Building computational models of biochemical processes is usually a demanding task, especially for experimental biologists without modeling experience. This chapter aims to provide a guide on how one can quickly build and simulate spatially resolved biochemical models with the Spatiocyte software. We started with the basic theory of the Spatiocyte method and continued with the installation and simulation procedures. The various modules available to Spatiocyte users were also explained with accompanying model examples.
We plan to continuously develop and improve the Spatiocyte software and user experience. The contents of this guide will also therefore, evolve with the addition of new features and enhancements. The latest version of this guide will be available along with the Spatiocyte source code, which at the time of writing, is hosted at GitHub. The Spatiocyte website, http://spatiocyte.org also contains the latest information about the Spatiocyte method and software.
In future, we would like to introduce the ability of subunits to polymerize on the membrane and in the cytoplasm. A polymerization strategy using the HCP lattice was proposed recently (Arjunan, 2009). Diffusion of compartments, and molecules with different shapes and sizes are also in the future development plan. Parallel implementation of the Spatiocyte method to run on multi-core architectures and graphics processing units is also being considered. We are also currently working on introducing compartments with complex surface geometries. Spatiocyte users are encouraged to submit feature requests and bug reports, while independent developers can submit their own algorithm modules, code improvements and bug fixes.
The author thanks Goh Su Hua for creating the cluster model in the parameter tuning example.
- Arjunan, S. N. V. (2009) Modeling three-dimensional spatial regulation of bacterial cell division. PhD Thesis, Keio University.
- Arjunan, S. N. V. and Tomita, M. (2009). Modeling reaction-diffusion of molecules on surface and in volume spaces with the E-Cell System. International Journal of Computer Science and Information Security. 3(1): 211–216.
- Arjunan, S. N. V. and Tomita, M. (2010). A new multicompartmental reaction-diffusion modeling method links transient membrane attachment of E. coli MinE to E-ring formation. Systems and Synthetic Biology 4(1): 35-53
- Collins, F. C. and Kimball, G. E. (1949). Diffusion-controlled reaction rates. J Colloid Sci 4(4):425–437.
- Gibson, M. and Bruck, J. (2000). Efficient exact stochastic simulation of chemical systems with many species and many channels. J Phys Chem A 104(9):1876–1889
- Gillespie, D. T. (1976). A general method for numerically simulating the stochastic time evolution of coupled chemical reactions. J Comput Phys 22(4):403–434
- Gillespie, D. T. (1977). Exact stochastic simulation of coupled chemical reactions. J Phys Chem 81(25):2340–2361
- Dix JA, Verkman AS. (2008). Crowding effects on diffusion in solutions and cells. Annu Rev Biophys. 37:247-63.
- Hall D, Hoshino M. (2010). Effects of macromolecular crowding on intracellular diffusion from a single particle perspective. Biophysical Reviews. 2(1):39-53.
- Takahashi, K., Kaizu, K., Hu, B. and Tomita, M. (2004). A multi-algorithm, multi-timescale method for cell simulation. Bioinformatics. 20(4):538–546