2001: An Introduction to Artificial Immune Systems

An Introduction to theAn Introduction to the
Artificial Immune SystemsArtificial Immune Systems
ICANNGA 2001ICANNGA 2001
Prague, 22-25th April, 2001Prague, 22-25th April, 2001
Leandro Nunes de Castro
lnunes@dca.fee.unicamp.br
http://www.dca.fee.unicamp.br/~lnunes
State University of Campinas - UNICAMP/Brazil
Financial Support: FAPESP - 98/11333-9

ICANNGA 2001 - An Introduction to the Artificial Im2
• 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

— 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

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

• 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 a n u lo c y t e s M a c r o p h a g e s
I n n a t e
B - c e lls T - c e lls
L y m p h o c y t e s
A d a p t a t iv e
I m m u n it y

• Anatomy
The Immune System (III)
Lymphatic vessels
Lymph nodes
Thymus
Spleen
Tonsils and
adenoids
Bone marrow
Appendix
Peyer’s patches
Primary lymphoid
organs
Secondary lymphoid
organs

• 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 complement
system
The Immune System (IV)

• The Adaptive Immune System
– the vertebrates have an anticipatory immune system
– the lymphocytes carry antigen receptors on their
surfaces. 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)

• Pattern Recognition: B-cell
The Immune System (VI)
Epitopes
B-cell Receptors (Ab)
Antigen
• Pattern Recognition: B-cell

• Pattern Recognition: T-cell
The Immune System (VII)

The Immune System (VIII)
after Nosssal, 1993

• 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

• Clonal Selection
The Immune System (X)

• Reinforcement Learning and Immune Memory
The Immune System (XI)
Antigen Ag1
Antigens
Ag1, Ag2
Primary Response Secondary Response
Lag
Response
to Ag1
AntibodyConcentration
Time
Lag
Response
to Ag2
Response
to Ag1
...
...
Cross-Reactive
Response
...
...
Antigen
Ag1’
Response to
Ag1’
Lag

• Affinity Maturation
– somatic hypermutation
– receptor editing
The Immune System (XII)

• 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)

The Immune System (XIV)
Clonal
deletion
AnergyUnaffected cell
Clonal Expansion Negative SelectionClonal Ignorance
Effector clone
Self-
antigen
OR receptor editing
Nonself
antigens

• 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)

• Immune Network Dynamics
The Immune System (XVI)
after Jerne, 1974

• 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
• Immune Network Theory
The Immune System
— Summary —

• 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 —

• 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)

• 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 (II)

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 on
immunology;
– Immune approaches for artificial life;
– Immune approaches for the security of information
systems;
– Immune metaphors for machine-learning.

• 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)

Artificial Immune Systems (V)

• 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
– affinity threshold
Artificial Immune Systems (VI)

• Recognition Via Regions of Complementarity
Antibody
Antigen
Artificial Immune Systems (VII)

• Shape-Space (S)
Artificial Immune Systems (VIII)
ε
Vε
ε
Vε
ε
Vε
V
×
×
×
×
×
×
×
after Perelson, 1989

• Representations
– Set of coordinates: m = 〈m1, m2, ..., mL〉, m ∈ SL
⊆ ℜL
– Ab = 〈Ab1, Ab2, ..., AbL〉, Ag = 〈Ag1, Ag2, ..., AgL〉
• Affinities: related to their distance
– Euclidean
– Manhattan
– Hamming
Artificial Immune Systems (IX)
∑=
−=
L
i
ii AgAbD
1
2
)(
∑=
−=
L
i
ii AgAbD
1
∑= 

 ≠
==
L
i
ii AgAb
D
1 otherwise0
if1
δwhereδ,

• Affinities in Hamming Shape-Space
Artificial Immune Systems (X)
Hamming r-contiguous bit Affinity measure
distance rule of Hunt
∑+= i
l
H
i
DD 2
Flipping one string

• 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)

• 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 A2 A3 A4 A5 A6 A7 A8
A3 D5C8B2
A3 D5C8B2
A3 B2 C8 D5
= four 16 bit segments
= a 64 bit chain
Expressed Ab molecule
B1 B2 B3 B4 B5 B6 B7 B8 C1 C2 C3 C4 C5 C6 C7 C8 D1 D2 D3 D4 D5 D6 D7 D8
after Perelson et al., 1996

• A Negative Selection Algorithm
– store information about the complement of the patterns
to be recognized
Artificial Immune Systems (XIII)
Self
strings (S)
Generate
random strings
(R0)
Match Detector
Set (R)
Reject
No
Yes
No
Yes
DetectorSet
(R)
Self
Strings (S)
Match
Non-self
Detected
Censoring Monitoring
phase phase
after Forrest et al., 1994

• A Clonal Selection Algorithm
– the clonal selection principle with applications to
machine-learning, pattern recognition and optimization
Artificial Immune Systems (XIV)
after
de Castro & Von Zuben, 2001a

• Somatic Hypermutation
– Hamming shape-space with an alphabet of length 8
– Real-valued vectors: inductive mutation
Artificial Immune Systems (XV)

• Affinity Proportionate Hypermutation
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
D*
α
ρ= 5
ρ= 10
ρ= 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
α
Iterations
after de Castro & Von Zuben, 2001a after Kepler & Perelson, 1993

• 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 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.

• Guidelines to Design an AIS
Artificial Immune Systems (XVIII)
1. Problem definition
2. 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

• 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 —

• 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)

Examples of AIS (II)

• Life-cycle of a detector
Examples of AIS (III)
Randomly created
Immature
Mature & Naive
Death
Activated
Memory
No match during
tolerization
010011100010.....001101
Exceed activation
threshold
Don’t exceed
activation threshold
No costimulation Costimulation
tolerization
Match
Match during
tolerization
after
Hofmeyr & Forrest, 2000

• 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, or
connection strength, specified to each connected edge
(de Castro & Von Zuben, 2001)
Examples of AIS (IV)

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

A TOUR ONA TOUR ON
CLONALG AND aiNet . . .CLONALG AND aiNet . . .

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 by
adaptive memories of the system
• Strongly related to other intelligent
approaches, like ANN, EC, FS, DNA
Computing, etc.

• The proposal of a general framework
in 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

• 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.
References (I)

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 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.

http://www.dca.fee.unicamp.br/~lnunes
or
lnunes@dca.fee.unicamp.br
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2001: An Introduction to Artificial Immune Systems

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