Laboratory and clinical studies of cells maintaining the body's health and battling disease are increasingly complemented by computational modeling of cellular processes and diagnostic tools. Modeling involving nanoscale structures and processes has been particularly successful in structural cell biology, cellular mechanics, and in nanosensor development. The PI's laboratory develops a software package, NAMD/VMD, that is used by thousands of NIH-funded researchers as well as by pharmacological and biotechnological companies studying viral infection, developing new antibiotics, and providing faster gene sequencing methods. The impact of the computational tools, which provide profound microscopic views not available otherwise, is often limited by computing speed, hardware costs, and modeling ac- curacy. Recent dramatic advances in computer technology, namely multi-core processors and graphics processing units (GPUs), promise now a means of accelerating biomedical computing, while decreasing hardware cost and permitting more accurate simulations. The main investment needed is programmer ingenuity and time, as effective programming of multi-core processors requires different strategies and algorithms than used for conventional processing units. The PI and two co-PIs, long time collaborators with complementary backgrounds in biophysics, computer science, and electrical engineering, seek funds to hire two programmers and a graduate student research assistant, to program multi-core processors and serve three missions in biomedicine: the determination of very large cellular structures as they arise in cellular processes, the investigation of mechanical mechanisms underlying cellular dynamics, and the improvement of nanodevice simulation accuracy to better guide the development of sensors in medical diagnostics. Five computational bottlenecks will be addressed through revolutionary solutions involving multi-core capable software. In structural biology the merging of crystallographic and electron microscopy data through so-called grid forces and cross-correlation optimization will be greatly enhanced;in micro-mechanics of the body's cells interactive simulation and better analysis will permit investigators to feel and see simulated cellular mechanical responses on-the-fly, rather than only after hours or days;in nanomedicine an atomic resolution computational microscope will offer design engineers more accurate views of device behavior than ever achieved before. The approach taken combines biomedicine with physics, chemistry, parallel programming, and computer processing unit know-how in a unique way. Software improvements achieved will not only serve the stated applications, but many further applications of modern computational biomedicine.
This project seeks to increase the speed and reduce the cost of biomedical computing through new software that can run on a new generation of computer chips. The software, presently already in use by thousands of investigators, aids in understanding how cells maintain health and battle disease, in developing drugs like new antibiotics, and in designing sensors for genetic diseases. The planned advances require new mathematical algorithms and programming strategies.
| Qi, Yifei; Lee, Jumin; Singharoy, Abhishek et al. (2017) CHARMM-GUI MDFF/xMDFF Utilizer for Molecular Dynamics Flexible Fitting Simulations in Various Environments. J Phys Chem B 121:3718-3723 |
| Felberg, Lisa E; Brookes, David H; Yap, Eng-Hui et al. (2017) PB-AM: An open-source, fully analytical linear poisson-boltzmann solver. J Comput Chem 38:1275-1282 |
| Goh, Boon Chong; Hadden, Jodi A; Bernardi, Rafael C et al. (2016) Computational Methodologies for Real-Space Structural Refinement of Large Macromolecular Complexes. Annu Rev Biophys 45:253-78 |
| Singharoy, Abhishek; Teo, Ivan; McGreevy, Ryan et al. (2016) Molecular dynamics-based refinement and validation for sub-5 Å cryo-electron microscopy maps. Elife 5: |
| Stone, John E; Sener, Melih; Vandivort, Kirby L et al. (2016) Atomic Detail Visualization of Photosynthetic Membranes with GPU-Accelerated Ray Tracing. Parallel Comput 55:17-27 |
| McGreevy, Ryan; Teo, Ivan; Singharoy, Abhishek et al. (2016) Advances in the molecular dynamics flexible fitting method for cryo-EM modeling. Methods 100:50-60 |
| Stone, John E; Sherman, William R; Schulten, Klaus (2016) Immersive Molecular Visualization with Omnidirectional Stereoscopic Ray Tracing and Remote Rendering. IEEE Int Symp Parallel Distrib Process Workshops Phd Forum 2016:1048-1057 |
| Stone, John E; Hallock, Michael J; Phillips, James C et al. (2016) Evaluation of Emerging Energy-Efficient Heterogeneous Computing Platforms for Biomolecular and Cellular Simulation Workloads. IEEE Int Symp Parallel Distrib Process Workshops Phd Forum 2016:89-100 |
| Stone, John E; Hynninen, Antti-Pekka; Phillips, James C et al. (2016) Early Experiences Porting the NAMD and VMD Molecular Simulation and Analysis Software to GPU-Accelerated OpenPOWER Platforms. High Perform Comput (2016) 9945:188-206 |
| Stone, John E; Messmer, Peter; Sisneros, Robert et al. (2016) High Performance Molecular Visualization: In-Situ and Parallel Rendering with EGL. IEEE Int Symp Parallel Distrib Process Workshops Phd Forum 2016:1014-1023 |
Showing the most recent 10 out of 18 publications
