Our Research
Our research focuses on leveraging statistical genetics, causal inference, and AI-driven computational biology to uncover novel interventions for improving healthspan. We explore the biology of exercise and physical activity to reveal new processes that drive health benefits, connecting cutting-edge discoveries to practical, real-world outcomes.
Causal Inference and Statistical Genetics
Mathematical modeling in causal inference presents both challenges and opportunities in biomedical research. In statistical genetics, certain biological principles—such as the random inheritance of genetic variants—enable causal analysis. Techniques like Mendelian Randomization, especially when applied to molecular omics data, help uncover causal relationships between genes, traits, and diseases, enabling the discovery of novel drug targets.
Source: Holmes et al., 2017
Artificial Intelligence for Biomedical Research
AI enables precise measurement of novel phenotypes, enhancing biological monitoring and molecular "omics" analyses. Beyond data collection, AI-driven models reveal hidden patterns, refine hypotheses, and guide experimental design—accelerating discovery.
An example from Somineni et al. where we used multimodal data to enhance genetic discovery
Biology of exercise adaptation and physical activity
Exercise is one of the most powerful interventions for extending healthspan, yet many questions remain unanswered. How can training programs be personalized for optimal results? What molecular changes drive adaptation to physical activity? And what role do genetics play in human performance? By combining molecular biology, genetics, and computational modeling, we aim to uncover the mechanisms behind exercise benefits and develop strategies for maximizing individual responses.
Multi-omics data analysis
Biological systems are complex and interconnected, requiring a holistic approach to understand their regulation. By integrating multiple layers of molecular data—such as genomics, transcriptomics, proteomics, and metabolomics—we can uncover interactions between different biological systems. This comprehensive view allows us to identify key regulatory patterns, bridging molecular mechanisms with physiological outcomes and advancing precision medicine.
