Crowdsourcing Self/Non-Self Discrimination in the Immune System
Grégoire Altan-Bonnet, Memorial Sloan-Kettering Cancer Center – New York NY

The immune system is a collection of cells whose function is to eradicate pathogenic infections and malignant tumors while protecting healthy tissues. Recent work has delineated key molecular and cellular mechanisms associated with the ability to discriminate self from non-self agents. For example, structural studies have quantified the biophysical characteristics of antigenic molecules (those prone to trigger lymphocyte activation and a subsequent immune response). However, such molecular mechanisms were found to be highly unreliable at the individual cellular level.

We will present recent efforts to build experimentally validated computational models of the immune responses at the collective cell level. Such models have become critical to delineate how higher-level integration through nonlinear amplification in signal transduction, dynamic feedback in lymphocyte differentiation and cell-to-cell communication allows the immune system to enforce reliable self/non-self discrimination at the organism level. In particular computational models and quantitative measurements demonstrate how phenotypic variability and noise in lymphocyte activation are critical to achieve plasticity yet reliability of the immune response.

We will then discuss how such quantitative understanding can be leveraged in clinical settings to improve diagnostics and generate new immunotherapies (e.g. against tumor tissues)

Background Review Article:

G. Altan-Bonnet and T. Emonet, Systems Immunology: A Primer for Biophysicists. In: Edward H. Egelman, editor: Comprehensive Biophysics, Vol 9, Simulation and Modeling, Harel Weinstein. Oxford: Academic Press, 2012. pp. 389-413. ISBN: 978-0-12-374920-8 © Copyright 2012 Elsevier B.V. Academic Press.

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