Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences

    We have developed a computational Grid that enables us to exploit through a single interface a range of local, national and international resources. It insulates the user as far as possible from issues concerning administrative boundaries, passwords and different operating system features. This work has been undertaken as part of the European Union ImmunoGrid project whose aim is to develop simulations of the immune system at the molecular, cellular and organ levels. The ImmunoGrid consortium has members with computational resources on both sides of the Atlantic. By making extensive use of existing Grid middleware, our Grid has enabled us to exploit consortium and publicly available computers in a unified way, notwithstanding the diverse local software and administrative environments. We took 40 000 polypeptide sequences from 4000 avian and mammalian influenza strains and used a neural network for class I T-cell epitope prediction tools for 120 class I alleles and haplotypes to generate over 14 million high-quality protein–peptide binding predictions that we are mapping onto the three-dimensional structures of the proteins. By contrast, the Grid is also being used for developing new methods for class T-cell epitope predictions, where we have running batches of 120 molecular dynamics free-energy calculations.


    • Almond J.& Snelling D.. 1999 UNICORE: uniform access to supercomputing as an element of electronic commerce. Future Gen. Comput. Syst. 15, 539–548.doi:10.1016/S0167-739X(99)00007-2. . Crossref, Web of ScienceGoogle Scholar
    • Bui H.-H., Peters B., Assarsson E., Mbawuike I.& Sette A.. 2007 Ab and T-cell epitiopes of influenza A virus, knowledge and opportunities. Proc. Natl Acad. Sci. USA. 104, 246–251.doi:10.1073/pnas.0609330104. . Crossref, PubMed, Web of ScienceGoogle Scholar
    • Coveney P.V., Saksena R.S., Zasada S.J., McKeown M.& Pickles S.. 2007 The application hosting environment: lightweight middleware for grid-based computational science. Comp. Phys. Commun. 176, 406–418.doi:10.1016/j.cpc.2006.11.011. . Crossref, Web of ScienceGoogle Scholar
    • Davies M.N., Sansom C.E., Beazley C.& Moss D.S.. 2003 A novel predictive technique for the MHC class II peptide-binding interaction. Mol. Med. 9, 220–225.doi:10.2119/2003-00032.Sansom. . Crossref, PubMed, Web of ScienceGoogle Scholar
    • Hénin J.& Chipot C.. 2004 Overcoming free energy barriers using unconstrained molecular dynamics simulations. J. Chem. Phys. 121, 2904 doi:10.1063/1.1773132. . Crossref, PubMed, Web of ScienceGoogle Scholar
    • Kertész, A. & Kacsuk, P. 2006 A taxonomy of Grid Resource Brokers. In Proc. 6th Austrian–Hungarian Workshop on Distributed and Parallel Systems (DAPSYS 2006) in conjunction with the Austrian Grid Symposium, Innsbruck, Austria 2006, pp. 21–23. Google Scholar
    • Kertész, A., Kacsuk, P., Rodero, I., Guim, F. & Corbalan, J. 2007 Meta-Brokering requirements and research directions in state-of-the-art Grid Resource Management. CoreGRID Technical report no. TR-0116. Google Scholar
    • Lundegaard C., Lund O., Kesmir C., Brunak S.& Nielsen M.. 2007 Modeling the adaptive immune system: predictions and simulations. Bioinform. 23, 3265–3275.doi:10.1093/bioinformatics/btm471. . Crossref, PubMed, Web of ScienceGoogle Scholar
    • Miotto O., Tan T.W.& Brusic V.. 2008 Rule-based knowledge aggregation for large-scale protein sequence analysis of influenza A viruses. BMC Bioinformatics. 9, Suppl. 1, S7 doi:10.1186/1471-2105-9-S1-S7. . Crossref, PubMed, Web of ScienceGoogle Scholar
    • Nielsen M., Lundegaard C., Worning P., Lauemoller S.L., Lamberth K., Buus S., Brunak S.& Lund O.. 2003 Reliable prediction of T-cell epitopes using neural networks with novel sequence representations. Protein Sci. 12, 1007–1017.doi:10.1110/ps.0239403. . Crossref, PubMed, Web of ScienceGoogle Scholar
    • Novotny, J., Tuecke, S. & Welch, V. 2001 An online credential repository for the Grid: MyProxy. In Proc. 10th Int. Symp. on High Performance Distributed Computing (HPDC-10), pp. 104–111. Piscataway, NJ: IEEE Press. Google Scholar
    • Pappalardo F., Musumeci S.& Motta S.. 2008 Modeling immune system control of atherogenesis. Bioinformatics. 24, 1715–1721.doi:10.1093/bioinformatics/btn306. . Crossref, PubMed, Web of ScienceGoogle Scholar
    • Phillips J.C., et al. 2005 Scalable molecular dynamics with NAMD. J. Comput. Chem. 26, 1781–1802.doi:10.1002/jcc.20289. . Crossref, PubMed, Web of ScienceGoogle Scholar
    • Richards, A., Schmidt, J., Dew, P. M., Youhanaie, F., Ford, M. & Geddes, N. 2004 Production quality e-Science Grid. In Proc. UK e-Science All Hands Meeting, Nottingham, UK, 31st August–3rd September 2004. Google Scholar
    • Sloan, T. M., Menday, R., Seed, T., Illingworth, M. & Trew, A. S. 2006 DESHL—standards based access to a heterogeneous European supercomputing infrastructure. In Proc. 2nd IEEE Int. Conf. on e-Science and Grid Computing—eScience 2006, 4th–6th December, Amsterdam, p. 91. Google Scholar
    • Zasada, S. J., Saksena, R. S., Coveney, P. V., McKeown, M. & Pickles, S. 2006 Facilitating user access to the Grid: a lightweight application hosting environment for grid enabled computational science. In Proc. 2nd IEEE Int. Conf. on e-Science and Grid Computing, p. 50. Google Scholar
    • Zasada, S. J., Cheney, B. G., Saksena, R. S., Suter, J. L., Coveney, P. V. & Essex, J. W. 2007 Production level scientific simulation management on international federated Grids. In Proc. 2nd TeraGrid Conference, Madison, WI, 4–8 June 2007. Google Scholar