In a previous work we have analysed a family of antibody and B-cell network models (basic AB models) of the immune system. This analysis focused principally on the physiological interpretation of their parameters. Our approach consisted in building a detailed and general mathematical model (referred
Studies on a Recent Class of Network Models of the Immune System
โ Scribed by Jose Faro; Santiago Velasco
- Publisher
- Elsevier Science
- Year
- 1993
- Tongue
- English
- Weight
- 735 KB
- Volume
- 164
- Category
- Article
- ISSN
- 0022-5193
No coin nor oath required. For personal study only.
โฆ Synopsis
It is argued that the realism of computer simulations of network models of the immune system depends basically on the coherence of these models with the essentials of the known physiology of the cells and molecules selected to be modelled and on the incorporation in them of the different compartments of activated (\mathrm{B}) cells. Focusing on these two aspects, here we analyse the simplifications and assumptions that go implicit in the formulation of a recently developed new class of network models that distinguish between immunoglobulins and (\mathrm{B}) cells. This is approached by frrst building a general model which incorporates explicitly the kinetics of diffierent B-cell compartments as well as a splenic compartment and a peripheric one for immunoglobulins, and then formally studying the simplifications on this model that are necessary to recover the initial simpler models. Following this procedure, it is shown that the effective coefficients of the different rate terms in the simpler models are particular combinations of the elementary rates obtained empirically. These relations refect the particular assumptions associated with each simplification step. Also, it is shown that the usual biological interpretation of some of the coefficients in the ordinary differential equations of the simpler models is inconsistent with the more exact general model, unless one makes certain unreasonable assumptions about B-cell physiology. The relevance of this approach in providing variables with a biologically identifiable reality and for realistic, testable, computer simulations is discussed.
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