PyMOOSE: Interoperable Scripting in Python for MOOSE

Python is emerging as a common scripting language for simulators. This opens up many possibilities for interoperability in the form of analysis, interfaces, and communications between simulators. We report the integration of Python scripting with the Multi-scale Object Oriented Simulation Environment (MOOSE). MOOSE is a general-purpose simulation system for compartmental neuronal models and for models of signaling pathways based on chemical kinetics. We show how the Python-scripting version of MOOSE, PyMOOSE, combines the power of a compiled simulator with the versatility and ease of use of Python. We illustrate this by using Python numerical libraries to analyze MOOSE output online, and by developing a GUI in Python/Qt for a MOOSE simulation. Finally, we build and run a composite neuronal/signaling model that uses both the NEURON and MOOSE numerical engines, and Python as a bridge between the two. Thus PyMOOSE has a high degree of interoperability with analysis routines, with graphical toolkits, and with other simulators.

• Publication year

  2008

• Venue

  Frontiers Neuroinformatics

• Publication date

  2008-10-13

• Fields of study

  Medicine, Computer Science

• Identifiers
  DOI 10.3389/neuro.11.006.2008PMID 19129924PMCID 2614320
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar, PubMed

• A composite neuronal/signaling model is built and run by using NEURON and MOOSE as separate numerical engines with Python acting as the bridge between them.

  Confidence 0.97

  박진우 (dztg5apj7m) extractionB (s683577b42) reviewmexicorea (qjvnbu8xg3) review
• Python numerical libraries are used to analyze MOOSE output online, and a Python/Qt GUI is developed for interacting with a MOOSE simulation.

  Confidence 0.92

  박진우 (dztg5apj7m) extractionB (s683577b42) reviewmexicorea (qjvnbu8xg3) review
• PyMOOSE integrates Python scripting with the MOOSE simulation environment for compartmental neuronal models and chemical-kinetics signaling models.

  Confidence 0.98

  박진우 (dztg5apj7m) extractionB (s683577b42) reviewmexicorea (qjvnbu8xg3) review

• composite neuronal/signaling model

  model

  An integrated model combining neuronal and signaling components within one simulation workflow.

  Aliases: hybrid neuronal/signaling model

  박진우 (dztg5apj7m) extractionB (s683577b42) reviewmexicorea (qjvnbu8xg3) review
• moose

  simulation environment

  A general-purpose simulation environment for compartmental neuronal models and chemical-kinetics signaling pathways.

  Aliases: Multi-scale Object Oriented Simulation Environment

  박진우 (dztg5apj7m) extractionB (s683577b42) reviewmexicorea (qjvnbu8xg3) review
• neuron

  simulation engine

  A numerical engine for neuronal simulation used alongside MOOSE in the composite model.

  박진우 (dztg5apj7m) extractionB (s683577b42) reviewmexicorea (qjvnbu8xg3) review
• pymoose

  software platform

  The Python-scripting version of MOOSE described as the interoperable scripting layer for the simulator.

  Aliases: Python-scripting version of MOOSE

  박진우 (dztg5apj7m) extractionB (s683577b42) reviewmexicorea (qjvnbu8xg3) review
• python numerical libraries

  software library, analysis tool

  Python-based scientific computation libraries used here for online analysis of simulation output.

  Aliases: Python numerics libraries

  박진우 (dztg5apj7m) extractionB (s683577b42) reviewmexicorea (qjvnbu8xg3) review
• python/qt gui

  user interface, software component

  A graphical user interface built with Python and Qt for controlling or viewing a MOOSE simulation.

  Aliases: GUI in Python/Qt, Python Qt interface

  박진우 (dztg5apj7m) extractionB (s683577b42) reviewmexicorea (qjvnbu8xg3) review
• python scripting

  programming approach

  The use of Python as the scripting layer for driving simulation, analysis, and interoperability tasks.

  Aliases: Python scripting in Python, Python as a bridge

  박진우 (dztg5apj7m) extractionB (s683577b42) reviewmexicorea (qjvnbu8xg3) review

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