Tyler O'Neal, Staff Editor DEVELOPER TOOLS

UVA develops new tools to battle cancer, advance genomics research

University of Virginia School of Medicine scientists have developed important new resources that will aid the battle against cancer and advance cutting-edge genomics research.

UVA's Chongzhi Zang, Ph.D., and his colleagues and students have developed a new computational method to map the folding patterns of our chromosomes in three dimensions from experimental data. This is important because the configuration of genetic material inside our chromosomes actually affects how our genes work. In cancer, that configuration can go wrong, so scientists want to understand the genome architecture of both healthy cells and cancerous ones. This will help them develop better ways to treat and prevent cancer, in addition to advancing many other areas of medical research. An image of chromosomes at 20 nanometers, captured by light microscopes. (National Cancer Institute/ Hao Zhang, Vadim Backman)

Using their new approaches, Zang and his colleagues and students have already unearthed a treasure trove of useful data, and they are making their techniques and findings available to their fellow scientists. To advance cancer research, they've even built an interactive website that brings together their findings with vast amounts of data from other resources. They say their new website, bartcancer.org, can provide "unique insights" for cancer researchers.

"The folding pattern of the genome is highly dynamic; it changes frequently and differs from cell to cell. Our new method aims to link this dynamic pattern to the control of gene activities," said Zang, a computational biologist with UVA's Center for Public Health Genomics and UVA Cancer Center. "A better understanding of this link can help unravel the genetic cause of cancer and other diseases and can guide future drug development for precision medicine." 

Bet on BART UVA's Chongzhi Zang, PhD, and his colleagues and students have developed a new computational method to map the folding patterns of our chromosomes in three dimensions. CREDIT Courtesy Zang lab at UVA

Zang's new approach to mapping the folding of our genome is called BART3D. Essentially, it compares available three-dimensional configuration data about one region of a chromosome with many of its neighbors. It can then extrapolate from this comparison to fill in blanks in the blueprints of genetic material using "Binding Analysis for Regulation of Transcription", or BART, a novel algorithm they recently developed. The result is a map that offers unprecedented insights into how our genes interact with the "transcriptional regulators" that control their activity. Identifying these regulators helps scientists understand what turns particular genes on and off - information they can use in the battle against cancer and other diseases.

The researchers have built a web server, BARTweb, to offer the BART tool to their fellow scientists. It's available, for free, at http://bartweb.org. The source code is available at https://github.com/zanglab/bart2. Test runs demonstrated that the server outperformed several existing tools for identifying the transcriptional regulators that control particular sets of genes, the researchers report.

The UVA team also built the BART Cancer database to advance research into 15 different types of cancer, including breast, lung, colorectal, and prostate cancer. Scientists can search the interactive database to see which regulators are more active and which are less active in each cancer.

"While a cancer researcher can browse our database to screen potential drug targets, any biomedical scientist can use our webserver to analyze their own genetic data," Zang said. "We hope that the tools and resources we develop can benefit the whole biomedical research community by accelerating scientific discoveries and future therapeutic development."

Texas A&M researchers develop Computational Fluid Dynamics-Discrete Element Methods model for studying the flow in the next-generation reactors to improve safety

Tyler O'Neal, Staff Editor DEVELOPER TOOLS

The model can better predict the physical phenomenon inside of very-high-temperature pebble-bed reactors

When one of the largest modern earthquakes struck Japan on March 11, 2011, the nuclear reactors at Fukushima-Daiichi automatically shut down, as designed. The emergency systems, which would have helped maintain the necessary cooling of the core, were destroyed by the subsequent tsunami. Because the reactor could no longer cool itself, the core overheated, resulting in a severe nuclear meltdown, the likes of which haven't been seen since the Chernobyl disaster in 1986.

Since then, reactors have improved exponentially in terms of safety, sustainability, and efficiency. Unlike the light-water reactors at Fukushima, which had liquid coolant and uranium fuel, the current generation of reactors has a variety of coolant options, including molten-salt mixtures, supercritical water, and even gases like helium.

Dr. Jean Ragusa and Dr. Mauricio Eduardo Tano Retamales from the Department of Nuclear Engineering at Texas A&M University have been studying a new fourth-generation reactor, pebble-bed reactors. Pebble-bed reactors use spherical fuel elements (known as pebbles) and a fluid coolant (usually a gas). Pebble-bed reactors use passive natural circulation to cool down, making it theoretically impossible for a core meltdown to occur. CREDIT Dr. Jean Ragusa and Dr. Mauricio Eduardo Tano Retamales/Texas A&M University Engineering

"There are about 40,000 fuel pebbles in such a reactor," said Ragusa. "Think of the reactor as a really big bucket with 40,000 tennis balls inside."

During an accident, as the gas in the reactor core begins to heat up, the cold air from below begins to rise, a process known as natural convection cooling. Additionally, the fuel pebbles are made from pyrolytic carbon and tristructural-isotropic particles, making them resistant to temperatures as high as 3,000 degrees Fahrenheit. As a very high-temperature reactor (VHTR), pebble-bed reactors can be cooled down by passive natural circulation, making it theoretically impossible for an accident like Fukushima to occur.

However, during normal operation, a high-speed flow cools the pebbles. This flow creates movement around and between the fuel pebbles, similar to the way a gust of wind changes the trajectory of a tennis ball. How do you account for the friction between the pebbles and the influence of that friction in the cooling process?

This is the question that Ragusa and Tano aimed to answer in their most recent publication in the journal Nuclear Technology titled "Coupled Computational Fluid Dynamics-Discrete Element Method Study of Bypass Flows in a Pebble-Bed Reactor."

"We solved for the location of these 'tennis balls' using the Discrete Element Method, where we account for the flow-induced motion and friction between all the tennis balls," said Tano. "The coupled model is then tested against thermal measurements in the SANA experiment."

The SANA experiment was conducted in the early 1990s and measured how the mechanisms in a reactor interchange when transmitting heat from the center of the cylinder to the outer part. This experiment allowed Tano and Ragusa to have a standard to which they could validate their models.

As a result, their teams developed a coupled Computational Fluid Dynamics-Discrete Element Methods model for studying the flow over a pebble bed. This model can now be applied to all high-temperature pebble-bed reactors and is the first computational model of its kind to do so. It's very high-accuracy tools such as this that allow vendors to develop better reactors.

"The computational models we create help us more accurately assess different physical phenomena in the reactor," said Tano. "As a result, reactors can operate at a higher margin, theoretically producing more power while increasing the safety of the reactor. We do the same thing with our models for molten-salt reactors for the Department of Energy."

As artificial intelligence continues to advance, its applications to super computational modeling and simulation grow. "We're in a very exciting time for the field," said Ragusa. "And we encourage any prospective students who are interested in computational modeling to reach out because this field will hopefully be around for a long time."

Weigel develops Python tools to help scientists view heliophysics data

Tyler O'Neal, Staff Editor DEVELOPER TOOLS

Robert Weigel, Associate Professor, Physics and Astronomy, and his collaborators are developing a suite of Python tools that will allow scientists to easily view and explore heliophysics data in 3-D.

For the software development side of the project, the researchers will (1) create a set of Python functions that use existing SpacePy and HAPI libraries to read in measurements and simulation output and use the Python/ParaView API to render the data; (2) create a library of functions that produce 3-D renderings of data in a way that is useful for domain scientists; and (3) develop a set of functions that allow for common and basic 3-D data reduction and manipulation that can be used for exploring 3-D data renderings.

The researchers' initial target for the types of simulation visualizations that can be created will be magnetosphere magnetohydrodynamic simulation output in the Community Coordinated Modeling Center CDF format, native Space Weather Modeling Framework simulation output, and Enlil solar wind model simulation output. {module INSIDE STORY}

The software will also have the ability to read in spacecraft measurements from NASA's CDAWeb from and SSCWeb using an existing HAPI library and overlay the spacecraft time series data on a 3-D rendering of simulation output.

The second part of this project will involve the development of a set of recommendations and conventions for 3-D visualization tools in Python in collaboration with members of the PyHC (Python Heliophysics) community.

Weigel received $74,996 from NASA Goddard Space Flight Center for this work. Funding began in January 2021 and will conclude in late December 2021.