Robert L. Martino

A system for the 3D reconstruction of retracted-septa PET data using the EM algorithm

We have implemented the EM reconstruction algorithm for volume acquisition from current generation retracted-septa PET scanners. Although the software was designed for a GE Advance scanner, it is easily adaptable to other 3D scanners. The reconstruction software was written for an Intel iPSC/860 parallel computer with 128 compute nodes. Running on 32 processors, the algorithm requires approximately 55 minutes per iteration to reconstruct a 128/spl times/128/spl times/35 image. No projection data compression schemes or other approximations were used in the implementation. Extensive use of EM system matrix (C/sub ij/) symmetries (including the 8-fold in-plane symmetries, 2-fold axial symmetries, and axial parallel line redundancies) reduces the storage cost by a factor of 188. The parallel algorithm operates on distributed projection data which are decomposed by base-symmetry angles. Symmetry operators copy and index the C/sub ij/ chord to the form required for the particular symmetry. The use of asynchronous reads, lookup tables, and optimized image indexing improves computational performance. >

Parallel computation in biological sequence analysis

A massive volume of biological sequence data is available in over 36 different databases worldwide, including the sequence data generated by the Human Genome project. These databases, which also contain biological and bibliographical information, are growing at an exponential rate. Consequently, the computational demands needed to explore and analyze the data contained in these databases is quickly becoming a great concern. To meet these demands, we must use high performance computing systems, such as parallel computers and distributed networks of workstations. We present two parallel computational methods for analyzing these biological sequences. The first method is used to retrieve sequences that are homologous to a query sequence. The biological information associated with the homologous sequences found in the database may provide important clues to the structure and function of the query sequence. The second method, which helps in the prediction of the function, structure, and evolutionary history of biological sequences, is used to align a number of homologous sequences with each other. These two parallel computational methods were implemented and evaluated on an Intel IPSC/860 parallel computer. The resulting performance demonstrates that parallel computational methods can significantly reduce the computational time needed to analyze the sequences contained in large databases.

Orientation determination in the 3D reconstruction of icosahedral viruses using a parallel computer

Three dimensional image reconstruction from cryoelectron micrographs allows the capsid structures of icosahedral viruses to be studied. The most computationally demanding stages of the reconstruction process are those that find and refine the orientations of the virus particles. We have devised and implemented parallel solutions to the problem of determining these orientations. By enabling determination of the orientations of far more particles than previously had been possible, parallel processing methods have contributed to improvements in the quality of recent reconstructions.<<ETX>>

High Performance Computational Methods for Biological Sequence Analysis

Preface. 1. Introduction. 2. Biological Background. 3. Sequence Analysis Algorithms. 4. High Performance Computing Architectures and Techniques. 5. Multiprocessor Sequence Alignment. 6. Multiprocessor Sequence Similarity Searching. 7. Biological Sequence Resources on the Internet. 8. Looking to the Future. References. Index.

Parallel computing strategies for determining viral capsid structure by cryoelectron microscopy

To calculate a full 3D structural model of a virus capsid, researchers analyzed cryoelectron micrographs that contain many randomly oriented images of the views. The authors use parallel computing techniques to improve the performance of the computational algorithms that determine each particles orientation and generate the 3D model. This enhanced computational performance allows analysis of many more particles and a more precise determination of their orientations, letting researchers study important details of virus capsids at higher resolutions.

A collaborative telemedicine environment for the Ireland - Northern Ireland - National Cancer Institute international partnership in cancer care

The Ireland - Northern Ireland - National Cancer Institute Cancer Consortium was established to improve clinical cancer services and patient care on the island of Ireland, and to foster joint collaboration in cancer research and development. An important part of this Consortium was to develop telemedicine systems to facilitate collaboration between participating partners. We have implemented a telemedicine environment for the Consortium based on the TELESYNERGY/spl reg/ Medical Consultation Workstation developed at the U.S. National Institutes of Health. The collaborative use of the system at the three sites has led to the improvement in quality of care in that each patient, regardless of location, can receive an expert assessment and given optimal therapy. The system will be a platform for collaborative clinical and translational research. In the future, this technology will be implemented at additional sites and applied to other medical specialties including cardiology, radiology, and infectious diseases.

Parallel Computing Methods for Analyzing Gene Expression Relationships

This paper presents a parallel program for assessing the codetermination of gene transcriptional states from large- scale simultaneous gene expression measurements with cDNA microarrays. The parallel program is based on a nonlinear statistical framework recently proposed for the analysis of gene interaction via multivariate expression arrays. Parallel computing is key in the application of the statistical framework to a large set of genes because a prohibitive amount of computer time is required on a classical single-CPU machine. Our parallel program, named the Parallel Analysis of Gene Expression (PAGE) program, exploits inherent parallelism exhibited in the proposed codetermination prediction models. By running PAGE on 64 processors in Beowulf, a clustered parallel system, an analysis of melanoma cDNA microarray expression data has been completed within 12 days of computer time, an analysis that would have required about one and half years on a single-CPU computing system. A data visualization program, named the Visualization of Gene Expression (VOGE) program, has been developed to help interpret the massive amount of quantitative information produced by PAGE. VOGE provides graphical data visualization and analysis tools with filters, histograms, and accesses to other genetic databanks for further analyses of the quantitative information.

Parallel homologous sequence searching in large databases

We present a parallel computational method for retrieving similar sequences from large genetic and protein databases using a dynamic programming comparison algorithm. Two previously published parallel methods for performing this task are first discussed and evaluated. The advantages of these two parallel methods are combined and incorporated into our new method to obtain better performance than either of the original two. Using the entire GenBank database (release 80.0), we compare the performance of the three methods on an Intel iPSC/860 parallel computer.<<ETX>>

A Web-based medical image archive system

This paper presents a Web-based medical image archive system in three-tier, client-server architecture for the storage and retrieval of medical image data, as well as patient information and clinical data. The Web-based medical image archive system was designed to meet the need of the National Institute of Neurological Disorders and Stroke for a central image repository to address questions of stroke pathophysiology and imaging biomarkers in stroke clinical trials by analyzing images obtained from a large number of clinical trials conducted by government, academic and pharmaceutical industry researchers. In the database management-tier, we designed the image storage hierarchy to accommodate large binary image data files that the database software can access in parallel. In the middle-tier, a commercial Enterprise Java Bean server and secure Web server manages user access to the image database system. User-friendly Web-interfaces and applet tools are provided in the client-tier for easy access to the image archive system over the Internet. Benchmark test results show that our three-tier image archive system yields fast system response time for uploading, downloading, and querying the image database.

Parallel Algorithms in Molecular Biology

Scalable parallel computer architectures provide the computational performance needed for advanced computing problems in molecular biology. Many scientific challenges in molecular biology have associated with them a computational requirement that must be solved before scientific progress can be made. We have developed a number of parallel algorithms and techniques useful in determining biological structure and function. Two example applications are the alignment of multiple DNA and protein sequences using speculative computation and the calculation of the solvent accessible surface area of proteins used to predict the three-dimensional conformation of these molecules from their primary structure. Timing results demonstrate substantial performance improvements with parallel implementations compared with conventional sequential systems. As the developed methods allow molecular biologists to perform computational tasks that would not otherwise be possible, we continue to develop parallel algorithms useful to this important scientific field.