Stable State Analysis of an Immune Network Model

The paper analyzes a model of immune system developed by different authors (Perelson, De Boer, Weisbuch and others). The model describes interactions among B-lymphocytes. It does not consider antibodies as interaction intermediaries, although it uses a typical activation curve. The relevant parameters are: an influx term, a threshold value, a proliferation rate, and a decay parameter. The study of the n-dimensional extension of the model and a bifurcation analysis of the stationary states with respect to the influx parameter show that the influx value for which biologically acceptable solutions exist decreases as n increases. When the influx term is neglected the stationary states are obtained analytically and their stability is discussed. Moreover, it is discussed how the stable solutions can be considered as "selective states", that is, with only one high idiotypic concentration, when we suppose a complete connectivity.

[1]  Pauline Hogeweg,et al.  Unreasonable implications of reasonable idiotypic network assumptions , 1989 .

[2]  M. Hirsch Systems of Differential Equations that are Competitive or Cooperative II: Convergence Almost Everywhere , 1985 .

[3]  Qualitative and quantitative stabilized behavior of truncated two-dimensional naver-stokes equations , 1991 .

[4]  Pauline Hogeweg,et al.  Memory but no suppression in low-dimensional symmetric idiotypic networks , 1989, Bulletin of mathematical biology.

[5]  P. Richter,et al.  A network theory of the immune system , 1975, European journal of immunology.

[6]  Geoffrey W. Hoffmann,et al.  A Mathematical Model of the Stable States of a Network Theory of Self-Regulation , 1979 .

[7]  L N Cooper,et al.  Mean-field theory of a neural network. , 1988, Proceedings of the National Academy of Sciences of the United States of America.

[8]  G. Parisi A simple model for the immune network. , 1990, Proceedings of the National Academy of Sciences of the United States of America.

[9]  M. Hirsch Systems of di erential equations which are competitive or cooperative I: limit sets , 1982 .

[10]  A S Perelson,et al.  Localized memories in idiotypic networks. , 1990, Journal of theoretical biology.

[11]  Valter Franceschini Two models of truncated Navier–Stokes equations on a two‐dimensional torus , 1983 .

[12]  A. Coutinho,et al.  Beyond Clonal Selection and Network , 1989, Immunological reviews.

[13]  D. Ruelle,et al.  Ergodic theory of chaos and strange attractors , 1985 .

[14]  A. Perelson Immune Network Theory , 1989, Immunological reviews.

[15]  G. Hoffmann A theory of regulation and self‐nonself discrimination in an immune network , 1975, European journal of immunology.

[16]  Gastone Castellani,et al.  CD45 isoforms expression on CD4+ and CD8+ T cells throughout life, from newborns to centenarians: implications for T cell memory , 1996, Mechanisms of Ageing and Development.

[17]  A. Perelson,et al.  Size and connectivity as emergent properties of a developing immune network. , 1991, Journal of theoretical biology.

[18]  E. Bienenstock,et al.  Theory for the development of neuron selectivity: orientation specificity and binocular interaction in visual cortex , 1982, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[19]  G W Hoffmann,et al.  A neural network model based on the analogy with the immune system. , 1986, Journal of theoretical biology.