Skip to main content


We're creating a new version of this page. See preview

  • Poster presentation
  • Open Access

Connecting MOOSE and NeuroRD through MUSIC: towards a communication framework for multi-scale modeling

  • 1, 2,
  • 2, 3, 4,
  • 2,
  • 2,
  • 3,
  • 2, 4 and
  • 1, 4Email author
BMC Neuroscience201112 (Suppl 1) :P77

  • Published:


  • Compartment Model
  • Electrical Model
  • Communication Framework
  • Parallel Solver
  • Single Model System

The nervous system encompasses structure and phenomena at different spatial and temporal scales from molecule to behavior. In addition, different scales are described by different physical and mathematical formalisms. The dynamics of second messenger pathways can be formulated as stochastic reaction-diffusion systems [1] while the electrical dynamics of the neuronal membrane is often described by compartment models and the Hodgkin-Huxley formalism. In neuroscience, there is an increasing need and interest to study multi-scale phenomena where multiple scales and physical formalisms are covered by a single model. While there exists simulators/frameworks, such as GENESIS and MOOSE [3], which spans such scales (kinetikit/HH-models), most software applications are specialized for a given domain. Here, we report about initial steps towards a framework for multi-scale modeling which builds on the concept of multi-simulations [2]. We aim to provide a standard API and communication framework allowing parallel simulators targeted at different scales and/or different physics to communicate on-line in a cluster environment. Specifically, we show prototype work on simulating electrical activity and Ca2+-dynamics in a dendritic spine using MOOSE and NeuroRD [4, 8].

Electrical properties of a simple compartment model with soma, dendrite and spine is simulated in MOOSE, while Ca2+ dependent reactions and diffusion in the spine is simulated in NeuroRD. In a prototype system, the two simulators are connected using PyMOOSE [5] and JPype [7]. The two models with their different physical properties (membrane currents in MOOSE, molecular biophysics in NeuroRD), are joined into a single model system. Ca2+ currents in the electrical model are translated to Ca2+ influx rates in NeuroRD, determining the dynamics of the biophysical model. In turn Ca2+ dependent events in the spine control properties such as Ca2+ dependent ion channels in the electrical model. The joint system, including details of solver methods, is also studied analytically with regard to stability and accuracy and a set of requirements for a generic API allowing parallel solvers to communicate in a multi-simulation is formulated. Experiences from couplers [6] used to couple field models in climate research is taken into consideration. A gap analysis with respect to the existing MUSIC framework [2] is performed.

We demonstrate the interaction of the numerical solvers of two simulators (MOOSE, NeuroRD) targeted at different spatiotemporal scales and different physics while solving a multi-scale problem. We analyze some of the problems that may arise in multi-scale multi-simulations and present requirements for a generic API for parallel solvers. This work represents an initial step towards a flexible modular framework for simulation of large-scale multi-scale multi-physics problems in neuroscience.

Authors’ Affiliations

PDC, Royal Institute of Technology - KTH, Stockholm, S-100 44, Sweden
CSC, Royal Institute of Technology - KTH, Stockholm, S-100 44, Sweden
National Centre for Biological Sciences, Bangalore, India
INCF, Karolinska Institutet - KI, Stockholm, S-171 77, Sweden


  1. Blackwell KT: An efficient stochastic diffusion algorithm for modeling second messengers in dendrites and spines. J Neurosci Meth. 2006, 157: 142-153. 10.1016/j.jneumeth.2006.04.003.View ArticleGoogle Scholar
  2. Djurfeldt M, Hjorth J, Eppler JM, Dudani N, Helias M, Potjans TC, Bhalla US, Diesmann M, Hellgren Kotaleski J, Ekeberg O: Run-Time Interoperability Between Neural Network Simulators Based on the MUSIC Framework. Neurinform. 2010, 8: 43-60. 10.1007/s12021-010-9064-z.View ArticleGoogle Scholar
  3. Dudani N, Ray S, George S, Bhalla US: Multiscale modeling and interoperability in MOOSE. Neuroscience. 2009, 10 (Suppl 1): 54.Google Scholar
  4. Oliveira RF, Terrin A, Di Benedetto G, Cannon RC, Koh W, Kim M, Zaccolo M, Blacwell KT: The Role of Type 4 Phosphodiesterases in Generating Microdomains of cAMP: Large Scale Stochastic Simulations. PloS one. 2010, 5 (7).Google Scholar
  5. Ray S, Bhalla US: PyMOOSE: interoperable scripting in Python for MOOSE. Front. Neuroinf. 2008, 2 (6).Google Scholar
  6. Valcke S, Redler R: Oasis 4 User Guide. CERFACS and NEC-CCRL. 2006Google Scholar
  7. Jpype Bridging the worlds of Java and Python. []
  8. NeuroRD. []


© Brandi et al; licensee BioMed Central Ltd. 2011

This article is published under license to BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.