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LEMS: a language for expressing complex biological models in concise and hierarchical form and its use in underpinning NeuroML 2.

Cannon, RC; Gleeson, P; Crook, S; Ganapathy, G; Marin, B; Piasini, E; Silver, RA; (2014) LEMS: a language for expressing complex biological models in concise and hierarchical form and its use in underpinning NeuroML 2. Front Neuroinform , 8 , Article 79. 10.3389/fninf.2014.00079. Green open access

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Abstract

Computational models are increasingly important for studying complex neurophysiological systems. As scientific tools, it is essential that such models can be reproduced and critically evaluated by a range of scientists. However, published models are currently implemented using a diverse set of modeling approaches, simulation tools, and computer languages making them inaccessible and difficult to reproduce. Models also typically contain concepts that are tightly linked to domain-specific simulators, or depend on knowledge that is described exclusively in text-based documentation. To address these issues we have developed a compact, hierarchical, XML-based language called LEMS (Low Entropy Model Specification), that can define the structure and dynamics of a wide range of biological models in a fully machine readable format. We describe how LEMS underpins the latest version of NeuroML and show that this framework can define models of ion channels, synapses, neurons and networks. Unit handling, often a source of error when reusing models, is built into the core of the language by specifying physical quantities in models in terms of the base dimensions. We show how LEMS, together with the open source Java and Python based libraries we have developed, facilitates the generation of scripts for multiple neuronal simulators and provides a route for simulator free code generation. We establish that LEMS can be used to define models from systems biology and map them to neuroscience-domain specific simulators, enabling models to be shared between these traditionally separate disciplines. LEMS and NeuroML 2 provide a new, comprehensive framework for defining computational models of neuronal and other biological systems in a machine readable format, making them more reproducible and increasing the transparency and accessibility of their underlying structure and properties.

Type: Article
Title: LEMS: a language for expressing complex biological models in concise and hierarchical form and its use in underpinning NeuroML 2.
Location: Switzerland
Open access status: An open access version is available from UCL Discovery
DOI: 10.3389/fninf.2014.00079
Publisher version: http://dx.doi.org/10.3389/fninf.2014.00079
Language: English
Additional information: PMCID: PMC4174883 Copyright © 2014 Cannon, Gleeson, Crook, Ganapathy, Marin, Piasini and Silver. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
Keywords: model description language, model sharing, simulation, spiking neural networks, standardization
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences > Div of Biosciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences > Div of Biosciences > Neuro, Physiology and Pharmacology
URI: https://discovery.ucl.ac.uk/id/eprint/1451517
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