Topic: Biology/Biomedicine

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.

libROM is a library designed to facilitate Proper Orthogonal Decomposition (POD) based Reduced Order Modeling (ROM).

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.

A multi-institutional team including LLNL is using Summit, America’s fastest supercomputer, to understand how certain proteins signal body cells to reproduce uncontrollably, triggering cancer.

A machine learning model developed by a team of LLNL scientists to aid in COVID-19 drug discovery efforts is a finalist for the Gordon Bell Special Prize for HPC-Based COVID-19 Research.

At the 2020 LLNL Computing Expo, Jim Brase outlined the Lab’s predictive biology efforts for new therapeutics, HPC capabilities, and making those resources available to researchers.

Ruby, a 6-petaflop cluster, will be used for the stockpile stewardship mission, open science, and the search for therapeutic drugs and antibodies against SARS-CoV-2.

Funded by the CARES Act, LLNL's new computing cluster, Mammoth, will be used to perform genomics analysis, nontraditional simulations, and graph analytics required by scientists working on COVID-19.

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

Funding by the CARES Act enabled LLNL and industry partners to more than double the speed of the Corona supercomputing cluster to in excess of 11 petaFLOPS of peak performance.

LLNL pairs 3D-printed human brain vasculature with computational flow simulations to understand tumor cell attachment to blood vessels, a step in secondary tumor formation during cancer metastasis.

LLNL and Duke University combine 3D bioprinting and computational flow models to analyze tumor cell behavior and the cells’ attachment to the vascular endothelium.

LLNL researchers and collaborators have combined machine learning, 3D printing, and HPC simulations to accurately model blood flow in the aorta.

This video provides an overview of LLNL projects in which data scientists work with domain scientists to address major challenges in healthcare.