I study metabolism and its role in disease, combining quantitative, computational, and experimental approaches to understand how cells rewire their metabolism, and what happens when that rewiring fails.
Cells constantly remodel their metabolism in response to their environment, and I'm interested in the molecular logic behind those decisions. How do cells sense nutrient availability and decide where to route carbon and electrons? When the usual pathways are blocked or the environment turns hostile (low oxygen, nutrient stress, a shifting metabolic demand) what alternative routes do cells take, and how do they coordinate those changes across the mitochondria, the nucleus, and the rest of the cell? I'm interested in metabolic flexibility as a fundamental property of living systems.
I'm curious about how metabolic changes at the level of a single cell scale up into the physiology of a whole organism. What determines energy balance, and how do these processes break down to produce the disease phenotypes we see in obesity or diabetes? Why do some interventions shift the body toward burning energy through thermogenesis and futile cycles rather than storing it? I'm motivated by questions that connect molecular mechanism to systemic outcome, and ultimately by the hope of finding metabolic vulnerabilities that can be targeted therapeutically.
Method development runs through all of my work. I rely heavily on liquid chromatography-mass spectrometry and metabolomics, paired with stable-isotope tracing, to measure metabolic flux and characterize the cellular metabolome. Just as important is multiomic integration: connecting metabolomic data with transcriptomics and proteomics to build a fuller picture of regulation. I'm particularly interested in applications of machine learning to metabolic data.