STEPS: Modeling and simulating complex reaction-diffusion systems with Python

Frontiers in Neuroinformatics

Volumn 3, Issue JUN, 2009, Pages

  Wils, Stefan ^a,b   De Schutter, Erik ^a,b  

^a UNIVERSITY OF ANTWERP   (Belgium)

^b OKINAWA INSTITUTE OF SCIENCE AND TECHNOLOGY GRADUATE UNIVERSITY   (Japan)

Author keywords

3D diffusion; Python; Reaction kinetics; Scripting; Signaling pathway; Simulator; Software

Indexed keywords

EID: 77950545445     PISSN: 16625196     EISSN: None     Source Type: Journal    
DOI: 10.3389/neuro.11.015.2009     Document Type: Article

References (34)

1. Andrews, S. S., and Bray, D. (2004). Stochastic simulation of chemical reactions with spatial resolution and single molecule detail. Phys. Biol. 1, 137-151
2. Bergmann, F. T., and Sauro, H. M. (2008). Comparing simulation results of SBML capable simulators. Bioinformatics 24, 1963-1965
3. Bhalla, U. S. (2004). Models of cell signaling pathways. Curr. Opin. Genet. Dev. 14, 375-381
4. Cannon, R. C., Gewaltig, M.-O., Gleeson, P., Bhalla, U. S., Cornelis, H., Hines, M. L., Howell, F. W., Muller, E., Stiles, J. R., Wils, S., and De Schutter, E. (2007). Interoperability of neuroscience modeling software: current status and future directions. Neuroinformatics 5, 127-138
5. Carnevale, T. C., and Hines, M. L. (2006). The NEURON Book. Cambridge, Cambridge University Press.
6. Doi, T., Kuroda, S., Michikawa, T., and Kawato, M. (2005). Spike-timing detection by calcium signaling pathways of cerebellar Purkinje cells in different forms of long-term depression. J. Neurosci. 25, 950-961
7. Ekeberg, Ö., and Djurfeldt, M. (2008). MUSIC-Multisimulation Coordinator: Request For Comments. Nature Precedings. Available at: http://dx.doi.org/10.1038/npre.2008.1830.1
8. Elf, J., and Ehrenberg, M. (2004). Spontaneous separation of bi-stable biochemical systems into spatial domains of opposite phases. Syst. Biol. 1, 230-236
9. Ferziger, J. H., and Peric, M. (2002). Computational Methods for Fluid Dynamics, 3rd Edn. Berlin, Springer-Verlag.
10. Gillespie, D. T. (1976). A general method for numerically simulating the stochastic time evolution of coupled chemical species. J. Comput. Phys. 22, 403-434
11. Gillespie, D. T. (1977). Exact stochastic simulation of coupled chemical reactions. J. Phys. Chem. 81, 2340-2361
12. Gillespie, D. T. (2001). Approximate accelerated stochastic simulation of chemically reacting systems. J. Chem. Phys. 115, 1716-1733
13. Gillespie, D. T. (2007). Stochastic simulation of chemical kinetics. Annu. Rev. Phys. Chem. 58, 35-55
14. Gillespie, D. T., Lampoudi, S., and Petzold, L. R. (2007). Effect of reactant size on discrete stochastic chemical kinetics. J. Chem. Phys. 126, 034302
15. Hattne, J., Fange, D., and Elf, J. (2005). Stochastic reaction-diffusion simulation with MesoRD. Bioinformatics 21, 2923-2924
16. Holmes, W. R. (2000). Models of calmodulin trapping and CaM kinase II activation in a dendritic spine. J. Comput. Neurosci. 8, 65-68
17. Hucka, M., Finney, A., Sauro, H. M., Bolouri, H., Doyle, J. C., Kitano, H., Arkin, A. P., Bornstein, B. J., Bray, D., Cornish-Bowden, A., Cuellar, A. A., Dronov, S., Gilles, E. D., Ginkel, M., Gor, V., Gorvanin, I. I., Hedley, W. J., Hodgman, T. C., Hofmeyr, J. H., Hunter, P. J., Juty, N. S., Kasberger, J. L., Kremling, A., Kummer, U., Le Novère, N., Loew, L. M., Lucio, D., Mendes, P., Minch, E., Mjolness, E. D., Nakayama, Y., Nelson, M. R., Nielsen, P. F., Sakurada, T., Schaff, J. C., Shapiro, B. E., Shimizu, T. S., Spence, H. D., Stelling, J., Takahashi, K., Tomita, M., Wagner, J., and Wang, J. (2003). The systems biology markup language (SBML): a medium for representation and exchange of biochemical network models. Bioinformatics 19, 524-531
18. Kloeden, P. E., and Platen, E. (1999). Numerical Solution of Stochastic Differential Equations, 3rd Edn. Berlin, Springer-Verlag.
19. Kuroda, S., Schweighofer, N., and Kawato, M. (2001). Exploration of signal transduction pathways in cerebellar long-term depression by kinetic simulation. J. Neurosci. 21, 5693-5702
20. Lemerle, C., Di Ventura, B., and Serrano, L. (2005). Space as the fi nal frontier in stochastic simulations of biological systems. FEBS Lett. 579, 1789-1794
21. Lindskog, M., Kim, M., Wikström, M. A., Blackwell, K. T., and Kotaleski, J. H. (2006). Transient calcium and dopamine increase PKA activity and DARPP-32 phosphorylation. PLoS Comput. Biol. 2, e119
22. Lopreore, C. L., Bartol, T. M., Coggan, J. S., Keller, D. X., Sosinsky, G. E., Ellisman, M. H., and Sejnowski, T. J. (2008). Computational modeling of three-dimensional electrodiffusion in biological systems: applications to the node of Ranvier. Biophys. J. 95, 2624-2635
23. Means, S., Smith, A. J., Shepherd, J., Shadid, J., Fowler, J., Wojcikiewicz, R. J. H., Mazel, T., Smith, G. D., and Wilson, B. S. (2006). Reaction diffusion modeling of calcium dynamics with realistic ER geometry. Biophys. J. 91, 537-557
24. Miller, P., Zhabotinsky, A. M., Lisman, J. E., and Wang, X. J. (2005). The stability of a stochastic CaMKII switch: dependence on the number of enzyme molecules and protein turnover. PLoS Biol. 3, e107
25. Press, W. H., Teukolsky, S. A., Vetterling, W. T., and Flannery, B. P. (2007). Numerical Recipes in C: The Art of Scientific Computing, 3rd Edn. Cambridge, Cambridge University Press.
26. Santamaria, F., Wils, S., De Schutter, E., and Augustine, G. J. (2006). Anomalous diffusion in Purkinje cell dendrites caused by spines. Neuron 52, 635-648
27. Schnell, S., and Turner, T. E. (2004). Reaction kinetics in intracellular environments with macromolecular crowding: simulations and rate laws. Prog. Biophys. Mol. Biol. 85, 235-260
28. Smolen, P., Baxter, D. A., and Byrne, J. H. (2006). A model of the roles of essential kinases in the induction and expression of late long-term potentiation. Biophys. J. 90, 2760-2775
29. Stefan, M. I., Edelstein, S. J., and Le Novère, N. (2008). An allosteric model of calmodulin explains differential activation of PP2B and CaMKII. Proc. Natl. Acad. Sci. U.S.A. 105, 10768-10773
30. Stiles, J. R., and Bartol, T. M. (2001). Monte Carlo methods for simulating realistic synaptic microphysiology using MCell. In Computational Neuroscience: Realistic Modeling for Experimentalists, E. De Schutter, ed. (Boca Raton, CRC Press).
31. van Kampen, N. G. (2007). Stochastic Processes in Physics and Chemistry, 3rd Edn. Amsterdam, Elsevier.
32. Vandevoorde, D., and Josuttis, N. M. (2003). C++ Templates: The Complete Guide. Reading, MA, Addison-Wesley.
33. Wang, S. S., and Augustine, G. J. (1995). Confocal imaging and local photolysis of caged compounds: dual probes of synaptic function. Neuron 15, 755-760
34. Wils, S., and De Schutter, E. (2009). STEPS: an algorithm for stochastic simulation of reaction-diffusion systems using tetrahedral meshes. In preparation.