Abstract:
MOTIVATION:The human immune system evolved a multi-layered control mechanism to eliminate self-reactive cells. Of these so-called tolerance induction mechanisms, lymphocytes T education in the thymus gland represents the very first one. This complicated process is not fully understood and quantitative models able to help in this endeavor are lacking. Here, we present a stochastic computational model of the thymus which combines data-driven prediction methods and a novel method based on protein-protein potential measurements for assessing molecular binding among cell receptors, major histocompatibility complex (MHC) molecules, and self-peptides. RESULTS:Of all possible specificities of immature T cells entering the thymus, only a small fraction is actually selected for maturation. Monte Carlo simulations of thymocytes selection in the thymus are performed varying the size of the self and a parameter determining the number of encounter with antigen-presenting cells (APCs). We score the fraction of self-reacting thymocytes leaving the thymus as mature naive T cells and show that self-reactivity is only marginally dependent on the number of self-molecules presented by APCs, while it is strongly affected by a parameter proportional to the time spent in the thymus. We study how this measure changes when we vary the number of MHC alleles and found an optimal number not too different from what we have in reality. The main result of this study is more methodological than biological as we show that immunoinformatics data and methods can be used in systemic level simulation of immune processes.
journal_name
Autoimmunityjournal_title
Autoimmunityauthors
Castiglione F,Santoni D,Rapin Ndoi
10.3109/08916934.2011.523272subject
Has Abstractpub_date
2011-06-01 00:00:00pages
261-70issue
4eissn
0891-6934issn
1607-842Xjournal_volume
44pub_type
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