Multiscale Modeling in MOOSE: Interfaces, Interoperability and Standards
Introduction Multiscale Object Oriented Simulation Environment (MOOSE) is a general purpose biological simulator. It runs on multi-core as well as multi-node computer systems while making load-balancing and messaging transparent to the user. It is multiscale in the sense of handling simulations from molecular 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. It has a graphical-user interface that allows for easy plotting, and 3D visualization of complex models and their state.
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 in brain regions. The times can be anywhere between microseconds to days (or millenia for evolutionary biologists). 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) 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 for each level of detail.
Impact on standards
There are multiple standards for model specification at various levels and MOOSE supports three of them: the GENESIS scripting language, SBML and NeuroML. 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 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.
Keywords:
Large scale modeling,
multiscale modeling,
3D visualization,
computational neuroscience,
Neural Networks (Computer)
Conference:
5th INCF Congress of Neuroinformatics, Munich, Germany, 10 Sep - 12 Sep, 2012.
Presentation Type:
Demo
Topic:
Neuroinformatics
Citation:
Ray
S,
Dudani
N,
Chintaluri
H,
Harsharani
G and
Bhalla
U
(2014). Multiscale Modeling in MOOSE: Interfaces, Interoperability and Standards.
Front. Neuroinform.
Conference Abstract:
5th INCF Congress of Neuroinformatics.
doi: 10.3389/conf.fninf.2014.08.00097
Copyright:
The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers.
They are made available through the Frontiers publishing platform as a service to conference organizers and presenters.
The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated.
Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed.
For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions.
Received:
21 Mar 2013;
Published Online:
27 Feb 2014.
*
Correspondence:
Dr. Subhasis Ray, NCBS, unset, India, ray.subhasis@gmail.com