Computational ImmunologyComputational Immunology

and and

Immunological ComputationImmunological Computation

Giuseppe NicosiaGiuseppe Nicosia

Cineca Cineca

High Performance SystemsHigh Performance Systems

Talk OutlineTalk Outline

1.1. Tour of Immunology.Tour of Immunology.

2.2. Computational Immunology.Computational Immunology.

3.3. Immunological Computation: what Immunological Computation: what are artificial immune systems?are artificial immune systems?

4.4. Future directions.Future directions.

Part IPart ITour of ImmunologyTour of Immunology

“The Immune System is a complex adaptive system of cells and molecules, distributed throughout our body, that provide us with a basic defense against pathogenic organisms”

What problem is it that the IS solves?What problem is it that the IS solves? The IS uses distributed detection to solve the problem of

distinguishing between self and nonself, which are elements of the body, and foreign elements respectively (actually, the success of the IS is more dependent on its ability to distinguish between harmful nonself, and everything else).

Why is this a hard problem?Why is this a hard problem? because there are so many patterns in nonselfnonself, on the order of

10101616, that have to be distinguished from 101066 self patterns self patterns; because the environment is highly distributed; because the body must continue to function all the time; because resources are scarce.

1. A gentle introduction to the Immune System1. A gentle introduction to the Immune System

How does the How does the IS solve this IS solve this problem?problem?

IS defenses are multi-layered

Most elementary is the skinskin, which is the first barrier to infection. Another barrier is physiologicalphysiological where conditions such as pH and temperature provide inappropriate living conditions for foreign organisms. Once pathogens have entered the body, they are dealt with by the innate ISinnate IS and by the acquired immune response acquired immune response systemsystem.

LymphocytesLymphocytes

• The acquired immune response consists of certain types of white blood cells, called lymphocytes, that cooperate to detect pathogens and assist in the destruction of those pathogens. These lymphocytes can be thought of as detectorsdetectors which are tiny compared to the body.

• Detection and elimination of pathogens is a consequence of millions of small cells - detectors - interacting through simple, very localised rulesinteracting through simple, very localised rules, to give rise to a truly efficient distributed system.

2. How do lymphocytes detect pathogens?2. How do lymphocytes detect pathogens?

Pathogens are detected when a molecular bond is established between the pathogen and receptors that cover the surface of the lymphocyte. Because of the large size and complexity of most pathogens, only parts of the pathogen, discrete sites called epitopes, get bound to the lymphocyte receptors.

Lymphoctyes recognize pathogens by bindingbinding to them.

Similarity Subset. A lymphocyte has approximately 105 receptors on its surface, but because all of these receptors have the same structure (a lymphocyte is monoclonalmonoclonal), a single lymphocyte can only bind to structurally related epitopes. These structurally related epitopes define the similarity subset that the lymphocyte detects.

Affinity. The number of receptors that bind to pathogens will determine the affinity that the lymphocyte has for a given pathogen. If a bond is very likely to occur, then many receptors will bind to pathogen epitopes, resulting in a high affinityhigh affinity for that pathogen; if a bond is unlikely to occur, then few receptors will bind to epitopes, and the lymphocyte will have a low affinitylow affinity for that pathogen.

Affinity threshold. Lymphocytes can only be activatedactivated by a pathogen if the lymphocyte's affinity for the pathogen exceeds a certain affinity threshold.

Some DefinitionsSome Definitions

3. Generating Receptor Diversity3. Generating Receptor Diversity

Because detection is carried out by binding with nonself, the IS must have a sufficient diversitysufficient diversity of lymphocyte receptors to ensure that at least some lymphocytes can bind to any given pathogen.

Generating sufficient diversity is a problemGenerating sufficient diversity is a problem because the human body only makes on the order of 106 different proteins, which the IS must use to construct receptors that can recognize potentially 1016 different proteins or patterns.

How does the IS produce the How does the IS produce the required diversity of receptors?required diversity of receptors?

One source of this diversity:One source of this diversity:Lymphocyte receptors are constructed from inherited gene segments or libraries. The receptors are made by randomly recombining elements from different libraries, resulting in an exponential number of possible combinations, and hence a huge diversity of receptor structures. This combinatorial explosion allows the IS to make potentially 1015 different kinds of receptors, although the actual number of distinct receptors present in the body at any given time is far less than this. Estimates place the number at between 108 and 1012 distinct receptors present at one instance.

Receptor diversity is generated by randomly Receptor diversity is generated by randomly recombining parts of inherited gene segments.recombining parts of inherited gene segments.

This process allow each B cell to create an antibody receptor by choosing among a number of interchangeable parts that are present in the DNA.

Somatic MutationSomatic Mutation

The other source of diversity, wich operates after a mature B cell has been stimulated by an antigen, is somatic mutation:

“a B cell can introduce point mutations into point mutations into the genesthe genes that code for its previously chosen antibody receptor”.

With somatic mutation, the possible number of Ab receptors may be as high as 1016.

If the diversity of receptors present is several orders If the diversity of receptors present is several orders of magnitude less than the diversity of pathogen of magnitude less than the diversity of pathogen

patterns, how does the IS detect most pathogens?patterns, how does the IS detect most pathogens?

By using approximate bindingapproximate binding: each monoclonal lymphocyte can bind to several variants of a pathogen, i.e. it can recognize a similarity subset of patterns.

By being very dynamicvery dynamic: lymphocytes are very short-lived (2 to 3 days) and are constantly replaced (100 million new lymphocytes are generated every day).

4. Affinity Maturation4. Affinity Maturation

To cover the space of all possible nonself patterns adequately requires a huge diversity of lymphocyte receptors, with relatively low affinity low affinity thresholdsthresholds. This enables the IS to detect just about any pathogen, but the detection may take some time, during which the pathogen will be replicating and causing harm. The IS needs to be able to both detect and eliminate pathogens as quickly as possible. Generalized lymphocytes will not be very fast at detecting specific pathogens, nor very efficient at capturing them.

How does the IS learn to How does the IS learn to recognize specific pathogens?recognize specific pathogens?

Through a process called affinity maturation, which is essentially a Darwinian process of variation and selection. Affinity maturation involves a subset of lymphocytes, B-cells. When a B-cell is activatedWhen a B-cell is activated (its affinity threshold is exceeded), by binding to pathogens, it does two thingsit does two things.

1. Firstly, it secretesit secretes a soluble form of its receptors, called antibodiesantibodies, which bind to pathogens and inactivate them, or identify them to phagocytes and other innate system defenses, which allows the innate system to eliminate them.

2. Secondly, the B-cell clones itselfthe B-cell clones itself, but the copies producted by this cloning are not perfect. Cloning is subject to very high mutation to very high mutation ratesrates, called somatic hypermutationsomatic hypermutation, which can result in daughter cells that have somewhat different receptors from the parent.

Activated B-cells Activated B-cells produce antibodies produce antibodies and mutated clones, and mutated clones, which are subject to which are subject to selection via selection via pathogen affinities.pathogen affinities.

The affinity loopThe affinity loopThese new B-cells will also have the opportunity to bind to pathogens, and if they have a high affinity for the pathogens, they in turn will be activated and cloned. The higher the affinity of a B-cell for pathogens present, the more likely it is that the B-cell will clone; B-cells end up competing for available pathogens, with the highest affinity B-cells being the "fittest" and hence replicating the most.

Thus

the variationvariation is provided by hypermutationhypermutation, and the selectionselection is provided by competitioncompetition for pathogens: Clonal selectionClonal selection.

5. Immune Memory5. Immune MemoryA successful primary immune response results in a proliferation of B-cells that have high affinities for the pathogens that caused the response. Typically these B-cells are so short-lived, that once the pathogen is eliminated, we would expect these B-cells to die out, which means that if the pathogen were encountered again, the IS would have to go through the whole affinity maturation process again. The IS avoids this by somehow retaining a memory of the information encoded in these adapted B-cells: adapted B-cells are "memory memory cellscells" that are very long-lived;

Primary responses to new pathogens are slow; memory Primary responses to new pathogens are slow; memory of previously seen pathogens allows the IS to mount of previously seen pathogens allows the IS to mount

much faster secondary responsesmuch faster secondary responses

Associative memory underlies the concept of inoculationAssociative memory underlies the concept of inoculation

Part IIPart IIComputational ImmunologyComputational Immunology

To use information technology to advance the study of immunology. The main research interests are at the overlap between immunology and computer science.

By combining these two domains one seek to identify potential targets for vaccinevaccine and immunotherapeutic immunotherapeutic drug designdrug design.

By simulating laboratory experimentssimulating laboratory experiments, we seek to

help find answers to some of (many) open questions about the IS.

The problem – deciphering the The problem – deciphering the human immune systemhuman immune system

large combinatorial space;

degeneracy of interactions;

difficulty performing in-vivo experiments.

Cellular Automata based models of the ISCellular Automata based models of the IS

Large complex automata: Model incorporates a lot of immunological details; Allow different investigations using the same model (it is

relatively easy to compare results – integrate hypothesis); More suitable as real in machinain machina experimental tool; Could be a good working tool for biologists; There are several advantages in a CA based model of the

IS: - it represents the entities and processes of interest in biological terms,- it is easy to modify the complexity of the interactions without introducing any new qualitative difficulties in solving the model.

The Celada-Seiden modelThe Celada-Seiden model

Among the CA based models, the Celada-Seiden (CS) automaton (1992) is one of the most prominent as to the biological fidelity.

The CS-model concentrates

on the process of clonal selectionclonal selection

during the humoral immune responsehumoral immune response.

The CS automaton 1/2The CS automaton 1/2

The CS can be defined as a extended lattice gasextended lattice gas:: the dynamics is probabilistic; entities move from site to site (diffusion process); the number of entities is not strictly constant; the CS represents a single lymph node; the CA is defined on a triangular 2-d lattice with

periodic boundary conditions (toroidal geometry), Each site has just six edges nearest neighbors.

Each time step corresponds to 8 hours of “real life”;

there is an “external environmentexternal environment” representing the bone marrowbone marrow and the thymusthymus. These components create new virgin cells during the simulation but they are NOT part of the CA;

the entities defined on the sites are cells, molecules and signal molecules.

The CS automaton 2/2The CS automaton 2/2

Cellular entities (B) Lymphocyte B (Th) Lymphocyte T Helper (PLB) Lymphocyte Plasma B cell (APC) Macrophage or generic antigen processing

cell.Molecular entities (Ag) Antigen (bacteria, parasites, and viruses) (Ab) Antibody (IC) Immuno complex or Ab-Ag binding.Signal (IL2) Interleukin (implicitly modeled)

Each entity has r(e) recettors and s(e) states.

Cell receptors and molecules are modeled as bit strings of length L.

The binding between two strings occurs with a certain probability which is function of the match m computed as the Hamming distance of the two bit-string.

Bit StringBit String

Matching BitsMatching Bits In this figure an example of perfect matching

corresponding to zero Hamming distance.

The probability to interact, affinity function, is a simple truncated exponential function with thresholdthreshold mmcc, (L/2< mc<L),

V(m)=(L-m)/(L-mc) for m mc

V(m)=0 for m< mc

In the case mc=L we set V(L)=1.

The parameter (0,1) determines the sharpness of the affinity.

Affinity functionAffinity function

InteractionsInteractions

External interactions B – Ag B – Th Ab – Ag Th – APC APC – IC APC – Ag

Internal interactions B – MHCII APC – MHCII

The MHC molecules containing the inserted antigenic peptides (MHC-peptide complexes) are then transported to the surface of the cell.

The main loop of the ARIMSY programFor (simTime=0; simTime<200; simTime++){ birth(); /* B cell proliferation */ produceAb(); /* PLB antibody production */ iB_Ag(); /* B cell + Ag interaction */ iAb_Ag(); /* Ab + Ag interaction */ death(); /* Cell death */ flow(); /* Flow of naive B cells from bone

marrow */ diffusion(); /* Diffusion within the body lattice*/ injectAg(); /* Check for scheduled injectionof Ag */}

ImmunizationImmunization – Cells population dynamics: AgAg (up-left), BB (up-right) and PLBPLB (down-left). Due to the memory of the system, the Ag is removed in decreasing time from the first to the second injection.

ImmunizationImmunization – Cells population dynamics: TT (up-left), ICIC (up-right) and AbAb (down-left).

A Network of CA for the simulation of the ISA Network of CA for the simulation of the IS(G. Nicosia et al., Int. J. of Modern Physics C, (G. Nicosia et al., Int. J. of Modern Physics C, Vol. 10 No. 4, 677-686,Vol. 10 No. 4, 677-686, 1999) 1999)

In each point of the CA-node one have only one cell and a certain maximum number nmax of molecules.

Some works with the CS-model 1/2Some works with the CS-model 1/2

B. Kohler (NYU) performed a systematic study in IMMSIM indicating that depending on the characteristics (speed of growth, size of burst, infectivity) of the virus, the vaccine to be successful should sometimes “correct” the balance between humoral and cellular responses.

S. Kleinstein (Princeton) simulated the dynamics of T cells and B cell hypermutation in Germinal centres.

Some work with the CS-model 2/2Some work with the CS-model 2/2 F. Castiglione (University of Cologne) studied the

physical properties of humoral immune response (Phys. Rev. E, Vol. 61, issue 2, 2000; Phys. Rev. Lett., Vol. 79, No. 22, 1997).

People at the University of Catania developed a different automaton approach which shows to reproduce the same behaviour for what concernes the humoral response. Moreover they studied the pattern recognition ability of the IS (G. Nicosia et al., Theory in Bioscience, to appear 2001).

Part II, future directionsPart II, future directions

• To improve visualisationvisualisation.

• To insert CD5 and CD72 recetors to implement the apoptosis processapoptosis process.

• Final goal of the simulations is to carry on realistic “in machina” experimentsrealistic “in machina” experiments.

Part III Part III Immune System metaphor for Immune System metaphor for

machine learning: machine learning:

Patterns Recognition with an Patterns Recognition with an

Artificial Immune SystemArtificial Immune System

It is a unique and fascinating computational system that has evolved to solve a unique problem. We hope that a study of the IS can suggest new solutions to computer science problems, or at least give us new ways of looking at these problems. Some of the properties of the IS that might be of interest to a computer scientist are:

Uniqueness: the IS of each individual is unique and therefore vulnerabilities differ from one system to the next.

Distributed detection: the detectors used by the IS are small and efficient, are highly distributed, and are not subject to centralized control or coordination.

Why are computer scientists Why are computer scientists interested in the immune system?interested in the immune system?

Imperfect detection: by not requiring absolute detection of every pathogen, the IS is more flexible.

Anomaly detection: the IS can detect and react to pathogens that the body has never before encountered.

Learning and memory (adaptability): the IS can learn the structures of pathogens, and remember those structures, so that future responses to the pathogens can be much faster.

These properties result in a systemsystem that is:

scalablescalable, resilient to subversionresilient to subversion, robustrobust,

very flexiblevery flexible, and that degrades gracefullydegrades gracefully.

Immunological ComputationImmunological Computation From an information-processing perspective, the IS is a

remarkable parallel and distributed adaptive systemparallel and distributed adaptive system.

It uses learning, memory, and associative retrievallearning, memory, and associative retrieval to solve recognition and classification tasks.

Also, the overall behavior of the system is an emergent emergent property of many local interactionsproperty of many local interactions.

These remarkable information-processing abilities of the IS provide several important aspects in the field of computation.

The Artificial Immune SystemsThe Artificial Immune Systems

The Immunological Computation makes use of immunolgical concepts in order to creates tools and algorithms (the Artificial Immune Systems) for solving machine-learning problems.

Other biologically inspired systems:

1.1. Artificial neural networks;Artificial neural networks;

2.2. Evolutionary computation;Evolutionary computation;

3.3. DNA computation.DNA computation.

ApplicationsApplicationsAIS is a new computational paradigm which is used in different areas of science and technology ( see D. Dasgupta, 1998, Springer).

In between the various application we remind: - data analysis (Very Important Application); - control autonomous mobile robot; - intelligent control applied to aircraft control; - commercial antivirus product (IBM antivirus); - security (V.I.A.); - anomaly detection (V.I.A); - function optimization.

Metaphors employedMetaphors employed

B cells, Antigens; Clonal selection; Somatic hypermutation; Adaptation; Primary and secondary immune response.

The AIS algorithmThe AIS algorithm (G. Nicosia et al., ”, accepted to Natural and Artificial Intelligent Systems,

ENAIS'2001, ICSC Academic Press, March 17-21, 2001, Dubai)Input(patterns, mc)

init(B population)

for(t=0; t<steps; t++)

{ interact() /* cells interact */

proliferate() /* clone expansion*/

if (pm != 0) {

hypermutation() /* mutation process*/

}

age() /* death*/

bone-marrow() /* new B cells*/

diffuse() /* diffusion of patterns e B cells */

}

Output(B population)

Recognition of one patternRecognition of one pattern

Learning multiple patternsLearning multiple patterns

Lower plot: the antigen population (learning phase).Upper plot: the population of the recognizing clones of B cells (recall phase). All the pattern are recognized.

Recognition CapacityRecognition Capacity

Recognition rate for different initial populations and number of different patterns presented.

Part III, conclusionsPart III, conclusions The AIS is able to recognize multiple presented

patterns and to cover a large repertoire thanks to the mutation process and the clonal selection.

The threshold parameter mc can be used to tune the tolerance in the recognition process.

The bottleneck seems to be the initial number of cells represented in the system.

Part III, Future worksPart III, Future works

More exploration of immunology metaphors:

1. T cells,

2. Macrophages (breaking up the antigens),

3. Using other matching methods. Associative memory with an AIS. Character recognition. Travelling Salesman Problem (TSP) and other

NP-problems.

Part IV, future directionsPart IV, future directions

Bioinformatics is the interdisciplinary science concerning the acquisition, archiving, analysis, and interpretation of biological information.

In particular, it is concerned with exploiting modern developments in computer science towards understanding the structure and function of proteins, cells, and the genome, and understanding the process of natural evolution.

Computational Tools in Bioinformatics

Hidden Markov Models; Neural Networks; Genetic algorithms; Tree Algorithms; Dynamic Programming; Interval Graphs; Entropy Maximization; Monte Carlo Methods; Simulated Annealing.

There are several opportunities in this area for the application of evolutionary application of evolutionary computation techniquescomputation techniques; much work is already under way, but there are lots of opportunities still unexplored (e.g., the AISAIS).

Final remarkFinal remark