Grand challenge problems like the ones addressed in this proposal cannot be solved by the effort of individual(s) in a single discipline. The framework for collaborations proposed here is solid and there is an initial project schedule which makes our goals more realistic. I believe that this interdisciplinary approach will bring fruits that go beyond the immediate goals of this proposal. By bridging gaps, particularly between biological and computational scientists, Notre Dame and the other participating institutions will be enriched with a foundation for further and more pervasive collaboration. This may lead to the creation of centers of excellence in computational biology and related areas. This goal is in line with departmental and institutional aspirations of Notre Dame. Since coming to Notre Dame, I have found tremendous institutional and departmental support. A generous start-up package has allowed me to quickly get started in establishing necessary links with scientific and industrial collaborators, and recruiting top students. Other centers at Notre Dame have also been very supportive. The Center for Applied Mathematics has supported two of my graduate students to attend a workshop on Mathematical and Computational Challenges in Mollecular and Cell Biology organized by the Mathematical Sciences Research Institute. They have granted me a faculty research grant to do exploratory work on issues addressed in this proposal and are funding one of my undergraduate research assistants this summer.
Through the center I have established an important collaboration with Profs. Mark Alber from mathematics and James Glazier from Physics. We were recently awarded an NSF Biocomplexity award to do multiscale simulation of avian limb development. My role in this project is related to numerical and computational aspects. Some components of the collaboratory software infrastructure will serve both this project and the biocomplexity one.
Since I started working in algorithms and software for MD in 1997, I have made significant contributions, primarily dealing with MD, their efficient parallel implementation, and the link with significant applications in bio-medicinal research. I have greatly benefited from the interaction with physical and computational scientists at the theoretical biophysics group at the Univ. of Illinois.
This project is an ambitious one. But, I am confident that with my experience in collaborative projects, the quality and commitment of my students and collaborators from both research universities and industry, and the outstanding institutional and departmental support, it will be carried out effectively. Also, the topics explored here will certainly extend beyond five years. As PROTOMOL is customized into a problem-solving environment, experience with our applications will allow for encapsulation of a great deal of domain knowledge that will make the software easier to use, and lead to greater applicability. Steering of simulations may prove tremendously important in the future, and our initial work on interactive molecular dynamics would give us a good position to enable future developments. The collaboration with massively parallel computers, although may not give immediate results in the first few years, will certainly be important when these technologies become available sometime in the next decade. And the applications that we are addressing may lead to more extensive collaboration with pharmaceutical industry and funding agencies such as NIH. For example, the structure based drug design group at Parke Davis, MI, is interested in our flexible docking project.
| Project schedule | Sub-goals |
| Year I | |
| PROTOMOL, algorithms | Master-Slave parallelization; MOLLY for constant pressure; |
| Web, TITANIUM and VMD APIs. | |
| Applications | Single QCA simulations; ER
bound to anti cancer drug; Small protein dynamics. |
| Education | Fall: Data Structures online materials; New scientific computing course in Fall |
| Spring: Workshop on Biomolecular Modeling | |
| Year II | |
| PROTOMOL, algorithms | Multilevel electrostatics; More efficient MOLLY; Distributed parallelization. |
| Applications | System QCA simulations; ER
bound to other drugs; Small protein folding. |
| Education | Fall: Scientific computing online materials; Spring: Biomolecular simulation with lab. |
| Year III | |
| PROTOMOL, algorithms | Wavelet-based Ewald sum; MOLLY HMC-MTS; Parallel multilevel; PDB connection. |
| Applications | QCA on surfaces; DNA/ER/drug interactions; Protein folding on Internet |
| Education | Fall: Interactive learning modules for data structures; |
| Spring: Workshop on Internet Computing | |
| Year IV | |
| PROTOMOL, algorithms | Flexible docking; Multiscale non-linear MOLLY; Parallel Ewald-like sum |
| Applications | Anti cancer drug with other receptors; Protein folding on MPP |
| Education | Fall: Interactive learning modules for scientific computing; |
| Spring: Congress on Petaflop Computing | |
| Year V | |
| PROTOMOL, algorithms | Multiscale MD-HMC integration; Rule encapsulation and structure generation |
| Applications | Healthy/malignant cell characterization; Interpretation and publications |
| Education | Computational biology undergraduate course (team-taught) |
Equally important, the educational initiatives described in the previous section will contribute towards educating computer science undergraduate and graduate students in computational science. Collaborative projects, and relevant examples and tutorials from applications, will serve to prepare them to participate in grand challenge problems where knowledge of architectural, algorithmic, large-scale, and high-performance software engineering issues is crucial.
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