In the summer of 2015, Adam Session was a postdoc working at the DOE Joint Genome Institute with Dan Rokhsar, who also holds a joint appointment with the University of California, Berkeley. Nowadays, Session is an Assistant Professor at Binghamton University in New York. He and Rokhsar have recently published a Nature Communications paper that builds off their early collaborations that could help crop breeders and researchers predict how crops and model organisms may evolve.
Breaking Barriers in Drug Delivery with Better Lipid Nanoparticles
Berkeley Lab and Genentech are collaborating to make the next generation of lipid nanoparticles (LNPs), the drug delivery technology used in the COVID-19 vaccines. With their combined expertise in structural biology and pharmaceutical science, the team is designing LNPs that can precisely deliver vaccines and therapeutics to target tissues while improving the product’s shelf life and duration of action.
A Roadmap for Gene Regulation in Plants
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.
Photosynthesis, Key to Life on Earth, Starts with a Single Photon
A new study published in Nature confirms for the first time that a single photon–the smallest quantity of light possible–can initiate the first step of photosynthesis, one of nature’s essential processes. The study, conducted by an interdisciplinary team led by Molecular Biophysics and Integrated Bioimaging (MBIB) senior faculty scientist Graham Fleming and Energy Sciences Area senior faculty scientist Birgitta Whaley, solidifies our current understanding of photosynthesis and will help answer questions about how life works on the smallest of scales, where quantum physics and biology meet.
Proteins Designed Using Reinforcement Learning Characterized on SIBYLS Beamline
The SIBYLS beamline at the Advanced Light Source was used to characterize proteins dreamt up by a reinforcement learning algorithm. The algorithm, developed by researchers in David Baker’s lab at the University of Washington, is powered by the machine learning strategy behind computer programs capable of defeating top human players at board games like chess and go. The advance could create a pathway to greater control when designing therapeutic proteins, vaccines, and other molecules.
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