Dr. Giannone is a R&D Staff Scientist at Oak Ridge National Laboratory and Adjunct Assistant Professor at the University of Tennessee with appointments to both the Graduate School of Genome Science and Technology at the College of Arts and Sciences and the Department of Biomedical and Diagnostic Sciences at the College of Veterinary Medicine. Dr. Giannone has 15+ years of experience in the field of bioanalytical mass spectrometry with a specific focus on LC-MS/MS-based proteomics. His research interests lie in the development and application of enhanced proteomic sampling methodologies for the quantitative analysis of complex biological systems. Though much of his work thus far has centered on the proteomic characterization of bioenergy-relevant organisms for biofuel production, he is keenly interested in microbial community dynamics, specifically with regard to how organisms within the same ecological niche functionally interact with each other and their local environment (Metaproteomics). These niches range in complexity from simple, two-member systems (i.e. defining the relationship between Ignicoccus hospitalis and Nanoarchaeum equitans), to larger, more diverse communities comprised of hundreds of members (i.e. organisms that thrive in the human gut microbiome). Despite varying complexities, the application of LC-MS/MS-based proteomics to unambiguously assign peptides to specific organisms, while concurrently providing protein abundance information, has enabled an unprecedented glimpse into microbial community dynamics, especially as it pertains to community membership, stability and re-organization to accommodate environmental cues and/or perturbations. Together with internal efforts to provide enhanced, high-resolution, accurate, intensity-based quantitation, Giannone and colleagues are able to study these complex dynamics across scales, deriving not only community-level functional signatures, but also “division-of-labor” amongst constituent microbes, host proteomic response (if applicable), as well as the identification of specific proteins/protein classes that drive community-relevant metabolic activities.