2006: Artificial Immune Systems - The Past, The Present, And The Future?

Leandro Nunes de Castro - ICARIS 20061

Artificial Immune Systems: The Past, the Present. And the Future?

Leandro Nunes de Castro

Catholic University of [email protected], Support: CNPq, FAPESP

ICARIS 2006, Institute Gulbenkian, Portugal


Outline

What this talk is about Immunology for Non-Immunologists AIS: The Past

– A Tutorial on AIS

AIS: The Present– Current Trends

AIS: The Future– ? ? ?


What this talk is about...

...and what it is not about About:

– Basically an introduction to Artificial Immune Systems (AIS)

– A brief review of the main current trends

Not About:– Making predictions


Immunology for Non-Immunologists

The immune system Anatomy Pattern recognition Innate/Adaptive immunity Some Theories:

– Clonal selection and affinity maturation– Self/Nonself discrimination– Immune network theory– Danger theory


Some perspectives on the IS (de Castro, 2003):– Self-recognition: dichotomy– Self-assertion: no fundamental difference between

self and non-self– Multi-systemic: integration with other systems

Classical concepts– 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)




All living beings present a type of defense mechanism

The Immune System– 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 an

immune response



Anatomy

Lymphatic vessels

Lymph nodes

Thymus

Spleen

Tonsils andadenoids

Bone marrow

Appendix

Peyer’s patches

Primary lymphoidorgans

Secondary lymphoidorgans



Innate and Adaptive Immunity



Innate immune system: – immediately available for combat

Adaptive immune system: – antibody (Ab) production specific to a determined infectious

agent

Main Players

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


Innate Immune System– first line of defense– controls bacterial infections– regulates adaptive immunity– composed mainly of phagocytes and the

complement system– PAMPs and PRRs



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



Pattern Recognition: B-cells


Epitopes

B-cell Receptors (Ab)

Antigen


Pattern Recognition: T-cells



Some Theories and Processes



Clonal Selection and Affinity Maturation




Immune Responses: Maturation and Cross-Reactivity

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


Self/Nonself Discrimination– repertoire completeness– co-stimulation– tolerance

Positive selection– recognition of a self-MHC by an immature T-cell, or

recognition of a nonself antigen by a mature B-cell Negative selection

– recognition of self-antigens in the central lymphoid organs, or peripheral recognition of self-antigens in the absence of co-stimulatory signals




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



Immune Network Connectivity


Danger Theory (Matzinger, 1994)



Part I AIS: The Past


The Early Days

From immunology to artificial immune systems:– Theoretical concepts/models– Empirical evidences– Abstractions/Metaphors

Main goals of AIS:– Perform tasks such as data mining, control, and

optimization


The Early Days: A Bit of History

Pioneer works:– Farmer et al. (1986): continuous model of the

immune network theory whose dynamics is observed in other biological systems. Argument that machine learning could benefit from the investigation of immune systems

– Hoffmann (1986): explored similarities and differences between nervous and immune systems to formulate new artificial neural networks



Pioneer works:– Ishida (1990): PDP immune networks– Bersini and Varela (1990): machine learning,

optimization and adaptive control– Forrest and Perelson (1991): use of GAs to

explore pattern recognition in the immune system– Forrest et al. (1994) and Kephart (1994): use of

immune metaphors to computer security



1996: First workshop organized by Y. Ishida From 1997 to 2001: special tracks organized

by D. Dasgupta Early edited volumes: Y. Ishida (Immunity-

Based Systems) and D. Dasgupta (Artificial Immune Systems)

Some numbers:– Late 2001: around 200 papers on AIS

From 2002 onwards: ICARIS conference series


Previous Years: Some Numbers

ICARIS Series:

ICARIS Submissions Acceptance

2002 ? 26

2003 41 26 (63%)

2004 58 34 (59%)

2005 68 37 (54%)

2006 60 34


An Immune Engineering Framework

Immune Engineering Framework– Introduced in 2001 as a more principled approach

to design AIS (de Castro, 2001; de Castro & Timmis, 2002)

Main feature of the framework– Problem-oriented (engineering perspective)


The Immune Engineering Framework


Immune Engineering Framework

Why the Immune System?– Uniqueness– Self-identity– Diversity– Autonomy– Anomaly detection– Pattern recognition– Dynamic

– Learning and memory– Self-organized– Integrated with other

systems– Disposability – Distributed– Robust



Some AIS Definitions:– “Artificial immune systems are intelligent

methodologies inspired by the immune system toward real-world problem solving” (Dasgupta, 1999).

– “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)





Basic elements:– Representation– Evaluating interactions: fitness and affinity

functions– Adaptation mechanisms: dynamics and

metadynamics



Representation: Shape-Space (Perelson & Oster, 1979)– Generalized shape of a molecule: m = m1, m2, ..., mL

– Cross-reactivity threshold:



Main types of Shape-Space:– Hamming, Euclidean, Integer, and Symbolic

Quantifying affinity (real-valued spaces):– Ab = Ab1, Ab2, ..., AbL; Ag = Ag1, Ag2, ..., AgL – Use of any norm, which will help define the type of

shape-space, e.g., p=2: Euclidean shape-space, p=1: Manhattan shape-space.

pL

i

piiD

/1

1

||

AgAb



Quantifying affinity (binary-spaces):– Hamming distance

– r-contiguous matching– r-chunks– Rogers and Tanimoto– Hunt’s measure (Hunt et al., 1995):

L

i

iiii

AgAbD

1 otherwise0

if1δwhere,δ

i

lH

iDD 2



Immune Algorithms:– Bone marrow algorithms: generation of immune

repertoires– Thymus algorithms: self-nonself discrimination– Clonal algorithms: immune response to antigens– Immune network algorithms: idiotypic interactions



Mechanism/Principle Usual Role

Bone-marrow models Generation of cellular and molecular repertoires

Affinity function Quantify affinities (match Ab-Ag; Ab-Ab)

Somatic hypermutation Introduction and/or maintenance of population diversity and/or variation

Affinity maturation Promote learning (adaptation) through somatic hypermutation and natural selection

Clonal selection Perform the dynamics of the system: how the immune cells and molecules are going to interact with antigens

Negative selection Generation of a set of nonself detectors for anomaly detection

Immune network Perform the dynamics and metadynamics of the system: how the immune cells and molecules are going to interact with each other and the antigens, and their survival



Bone Marrow Algorithms– Simplest approach: random generation– More biologically plausible: based on gene

libraries (Oprea & Forrest, 1998)



Thymus Algorithms:– Positive selection (Seiden & Selada, 1992)



Thymus Algorithms:– Negative selection (Forrest et al., 1994)



Thymus Algorithms:– The Monitoring Phase



Clonal Selection Algorithms: – A GA without crossover is a suitable model of

clonal selection (Forrest et al., 1993)– An immuno-genetic clonal selection algorithm –

CLONALG (de Castro & Von Zuben, 2000-2002)– Nicosia et al. (2001): pattern recognition in the

immune system by primary and secondary response



Immune Network Algorithms:– Continuous dynamics (e.g., Farmer et al., 1986;

Varela & Coutinho, 1991)– Discrete dynamics (e.g., de Castro & Von Zuben,

2000; Timmis, 2000)

Rate of population variation

Network stimulation

Network suppression

Death of unstimulated

elements

Influx of new elements= - + -



Immune Network Algorithms:– Continuous dynamics (Varela & Coutinho, 1991):

.)(σ1

,

N

jjiji fmt

iiiiii fkfkMatbkdt

df321 σ)( σ

iiii bkiMetaolPrbkdt

db54 ][)σ(

Network sensitivity for the idiotype

Change in antibody concentration

Change in cell-surface molecules



Immune Network Algorithms:– An example of discrete dynamics (de Castro & Von

Zuben, 2000)

For each antigen, doClonal selection and expansionAffinity maturationClonal interactionsClonal suppressionNetwork construction

End ForNetwork suppressionDiversityEnd


Part II AIS: The Present

Features, Difficulties and Current Investigations


AIS: The Present

Identification of several difficulties of AIS:– Clonal selection algorithms: inherently evolutionary.

Strong inter-relationship with other approaches, e.g., evolutionary algorithms

– Negative selection: akin to binary classification. Also, it is usually inefficient to map the entire self or nonself space

– Network algorithms: connectionist models with evolutionary stages. Significantly different from neural networks, as the nodes and connections have different meanings; also have different dynamics


AIS: The Present

Important questions:– Is the field growing? Are we moving somewhere?

The usefulness criterion: uniqueness/efficiency (Garrett, 2005)

How to tackle these difficulties? Main trends:

– New applications areas– Algorithmic improvements– Theoretical investigation– Novel algorithms


AIS: The Present

A Sample of new application areas:– Dynamic environments– Web applications, e.g., e-mail classification, text

mining– Bioinformatics – A number of commercial applications


AIS: The Present

A Sample of algorithmic improvements:– Many versions of immune networks and clonal

selection algorithms: aiNets; B-cell algorithm; Bersini’s, Neal’s, Hart’s networks

– Real-valued negative selection– New operators, e.g., mutation, match functions

Same basic principles, but variations in representation, methods of calculating stimulation, suppression, dynamics and metadynamics


AIS: The Present

Theoretical aspects:– Convergence analysis– Markov chain models

Novel algorithms:– Hybrids: neuro-immune, evolutionary-immune,

homeostatic algorithms, etc.– Dendritic cell algorithm*– Danger algorithms*


Part III:And the Future?


Broadening the Viewpoint

Looking at other new approaches:– Ant-based algorithms– Particle swarm– Differential evolution*– Cultural algorithms*

Maybe these fields satisfy the uniqueness/efficiency criteria respecting some constraints and for some specific problems, but do not seem to grow much as well!


And the Future?

*“It is hard to make predictions, mainly about the future”*

Potential frontlines:– Strengthen theoretical developments; improvement

and analysis (usefulness by efficiency and understanding)

– Deeper look into immunology: modeling x engineering (usefulness by novelty and fidelity)


And the Future?

More specifically (Aickelin & Dasgupta, 2005; Hart & Timmis, 2005):– Closer look into innate immunity– Danger algorithms– Applications to dynamic environments

Some questions already raised:– Should we have a ‘killer’ application?– Should we have one main algorithm?


To Conclude…

A good aspect of the AIS community:– Everybody is very critical and concerned about

what we are doing and where we are heading

But what exactly are we looking for:– Uniqueness?– Efficiency?– “Boosting” the field?– Modeling the IS?


References

Dasgupta, D. (Ed.) (1998), Artificial Immune Systems and Their Applications, Springer-Verlag.

de Castro, L. N. & Von Zuben, F. J., (2002), “Learning and Optimization Using the Clonal Selection Principle”, IEEE Transactions on Evolutionary Computation, 6(3), pp. 239-251.

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.

de Castro, L. N. (2006), “Fundamentals of Natural Computing: Basic Concepts, Algorithms, and Applications”, CRC Press LLC.

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.

Jerne, N. K. (1974), “Towards a Network Theory of the Immune System”, Ann. Immunol. (Inst. Pasteur) 125C, pp. 373-389.

Klein, J. (1990), Immunology, Blackwell Scientific Publications. Matzinger, P. (1994), “Tolerance, Danger and the Extended Family”, Annual

Reviews of Immunology, 12, pp. 991-1045. Garrett, S. (2005), “How Do We Evaluate Artificial Immune Systems”, Evolutionary

Computation, 13(2), pp. 145-178.


References

Oprea, M. & Forrest, S. (1998), “Simulated Evolution of Antibody Gene Libraries Under Pathogen Selection”, Proc. of the IEEE SMC’98.

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.

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.

Hart, E. & Timmis, J. (2005), “Application Areas of AIS: The Past, the Present and the Future”, Lecture Notes in Computer Science 3627, pp. 483-498.

Aickelin, U. & Dasgupta, D. (2005): “Artificial Immune Systems Tutorial”, Search Methodologies - Introductory Tutorials in Optimization and Decision Support Techniques (eds. E. Burke and G. Kendall), pp 375-399, Kluwer.

Nicosia, G., Castiglione, F., and Motta, S. (2001), “Pattern Recognition by Primary and Secondary Response of an Artificial Immune System”, Theory in Biosciences, 120(2), pp. 93-106.


References

• Bersini, H. & Varela, F. J. (1990), “Hints for Adaptive Problem Solving Gleaned from Immune Networks”, Parallel Problem Solving from Nature, pp. 343-354.

• Farmer, J. D., Packard, N. H. & Perelson, A. S. (1986), “The Immune System, Adaptation, and Machine Learning”, Physica 22D, pp. 187-204.

• Forrest, S. & A. Perelson (1991), “Genetic Algorithms and the Immune System”, Proc. of the Parallel Problem Solving form Nature, H-. P. Schwefel & R. Manner (eds.), Springer-Verlag.

• Hoffmann, G. W. (1986), “A Neural Network Model Based on the Analogy with the Immune System”, J. theor. Biol., 122, pp. 33-67.

• Ishida, Y. (1990), “Fully Distributed Diagnosis by PDP Learning Algorithm: Towards Immune Network PDP Model”, Proc. of the Int. Joint Conf. on Neural Networks, pp. 777-782.

• Hunt, J. E., Cooke, D. E. & Holstein, H. (1995), “Case Memory and Retrieval Based on the Immune System”, 1st Int. Conference on Case-Based Reasoning, Published as Case-Based Reasoning Research and Development, Manuela Weloso and Agnar Aamodt (eds.), Lecture Notes in Artificial Intelligence, 1010, pp 205 -216.

• Seiden, P. E. & Celada, F. (1992), “A Model for Simulating Cognate Recognition and Response in the Immune System”, J. theor. Biol., 158, pp. 329-357.

• de Castro, L. N. (2003), “Immune Cognition, Micro-evolution, and a Personal Account on Immune Engineering”, S.E.E.D. Journal (Semiotics, Evolution, Energy, and Development). Universidade de Toronto, 3(3), pp. 134-155.

Technology

2006: Artificial Immune Systems - The Past, The Present, And The Future?