DNA computing

DNA computing is a branch of computing which uses DNA, biochemistry, and molecular biology hardware, instead of the traditional silicon-based computer technologies. Research and development in this area concerns theory, experiments, and applications of DNA computing. The term "molectronics" has sometimes been used, but this term has already been used for an earlier technology, a then-unsuccessful rival of the first integrated circuits;[1] this term has also been used more generally, for molecular-scale electronic technology.[2]


Leonard Adleman of the University of Southern California initially developed this field in 1994.[3] Adleman demonstrated a proof-of-concept use of DNA as a form of computation which solved the seven-point Hamiltonian path problem. Since the initial Adleman experiments, advances have occurred and various Turing machines have been proven to be constructible.[4][5]

While the initial interest lay in using this novel approach to tackle NP-hard problems, researchers soon realized that DNA computing may not be best suited for this type of computation, and several proposals have been made to find a "killer application" for this approach. In 1997 computer scientist Mitsunori Ogihara, working with biologist Animesh Ray, suggested one to be the evaluation of Boolean circuits and described an implementation.[6][7]

In 2002 researchers from the Weizmann Institute of Science in Rehovot, Israel, unveiled a programmable molecular computing machine composed of enzymes and DNA molecules instead of silicon microchips.[8] On April 28, 2004, Ehud Shapiro, Yaakov Benenson, Binyamin Gil, Uri Ben-Dor, and Rivka Adar at the Weizmann Institute announced in the journal Nature that they had constructed a DNA computer coupled with an input-and-output module which would theoretically be capable of diagnosing cancerous activity within a cell and of releasing an anti-cancer drug upon diagnosis.[9]

In January 2013 researchers were able to store a JPEG photograph, a set of Shakespearean sonnets, and an audio file of Martin Luther King, Jr.'s "I Have a Dream" speech on DNA digital data storage.[10]

In March 2013 researchers built a transcriptor (a biological transistor).[11]

In August 2016 researchers used the CRISPR gene-editing system to insert a GIF of a galloping horse and rider into the DNA of living bacteria.[12]

Subsequent research on DNA computing has produced reversible DNA computing, bringing the technology one step closer to the silicon-based computing used in (for example) PCs. In particular, John Reif and his group at Duke University have proposed two different techniques to reuse the computing DNA complexes. The first design uses dsDNA gates,[13] while the second design uses DNA hairpin complexes.[14] While both the designs face some issues (such as reaction leaks), this appears to represent a significant breakthrough in the field of DNA computing.


The organisation and complexity of all living beings is based on a coding system functioning with four key components of the DNA-molecule. Because of this, the DNA is very suited as a medium for data processing.[15] According to different calculations a DNA-computer with one liter of fluid containing six grams of DNA could potentially have a memory capacity of 3072 exabytes. The theoretical maximum data transfer speed would also be enormous due to the massive parallelism of the calculations. Therefore, about 1000 petaFLOPS could be reached, while today's most powerful computers do not go above much (200 petaFLOPS being the current record).[16]

Pros and cons

The slow processing speed of a DNA-computer (the response time is measured in minutes, hours or days, rather than milliseconds) is compensated by its potential to make a high amount of multiple parallel computations. This allows the system to take a similar amount of time for a complex calculation as for a simple one. This is achieved by the fact that millions or billions of molecules interact with each other simultaneously. However, it is much harder to analyze the answers given by a DNA-Computer than by a digital one.


In 1994 Leonard Adleman presented the first prototype of a DNA-Computer. The TT-100 was a test tube filled with 100 microliters of a DNA-solution. He managed to solve an instance of the directed Hamiltonian path problem.[17]

In Adleman's experiment the Hamiltonian Path Problem was implemented notationally as “travelling salesman problem”. For this purpose, different DNA-fragments were created, each one of them representing a city that had to be visited. Every one of these fragments is capable of a linkage with the other fragments created. These DNA-fragments were produced and mixed in a test tube. Within seconds, the small fragments form bigger ones, representing the different travel routes. Through a chemical reaction (that lasts a few days), the DNA-fragments representing the longer routes were eliminated. The remains are the solution to the problem. However, current technical limitations prevent evaluation of the results. Therefore, the experiment isn’t suitable for application, but it is nevertheless a proof of concept.

Combinatorial problems

First results to these problems were obtained by Leonard Adleman (NASA JPL)

Tic-tac-toe game

In 2002, J. Macdonald, D. Stefanovic and Mr. Stojanovic created a DNA computer able to play tic-tac-toe against a human player.[18] The calculator consists of nine bins corresponding to the nine squares of the game. Each bin contains a substrate and various combinations of DNA enzymes. The substrate itself is composed of a DNA strand onto which was grafted a fluorescent chemical group at one end, and the other end, a repressor group. Fluorescence is only active if the molecules of the substrate are cut in half. The DNA enzymes simulate logical functions. For example, such a DNA will unfold if two specific types of DNA strand are introduced to reproduce the logic function AND.

By default, the computer is considered to have played first in the central square. The human player starts with eight different types of DNA strands corresponding to the eight remaining boxes that may be played. To play box number i, the human player pours into all bins the strands corresponding to input #i. These strands bind to certain DNA enzymes present in the bins, resulting, in one of these bins, in the deformation of the DNA enzymes which binds to the substrate and cuts it. The corresponding bin becomes fluorescent, indicating which box is being played by the DNA computer. The DNA enzymes are divided among the bins in such a way as to ensure that the best the human player can achieve is a draw, as in real tic-tac-toe.

Neural network based computing

Kevin Cherry and Lulu Qian at Caltech developed a DNA-based artificial neural network that can recognize 100-bit hand-written digits. They achieve this by programming on computer in advance with appropriate set of weights represented by varying concentrations weight molecules which will later be added to the test tube that holds the input DNA strands.[19][20]


DNA computing is a form of parallel computing in that it takes advantage of the many different molecules of DNA to try many different possibilities at once.[21] For certain specialized problems, DNA computers are faster and smaller than any other computer built so far. Furthermore, particular mathematical computations have been demonstrated to work on a DNA computer. As an example, DNA molecules have been utilized to tackle the assignment problem.[22]

Jian-Jun Shu and colleagues built a DNA GPS[23] system and also conduct an experiment to show that magnetic fields can enhance charge transport through DNA[24] (or protein), which may allow organisms to sense magnetic fields.

DNA computing does not provide any new capabilities from the standpoint of computability theory, the study of which problems are computationally solvable using different models of computation. For example, if the space required for the solution of a problem grows exponentially with the size of the problem (EXPSPACE problems) on von Neumann machines, it still grows exponentially with the size of the problem on DNA machines. For very large EXPSPACE problems, the amount of DNA required is too large to be practical.


There are multiple methods for building a computing device based on DNA, each with its own advantages and disadvantages. Most of these build the basic logic gates (AND, OR, NOT) associated with digital logic from a DNA basis. Some of the different bases include DNAzymes, deoxyoligonucleotides, enzymes, toehold exchange.


Catalytic DNA (deoxyribozyme or DNAzyme) catalyze a reaction when interacting with the appropriate input, such as a matching oligonucleotide. These DNAzymes are used to build logic gates analogous to digital logic in silicon; however, DNAzymes are limited to 1-, 2-, and 3-input gates with no current implementation for evaluating statements in series.

The DNAzyme logic gate changes its structure when it binds to a matching oligonucleotide and the fluorogenic substrate it is bonded to is cleaved free. While other materials can be used, most models use a fluorescence-based substrate because it is very easy to detect, even at the single molecule limit.[25] The amount of fluorescence can then be measured to tell whether or not a reaction took place. The DNAzyme that changes is then “used,” and cannot initiate any more reactions. Because of this, these reactions take place in a device such as a continuous stirred-tank reactor, where old product is removed and new molecules added.

Two commonly used DNAzymes are named E6 and 8-17. These are popular because they allow cleaving of a substrate in any arbitrary location.[26] Stojanovic and MacDonald have used the E6 DNAzymes to build the MAYA I[27] and MAYA II[28] machines, respectively; Stojanovic has also demonstrated logic gates using the 8-17 DNAzyme.[29] While these DNAzymes have been demonstrated to be useful for constructing logic gates, they are limited by the need for a metal cofactor to function, such as Zn2+ or Mn2+, and thus are not useful in vivo.[25][30]

A design called a stem loop, consisting of a single strand of DNA which has a loop at an end, are a dynamic structure that opens and closes when a piece of DNA bonds to the loop part. This effect has been exploited to create several logic gates. These logic gates have been used to create the computers MAYA I and MAYA II which can play tic-tac-toe to some extent.[31]


Enzyme based DNA computers are usually of the form of a simple Turing machine; there is analogous hardware, in the form of an enzyme, and software, in the form of DNA.[32]

Benenson, Shapiro and colleagues have demonstrated a DNA computer using the FokI enzyme[33] and expanded on their work by going on to show automata that diagnose and react to prostate cancer: under expression of the genes PPAP2B and GSTP1 and an over expression of PIM1 and HPN.[9] Their automata evaluated the expression of each gene, one gene at a time, and on positive diagnosis then released a single strand DNA molecule (ssDNA) that is an antisense for MDM2. MDM2 is a repressor of protein 53, which itself is a tumor suppressor.[34] On negative diagnosis it was decided to release a suppressor of the positive diagnosis drug instead of doing nothing. A limitation of this implementation is that two separate automata are required, one to administer each drug. The entire process of evaluation until drug release took around an hour to complete. This method also requires transition molecules as well as the FokI enzyme to be present. The requirement for the FokI enzyme limits application in vivo, at least for use in "cells of higher organisms".[35] It should also be pointed out that the 'software' molecules can be reused in this case.

Toehold exchange

DNA computers have also been constructed using the concept of toehold exchange. In this system, an input DNA strand binds to a sticky end, or toehold, on another DNA molecule, which allows it to displace another strand segment from the molecule. This allows the creation of modular logic components such as AND, OR, and NOT gates and signal amplifiers, which can be linked into arbitrarily large computers. This class of DNA computers does not require enzymes or any chemical capability of the DNA.[36]

Algorithmic self-assembly

DNA nanotechnology has been applied to the related field of DNA computing. DNA tiles can be designed to contain multiple sticky ends with sequences chosen so that they act as Wang tiles. A DX array has been demonstrated whose assembly encodes an XOR operation; this allows the DNA array to implement a cellular automaton which generates a fractal called the Sierpinski gasket. This shows that computation can be incorporated into the assembly of DNA arrays, increasing its scope beyond simple periodic arrays.[37]

Alternative technologies

A partnership between IBM and Caltech was established in 2009 aiming at "DNA chips" production.[38] A Caltech group is working on the manufacturing of these nucleic-acid-based integrated circuits. One of these chips can compute whole square roots.[39] A compiler has been written[40] in Perl.

See also


  1. "Molectronic Computer Shown by Texas Instr.", unknown publication, circa 1963, in Box 2, Folder 3, listed in Jack Kilby Papers: A Guide to the Collection, Southern Methodist University. .
  2. "Application-specific methods for testing molectronic or nanoscale devices" (filed April 1, 2004), Patent US 7219314 B1. .
  3. Adleman, L. M. (1994). "Molecular computation of solutions to combinatorial problems". Science. 266 (5187): 1021–1024. Bibcode:1994Sci...266.1021A. CiteSeerX doi:10.1126/science.7973651. PMID 7973651. The first DNA computing paper. Describes a solution for the directed Hamiltonian path problem. Also available here: "Archived copy" (PDF). Archived from the original (PDF) on 2005-02-06. Retrieved 2005-11-21.CS1 maint: archived copy as title (link)
  4. Boneh, D.; Dunworth, C.; Lipton, R. J.; Sgall, J. Í. (1996). "On the computational power of DNA". Discrete Applied Mathematics. 71 (1–3): 79–94. doi:10.1016/S0166-218X(96)00058-3. Describes a solution for the boolean satisfiability problem. Also available here: "Archived copy" (PDF). Archived from the original (PDF) on 2012-04-06. Retrieved 2011-10-14.CS1 maint: archived copy as title (link)
  5. Lila Kari; Greg Gloor; Sheng Yu (January 2000). "Using DNA to solve the Bounded Post Correspondence Problem". Theoretical Computer Science. 231 (2): 192–203. doi:10.1016/s0304-3975(99)00100-0. Describes a solution for the bounded Post correspondence problem, a hard-on-average NP-complete problem. Also available here:
  6. M. Ogihara and A. Ray, "Simulating Boolean circuits on a DNA computer". Algorithmica 25:239–250, 1999.
  7. "In Just a Few Drops, A Breakthrough in Computing", The New York Times, May 21, 1997
  8. Lovgren, Stefan (2003-02-24). "Computer Made from DNA and Enzymes". National Geographic. Retrieved 2009-11-26.
  9. Benenson, Y.; Gil, B.; Ben-Dor, U.; Adar, R.; Shapiro, E. (2004). "An autonomous molecular computer for logical control of gene expression". Nature. 429 (6990): 423–429. Bibcode:2004Natur.429..423B. doi:10.1038/nature02551. PMC 3838955. PMID 15116117.. Also available here: An autonomous molecular computer for logical control of gene expression
  10. DNA stores poems, a photo and a speech | Science News
  11. Bonnet, Jerome; Yin, Peter; Ortiz, Monica E.; Subsoontorn, Pakpoom; Endy, Drew (2013). "Amplifying Genetic Logic Gates". Science. 340 (6132): 599–603. Bibcode:2013Sci...340..599B. doi:10.1126/science.1232758. PMID 23539178.
  12. Shipman, Seth L.; Nivala, Jeff; Macklis, Jeffrey D.; Church, George M. (12 July 2017). "CRISPR–Cas encoding of a digital movie into the genomes of a population of living bacteria". Nature. 547 (7663): 345–349. Bibcode:2017Natur.547..345S. doi:10.1038/nature23017. PMC 5842791. PMID 28700573.
  13. Garg, Sudhanshu; Shah, Shalin; Bui, Hieu; Song, Tianqi; Mokhtar, Reem; Reif, John (2018). "Renewable Time-Responsive DNA Circuits". Small. 14 (33): 1801470. doi:10.1002/smll.201801470. ISSN 1613-6829. PMID 30022600.
  14. Eshra, A.; Shah, S.; Song, T.; Reif, J. (2019). "Renewable DNA hairpin-based logic circuits". IEEE Transactions on Nanotechnology. 18: 252–259. arXiv:1704.06371. doi:10.1109/TNANO.2019.2896189. ISSN 1536-125X.
  15. Amos, Martyn; et al. (2002). "Topics in the theory of DNA computing". Theoretical Computer Science. 287 (1): 3–38. doi:10.1016/s0304-3975(02)00134-2.
  16. Pierce; et al. (2016). "Current resolution of data in DNA computing". Bioinformatics Research. 88 (34): 435–451.
  17. Braich, Ravinderjit S., et al. "Solution of a satisfiability problem on a gel-based DNA computer." DNA Computing. Springer Berlin Heidelberg, 2001. 27-42.
  18. [FR] - J. Macdonald, D. Stefanovic et M. Stojanovic, Des assemblages d'ADN rompus au jeu et au travail, Pour la Science, No. 375, January 2009, p. 68-75
  19. Qian, Lulu; Winfree, Erik; Bruck, Jehoshua (July 2011). "Neural network computation with DNA strand displacement cascades". Nature. 475 (7356): 368–372. doi:10.1038/nature10262. ISSN 0028-0836. PMID 21776082.
  20. Cherry, Kevin M.; Qian, Lulu (2018-07-04). "Scaling up molecular pattern recognition with DNA-based winner-take-all neural networks". Nature. 559 (7714): 370–376. doi:10.1038/s41586-018-0289-6. ISSN 0028-0836. PMID 29973727.
  21. Lewin, D. I. (2002). "DNA computing". Computing in Science & Engineering. 4 (3): 5–8. doi:10.1109/5992.998634.
  22. Shu, Jian-Jun; Wang, Q.-W.; Yong, K.-Y. (2011). "DNA-based computing of strategic assignment problems". Physical Review Letters. 106 (18): 188702. Bibcode:2011PhRvL.106r8702S. doi:10.1103/PhysRevLett.106.188702. PMID 21635133.
  23. Shu, Jian-Jun; Wang, Q.-W.; Yong, K.-Y.; Shao, F.; Lee, K.J. (2015). "Programmable DNA-mediated multitasking processor". Journal of Physical Chemistry B. 119 (17): 5639–5644. arXiv:1508.03509. doi:10.1021/acs.jpcb.5b02165. PMID 25874653.
  24. Wong, J.R.; Lee, K.J.; Shu, Jian-Jun; Shao, F. (2015). "Magnetic fields facilitate DNA-mediated charge transport". Biochemistry. 54 (21): 3392–3399. arXiv:1508.03512. doi:10.1021/acs.biochem.5b00295. PMID 25946473.
  25. Weiss, S. (1999). "Fluorescence Spectroscopy of Single Biomolecules". Science. 283 (5408): 1676–1683. Bibcode:1999Sci...283.1676W. doi:10.1126/science.283.5408.1676. PMID 10073925.. Also available here: http://www.lps.ens.fr/~vincent/smb/PDF/weiss-1.pdf
  26. Santoro, S. W.; Joyce, G. F. (1997). "A general purpose RNA-cleaving DNA enzyme". Proceedings of the National Academy of Sciences. 94 (9): 4262–4266. Bibcode:1997PNAS...94.4262S. doi:10.1073/pnas.94.9.4262. PMC 20710. PMID 9113977.. Also available here:
  27. Stojanovic, M. N.; Stefanovic, D. (2003). "A deoxyribozyme-based molecular automaton". Nature Biotechnology. 21 (9): 1069–1074. doi:10.1038/nbt862. PMID 12923549.. Also available here:
  28. MacDonald, J.; Li, Y.; Sutovic, M.; Lederman, H.; Pendri, K.; Lu, W.; Andrews, B. L.; Stefanovic, D.; Stojanovic, M. N. (2006). "Medium Scale Integration of Molecular Logic Gates in an Automaton". Nano Letters. 6 (11): 2598–2603. Bibcode:2006NanoL...6.2598M. doi:10.1021/nl0620684. PMID 17090098.. Also available here:
  29. Stojanovic, M. N.; Mitchell, T. E.; Stefanovic, D. (2002). "Deoxyribozyme-Based Logic Gates". Journal of the American Chemical Society. 124 (14): 3555–3561. doi:10.1021/ja016756v. PMID 11929243.. Also available at
  30. Cruz, R. P. G.; Withers, J. B.; Li, Y. (2004). "Dinucleotide Junction Cleavage Versatility of 8-17 Deoxyribozyme". Chemistry & Biology. 11 (1): 57–67. doi:10.1016/j.chembiol.2003.12.012. PMID 15112995.
  31. Darko Stefanovic's Group, Molecular Logic Gates Archived 2010-06-18 at the Wayback Machine and MAYA II, a second-generation tic-tac-toe playing automaton Archived 2010-06-18 at the Wayback Machine.
  32. Shapiro, Ehud (1999-12-07). "A Mechanical Turing Machine: Blueprint for a Biomolecular Computer". Weizmann Institute of Science. Archived from the original on 2009-01-03. Retrieved 2009-08-13.
  33. Benenson, Y.; Paz-Elizur, T.; Adar, R.; Keinan, E.; Livneh, Z.; Shapiro, E. (2001). "Programmable and autonomous computing machine made of biomolecules". Nature. 414 (6862): 430–434. Bibcode:2001Natur.414..430B. doi:10.1038/35106533. PMC 3838952. PMID 11719800.. Also available here: Archived 2012-05-10 at the Wayback Machine
  34. Bond, G. L.; Hu, W.; Levine, A. J. (2005). "MDM2 is a Central Node in the p53 Pathway: 12 Years and Counting". Current Cancer Drug Targets. 5 (1): 3–8. doi:10.2174/1568009053332627. PMID 15720184.
  35. Kahan, M.; Gil, B.; Adar, R.; Shapiro, E. (2008). "Towards molecular computers that operate in a biological environment". Physica D: Nonlinear Phenomena. 237 (9): 1165–1172. Bibcode:2008PhyD..237.1165K. doi:10.1016/j.physd.2008.01.027.. Also available here:
  36. Seelig, G.; Soloveichik, D.; Zhang, D. Y.; Winfree, E. (8 December 2006). "Enzyme-free nucleic acid logic circuits". Science. 314 (5805): 1585–1588. Bibcode:2006Sci...314.1585S. doi:10.1126/science.1132493. PMID 17158324.
  37. Rothemund, P. W. K.; Papadakis, N.; Winfree, E. (2004). "Algorithmic Self-Assembly of DNA Sierpinski Triangles". PLoS Biology. 2 (12): e424. doi:10.1371/journal.pbio.0020424. PMC 534809. PMID 15583715.
  38. (Caltech's own article) Archived October 14, 2011, at the Wayback Machine
  39. Scaling Up Digital Circuit Computation with DNA Strand Displacement Cascades
  40. Online

Further reading

  • Martyn Amos (June 2005). Theoretical and Experimental DNA Computation. Natural Computing Series. Springer. ISBN 978-3-540-65773-6. The first general text to cover the whole field.
  • Gheorge Paun, Grzegorz Rozenberg, Arto Salomaa (October 1998). DNA Computing - New Computing Paradigms. Springer-Verlag. ISBN 978-3-540-64196-4.CS1 maint: multiple names: authors list (link) The book starts with an introduction to DNA-related matters, the basics of biochemistry and language and computation theory, and progresses to the advanced mathematical theory of DNA computing.
  • JB. Waldner (January 2007). Nanocomputers and Swarm Intelligence. ISTE. p. 189. ISBN 978-2-7462-1516-0.
  • Zoja Ignatova; Israel Martinez-Perez; Karl-Heinz Zimmermann (January 2008). DNA Computing Models. Springer. p. 288. ISBN 978-0-387-73635-8. A new general text to cover the whole field.
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