This award will provide funds to the Department of Mathematics at University of Wisconsin Madison in collaboration with mathematics faculty at UW Eau Claire, UW Whitewater and the Madison Area Technical College (MATC) to implement a network of high performance computing environment for interdisciplinary research in the mathematical sciences and its applications to biological and basic medical sciences. This academic network will collaborate closely with pioneering American industrial partners (SUN Grid Network.com, nVIDIA and PBC Linear automation technologies) to provide a novel approach that enables a broader participation of students and faculty from the State of Wisconsin in advancing applications of distributed massively parallel GPU-based computation along with the Madison research groups in the computer sciences, genetics and mathematics.
The project outcomes include: applications of topology to accurately determine structure of large proteins, topological methods in fluid dynamics, mathematical modeling and computations to determine functions of genes in development of plant root systems. Further, novel algorithms will enable researchers to overcome current obstacles for visualization and analysis of very high dimensional massive data sets that are the hallmarks of the 21st century biotechnology in agronomy, drug discovery, and systems biology.
The main objectives of this project were: (1) develop the computational infrastructure to enable a multi-disciplinary team of researchers explore, study and solve a number of outstanding problems in the biological, computer and mathematical sciences; (2) implement research strategies that require "state-of-art super-computing" and automated imaging with very large outputs and highly cost-effective operation overhead. Intellectual Merit - The PIs acquired hardware parts and software packages, designed and engineered a high-performance computational cluster and automated imaging systems. In addition, the PIs completed the mathematical computation, modeling, design of novel algorithms, and their implementation using state-of-art programming languages and tools. The combination of superior performance by hardware, software and the computational findings enabled the PIs and their collaborators to answer outstanding questions in plant biology, computational neuroscience, theoretical computer sciences, numerical fluid dynamics and mathematical biology. Broader Impact - The images represent samples of applications of research findings that became possible thanks to the availability infrastructure (1)(2) above. These include novel algorithms for discovery of functions of genes and proteins in molecular plant biology of plant roots and shoots, applicable to modern agriculture. The mathematical calculations and statistical models for analysis of growth and morphology of plant root images have broader applicability, indeed, to "branched structures" such as lood vessels and neuronal networks. A notable mathematical outcome of this project is an application to early detection of pathological changes in the human eye (retina). Such malformations are typically occur in diabetic patients (Diabetic Retinopathy), and they account for most cases of vision loss in the US and Europe. Another application is to BIGDATA, namely, design of novel machine learning algorithms that automatically "read and process" thousands of scientific publications in order to extract hard-to-find relationships, facts and pieces of information from diverse subjects. Additional discoveries (work in progress) are in the medical field, known as Angiogenesis, development and growth of tiny blood vessels in mature animals. Angiogenesis is often pathological, for example, due to diabetes in the eye or kidney, and its occurances in different organs could be quite different. In heart tissue and wound-healing, Angiogenesis serves critical therapeutic functions, while in cancer tumors, the malformed invasive networks of blood vessels spread malignancy and are fatal. PI/PD Assadi, UW Madison students and collaborators have developed novel algorithms in image analysis that successfully identify the effects of different classes of anti-angiogenesis drugs on human cells that drive angiogenesis in health and disease. Implications of this discovery point to new mathematical models in personalized medicine in cancer and diabetes research. Education, Training and Development of Human Resources - A large niumber of undergraduate and graduate students were mentored in research activities that were mentioned above. Further, K-16 students, including under-represented groups, had the opportunity to work with the imaging systems, investigate their functions in hardware and user-friendly software interfaces, and wrote comprehensive reports on capabilities, shortcomings and potential future improvements. National and International Collaborations - Research labs from Europe, Japan, Korea and Brazil have established long-term collaborative projects related to agriculture, bioenergy, and environmental studies. These collaborations offer the collaborating labs access to the imaging hardware, the benefits and versatility of the automated imaging system, and fully automated analysis of the very large data sets of images that they generate. These collaborations are expected to be organized in Research Networks, which will save costs in research and training, and opens new possibilities for large-scale team approach to the challenging multi-disciplinary research problems.
