Janet Jansson is Chief Scientist for Biology in the Biological Sciences Division and a Laboratory Fellow at the Pacific Northwest National Laboratory (PNNL). Jansson has more than 30 years of experience in microbial ecology. Jansson earned an M.S.in Soil Microbiology at Colorado State University...

Hyun Song’s research is directed toward developing modeling and computational tools for the simulation of cellular metabolism and microbial community dynamics. His research interest in metabolic modeling includes 1) omics data-guided estimation of flux distribution in a genome-scale metabolic...

Dr. Nelson’s background is in genomic analysis of microbial organisms. He spent 10 years at The Institute for Genomic Research (TIGR), first as a data analyst and then as the team lead for Microbial Annotation. He was involved in some of the earliest bacterial genome sequencing projects, including...

Staff Scientist at Pacific Northwest National Laboratory

Education Ph.D. 1998, Oregon State University. Forest Science. Area(s) of Specialization Mycology; fungal ecology; fungus-plant interactions. Research Focus My laboratory studies the environmental selection processes that dictate the extant community structures. Towards these goals, we have adopted...

Publications: 2018 Stegen JC, TC Johnson, JK Fredrickson, MJ Wilkins, AE Konopka, WC Nelson, EV Arntzen, WB Chrisler, RK Chu, SJ Fansler, EB Graham, DW Kennedy, CT Resch, MM Tfaily, and JM Zachara. 2018. "Influences of organic carbon speciation on hyporheic corridor biogeochemistry and microbial...

Earth Scientist at PNNL Disciplines and Skills: Biogeochemical Cycles Carbon Cycle Isotopes Microbial Communities Microbial Ecology Microbiology Microbiome Science Nitrogen Education: Morningside College : Bachelor of Science, Biology

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 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.