Moose

Moose simulates multi-scale neural and biochemical systems to model compartmental neuronal dynamics and signaling pathways based on chemical kinetics across subcellular to system-level scales.

Key Features:

• Multi-scale modeling: Supports models from subcellular components and biochemical reactions to single neurons, large networks, and system-level processes.
• Compartmental neuron support: Handles compartmental neuronal models for spatially resolved electrical dynamics.
• Chemical-kinetics signaling: Represents signaling pathways using chemical kinetics for biochemical reaction networks.
• Python integration (PyMOOSE): Exposes simulation control and model construction via PyMOOSE for scripting in Python.
• Simulator interoperability: Enables communication and bridging between numerical engines such as NEURON and MOOSE.
• Composite model construction: Allows building and executing models that integrate neuronal dynamics with signaling pathways using Python as the unifying language.
• Real-time analysis: Integrates Python numerical libraries to perform analysis of simulation outputs during execution.

Scientific Applications:

• Computational neuroscience: Modeling electrical behavior of neurons and networks at multiple spatial and temporal scales.
• Biochemical pathway simulation: Simulating intracellular signaling and biochemical reaction networks using chemical kinetics.
• Multi-scale interaction studies: Investigating interactions between neuronal dynamics and molecular signaling pathways.
• Cross-simulator model integration: Constructing composite models that combine components simulated in NEURON and MOOSE.

Methodology:

Uses compartmental modeling and chemical-kinetics representations, Python scripting via PyMOOSE, integration of Python numerical libraries for analysis, and bridging between numerical engines such as NEURON and MOOSE to construct and run composite models.