ais basics

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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

[email protected]

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