Understanding molecular modifications of proteins in the red yeast could help scientists re-engineer the organism’s phenotype.

PNNL's E-COMP initiative is helping unleash American energy innovation with advanced theories, models, and software tools to better operate power systems that rely heavily on high-speed power electronic control.

Continued studies will deepen scientists’ understanding of virus-host interactions at the molecular level and also pave the way for developing better drugs to fight emerging viruses.

Flexible, high-throughput proteomics approach identifies protein modifications related to pancreatic stress and insulin production.

Scientists uncover how cyanobacteria's circadian cycle controls carbon and energy metabolism, increasing carbon dioxide-to-bioproducts yields.

Scientists map how transitions from day to night control gene regulatory networks in cyanobacteria, revealing key orchestrators of metabolic switching.

PNNL is hosting a scientific conference focused directly on understanding and predicting the collection of an organism’s traits.

Circadian rhythms of blue-green microbes offer new window into predictive phenomics—how to predict and control the traits of an organism.

By combining computational modeling with experimental research, scientists identified a promising composition that reduces the need for a critical material in an alloy that can withstand extreme environments.

This week at AAAS, PNNL scientists discuss phenomics and the factors that influence DNA instructions to bring about the world around us.

The Generator Scorecard, developed by PNNL in partnership with BPA, automates generator evaluations, reducing engineering workloads and improving grid reliability.

PNNL scientists use proteomics to unveil how high-density lipoproteins protect against cholesterol accumulation in the artery wall.

Computing and artificial intelligence aid scientists who seek to understand function-driven design and control of biological systems.

PNNL researchers have developed a new, physics-informed machine learning model that accurately predicts how heat accumulates and dissipates during friction stir processing.

EGRASS wins in the software/services category and is awarded a gold medal as special recognition in corporate social responsibility.

The National Transmission Planning Study presents several transmission expansion scenarios that would reliably support the growing demand for energy across the nation.

Three PNNL technologies have been named 2024 R&D 100 Award winners.

PNNL computing experts Robert Rallo and Court Corley contribute their knowledge to a recent DOE report on applications of AI to energy, materials, and the power grid.