Adapting rule-based model descriptions for simulating in continuous and hybrid space

Proceedings of the 9th International Conference on Computational Methods in Systems Biology, CMSB'11

Volumn , Issue , 2011, Pages 161-170

  Bittig, Arne T ^a   Haack, Fiete ^a   Maus, Carsten ^a   Uhrmacher, Adelinde M ^a  

^a UNIVERSITY OF ROSTOCK   (Germany)

Author keywords

model description languages; spatial simulation

Indexed keywords

BIOLOGICAL MODELING; BROWNIAN DYNAMICS; COLOCALIZATION; CONTINUOUS SPACES; EXCLUDED VOLUME EFFECTS; LIPID RAFT; MODEL DESCRIPTION LANGUAGES; MULTI-LEVEL; REACTION DIFFUSION SYSTEMS; RULE-BASED LANGUAGE; RULE-BASED MODELS; SPATIAL DYNAMICS; SPATIAL SIMULATION;

BROWNIAN MOVEMENT; COMPUTATIONAL METHODS; COMPUTER SIMULATION LANGUAGES; DYNAMICS;

COMPUTER SIMULATION;

EID: 80054814075     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2037509.2037533     Document Type: Conference Paper

References (32)

1. E. Bartocci, F. Corradini, M. R. Di Berardini, E. Merelli, and L. Tesei. Shape calculus. A spatial mobile calculus for 3D shapes. Scientific Annals of Computer Science, 20(20):1-31, 2010.
2. D. Bernstein. Simulating mesoscopic reaction-diffusion systems using the gillespie algorithm. Physical Review E, 71(4):041103+, Apr. 2005.
3. A. T. Bittig and A. M. Uhrmacher. Spatial modeling in cell biology at multiple levels. In B. Johansson, S. Jain, J. Montoya-Torres, J. Hugan, and E. Yücesan, editors, Proceedings of the 2010 Winter Simulation Conference, pages 608-619. IEEE Computer Science, Dec. 2010.
4. L. Cardelli. Brane calculi. In Computational Methods in Systems Biology, pages 257-278. 2005.
5. F. Ciocchetta, A. Degasperi, J. Hillston, and M. Calder. Some investigations concerning the ctmc and the ode model derived from bio-pepa. Electr. Notes Theor. Comput. Sci., 229(1):145-163, 2009.
6. M. Deaconu and A. Lejay. Simulation of exit times and positions for brownian motions and diffusions. Proc. Appl. Math. Mech., 7(1):1081401-1081402, 2007.
7. J. Elf and M. Ehrenberg. Spontaneous separation of bi-stable biochemical systems into spatial domains of opposite phases. Systems biology, 1(2):230-236, Dec. 2004.
8. S. Engblom, L. Ferm, A. Hellander, and P. Lötstedt. Simulation of stochastic reaction-diusion processes on unstructured meshes. ArXiv e-prints, Apr. 2008.
9. R. Ewald, J. Himmelspach, M. Jeschke, S. Leye, and A. M. Uhrmacher. Flexible experimentation in the modeling and simulation framework JAMES II-implications for computational systems biology. Brief Bioinform, 11(3):290-300, Jan. 2010.
10. D. Fange, O. G. Berg, P. Sjöberg, and J. Elf. Stochastic reaction-diffusion kinetics in the microscopic limit. Proceedings of the National Academy of Sciences, 2010.
11. L. Ferm, A. Hellander, and P. Lötstedt. An adaptive algorithm for simulation of stochastic reaction-diffusion processes. Journal of Computational Physics, 229(2):343-360, Jan. 2010.
12. G. Gruenert, B. Ibrahim, T. Lenser, M. Lohel, T. Hinze, and P. Dittrich. Rule-based spatial modeling with diffusing, geometrically constrained molecules. BMC Bioinformatics, 11(1):307+, 2010.
13. J. F. Hancock. Lipid rafts: contentious only from simplistic standpoints. Nat Rev Mol Cell Biol, 7(6):456-462, June 2006.
14. L. A. Harris, J. S. Hogg, and J. R. Faeder. Compartmental rule-based modeling of biochemical systems. In M. D. Rossetti, R. R. Hill, B. Johansson, A. Dunkin, and R. G. Ingalls, editors, Proceedings of the 2009 Winter Simulation Conference, pages 908-919. IEEE Computer Science, Dec. 2009.
15. M. Jeschke. Efficient Non-spatial and Spatial Simulation of Biochemical Reaction Networks. PhD thesis, University of Rostock, 18051 Rostock, Oct. 2010.
16. M. Jeschke, R. Ewald, and A. M. Uhrmacher. Exploring the performance of spatial stochastic simulation algorithms. J. Comput. Phys., 230:2562-2574, April 2011.
17. M. Jeschke and A. M. Uhrmacher. Multi-resolution spatial simulation for molecular crowding. In S. J. Mason, R. R. Hill, L. Moench, and O. Rose, editors, Proceedings of the 2008 Winter Simulation Conference, pages 1384-1392. IEEE, Dec. 2008.
18. M. John, R. Ewald, and A. M. Uhrmacher. A spatial extension to the π calculus. Electron. Notes Theor. Comput. Sci., 194(3):133-148, Jan. 2008.
19. M. John, C. Lhoussaine, J. Niehren, and A. Uhrmacher. The attributed Pi-Calculus with priorities. Transactions on Computational Systems Biology XII, 5945:13-76, 2010.
20. M. John, C. Lhoussaine, J. Niehren, and C. Versari. Biochemical reaction rules with constraints. In G. Barthe, editor, ESOP, volume 6602 of Lecture Notes in Computer Science, pages 338-357. Springer, 2011.
21. B. N. Kholodenko. Cell-signalling dynamics in time and space. Nature Reviews Molecular Cell Biology, 7(3):165-176, Feb. 2006.
22. D. Lingwood and K. Simons. Lipid rafts as a Membrane-Organizing principle. Science, 327(5961):46-50, Jan. 2010.
23. C. Maus, M. John, S. Rybacki, and A. M. Uhrmacher. Towards Rule-Based Multi-Level modeling. Poster, 2010.
24. C. Maus, S. Rybacki, and A. M. Uhrmacher. Rule-based multi-level modeling of cell biological systems. submitted, 2011.
25. D. V. Nicolau, K. Burrage, R. G. Parton, and J. F. Hancock. Identifying optimal lipid raft characteristics required to promote nanoscale Protein-Protein interactions on the plasma membrane. Mol. Cell. Biol., 26(1):313-323, Jan. 2006.
26. C. Priami and P. Quaglia. Beta binders for biological interactions. In Computational Methods in Systems Biology, pages 20-33. Springer Berlin Heidelberg, April 2005.
27. K. Radhakrishnan, A. Halász, D. Vlachos, and J. S. Edwards. Quantitative understanding of cell signaling: the importance of membrane organization. Current Opinion in Biotechnology, 21(5):677-682, Oct. 2010.
28. A. Regev, E. Panina, W. Silverman, L. Cardelli, and E. Shapiro. Bioambients: an abstraction for biological compartments. Theor. Comput. Sci., 325:141-167, September 2004.
29. S. Rybacki, J. Himmelspach, and A. M. Uhrmacher. Experiments with single core, multi-core, and gpu based computation of cellular automata. In Proceedings of the 2009 First International Conference on Advances in System Simulation, pages 62-67, Washington, DC, USA, 2009. IEEE Computer Society.
30. K. Takahashi, S. Arjunan, and M. Tomita. Space in systems biology of signaling pathways - towards intracellular molecular crowding in silico. FEBS Letters, 579(8):1783-1788, Mar. 2005. (Pubitemid 40342654)
31. K. Takahashi, S. Tǎ;nase-Nicola, and P. R. ten Wolde. Spatio-temporal correlations can drastically change the response of a MAPK pathway. Proceedings of the National Academy of Sciences, 107(6):2473-2478, Feb. 2010.
32. D. S. Wishart, R. Yang, D. Arndt, P. Tang, and J. Cruz. Dynamic cellular automata: An alternative approach to cellular simulation. In Silico Biology, 5(2):139-161, Jan. 2005. (Pubitemid 40983548)