One of Livermore’s most powerful
supercomputers simulates a beating human heart.
The DOE is leveraging advanced computing
technologies to improve drug efficacy.
The DOE is developing an unsupervised machine-learning
ecosystem for targeted therapy against RAS-driven cancer growth.
The DOE is helping to lay the foundation for an integrative,
data-driven approach to modeling cancer outcomes.
Supercomputing-based modeling may
help validate and accelerate drug research.
A first-of-its-kind supercomputer is helping
scientists rapidly characterize pathogens.
Lawrence Livermore National Laboratory (LLNL) is a premier applied science laboratory based in the San Francisco Bay Area. As part of the National Nuclear Security Administration within the Department of Energy (DOE), the Lab endeavors to strengthen national security through world-class science, technology, and engineering.
With the evolution of diseases and biological threats comes the need to revolutionize our responses to them and the Computational Predictive Biology initiative at LLNL is doing just that. As part of a national collaboration to mitigate biological threats, Livermore and its partners—other DOE laboratories, government agencies, academic institutions, and industry members—are working together to apply advanced computing to biological research. By integrating supercomputing, data analysis, and predictive simulations at scale, LLNL and its partners are changing the way biological research is done to make the world a healthier and safer place. This major endeavor comprises five main action areas: predictive physiology, predictive pharmacology, predictive pathophysiology, predictive oncology, and predictive microbial communities.
The initiative’s overall strategy is to use high performance computing (HPC) to develop a suite of predictive analytics that will expedite the characterization of biological threats, predict the paths of diseases through the population, and help accelerate the development of technologies and treatments. From virtual screenings that identify the most promising drug candidates to scalable codes that can replicate the electrophysiology of the human heart, computational predictive biology is changing the way we approach human health.