Genome-scale computational approaches to memory-intensive applications in systems biology

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dc.contributor.author Abu-Khzam, F.N.
dc.contributor.author Zhang, Yun
dc.contributor.author Baldwin, N.E.
dc.contributor.author Chesler, E.J.
dc.contributor.author Langston, M.A.
dc.contributor.author Samatova, N.F.
dc.date.accessioned 2017-03-20T11:30:25Z
dc.date.available 2017-03-20T11:30:25Z
dc.date.issued 2017-03-20
dc.identifier.isbn 1-59593-061-2 en_US
dc.identifier.uri http://hdl.handle.net/10725/5407
dc.description.abstract Graph-theoretical approaches to biological network analysis have proven to be effective for small networks but are computationally infeasible for comprehensive genome-scale systems-level elucidation of these networks. The difficulty lies in the NP-hard nature of many global systems biology problems that, in practice, translates to exponential (or worse) run times for finding exact optimal solutions. Moreover, these problems, especially those of an enumerative flavor, are often memory-intensive and must share very large sets of data effectively across many processors. For example, the enumeration of maximal cliques - a core component in gene expression networks analysis, cis regulatory motif finding, and the study of quantitative trait loci for high-throughput molecular phenotypes can result in as many as 3^n/3 maximal cliques for a graph with n vertices. Memory requirements to store those cliques reach terabyte scales even on modest-sized genomes. Emerging hardware architectures with ultra-large globally addressable memory such as the SGI Altix and Cray X1 seem to be well suited for addressing these types of data-intensive problems in systems biology. This paper presents a novel framework that provides exact, parallel and scalable solutions to various graph-theoretical approaches to genome-scale elucidation of biological networks. This framework takes advantage of these large-memory architectures by creating globally addressable bitmap memory indices with potentially high compression rates, fast bitwise-logical operations, and reduced search space. Augmented with recent theoretical advancements based on fixed-parameter tractability, this framework produces computationally feasible performance for genome-scale combinatorial problems of systems biology. en_US
dc.language.iso en en_US
dc.publisher IEEE en_US
dc.title Genome-scale computational approaches to memory-intensive applications in systems biology en_US
dc.type Conference Paper / Proceeding en_US
dc.author.school SAS en_US
dc.author.idnumber 200302941 en_US
dc.author.department Computer Science and Mathematics en_US
dc.description.embargo N/A en_US
dc.keywords Genomics en_US
dc.keywords Bioinformatics en_US
dc.keywords Biology computing en_US
dc.keywords Systems biology en_US
dc.keywords Biological system modeling en_US
dc.keywords Computational modeling en_US
dc.keywords Hardware en_US
dc.keywords Space technology en_US
dc.keywords Government en_US
dc.keywords Computer science en_US
dc.identifier.doi http://dx.doi.org/10.1109/SC.2005.29 en_US
dc.identifier.ctation Zhang, Y., Abu-Khzam, F. N., Baldwin, N. E., Chesler, E. J., Langston, M. A., & Samatova, N. F. (2005, November). Genome-scale computational approaches to memory-intensive applications in systems biology. In Supercomputing, 2005. Proceedings of the ACM/IEEE SC 2005 Conference (pp. 12-12). IEEE. en_US
dc.author.email faisal.abukhzam@lau.edu.lb en_US
dc.conference.date 12-18 Nov. 2005 en_US
dc.conference.subtitle Proceedings of the 2005 ACM/IEEE Conference on Supercomputing en_US
dc.conference.title SC '05 en_US
dc.identifier.tou http://libraries.lau.edu.lb/research/laur/terms-of-use/articles.php en_US
dc.identifier.url http://ieeexplore.ieee.org/abstract/document/1559964/ en_US
dc.orcid.id https://orcid.org/0000-0001-5221-8421
dc.author.affiliation Lebanese American University en_US

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