We present a method for deriving shape space parameters that are consistent with immunological data, and illustrate the method by deriving shape space parameters for a model of cross-reactive memory. Cross-reactive memory responses occur when the immune system is primed by one strain of a pathogen and challenged with a related, but different, strain. Much of the nature of a cross-reactive response is determined by the quantity and distribution of the memory cells, raised to the primary antigen, that cross-react with the secondary antigen. B cells with above threshold affinity for an antigen lie in a region of shape space that we call a ball of stimulation. In a cross-reactive response, the intersection of the balls of stimulation of the primary and secondary antigens contains the cross-reactive B cells and thus determines the degree of cross-reactivity between the antigens. We derive formulas for the volume of intersection of balls of stimulation in different shape spaces, and show th...

We describe a method of implementing efficient computer simulations of immune systems that have a large number of unique B and/or T cell clones. The method uses an implementation technique called lazy evaluation to create the illusion that all clones are being simulated, while only actually simulating a much smaller number of clones that can respond to the antigens in the simulation. The method is effective because only 0.001 % to 0.01 % of clones can typically be stimulated by an antigen, and because many simulations involve only a small number of distinct antigens. A lazy simulation of a realistic number of clones and 10 distinct antigens is 1,000 times faster and 10,000 times smaller than a conventional simulation—making simulations of immune systems with realistic-size repertoires computationally tractable.

Artificial Immune Systems (AIS) have been shown to be useful, practical and realisable approaches to real-world problems [5]. Most AIS implementations are based around a canonical algorithm such as clonotypic learning [4], which we may think of as individual, lifetime learning. Yet a species also learns. Gene libraries are often thought of as a biological mechanism for generating combinatorial diversity of antibodies. However, they also bias the antibody creation process, so that they can be viewed as a way of guiding the lifetime learning mechanisms. Over time, the gene libraries in a species will evolve to an appropriate bias for the expected environment (based on species memory). Thus gene libraries are a form of meta-learning which could be useful for AIS. Yet they are hardly ever used. In this paper we consider some of the possible benefits and implications of incorporating the evolution of gene libraries into AIS practice. We examine some of the issues that must be considered if the implementation is to be successful and beneficial.

this report (De Castro & Von Zuben, 1999) is intended to present the basic theory and concepts necessary for the development of immune-based systems. It brings an instructive introduction to the mammal immune system and depicts its most relevant aspects from the viewpoint of engineering. Mechanisms like the clonal selection theory, the immune response along with its affinity maturation process and the immune network hypothesis are emphasized. A few computational algorithms were developed and applied to several different types of problems in order to demonstrate how principles gleaned from the immune system can and must be used in the design of engineering tools for solving complex tasks. In addition, it is introduced an emerging area of research, called immune engineering. The immune engineering is comprised of several strategies, like artificial immune systems, immune-based systems, immunogenetic approaches, etc., and is supposed to include any technique developed using ideas from immunology.

The biological immune system exhibits powerful information processing capabil-ities, and therefore is of great interest to the computer scientist. A rapidly expanding research area has attempted to model many of the features inherent in the natural im-mune system in order to solve complex computational problems. This thesis examines the metaphor in detail, in an effort to understand and capitalise on those features of the metaphor which distinguish it from other existing methodologies. Two problem domains are considered — those of scheduling and data-clustering. It is argued that these domains exhibit similar characteristics to the environment in which the biological immune system operates and therefore that they are suitable candidates for application of the metaphor. For each problem domain, two distinct models are developed, incor-porating a variety of immunological principles. The models are tested on a number of artifical benchmark datasets. The success of the models on the problems considered confirms the utility of the metaphor. i

The dynamic clonal selection algorithm (dynamiCS) was created to tackle the difficulties of anomaly detection in continuously changing environments (Kim and Bentley, 2002a). This algorithm was extended in a sister paper (Kim and Bentley, 2002b), so that memory detectors that are no longer valid are automatically deleted. Here we describe a further extension to the system: the use of hypermutation on deleted memory detectors to produce, in effect, a "virtual gene library" which seeds the immature detector population.

© This paper is not for reproduction without permission of the author. The immune system has a complexity sometimes compared to that of the brain. The vast and diverse number of molecules, cells and tissues, and their complicated pathways of communication (with each other and other bodily systems), endow the immune system with cognitive abilities capable of complementing nervous cognition. In addition, there are several processes and theories used to explain the immune functioning that bring to discussion several key aspects of biology and biologically-inspired computing. This paper thus provides two forms of studying the immune system. The first is more of an analytical approach; it presents some cognitive views of the immune system, the intrinsic evolutionary nature of an adaptive immune response, and how immunity influences the evolution of species. The second study is of a synthetic nature; it describes the immune engineering concept as a meta-synthetic process used for the design of computational intelligence approaches by borrowing inspiration from the immune systems. The latter discussion is a personal account, describing how I used ideas from the immune system to solve complex engineering problems. But these are supposed to provide the reader with some insights about the development of biologically-inspired systems. 1