P252 Developments around MOOSE for modeling in Systems Biology and Neuroscience
Subhasis Ray*1,2, G.V. Harsharani3,4, Anal Kumar3, Ashish Anshuman3, Parita Mehta3, Jayesh Poojari3, Deepa SM3, and Upinder S. Bhalla3,4
1CHINTA, TCG CREST, Kolkata, India 2IAI, TCG CREST, Kolkata, India 3NCBS-TIFR, Bangalore, India 4Centre for Brain and Mind, NCBS-TIFR, Bangalore, India *Email: ray.subhasis@gmail.com
Introduction
Public databases for neuroscience, including those for connectomes, cell morphologies, and electrophysiological recordings, are accelerating data-driven neuroscience. Tools supporting such databases and standard formats for model and data exchange are critical for maximizing the utility of these resources. MOOSE, the Multiscale Object Oriented Simulation Environment [1], is a stable software for computational modeling and simulation in Systems Biology and Neuroscience. It emphasizes models that span molecular and electrical signaling from synapses to networks. As MOOSE-development emphasized standards and interoperability early on, it is well placed to facilitate the development of biological neural models utilising public model- and data-repositories. Methods The core of MOOSE is written in C++ for speed, while its Python API allows integration with the Python ecosystem. Extensive documentation is supplemented with a wide range of tutorials using Python graphics and browser-based 3-D graphics. We use existing Python modules for various model and data description formats to support them in MOOSE, and web frameworks to utilize public APIs of the neuroscience databases. We actively conduct outreach activities and user-research to enhance the user experience and documentation of MOOSE, and workshops for training students and researchers on modeling and simulation in Systems Biology and Computational Neuroscience. Results MOOSE covers multiple scales of modeling, from chemical reactions and signaling pathways to large biological neural networks. Currently it supports standard formats like SBML and NeuroML for model description, SWC for morphology, and NSDF for simulated data. It includes Python tools to easily create multiscale models from a library of model components. We are also developing clients for accessing public repositories of model and data, enabling users to seamlessly integrate model components from such sources into their composite models. Discussion A major goal of MOOSE is to make biological modeling accessible to students and researchers from diverse backgrounds. Users can seamlessly incorporate published and curated models in their simulation experiments using software tools developed around MOOSE. The new developments in the MOOSE ecosystem will help accelerate data-driven research in Systems Biology and Neuroscience.
Acknowledgements We thank the Kavli Foundation, and DBT and DST of the Govt. of India, for supporting MOOSE development. NCBS/TIFR receives support from the Department of Atomic Energy, Government of India, under Project Identification No. RTI 4006.
