Mitigating and exploiting stochasticity in the immune system

In the adaptive immune system of higher organisms, T cells are responsible for detecting infections and mounting a response. It is of great importance that T cells respond accurately to very small traces of pathogenic signal in a background sea of healthy cells, to which mounting an immune response in the absence of viral infection could prove fatal for the organism. T cells detect pathogenic signal through noisy protein interaction networks. The goal of this work is to understand how the noise intrinsic to signal transduction mechanisms is mitigated and in some cases exploited to outperform corresponding deterministic mechanisms. Two broad areas of research are presented in this work: 1). Due to fluctuating conformations of proteins, the rate constants of various chemical reactions are not fixed but fluctuate stochastically throughout the course of a signaling cascade. For modeling purposes, this implies that signal detection is based on samples from a large, continuous-time Markov chain whose rate constants follow their own stochastic process. We seek to understand how this behavior limits the information that a network can transmit, and how these limitations can be mitigated based on the specific network topology, or exploited in biological systems to limit autoimmunity. We develop algorithms to detect and characterize the distribution that rate constants sample from. 2). The topology of very early stages in T cell signaling is critical for mounting an accurate immune response. We explore a mechanism that contrasts with the conventional signaling network topology, that outperforms the original by all metrics considered and explains recent experimental results. We study the role that stochasticity in the dwell time of a T cell at an APC plays in achieving a robust cellular response, and explore models of sequential decision making in the immune system.

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Chemistry.

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2016

Author dc:creator

• Thill, Peter (Peter Daniel)

Advisor dc:contributor.advisor

• Arup' K. 'Cha'kraborty.

• Chemistry.

Statement dc:rights

• M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/1721.1/105051

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/105051