Multiscale Models in MOOSE: Interoperability and Standardization
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1
TIFR, National Centre for Biological Sciences, India
Introduction:
Multiscale Object Oriented Simulation Environment (MOOSE) is a general purpose biological simulator that can utilize multi-core as well as multi-node computer systems while making the complexities of load-balancing and messaging in these systems transparent to the user. It is multiscale in the sense of handling simulations from molecular on up to large network scales, events from microseconds to days, and in terms of running on hardware scaling from laptops to large clusters . It provides a Python based interface and can be used synergistically with other libraries and simulators that use Python.
Multiple scales of modeling:
The scales in biology can range from a few molecules bouncing around in a vesicle to networks of thousands of neurons modeling whole brain regions. The times can be anywhere between microseconds to days (or millenia for evolutionary biologists). Although practically any well-defined regime of simulation can be incorporated into MOOSE, the current focus is on chemical kinetics and neuronal networks. Fast solvers have been implemented/interfaced for reaction-diffusion chemical kinetics (GNU Scientific Library), stochastic chemical kinetics for small volumes (Gillespie algorithm), spatial Monte Carlo calculations for individual molecules (Smoldyn [1]) and realistic compartmental modeling of neurons (Hines’ algorithm). A key area of development in MOOSE is to integrate models of signaling pathways with compartmental models for studying emergent properties at the interface between biochemical and electrical signaling. MOOSE presents an intuitive object-oriented interface to the user, while transparently handling fast calculations with specialized numerical engines which are implemented for each level of detail.
Impact on standards:
Although progress in computer hardware and software is making it more feasible to study multiscale models, integrating existing models is often a tedious process. There are multiple standards for model specification at various levels and MOOSE supports three of them: the GENESIS scripting language, SBML [3] and NeuroML [2]. Moreover, it aims to support the Network Interchange format for NEuroscience (NineML) as the specification matures. In the absence of a common framework to combine model components specified in different formats, the end user has to put significant effort in developing composite models and such models remain non-standard. However, as simulating composite models out of existing ones becomes easier, it will be important for the community to find a way to integrate the existing standards for maximum productivity. MOOSE is one of the first simulators with this cross-scale capability, and provides a key test-bed for implementations of multiscale model definition standards.
References:
1. Andrews SS, Bray D: Stochastic simulation of chemical reactions with spatial resolution and single molecule detail, Phys. Biol. 2004, 1: 137–151
2. Gleeson P et. al.: NeuroML: A Language for Describing Data Driven Models of Neurons and Networks with a High Degree of Biological Detail, PLoS Comp. Bio. 2010, 6(6):e1000815
3. Hucka M. et. al.: The Systems Biology Markup Language (SBML): A Medium for Representation and Exchange of Biochemical Network Models, Bioinformatics, 9(4):524–531
Keywords:
computational neuroscience,
digital atlasing
Conference:
4th INCF Congress of Neuroinformatics, Boston, United States, 4 Sep - 6 Sep, 2011.
Presentation Type:
Demo Presentation
Topic:
Computational neuroscience
Citation:
Bhalla
U,
Ray
S,
Dudani
N,
George
S,
Gilra
A and
Harsharani
G
(2011). Multiscale Models in MOOSE: Interoperability and Standardization.
Front. Neuroinform.
Conference Abstract:
4th INCF Congress of Neuroinformatics.
doi: 10.3389/conf.fninf.2011.08.00071
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Received:
17 Oct 2011;
Published Online:
19 Oct 2011.
*
Correspondence:
Dr. Upinder Bhalla, TIFR, National Centre for Biological Sciences, Bangalore, India, bhalla@ncbs.res.in