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Texas, Texas, United States

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News Article | May 9, 2017
Site: www.eurekalert.org

Some scientists dream about big data. The dream bridges two divided realms. One realm holds lofty peaks of number-crunching scientific computation. Endless waves of big data analysis line the other realm. A deep chasm separates the two. Discoveries await those who cross these estranged lands. Unfortunately, data cannot move seamlessly between Hadoop (HDFS) and parallel file systems (PFS). Scientists who want to take advantage of the big data analytics available on Hadoop must copy data from parallel file systems. That can slow workflows to a crawl, especially those with terabytes of data. Computer Scientists working in Xian-He Sun's group are bridging the file system gap with a cross-platform Hadoop reader called PortHadoop, short for portable Hadoop. "PortHadoop, the system we developed, moves the data directly from the parallel file system to Hadoop's memory instead of copying from disk to disk," said Xian-He Sun, Distinguished Professor of Computer Science at the Illinois Institute of Technology. Sun's PortHadoop research was funded by the National Science Foundation and the NASA Advanced Information Systems Technology Program (AIST). The concept of 'virtual blocks' helps bridge the two systems by mapping data from parallel file systems directly into Hadoop memory, creating a virtual HDFS environment. These 'virtual blocks' reside in the centralized namespace in HDFS NameNode. The HDFS MapReduce application cannot see the 'virtual blocks'; a map task triggers the MPI file read procedure and fetches the data from the remote PFS before its Mapper function processes its data. In other words, a dexterous slight-of-hand from PortHadoop tricks the HDFS to skip the costly I/O operations and data replications it usually expects. Sun said he sees PortHadoop as the consequence of the strong desire for scientists to merge high performance computing with cloud computing, which companies such as Facebook and Amazon use to 'divide and conquer' data-intensive MapReduce tasks among its sea of servers. "Traditional scientific computing is merging with big data analytics," Sun said. "It creates a bigger class of scientific computing that is badly needed to solve today's problems." PortHadoop was extended to PortHadoop-R to seamlessly link cross-platform data transfer with data analysis and virtualization. Sun and colleagues developed PortHadoop-R specifically with the needs of NASA's high-resolution cloud and regional scale modeling applications in mind. High performance computing has served NASA well for their simulations, which crunch data through various climate models. Sun said the data generated from models combined with observational data are unmanageably huge and have to be analyzed and also visualized to more fully understand chaotic phenomena like hurricanes and hail storms in a timely fashion. PortHadoop faced a major problem in preparation to work with NASA applications. NASA's production environment doesn't allow any testing and development on its live data. PortHadoop developers overcame the problem with the Chameleon cloud testbed system, funded by the National Science Foundation (NSF). Chameleon is a large-scale, reconfigurable environment for cloud computing research co-located at the Texas Advanced Computing Center of the University of Texas at Austin and also at the the Computation Institute of the University of Chicago. Chameleon allows researchers bare-metal access, i.e., allows them to fully reconfigure the environment on its nodes including support for operations such as customizing the operating system kernel and console access. What's more, the Chameleon system of ~15,000 cores with Infiniband interconnect and 5 petabytes of storage adeptly blends in a variety of heterogeneous architectures, such as low-power processors, graphical processing units, and field-programmable gate arrays. "Chameleon helped us in different ways," Sun said. "First, it made it possible for us to create two different environments," each on a separate computer cluster of the bare metal system to mimic the NASA environment. "We are really happy that we were able to use Chameleon. The system helped us a great deal in our development," Sun added. Sun and colleagues installed some nodes with the traditional MPI, and on the other cluster they installed MapReduce. "Then we ran programs on these two different clusters and did the data integration, cross-platform data access, data analysis and visualization, all on Chameleon," Sun added. "Chameleon provides all functionality and scale of this computing facility, as well as the option of creating different programming environments on hardware resources with both HPC and data-intensive characteristics for our integration research." Sun and colleagues put PortHadoop-R to the test by using it in the NASA Cloud library project for empowering data management, diagnostics, and visualization of Cloud-Resolving Models (CRMs) of climate modeling. The NASA Cloud library has big data from satellites and human observations, with more than 70,000 datasets downloaded since April 2010 by 155 distinct users. The data are used for real-time forecasts of hurricane and other natural disasters; and for long-term climate prediction. "The ultimate goal is to generate a core cloud library that is dynamic and interactive with the user," said Wei-Kuo Tao, a senior research meteorologist at the NASA Goddard Space Flight Center. He leads the Goddard Mesoscale Dynamic and Modeling Group and is the principal investigator of the NASA AIST program. Tao and colleagues combine large-scale CRM simulation data at real time for data analysis and visualization. "The idea of the dynamic visualization and analysis with the Hadoop reader is that you don't have to copy the data," Tao said. "You can produce the visualization at the same time." Xian-He Sun spoke of the work that bridged the two types of storage systems. "We tested our PortHadoop-R strategy on Chameleon, and later confirmed these tests on NASA machines in practice. The result is fantastic, and beyond our expectation. We expected a 2-fold speedup. The result is a 15x speedup. The reason is that PortHadoop-R not only reduced one round disk copy but also utilized the concurrency of parallel file systems and Hadoop systems in a level which for general users would be difficult to achieve. In other words, PortHadoop-R has integrated the MPI and Hadoop systems," Sun said. "Chameleon was really helpful to provide the flexible environment so we can install, or simulate different environments. We have an HPC environment. We have a cloud environment. And we have them together and test them together. Chameleon in this sense provides us the ability to scale our computing resource and the privilege to control, optimize, and install our custom designed software environment to develop our software systems," Sun said. Building on the success with PortHadoop-R on real applications at NASA, Sun added that "the next step is to make PortHadoop-R more user-friendly. And, we would like to expand PortHadoop-R to support different application interfaces, so different users can use it easily." "In the long run, we would like to extend the merge at the OS level and at the user level. So, there are still a lot of things we need to do to support the seamless integration of the high-performance computing and big data analytics," Sun said.


News Article | May 10, 2017
Site: phys.org

Unfortunately, data cannot move seamlessly between Hadoop (HDFS) and parallel file systems (PFS). Scientists who want to take advantage of the big data analytics available on Hadoop must copy data from parallel file systems. That can slow workflows to a crawl, especially those with terabytes of data. Computer Scientists working in Xian-He Sun's group are bridging the file system gap with a cross-platform Hadoop reader called PortHadoop, short for portable Hadoop. "PortHadoop, the system we developed, moves the data directly from the parallel file system to Hadoop's memory instead of copying from disk to disk," said Xian-He Sun, Distinguished Professor of Computer Science at the Illinois Institute of Technology. Sun's PortHadoop research was funded by the National Science Foundation and the NASA Advanced Information Systems Technology Program (AIST). The concept of 'virtual blocks' helps bridge the two systems by mapping data from parallel file systems directly into Hadoop memory, creating a virtual HDFS environment. These 'virtual blocks' reside in the centralized namespace in HDFS NameNode. The HDFS MapReduce application cannot see the 'virtual blocks'; a map task triggers the MPI file read procedure and fetches the data from the remote PFS before its Mapper function processes its data. In other words, a dexterous slight-of-hand from PortHadoop tricks the HDFS to skip the costly I/O operations and data replications it usually expects. Sun said he sees PortHadoop as the consequence of the strong desire for scientists to merge high performance computing with cloud computing, which companies such as Facebook and Amazon use to 'divide and conquer' data-intensive MapReduce tasks among its sea of servers. "Traditional scientific computing is merging with big data analytics," Sun said. "It creates a bigger class of scientific computing that is badly needed to solve today's problems." PortHadoop was extended to PortHadoop-R to seamlessly link cross-platform data transfer with data analysis and virtualization. Sun and colleagues developed PortHadoop-R specifically with the needs of NASA's high-resolution cloud and regional scale modeling applications in mind. High performance computing has served NASA well for their simulations, which crunch data through various climate models. Sun said the data generated from models combined with observational data are unmanageably huge and have to be analyzed and also visualized to more fully understand chaotic phenomena like hurricanes and hail storms in a timely fashion. PortHadoop faced a major problem in preparation to work with NASA applications. NASA's production environment doesn't allow any testing and development on its live data. PortHadoop developers overcame the problem with the Chameleon cloud testbed system, funded by the National Science Foundation (NSF). Chameleon is a large-scale, reconfigurable environment for cloud computing research co-located at the Texas Advanced Computing Center of the University of Texas at Austin and also at the the Computation Institute of the University of Chicago. Chameleon allows researchers bare-metal access, i.e., allows them to fully reconfigure the environment on its nodes including support for operations such as customizing the operating system kernel and console access. What's more, the Chameleon system of ~15,000 cores with Infiniband interconnect and 5 petabytes of storage adeptly blends in a variety of heterogeneous architectures, such as low-power processors, graphical processing units, and field-programmable gate arrays. "Chameleon helped us in different ways," Sun said. "First, it made it possible for us to create two different environments," each on a separate computer cluster of the bare metal system to mimic the NASA environment. "We are really happy that we were able to use Chameleon. The system helped us a great deal in our development," Sun added. Sun and colleagues installed some nodes with the traditional MPI, and on the other cluster they installed MapReduce. "Then we ran programs on these two different clusters and did the data integration, cross-platform data access, data analysis and visualization, all on Chameleon," Sun added. "Chameleon provides all functionality and scale of this computing facility, as well as the option of creating different programming environments on hardware resources with both HPC and data-intensive characteristics for our integration research." Sun and colleagues put PortHadoop-R to the test by using it in the NASA Cloud library project for empowering data management, diagnostics, and visualization of Cloud-Resolving Models (CRMs) of climate modeling. The NASA Cloud library has big data from satellites and human observations, with more than 70,000 datasets downloaded since April 2010 by 155 distinct users. The data are used for real-time forecasts of hurricane and other natural disasters; and for long-term climate prediction. "The ultimate goal is to generate a core cloud library that is dynamic and interactive with the user," said Wei-Kuo Tao, a senior research meteorologist at the NASA Goddard Space Flight Center. He leads the Goddard Mesoscale Dynamic and Modeling Group and is the principal investigator of the NASA AIST program. Tao and colleagues combine large-scale CRM simulation data at real time for data analysis and visualization. "The idea of the dynamic visualization and analysis with the Hadoop reader is that you don't have to copy the data," Tao said. "You can produce the visualization at the same time." Xian-He Sun spoke of the work that bridged the two types of storage systems. "We tested our PortHadoop-R strategy on Chameleon, and later confirmed these tests on NASA machines in practice. The result is fantastic, and beyond our expectation. We expected a 2-fold speedup. The result is a 15x speedup. The reason is that PortHadoop-R not only reduced one round disk copy but also utilized the concurrency of parallel file systems and Hadoop systems in a level which for general users would be difficult to achieve. In other words, PortHadoop-R has integrated the MPI and Hadoop systems," Sun said. "Chameleon was really helpful to provide the flexible environment so we can install, or simulate different environments. We have an HPC environment. We have a cloud environment. And we have them together and test them together. Chameleon in this sense provides us the ability to scale our computing resource and the privilege to control, optimize, and install our custom designed software environment to develop our software systems," Sun said. Building on the success with PortHadoop-R on real applications at NASA, Sun added that "the next step is to make PortHadoop-R more user-friendly. And, we would like to expand PortHadoop-R to support different application interfaces, so different users can use it easily." "In the long run, we would like to extend the merge at the OS level and at the user level. So, there are still a lot of things we need to do to support the seamless integration of the high-performance computing and big data analytics," Sun said. Explore further: High-performance computation is available by cloud computing


News Article | May 10, 2017
Site: phys.org

"Historically, radiation has been a blunt tool," said Matt Vaughn, Director of Life Science Computing at the Texas Advanced Computing Center. "However, it's become ever more precise because we understand the physics and biology of systems that we're shooting radiation into, and have improved our ability to target the delivery of that radiation." The science of calculating and assessing the radiation dose received by the human body is known as dosimetry - and here, as in many areas of science, advanced computing plays an important role. Current radiation treatments rely on imaging from computed tomography (CT) scans taken prior to treatment to determine a tumor's location. This works well if the tumor lies in an easily detectable and immobile location, but less so if the area is moving, as in the case of lung cancer. At the University of Texas MD Anderson Cancer Center, scientists are tackling the problem of accurately attacking tumors using a new technology known as an MR-linac that combines magnetic resonance (MR) imaging with linear accelerators (linacs). Developed by Elekta in cooperation with UMC Utrecht and Philips, the MR-linac at MD Anderson is the first of its kind in the U.S. MR-linacs can image a patient's anatomy while the radiation beam is being delivered. This allows doctors to detect and visualize any anatomical changes in a patient during treatment. Unlike CT or other x-ray based imaging modalities, which provide additional ionizing radiation, MRI is harmless to healthy tissue. The MR-linac method offers a potentially significant improvement over current image-guided cancer treatment technology. However, to ensure patients are treated safely, scientists must first correct for the influence of the MRI's magnetic field on the measurements used to calibrate the radiation dose being delivered. Researchers use software called Geant4 to simulate radiation within the detectors. Originally developed by CERN to simulate high energy particle physics experiments, the MD Anderson team has adapted Geant4 to incorporate magnetic fields into their computer dosimetry model. "Since the ultimate aim of the MR-linac is to treat patients, it is important that our simulations be very accurate and that the results be very precise," said Daniel O'Brien, a postdoctoral fellow in radiation physics at MD Anderson. "Geant4 was originally designed to study radiation at much higher energies than what is used to treat patients. We had to perform tests to make sure that we had the accuracy that we needed." Using the Lonestar supercomputer at the Texas Advanced Computing Center (TACC), the research team simulated nearly 17 billion particles of radiation per detector to get the precision that they needed for their study. In August 2016, they published magnetic field correction factors in Medical Physics for six of the most-used ionization chamber detectors (gas-filled chambers that are used to ensure the dose delivered from a therapy unit is correct). They are now working on verifying these results experimentally. "The MR-linac is a very promising technology but it also presents many unique challenges from a dosimetry point of view," O'Brien said. "Over time, our understanding of these effects has improved considerably, but there is still work to be done and resources like TACC are an invaluable asset in making these new technologies safe and reliable." "Our computer simulations are important because their results will serve as the foundation to extend current national and international protocols to perform calibration of conventional linacs to MR-linacs," said Gabriel Sawakuchi, assistant professor of Radiation Physics at MD Anderson. "However, it is important that our results be validated against measurements and independent simulations performed by other groups before used clinically." X-ray radiation is the most frequently used form of high-energy treatment, but a new treatment is emerging that uses a beam of protons to deliver energy directly to the tumor with minimal damage to surrounding tissues and without the side effects of x-ray therapy. Like x-ray radiation, proton therapy blasts tumors with beams of particles. But whereas traditional radiation uses photons, or focused light beams, proton therapy uses ions - hydrogen atoms that have lost an electron. Proton beams have a unique physical characteristic known as the 'Bragg peak' that allows the greatest part of its energy to be transferred to a specific area within the body, where it has maximum destructive effect. X-ray radiation, on the other hand, deposits energy and kills cells along the whole length of the beam. This can lead to unintended cell damage and even secondary cancer that can develop years later. In comparison with current radiation procedures, proton therapy saves healthy tissue in front of and behind the tumor. Since the patient is irradiated from all directions and the intensity of beams can be well modulated, the method provides further reduction of adverse effects. Proton therapy is particularly effective when irradiating tumors near sensitive organs—for instance near the neck, spine, brain or lungs—where stray beams can be particularly damaging. Medical physicists and radiation oncologists from Mayo Clinic in Phoenix, Arizona in collaboration with MD Anderson researchers, recently published a series of papers describing improved planning and use of proton therapy. Writing in Medical Physics in January 2017, they showed that in the three clinical cases included in this study, their chance-constrained model was better at sparing organs at risk than the current method. The model also provided a flexible tool for users to balance between plan robustness and plan quality and was found to be much faster than the commercial solution. The research used the Stampede supercomputer at TACC to conduct computationally intensive studies of the hundreds of factors that go into maximizing the effectiveness of, and minimizing the risk and uncertainties involved in, these treatments. Proton therapy was first developed in the 1950s and came into mainstream in the 1990s. There are currently 12 proton therapy centers nation-wide and the number is growing. However, the cost of the proton beam devices—$200 million dollars, or 30 to 50 times more expensive than a traditional x-ray system—means they are still rare. They are applied only in cases that require extra precision and doctors must maximize their benefit when they are used. Mayo Clinic and MD Anderson operate the most advanced versions of these devices, which perform scanning beam proton therapy and are able to modulate the intensity of the beam. Wei Liu, one of the lead proton therapy researchers at Mayo Clinic, likens the process to 3-D printing, "painting the tumor layer by layer." However, this is accomplished at a distance, through a protocol that must be planned in advance. The specificity of the proton beam, which is its greatest advantage, means that it must be precisely calibrated and that discrepancies from the ideal must be considered. For instance, hospital staff situate patients on the operating surface of the device, and even placing a patient a few millimeters off-center can impact the success of the treatment. Moreover, every patient's body has a slightly different chemical composition, which can make the proton beam stop at a different position from what is intended. Even patients' breathing can throw off the location of the beam placement. "If a patient has a tumor close to the spinal cord and this level of uncertainty exists, then the proton beam can overdose and paralyze the patient," Liu said. The solution to these challenges is robust optimization, which uses mathematical techniques to generate a plan that can manage and mitigate the uncertainties and human errors that may arise. "Each time, we try to mathematically generate a good plan," he said. "There are many unknown variables. You can choose different beam angles or energy or intensity. There are 25,000 variables or more, so generating a plan that is robust to these mistakes and can still get the proper dose distribution to the tumor is a large-scale optimization problem." To solve these problems, Liu and his team use supercomputers at the Texas Advanced Computing Center. "It's very computationally expensive to generate a plan in a reasonable timeframe," he continued. "Without a supercomputer, we can do nothing." Liu has been working on developing the proton beam planning protocols for many years. Leading commercial companies have adopted methods similar to those that Liu and his collaborators developed as the basis for their radiation planning solutions. Recently, Liu and his collaborators extended their studies to include the uncertainties presented by breathing patients, which they call "4D robust optimization," since it takes into account the time component and not just spatial orientation. In the May 2016 issue of the International Journal of Radiation Oncology, they showed that compared to its 3D counterpart, 4D robust optimization for lung cancer treatment provided more robust target dose distribution and better target coverage, while still offering normal tissue protection. "We're trying to provide the patient with the most effective, most reliable, and most efficient proton therapy," Liu said. "Because it's so expensive, we have to do the best job to take advantage of this new technology." Like many forms of cancer therapy, clinicians know that proton therapy works, but precisely how it works is a bit of a mystery. The basic principle is not in question: proton ions collide with water molecules, which make up 70 percent of cells, triggering the release of electrons and free radicals that damage the DNA of cancerous cells. The proton ions also collide with the DNA directly, breaking bonds and crippling DNA's ability to replicate. Because of their high rate of division and reduced ability to repair damaged DNA, cancerous cells are much more vulnerable to DNA attacks than normal cells and are killed at a higher rate. Furthermore, a proton beam can be focused on a tumor area, thus causing maximum damage on cancerous cells and minimum damage on surrounding healthy cells. However, beyond this general microscopic picture, the mechanics of the process have been hard to determine. "As happens in cancer therapy, they know empirically that it works but they don't know why," said Jorge A. Morales, a professor of chemistry at Texas Tech University and a leading proponent of the computational analysis of proton therapy. "To do experiments with human subjects is dangerous, so the best way is through computer simulation." Morales has been running computer simulations of proton-cell chemical reactions using quantum dynamics models on TACC's Stampede supercomputer to investigate the fundamentals of the process. Computational experiments can mimic the dynamics of the proton-cell interactions without causing damage to a patient and can reveal what happens when the proton beam and cells collide from start to finish, with atomic-level accuracy. Quantum simulations are necessary because the electrons and atoms that are the basis for proton cancer therapy's effectiveness do not behave according to the laws of classical physics. Rather they are guided by the laws quantum mechanics which involve probabilities of location, speed and reactions' occurrences rather than to the precisely defined versions of those three variables. Morales' studies on Stampede, reported in PLOS One in March 2017, as well as in Molecular Physics, and Chemical Physics Letters (both 2014), have determined the basic byproducts of protons colliding with water within the cell, and with nucleotides and clusters of DNA bases - the basic units of DNA. The studies shed light on how the protons and their water radiolysis products damage DNA. The results of Morales' computational experiments match the limited data from physical chemistry experiments, leading to greater confidence in their ability to capture the quantum behavior in action. Though fundamental in nature, the insights and data that Morales' simulations produce help researchers understand proton cancer therapy at the microscale, and help modulate factors like dosage and beam direction. "The results are all very promising and we're excited to extend our research further," Morales said. "These simulations will bring about a unique way to understand and control proton cancer therapy that, at a very low cost, will help to drastically improve the treatment of cancer patients without risking human subjects." Explore further: What is cancer radiotherapy, and why do we need proton beam therapy? More information: Austin J. Privett et al, Exploring water radiolysis in proton cancer therapy: Time-dependent, non-adiabatic simulations of H+ + (H2O)1-6, PLOS ONE (2017). DOI: 10.1371/journal.pone.0174456


News Article | May 10, 2017
Site: www.eurekalert.org

Researchers use supercomputers at the Texas Advanced Computing Center to improve, plan, and understand the basic science of, radiation therapy Radiation therapy shoots high-energy particles into the body to destroy or damage cancer cells. Over the last century, the technologies used have constantly improved and it has become a highly effective way to treat cancer. However, physicians must still walk a fine line between delivering enough radiation to kill tumors, while sparing surrounding healthy tissue. "Historically, radiation has been a blunt tool," said Matt Vaughn, Director of Life Science Computing at the Texas Advanced Computing Center. "However, it's become ever more precise because we understand the physics and biology of systems that we're shooting radiation into, and have improved our ability to target the delivery of that radiation." The science of calculating and assessing the radiation dose received by the human body is known as dosimetry - and here, as in many areas of science, advanced computing plays an important role. Current radiation treatments rely on imaging from computed tomography (CT) scans taken prior to treatment to determine a tumor's location. This works well if the tumor lies in an easily detectable and immobile location, but less so if the area is moving, as in the case of lung cancer. At the University of Texas MD Anderson Cancer Center, scientists are tackling the problem of accurately attacking tumors using a new technology known as an MR-linac that combines magnetic resonance (MR) imaging with linear accelerators (linacs). Developed by Elekta in cooperation with UMC Utrecht and Philips, the MR-linac at MD Anderson is the first of its kind in the U.S. MR-linacs can image a patient's anatomy while the radiation beam is being delivered. This allows doctors to detect and visualize any anatomical changes in a patient during treatment. Unlike CT or other x-ray based imaging modalities, which provide additional ionizing radiation, MRI is harmless to healthy tissue. The MR-linac method offers a potentially significant improvement over current image-guided cancer treatment technology. However, to ensure patients are treated safely, scientists must first correct for the influence of the MRI's magnetic field on the measurements used to calibrate the radiation dose being delivered. Researchers use software called Geant4 to simulate radiation within the detectors. Originally developed by CERN to simulate high energy particle physics experiments, the MD Anderson team has adapted Geant4 to incorporate magnetic fields into their computer dosimetry model. "Since the ultimate aim of the MR-linac is to treat patients, it is important that our simulations be very accurate and that the results be very precise," said Daniel O'Brien, a postdoctoral fellow in radiation physics at MD Anderson. "Geant4 was originally designed to study radiation at much higher energies than what is used to treat patients. We had to perform tests to make sure that we had the accuracy that we needed." Using the Lonestar supercomputer at the Texas Advanced Computing Center (TACC), the research team simulated nearly 17 billion particles of radiation per detector to get the precision that they needed for their study. In August 2016, they published magnetic field correction factors in Medical Physics for six of the most-used ionization chamber detectors (gas-filled chambers that are used to ensure the dose delivered from a therapy unit is correct). They are now working on verifying these results experimentally. "The MR-linac is a very promising technology but it also presents many unique challenges from a dosimetry point of view," O'Brien said. "Over time, our understanding of these effects has improved considerably, but there is still work to be done and resources like TACC are an invaluable asset in making these new technologies safe and reliable." "Our computer simulations are important because their results will serve as the foundation to extend current national and international protocols to perform calibration of conventional linacs to MR-linacs," said Gabriel Sawakuchi, assistant professor of Radiation Physics at MD Anderson. "However, it is important that our results be validated against measurements and independent simulations performed by other groups before used clinically." X-ray radiation is the most frequently used form of high-energy treatment, but a new treatment is emerging that uses a beam of protons to deliver energy directly to the tumor with minimal damage to surrounding tissues and without the side effects of x-ray therapy. Like x-ray radiation, proton therapy blasts tumors with beams of particles. But whereas traditional radiation uses photons, or focused light beams, proton therapy uses ions - hydrogen atoms that have lost an electron. Proton beams have a unique physical characteristic known as the 'Bragg peak' that allows the greatest part of its energy to be transferred to a specific area within the body, where it has maximum destructive effect. X-ray radiation, on the other hand, deposits energy and kills cells along the whole length of the beam. This can lead to unintended cell damage and even secondary cancer that can develop years later. In comparison with current radiation procedures, proton therapy saves healthy tissue in front of and behind the tumor. Since the patient is irradiated from all directions and the intensity of beams can be well modulated, the method provides further reduction of adverse effects. Proton therapy is particularly effective when irradiating tumors near sensitive organs -- for instance near the neck, spine, brain or lungs -- where stray beams can be particularly damaging. Medical physicists and radiation oncologists from Mayo Clinic in Phoenix, Arizona in collaboration with MD Anderson researchers, recently published a series of papers describing improved planning and use of proton therapy. Writing in Medical Physics in January 2017, they showed that in the three clinical cases included in this study, their chance-constrained model was better at sparing organs at risk than the current method. The model also provided a flexible tool for users to balance between plan robustness and plan quality and was found to be much faster than the commercial solution. The research used the Stampede supercomputer at TACC to conduct computationally intensive studies of the hundreds of factors that go into maximizing the effectiveness of, and minimizing the risk and uncertainties involved in, these treatments. Proton therapy was first developed in the 1950s and came into mainstream in the 1990s. There are currently 12 proton therapy centers nation-wide and the number is growing. However, the cost of the proton beam devices -- $200 million dollars, or 30 to 50 times more expensive than a traditional x-ray system -- means they are still rare. They are applied only in cases that require extra precision and doctors must maximize their benefit when they are used. Mayo Clinic and MD Anderson operate the most advanced versions of these devices, which perform scanning beam proton therapy and are able to modulate the intensity of the beam. Wei Liu, one of the lead proton therapy researchers at Mayo Clinic, likens the process to 3-D printing, "painting the tumor layer by layer." However, this is accomplished at a distance, through a protocol that must be planned in advance. The specificity of the proton beam, which is its greatest advantage, means that it must be precisely calibrated and that discrepancies from the ideal must be considered. For instance, hospital staff situate patients on the operating surface of the device, and even placing a patient a few millimeters off-center can impact the success of the treatment. Moreover, every patient's body has a slightly different chemical composition, which can make the proton beam stop at a different position from what is intended. Even patients' breathing can throw off the location of the beam placement. "If a patient has a tumor close to the spinal cord and this level of uncertainty exists, then the proton beam can overdose and paralyze the patient," Liu said. The solution to these challenges is robust optimization, which uses mathematical techniques to generate a plan that can manage and mitigate the uncertainties and human errors that may arise. "Each time, we try to mathematically generate a good plan," he said. "There are many unknown variables. You can choose different beam angles or energy or intensity. There are 25,000 variables or more, so generating a plan that is robust to these mistakes and can still get the proper dose distribution to the tumor is a large-scale optimization problem." To solve these problems, Liu and his team use supercomputers at the Texas Advanced Computing Center. "It's very computationally expensive to generate a plan in a reasonable timeframe," he continued. "Without a supercomputer, we can do nothing." Liu has been working on developing the proton beam planning protocols for many years. Leading commercial companies have adopted methods similar to those that Liu and his collaborators developed as the basis for their radiation planning solutions. Recently, Liu and his collaborators extended their studies to include the uncertainties presented by breathing patients, which they call "4D robust optimization," since it takes into account the time component and not just spatial orientation. In the May 2016 issue of the International Journal of Radiation Oncology, they showed that compared to its 3D counterpart, 4D robust optimization for lung cancer treatment provided more robust target dose distribution and better target coverage, while still offering normal tissue protection. "We're trying to provide the patient with the most effective, most reliable, and most efficient proton therapy," Liu said. "Because it's so expensive, we have to do the best job to take advantage of this new technology." Like many forms of cancer therapy, clinicians know that proton therapy works, but precisely how it works is a bit of a mystery. The basic principle is not in question: proton ions collide with water molecules, which make up 70 percent of cells, triggering the release of electrons and free radicals that damage the DNA of cancerous cells. The proton ions also collide with the DNA directly, breaking bonds and crippling DNA's ability to replicate. Because of their high rate of division and reduced ability to repair damaged DNA, cancerous cells are much more vulnerable to DNA attacks than normal cells and are killed at a higher rate. Furthermore, a proton beam can be focused on a tumor area, thus causing maximum damage on cancerous cells and minimum damage on surrounding healthy cells. However, beyond this general microscopic picture, the mechanics of the process have been hard to determine. "As happens in cancer therapy, they know empirically that it works but they don't know why," said Jorge A. Morales, a professor of chemistry at Texas Tech University and a leading proponent of the computational analysis of proton therapy. "To do experiments with human subjects is dangerous, so the best way is through computer simulation." Morales has been running computer simulations of proton-cell chemical reactions using quantum dynamics models on TACC's Stampede supercomputer to investigate the fundamentals of the process. Computational experiments can mimic the dynamics of the proton-cell interactions without causing damage to a patient and can reveal what happens when the proton beam and cells collide from start to finish, with atomic-level accuracy. Quantum simulations are necessary because the electrons and atoms that are the basis for proton cancer therapy's effectiveness do not behave according to the laws of classical physics. Rather they are guided by the laws quantum mechanics which involve probabilities of location, speed and reactions' occurrences rather than to the precisely defined versions of those three variables. Morales' studies on Stampede, reported in PLOS One in March 2017, as well as in Molecular Physics, and Chemical Physics Letters (both 2014), have determined the basic byproducts of protons colliding with water within the cell, and with nucleotides and clusters of DNA bases - the basic units of DNA. The studies shed light on how the protons and their water radiolysis products damage DNA. The results of Morales' computational experiments match the limited data from physical chemistry experiments, leading to greater confidence in their ability to capture the quantum behavior in action. Though fundamental in nature, the insights and data that Morales' simulations produce help researchers understand proton cancer therapy at the microscale, and help modulate factors like dosage and beam direction. "The results are all very promising and we're excited to extend our research further," Morales said. "These simulations will bring about a unique way to understand and control proton cancer therapy that, at a very low cost, will help to drastically improve the treatment of cancer patients without risking human subjects."


News Article | May 18, 2017
Site: www.marketwired.com

PITTSBURGH, PA--(Marketwired - May 18, 2017) - Avere Systems, a leading provider of hybrid cloud enablement solutions, announced today that BioTeam, Inc. is incorporating Avere FXT Edge filers into its Convergence Lab, a testing environment hosted at the Texas Advanced Computing Center (TACC), in Austin, Texas. In cooperation with vendors and TACC, BioTeam utilizes the lab to evaluate solutions for its clients by standing up, configuring and testing new infrastructure under conditions relevant to life sciences in order to deliver on its mission of providing objective, vendor agnostic solutions to researchers. The life sciences community is producing increasingly large amounts of data from sources ranging from laboratory analytical devices, to research, to patient data, which is putting IT organizations under pressure to support these growing workloads. Avere's technology offers life science organizations the ability to flexibly process and store these growing datasets where it makes the most sense -- at performance levels that help to improve the rate of discovery. Avere Edge filers allow seamless integration of multiple storage destinations, including multiple public clouds and on-premises data centers, increasing the options that organizations like BioTeam can provide its customers for data center optimization. BioTeam plans on utilizing the FXT filers to test burst buffer workloads and hybrid storage strategies for life sciences data and workloads in order to develop effective recommendations for their customers under the right conditions. Avere's technology provides many world-renowned life science research facilities with flexibility and performance benefits, in addition to the ability to support the large data sets common to BioIT workflows. By reducing the dependency on traditional storage and facilitating modernization with hybrid cloud infrastructures, Avere also helps organizations keep their IT costs in check. The BioTeam Lab takes an integrative approach to streamlining computer-aided research from the lab bench to knowledge. Solutions are driven by BioTeam's clients and tailored to meet the scientific needs of the organization. Inside the lab, BioTeam works with vendors to understand the end-to-end experience of using their technologies and handles everything including the racking, installations, configuration, testing and integration, vendor communication and return shipping. Remote access to the lab is available from virtually any location with an internet connection. TACC provides the space, power, cooling, connectivity, support and deep collaboration on lab projects. "BioTeam is a fast-growing consulting company that is comprised of a highly cross-functional and creative group of scientists and engineers. Our unique cross section of experience allows us to enable computer-aided discovery in life sciences by creating and adapting IT infrastructure and services to fit the scientific goals of the organizations we work with," said Ari Berman, Vice President and General Manager of Consulting, BioTeam. "As part of our larger suite of hardware and software, having Avere in our lab gives us the hands-on ability to test Avere-based hybrid storage scenarios in a controlled and optimized life sciences environment, utilizing real workloads. These scenarios will allow BioTeam to understand where Avere technology best fits in the life sciences and healthcare domain and will allow us to innovate next-generation strategies for storage and analytics workflows. Having this opportunity allows us to deepen our understanding of the overall storage landscape and to be able to recommend fit for purpose solutions to our customers." "Working with BioTeam is a natural fit for Avere. Our technology has a solid track record of helping life science organizations leverage the cloud for large workloads for both cloud compute and storage resources," said Jeff Tabor, Senior Director of Product Management and Marketing at Avere Systems. "We look forward to collaborating with the BioTeam and continuing to help the industry effectively integrate cloud into their data center strategies and seamlessly use multiple cloud vendors." Next week at the BioIT World Conference in Boston, BioTeam and Avere will co-present "Freeing Data: How to Win the War with Hybrid Clouds." BioTeam Senior Scientific Consultant Adam Kraut and Avere CEO Ron Bianchini will take the stage on May 25, 2017 at 12:20pm ET. Avere Systems is exhibiting at the show, booth #536, from May 23 - 25, 2017. About Avere Systems Avere helps enterprise IT organizations enable innovation with high-performance data storage access, and the flexibility to compute and store data where necessary to match business demands. Customers enjoy easy reach to cloud-based resources, without sacrificing the consistency, availability or security of enterprise data. A private company based in Pittsburgh, Pennsylvania, Avere is led by industry experts to support the demanding, mission-critical hybrid cloud systems of many of the world's most recognized companies and organizations. Learn more at www.averesystems.com. About BioTeam, Inc. BioTeam, Inc. has a well-established history of providing complete, and forward-thinking solutions to the life sciences. With a cross-section of expertise that includes classical laboratory scientific training, applications development, informatics, large data center installations, HPC, enterprise and scientific network engineering, and high-volume as well as high-performance storage, BioTeam leverages the right technologies customized to its client's unique needs in order to enable them to reach their scientific objectives. For more information, please visit the company website. About Texas Advanced Computing Center TACC designs and deploys the world's most powerful advanced computing technologies and innovative software solutions to enable researchers to answer complex questions like these and many more. Every day, researchers rely on our computing experts and resources to help them gain insights and make discoveries that change the world. Find out more at https://www.tacc.utexas.edu/.


News Article | April 10, 2017
Site: www.scientificcomputing.com

An innovative supercomputing program could assist psychologists with diagnosing mental health conditions. Researchers are using the Stampede Supercomputer, stationed at the Texas Advanced Computing Center, to teach a machine-learning algorithm that can sift through diverse data sets and potentially predict which patients are at risk of developing depression and anxiety. The team conducted a study where they had 52 treatment-seeking participants with depression and 45 healthy control participants receive diffusion tensor imaging (DTI) MRI scans. This process entails tagging water molecules to analyze the level of which these particles are microscopically diffused in the brain over a certain period of time. "We feed in whole brain data or a subset and predict disease classifications or any potential behavioral measure such as measures of negative information bias," said David Schnyer, a psychology professor and cognitive neuroscientists at the University of Texas at Austin, in a statement. Measuring these diffusions in multiple spatial directions generates vectors for each voxel, according to the official announcement. Voxels are three-dimensional cubes that signify either structure or neural activity throughout the brain. These outcomes are then morphed into metrics that indicate the integrity of white matter pathways residing in the cerebral cortex. The algorithm sorted through this data and was able to predict whether a volunteer in this study had a form of depression with roughly 75 percent accuracy. "Not only are we learning that we can classify depressed versus non-depressed people using DTI data, we are also learning something about how depression is represented within the brain," said Christopher Beevers, a professor of psychology and director of the Institute for Mental Health Research at UT Austin who participated in this research. "Rather than trying to find the area that is disrupted in depression, we are learning that alterations across a number of networks contribute to the classification of depression." Both researchers were impressed with these findings, but plan on adding more data from several hundred volunteers to strengthen the system’s predictive capabilities. Machine learning is a growing field in the healthcare sector. Researchers are designing these innovative programs for tasks like obtaining data from cancer pathology reports, improving cancer surveillance on the national, state, and local levels, and diagnoses for voice disorders.


News Article | April 10, 2017
Site: www.scientificcomputing.com

An innovative supercomputing program could assist psychologists with diagnosing mental health conditions. Researchers are using the Stampede Supercomputer, stationed at the Texas Advanced Computing Center, to teach a machine-learning algorithm that can sift through diverse data sets and potentially predict which patients are at risk of developing depression and anxiety. The team conducted a study where they had 52 treatment-seeking participants with depression and 45 healthy control participants receive diffusion tensor imaging (DTI) MRI scans. This process entails tagging water molecules to analyze the level of which these particles are microscopically diffused in the brain over a certain period of time. "We feed in whole brain data or a subset and predict disease classifications or any potential behavioral measure such as measures of negative information bias," said David Schnyer, a psychology professor and cognitive neuroscientists at the University of Texas at Austin, in a statement. Measuring these diffusions in multiple spatial directions generates vectors for each voxel, according to the official announcement. Voxels are three-dimensional cubes that signify either structure or neural activity throughout the brain. These outcomes are then morphed into metrics that indicate the integrity of white matter pathways residing in the cerebral cortex. The algorithm sorted through this data and was able to predict whether a volunteer in this study had a form of depression with roughly 75 percent accuracy. "Not only are we learning that we can classify depressed versus non-depressed people using DTI data, we are also learning something about how depression is represented within the brain," said Christopher Beevers, a professor of psychology and director of the Institute for Mental Health Research at UT Austin who participated in this research. "Rather than trying to find the area that is disrupted in depression, we are learning that alterations across a number of networks contribute to the classification of depression." Both researchers were impressed with these findings, but plan on adding more data from several hundred volunteers to strengthen the system’s predictive capabilities. Machine learning is a growing field in the healthcare sector. Researchers are designing these innovative programs for tasks like obtaining data from cancer pathology reports, improving cancer surveillance on the national, state, and local levels, and diagnoses for voice disorders.


News Article | May 2, 2017
Site: www.biosciencetechnology.com

Surgery and radiation remove, kill, or damage cancer cells in a certain area. But chemotherapy -- which uses medicines or drugs to treat cancer -- can work throughout the whole body, killing cancer cells that have spread far from the original tumor. Finding new drugs that can more effectively kill cancer cells or disrupt the growth of tumors is one way to improve survival rates for ailing patients. Increasingly, researchers looking to uncover and test new drugs use powerful supercomputers like those developed and deployed by the Texas Advanced Computing Center (TACC). "Advanced computing is a cornerstone of drug design and the theoretical testing of drugs," said Matt Vaughn, TACC's Director of Life Science Computing. "The sheer number of potential combinations that can be screened in parallel before you ever go in the laboratory makes resources like those at TACC invaluable for cancer research." Three projects powered by TACC supercomputer, which use virtual screening, molecular modeling and evolutionary analyses, respectively, to explore chemotherapeutic compounds, exemplify the type of cancer research advanced computing enables. Shuxing Zhang, a researcher in the Department of Experimental Therapeutics at the University of Texas MD Anderson Cancer Center, leads a lab dedicated to computer-assisted rational drug design and discovery of novel targeted therapeutic agents. The group develops new computational methods, using artificial intelligence and high-performance computing-based virtual screening strategies, that help the entire field of cancer drug discovery and development. Identifying a new drug by intuition or trial and error is expensive and time consuming. Virtual screening, on the other hand, uses computer simulations to explore how a large number of small molecule compounds "dock", or bind, to a target to determine if they may be candidates for future drugs. "In silico virtual screening is an invaluable tool in the early stages of drug discovery," said Joe Allen, a research associate at TACC. "It paints a clear picture not only of what types of molecules may bind to a receptor, but also what types of molecules would not bind, saving a lot of time in the lab." One specific biological target that Zhang's group investigates is called TNIK (TRAF2- and NCK-interacting kinase). TNIK is an enzyme that plays a key role in cell signaling related to colon cancer. Silencing TNIK, it is believed, may suppress the proliferation of colorectal cancer cells. Writing in Scientific Reports in September 2016, Zhang and his collaborators reported the results of a study that investigated known compounds with desirable properties that might act as TNIK inhibitors. Using the Lonestar supercomputer at TACC, they screened 1,448 Food and Drug Administration-approved small molecule drugs to determine which had the molecular features needed to bind and inhibit TNIK. They discovered that one -- mebendazole, an approved drug that fights parasites -- could effectively bind to the target. After testing it experimentally, they further found that the drug could also selectively inhibit TNIK's enzymatic activity. As an FDA-approved drug that can be used at higher dosages without severe side effects, mebendazole may is a strong candidate for further exploration and may even exhibit a 'synergic anti-tumor effect' when used with other anti-cancer drugs. "Such advantages render the possibility of quickly translating the discovery into a clinical setting for cancer treatment in the near future," Zhang and his collaborators wrote. In separate research published in Cell in 2013, Zhang's group used Lonestar to virtually screen an even greater number of novel inhibitors of Skp2, a critical oncogene that controls the cell cycle and is frequently observed as being overexpressed in human cancer. "Molecular docking is a computationally-expensive process and the screening of 3 million drug-like compounds needs more than 2,000 days on a single CPU [computer processing unit]," Zhang said. "By running the process on a high-performance computing cluster, we were able to screen millions of compounds within days instead of years." Their computational approaches identified a specific Skp2 inhibitor that can selectively impair Skp2 activity and functions, thereby exhibiting potent anti-tumor activity. "Our work at TACC has resulted in multiple potential drug candidates currently at the different stages of preclinical and clinical studies," said Zhang. "We hope to continue using the resources to identify more effective and less toxic therapeutics." Described as "the guardian of the genome", tumor protein 53 (p53) plays a crucial role in multicellular organisms, conserving the stability of DNA by preventing mutations and thereby acting as a tumor suppressor. However, in approximately 50 percent of all human cancers, p53 is mutated and rendered inactive. Therefore, reactivation of mutant p53 using small molecules has been a long-sought-after anticancer therapeutic strategy. Rommie Amaro, professor of Chemistry and Biochemistry at the University of California, San Diego has been studying this important molecule for years trying to understand how it works. In September 2016, writing in the journal Oncogene, she reported results from the largest atomic-level simulation of the tumor suppression protein to date -- comprising more than 1.5 million atoms. The simulations helped to identify new "pockets" -- binding sites on the surface of the protein -- where it may be possible to insert a small molecule that could reactivate p53. They revealed a level of complexity that is very difficult, if not impossible, to experimentally test. "We could see how when the full-length p53 was bound to a DNA sequence that was a recognition sequence, the tetramer clamps down and grips onto the DNA - which was unexpected," Amaro said. In contrast, with the negative control DNA, p53 stays more open. "It actually relaxes and loosens its grip on the DNA," she said. "It suggested a mechanism by which this molecule could actually change its dynamics depending on the exact sequence of DNA." According to Amaro, computing provides a better understanding of cancer mechanisms and ways to develop possible novel therapeutic avenues. "When most people think about cancer research they probably don't think about computers, but biophysical models are getting to the point where they have a great impact on the science," she said. Chemicals created by plants are the basis for the majority of the medicines used today. One such plant, the periwinkle (Catharanthus roseus), is used in chemotherapy protocols for leukemia and Hodgkin's lymphoma. A completely different approach to drug discovery involves studying the evolution of plants that are known to be effective chemotherapeutic agents and their genetic relatives, since plants that share an evolutionary history often share related collections of chemical compounds. University of Texas researchers -- working with researchers from King Abdulaziz University in Saudi Arabia, the University of Ottawa and Université de Montréal -- have been studying Rhazya stricta, an environmentally stressed, poisonous evergreen shrub found in Saudi Arabia that is a member of the family that includes the periwinkle. To understand the genome and evolutionary history of Rhayza stricta, the researchers performed genome assemblies and analyses on TACC's Lonestar, Stampede and Wrangler systems. According Robert Jansen, professor of Integrative Biology at UT and lead researcher on the project, the computational resources at TACC were essential for constructing and studying the plant's genome. The results were published in Scientific Reports in September 2016. "These analyses allowed the identification of genes involved in the monoterpene indole alkaloid pathway, and in some cases expansions of gene families were detected," he said. The monoterpene indole alkaloid pathway produces compounds that have known therapeutic properties against cancer. From the annotated Rhazya genome, the researchers developed a metabolic pathway database, RhaCyc, that can serve as a community resource and help identify new chemotherapeutic molecules. Jansen and his team hope that by better characterizing the genome and evolutionary history using advanced computational methods, and making the metabolic pathway database available as a community resource, they can speed the development of new medicines in the future. "There are a nearly infinite number of possible drug compounds," Vaughn said. "But knowing the principles of what a good drug might look like - how it might bind to a certain pocket or what it might need to resemble - helps narrow the scope immensely, accelerating discoveries, while reducing costs."


News Article | May 1, 2017
Site: www.eurekalert.org

Researchers use TACC's advanced computers to virtually discover and experimentally test new chemotherapy drugs and targets Surgery and radiation remove, kill, or damage cancer cells in a certain area. But chemotherapy -- which uses medicines or drugs to treat cancer -- can work throughout the whole body, killing cancer cells that have spread far from the original tumor. Finding new drugs that can more effectively kill cancer cells or disrupt the growth of tumors is one way to improve survival rates for ailing patients. Increasingly, researchers looking to uncover and test new drugs use powerful supercomputers like those developed and deployed by the Texas Advanced Computing Center (TACC). "Advanced computing is a cornerstone of drug design and the theoretical testing of drugs," said Matt Vaughn, TACC's Director of Life Science Computing. "The sheer number of potential combinations that can be screened in parallel before you ever go in the laboratory makes resources like those at TACC invaluable for cancer research." Three projects powered by TACC supercomputer, which use virtual screening, molecular modeling and evolutionary analyses, respectively, to explore chemotherapeutic compounds, exemplify the type of cancer research advanced computing enables. Shuxing Zhang, a researcher in the Department of Experimental Therapeutics at the University of Texas MD Anderson Cancer Center, leads a lab dedicated to computer-assisted rational drug design and discovery of novel targeted therapeutic agents. The group develops new computational methods, using artificial intelligence and high-performance computing-based virtual screening strategies, that help the entire field of cancer drug discovery and development. Identifying a new drug by intuition or trial and error is expensive and time consuming. Virtual screening, on the other hand, uses computer simulations to explore how a large number of small molecule compounds "dock", or bind, to a target to determine if they may be candidates for future drugs. "In silico virtual screening is an invaluable tool in the early stages of drug discovery," said Joe Allen, a research associate at TACC. "It paints a clear picture not only of what types of molecules may bind to a receptor, but also what types of molecules would not bind, saving a lot of time in the lab." One specific biological target that Zhang's group investigates is called TNIK (TRAF2- and NCK-interacting kinase). TNIK is an enzyme that plays a key role in cell signaling related to colon cancer. Silencing TNIK, it is believed, may suppress the proliferation of colorectal cancer cells. Writing in Scientific Reports in September 2016, Zhang and his collaborators reported the results of a study that investigated known compounds with desirable properties that might act as TNIK inhibitors. Using the Lonestar supercomputer at TACC, they screened 1,448 Food and Drug Administration-approved small molecule drugs to determine which had the molecular features needed to bind and inhibit TNIK. They discovered that one -- mebendazole, an approved drug that fights parasites -- could effectively bind to the target. After testing it experimentally, they further found that the drug could also selectively inhibit TNIK's enzymatic activity. As an FDA-approved drug that can be used at higher dosages without severe side effects, mebendazole may is a strong candidate for further exploration and may even exhibit a 'synergic anti-tumor effect' when used with other anti-cancer drugs. "Such advantages render the possibility of quickly translating the discovery into a clinical setting for cancer treatment in the near future," Zhang and his collaborators wrote. In separate research published in Cell in 2013, Zhang's group used Lonestar to virtually screen an even greater number of novel inhibitors of Skp2, a critical oncogene that controls the cell cycle and is frequently observed as being overexpressed in human cancer. "Molecular docking is a computationally-expensive process and the screening of 3 million drug-like compounds needs more than 2,000 days on a single CPU [computer processing unit]," Zhang said. "By running the process on a high-performance computing cluster, we were able to screen millions of compounds within days instead of years." Their computational approaches identified a specific Skp2 inhibitor that can selectively impair Skp2 activity and functions, thereby exhibiting potent anti-tumor activity. "Our work at TACC has resulted in multiple potential drug candidates currently at the different stages of preclinical and clinical studies," said Zhang. "We hope to continue using the resources to identify more effective and less toxic therapeutics." Described as "the guardian of the genome", tumor protein 53 (p53) plays a crucial role in multicellular organisms, conserving the stability of DNA by preventing mutations and thereby acting as a tumor suppressor. However, in approximately 50 percent of all human cancers, p53 is mutated and rendered inactive. Therefore, reactivation of mutant p53 using small molecules has been a long-sought-after anticancer therapeutic strategy. Rommie Amaro, professor of Chemistry and Biochemistry at the University of California, San Diego has been studying this important molecule for years trying to understand how it works. In September 2016, writing in the journal Oncogene, she reported results from the largest atomic-level simulation of the tumor suppression protein to date -- comprising more than 1.5 million atoms. The simulations helped to identify new "pockets" -- binding sites on the surface of the protein -- where it may be possible to insert a small molecule that could reactivate p53. They revealed a level of complexity that is very difficult, if not impossible, to experimentally test. "We could see how when the full-length p53 was bound to a DNA sequence that was a recognition sequence, the tetramer clamps down and grips onto the DNA - which was unexpected," Amaro said. In contrast, with the negative control DNA, p53 stays more open. "It actually relaxes and loosens its grip on the DNA," she said. "It suggested a mechanism by which this molecule could actually change its dynamics depending on the exact sequence of DNA." According to Amaro, computing provides a better understanding of cancer mechanisms and ways to develop possible novel therapeutic avenues. "When most people think about cancer research they probably don't think about computers, but biophysical models are getting to the point where they have a great impact on the science," she said. Chemicals created by plants are the basis for the majority of the medicines used today. One such plant, the periwinkle (Catharanthus roseus), is used in chemotherapy protocols for leukemia and Hodgkin's lymphoma. A completely different approach to drug discovery involves studying the evolution of plants that are known to be effective chemotherapeutic agents and their genetic relatives, since plants that share an evolutionary history often share related collections of chemical compounds. University of Texas researchers -- working with researchers from King Abdulaziz University in Saudi Arabia, the University of Ottawa and Université de Montréal -- have been studying Rhazya stricta, an environmentally stressed, poisonous evergreen shrub found in Saudi Arabia that is a member of the family that includes the periwinkle. To understand the genome and evolutionary history of Rhayza stricta, the researchers performed genome assemblies and analyses on TACC's Lonestar, Stampede and Wrangler systems. According Robert Jansen, professor of Integrative Biology at UT and lead researcher on the project, the computational resources at TACC were essential for constructing and studying the plant's genome. The results were published in Scientific Reports in September 2016. "These analyses allowed the identification of genes involved in the monoterpene indole alkaloid pathway, and in some cases expansions of gene families were detected," he said. The monoterpene indole alkaloid pathway produces compounds that have known therapeutic properties against cancer. From the annotated Rhazya genome, the researchers developed a metabolic pathway database, RhaCyc, that can serve as a community resource and help identify new chemotherapeutic molecules. Jansen and his team hope that by better characterizing the genome and evolutionary history using advanced computational methods, and making the metabolic pathway database available as a community resource, they can speed the development of new medicines in the future. "There are a nearly infinite number of possible drug compounds," Vaughn said. "But knowing the principles of what a good drug might look like - how it might bind to a certain pocket or what it might need to resemble - helps narrow the scope immensely, accelerating discoveries, while reducing costs."


News Article | May 1, 2017
Site: phys.org

Finding new drugs that can more effectively kill cancer cells or disrupt the growth of tumors is one way to improve survival rates for ailing patients. Increasingly, researchers looking to uncover and test new drugs use powerful supercomputers like those developed and deployed by the Texas Advanced Computing Center (TACC). "Advanced computing is a cornerstone of drug design and the theoretical testing of drugs," said Matt Vaughn, TACC's Director of Life Science Computing. "The sheer number of potential combinations that can be screened in parallel before you ever go in the laboratory makes resources like those at TACC invaluable for cancer research." Three projects powered by TACC supercomputer, which use virtual screening, molecular modeling and evolutionary analyses, respectively, to explore chemotherapeutic compounds, exemplify the type of cancer research advanced computing enables. Shuxing Zhang, a researcher in the Department of Experimental Therapeutics at the University of Texas MD Anderson Cancer Center, leads a lab dedicated to computer-assisted rational drug design and discovery of novel targeted therapeutic agents. The group develops new computational methods, using artificial intelligence and high-performance computing-based virtual screening strategies, that help the entire field of cancer drug discovery and development. Identifying a new drug by intuition or trial and error is expensive and time consuming. Virtual screening, on the other hand, uses computer simulations to explore how a large number of small molecule compounds "dock", or bind, to a target to determine if they may be candidates for future drugs. "In silico virtual screening is an invaluable tool in the early stages of drug discovery," said Joe Allen, a research associate at TACC. "It paints a clear picture not only of what types of molecules may bind to a receptor, but also what types of molecules would not bind, saving a lot of time in the lab." One specific biological target that Zhang's group investigates is called TNIK (TRAF2- and NCK-interacting kinase). TNIK is an enzyme that plays a key role in cell signaling related to colon cancer. Silencing TNIK, it is believed, may suppress the proliferation of colorectal cancer cells. Writing in Scientific Reports in September 2016, Zhang and his collaborators reported the results of a study that investigated known compounds with desirable properties that might act as TNIK inhibitors. Using the Lonestar supercomputer at TACC, they screened 1,448 Food and Drug Administration-approved small molecule drugs to determine which had the molecular features needed to bind and inhibit TNIK. They discovered that one—mebendazole, an approved drug that fights parasites—could effectively bind to the target. After testing it experimentally, they further found that the drug could also selectively inhibit TNIK's enzymatic activity. As an FDA-approved drug that can be used at higher dosages without severe side effects, mebendazole may is a strong candidate for further exploration and may even exhibit a 'synergic anti-tumor effect' when used with other anti-cancer drugs. "Such advantages render the possibility of quickly translating the discovery into a clinical setting for cancer treatment in the near future," Zhang and his collaborators wrote. In separate research published in Cell in 2013, Zhang's group used Lonestar to virtually screen an even greater number of novel inhibitors of Skp2, a critical oncogene that controls the cell cycle and is frequently observed as being overexpressed in human cancer. "Molecular docking is a computationally-expensive process and the screening of 3 million drug-like compounds needs more than 2,000 days on a single CPU [computer processing unit]," Zhang said. "By running the process on a high-performance computing cluster, we were able to screen millions of compounds within days instead of years." Their computational approaches identified a specific Skp2 inhibitor that can selectively impair Skp2 activity and functions, thereby exhibiting potent anti-tumor activity. "Our work at TACC has resulted in multiple potential drug candidates currently at the different stages of preclinical and clinical studies," said Zhang. "We hope to continue using the resources to identify more effective and less toxic therapeutics." Described as "the guardian of the genome", tumor protein 53 (p53) plays a crucial role in multicellular organisms, conserving the stability of DNA by preventing mutations and thereby acting as a tumor suppressor. However, in approximately 50 percent of all human cancers, p53 is mutated and rendered inactive. Therefore, reactivation of mutant p53 using small molecules has been a long-sought-after anticancer therapeutic strategy. Rommie Amaro, professor of Chemistry and Biochemistry at the University of California, San Diego has been studying this important molecule for years trying to understand how it works. In September 2016, writing in the journal Oncogene, she reported results from the largest atomic-level simulation of the tumor suppression protein to date—comprising more than 1.5 million atoms. The simulations helped to identify new "pockets"—binding sites on the surface of the protein—where it may be possible to insert a small molecule that could reactivate p53. They revealed a level of complexity that is very difficult, if not impossible, to experimentally test. "We could see how when the full-length p53 was bound to a DNA sequence that was a recognition sequence, the tetramer clamps down and grips onto the DNA - which was unexpected," Amaro said. In contrast, with the negative control DNA, p53 stays more open. "It actually relaxes and loosens its grip on the DNA," she said. "It suggested a mechanism by which this molecule could actually change its dynamics depending on the exact sequence of DNA." According to Amaro, computing provides a better understanding of cancer mechanisms and ways to develop possible novel therapeutic avenues. "When most people think about cancer research they probably don't think about computers, but biophysical models are getting to the point where they have a great impact on the science," she said. Chemicals created by plants are the basis for the majority of the medicines used today. One such plant, the periwinkle (Catharanthus roseus), is used in chemotherapy protocols for leukemia and Hodgkin's lymphoma. A completely different approach to drug discovery involves studying the evolution of plants that are known to be effective chemotherapeutic agents and their genetic relatives, since plants that share an evolutionary history often share related collections of chemical compounds. University of Texas researchers—working with researchers from King Abdulaziz University in Saudi Arabia, the University of Ottawa and Université de Montréal—have been studying Rhazya stricta, an environmentally stressed, poisonous evergreen shrub found in Saudi Arabia that is a member of the family that includes the periwinkle. To understand the genome and evolutionary history of Rhayza stricta, the researchers performed genome assemblies and analyses on TACC's Lonestar, Stampede and Wrangler systems. According Robert Jansen, professor of Integrative Biology at UT and lead researcher on the project, the computational resources at TACC were essential for constructing and studying the plant's genome. The results were published in Scientific Reports in September 2016. "These analyses allowed the identification of genes involved in the monoterpene indole alkaloid pathway, and in some cases expansions of gene families were detected," he said. The monoterpene indole alkaloid pathway produces compounds that have known therapeutic properties against cancer. From the annotated Rhazya genome, the researchers developed a metabolic pathway database, RhaCyc, that can serve as a community resource and help identify new chemotherapeutic molecules. Jansen and his team hope that by better characterizing the genome and evolutionary history using advanced computational methods, and making the metabolic pathway database available as a community resource, they can speed the development of new medicines in the future. "There are a nearly infinite number of possible drug compounds," Vaughn said. "But knowing the principles of what a good drug might look like - how it might bind to a certain pocket or what it might need to resemble - helps narrow the scope immensely, accelerating discoveries, while reducing costs." Explore further: Supercomputing the p53 protein as a promising anticancer therapy

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