Immunologic Research

, Volume 53, Issue 1–3, pp 251–265 | Cite as

Systems immunology: a survey of modeling formalisms, applications and simulation tools

  • Vipin Narang
  • James Decraene
  • Shek-Yoon Wong
  • Bindu S. Aiswarya
  • Andrew R. Wasem
  • Shiang Rong Leong
  • Alexandre GouaillardEmail author
Singapore Immunology Network


Immunological studies frequently analyze individual components (e.g., signaling pathways) of immune systems in a reductionist manner. In contrast, systems immunology aims to give a synthetic understanding of how these components function together as a whole. While immunological research involves in vivo and in vitro experiments, systems immunology research can also be conducted in silico. With an increasing interest in systems-level studies spawned by high-throughput technologies, many immunologists are looking forward to insights provided by computational modeling and simulation. However, modeling and simulation research has mainly been conducted in computational fields, and therefore, little material is available or accessible to immunologists today. This survey is an attempt at bridging the gap between immunologists and systems immunology modeling and simulation. Modeling and simulation refer to building and executing an in silico replica of an immune system. Models are specified within a mathematical or algorithmic framework called formalism and then implemented using software tools. A plethora of modeling formalisms and software tools are reported in the literature for systems immunology. However, it is difficult for a new entrant to the field to know which of these would be suitable for modeling an immunological application at hand. This paper covers three aspects. First, it introduces the field of system immunology emphasizing on the modeling and simulation components. Second, it gives an overview of the principal modeling formalisms, each of which is illustrated with salient applications in immunological research. This overview of formalisms and applications is conducted not only to illustrate their power but also to serve as a reference to assist immunologists in choosing the best formalism for the problem at hand. Third, it lists major software tools, which can be used to practically implement models in these formalisms. Combined, these aspects can help immunologists to start experimenting with in silico models. Finally, future research directions are discussed. Particularly, we identify integrative frameworks to facilitate the coupling of different modeling formalisms and modeling the adaptation properties through evolution of immune systems as the next key research efforts necessary to further develop the multidisciplinary field of systems immunology.


Systems immunology Modeling and simulation Multiscale modeling Integration Modeling formalisms Software 



The authors thank P.S. Thiagarajan for helpful comments.


  1. 1.
    Hooke RC. Micrographia: or some physiological descriptions of miniature bodies made by magnifying glasses. London: Jo. Martyn, and Ja. Allestry; 1665.Google Scholar
  2. 2.
    Ge H, Walhout AJM, Vidal M. Integrating ‘omic’ information: a bridge between genomics and systems biology. Trends Genet. 2003;19(10):551.PubMedCrossRefGoogle Scholar
  3. 3.
    Joyce AR, Palsson B. The model organism as a system: integrating ‘omics’ data sets. Nat Rev Mol Cell Biol. 2006;7(3),198. doi: 10.1038/nrm1857.
  4. 4.
    Regenmortel MHVV. Reductionism and complexity in molecular biology. EMBO Rep. 2004;5(11):1016. doi: 10.1038/sj.embor.7400284.
  5. 5.
    Schnell S, Grima R, Maini PK. Multiscale modeling in biology. Am Sci. 2007;95(2):134. doi: 10.1511/2007.64.1018.Google Scholar
  6. 6.
    Young D, Stark J, Kirschner D. Systems biology of persistent infection: tuberculosis as a case study. Nat Rev Microbiol. 2008; 6(7):520. doi: 10.1038/nrmicro1919.Google Scholar
  7. 7.
    Benoist C, Germain RN, Mathis D. A plaidoyer for systems immunology. Immunol Rev. 2006;210(1):229. doi: 10.1111/j.0105-2896.2006.00374.x. URL Scholar
  8. 8.
    Kitano H. Systems biology: a brief overview. Science. 2002;295(5560):1662. doi: 10.1126/science.1069492.Google Scholar
  9. 9.
    Bokulich A. How scientific models can explain. Synthese. 2009; eprint 1. doi: 10.1007/s11229-009-9565-1.
  10. 10.
    Craver C. When mechanistic models explain. Synthese. 2006;153(3):355. doi: 10.1007/s11229-006-9097-x.Google Scholar
  11. 11.
    Vodovotz Y. Deciphering the complexity of acute inflammation using mathematical models. Immunol Res. 2006;36(1–3):237. doi: 10.1385/IR:36:1:237.
  12. 12.
    Materi W, Wishart DS. Computational systems biology in cancer: modeling methods and applications. Gene Regul Syst Bio. 2007;1:91.PubMedGoogle Scholar
  13. 13.
    Guo Z, Sloot PMA, Tay JC. A hybrid agent-based approach for modeling microbiological systems. J Theor Biol. 2008;255(2):163. doi: 10.1016/j.jtbi.2008.08.008.Google Scholar
  14. 14.
    Newman SA, Christley S, Glimm T, Hentschel HGE, Kazmierczak B, Zhang YT, Zhu J, Alber M. Multiscale models for vertebrate limb development. Curr Top Dev Biol. 2008;81:311. doi: 10.1016/S0070-2153(07)81011-8.Google Scholar
  15. 15.
    Fisher J, Henzinger TA. Executable cell biology. Nat Biotechnol. 2007;25(11):1239. doi: 10.1038/nbt1356.Google Scholar
  16. 16.
    Kanehisa M, Goto S, Kawashima S, Okuno Y, Hattori M. The kegg resource for deciphering the genome. Nucleic Acids Res. 2004;32(Database issue):D277. doi: 10.1093/nar/gkh063.
  17. 17.
    Nagasaki M, Doi A, Matsuno H, Miyano S. A versatile petri net based architecture for modeling and simulation of complex biological processes. Genome Inform. 2004;15(1):180.PubMedGoogle Scholar
  18. 18.
    Eils J, Lawerenz C, Astrahantseff K, Ginkel M, Eils R. Computational systems biology (Elsevier, Amsterdam [u.a.]), chap. Databases for systems biology; 2005. p. 15–38.Google Scholar
  19. 19.
    Ng A, Bursteinas B, Gao Q, Mollison E, Zvelebil M. Resources for integrative systems biology: from data through databases to networks and dynamic system models. Brief Bioinform. 2006;7(4):318. doi: 10.1093/bib/bbl036.Google Scholar
  20. 20.
    van Gend C, Snoep JL. Systems biology model databases and resources. Essays Biochem. 2008;45:223. doi: 10.1042/BSE0450223.Google Scholar
  21. 21.
    Gillespie DT. Stochastic simulation of chemical kinetics. Annu Rev Phys Chem. 2007;58:35. doi: 10.1146/annurev.physchem.58.032806.104637.Google Scholar
  22. 22.
    Yates A, Chan CC, Callard RE, George AJ, Stark J. An approach to modelling in immunology. Brief Bioinform. 2001;2(3):245.PubMedCrossRefGoogle Scholar
  23. 23.
    Andrew SM, Baker CT, Bocharov GA. Rival approaches to mathematical modelling in immunology. J Comput Appl Math. 2007;205(2):669. URL Scholar
  24. 24.
    Kim PS, Levy D, Lee PP. Modeling and simulation of the immune system as a self-regulating network. Methods Enzymol. 2009;467:79. doi: 10.1016/S0076-6879(09)67004-X.
  25. 25.
    Klotz C, Ziegler T, Figueiredo A, Rausch S, Hepworth M, Obsivac N, Sers C, Lang R, Hammerstein P, Lucius R, et al. A helminth immunomodulator exploits host signaling events to regulate cytokine production in macrophages. PLoS Pathogens. 2011;7(1):e1001248.PubMedCrossRefGoogle Scholar
  26. 26.
    Smieja J, Jamaluddin M, Brasier A, Kimmel M. Model-based analysis of interferon-β induced signaling pathway. Bioinformatics. 2008;24(20):2363.PubMedCrossRefGoogle Scholar
  27. 27.
    Hoffmann A, Levchenko A, Scott ML, Baltimore D. The ikappab-nf-kappab signaling module: temporal control and selective gene activation. Science. 2002;298(5596):1241. doi: 10.1126/science.1071914.Google Scholar
  28. 28.
    Lipniacki T, Paszek P, Brasier ARAR, Luxon B, Kimmel M. Mathematical model of nf-kappab regulatory module. J Theor Biol. 2004;228(2):195. doi: 10.1016/j.jtbi.2004.01.001.Google Scholar
  29. 29.
    de Pillis L, Radunskaya A, Wiseman C. A validated mathematical model of cellmediated immune response to tumor growth. Cancer Research. 2005;65(17):7950.PubMedGoogle Scholar
  30. 30.
    Pennisi M, Bianca C, Pappalardo F, Motta S. Modeling artificial immunity against mammary carcinoma. In: Proceedings of the 10th International Conference on Mathematical Methods in Science and Engineering (CMMSE 2010); 2010. p. 753–756.Google Scholar
  31. 31.
    Pennisi M, Bianca C, Pappalardo F, Motta S. Compartmental mathematical modeling of immune system—melanoma competition. In: Proceedings of the 10th International Conference on Mathematical Methods in Science and Engineering (CMMSE 2011); 2011. pp. 930–934.Google Scholar
  32. 32.
    Merill SJ. A model of the role of natural killer cells in immune surveillance. J Math Biol. 1981;12:363.CrossRefGoogle Scholar
  33. 33.
    Varela F, Stewart J. Dynamics of a class of immune networks i. global stability of idiotype interactions. J Theor Biol. 1990;144(1):93.PubMedCrossRefGoogle Scholar
  34. 34.
    De Boer R, Perelson A. Size and connectivity as emergent properties of a developing immune network. J Theor Biol. 1991;149(3):381.PubMedCrossRefGoogle Scholar
  35. 35.
    Ougrinovskaia A, Thompson RS, Myerscough MR. An ode model of early stages of atherosclerosis: mechanisms of the inflammatory response. Bull Math Biol 2010;72(6):1534. URL Scholar
  36. 36.
    Essunger P, Perelson AS. Modeling hiv infection of cd4+ t-cell subpopulations. J Theor Biol. 1994;170(4):367.PubMedCrossRefGoogle Scholar
  37. 37.
    Funk G, Barbour A, Hengartner H, Kalinke U. Mathematical model of a virusneutralizing immunglobulin response. J Theor Biol. 1998;195(1):41.PubMedCrossRefGoogle Scholar
  38. 38.
    Wodarz D, Thomsen A. Effect of the ctl proliferation program on virus dynamics. Int Immunol. 2005;17(9):1269.PubMedCrossRefGoogle Scholar
  39. 39.
    Pennisi M, Pappalardo F, Chiacchio F, Motta S. A model of cytotoxic t antitumor activation stimulated by pulsed dendritic cells. In: Simos TE, Psihoyios G, Tsitouras C, Anastassi Z, editors. American Institute of Physics Conference Series, American Institute of Physics Conference Series, vol. 1389, American Institute of Physics Conference Series. 2011. p. 1236–1239.Google Scholar
  40. 40.
    Werner S, Kearns J, Zadorozhnaya V, Lynch C, ODea E, Boldin M, Ma A, Baltimore D, Hoffmann A. Encoding nf-κb temporal control in response to tnf: distinct roles for the negative regulators iκbα and a20. Genes Dev. 2008;22(15):2093.PubMedCrossRefGoogle Scholar
  41. 41.
    Shih VF, Kearns JD, Basak S, Savinova OV, Ghosh G, Hoffmann A. Kinetic control of negative feedback regulators of NF-kappaB/RelA determines their pathogen- and cytokine-receptor signaling specificity. Proc Nat Acad Sci. 2009;106(24):9619. doi: 10.1073/pnas.0812367106.Google Scholar
  42. 42.
    Kepler TB, Elston TC. Stochasticity in transcriptional regulation: origins, consequences, and mathematical representations. Biophys J. 2001;81(6):3116. doi: 10.1016/S0006-3495(01)75949-8.Google Scholar
  43. 43.
    Arkin A, Ross J, McAdams HH. Stochastic kinetic analysis of developmental pathway bifurcation in phage lambda-infected escherichia coli cells. Genetics 1998;149(4):1633.PubMedGoogle Scholar
  44. 44.
    Zhang Q, Bhattacharya S, Kline DE, Crawford RB, Conolly RB, Thomas RS, Kaminski NE, Andersen ME. Stochastic modeling of b lymphocyte terminal differentiation and its suppression by dioxin. BMC Syst Biol.2010;4:40. doi: 10.1186/1752-0509-4-40.Google Scholar
  45. 45.
    Lipniacki T, Paszek P, Marciniak-Czochra A, Brasier AR, Kimmel M. Transcriptional stochasticity in gene expression. J Theor Biol. 2006;238(2):348. doi: 10.1016/j.jtbi.2005.05.032.Google Scholar
  46. 46.
    Figge M. Optimization of immunoglobulin substitution therapy by a stochastic immune response model. PloS one. 2009;4(5):e5685.PubMedCrossRefGoogle Scholar
  47. 47.
    Srivastava R, You L, Summers J, Yin J. Stochastic vs. deterministic modeling of intracellular viral kinetics. J Theor Biol. 2002;218(3):309.PubMedCrossRefGoogle Scholar
  48. 48.
    Hucka M, Finney A, Sauro HM, Bolouri H, Doyle JC, Kitano H, Arkin AP, Bornstein BJ, Bray D, Cornish-Bowden A, Cuellar AA, Dronov S, Gilles ED, Ginkel M, Gor V, Goryanin II, Hedley WJ, Hodgman TC, Hofmeyr JH, Hunter PJ, Juty NS, Kasberger JL, Kremling A, Kummer U, Novre NL, Loew LM, Lucio D, Mendes P, Minch E, Mjolsness ED, Nakayama Y, Nelson MR, Nielsen PF, Sakurada T, Schaff JC, Shapiro BE, Shimizu TS, Spence HD, Stelling J, Takahashi K, Tomita M, Wagner J, Wang J, Forum SBML. The systems biology markup language (sbml): a medium for representation and exchange of biochemical network models. Bioinformatics. 2003;19(4):524.PubMedCrossRefGoogle Scholar
  49. 49.
    Lloyd CM, Halstead MDB, Nielsen PF. Cellml: its future, present and past. Prog Biophys Mol Biol. (2004);85(2–3):433. doi: 10.1016/j.pbiomolbio.2004.01.004.
  50. 50.
    Bergmann FT, Sauro HM. Sbw—a modular framework for systems biology. In: WSC ’06: Proceedings of the 38th conference on Winter simulation (Winter Simulation Conference); 2006. p. 1637–1645.Google Scholar
  51. 51.
    Wolfram S. Cellular automata as models of complexity. Nature. 1984;311(5985):419.CrossRefGoogle Scholar
  52. 52.
    Celada F, Seiden P. Affinity maturation and hypermutation in a simulation of the humoral immune response. Eu J Immunol. 1996;26(6):1350.CrossRefGoogle Scholar
  53. 53.
    Stewart J, Agosto H, Litwin S, Welsh J, Shlomchik M, Weigert M, Seiden P. A solution to the rheumatoid factor paradox: pathologic rheumatoid factors can be tolerized by competition with natural rheumatoid factors. J Immunol. 1997;159(4):1728.PubMedGoogle Scholar
  54. 54.
    Patel AA, Gawlinski ET, Lemieux SK, Gatenby RA. A cellular automaton model of early tumor growth and invasion. J Theor Biol. 2001;213(3):315. doi: 10.1006/jtbi.2001.2385.Google Scholar
  55. 55.
    Dormann S, Deutsch A. Modeling of self-organized avascular tumor growth with a hybrid cellular automaton. In Silico Biol. 2002;2(3):393.PubMedGoogle Scholar
  56. 56.
    Gevertz JL, Torquato S. Modeling the effects of vasculature evolution on early brain tumor growth. J Theor Biol. 2006;243(4):517. doi: 10.1016/j.jtbi.2006.07.002.
  57. 57.
    Mallet DG, Pillis LGD. A cellular automata model of tumor-immune system interactions. J Theor Biol. 2006;239(3):334. doi: 10.1016/j.jtbi.2005.08.002.Google Scholar
  58. 58.
    Bankhead A, Magnuson NS, Heckendorn RB. Cellular automaton simulation examining progenitor hierarchy structure effects on mammary ductal carcinoma in situ. J Theor Biol. 2007;246(3):491. doi: 10.1016/j.jtbi.2007.01.011.Google Scholar
  59. 59.
    Gerlee P, Anderson ARA. An evolutionary hybrid cellular automaton model of solid tumour growth. J Theor Biol. 2007;246(4):583. doi: 10.1016/j.jtbi.2007.01.027.Google Scholar
  60. 60.
    Gerlee P, Anderson ARA. A hybrid cellular automaton model of clonal evolution in cancer: the emergence of the glycolytic phenotype. J Theor Biol. 2008;250(4):705. doi: 10.1016/j.jtbi.2007.10.038.
  61. 61.
    Basanta D, Strand DW, Lukner RB, Franco OE, Cliffel DE, Ayala GE, Hayward SW, Anderson ARA. The role of transforming growth factor-beta-mediated tumor-stroma interactions in prostate cancer progression: an integrative approach. Cancer Res. 2009;69(17):7111. doi: 10.1158/0008-5472.CAN-08-3957.Google Scholar
  62. 62.
    Mallet DG, Heymer KJ, Rank RG, Wilson DP. Chlamydial infection and spatial ascension of the female genital tract: a novel hybrid cellular automata and continuum mathematical model. FEMS Immunol Med Microbiol. 2009;57(2):173. doi: 10.1111/j.1574-695X.2009.00596.x.
  63. 63.
    Gerlee P, Anderson ARA. Diffusion-limited tumour growth: simulations and analysis. Math Biosci Eng. 2010;7(2):385.PubMedCrossRefGoogle Scholar
  64. 64.
    Smallbone K, Maini PK, Gatenby RA. Episodic, transient systemic acidosis delays evolution of the malignant phenotype: Possible mechanism for cancer prevention by increased physical activity. Biol Direct. 2010;5:22. doi: 10.1186/1745-6150-5-22.
  65. 65.
    Zorzenon dos Santos R, Coutinho S. Dynamics of hiv infection: a cellular automata approach. Phys Rev Lett. 2001;87(16):168102.CrossRefGoogle Scholar
  66. 66.
    Strain M, Richman D, Wong J, Levine H. Spatiotemporal dynamics of hiv propagation. J Theor Biol. 2002;218(1):85.PubMedCrossRefGoogle Scholar
  67. 67.
    Castiglione F, Duca K, Jarrah A, Laubenbacher R, Hochberg D, Thorley-Lawson D. Simulating epstein-barr virus infection with c-immsim. Bioinformatics. 2007;23(11):1371.PubMedCrossRefGoogle Scholar
  68. 68.
    Warrender C, Forrest S, Koster F. Modeling intercellular interactions in early mycobacterium infection. Bull Math Biol. 2006;68(8):2233.PubMedCrossRefGoogle Scholar
  69. 69.
    Marino S, Linderman J, Kirschner D. A multifaceted approach to modeling the immune response in tuberculosis. Wiley Interdisciplinary Reviews: Systems Biology and Medicine; 2010.Google Scholar
  70. 70.
    Kam N. The immune system as a reactive system: Modeling t cell activation with statecharts. In: Human-centric computing languages and environments, IEEE CS International Symposium on, vol. 0, ed. by Cohen IR, Harel D. 2001;vol. 0, p. 15–15. doi: 10.1109/HCC.2001.995228.
  71. 71.
    Efroni S, Harel D, Cohen IR. Toward rigorous comprehension of biological complexity: modeling, execution, and visualization of thymic t-cell maturation. Genome Res. 2003;13(11), 2485. doi: 10.1101/gr.1215303.
  72. 72.
    Efroni S, Harel D, Cohen IR. Emergent dynamics of thymocyte development and lineage determination. PLoS Comput Biol. 2007;3(1):e13. doi: 10.1371/journal.pcbi.0030013.
  73. 73.
    Naamah S, David CIRH. The lymph node b cell immune response: dynamic analysis in-silico. Proc IEEE. 2008;96(8):1421.Google Scholar
  74. 74.
    Celada F, Seiden P. A computer model of cellular interactions in the immune system. Immunol Today. 1992;13(2):56.PubMedCrossRefGoogle Scholar
  75. 75.
    Puzone R, Kohler B, Seiden P, Celada F. Immsim, a flexible model for in machina experiments on immune system responses. Fut Gen Comput Syst. 2002;18(7):961.CrossRefGoogle Scholar
  76. 76.
    Rapin N, Lund O, Castiglione F. Immune system simulation online. Bioinformatics. 2011;27(14):2013.PubMedCrossRefGoogle Scholar
  77. 77.
    Kugler H, Larjo A, Harel D. Biocharts: a visual formalism for complex biological systems. J R Soc Interface. 2009. URL
  78. 78.
    An G. Agent-based computer simulation and sirs: building a bridge between basic science and clinical trials. Shock. 2001;16(4):266.PubMedCrossRefGoogle Scholar
  79. 79.
    An G. Concepts for developing a collaborative in silico model of the acute inflammatory response using agent-based modeling. J Crit Care. 2006;21(1):105. doi: 10.1016/j.jcrc.2005.11.012.
  80. 80.
    An G, Hunt CA, Clermont G, Neugebauer E, Vodovotz Y. Challenges and rewards on the road to translational systems biology in acute illness: four case reports from interdisciplinary teams. J Crit Care. 2007;22(2):169. doi: 10.1016/j.jcrc.2006.12.011.
  81. 81.
    Cauwels A, Buys ES, Thoonen R, Geary L, Delanghe J, Shiva S, Brouckaert P. Nitrite protects against morbidity and mortality associated with tnf- or lps-induced shock in a soluble guanylate cyclase-dependent manner. J Exp Med. 2009;206(13):2915. doi: 10.1084/jem.20091236.Google Scholar
  82. 82.
    Bailey AM, Thorne BC, Peirce SM. Multi-cell agent-based simulation of the microvasculature to study the dynamics of circulating inflammatory cell trafficking. Ann Biomed Eng. 2007;35(6):916. doi: 10.1007/s10439-007-9266-1.
  83. 83.
    Mi Q, Rivire B, Clermont G, Steed DL, Vodovotz Y. Agent-based model of inflammation and wound healing: insights into diabetic foot ulcer pathology and the role of transforming growth factor-beta1. Wound Repair Regen. 2007;15(5):671. doi: 10.1111/j.1524-475X.2007.00271.x.
  84. 84.
    An G. Introduction of an agent-based multi-scale modular architecture for dynamic knowledge representation of acute inflammation. Theor Biol Med Model. 2008;5:11. doi: 10.1186/1742-4682-5-11.
  85. 85.
    Dong X, Foteinou PT, Calvano SE, Lowry SF, Androulakis IP. Agent-based modeling of endotoxin-induced acute inflammatory response in human blood leukocytes. PLoS One. 2010;5(2):e9249. doi: 10.1371/journal.pone.0009249.
  86. 86.
    Galvo V, Miranda JGV, dos Santos RR. Development of a two-dimensional agent-based model for chronic chagasic cardiomyopathy after stem cell transplantation. Bioinformatics. 2008;24(18):2051. doi: 10.1093/bioinformatics/btn362.
  87. 87.
    Galvo V, Miranda JGV. A three-dimensional multi-agent-based model for the evolution of chagas’ disease. Biosystems. 2010;100(3):225. doi: 10.1016/j.biosystems.2010.03.007.
  88. 88.
    Li NYK, Verdolini K, Clermont G, Mi Q, Rubinstein EN, Hebda PA, Vodovotz Y. A patient-specific in silico model of inflammation and healing tested in acute vocal fold injury. PLoS One. 2008;3(7):e2789. doi: 10.1371/journal.pone.0002789.
  89. 89.
    Bailey AM, Lawrence MB, Shang H, Katz AJ, Peirce SM. Agent-based model of therapeutic adipose-derived stromal cell trafficking during ischemia predicts ability to roll on p-selectin. PLoS Comput Biol. 2009;5(2):e1000294. doi: 10.1371/journal.pcbi.1000294.
  90. 90.
    Tang J, Hunt CA. Identifying the rules of engagement enabling leukocyte rolling, activation, and adhesion. PLoS Comput Biol. 2010;6(2):e1000681. doi: 10.1371/journal.pcbi.1000681.
  91. 91.
    Adra S, Sun T, MacNeil S, Holcombe M, Smallwood R. Development of a three dimensional multiscale computational model of the human epidermis. PLoS One. 2010;5(1):e8511. doi: 10.1371/journal.pone.0008511.
  92. 92.
    Segovia-Juarez JL, Ganguli S, Kirschner D. Identifying control mechanisms of granuloma formation during m. tuberculosis infection using an agent-based model. J Theor Biol. 2004; 231(3):357. doi: 10.1016/j.jtbi.2004.06.031.
  93. 93.
    Pappalardo F, Musumeci S, Motta S. Modeling immune system control of atherogenesis. Bioinformatics. 2008;24(15):1715. doi: 10.1093/bioinformatics/btn306.Google Scholar
  94. 94.
    Duca KA, Shapiro M, Delgado-Eckert E, Hadinoto V, Jarrah AS, Laubenbacher R, Lee K, Luzuriaga K, Polys NF, Thorley-Lawson DA. A virtual look at epstein-barr virus infection: biological interpretations. PLoS Pathog. 2007;3(10):1388. doi: 10.1371/journal.ppat.0030137.Google Scholar
  95. 95.
    Casal A, Sumen C, Reddy TE, Alber MS, Lee PP. Agent-based modeling of the context dependency in t cell recognition. J Theor Biol. 2005;236(4):376. doi: 10.1016/j.jtbi.2005.03.019.Google Scholar
  96. 96.
    Nudelman G, Weigert M, Louzoun Y. In-silico cell surface modeling reveals mechanism for initial steps of b-cell receptor signal transduction. Mol Immunol. 2009;46(15):3141. doi: 10.1016/j.molimm.2009.03.027.Google Scholar
  97. 97.
    Bogle G, Dunbar PR. Agent-based simulation of t-cell activation and proliferation within a lymph node. Immunol Cell Biol. 2010;88(2):172. doi: 10.1038/icb.2009.78.
  98. 98.
    Meyer-Hermann ME, Maini PK, Iber D. An analysis of b cell selection mechanisms in germinal centers. Math Med Biol. 2006;23(3):255. doi: 10.1093/imammb/dql012.Google Scholar
  99. 99.
    Santoni D, Pedicini M, Castiglione F. Implementation of a regulatory gene network to simulate the th1/2 differentiation in an agent-based model of hypersensitivity reactions. Bioinformatics. 2008;24(11):1374. doi: 10.1093/bioinformatics/btn135.
  100. 100.
    Cheng Y, Ghersi D, Calcagno C, Selin LK, Puzone R, Celada F. A discrete computer model of the immune system reveals competitive interactions between the humoral and cellular branch and between cross-reacting memory and nave responses. Vaccine. 2009;27(6):833. doi: 10.1016/j.vaccine.2008.11.109.Google Scholar
  101. 101.
    Baldazzi V, Castiglione F, Bernaschi M. An enhanced agent based model of the immune system response. Cell Immunol. 2006;244(2):77. doi: 10.1016/j.cellimm.2006.12.006.Google Scholar
  102. 102.
    Folcik VA, An GC, Orosz CG. The basic immune simulator: an agent-based model to study the interactions between innate and adaptive immunity. Theor Biol Med Model. 2007;4:39. doi: 10.1186/1742-4682-4-39.
  103. 103.
    Mitha F, Lucas TA, Feng F, Kepler TB, Chan C. The multiscale systems immunology project: software for cell-based immunological simulation. Source Code Biol Med. 2008;3:6. doi: 10.1186/1751-0473-3-6.
  104. 104.
    Halling-Brown M, Pappalardo F, Rapin N, Zhang P, Alemani D, Emerson A, Castiglione F, Duroux P, Pennisi M, Miotto O, Churchill D, Rossi E, Moss DS, Sansom CE, Bernaschi M, Lefranc MP, Brunak S, Lund O, Motta S, Lollini PL, Murgo A, Palladini A, Basford KE, Brusic V, Shepherd AJ. Immunogrid: towards agent-based simulations of the human immune system at a natural scale. Philos Transact A Math Phys Eng Sci. 2010;368(1920):2799. doi: 10.1098/rsta.2010.0067.
  105. 105.
    Rapin N, Lund O, Bernaschi M, Castiglione F. Computational immunology meets bioinformatics: the use of prediction tools for molecular binding in the simulation of the immune system. PLoS One. 2010;5(4):e9862. doi: 10.1371/journal.pone.0009862.
  106. 106.
  107. 107.
  108. 108.
    Wilensky U. Netlogo. (1999).
  109. 109.
    An G, Wilensky U. Artificial life models in software. (Springer, London), chap. From artificial life to in silico medicine: NetLogo as a means of translational knowledge representation in biomedical research; 2009. p. 183–214.Google Scholar
  110. 110.
    Remy E, Ruet P, Mendoza L, Thieffry D, Chaouiya C. From logical regulatory graphs to standard petri nets: Dynamical roles and functionality of feedback circuits. In: Priami C, Ingolfsdottir A, Mishra B, Riis Nielson H, editors. Transactions on Computational Systems Biology VII, Lecture Notes in Computer Science, vol. 4230. Springer, Berlin/Heidelberg; 2006. p. 56–72.Google Scholar
  111. 111.
    Mendoza L. A network model for the control of the differentiation process in th cells. Biosystems. 2006;84(2):101. doi: 10.1016/j.biosystems.2005.10.004.Google Scholar
  112. 112.
    Saez-Rodriguez J, Simeoni L, Lindquist JA, Hemenway R, Bommhardt U, Arndt B, Haus UU, Weismantel R, Gilles ED, Klamt S, Schraven B. A logical model provides insights into t cell receptor signaling. PLoS Comput Biol. 2007;3(8):e163. doi: 10.1371/journal.pcbi.0030163.
  113. 113.
    Saez-Rodriguez J, Alexopoulos LG, Epperlein J, Samaga R, Lauffenburger DA, Klamt S, Sorger PK. Discrete logic modelling as a means to link protein signalling networks with functional analysis of mammalian signal transduction. Mol Syst Biol. 2009;5:331. doi: 10.1038/msb.2009.87.Google Scholar
  114. 114.
    Franke R, Mller M, Wundrack N, Gilles ED, Klamt S, Khne T, Naumann M. Hostpathogen systems biology: logical modelling of hepatocyte growth factor and helicobacter pylori induced c-met signal transduction. BMC Syst Biol. 2008;2:4. doi: 10.1186/1752-0509-2-4.
  115. 115.
    Chaouiya C. Petri net modelling of biological networks. Brief Bioinform. 2007;8(4):210. doi: 10.1093/bib/bbm029.Google Scholar
  116. 116.
    Regev A, Silverman W, Shapiro E. Representation and simulation of biochemical processes using the pi-calculus process algebra. Pac Symp Biocomput; 2001:459–470.Google Scholar
  117. 117.
    Clarke EM, Grumberg O, Peled DA. Model checking. MIT Press; 2000.Google Scholar
  118. 118.
    Na D, Park I, Lee KH, Lee D. Integration of immune models using petri nets. In: Nicosia Giuseppe, Cutello Vincenzo, Bentley Peter J, et al, editors. Proceedings of Artificial Immune Systems: Third International Conference, ICARIS 2004, Catania, Sicily, Italy, September 13–16, 2004—Vol 3239 of Lecture Notes in Computer Science. Berlin: Springer; 2004. p. 205–216.Google Scholar
  119. 119.
    Monroy R. A process algebra model of the immune system. In: Proceedings of the 8th Knowledge-Based Intelligent Information & Engineering Systems, KES 2004. Lecture Notes in Artificial Intelligence; 2004.Google Scholar
  120. 120.
    Guerriero ML, Prandi D, Priami C, Quaglia P. Process calculi abstractions for biology. Tech. Rep. Technical Report TR-13-2006, CoSBi (Center for Computational and Systems Biology), University of Trento; 2006.Google Scholar
  121. 121.
    Spicher A, Michel O, Cieslak M, Giavitto JL, Prusinkiewicz P. Stochastic p systems and the simulation of biochemical processes with dynamic compartments. Biosystems. 2008;91(3):458. doi: 10.1016/j.biosystems.2006.12.009.Google Scholar
  122. 122.
    Corne DW, Frisco P. Dynamics of hiv infection studied with cellular automata and conformon-p systems. Biosystems. 2008;91(3):531. doi: 10.1016/j.biosystems.2007.01.007.Google Scholar
  123. 123.
    Chopard B, Falcone J, Hoekstra A, Borgdorff J. A framework for multiscale and multiscience modeling and numerical simulations. Unconvent Comput; 2011:2–8.Google Scholar
  124. 124.
    Eissing T, Kuepfer L, Becker C, Block M, Coboeken K, Gaub T, Goerlitz L, Jaeger J, Loosen R, Ludewig B, et al. A computational systems biology software platform for multiscale modeling and simulation: Integrating whole-body physiology, disease biology, and molecular reaction networks. Front Physiol. 2011;2.Google Scholar
  125. 125.
    Kawashima Y, Pfafferott K, Frater J, Matthews P, Payne R, Addo M, Gatanaga H, Fujiwara M, Hachiya A., Koizumi H., et al. Adaptation of hiv-1 to human leukocyte antigen class i. Nature. 2009;458(7238):641.PubMedCrossRefGoogle Scholar
  126. 126.
    Obbard D, Welch J, Kim K, Jiggins F. Quantifying adaptive evolution in the drosophila immune system. PLoS Genet. 2009;5(10):e1000698.PubMedCrossRefGoogle Scholar
  127. 127.
    Farmer J, Packard N, Perelson A. The immune system, adaptation, and machine learning. Phys D: Nonlinear Phenomena. 1986;22(1–3):187.CrossRefGoogle Scholar
  128. 128.
    Farmer J, Kauffman S, Packard N, Perelson A. Adaptive dynamic networks as models for the immune system and autocatalytic sets. Ann N Y Acad Sci. 1987;504(1):118.PubMedCrossRefGoogle Scholar
  129. 129.
    Forrest S, Perelson A. Genetic algorithms and the immune system. Parallel Problem Solving from Nature; 1991. p. 319–325.Google Scholar
  130. 130.
    Oprea M, Forrest S. Simulated evolution of antibody gene libraries under pathogen selection. In: Systems, Man, and Cybernetics, 1998. 1998 IEEE International Conference on, vol 4. 1998;4:3793–3798.Google Scholar
  131. 131.
    Kim J, Bentley P. Immune memory and gene library evolution in the dynamic clonal selection algorithm. Genet Program Evolvable Mach. 2004;5(4):361.CrossRefGoogle Scholar
  132. 132.
    De Jong K. Evolutionary computation; 2002.Google Scholar
  133. 133.
    Forrest S, Beauchemin C. Computer immunology. Immunol Rev. 2007;216:176. doi: 10.1111/j.1600-065X.2007.00499.x.Google Scholar
  134. 134.
    Izmailian N, Papoyan V, Priezzhev V, Hu C, et al. Self-organizing behavior in a lattice model for co-evolution of virus and immune systems. Phys Rev E, Stat Nonlinear Soft Matter Phys. 2007;75(4 Pt 1):041104.CrossRefGoogle Scholar
  135. 135.
    Guttenberg N, Ali Tabei SM, Dinner AR. Short-time evolution in the adaptive immune system. Phys Rev E. 2011;84(3):031932.CrossRefGoogle Scholar
  136. 136.
    Lieberman T, Michel J, Aingaran M, Potter-Bynoe G, Roux D, Davis M Jr, Skurnik D, Leiby N, LiPuma J, Goldberg J, et al. Parallel bacterial evolution within multiple patients identifies candidate pathogenicity genes. Nat Genet; 2011.Google Scholar
  137. 137.
    Aldridge BB, Burke JM, Lauffenburger DA, Sorger PK. Physicochemical modelling of cell signalling pathways. Nat Cell Biol. 2006;8(11):1195. doi: 10.1038/ncb1497.Google Scholar
  138. 138.
    Alves R, Antunes F, Salvador A. Tools for kinetic modeling of biochemical networks. Nat Biotechnol. 2006;24(6):667. doi: 10.1038/nbt0606-667.Google Scholar
  139. 139.
    Wilkinson DJ. Stochastic modelling for quantitative description of heterogeneous biological systems. Nat Rev Genet. 2009.10(2):122. doi: 10.1038/nrg2509.Google Scholar
  140. 140.
    de Jong H, Ropers D. System modeling in cellular biology: from concepts to nuts and bolts, chap. Qualitative approaches towards the analysis of genetic regulatory networks. Cambridge, MA: MIT Press. 2006. p. 125–148.Google Scholar
  141. 141.
    Albert R, Wang RS. Discrete dynamic modeling of cellular signaling networks. Methods Enzymol. 2009;467:281. doi: 10.1016/S0076-6879(09)67011-7.Google Scholar
  142. 142.
    Cohen IR, Harel D. Explaining a complex living system: dynamics, multi-scaling and emergence. J R Soc Interface. 2007;4(13):175. doi: 10.1098/rsif.2006.0173.
  143. 143.
    Fisher J, Piterman N. The executable pathway to biological networks. Brief Funct Genomics. 2010;9(1):79. doi: 10.1093/bfgp/elp054.Google Scholar
  144. 144.
    Romero-Campero FJ, Twycross J, Camara M, Bennett M, Gheorghe M, Krasnogor N. Modular assembly of cell systems biology models using p systems. Int J Found Comput Sci. 2009;3:427 doi: 10.1142/S0129054109006668.Google Scholar
  145. 145.
  146. 146.
    Hlavacek WS, Faeder JR, Blinov ML, Posner RG, Hucka M, Fontana W. Rules for modeling signal-transduction systems. Sci STKE. 2006;2006(344):re6. doi: 10.1126/stke.3442006re6.

Copyright information

© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  • Vipin Narang
    • 1
  • James Decraene
    • 1
  • Shek-Yoon Wong
    • 1
  • Bindu S. Aiswarya
    • 1
  • Andrew R. Wasem
    • 1
  • Shiang Rong Leong
    • 1
  • Alexandre Gouaillard
    • 1
    Email author
  1. 1.Singapore Immunology Network, BMSISingaporeSingapore

Personalised recommendations