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2005: An Introduction to Artificial Immune Systems

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BIC 2005 (Biologically Inspired Computing Conference), Johor, Malaysia

BIC 2005 (Biologically Inspired Computing Conference), Johor, Malaysia

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  • 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 [email_address] Catholic University of Santos - UniSantos/Brazil
  • 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. 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. 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.
    • Innate immune system:
      • immediately available for combat
    • Adaptive immune system:
      • antibody (Ab) production specific to a determined infectious agent
    The Immune System (II)
  • 7.
    • Anatomy
    The Immune System (III)
  • 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.
    • 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.
    • Pattern Recognition: B-cell
    The Immune System (VI)
  • 11.
    • Pattern Recognition: T-cell
    The Immune System (VII)
  • 12. The Immune System (VIII) after Nosssal, 1993
    • Basic Immune Recognition and Activation Mechanisms
  • 13.
    • Antibody Synthesis:
    The Immune System (IX) after Oprea & Forrest, 1998
  • 14.
    • Clonal Selection and Affinity Maturation
    The Immune System (X)
  • 15.
    • Maturation and Cross-Reactivity of Immune Responses
    The Immune System (XI)
  • 16.
    • Affinity Maturation
      • somatic hypermutation
      • receptor editing
    The Immune System (XII)
  • 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.
    • Self/Nonself Discrimination
    The Immune System (XIV)
  • 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.
    • Immune Network Dynamics
    The Immune System (XVI) after Jerne, 1974
  • 21. The Immune System (XVII)
    • Danger Theory
    after Matzinger, 1994
  • 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.
    • 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.
    • 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.
    • 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.
    • 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
    Artificial Immune Systems (III)
  • 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.
    • 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
    Artificial Immune Systems (V)
  • 30. The Early Events
  • 31. Part III A Framework to Engineer AIS
  • 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.
    • Recognition Via Regions of Complementarity and Shape Space ( S )
    • Cross-Reactivity
    A Framework for AIS (II) after Perelson, 1989
  • 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. A Framework for AIS (IV)
    • Affinities: related to distance/similarity
    • Examples of affinity measures
      • Euclidean
      • Manhattan
      • Hamming
  • 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.
    • 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.
    • A Bone Marrow Algorithm
    A Framework for AIS (VII) after Perelson et al., 1996
  • 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) Censoring Monitoring phase phase after Forrest et al., 1994
  • 40.
    • A Clonal Selection Algorithm
    A Framework for AIS (IX) after de Castro & Von Zuben, 2001a
  • 41.
    • Somatic Hypermutation
      • Hamming shape-space with an alphabet of length 8
      • Real-valued vectors: inductive mutation
    A Framework for AIS (X)
  • 42.
    • Affinity Proportionate Hypermutation
    A Framework for AIS (XI) after de Castro & Von Zuben, 2001a after Kepler & Perelson, 1993
  • 43.
    • A Discrete Immune Network Model: aiNet
    A Framework for AIS (XII)
  • 44.
    • Guidelines to Design an AIS
    A Framework for AIS (XIII)
  • 45. Part IV Discussion and Main Trends
  • 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.
    • 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.
      • 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. 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. [email_address] Thank You! Questions? Comments?