Research in Professor Stultz’ group revolves around two general themes.  Firstly, a major thrust of the group is to use computational methods to understand conformational changes in macromolecules and the effect of structural transitions on common human diseases. Secondly, his group draws upon concepts in signal processing and machine learning to develop computational biomarkers that identify patients at high risk of adverse cardiovascular events.  His research group employs an interdisciplinary approach that utilizes techniques drawn from computational chemistry, signal processing, and basic biochemistry.

 

Modeling the Unfolded State of Disordered Proteins
We have developing novel methods to model the unfolded states of intrinsically disordered systems.  This exercise is of paramount importance as many disordered proteins have been implicated in a number of neurodegenerative disorders, such as Alzheimer’s disease.. We have applied our methods to understand the unfolded state of the intrinsically disordered proteins, tau protein, alpha synuclein, and abeta. Our goal is to understand how these proteins contribute to the pathogenesis of disease using a combination of physically based calculations and biochemical data.

 

Risk Stratification for Patients with Cardiovascular Disease

We are interested in developing automated methods that can identify patients with cardiovascular disease who are at high risk of adverse outcomes.  To do this we employ a variety of different methods grounded in signal progressing and machine learning. Our methods combine disparate types of clinical information (e.g., medical history, genetic information, physiologic signals) to arrive at models that can guide clinical decision making.