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An Introduction to theArtificial Immune Systems
ICANNGA 2001
Prague, 22-25th April, 2001
Leandro Nunes de Castro
[email protected]://www.dca.fee.unicamp.br/~lnunes
State University of Campinas - UNICAMP/Brazil
Financial Support: FAPESP - 98/11333-9
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ICANNGA 2001 - An Introduction to the Artificial Immune Systems 2
Part I
Introduction to the Immune System Part II
Artificial Immune Systems (AIS)
Part III
Examples of AIS and Applications
Discussion and Future Trends
Presentation Topics
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Part I Introduction to the Immune System
Fundamentals and Main Components Anatomy
Innate Immune System
Adaptive Immune System Pattern Recognition in the Immune System
A General Overview of the Immune Defenses
Clonal Selection and Affinity Maturation Self/Nonself Discrimination
Immune Network 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 presents learning and memory
Microorganisms that might cause diseases (pathogen):
viruses, funguses, 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 r an u lo cyte s M a c ro ph a ge s
In n a te
B -c ells T-c ells
L ym ph ocytes
Adaptat ive
Im m u n ity
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Anatomy
The Immune System (III)
Lymphatic vessels
Lymph nodes
Thymus
Spleen
Tonsils and
adenoids
Bone marrow
Appendix
Peyers 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 the adaptive immunity
important for self/nonself discrimination
composed mainly of phagocytes and the complementsystem
The Immune System (IV)
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The Adaptive Immune System
the vertebrates have an anticipatory immune system
the lymphocytes carry antigen receptors on theirsurfaces. These receptors are specific to a given antigen
Clonal selection: B-cells that recognize antigens are
stimulated, proliferate (clone) and differentiate into
memory and plasma cells
confer resistance against future infections
is capable of fine-tuning the cell receptors of the
selected cells to the selective antigens
The Immune System (V)
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Pattern Recognition: B-cell
The Immune System (VI)
B-cell
BCR or Antibody
Epitopes
B-cell Receptors (Ab)
Antigen
Pattern Recognition: B-cell
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Pattern Recognition: T-cell
The Immune System (VII)
T-cell
TCR
APC
MHC-II Protein Pathogen
Peptide
TH cell
MHC/peptide complex
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The Immune System (VIII)
APC
MHC protein Pathogen
Peptide
T cell
Activated T cell
B cell
Lymphokines
Activated B cell(Plasma cell)
( I )
( II )
( III )
( IV )
( V )
( VI )
after Nosssal, 1993
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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
The Immune System (X)
Foreign antigens
Proliferation
Differentiation
Plasma cells
Memory cellsSelection
M
M
Antibody
Self-antigen
Self-antigen
Clonal deletion(negative selection)
Clonal deletion(negative selection)
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Reinforcement Learning and Immune Memory
The Immune System (XI)
Antigen Ag1AntigensAg1, Ag2
Primary Response Secondary Response
Lag
Responseto Ag1
AntibodyConcentration
Time
Lag
Responseto Ag2
Response
to Ag1
...
...
Cross-ReactiveResponse
...
...
AntigenAg1
Response to
Ag1
Lag
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Affinity Maturation
somatic hypermutation
receptor editing
The Immune System (XII)
1ary
2ary
3ary
Affinity(logsc
ale)
Response
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Self/Nonself Discrimination
repertoire completeness
co-stimulation
tolerance
Positive selection
B- and T-cells are selected asimmunocompetent 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)
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Self/Nonself Discrimination
The Immune System (XIV)
Clonaldeletion
AnergyUnaffected cell
Clonal Expansion Negative SelectionClonal Ignorance
Effector clone
Self-antigen
OR receptor editing
Nonselfantigens
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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 setsof 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)
Ant ibody
Paratope
Id iotope
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Immune Network Dynamics
pa
ia
pb
ib
pc
ic
Ag(epitope)
Dynamics of the immune system
Foreign stimulus
Internal image
Anti-idiotypic set
Recognition
ia
px
Non-specific set
The Immune System (XVI)
after Jerne, 1974
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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
The Immune System
Summary
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Artificial Immune Systems (AIS)
Remarkable Immune Properties
Concepts, Scope and Applications
History of the AIS
The Shape-Space Formalism
Representations and Affinities
Algorithms and Processes
A Discrete Immune Network Model
Guidelines to Design an AIS
Part II
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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)
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Concepts
Artificial immune systems are data manipulation,
classification, reasoning and representation
methodologies, that follow a plausible biologicalparadigm: 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 (II)
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Artificial Immune Systems (III)
Scope (Dasgupta, 1998):
Computational methods inspired by immune principles;
Multi-agent systems based on immunology;
Self-organized systems based on immunology;
Immunity-based systems for the development of
collective behavior;
Search and optimization methods based onimmunology;
Immune approaches for artificial life;
Immune approaches for the security of informationsystems;
Immune metaphors for machine-learning.
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Artificial Immune Systems (V)Year Event Activity Organizer/Speaker
1996 IMBS Workshop Y. Ishida
1997 SMC Special Track / Tutorial D. Dasgupta
SMC Special Track D. Dasgupta1998
Book First edited book D. Dasgupta (ed.)
1999 SMC Special Track D. Dasgupta
Workshop D. Dasgupta
GECCO Talk: Why Does a Computer Need an
Immune SystemS. Forrest
Tutorial: Immunological ComputationSMC
Special TrackD. Dasgupta
2000
SBRNTutorial: Artificial Immune Systems
and Their ApplicationsL. N. de Castro
ICANNGATutorial: An Introduction to theArtificial Immune Systems
L. N. de Castro
CECTutorial: Artificial Immune Systems:
An Emerging TechnologyJ. I. Timmis2001
Journal(Special Issue) IEEE-TEC: Special Issue on ArtificialImmune Systems D. Dasgupta (ed.)
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How do we mathematically represent immunecells 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
affinity threshold
Artificial Immune Systems (VI)
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Recognition Via Regions of Complementarity
Antibody
Antigen
Artificial Immune Systems (VII)
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Shape-Space (S)
Artificial Immune Systems (VIII)
V
V
V
V
after Perelson, 1989
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Representations
Set of coordinates: m=m1,m2,...,mL, m SLL
Ab=Ab1,Ab2,...,AbL, Ag=Ag1,Ag2,...,AgL
Affinities: related to their distance
Euclidean
Manhattan
Hamming
Artificial Immune Systems (IX)
=
=L
i
ii AgAbD1
2)(
=
=L
i
ii AgAbD1
=
==L
i
ii AgAbD
1 otherwise0
if1/where/
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Affinities in Hamming Shape-Space
Artificial Immune Systems (X)
Affinity: 6
0 0 1 1 0 0 1 1
1 1 1 0 1 1 0 1
1 1 0 1 1 1 1 0
0 0 1 1 0 0 1 1
1 1 1 0 1 1 0 1
1 1 0 1 1 1 1 0
Affinity: 4
0 0 1 1 0 0 1 1
1 1 1 0 1 1 0 1
1 1 0 1 1 1 1 0
Affinity: 6+2 =22
Hamming r-contiguous bit Affinity measure
distance rule of Hunt
+= il
HiDD 2
0 0 1 1 0 0 1 1
1 1 1 0 1 1 0 1
0 0 1 1 0 0 1 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
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Algorithms and Processes
Main generic algorithms that model specific
immune principles Examples
Generation of initial antibody repertoires (Bone
Marrow)
A Negative selection algorithm
A Clonal selection algorithm
Affinity maturation
Immune network models
Artificial Immune Systems (XI)
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Generation of Initial Antibody Repertoires
Artificial Immune Systems (XII)
An individual genome corresponds to four libraries:
Library 1 Library 2 Library 3 Library 4
A1 A2A3A4A5A6A7A8
A3 D5C8B2
A3 D5C8B2
A3 B2 C8 D5
= four 16 bit segments
= a 64 bit chain
Expressed Ab molecule
B1 B2B3B4B5B6B7B8 C1 C2 C3 C4C5C6 C7C8 D1 D2D3D4D5D6 D7D8
after Perelson et al., 1996
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A Negative Selection Algorithm
store information about the complement of the patterns
to be recognized
Artificial Immune Systems (XIII)
Selfstrings (S)
Generaterandom strings
(R0)
Match DetectorSet (R)
Reject
No
Yes
No
Yes
DetectorSet(R)
SelfStrings (S)
Match
Non-selfDetected
Censoring Monitoring
phase phase
after Forrest et al., 1994
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A Clonal Selection Algorithm the clonal selection principle with applications to
machine-learning, pattern recognition and optimization
Artificial Immune Systems (XIV)
Ab (1)
(2)
(3)
(4)
Ab{d}(8)
Maturate
Select
Clone
Ab{n}
C
C*
Re-select
f
f
(7)
(6)
(5)
Ab{n}
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
Artificial Immune Systems (XV)
3 1 2 4 6 1 8 7
3 1 6 4 2 1 8 7
Single-point mutation
Original string
Mutated string
Bits to be swapped
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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
Artificial Immune Systems (XVI)
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
Artificial Immune Systems (XVII)
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 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 affinities
are inferior to a pre-specified threshold.
A ifi i l I S (XVIII)
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Guidelines to Design an AIS
Artificial Immune Systems (XVIII)
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 othe AIS (dynamics)
2.5 Controlling the metadynamics of the AIS3. Reverse mapping from AIS to the real problem
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Examples of Artificial Immune Systems
Network Intrusion Detection by Hofmeyr &Forrest (2000)
aiNet: An Artificial Immune Network Model by
de Castro & Von Zuben (2001)
A Tour on the Clonal Selection Algorithm
(CLONALG) and aiNet
Discussion and Future Trends
Part III
E l f AIS (I)
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Computer Security
direct metaphor
virus and network intrusion detection
Network Intrusion Detection by Hofmeyr &
Forrest (2000)
Rationale: protect a computer network against illegal
users
Basic cell type: detector that can assume several states,
such as thymocyte, naive B-cell, memory B-cell
Representation: Hamming shape-space and r-contiguous bits rule
Examples of AIS (I)
E l f AIS (II)
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Examples of AIS (II)
Immune System Artificial Immune System
Receptor Bitstring
Lymphocyte (B- and T-cell) Detector
Memory cell Memory detector
Pathogen Non-self bitstring
Binding Approximate string matching
Circulation Mobile detectors
MHC Representation parameters
Cytokines Sensitivity level
Tolerization Distributed negative selection
Co-stimulation: signal one A match exceeding the
activation threshold
Co-stimulation: signal two Human operator
Lymphocyte cloning Detector replication
E l f AIS (III)
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Life-cycle of a detector
Examples of AIS (III)
Randomly created
Immature
Mature & Naive
Death
Activated
Memory
No match duringtolerization
010011100010.....001101
Exceed activationthreshold
Dont exceed
activation thresholdNo costimulation Costimulation
Match
Match duringtolerization
after
Hofmeyr & Forrest, 2000
E amples of AIS (IV)
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aiNet: An Artificial Immune Network Model The aiNet is a disconnected graph composed of a set of
nodes, called cells or antibodies, and sets of node pairs
called edges with a number assigned called weight, orconnection strength, specified to each connected edge
(de Castro & Von Zuben, 2001)
Examples of AIS (IV)
1
20.11
0.4
0.1
0.3 0.25
4
5
68 70.10.1
30.06
0.05
Examples of AIS (V)
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Examples of AIS (V)
Rationale:
To use the clonal selection principle together with the
immune network theory to develop an artificial network
model using a different paradigm from the ANN.
Applications:
Data compression and analysis.
Properties:
Knowledge distributed among the cells
Competitive learning (unsupervised)
Constructive model with pruning phases
Generation and maintenance of diversity
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A TOUR ON
CLONALG AND aiNet . . .
Discussion
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Discussion
Growing interest for the AIS
Biologically Motivated Computing
utility and extension of biology improved comprehension of natural phenomena
Example-based learning, where different
pattern categories are represented byadaptive memories of the system
Strongly related to other intelligent
approaches, like ANN, EC, FS, DNAComputing, etc.
Future Trends
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The proposal of a general frameworkin which to design AIS
Relate AIS with ANN, EC, FS, etc.
Similarities and differences
Equivalencies
Applications
Optimization
Data Analysis
Machine-Learning
Pattern Recognition
Hybrid algorithms
Future Trends
References (I)
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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 ImmuneNetwork 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 onResearch 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.
References (I)
References (II)
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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 SMC98.
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 Publishing,
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.
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http://www.dca.fee.unicamp.br/~lnunesor
Further Information: