Methodologies: Machine Learning, Optimization

Aaron Frank

Assistant Professor, Biophysics



In order to understand the relationship between molecular structure and dynamics and biological function, the Frank research group seeks to develop and deploy integrative modeling tools to elucidate the structure and dynamics of biologically relevant molecules. Our methods will utilize readily accessible experimental observables from a variety of sources to first guide structure prediction efforts and then guide atomistic simulations to map the entire conformational landscape of these molecules. We are primarily interested in using our methods to understand how functional ribonucleic acids, either by themselves or in concert with other molecules, achieve specific cellular functions. Our research makes heavy use of advanced machine learning  and  optimization techniques.

Integrative modeling and simulations of biomolecules

Integrative modeling and simulations of biomolecules