Immunological Computation: Theory and Applications

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ISBN: 9781420065459 | 1420065459
Cover: Hardcover
Copyright: 9/12/2008

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Clearly, nature has been very effective in creating organisms that are capable of protecting themselves against a wide variety of pathogens such as bacteria, fungi, and parasites. The powerful information-processing capabilities of the immune system, such as feature extraction, pattern recognition, learning, memory, and its distributive nature provide rich metaphors that researchers are finding very useful for the development of computational models. While some of these models are designed to give us a better understanding of the immune system, other models are being developed to solve complex real-world problems such as anomaly detection, pattern recognition, data analysis (clustering), function optimization, and computer security.

Preface	p. xiii
Acknowledgments	p. xv
Authors	p. xvii
Immunology Basics	p. 1
Functional Elements of the Immune System	p. 2
Organs	p. 2
Bone Marrow	p. 2
Thymus	p. 3
Spleen	p. 3
Lymph Node	p. 3
Immune Cells and Molecules	p. 3
Lymphocytes, T Lymphocytes, and B Lymphocytes	p. 4
Antibodies	p. 5
Cytokines, Lymphokines, and Interleukins	p. 5
Peptides, Major Histocompatibility Complex, and Antigen Presenting Cells	p. 6
Macrophages and Dendritic Cells	p. 7
The Complement System	p. 7
Layers of the Immune System	p. 8
Anatomic Barrier	p. 8
Innate Immunity	p. 8
Adaptive Immunity	p. 9
Immune System Dynamics	p. 9
Immune Recognition: Matching and Binding	p. 10
Response to Antigens	p. 11
T Cell Maturation	p. 12
B Cell Proliferation: Affinity Maturation	p. 14
Germinal Center	p. 15
Apoptosis and Lysis	p. 17
Circulatory Mechanism	p. 18
Regulatory Mechanisms	p. 19
Signaling and Message-Passing Mechanism	p. 20
Summary	p. 22
Review Questions	p. 22
References	p. 24
Theoretical Models of Immune Processes	p. 27
Clonal Selection Theory	p. 27
Immune Network Theory	p. 29
First-Generation Immune Networks	p. 29
Second-Generation Immune Networks	p. 31
Third-Generation Immune Networks	p. 32
Multiepitope Immune Network	p. 33
Modeling the Germinal Center	p. 33
Danger Theory	p. 38
Computational Aspects of the Immune System	p. 40
Summary	p. 41
Review Questions	p. 42
References	p. 43
Immunity-Based Computational Models	p. 45
Shape-Space and Affinity	p. 46
Representation Schemes	p. 48
Affinity Measures	p. 48
String-Matching Rules	p. 50
Hamming Distance	p. 50
Binary Distance	p. 50
Edit Distance	p. 52
Value Difference Metric	p. 52
Landscape-Affinity Matching	p. 53
R-Contiguous Bits Matching	p. 53
R-Chunk Matching Rule	p. 54
Real-Valued Vector Matching Rules	p. 54
Euclidean Distance	p. 54
Partial (Euclidean) Distance	p. 54
Minkowski Distance	p. 55
Chebyshev Distance	p. 55
Mixed Representation	p. 55
Heterogeneous Euclidean-Overlap Metric	p. 55
Heterogeneous Value Difference Metric	p. 56
Considerations about Representation	p. 56
Affinity Maturation	p. 57
Solving Problems Applying Immunity-Based Models	p. 57
Summary	p. 58
Review Questions	p. 58
References	p. 59
T Cell-Inspired Algorithms	p. 61
Self/Nonself Discrimination	p. 61
Negative Selection Algorithms	p. 62
Negative Detector Generation Schemes	p. 64
An Exhaustive Approach (Generating Detectors Randomly)	p. 64
A Dynamic Programming Approach (Linear-Time Algorithm)	p. 65
Phase I: Solving the Counting Recurrence	p. 66
Phase II: Generating Strings Unmatched by S	p. 67
A Greedy Algorithm for Detector Generation	p. 69
Other Variants in Detector Generation	p. 70
NSMutation Algorithm	p. 70
Binary Template	p. 71
DynamiCS	p. 72
Schemata-Based Detection Rules	p. 72
Analysis of Negative Selection Algorithms	p. 73
Complexity of Detector Generation	p. 73
Immunological Holes	p. 74
Empirical Analysis: Binary Matching Rules and Detector Coverage	p. 77
Real-Valued Negative Selection Algorithms	p. 79
Detector Generation Using Evolutionary Algorithms	p. 82
Negative Selection with Fuzzy Detection Rules	p. 85
Randomized Approaches in Generating (Fixed Size) Spherical Detectors	p. 88
Estimation of Detector Volume and Overlap	p. 90
An Iterative Approach in Generating (Variably Sized) Negative Detectors	p. 92
Testing Process	p. 94
A Statistical Method: V-Detector Algorithm	p. 95
Multishaped Negative Detector Generation	p. 97
Applicability Issues of Real-Valued Negative Selection Algorithms	p. 100
Positive Selection (Detection)	p. 101
Negative Database	p. 103
Negative Database Representation	p. 103
Representation of Negative Databases as Satisfiability Problems	p. 104
Approaches to Generate Negative Databases	p. 104
Prefix Algorithm	p. 105
Randomized Algorithm	p. 105
Operations on Negative Database	p. 107
Negative Algebra	p. 107
Summary	p. 109
Research Questions	p. 111
Review Questions	p. 111
References	p. 113
B Cell-Inspired Algorithms	p. 117
Clonal Selection Algorithms	p. 117
Immune Network Models	p. 120
Continuous Immune Network Models	p. 123
Jerne's Idiotypical Network	p. 124
Coutinho and Varela's Idiotypical Network	p. 125
Farmer, Packard, and Perelson's Idiotypical Network	p. 125
Parisi's Idiotypical Network	p. 127
Stewart and Carneiro's Idiotypical Network	p. 128
Discrete Immune Network Models	p. 129
Hunt and Cooke's Immune Network Model and Its Variation	p. 129
Fractal Immune Network	p. 130
AiNet and Its Variations	p. 132
Opt-aiNet	p. 132
Dynamic Optimization AiNet	p. 135
Fuzzy Immune Network Model	p. 139
A General Model of Artificial Immune Network	p. 144
Summary	p. 147
Exercises	p. 149
General Questions	p. 149
References	p. 150
Latest Immune Models and Hybrid Approaches	p. 153
The Danger Theory	p. 153
Danger Theory-Based Algorithms	p. 155
Combining Dendritic Cells and Danger Theory	p. 159
Multilevel Immune Learning Algorithm	p. 160
Major Histocompatibility Complex-Based Systems	p. 162
Cytokine Network Model	p. 162
Phylogenies of T Cells	p. 164
Combining Negative Selection and Classification Techniques	p. 165
Summary	p. 167
Review Questions	p. 167
References	p. 168
Real-World Applications	p. 171
Solving Problems Using Immunological Computation	p. 171
Applications in Computer Security	p. 172
Virus Detection	p. 172
An Alternative Approach to Virus Detection	p. 173
UNIX Process Monitoring	p. 175
Immunity-Based Intrusion Detection Systems	p. 176
Immune Agent Architecture	p. 178
Immunogenetic Approaches in Intrusion Detection	p. 178
Danger Theory in Network Security	p. 179
Dendritic Cell Algorithm	p. 179
TLR Algorithm	p. 179
Applications in Fraud Detection	p. 182
Application in Robotics and Control	p. 183
Application in Fault Detection and Diagnosis	p. 185
Application to Scheduling	p. 188
AIS in Data Mining	p. 189
Applications in Web Mining	p. 190
Application in Anomaly Detection	p. 190
Solving Optimization Problems	p. 192
Other Applications	p. 193
Developing Associative Memories	p. 193
Applications in Games	p. 193
Applications in Software Testing	p. 194
Hybrid Approaches	p. 195
Application in Neural Networks	p. 195
Applications in Genetic Algorithms	p. 195
Summary	p. 196
Review Questions	p. 197
References	p. 198
Indexed Bibliography	p. 205
Index	p. 269
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Immunological Computation: Theory and Applications

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