ENTERTAINMENT
From clay to blood to drugs
Various projects within Peter Coveney's research group have benefited from the high-performance computing resources at NCSA and other TeraGrid centers. One is large-scale atomistic simulations of cationic and anionic clay nanomaterials. To model realistic-sized clay sheets, the team's simulations must contain more than 1 million atoms. The team has simulated montmorillonite clay systems containing approximately 10 million atoms whose dimensions approached those of a realistic clay platelet. The simulations examined the collective thermal motion of clay sheet atoms over lengths greater than 150 Å. The motion produced low-amplitude, long-wavelength undulations of the clay sheets, which were used to calculate material properties that are normally difficult to obtain experimentally due to the small size of the clay platelets. Montmorillonite is commonly used as a filler in clay-polymer nanocomposites; estimation of the elastic properties of the composite requires accurate knowledge of the elastic moduli of the components. This work, which was published in the Journal of Physical Chemistry C, was partially supported by the United Kingdom's Engineering and Physical Sciences Research Council (EPSRC), the U.S. National Science Foundation (NSF), and the Distributed European Infrastructure for Supercomputing Applications (DEISA) Consortium.
In the biological domain, Coveney's team has been part of the GENIUS project, which aims to study cerebral blood flow using patient-specific image-based models using a bespoke lattice-Boltzmann solver distributed across an international federated grid of supercomputers. Modeling and simulation have a crucial role to play in neurosurgical blood flow treatments, due to the limitations of experimental methods. Simulation offers the clinician the possibility of performing non-invasive virtual experiments in order to plan and study the effects of certain courses of treatment with no danger to the patient. The team developed HemeLB, an application to model cerebral blood flow, and used tools to steer and visualize these simulations as they were in progress. In addition, they performed cross-site simulations using MPIg to allow the consolidation of machines at NCSA and the San Diego Supercomputer Center. This project, which was partially funded by EPSRC and NSF, will provide a prototype brain blood flow modeling environment that will be of considerable value to clinicians; the team will continue to work beyond the scope of this project to exploit this technology for use in everyday surgical procedure planning. HemeLB was the focus of a paper published in Computer Physics Communications, and the cross-site run was the subject of a presentation at the 17th IEEE International Symposium on High-Performance Computing. The team also has recently seen success in simulating the efficacy of an HIV drug in blocking a key protein used by the virus. The method (an early example of the Virtual Physiological Human in action) could one day be used to tailor personal drug treatments, for example for HIV patients developing resistance to their drug's resistant mutants of HIV-1 protease, a protein produced by the virus to propagate itself. These protease mutations are associated with the disease's resistance to saquinavir, an HIV-inhibitor drug. Although nine drugs are available to inhibit HIV-1 protease, doctors have no way of matching a drug to the unique profile of the virus as it mutates in each patient. Instead, they prescribe a course of drugs and then test whether these are working by analyzing the patient's immune response. One of the goals of the Virtual Physiological Human is for such "trial and error" methods to eventually be replaced by patient-specific treatments tailored to a person's unique genotype. This study, which was partially funded by EPSRC, the European Union-supported ViroLab project, and NSF, represents a first step toward the ultimate goal of "on-demand" medical computing, where doctors could one day "borrow" supercomputing time from the national grid to make critical decisions on life-saving treatments. A doctor could perform an assay to establish the patient's genotype and then rank the available drugs' efficacy against that patient's profile based on a rapid set of large-scale simulations, enabling the doctor to tailor the treatment accordingly. This work was published in the Journal of the American Chemical Society.
