Cell Modeling Virtual Workshop 2020

*Due to the associated travel restrictions related to the spread of COVID-19, and to safeguard workshop participants, we have decided to restructure the Cell Modeling Workshop into a "virtual workshop."  Applicants will be updated with more details soon. We regret the inconvenience for this change and appreciate your patience as we deal with these exceptional circumstances.

Dates: June 22-26, 2020
Workshop details: Additional details coming soon!
Application deadline: May 15

Registration is now closed. Thank you.

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

During the workshop, participants will learn 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. The workshop focuses on the following tools:

  1. The MCell simulation environment, including new Monte Carlo methods for 3-D simulation of reactions in solution and on arbitrarily shaped biological surfaces. The newest version of CellBlender our MCell model creation and visualization framework.
  2. The BioNetGen software for specifying, simulating, and analyzing biochemical networks using a modular, rule-based approach.
  3. The CellOrganizer system for creating image-derived models of cell shape and intracellular organization that can be used to compare cell populations and to create cell simulations for diverse cell geometries to explore the effect of variation in cell organization on microphysiology.

Workshop instructors:

References

  1. R. Kerr et al. (2008) Fast Monte Carlo simulation methods for biological reaction-diffusion systems in solution and on surfaces. SIAM J Sci Comput 30:3126–3149.
  2. L. A. Harris et al. (2016) BioNetGen 2.2: Advances in Rule-Based Modeling. Bioinformatics 32:3366–8.
  3. T. Majarian, I. Cao-Berg, X. Ruan, and R. F. Murphy (2019) CellOrganizer: Learning and Using Cell Geometries for Spatial Cell Simulations. Methods in Molecular Biology 1945:251-264.
  4. Gupta, S,  et al. (2018) Spatial Stochastic Modeling with MCell and CellBlender. In Quantitative Biology: Theory, Computational Methods and Examples of Models, B. Munsky, W. Hlavacek, and L. Tsimring, Eds. MIT Press, ISBN:9780262038089.

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