Jason Bazil

Jason Bazil is an Assistant Professor in the Department of Physiology at Michigan State University's College of Natural Science, with affiliations to the BioMolecular Science Gateway program. His research laboratory, located in the Biomedical Physical Sciences Building, focuses on cardiac energetics and mitochondrial physiology in ischemic heart disease.

Dr. Bazil investigates mitochondrial mechanisms underlying lethal reperfusion injury through an integrative computational-experimental approach. His lab characterizes calcium handling, oxidative stress responses, and mitochondrial volume regulation in cardiac tissue, aiming to identify therapeutic targets for ischemic injury. Additional work develops dynamical modeling frameworks for large-scale biomedical data analysis in pharmacogenomics and systems pharmacology.

Recent publications demonstrate a cohesive research trajectory centered on mitochondrial dynamics in cardiac pathophysiology. Key themes include calcium overload-induced permeability transition, adenine nucleotide transporter regulation, polyphosphate metabolism, and ferroptosis mechanisms. The consistent integration of cryo-EM, respirometry, and computational modeling across publications highlights a signature methodological approach for elucidating causal relationships in energy metabolism.

No scientific awards are explicitly documented in the provided materials.

Dr. Bazil leads the Bazil Lab (bazillab.com) with significant external funding, including an NSF CAREER award as evidenced by 2023 publications. While specific student names aren't listed, his laboratory trains researchers in interdisciplinary cardiac metabolism studies using state-of-the-art instrumentation for mitochondrial analysis.

The Bazil Lab operates within MSU's BioMolecular Science Gateway infrastructure, utilizing advanced cryo-electron microscopy, high-resolution respirometry, and computational platforms. Current projects examine mitochondrial ultrastructural remodeling during calcium overload, therapeutic interventions for reperfusion injury, and big data applications in predicting drug responses for cardiac conditions.