Concepts And Inspiration From Vertebrate Immune System Biology Essay


Bio inspiration for AIS. The vertebrate biological immune system is an elegant defense system which has evolved over thousands of years. While many details of the immune mechanisms and processes are still unknown to humans, researchers have identified that the immune system is a multilevel defense system which acts in both parallel and sequential manner.[++]

Depending on the type of the pathogen, and the way it gets into the body, the immune system uses different response mechanisms either to neutralize the pathogenic effect or to destroy the infected cells. [Kubi (2002)].

Kubi, J., 2002, Kubi Immunology, 5th edn, Richard A. Goldsby, Thomas

J. Kindt and Barbara A. Osborne, eds, Freeman, San Francisco.

Matzinger, P., 2003,

Matzinger, P., 2001, The danger model in its historical context, Scand.

J. Immunol. 54:4-9.

The immune features that are of special interest are matching, diversity and distributed control. Matching refers to the binding between antibodies and antigens. Diversity refers to the fact that, in order to achieve optimal antigen space coverage [ (see Hightower et al., 1995)]++.

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Distributed control refers that there is no central controller; rather, the immune system is governed by local interactions between immune cells and antigens.

The most important cells in the immune process are white blood cells, which are of two kinds called T-cells and B-cells. Both of these originate in the bone marrow, but T-cells pass on to the thymus to mature, before circulating in the blood and lymphatic vessels.

The T-cells are of three types: helper T-cells which are essential to the activation of B-cells, killer T-cells which bind to foreign invaders and inject poisonous chemicals into them causing their destruction, and suppressor T-cells which inhibit the action of other immune cells thus preventing allergic reactions and autoimmune diseases. Further T-cells produce molecules called antibodies, which have the ability to bind themselves to specific molecules called pathogens that are found in the invader foreign bodies. Depending on their structure, different antibodies will bind to different types of pathogens, and this ability is called the affinity of the antibodies.

B-cells are responsible for the production and secretion of antibodies, which are specific proteins that bind to the antigen. Each B-cell can only produce one particular antibody. The antigen is found on the surface of the invading organism and the binding of an antibody to the antigen is a signal to destroy the invading cell as shown in Fig. 13.1.

As stated earlier the human body is protected against foreign invaders in several levels, The immune system is composed of physical barriers such as the skin and respiratory system; also physiological barriers such as enzymes and stomach acids; and the immune system which can be viewed as of two types: innate immunity and adaptive immunity.

Adaptive immunity can again be subdivided into two types: humoral immunity and cell-mediated immunity. Innate immunity is present at birth. Adaptive immunity is the main focus of interest in artificial immune systems as learning, adaptability, and memory are important characteristics of adaptive immunity.

Humoral immunity is mediated by antibodies contained in body fluids (known as humors). The humoral immune system involves interaction of B-cells with antigen and their subsequent proliferation and differentiation into antibody-secreting plasma cells. Antibody functions as the effectors of the humoral response by binding to antigen and eliminating them, when an antigen is coated with antibody, it can be eliminated in several ways. For example, antibody can cross-link the antigen, forming clusters that are more readily ingested by phagocytic cells. Binding of antibody to antigen on a micro-organism also can activate the complement system, resulting in lysis of the foreign organism.

Cellular immunity is cell-mediated; effector T-cells generated in response to antigen are responsible for cell-mediated immunity. Cytotoxic T-lymphocytes (CTLs) participate in cell-mediated immune reactions by killing altered self-cells; they play an important role in the killing of virus-infected and tumor cells. Cytokines secreted by TDH can mediate the cellular immunity, and activate various phagocytic cells, enabling them to phagocytose and kill micro-organisms more effectively. This type of cell-mediated immune response is especially important in host defense against intracellular bacteria and protozoa.

[(for more details see Farmer et al., 1986; Kubi, 2002; Jerne, 1973), ]++

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The essential process is the matching of antigen and antibody, which leads to increased concentrations (proliferation) of more closely, matched antibodies. In particular, idiotypic network theory, negative selection mechanism, and the "clonal selection" and "somatic hypermutation" theories are primarily used in Artificial Immune System models.

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Artificial Immune Systems

As earlier stated the vertebrate immune system possesses the ability to recognize, adapt to, and eventually eliminate invasive foreign bodies with accuracy. Because of this unique capability, the immune system provides the basis for number of bio-inspired problem solving approaches in engineering. These approaches are collectively called Artificial Immune Systems (AIS)

[(Castro & Timmis, 2003)]++.

As the affinity of the antibodies to bind with foreign invaders [pdf]++ , antibodies must not bind to the molecules produced by their own organism. The ability to distinguish between own cells (called Self) and pathogens (called Nonself) is termed Self-Nonself discrimination. Self-Nonself discrimination is a important feature of the antibodies, and idea of Self-Nonself discrimination have been successfully applied to many AIS based anomaly detection applications .[(cf.Aickelin et al. 2004).]++

Under normal circumstances, Self-Nonself discrimination could work by using positive characterization,

i.e. train the antibodies to recognize known samples of foreign cells. For example, a typical application of

positive characterization is a computer anti-virus program. The virus definitions have to be periodically

updated to enable the system to identify new threats. However, it is impossible to predict all possible

contaminations by foreign cells, and such a system would be unable to react to threats which is has not

encountered before. The vertebrate immune system is, therefore, based on negative characterization or

negative selection instead (Aickelin et al. 2004; Luh & Chen, 2005). This is accomplished by continuously

creating a large variety of antibodies. These antibodies are then presented to the body's own cells.

If an antibody is found to bind to any of the latter cells, it is simply eliminated from the bloodstream.

This is done so that the immune system does not develop an adverse autoimmune reaction. Otherwise,

the antibody is released in the bloodstream. The detection process is then straightforward: if an antibody

binds to any cell, it is assumed to be foreign and is then destroyed by the immune system.

Artificial immune system algorithms based on negative selection are the mainstay of anomaly detection

methods. In a manner reminiscent of their biological counterpart, these algorithms generate a

repertoire of detectors. These detectors are generated in substantial numbers with the expectation of

covering the entire Nonself region. Subsequently, any input that is detected by any detector is classified

as an anomalous input.

Negative selection algorithms have been successfully applied to many anomaly detection problems.

Probably the most intuitive applications are in enhanced computer security (Dasgupta & Gonzales,

2002; Harmer et al., 2002; Nia et al., 2003). Other applications use negative selection to detect faults

in squirrel cage induction motors (Branco et al., 2003), refrigeration systems (Taylor & Corne, 2003),

aircraft systems (Dasgupta et al., 2004), and power systems (Gui et al., 2007). They also have been used

to detect anomalies in time series data (Nunn & White, 2005) and to recognize patterns, for example the

Indian Telugu characters (Ji & Dasgupta, 2004; Ji & Dasgupta, 2006). More recently, a method that uses

negative selection in conjunction with an optimization algorithm has been applied to classify faults in

rotor rigs from vibration data (Strackeljan & Leiviskä, 2008).

This chapter begins with a description of the basic idea behind negative selection. Next, it focuses

on a subclass of detectors, called V-detectors, or variable-sized detectors, which is a popular choice in

many recent engineering applications. Several recent methods derived from basic negative selection

have been outlined, which are divided into three broad categories: self-organizing detectors, evolving

detectors and proliferating detectors. In order to demonstrate the effectiveness of negative selection, this

chapter describes in detail the proliferation mechanism and proposes extending the detector proliferation


Artificial Immune Systems for Anomaly Detection

method beyond anomaly detection to also perform multi-category classification.

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As a case study, the V-detector and proliferating V-detectors algorithms are applied to a real-world

engineering problem of detecting anomalous data for an automated bearing testing machine. The goal

of the application is to determine when a bearing is near the end of its useful life, based on several observed

parameters, some of which may migrate outside of their normal ranges of operation towards the

end of the product's normal lifetime.

As a second case study, this chapter also demonstrates the use of the proliferating V-detectors algorithm

to solve a power quality disturbance classification problem. Four normalized, statistical features for

power quality disturbances are extracted from raw data using the S-transform technique. The proposed

classification method, based on the proliferating V-detectors algorithm, is then applied.

Simulation results for both case studies verify the effectiveness of the proposed method, which is

based on negative selection.