Minjoung Kyoung, Department of Chemistry and Biochemistry
Michelle Ramsahoye, Department of Mathematics and Statistics
The objective of this proposal is to quantitatively understand how membranelesseless enzyme compartments in glucose metabolism are spatially and functionally networked with mitochondria in living cells. We envision that the proposed machine learning methods to analyze 4D live cell data will provide unprecedented insights of how metabolic network is operated in space and time by the enzyme compartments, readily tuned by cellular demands, employing liquid-liquid phase separation. Therefore, the proposed research will significantly advance our understanding of 4 dimensional metabolic network of glucose metabolism with mitochondria and its dysregulation in human metabolic diseases.