Immune Network Theory

Abstract

Theoretical ideas have played a profound role in the development of idiotypic network theory. Mathematical models can help in the precise translation of speculative ideas into quantitative predictions. They can also help establish general principles and frameworks for thinking. Using the idea of shape space, criteria were introduced for evaluating the completeness and overlap in the antibody repertoire. Thinking about the distribution of clones in shape space naturally leads to considerations of stability and controllability. An immune system which is too stable will be sluggish and unresponsive to antigenic challenge; one which is unstable will be driven into immense activity by internal fluctuations. This led us to postulate that the immune system should be stable but not too stable. In many biological contexts the development of pattern requires both activation and inhibition but on different spatial scales. Similar ideas can be applied to shape space. The principle of short-range activation and long-range inhibition translates into specific activation and less specific inhibition. Application of this principle in model immune systems can lead to the stable maintenance of non-uniform distributions of clones in shape space. Thus clones which are useful and recognize antigen or internal images of antigen can be maintained at high population levels whereas less useful clones can be maintained at lower population levels. Pattern in shape space is a minimal requirement for a model. Learning and memory correspond to the development and maintenance of particular patterns in shape space. Representing antibodies by binary strings allows one to develop models in which the binary string acts as a tag for a specific molecule or clone. Thus models with huge numbers of cells and molecules can be developed and analyzed using computers. Using parallel computers or finite state models it should soon be feasible to study model immune systems with 10(5) or more elements. Although idiotypic networks were the focus of this paper, these modeling strategies are general and apply equally well to non-idiotypic models. Using bit string or geometric models of antibody combining sites, the affinity of interaction between any two molecules, and hence the connections in a model idiotypic network, can be determined. This approach leads to the prediction of a phase transition in the structure of idiotypic networks. On one side of the transition networks are small localized structures much as might be predicted by clonal selection and circuit ideas.(ABSTRACT TRUNCATED AT 400 WORDS)

Tumor-Associated and Disease-Associated Autoantibody Repertoires in Healthy Colostrum and Maternal and Newborn Cord Sera

“…The immune system is composed of complex networks of molecules and cells that act together to orchestrate the beneficial inflammation needed to maintain and repair the body as well as to protect it from neoplastic cells and invading pathogens (1)(2)(3)(4)(5)(6). Abs binding to body molecules-autoantibodieswould appear to mark the self-reactivity needed for tissue healing (7) and for tumor surveillance (8); autoantibodies also mark autoimmune diseases (9).…”

An artificial immune system for data analysis

“…These stay behind in the system after the infection has gone. A more contentious theory is the immune network theory first proposed by Jerne (Jerne, 1974) and reviewed by Perelson (Perelson, 1989). This theory proposes that a network dynamically maintains the immune memory using feedback mechanisms.…”

“…This work took as its inspiration, the immune network theory (Perelson, 1989) and applied it to the field of machine learning using artificial immune networks as a basis. The work presented here is a consolidation of the work described in Cooke et al (1995), Hunt et al (1998), Neal et al (1998) and defines what is meant by an artificial immune system (AIS), including the fundamental components and how they interact to exhibit learning.…”

A new bell-shaped function for idiotypic interactions based on cross-linking

“…In order to have o influence the width only, we rescale equation (9) The two-field function defined by equation (10) has a certain similarity to the function studied by Segel and Perelson (1988, 1989. They defined a proliferation function that depended on two fields, and h,, a suppressive field, i.e.…”