A team of researchers from the Joint BioEnergy Institute’s (JBEI) Feedstocks Division has, for the first time, developed a genome-scale way to map the regulatory role of transcription factors, the proteins that play a key role in gene expression and determining a plant’s physiological traits. Their work reveals unprecedented insights into gene regulatory networks and identifies a new library of DNA parts that can be used to optimize genetic engineering efforts in plants.
Dylan Chivian, Microbial Explorer
Dylan Chivian’s upbringing motivated him to help humanity and the natural world. Now a microbial scientist and coding engineer with the Department of Energy Systems Biology Knowledgebase (KBase), he’s building software tools that aim to share microbial genomic information and promote collaboration across the broader scientific community.
A Biofuel Breakthrough, Courtesy of Fungi
It’s a tough job, but someone’s got to do it. In this case, the “job” is the breakdown of lignin, the structural molecule that gives plants strength and rigidity. One of the most abundant terrestrial polymers (large molecules made of repeating subunits called monomers) on Earth, lignin surrounds valuable plant fibers and other molecules that could be converted into biofuels and other commodity chemicals – if we could only get past that rigid plant cell wall.
Dub-seq Used to Screen Phage Proteins for Antibiotic Properties
As conventional antibiotics continue to lose effectiveness against evolving pathogens, scientists are keen to employ the bacteria-killing techniques perfected by bacteriophages (phages), the viruses that infect bacteria. One major challenge is the difficulty of studying individual phage proteins and determining precisely how the virus wields these tools to kill their host bacteria. A team of researchers from Berkeley Lab, UC Berkeley, and Texas A&M University worked together on a high-throughput genetic screen to identify which part of the bacteria the phages were targeting.
Machine Learning Tackles Long COVID
A new machine learning tool developed by a team of researchers led by Justin Reese of Berkeley Lab and Peter Robinson of Jackson Lab analyzes electronic health records to find symptoms in common between people who have been diagnosed with long COVID and to define subtypes of the condition.
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