Topic: Biology/Biomedicine

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.

LLNL will lend its expertise in vaccine research and computing resources to the Human Vaccines Project consortium to aid development of a universal coronavirus vaccine and improve understanding of immune response.

Computational biology is using HPC to rapidly design and develop ways to treat cancer and COVID. LLNL researcher Felice Lightstone discusses ATOM (Accelerated Therapeutic Opportunities in Medicine) in this edition of SC21 TV.

From studying radioactive isotope effects to better understanding cancer metastasis, the Laboratory’s relationship with cancer research endures some 60 years after it began, with historical precedent underpinning exciting new research areas.

LLNL has turned to AMD and Penguin Computing to upgrade a supercomputer to help in the fight against the novel coronavirus. The computer's name is... Corona.

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

COVID-19 HPC Consortium scientists and stakeholders met virtually to mark the consortium’s one-year anniversary, discussing the progress of research projects and the need to pursue a broader organization to mobilize supercomputing access for future crises.

In recognition of March as International Women’s History Month, SC21 profiled six women doing trailblazing work, including LLNL's Hiranmayi Ranganathan.

The ATOM consortium, of which LLNL is part, announced the DOE’s Argonne, Brookhaven, and Oak Ridge national labs are joining the consortium to develop ATOM’s AI-driven drug discovery platform.

LLNL and IBM research on deep learning models to accurately diagnose diseases from x-ray images won the Best Paper award for Computer-Aided Diagnosis at the SPIE Medical Imaging Conference.

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.