Abstract
Space plays an ever increasing role in cell biological modeling and simulation. This ranges from compartmental dynamics, via mesh-based approaches, to individuals moving in continuous space. An attributed, multi-level, rule-based language, ML-Space, is presented that allows to integrate these different types of spatial dynamics within one model. The associated simulator combines Gillespie's method, the Next Subvolume method, and Brownian dynamics. This allows the simulation of reaction diffusion systems as well as taking excluded volume effects into account. A small example illuminates the potential of the approach in dealing with complex spatial dynamics like those involved in studying the dynamics of lipid rafts and their role in receptor co-localization.
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