Introduction to Artificial Introduction to Artificial Immune Systems (AIS) Immune Systems (AIS) BIC 2005: BIC 2005: International Symposium on Bio-Inspired Computing International Symposium on Bio-Inspired Computing Johor, MY, 5-7 September 2005 Johor, MY, 5-7 September 2005 Dr. Leandro Nunes de Castro [email protected]Catholic University of Santos - UniSantos/Brazil
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2005: An Introduction to Artificial Immune Systems
BIC 2005 (Biologically Inspired Computing Conference), Johor, Malaysia
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Introduction to Artificial Introduction to Artificial Immune Systems (AIS)Immune Systems (AIS)
BIC 2005: BIC 2005: International Symposium on Bio-Inspired ComputingInternational Symposium on Bio-Inspired Computing
Johor, MY, 5-7 September 2005Johor, MY, 5-7 September 2005
BIC 2005 - Introduction to Artificial Immune Systems - Dr. Leandro Nunes de Castro
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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
Part I
Brief Introduction to the Immune System
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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
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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
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Innate immune system: immediately available for combat
Adaptive immune system: antibody (Ab) production specific to a
determined infectious agent
The Immune System (II)
G ran u locytes M ac rop h ag es
In n a te
B -ce lls T-ce lls
L ym p h ocytes
A d ap ta tive
Im m u n ity
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Anatomy
The Immune System (III)
Lymphatic vessels
Lymph nodes
Thymus
Spleen
Tonsils andadenoids
Bone marrow
Appendix
Peyer’s patches
Primary lymphoidorgans
Secondary lymphoidorgans
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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)
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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)
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Pattern Recognition: B-cell
The Immune System (VI)
Epitopes
B-cell Receptors (Ab)
Antigen
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Pattern Recognition: T-cell
The Immune System (VII)
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The Immune System (VIII)
after Nosssal, 1993
Basic Immune Recognition and Activation Mechanisms
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Antibody Synthesis:
The Immune System (IX)
... ... ... V V
V library
D D
D library
J J
J library
Gene rearrangement
V D J Rearranged DNA
after Oprea & Forrest, 1998
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Clonal Selection and Affinity Maturation
The Immune System (X)
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Maturation and Cross-Reactivity of Immune Responses
The Immune System (XI)
Antigen Ag1AntigensAg1, Ag2
Primary Response Secondary Response
Lag
Responseto Ag1
Ant
ibo
dy
Con
cen
tra
tion
Time
Lag
Responseto Ag2
Responseto Ag1
...
...
Cross-ReactiveResponse
...
...
AntigenAg1’
Response toAg1’
Lag
BIC 2005 - Introduction to Artificial Immune Systems - Dr. Leandro Nunes de Castro
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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)
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Immune Network Dynamics
The Immune System (XVI)
after Jerne, 1974
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The Immune System (XVII)
Danger Theory
after Matzinger, 1994
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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
The Immune System— Summary —
Part II
Artificial Immune Systems
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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
BIC 2005 - Introduction to Artificial Immune Systems - Dr. Leandro Nunes de Castro
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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)
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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
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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)
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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
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The Early Events
Part III
A Framework to Engineer AIS
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Representation How do we mathematically represent immune
cells and molecules? How do we quantify their interactions or
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Thymus Algorithms: Negative Selection Store information about the patterns to be
recognized based on a set of known patterns
A Framework for AIS (VIII)
Selfstrings (S)
Generaterandom strings
(R0)Match Detector
Set (R)
Reject
No
Yes
No
Yes
Detector Set(R)
SelfStrings (S)
Match
Non-selfDetected
Censoring Monitoring phase phase
after Forrest et al., 1994
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A Clonal Selection Algorithm
A Framework for AIS (IX)
after de Castro & Von Zuben, 2001a
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Somatic Hypermutation Hamming shape-space with an alphabet of
length 8
Real-valued vectors: inductive mutation
A Framework for AIS (X)
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Affinity Proportionate Hypermutation
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
D*
= 5
= 10
= 20
A Framework for AIS (XI)
0 20 40 60 80 100 120 140 160 180 200
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
Iterations
after de Castro & Von Zuben, 2001a after Kepler & Perelson, 1993
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A Discrete Immune Network Model: aiNet
A Framework for AIS (XII)
1. For each antigenic pattern Agi,1.1 Clonal selection: Apply the pattern recognition version
of CLONALG that will return a matrix of memoryclones for Agi;
1.2 Apoptosis: Eliminate all memory clones whose affinitywith antigen are below a threshold;
1.3 Inter-cell affinity: Determine the affinity between allclones generated for Agi;
1.4 Clonal Suppression: Eliminate those clones whoseaffinities are inferior to a pre-specified threshold;
1.5 Total repertoire: Concatenate the clone generated forantigen Agi with all network cells
2. Inter-cell affinity: Determine the affinity between all networkcells;
3. Network suppression: Eliminate all aiNet cells whose affinitiesare inferior to a pre-specified threshold.
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Guidelines to Design an AIS
A Framework for AIS (XIII)
1. Problem definition2. Mapping the real problem into the AIS domain
2.1 Defining the types of immune cells and moleculesto be used
2.2 Deciding the immune principle(s) to be used in thesolution
2.3 Defining the mathematical representation for theelements of the AIS
2.4 Evaluating the interactions among the elements ofthe AIS (dynamics)
2.5 Controlling the metadynamics of the AIS3. Reverse mapping from AIS to the real problem
Part IV
Discussion and Main Trends
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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
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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
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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)
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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., 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. 359-375.
de Castro, L. N., & Timmis, J. (2002), Artificial Immune Systems: A New Computational Intelligence Approach, Springer-Verlag.