A Computational Model of Degeneracy in a Lymph Node

A Computational Model of Degeneracy in a Lymph Node Paul S. Andrews1 and Jon Timmis1,2 1

Department of Computer Science, University of York, UK 2 Department of Electronics, University of York, UK {psa,jtimmis}@cs.york.ac.uk

Abstract. This paper highlights degeneracy as being an important property in both the immune system and biology in general. From this, degeneracy is chosen as a candidate to inspire artificial immune systems. As a first step in exploiting the power of degeneracy, we follow the conceptual framework approach and build an abstract computational model in order to understand the properties of degenerate detectors free of any application bias. The model we build is based on the activation of TH cell in the lymph node, as lymph nodes are the sites in the body where the adaptive immune response to foreign antigen in the lymph are activated. The model contains APC, antigen and TH cell agents that move and interact in a 2-dimensional cellular space. The TH cell agent receptors are assumed to be degenerate and their response to different antigen agents is measured. Initial observations and results of our model are presented and highlight some of the possibilities of degenerate detector recognition.

1

Introduction

In a previous work [1] we outlined an approach to exploiting immune ideas not yet used for artificial immune system (AIS) inspiration. We concluded that even though competing and conflicting immune theories are used to inspire AIS, these AIS are still able to perform their tasks well. However, it was observed that many of these AIS were designed with too much of an engineering approach, failing to adequately capture the immunological processes on which they were built. In addition, Hart and Timmis [2] state that current AIS do not offer sufficient advantage over other paradigms available to the engineer. To address this it was suggested that alternative immune ideas should be actively investigated in order to identify useful immune processes that could inspire unique and powerful AIS. As an example of an alternative view of the immune system, we presented the ideas of Cohen [3–5] who describes the immune system as a complex adaptive system, the role of which is body maintenance. It was clear from this view that there are many ideas that could inspire the development of new AIS, and the example of receptor degeneracy (the ability of an antigen receptor to respond to different ligands [6]) was highlighted. In order to exploit such an idea we suggested adopting a suitable methodology such as the conceptual framework

approach [7], which promotes the use of an interdisciplinary set of stages to develop and analyse bio-inspired algorithms in a more principled way. As a continuation of these ideas we present in this paper the initial stages of our work aimed at exploiting the notion of degenerate immune receptors for use in AIS. By following the conceptual framework approach [7], we have used biological detail drawn from the immunological research literature to build a computational model based on the process of TH cell activation in the paracortex of a lymph node, in which the TH cell receptors are assumed to be degenerate. We believe that the model we have developed is a novel tool with a richness of behaviour to enable the investigation of abstract degenerate detectors. Ultimately this investigation aims to generate enough insight to extract algorithmic design principles that will benefit the development of an AIS for pattern classification. It is noted that the degeneracy issues we explore here are not explicitly connected to the ideas of immune networks.

2

Degeneracy

Degeneracy is a property that is not only seen in the immune system, but, according to Edelman and Gally [8], is a ubiquitous biological property present at most levels of biological organisation. They define degeneracy in biology as: “the ability of elements that are structurally different to perform the same function or yield the same output” Examples they give include the genetic code, where different sequences can encode the same polypeptide, and human language, where there many different ways to transmit the same message. They go on to argue that the omnipresence of degeneracy in biology is a result of it being conserved and favoured by natural selection. Additionally, it is noted that degeneracy in biological systems is typically accompanied with complexity, and it is suggested that degeneracy plays a key role in complex systems. Parnes [9] states that even though degeneracy is a term that has been used in immunology for the last 35 years, it has escaped rigorous definition. For our work, we have adopted the definition given by Cohen [6], which describes antigen receptor degeneracy as the: “capacity of any single antigen receptor to bind and respond to (recognize) many different ligands” Cohen [6] reports that the main consequence of the degeneracy of antigen receptors is poly-recognition, whereby a single lymphocyte clone can recognise different antigen epitopes. This causes a problem for the traditional clonal selection theory view of immunology [10] that relies on the strict specificity of lymphocyte clones. In [9], Parnes notes that in immunology there is a notable confusion between the ideas of ‘degeneracy’, ‘cross-reactivity’ and ‘promiscuity’. The interested reader is referred to the Parnes [9] article for a detailed description of this issue.

As an example of the power of receptor degeneracy, Cohen [6] discusses the example of colour vision in the human eye. The eye possesses millions of colour receptors called cones of which there are only three types (red, green and blue). These receptors are degenerate, each responding to broad range of light wavelengths that overlap between the different cone types. The human brain, however, is able to perceive thousands of specific different colours, thus colour specificity is not encoded by the cones, but achieved via subsequent neuronal firings. Likewise, Cohen [6] envisages a similar recognition scenario in the immune system. 2.1

Exploiting Degeneracy

The description of degeneracy just presented pitches it as an important, advantageous and powerful property at all levels of biological organisation including the immune system. Based on this, we have chosen to investigate the property of degenerate detectors to inspire AIS development. At present there are no instances within the AIS literature where degenerate detectors have been directly addressed, although degeneracy is an issue that is both being discussed [6, 9, 11] and modelled [12] by immunologists. It is clear that incorporating degenerate detectors into AIS will affect the dynamics of the immune algorithm. Instead of recognition being the responsibility of a single detector, recognition will emerge from the collective response of a set of detectors. The assumed benefit of an AIS with degenerate detectors will be to provide greater scalability and generalisation over existing classifier AIS. Greater scalability can be achieved as the capacity to discriminate patterns collectively by a set of degenerate detectors should be greater than by single detectors. Thus, as the number of patterns to be recognised increases, the number of detectors needed in an AIS with degenerate recognition should be less than that of existing AIS. Better generalisation ability to recognise unseen patterns could be achieved as similar patterns should produce a similar pattern of response from the set of detectors. To investigate and exploit degeneracy for the benefit of AIS we follow the approach previously outlined by us in [1], which advocates the use of the conceptual framework approach [7] to bio-inspired algorithm design. Following this, and as a first step before building an AIS, we investigate the biology free of any algorithmic application bias via a process of computational modelling. Based on the notion that antigen receptors of lymphocytes are degenerate, the aim of this modelling exercise is to assess the computational impact of lymphocyte antigen receptor degeneracy on epitope/antigen recognition. This includes investigating the recognition properties of sets of degenerate receptors when presented with sets of target antigens. In order to build such a model we first needed to identify a biological process where recognition by degenerate receptors might take place. An investigation of suitable immunological literature identified the lymph nodes as suitable candidate as they are the immune organs where the adaptive immune response to antigen in the lymph are triggered [13]. Biological details of the lymph node and TH cell activation follow in section 3, which are then used in the design of an abstract computational model of degeneracy in a lymph node presented in section 4.

3

Lymph Nodes

Lymph nodes are examples of the secondary, or peripheral, immune organs, which are the sites where the adaptive immune responses to foreign antigen are initiated. The human body contains many hundred lymph nodes situated at various points in the lymphatic system (lymphatics). They are rich in both lymphocytes and antigen presenting cells (APCs) and so provide an environment where immune responses to antigen in the lymph may be triggered and develop. They thus act as filters of the lymph before it returns to the blood, capturing and responding to foreign antigen that have entered the body via portals of entry such as the skin [14, 13]. Lymph nodes are small bean shaped structures connected to the lymphatics via a number of afferent lymph vessels through which lymph enters the node, and a single efferent lymph vessel through which the lymph leaves the node. Each lymph node is also connected to the circulatory system via a lymphatic artery and vein. It is through the lymphatic artery that lymphocytes (mainly naive T and B cells) enter the lymph node. As lymph drains though the node, any antigen present is captured and processed by APCs for presentation to lymphocytes, which consequently initiates the chain of events that results in the adaptive immune response. Antigen may also be transported into the lymph node by APCs, called dendritic cells, that have captured the antigen close to the portal of entry and then migrated to the node via the lymphatics. The lymph node can be functionally separated into three distinct areas each supporting a different cellular environment: the cortex, the paracortex and the medulla. The cortex supports supports mainly B cells and various APCs (macrophages and dendritic cells), the paracortex supports mainly naive TH cells and dendritic cells, and the medulla contains mostly lymphocytes including the antibody producing plasma cells. As lymph drains through the lymph node, it slowly percolates though each of these three regions. In the paracortex, the dendritic cells trap and process any foreign antigen and presents it via MHCII to the naive TH cells resulting in their activation. These TH cells then play their part in activating B cells on the edge of the paracortex leading to B cell proliferation. This proliferation takes place in the germinal centres of the cortex, and results in antibody producing plasma cells, some of which migrate to the medulla. This whole process results in the lymph leaving the lymph node being enriched with antibodies and lymphocytes [14]. The segmentation of the lymph node into the three different areas is due to the presence of a particular variety of signalling molecules called chemokines. Both naive TH cells and dendritic cells activated due to exposure to antigen, express the same cell-surface receptor for a chemokine produced only in the paracortex. This has the effect of attracting both of these cell types into the same area, thus enabling their interaction. Likewise, naive B cells are concentrated in the cortex as they express a receptor for a different chemokine produced only in the cortex. Once TH and B cells have been activated by antigen/APCs, they lose their chemokine receptors from the cell surface, and therefore migrate towards each other. Thus the structure of the lymph node keeps each of the T and B

cells populations in close proximity to the appropriate APCs and also apart from each other until they are in a state in which they are ready to interact with each other [13]. 3.1

TH Activation in a Lymph Node

Naive TH cells become activated by APCs presenting MHC-II to which antigenic peptides are bound (MHC-P). In order for this activation to take place, a certain level of stimulation is required, an issue determined by two concepts known as affinity and avidity. Affinity is simply the strength of binding between a single binding site (e.g. T cell receptor) and a single ligand (e.g. an MHC-P complex). It can be quantitatively measured using a dissociation constant K d , which is the concentration of a molecule X required to occupy half of the combining sites of another molecule Y present in a solution. Hence, a smaller Kd represents a stronger or higher affinity [13]. Affinity differs from avidity, which is a measure of the strength of binding between molecules or cells when there is more than one binding site present [15]. T cells become activated when the concentration of MHC-P complexes on an APC reaches a sufficient threshold level [16]. In other words, T cells become activated when an avidity threshold is met, and so T cell activation is affected by both the affinity between the T cell receptor and antigenic peptides presented by the APC, and the concentration of these ligands present. It is possible, therefore, for an APC presenting high concentration of MHC-P complexes with weak affinity to activate a T cell, and conversely an APC presenting a low concentration of MHC-P complexes with high affinity not to activate a T cell. Once a naive T cell has become activated it initiates a process of cellular proliferation and differentiation into effector T cells that can perform their allotted immune functions. In the case of effector TH cells, they play a crucial role in activating both B and TC cells which are then in turn able to neutralise pathogens.

4

Degenerate Receptor Lymph Node Model

The previous section described how the activation of naive TH cells in the paracortex of the lymph node provides the initial recognition event of the adaptive immune response to lymph-borne antigen. The computational model that is described in this section aims to understand how this recognition event is affected by notion that the antigen receptors of TH cells are degenerate. Specifically, the model is an abstract representation of the activation of TH cells in the paracortex of the lymph node based on the biological detail presented in section 3, and the assumption that the TH cell receptors can bind to more than one antigen epitope. 4.1

Overview

The first step in building the model was to extract the relevant details from the biology to enable the identification of a suitable model type. The process

of TH cell activation requires the interaction of three immune agents: dendritic cells (which we shall call APCs from this point forth), foreign antigen and TH cells. For these agents to interact, they must be spatially close and able to move appropriately. From a computational point of view, these immune agents can be considered as specific agent types within a model, each with its own set of movement and interaction behaviours. Based on these observations a two-layer cellular automaton (CA) type approach in which APC, antigen and TH cell agents move and interact was chosen as the modelling tool. This was deemed suitable as in a CA each element of the system is modelled individually in a physical space. Having chosen to use this approach, it was possible to reduce some of the complexity present in the real lymph node by reducing it to 2 spatial dimensions. Whilst reducing the spatial complexity of the system, this still enables the elements of the system to move in a non-trivial way. The approach we have taken to model the immune agents and their movement due to a chemokine, is similar to that of Maree et al. [17] who have modelled the movement of Dictyostelium disciodeum amoebae due to a chemical gradient. They use a hybrid CA/partial differential equation model, where the CA is used to represent the physical details of the amoebae and the partial differential equation models the chemical gradient. In our model, two separate layers exist: a chemical space and an agent space. The chemical space models the action of the chemokine produced by the paracortex to attract naive TH cells and APCs presenting antigen. The agent space provides the environment where the agents of the model can move and interact. Both layers are implemented as 2 dimensional grids of cells, with the agent space placed directly on top of the chemical space. Both grids therefore share the same dimensions and co-ordinate system, so for example grid reference (2, 3) in the agent space would relate directly to the same grid reference in the chemical space. The contents of the cells in the chemical space are integer values representing a level of chemokine, and the contents of the cells in the agent space can either be one of the agent types or empty. See Fig. 1 for pictorial example. Wrap around occurs between the right and left edges of the cellular spaces, but not at the top and bottom. This produces an effect whereby the top of the space represents the afferent lymph vessels where lymph enters the node, and the bottom of the space represents the efferent lymph node through which the lymph leaves the node. Time is represented in the model by discrete steps called iterations, and when the model is simulated it runs for a user defined number of iterations. At each model iteration all the cells in the chemical space update, followed by agent movements in the agent space, and lastly agent interactions. 4.2

Chemical Space

Upon initialisation of the model each cell in the chemical space is set to an integer value representing a chemokine concentration. These values are randomly generated integers between 0 and a user defined maximum value. At each iteration of the model, the chemokine values update according to a diffusion rule, whereby the value at each cell is shared out equally to all the its neighbours.

The neighbourhood used for this comprises nine cells: the original cell and the eight Moore neighbours (the cells to the north, northeast, east, southeast, south, southwest, west and northwest) shown in Fig. 2. When applying the diffusion rule the chemokine value for a cell is integer divided by 9 and the resulting value shared between the neighbours. The remainder, R, from this division is then shared out randomly between the neighbours by generating R random numbers between 0 and 8 inclusive that relate to the positions of the nine neighbours, and incrementing the value of these neighbours by 1. An example of applying this diffusion rule for a cell with a chemokine value of 95 is shown in Fig. 3. Here, each neighbour is first assigned a value of 10 from the integer division step, then five random numbers (e.g. 0, 4, 5, 5 and 7) are generated resulting in the allocation of the remainder, R, to random neighbours. When applying this rule to the entire grid of cells in the chemical space, all cells are initially set to a value of 0 then the diffusion rule is applied to each cell in turn using the old chemokine value of that cell. The effect of the diffusion rule over a number of iterations is to smooth the chemokine concentration over the entire chemical space, whilst leaving a level of stochasticity at the local level. This stochasticity is important as it provides a small amount of randomness to the agent movements in the model. To simulate the production of chemokine in the paracortex, there is a user defined parameter determining an area in the middle of the chemical space in which chemokine can be added. To provide a stable chemokine gradient, the level of chemokine that is lost at the top and bottom of the chemical space during an update (due to no cell wrap around) is counted and re-injected in the paracortex region. This re-injection takes place once an update of the entire chemical space has taken

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Fig. 1. The two layers of the cellular space with typical values

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Fig. 2. The Moore neighbourhood where C = central cell, N = north, NE = northeast, E = east, SE = southeast, S = south, SW = southwest, W = west and NW = northwest 0

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Fig. 3. An example of the stages involved in the diffusion rule in the chemical space

place, and the paracortex cells to which the chemical is added are determined by random numbers. The chemokine gradient produced by these rules can be seen visually in Fig. 4, where the value of chemical is represented by a greyscale value with black being the lowest, and white the highest. Here, a lighter band can be seen in the centre of the space representing the paracortex region. 4.3

Agent Space

Cells in the agent space layer of the model can be either empty or contain one of the following agents types: antigen, APC or TH cell. Upon initialisation, agents are placed at random positions in the agent space. When updating, all agents within the space move according to rules defined by their agent type. This is followed by all agents interacting with other agents present in their Moore neighbourhood, again according to the rules of their agent type. The functionality of the three agent types, based on the biological details presented in section 3, are described below in turn. The antigen agents have associated with them a bit string that represents their molecular shape. The movement of the antigen agents mimics the movement of real antigen in the lymph node which drain through the node, entering at the top through afferent lymph vessels and exiting at the bottom via the efferent lymph vessel. The antigen agents, therefore, can only move down or sideways in the agent space, i.e. movements to the east, west, south, southeast and southwest neighbours. At each iteration, a random number determines which of the neighbours the agent moves to. Once an antigen leaves the bottom of the agent space it is automatically reinserted to a cell at the top of the agent space

Fig. 4. A snapshot of the simulator user interface with a visual representation of the agent space on top of the chemical space in the left panel

to mimic a constant flow of antigen through the node. The antigen agents do not themselves initiate any interactions with other agents, although APCs do interact with them which is described below. The APC agents can be in one of two states that dictate their behaviour: not presenting antigen (naive) or presenting antigen (activated). All APC agents start off in the naive state and move to the activated state upon ingestion of an antigen agent. In the naive state, real dendritic cells (APCs) lack the receptor for the chemokine produced in the paracortex, thus naive APCs in this model move to a Moore neighbour determined by a random number at each iteration. Once activated, real dendritic cells produce the chemokine receptor and move towards the paracortex region. This is mimicked in the model by activated APC agents consulting the chemical space level of the model, and moving to the unoccupied Moore neighbour with the highest level of chemokine, thus following the chemokine gradient. APC agents in both the naive and activated states initiate interactions with antigen agents in their Moore neighbourhood. If the APC is naive it ingests the antigen agent, thus removing it from the agent space. Upon ingesting, the APC becomes activated, and an antigen concentration count for that APC is set to 1. A peptide bit string is generated from the bit string of the antigen that represents the peptide presented by real APCs via MHC for recognition by TH cells. This peptide bit string is the same length as the TH cell agent receptors in the model, and is generated as a sub-string of the

antigen bit string that was ingested. If the APC agent is activated, it can ingest further antigen agents it interacts with, depending on a model parameter that determines how many antigen agents each APC is allowed to ingest. APC agents also interact with naive TH cell agents, but this interaction is initiated by the TH cell agent, and is described below. All TH cell agents in the model have associated with them a unique bit string that represents its antigen receptor. Like the APC agents, the TH cell agents can be in one of two states that affect their behaviour: naive or activated. Again, all TH cell agents start off in the naive state and move to the activated state upon interaction with a suitable APC agent. Like activated dendritic cells, real naive TH cells have the receptor for the chemokine produced in the paracortex, thus the movement of naive TH cell agents in the model is the same as activated APC agents. When real TH cells become activated, they lose this chemokine receptor and so in the model activated TH cell agents move to random Moore neighbours. TH cell agents have associated with them an affinity measure type and activation threshold that is used when they interact with APC agents. Only naive TH cell agents interact with APC agents, and these APC agents must be in the activated state, and thus, presenting a peptide bit string. When such an APC agent is in the Moore neighbourhood of a naive TH cell agent, the avidity between the two is calculated. The affinity between the peptide being presented by the APC and the receptor of the TH cell is calculated. As the antigen and TH cell receptor are implemented as bit strings, two affinity measures are defined in the model which are the Hamming distance and R-contiguous bits measure. The Hamming distance returns as the affinity the result of applying the XOR operator to the strings, while the R-contiguous bits measure returns the size of the longest run of complementary bits between the strings. This affinity measure is then multiplied by the antigen concentration level of the APC to provide the avidity. This avidity is then compared to the user-defined activation threshold to determine whether the TH cell becomes activated or not. 4.4

The Simulator

To run useful experiments, a simulator written in the Java programming language is used to execute the model just described. This can be run either interactively via a graphical user interface (see Fig. 4) or on the command line allowing for batch simulation runs. The results of a simulation run, such as the TT cells that have become activated, can be saved to a log file for future analysis. The simulator has the following user defined parameters which are set via a configuration file: – Width, w: The width of the cellular space in number of cells. Typically in the range 50 to 200. – Height, h: The height of the cellular space in number of cells. Typically the same as the width. – Number of Pre-Iterations, pre itns: The number of simulation iterations before the agents are inserted. During this time only the chemical space settings up date thus allowing it time to settle down from a random initialisation.

– Number of Iterations, itns: The number of simulation iterations once the agents have been inserted. – Chemokine Producer Percentage, chem prod: The percentage of the total chemical space set to be the chemokine producing area. – Maximum Chemokine Level, chem max: The maximum allowed chemokine value of a chemical space cell upon initialisation of the simulator. – Maximum Antigen Ingestion, ag max: The maximum number of antigen agents a single APC is allowed to ingest. – Number of APCs, apc num: The number of APC agents. – Number of Antigens, ag num: The number of antigen agents. – Number of TH Cells, th num: The number of TH cell agents. – Recognition Threshold, recog: A user defined avidity threshold to determine whether a TH cell becomes activated upon interaction with an APC. – Affinity Measure, aff: The type of metric used to calculate the affinity between an APC peptipe string and a TH cell receptor. – Antigen String, ag: The bit string that represents the antigen shape. This is the same for all antigen agents in the simulation. – TH Cell Receptors, ths: The list of bit strings that represent the unique receptors for each TH cell in the simulation. The size of the list equals the number of TH cells parameter.

5

Initial Results and Observations

In this section we first describe the behaviour of the simulator, and then show the type of results it generates. During a typical run of the simulator, a number of emergent behaviours can be seen that result from the rules of the model described above. Firstly, during the pre-iterations stage when only the chemical space updates, a visually stable chemokine gradient emerges that flows from a high concentration in the central paracortex region to a low concentration at the top and bottom of the chemical space. After the pre-iterations have finished, all the agents are inserted into the agent space at random positions and start to move and update as the iterations proceed. As the antigen agents cannot move upwards in the agent space, they cycle as a population from the top to the bottom of the agent space, being ingested as they encounter APC agents. As a results, the number of free antigen agents decreases during a run of the simulator. All the TH cell agents are inserted in the naive state, so they immediately start to follow the chemokine gradient in the chemical space and soon settle in the centre of the paracortex area where the chemokine gradient is at its greatest. Once in the centre, the naive TH cell agents continue to move due to the stochasticity in chemokine values at the local level of the chemical space. Like the TH cell agents, all the APC agents are naive when inserted and thus move randomly until the ingest antigen and become activated. Once activated, they follow the same movement behaviour of the naive TH cell agents, gravitating to the centre of the paracortex region. Once in the centre, the activated APC agents are close enough to the naive TH cell agents for them to interact, which results in some

of the TH cell agents becoming activated depending on whether the avidity with the APC agents is above the recognition threshold. Once activated, the TH cell agents lose their chemokine receptor and start move randomly resulting in them drifting away from the paracortex region. At the end of the simulation iterations, the activated TH cell agents are noted. Even though the overall behaviour of the agents in the model may be what is expected, it goes some way to justify the model as the individual pieces of biology detail that has been used to build it, combines to produce behaviour (i.e. movement and interactions of immune agents) similar to that seen in real lymph nodes. Due to the number of parameters that can be changed in the simulator, many different experiments can be run to investigate different issues and effects relating to the behaviour of the model. It is noted that a large numbers of parameters can often hinder the experimentation and results gained from simulations such as ours. However, some initial parameter investigations suggest that the behaviour of the simulator is insensitive to appropriate changes in many of the parameters such as the cellular space sizes and chemical space parameters. These parameters can therefore be kept constant for experimentation into the degenerate receptors. This leaves the simulator with only a small manageable subset of the parameters described above (such as the recognition threshold, antigen receptor and TH cell receptors) that have a real effect on degenerate recognition in the model. By investigating the effects of these parameters, useful design principles for an AIS algorithm employing similar parameters should become apparent. As an example, we present the results from an experiment investigating the patterns of 10 unique TH cell agents with 8-bit receptors that become activated when the simulator is run separately with 20 different 16-bit antigens. For each antigen, the simulator is run 50 times and the percentage of simulations in which each TH cell agent becomes activated is calculated. The results are shown in Table 1, where a blank entry means that the TH cell agent did not become activated. The parameters used for this experiment were: w = 50, h = 50, pre itns = 100, itns = 500, chem prod = 25%, chem max = 500, ag max = 1, apc num = 10, ag num 20, th num = 10, recog = 4 and aff = R-contiguous bits. The degeneracy of the TH cells can clearly be seen in the results as each TH cell is reacting to different antigen ligands (see definition in section 2). We can also see that each of the 20 antigens invokes a unique set (pattern) of T H cells to become activated. These sets are of different sizes for different antigens, ranging from 2 to 6 TH cells being activated. It is interesting to note that the 2 TH cells that become activated by Antigen 9 are also activated by Antigen 8, but the sets differ as Antigen 8 also activated 2 more TH cells. The percentage values for the TH cell activations can be seen as a sensitivity that the TH cell has for the antigen. In general, the results highlight the ability of 10 randomly generated degenerate detectors to collectively distinguish between at least 20 different patterns based on the pattern of response of the detectors. This shows that our model contains degerate detectors capable of reacting in different ways to different patterns, and is therefore a tool we can use for further investigations into the properties degeneracy.

Table 1. Results of a sample experiment showing the percentage of 50 simulations in which the 10 unique TH cell agents become activated for 20 different 16-bit antigens TH 1 TH 2 TH 3 TH 4 TH 5 TH 6 TH 7 TH 8 TH 9 TH 10 Antigen Antigen Antigen Antigen Antigen Antigen Antigen Antigen Antigen Antigen Antigen Antigen Antigen Antigen Antigen Antigen Antigen Antigen Antigen Antigen

6

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

62

86 64 60

66

62

66

58 66

70

68 52 82

62

58

82

60

64 96

68

60

90

62

50 84 62

94 68

58 56

82

62 58

50

70

60

52 90 74 60

74 56

68 72

64 60 58

68 62 84 58 80

62 60

62 56 54

70

96

84 56 48

58 66 60

64 56

64 60

88 58

88

66 90

88 58 66 52

Conclusions and Future Work

In this paper we began with a desire to investigate alternative immune ideas for AIS inspiration and identified degeneracy as a possible candidate. By following the suggestions of the conceptual framework approach to bio-inspired algorithm design [7], an abstract computational modelling exercise was chosen to investigate degeneracy as the first step towards AIS design. Lymph nodes were identified as being possible places where degenerate recognition would take place by lymphocytes (in particular TH cells) as they are the places where the adaptive immune response to foreign antigen in the lymph are initiated. By considering TH cells to be degenerate, and investigating the biological details of the lymph node and TH cell activation, an abstract two-layer cellular space model of degeneracy in the lymph node was designed and built, with sample results highlighting the ability of randomly generated detectors to distinguish between patterns based on their collective response. The purpose of the model we have designed is not to explain how the collective TH response leads to the different ways the immune system responds pathogens. It is, rather, an investigation into the computational recognition capabilities of detectors based on the assumption that these detectors are inherently degenerate. Further work will concentrate on continuing the conceptual framework [7] path to design and build an AIS that utilises degenerate detectors. Firstly, com-

prehensive experimentation with the model described in this paper will be used to understand better the recognition abilities of degenerate detectors. This insight will then lead to the identification of design principles for using degenerate detectors. Based on these an AIS with degenerate detectors for the task of pattern classification will be designed and built, that may provide greater scalability and generalisation performance over existing classification AIS.

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