2006: Artificial Immune Systems - The Past, The Present, And The Future?


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ICARIS 2006 (International Conference on Artificial Immune Systems), Instituto Gulbenkian, Portugal

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2006: Artificial Immune Systems - The Past, The Present, And The Future?

  1. 1. Artificial Immune Systems: The Past, the Present. And the Future? Leandro Nunes de Castro Catholic University of Santos [email_address] , Support: CNPq, FAPESP ICARIS 2006, Institute Gulbenkian, Portugal
  2. 2. Outline <ul><li>What this talk is about </li></ul><ul><li>Immunology for Non-Immunologists </li></ul><ul><li>AIS: The Past </li></ul><ul><ul><li>A Tutorial on AIS </li></ul></ul><ul><li>AIS: The Present </li></ul><ul><ul><li>Current Trends </li></ul></ul><ul><li>AIS: The Future </li></ul><ul><ul><li>? ? ? </li></ul></ul>
  3. 3. What this talk is about... <ul><li>...and what it is not about </li></ul><ul><li>About: </li></ul><ul><ul><li>Basically an introduction to Artificial Immune Systems (AIS) </li></ul></ul><ul><ul><li>A brief review of the main current trends </li></ul></ul><ul><li>Not About: </li></ul><ul><ul><li>Making predictions </li></ul></ul>
  4. 4. Immunology for Non-Immunologists <ul><li>The immune system </li></ul><ul><li>Anatomy </li></ul><ul><li>Pattern recognition </li></ul><ul><li>Innate/Adaptive immunity </li></ul><ul><li>Some Theories: </li></ul><ul><ul><li>Clonal selection and affinity maturation </li></ul></ul><ul><ul><li>Self/Nonself discrimination </li></ul></ul><ul><ul><li>Immune network theory </li></ul></ul><ul><ul><li>Danger theory </li></ul></ul>
  5. 5. <ul><li>Some perspectives on the IS (de Castro, 2003): </li></ul><ul><ul><li>Self-recognition: dichotomy </li></ul></ul><ul><ul><li>Self-assertion: no fundamental difference between self and non-self </li></ul></ul><ul><ul><li>Multi-systemic: integration with other systems </li></ul></ul><ul><li>Classical concepts </li></ul><ul><ul><li>Immunology is the study of the defense mechanisms that confer resistance against diseases (Klein, 1990) </li></ul></ul><ul><ul><li>The immune system (IS) is the one responsible to protect us against the attack from external microorganisms (Tizard, 1995) </li></ul></ul>Immunology for Non-Immunologists
  6. 6. Immunology for Non-Immunologists <ul><li>All living beings present a type of defense mechanism </li></ul><ul><li>The Immune System </li></ul><ul><ul><li>Several defense mechanisms in different levels; some are redundant </li></ul></ul><ul><ul><li>The IS is adaptable (presents learning and memory ) </li></ul></ul><ul><ul><li>Microorganisms that might cause diseases (pathogen): viruses, fungi, bacteria and parasites </li></ul></ul><ul><ul><li>Antigen: any molecule that can stimulate an immune response </li></ul></ul>
  7. 7. Immunology for Non-Immunologists <ul><li>Anatomy </li></ul>
  8. 8. Immunology for Non-Immunologists Innate and Adaptive Immunity
  9. 9. Immunology for Non-Immunologists <ul><li>Innate immune system: </li></ul><ul><ul><li>immediately available for combat </li></ul></ul><ul><li>Adaptive immune system: </li></ul><ul><ul><li>antibody (Ab) production specific to a determined infectious agent </li></ul></ul><ul><li>Main Players </li></ul>
  10. 10. <ul><li>Innate Immune System </li></ul><ul><ul><li>first line of defense </li></ul></ul><ul><ul><li>controls bacterial infections </li></ul></ul><ul><ul><li>regulates adaptive immunity </li></ul></ul><ul><ul><li>composed mainly of phagocytes and the complement system </li></ul></ul><ul><ul><li>PAMPs and PRRs </li></ul></ul>Immunology for Non-Immunologists
  11. 11. <ul><li>Adaptive Immune System </li></ul><ul><ul><li>vertebrates have an adaptive immune system that confers resistance against future infections by the same or similar antigens </li></ul></ul><ul><ul><li>lymphocytes carry antigen receptors on their surfaces. </li></ul></ul><ul><ul><ul><li>These receptors are specific to a given antigen </li></ul></ul></ul><ul><ul><li>is capable of fine-tuning the cell receptors of the selected cells to the selective antigens </li></ul></ul><ul><ul><li>is regulated and down regulated by the innate immunity </li></ul></ul>Immunology for Non-Immunologists
  12. 12. <ul><li>Pattern Recognition: B-cells </li></ul>Immunology for Non-Immunologists
  13. 13. <ul><li>Pattern Recognition: T-cells </li></ul>Immunology for Non-Immunologists
  14. 14. Some Theories and Processes Immunology for Non-Immunologists
  15. 15. <ul><li>Clonal Selection and Affinity Maturation </li></ul>Immunology for Non-Immunologists
  16. 16. Immunology for Non-Immunologists <ul><li>Immune Responses: Maturation and Cross-Reactivity </li></ul>
  17. 17. <ul><li>Self/Nonself Discrimination </li></ul><ul><ul><li>repertoire completeness </li></ul></ul><ul><ul><li>co-stimulation </li></ul></ul><ul><ul><li>tolerance </li></ul></ul><ul><li>Positive selection </li></ul><ul><ul><li>recognition of a self-MHC by an immature T-cell, or recognition of a nonself antigen by a mature B-cell </li></ul></ul><ul><li>Negative selection </li></ul><ul><ul><li>recognition of self-antigens in the central lymphoid organs, or peripheral recognition of self-antigens in the absence of co-stimulatory signals </li></ul></ul>Immunology for Non-Immunologists
  18. 18. Immunology for Non-Immunologists <ul><li>Immune Network Theory </li></ul><ul><ul><li>The immune system is composed of an enormous and complex network of paratopes that recognize sets of idiotopes, and of idiotopes that are recognized by sets of paratopes, thus each element can recognize as well as be recognized (Jerne, 1974) </li></ul></ul><ul><li>Features (Varela et al., 1988) </li></ul><ul><ul><li>Structure </li></ul></ul><ul><ul><li>Dynamics </li></ul></ul><ul><ul><li>Metadynamics </li></ul></ul>
  19. 19. Immunology for Non-Immunologists <ul><li>Immune Network Connectivity </li></ul>
  20. 20. <ul><li>Danger Theory (Matzinger, 1994) </li></ul>Immunology for Non-Immunologists
  21. 21. Part I AIS: The Past
  22. 22. The Early Days <ul><li>From immunology to artificial immune systems: </li></ul><ul><ul><li>Theoretical concepts/models </li></ul></ul><ul><ul><li>Empirical evidences </li></ul></ul><ul><ul><li>Abstractions/Metaphors </li></ul></ul><ul><li>Main goals of AIS: </li></ul><ul><ul><li>Perform tasks such as data mining, control, and optimization </li></ul></ul>
  23. 23. The Early Days: A Bit of History <ul><li>Pioneer works: </li></ul><ul><ul><li>Farmer et al. (1986): continuous model of the immune network theory whose dynamics is observed in other biological systems. Argument that machine learning could benefit from the investigation of immune systems </li></ul></ul><ul><ul><li>Hoffmann (1986): explored similarities and differences between nervous and immune systems to formulate new artificial neural networks </li></ul></ul>
  24. 24. The Early Days: A Bit of History <ul><li>Pioneer works: </li></ul><ul><ul><li>Ishida (1990): PDP immune networks </li></ul></ul><ul><ul><li>Bersini and Varela (1990): machine learning, optimization and adaptive control </li></ul></ul><ul><ul><li>Forrest and Perelson (1991): use of GAs to explore pattern recognition in the immune system </li></ul></ul><ul><ul><li>Forrest et al. (1994) and Kephart (1994): use of immune metaphors to computer security </li></ul></ul>
  25. 25. The Early Days: A Bit of History <ul><li>1996: First workshop organized by Y. Ishida </li></ul><ul><li>From 1997 to 2001: special tracks organized by D. Dasgupta </li></ul><ul><li>Early edited volumes: Y. Ishida (Immunity-Based Systems) and D. Dasgupta (Artificial Immune Systems) </li></ul><ul><li>Some numbers: </li></ul><ul><ul><li>Late 2001: around 200 papers on AIS </li></ul></ul><ul><li>From 2002 onwards: ICARIS conference series </li></ul>
  26. 26. Previous Years: Some Numbers <ul><li>ICARIS Series: </li></ul>34 60 2006 37 (54%) 68 2005 34 (59%) 58 2004 26 (63%) 41 2003 26 ? 2002 Acceptance Submissions ICARIS
  27. 27. An Immune Engineering Framework <ul><li>Immune Engineering Framework </li></ul><ul><ul><li>Introduced in 2001 as a more principled approach to design AIS (de Castro, 2001; de Castro & Timmis, 2002) </li></ul></ul><ul><li>Main feature of the framework </li></ul><ul><ul><li>Problem-oriented (engineering perspective) </li></ul></ul>
  28. 28. The Immune Engineering Framework
  29. 29. Immune Engineering Framework <ul><li>Why the Immune System? </li></ul><ul><ul><li>Uniqueness </li></ul></ul><ul><ul><li>Self-identity </li></ul></ul><ul><ul><li>Diversity </li></ul></ul><ul><ul><li>Autonomy </li></ul></ul><ul><ul><li>Anomaly detection </li></ul></ul><ul><ul><li>Pattern recognition </li></ul></ul><ul><ul><li>Dynamic </li></ul></ul><ul><ul><li>Learning and memory </li></ul></ul><ul><ul><li>Self-organized </li></ul></ul><ul><ul><li>Integrated with other systems </li></ul></ul><ul><ul><li>Disposability </li></ul></ul><ul><ul><li>Distributed </li></ul></ul><ul><ul><li>Robust </li></ul></ul>
  30. 30. Immune Engineering Framework <ul><li>Some AIS Definitions: </li></ul><ul><ul><li>“ Artificial immune systems are intelligent methodologies inspired by the immune system toward real-world problem solving” (Dasgupta, 1999). </li></ul></ul><ul><ul><li>“ Artificial immune systems (AIS) are adaptive systems, inspired by theoretical immunology and observed immune functions, principles and models, which are applied to problem solving.” (de Castro & Timmis, 2002) </li></ul></ul>
  31. 31. Immune Engineering Framework
  32. 32. Immune Engineering Framework <ul><li>Basic elements: </li></ul><ul><ul><li>Representation </li></ul></ul><ul><ul><li>Evaluating interactions: fitness and affinity functions </li></ul></ul><ul><ul><li>Adaptation mechanisms: dynamics and metadynamics </li></ul></ul>
  33. 33. Immune Engineering Framework <ul><li>Representation: Shape-Space (Perelson & Oster, 1979) </li></ul><ul><ul><li>Generalized shape of a molecule: m  =   m 1 ,  m 2 , ...,  m L  </li></ul></ul><ul><ul><li>Cross-reactivity threshold:  </li></ul></ul>
  34. 34. Immune Engineering Framework <ul><li>Main types of Shape-Space: </li></ul><ul><ul><li>Hamming, Euclidean, Integer, and Symbolic </li></ul></ul><ul><li>Quantifying affinity (real-valued spaces): </li></ul><ul><ul><li>Ab  =   Ab 1 ,  Ab 2 , ...,  Ab L  ; Ag  =   Ag 1 ,  Ag 2 , ...,  Ag L  </li></ul></ul><ul><ul><li>Use of any norm, which will help define the type of shape-space, e.g., p =2: Euclidean shape-space, p =1: Manhattan shape-space. </li></ul></ul>
  35. 35. Immune Engineering Framework <ul><li>Quantifying affinity (binary-spaces): </li></ul><ul><ul><li>Hamming distance </li></ul></ul><ul><ul><li>r-contiguous matching </li></ul></ul><ul><ul><li>r-chunks </li></ul></ul><ul><ul><li>Rogers and Tanimoto </li></ul></ul><ul><ul><li>Hunt’s measure (Hunt et al., 1995): </li></ul></ul>
  36. 36. Immune Engineering Framework <ul><li>Immune Algorithms: </li></ul><ul><ul><li>Bone marrow algorithms: generation of immune repertoires </li></ul></ul><ul><ul><li>Thymus algorithms: self-nonself discrimination </li></ul></ul><ul><ul><li>Clonal algorithms: immune response to antigens </li></ul></ul><ul><ul><li>Immune network algorithms: idiotypic interactions </li></ul></ul>
  37. 37. Immune Engineering Framework Perform the dynamics and metadynamics of the system: how the immune cells and molecules are going to interact with each other and the antigens, and their survival Immune network Generation of a set of nonself detectors for anomaly detection Negative selection Perform the dynamics of the system: how the immune cells and molecules are going to interact with antigens Clonal selection Promote learning (adaptation) through somatic hypermutation and natural selection Affinity maturation Introduction and/or maintenance of population diversity and/or variation Somatic hypermutation Quantify affinities (match Ab-Ag; Ab-Ab) Affinity function Generation of cellular and molecular repertoires Bone-marrow models Usual Role Mechanism/Principle
  38. 38. Immune Engineering Framework <ul><li>Bone Marrow Algorithms </li></ul><ul><ul><li>Simplest approach: random generation </li></ul></ul><ul><ul><li>More biologically plausible: based on gene libraries (Oprea & Forrest, 1998) </li></ul></ul>
  39. 39. Immune Engineering Framework <ul><li>Thymus Algorithms: </li></ul><ul><ul><li>Positive selection (Seiden & Selada, 1992) </li></ul></ul>
  40. 40. Immune Engineering Framework <ul><li>Thymus Algorithms: </li></ul><ul><ul><li>Negative selection (Forrest et al., 1994) </li></ul></ul>
  41. 41. Immune Engineering Framework <ul><li>Thymus Algorithms: </li></ul><ul><ul><li>The Monitoring Phase </li></ul></ul>
  42. 42. Immune Engineering Framework <ul><li>Clonal Selection Algorithms: </li></ul><ul><ul><li>A GA without crossover is a suitable model of clonal selection (Forrest et al., 1993) </li></ul></ul><ul><ul><li>An immuno-genetic clonal selection algorithm – CLONALG (de Castro & Von Zuben, 2000-2002) </li></ul></ul><ul><ul><li>Nicosia et al. (2001): pattern recognition in the immune system by primary and secondary response </li></ul></ul>
  43. 43. Immune Engineering Framework <ul><li>Immune Network Algorithms: </li></ul><ul><ul><li>Continuous dynamics (e.g., Farmer et al., 1986; Varela & Coutinho, 1991) </li></ul></ul><ul><ul><li>Discrete dynamics (e.g., de Castro & Von Zuben, 2000; Timmis, 2000) </li></ul></ul>Rate of population variation Network stimulation Network suppression Death of unstimulatedelements Influx of new elements = - + -
  44. 44. Immune Engineering Framework <ul><li>Immune Network Algorithms: </li></ul><ul><ul><li>Continuous dynamics (Varela & Coutinho, 1991): </li></ul></ul>Network sensitivity for the idiotype Change in antibody concentration Change in cell-surface molecules
  45. 45. Immune Engineering Framework <ul><li>Immune Network Algorithms: </li></ul><ul><ul><li>An example of discrete dynamics (de Castro & Von Zuben, 2000) </li></ul></ul>For each antigen, do Clonal selection and expansion Affinity maturation Clonal interactions Clonal suppression Network construction End For Network suppression Diversity End
  46. 46. Part II AIS: The Present Features, Difficulties and Current Investigations
  47. 47. AIS: The Present <ul><li>Identification of several difficulties of AIS: </li></ul><ul><ul><li>Clonal selection algorithms: inherently evolutionary. Strong inter-relationship with other approaches, e.g., evolutionary algorithms </li></ul></ul><ul><ul><li>Negative selection: akin to binary classification. Also, it is usually inefficient to map the entire self or nonself space </li></ul></ul><ul><ul><li>Network algorithms: connectionist models with evolutionary stages. Significantly different from neural networks, as the nodes and connections have different meanings; also have different dynamics </li></ul></ul>
  48. 48. AIS: The Present <ul><li>Important questions: </li></ul><ul><ul><li>Is the field growing? Are we moving somewhere? </li></ul></ul><ul><li>The usefulness criterion: uniqueness/efficiency (Garrett, 2005) </li></ul><ul><li>How to tackle these difficulties? </li></ul><ul><li>Main trends: </li></ul><ul><ul><li>New applications areas </li></ul></ul><ul><ul><li>Algorithmic improvements </li></ul></ul><ul><ul><li>Theoretical investigation </li></ul></ul><ul><ul><li>Novel algorithms </li></ul></ul>
  49. 49. AIS: The Present <ul><li>A Sample of new application areas: </li></ul><ul><ul><li>Dynamic environments </li></ul></ul><ul><ul><li>Web applications, e.g., e-mail classification, text mining </li></ul></ul><ul><ul><li>Bioinformatics </li></ul></ul><ul><ul><li>A number of commercial applications </li></ul></ul>
  50. 50. AIS: The Present <ul><li>A Sample of algorithmic improvements: </li></ul><ul><ul><li>Many versions of immune networks and clonal selection algorithms: aiNets; B-cell algorithm; Bersini’s, Neal’s, Hart’s networks </li></ul></ul><ul><ul><li>Real-valued negative selection </li></ul></ul><ul><ul><li>New operators, e.g., mutation, match functions </li></ul></ul><ul><li>Same basic principles, but variations in representation, methods of calculating stimulation, suppression, dynamics and metadynamics </li></ul>
  51. 51. AIS: The Present <ul><li>Theoretical aspects: </li></ul><ul><ul><li>Convergence analysis </li></ul></ul><ul><ul><li>Markov chain models </li></ul></ul><ul><li>Novel algorithms: </li></ul><ul><ul><li>Hybrids: neuro-immune, evolutionary-immune, homeostatic algorithms, etc. </li></ul></ul><ul><ul><li>Dendritic cell algorithm* </li></ul></ul><ul><ul><li>Danger algorithms* </li></ul></ul>
  52. 52. Part III: And the Future?
  53. 53. Broadening the Viewpoint <ul><li>Looking at other new approaches: </li></ul><ul><ul><li>Ant-based algorithms </li></ul></ul><ul><ul><li>Particle swarm </li></ul></ul><ul><ul><li>Differential evolution* </li></ul></ul><ul><ul><li>Cultural algorithms* </li></ul></ul><ul><li>Maybe these fields satisfy the uniqueness/efficiency criteria respecting some constraints and for some specific problems, but do not seem to grow much as well! </li></ul>
  54. 54. And the Future? <ul><li>*“It is hard to make predictions, mainly about the future”* </li></ul><ul><li>Potential frontlines: </li></ul><ul><ul><li>Strengthen theoretical developments; improvement and analysis (usefulness by efficiency and understanding) </li></ul></ul><ul><ul><li>Deeper look into immunology: modeling x engineering (usefulness by novelty and fidelity) </li></ul></ul>
  55. 55. And the Future? <ul><li>More specifically (Aickelin & Dasgupta, 2005; Hart & Timmis, 2005): </li></ul><ul><ul><li>Closer look into innate immunity </li></ul></ul><ul><ul><li>Danger algorithms </li></ul></ul><ul><ul><li>Applications to dynamic environments </li></ul></ul><ul><li>Some questions already raised: </li></ul><ul><ul><li>Should we have a ‘killer’ application? </li></ul></ul><ul><ul><li>Should we have one main algorithm? </li></ul></ul>
  56. 56. To Conclude… <ul><li>A good aspect of the AIS community: </li></ul><ul><ul><li>Everybody is very critical and concerned about what we are doing and where we are heading </li></ul></ul><ul><li>But what exactly are we looking for: </li></ul><ul><ul><li>Uniqueness? </li></ul></ul><ul><ul><li>Efficiency? </li></ul></ul><ul><ul><li>“ Boosting” the field? </li></ul></ul><ul><ul><li>Modeling the IS? </li></ul></ul>
  57. 57. References <ul><li>Dasgupta, D. (Ed.) (1998), Artificial Immune Systems and Their Applications, Springer-Verlag. </li></ul><ul><li>de Castro, L. N. & Von Zuben, F. J., (2002), “Learning and Optimization Using the Clonal Selection Principle”, IEEE Transactions on Evolutionary Computation , 6(3), pp. 239-251. </li></ul><ul><li>de Castro, L. N. & Von Zuben, F. J. (2001), &quot;aiNet: An Artificial Immune Network for Data Analysis&quot;, Book Chapter in Data Mining: A Heuristic Approach, Hussein A. Abbass, Ruhul A. Sarker, and Charles S. Newton (Eds.), Idea Group Publishing, USA. </li></ul><ul><li>de Castro, L. N. (2006), “Fundamentals of Natural Computing: Basic Concepts, Algorithms, and Applications”, CRC Press LLC. </li></ul><ul><li>Forrest, S., A. Perelson, Allen, L. & Cherukuri, R. (1994), “Self-Nonself Discrimination in a Computer”, Proc. of the IEEE Symposium on Research in Security and Privacy, pp. 202-212. </li></ul><ul><li>Jerne, N. K. (1974), “Towards a Network Theory of the Immune System”, Ann. Immunol. (Inst. Pasteur) 125C, pp. 373-389. </li></ul><ul><li>Klein, J. (1990), Immunology , Blackwell Scientific Publications. </li></ul><ul><li>Matzinger, P. (1994), “Tolerance, Danger and the Extended Family”, Annual Reviews of Immunology , 12, pp. 991-1045. </li></ul><ul><li>Garrett, S. (2005), “How Do We Evaluate Artificial Immune Systems”, Evolutionary Computation , 13(2), pp. 145-178. </li></ul>
  58. 58. References <ul><li>Oprea, M. & Forrest, S. (1998), “Simulated Evolution of Antibody Gene Libraries Under Pathogen Selection”, Proc. of the IEEE SMC’98 . </li></ul><ul><li>Perelson, A. S. & Oster, G. F. (1979), “Theoretical Studies of Clonal Selection: Minimal Antibody Repertoire Size and Reliability of Self-Nonself Discrimination”, J. theor.Biol. , 81, pp. 645-670. </li></ul><ul><li>Tizard, I. R. (1995), Immunology An Introduction , Saunders College Pub., 4 th Ed. </li></ul><ul><li>Varela, F. J., Coutinho, A. Dupire, E. & Vaz, N. N. (1988), “Cognitive Networks: Immune, Neural and Otherwise”, Theoretical Immunology , Part II, A. S. Perelson (Ed.), pp. 359-375. </li></ul><ul><li>de Castro, L. N., & Timmis, J. (2002), Artificial Immune Systems: A New Computational Intelligence Approach , Springer-Verlag. </li></ul><ul><li>Hart, E. & Timmis, J. (2005), “Application Areas of AIS: The Past, the Present and the Future”, Lecture Notes in Computer Science 3627 , pp. 483-498. </li></ul><ul><li>Aickelin, U. & Dasgupta, D. (2005): “Artificial Immune Systems Tutorial”, Search Methodologies - Introductory Tutorials in Optimization and Decision Support Techniques (eds. E. Burke and G. Kendall), pp 375-399, Kluwer. </li></ul><ul><li>Nicosia, G., Castiglione, F., and Motta, S. (2001), “Pattern Recognition by Primary and Secondary Response of an Artificial Immune System”, Theory in Biosciences , 120(2), pp. 93-106. </li></ul>
  59. 59. References <ul><li>Bersini, H. & Varela, F. J. (1990), “Hints for Adaptive Problem Solving Gleaned from Immune Networks”, Parallel Problem Solving from Nature, pp. 343-354. </li></ul><ul><li>Farmer, J. D., Packard, N. H. & Perelson, A. S. (1986), “The Immune System, Adaptation, and Machine Learning”, Physica 22D, pp. 187-204. </li></ul><ul><li>Forrest, S. & A. Perelson (1991), “Genetic Algorithms and the Immune System”, Proc. of the Parallel Problem Solving form Nature, H-. P. Schwefel & R. Manner (eds.), Springer-Verlag. </li></ul><ul><li>Hoffmann, G. W. (1986), “A Neural Network Model Based on the Analogy with the Immune System”, J. theor. Biol., 122, pp. 33-67. </li></ul><ul><li>Ishida, Y. (1990), “Fully Distributed Diagnosis by PDP Learning Algorithm: Towards Immune Network PDP Model”, Proc. of the Int. Joint Conf. on Neural Networks, pp. 777-782. </li></ul><ul><li>Hunt, J. E., Cooke, D. E. & Holstein, H. (1995), “Case Memory and Retrieval Based on the Immune System”, 1st Int. Conference on Case-Based Reasoning, Published as Case-Based Reasoning Research and Development, Manuela Weloso and Agnar Aamodt (eds.), Lecture Notes in Artificial Intelligence, 1010, pp 205 -216. </li></ul><ul><li>Seiden, P. E. & Celada, F. (1992), “A Model for Simulating Cognate Recognition and Response in the Immune System”, J. theor. Biol., 158, pp. 329-357. </li></ul><ul><li>de Castro, L. N. (2003), “Immune Cognition, Micro-evolution, and a Personal Account on Immune Engineering”, S.E.E.D. Journal (Semiotics, Evolution, Energy, and Development). Universidade de Toronto, 3 (3), pp. 134-155. </li></ul>