Abstract
In this paper, we present the Cellular Dynamic Simulator (CDS) for simulating diffusion and chemical reactions within crowded molecular environments. CDS is based on a novel event driven algorithm specifically designed for precise calculation of the timing of collisions, reactions and other events for each individual molecule in the environment. Generic mesh based compartments allow the creation / importation of very simple or detailed cellular structures that exist in a 3D environment. Multiple levels of compartments and static obstacles can be used to create a dense environment to mimic cellular boundaries and the intracellular space. The CDS algorithm takes into account volume exclusion and molecular crowding that may impact signaling cascades in small sub-cellular compartments such as dendritic spines. With the CDS, we can simulate simple enzyme reactions; aggregation, channel transport, as well as highly complicated chemical reaction networks of both freely diffusing and membrane bound multi-protein complexes. Components of the CDS are generally defined such that the simulator can be applied to a wide range of environments in terms of scale and level of detail. Through an initialization GUI, a simple simulation environment can be created and populated within minutes yet is powerful enough to design complex 3D cellular architecture. The initialization tool allows visual confirmation of the environment construction prior to execution by the simulator. This paper describes the CDS algorithm, design implementation, and provides an overview of the types of features available and the utility of those features are highlighted in demonstrations.
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Acknowledgments
This work was supported by U.S. National Institutes of Health grant NS038310. We would like to thank Pete Swulius and Dave Howard for their invaluable help on developing an early version of the visualization tool.
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Byrne, M.J., Waxham, M.N. & Kubota, Y. Cellular Dynamic Simulator: An Event Driven Molecular Simulation Environment for Cellular Physiology. Neuroinform 8, 63–82 (2010). https://doi.org/10.1007/s12021-010-9066-x
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DOI: https://doi.org/10.1007/s12021-010-9066-x