Editing DNA computing (section)

== Applications, examples, and recent developments ==
In 1994 [[Leonard Adleman]] presented the first prototype of a DNA computer. The [[:de:TT-100|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.<ref>Braich, Ravinderjit S., et al. "Solution of a satisfiability problem on a gel-based DNA computer." ''DNA Computing''. Springer Berlin Heidelberg, 2001. 27-42.</ref> In Adleman's experiment, the Hamiltonian Path Problem was implemented notationally as the "[[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, the DNA fragments representing the longer routes were eliminated. The remains are the solution to the problem, but overall, the experiment lasted a week.<ref>{{cite journal | last1 = Adleman | first1 = Leonard M | year = 1998 | title = Computing with DNA | journal = Scientific American | volume = 279 | issue = 2| pages = 54–61 | doi = 10.1038/scientificamerican0898-54 | bibcode = 1998SciAm.279b..54A }}</ref> However, current technical limitations prevent the evaluation of the results. Therefore, the experiment isn't suitable for the application, but it is nevertheless a [[proof of concept]].

=== Combinatorial problems ===
First results to these problems were obtained by [[Leonard Adleman]].

* In 1994: Solving a [[Hamiltonian path problem|Hamiltonian path]] in a graph with seven summits.
* In 2002: Solving a [[NP-complete]] problem as well as a [[3-satisfiability|3-SAT]] problem with 20 variables.

=== Tic-tac-toe game ===
In 2002, J. Macdonald, D. Stefanović and M. Stojanović created a DNA computer able to play [[tic-tac-toe]] against a human player.<ref>[FR] - J. Macdonald, D. Stefanovic et M. Stojanovic, ''Des assemblages d'ADN rompus au jeu et au travail'', [[:fr:Pour la Science|Pour la Science]], No.&nbsp;375, January 2009, {{p.|68-75}}</ref> 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 [[Logic function|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 achieved this by programming on a computer in advance with the appropriate set of weights represented by varying concentrations weight molecules which are later added to the test tube that holds the input DNA strands.<ref>{{Cite journal|last1=Qian|first1=Lulu|last2=Winfree|first2=Erik|last3=Bruck|first3=Jehoshua|date=July 2011|title=Neural network computation with DNA strand displacement cascades|journal=Nature|language=En|volume=475|issue=7356|pages=368–372|doi=10.1038/nature10262|pmid=21776082|s2cid=1735584|issn=0028-0836}}</ref><ref name=":4">{{Cite journal|last1=Cherry|first1=Kevin M.|last2=Qian|first2=Lulu|date=2018-07-04|title=Scaling up molecular pattern recognition with DNA-based winner-take-all neural networks|journal=Nature|language=En|volume=559|issue=7714|pages=370–376|doi=10.1038/s41586-018-0289-6|pmid=29973727|issn=0028-0836|bibcode=2018Natur.559..370C|s2cid=49566504|url=https://authors.library.caltech.edu/84840/}}</ref>

=== Improved speed with Localized (cache-like) Computing ===
One of the challenges of DNA computing is its slow speed. While DNA is a biologically compatible substrate, i.e., it can be used at places where silicon technology cannot, its computational speed is still very slow. For example, the square-root circuit used as a benchmark in the field takes over 100 hours to complete.<ref name=":5">{{Cite journal|last1=Qian|first1=L.|last2=Winfree|first2=E.|s2cid=10053541|date=2011-06-02|title=Scaling Up Digital Circuit Computation with DNA Strand Displacement Cascades|journal=Science|volume=332|issue=6034|pages=1196–1201|doi=10.1126/science.1200520|pmid=21636773|issn=0036-8075|bibcode=2011Sci...332.1196Q}}</ref> While newer ways with external enzyme sources are reporting faster and more compact circuits,<ref name=":6">{{Cite journal|last1=Song|first1=Tianqi|last2=Eshra|first2=Abeer|last3=Shah|first3=Shalin|last4=Bui|first4=Hieu|last5=Fu|first5=Daniel|last6=Yang|first6=Ming|last7=Mokhtar|first7=Reem|last8=Reif|first8=John|date=2019-09-23|title=Fast and compact DNA logic circuits based on single-stranded gates using strand-displacing polymerase|journal=Nature Nanotechnology|volume=14|issue=11|pages=1075–1081|doi=10.1038/s41565-019-0544-5|pmid=31548688|issn=1748-3387|bibcode=2019NatNa..14.1075S|s2cid=202729100}}</ref> Chatterjee et al. demonstrated an interesting idea in the field to speed up computation through localized DNA circuits,<ref name="spacearch">{{Cite journal|last1=Chatterjee|first1=Gourab|last2=Dalchau|first2=Neil|last3=Muscat|first3=Richard A.|last4=Phillips|first4=Andrew|last5=Seelig|first5=Georg|date=2017-07-24|title=A spatially localized architecture for fast and modular DNA computing|journal=Nature Nanotechnology|volume=12|issue=9|pages=920–927|doi=10.1038/nnano.2017.127|pmid=28737747|issn=1748-3387|bibcode=2017NatNa..12..920C}}</ref> a concept being further explored by other groups.<ref name=":9">{{Cite journal|last1=Bui|first1=Hieu|last2=Shah|first2=Shalin|last3=Mokhtar|first3=Reem|last4=Song|first4=Tianqi|last5=Garg|first5=Sudhanshu|last6=Reif|first6=John|date=2018-01-25|title=Localized DNA Hybridization Chain Reactions on DNA Origami|journal=ACS Nano|volume=12|issue=2|pages=1146–1155|doi=10.1021/acsnano.7b06699|pmid=29357217|issn=1936-0851}}</ref> This idea, while originally proposed in the field of computer architecture, has been adopted in this field as well. In computer architecture, it is very well-known that if the instructions are executed in sequence, having them loaded in the cache will inevitably lead to fast performance, also called the principle of localization. This is because with instructions in fast cache memory, there is no need swap them in and out of main memory, which can be slow.<ref name="spacearch"/> Similarly, in localized DNA computing, the DNA strands responsible for computation are fixed on a breadboard-like substrate ensuring physical proximity of the computing gates. Such localized DNA computing techniques have been shown to potentially reduce the computation time by orders of magnitude.<ref name="spacearch"/>

=== Renewable (or reversible) DNA computing ===
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) [[Personal computer|PC]]s. 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,<ref>{{Cite journal|last1= Garg|first1= Sudhanshu|last2= Shah|first2= Shalin|last3= Bui|first3= Hieu|last4= Song|first4= Tianqi|last5= Mokhtar|first5= Reem|last6= Reif|first6= John|date= 2018|title= Renewable Time-Responsive DNA Circuits|journal= Small|language= en|volume= 14|issue= 33|pages= 1801470|doi= 10.1002/smll.201801470|pmid= 30022600|issn= 1613-6829|doi-access= free}}</ref> while the second design uses DNA hairpin complexes.<ref>
{{Cite journal
|last1= Eshra|first1= A.|last2= Shah|first2= S.
|last3= Song|first3= T.|last4= Reif|first4= J.
|date= 2019
|title= Renewable DNA hairpin-based logic circuits
|journal= [[IEEE Transactions on Nanotechnology]]
|volume= 18|pages= 252–259
|doi= 10.1109/TNANO.2019.2896189|issn= 1536-125X
|arxiv= 1704.06371
|bibcode= 2019ITNan..18..252E|s2cid= 5616325}}
</ref>
While both designs face some issues (such as reaction leaks), this appears to represent a significant breakthrough in the field of DNA computing. Some other groups have also attempted to address the gate reusability problem.<ref>{{Cite journal|last1=Song|first1=Xin|last2=Eshra|first2=Abeer|last3=Dwyer|first3=Chris|last4=Reif|first4=John|date=2017-05-25|title=Renewable DNA seesaw logic circuits enabled by photoregulation of toehold-mediated strand displacement|journal=RSC Advances|language=en|volume=7|issue=45|pages=28130–28144|doi=10.1039/C7RA02607B|bibcode=2017RSCAd...728130S|issn=2046-2069|doi-access=free}}</ref><ref>{{Cite book|last1=Goel|first1=Ashish|last2=Ibrahimi|first2=Morteza|chapter=Renewable, Time-Responsive DNA Logic Gates for Scalable Digital Circuits |date=2009|editor-last=Deaton|editor-first=Russell|editor2-last=Suyama|editor2-first=Akira|title=DNA Computing and Molecular Programming|series=Lecture Notes in Computer Science|volume=5877|language=en|location=Berlin, Heidelberg|publisher=Springer|pages=67–77|doi=10.1007/978-3-642-10604-0_7|isbn=978-3-642-10604-0}}</ref>

Using strand displacement reactions (SRDs), reversible proposals are presented in the "Synthesis Strategy of Reversible Circuits on DNA Computers" paper for implementing reversible gates and circuits on DNA computers by combining DNA computing and reversible computing techniques. This paper also proposes a universal reversible gate library (URGL) for synthesizing n-bit reversible circuits on DNA computers with an average length and cost of the constructed circuits better than the previous methods.<ref>{{Cite journal|last1=Rofail|first1=Mirna|last2=Younes|first2=Ahmed|date=July 2021|title=Synthesis Strategy of Reversible Circuits on DNA Computers|journal=Symmetry|language=en|volume=13|issue=7|pages=1242|doi=10.3390/sym13071242|bibcode=2021Symm...13.1242R|doi-access=free}}</ref>