Multiscale Object Oriented Simulation Environment

Designed to simulate neural systems ranging from subcellular components and biochemical reactions to complex models of single neurons, circuits, and large networks.

MOOSE Features

Python Interface
Available as a Python module with predefined classes that map directly to biological components.
Modeling across domains
Supports both biochemistry and biophysics for integrated simulations.
Scalability
Built-in solvers with optimized data structures and algorithms handle complex models efficiently, yet maintain a biologically meaningful view for the user
Multiscale Capability
Enables seamless modeling from stochastic chemical processes and reaction-diffusion systems to multi-compartmental neuron models and large-scale biological neural networks.
Format Support
Handles multiple model formats, including SBML, NeuroML, and GENESIS(kkit, cell.p) Data can be saved in text, HDF5 based NSDF, or any other format compatible with numpy arrays
Rdesigneur
Simplifies the model-building process for faster creation and testing of integration models using a library of prototype components.
Mode Switching
Easily toggles between deterministic and stochastic simulation modes for biochemical models.

Single Neuron Electrical Aspects

Neurons modeled as equivalent electrical circuits.

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Chemical Bistables

A bistable system has two stable equilibrium states.

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Publications

  • 01
    Bhalla, U. S. (2011a). Multiscale interactions between chemical and electric signaling in LTP induction, LTP reversal and dendritic excitability. Neural Networks, 24(9), 943–949.
  • 02
    Bhalla, U. S. (2011b). Trafficking motifs as the basis for two-compartment signaling systems to form multiple stable states. Biophysical Journal, 101(1), 21–32.
  • 03
    Bhalla, U. S. (2017). Synaptic input sequence discrimination on behavioral timescales mediated by reaction-diffusion chemistry in dendrites. Elife, 6, e25827.
  • 04
    Bhalla, U. S. (2021). HillTau: A fast, compact abstraction for model reduction in biochemical signaling networks. PLoS Computational Biology, 17(11), e1009621.
  • 05
    Bhatia, A., Moza, S., & Bhalla, U. S. (2019). Precise excitation-inhibition balance controls gain and timing in the hippocampus. Elife, 8, e43415.
  • 06
    Brocke, E., Bhalla, U. S., Djurfeldt, M., Hellgren Kotaleski, J., & Hanke, M. (2016). Efficient integration of coupled electrical-chemical systems in multiscale neuronal simulations. Frontiers in Computational Neuroscience, 10, 97.
  • 07
    Djurfeldt, M., Hjorth, J., Eppler, J. M., Dudani, N., Helias, M., Potjans, T. C., Bhalla, U. S., Diesmann, M., Hellgren Kotaleski, J., & Ekeberg, Ö. (2010). Run-time interoperability between neuronal network simulators based on the MUSIC framework. Neuroinformatics, 8, 43–60.
  • 08
    Dudani, N., Bhalla, U. S., & Ray, S. (2014). MOOSE, the Multiscale Object-Oriented Simulation Environment. In D. Jaeger & R. Jung (Eds.), Encyclopedia of Computational Neuroscience. Springer New York. https://doi.org/10.1007/978-1-4614-7320-6_257-1
  • 09
    Gilra, A., & Bhalla, U. S. (2015). Bulbar microcircuit model predicts connectivity and roles of interneurons in odor coding. PLoS One, 10(5), e0098045.
  • 10
    Gleeson, P., Crook, S., Cannon, R. C., Hines, M. L., Billings, G. O., Farinella, M., Morse, T. M., Davison, A. P., Ray, S., Bhalla, U. S., & others. (2010). NeuroML: a language for describing data driven models of neurons and networks with a high degree of biological detail. PLoS Computational Biology, 6(6), e1000815.
  • 11
    Jain, P., & Bhalla, U. S. (2009). Signaling logic of activity-triggered dendritic protein synthesis: an mTOR gate but not a feedback switch. PLoS Computational Biology, 5(2), e1000287.
  • 12
    Jain, P., & Bhalla, U. S. (2014). Transcription control pathways decode patterned synaptic inputs into diverse mRNA expression profiles. PloS One, 9(5), e95154.
  • 13
    Ray, S., & Bhalla, U. S. (2008). PyMOOSE: Interoperable scripting in Python for MOOSE. Frontiers in Neuroinformatics, 2. https://doi.org/10.3389/neuro.11.006.2008
  • 14
    Somashekar, B. P., & Bhalla, U. S. (2025). Discriminating neural ensemble patterns through dendritic computations in randomly connected feedforward networks. eLife, 13, RP100664.
  • 15
    Viswan, N. A., HarshaRani, G. V., Stefan, M. I., & Bhalla, U. S. (2018). FindSim: a framework for integrating neuronal data and signaling models. Frontiers in Neuroinformatics, 12, 38.

Funders

NCBSDBTDAEKalviTIFR

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