BIO COMPUTERS

INTRODUCTION  Growing needs of mankind-Rapid Development.  Rapid advancement in computer technology will lose its momentum when silicon chip reaches its full capacity & miniaturization  Solving complex problems which today's supercomputers are unable to perform in stipulated period of time. WHAT COULD BE A REMEDY TO THIS CONCERN?????

What is Biological Computer?  Biological Computers are computers which use synthesized biological components to store and manipulate data analogous to processes in the human body.  The result is small yet faster computer that operates with great accuracy.  Main biological component used in a Biological Computer is :

What is DNA?  DNA Stands for DeOxyRiboNucleic Acid.  A hereditary material found in almost all living organisms.  Located inside the nucleus of a cell.  Helps in long term storage of information.  Information in DNA is stored as a code made of four chemical bases (A,T,G & C).  Order & sequence of these bases determine the kind of information stored.

Graphical Representation of Inherent Bonding Properties of DNA

What is a DNA Computer? DNA Computers are small, fast and highly efficient computers which includes the following properties:-  Dense data storage.  Massively parallel computation.  Extraordinary energy efficiency.

How Dense is the Data 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 Efficiency?  Modern supercomputers only operate at 109 operations per joule.  Adleman figured his computer was running 2 x operations per joule.

Adleman- Inventor of Biological Computers  His article released in 1994,described how to use DNA to solve a well-known mathematical problem, called the directed Hamilton Path problem.  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.

Steps in Adleman’s Experiment  Strands of DNA represent the seven cities. 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.

Hamilton Path Problem (also known as the travelling salesperson problem) Darwin PerthAlice Spring Brisbane Melbourne Sydney Is there any Hamiltonian path from Darwin to Alice Spring?

Adleman’s Experiment (continued…)  Encode each city with complementary base - vertex molecules Sydney - TTAAGG Perth - AAAGGG Melbourne - GATACT Brisbane - CGGTGC Alice Spring - CGTCCA Darwin - CCGATG

Adleman’s Experiment (continued…)  Encode all possible paths using the complementary base – edge molecules Sydney  Melbourne – AGGGAT Melbourne  Sydney – ACTTTA Melbourne  Perth – ACTGGG etc…

Adleman’s Experiment (continued…)  Merge vertex molecules and edge molecules. All complementary base will adhere to each other to form a long chains of DNA molecules Solution with vertex DNA molecules Solution with edge DNA molecules Merge & Anneal Long chains of DNA molecules (All possible paths exist in the graph)

Adleman’s Experiment (continued…)  Select a path that starts with proper city and ends with final city.  Select paths with correct number of cities.  Select path which contains each city only once.

Adleman’s Experiment (continued…)  The solution is a double helix molecule: Hence Adleman proved DNA can be used to solve complex problems………. CCGATG – CGGTGC – TTAAGG – GATACT – AAAGGG – CGTCCA TACGCC – ACGAAT – TCCCTA – TGATTT – CCCGCA Alice SpringPerth Melbourne SydneyBrisbane Darwin  Brisbane Brisbane  Sydney Sydney  Melbourne Melbourne  Perth Perth  Alice Spring

Conventional vs. Biological Computers ConventionalBiological Component materials Inorganic, e.g. siliconBiological, e.g. DNA Processing schemeSequential and limited massively parallel Massively parallel Current max. operations Op.s per sec Op.s per sec. Quantum effects a problem? YesNo Toxic components?YesNo Energy efficient?NoYes

Applications  Can be a general purpose tool for a variety of problems  Many possible applications: Pattern recognition Cryptography Evaluating gene sequence  Medical Application: ‘developing disease’ treatments such as cancer

Advantages of Biological Computers Parallel Computing- Biological computers are massively parallel. Incredibly light weight- With only 1 LB of DNA you have more computing power than all the computers ever made. 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.

Advantages (Continued…) 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.

Limitations Error: Molecular operations are not perfect. Efficiency: How many molecules contribute? Encoding problem in molecules is difficult 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 answer Reliability- There is sometime errors in the pairing of DNA strands DNA in vitro decays through time, so lab procedures should not take too long.

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.

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