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mcell - A Multicellular Modeling Framework
mcell is a small set of Python classes that allows the enterprising modeler to:
- Easily create very flexible models of of multicellular dynamics
- Manage the models already created through a simple command-line interface
- Easily change defined parameters in models
- Render the runs of the models in a convenient, simple way
- Analyze the runs of the models through histograms and clustering
mcell is a thin layer on top of the combination of [BionetSolver], which model internal cell states as systems of ODEs, and [CompuCell3D], which models cell shape dynamics using the Glazier-Graner-Hogeweg methos. It was created largely in reaction to inconveniences experienced when using BionetSolver and CompuCell3D as detailed [here]. It is in continuous development.
Quick
Installation
How it works
In mcell models, BionetSolver models the internal cell states, and CompuCell3D models the cell dynamics.
BionetSolver
BionetSolver reads one or several [SBML] files, each of which define a circuit as a system of chemical reactions in several containers with given rate laws. SBML models are simply XML files, and can be written with a text editor; however, it is much easier to define them using a graphical designer, like [JDesigner], or using a simple scripting language, like [Jarnac]. (Both of those editors can be obtained by installing the [Synthetic Biology Workbench].
After loading the circuits, BionetSolver is in posession of a system of ODEs that define the internal state of each modeled cell. To simulate each cell, BionetSolver simply Euler-steps its ODE system forward with a fixed time step. Some of the variables in the ODE model (like, say, the concentration of a protein on the neighboring cells) are actually parameters that are continually updated from the CompuCell3D thread.
CompuCell3D
CompuCell3D uses
