Modelling the tunability of early T cell signalling events

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 5132 LNCS, Issue , 2008, Pages 12-23

  Owens, Nick D L ^a   Timmis, Jon ^a   Greensted, Andrew ^a   Tyrrell, Andy ^a  

^a UNIVERSITY OF YORK   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

IMMUNOLOGY; MATHEMATICAL MODELS; MODEL CHECKING; PATIENT MONITORING; STOCHASTIC MODELS;

ARTIFICIAL IMMUNE SYSTEMS; CELL SIGNALLING; INTERNATIONAL CONFERENCES;

CYTOLOGY;

EID: 51049122630     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-85072-4_2     Document Type: Conference Paper

References (22)

1. Grossman, Z., Paul, W.E.: Adaptive Cellular Interactions in the Immune System: The Tunable activation threshold and significance of subthreshold. PNAS 89, 10365-10369 (1992)
2. Grossman, Z., Paul, W.E.: Autoreactivity, dynamic tuning and selectivity. Current Opinion in Immunology 13, 687-698 (2001)
3. Carneiro, J., Paixoa, T., Milutinovicb, D., Sousaa, J., Leona, K., Gardnera, R., Faroa, J.: Immunological self-tolerance: Lessons from mathematical modeling. Journal of Computational and Applied Mathematics 184, 77-100 (2005)
4. Germain, R.N., Stefanov, I.: The dynamics of T cell receptor signaling: complex orchestration and the key roles of tempo and cooperation. Annu. Rev. Immunol. 17, 467-522 (1999)
5. Altan-Bonnet, G., Germain, R.N.: Modeling T cell antigen discrimination based on feedback control of digital ERK responses. PLoS Biol. 3, e356 (2005)
6. Timmis, J., Andrews, P., Owens, N., Clark, E.: An Interdisciplinary Perspective on Artificial Immune Systems. Evolutionary Intelligence 1, 5-26 (2008)
7. McKeithan, T.W.: Kinetic proofreading in T-cell receptor signal transduction. PNAS 92, 5042-5046 (1995)
8. Huang, C.Y., Ferrell, J.E.: Ultrasensitivity in the mitogen-activated protein kinase cascade. PNAS 93, 10078-10083 (1996)
9. Milner, R.: Communicating and Mobile Systems: the π-Calculus. Cambridge University Press, Cambridge (1999)
10. Priami, C.: Stochastic π-Calculus. The Computer Journal 38, 578-589 (1995)
11. Phillips, A., Cardelli, L.: Efficient Correct Simulation of Biological Processes in Stocastic Pi-calculus. In: Calder, M., Gilmore, S. (eds.) CMSB 2007. LNCS (LNBI), vol. 4695, pp. 184-199. Springer, Heidelberg (2007)
12. Janeway, C., Travers, P., Walport, M., Shlomchik, M.: Immunobiology: The Immune System is Health and Disease Garland Science (2004)
13. Feinerman, O., Germain, R.N., Altan-Bonnet, G.: Quantitative challenges in understanding ligand discrimination by alphabeta T cells. Mol. Immunol. 45, 619-631 (2008)
14. Štefanová, I., Hemmer, B., Vergelli, M., Martin, R., Biddison, W.E., Germain, R.N.: TCR. ligand discrimination is enforced by competing ERK positive and SHP-1 negative feedback pathways. Nat. Immunol. 4, 248-254 (2003)
15. Wylie, D.C., Das, J., Chakraborty, A.K.: Sensitivity of T cells to antigen and antagonism emerges from differential regulation of the same molecular signaling module. PNAS 104, 5533-5538 (2007)
16. Gillespie, D.T.: Exact Stochastic Simulation of Coupled Chemical Reactions. The Journal of Physical Chemistry 81, 2340-2361 (1977)
17. Norman, G., Palamidessi, C., Parker, D., Wu, P.: Model checking the probabilistic π-calculus. In: Proc. QEST 2007, pp. 169-178. IEEE Computer Society, Los Alamitos (2007)
18. Priami, C., Regev, A., Shapiro, E.: Application of a stocastic name-passing calculus to representation for biological processes in the stochastic π-calculus. Information Processing Letters 80, 25-31 (2001)
19. Heath, J., Kwiatkowska, M., Norman, G., Parker, D., Tymchyshyn, O.: Probabilistic model checking of complex biological pathways. Theoretical Computer Science 319, 239-257 (2008)
20. Hermanns, H.: Interactive Markov Chains. LNCS, vol. 2428. Springer, Heidelberg (2002)
21. Model description: Reactions, SPiM and PRISM code, http://www-users.york. ac.uk/~ndlo100/icaris2008/
22. Hopfield, J.J.: Kinetic Proofreading: A New Mechanism for Reducing Errors in Biosynthetic Processes Requiring High Specificity. PNAS 71(10), 4135-4139 (1974)