Integrating experiments and modeling show how a fungal exudate-inspired substrate mixture elicits an emergent growth phenotype and metabolic responses in Pseudomonas putida.

Uncovered how the choice of carbon source influences the stress tolerance, redox balance, and regulatory responses of yeast, which can help provide resilience against scale-up challenges.

Study shows the advantages of top-town proteomics for characterizing histones.

A microbial community expresses a phenotype of interest ~20 percent higher than the sum of each member species.

Structural proteomics reveal how cyanobacteria adapt to changing light.

Researchers use multiomics to confirm role of lipid synthesis during infection, leading to model of how to restrict virus replication.

Capturing protein structural changes in host cells exposes vulnerabilities exploited by viruses to hijack cellular machinery.

PNNL researchers developed a new computational workflow to study day-to-night gene expression dynamics of cyanobacterium in real time.

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

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 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.

The new PTM-Psi workflow opens the door for uncovering molecular insights within proteomics datasets.

Scientists developed a process (or pipeline) that combined molecular probes—a specific chemical that binds to microbes carrying out a particular function—with a method that isolated these cells from their complex community.

Scientists screen for nanobodies that recognize wild type and mutant functional proteins to develop a framework to disrupt protein interactions that can cause disease.