thpark dna computing
Post on 06-Apr-2018
232 Views
Preview:
TRANSCRIPT
-
8/3/2019 Thpark DNA Computing
1/29
DNA-Based Computing& Development of
Efficient Operatorsfor Solving Traveling Salesman ProblemTai Hyun ParkTai Hyun Park
School of Chemical EngineeringSchool of Chemical Engineering
Seoul National UniversitySeoul National University
-
8/3/2019 Thpark DNA Computing
2/29
ContentsContents IntroductionIntroduction
DNA computing for solving traveling salesman problemDNA computing for solving traveling salesman problem
Traveling salesman problem (TSP)
Molecular algorithm for TSP Experimental implementation
Unit operators in DNA computingUnit operators in DNA computing Denaturation temperature gradient polymerase chain reaction
(DTG-PCR)
Initial pool generation using parallel overlap assembly (POA)
-
8/3/2019 Thpark DNA Computing
3/29
Rise and Growth of DNA ComputingRise and Growth of DNA Computing AdlemanAdlemanss work in 1994work in 1994
Hamiltonian path problem (graph problem, NP problem)
City and road information representation using DNA sequences(indicative information)
Solution-based DNA computing
LiuLius work in 2000s work in 2000 SAT problem
Surface-based DNA computing
BenensonBenensonss work in 2004work in 2004 Application to disease diagnosis and drug (antisense)
administration
-
8/3/2019 Thpark DNA Computing
4/29
Our Work in DNA Computing EraOur Work in DNA Computing Era Solving traveling salesman problemSolving traveling salesman problem
Graph problem with weighted edges
Representation of numerical information
Expansion to larger problems (in progress)
Development of unit operatorsDevelopment of unit operators
Denaturation temperature gradient PCR (DTG-PCR)
Initial pool generation for TSP using parallel overlap assembly
(POA)
Modeling and simulation of DTG-PCR and POA
New application to medical diagnosisNew application to medical diagnosis
Disease diagnosis model development
Experimental verification (in progress)
-
8/3/2019 Thpark DNA Computing
5/29
Objective (traveling salesman problem)Objective (traveling salesman problem) Application of DNA computing to mathematicalApplication of DNA computing to mathematical
problemsproblems 7-city traveling salesman problem
Graph problem with weighted edges
Representation of numerical values (weighted edges)Representation of numerical values (weighted edges)
Expansion to practical problemsExpansion to practical problems 26-variable TSP 26 subway stations in Seoul
-
8/3/2019 Thpark DNA Computing
6/29
Traveling Salesman ProblemTraveling Salesman Problem FindFind
The cheapest way of visiting all the cities and returning to the
starting point
when a number of cities to visit and the traveling cost between
each pair of cities are given.
Previous work for weight (cost) representationPrevious work for weight (cost) representation DNA length
DNA concentration
Our method for weight (cost) representationOur method for weight (cost) representation Thermal stability of DNA duplex
Melting temperature (Tm), GC content
-
8/3/2019 Thpark DNA Computing
7/29
Target Problem & Encoding MethodTarget Problem & Encoding Method
1
0
3
2 5
6
4
3
5 33
7 11
3
3
9
11
3
3
start & end
city 7-city traveling salesman problem- 7 cities (0 to 6), 23 roads, 5 costs
- optimal path: 01234560
7-city traveling salesman problem
- 7 cities (0 to 6), 23 roads, 5 costs
- optimal path: 01234560
Oligonucleotides & Encoding- cities and costs are 20-base ssDNA
- roads are 40-base ssDNA
- 35 oligonucleotides
(7 cities, 23 roads, 5 costs)
Oligonucleotides & Encoding- cities and costs are 20-base ssDNA
- roads are 40-base ssDNA
- 35 oligonucleotides
(7 cities, 23 roads, 5 costs)
(20 bases) (20bases) (20 bases)
city A cost A to B city B5 3
3 5road A to B
(40 bases)
-
8/3/2019 Thpark DNA Computing
8/29
Weight (Cost) EncodingWeight (Cost) Encoding
Seoul $ 500 Tokyo $ 200 Sapporo
Road from Seoul to Tokyo Road from Tokyo to Sapporo
20-base ssDNAwith 50% GC
content
20-base ssDNAwith 50% GC
content
20-base ssDNAwith higher GC
content
20-base ssDNAwith higher GC
content
20-base ssDNAwith lower GC
content
20-base ssDNAwith lower GC
content
40-base ssDNAcomplementary
to city and cost sequence
40-base ssDNAcomplementary
to city and cost sequence
More amplification of
DNA strand which has
lower sum of cost
More amplification ofDNA strand which has
lower sum of cost
Separation
and detection
Separation
and detection
DTG-PCR
TGGE
Lower costLower cost
Lower TmLower Tm
NACST/seq
-
8/3/2019 Thpark DNA Computing
9/29
DenaturationDenaturation Temperature GradientTemperature Gradient
Polymerase Chain Reaction (DTGPolymerase Chain Reaction (DTG--PCR)PCR)
92~95
Denaturation
68~72Extension
(Tm
-5)
Annealing
Cycle
Conventional PCRConventional PCR
Denaturation (Td)
Annealing (Ta)
Extention (Te)
Modification of conventionalModification of conventional
PCR protocolPCR protocol
Variation in Td
-
8/3/2019 Thpark DNA Computing
10/29
DenaturationDenaturation Temperature GradientTemperature Gradient
Initial denaturationTemperature ~ 70
Final denaturationtemperature~ 94
High Td
Amplification regardless of Tm
Low Td
More amplification of DNA strand
which has lower Tm (lower cost)
Cycle pr ogress
Biased operator:Biased operator: more amplification of DNA strands with lower Tmore amplification of DNA strands with lower Tmm
biased search for lower cost
-
8/3/2019 Thpark DNA Computing
11/29
Molecular AlgorithmMolecular Algorithm
1
3
(1) Generation of
all random pathsHybridization/ligation
(2) Selection of paths
satisfying TSP conditionsPCR/electrophoresis/affinity
2
(3) More amplificationof the cheapest path
DTG-PCR
4Readout!Sequencing
5
(5) Elution ofthe cheapest path
Gel elution
(4) Separation ofthe cheapest path
TGGE
-
8/3/2019 Thpark DNA Computing
12/29
Sequence Design for Cities and CostsSequence Design for Cities and Costs
Using NACST/Using NACST/seqseq
NonNon--cross hybridizationcross hybridization
Similar TSimilar Tmm among citiesamong cities
Different TDifferent Tmm among costsamong costs
-
8/3/2019 Thpark DNA Computing
13/29
Experimental ImplementationExperimental Implementationfor TSP Conditionsfor TSP Conditions
StratingStrating and ending with city 0and ending with city 0
PCR using primers complementary to city 0
Visiting every cityVisiting every city
A series of affinity chromatography
Each affinity column contains ssDNA complementary
to each city.
Cheapest pathCheapest path
DTG-PCR
-
8/3/2019 Thpark DNA Computing
14/29
Experimental ResultsExperimental Results Random path generation
(by hybridization and ligation)
Selective amplification of paths startingand ending with city 0(by PCR using primers complementary to city 0)
M 1 2 3
M: 50 bp ladderlane 1,2:
after hybridization/ligation
lane 3: mixture of ssDNA
M 1 M 1
300bp
-
8/3/2019 Thpark DNA Computing
15/29
Separation of paths containingSeparation of paths containing
every cityevery city
(by a series of affinity chromatography)
More amplification of pathsMore amplification of paths
with lower costswith lower costs
(by DTG-PCR)
M 1
MagneticBead
biotin
streptavidin
City 0Cost
City 1Cost
City 2Cost
300bp
(8 cities)
-
8/3/2019 Thpark DNA Computing
16/29
Separation of the pathSeparation of the pathwith lowest costwith lowest cost(by TGGE)
TGGETGGE(Temperature Gradient(Temperature Gradient
Gel Electrophoresis)Gel Electrophoresis)
T
DNAmolecules
1 2 3
major
band
-
8/3/2019 Thpark DNA Computing
17/29
ReadoutReadout
(by cloning and sequencing)
TTCTGCGTTGTTTCGGGGTACAGTGGCTCCTCCGTTCCGCCTGCACTGTGGAGAGGTGAGCAGTGGCTCCTCCGTTCCGCGTGGATTCACAAGGCCATCGCAGTGGCTCCTCCGTTCCGCATACGGCGTGGTTTTTCGGGCAGTGGCTCCTCCGTTCCGCAAACGGTCGTAAGTGATGAACAGTGGCTCCTCCGTTCCGCGCACAGTCCACCTGTAGACACAGTGGCTCCTCCGTTCCGCTATGTCCAGCTGTCGCAAAGCAGTGGCTCCTCCGTTCCGCTTCTGCGTTGTTTCGGGGTA
City 0
Cost 3
City 1
Cost 3
City 2
Cost 3
City 3
Cost 3
City 4
Cost 3
City 5
Cost 3
City 6
Cost 3
City 0
-
8/3/2019 Thpark DNA Computing
18/29
1
2
24
2
32
5
2
4
1
2
2
2
2
17
8 2
3
5
21
1
11
31
1
21
1
22
2
3 4
711
3
2
1 1
4
4
1
Subway routes in SeoulSubway routes in Seoul
Toward Larger ProblemsToward Larger Problems
-
8/3/2019 Thpark DNA Computing
19/29
Target Problem:Target Problem:2626--City TSPCity TSP
4
12
16
25
18 19924
2
10 17 21
7
8 23
13
14
120
6 3
15
5 11
22
1
2
24
2
32
5
2
4
1
2
2
2
2
17
8 2
3
5
21
1
11
3
1
1
21
1
22
23 4
7
11
3
2
1 1
4
4
1
0
Graph with 26 vertexes (cities) and 92 edges (roads)Vertex: station connected with more than two stationsWeight: number of stations between vertex stations
-
8/3/2019 Thpark DNA Computing
20/29
Initial Pool Generation with POAInitial Pool Generation with POA
Initial poolInitial pool
A combinatorial library that contains numerical or indicativeinformation
Initial pool generationInitial pool generation
Prerequisite step of most molecular algorithms formathematical problems
Initial pool generation methodsInitial pool generation methods
Hybridization and ligation method Parallel overlap assembly method
-
8/3/2019 Thpark DNA Computing
21/29
Initial Pool Generation for TSPInitial Pool Generation for TSP:: POA vs. Hybiridzation/Ligation
Oligonucleotides
First cycle extension
Second cycle extension
Repeat of denaturation/annealing
/extension steps
Ligation: ligase
Phosphorylation
Hybridization: slow cooling
-
8/3/2019 Thpark DNA Computing
22/29
Experimental ResultsExperimental Results
Marker Lane 1 Lane 2 Lane 3
900700
500
300
200
100
Lane M: 50 bp ladder
Lane 1: mixture of ssDNA
Lane 2: hybridization/ligation
Lane 3: POA
900
700
500
300
200
100
M 1 2 3
Quantification by image analysisQuantification by image analysis
-
8/3/2019 Thpark DNA Computing
23/29
Comparison of Two MethodsComparison of Two Methods
Parallel overlap assembly Viewpoint Hybridization/ligation
Population size is almost
preserved throughout the
process.
Scalability
Economy
Fidelity
Population size decreases as
hybridization proceeds.
No need of 5-phosphorylation 5-phosphorylation is necessary
Less time & reagents consuming More time & reagents consuming
High error rate by non-specific
priming
Low error rate (higher
hybridization specificity)
-
8/3/2019 Thpark DNA Computing
24/29
SummarySummary
Development of a molecular algorithm for TSP based onDevelopment of a molecular algorithm for TSP based on
the melting temperature differencethe melting temperature difference
Development of DTGDevelopment of DTG--PCR as an efficient operator forPCR as an efficient operator for
the graph problem with weighted edgethe graph problem with weighted edge
Success in solving 7Success in solving 7--city TSPcity TSP
2626--city traveling salesman problemcity traveling salesman problem
POA as an efficient operator for large size TSPPOA as an efficient operator for large size TSP
-
8/3/2019 Thpark DNA Computing
25/29
AcknowledgmentsAcknowledgments
Prof. ByoungProf. Byoung--Tak ZhangTak Zhang
(School of Computer Science and Engineering, SNU)(School of Computer Science and Engineering, SNU)
JiJi YounYoun LeeLee
(School of Chemical Engineering, SNU)(School of Chemical Engineering, SNU)
SooSoo--Yong Shin (SNU)Yong Shin (SNU)
(School of Computer Science and Engineering, SNU)(School of Computer Science and Engineering, SNU)
The Ministry of Commerce, Industry, and EnergyThe Ministry of Commerce, Industry, and Energy
-
8/3/2019 Thpark DNA Computing
26/29
-
8/3/2019 Thpark DNA Computing
27/29
-
8/3/2019 Thpark DNA Computing
28/29
-
8/3/2019 Thpark DNA Computing
29/29
top related