Nick D. L. Owens

An interdisciplinary perspective on artificial immune systems

This review paper attempts to position the area of Artificial Immune Systems (AIS) in a broader context of interdisciplinary research. We review AIS based on an established conceptual framework that encapsulates mathematical and computational modelling of immunology, abstraction and then development of engineered systems. We argue that AIS are much more than engineered systems inspired by the immune system and that there is a great deal for both immunology and engineering to learn from each other through working in an interdisciplinary manner.

T Cell Receptor Signalling Inspired Kernel Density Estimation and Anomaly Detection

The T cell is able to perform fine-grained anomaly detection via its T Cell Receptor and intracellular signalling networks. We abstract from models of T Cell signalling to develop a new Artificial Immune System concepts involving the internal components of the TCR. We show that the concepts of receptor signalling have a natural interpretation as Parzen Window Kernel Density Estimation applied to anomaly detection. We then demonstrate how the dynamic nature of the receptors allows anomaly detection when probability distributions vary in time.

Modelling the Tunability of Early T Cell Signalling Events

The Tunable Activation Threshold hypothesis of T Cells is investigated through computational modelling of T cell signalling pathways. Modelling techniques involving the i¾?-calculus and the PRISM model checker are presented, and are applied to produce a stochastic model of T cell signalling. Initial results which demonstrate tuning of T cells are presented.

On immune inspired homeostasis for electronic systems

Many electronic systems would benefit from the inclusion of self-regulatory mechanisms. We strive to build systems that can predict, or be aware of, imminent threats upon their specified operation. Then, based on this prediction, the system can alter its operation or configuration to circumvent the effects of the threat. In this position paper, we discuss the role of the immune system can play in serving as inspiration for the development of homeostatic engineered systems, through the development of an immune inspired extensible architecture. We outline the major requirements for such an architecture, and discuss issues that arise as a result and propose possible solutions: things are never as simple as they first appear.

Elucidation of T cell signalling models

A potential mechanism that allows T cells to reliably discriminate pMHC ligands involves an interplay between kinetic proofreading, negative feedback and a destruction of this negative feedback. We analyse a detailed model of these mechanisms which involves the TCR, SHP1 and ERK. We discover that the behaviour of pSHP1 negative feedback is of primary importance, and particularly the influence of a kinetic proofreading base negative feedback state on pSHP1 dynamics. The CD8 co-receptor is shown to benefit from a kinetic proofreading locking mechanism and is able to overcome pSHP1 negative influences to sensitise a T cell.

Chemical Detection Using the Receptor Density Algorithm

This paper describes the application of the receptor density algorithm, an artificial immune system, as used to detect chemicals from data provided by various spectrometers. The system creates chemical signatures which are matched to a library of known chemicals, allowing the positive identification of hazardous substances. The performance of the system is tested against a publicly available mass-spectrometry dataset, against which it has previously been demonstrated as an effective anomaly detection algorithm. An autonomous chemical-detection device is then discussed, in which the algorithm is running on hardware embedded in a Pioneer robot carrying a portable chemical agent monitor.

The Game of Life Rules on Penrose Tilings: Still Life and Oscillators

John Horton Conway’s Game of Life is a simple two-dimensional, two state cellular automaton (CA), remarkable for its complex behaviour. That behaviour is known to be very sensitive to a change in the CA rules. Here we continue our investigations into its sensitivity to changes in the lattice, by the use of an aperiodic Penrose tiling lattice.

Immune Systems and Computation: An Interdisciplinary Adventure

Artificial Immune Systems (AIS) is a diverse area of research that attempts to bridge the divide between immunology and engineering and are developed through the application of techniques such as mathematical and computational modeling of immunology, abstraction from those models into algorithm (and system) design and implementation in the context of engineering. Whilst AIS has become known as an area of computer science and engineering that uses immune system metaphors for the creation of novel solutions to problems, we argue that the area of AIS is much wider and is not confined to the simple development of new algorithms. In this paper we would like to broaden the understanding of what AIS are all about, thus driving the area into a true interdisciplinary one of genuine interaction between immunology, mathematics and engineering.

Parameter optimisation in the receptor density algorithm

In this paper a system which optimises parameter values for the Receptor Density Algorithm (RDA), an algorithm inspired by T-cell signalling, is described. The parameter values are optimised using a genetic algorithm. This system is used to optimise the RDA parameters to obtain the best results when finding anomalies within a large prerecorded dataset, in terms of maximising detection of anomalies and minimising false-positive detections. A trade-off front between the objectives is extracted using NSGA-II as a base for the algorithm. To improve the run-time of the optimisation algorithm with the goal of achieving real-time performance, the system exploits the inherent parallelism of GPGPU programming techniques, making use of the CUDA language and tools developed by NVidia to allow multiple evaluations of a given data set in parallel.

The Receptor Density Algorithm

This paper describes the biological and theoretical foundations of a new Artificial Immune System the Receptor Density Algorithm. The algorithm is developed with inspiration from T cell signalling processes and has application in anomaly detection. Connections between the Receptor Density Algorithm and kernel density estimation with exponential smoothing are demonstrated. Finally, the paper evaluates the algorithms performance on two types of anomaly detection problem.