Goal: To create predictive models of cellular behavior, with a focus on modeling signal transduction dynamics.
Our lab runs a world-class mass spectrometer (LTQ-Orbitrap XL +ETD) that allows us to identify and quantify hundreds to thousands of proteins, lipids, and other metabolites in a single run. With this instrument, we can pose highly informative queries of cell state. We are currently focusing our efforts on these specific objectives:
Proteins interact with one another in order to transmit signals from the outside environment (signal transduction), form complex molecular machines, and localize to specific areas of the cell. Current methods for capturing protein-protein interactions (PPIs) lack context and/or specificity. We are developing methods that will enable us to capture in vivo PPIs involving new biochemical, mass spectrometric, and computational techniques. See PPIX project page.
Proteins undergo state changes through post-translational modifications. These chemical modifications determine the activity of enzymes, the localization of proteins, and a protein's interactions. We are working to perfect high-throughput methodologies to measure global protein phosphorylation.
In collaboration with Emily Bates' lab, we are currently analyzing differential phosphorylation in brain tissue between migraine susceptible and wild-type mice. See Mouse Brain Phosphoproteomics project page.
Building on the work and expertise of the BYU Lipidomics Team, we are collaborating with Srirama Rao to apply lipidomic and proteomic techniques to mouse asthma models and human lung tissue. Specifically, we are asking:
The integration of measures of protein state to create meaningful models of cellular behavior is an ongoing challenge in Systems Biology.
We are beginning work with Sean Warnick's lab to apply mathematical models to proteomic measurements in order to deduce protein network structure. To accomplish this, prescribed sets of time series data and over/under expression of particular nodes are used with Dynamical Structure Analysis (DSA), yielding a useful model of protein interactions and dynamics. We are addressing how to:
Mass spectrometry based proteomic data sets are challenging to analyze due to their enormous size, complexity, and changing specifications. The development of fast, easily modifiable software enables researchers to glean much additional information from existing data sets and rapidly test new analytical approaches.
We are the main support behind the mspire libraries, enabling programmatic access to a variety of mass spectrometry data. In particular, we offer:
We are also currently working on: