Miles, Alexander S.
(2017)
Mathematical Modelling and Statistical
Inference from Immune Response Data.
Masters thesis, National University of Ireland Maynooth.
Abstract
A hallmark of the adaptive immune response is the proliferation of pathogen-specific lymphocytes
that leave in their wake a long lived population of cells that provide lasting immunity. A subject
of debate is at which time point post infection those memory cells are produced during an adaptive
immune response. In two ground-breaking studies, [Buchholz et al., 2013] and [Gerlach et al., 2013]
introduced a new experimental method that allowed them to determine the number offspring from
individual lymphocytes in vivo at a single harvesting time point. Through the development, application
and fitting of a mathematical model, the authors of [Buchholz et al., 2013] concluded that
memory cell precursors are produced before the effector cells that clear the original pathogen, contrary
to prior understanding. Cohort level cell data in the paper [Kinjyo et al., 2015], however, challenges
that deduction. In this thesis we sought to quantitatively reconcile these two reports by adopting the
mathematical methodology of [Buchholz et al., 2013] to make it suitable for drawing inferences from
the data in [Badovinac et al., 2007], [Schlub et al., 2010] and [Kinjyo et al., 2015]. When fitting to
spleen and blood data reported in these papers, under the assumptions of the model, our conclusion is
consistent with [Buchholz et al., 2013]: memory precursor cells appear before effector cells. However,
an alternative possibility supported by the data in [Kinjyo et al., 2015] is that memory is created after
the expansion phase, a deduction not possible from the data or mathematical methods in [Buchholz
et al., 2013].
| Item Type: |
Thesis
(Masters)
|
| Keywords: |
Mathematical Modelling; Statistical Inference; Immune Response Data; |
| Academic Unit: |
Faculty of Science and Engineering > Research Institutes > Hamilton Institute |
| Item ID: |
8850 |
| Depositing User: |
IR eTheses
|
| Date Deposited: |
27 Sep 2017 08:39 |
| URI: |
|
| Use Licence: |
This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available
here |
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