D DN NA A Deoxyribonucleic Acid Genetic material of all - - PDF document

“It is not that the bear dances so well, it is that he dances at all”.

• L. Adleman, in reference to DNA computing

Eric Yeung Eric Yeung CPSC 607 – Winter 2004

D DN NA A

• Deoxyribonucleic Acid
• Genetic material of all cellular organisms

and most viruses.

• Carries information required for protein

synthesis and replication.

• DNA is organized on chromosome

located in the nucleus of the cell.

DNA Structure DNA Structure

• double helix structure
• twisted like a winding staircase
• strands composed of chemical compounds

called nucleotides.

Nucleotides Nucleotides Nucleotides Nucleotides

Each nucleotides consists of 3 units

• a sugar molecule called deoxyribose
• a phosphate group
• 1 of 4 different nitrogen compounds

Adenine Thymine Cystosine Guanine

• each nucleotide is paired in a complementary fashion

A <> T G <> C

Founders of DNA Founders of DNA

James D. Watson James D. Watson

• American biochemist

American biochemist

Francis Crick Francis Crick

• British biophysicist

British biophysicist

Watson & Crick Watson & Crick

• In 1953 James Watson, left,

and Francis Crick, right, described the structure of the DNA molecule as a double helix, somewhat like a spiral staircase with many individual steps.

• In 1962 Crick, and Watson

received the Nobel Prize for their pioneering work on the structure of the DNA molecule.

Inventor of DNA Computing Inventor of DNA Computing

Leonard M. Adleman

• Professor of Computer Science
• Professor of Molecular Biology
• University of Southern California

In 1994, published a paper in Science describe how he used DNA to compute a solution to the “traveling salesman problem”

Cracking Encryptions Cracking Encryptions

Three researchers

• Richard J Lipton
• Daniel Boneh
• Christopher T Dunworth
• Outlined a way for a DNA computer to crack

messages encrypted with the US government’s own data encryption standards (DES).

• Messages like classified telephone conversations

and data transmissions between banks and the Federal Reserve.

Cracking Encryptions Cracking Encryptions (

(con con’ ’t t) )

• The coding relies on one of the 72 quadrillion

“keys”

• Testing all possible keys on an electronic

computer would take an enormous amount of time.

• However, DNA computer could test all of

the keys at the same time.

DES overview DES overview

• encrypts 64 bit plain text into 64 cipher text using

a 56 bit key.

DES(M, k) == encryption of plain text M using the key k

• run the DES circuit on a fixed 64 bit string M using all

possible keys k

• decryption is denoted by DES-1

That is, if E = DES(M, k), then M = DES-1(E, k). f(k) = DES(M, k) for all possible k

DES circuit diagram DES circuit diagram

DES circuit DES circuit

• 16 levels called rounds
• circuit diagram shows

first 4 rounds and last

• the high order 32 bits

denoted by Mh

• the low order 32 bits

denoted by Ml

DES circuit DES circuit con con’ ’t t

P-box

• permutes the bits of its input
• Suppose a P-box contains x bits and the output contains y bits
• If x = y, then the box permutes the bits of the input

e.g. swap 2nd and 3rd bits, mapping 001 to 010

• If x > y, then the box outputs a subset of bits of the input

in some order

• If x < y, then the box replicates some of the bits of the

input to obtain a y bit output

However, they found the P-box to be insignificant and may be ignored.

DES circuit DES circuit con con’ ’t t

S-box

• takes 48 bits of input and outputs 32 bits
• 8 groups of 6 bits each
• 6 bits into a lookup table and outputs 4 bits

DNA notations DNA notations

• Represent strings over the alphabet {A, C, G, T}
• Strings, not a strand
• no orientation
• strings concatenated
• Watson-Crick complement of x
• Reverse of a string x
• Reverse & complement of a string x
• Single DNA strand, from 5’ to 3’
• complement of above, from 3’ to 5’
• x as a double strand

Biological Operations Biological Operations

Extract

• If we want all strands containing
• simply create strands of
• will anneal to
• A wash procedure will whisk away all strands that did not

anneal

Polymerization via DNA Polymerase

• already discussed in class

Amplification via PCR

Representing Binary Strings Representing Binary Strings

• Let x = x1 … xn be an n-bit binary string
• The idea is to assign a unique 30-mer, a special primer,

to each bit position and bit value.

• let Bi(0) be the 30-mer used to encode the i-th bit of x is 0.
• for i = 0, ..., n let Si be a 30-mer as a separator between

consecutive bits.

• The DNA strand representing the binary string
• For convenience, given an n-bit string x, we denote by Ri(x)

the string encoding x at position i

Operations on Binary Strings Operations on Binary Strings

• Let T be a test tube containing a collection of DNA strands

which represent some binary strings.

• Suppose we wish to extract all strands in T whose ith bit is 1.
• This operation is denoted by;

Extract (T, xi = 1)

• The operation can be expanded to;

Extract (T, xixi+1 = 10)

• where we extract strands in T that has 1 at ith

position and a 0 at the (i+1)th More possible operations;

• Extract (T, xixi+1xi+2 = 100 or xixi+1xi+2 = 101 )
• Extract (T, xixi+1xi+2 = 100 and xi+9xi+10xi+11 = 111 )

Plan of DES attack Plan of DES attack

• Given a message M it is possible to create a solution that

contains for each k _ {0, 1}56 a DNA strand of the form; _S0 R1(k) R57( DES(M, k) )

• Each strand in this solution encodes a key k and the encrypted

message of M using the key k

• Let (M, E) be a (plain text, cipher-text) pair. We wish to find a

key k s.t. M = DES-1(E, k)

• 1. Create the solution DES-1(E) where _S0 R1(k) R57( DES-1(E, k) )
• 2. Extract from DES-1(E) all strands that contain the patter R57(M)
• 3. The extracted strands encode pairs of strings (k, M) where M =

DES-1(E, k). The key k can be recovered by sequencing any of the extracted DNA strands.

Plan of DES attack ( Plan of DES attack (con con’ ’t t) )

• Steps 2 and 3 can be done very quickly.
• Laborious part is step 1.
• Once the solution DES-1(E0) is generated for

some 64 bit E0, any DES system can be broken into.

DNA Logic Gates DNA Logic Gates

In 1997, at the First International Conference on Computational Molecular Biology

• Animesh Ray and Mitsu Ogihara, scientists at the

University of Rochester, announced that they had built the first DNA computer hardware ‘ever’: logic gates made out

• f DNA.
• using only the most commonplace biological laboratory

techniques, such as DNA ligation and gel electrophoresis.

• unlike today’s computers, DNA logic gates do not rely on

electrical signal; but rather on DNA codes.

DNA Logic Gates DNA Logic Gates (

(con con’ ’t t) )

• They detect fragments of genetic

material as input.

• Splicing fragments together to form a

single output. For example, a genetic ‘AND’ gate links two DNA inputs by chemically binding so they are locked in an end-to-end structure, just like the lego below.

DNA Logic Gates ( DNA Logic Gates (con con’ ’t t) )

• one of the first to consider whether DNA computers might be

used for problems now routinely done by electronic computers, and to emulate the way electronic computers "think."

• DNA computers using these logic gates are more efficient that

today’s digital computers.

• instead of running DNA strands through slow gel electrophoresis,
• labeled strands can be added to a DNA chip, where many

different known strands of DNA can bind with the complementary sequence

• scientists can use the labeled strands to detect the answer more

quickly

MAYA MAYA

Molecular Array of YES and ANDANDNOT gates

• Milan Stojanovic – Columbia University
• Darko Stefanovic – University of New Mexico
• fashioned a device that uses DNA to play tic-tac-toe
• device is made of 9 wells, contains solutions of DNA
• DNA in the wells act like logic gates
• As long as the automaton makes the first move, it cannot be

beaten.

• DNA in the wells act like logic gates

MAYA ( MAYA (con con’ ’t t) )

• contains 24 logic gates distributed in the nine wells of solution.
• logic gates perform Boolean calculation when oligonucleotides are

added

• addition triggers an enzyme to react with DNA
• the reaction exposes a fluorescent molecule, which makes the well

glow to indicate the move.

Mealy Automaton

• like a DFA
• takes an input a and outputs w

MAYA ( MAYA (con con’ ’t t) ) MAYA ( MAYA (con con’ ’t t) )

Game Tree

MAYA ( MAYA (con con’ ’t t) )

• automaton always makes the first move, (square 5)
• human player always start in square 1 or 4
• 19 possible games
• 10 end in victory for the automaton after 2 human moves
• 7 after 3 moves
• 1 after 4 moves
• 1 game ends in a draw

MAYA ( MAYA (con con’ ’t t) )

• unlike the Adleman-Lipton paradigm,

MAYA is not trying to use DNA’s massive parallelism

• Their approach is silicomimetic
• use molecules that behave as logic gates, and

arrange these logic gates into more complicated circuits by mixing them in solution

MAYA ( MAYA (con con’ ’t t) )

• researchers are aiming to eventually use the

method to control nano devices in the human body

• to make decisions in the living whether to

release a toxic compound or not, or to kill a cell or not

• such devices could be used to monitor

in vivo disease signatures of astronauts

• n long space flights

Olympus Olympus’ ’ DNA Computer DNA Computer

• First Commercially

practical DNA Computer

• Specializes in gene analysis

Olympus ( Olympus (con con’ ’t t) )

Gene Analysis

• usually done manually
• by arranging DNA fragments and observing chemical reactions
• very time consuming (6 days)

Akira Toyama – Tokyo University

• developed principles for gene analysis using a DNA computer
• This DNA computer takes 3 hours

Olympus ( Olympus (con con’ ’t t) )

Genome Informatics

• combines two disciplines
• information processing engineering
• molecular biology
• formed a joint venture NovousGene Inc

Olympus’ computer is divided into 2 sections

• a molecular calculation component
• calculates DNA combinations of molecules
• implements chemical reactions
• searches and pulls out the right DNA results
• an electronic calculation component
• executes processing programs
• analyzes the results

GeneChip GeneChip

Developed by Affymetrix

• "DNA chips," where DNA strands are attached to

a silicon substrate in large arrays

• applied in a wide variety of DNA and mRNA

analyses

http://www.affymetrix.com

• the discovery of a new class of leukemia
• development of new assays to track drug metabolism

Conferences Conferences

http://analytical.chem.wisc.edu/DNA9/

DNA 9 DNA 9 – – annual DNA Computing conference

annual DNA Computing conference

DNA 10 DNA 10 – Milan, Italy

12 12th

th International Conference on Intelligent Systems for Molecular Biology

International Conference on Intelligent Systems for Molecular Biology 3 3rd

rd European Conference on Computational Biology

European Conference on Computational Biology http://www.iscb.org/ismbeccb2004/index.html

ISMB/ECCB ISMB/ECCB

Successor to Silicon? Successor to Silicon?

Advantages

• Perform millions of operations simultaneously
• Generate a complete set of potential solutions
• Conduct large parallel searches
• Efficiently handle massive amounts of working memory

Successor to Silicon? Successor to Silicon?

Drawbacks

• Each stage of parallel operations requires time measured in hours or

days, with extensive human or mechanical intervention between steps

• Generating solution sets, even for some relatively simple problems,

may require impractically large amounts of memory

• Many empirical uncertainties; e.g. actual error rates, the generation of
• ptimal encoding techniques, and the ability to perform necessary bio-
• perations conveniently in vitro or in vivo

DNA DNA vs vs Conventional Conventional

DNA over Conventional

• when problems are able to be divided into separate, non-

sequential tasks,

• due to the fact that they can hold much more memory and

perform more operations at once

Conventional over DNA

• problems that require many sequential operations are likely

to remain much more efficient on a conventional computer

Relations with other sciences Relations with other sciences

• high levels of collaboration between academic

disciplines is extremely important (e.g. chemistry, biology, medicine)

• collaborations toward the development of a DNA

computer may lead;

• increase understand of DNA
• other biological mechanisms
• need for precision demands progress in biomolecular

techniques that might not otherwise be considered

The Future? The Future?

With advancements in DNA computing

• enhance understanding of both the natural and computer sciences
• help explore and understand the limits of computing

Even if a practical DNA computer cannot be built;

• DNA based computation methods as a means of simulating and

predicting the emergent behavior of complex systems

e.g. fields pertaining to weather forecasting, economics

• medium for use of evolutionary programming
• possible a true fuzzy logic system

References References

1.

• J. Adams. Application of DNA Based Computation. (1998) Retrieved January 24, 2004. from

University of Western Ontario Web site: http://publish.uwo.ca/~jadams/dnaapps1.htm 2.

• M. Stojanovic. D. Stefanovic (2003) A deoxyribozyme-base molecular automaton. In Nature
• Biotechnology. vol.21 no.9 September 2003. Retrieved January 22, 2004. from

http://www.nature.com/cgi-taf/DynaPage.taf?file=/nbt/journal/v21/n9/full/nbt862.html 3.

• D. Boneh. C. Dunworth. R. Lipton. Breaking DES Using a Molecular Computer. Princeton.
• 1995. Retrieved January 22, 2004. from http://citeseer.nj.nec.com/boneh95breaking.html

4.

• K. Patch (2003) DNA plays tic-tac-toe. Retrieved January 22, 2004. from

http://www.trnmag.com/Stories/2003/082703/DNA_plays_tic-tac-toe_082703.html10 5.

• K. Bonsor. How DNA Computers Will Work. Retrieved January 24, 2004. from

http://computer.howstuffworks.com/dna-computer.htm/printable 6.

• S. Bradt. Everyday technology underlies first DNA computer logic gates. (1997) Retrieved

January 22, 2004. from University of Rochester Web Site: http://www.rochester.edu/pr/releases/bio/ray2.htm 7.

• W. Ryu. DNA Computing: A Primer. Retrieved January 22, 2004. from

http://www.arstechnica.com/reviews/2q00/dna/dna-1.html 8.

• O. Quraishi DNA Computing. (2002) Retrieved January 20, 2004. from University of Calgary

Web Site: http://pages.cpsc.ucalgary.ca/~jacob/Courses/Winter2003/CPSC601-73/Slides/05- DNA-Computing-Apps.pdf