Basis Biological Concepts for Artiﬁcial Immune Systems · inspired computing that attempts to exploit theories, principles, and concepts of modern immunology to design immune system

Basis Biological Concepts for

Artificial Immune Systems

Mario Pavone

IBM-KAIST Bio-Computing Research Center

Korea Advanced Institute of Science and Technology

[email protected]

and

Department of Mathematics and Computer Science

University of Catania

[email protected] Pavone, IBM-KAIST Bio-Computing Research Center – p.1/41

My research focuses on combinatorial and numerical optimization

problems, and computational biology problems, using biologically

inspired methodologies, in particular Clonal Selection Algorithms

opt-IMMALG – Optimization Immune Algorithm

[GECCO 2003, CEC 2004, ICARIS 2004, SAC2006, IEEE Trans. on Evol. Comp.]

I’m a member of the AI ·COM, Artificial Intelligence and COmputational

Methodologies, Research Group, of the University of Catania, led by

Prof. Vincenzo Cutello

www.dmi.unict.it/∼aicom

more informations about me on www.dmi.unict.it/∼mpavone

Mario Pavone, IBM-KAIST Bio-Computing Research Center – p.2/41

Talk outline

What is an Artificial Immune System?

Concepts of Biological Immune System

Processes of the Immune System

Clonal Selection

Negative Selection

Immune Network

Danger Theory


What is an Artificial Immune System?

Artificial Immune Systems (AIS) represent a field of biologically

inspired computing that attempts to exploit theories, principles,

and concepts of modern immunology to design immune system

based applications to solve problems in science and engineering

AIS are population based, as a typical evolutionary algorithm (EA)

Computational Intelligence and EAs are optimization methods

based on an evolutionary metaphor that showed effective in

solving difficult problems


Computational Intelligence


Artificial Immune Systems

Computational Immunology: AIS is a model of the immune

system that can be used by immunologists for explanation,

experimentation and prediction activities that would be difficult or

impossible in laboratory experiments

AIS is an abstraction of one or more immunological processes to

tackle complex problem domains


Biological Immune System (1/4)

Immunology is the study of the defense mechanisms that confer

resistance against diseases

Immune System (IS) is the main responsible to protect the

organism against the attack from external microorganisms, that

might cause diseases (pathogen: viruses, funguses, bacteria and parasites)

The biological IS has to assure recognition of each potentially

dangerous molecule or substance (antigen – Ag)

Antigen is any molecule that can stimulate the IS

IS, first, distinguishes between the cells of the organism (self )

and those that do not belong to it (nonself ), and then it

eliminates the dangerous or extraneous cells and those that

have been infected, so as to avoid or block the disease.



The process of distinguishing between what cells belong and does

not belong to the organism is called self/nonself discrimination

Bone marrow: organs where the blood cells are generated and

developed

Thymus: organs where a class of immune cells migrates and

matures

Lymphocytes: are white blood cells, specialized in the

recognition of pathogens

B cells, which develop within the bone marrow

T cells, which migrate and develop within the thymus

There are two kinds of T cells: cytotoxic (or killer) T cells and

helper T cellsMario Pavone, IBM-KAIST Bio-Computing Research Center – p.8/41


Both lymphocytes present receptor molecules on their surfaces,

which are responsible to recognize the Ag

TCR and BCR (or antibody)

The purposes of the Ab are:

recognize and bind with ad Ag

perform an effector function

While the antibody can recognize and bind only antigens free in

solution, TCRs can only recognize and bind with antigens

presented by self molecules

major histocompatibility complex - MHC



Each receptor has a specific shape and can only react with a

certain antigen

Recognition in the immune system is based on shape

complementary

If a receptor binds an antigen then the cell will be activated

through the binds, a virus may be inactivated

The binding between receptors and antigens, trigger the immune

response


B cell receptor – Antibody (1/2)


B cell receptor – Antibody (2/2)


T cell receptor


Humoral Immune Response

An immune response is provoked when the immune system encounters a foreign

molecule

A number of receptors will be produced by the immune system in response to the

infection, which will help to eliminate the antigen

All receptors that better recognize the antigen, will be selected to have long life

spans (Immune Memory)

The production of cells with longer expected lifetime assures the organism a

higher specific responsiveness to that antigenic pattern

Primary Response: the antigen is recognized and the memory is developed

Secondary Response: a rapid and more abundant production of antibodies is

obtained from the stimulation of cells already specialized and present as memory

cells, when the same antigen is encountered again

This means that the body is ready to combat any re-infection


Primary and Secondary

Immune Responses


Processes of the Immune System

Clonal Selection

Proliferation and differentiation of the cells, which better

recognize nonself entities

Negative Selection

Eliminating of T cells in the thymus, which recognize self

entities

Immune Network

How do the cells of the immune system interact with each

other?

Danger Theory

Why there is an immune response for harmful self entities,

and not for harmless nonself ?Mario Pavone, IBM-KAIST Bio-Computing Research Center – p.16/41

Clonal Selection

The focus of the clonal selection is on how B cells can adapt to

match and kill invaders

When a B cell matches an antigen, this causes a B cell to be

cloned, proportionally to its match’s quality

Among all possible cells present in the organism, those who are

able to recognize the antigen, will start to proliferate by duplication

(cloning) [Burnet, Cambridge University Press, 1959]

Clonal Expansion phase: is the process where the B cells

produce many clones, when they are activated by binding an

antigen (higher the affinity of a B cell to recognize antigens, more

likely it will clone)


Clonal Expansion


Affinity Maturation

Affinity is the degree of binding of the cell receptor with the

antigen: higher the affinity the stronger the binding and thus better

will be the immune recognition and response

Each receptor recognizes one antigen, which invades the

organism, with different degrees of affinity

Affinity Maturation: during the immune response, B cells and T

cells increase the affinity of the cloned receptors

The immune response is said to be adaptive because it allows the

cells receptors to adapt themselves to antigens, by mutation and

selection

Clonal selection affect both B cells and T cells, but affinity

maturation has been observed in B cells, only


Binding Shapes


Somatic Hypermutation

Changes to the shape of the receptors are caused by mutations

The cloned B cells will undergo a Somatic Hypermutation,

creating B cells with mutated receptors

Why we use the terminology hypermutation ?

Hypermutation rate is Inversely Proportional to the cell affinity

Such kind of hypermutation, help to preserve high affinity of the

cloned cells, and to produce several variants of the receptor

selected

Thanks to hypermutation, all cloned cells will present slight

differences, with respect their parent cells


The Theory of the Clonal Selection


Clonal Selection Algorithms (1/2)

CSA are inspired by the human’s clonal selection principle to

produce effective methods for search and optimization

Clonal Expansion triggers the growth of a new population of

high-value B cells centered on a higher affinity value

Hypermutation can be seen as a local search procedure that

leads to a fast maturation

CSA provides an excellent example of bottom up intelligent

strategy [Cutello, Nicosia, Pavone, LNCS 2723, GECCO, 2003]

adaptation operates at the local level of cells and molecules,

and useful behavior emerges at the global level with the

immune humoral and cellular responses.


Clonal Selection Algorithms (2/2)

CSA can be seen as a problem learning and solving system:

the Ag is the problem to solve

the Ab is the generated solution

[Forrest, et al., Oxford University Press, 2000]

At the beginning of the primary response the antigen-problem is

recognized by poor candidate solutions

At the end of the primary response the antigen-problem is

defeated-solved by good candidate solutions

The primary response corresponds to a training phase, whereas

the secondary response is the testing phase


Negative Selection

Is the process, which eliminates all T cells, whose receptors are

able to recognize and bind with self entities, presented in the

thymus

A blood thymic barrier avoids that nonself entities can be present

within the thymus. All cells within it are self entities

All T cells, which are not able to recognize any self entities

become immunocompetent T cells, i.e. able to perform an

immune response

These kinds of cells will be released into the blood stream, with

the purpose to patrol the body from the nonself entities

This set of T cells, called detectors, can detect any change in self

entities or any form of nonself





How does it works


Negative Selection Algorithm (1/2)

Negative Selection (or negative detection) can be used to perform

tasks like pattern recognition by storing information about the set

of patterns that are unknown to the system

Goal: to detect when elements of a set of self string havechanged from an established norm

Given a set of self string S, the standard negative selection algorithm, is as follow:

[Forrest et al., IEEE Symp. on Research in Security and Privacy, 1994]

Randomly generate a set of strings, R;

Evaluate the match affinity of all strings in R with all strings of S. If the match

affinity of a string of R with at least one string of S is greater or equal to a

given threshold ε, then it will be eliminated (self-string).

Monitor S for changes by continually matching the detectors in R against S

The repertoire subset R is known as the detector set


Negative Selection Algorithm (2/2)


Negative Selection Approaches (1/3)

The first negative selection algorithm was proposed using a

binary representation {0, 1}

[Forrest et al., IEEE Symp. on Research in Security and Privacy, 1994]

The r–contiguous matching rule was applied to determine the

affinity between a detector and an element

two elements, with the same length, match if at least r contiguous

characters are identical

An improved variation of the r–contiguous matching rule is the

r–chunk [Dasgupta, et. al., LNCS 2723, GECCO, 2003]

given an element e and detector d, they match if exist a position p,

where all characters of e and d are identical, over a sequence length r



All matching rules cause undetectable elements (i.e. self

elements not seen during the training phase)

Crossover closure was proposed with the purpose to find holes

each self string will be stored as relations with an attribute for each bit

to reconstruct the original strings one computes the natural join of the

relations, producing more than original strings

it will return all possible crossovers of the original strings: the total

set of strings that are undetectable

[Forrest et al., ICARIS, 2003]



Dasgupta et al. (ICARIS 2003), proposed a negative selection algorithm, which

operates on a unitary hypercube

detector d = (c, rns), having a center c and a nonself recognition radius rns

motivation: consider all elements, which are close to the self-center, as self

if an element lies within a detectors the it is classified as nonself, otherwise as

self

an element e lies within a detector d if the Euclidean distance is smaller than

rns

Dasgupta et al. (GECCO 2004), proposed a real-valued negative selection

algorithm with variable size detectors (V-detectors)

the center of a detector is positioned randomly and must not lie within the

hypersphere of a self element

Esponda and Forrest (ICARIS 2004), proposed a prototype negative database

based on the principles of negative selection

ND stores information about the inverse of the data we wish to store.Mario Pavone, IBM-KAIST Bio-Computing Research Center – p.34/41

Immune Network Theory (1/2)

The immune network theory suggest that antibody have portions

of their receptors that can be recognized by other antibodies

Part of an antibody (paratope) will bind to part of an antigen

(epitope)

Also, antibody have epitopes, which can be bound by other

antibodies’ paratope

Arise a network of communication: immune network

The entities presenting bound epitope will be eliminated or

repressed, whereas the antibodies presenting the active paratope

will proliferated

Such network of stimulatory and suppressive interactions allow a

form of associative memory [Hart and Ross, ICARIS, 2002]


Immune Network Theory (2/2)

The immune network algorithm are tightly linked to two equations:

matching affinity and change of the antibody’s concentration

The first models were based on differential equations, which

govern the variations in population sizes

continuous immune network

Immune network are used also as inspiration to the development

of machine learning network models with applications in data

analysis

These kind of models are mainly based on iterative procedures of

adaptation

discrete immune network


Immune Network Example


Danger Theory (DT–AIS) (1/3)

The central idea in DT-AIS is that the immune system doesn’t respond to nonself

entity, but to danger

Instead to respond to foreignness, the immune system reacts to danger

There is no need to attack everything that is foreign

Danger Theory is measured by damage to cells indicated by distress signals that

are sent out when cells die an unnatural death

Cells can die in two ways:

apoptotic: normal death that has been requested by the body’s internal

signaling system

necrosis: a form of unexpected death caused by something going wrong with

the cell, which often causes an inflammatory response

Immune response is contextualized to the location in which necrosis is occurring

the danger signal establishes a danger zone around itself



B cells producing antibodies that match antigens within the

danger zone

All B cells that don’t match or are too far away from danger

zone do not get stimulated

In the natural immune system is not immediately clear which

signals are danger signal

the exact nature of the danger signal(s) is still unclear

Danger Theory can help to study and understand the intrusion

detection systems

self-nonself discrimination =⇒ danger-nondanger discrimination

The concepts of self-nonself may change over time, whereas the

ones danger-nondanger are grounded in undesirable eventsMario Pavone, IBM-KAIST Bio-Computing Research Center – p.39/41


”Instead of responding directly to a nonself entity, the immune system responds to cells,

which are under stress?”[Matzinger, Science, 2002]


References

D. Dasgupta, Artificial Immune Systems and Their Applications,

Springer-Verlag, 1999

L. N. de Castro and J. Timmis, Artificial Immune Systems: A New

Computational Intelligence Approach, Springer-Verlag, 2002

A. O. Tarakanov, V. A.Skrormin and S. P. Sokolova,

Immunocomputing: Principles and Appilcations, Springer-Verlag, 2003

International Conference on Artificial Immune Systems (ICARIS), 2002,

2003, 2004, 2005

www.artificial-immune-systems.org


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Basis Biological Concepts for Artiﬁcial Immune Systems · inspired computing that attempts to exploit theories, principles, and concepts of modern immunology to design immune system