Hostname: page-component-cd9895bd7-mkpzs Total loading time: 0 Render date: 2024-12-23T06:26:09.220Z Has data issue: false hasContentIssue false
  • English
  • Français

A Modeling Framework For Immune-related Diseases

Published online by Cambridge University Press:  06 June 2012

F. Castiglione ,
S. Motta ,
F. Pappalardo  and
M. Pennisi
F. Castiglione*
Affiliation:
National Research Council of Italy, Rome, Italy
S. Motta
Affiliation:
University of Catania, Catania, Italy
F. Pappalardo
Affiliation:
University of Catania, Catania, Italy
M. Pennisi
Affiliation:
University of Catania, Catania, Italy
*
⋆ Corresponding author. E-mail: [email protected]
Get access

Abstract

About twenty five years ago the first discrete mathematical model of the immune systemwas proposed. It was very simple and stylized. Later, many other computational models havebeen proposed each one adding a certain level of sophistication and detail to thedescription of the system. One of these, the Celada-Seiden model published back in 1992,was already mature at its birth, setting apart from the topic-specific nature of the othermodels. This one was not just a model but rather a framework with which one couldimplement his own immunological theories.

Here we describe this computational framework, developed to perform simulations ofdifferent pathologies that are directly or indirectly connected to the immune system. Webriefly describe the system first, then we report on few applications so to give thereader a clear idea of its practical utility in clinical research problems.

Keywords

computational biologyCS-modelimmunologyagent-based modeling
Type
Research Article
Information
Mathematical Modelling of Natural Phenomena , Volume 7 , Issue 3: Epidemiology , 2012 , pp. 40 - 48
Copyright
© EDP Sciences, 2012

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

A.S. Perelson, G. Weisbuch (Eds.). Theoretical Immunology, Part One. Springer, Berlin, 1988.
A.S. Perelson (Ed.). Theoretical Immunology, Part Two. Addison Wesley, Redwood City, CA, 1988.
Perelson, A.S., Weisbuch, G.. Immunology for physicists. Rev. Mod. Phys., 69 (1997), 1219–1267. CrossRefGoogle Scholar
Puzone, R., Kohler, B., Seiden, P.E., Celada, F.. IMMSIM, a flexible model for in machina experiments on immune system responses. Future Gener. Comput. Syst., 18 (2002), No. 7, 961–972. CrossRefGoogle Scholar
M. Meier-Schellersheim, G. Mack. Simmune : A tool for simulating and analyzing immune system behavior. Das Deutsche Elektronen-Synchrotron Technical Report, 1999.
Castiglione, F. Agent based modeling. Scholarpedia, 1 (2006), No. 10, 1562. CrossRefGoogle Scholar
Kaufman, M., Urbain, J., Thomas, R.. Towards a logical analysis of the immune response. J. Th. Biol., 114 (1985), 527–561. CrossRefGoogle Scholar
Celada, F., Seiden, P.E.. Affinity maturation and hypermutation in a simulation of the humoral immune response. Eur. J. Immunol., 26 (1996), No. 6, 1350–1358. CrossRefGoogle Scholar
Celada, F., Seiden, P.E.. A computer model of cellular interactions in the immune system. Immunol. Today, 13 (1992), No. 2, 56–62. CrossRefGoogle ScholarPubMed
Seiden, P.E., Celada, F.. A model for simulating cognate recognition and response in the immune system. J. Th. Biol., 158 (1992), No. 3, 329–357. CrossRefGoogle Scholar
Morpurgo, D., Serenthà, R., Seiden, P.E., Celada, F.. Modelling thymic functions in a cellular automaton. Int. Immunol., 7 (1995), No. 4, 505–516. CrossRefGoogle Scholar
Castiglione, F., Poccia, F., D’Offizi, G., Bernaschi, M.. Mutation, fitness, viral diversity and predictive markers of disease p rogression in a computational model of HIV-1 infection. AIDS Res. Human Retrovirus, 20 (2004), No. 12, 1316–1325. CrossRefGoogle Scholar
Castiglione, F., Duca, K.A., Jarrah, A.S., Laubenbacher, R., Luzuriaga, K., Hochberg, D., Thorley-Lawson, D.A.. Simulating Epstein-Barr virus infection with C-ImmSim. Bioinformatics, 23 (2007), 1371–1377. CrossRefGoogle ScholarPubMed
F. Castiglione, V. Sleitser, Z. Agur. Analyzing hypersensitivity to chemotherapy in a Cellular Automata model of the immune system, in Cancer Modeling and Simulation, L. Preziosi (Ed.), pages 333–365. Chapman & Hall/CRC Press, London, UK, 2003.
Pappalardo, F., Lollini, P-L., Castiglione, F., Motta, S.. Modelling and simulation of cancer immunoprevention vaccine. Bioinformatics, 21 (2005), No. 12, 2891–2897. CrossRefGoogle Scholar
Paci, P., Carello, R., Bernaschi, M., D’Offizi, G., Castiglione, F.. Immune control of HIV-1 infection after therapy interruption : immediate versus deferred antiretroviral therapy. BMC Infect. Dis., 9 (2009), 172. CrossRefGoogle ScholarPubMed
Farmer, J.D., Packard, N.H., and Perelson, A.S.. The immune system, adaptation and machine learning. Physica D, 22 (1986), 187–204. CrossRefGoogle Scholar
P. Paci, F. Castiglione, M. Bernaschi, V. Baldazzi. A discrete/continuous model of anti-HIV response and therapy. IEEE Computer Society, Digital library Proceedins UKSIM, pages 481–486, 2008.
Baldazzi, V., Paci, P., Bernaschi, M., Castiglione, F.. Modeling lymphocytes homing and encounters in lymph nodes. BMC Bioinformatics, 10 (2009), 387. CrossRefGoogle ScholarPubMed
Paci, P., Martini, F., Bernaschi, M., D’Offizi, F., Castiglione, F.. Earlier is better : a timely HAART initiation may pave the way for best controllers. BMC Infectious Diseases, 11 (2011), 56. CrossRefGoogle Scholar
Castiglione, F., Paci, P.. Criticality of timing for anti-HIV therapy initiation. PLoS ONE, 5 (2010), No. 12, e15294. CrossRefGoogle ScholarPubMed
Laichalk, L.L., Thorley-Lawson, D.A.. Terminal differentiation into plasma cells initiates the replicative cycle of Epstein-Barr virus in vivo. J. Virol., 79 (2005), 1296–1307. CrossRefGoogle ScholarPubMed
Kay AB, A.B.. Allergy and allergic diseases. First Part. N. Engl. J. Med., 344 (2001), 30–37. CrossRefGoogle Scholar
R.A. Goldsby, T.J. Kindt, B.A. Osborne. Kuby Immunology, IV ed.. W.H. Freeman & Co., NY, 2000.
Kay, A.B.. Allergy and allergic diseases. Second Part. N. Engl. J. Med., 344 (2001), 109–113. CrossRefGoogle Scholar
Holgate, S.T.. Allergic disorders. British Med. J., 320 (2000), 231–234. CrossRefGoogle ScholarPubMed
Santoni, D., Pedicini, M., Castiglione, F.. Implementation of a regulatory gene network to simulate the TH1/2 differentiation in an agent-based model of hyper-sensitivity reactions. Bioinformatics, 24 (2008), 1374–1380. CrossRefGoogle Scholar
Hansson, G.K.. Inflammation, atherosclerosis, and coronary artery disease. N. Engl. J. Med., 352 (2002), No. 16, 1685–1695. CrossRefGoogle ScholarPubMed
Romero-Corral, A., Somers, V.K., Korinek, J., Sierra-Johnson, J., Thomas, R.J., Allison, T.G., Lopez-Jimenez, F.. Update in prevention of atherosclerotic heart disease : management of major cardiovascular risk factors. Rev. Invest. Clin., 58 (2006), No. 3, 237–244. Google Scholar
Weber, C., Zernecke, A., Libby, P.. The multifaceted contributions of leukocyte subsets to atherosclerosis : lessons from mouse models. Nat. Rev. Immunol., 8 (2008), No. 10, 802–815. CrossRefGoogle Scholar
Pappalardo, F., Musumeci, S., Motta, S.. Modeling immune system control of atherogenesis. Bioinformatics, 24 (2008), No. 15, 1715–1721. CrossRefGoogle ScholarPubMed
M. Meier-Schellersheim. The immune system as a complex system : Description and simulation of the interactions of its constituents. Ph.D. thesis, University of Hamburg, Germany, 2001.
Efroni, S., Harel, D., Cohen, I.R.. Toward rigorous comprehension of biological complexity : modeling, execution, and visualization of thymic T-cell maturation. Genome Res., 13 (2003), No. 11, 2485–2497. CrossRefGoogle ScholarPubMed
Kalita, J.K., Chandrashekar, K., Hans, R., Selvam, R.. Computational modelling and simulation of the immune system. Int. J. Bioinf. Res. Appl., 2 (2006), No. 1, 63–88. CrossRefGoogle Scholar
Folcik, V.A., An, G.C., Orosz, C.G.. The basic immune simulator : an agent-based model to study the interactions between innate and adaptive immunity. Theor. Biol. Med. Model., 4 (2007), 39. CrossRefGoogle ScholarPubMed
R.C. Laubenbacher, A.S. Jarrah, H.S. Mortveit, S.S. Ravi. A mathematical formalism for agent-based modeling. CoRR., (2008), abs/0801.0249.
S.Y. Del Valle, P.D. Stroud, J.P. Smith, S.M. Mniszewski, J.M. Riese, S.J. Sydoriak, D.A. Kubicek. Episims : Epidemic simulation system. Los Alamos Unlimited Release (LAUR), (2006), 06-06714.
S. Mniszewski, S.Y. Del Valle, P. Stroud, J. Riese, S. Sydoriak. Episims simulation of a multi-component strategy for pandemic influenza. Proceedings of the 2008 Spring Simulation Multiconference, 2008.