Transdisciplinary Strategies to Study the Mechanisms of CD4+ T cell Differentiation and Heterogeneity

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Date
2014-08-25
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Virginia Tech
Abstract

CD4+ T cells mediate and orchestrate a tremendous panoply of lymphoid cell subsets in the human immune system. CD4+ T cells are able to differentiate into either effector pro-inflammatory or regulatory anti-inflammatory subsets depending on the cytokine milieu in their environment. This complex process is mediated through a variety of cytokines and soluble factors. Yet, the mechanisms of action underlying the process of differentiation and plasticity of this interesting immune subset are incompletely understood. To gain a better understanding of the CD4+ T cell differentiation and function, here we present an array of different strategies to model and validate CD4+ T cell differentiation and heterogeneity. The approaches presented here vary from ordinary-differential equation-based to agent-based simulations, from data-driven to theory-based approaches, and from intracellular mathematical to tissue-level or cellular modeling. The knowledge generated throughout this dissertation exemplifies how a combination of computational modeling with experimental immunology can efficiently advance the scene on CD4+ T cell differentiation. In this thesis I present i) an overview on CD4+ T cell differentiation and an introduction to which computational strategies have been adopted in the field to tackle with this problem, ii) ODE-based modeling and predictions on Th17 plasticity modulated by PPARγ, iii) ODE- and ABM-based cellular level modeling of immune responses towards Helicobacter pylori and the role of CD4+ T cell subsets on it, iv) Intracellular strategies to validate a potential therapeutic target within a CD4+ T cell to treat H. pylori infection, and finally v) data-driven strategies to model Th17 differentiation based on sequencing or microarray data to generate novel predictions on specific components. I present both mathematical and computational work as well as experimental work, in vitro and in vivo with animal models, to demonstrate how computational immunology and immunoinformatics can help, not only in understanding this complex process, but also in the development of immune therapeutics for infectious, allergic and immune-mediated diseases.

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Keywords
Immunology, CD4+ T cells, computational modeling
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