ix
TABLE OF CONTENTS
CHAPTER
1
2
TITLE
PAGE
DECLARATION
ii
ACKNOWLEDGEMENTS
v
ABSTRACT
vi
ABSTRAK
vii
TABLE OF CONTENTS
viii
LIST OF TABLES
xii
LIST OF FIGURES
xv
LIST OF ABBREVIATIONS
xviii
LIST OF SYMBOLS
xx
INTRODUCTION
1
1.1
Background
1
1.2
Theory of DNA
2
1.3
DNA Computing
4
1.4
ACS based DNA Sequence Design
7
1.5
Objective
7
1.6
Scope of Work
8
1.7
Thesis Organization
8
LITERATURE REVIEW
10
2.1
Introduction
10
2.2
DNA Sequence Design Approaches
10
2.2.1
Theoretical Method
10
2.2.2
Simple Method
11
2.2.3
Heuristic Method
12
x
2.3
2.4
3
Evolutionary Method
13
2.2.5
Population-based search Method
14
Ant Colony System
15
2.3.1
Routing Problem
15
2.3.2
Schedulling Problem
17
2.3.3
Assignment Problem
17
2.3.4
Others
18
Chapter Summary
18
FORMULATION OF OBJECTIVE FUNCTIONS
AND CONSTRAIN IN DNA SEQUENCE DESIGN
19
3.1
Introduction
19
3.2
Design Criteria
20
3.3
Objective Functions in DNA Sequence Design
24
3.3.1
Hmeasure
26
3.3.2
Similarity
28
3.3.3
Continuity
27
3.3.4
Hairpin
28
3.4
3.6
4
2.2.4
Constraints in DNA Sequence Design
29
3.4.1
GCcontent
29
3.4.2
Melting Temperature
29
Chapter Summary
31
ANT COLONY OPTIMIZATION FOR DNA
SEQUENCE DESIGN
32
4.1
Introduction
32
4.2
Background
32
4.3
Ant Colony System Algorithms
34
4.3.1
State Transition Rule
34
4.3.2
Global Update Rle
37
4.3.3
Local Update Rule
38
4.4
Ant Colony System for DNA Sequence Design
38
4.5
Chapter Summary
43
xi
5
6
RESULTS AND DISCUSSION
44
5.1
Introduction
44
5.2
Computer Interface
44
5.3
Results
48
5.4
Number of Ants and Computational Time
52
5.5
Comparison with Other Algorithm
54
5.6
Chapter Summary
67
CONCLUSIONS AND SUGGESTION FOR FUTURE
RESEARCH
69
6.1
Summary
69
6.2
Conclusion
70
6.3
Limitation
71
6.4
Suggestions for Future Research
71
REFERENCES
Appendix A
73 - 78
79
xii
LIST OF TABLES
TABLE NO.
1.1
TITLE
PAGE
Encoding of the vertices of Hamiltonian Path Problem
in DNA
5
Encoding of the edges of Hamiltonian Path Problem
in DNA
6
2.1
Previous approaches used in DNA sequence design
12
2.2
Several problem solved using ACS algorithm
18
3.1
Summary of the objective functions and constraints
employed in previous works
25
3.2
Basic notations
27
3.3
ΔH and ΔS values of nearest-neighbour model
35
4.1
Initialization Parameters for Ant Colony System
62
4.2
DNA Sequence Parameter
63
5.1
Total average objective function, standard deviation,
minimum and maximum reading for each ant model.
73
Average value of each objective functions for each ant
model
73
5.3
Best result for 5-Ants model
74
5.4
Best result for 10-Ants model
74
5.5
Best result for 15-Ants model
75
5.6
Best result for 20-Ants model
75
5.7
Best result for 25-Ants model
76
1.2
5.2
xiii
5.8
Best result for 30-Ants model
76
5.9
Best result for 35-Ants model
79
5.10
Best result for 40-Ants model
81
5.11
Average values of computational time and convergence
results
82
5.12
Previous approaches used in DNA sequence design
84
5.13
Comparison results of sequences generated using GA
by Deaton et al. with the proposed ACS model.
86
Comparison results of sequences generated using SA
by Tanaka et al. with the proposed ACS model.
86
Comparison results of sequences generated using MOEA
by Shin et al. with the proposed ACS model.
87
Comparison results of sequences generated using MOPSO
by Zhao et al. with the proposed ACS model.
87
Comparison results of sequences generated using PSO
by GuangZhou et al. with the proposed ACS model
88
Comparison results of sequences generated using BinPSO
by Khalid et al. with the proposed ACS model.
89
5.14
5.15
5.16
5.17
5.18
5.19
Comparison results of sequences generated using P-ACO by
Kurniawan et al. with the proposed ACS model.
90
5.20
Comparison results of sequences generated using original
ACS model by Yakop et al. with the proposed ACS model
91
xiv
LIST OF FIGURES
FIGURE NO.
TITLE
PAGE
1.1
Chemical structure of DNA molecule
3
1.2
Backbone of the DNA structure
4
1.3
Hamiltonian Path Problem. The bold lines represent the
only correct path that is 0→1, 1→2, 2→3, 3→4, 4→5,
5→6
5
3.1
Example of Hmeasure measure (Kurniawan et al.)
28
3.2
Example of similarity measure (Kurniawan et al.)
30
3.3
Illustration for hairpin calculation (Kurniwan et al.)
32
4.1
Setup for Double Bridge Experimen (Dorigo, Birattari,
and Stutzle, 2006)
48
4.2
The proposed model
50
4.3
Modeling of DNA sequences design
59
5.1
Graphical user interface of the DNA sequence generator
69
5.2
Parameter setting tab of the DNA sequence generator
70
5.3
Example of best sequence generated by the proposed
model which is stored under the [Best Result] tab
70
Display of the [Convergence Curve] tab in the
DNA sequence generator
71
Display of the [Pheromone Table] tab in the
DNA sequence generator
71
Plot for the average of total objective value of each ant
model
72
5.4
5.5
5.6
xv
5.7
Comparison results of average objective measure
between Deaton et al. and proposed ACS model
72
5.8
Sample of convergence to generate 7 sequences of 20-mer
76
5.9
Comparison results of average objective measure between
Tanaka et al. and proposed ACS model
77
5.10
Sample of convergence to generate 14 sequences of 20-mer
78
5.11
Comparison results of average objective measure between
Shin et al. and proposed ACS model
78
Comparison results of average objective measure between
Zhao et al. and proposed ACS model
80
Comparison results of average objective measure
between GuangZhou et al. and proposed ACS model
81
5.14
Sample of convergence to generate 20 sequences of 20-mer
83
5.15
Comparison results of average objective measure between
Khalid et al. and proposed ACS model
85
Comparison results of average objective measure
between Kurniawan et al. and proposed ACS model
88
Comparison results of average objective measure between
Yakop et al. and proposed ACS model
89
5.12
5.13
5.16
5.17
xvi
LIST OF ABBREVIATIONS
-PO4
-
Phosphate
-OH
-
Hydroxyl
A
-
Adenine
ACO
-
Ant Colony Optimization
ACS
-
Ant Colony System
AFSA
-
Artificial Fish Swarm Algorithms
AS
-
Ant System
C
-
Cytosine
CTP
-
Course Timetabling Problem
DNA
-
Deoxyribonucleic Acid
G
-
Guanine
GA
-
Genetic Algorithms
MMAS-CTP
-
Min-Max AS version for Course Timetabling Problem
MOEA
-
Multi-Objective Evolutionary Algorithms
MOPSO
-
Multi-Objective Particle Swarm Optimization
MTTP
-
Minimum Tardy Task Problem
P-ACO
-
Population-Based Ant Colony Optimization
PSO
-
Particle Swarm Optimization
PSP
-
Project Scheduling Problem
QAP
-
Quadratic Assignment Problem
RCPSP
-
Resource Constrained Project Scheduling Problem
xvii
RLP
-
Resource Levelling Problem
RNA
-
Ribonucleic Acid
SA
-
Simulated Annealing
SI
-
Swarm Intelligence
T
-
Thymine
Tm
-
Melting Temperature
TSP
-
Travelling Salesman Problem
TWTS
-
Total Weighted Tardiness Scheduling
VRP
-
Vehicle Routing Problem
VRPPD
Vehicle Routing Problem with Pickup and Delivery
VRPTW
Vehicle Routing Problem with Time Windows
xviii
LIST OF SYMBOLS
fDNA
-
the total objective functions

-
bases of DNA {A, C, G, T}
x, y
-
a DNA sequence
|x|
-
length of x (DNA sequences)
xi (1 ≤ i ≤ |x|)
-
i-th nucleotide from 5’-end of sequence x
Σ
-
a set of n sequences with the same length l
Σi
-
i-th member of Σ
ā
-
complementary base of a
l
-
length of sequence
n
-
number of sequences
CT
-
the total oligonucleotide strand concentration
R
-
the universal gas constant (Boltzmann’s constant)
ΔH
-
enthalpy changes of the annealing reaction
ΔS
-
entropy changes of the annealing reaction