DNA computing is a form of computing which uses DNA, biochemistry and molecular biology, instead of the traditional silicon-based computer technologies.
DNA computing, or, more generally, molecular computing, is a fast developing interdisciplinary area. Research and development in this area concerns theory, experiments and applications of DNA computing.
History: This field was initially developed by Leonard Adleman of the University of Southern California, in 1994. 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 been made and various Turing machines have been proven to be constructible.
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. 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. This was coupled with an input and output module and is capable of diagnosing cancerous activity within a cell, and then releasing an anti-cancer drug upon diagnosis.
What is really a DNA Computer? DNA replicates according to its sequences. So, based on initial input sequences (the problem to be solved), DNA replicates and create trillions of new sequences. The solution to the problem is one of those new sequence strands created that could be achieved by an elimination process.
Practically, the DNA computer looks like a solution in a test tube. There is no mechanical device. Instead of showing up on a computer screen, results are analyzed using a technique that allows scientists to see the length of the DNA output molecule.
In order to enable a convenient input of sequences and present the output to the naked eye, human manipulation is needed. In other words, no I/O devices exist yet for this kind of computer. This kind of computer is not practical yet and is in its initial stages.
Capabilities: DNA computing is fundamentally similar to parallel computing in that it takes advantage of the many different molecules of DNA to try many different possibilities at once.
DNA computing also offers much lower power consumption than traditional silicon computers. DNA uses adenosine triphosphate (ATP) as fuel to allow ligation or as a means to heat the strand to cause disassociation. Both strand hybridization and the hydrolysis of the DNA backbone can occur spontaneously, powered by the potential energy stored in DNA. Consumption of two ATP molecules releases 1.5 x 10−19 J. Even with a large number of transitions per second using two ATP molecules, power output is still low. For instance, Kahan reports 109 transitions per second with an energy consumption of 10−10 W, and similarly Shapiro reports a system producing 7.5 x 1011 outputs in 4000 sec resulting in an energy consumption rate of ~ 10−10 W.
For certain specialized problems, DNA computers are faster and smaller than any other computer built so far. But 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. (Quantum computing, on the other hand, does provide some interesting new capabilities).
DNA computing overlaps with, but is distinct from, DNA nanotechnology. The latter uses the specificity of Watson-Crick basepairing and other DNA properties to make novel structures out of DNA. These structures can be used for DNA computing, but they do not have to be. Additionally, DNA computing can be done without using the types of molecules made possible by DNA nanotechnology.
Methods: 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, DNA tiling, and polymerase chain reaction.
DNAzymes: 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. 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. Stojanovic and MacDonald have used the E6 DNAzymes to build the MAYA I and MAYA II machines, respectively; Stojanovic has also demonstrated logic gates using the 8-17 DNAzyme. 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.
Enzymes: 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.
Shapiro has demonstrated a DNA computer using the FokI enzyme and expanded on his 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. His 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. 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”. It should also be pointed out that the 'software' molecules can be reused in this case.
Examples of DNA computing: MAYA-II (Molecular Array of YES and AND logic gates) is a DNA computer, developed by scientists at Columbia University and the University of New Mexico.
Replacing the normally silicon-based circuits, this chip has DNA strands to form the circuit. It is said that the speed that such DNA-circuited computer chips can attain will rival the silicon-based ones, they will be of use in blood samples and in the body and might part-take in single cell signaling.
It is the successor to the MAYA I which was composed of only 25 logic gates and could only complete a partial game of tic-tac-toe. MAYA-II has more than 100 DNA circuits and can now thoroughly play a game of tic-tac-toe. It is very slow - one move in a game of tic-tac-toe can take up to 30 minutes making it more of a demonstration than an actual application.
The arrangement of this device looks like that of a tic-tac-toe grid and consists of nine wells coated with culture cells. The cell-containing wells are filled with a solution that contain DNA strands coding for red or green fluorescent dyes.
This technology was used to deepen the quality of diagnostics given to patients infected with the West Nile virus. Joanne Macdonald, a Columbia University virologist, hopes this device can be implanted in the human body and control the presence of cancer cells or the levels of insulin for diabetic patients.
One of the suggested uses put forward by MAYA's creators is that technology such as this can be used in situations where fluid is involved, such as in a sample of blood or a body, since it does not use traditional silicon components.
Topics of Interest
Biocomputers use systems of biologically derived molecules, such as DNA and proteins, to perform computational calculations involving storing, retrieving, and processing data.
A computational gene is a molecular automaton consisting of a structural part and a functional part; and its design is such that it might work in a cellular environment.
Molecular electronics (sometimes called moletronics) is an interdisciplinary theme that spans physics, chemistry, and materials science. The unifying feature of this area is the use of molecular building blocks for the fabrication of electronic components, both passive (e.g. resistive wires) and active (e.g. transistors). The concept of molecular electronics has aroused much excitement both in science fiction and among scientists due to the prospect of size reduction in electronics offered by molecular-level control of properties. Molecular electronics provides means to extend Moore's Law beyond the foreseen limits of small-scale conventional silicon integrated circuits.
Peptide computing is a form of computing which uses peptides and molecular biology, instead of traditional silicon-based computer technologies. The basis of this computational model is the affinity of antibodies towards peptide sequences. Similar to DNA computing, the parallel interactions of peptide sequences and antibodies have been used by this model to solve a few NP-complete problems. Specifically, the hamiltonian path problem (HPP) and some versions of the set cover problem are a few NP-complete problems which have been solved using this computational model so far. This model of computation has also been shown to be computationally universal (or Turing complete).
DNA nanotechnology is a subfield of nanotechnology which seeks to use the unique molecular recognition properties of DNA and other nucleic acids to create novel, controllable structures out of DNA. The DNA is thus used as a structural material rather than as a carrier of genetic information, making it an example of bionanotechnology. This has possible applications in molecular self-assembly and in DNA computing.
Natural computing, also called Natural computation, is a terminology introduced to encompass three classes of methods: 1) those that take inspiration from nature for the development of novel problem-solving techniques; 2) those that are based on the use of computers to synthesize natural phenomena; and 3) those that employ natural materials (e.g., molecules) to compute. The main fields of research that compose these three branches are artificial neural networks, evolutionary algorithms, swarm intelligence, artificial immune systems, fractal geometry, artificial life, DNA computing, and quantum computing, among others.
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