Skip to main content
Log in

Memory in idiotypic networks due to competition between proliferation and differentiation

  • Published:
Bulletin of Mathematical Biology Aims and scope Submit manuscript

Abstract

A model employing separate dose-dependent response functions for proliferation and differentiation of idiotypically interacting B cell clones is presented. For each clone the population dynamics of proliferating B cells, non-proliferating B cells and free antibodies are considered. An effective response function, which contains the total impact of proliferation and differentiation at the fixed points, is defined in order to enable an exact analysis. The analysis of the memory states is restricted in this paper to a two-species system. The conditions for the existence of locally stable steady states with expanded B cell and antibody populations are established for various combinations of different field-response functions (e.g. linear, saturation, log-bell functions). The stable fixed points are interpreted as memory states in terms of immunity and tolerance. It is proven that a combination of linear response functions for both proliferation and differentiation does not give rise to stable fixed points. However, due to competition between proliferation and differentiation saturation response functions are sufficient to obtain two memory states, provided proliferation preceeds differentiation and also saturates earlier. The use of log-bell-shaped response functions for both proliferation and differentiation gives rise to a “mexican-hat” effective response function and allows for multiple (four to six) memory states. Both a primary response and a much more pronounced secondary response are observed. The stability of the memory states is studied as a function of the parameters of the model. The attractors lose their stability when the mean residence time of antibodies in the system is much longer than the B cells' lifetime. Neither the stability results nor the dynamics are qualitatively chanbed by the existence of non-proliferating B cells: memory states can exist and be stable without non-proliferating B cells. Nevertheless, the activation of non-proliferating B cells and the competition between proliferation and differentiation enlarge the parameter regime for which stable attractors are found. In addition, it is shown that a separate activation step from virgin to active B cells renders the virgin state stable for any choice of biologically reasonable parameters.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

Literature

  • Behn, U. and J. L. van Hemmen. 1989a. Network description of the immune system: Dormant B cells stabilize cycles.J. Stat. Phys. 56, 533–545.

    Article  MATH  Google Scholar 

  • Behn, U. and J. L. van Hemmen. 1989b. On the theory of networks for the immune system. In:Dynamical Networks. W. Ebeling and M. Peschel (Eds.), pp. 162–172. Berlin: Academic Verlag.

    Google Scholar 

  • Behn, U., J. L. van Hemmen and B. Sulzer. 1992. Memory B cells stabilize cycles in a repressive network. InTheoretical and Experimental Insight into Immunology. A. S. Perelson, G. Weisbuch and A. Coutinho (Eds), pp. 249–260. Berlin: Springer.

    Google Scholar 

  • Behn, U., J. L. van Hemmen and B. Sulzer. 1993. Memory to antigenic challenge of the immune system: Synergy of idiotypic interactions and memory B cells.J. theor. Biol., in press.

  • Bell, G. 1970. Mathematical model of clonal selection and antibody production.J. theor. Biol. 29, 191–232.

    Article  Google Scholar 

  • Bell, G. 1971. Mathematical model of clonal selection and antibody production. II–III.J. theor. Biol. 33, 339–398.

    Article  Google Scholar 

  • Coutinho, A. 1989. Beyond clonal selection and network.Immunol. Rev. 110, 63–87.

    Article  Google Scholar 

  • De Boer, R. J. 1983. GRIND. Great Integrator Differential Equations, Bioinformatics Group, University of Utrecht, The Netherlands

    Google Scholar 

  • De Boer, R. J. and P. Hogeweg. 1989a. Memory but no suppression in low-dimensional symmetric idiotypic networks.Bull. math. Biol. 51, 223–246.

    Article  MATH  Google Scholar 

  • De Boer, R. J. and P. Hogeweg. 1989b. Unreasonable implications of reasonable idiotypic network assumptions.Bull. math. Biol. 51, 381–408.

    Article  MATH  Google Scholar 

  • De Boer, R. J., I. G. Kevrekidis and A. S. Perelson. 1990. A simple idiotypic network model with complex dynamics.Chem. Engng Sci. 45, 2375–2382.

    Article  Google Scholar 

  • De Boer, R. J., A. U. Neumann, A. S. Perelson, L. A. Segel and G. Weisbuch. 1992. Recent approaches to immune networks. Santa Fe Institute Working Papers Series, 91-10-040.

  • De Boer, R. J., I. G. Kevrekidis and A. S. Perelson. 1993a. Immune network behavior I: From stationary states to limit cycle oscillations.Bull. math. Biol. 55, 745–780.

    Article  MATH  Google Scholar 

  • De Boer, R. J., I. G. Kevrekidis and A. S. Perelson. 1993b. Immune network behavior II: From oscillations to chaos and stationary states.Bull. Math. Biol. 55, 781–816.

    Article  MATH  Google Scholar 

  • Goldstein, B. 1988. Desensitization, histamine release and the aggregation of IgE on human basophiles. In:Theoretical Immunology, Part Two,SFI Studies in the Science of Complexity. A. S. Perelson (Ed.), Vol. III, pp. 3–40, Redwood City, CA: Addison-Wesley.

    Google Scholar 

  • Gray, D. and T. Leanderson. 1990. Expansion, selection and maintenance of memory B-cell clones. In:Immunological Memory, Current Topics in Microbiol. and Immunol. D. Gray and J. Sprent (Eds), Vol. 159, pp. 1–17.

  • Grossman, Z., R. Asofsky and C. DeLisi. 1980. The dynamics of antibody secreting cell production: Regulation of growth and oscillations in the response to T-independent antigens.J. theor. Biol. 84, 49–92.

    Article  Google Scholar 

  • Hoffmann, G. W. 1975. A theory of regulation and self-nonself discrimination in an immune network.Eur. J. Immunol. 5, 638–647.

    Google Scholar 

  • Jerne, N. K. 1974. Towards a net work theory of the immune system.Ann. Immunol. (Inst. Pasteur) 125C, 373–389.

    Google Scholar 

  • Jerne, N. K. 1984. Idiotypic networks and other preconceived ideas.Immunol. Rev. 79, 5–24.

    Article  Google Scholar 

  • Jerne, N. K. 1985. The generative grammar of the immune system.EMBO J. 4, 243–247.

    Google Scholar 

  • Linton, J.-P., D. J. Decker and N. R. Klinman. 1989. Primary antibody-forming cells and secondary B cells are generated from separate precursor cell populations.Cell 59, 1049–1059.

    Article  Google Scholar 

  • Merrill, S. J. 1978. A model of the stimulation of B-cells by replicating antigen.Math. Biosci. 41, 125.

    Article  MATH  MathSciNet  Google Scholar 

  • Murray, J. D. 1989.Mathematical Biology, pp. 702–705. Berlin: Springer.

    MATH  Google Scholar 

  • Neumann, A. U. and G. Weisbuch. 1992a. Window automata analysis of population dynamics of the immune system.Bull. math. Biol. 54, 21–44.

    MATH  Google Scholar 

  • Neumann, A. U. and G. Weisbuch. 1992b. Dynamics and topology of immune networks.Bull. math. Biol. 54, 699–726.

    Article  MATH  Google Scholar 

  • Perelson, A. S. 1988. Theoretical Immunology, Part Two,SFI Studies in the Science of Complexity. A. S. Perelson (Ed.), Vol. III, Redwood City, CA: Addison-Wesley.

    Google Scholar 

  • Perelson, A. S. 1989. Immune network theory.Immunol. Rev. 110, 5–36.

    Article  Google Scholar 

  • Perelson, A. S. and C. DeLisi. 1980. Receptor clustering on a cell surface. I. Theory of receptor cross-linking by ligands bearing two chemically identical functional groups.Math. Biosci. 48, 71–110.

    Article  MATH  MathSciNet  Google Scholar 

  • Perelson, A. S., M. Mirmirani, G. F. Oster. 1976. Optimal strategies in immunology. I. B-cell differentiation and proliferation.J. math. Biol. 3, 325–367.

    MATH  MathSciNet  Google Scholar 

  • Prikrylová, D., M. Jilek and J. Waniewski. 1992.Mathematical Modeling of the Immune Response, pp. 91–112. Boca Raton, FL: CRC Press.

    MATH  Google Scholar 

  • Rasure, J. and M. Young. 1992. An open environment for image processing software development. In:SPIE Proceedings, Vol. 1659.

  • Richter, P. H. 1975. A network theory of the immune system.Eur. J. Immunol. 5, 350–354.

    Google Scholar 

  • Richter, P. H. 1978. The network idea and the immune response. In:Theoretical Immunology. G. I. Bell, A. S. Perelson and G. H. Pimbley (Eds), pp. 539–569. New York: Marcel Dekker.

    Google Scholar 

  • Schittek, B. and K. Rajewski. 1990. Maintenance of B-cell memory by long-lived cells generated from proliferating precursors.Nature 346, 749–752.

    Article  Google Scholar 

  • Segel, L. A. and A. S. Perelson. 1990. Some reflections on memory in shape space. In:Theories of Immune Networks. H. Atlan and I. R. Cohen (Eds.), pp. 63–70. Berlin, Springer.

    Google Scholar 

  • Segel, L. A. and A. S. Perelson. 1991. Exploiting the diversity of time scales in the immune system: A B-cell antibody model.J. stat. Phys. 63, 1113–1131.

    Article  Google Scholar 

  • Varela, F. J. and A. Coutinho. 1991. Second generation immune networks.Immunol. Today 12, 159–166.

    Google Scholar 

  • Varela, F. J. and J. Stewart. 1990. Dynamics of a class of immune networks: Global stability of idiotype interactions.J. theor. Biol. 144, 93–101.

    MathSciNet  Google Scholar 

  • Vitetta, E. S., M. T. Berton, C. Burger, M. Kepron, W. T. Lee and X.-M. Yin. 1991. Memory B and T cells.Ann. Rev. Immunol. 9, 193–217.

    Article  Google Scholar 

  • Weisbuch, G., R. J. de Boer and A. S. Perelson. 1990. Localized memory in idiotypic networks.J. theor. Biol. 146, 483–499.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Rights and permissions

Reprints and permissions

About this article

Cite this article

Sulzer, B., Leo van Hemmen, J., Neumann, A.U. et al. Memory in idiotypic networks due to competition between proliferation and differentiation. Bltn Mathcal Biology 55, 1133–1182 (1993). https://doi.org/10.1007/BF02460702

Download citation

  • Received:

  • Revised:

  • Issue Date:

  • DOI: https://doi.org/10.1007/BF02460702

Keywords

Navigation