Abstract
In this paper we present the ImmunoGrid project, whose goal is to develop an immune system simulator which integrates molecular and system level models with Grid computing resources for large-scale tasks and databases. We introduce the models and the technologies used in the ImmunoGrid Simulator, showing how to use them through the ImmunoGrid web interface. The ImmunoGrid project has proved that simulators can be used in conjunction with grid technologies for drug and vaccine discovery, demonstrating that it is possible to drastically reduce the developing time of such products.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Castiglione, F., Bernaschi, M., Succi, S.: Simulating the immune response on a distributed parallel computer. Int. J. Mod. Phys. C 8, 527–545 (1997)
Motta, S., Castiglione, F., Lollini, P., Pappalardo, F.: Modelling vaccination schedules for a cancer immunoprevention vaccine. Immunome Res. 1, 5 (2005)
Lin, H.H., Ray, S., Tongchusak, S., et al.: Evaluation of MHC class I peptide binding prediction servers: applications for vaccine research. BMC Immunol. 9, 8 (2008)
Lin, H.H., Zhang, G.L., Tongchusak, S., et al.: Evaluation of MHC-II peptide binding prediction servers: applications for vaccine research. BMC Bioinformatics 9(Suppl. 12), S22 (2008)
Lefranc, M.P.: IMGT, the international ImMunoGeneTics information system ®: a standardized approach for immunogenetics and immunoinformatics. Immunome Res. 1, 3 (2005) [ imgt.cines.fr ]
Lefranc, M.P., Giudicelli, V., Duroux, P.: IMGT ®, a system and an ontology that bridge biological and computational spheres in bioinformatics. Brief Bioinform. 9, 263–275 (2008)
Motta, S., Brusic, V.: Mathematical modeling of the immune system. In: Ciobanu, G., Rozenberg, G. (eds.) Modelling in Molecular Biology. Natural Computing Series, pp. 193–218. Springer, Berlin (2004)
Louzoun, Y.: The evolution of mathematical immunology. Immunol. Rev. 216, 9–20 (2007)
Castiglione, F., Liso, A.: The role of computational models of the immune system in designing vaccination strategies. Immunopharmacol. Immunotoxicol. 27, 417–432 (2005)
Falus, A. (ed.): Immunogenomics and HumanDisease. Wiley, Hoboken (2006)
Purcell, A.W., Gorman, J.J.: Immunoproteomics: Massspectrometry-based methods to study the targets of the immune response. Mol. Cell Proteomics 3, 193–208 (2004)
Brusic, V., Marina, O., Wu, C.J., Reinherz, E.L.: Proteome informatics for cancer research: from molecules to clinic. Proteomics 7, 976–991 (2007)
Schönbach, C., Ranganathan, S., Brusic, V. (eds.): Immunoinformatics. Springer, Heidelberg (2007)
Tegnér, J., Nilsson, R., Bajic, V.B., et al.: Systems biology of innate immunity. Cell Immunol. 244, 105–109 (2006)
Sachdeva, N., Asthana, D.: Cytokine quantitation: technologies and applications. Front Biosci. 12, 4682–4695 (2007)
Harnett, M.M.: Laser scanning cytometry: understanding the immune system in situ. Nat. Rev. Immunol. 7, 897–904 (2007)
Brusic, V., Bucci, K., Schon̈bach, C., et al.: Efficient discovery of immune response targets by cyclical refinement of QSAR models of peptide binding. J. Mol. Graph Model 19, 405–411 (2001)
Pappalardo, F., Motta, S., Lollini, P.L., Mastriani, E.: Analysis of vaccines schedules using models. Cell Immunol. 244, 137–140 (2006)
Yates, A., Chan, C.C., Callard, R.E., et al.: An approach to modelling in immunology. Brief Bioinform. 2, 245–257 (2001)
Celada, F., Seiden, P.E.: A computer model of cellular inter- action in the immune system. Immunol. Today 13, 56–62 (1992)
Castiglione, F., Poccia, F., D’Offizi, G., Bernaschi, M.: Mutation, fitness, viral diversity and predictive markers of disease progression in a computational model of HIV-1 infection. AIDS Res. Hum. Retroviruses 20, 1316–1325 (2004)
Baldazzi, V., Castiglione, F., Bernaschi, M.: An enhanced agent based model of the immune system response. Cell Immunol. 244, 77–79 (2006)
Castiglione, F., Duca, K., Jarrah, A., et al.: Simulating Epstein- Barr virus infection with C-ImmSim. Bioinformatics 23, 1371–1377 (2007)
Castiglione, F., Toschi, F., Bernaschi, M., et al.: Computational modeling of the immune response to tumor antigens: implications for vaccination. J. Theo. Biol. 237/4, 390–400 (2005)
Lollini, P.L., Motta, S., Pappalardo, F.: Discovery of cancer vaccination protocols with a genetic algorithm driving an agent based simulator. BMC Bioinformatics 7, 352 (2006)
Pappalardo, F., Lollini, P.L., Castiglione, F., Motta, S.: Modeling and simulation of cancer immunoprevention vaccine. Bioinformatics 21, 2891–2897 (2005)
Pappalardo, F., Musumeci, S., Motta, S.: Modeling immune system control of atherogenesis. Bioinformatics 24, 1715–1721 (2008)
He, X., Luo, L.: Theory of the lattice Boltzmann method: from the Boltzmann equation to the lattice Boltzmann equation. Phys. Rev. E 56, 6811–6817 (1997)
Ferreira Jr., S.C., Martins, M.L., Vilela, M.J.: Morphology transitions induced by chemotherapy in carcinomas in situ. Phys. Rev. E 67, 051914 (2003)
Catron, D.M., Itano, A.A., Pape, K.A., et al.: Visualizing the first 50hr of the primary immune response to a soluble antigen. Immunity 21, 341–347 (2004)
Garside, P., Ingulli, E., Merica, R.R., et al.: Visualization of specific B and T lymphocyte interactions in the lymph node. Science 281, 96–99 (1998)
Mempel, T.R., Henrickson, S.E., Von Andrian, U.H.: T-cell priming by dendritic cells in lymph nodes occurs in three distinct phases. Nature 427, 154–159 (2004)
Brusic, V., Rudy, G., Harrison, L.C.: MHCPEP, a database of MHC-binding peptides: update 1997. Nucleic Acids Res 26, 368–371 (1998)
Rammensee, H., Bachmann, J., Emmerich, N.P., et al.: SYFPEITHI: database for MHC ligands and peptide motifs. Immunogenetics 50, 213–219 (1999)
Toseland, C.P., Clayton, D.J., McSparron, H., et al.: AntiJen: a quantitative immunology database integrating functional, thermodynamic, kinetic, biophysical, and cellular data. Immunome Res. 1, 4 (2005)
Sette, A., Bui, H., Sidney, J., et al.: The immune epitope database and analysis resource. In: Rajapakse, J.C., Wong, L., Acharya, R. (eds.) PRIB 2006. LNCS (LNBI), vol. 4146, pp. 126–132. Springer, Heidelberg (2006)
Nielsen, M., Lundegaard, C., Lund, O., Kesmir, C.: The role of the proteasome in generating cytotoxic T-cell epitopes: insights obtained from improved predictions of proteasomal cleavage. Immunogenetics 57, 33–41 (2005)
Larsen, M.V., Lundegaard, C., Lamberth, K., et al.: An integrative approach to CTL epitope prediction: a combined algorithm integrating MHC class I binding, TAP transport efficiency, and proteasomal cleavage predictions. Eur. J. Immunol. 35, 2295–2303 (2005)
Nielsen, M., Lundegaard, C., Lund, O.: Prediction of MHC class II binding affinity using SMM-align, a novel stabilization matrix alignment method. BMC Bioinformatics 8, 238 (2007)
Nielsen, M., Lundegaard, C., Blicher, T., et al.: NetMHCpan, a method for quantitative predictions of peptide binding to any HLA-A and -B locus protein of known sequence. PLoS ONE 2, e796 (2007)
Larsen, J.E., Lund, O., Nielsen, M.: Improved method for predicting linear B-cell epitopes. Immunome Res. 2, 2 (2006)
Andersen, P.H., Nielsen, M., Lund, O.: Prediction of residues in discontinuous B-cell epitopes using protein 3D structures. Protein Sci. 15, 2558–2567 (2006)
Brusic, V., Bajic, V.B., Petrovsky, N.: Computational methods for prediction of T-cell epitopes a framework for modelling, testing, and applications. Methods 34, 436–443 (2004)
Tong, J.C., Tan, T.W., Ranganathan, S.: Methods and protocols for prediction of immunogenic epitopes. Brief Bioinform. 8, 96–108 (2007)
Reche, P.A., Glutting, J.P., Zhang, H., Reinherz, E.L.: Enhancement to the RANKPEP resource for the prediction of peptide binding to MHC molecules using profiles. Immunogenetics 56, 405–419 (2004)
Zhang, G.L., Khan, A.M., Srinivasan, K.N., et al.: MULTIPRED: a computational system for prediction of promiscuous HLA binding peptides. Nucleic Acids Res. 33, 17–29 (2005)
Zhang, G.L., Bozic, I., Kwoh, C.K., et al.: Prediction of supertype-specific HLA class I binding peptides using support vector machines. J. Immunol. Meth. 320, 143–154 (2007)
Peters, B., Bui, H.H., Frankild, S.: A community resource benchmarking predictions of peptide binding to MHC-I molecules. PLoS Comput. Biol. 2, e65 (2006)
Larsen, M.V., Lundegaard, C., Lamberth, K., et al.: Large-scale validation of methods for cytotoxic T-lymphocyte epitope prediction. BMC Bioinformatics 8, 424 (2007)
Lin, H.H., Ray, S., Tongchusak, S., et al.: Evaluation of MHC class I peptide binding prediction servers: applications for vaccine research. BMC Immunol. 9, 8 (2008)
You, L., Zhang, P., Bodén, M., Brusic, V.: Understanding prediction systems for HLA-binding peptides and T-cell epitope identification. In: Rajapakse, J.C., Schmidt, B., Volkert, L.G. (eds.) PRIB 2007. LNCS (LNBI), vol. 4774, pp. 337–348. Springer, Heidelberg (2007)
Lin, H.H., Zhang, G.L., Tongchusak, S., et al.: Evaluation of MHC-II peptide binding prediction servers: applications for vaccine research. BMC Bioinformatics 9(Suppl. 12), S22 (2008)
Gowthaman, U., Agrewala, J.N.: In silico tools for predicting peptides binding to HLA-class II molecules: more confusion than conclusion. J. Proteome Res. 7, 154–163 (2008)
Rajapakse, M., Schmidt, B., Feng, L., Brusic, V.: Predicting peptides binding to MHC class II molecules using multi- objective evolutionary algorithms. BMC Bioinformatics 8, 459 (2007)
Nielsen, M., Lundegaard, C., Worning, P., et al.: Improved prediction of MHC class I and class II epitopes using a novel Gibbs sampling approach. Bioinformatics 20, 1388–1397 (2004)
Karpenko, O., Huang, L., Dai, Y.: A probabilistic meta- predictor for the MHC class II binding peptides. Immunogenetics 60, 25–36 (2008)
Zhang, C., Crasta, O., Cammer, S., et al.: An emerging cyberinfrastructure for biodefense pathogen and pathogen-host data. Nucleic Acids Res. 36, 884–891 (2008)
Laghaee, A., Malcolm, C., Hallam, J., Ghazal, P.: Artificial intelligence and robotics in high throughput post-genomics. Drug Discov. Today 10, 12539 (2005)
Fogel, G.: Computational Intelligence approaches for pattern discovery in biological systems. Brief Bioinform. 9, 307–316 (2008)
Rauwerda, H., Roos, M., Hertzberger, B.O., Breit, T.M.: The promise of a virtual lab in drug discovery. Drug Discov. Today 11, 228–236 (2006)
Becciani, U.: The Cometa Consortium and the PI2S2 project. Mem. S.A.It 13(Suppl.) (2009)
Romberg, M.: The UNICORE Architecture: Seamless Access to Distributed Resources, High Performance Distributed Computing. In: Proceedings of the 8th IEEE International Symposium on High Performance Distributed Computing, August 03-06 (1999)
Coveney, P.V., Saksena, R.S., Zasada, S.J., McKeown, M., Pickles, S.: The Application Hosting Environment: Lightweight Middleware for Grid-Based Computational Science. Computer Physics Communications 176(6), 406–418
Sloan, T.M., Menday, R., Seed, T.P., Illingworth, M., Trew, A.S.: DESHL–Standards Based Access to a Heterogeneous European Supercomputing Infrastructure. In: Proceedings of the Second IEEE International Conference on e-Science and Grid Computing, p. 91 (2006)
McGougha, A.S., Leeb, W., Dasc, S.: A standards based approach to enabling legacy applications on the Grid. Future Generation Computer Systems 24(7), 731–743 (2008)
Foster, I., Kesselman, C.: Globus: a Metacomputing Infrastructure Toolkit. International Journal of High Performance Computing Applications 11(2), 115–128 (1997), doi:10.1177/109434209701100205
Niederberger, R.: DEISA: Motivations, strategies, technologies. In: Proc. of the Int. Supercomputer Conference, ISC 2004 (2004)
Mastriani, E., Halling-Brown, M., Giorgio, E., Pappalardo, F., Motta, S.: P2SI2-ImmunoGrid services integration: a working example of web based approach. In: Proceedings of the Final Workshop of Grid Projects, PON Ricerca 2000-2006, vol. 1575, pp. 438–445 (2009); ISBN: 978-88-95892-02-3
Halling-Brown, M.D., Moss, D.S., Sansom, C.J., Shepherd, A.J.: Computational Grid Framework for Immunological Applications. Philosophical Transactions of the Royal Society A (2009)
Halling-Brown, M.D., Moss, D.S., Shepherd, A.J.: Towards a lightweight generic computational grid framework for biological research. BMC Bioinformatics 9, 407 (2008)
Halling-Brown, M.D., Moss, D.S., Sansom, C.S., Sheperd, A.J.: Web Services, Workflow & Grid Technologies for Immunoinformatics. In: Proceedings of Intern. Congress of Immunogenomics and Immunomics, vol. 268 (2006)
Kumar, N., Hendriks, B.S., Janes, K.A., De Graaf, D., Lauffenburger, D.A.: Applying computational modeling to drug discovery and development. Drug discovery today 11(17-18), 806–811 (2006)
Davies, M.N., Flower, D.R.: Harnessing bioinformatics to discover new vaccines. Drug Discovery Today 12(9-10), 389–395 (2007)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Pappalardo, F. et al. (2010). The ImmunoGrid Simulator: How to Use It. In: Masulli, F., Peterson, L.E., Tagliaferri, R. (eds) Computational Intelligence Methods for Bioinformatics and Biostatistics. CIBB 2009. Lecture Notes in Computer Science(), vol 6160. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14571-1_1
Download citation
DOI: https://doi.org/10.1007/978-3-642-14571-1_1
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-14570-4
Online ISBN: 978-3-642-14571-1
eBook Packages: Computer ScienceComputer Science (R0)