Rapid Creation, Monte Carlo Simulation, and Visualization of Realistic 3D Cell Models

  • Jacob Czech
  • Markus Dittrich
  • Joel R. StilesEmail author
Part of the Methods in Molecular Biology book series (MIMB, volume 500)


Spatially realistic diffusion-reaction simulations supplement traditional experiments and provide testable hypotheses for complex physiological systems. To date, however, the creation of realistic 3D cell models has been difficult and time-consuming, typically involving hand reconstruction from electron microscopic images. Here, we present a complementary approach that is much simpler and faster, because the cell architecture (geometry) is created directly in silico using 3D modeling software like that used for commercial film animations. We show how a freely available open source program (Blender) can be used to create the model geometry, which then can be read by our Monte Carlo simulation and visualization softwares (MCell and DReAMM, respectively). This new workflow allows rapid prototyping and development of realistic computational models, and thus should dramatically accelerate their use by a wide variety of computational and experimental investigators. Using two self-contained examples based on synaptic transmission, we illustrate the creation of 3D cellular geometry with Blender, addition of molecules, reactions, and other run-time conditions using MCell's Model Description Language (MDL), and subsequent MCell simulations and DReAMM visualizations. In the first example, we simulate calcium influx through voltage-gated channels localized on a presynaptic bouton, with subsequent intracellular calcium diffusion and binding to sites on synaptic vesicles. In the second example, we simulate neurotransmitter release from synaptic vesicles as they fuse with the presynaptic membrane, subsequent transmitter diffusion into the synaptic cleft, and binding to postsynaptic receptors on a dendritic spine.


Blender MCell DReAMM MDL Cell modeling Cell architecture Cell geometry Stochastic Diffusion-reaction 



The authors thank Aji Janis and Gary Blumenthal for careful reading and testing of the examples in this chapter. This work was supported by NIH R01 GM068630 (JRS), P41 RR06009 (JRS), and F32 GM083473 (MD).


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Copyright information

© Humana Press 2009

Authors and Affiliations

  1. 1.National Resource for Biomedical Supercomputing, Pittsburgh Supercomputing CenterCarnegie Mellon UniversityPittsburghUSA

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