ImmunoGrid: towards agent-based simulations of the human immune system at a natural scale

M. Halling-Brown, F. Pappalardo, Nicolas Rapin, P. Zhang, D. Alemani, A. Emerson, F. Castiglione, P. Duroux, M. Pennisi, O. Miotto, D. Churchill, E. Rossi, D.S. Moss, C.E. Sansom, M. Bernaschi, M.P. Lefranc, Søren Brunak, Ole Lund, S. Motta, P.L. LolliniA. Murgo, A. Palladini, K.E. Basford, V. Brusic, A.J. Shepherd

    Research output: Contribution to journalJournal articleResearchpeer-review

    Abstract

    The ultimate aim of the EU-funded ImmunoGrid project is to develop a natural-scale model of the human immune system-that is, one that reflects both the diversity and the relative proportions of the molecules and cells that comprise it-together with the grid infrastructure necessary to apply this model to specific applications in the field of immunology. These objectives present the ImmunoGrid Consortium with formidable challenges in terms of complexity of the immune system, our partial understanding about how the immune system works, the lack of reliable data and the scale of computational resources required. In this paper, we explain the key challenges and the approaches adopted to overcome them. We also consider wider implications for the present ambitious plans to develop natural-scale, integrated models of the human body that can make contributions to personalized health care, such as the European Virtual Physiological Human initiative. Finally, we ask a key question: How long will it take us to resolve these challenges and when can we expect to have fully functional models that will deliver health-care benefits in the form of personalized care solutions and improved disease prevention?
    Original languageEnglish
    JournalRoyal Society of London. Philosophical Transactions. Mathematical, Physical and Engineering Sciences
    Volume368
    Issue number1920
    Pages (from-to)2799-2815
    ISSN1364-503X
    DOIs
    Publication statusPublished - 2010

    Keywords

    • vaccine discovery
    • immunoinformatics
    • Grid computing
    • systems biology
    • agent-based simulation

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