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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 [email protected] Catholic University of Santos - UniSantos/Brazil Outline 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 BIC 2005 - Introduction to Artificial Immune Systems - Dr. Leandro Nunes de Castro 2 Part I Brief Introduction to the Immune System 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 BIC 2005 - Introduction to Artificial Immune Systems - Dr. Leandro Nunes de Castro 4 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 BIC 2005 - Introduction to Artificial Immune Systems - Dr. Leandro Nunes de Castro 5 The Immune System (II) Immunity Innate Granulocytes Macrophages Innate immune system: Adaptative Lymphocytes B-cells T-cells immediately available for combat Adaptive immune system: antibody (Ab) production specific to a determined infectious agent BIC 2005 - Introduction to Artificial Immune Systems - Dr. Leandro Nunes de Castro 6 The Immune System (III) Anatomy Primary lymphoid organs Secondary lymphoid organs Tonsils and adenoids Thymus Spleen Peyer’s patches Appendix Bone marrow Lymph nodes Lymphatic vessels BIC 2005 - Introduction to Artificial Immune Systems - Dr. Leandro Nunes de Castro 7 The Immune System (IV) 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 BIC 2005 - Introduction to Artificial Immune Systems - Dr. Leandro Nunes de Castro 8 The Immune System (V) 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 BIC 2005 - Introduction to Artificial Immune Systems - Dr. Leandro Nunes de Castro 9 The Immune System (VI) Pattern Recognition: B-cell BCR or Antibody B-cell Receptors (Ab) Epitopes Antigen B-cell BIC 2005 - Introduction to Artificial Immune Systems - Dr. Leandro Nunes de Castro 10 The Immune System (VII) Pattern Recognition: T-cell TCR T-cell BIC 2005 - Introduction to Artificial Immune Systems - Dr. Leandro Nunes de Castro 11 The Immune System (VIII) Basic Immune Recognition and Activation Mechanisms after Nosssal, 1993 BIC 2005 - Introduction to Artificial Immune Systems - Dr. Leandro Nunes de Castro 12 The Immune System (IX) Antibody Synthesis: V D V ... D ... V library D library J J ... J library Gene rearrangement V D J Rearranged DNA after Oprea & Forrest, 1998 BIC 2005 - Introduction to Artificial Immune Systems - Dr. Leandro Nunes de Castro 13 The Immune System (X) Clonal Selection and Affinity Maturation Selection Proliferation Differentiation M M Memory cells Plasma cells Antigens BIC 2005 - Introduction to Artificial Immune Systems - Dr. Leandro Nunes de Castro 14 The Immune System (XI) Maturation and Cross-Reactivity of Immune Responses Antibody Concentration Cross-Reactive Response Secondary Response Primary Response Lag Lag Response to Ag1 Lag Response to Ag1 ... ... Antigen Ag1 Antigens Ag1, Ag2 ... Response to Ag1’ Response to Ag2 ... Antigen Ag1’ BIC 2005 - Introduction to Artificial Immune Systems - Dr. Leandro Nunes de Castro Time 15 The Immune System (XII) Affinity Maturation Affinity (log scale) somatic hypermutation receptor editing 1ary 2ary 3ary Response BIC 2005 - Introduction to Artificial Immune Systems - Dr. Leandro Nunes de Castro 16 The Immune System (XIII) Self/Nonself Discrimination Positive selection repertoire completeness co-stimulation tolerance 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 BIC 2005 - Introduction to Artificial Immune Systems - Dr. Leandro Nunes de Castro 17 The Immune System (XIV) Self/Nonself Discrimination Nonself antigens Selfantigen Clonal Expansion Effector clone Clonal Ignorance Negative Selection Unaffected cell Clonal deletion Anergy OR receptor editing BIC 2005 - Introduction to Artificial Immune Systems - Dr. Leandro Nunes de Castro 18 The Immune System (XV) 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) Paratope Features (Varela et al., 1988) Structure Dynamics Metadynamics Idiotope Antibody BIC 2005 - Introduction to Artificial Immune Systems - Dr. Leandro Nunes de Castro 19 The Immune System (XVI) Immune Network Dynamics Dynamics of the immune system pb Foreign stimulus ib Ag (epitope) pa Internal image ia pc Recognition ic ia Anti-idiotypic set px after Jerne, 1974 Non-specific set BIC 2005 - Introduction to Artificial Immune Systems - Dr. Leandro Nunes de Castro 20 The Immune System (XVII) Danger Theory after Matzinger, 1994 BIC 2005 - Introduction to Artificial Immune Systems - Dr. Leandro Nunes de Castro 21 The Immune System — Summary — Pathogen, Antigen, Antibody Lymphocytes: B- and T-cells Affinity 1ary, 2ary and cross-reactive response Learning and memory increase in clone size and affinity maturation Self/Nonself Discrimination Immune Network Theory Danger Signals BIC 2005 - Introduction to Artificial Immune Systems - Dr. Leandro Nunes de Castro 22 Part II Artificial Immune Systems Artificial Immune Systems: Outline 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 BIC 2005 - Introduction to Artificial Immune Systems - Dr. Leandro Nunes de Castro 24 Artificial Immune Systems (I) Remarkable Immune Properties uniqueness diversity robustness autonomy multilayered self/nonself discrimination* distributivity reinforcement learning and memory predator-prey behavior noise tolerance (imperfect recognition) BIC 2005 - Introduction to Artificial Immune Systems - Dr. Leandro Nunes de Castro 25 Artificial Immune Systems (II) 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) BIC 2005 - Introduction to Artificial Immune Systems - Dr. Leandro Nunes de Castro 26 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 BIC 2005 - Introduction to Artificial Immune Systems - Dr. Leandro Nunes de Castro 27 Artificial Immune Systems (IV) 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. BIC 2005 - Introduction to Artificial Immune Systems - Dr. Leandro Nunes de Castro 28 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 BIC 2005 - Introduction to Artificial Immune Systems - Dr. Leandro Nunes de Castro 29 The Early Events Year Event 1996 IMBS Workshop Y. Ishida 1997 SMC Special Track / Tutorial D. Dasgupta SMC Special Track D. Dasgupta Book First edited book D. Dasgupta (ed.) SMC Special Track D. Dasgupta Workshop D. Dasgupta Talk: “Why Does a Computer Need an Immune System” S. Forrest 1998 1999 GECCO 2000 SMC SBRN 2001 2002 2003 Activity Tutorial: “Immunological Computation” Special Track Tutorial: “Artificial Immune Systems and Their Applications” Organizer/Speaker D. Dasgupta L. N. de Castro Tutorial: “An Introduction to the Artificial Immune Systems” L. N. de Castro CEC Tutorial: “Artificial Immune Systems: An Emerging Technology” J. Timmis Journal (Special Issue) IEEE-TEC: Special Issue on Artificial Immune Systems D. Dasgupta (ed.) Book Artificial Immune Systems: A New Computational Intelligence Approach L. N. de Castro & J. Timmis ICARIS First International Conference on AIS Various ICARIS, Special Tracks, Special Issues, etc. ICANNGA J. Timmis & P. Bentley Various BIC 2005 - Introduction to Artificial Immune Systems - Dr. Leandro Nunes de Castro 30 Part III A Framework to Engineer AIS A Framework for AIS (I) 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 BIC 2005 - Introduction to Artificial Immune Systems - Dr. Leandro Nunes de Castro 32 A Framework for AIS (II) Recognition Via Regions of Complementarity and Shape Space (S) Cross-Reactivity V V V V after Perelson, 1989 BIC 2005 - Introduction to Artificial Immune Systems - Dr. Leandro Nunes de Castro 33 A Framework for AIS (III) Representation Set of coordinates: m = m1, m2, ..., mL, m SL L Ab = Ab1, Ab2, ..., AbL, Ag = Ag1, Ag2, ..., AgL Some Types of Shape Space Hamming Euclidean Manhattan Symbolic BIC 2005 - Introduction to Artificial Immune Systems - Dr. Leandro Nunes de Castro 34 A Framework for AIS (IV) Affinities: related to distance/similarity Examples of affinity measures Euclidean L ( Ab Ag ) D i 1 Manhattan i 2 i L D Abi Agi i 1 Hamming L D δ, where i 1 1 if Abi Agi δ 0 otherwise BIC 2005 - Introduction to Artificial Immune Systems - Dr. Leandro Nunes de Castro 35 A Framework for AIS (V) Affinities in Hamming Shape-Space 0 0 1 1 0 0 1 1 0 0 1 1 0 0 1 1 0 0 1 1 0 0 1 1 1 1 1 0 1 1 0 1 1 1 1 0 1 1 0 1 1 1 1 0 1 1 0 1 XOR : 1 1 0 1 1 1 1 0 Affinity: 6 XOR : 1 1 0 1 1 1 1 0 Affinity: 4 XOR : 1 1 0 1 1 1 1 0 4 Affinity: 6+2 =22 Hamming distance r-contiguous bit rule Affinity measure of Hunt D DH i 2l i 0 0 1 1 0 0 1 1 0 0 1 1 0 0 1 1 1 1 1 0 1 1 0 1 1 1 1 1 0 1 1 0 ... 0 0 1 1 0 0 1 1 1 1 0 1 1 0 1 1 Affinity: 6 + 4 + ... + 4 = 32 Flipping one string BIC 2005 - Introduction to Artificial Immune Systems - Dr. Leandro Nunes de Castro 36 A Framework for AIS (VI) 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 BIC 2005 - Introduction to Artificial Immune Systems - Dr. Leandro Nunes de Castro 37 A Framework for AIS (VII) A Bone Marrow Algorithm An individual genome corresponds to four libraries: Library 1 A1 A2 A3 A4 A5 A6 A7 A8 A3 Library 2 Library 3 B1 B2 B3 B4 B5 B6 B7 B8 Library 4 C1 C2 C3 C4 C5 C6 C7 C8 B2 D1 D2 D3 D4 D5 D6 D7 D8 C8 A3 B2 C8 D5 A3 B2 C8 D5 D5 = four 16 bit segments = a 64 bit chain Expressed Ab molecule after Perelson et al., 1996 BIC 2005 - Introduction to Artificial Immune Systems - Dr. Leandro Nunes de Castro 38 A Framework for AIS (VIII) Thymus Algorithms: Negative Selection Store information about the patterns to be recognized based on a set of known patterns Detector Set (R) Self strings (S) Generate random strings (R0) Match No Detector Set (R) Self Strings (S) Match Yes Yes Reject Censoring phase No Non-self Detected after Forrest et al., 1994 Monitoring phase BIC 2005 - Introduction to Artificial Immune Systems - Dr. Leandro Nunes de Castro 39 A Framework for AIS (IX) A Clonal Selection Algorithm (8) Ab{d} Ab (1) f (2) Ab{n} (7) Re-select Select (3) (6) f C* Ab{n} after de Castro & Von Zuben, 2001a Clone (5) (4) Maturate C BIC 2005 - Introduction to Artificial Immune Systems - Dr. Leandro Nunes de Castro 40 A Framework for AIS (X) Somatic Hypermutation Hamming shape-space with an alphabet of length 8 Real-valued vectors: inductive mutation BIC 2005 - Introduction to Artificial Immune Systems - Dr. Leandro Nunes de Castro 41 A Framework for AIS (XI) Affinity Proportionate Hypermutation 1 0.9 0.9 0.8 0.8 0.7 0.7 0.6 =5 0.6 0.5 0.4 = 10 0.4 0.5 0.3 = 20 0.3 0.2 0.2 0.1 0.1 0 0 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 D* after de Castro & Von Zuben, 2001a 0 20 40 60 80 100 120 140 160 180 200 Iterations after Kepler & Perelson, 1993 BIC 2005 - Introduction to Artificial Immune Systems - Dr. Leandro Nunes de Castro 42 A Framework for AIS (XII) A Discrete Immune Network Model: aiNet 1. For each antigenic pattern Agi, 1.1 Clonal selection: Apply the pattern recognition version of CLONALG that will return a matrix of memory clones for Agi; 1.2 Apoptosis: Eliminate all memory clones whose affinity with antigen are below a threshold; 1.3 Inter-cell affinity: Determine the affinity between all clones generated for Agi; 1.4 Clonal Suppression: Eliminate those clones whose affinities are inferior to a pre-specified threshold; 1.5 Total repertoire: Concatenate the clone generated for antigen Agi with all network cells 2. Inter-cell affinity: Determine the affinity between all network cells; 3. Network suppression: Eliminate all aiNet cells whose affinities are inferior to a pre-specified threshold. BIC 2005 - Introduction to Artificial Immune Systems - Dr. Leandro Nunes de Castro 43 A Framework for AIS (XIII) Guidelines to Design an AIS 1. 2. Problem definition Mapping the real problem into the AIS domain 2.1 Defining the types of immune cells and molecules to be used 2.2 Deciding the immune principle(s) to be used in the solution 2.3 Defining the mathematical representation for the elements of the AIS 2.4 Evaluating the interactions among the elements of the AIS (dynamics) 2.5 Controlling the metadynamics of the AIS 3. Reverse mapping from AIS to the real problem BIC 2005 - Introduction to Artificial Immune Systems - Dr. Leandro Nunes de Castro 44 Part IV Discussion and Main Trends 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 BIC 2005 - Introduction to Artificial Immune Systems - Dr. Leandro Nunes de Castro 46 Main Trends 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 BIC 2005 - Introduction to Artificial Immune Systems - Dr. Leandro Nunes de Castro 47 References (I) 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. BIC 2005 - Introduction to Artificial Immune Systems - Dr. Leandro Nunes de Castro 48 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 VRegion 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., 4th 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. 359375. de Castro, L. N., & Timmis, J. (2002), Artificial Immune Systems: A New Computational Intelligence Approach, Springer-Verlag. BIC 2005 - Introduction to Artificial Immune Systems - Dr. Leandro Nunes de Castro 49 Thank You! Questions? Comments? [email protected]