The unfolded protein response (UPR) is a cellular mechanism whose primary functions are to sense perturbations in the protein-folding capacity of the endoplasmic reticulum and to take corrective steps to restore homeostasis. Although the UPR is conserved across all eukaryotic cells, it is considerably more complex in mammalian cells, due to the presence of three interconnected pathways triggered by separate sensor proteins, a translation attenuation mechanism, and a negative feedback loop. The mechanisms of these interacting biochemical pathways in the mammalian UPR allow for a better fine-tuning of the response than in the case of lower eukaryotes, such as yeasts.
The present thesis develops a quantitative mathematical model for the dynamics of the UPR in mammalian cells, which incorporates all the proteins and interactions between them that are known to play a role in this response. This model can be used to provide quantitative information about the levels of its components throughout the response, and to analyze the ramifications of perturbations of the UPR. The model uses a system of ordinary nonlinear differential equations based on biochemical rate equations to describe the dynamics of the UPR as a network of interacting proteins and mRNAs. An early model is presented as a first attempt to investigate the UPR network and construct an inclusive wiring diagram, as well as suggesting a framework to model the differential equations. Then, a refined, quantitative model is designed based on experimental data collected on Mouse Embryonic Fibroblasts treated with Thapsigargin to induce stress and trigger the UPR. The model defines the differential equations and determines the unknown kinetic parameters by optimizing the fit of the system's solution to the experimental data. It includes the UPR's intrinsic feedback loops and allows for the integration of various forms of external stress signals. To the best of our knowledge, it is the first, data-validated, quantitative model in the literature for the UPR in mammalian cells.
The last chapters of the thesis address, from a modeling point of view, two important questions for the UPR: (1) cell survival versus apoptosis; and (2) incompleteness of the biological wiring diagram. Recent experimental results show that the UPR is capable of producing qualitatively different results leading to cell survival or death depending on the nature, strength, and persistence of the inducing stress. This thesis proposes several approaches by which the equations can be modified to model the transition from adaptation to apoptosis as a dynamic switch, while taking into account the various hypotheses of cell death mechanisms. Finally, we use recently-developed computational algebra techniques to infer an optimal structure of the UPR network, based solely on the experimental data; the resulting wiring diagram provides insights on elements of the structure of the model that may have been overlooked during the classical (mechanistic) approach to our original data-based model.
| Identifer | oai:union.ndltd.org:uiowa.edu/oai:ir.uiowa.edu:etd-3285 |
| Date | 01 July 2012 |
| Creators | Diedrichs, Danilo Roberto |
| Contributors | Curtu, Rodica |
| Publisher | University of Iowa |
| Source Sets | University of Iowa |
| Language | English |
| Detected Language | English |
| Type | dissertation |
| Format | application/pdf |
| Source | Theses and Dissertations |
| Rights | Copyright 2012 Danilo Diedrichs |
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