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There is now a CONTENT FREEZE for Mercury while we switch to a new platform. It began on Friday, March 10 at 6pm and will end on Wednesday, March 15 at noon. No new content can be created during this time, but all material in the system as of the beginning of the freeze will be migrated to the new platform, including users and groups. Functionally the new site is identical to the old one. webteam@gatech.edu
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Advisor:
Mark Styczynski, Ph.D. (Georgia Institute of Technology - ChBE)
Committee:
Lily Cheung, Ph.D. (Georgia Institute of Technology - ChBE)
Meleah Hickman, Ph.D. (Emory University - Biology)
Hang Lu, Ph.D. (Georgia Institute of Technology - ChBE)
Ronghu Wu, Ph.D. (Georgia Institute of Technology - Chem)
Metabolomics Analysis of Respirofermentative Phenotypes
Some yeasts exhibit a metabolic phenomenon known as the Crabtree effect, which is a poorly-understood behavior characterized by a preference to undergo fermentation in glucose- and oxygen-rich environments. This respirofermentative metabolism has major implications in biotechnological applications and cancer research; therefore, discovering the underlying mechanisms of this phenotype would improve our ability to control cells and aid in the development of therapeutics. The main goal of this proposal is to study the Crabtree effect by evaluating the metabolic responses of yeast to gene deletions involved in fermentative and respiratory metabolism. An in-depth metabolomics analysis considering a breadth of genes in a narrow range of species will be completed first, followed by a second set of experiments focusing on the key genes involved in the CT effect across a wide range of species. Lastly, we will select genes from the previous experiments and assess the impact of deletion of these genes on the metabolism of human cancer cells. With these studies, we intend to evaluate the metabolic mechanisms of the CT effect in yeast and assess its relation to cancer metabolism to ultimately provide a deeper understanding of yeast evolution, innovative metabolic engineering approaches, and improved models for cancer.
