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Complexity and High-End Computing in Biology and Medicine

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Software Tools and Algorithms for Biological Systems

Part of the book series: Advances in Experimental Medicine and Biology ((AEMB,volume 696))

Abstract

Biomedical systems involve a large number of entities and intricate interactions between these. Their direct analysis is, therefore, difficult, and it is often necessary to rely on computational models. These models require significant resources and parallel computing solutions. These approaches are particularly suited, given parallel aspects in the nature of biomedical systems. Model hybridisation also permits the integration and simultaneous study of multiple aspects and scales of these systems, thus providing an efficient platform for multidisciplinary research.

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Notes

  1. 1.

    Patterns of system evolution arising from an abundance of simple, low-level, interactions [7].

  2. 2.

    Increased complexity obtained without intervention from an outside source [12].

  3. 3.

    IBM’s ASCI White (fastest computer from 11/2000 to 06/2002) cost $110 million. IBM’s Roadrunner (ranked first since 06/2008) $130 million, but delivered 200 times the computing power.

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Acknowledgements

The author warmly acknowledges financial support from the Irish Research Council for Science, Engineering and Technology (Embark Initiative, immune modelling), Science Foundation Ireland (Research Frontiers Programme 07/RFP/CMSR724, epigenetic modelling), and Dublin City University (Career Start Award, socio-epidemic modelling). The author also wishes to thank both the SFI/HEA Irish Centre for High-End Computing, and the Centre for Scientific Computing & Complex Systems Modelling, for the provision of computational facilities and support.

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Authors and Affiliations

  1. Centre for Scientific Computing & Complex Systems Modelling, Dublin City University, Glasnevin, Dublin 9, Ireland

    Dimitri Perrin

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  1. Dimitri Perrin
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Correspondence to Dimitri Perrin .

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Editors and Affiliations

  1. Dept. Computer Science, University of Georgia, Athens, 30602-7404, Georgia, USA

    Hamid R. Arabnia

  2. , Department of Computer Science, Lamar University, Beaumont, 77710, Texas, USA

    Quoc-Nam Tran

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Perrin, D. (2011). Complexity and High-End Computing in Biology and Medicine. In: Arabnia, H., Tran, QN. (eds) Software Tools and Algorithms for Biological Systems. Advances in Experimental Medicine and Biology, vol 696. Springer, New York, NY. https://doi.org/10.1007/978-1-4419-7046-6_38

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