DNA computing
Although the field originally started with the demonstration of a computing application by Len Adleman in 1994, it has now been expanded to several other avenues such as the development of storage technologies,[1][2][3] nanoscale imaging modalities,[4][5][6] synthetic controllers and reaction networks,[7][8][9][10] etc.
[11] Adleman demonstrated a proof-of-concept use of DNA as a form of computation which solved the seven-point Hamiltonian path problem.
This expanded the horizon of DNA computing into the realm of memory technology although the in vitro demonstrations were made after almost a decade.
[15] Ned's original idea in the 1980s was to build arbitrary structures using bottom-up DNA self-assembly for applications in crystallography.
While the demonstration by Adleman showed the possibility of DNA-based computers, the DNA design was trivial because as the number of nodes in a graph grows, the number of DNA components required in Adleman's implementation would grow exponentially.
[22] Before 2002, Lila Kari showed that the DNA operations performed by genetic recombination in some organisms are Turing complete.
[23] In 2003, John Reif's group first demonstrated the idea of a DNA-based walker that traversed along a track similar to a line follower robot.
In 2002, J. Macdonald, D. Stefanović and M. Stojanović created a DNA computer able to play tic-tac-toe against a human player.
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.
Kevin Cherry and Lulu Qian at Caltech developed a DNA-based artificial neural network that can recognize 100-bit hand-written digits.
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.
[29] While newer ways with external enzyme sources are reporting faster and more compact circuits,[30] Chatterjee et al. demonstrated an interesting idea in the field to speed up computation through localized DNA circuits,[31] a concept being further explored by other groups.
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.
In particular, John Reif and his group at Duke University have proposed two different techniques to reuse the computing DNA complexes.
[34] While both designs face some issues (such as reaction leaks), this appears to represent a significant breakthrough in the field of DNA computing.
The most fundamental operation in DNA computing and molecular programming is the strand displacement mechanism.
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.
At the highest level, a C-like general purpose programming language is expressed using a set of chemical reaction networks (CRNs).
In 2010, Erik Winfree's group showed that DNA can be used as a substrate to implement arbitrary chemical reactions.
This opened the way to design and synthesis of biochemical controllers since the expressive power of CRNs is equivalent to a Turing machine.
Catalytic DNA (deoxyribozyme or DNAzyme) catalyze a reaction when interacting with the appropriate input, such as a matching oligonucleotide.
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.
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.
[43] While these DNAzymes have been demonstrated to be useful for constructing logic gates, they are limited by the need of a metal cofactor to function, such as Zn2+ or Mn2+, and thus are not useful in vivo.
