In May 2020, the U.S. Food and Drug Administration authorized the antiviral drug remdesivir for emergency treatment of COVID-19. Scientists are investigating ways to improve its effectiveness.
Many patients have difficulty recovering from traumatic brain injuries. Stem cell therapy has shown promise in various studies, however, and now, researchers are using supercomputers to help investigate this novel treatment.
As group lead of the NASA Jet Propulsion Laboratory’s Robotic Surface Mobility Group, Masahiro Ono and his colleagues are preparing for a new, smarter generation of Mars rovers – and they’re using supercomputing to do it.
Published on July 28, 2020 by Austin Technology Incubator
A collaborative group of more than 50 organizations in the academic, public and private sectors has formed the Texas Global Health Security Innovation Consortium (TEXGHS) to coordinate efforts to mitigate the effects of COVID-19 and future pandemics by supporting innovators working toward pandemic readiness, response, recovery and resiliency.
Across the world, hundreds of scientists log into the mainframe, creating simulations as grand as a replica of the city of Austin for tracking the coronavirus’ spread, and as microscopic as a model of the virus itself that can help vaccine scientists destroy it.
The collaboration between the three UT System institutions, based in Houston and Austin, will support the developmentof teams that bring together MD Anderson’s oncology expertise and data with novel mechanism-based computational modeling techniques led by researchers at Oden Institute and TACC.
SARS-CoV-2, the novel coronavirus behind the current Covid-19 pandemic […] has a coating of complex structures composed of interlocking sugar molecules known as glycans. This coating is thought to help the virus to slip past immune systems undetected.
UT epidemiologist Lauren Ancel Meyers spent her career planning for infectious disease outbreaks. She has had to rapidly adapt to the very different challenges posed by the novel coronavirus.
High performance computing systems at TACC are being used in coronavirus-related projects that address everything from the search for treatments and vaccines to understanding the nature of the virus to modeling the spread of the outbreak.
Supercomputers provide scientists who are studying COVID-19 with unique capabilities: they can explore the structure and behavior of the virus at the molecular level while designing drugs and forecasting the spread of the disease much faster than would otherwise be possible.
Published on May 15, 2020 by COVID-19 HPC Consortium
The two virus strains are physically very similar, with the exception of minor variations in the amino acids, including some that bind to the human ACE2 receptor. So when in March the WHO declared the current outbreak a pandemic, some in the scientific community may have assumed we’d be facing a similar threat. They couldn’t have been more mistaken.
As the world prays for the quick discovery of a cure for COVID-19, an international team of computational scientists, medicinal chemists, biochemists, and virologists — led by Texas researchers — have coalesced to rapidly identify drug-like molecules that inhibit SARS-CoV-2 replication.
The computing power of more than a million laptops combined. […] From atomic scale models of the virus' structure, to modeling the spread of the virus in enclosed spaces, these powerful, high-performance resources are helping facilitate COVID-19 research, and possible solutions, faster than ever before!
The Texas Advanced Computing Center (TACC)'s expert data science team has facilitated social media analysis in the past, and has developed machine learning tools to better pull needles of insight out of the vast haystacks of the Twitterverse.
Published on April 29, 2020 by Technology Networks
Thomas Cheatham -- a professor of medicinal chemistry and director of the Center for High Performance Computing at the University of Utah -- and Rodrigo Galindo -- a research professor in his group -- use powerful supercomputers to predict the characteristics of novel drugs.
The world’s supercomputers are engaged in an urgent scavenger hunt, poring over as many molecules as possible in the hopes of finding one that bonds to COVID-19 effectively enough to be used as a drug. There’s a daunting backlog of molecules that need testing, however – numbering in the billions. Now, researchers at Argonne National Laboratory are leveraging supercomputing-powered AI to fast-track identification of the most promising molecules.
About 100 organizations worldwide have already contributed genomic data to the study of the pandemic, mainly academic labs and genome sequencing facilities. Genomic data is critical because it helps identify how the virus is evolving, which can provide critical clues to how to stop it. A number of these teams have experience with response efforts to rapidly ramp up genome sequencing as has been done in the past with HIV, Ebola, Zika, influenza, and Hepatitis C.
The United States is waiting with bated breath to see its crucial coronavirus curves – daily cases, hospitalizations, and deaths – flatten, peak, and begin to decrease. A number of models have attempted to predict these milestones, most notably a model from the University of Washington’s Institute for Health Metrics and Evaluation: the widely cited IHME model. A new study from the University of Texas at Austin, enabled by resources from the Texas Advanced Computing Center (TACC), has differentiated itself from the IHME model in several ways, coming to its own conclusions about the trajectory of peak COVID-19 deaths in the United States.
