challenges for artificial immune systems

8/8/2019 Challenges for Artificial Immune Systems

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Challenges for Artificial Immune Systems

Jon Timmis

Department of Computer Science and Department of ElectronicsUniversity of York, Heslington, York. YO10 5DD.

[email protected],

http://www-users.cs.york.ac.uk/jtimmis

Abstract. In this position paper, we argue that the field of ArtificialImmune Systems (AIS) has reached an impass. For many years, immuneinspired algorithms, whilst having some degree of success, have beenlimited by the lack of theorectical advances, the adoption of a limitedimmune inspired approach and the limited application of AIS to hardproblems.

1 Introduction

The UK research community has proposed a number of Grand Challenges forComputer Science research1 and ambitious plans for the development of a va-riety of research areas. Grand Challenge 7 (GC-7)2 [1] addresses the area ofNon-Classical Computation, which includes exploring areas of both biologicallyinspired paradigms, and the exploitation of the natural world (for example, DNAcomputing and quantum computing) to develop new notions of computation.GC7 consists of a number of journeys of which one is concerned with ArtificialImmune Systems. In the spirit of GC7, this position paper proposes a number ofchallenges to the AIS community, and initial thoughts on how we might go aboutaddressing those challenges. We begin by exploring the current immunologicalmind set of the AIS practitioner with a simple overview of the immunology thathas served as inspiration to date. We then provide a brief overview of the area ofAIS, and describe AIS in terms of a simple framework (as defined by De Castroand Timmis [2]). We then argue that rather than viewing the immune system inisolition from other bioligical systems, consideration should be given to immune,neural and endocrine interactions and how this may impact on the developmentof immune inspired approaches. We then conclude with a number of challengesto the AIS community. It should be said, that many of these ideas are not new,

indeed not my own, but have come from many discussions with people in theAIS community. This paper is intended to be both a review and position paperthat will hopefully draw together many ideas and stimulate further discussionand research.

1 http://www.nesc.ac.uk/esi/events/Grand Challenges/2 http://www.cs.york.ac.uk/nature/gc7/index.htm


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2 The Immune System: From an Artificial Immune

Systems Perspective

Recent developments within AIS, have focussed on three main immunologicaltheories: clonal selection [3], immune networks [4] and negative selection [5].Researchers in AIS have concentrated, for the most part, on the learning andmemorymechanisms of the immune system (typically taking clonal selection andimmune network theories as a basis), and the selection of detectors for identifyinganomalous entities (typically undertaken with negative selection theory).

It is becoming increasingly apparent that the biological inspiration behindAIS has been somewhat naive and that the mechanisms and processes within theimmune system (not to mention the role of the immune system) exploited by theAIS community has taken a limited perspective. Indeed, as argued by [6] AIS,and the majority of bio-inspired paradigms, have been guilty of a reasoning by

metaphor approach. I would agree with this, and despite the success to date ofAIS (and this should not be ignored) the restricted view of the immune systemadopted by the AIS practitioners will no doubt limit the success of AIS.

In this section, we review the immunology, that has served as the founda-tions for much of AIS. We outline the main immunological theories that haveacted as a source of inspiration, notably: clonal selection, immune networks andnegative selection theories, and provide a brief explanation of how these havebeen exploited within AIS. A full review of these can be found in [2].

2.1 Immunity

The vertebrate immune system is composed of diverse sets of cells and molecules

that work together with other systems, such as neural and endocrine, in orderto maintain a steady state within the host. One traditionally held view on therole of the immune system is to protect our bodies from infectious agents suchas viruses, bacteria, fungi and other parasites. On the surface of these agentsare antigens that allow the identification of the invading agents (pathogens) bythe immune cells and molecules, thus provoking an immune response. There aretwo basic types of immunity, innate and adaptive. Innate immunity [7] is notdirected towards specific invaders, but against general pathogens that enter thebody. The innate immune system plays a vital role in the initiation and regu-lation of immune responses, including adaptive immune responses. Specializedcells of the innate immune system evolved so as to recognize and bind to commonmolecular patterns found only in microorganisms. However, the innate immunesystem is by no means a complete solution to protecting the body.

Adaptive or acquired immunity [8], allows the immune system to launch anattack against any invader that the innate system cannot remove. The adaptivesystem is directed against specific invaders, and is modified by exposure to suchinvaders. The adaptive immune system mainly consists of lymphocytes, whichare white blood cells, more specifically B and T-cells. These cells aid in the


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process of recognizing and destroying specific substances. Any substance that iscapable of generating such a response from the lymphocytes is called an anti-

gen or immunogen. Antigens are not the invading microorganisms themselves;they are substances such as toxins or enzymes in the microorganisms that theimmune system considers foreign. Adaptive immune responses are normally di-rected against the antigen that provoked them and are said to be antigen-specific.

2.2 Clonal Selection

A large part of AIS work has been based on the clonal selection theory. Whenantibodies on a B-cell bind with an antigen, the B-cell becomes activated andbegins to proliferate. New B-cell clones are produced that are an exact copyof the parent B-cell, but then undergo somatic hypermutation [9] and produceantibodies that are specific to the invading antigen. The clonal selection principle[3] is the term used to describe the basic properties of an adaptive immuneresponse to an antigenic stimulus. It establishes the idea that only those cellscapable of recognizing an antigenic stimulus will proliferate, thus being selectedagainst those that do not. Clonal selection operates on both T-cells and B-cells. The B-cells, in addition to proliferating or differentiating into plasma cells,can differentiate into long-lived B memory cells. Memory cells circulate throughthe blood, lymph and tissues. When exposed to a second antigenic stimulusthey commence differentiating into large lymphocytes (plasma cells) capable ofproducing high affinity antibody.

In order for the immune system to be protective over periods of time, antigenrecognition is insufficient. In the normal course of the evolution of the immunesystem, an organism would be expected to encounter a given antigen repeatedlyduring its lifetime. The initial exposure to an antigen that stimulates an adaptive

immune response (an immunogen) is handled by a small number of B-cells, eachproducing antibodies of different affinity. Storing some high affinity antibodyproducing cells from the first infection, so as to form a large initial specificB-cell sub-population (clone) for subsequent encounters, considerably enhancesthe effectiveness of the immune response to secondary encounters. These arereferred to as memory cells. Rather than starting from a tabula rosa every time,such a strategy ensures that both the speed and accuracy of the immune responsebecomes successively stronger after each infection.

Computationally then, this has led to the development of clonal selectionbased algorithms. There are many in the literature, most of which have focussedon developing optimisation approaches, such as the work by De Castro and VonZuben on CLONALG [10], the work by Nicoisa et al. [11] and the work byGarrett on parameter free clonal selection [12]. Clonal selection has also formedthe basis of learning algorithms (mainly supervised) such as AIRS by Watkins[13] and a parallel and a distributed version of AIRS [14], a distributed version ofCLONALG by Watkins et al [15], the work by Kim and Bentley with DynamicCSapplied to computer security [16], work by Secker et al, on email filtering [17] andmany, many more. From a computational perspective, application of the clonalselection theory leads to algorithms that evolve (through a cloning, mutation


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and selection phase), candidate solutions in terms of optimisation or patterndetectors in terms of learning. Each of these algorithms have populations of B-

cells (candidate solution) that match against antigens (function to be optimised).These B-cells then undergo cloning (usually in proportion to the strength ofthe match) and mutation (usually, inversely proportional to the strength of thematch). High affinity B-cells are then selected to remain in the population, somelow affinity cells are removed and new random cells are generated. In essence, thisis a high level abstraction of the clonal selection process. Through this process,good solutions can be found, and in terms of dynamic environments (such as[16, 17]) these solutions can be maintained over long periods of time.

2.3 Immune Networks

In the mid 1980s there was a great interest in the idea that B-cells formed an

idiotypic network [4]. In that paper, Jerne proposed that the immune systemis capable of achieving immunological memory by the existence of a mutuallyreinforcing network of B-cells. These cells not only stimulate each other but alsosuppress connected B-cells, though to a lesser degree. This suppression functionis a mechanism by which to regulate the over stimulation of B-cells in order tomaintain a stable memory. This network of B-cells occurs due to the ability ofparatopes, located on B-cells, to match against idiotopes on other B-cells. Thebinding between idiotopes and paratopes has the effect of stimulating the B-cells. This is because the paratopes on B-cells react to the idiotopes on similarB-cells, as it would an antigen. However, to counter the reaction there is a certainamount of suppression between B-cells which acts as a regulatory mechanism.

From a computational perspective, this has led to the development of a num-ber of immune network algorithms. Work by De Castro et al. on aiNET [18, 19],by Timmis et al on AINE [20, 21], by Neal on a self-stabilising immune network[22], by Bersini [23] and by Ishida [24]. There is little to unify these immunenetwork algorithms. The main unifying theme is that they are all, in one wayor another, based on the ideas of clonal selection (as outlined above). The mainaddition is that the candidate solutions (or detectors) interact based on stimula-tion and suppression mechanisms employed from the immune network. Immunenetwork algorithms have tended to suffer a great deal from a large overhead ofcomputational complexity, which has made their applicability to date, ratherlimited.

2.4 Negative Selection

Negative selection is a process of selection that takes place in the thymus gland.T-cells are produced in the bone marrow and before they are released into thelymphatic system, undergo a maturation process in the thymus gland. The mat-uration of the T-cells is conceptually very simple. T-cells are exposed to self-proteins in a binding process. If this binding activates the T-cell, then the T-cellis killed, otherwise it is allowed into the lymphatic system.


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This process was one of the first to really catch the eye of the AIS com-munity. Pioneering work by Forrest [25, 26] and more recently [27] viewed the

negative selection process analogous to the production to detectors that candetect a change in the normal behavior of the system. The negative selectionprinciple is used to filter randomly produced change detectors (filtered againstwhat is considered to be normal behavior) to populate a system that monitorsa data stream. Deviations from the normal behavior are then flagged by thesystem. Whilst this is intuitively appealing, and indeed, has demonstrated somesuccess, there are issues to do with scalability of the approach [28], rather thanapplicability.

3 Artificial Immune Systems

In an attempt to create a common basis for AIS, work in [2] proposed the idea of a

framework for AIS. The authors argued the case for proposing such as frameworkfrom the standpoint that in the case of other biologically inspired approaches,such as artificial neural networks (ANN) and evolutionary algorithms (EAs)such a basic idea exists and helps considerably with the understanding andconstruction of such systems. For example, de Castro and Timmis [2] considera set of artificial neurons, which can be arranged together so as to form anartificial neural network. In order to acquire knowledge, these neural networksundergo an adaptive process, known as learning or training, which alters (someof) the parameters within the network. Therefore, the authors argued that in asimplified form, a framework to design an ANN is composed of: a set of artificialneurons, a pattern of interconnection for these neurons, and a learning algorithm.Similarly, the authors argued that in evolutionary algorithms, there is a set of

artificial chromosomes representing a population of individuals that iterativelysuffer a process of reproduction, genetic variation, and selection. As a result ofthis process, a population of evolved artificial individuals arises. A framework,in this case, would correspond to the genetic representation of the individualsof the population, plus the procedures for reproduction, genetic variation, andselection. Therefore, the authors adopted the viewpoint that a framework todesign a biologically inspired algorithm requires, at least, the following basicelements:

A representation for the components of the system (known as shape space); A set of mechanisms to evaluate the interaction of individuals with the en-

vironment and each other. The environment is usually simulated by a set ofinput stimuli, one or more fitness function(s), or other mean(s) and;

Procedures of adaptation that govern the dynamics of the system, i.e., howits behavior varies over time.

The framework can be thought of as a layered approach. In order to builda system, one typically requires an application domain or target function. Fromthis basis, the way in which the components of the system will be representedwill be considered. For example, the representation of network traffic may well be


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different than the representation of a real time embedded system. Once the rep-resentation has been chosen, one or more affinity measures are used to quantify

the interactions of the elements of the system. There are many possible affinitymeasures (which are partially dependent upon the representation adopted), suchas Hamming and Euclidean distances. The final layer involves the use of algo-rithms, which govern the behavior (dynamics) of the system. Here, in the originalframework proposal, algorithms based on the following immune processes werepresented: negative and positive selection, clonal selection, bone marrow, andimmune network algorithms.

3.1 Building Artificial Immune Systems

When constructing an AIS, there are many computational and practical issuesto consider. The first is computational complexity of the approach. This relatesto the time and space required to generate the suitable number of detectors(members of a population) that are required for the job [29]. For example, thereare a number of works that outline the unacceptable computational complexity ofthe negative selection approach from AIS [30, 28, 31] as there is an exponentialrelationship between the size of the data set to be used, and the number ofdetectors that it is possible to generate. However, other approaches within AIS,such as clonal selection and immune networks, seem not to suffer quite thesame problem. The second aspect to consider is the data to be used. In thecontext of embedded systems for example, if one abstracts away from the systemcomponents and uses state machines, then one has to be careful that there is

an accurate mapping between the state machine and the actual system, andensure that the state machine adequately scopes the space to be immunised [29].Consideration here also has to be given to the way in which data is represented.The shape space paradigm proposes varying ways of data representation andinteraction. However, when dealing with discrete values, such as those found inembedded systems, the method of defining affinity (i.e. seeing how similar oneitem is to another) is not as clear-cut as it may seem. This is coupled withthe fact that mutation, even what might be thought of as a small amount,could have a huge impact on the meaning of the data. Should a binary shapespace be employed, the mere flipping of one bit could indicate a huge shift inmeaning of the state, rather than the small shift that may be desired. In bothof these situations, domain knowledge can play a pivotal role in the success orfailure of such as system [29]. For example, recent studies by [32], point out thatthe main effect on immune network algorithms may well be the way in whichinteraction is defined. Through the development of a simple model Hart andRoss demonstrate the evolution of various immune network structures whichare considerably affected by the choice of affinity measure between two B-cells,which in turn effects how B-cells interact with each other. Whilst no concreteconclusions are drawn here, the message is clear: think before you design.


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4 Reflections on AIS

The original AIS were with an interdisciplinary slant. For example, Bersini [23,33, 34] pays clear attention to the development of immune network models, andthen applies these models to a control problem characterised by a discrete statevector in a state space. Bersinis proposal relaxes the conventional control strate-gies, which attempt to drive the process under control to a specific zone of thestate space; he instead argues that the metadynamics of the immune network isakin to a meta-control whose aim is to keep the concentration of the antibodiesin a certain range of viability so as to continuously preserve the identity of thesystem.

There are other examples of interdisciplinary work, such as the developmentof immune gene libraries and ultimately a bone marrow algorithm employed inAIS [35], and the development of the negative selection algorithm and the first

application to computer security [25]. However, in more recent years, work onAIS has drifted away from the more biologically-appealing models and atten-tion to biological detail, with a focus on more engineering-oriented approach.This has led to systems that are examples of reasoning by metaphor [6]. Theseinclude simple models of clonal selection, immune networks and negative selec-tion algorithms as outlined above. For example, the clonal selection algorithm(CLONALG), whilst intuitively appealing, lacks any notion of interaction of B-cells with T-cells, MHC or cytokines. In addition, the large number of parametersassociated with the algorithm, whilst well understood, make the algorithm lessappealing from a computational perspective. aiNET, again, whilst somewhat af-fective, does not employ the immune network theory to a great extent. Onlysuppression between B-cells is employed, whereas in the immune network the-ory, there is suppression and stimulation between cells. With regards to negative

selection, the simple random search strategy employed, combined with using abinary representation, makes the algorithm computational so expensive, that itis almost unusable in a real world setting [28, 31].

4.1 Problem Oriented Perspective

Freitas and Timmis [36] outline the need to consider carefully the applicationdomain when developing AIS. They review the role AIS have played in the devel-opment of a number of machine learning tasks, including that of classification.However, Freitas and Timmis point out that there is a lack of appreciation forpossible inductive bias within algorithms and positional bias within the choiceof representation and affinity measures. Indeed, this observation is reinforced bythe work of Hart and Ross [32] with the development of their simple immunenetwork simulator with various affinity metrics. They make the argument thatseemingly generic AIS algorithms, are maybe not so generic after all, and eachhas to be tailored to specific application areas. This may be facilitated by thedevelopment of more theoretical aspects of AIS, which will help us to understandhow, when and where to apply various AIS techniques.


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It should be noted that there have been some previous attempts at providingdesign principles for immune systems, such as work by Segal et al. [37] and

Bersini and Varela [34]. However, work by Segal, whilst extremely interesting,focussed primarily on network signalling, and did not provide a comprehensiveset of general design principles, or provide any test application areas for thoseprinciples. Work by Bersini, focussed on the immune network and self assertionideas of the immune system to create his design principles and whilst being moreconcrete, are still quite high level:

Principle 1: The control of any process is distributed around many operatorsin a network structure. This allows for the development of a self-organisingsystem that can display emerging properties.

Principle 2: The controller should maintain the viability of the process beingcontrolled. This is keeping the system within certain limits and preventingthe system from being driven in one particular way.

Principle 3: While there may be perturbations that can affect the process, thecontroller learns to maintain the viability of the process through adaptation.This learning and adaptation requires two kinds of plasticity: a parametricplasticity, which keeps a constant population of operators in the process,but modifies parameters associated with them; and a structural plasticitywhich is based on the recruitment mechanism which can modify the currentpopulation of operators.

Principle 4: The learning and adaptation are achieved by using a reinforce-ment mechanism between operators. Operators interact to support commonoperations or controls.

Principle 5: The dynamics and metadynamics of the system can be affectedby the sensitivity of the population.

Principle 6: The system retains a population-based memory, which can main-tain a stable level in a changing environment.

These are potentially useful principles, that should be refined in light of immuno-logical advances and possibly taken on board (to some degree) by the community.These need to be tested in various application areas, and refined to allow for thecreation of not only a generic set of AIS design principles that are useful to thecommunity, but also specific ones for specific application areas. With this, maycome a better understanding of how to apply AIS, and not fall into the trapshighlighted by Freitas and Timmis.

4.2 Theoretical Perspective

There is very limited work on the more theoretical aspects of AIS. In termsof convergence proofs, one paper, [38], presents a complete proof for a specificmulti-objective clonal selection algorithm using markov chains [39]. As pointedout by Hone and Kelsey [40] a useful and valued avenue to explore would be intothe dynamics of immune algorithms based on nonlinear dynamical systems in-spired by biological models [41], and stochastic differential equations [42]. Given


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the use of clonal selection based algorithms within AIS, a great deal could begained by the community with further theoretical investigations such as, the role

of mutation operators, which could be used to provide information for optimalmutation rates for specific functions. There is some advancement in the theo-retical aspects of negative selection, with a firmer understanding of the role ofaffinity measures such as r-chunkmatching and the scalability issues relevant tocertain shape-spaces employed with negative selection [28, 27].

4.3 The Immune System is not an Island

Homeostasis is the ability of an organism to achieve a steady state of internalbody function in a varying environment [43]. This is achieved via complex in-teractions between a number of processes and systems within the organism, inparticular the immune, neural and endocrine systems. We propose that by exam-ining these systems, and their interactions, it should be possible to gain insightinto how organisms achieve homeostasis, and therefore exploit these interactionsin the realm of computer science and engineering. There is a large body of workinvestigating the interactions of these systems, but little (to be honest hardlyany) work in the area of AIS has payed any attention to this, however for a moredetailed overview and for some initial ideas see [44].

Whilst the immune system is clearly an interesting system to investigate,if viewed in isolation, many key emergent properties arising from interactionswith other systems will be missed. Such systems do not operate in isolation inbiology, therefore, consideration should to be given to the interactions of the im-mune, neural and endocrine systems, and how, together, they allow for emergentproperties to arise [4547]. Immune, neural and endocrine cells express receptorsfor each other. This allows interaction and communication between cells and

molecules in each direction. It appears that products from immune and neuralsystems can exist in lymphoid, endocrine and neural tissue at the same time. Thisindicates that there is a bi-directional link between the nervous system and im-mune system. Therefore, it would seem that both endocrine and neural systemscan affect the immune system. There is evidence to suggest that by stimulatingareas of the brain it is possible to affect certain immune responses, and alsothat stress (which is regulated by the endocrine system) can suppress immuneresponses: this is also reciprocal in that immune cells can affect endocrine andneural systems. The action of various endocrine products on the neural systemis accepted to be an important stimulus of a wide variety of behaviors. Theserange from behaviors such as flight and sexual activity to sleeping and eating[44].

Computationly then, what does this have to say to AIS? It should be possibleto explore the role of interaction between these three systems. One interestingavenue would be to design an AIS to help select the types of components whichwill be most useful when added to a control system at any moment (differentia-tion) and to remove components when they are proving harmful to the controlsystem (apoptosis). The biological immune system cells select which action toperform by detecting properties of the cells and chemical environment through


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molecular interactions at membrane receptors. In an artificial system, similarproperties can be detected by looking at activation states of artificial neurons

and endocrine cells as well as global state information such as current consump-tion and battery levels. Thus the artificial immune system components can makesimilar decisions within the artificial context.

4.4 Methodology

Work in [6] proposes a conceptual framework that allows for the development ofmore biologically grounded AIS, through the adoption of an interdisciplinary ap-proach. Metaphors employed have typically been simple, but somewhat effective.However, as proposed in [6], through greater interaction between computer scien-tists, engineers, biologists and mathematicians, better insights into the workingsof the immune system, and the applicability (or otherwise) of the AIS paradigm

will be gained. These interactions should be rooted in a sound methodology inorder to fully exploit the synergy. Modelling techniques can be employed such asCellular automata [47] , petri nets [48, 49] and process calculi such as -calculus[50], stochastic -calculus [51], and ambient calculus [52]. These techniques existand a great deal of work has been done in this area to understand the workingsof numerous biological systems: as a community of AIS practitioners, it is timeto take a look.

5 Conclusions: Challenges for Artificial Immune Systems

Based on the above observations, there are a number of challenges that are noteasy to solve in a day:

Novel and Accurate Metaphors. Typically naive approaches to extract-ing metaphors from the immune system have been taken. This has occurredas an accident of history, and AIS has slowly drifted away from its immuno-logical roots. Time is now ripe for greater interaction with immunologistsand mathematicians to undertake specific experimentation and create usefulmodels, all of which can be used as a basis for abstraction into powerfulalgorithms;

Integration of Immune and Other Systems. The immune system doesnot work in isolation. Therefore, attention should not only be paid to thepotential of the immune system as inspiration, but also other systems withwhich the immune system interacts, in particular the immune, neural andendocrine systems. This will pave the way for a greater understanding of therole and function of the immune system and develop a new breed of immuneinspired algorithms.

Theoretical basis for AIS. Much work on AIS has concentrated on simpleextraction of metaphors and direct application. Despite the creation of aframework for developing AIS, it still lacks significant formal and theoreticalunderpinning. AIS have been applied to a wide variety of problem domains,


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but a significant effort is still required to understand the nature of AIS andwhere they are best applied. For this, a more theoretical understanding is

required; Application of AIS. Work to date in the realm of AIS has mainly concen-

trated on what other paradigms do, such as simple optimisation, learning andthe like. this has happened as an accident of history, and whilst productive,the time is here to look for the killer application of AIS, or if not that rad-ical, then applications where the benefit of adopting the immune approachis clear;

Acknowledgments This paper is a result of many useful interactions with awide variety of people in the AIS community and in particular during meet-ings that have taken place under the aegis of the EPSRC funded ARTIST3

network in the UK. Particular thanks to: Mark Neal, Emma Hart, Susan Step-ney, Andy Tyrrell, Andy Greenstead, Andy Hone, Hugo Van-den-Berg, Adrian

Robins, Jamie Tycross, Al Lawson, Colin Johnson, Qi Chen and Paul Andrewsfor stimulating discussions.

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