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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 Links

• Why?
• Installation
• How It Works
• Management of Models
• Rendering and Analysis of Runs

Why?

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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

Model Management

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Rendering and Analysis

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