Download DNA COMPUTING

Technical Seminar Report On
“DNA COMPUTING”
Introduction
Double-stranded molecule twisted into a helix
Each strand, comprised of a
sugar-phosphate backbone and
attached bases, is connected to
a complementary strand by non
-covalent hydrogen bonding
between paired bases
Bases are:
adenine (A)
guanine (G).
thymine (T)
cytosine (C)
A and T are connected by two hydrogen bonds. G and C
are connected by three hydrogen bonds
DNA As Computing Machine
A DNA-based finite automaton computes via repeated
cycles of self assembly and processing.
DNA molecules serve as input, output, and software, and
the hardware consists of DNA restriction and ligation
Enzymes Using ATP as fuel
The reversible self-assembly is driven by hybridization
energy between input/software complementary sticky ends,
followed by an irreversible processing step i.e. an
irreversible software-directed cleavage (hydrolysis of the
Input DNA backbone) of the input molecule, which drives the
computation forward by increasing entropy and releasing
heat and hence does not require ATP or heating.
Continued…
The cleavage uses the restriction enzyme FokI, which serves
as the hardware, to operate on a non covalent software/input
hybrid.
This automaton use a fixed amount of software and hardware
molecules to process any input molecule of any length
without external energy supply.
This automaton demonstrate 3 1012 automata per µl
10
6
.
6

10
performing
transitions per second per µl
29
dissipating about 5  10 W/µl as heat .
Energy Dissipation Calculation
 A computation is a series of single symbol cleavages, which
occur sequentially for each input molecule
 if  i (t ) = the number of moles of each intermediate at a
time t then
n
 i (t ) =  j (t )
j i 1
Where  i (t ) = the number of moles of each cleaved symbol  i (t )
average energy dissipation between time point’s t1 and t2 is
∆G =∆G (281K)+RTlnQ , Q =   V
0
i
i 1
i
i
1
i
i
where V is the reaction volume.
The ∆G has the units of J/mole
 i and  i = the average number of moles of each intermediate
State Machines and Finite Automata
A finite automaton is a unidirectional read-only
Turing machine.
Its input is a finite string of symbols.
It is initially positioned on the leftmost input symbol in a
default initial state, and in each transition moves one
symbol to the right, possibly changing its internal state.
Its software consists of transition rules, each specifying a
next state based on the current state and current symbol.
A computation terminates after the last input
symbol is processed, the final state being its ‘‘output.’’
An automaton accepts an input if there is a computation
with this input that ends in an accepting final state
Molecular Finite Automaton
Encoding of a, b, and terminator (sense strands) and the
<state, symbol> Interpretation of exposed 4-nt sticky ends,
the leftmost representing the current symbol and the state S1
,similarly the rightmost for S0 (fig : A).
Hardware: The FokI restriction enzyme, which recognizes
the sequence GGATG and cleaves 9 and 13 nt apart (fig :B)
Software: Each DNA molecule realizes a different transition
rule by detecting a current state and symbol and determining
a next state. It consists of a<state, symbol> Detector (yellow),
a FokI recognition site (blue), and a spacer (gray) of variable
length that determines the FokI cleavage site inside the next
symbol, which in turn defines the next state.
How it computes ???
Double-stranded DNA molecules with sticky ends realize
both the software (Fig. C) and the input (Fig. D)
The computation proceeds via a cascade of transition cycles,
each cleaving and scattering one input symbol, Both
hardware and software molecules are recycled
Each computational step cleaves and scatters one input
symbol.
In the core computational step, the software molecule used
in one step is not consumed as it dissociates spontaneously
from the cleaved input symbol (Fig. E), rendering it reusable
for subsequent transitions.
The computation proceeds until no software molecule
matches the state-symbol pair encoded by the exposed
sticky end or until the special terminator symbol is cleaved
Advantages of DNA Computers
 Parallelism
 Gigantic Memory Capacity
information density =1 bit per cubic nanometer
data density = 18 Megabits per inch
If assumed one base per square nanometer, the data density ≥ one
million Gigabits per square inch but data density of a typical high
performance hard driver, which is about 7 gigabits per square inch
 Low Power Dissipation
 Clean, Cheap and Available
clean because people do not use any harmful material to
produce it and also no pollution generates
cheap and available because you can easily find DNA from
nature while it’s not necessary to exploit mines
Disadvantages
 Occasionally Slow
 Hydrolysis
The DNA molecules can fracture. Over the six months you're
computing your DNA system is gradually turning to water
 Information Untransmittable
Current DNA algorithms compute successfully without passing
any information from one processor to the next in a multiprocessor
connection-bus
 Reliability Problems
Errors in DNA Computers happen due to many factors
Annealing (or hybridization) Errors while combine with the proper
DNA complements
Misincorporation errors while synthesizing the copies of the
DNA strands in Polymerase Chain Reaction (PCR)
Application of DNA Based Computation
Massively Parallel Processing
Solving NP-Complete and Hard Computational Problems
Storage and Associative Memory
DNA2DNA Applications
Implications to Biology, Chemistry, and Medicine
Solution to Hamiltonian path problem
The Hamilton path problem —commonly known as the
traveling salesman problem —is a hard NP problem
If there are N cities then , there are N! /2 possible paths and
the goal is to find a path from the start city to the end city
going through every city only once
STEP 1: Represent each city by a single DNA strand
containing 20 randomly chosen amino acid bases
STEP 2: Represent the route between any two cities by a
single DNA strand where the 1st 10 amino acid
bases are the complementary bases to the last
10 bases in City 1 and the 2nd 10 bases are the
complementary bases to the first 10 bases in
City 2.
Continued…
STEP 3: Millions of stands of DNA representing every
city and every possible route between any two
cities are placed in a test tube where the strands
combine. The end result is a large number of long
strings of variable lengths formed by the strands
combining.
To determine the solution:
Look only for strings that have City 1 at one end and
City 7 at the other
Among these strands look for only the strings that had
seven cities
Among what was left, look for a string with seven
different cities and that is the solution
Conclusion
I have described here:
What is a DNA and how it is helpful in computing?
What is a molecular finite automata and how it computes?
What are the advantages and disadvantages of
DNA computing?
What are the applications and how it is helpful in solving
the Hamiltonian path problem?
But what ever I have given that is just a bird’s eye vision to
this evolving computational field and I hope this paper will
inspire readers to do further research in for removing the
drawbacks like Self-assembly problems, Hydrolysis problems
Stability problems etc.
Thank You !!!