Environmental Effects on Protein-Protein Interactions
A complete understanding of how environmental factors influence HIV transmission is needed for the development of an effective HIV vaccine. I develop and utilize state-of-the-art modelling and simulation methods to investigate protein-protein interactions involved in viral transmission at the molecular level, and determine how relevant environmental factors (such as salt concentration and pH) affect these interactions. This project is a collaboration with researchers at Los Alamos National Laboratory.
Mutational Analysis of Metalloproteins
Modelling and simulation provide a rational approach for predicting how mutations to metalloprotein enzymes will degrade or enhance catalysis of viable substrates. These predictions greatly aid the search for useful mutations by prioritizing the screening order of mutants in the laboratory, lowering costs and producton times. Key targets have been enzymes which degrade chemical warfare nerve agents and may be used for therapeutic or sanitation purposes. This project is a collaboration with researchers at Los Alamos National Laboratory.
Multiscale Modeling and Simulation of Multidrug Resistance
Bacteria have developed several mechanisms which contribute to multidrug resistance and hinder the development of new antibiotic treatments. If the mechanisms involved in resistance were more fully understood, novel treatments might be developed which could rescue our current stream of antibiotics. I use modelling and simulation to study the structure and function of multidrug resistance efflux pumps, one of the main contributors to antibiotic resistance. I aim to uncover a complete mechanistic understanding of pump function in order to aid efforts to effectively subvert their function. This project is a collaboration with researchers at Los Alamos National Laboratory.
Molecular Simulation Analysis
There are many biological and chemical processes which can be studied in remarkable detail using computational modeling and simulation. The significant computational cost of these approaches suggests that rigorous analysis of the resulting data is necessary to justify the consumed computational resources, and state-of-the-art statistical and machine learning methods are poised to fill this need. However, these methods are often developed outside of the scientific domains where they are applied, and the nuances faced when working with real data make it difficult to discern when these approaches are achieving their intended purpose. I use domain knowledge to construct model-based validation frameworks which help to resolve such issues. Past domains of interest include intrinsically disordered and natively folded proteins. This project is a collaboration with researchers at the University of California, Merced.
Robot Prefrontal Cortex (PFC) Working Memory Toolkit (WMtk)
One past project focused on the development of biologically inspired computational mechanisms for effective robot learning and control. In particular, David Noelle (Univ. of Calif., Merced), and I developed a software toolkit that allows for the easy integration of a powerful computational neuroscience model of working memory into robotic systems. Current work involves combining the toolkit with models of other brain systems and creating more efficient knowledge representation structures which are more flexible and comprehensive than those currently used by toolkit. This model of working memory has been used to train robots to perform standard laboratory tests of working memory function, such as the delayed saccade task, as well tasks in robot navigation, motor skill learning, and object manipulation.