1. Introduction to Artificial Immune Systems (AIS) BIC 2005: International Symposium on Bio-Inspired Computing Johor, MY, 5-7 September 2005 Dr. Leandro Nunes de Castro lnunes@unisantos.br Catholic University of Santos - UniSantos/Brazil

2. BIC 2005 - Introduction to Artificial Immune Systems - Dr. Leandro Nunes de Castro 2 Introduction to the Immune System Artificial Immune Systems A Framework to Design Artificial Immune Systems (AIS) Representation Schemes Affinity Measures Immune Algorithms Discussion and Main Trends Outline

3. Part I Brief Introduction to the Immune System

4. BIC 2005 - Introduction to Artificial Immune Systems - Dr. Leandro Nunes de Castro 4 Brief Introduction to the Immune System: Outline Fundamentals and Main Components Anatomy Innate Immune System Adaptive Immune System Pattern Recognition in the Immune System Basic Immune Recognition and Activation Clonal Selection and Affinity Maturation Self/Nonself Discrimination Immune Network Theory Danger Theory

5. BIC 2005 - Introduction to Artificial Immune Systems - Dr. Leandro Nunes de Castro 5 The Immune System (I) Fundamentals: Immunology is the study of the defense mechanisms that confer resistance against diseases (Klein, 1990) The immune system (IS) is the one responsible to protect us against the attack from external microorganisms (Tizard, 1995) Several defense mechanisms in different levels; some are redundant The IS is adaptable (presents learning and memory) Microorganisms that might cause diseases (pathogen): viruses, fungi, bacteria and parasites Antigen: any molecule that can stimulate the IS

6. BIC 2005 - Introduction to Artificial Immune Systems - Dr. Leandro Nunes de Castro 6 Innate immune system: immediately available for combat Adaptive immune system: antibody (Ab) production specific to a determined infectious agent The Immune System (II)

7. BIC 2005 - Introduction to Artificial Immune Systems - Dr. Leandro Nunes de Castro 7 Anatomy The Immune System (III)

8. BIC 2005 - Introduction to Artificial Immune Systems - Dr. Leandro Nunes de Castro 8 All living beings present a type of defense mechanism Innate Immune System first line of defense controls bacterial infections regulates adaptive immunity composed mainly of phagocytes and the complement system PAMPs and PRRs The Immune System (IV)

9. BIC 2005 - Introduction to Artificial Immune Systems - Dr. Leandro Nunes de Castro 9 Adaptive Immune System vertebrates have an adaptive immune system that confers resistance against future infections by the same or similar antigens lymphocytes carry antigen receptors on their surfaces. These receptors are specific to a given antigen is capable of fine-tuning the cell receptors of the selected cells to the selective antigens is regulated and down regulated by the innate immunity The Immune System (V)

10. BIC 2005 - Introduction to Artificial Immune Systems - Dr. Leandro Nunes de Castro 10 Pattern Recognition: B-cell The Immune System (VI)

11. BIC 2005 - Introduction to Artificial Immune Systems - Dr. Leandro Nunes de Castro 11 Pattern Recognition: T-cell The Immune System (VII)

12. BIC 2005 - Introduction to Artificial Immune Systems - Dr. Leandro Nunes de Castro 12 The Immune System (VIII) after Nosssal, 1993 Basic Immune Recognition and Activation Mechanisms

13. BIC 2005 - Introduction to Artificial Immune Systems - Dr. Leandro Nunes de Castro 13 Antibody Synthesis: The Immune System (IX) after Oprea & Forrest, 1998

14. BIC 2005 - Introduction to Artificial Immune Systems - Dr. Leandro Nunes de Castro 14 Clonal Selection and Affinity Maturation The Immune System (X)

15. BIC 2005 - Introduction to Artificial Immune Systems - Dr. Leandro Nunes de Castro 15 Maturation and Cross-Reactivity of Immune Responses The Immune System (XI)

16. BIC 2005 - Introduction to Artificial Immune Systems - Dr. Leandro Nunes de Castro 16 Affinity Maturation somatic hypermutation receptor editing The Immune System (XII)

17. BIC 2005 - Introduction to Artificial Immune Systems - Dr. Leandro Nunes de Castro 17 Self/Nonself Discrimination repertoire completeness co-stimulation tolerance Positive selection B- and T-cells are selected as immunocompetent cells Recognition of self-MHC molecules Negative selection Tolerance of self: those cells that recognize the self are eliminated from the repertoire The Immune System (XIII)

18. BIC 2005 - Introduction to Artificial Immune Systems - Dr. Leandro Nunes de Castro 18 Self/Nonself Discrimination The Immune System (XIV)

19. BIC 2005 - Introduction to Artificial Immune Systems - Dr. Leandro Nunes de Castro 19 Immune Network Theory 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) Features (Varela et al., 1988) Structure Dynamics Metadynamics The Immune System (XV)

20. BIC 2005 - Introduction to Artificial Immune Systems - Dr. Leandro Nunes de Castro 20 Immune Network Dynamics The Immune System (XVI) after Jerne, 1974

21. BIC 2005 - Introduction to Artificial Immune Systems - Dr. Leandro Nunes de Castro 21 The Immune System (XVII) Danger Theory after Matzinger, 1994

22. BIC 2005 - Introduction to Artificial Immune Systems - Dr. Leandro Nunes de Castro 22 Pathogen, Antigen, Antibody Lymphocytes: B- and T-cells Affinity 1 ary, 2 ary and cross-reactive response Learning and memory increase in clone size and affinity maturation Self/Nonself Discrimination Immune Network Theory Danger Signals The Immune System — Summary —

23. Part II Artificial Immune Systems

24. BIC 2005 - Introduction to Artificial Immune Systems - Dr. Leandro Nunes de Castro 24 Artificial Immune Systems (AIS) Remarkable Immune Properties Concepts, Scope and Applications Brief History of AIS An Engineering Framework for AIS The Shape-Space Formalism Measuring Affinities Algorithms and Processes Artificial Immune Systems: Outline

25. BIC 2005 - Introduction to Artificial Immune Systems - Dr. Leandro Nunes de Castro 25 Remarkable Immune Properties uniqueness diversity robustness autonomy multilayered self/nonself discrimination* distributivity reinforcement learning and memory predator-prey behavior noise tolerance (imperfect recognition) Artificial Immune Systems (I)

26. BIC 2005 - Introduction to Artificial Immune Systems - Dr. Leandro Nunes de Castro 26 Concepts Artificial immune systems are data manipulation, classification, reasoning and representation methodologies, that follow a plausible biological paradigm: the human immune system (Starlab) An artificial immune system is a computational system based upon metaphors of the natural immune system (Timmis, 2000) The artificial immune systems are composed of intelligent methodologies, inspired by the natural immune system, for the solution of real-world problems (Dasgupta, 1998) 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) Artificial Immune Systems (II)

27. BIC 2005 - Introduction to Artificial Immune Systems - Dr. Leandro Nunes de Castro 27 Artificial Immune Systems (III) Scope (de Castro & Timmis, 2002): Pattern recognition Fault and anomaly detection Data analysis (classification, clustering, etc.) Agent-based systems Search and optimization Machine-learning Autonomous navigation and control Artificial life Security of information systems

28. BIC 2005 - Introduction to Artificial Immune Systems - Dr. Leandro Nunes de Castro 28 Examples of Applications Pattern recognition; Function approximation; Optimization; Data analysis and clustering; Machine learning; Associative memories; Diversity generation and maintenance; Evolutionary computation and programming; Fault and anomaly detection; Control and scheduling; Computer and network security; Generation of emergent behaviors. Artificial Immune Systems (IV)

29. BIC 2005 - Introduction to Artificial Immune Systems - Dr. Leandro Nunes de Castro 29 Artificial Immune Systems (V) The Early Days: Developed from the field of theoretical immunology in the mid 1980’s. Suggested we “might look” at the IS 1990 – Bersini first use of immune algorithms to solve problems Forrest et al – Computer Security mid 1990’s Work by IBM on virus detection Hunt et al, mid 1990’s – Machine learning

30. BIC 2005 - Introduction to Artificial Immune Systems - Dr. Leandro Nunes de Castro 30 The Early Events

31. Part III A Framework to Engineer AIS

32. BIC 2005 - Introduction to Artificial Immune Systems - Dr. Leandro Nunes de Castro 32 Representation How do we mathematically represent immune cells and molecules? How do we quantify their interactions or recognition? Shape-Space Formalism (Perelson & Oster, 1979) Quantitative description of the interactions between cells and molecules Shape-Space (S) Concepts generalized shape recognition through regions of complementarity recognition region (cross-reactivity) affinity threshold A Framework for AIS (I)

33. BIC 2005 - Introduction to Artificial Immune Systems - Dr. Leandro Nunes de Castro 33 Recognition Via Regions of Complementarity and Shape Space (S) Cross-Reactivity A Framework for AIS (II) after Perelson, 1989

34. BIC 2005 - Introduction to Artificial Immune Systems - Dr. Leandro Nunes de Castro 34 Representation Set of coordinates: m =  m 1, m 2,..., m L , m  S L   L Ab =  Ab 1, Ab 2,..., Ab L , Ag =  Ag 1, Ag 2,..., Ag L  Some Types of Shape Space Hamming Euclidean Manhattan Symbolic A Framework for AIS (III)

35. BIC 2005 - Introduction to Artificial Immune Systems - Dr. Leandro Nunes de Castro 35 A Framework for AIS (IV) Affinities: related to distance/similarity Examples of affinity measures Euclidean Manhattan Hamming

36. BIC 2005 - Introduction to Artificial Immune Systems - Dr. Leandro Nunes de Castro 36 Affinities in Hamming Shape-Space A Framework for AIS (V) Hamming r-contiguous bit Affinity measure distance rule of Hunt Flipping one string

37. BIC 2005 - Introduction to Artificial Immune Systems - Dr. Leandro Nunes de Castro 37 Algorithms and Processes Generic algorithms based on specific immune principles, processes or theoretical models Main Types Bone marrow algorithms Thymus algorithms Clonal selection algorithms Immune network models A Framework for AIS (VI)

38. BIC 2005 - Introduction to Artificial Immune Systems - Dr. Leandro Nunes de Castro 38 A Bone Marrow Algorithm A Framework for AIS (VII) after Perelson et al., 1996

39. BIC 2005 - Introduction to Artificial Immune Systems - Dr. Leandro Nunes de Castro 39 Thymus Algorithms: Negative Selection Store information about the patterns to be recognized based on a set of known patterns A Framework for AIS (VIII) CensoringMonitoring phase phase after Forrest et al., 1994

40. BIC 2005 - Introduction to Artificial Immune Systems - Dr. Leandro Nunes de Castro 40 A Clonal Selection Algorithm A Framework for AIS (IX) after de Castro & Von Zuben, 2001a

41. BIC 2005 - Introduction to Artificial Immune Systems - Dr. Leandro Nunes de Castro 41 Somatic Hypermutation Hamming shape-space with an alphabet of length 8 Real-valued vectors: inductive mutation A Framework for AIS (X)

42. BIC 2005 - Introduction to Artificial Immune Systems - Dr. Leandro Nunes de Castro 42 Affinity Proportionate Hypermutation A Framework for AIS (XI) after de Castro & Von Zuben, 2001aafter Kepler & Perelson, 1993

43. BIC 2005 - Introduction to Artificial Immune Systems - Dr. Leandro Nunes de Castro 43 A Discrete Immune Network Model: aiNet A Framework for AIS (XII)

44. BIC 2005 - Introduction to Artificial Immune Systems - Dr. Leandro Nunes de Castro 44 Guidelines to Design an AIS A Framework for AIS (XIII)

45. Part IV Discussion and Main Trends

46. BIC 2005 - Introduction to Artificial Immune Systems - Dr. Leandro Nunes de Castro 46 Discussion Growing interest for the AIS Biologically Inspired Computing utility and extension of biology improved comprehension of natural phenomena Example-based learning, where different pattern categories are represented by adaptive memories of the system A new computational intelligence approach

47. BIC 2005 - Introduction to Artificial Immune Systems - Dr. Leandro Nunes de Castro 47 The use of a general framework to design AIS Main application domains Optimization, Data Analysis, Machine-Learning, Pattern Recognition Main trends Innate immunity, hybrid algorithms, use of danger theory, formal aspects of AIS, mathematical analysis, development of more theoretical models Main Trends

48. BIC 2005 - Introduction to Artificial Immune Systems - Dr. Leandro Nunes de Castro 48 Dasgupta, D. (Ed.) (1998), Artificial Immune Systems and Their Applications, Springer-Verlag. de Castro, L. N., & Von Zuben, F. J., (2001a), “Learning and Optimization Using the Clonal Selection Principle”, submitted to the IEEE Transaction on Evolutionary Computation (Special Issue on AIS). de Castro, L. N. & Von Zuben, F. J. (2001), "aiNet: An Artificial Immune Network for Data Analysis", Book Chapter in Data Mining: A Heuristic Approach, Hussein A. Abbass, Ruhul A. Sarker, and Charles S. Newton (Eds.), Idea Group Publishing, USA. 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. Hofmeyr S. A. & Forrest, S. (2000), “Architecture for an Artificial Immune System”, Evolutionary Computation, 7(1), pp. 45-68. Jerne, N. K. (1974a), “Towards a Network Theory of the Immune System”, Ann. Immunol. (Inst. Pasteur) 125C, pp. 373-389. Kepler, T. B. & Perelson, A. S. (1993a), “Somatic Hypermutation in B Cells: An Optimal Control Treatment”, J. theor. Biol., 164, pp. 37-64. Klein, J. (1990), Immunology, Blackwell Scientific Publications. Matzinger, P. (1994), “Tolerance, Danger and the Extended Family”, Annual Reviews of Immunology, 12, pp. 991-1045. References (I)

49. BIC 2005 - Introduction to Artificial Immune Systems - Dr. Leandro Nunes de Castro 49 References (II) Nossal, G. J. V. (1993a), “Life, Death and the Immune System”, Scientific American, 269(3), pp. 21-30. Oprea, M. & Forrest, S. (1998), “Simulated Evolution of Antibody Gene Libraries Under Pathogen Selection”, Proc. of the IEEE SMC’98. Perelson, A. S. (1989), “Immune Network Theory”, Imm. Rev., 110, pp. 5-36. 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. Perelson, A. S., Hightower, R. & Forrest, S. (1996), “Evolution and Somatic Learning in V- Region Genes”, Research in Immunology, 147, pp. 202-208. Starlab, URL: http://www.starlab.org/genes/ais/ Timmis, J. (2000), Artificial Immune Systems: A Novel Data Analysis Technique Inspired by the Immune Network Theory, Ph.D. Dissertation, Department of Computer Science, University of Whales, September. Tizard, I. R. (1995), Immunology An Introduction, Saunders College Pub., 4 th Ed. 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. de Castro, L. N., & Timmis, J. (2002), Artificial Immune Systems: A New Computational Intelligence Approach, Springer-Verlag.

50. lnunes@unisantos.br Thank You! Questions? Comments?