Immunology as a Metaphor for Computational ... - Napier University

More documents

Recommendations

Info

Chapter 7. Conclusion 185In addition, COSDM and SOSDM also incorporate some other biological featuresnot mentioned in table 7.1. Binding of antigens to specific antibodies based on theattraction of the antibody for the antigen is consistent with the idea of shape-space,first introduced as an abstract concept by [Perelson and Oster, 1979]. In this model,antibodies and antigens are considered as points in a ’shape-space’, and antibodieswithin the affinity cut-off for clonal selection by an antigen form a ball in the shapespaceknown as the ball of stimulation. [Perelson and Oster, 1979] attempted to makethe shape-space quantitative by representing antibodies and antigen with real-valuedcoordinates, however an alternative to this kind of Euclidean shape-space is the Hammingshape-space used by for example [Farmer et al., 1986, Hightower et al., 1995,Perelson et al., 1996]. Thus, in COSDM, the size of the shape-space is directly determinedby the recognition radius of each location, and the corresponding quantitycan be calculated in SOSDM by determining the maximum distance between an antibodyand an antigen recognised by it. Furthermore, due to its self-organising nature,SOSDM also exhibits the meta-dynamic behaviour observed in immune-networks anddiscussed in chapter 2. Every time the antigen data is presented to the system, thedefinition of the antibodies may be perturbed, but the system eventually settles into astable representation of the current input data, representing the core clusters.Thus, of the four models presented in this thesis, SOSDM comes closest to modellingall of the features of the real immune system. Everyone of the features listedin table 7.1 is apparent in the model, ( or could potentially be added) and it containsthe core components of an immune system, i.e. antigens and antibodies, andthe ability of one species to recognise the other. Therefore, it seems justified to labelthe system as an immune system. Furthermore, the next section shows the hybridSOSDM/COSDM systems encapsulates precisely the properties that are required of adata-clustering system, and can offer some advantages over standard clustering algorithms,and that therefore approaching the design of the system from an immunologicalperspective has proved beneficial.
Chapter 7. Conclusion 1867.3 Is Immunology a Useful Metaphor ?In the 14th century, William of Occam, a logician and Franciscan friar is said to haveremarked “Entia non sunt multiplicanda praeter necessitatem”, or “Entities shouldnot be multiplied more than necessary”. This guiding principle, known as Occam’sRazor, is usually interpreted as meaning ’the most simple explanation is the one to bepreferred’. Thus, with this in mind, it is necessary to consider whether, in the context ofthe work presented in this thesis, the immune system has proved a useful metaphor, orwhether the same problems could have been tackled by other methods. In this section,we examine some of the systems to which immune systems have been compared, tosee if they could have been applied to the problems considered.7.3.1 Other approaches to schedulingIn [Farmer et al., 1986], the analogy between the immune system and classifier systemswas discussed in detail. The idea of the classifier system was first introduced byHolland in [Holland et al., 1986], and there exist many variations of the basic system.However, in its simplest form, it consists of a number of classifiers, which are simplyrules comprising of a condition and an action. If the condition part of any rule ismatched by a message from the environment, then the rule bids to be able to fire andhence execute its action. Each bid is a fraction of the rule’s associate strength — thewinning rule gets to fire and executes its action, and then may earn some reward fromthe external environment. This reward is paid to the winning rule, though in somemore elaborate systems, the winning rules also pay some of their strength to thoserules which made it possible for them to fire, in a kind of ’trickle-down informationeconomy’. New classifiers are generated either at random, or using a GA, which periodicallyruns in order to generate new rules and replace existing ones of low strength.The paper by Farmer illustrates the analogy in detail. Could the type of schedulingproblem described in chapter 3 be tackled by a classifier system approach, rather thanusing the immune metaphor ?First note that the analogy as described by Farmer applies to immune-networks,and not necessarily to the type of immune model presented in chapter 3. In this chap-
Page 2 and 3:
AbstractThe biological immune syste
Page 4 and 5:
DeclarationI declare that this thes
Page 6:
Table of Contents1 Introduction 11.
Page 9 and 10:
5.3.1 Default Parameters . . . . .
Page 11:
List of Figures2.1 ’Lock and Key
Page 14 and 15:
List of Tables2.1 Structural and fu
Page 16 and 17:
Chapter 1IntroductionThe study of b
Page 18 and 19:
Chapter 1. Introduction 3scientists
Page 20 and 21:
Chapter 1. Introduction 5Thus, the
Page 22 and 23:
Chapter 1. Introduction 7of the cha
Page 24 and 25:
Chapter 1. Introduction 94. Analysi
Page 26 and 27:
Chapter 2. Background 11paratopeepi
Page 28 and 29:
Chapter 2. Background 13antigenepit
Page 30 and 31:
£Chapter 2. Background 15low the m
Page 32 and 33:
Chapter 2. Background 17Antigene 0p
Page 34 and 35:
Chapter 2. Background 19maintained
Page 36 and 37:
E £ ρ¥ £Chapter 2. Background 2
Page 38 and 39:
Chapter 2. Background 23genetically
Page 40 and 41:
Chapter 2. Background 251. Choose a
Page 42 and 43:
Chapter 2. Background 27non-interbr
Page 44 and 45:
Chapter 2. Background 29been made i
Page 46 and 47:
Chapter 2. Background 31patterns wi
Page 48 and 49:
Chapter 2. Background 33reported on
Page 50 and 51:
Chapter 2. Background 35Immunos 81
Page 52 and 53:
Chapter 2. Background 37recognition
Page 54 and 55:
Chapter 2. Background 391. Create a
Page 56 and 57:
Chapter 2. Background 41environment
Page 58 and 59:
Chapter 2. Background 43a)input dat
Page 60 and 61:
Chapter 2. Background 45antibody wi
Page 62 and 63:
Chapter 3. Immune Systems for Sched
Page 64 and 65:
Page 66 and 67:
Page 68 and 69:
Page 70 and 71:
Page 72 and 73:
Page 74 and 75:
Page 76 and 77:
Page 78 and 79:
Page 80 and 81:
Page 82 and 83:
Page 84 and 85:
££Chapter 3. Immune Systems for S
Page 86 and 87:
Page 88 and 89:
Page 90 and 91:
Page 92 and 93:
Page 94 and 95:
Page 96 and 97:
Page 98 and 99:
Page 100 and 101:
Page 102 and 103:
Page 104 and 105:
Page 106 and 107:
Page 108 and 109:
Page 110 and 111:
Page 112 and 113:
Page 114 and 115:
Page 116 and 117:
Page 118 and 119:
Chapter 4Applying an Immune System
Page 120 and 121:
Chapter 4. Applying an Immune Syste
Page 122 and 123:
Page 124 and 125:
Page 126 and 127:
Page 128 and 129:
Page 130 and 131:
Chapter 5. EA Based Model — COSDM
Page 132 and 133:
Page 134 and 135:
Page 136 and 137:
JH £ c i ¤ a¥ :Chapter 5. EA Bas
Page 138 and 139:
Page 140 and 141:
Page 142 and 143:
Page 144 and 145:
Page 146 and 147:
Page 148 and 149:
Page 150 and 151: Chapter 5. EA Based Model — COSDM
Page 156 and 157: Chapter 6. A Self-Organising SDM
Page 166 and 167: J:¡Chapter 6. A Self-Organising SD
Page 168 and 169: ¡¡¡Chapter 6. A Self-Organising
Page 176 and 177: £Chapter 6. A Self-Organising SDM
Page 178 and 179: g£££1 At iteration 0 of the algo
Page 194 and 195: Chapter 7Conclusion7.1 OverviewThis
Page 196 and 197: Chapter 7. Conclusion 181however wh
Page 198 and 199: Chapter 7. Conclusion 183features m
Page 202 and 203: Chapter 7. Conclusion 187ter, sched
Page 204 and 205: Chapter 7. Conclusion 189Clustering
Page 206 and 207: Chapter 7. Conclusion 191nature in
Page 208 and 209: Appendix ACoincidences in permutati
Page 210 and 211: Appendix BExperimental results obta
Page 212 and 213: Appendix B. Experimental results ob
Page 218 and 219: Bibliography 203[Cooke and Hunt, 19
Page 220 and 221: Bibliography 205[Farmer et al., 198
Page 222 and 223: Bibliography 207[Hart et al., 1998]
Page 224 and 225: Bibliography 209[Kohonen, 1982b] Ko
Page 226 and 227: Bibliography 211[Potter and De Jong
Page 228: Bibliography 213[Timmis et al., 199
show all

Immunology as a Metaphor for Computational ... - Napier University

Create successful ePaper yourself

Delete template?

Save as template?