Reactions in signaling pathways can vary according to the location of components. A typical example of this kind of pathway is the receptor-mediated endocytotic pathway. To faithfully model these pathways, there is a need to explicitly represent different compartments of the cell to finely describe the relocation of components. In our work, we consider a spatial model for different sorting of receptors of Fibroblast Growth Factor via the endocytotic pathway.
For computational model, we use the stochastic version of BioAmbients, a process calculus that allows to model both signaling pathways and the spatial distributions of signaling. The stochastic simulation is carried out using BAM (BioAmbient Machine), a Java implementation of BioAmbients via a spatial version of the Gillespie Algorithm. Our model, and the associated results of the simulations, confirm known experimental data. Our work  sheds light on different mechanisms that influence the spatial distribution of the different components in the pathway.
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