Literature DB >> 8647216

Affinity maturation and hypermutation in a simulation of the humoral immune response.

F Celada1, P E Seiden.   

Abstract

By experimenting with a cellular automaton model of the immune system, we have reproduced affinity maturation of the antibody response, a somatic adaptation to a changing environment. The simulation allowed the isolation of a number of variables, e.g. the fraction of repertoire available, the magnitude of the change in affinity with mutation, the mutation frequency and its focus on the complementarity-determining regions (CDR) of the antibody. Multiple series of immunizations were run in machina where the contribution of each variable was evaluated against the maturation observed. We found that hypermutation is not necessary for affinity maturation if the repertoire of B cell specificities is sufficiently complete, but is essential when the B cell diversity is limited (which happens to be the case in vivo), as it fills the holes in the repertoire and allows selection by antigen. Maturation also depends on the magnitude of the change in affinity with mutation, and we supply some necessary limits on this parameter. For mutations confined to the CDR, the most efficient maturation occurs at mutation rates of 0.2 per paratope and per cell division. When mutations also affect the framework regions, the peak of the most effective CDR mutation rate moves progressively to lower values. A most sensitive parameter is the speed of maturation, which reflects the rate of expansion of mutated clones. Comparing it with biological observations can help to discriminate between alternative hypotheses on the phenomena of hypermutation and affinity.

Mesh:

Year:  1996        PMID: 8647216     DOI: 10.1002/eji.1830260626

Source DB:  PubMed          Journal:  Eur J Immunol        ISSN: 0014-2980            Impact factor:   5.532


  23 in total

Review 1.  Computer simulations of heterologous immunity: highlights of an interdisciplinary cooperation.

Authors:  Claudia Calcagno; Roberto Puzone; Yanthe E Pearson; Yiming Cheng; Dario Ghersi; Liisa K Selin; Raymond M Welsh; Franco Celada
Journal:  Autoimmunity       Date:  2011-01-27       Impact factor: 2.815

2.  Evolutionary relationships of major histocompatibility complex class I genes in simian primates.

Authors:  Hiromi Sawai; Yoshi Kawamoto; Naoyuki Takahata; Yoko Satta
Journal:  Genetics       Date:  2004-04       Impact factor: 4.562

Review 3.  Systems immunology: a survey of modeling formalisms, applications and simulation tools.

Authors:  Vipin Narang; James Decraene; Shek-Yoon Wong; Bindu S Aiswarya; Andrew R Wasem; Shiang Rong Leong; Alexandre Gouaillard
Journal:  Immunol Res       Date:  2012-09       Impact factor: 2.829

Review 4.  Systems biology in immunology: a computational modeling perspective.

Authors:  Ronald N Germain; Martin Meier-Schellersheim; Aleksandra Nita-Lazar; Iain D C Fraser
Journal:  Annu Rev Immunol       Date:  2011       Impact factor: 28.527

5.  Problems in using statistical analysis of replacement and silent mutations in antibody genes for determining antigen-driven affinity selection.

Authors:  Biplab Bose; Subrata Sinha
Journal:  Immunology       Date:  2005-10       Impact factor: 7.397

6.  Narrowed TCR repertoire and viral escape as a consequence of heterologous immunity.

Authors:  Markus Cornberg; Alex T Chen; Lee A Wilkinson; Michael A Brehm; Sung-Kwon Kim; Claudia Calcagno; Dario Ghersi; Roberto Puzone; Franco Celada; Raymond M Welsh; Liisa K Selin
Journal:  J Clin Invest       Date:  2006-04-13       Impact factor: 14.808

7.  Modifying the sequence of an immunoglobulin V-gene alters the resulting pattern of hypermutation.

Authors:  B Goyenechea; C Milstein
Journal:  Proc Natl Acad Sci U S A       Date:  1996-11-26       Impact factor: 11.205

8.  Agent-based modeling of host-pathogen systems: The successes and challenges.

Authors:  Amy L Bauer; Catherine A A Beauchemin; Alan S Perelson
Journal:  Inf Sci (N Y)       Date:  2009-04-29       Impact factor: 6.795

9.  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

10.  Optimality of mutation and selection in germinal centers.

Authors:  Jingshan Zhang; Eugene I Shakhnovich
Journal:  PLoS Comput Biol       Date:  2010-06-03       Impact factor: 4.475

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