DNA Computation

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          Presentation Outline

 Basic concepts of DNA
 Origin of DNA Computing
 Solution for NP-Complete Problems
 Advantages of DNA Computing
 Problems with Adleman’s Experiment
 DNA Computers
 Current research
 Conclusion
                What is DNA?

 DNA stands for Deoxyribonucleic Acid
 DNA represents the genetic blueprint of living
 DNA contains “instructions” for assembling
 Every cell in human body has a complete set
  of DNA
 DNA is unique for each individual
Double Helix shape of DNA

         The two strands of a DNA
          molecule are anti parallel where
          each strand runs in an opposite
         Complementary base pairs
          Adenine & Thymine
          Guanine & Cytosine
         Two strands are held together
          by weak hydrogen bonds
          between the complementary base
Graphical Representation of inherent bonding
          properties of DNA
              Instructions in DNA
                      Sequence to indicate the
                       start of an instruction


        Instruction that triggers
                                    Instruction for hair cells
        Hormone injection
 Instructions are coded in a sequence of the DNA
 A segment of DNA is exposed, transcribed and
  translated to carry out instructions
DNA Duplication
            Protein Synthesis
 DNA  RNA  Proteins  actions
                    Basics and
              Origin of DNA Computing
 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
  faster than utilizing a conventional computer

 Leonard Adleman proposed that the makeup of DNA and
  its multitude of possible combining nucleotides could
  have application in computational research techniques
        Dense Information Storage
 This image shows 1 gram
  of DNA on a CD. The CD
  can hold 800 MB of data.

 The 1 gram of DNA can
  hold about 1x1014 MB of

 The number of CDs
  required to hold this
  amount of information,
  lined up edge to edge,
  would circle the Earth 375
  times, and would take
  163,000 centuries to listen
     How Dense is the Information Storage?

 with bases spaced at 0.35 nm along DNA, data
  density is over a million Gbits/inch compared to
  7 Gbits/inch in typical high performance HDD.
 Check this out………..
      How enormous is the parallelism?

 A test tube of DNA can contain trillions of
  strands. Each operation on a test tube of DNA is
  carried out on all strands in the tube in parallel !

 Check this out……. We Typically use
         How extraordinary is the energy
    Adleman figured his computer was running
    2 x 1019 operations per joule.
          NP Complete Problems
   A hard NP problem is one in which the time required
    for algorithms to find a solution increases exponentially
    with the number of variables involved.
   A hard NP problem can eat up a lot of computer cycles
    if carried out by brute force. For example, the Hamilton
    path problem —commonly known as the traveling
    salesman problem —is a hard NP problem.
   If there are N cities in a Hamilton path problem, there
    are N!/2 possible paths, where N! is N factorial, which
    is the multiplication of every integer from 1 to N —for
    example, 4!= 1 x 2 x 3 x 4.
    Inventor Of DNA Computing: Adleman

 Adleman is often called the inventor of DNA computers.
  His article in a 1994 issue of the journal Science outlined
  how to use DNA to solve a well-known mathematical
  problem, called the directed Hamilton Path problem, also
  known as the "traveling salesman" problem.
 The goal of the problem is to find the shortest route
  between a number of cities, going through each city only
  once. As you add more cities to the problem, the
  problem becomes more difficult. Adleman chose to find
  the shortest route between seven cities.
         Steps for Adleman’s Experiment

 Strands of DNA represent the seven cities. In genes,
  genetic coding is represented by the letters A, T, C and
  G. Some sequence of these four letters represented
  each city and possible flight path.
 These molecules are then mixed in a test tube, with
  some of these DNA strands sticking together. A chain of
  these strands represents a possible answer.
 Within a few seconds, all of the possible combinations of
  DNA strands, which represent answers, are created in
  the test tube.
 Adleman eliminates the wrong molecules through
  chemical reactions, which leaves behind only the flight
  paths that connect all seven cities.
  Adleman’s Experiment

 Hamilton Path Problem
  (also known as the travelling salesperson problem)

 Perth                      Alice Spring             Brisbane



         Is there any Hamiltonian path from Darwin to Alice Spring?
     Adleman’s Experiment (Cont’d)

 Solution by inspection is:
  Darwin  Brisbane  Sydney  Melbourne  Perth 
  Alice Spring
 BUT, there is no deterministic solution to this
  problem, i.e. we must check all possible
  combinations.             Darwin
                          Alice Spring


    Adleman’s Experiment (Cont’d)

1. Encode each city with complementary base -
   vertex molecules
   Sydney - TTAAGG
   Perth - AAAGGG
   Melbourne - GATACT
   Brisbane - CGGTGC
   Alice Spring – CGTCCA
   Darwin - CCGATG
     Adleman’s Experiment (Cont’d)

2. Encode all possible paths using the
   complementary base – edge molecules
   Sydney  Melbourne – AGGGAT
   Melbourne  Sydney – ACTTTA
   Melbourne  Perth – ACTGGG
   Adleman’s Experiment (Cont’d)

3. Merge vertex molecules and edge molecules.
   All complementary base will adhere to each other to
   form a long chains of DNA molecules
   Solution with       Merge       Solution with
    vertex DNA          &           edge DNA
     molecules         Anneal        molecules

          Long chains of DNA molecules (All
           possible paths exist in the graph)
Adleman’s Experiment (Cont’d)

    The solution is a double helix molecule:
Darwin   Brisbane     Sydney    Melbourne     Perth    Alice Spring


    Darwin      Brisbane   Sydney       Melbourne Perth
    Brisbane   Sydney    Melbourne   Perth    Alice Spring
The success of the Adleman DNA computer proves that
DNA can be used to calculate complex mathematical
Three years after Adleman's experiment, researchers at
the University of Rochester developed logic gates made of
Currently, logic gates interpret input signals from silicon
transistors, and convert those signals into an output signal
that allows the computer to perform complex functions.
But the logic gates made up DNA instead of using
electrical signals to perform logical, rely on DNA code.
They detect fragments of genetic material as input, splice
together these fragments and form a single output.
 For instance, a genetic gate called the "And gate" links
  two DNA inputs by chemically binding them so they're
  locked in an end-to-end structure.

 The researchers believe that these logic gates might be
  combined with DNA microchips to create a breakthrough
  in DNA computing.

 breaking the weak hydrogen bonds in a double helix
 to form two DNA strands which are complement to
 each other
 reconnecting the hydrogen bonds between
 complementary DNA strands
  Operations (Cont’d)

 Merging
  mixing two test tubes with many DNA molecules
 Amplification
  DNA replication to make many copies of the original
  DNA molecules
 Selection
  elimination of errors (e.g. mutations) and selection of
  correct DNA molecules
  given a test tube T and a strand s, it is possible to extract all the strands in T
   that contain s as a subsequence, and to separate them from those that do not
   contain it.

                                                             Spooling the DNA with a metal
                                                                hook or similar device

                               Precipitation of more DNA
                                   strands in alcohol
Formation of DNA strands.
     Advantages of a DNA Computer
 Parallel Computing- DNA computers are massively

 Incredibly light weight- With only 1 LB of DNA you
  have more computing power than all the computers ever

 Low power- The only power needed is to keep DNA
  from denaturing.

 Solves Complex Problems quickly- A DNA computer
  can solve hardest of problems in a matter of weeks.
 Perform millions of operations simultaneously.

 Generate a complete set of potential solutions.

 Efficiently handle massive amounts of working memory.

 cheap, clean, readily available materials.

 amazing ability to store information.
Current Research
          Soft Molecular Computing
 DNA Computing utilizes the complex interaction of bio
  molecules and molecular biology to effect computation

 Lab experiments in DNA Computing are unreliable,
  inefficient, unscalable and expensive compared to
  conventional computing standards

 A critical issue in DNA Computing is to test protocols

 So we will describe a platform EDNA, to address this
     EDNA,integrated software platform
 Address the basic problems of reliability, efficiency and
  scalability for molecular protocols using DNA molecules.

 Allows to take advantage of digital computers to gain
  realistic insights on actual test tube performance of a
  protocol before they are carried out in the lab.

 It is a research tool that makes it possible to use the
  advantages of conventional computing to bring to DNA
  computing comparable levels of reliability and efficiency.
 EDNA is object oriented and extensible, so that it can
  easily evolve as the field progresses.

 EDNA is therefore a research tool that makes it possible
  to use the advantages of conventional computing to
  make DNA computing reliable.

 EDNA includes graphical interfaces and click-and-drag
  facilities to enable easy use.
             DNA Authentication
 Taiwan introduced the world's first DNA authentication

 The first DNA chip in the world has finally been
  developed by Biowell Technology Inc. after two years of

 Inside the chip is synthesized DNA, which can be
  identified by a device similar to an identification card or a
  credit card reader.

 Suggestions have been made to make use of DNA chips
  on national identification cards in order to crack down on
  frauds using fake ID cards.
               DNA Authentication
 The synthesized DNA inside the chip generates DNA
  signals which only the company's readers can detect
  and authenticate in two seconds.

 The DNA chip can also be used on passports, credit
  cards, debit cards, membership cards, driver's licenses,
  automobile license plates, CDs, VCDs, DVDs,
  notebooks, PDAs, computer software.

 In addition to the absolute security of the DNA
  authentication systems, the price of the DNA
  authentication product is comparable to that of IC chip.
DNA Chip
        What are the challenges?
 Error: Molecular operations are not perfect.

 Reversible and Irreversible Error

 Efficiency: How many molecules contribute?

 Encoding problem in molecules is difficult

 Scaling to larger problems
  What are the challenges for
    Computer Science?
 Discover problems DNA Computers are
  good at
  o Messy reactions as positive
  o Evolvable, not programmable
 Characterize complexity for DNA
  computations with bounded resources
 New notions of what a ―computation‖ is?
       What are the challenges for
        molecular biology?
 Develop computation-specific protocols
 Better understanding of basic mechanisms
  and properties
 Better characterization of processes
 Measures of reliability and efficiency
 Advanced understanding of biomolecules
  other than DNA and RNA
    What developments can we
     expect in the near-term?
 Increased use of molecules other than DNA
 Evolutionary approaches
 Continued impact by advances in molecular
 Some impact on molecular biology by DNA
 Increased error avoidance and detection
      What are the long-term
 Cross-fertilization among evolutionary
  computing, DNA computing, molecular
  biology, and computation biology

 Niche uses of DNA computers for problems
  that are difficult for electronic computers

 Increased movement into exploring the
  connection between life and computation?
      DNA Vs Electronic computers

 At Present,NOT competitive with the state-of-the-
  art algorithms on electronic computers
  o Only small instances of HDPP can be solved.Reason?..for
    n vertices, we require 2^n molecules.
  o Time consuming laboratory procedures.
  o Good computer programs that can solve TSP for 100
    vertices in a matter of minutes.
  o No universal method of data representation.
             Size restrictions
 Adleman’s process to solve the traveling
  salesman problem for 200 cities would require
  an amount of DNA that weighed more than the

 The computation time required to solve
  problems with a DNA computer does not grow
  exponentially, but amount of DNA required
          Error Restrictions
 DNA computing involves a relatively large
  amount of error.

 As size of problem grows, probability of
  receiving incorrect answer eventually becomes
  greater than probability of receiving correct

High cost is time.

Occasionally slower-Simple problems are solved
much faster on electronic computers.

Reliability- There is sometime errors in the pairing
of DNA strands
     Some more……….

 Different problems need different approaches.

 requires human assistance!

 DNA in vitro decays through time,so lab procedures should
  not take too long.

 No efficient implementation has been produced for testing,
  verification and general experimentation.
                        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 recent years, and future
  advances may make DNA computers more efficient.

 The University of Wisconsin is experimenting with chip-based DNA computers.

 DNA computers are unlikely to feature word processing, emailing and solitaire

 Instead, their powerful computing power will be used for areas of encryption,
  genetic programming, language systems, and algorithms or by airlines wanting to
  map more efficient routes. Hence better applicable in only some promising areas.
            THANK YOU!!!!!

It will take years to develop a practical,
 workable DNA computer.

But…Let’s all hope that this DREAM comes

                            Pratibha Rathore
                                VIII Sem

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