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Characterization of Sphingolipid-Based Stress Responses in the Yeast Saccharomyces cerevisiae through Reverse Engineering
Advisor:
Eberhard O. Voit, PhD
Committee:
Edward A. Botchwey, III, Ph.D. (BME, Georgia Tech)
Yusuf A. Hannun, M.D., Ph.D. (Stony Brook Cancer Center)
Melissa L. Kemp, Ph.D. (BME, Georgia Tech)
Mark P. Styczynski, Ph.D. (ChBE, Georgia Tech)
Cells and organisms must respond to environmental changes on a regular basis. Small fluctuations are usually tolerated quite easily, whereas stronger perturbations can cause stresses that require fast, coordinated responses. Stress responses are intriguing, as they provide a window into the mechanisms with which cells interact with their environments. The coordination of stress responses consists of activities at multiple layers of the biological hierarchy, including biochemical changes, targeted expression of genes, de novo protein biosynthesis, and altered metabolic flux redistributions. All these responses require the effective transduction of signals of different types.
The proposed work aims to unravel some of the intertwined “survival secrets” of the yeast Saccharomyces cerevisiae with methods of mathematical modeling and reverse engineering. The specific focus is on sphingolipids, which have been shown to play important roles in regulating key cellular functions in eukaryotes. Of particular importance here is the observation that sphingolipids respond to stresses very quickly and, in turn, effect specific changes in gene expression that are the basis for mounting a longer-term stress response. The sphingolipid concentrations are driven by the activities of enzymes within the highly regulated metabolic pathway system of sphingolipid biosynthesis and degradation, and global stress responses of the system rely on adjustments in the activities of these enzymes.
The proposed work has three specific aims. In Aim 1, I will develop computational inverse methods that allow the theoretical and computational inference of response mechanisms from data characterizing a shift in steady state from normal to stress conditions. In Aim 2, I will adapt an existing mathematical model of sphingolipid metabolism and develop a new reverse engineering method to infer the dynamics of enzymatic alterations under heat stress. In Aim 3, I will refine this inference by accounting for different species within the class of long-chain bases and ceramides. The refined methods will be applied again to heat stress as well as hydroxyurea stress. Preliminary results suggest that the proposed methods are feasible and likely to succeed.
