vii
TABLE OF CONTENTS
CHAPTER
1
TITLE
PAGE
DECLARATION
ii
DEDICATION
iii
ACKNOLEDGEMENTS
iv
ABSTRACT
v
ABSTRAK
vi
TABLE OF CONTENTS
vii
LIST OF TABLES
xi
LIST OF FIGURES
xvi
LIST OF SYMBOLS
xviii
LIST OF ABBREVIATIONS
xx
LIST OF APPENDICES
xxii
INTRODUCTION
1
1.1
Deoxyribonucleic Acid (DNA)
1
1.2
Basic Biotechnology
4
1.2.1
Syhthesizing DNA
4
1.2.2
Hybridization and Denaturation
4
1.2.3
Ligation
6
1.2.4
Polymerization
6
1.2.5
Polymerase Chain Reaction (PCR)
7
1.2.6
Gel Electrophoresis
9
viii
1.2.7
1.3
DNA Extraction
10
DNA Computing Paradigm
11
1.3.1
Hamiltonian Path Problem
11
1.3.2
From Turing Machine to DNA Computing 12
1.4
Emergence of DNA Computing
14
1.5
Reviewes of Output Visualization Technologies in
17
DNA Computing
2
1.5.1
Polymerase Chain Reaction
17
1.5.2
DNA Sequencing
18
1.5.3
Biochip
19
1.5.4
Fluorescence Detection
20
1.5.5
Atomic Force Microscope
20
1.6
Problem Statement
21
1.7
Objective
22
1.8
Scope of Work
23
1.9
Contribution
25
1.10
Publication List
26
1.11
Thesis Organization
27
DNA COMPUTING READOUT METHOD BASED ON 29
REAL-TIME POLYMERASE CHAIN REACTION
2.1
Introduction
29
2.2
Real-Time PCR
31
2.3
Basic Notation
34
2.4
Readout Approach
36
2.5
Experiment
39
2.5.1
Preparation of Input Molecules
39
2.5.2
Real-Time PCR Experiments
44
2.6
Results
49
2.7
Discussion
53
2.8
Chapter Summary
54
ix
3
CLUSTERING IMPLEMENTATION ON DNA
56
COMPUTING READOUT METHOD BASED ON
LIGHTCYCLER SYSTEM
3.1
Introduction
56
3.2
Data Clustering
58
3.3
K-Means Algorithm
60
3.4
Fuzzy C-Means
62
3.5
Classification of TaqMan reactions Using FCM
63
Clustering Algorithm
4
3.6
Results
68
3.7
Discussion
71
3.8
Chapter Summary
74
CLUSTERING IMPLEMENTATION ON DNA
75
COMPUTING READOUT METHOD BASED ON DNA
ENGINE OPTICON 2 SYSTEM
4.1
Introduction
75
4.2
Fuzzy C-Means Implementations
77
4.2.1
Methodology
77
4.2.2
Results
78
4.2.3
Discussion
82
4.3
4.4
5
Alternative Fuzzy C-Means Implementation
83
4.3.1
Methodology
83
4.3.2
Results
85
4.3.3
Discussion
88
Chapter Summary
89
CONCLUSIONS
90
5.1
Thesis Summary
90
5.2
Conclusions
91
5.3
Future Research
92
x
REFERENCES
Appendix A
93
103-105
xi
LIST OF TABLES
TABLE NO.
2.1
TITLE
11 ssDNAs used for generation of input molecules
PAGE
40
readout of V0→V2→V4→V1→V3→V5
2.2
Transformation index from
41
V0→V2→V4→V1→V3→V5 to
V0→V4→V1→V2→V3→V5
2.3
The required 13 ssDNAs for readout of
42
Hamiltonian Path V0→V1→V4→V2→V5→V3→V6
2.4
Transformation index for another seven nodes HPP
43
2.5
Sequences for forward and reverse
44
primers for V0→V2→V4→V1→V3→V5.
2.6
Sequences for TaqMan dual-labeled
45
probes for V0→V2→V4→V1→V3→V5.
2.7
Sequences for forward and reverse primers for
45
V0→V4→V1→V2→V3→V5.
2.8
Sequences for TaqMan dual-labeled probes for
45
V0→V4→V1→V2→V3→V5.
2.9
Sequences for forward and reverse primers for
47
V0→V1→V4→V2→V5→V3→V6.
2.10
Sequences for TaqMan dual-labeled probes for
47
V0→V1→V4→V2→V5→V3→V6.
2.11
Sequences for forward and reverse primers for
V0→V1→V3→V5→V4→V2→V6.
47
xii
2.12
Sequences for TaqMan dual-labeled probes for
48
V0→V1→V3→V5→V4→V2→V6.
2.13
Sequences for forward and reverse primers for
48
V0→V1→V5→V3→V4→V2→V6
2.14
Sequences for TaqMan dual-labeled probes for
48
V0→V1→V5→V3→V4→V2→V6
2.15
Summary of the results obtained from both LightCycler
52
and DNA Engine Opticon 2 System.
2.16
Comparison of two different outputs by using standard
53
and modified in silico algorithm
3.1
Partition matrix values for each real-time PCR reaction
65
calculated based on FCM clustering algorithm for test data.
3.2
Partition matrix values for each real-time PCR reaction
66
calculated based on FCM clustering algorithm for test data.
3.3
Partition matrix values for each TaqMan reaction based on 69
K-means clustering algorithm for data1
3.4
Partition matrix values for each TaqMan reaction based on 69
K-means clustering algorithm for data2.
3.5
Partition matrix values for each TaqMan reaction based on 70
FCM clustering algorithm for data1
3.6
Partition matrix values for each TaqMan reaction based
71
on FCM clustering algorithm for data2.
3.7
Comparison of K-means and FCM for data1
73
(100 iteration runs).
3.8
Comparison of K-means and FCM for data2
73
(100 iteration runs).
4.1
Partition matrix value for each TaqMan reaction
79
based on FCM clustering algorithm for data3.
4.2
Partition matrix value for each TaqMan reaction
80
based on FCM clustering algorithm for data5.
4.3
Partition matrix value for each TaqMan reaction
81
based on FCM clustering algorithm for data5.
4.4
Outliers classification in DNA Engine Opticon 2 data set. 82
xiii
4.5
Partition matrix value for each TaqMan reaction
86
based on AFCM clustering algorithm for data3.
4.6
Partition matrix value for each TaqMan reaction
87
based on AFCM clustering algorithm for data4.
4.7
Partition matrix value for each TaqMan reaction
88
based on AFCM clustering algorithm for data5.
4.8
100 independent runs of AFCM clustering algorithm
89
xiv
LIST OF FIGURES
FIGURE NO.
TITLE
PAGE
1.1
A nucleotide
1
1.2
A single-stranded DNA
2
1.3
Double helix structure of DNA
3
1.4
Bi-molecular hybridization and denaturation of DNA
5
1.5
An example of hairpin formation of DNA
5
1.6
Ligation
6
1.7
DNA polymerization
7
1.8
Polymerase chain reaction
8
1.9
Gel electrophoresis
9
1.10
Example of a gel image
9
1.11
An example of DNA extraction by using
10
streptavidin-coated magnetic bead.
1.12(a)
A directed graph for Hamiltonian path problem
11
1.12(b)
The answer of Hamiltonian path problem.
11
1.13
The overall procedure of Adleman HPP base
14
DNA computing.
1.14
Scope of work and contribution
24
1.15
The whole process of readout method based on
24
real-time PCR
2.1
Overview of the research. The in vitro part is highlighted
as the main work in this chapter. The improvement
of in silico algorithm is depicted as a small
contribution in this chapter.
30
xv
2.2
Illustration of the structure of a TaqMan DNA probe.
32
Here, R and Q denote the reporter and quencher
fluorophores, respectively
2.3
Mechanism of real-time PCR based on TaqMan probe
33
2.4
An example of amplification plots corresponding to
35
TaqMan(v0,vk,vl) = YES (first condition) and
TaqMan(v0,vk,vl) = NO (second condition) implemented
on LightCycler System.
2.5
An example of amplification plots corresponding to
35
TaqMan(v0,vk,vl) = YES (first condition) and
TaqMan(v0,vk,vl) = NO (second condition) implemented
on DNA Engine Opticon 2 System.
2.6
Gel image for the preparation of 120-bp input molecules.
42
Lane M denotes a 20-bp molecular marker, lane 1 is the
product of initial pool generation based on parallel
overlap assembly, and lane 2 is the amplified PCR product.
2.7
Gel image for the preparation of 140-bp input molecules.
43
2.8
Output of real-time PCR for readout of
49
V0→V2→V4→V1→V3→V5 implemented on
LightCycler System. Reaction 1 to 6 indicate the
TaqMan(v0,vk,vl) reactions.
2.9
Output of real-time PCR for readout of
50
V0→V4→V1→V2→V3→V5 implemented on
LightCycler System. Reaction 1 to 6 indicate the
TaqMan(v0,vk,vl) reactions.
2.10
Output of real-time PCR for
50
readout of V0→V1→V4→V2→V5→V3→V6
implemented on DNA Engine Opticon 2 System.
Reaction 1 to 10 indicate the TaqMan(v0,vk,vl) reactions.
2.11
Output of real-time PCR for
readout of V0→V1→V3→V5→V4→V2→V6 implemented
on DNA Engine Opticon 2 System. Reaction 1 to 10
indicate the TaqMan(v0,vk,vl) reactions.
51
xvi
2.12
Output of real-time PCR for
51
readout of V0→V1→V5→V3→V4→V2→V6 implemented
on DNA Engine Opticon 2 System. Reaction 1 to 10
indicate the TaqMan(v0,vk,vl) reactions.
3.1
Scope of work and contribution of this thesis. The
57
implementation of clustering on LightCycler System
is highlighted as the main contribution in this chapter.
3.2
A simple example of cluster.
59
3.3
Graphical representation of hard and soft
60
partitioning cluster.
3.4
The K-means algorithm.
61
3.5
The FCM algorithm.
63
3.6
Test data obtained from LightCycler System.
64
3.7
Output of real-time PCR with y1 and y2 centers,
64
calculated using FCM clustering algorithm.
3.8
Output of real-time PCR with y1 and y2 centers,
66
calculated using FCM clustering algorithm.
3.9
Classification procedure of TaqMan reactions
67
using K-means algorithm
3.10
Classification procedure of TaqMans reaction using
68
FCM algorithm
3.11
Output of real-time PCR with “YES” and “NO” centers,
68
implemented based on K-means clustering algorithm
for data1.
3.12
Output of real-time PCR with “YES” and “NO” centers
69
implemented based on K-means clustering algorithm
for data2.
3.13
Output of real-time PCR with “YES” and “NO” centers
70
implemented based on FCM clustering algorithm for
data1 with ࢟ଵ(ସହ) > ࢟ଶ(ସହ) .
3.14
Output of real-time PCR with “YES” and “NO” centers
71
implemented based on FCM clustering algorithm
for data2.
3.15
The comparison of convergence behaviors for the
72
xvii
K-means and FCM clustering algorithms implemented
on data1.
3.16
The comparison of convergence behaviors for the
72
K-means and FCM clustering algorithms implemented
on data2.
4.1
Scope of work and contribution of this thesis. The
76
implementation of clustering on DNA Engine
Opticon 2 System is highlighted as the main
contribution in this chapter.
4.2
Classification of TaqMan reaction using FCM algorithm
78
4.3
Output of real-time PCR with “YES” and “NO” centers,
79
implemented by FCM clustering algorithm for
data3 with ‫ݕ‬ଵ(ସ଺) > ‫ݕ‬ଶ(ସ଺) .
4.4
Output of real-time PCR with “YES” and “NO” centers,
80
implemented by FCM clustering algorithm for
data4 with ‫ݕ‬ଵ(ସ଺) > ‫ݕ‬ଶ(ସ଺) .
4.5
Output of real-time PCR with “YES” and “NO” centers,
81
implemented by FCM clustering algorithm for
data5 with ‫ݕ‬ଵ(ସ଺) > ‫ݕ‬ଶ(ସ଺) .
4.6
Classification of TaqMan reaction using AFCM
84
algorithm
4.7
Output of real-time PCR with “YES” and “NO” centers
85
implemented by AFCM clustering algorithm for
data3 with ‫ݕ‬ଵ(ସ଺) > ‫ݕ‬ଶ(ସ଺) .
4.8
Output of real-time PCR with “YES” and “NO” centers
86
implemented by AFCM clustering algorithm for
data4 with ‫ݕ‬ଵ(ସ଺) > ‫ݕ‬ଶ(ସ଺) .
4.9
Output of real-time PCR with “YES” and “NO” centers
87
implemented by AFCM clustering algorithm for
data5 with ‫ݕ‬ଵ(ସ଺) > ‫ݕ‬ଶ(ସ଺) .
4.10
Convergence behaviors for the AFCM clustering
algorithms implemented on data3, data4, and data5.
89
xviii
LIST OF SYMBOLS
°C
-
degree celcius
Ts
-
DNA strand
S
-
DNA strand
S*
-
DNA complement of S
F
-
DNA strand
G
-
directed graph
V
-
set of vertices
eij
-
edges
Vin
-
start node
Vout
-
end node
nm
-
nanometer
kg
-
kilogram
vi
-
double stranded DNA
Vi
-
node
|V|
-
number of nodes
L
-
array of location of nodes
A
-
array of aggregation values
N
-
array of Hamiltonian path node
µl
-
microliter
v̅i
-
reverse primer
µM
-
micro Molar
rpm
-
revolution per minute
s
-
second
J
-
cost function
U
-
partition matrix
xix
Y
-
set of cluster centers
X
-
set of data
C
-
number of clusters
N
-
number of data
m
-
fuzziness value index
x
-
data point
y
-
cluster center
µ
-
membership value
d (x,y) -
distance
ߝ
-
error
t
-
iteration step
GHz
-
Giga Herzt
GB
-
Giga Byte
η
-
scale parameter
β
-
positive constant
xx
LIST OF ABBREVIATIONS
DNA
-
Deoxyribonucleic acid
PCR
-
Polymerase Chain Reaction
HPP
-
Hamiltonian Path Problem
A
-
Adenine
C
-
Cytosine
G
-
Guanine
T
-
Thymine
ssDNA
-
single-stranded DNA
dsDNA
-
double stranded DNA
ATP
-
Adenosine-5'-triphosphate
NAD
-
Nicotinamide adenine dinucleotide
PO −4
-
phosphate
dNTP
-
deoxynucleotide triphosphate
NP
-
Nondeterministic polynomial
RNA
-
Ribonucleic acid
PAGE
-
Polyacrylamide Gel Electrophoresis
UV
-
ultra violet
SAT
-
satisfiability problem
SA
-
simulated annealing
EA
-
Evolutionary Algorithm
ACO
-
Ant Colony Optimization
PSO
-
Particle Swarm Optimization
AFM
-
Atomic Force Microscope
DHP
-
Directed Hamiltonian Path
FCM
-
Fuzzy C-Means
+
xxi
AFCM
-
Alternative Fuzzy C-Means
EtBr
-
ethidium bromide.
FAM
-
6-carboxyfluorescein
TAMRA
-
tetramethylrhodamine
FRET
-
fluorescence resonance energy transfer
R
-
reporter dye and
Q
-
quencher dye
Taq
-
Thermus aquaticus
bp
-
base pairs
POA
-
Parallel Overlap Assembly
ddH2O
-
double distilled water
MgCl2
-
magnesium chloride
dUTP-2'
-
deoxyuridine 5'-triphosphate
dTTP
-
deoxythymidine triphosphate
EM
-
Expectation Maximization
PCA
-
Principal Component Analysis
PCM
-
Possibilistic C-Means
TSP
-
Travelling Salesman Problem
SPP
-
Shortest Path Problem
xxii
LIST OF APPENDICES
APPENDIX
A
TITLE
List of publications
PAGE
103