Martin Harvey Schultz - US grants

Programming supercomputers is currently difficult and time consuming. Scientists and engineers who use these machines have an urgent need for a programming environment that is portable, interactive, flexible, and convenient. There is a prototype that serves as an existence proof for the feasibility of constructing a useful environment for scientific computing. This software tool, called CLAMth (the Computatinal Linear Algebra Machine), currently exists as a prototype version written in Fortran 77. Although CLAM is successful, we view it as a "first generation" programming environment for supercomputers. Using a modern programming language (T), we propose to build a "second generation" environment that will: oAccept the programmer's preferred language, be it T, CLAM, or Fortran. oHave an interpretive debugger that will promote fast prototyping. oBe fully portable from workstation to mainframe to supercomputer. oUtilize state-of-the-art algorithms for large scale numerical analysis tasks. It will handle vector and matrix data structures automatically and also contain an "expert" capability to select appropriate algorithms from its own internal library to match the solution techniques to the problem size and characteristics. In short, this environment will provide the applications- oriented engineer or scientists with the fastest possible solution for any particular computation, without requiring the user to make decisions about the appropriate algorithms and data structures to be employed. ---------------------------------------------------------------- This proposal focuses on the development of programming tools for scientists and engineers. Specifically the proposal refines the programming language associated with CLAM, a previously developed software package, for improved programming environments. This new version of CLAM will have several improvements such a portability, automatic parallelization, etc. and can be easily incorporated into code generated by other languages. Thus 36 month proposal will be funded all at one time as follows: $150.0k CSE (CISE) $ 50.0k Comp. Math (DMS) $ 50.0k New Tech (DASC) --------- $250.0k 36 mos. paid entirely in first year. ***

Advances in technologies are changing the field of biology to move beyond genomes to transcriptomes, proteomes and metabolomes. It has become clear that the combination of predictive modeling with systematic experimental verification will be required to gain a deeper insight into living organisms, therapeutic targeting and bioengineering. Although the importance of integrating various types of biological data to address scientific questions is well recognized and appreciated, the potential information carried in different types of data may not be fully realized without a sound and comprehensive statistical framework to integrate these data. In addition, close collaborations among statisticians, biologists, bioinformaticians, and computer scientists are essential to ensure that these statistical methods provide a reasonable description of the biological processes studied and the validity of these methods should be rigorously tested through biological experiments. In this project, a team of researchers with expertise in statistics, genomics and proteomics, bioinformatics, and computer science will develop an integrated approach to reconstructing biological pathways. Statistical and computational methods will be developed to better identify transcription factor targets, to integrate yeast two-hybrid data, protein complex data, protein localization data, and gene expression data to infer protein interaction networks, and to further integrate DNA- protein binding data to reconstruct transcriptional regulatory networks. This project focuses on the G1/S transition during the yeast cell cycle to statistically model and experimentally validate inferred regulatory networks. In addition, parallel computing methods will be developed to overcome the computing bottleneck in the analysis of large-scale networks. The resources generated from this project, both computer programs and network information will be made available to the scientific community. It is anticipated that this project will lead to a statistical framework that can be utilized to dissect biological pathways and also will lead to an approach to integrating expertise from diverse disciplines to address important scientific problems in the post-genome era.
With recent progresses in biotechnologies, it has become reality to collect tens of thousands of gene expression and protein expression levels in humans and other organisms. In addition, scientists now are able to monitor interactions among proteins and interactions between proteins and DNA sequences, to investigate the location that each gene is expressed, and to study the overall effects on the whole organism of individual genes through large collections of mutation strains. The availability of such data has led to a revolution in biological and biomedical sciences. Although there is a great potential and an enormous amount of information in these data, the major challenge is how to best integrate, analyze, and interpret these data to understand biological pathways. In this project, statistical and computational methods will be developed to integrate various types of data in an effort to reconstruct biological pathways with a focus on the understanding of gene regulations in cell cycle. The statistical models to be developed will be validated with biological experiments. Computer programs will be developed and distributed to the scientific community after extensive testing to allow biologists and medical researchers to use these tools to study other biological pathways. This project will also develop high-performance computing approaches to implementing the developed methods and will involve training activities in the general area of computational biology and bioinformatics. This grant is made under the Joint DMS/NIGMS Initiative to Support Research Grants in the Area of Mathematical Biology. This is a joint competition sponsored by the Division of Mathematical Sciences (DMS) at the National Science Foundation and the National Institute of General Medical Sciences (NIGMS) at the National Institutes of Health.