Major discoveries often come at the interface of multiple scientific domains. Therefore, it is extremely important to foster cross-disciplinary research. My overarching goal is to integrate the methods and approaches of the physical and life sciences to address questions relevant to biomedical research and health science. To this end, I apply quantitative techniques, (using math-based reasoning and computational and theoretical physics) to generate hypotheses which can then be tested experimentally. The power of physics-based approaches lies in their ability to uncover functional modules in biomolecular networks, to chart the connectivities between perturbed reactions and to reveal mechanistic understanding of specific genes.
The work I have conducted during my graduate and post-graduate career has focused on gaining expertise in the area of physics-based modeling and computational systems biology. In my research, I have been able to demonstrate how integrating multiple disparate data types (i.e. genomic, proteomic and metabolomic) with systems biology models and machine learning approaches provides clues on how complex pathways and regulatory networks in healthy cells are rewired in stress conditions, like cancer.
Link to my published work:
google scholar citations
ebrunk at ucsd dot edu