PNNL Publications 2020 Ogden A.J., T.W. Wietsma, T.E. Winkler, Y. Farris, G.L. Myers, and A. Ahkami. 2020. "Dynamics of Global Gene Expression and Regulatory Elements in Growing Brachypodium Root System." Scientific Reports 10. PNNL-SA-145907. doi:10.1038/s41598-020-63224-z 2019 Boiteau R.M., S.J...

PNNL Publications 2020 Smith J.N., K.J. Tyrrell, J.P. Smith, J.P. Smith, K.K. Weitz, and W.D. Faber. 2020. "Linking Internal Dosimetries of the Propyl Metabolic Series in Rats and Humans Using Physiologically Based Pharmacokinetic (PBPK) Modeling." Regulatory Toxicology and Pharmacology 110. PNNL...

Tom Metz is a Principal Investigator within the Integrative Omics group at PNNL and the Metabolomics Team Lead for a group of scientists that focuses on development and applications of high throughput metabolomics and lipidomics methods to various biological questions. He has worked to develop state...

Aaron Wright leads the Chemical Biology & Exposure Sciences Group in the Biological Sciences Division at PNNL. His highly collaborative and diverse chemical biology research team is focused on gaining an improved functional and mechanistic understanding of biological processes including: (a)...

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...

The Illumina MiSeq System Sequencer is a high-throughput DNA sequencer machine developed and manufactured by Illumina, and is designed for sequencing data acquisition using synthesis technology to provide an end-to-end solution (cluster generation, amplification, sequencing, and data analysis) in a...

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...

Dr. Jason McDermott, senior research scientist, has extensive research experience in molecular and structural virology and data resource design, data integration and prediction of biological networks, bridging experimental and computational biology. Currently, his research interests include data...

Last updated on 2023-02-23T19:37:46+00:00 by LN Anderson Pending Upload Mass spectrometry data analysis of SoDA-PAL photoaffinity probe labeled proteins in profiling the Sorgoleone interactome of microbial isolate A. pittii SO1. Reference Taxonomy Acinetobacter pittii SO1 [NCBITAXON:48296] Funding...

Last updated on 2023-04-24T18:38:03+00:00 by LN Anderson PerCon SFA: Sorgoleone Microbial Specie Isolate Genome Sequencing Collection GenBank IDs Pending Whole genome sequencing of Sorgoleone utilizing rhizobacteria strains, as potential plant growth promoting microbes (PGPM), derived from Sorghum...

Inclusion levels of alternative splicing (AS) events of five different varieties (i.e. skipped exon (SE), retained intron (RI), alternative 5’ splice site (A5SS), alternative 3’ splice site (A3SS), and mutually exclusive exons (MXE)) were measured in human blood samples from two separate cohorts of...

The Environmental Determinants of Diabetes in the Young (TEDDY) study is searching for factors influencing the development of type 1 diabetes (T1D) in children. Research has shown that there are certain genes that correlate to higher risk of developing T1D, but not all children with these genes...

The Human Islet Research Network (HIRN) is a large consortia with many research projects focused on understanding how beta cells are lost in type 1 diabetics (T1D) with a goal of finding how to protect against or replace the loss of functional beta cells. The consortia has multiple branches of...

The Diabetes Autoimmunity Study in the Young (DAISY) seeks to find environmental factors that can trigger the development of type 1 diabetes (T1D) in children. DAISY follows children with high-risk of developing T1D based on family history or genetic markers. Genes, diets, infections, and...

Machine learning is a core technology that is rapidly advancing within type 1 diabetes (T1D) research. Our Human Islet Research Network (HIRN) grant is studying early cellular response initiating β cell stress in T1D through the generation of heterogenous low- and high-throughput molecular...