Topic: Biology/Biomedicine

Thirteen students traveled to Livermore in early December for a computer science course simulating pond ecology and evolution.

LLNL is participating in the 35th annual Supercomputing Conference (SC23), which will be held both virtually and in Denver on November 12–17, 2023.

With this year’s results, the Lab has now collected a total of 179 R&D 100 awards since 1978. The awards will be showcased at the 61st R&D 100 black-tie awards gala on Nov. 16 in San Diego.

The event brought together 35 University of California students—ranging from undergraduates to graduate-level students from a diversity of majors—to work in groups to solve four key tasks, using actual electrocardiogram data to predict heart health.

The new model addresses a problem in simulating RAS behavior, where conventional methods come up short of reaching the time- and length-scales needed to observe biological processes of RAS-related cancers.

Combining specialized software tools with heterogeneous HPC hardware requires an intelligent workflow performance optimization strategy.

Highlights include MFEM community workshops, compiler co-design, HPC standards committees, and AI/ML for national security.

Presented at the 2022 International Conference on Computational Science, the team’s research introduces metrics that can improve the accuracy of blood flow simulations.

An LLNL team will be among the first researchers to perform work on the world’s first exascale supercomputer—Oak Ridge National Laboratory’s Frontier—when they use the system to model cancer-causing protein mutations.

The Data Science Institute's career panel series continued on June 28 with a discussion of LLNL’s COVID-19 research and development. Four data scientists talked about their work in drug screening, protein–drug compounds, antibody–antigen sequence analysis, and risk factor identification.

For the first time in the DSC series since the COVID-19 pandemic began in 2020, Lab mentors visited the college campus to provide in-person guidance for five teams of UC Merced students.

The Accelerating Therapeutic Opportunities in Medicine (ATOM) consortium is showing “significant” progress in demonstrating that HPC and machine learning tools can speed up the drug discovery process, ATOM co-lead Jim Brase said at a recent webinar.

Kevin McLoughlin has always been fascinated by the intersection of computing and biology. His LLNL career encompasses award-winning microbial detection technology, a COVID-19 antiviral drug design pipeline, and work with the ATOM consortium.

From molecular screening, a software platform, and an online data to the computing systems that power these projects.

LLNL researchers and collaborators have developed a highly detailed, ML–backed multiscale model revealing the importance of lipids to RAS, a family of proteins whose mutations are linked to many cancers.

Highlights include power grid challenges, performance analysis, complex boundary conditions, and a novel multiscale modeling approach.

Highlights include scalable deep learning, high-order finite elements, data race detection, and reduced order models.

The SAMRAI library is the code base in CASC for exploring application, numerical, parallel computing, and software issues associated with structured adaptive mesh refinement.

Highlights include response to the COVID-19 pandemic, high-order matrix-free algorithms, and managing memory spaces.

Highlights include the HYPRE library, recent data science efforts, the IDEALS project, and the latest on the Exascale Computing Project.

Livermore researchers are enhancing HARVEY, an open-source parallel fluid dynamics application designed to model blood flow in patient-specific geometries.

This genome sequencing technology helps accelerate the comparison of genetic fragments with reference genomes and improve the accuracy of the results as compared to previous technologies.