Toward Quantitative Models of Germinal Center Dynamics (Thesis)

Abstract:

This dissertation uses modeling and simulation in order to better understand
the immune response. Numerous in vivo and in vitro studies have elucidated
the basic cellular mechanisms that underlie many aspects of immune response
dynamics. However, in most cases it is not well understood how these
mechanisms fit together. This is particularly true for the germinal center
reaction (an important component of the immune response). Although many
models of the germinal center have been proposed, most only attempt to
explain the average-case, qualitative behavior of the system. This
dissertation demonstrates that such a methodology can be misleading and
presents a number of contributions toward the development of
discrete/stochastic, quantitative models of the germinal center reaction.

The work presented in this dissertation is a fusion of computer science and
theoretical immunology. Formulas for estimating response-specific model
parameters, algorithms for developing discrete/stochastic simulations, and
quantitative constraints are combined to provide a framework for asking
precise questions about how well germinal center models can explain
experimental observations. This framework is used to highlight significant
weaknesses in well-known models of the germinal center reaction. To address
these problems, new models are proposed and validated. All of these models
are used to make specific predictions that can be tested by in vivo
experiments to obtain further validation and drive the development of
improved models.