1. ФИЗИКО-ХИМИЧЕСКИЕ ОСНОВЫ НАНОТЕХНОЛОГИИ
Профессор Н.Г. Рамбиди

2. ДНК-компьютинг

3. Структура ДНК

4. Розалинда Франклин

5. Морис Уилкинс

6. Джеймс Ватсон и Френсис Крик
We wish to suggest a structure for the salt of deoxi-ribose nucleic acid (D.N.A.). This structure has novel features which are of considerable biological interest

7. Nucleotide (DeoxyriboNucleic Acid)
DNA Structure N O
NH2
Base P O O-
Phosphate O
CH2 OH
Sugar (deoxyribose) 5‘ 4‘ 1‘ 3‘ 2‘
Nucleotide (DeoxyriboNucleic Acid)

8. DNA Structure - Single Strand5‘ A
4 Bases
Adenine (A)
Thymine (T)
Cytidine (C)
Guanine (G) T C
Distinctive ends
5‘ (5-prime) end
3‘ end
Sequence
5‘-ATCG-3‘ 3‘ G

9. DNA Structure – Double Strand3‘ A G C 5‘
Annealing
G lines up with C
3 hydrogen bonds
A lines up with T
2 hydrogen bonds 3‘ G 5‘ A T C
Watson-Crick complement
5‘-ATCG-3‘
3‘-TAGC-5‘
"Double Helix"
2 intertwined strands
hydrogen bonds
(weak)
covalent bond
(strong)

10. DNA Structure (3) base pairing
Complimentary base pairing accounts for the Chargaff’s rule (A=T, G=C).

11. DNA Structure (4) – double helix
Watson-Crick complement

12. Вторичная структура ДНК

13. Структурные варианты ДНК

14. ДНК – электронная микроскопия

15. Сложные структуры ДНК

16. Ферменты – принцип «ключ-замок»

17. Механизм ферментативной реакции

18. Репликация ДНК

19. Вычисления на основе ДНК

20. A different approach What DNA computing is: What DNA computing isn’t:
a completely new method among a few others (e.g., quantum computing) of general computation alternative to electronic/semiconductor technology
uses biochemical processes based on DNA
What DNA computing isn’t:
not to confuse with bio-computing which applies biological laws (evolution, selection) to computer algorithm design.

21. Why try new stuff ?
Will need a dramatically new technology to overcome some CMOS limitations and offer new opportunity.
Certain types of problems (learning, pattern recognition, fault-tolerant system, large set search algorithms) are intrinsically very difficult to solve even with fast evolution of CMOS.
Hope of achieving massive parallelism.

22. Conventional vs. Biological Computing
Component materials
Inorganic, e.g. silicon
Biological, e.g. DNA
Processing scheme
Sequential and limited massively parallel
Massively parallel
Current max. operations
1012 Op.s per sec.
1014 Op.s per sec.
Quantum effects a problem?
Yes No
Toxic components?
Energy efficient?

23. Historical timeline Research Commercial 1950’s … 1994 1995 2000 2005
R.Feynman’s
paper on sub microscopic
computers
L.Adleman solves Hamiltonian path problem using DNA.
Field started
D.Boneh paper
on breaking DES
with DNA
Lucent
builds DNA
“motor”
DNA computer
architecture ?
Commercial
1970’s …
1996
2000
2015
DNA used
in bio application
Affymetrix sells GeneChip
DNA analyzer
Human
Genome
Sequenced
Commercial computer ?

24. Проблема коммивояжера

25. Структура проблемы «Коммивояжер»
{ Vin,1,2,3..N, Vout }
fixed Vin ,Vout
P{1,2,3..N}
N! paths

27. First special DNA computer
Special problem: given N points, find a path visiting each and every point only once, and starting and ending at a given locations. (hamiltonian path problem)
Solved with a DNA computer by Leonard Adleman in ‘94 for N=6
Basic approach: code each point as an 8 unit DNA string, code each possible path, allow DNA bonding, suppress DNA with incorrect start/end points.

28. Travelling salesman 1. Locations and complements:x0 x2 x4 x1 x3 x5
p01
p02
p13
1. Locations and complements:
X0 = ACTTgcag; X0’ = TGAAcgtc
X1 = TCGGactg; X1’ = AGCCtgac
X2 = CCGAatgc; X2’ = GGCTtacg ……
2. Paths coded using Xn’:
p01 = cgtcAGCC; p12 = tgacGGCT
p02 = cgtcGGCT ….
locations DNA
paths DNA
ACTTgcag
cgtcAGCC
TCGGactg
tgac….
p01
<-- Solution
Bonding &
Filtering x0 x1

29. Procedure Generate random paths Hybridization & Ligation
through the graph
Hybridization & Ligation
Keep only those paths that
begin with vin and end with vout
PCR with vin and vout
If the graph has n vertices,
then keep only those paths
that enter exactly n vertices
Gel electrophoresis
Keep only those paths that
enter all of the vertices
of the graph at least once.
Antibody bead separation
With vi
If any paths remain, say “Yes”;
otherwise, say “No.”
Gel electrophoresis &
Sequecing

30. DNA Operators The bio-lab technology. Hybridization Ligation
Polymerase Chain Reaction (PCR)
Gel electrophoresis
Affinity separation (Bead)
Enzymes: restriction enzyme…

31. Hybridization & Ligation
base-pairing between two complementary single-strand molecules to form a double stranded DNA molecule
Ligation
Joining DNA fragments together
Solution generation step!

32. DNA Hybridization & Ligation
AGCTTAGGATGGCATGG
AATCCGATGCATGGC +
CGTACCTTAGGCT
Hybridization
AGCTTAGGATGGCATGG
AATCCGATGCATGGC
CGTACCTTAGGCT
Ligation
AGCTTAGGATGGCATGGAATCCGATGCATGGC
CGTACCTTAGGCT
Dehybridization
AGCTTAGGATGGCATGGAATCCGATGCATGGC +
CGTACCTTAGGCT

33. PCR (Polymerase Chain Reaction)
Mullis: Nobel Prize (1993)
Amplifies (produces identical copies of) selected dsDNA molecules.
Make 2n copies (n : number of iteration)
Solution filtering or amplification step!

34. The Invention of PCR Invented by Kary Mullis in 1983.
First published account appeared in 1985.
Awarded Nobel Prize for Chemistry in 1993.

35. Did He Really Invent PCR?
The basic principle of replicating a piece of DNA using two primers had already been described by Gobind Khorana in 1971:
Kleppe et al. (1971) J. Mol. Biol. 56,
Progress was limited by primer synthesis and polymerase purification issues.
Mullis properly exploited amplification.

36. It is hard to exaggerate the impact of the polymerase chain reaction
It is hard to exaggerate the impact of the polymerase chain reaction. PCR, the quick, easy method for generating unlimited copies of any fragment of DNA, is one of those scientific developments that actually deserves timeworn superlatives like "revolutionary" and "breakthrough." 
- Tabitha M. Powledge

37. PCR Cycles Denature DNA (950 C) Anneal Primers (Usually 50 – 65 0C)
Extend Primers (720 C)
Repeat

41. Gel electrophoresis Bead Separation
Molecular size fraction technique
Bead Separation
Detect the specific DNA
Solution detection or filtering step!

42. Gel Electrophoresis

43. Bead Separation (1)
Magnetic Beads
Magnet
Complementary

44. Bead Separation (2)
Biotin (Vitamin H)

45. Restriction enzyme Cut the specific DNA site.
Solution detection or filtering step!
A A G C T T
T T C G A A
OH 3’ A
5’ P
A C G T T
EcoRI
T T C G A A
P 5’
3’ OH

46. Results
Adleman estimated the speed of his graph problem calculation at 1014 operations/sec !
Energy efficiency: 2x1019 operations per joule
This approach seems appropriate for a small range mostly combinatorial problems or problems that can be cast as such (e.g., encryption/decryption)
The range of problems is still very limited
Correctness of the computation highly depends on the fidelity of the process

47. Sources of errors a) Common operation in handling DNA is filtering:
DNA filter
5% error rate
Heterogenous
mix
Single DNA
solution
Leads to high output error rate
b) Chain Reaction for DNA amplification.
error
ATTGA..
ATGGA..
Rare type of error, but does present a problem

48. Sources of errors (cont’d)
c) Binding error. Expect binding between complements C G
Formation of
double helix T A G C A C T A
error causes bulges, missed bonds -- translates into output errors
Error resilient computation techniques required:
classic error correction with redundant representation
careful data encoding - trade off density for resiliency
varying environment conditions may improve performance
Question: are errors always bad ? In this case, an occasional mutation may lead to improved learning

49. What’s up to date
Research still in very early stages but promise is big
First groundbreaking work by Adleman at USC only 6 years ago
No commercialization in sight within ~10 years
Main work on settling for a logic abstraction model, ways of using DNA to compute
Research sponsored by universities (Princeton, MIT, USC, Rutgers, etc) and NEC, Lucent Bell Labs, Telcordia, IBM
One way or another, DNA computing will have a significant impact