ImmunoGrid, an integrative environment for large-scale simulation of the immune system for vaccine discovery, design and optimization

Briefings in Bioinformatics

Volumn 10, Issue 3, 2009, Pages 330-340

  Pappalardo, Francesco ^a   Halling Brown, Mark D ^b   Rapin, Nicolas ^c   Zhang, Ping ^d   Alemani, Davide ^e   Emerson, Andrew ^e   Paci, Paola ^f   Duroux, Patrice ^g   Pennisi, Marzio ^a   Palladini, Arianna ^h   Miotto, Olivo ⁱ   Churchill, Daniel ^j   Rossi, Elda ^k   Shepherd, Adrian J ^l   Moss, David S ^l   Castiglione, Filippo ^f   Bernaschi, Massimo ^m   Lefranc, Marie Paule ⁿ   Brunak, Søren ^o,p   Motta, Santo ^a   Lollini, Pier Luigi ^q   Basford, Kaye E ^d   Brusic, Vladimir ^r   more..

^a UNIVERSITY OF CATANIA   (Italy)

^b Department of Bioinformatics Computational Immunology Chemogenomics and Grid Computing

^c UNIVERSITY OF COPENHAGEN   (Denmark)

^d UNIVERSITY OF QUEENSLAND   (Australia)

^e EPFL   (Switzerland)

^f CNR   (Italy)

^g UNIV MONTPELLIER   (France)

^h Italian Foundation for Cancer Research   (Italy)

ⁱ UNIVERSITY OF OXFORD   (United Kingdom)

^j UNIVERSITY OF HONG KONG   (Hong Kong)

^k CINECA   (Italy)

^l UNIVERSITY OF LONDON   (United Kingdom)

^m SAPIENZA UNIVERSITY OF ROME   (Italy)

ⁿ UNIVERSITÉ PARIS EST   (France)

^o University of Copenhagen   (Denmark)

^p TECHNICAL UNIVERSITY OF DENMARK   (Denmark)

^q UNIVERSITY OF BOLOGNA   (Italy)

^r DANA FARBER CANCER INSTITUTE   (United States)

more..

Author keywords

Computational modeling; Grid computing; Immunoinformatics; Simulations; Vaccine discovery

Indexed keywords

VACCINE;

ANTIGEN PRESENTATION; ARTICLE; ATHEROSCLEROSIS; BIOINFORMATICS; CANCER IMMUNIZATION; COMPUTER PREDICTION; COMPUTER SIMULATION; DRUG DESIGN; DRUG RESEARCH; IMMUNE SYSTEM; INFECTION; INFORMATION PROCESSING; MATHEMATICAL MODEL; MOLECULAR MODEL; ONLINE SYSTEM; PROCESS OPTIMIZATION;

COMPUTATIONAL BIOLOGY; COMPUTER SYSTEMS; DATABASE MANAGEMENT SYSTEMS; DATABASES, FACTUAL; DRUG DESIGN; HUMANS; IMMUNE SYSTEM; MAJOR HISTOCOMPATIBILITY COMPLEX; MODELS, BIOLOGICAL; SYSTEMS INTEGRATION; VACCINES;

EID: 65549157113     PISSN: 14675463     EISSN: 14774054     Source Type: Journal    
DOI: 10.1093/bib/bbp014     Document Type: Article

References (84)

1. Andre FE. Vaccinology: Past achievements, present road-blocks and future promises, Vaccine 2003;21:593-5.
2. Plotkin SA. Six revolutions in vaccinology. Pediatr Infect Dis J 2005;24:1-9.
3. Kurstak E. Towards the new global vaccinology era in prevention and control of diseases, Vaccine 2003;21:580-1.
4. Brusic V, August JT, Petrovsky N. Information technologies for vaccine research. ExpertRev Vaccines 2005;4:407-17.
5. Brusic V, August JT. The changing field of vaccine development in the genomics era. Pharmacogenomics 2004; 5:597-600.
6. Muzzi A, Masignani V, Rappuoli R. The pan-genome: Towards a knowledge-based discovery of novel targets for vaccines and antibacterials. Drug Discov Today 2007;12: 429-39.
7. Purcell AW, McCluskey J, Rossjohn J. More than one reason to rethink the use of peptides in vaccine design. Nat Rev Drug Discov 2007;6 404-14.
8. Heiny AT, Miotto O, Srinivasan KN, et al. Evolutionarily conserved protein sequences of influenza a viruses, avian and human, as vaccine targets. PLoS ONE 2007;2: E1190.
9. Nickle DC, Rolland M, Jensen MA, et al. Coping with viral diversity in HIV vaccine design. PLoS Comput Biol 2007;3:e75.
10. Fraser CK, Diener KR, Brown MP, Hayball JD. Improving vaccines by incorporating immunological coadjuvants. Expert Rev Vaccines 2007;6:559-78.
11. Denis F, Alain S, Ploy MC. New routes of administration: Epidermal, transcutaneous mucosal ways of vaccination. Med Sci (Paris) 2007;23:379-85.
12. Singh M, Chakrapani A, O'Hagan D. Nanoparticles and microparticles as vaccine-delivery systems. Expert Rev Vaccines 2007;6:797-808.
13. Liniger M, Zuniga A, Naim HY. Use of viral vectors for the development of vaccines. Expert Rev Vaccines 2007;6: 255-66.
14. Frey SE, Newman FK, Cruz J, et al. Dose-related effects of smallpox vaccine. N Engl J Med 2002;346:1275-80.
15. Motta S, Castiglione F, Lollini P, Pappalardo F. Modelling vaccination schedules for a cancer immunoprevention vaccine. Immunome Res 2005;1:5.
16. Pinto LA, Castle PE, Roden RB, et al. HPV-16 L1 VLP vaccine elicits a broad-spectrum of cytokine responses in whole blood. Vaccine 2005;23:3555-64.
17. Pukhalsky AL, Shmarina GV, Bliacher MS, et al. Cytokine profile after rubella vaccine inoculation: Evidence of the immunosuppressive effect of vaccination. Mediators Inflamm 2003;12:203-7.
18. The STEP Consortium. Seeding the EuroPhysiome: A Roadmap to the Virtual Physiological Human. [Online] 5 July 2007, .
19. Lefranc MP. IMGT, the international ImMunoGeneTics information system®: a standardized approach for immunogenetics and immunoinformatics. Immunome Res 2005;1:3. [imgt.cines.fr]
20. Lefranc MP, Giudicelli V, Duroux P. IMGT®, a system and an ontology that bridge biological and computational spheres in bioinformatics. Brief Bioinform 2008;9: 263-75.
21. Lefranc MP, Giudicelli V, Ginestoux C, et al. IMGT-ONTOLOGY for Immunogenetics and Immunoinformatics. In Silico Biol 2004;4 17-29.
22. Lefranc MP, Clément O, Kaas Q, et al. IMGT-Choreography for Immunogenetics and Immunoinformatics. In Silico Biol 2005;29 185-203.
23. Duroux P, Kaas Q, Brochet X, et al. IMGTKaleidoscope, the formal IMGT-ONTOLOGY paradigm. Biochimie 2008;4:570-83.
24. Lefranc M-P. IMGT-ONTOLOGY, IMGT® databases, tools and Web resources for Immunoinformatics. In: Schönbach C, Nanganathan S, Brusic V (eds). Immunoinformatics. Immunomics Reviews. New York, USA: Springer, 2008;1-18.
25. Lundegaard C, Lund O, Kecmir C, et al. Modeling the adaptive immune system: Predictions and simulations. Bioinformatics 2007; 23:3265-75.
26. Brusic V, Rudy G, Harrison LC. MHCPEP, a database of MHC-binding peptides: Update 1997. Nucleic Acids Res 1998;26:368-71.
27. Rammensee H, Bachmann J, Emmerich NP, et al. SYFPEITHI: Database for MHC ligands and peptide motifs. Immunogenetics 1999;50 213-9.
28. Toseland CP, Clayton DJ, McSparron H, et al. AntiJen: A quantitative immunology database integrating functional, thermodynamic, kinetic, biophysical, and cellular data. Immunome Res 2005;1 4.
29. Sette A, Bui H, Sidney J, et al. The immune epitope database and analysis resource. Lecture Notes Comput Sci 2006;4146:126-32.
30. Brusic V, Bajic VB, Petrovsky N. Computational methods for prediction of T-cell epitopes - a framework for modelling, testing, and applications. Methods 2004;34: 436-43.
31. Tong JC, Tan TW, Ranganathan S. Methods and protocols for prediction of immunogenic epitopes. Brief Bioinform 2007;8:96-108.
32. 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 2005;57 33-41.
33. Larsen MV, 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 2005; 35:2295-303.
34. Nielsen M, Lundegaard C, Lund O. Prediction of MHC class II binding affinity using SMM-align, a novel stabilization matrix alignment method. BMC Bioinformatics 2007;8:238.
35. 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 2007;2:e796.
36. Larsen JE, Lund O, Nielsen M. Improved method for predicting linear B-cell epitopes. Immunome Res 2006;2:2.
37. Andersen PH, Nielsen M, Lund O. Prediction of residues in discontinuous B-cell epitopes using protein 3D structures. Protein Sci 2006; 15:2558-67.
38. Reche PA, Glutting JP, Zhang H, Reinherz EL. Enhancement to the RANKPEP resource for the prediction of peptide binding to MHC molecules using profiles. Immunogenetics 2004;56:405-19.
39. Zhang GE, Khan AM, Srinivasan KN, et al. MULTIPRED: A computational system for prediction of promiscuous HLA binding peptides. Nucleic Acids Res 2005;33:W172-9.
40. Zhang GL, Bozic I, Kwoh CK, et al. Prediction of supertype-specific HLA class I binding peptides using support vector machines. J Immunol Meth 2007;320: 143-54.
41. Peters B, Bui HH, Frankild S. A community resource benchmarking predictions of peptide binding to MHC-I molecules. PLoS Comput Biol 2006;2:e65.
42. Larsen MV, Lundegaard C, Lamberth K, et al. Large-scale validation of methods for cytotoxic T-lymphocyte epitope prediction. BMC Bioinformatics 2007;8:424.
43. Lin HH, Ray S, Tongchusak S, et al. Evaluation of MHC class I peptide binding prediction servers: Applications for vaccine research. BMC Immunol 2008;9:8.
44. You L, Zhang P, Bodén M, Brusic V. Understanding prediction systems for HLA-binding peptides and T-cell epitope identification. Lect Notes Bioinform 2007;4774: 337-48.
45. Lin HH, Zhang GL, Tongchusak S, et al. Evaluation of MHC-II peptide binding prediction servers: Applications for vaccine research. BMC Bioinformatics 2008;9(Suppl 12):S22.
46. Gowtharnan U, Agrewala JN. In silico tools for predicting peptides binding to HLA-class II molecules: More confusion than conclusion. J Proteome Res 2008;7:154-63.
47. Rajapakse M, Schmidt B, Feng L, Brusic V. Predicting peptides binding to MHC class II molecules using multi-objective evolutionary algorithms. BMC Bioinformatics 2007;8:459.
48. Nielsen M, Lundegaard C, Worming P, et al. Improved prediction of MHC class I and class II epitopes using a novel Gibbs sampling approach. Bioinformatics 2004;20: 1388-97.
49. Karpenko O, Huang L, Dai Y. A probabilistic meta-predictor for the MHC class II binding peptides. Immunogenetics 2008;60:25-36.
50. Motta S, Brusic V. Mathematical modeling of the immune system. In: Ciobanu G, Rozenberg G (eds). Modelling in Molecular Biology. Berlin: Natural Computing Series, Springer Verlag, 2004;193-218. ISBN 3-540-40799-5.
51. Louzoun Y. The evolution of mathematical immunology. Immunol Rev 2007;216:9-20.
52. Castiglione F, Liso A. The role of computational models of the immune system in designing vaccination strategies. Immunopharmacol Immunotoxicol 2005;27:417-32.
53. Falus A (ed). Immunogenomics and Human Disease. Hoboken, New Jersey: Wiley, 2006. ISBN: 0-470-01530-6.
54. Purcell AW, Gorman JJ. Immunoproteomics: Mass spectrometry-based methods to study the targets of the immune response. Mol Cell Proteomics 2004;3:193-208.
55. Brusic V, Marina O, Wu CJ, Reinherz EL. Proteome informatics for cancer research: From molecules to clinic. Proteomics 2007;7:976-91.
56. Schönbach C, Ranganathan S, Brusic V (eds). Immunoinformatics. Springer 2007. ISBN: 978-0-387-72967-1
57. Tegnér J, Nilsson R, Bajic VB, et al. Systems biology of innate immunity. Cell Immunol 2006;244:105-109.
58. Sachdeva N, Asthana D. Cytokine quantitation: Technologies and applications. Front Biosci 2007;12:4682-95.
59. Harnett MM. Laser scanning cytometry: Understanding the immune system in situ. Nat Rev Immunol 2007;7: 897-904.
60. Brusic V, Bucci K, Schönbach C, et al. Efficient discovery of immune response targets by cyclical refinement of QSAR models of peptide binding. J Mol Graph Model 2001;19:405-11.
61. Pappalardo F, Motta S, Lollini PL, Mastriani E. Analysis of vaccine's schedules using models. Cell Immunol 2006;244: 137-40.
62. Celada F, Seiden PE. A computer model of cellular interaction in the immune system. Immunol Today 1992;13:56-62.
63. Castiglione F, Bernaschi M, Succi S. Simulating the immune response on a distributed parallel computer. Int J Mod Phys C 1997;8 527-45.
64. 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 2004;20:1316-25.
65. Baldazzi V, Castiglione F, Bernaschi M. An enhanced agent based model of the immune system response. Cell Immunol 2006;244:77-9.
66. Castiglione F, Duca K, Jarrah A, et al. Simulating Epstein-Barr virus infection with C-ImmSim. Bioinformatics 2007;23: 1371-7.
67. Castiglione F, Toschi F, Bernaschi M, et al. Computational modeling of the immune response to tumor antigens: Implications for vaccination. J Theo Biol 2005;237/4: 390-400.
68. Lollini PL, Motta S, Pappalardo F. Discovery of cancer vaccination protocols with a genetic algorithm driving an agent based simulator. BMC Bioinformatics 2006;7:352.
69. Pappalardo F, Lollini PL, Castiglione F, Motta S. Modeling and simulation of cancer immunoprevention vaccine. Bioinformatics 2005;21:2891-7.
70. Pappalardo F, Musumeci S, Motta S. Modeling immune system control of atherogenesis. Bioinformatics 2008;24: 1715-21.
71. He X, Luo L. Theory of the lattice Boltzmann method: From the Boltzmann equation to the lattice Boltzmann equation. Phys Re E 1997;56 6811-7.
72. Ferreira SC Jr, Martins ML, Vilela MJ. Morphology transitions induced by chemotherapy in carcinomas in situ. Phys Rev E 2003;67 051914.
73. Yates A, Chan CC, Callard RE, et al. An approach to modelling in immunology. Brief Bioinform 2001;2: 245-57.
74. Catron DM, Itano AA, Pape KA, et al. Visualizing the first 50 hr of the primary immune response to a soluble antigen. Immunity 2004;21:341-7.
75. Garside P, Ingulli E, Merica RR, et al. Visualization of specific B and T lymphocyte interactions in the lymph node. Science 1998; 281:96-9.
76. Mempel TR, Henrickson SE, Von Andrian UH. T-cell priming by dendritic cells in lymph nodes occurs in three distinct phases. Nature 2004;427:154-9.
77. Zhang C, Crasta O, Cammer S, et al. An emerging cyberinfrastructure for biodefense pathogen and pathogen-host data. Nucleic Acids Res 2008;36:D884-91.
78. Laghaee A, Malcohn C, Hallam J, Ghazal P. Artificial intelligence and robotics in high throughput post-genomics. Drug Discov Today 2005;10:1253-9.
79. Fogel G. Computational Intelligence approaches for pattern discovery in biological systems. Brief Bioinform 2008;9: 307-16.
80. Rauwerda H, Roos M, Hertzberger BO, Breit TM. The promise of a virtual lab in drug discovery. Drug Discov Today 2006;11:228-36.
81. Emerson A, Rossi E. ImmunoGrid - the virtual human immune system project. Stud Health Technol Inform 2007;126:87-92.
82. ImmunoGrid - The European Virtual Human Immune System Project: Educational and Research Portal. http://igrid-ext.cryst.bbk.ac.uk/ immunogrid/ (22 March 2009, date last accessed).
83. Churchill D. Towards a useful classification of learning objects. Educ Technol Res Dev 2007;55:479-97.
84. Hunter PJ, Crampin EJ, Nielsen PMF. Bioinformatics, multiscale modeling and the IUPS Physiome Project. Brief Bioinform 2008;9:333-43.