The science objective of this project is to apply structural proteomics technologies to map the molecular interactome.

The research objective of this project is to develop an integrative and automated multi-PTM profiling capability with deep proteome coverage.

The research goal of this project is to use stimuli-specific, synthetic nanobodies to target functional mediators without prior knowledge of the response networks or manipulating the biological system.

The research goal of this project is to identify and control host functions hijacked during viral infection through use of PNNL ‘omics technologies and modeling capabilities.

The research goal of this project is to build and understand model communities that show carbon storage phenotypes

This data is a model of synthetic adversarial activity surrounded by noise and was funded by DARPA. The various versions include gradually more complex networks of activities.

The influence of tidal inundation dynamics on below ground carbon pools is poorly understood across coastal terrestrial-aquatic interface (TAI) ecosystems. The dynamic environmental conditions of tidally-influenced landscapes, the chemically complex nature of carbon compounds, the diverse nature of...

The research goal of this project is to develop computational methods to predict cell regulation phenotypes using small molecule and proteome data to understand outcomes in complex biological systems.

The research goal of this project is to develop a biologically informed machine learning (ML) model that integrates datasets from different studies, and leverages current biological knowledge in an automated manner, to improve predictions in biological data analysis.

We are constructing a streamlined approach to identify phenotype-relevant signatures by integrating various proteomics data. Leveraging protein structures and interaction networks, we will map structural changes and post-translational modifications to identify molecular drivers and subsequently...

By developing explainable, predictive metabolic models of individual microbes, we aim to design consortia that convert light and abundant atmospheric gases into high-value molecules through microbial division of labor.

The research goal of this project is to develop new theory and tools that leverage evolutionary perspectives and knowledge of the energetics of reactions to predict the most likely regulation in a given environment. These methods will accelerate exploration, modeling and understanding of cell...

Assistant Professor, Mechanical Engineering at Texas A&M University https://engineering.tamu.edu/mechanical/profiles/pharr-matt.html

Biography Dr. Fifield is an experienced materials scientist and research leader interested in material synthesis, formulation, processing, characterization, application, simulation, and lifetime prediction. He leads nuclear cable aging research at PNNL for the Light Water Reactor Sustainability...

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