Computational Methods for Spatially Realistic Microphysiological Simulations 2014

Dates: Mon, April 28 - Wed, April 30, 2014

Workshop details:  Home    Agenda    Instructors   Evaluations

Video Recording of Modeling Workshop

The 2014 Workshop on Computational Methods for Spatially Realistic Microphysiological Simulations was recorded in its entirety and can be watched using the following YouTube playlist. 

 

This workshop covered theory and practice for the design and simulation of models focused on diffusion-reaction systems such as neurotransmission, signaling cascades, and other forms of biochemical networks.

During the workshop, users learned how to use the tools developed by MMBioS to create, run, and analyze models of cellular microphysiology and apply them to their own research questions. In particular, the workshop focused on:

  1. The lastest version of the MCell simulation environment, including new Monte Carlo methods for 3-D simulation of reactions in solution and on arbitrarily shaped biological surfaces.
  2. Novel tools to incorporate rule-based modeling techniques into MCell simulations based on the BioNetGen software (Faeder et al., Methods in Molecular Biology, 500:113-167, 2009).
  3. The CellOrganizer system (Murphy, Methods in Cell Biology 110:179-193, 2012) for creating image-derived models of cell shape and organization that can be used in MCell.
  4. The newest version of CellBlender, our MCell model creation and visualization framework (Czech et al., Methods in Molecular Biology 500: 237-287, 2009).

This workshop lasted three full days, and attendees were strongly encouraged to bring ideas/data for their own simulation projects. The daily schedule included theoretical and practical lectures, instructor-guided hands-on tutorials, and opportunities for one-on-one interaction with the instructors on individual project development.

Workshop instructors:

  • James Faeder (University of Pittsburgh)
  • Thomas Bartol (Salk Institute for Biological Studies)
  • Markus Dittrich (Pittsburgh Supercomputing Center)
  • Robert F. Murphy (Carnegie Mellon University)

 

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