Biophysics is the science of understanding how biological systems work at the molecular level. It is a multidisciplinary field that combines physics, chemistry, biology, and mathematics to study phenomena such as protein folding, enzyme catalysis, membrane transport, and gene regulation. Biophysics has many applications in medicine, biotechnology, nanotechnology, and bioengineering.
However, biophysics is a challenging field that demands advanced experimental techniques and computational methods to investigate the intricate and ever-changing characteristics of biomolecules. Conventional supercomputers and algorithms frequently fall short in comprehensively capturing the diverse range of biophysical processes, which encompass various temporal and spatial scales.
That is why the upcoming generation of supercomputers and artificial intelligence tools are revolutionizing the field of biophysics, allowing researchers to simulate intricate biological processes with unparalleled precision and correctness. These advanced technologies are unveiling fresh possibilities for biophysics exploration and advancement.
One example of this transformation is the work of Dr. Rafael Bernardi and Dr. Marcelo Melo from Auburn University, who have published a perspective article in the Biophysical Journal on the fusion of computational modeling and experimental biophysics. They argue that exascale computing and AI can help biophysicists challenge longstanding assumptions, create new proteins, and design novel molecular circuits.
The researchers at Auburn University explore the seamless integration of computational modeling and experimental biophysics, offering a vision for a future where breakthroughs are achieved with unprecedented accuracy. Instead of being passive onlookers, modern biophysicists, aided by advanced high-performance computing (HPC), have become pioneers who can question long-held biological beliefs, shed light on complex intricacies, and even engineer new proteins or devise innovative molecular circuits.
One of the most important aspects discussed in their perspective article is the new ability of computational biophysicists to simulate complex biological processes that range from subatomic processes to whole-cell models, with extraordinary detail. As Dr. Bernardi articulates, “The new exascale computers allow computational biophysicists to go beyond what can done experimentally and simulate biological processes with a much higher level of detail. For instance, we can now understand how pathogenic bacteria bind to humans during infection at an atomistic level, generating data for AI models and opening new roads of exploration.”
Exascale computing refers to the ability to perform more than one quintillion calculations per second, which is about 50 times faster than the current fastest supercomputer in the world. The first public exascale supercomputer, Frontier, was deployed by the Oak Ridge National Laboratory in late 2021.
AI is essential for exascale computing because it can help optimize the performance, reliability, and efficiency of the systems, as well as accelerate the analysis and interpretation of the data. AI can also help improve the accuracy and realism of the simulations by incorporating experimental data and machine learning models.
Relevant articles:
– A New Era in Biophysics Discovery Unleashed by Exascale Supercomputers, SciTechDaily, August 25, 2023
– Exascale computing: The next frontier for AI and HPC, TechRepublic, August 26, 2023
– How AI and exascale computing are transforming biophysics, The Next Web, August 27, 2023
– Exascale supercomputers could help design new drugs for Alzheimer’s disease, New Scientist, August 28, 2023