Regulation of natural antiallotype antibody responses by idiotype network-induced auto-antiidiotypic antibodies.

Rodkey, L. Scott; Adler, Frank L.

doi:10.1084/jem.157.6.1920

Cited by 27 publications

(9 citation statements)

References 31 publications

(27 reference statements)

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“…For dc 10-3, these peaks typically correspond to periods between 30 and 150 days. This is in agreement with empirical data of Rodkey & Adler (1983) which shows an 80-day period in rabbits. Networks with larger rates of complex formation failed to have sustained oscillatory behavior.…”

Section: Periodssupporting

confidence: 93%

Size and connectivity as emergent properties of a developing immune network

Boer

Perelson

1991

Journal of Theoretical Biology

120

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The development of the immune repertoire during neonatal life involves a strong selection process among different clones. The immune system is genetically capable of producing a much more diverse set of lymphocyte receptors than are expressed in the actual repertoire. By means of a model we investigate the hypothesis that repertoire selection is carried out during early life by the immune network. We develop a model network in which possibly hundreds of B cell clones proliferate and produce antibodies following stimulation. Stimulation is viewed as occurring through receptor crosslinking and is modeled via a log bell-shaped dose-response function. Through secretion of free antibody B cell clones can stimulate one another if their receptors have complementary shapes. Receptor shapes are modeled as binary strings and complementarity is evaluated by a string matching algorithm. The dynamic behavior of our model is typically oscillatory and for some parameters chaotic. In the case of two complementary B cell clones, the chaotic attractor has a number of features in common with the Lorenz attractor. The networks we model do not have a predetermined size or topology. Rather, we model the bone marrow as a source which generates novel clones. These novel clones can either be incorporated into the network or remain isolated, mimicking the non-network portion of the immune system. Clones are removed from the network if they fail to expand. We investigate the properties of the network as a function of P(match), the probability that two randomly selected immunoglobulins have complementary shapes. As the model networks evolve they develop a number of self-regulatory features. Most importantly, networks attain a specific equilibrium size and generate a characteristic amount of "natural" antibody. Because the network reaches an asymptotic size even though the bone marrow keeps supplying novel clones, clones must compete for presence in the network, i.e. repertoire selection takes place. Networks comprised of cells with multireactive receptors remain small, whereas networks consisting of cells with specific receptors become much larger. We find an inverse relationship between n, the number of clones in a network, and P(match), and a linear relationship between n and M, the rate at which novel clones are produced in the bone marrow. We present a simple phenomenological model for the number of clones in the network that accounts for the inverse relationship between n and P(match), and that can account for the relationship between n and M. Additionally, the phenomenological model suggests that there are two qualitatively different network equilibria.(ABSTRACT TRUNCATED AT 400 WORDS)

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Section: Periodssupporting

confidence: 93%

Size and connectivity as emergent properties of a developing immune network

Boer

Perelson

1991

Journal of Theoretical Biology

120

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“…Figure 11 a shows that under quite reasonable parameter choices the system can undergo sustained oscillations with the concentrations of idiotypic and anti-idiotypic antibodies fluctuating inversely with a period of approximately 80 days. These oscillations are reminiscent of those found by Rodkey & Adler (1983) in a year-long study of naturally induced auto-antiidiotypic antibodies in the rabbit, in which one could also identify peaks separated by 80 d. The oscillations found by Rodkey & Adler were much more irregular than those in Fig. lla.…”

Section: Al +S2b2^aj'b2-supporting

confidence: 68%

“…lib). Figs 2 and 4A of Rodkey & Adler (1983) show similar peaks at similar times (d 120 and 260, and 200 and 280, respectively). Inverse idiotypic and anti-idiotypic fluctuations have also been found by Kelso & Cerny (1979) and Bona (1982).…”

Section: Al +S2b2^aj'b2-mentioning

confidence: 61%

Immune Network Theory

Perelson

1989

Immunological Reviews

318

131

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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)

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“…Studies in rabbits and mice have shown that maternal IgG antibodies can both augment and suppress neonatal responsiveness (Okamoto et al, 1989;Rodkey and Adler, 1983;Rubinstein et al, 1982;Wikler et al, 1980). The suppression of IgE responsiveness is noteworthy (Bednar-Tantscher et al, 2001;Jarret and Hall, 1979;Roberts and Turner, 1983), which might explain the delayed onset of atopy among breastfed infants (Halpern et al, 1973).…”

Section: Immunomodulation By Passive Igs and Antibodiesmentioning

confidence: 99%