

    Article  Google Scholar 

  25. 25

    Landweber L F, Lipton R J, Rabin M O. DNA2DNA computations: a potential “killer app”? In: Proceedings of International Colloquium on Automata, Languages, and Programming (ICALP). Berlin: Springer, 1997. 56–64

    Google Scholar 

  26. 26

    Watada J, binti abu Bakar R. DNA computing and its applications. In: Proceedings of the 8th International Conference on Intelligent Systems Design and Applications, Kaohsiung, 2008. 288–294

    Google Scholar 

  27. 27

    Gehani A, LaBean T, Reif J. DNA-based cryptography. Asp Mol Comput, 2003, 2950: 167–188

    Article  MATH  Google Scholar 

  28. 28

    Miyamoto T, Razavi S, DeRose R, et al. Synthesizing biomolecule-based Boolean logic gates. ACS Synth Biol, 2012, 2: 72–82

    Article  Google Scholar 

  29. 29

    Jiang H, Riedel M D, Parhi K K. Digital logic with molecular reactions. In: Proceedings of International Conference on Computer-Aided Design (ICCAD), San Jose, 2013. 721–727

    Google Scholar 

  30. 30

    Zhang C, Ge L L, Zhong Z W, et al. Karnaugh map-aided combinational logic design approach with bistable molecular reactions. In: Proceedings of IEEE International Conference on Digital Signal Processing (DSP), Singapore, 2015. 1288–1292

    Google Scholar 

  31. 31

    Ge L, Zhong Z, Wen D, et al. A formal combinational logic synthesis with chemical reaction networks. IEEE Trans Mol Biol Multi-Scale Commun, 2017, 3: 33–47

    Article  Google Scholar 

  32. 32

    Wen D L, Ge L L, Lu Y X, et al. A DNA strand displacement reaction implementation-friendly clock design. In: Proceedings of IEEE International Conference on Communications (ICC), Paris, 2017

    Google Scholar 

  33. 33

    Zhang X C, Ge L L, You X H, et al. Synthesizing LDPC belief propagation decoding with molecular reactions. In: Proceedings of IEEE International Conference on Communications (ICC), Kansas City, 2018

    Google Scholar 

  34. 34

    Zhong Z W, Li Z, Ge L L, et al. Implementation of Mealy machine with molecular reactions. In: Proceedings of IEEE International Conference on Communications (ICC), Kansas City, 2018

    Google Scholar 

  35. 35

    Lu Y X, Ge L L, You X H, et al. Implementation of sinusoids and pulse width modulation with chemical reactions. In: Proceedings of IEEE International Conference on Communications (ICC), Kansas City, 2018

    Google Scholar 

  36. 36

    Li M H, Ge L L, You X H, et al. Basic arithmetics based on analog signal with molecular reactions. In: Proceedings of IEEE International Conference on Communications (ICC), Kansas City, 2018

    Google Scholar 

  37. 37

    Shen Z, Ge L, Wei W, et al. Molecular synthesis for probability theory and stochastic process. J Sign Process Syst, 2018, 90: 1479–1494

    Article  Google Scholar 

  38. 38

    Fang C, Shen Z, Zhang Z, et al. Synthesizing a neuron using chemical reactions. In: Proceedings of IEEE International Workshop on Signal Processing Systems (SiPS), Cape Town, 2018

    Google Scholar 

  39. 39

    Zhuang Y, Zhang Z, You X, et al. Arithmetic computations based on chemical reaction networks. In: Proceedings of IEEE International Workshop on Signal Processing Systems (SiPS), Cape Town, 2018

    Google Scholar 

  40. 40

    Zhong Z, Ge L, Shen Z, et al. CRN-based design methodology for synchronous sequential logic. In: Proceedings of IEEE International Workshop on Signal Processing Systems (SiPS), Lorient, 2017

    Google Scholar 

  41. 41

    Shen Z Y, Ge L L, Wei W, et al. Synthesizing Markov chain with reversible unimolecular reactions. In: Proceedings of International Conference on Wireless Communications and Signal Processing (WCSP), Nanjing, 2017

    Google Scholar 

  42. 42

    Zhuang Y C, Ge L L, Wei W, et al. A synthesis flow for fast convolution unit based on molecular reactions. In: Proceedings of International Conference on Wireless Communications and Signal Processing (WCSP), Nanjing, 2017

    Google Scholar 

  43. 43

    Shen Z, Zhang C, Ge L, et al. Synthesis of probability theory based on molecular computation. In: Proceedings of IEEE International Workshop on Signal Processing Systems (SiPS), Dallas, 2016

    Google Scholar 

  44. 44

    Ge L, Zhang C, Zhong Z, et al. A formal design methodology for synthesizing a clock signal with an arbitrary duty cycle of M/N. In: Proceedings of IEEE International Workshop on Signal Processing Systems (SiPS), Hangzhou, 2015

    Google Scholar 

  45. 45

    Jiang H, Riedel M D, Parhi K K. Synchronous sequential computation with molecular reactions. In: Proceedings of the 48th Design Automation Conference (DAC), San Diego, 2011. 836–841

    Google Scholar 

  46. 46

    Salehi S A, Riedel M D, Parhi K K. Asynchronous discrete-time signal processing with molecular reactions. In: Proceedings of the 48th Asilomar Conference on Signals, Systems and Computers, Pacific Grove, 2014

    Google Scholar 

  47. 47

    Senum P, Riedel M D. Rate-independent constructs for chemical computation. PLoS ONE, 2011, 6: e21414

    Article  Google Scholar 

  48. 48

    Howard P. Analysis of ODE models. 2009. http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.441.4759& rep=rep1&type=pdf

    Google Scholar 

  49. 49

    Strogatz S H. Nonlinear Dynamics and Chaos: With Applications to Physics, Biology, Chemistry, and Engineering. Boulder: Westview Press, 2014

    Google Scholar 

  50. 50

    Zauderer E. Partial Differential Equations of Applied Mathematics. Hoboken: John Wiley & Sons, 2011

    Google Scholar 

  51. 51

    Hale J K, Lunel S M V. Introduction to Functional Differential Equations. Berlin: Springer, 2013

    Google Scholar 

  52. 52

    Érdi P, Tóth J. Mathematical Models of chemical Reactions: Theory and Applications of Deterministic and Stochastic Models. Manchester: Manchester University Press, 1989

    Google Scholar 

  53. 53

    Horn F, Jackson R. General mass action kinetics. Arch Rational Mech Anal, 1972, 47: 81–116

    MathSciNet  Article  Google Scholar 

  54. 54

    Crick F. Central dogma of molecular biology. Nature, 1970, 227: 561–563

    Article  Google Scholar 

  55. 55

    Soloveichik D, Seelig G, Winfree E. DNA as a universal substrate for chemical kinetics. Proc Natl Acad Sci USA, 2010, 107: 5393–5398

    Article  Google Scholar 

  56. 56

    Zhang D Y, Seelig G. Dynamic DNA nanotechnology using strand-displacement reactions. Nat Chem, 2011, 3: 103–113

    Article  Google Scholar 

  57. 57

    Zhang D Y, Winfree E. Control of DNA strand displacement kinetics using toehold exchange. J Am Chem Soc, 2009, 131: 303–314

    Google Scholar 

  58. 58

    Phillips A, Cardelli L. A programming language for composable DNA circuits. J R Soc Interface, 2009, 6: S419–S436

    Article  Google Scholar 

  59. 59

    SantaLucia Jr J, Hicks D. The thermodynamics of DNA structural motifs. Annu Rev Biophys Biomol Struct, 2004, 33: 415–440

    Article  Google Scholar 

  60. 60

    Shapiro E, Ran T. DNA computing: molecules reach consensus. Nat Nanotech, 2013, 8: 703–705

    Article  Google Scholar 

  61. 61

    Zhang D Y. Dynamic DNA strand displacement circuits. Dissertation for Ph.D. Degree. Pasadena: California Institute of Technology, 2010

    Google Scholar 

  62. 62

    Leavitt S. Deciphering the genetic code: Marshall Nirenberg. Office of NIH History, 2004

    Google Scholar 

  63. 63

    Sarpeshkar R. Analog versus digital: extrapolating from electronics to neurobiology. Neural Comput, 1998, 10: 1601–1638

    Article  Google Scholar 

  64. 64

    Sauro H M, Kim K. Synthetic biology: It’s an analog world. Nature, 2013, 497: 572–573

    Article  Google Scholar 

  65. 65

    Song T, Garg S, Mokhtar R, et al. Analog computation by DNA strand displacement circuits. ACS Synth Biol, 2016, 5: 898–912

    Article  Google Scholar 

  66. 66

    Yordanov B, Kim J, Petersen R L, et al. Computational design of nucleic acid feedback control circuits. ACS Synth Biol, 2014, 3: 600–616

    Article  Google Scholar 

  67. 67

    Chen Y J, Dalchau N, Srinivas N, et al. Programmable chemical controllers made from DNA. Nat Nanotech, 2013, 8: 755–762

    Article  Google Scholar 

  68. 68

    Sarpeshkar R. Analog synthetic biology. Philos Trans R Soc A-Math Phys Eng Sci, 2014, 372: 20130110

    Article  Google Scholar 

  69. 69

    Daniel R, Rubens J R, Sarpeshkar R, et al. Synthetic analog computation in living cells. Nature, 2013, 497: 619–623

    Article  Google Scholar 

  70. 70

    Salehi S A, Jiang H, Riedel M D, et al. Molecular sensing and computing systems. IEEE Trans Mol Biol Multi-Scale Commun, 2015, 1: 249–264

    Article  Google Scholar 

  71. 71

    Frezza B M, Cockroft S L, Ghadiri M R. Modular multi-level circuits from immobilized DNA-based logic gates. J Am Chem Soc, 2007, 129: 875–879

    Article  Google Scholar 

  72. 72

    Chiniforooshan E, Doty D, Kari L, et al. Scalable, time-responsive, digital, energy-efficient molecular circuits using DNA strand displacement. In: Proceedings of the 16th International Conference on DNA Computing and Molecular Programming, Hong Kong, 2010. 25–36

    Google Scholar 

  73. 73

    Qian L, Winfree E. Scaling up digital circuit computation with DNA strand displacement cascades. Science, 2011, 332: 1196–1201

    Article  Google Scholar 

  74. 74

    Nielsen A A, Der B S, Shin J, et al. Genetic circuit design automation. Science, 2016, 352: aac7341

    Article  Google Scholar 

  75. 75

    Roquet N, Lu T K. Digital and analog gene circuits for biotechnology. Biotech J, 2014, 9: 597–608

    Article  Google Scholar 

  76. 76

    Weiss R, Basu S, Hooshangi S, et al. Genetic circuit building blocks for cellular computation, communications, and signal processing. Nat Comput, 2003, 2: 47–84

    Article  Google Scholar 

  77. 77

    Zadegan R M, Jepsen M D E, Hildebrandt L L, et al. Construction of a fuzzy and Boolean logic gates based on DNA. Small, 2015, 11: 1811–1817

    Article  Google Scholar 

  78. 78

    Zhang Y, Wirkert S J, Iszatt J, et al. Tissue classification for laparoscopic image understanding based on multispectral texture analysis. J Med Imag, 2017, 4: 015001

    Article  Google Scholar 

  79. 79

    Lu C H, Willner B, Willner I. DNA nanotechnology: from sensing and DNA machines to drug-delivery systems. ACS Nano, 2013, 7: 8320–8332

    Article  Google Scholar 

  80. 80

    Li J, Pei H, Zhu B, et al. Self-assembled multivalent DNA nanostructures for noninvasive intracellular delivery of immunostimulatory CpG oligonucleotides. ACS Nano, 2011, 5: 8783–8789

    Article  Google Scholar 

  81. 81

    Qian L, Winfree E, Bruck J. Neural network computation with DNA strand displacement cascades. Nature, 2011, 475: 368–372

    Article  Google Scholar 

  82. 82

    Schneider G, Wrede P. Artificial neural networks for computer-based molecular design. Prog Biophys Mol Biol, 1998, 70: 175–222

    Article  Google Scholar 

  83. 83

    Noordewier M O, Towell G G, Shavlik J W. Training knowledge-based neural networks to recognize genes in DNA sequences. In: Proceedings of Advances in Neural Information Processing Systems, Denver, 1991. 530–536

    Google Scholar 

  84. 84

    Zuber J, Sun H, Zhang X, et al. A sensitivity analysis of RNA folding nearest neighbor parameters identifies a subset of free energy parameters with the greatest impact on RNA secondary structure prediction. Nucleic Acids Res, 2017, 45: 6168–6176

    Article  Google Scholar 

  85. 85

    Brady M. Artificial intelligence and robotics. Artif Intell, 1985, 26: 79–121

    Article  Google Scholar 

  86. 86

    Ray K S, Mondal M. Similarity-based fuzzy reasoning by DNA computing. Int J Bio-Inspired Comput, 2011, 3: 112–122

    Article  Google Scholar 

  87. 87

    Jeng D J, Watada J, Wu B, et al. Fuzzy forecasting with DNA computing. In: Proceedings of International Workshop on DNA-Based Computers. Berlin: Springer, 2006. 324–336

    Google Scholar 

Download references

Acknowledgements

This work was supported in part by National Natural Science Foundation of China (Grant Nos. 61871115, 61501116), Jiangsu Provincial Natural Science Foundation for Excellent Young Scholars, Huawei HIRP Flagship under (Grant No. YB201504), the Fundamental Research Funds for the Central Universities, the SRTP of Southeast University, State Key Laboratory of ASIC & System (Grant No. 2016KF007), ICRI for MNC, and the Project Sponsored by the SRF for the Returned Overseas Chinese Scholars of MoE.

Author information

Affiliations

Authors

Corresponding author

Correspondence to Chuan Zhang.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Zhang, C., Ge, L., Zhuang, Y. et al. DNA computing for combinational logic. Sci. China Inf. Sci. 62, 61301 (2019). https://doi.org/10.1007/s11432-018-9530-x

Download citation

Keywords

  • synthetic biology
  • DNA computing
  • DNA strand displacement reactions
  • chemical reaction networks
  • combinational logic