Literature DB >> 7547676

Modelling thymic functions in a cellular automaton.

D Morpurgo1, R Serenthà, P E Seiden, F Celada.   

Abstract

Along the lines developed by Celada and Seiden, for simulating an immune system by means of cellular automata, we have constructed a 'thymus' where T cells undergo positive and negative selection. The populations thus 'matured' have been analyzed and their performance has been tested in machina. The key feature of this thymus is to allow chance meeting and possible interaction between newly born T cells and antigen presenting cells. The latter represent both the epithelial and the dendritic cells of the biological organ and are equipped with MHC molecules that can accommodate selected self peptides. All possible specificities are represented among the virgin T cells entering the thymus, but this diversity is drastically reduced by the time they exit as mature elements. In the model organ the fate of T cells, i.e. whether they will undergo proliferation or apoptosis, is governed by their capacity to recognize MHCs and the affinity of this interaction. Crucial parameters turn out to be the concentration of presenting cells, the number of types of MHC per cell, the 'size of self' in terms of the number of different peptides and their prevalence. According to the results, events in the automaton can realize unforeseen cooperations and competitions among receptors, depending upon the interaction order and frequency, and ultimately determine the rescue or the killing of thymocytes. Thus the making of the mature T repertoire has a random component and cannot be completely predicted.

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Mesh:

Year:  1995        PMID: 7547676     DOI: 10.1093/intimm/7.4.505

Source DB:  PubMed          Journal:  Int Immunol        ISSN: 0953-8178            Impact factor:   4.823


  5 in total

1.  Computational immunology meets bioinformatics: the use of prediction tools for molecular binding in the simulation of the immune system.

Authors:  Nicolas Rapin; Ole Lund; Massimo Bernaschi; Filippo Castiglione
Journal:  PLoS One       Date:  2010-04-16       Impact factor: 3.240

2.  A virtual look at Epstein-Barr virus infection: simulation mechanism.

Authors:  M Shapiro; K A Duca; K Lee; E Delgado-Eckert; J Hawkins; A S Jarrah; R Laubenbacher; N F Polys; V Hadinoto; D A Thorley-Lawson
Journal:  J Theor Biol       Date:  2008-02-16       Impact factor: 2.691

3.  Computer Modeling of Clonal Dominance: Memory-Anti-Naïve and Its Curbing by Attrition.

Authors:  Filippo Castiglione; Dario Ghersi; Franco Celada
Journal:  Front Immunol       Date:  2019-07-03       Impact factor: 7.561

Review 4.  Dynamical and Mechanistic Reconstructive Approaches of T Lymphocyte Dynamics: Using Visual Modeling Languages to Bridge the Gap between Immunologists, Theoreticians, and Programmers.

Authors:  Véronique Thomas-Vaslin; Adrien Six; Jean-Gabriel Ganascia; Hugues Bersini
Journal:  Front Immunol       Date:  2013-10-01       Impact factor: 7.561

5.  Modeling the development of the post-natal mouse thymus in the absence of bone marrow progenitors.

Authors:  Daniela Zaharie; Radu D Moleriu; Felix A Mic
Journal:  Sci Rep       Date:  2016-11-08       Impact factor: 4.379

  5 in total

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