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DNA COMPUTING

Obaidur Rehman Khan

0404331029

EC, Final Year


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WhatisDNA?

All organisms on this planet are made of the same type ofgenetic blueprint.

Within the cells of any organism is a substance called DNAwhich is a double-stranded helix of nucleotides.

DNA carries the genetic information of a cell. This information is the code used within cells to form proteins

and is the building block upon which life is formed. Strands of DNA are long polymers of millions of linked

nucleotides.


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Graphical Representation ofinherent bonding properties of

DNA


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Basics And Origin of DNAComputing DNA computing is utilizing the property of DNA for

massively parallel computation.

With an appropriate setup and enough DNA, one can

potentially solve huge problems by parallel search.

Utilizing DNA for this type of computation can be much fasterthan utilizing a conventional computer

Leonard Adleman proposed that the makeup of DNA and itsmultitude of possible combining nucleotides could haveapplication in computational research techniques.


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BiochemistryBasics

Extraction

given a test tube T and a strand s, it is possible to extract all thestrands in T that contain s as a subsequence, and to separate them

from those that do not contain it.

Formation of DNA strands.

Precipitation of more DNA

strands in alcohol

Spooling the DNA with a metal

hook or similar device


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Annealing

Curves represent single strands of DNA ogilonucleotides. The half arrow head represents the 3 end

of the strand. The dotted lines indicate the hydrogen bonding joining the strands.

The hydrogen bonding

between two

complimentary

sequences is weaker

than the one that links

nucleotides of the

same sequence.It is

possible to

pair(anneal) and

separate(melt) two

antiparallel and

complementary single

strands.


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Polymerase ChainReaction

PCR: One way toamplify DNA.

PCR alternates

between two phases:

separate DNA intosingle strands using

heat; convert into

double strands using

primer and

polymerase reaction.

PCR rapidlyamplifies a single

DNA molecule into

billions of molecules


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Gel Electrophoresis

Used to measure the length of a DNA molecule. Based on the fact that DNA molecules are vely

charged.

Gel Electrophoresis


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Adlemans solution of theHamiltonian Directed Path

Problem (HDPP)


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Algorithm

Given a graph with n vertices,

1. Generate a set of random paths through the graph.

2. for each path in the set:

a. Check whether that path starts at the start vertex and ends withthe end vertex. If not, remove that path from the set.b. Check if that path passes through exactly n vertices. If not,remove that path from the set.

c. For each vertex, check if that path passes through that vertex. Ifnot, remove that path from the set.

3. If the set is not empty, then report that there is a Hamiltonian path.If the set is empty, report that there is no Hamiltonian path.


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Assign a random DNA sequence to each city.

So, Atlanta becomes ACTTGCAG, Boston TCGGACTG and so on such asfirst half of the DNA sequence as the first name of the city and the secondhalf as the last name. So Atlanta's last name is GCAG, whereas Boston'sfirst name is TCGG.

Give each nonstop flight a DNA "flight number," obtained byconcatenating the last name of the city of origin with the first name of thecity of destination. In the example, the Atlanta-to-Boston flight number

becomes GCAGTCGG.

the Atlanta-to Boston flight number (GCAGTCGG) and thecomplementary name of Boston (AGCCTGAC) might meet by chance. Bydesign, the former sequence ends with TCGG, and the latter starts withAGCC. Because these sequences are complementary, they will sticktogether.

If the resulting complex now encounters the Boston-to-Chicago flightnumber (ACTGGGCT), it, too, will join the complex because the end ofthe former (TGAC) is complementary to the beginning of the latter(ACTG). In this manner, complexes will grow it length, with DNA flightnumbers splinted together by complementary DNA city names.


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Polymerase chain reaction (PCR).

This important technique requires many copies of two short pieces of DNAas primers to signal the DNA polymerase to start its Watson-Crickreplication. The primers used were the last name of the start city (GCAG

for Atlanta) and the Watson-Crick complement of the first name of the endcity (GGCT for Detroit). These two primers worked in concert: the firstalerted DNA polymerase to copy complements of sequences that had theright start city, and the second initiated the duplication of molecules thatencoded the correct end city.

The result was that molecules with both the right start and end cities were

reproduced at an exponential rate. In contrast, molecules that encoded theright start city but an incorrect end city, or vice versa, were duplicated in amuch slower, linear fashion. DNA sequences that had neither the right startnor end were not duplicated at all. Thus, by taking a small amount of themixture after the PCR was completed, we obtained a solution containingmany copies of the molecules that had both the right start and end cities

Next, we used gel electrophoresis to identify those molecules that had theright length (in the example a length of 24). All other molecules were

discarded. This completed step 2b of the algorithm.


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To check the remaining sequences for whether their paths passed through all theintermediary cities, we took advantage of Watson Crick annealing in a procedurecalled affinity separation. This process uses multiple copies of a DNA "probe"molecule that encodes the complementary name of a particular city (for example,Boston). These probes are attached to microscopic iron balls, each approximately

one micron in diameter. We suspended the balls in the tube containing theremaining molecules under conditions that encouraged Watson Crick pairing. Onlythose molecules that contained the desired city's name (Boston) would anneal to theprobes.

Then a magnet is placed against the wall of the test tube to attract and hold themetal balls to the side while poured out the liquid phase containing molecules thatdid not have the desired city's name. Then add new solvent and removed the

magnet in order to re suspend the balls. Raising the temperature of the mixturecaused the molecules to break free from the probes and re dissolve in the liquid.Next, reapplied the magnet to attract the bails again to the side of the test tube, butthis time without any molecules attached.

The liquid, which now contained the desired DNA strands (in the example,encoding paths that went through Boston), could then be poured into a new tube forfurther screening. The process was repeated for the remaining intermediary cities

(Chicago, in this case). At the conclusion of the affinity separations, step 2c of the algorithm was over, andthe DNA molecules left in the tube should be precisely those encoding Hamiltonianpaths. Hence, if the tube contained any DNA at all, it could conclude that aHamiltonian path existed in the graph. No DNA would indicate that no such pathexisted.


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Advantages of DNA Computing

Perform millions of operations simultaneously

Generate a complete set of potential solutions

Conduct large parallel searches

Efficiently handle massive amounts of working memory

cheap, clean, readily available materials

amazing ability to store information


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THE FUTURE! Algorithm used by Adleman for the traveling salesman problem was

simple. As technology becomes more refined, more efficient algorithms

may be discovered.

DNA Manipulation technology has rapidly improved in recentyears, and future advances may make DNA computers moreefficient.

DNA computers are unlikely to feature word processing, emailingand solitaire programs.

Instead, their powerful computing power will be used for areas ofencryption, genetic programming, language systems, and algorithmsor by airlines wanting to map more efficient routes. Hence betterapplicable in only some promising areas.


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Conclusion

The beauty of DNA research trends is found in the possibilityof mankinds utilization of its very life building blocks tosolve its most difficult problems.

The field of DNA computing is still in its infancy and theapplications for this technology are still not fully understood.

Is DNA computing viable perhaps, but the obstacles that

face the field such as the extrapolation and practicalcomputational environments required are daunting.


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THANK YOU!

It will take years to develop a practical,

workable DNA computer.

ButLets all hope that this DREAM comes

true!!!


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References

Molecular computation of solutions to combinatorial

problems- Leonard .M. Adleman

Introduction to computational molecular biology by

Joao Setubal and Joao Meidans -Sections 9.1 and9.3

DNA computing, new computing paradigms by

G.Paun, G.Rozenberg, A.Salomaa-chapter 2

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