Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Physics, 2011.Cataloged from PDF version of thesis.Includes bibliographical references (p. 147-158).Higher organisms, such as humans, have an adaptive immune system that usually enables them to successfully combat diverse (and evolving) microbial pathogens. The adaptive immune system is not preprogrammed to respond to prescribed pathogens, yet it mounts pathogen-specific responses against diverse microbes, and establishes memory of past infections (the basis of vaccination). Although major advances have been made in understanding pertinent molecular and cellular phenomena, the mechanistic principles that govern many aspects of an immune response are not known. In this thesis, I illustrate how complementary approaches from the physical and life sciences can help confront this challenge. Specifically, I describe work that brings together statistical mechanics and cell biology to shed light on how key regulators of the adaptive immune system, T cells, are selected to enable pathogen-specific responses. A model of T cell development is introduced and analyzed (computationally and analytically) by employing methods from statistical physics, such as extreme value distributions and Hamiltonian minimization. Results show that selected T cell receptors are enriched in weakly interacting amino acids. Such T cell receptors recognize (i.e. bind sufficiently strongly to) pathogens through several contacts of moderate strength, each of which makes a significant contribution to overall binding. Disrupting any contact by mutating the pathogen is statistically likely to abrogate T cell recognition of the mutated pathogen. We propose that this is the mechanism for the specificity of T cells for unknown pathogens. The T cell development model is also used to discuss one way in which host genetics can influence the selection of T cells and concomitantly the control of HIV infection. A model of the T cell selection process as diffusion in a random field of immobile traps that intermittently turn "on" and "off" is developed to estimate the escape probability of dangerous T cells that could cause autoimmune disease. Finally, and importantly, throughout this thesis, I describe, how the theoretical studies are closely synergistic/complementary with biological experiments and human clinical data.by Andrej Košmrlj.Ph.D
