BackgroundGraphs and networks are common analysis representations for biological systems. Many traditional graph algorithms such as k-clique, k-coloring, and subgraph matching have great potential as analysis techniques for newly available data in biology. Yet, as the amount of genomic and bionetwork information rapidly grows, scientists need advanced new computational strategies and tools for dealing with the complexities of the bionetwork analysis and the volume of the data.ResultsWe introduce a computational framework for graph analysis called the Biological Graph Environment (BioGraphE), which provides a general, scalable integration platform for connecting graph problems in biology to optimized computational solvers and high-performance systems. This framework enables biology researchers and computational scientists to identify and deploy network analysis applications and to easily connect them to efficient and powerful computational software and hardware that are specifically designed and tuned to solve complex graph problems. In our particular application of BioGraphE to support network analysis in genome biology, we investigate the use of a Boolean satisfiability solver known as Survey Propagation as a core computational solver executing on standard high-performance parallel systems, as well as multi-threaded architectures.ConclusionIn our application of BioGraphE to conduct bionetwork analysis of homology networks, we found that BioGraphE and a custom, parallel implementation of the Survey Propagation SAT solver were capable of solving very large bionetwork problems at high rates of execution on different high-performance computing platforms.

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This paper introduces three component technology initiatives within the SciDAC Center for Technology for Advanced Scientific Component Software (TASCS) that address everincreasing productivity challenges in creating, managing, and applying simulation software to scientific discovery. By leveraging the Common Component Architecture (CCA), a new component standard for high-performance scientific computing, these initiatives tackle difficulties at different but related levels in the development of component-based scientific software: (1) deploying applications on massively parallel and heterogeneous architectures, (2) investigating new approaches to the runtime enforcement of behavioral semantics, and (3) developing tools to facilitate dynamic composition, substitution, and reconfiguration of component implementations and parameters, so that application scientists can explore tradeoffs among factors such as accuracy, reliability, and performance.