MOOSE is the Multiscale Object-Oriented Simulation Environment. It is designed to simulate neural systems ranging from subcellular components and biochemical reactions to complex models of single neurons, circuits, and large networks. MOOSE can operate at many levels of detail, from stochastic chemical computations, to multicompartment single-neuron models, to spiking neuron network models.
MOOSE is multiscale: It can do all these calculations together. For example it handles interactions seamlessly between electrical and chemical signaling. MOOSE is object-oriented. Biological concepts are mapped into classes, and a model is built by creating instances of these classes and connecting them by messages. MOOSE also has classes whose job is to take over difficult computations in a certain domain, and do them fast. There are such solver classes for stochastic and deterministic chemistry, for diffusion, and for multicompartment neuronal models. MOOSE is a simulation environment, not just a numerical engine: It provides data representations and solvers (of course!), but also a scripting interface with Python, graphical displays with Matplotlib, PyQt, and OpenGL, and support for many model formats. These include SBML, NeuroML, GENESIS kkit and cell.p formats, HDF5 and NSDF for data writing.
This is MOOSE 3.0.2pre "Ghevar"
The Ghevar release is the third of series 3 of MOOSE releases.
Ghevar is a Rajasthani sweet with a stiff porous body soaked in sugar syrup.
MOOSE 3.0.2pre is an evolutionary increment over 3.0.1::
- There has been substantial development on the multiscale modeling front, with the implementation of the rdesigneur class and affiliated features.
- MOOSE can now read NeuroMorpho .swc files natively.
MOOSE is released under the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.
Old SourceForge repository is no longer maintained. Current source repository is hosted on github with almost all revision history.
- g++ (>= 4.6.x) REQUIRED For building the C++ MOOSE core.
- GSL (>=1.16.x) REQUIRED For core moose numerical computation
- OpenMPI (1.8.x) OPTIONAL For running moose in parallel on clusters
- SBML (5.9.x) OPTIONAL For reading and writing signalling models in SBML format
- HDF5 (1.8.x) OPTIONAL For reading and writing data in HDF5 based formats
Python interface for core MOOSE API
In addition to core MOOSE requirements:
- Python2 ( >= 2.7.x) REQUIRED For building the MOOSE Python bindings
- Python-dev ( >= 2.7.x) REQUIRED Python development headers and libraries
- NumPy ( >= 1.6.x) REQUIRED For array interface
- PyQt4 (4.8.x) REQUIRED For Python GUI
- Matplotlib ( >= 1.1.x) REQUIRED For plotting simulation results
- suds (0.4) REQUIRED For accessing models hosted on biomodels database.
- OSG (3.2.x) REQUIRED For 3D rendering and simulation of neuronal models
- Qt4 (4.8.x) REQUIRED For C++ GUI of Moogli
- PyQt4 (4.8.x) OPTIONAL GUI in squid demo, Traub et al 2005 demo.
- NetworkX (1.x) OPTIONAL display neuronal topology in Traub et al 2005 demo.
- Pygraphviz (1.x) OPTIONAL display neuronal topology in Traub et al 2005 demo.
- Upinder S. Bhalla - Primary Architect, Chemical kinetic solvers
- Niraj Dudani - Neuronal solver
- Subhasis Ray - PyMOOSE Design and Documentation, Python Plugin Interface, NSDF Format
- G.V.HarshaRani - Web page design, SBML support, Kinetikit Plugin Development
- Aditya Gilra - NeuroML reader development, integrate-and-fire neurons/networks, STDP
- Aviral Goel - Moogli/Neurokit Development
- Dilawar Singh - Packaging
You can join the MOOSE generic mailing list for your queries - https://lists.sourceforge.net/lists/listinfo/moose-generic
You can file bug reports and feature requets at the sourceforge tracker - http://sourceforge.net/p/moose/bugs/
Moose is usually run within Python. Here is a template Python script that will load and run various kinds of predefined model files:
>>> import moose
>>> help( moose ) # See what you can do with MOOSE.
>>> moose.loadModel( file_path, modelname ) # Load in your model
>>> moose.reinit() # Set initial conditions
>>> moose.start( runtime ) # Run the model
Using Matplotlib and a few more lines, one can plot the output of this simulation, which will have been been stored in tables somewhere in the model:
>>> import pylab
>>> import numpy
>>> for x in moose.wildcardFind( '/modelname/##[ISA=Table]' ):
>>> t = numpy.arange( 0, x.vector.size, 1, ) * dt
>>> pylab.plot( t, x.vector, label = x.name )
>>> pylab.legend()
>>> pylab.show()
Look in the moose-examples repository for sample code.
- tutorials: Standalone scripts meant for teaching. Students are expected to modify the scripts to learn the principles of the models.
- squid: The Hodkin-Huxley squid model, fully graphical interface.
- Genesis_files: A number of kinetics models used in MOOSE demos.
- neuroml: A number of NeuroML models used in MOOSE demos
- traub_2005: Example scripts for each of the individual cell models from the Traub 2005 thalamocortical model.
- snippets: Code snippets that can be used as building blocks and to illustrate how to use certain kinds of objects in MOOSE. These snippets are all meant to run as individual files.
MOOSE comes with a NeuroML reader. Demos/neuroml has some python scripts showing how to load NeuroML models.
MOOSE is backward compatible with GENESIS kinetikit. Demos/Genesis_files has some examples. You can load a kinetikit model with the loadModel function:
moose.loadModel(kkit_file_path, modelname )
MOOSE is backward compatible with GENESIS .p files used for neuronal model specification. The same loadModel function can be used for this but you need to have all the channels used in the .p file preloaded in /library:
moose.loadModel(prototype_file_path, modelname )
MOOSE can also read .swc files from NeuroMorpho.org.
Top level moose documentation can be accessed in the Python interpreter the usual way:
import moose
help(moose)
MOOSE classes have built-in documentation that can be accessed via
the doc() function -
moose.doc(classname)
This will give the full documentation for the class including the fields available.
`moose.doc(classname.fieldname)`
will give you information about a particular field in a class.
Complete MOOSE Documentation can be found at - http://moose.ncbs.res.in/content/view/5/6/