vii
TABLE OF CONTENTS
CHAPTER TITLE PAGE
DECLARATION ii
DEDICATION iii
ACKNOWLEDGEMENTS iv
ABSTRACT v
ABSTRAK vi
TABLE OF CONTENTS vii
LIST OF TABLES xi
LIST OF FIGURES xii
LIST OF ABBREVIATIONS xiv
1 INTRODUCTION 1
1.1 Background 1
1.2 Existing Graph Partitioning Algorithms for Protein
Interaction Network 3
1.3 Challenges in Graph Partitioning Algorithms 4
1.4 Statement of the Problem 5
1.6 Significance and Scope of Study 8
1.7 Thesis Outline 9
1.8 Summary 10
2 LITERATURE REVIEW 11
2.1 Introduction 11
2.2 Overview of Molecular Biology 13
2.2.1 The DNA 14
2.2.2 The RNA 14
2.2.3 The Protein 16
2.3 High-Throughput Technologies for Protein
Interaction Network Detection 18 2.4 Graph Modelling in Protein Interaction Network 20 2.4.1 Global Network Analysis 22 2.4.2 Network Modularity 24 2.5 Graph Partitioning Strategies for Detecting
Functional Modules 25
2.5.1 Divisive Approach 25
2.5.2 Highly Interacted Module Approach 27 2.5.3 Clique Finding Approach 28 2.6 Comparative Analysis of Graph Partitioning
Strategies 2.7 Summary 30 33 3 RESEARCH METHODOLOGY 34 3.1 Introduction 34 3.2 Research Framework 35 3.3 Testing Datasets 37
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3.4 Validation Datasets 38
3.5 Post-Processing 39
3.6 Evaluation Measurement 40
3.6.1 Biological Significance Measurement 40 3.6.2 Accuracy Performance Measurement 41 3.7 Hardware and Software Requirements 43
3.8 Summary 44
4 MINING RELIABLE FUNCTIONAL MODULES
FROM INCOMPLETE AND NOISY PROTEIN
INTERACTION NETWORK 45
4.1 Introduction 45
4.2 Experimental Framework 46
4.3 The Reliable Local Dense Neighbourhood
(RELODEN) Algorithm 48
4.4 Experimental Results 53
4.4.1 Biological Significance of Detected
Modules 53
4.4.2 Accuracy Performance of Proposed
Algorithm 57
4.5 Discussion 61
4.6 Summary 64
5 DETECTING OVERLAPPING FUNCTIONAL
MODULES FROM PROTEIN INTERACTION
NETWORK 66
5.1 Introduction 66
5.3 The Overlap-RELODEN Algorithm 69
5.4 Experimental Results 72
5.4.1 Biological Significance of Detected
Modules 73
5.4.2 Accuracy Performance of Proposed
Algorithm 76
5.4.3 Discard and Overlapping Rate of Proposed
Algorithm 80
5.5 Discussion 83
5.6 Summary 87
6 CONCLUSION AND FUTURE WORKS 88
6.1 Conclusion 88
6.2 Future Works 92
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LIST OF TABLES
TABLE NO. TITLE PAGE
2.1 Comparative study of different graph partitioning
strategies 31
2.2 Advantages and disadvantages of graph partitioning
strategies 32
3.1 Protein-protein interaction datasets 38
4.1 Biological significance of detected modules 54
4.2 Number of proteins predicted and matched with protein
complexes 59
4.3 The comparison of overall accuracy performance 59
5.1 Biological significance of detected modules 74
LIST OF FIGURES
FIGURE NO. TITLE PAGE
2.1 Central dogma of molecular biology (copyrighted by John
Wiley and Sons, Inc., 1997) 13
2.2 The different between RNA and DNA (retrieved from
National Human Genome Research Institute, 2009) 15 2.3 Nicotinic acid phosphoribosyltransferase protein structure
(downloaded from National Institute of General Medical
Science, 2009) 17
2.4 Y2H screening process (Pandey and Mann, 2000) 19
2.5 TAP process (Huber, 2003) 20
2.6 Example of graph modelling for protein interaction network
(Jonsson et al., 2006b) 21
2.7 Global network analysis of yeast protein interaction
network 23
2.8 Example of divisive approach (Fortunato and Castellano,
2007) 26
2.9 Example of module detected by highly interacted module
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2.10 Overlapping modules detected by clique finding approach
(Palla et al., 2005) 29
3.1 Research framework 36
3.2 Graph modelling in protein interaction network 37
4.1 Experimental framework 47
4.2 Proposed local clique searching procedure 49
4.3 Example of clique detected in a graph 50
4.4 Proposed local dense sub-graph detection procedure 51
4.5 Example of dense sub-graph detection process 52
4.6 Example of functional modules detected by RELODEN
algorithm 56
4.7 The comparison of recall and precision score for four algorithms using MIPS and DIP dataset
58
4.8 The comparison of the number of known complexes
predicted by four algorithms using MIPS and DIP dataset 60
5.1 Experimental framework 68
5.2 Proposed informative protein selection procedure 69 5.3 Proposed informative sub-graph construction and dense
sub-graph searching procedure 71
5.4 Example of dense sub-graph detection 72
5.5 The comparison of recall and precision scores for three
algorithms using MIPS and DIP dataset 77
5.6 The comparison of the number of detected modules by three
algorithms using MIPS and DIP dataset 79
5.7 The comparison of the number of detected modules by three
algorithms using MIPS and DIP dataset 82
5.8 The overlapping rate of different degree in detected
LIST OF ABBREVIATIONS
CPM - Clique Percolation Method
CYGD - Comprehensive Yeast Genome Database DIP - Database of Interacting Protein
DNA - Deoxyribonucleic Acid
Dr - Discard Rate
FDR - False Discovery Rate FN - False Negative
FP - False Positive
G-N - Girvan and Newman Algorithm GO - Gene Ontology
HCS - Highly Connected Sub-graph MCL - Markov Clustering
MCODE - Molecular Complex Detection
MIPS - Munich Information for Protein Sequences mRNA - Messenger Ribonucleic Acid
PI - Informative Proteins
PPI - Protein-Protein Interaction
RELODEN - Reliable Local Dense Neighbourhood RNA - Ribonucleic Acid
RNSC - Restricted Network Searching Clustering SAGA - Spt-Ada-Gcn5 acetyltransferase
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SNAP - S-Nitroso-N-acetylpenicillamine TAP - Tandem Affinity Purification TP - True Positive
