LEMS: a language for expressing complex biological models in concise and hierarchical form and its use in underpinning NeuroML 2.
LEMS: a language for expressing complex biological models in concise and hierarchical form and its use in underpinning NeuroML 2.
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DOI：
10.3389/fninf.2014.00079
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发表时间：
2014
影响因子：
3.5
通讯作者：
Silver RA
中科院分区：
文献类型：
--
作者：
Cannon RC;Gleeson P;Crook S;Ganapathy G;Marin B;Piasini E;Silver RA
计算模型对于研究复杂的神经生理系统越来越重要。作为科学工具，这些模型必须能够被一系列科学家复制和批判性地评估。然而，已发布的模型目前使用一组不同的建模方法，仿真工具和计算机语言，使它们无法访问，难以复制。模型通常还包含与特定领域模拟器紧密链接的概念，或者依赖于仅在基于文本的文档中描述的知识。为了解决这些问题，我们已经开发了一个紧凑的，分层的，基于XML的语言称为LEMS（低熵模型规范），可以定义的结构和动态范围广泛的生物模型在一个完全机器可读的格式。我们描述了LEMS如何支撑最新版本的NeuroML，并表明该框架可以定义离子通道，突触，神经元和网络的模型。单元处理，在重用模型时经常是错误的来源，是通过在模型中指定基本维度的物理量来构建到语言的核心。我们展示了如何LEMS，连同我们已经开发的开源Java和Python库，促进了多个神经元模拟器的脚本生成，并提供了一个模拟器免费代码生成的路线。我们建立了LEMS可用于定义系统生物学模型，并将其映射到神经科学领域特定的模拟器，使这些传统上独立的学科之间共享模型。LEMS和NeuroML 2提供了一个新的，全面的框架，用于以机器可读格式定义神经元和其他生物系统的计算模型，使其更具可重复性，并增加其底层结构和属性的透明度和可访问性。
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.
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神经结构：在3D空间中建模神经元网络的工具。
影响因子：
16.2
作者：
Gleeson P;Steuber V;Silver RA
通讯作者：
Silver RA
DOI：
10.1038/nrmicro1949
发表时间：
2009-02
期刊：
Nature reviews. Microbiology
影响因子：
--
作者：
通讯作者：
--
影响因子：
4.3
作者：
Farinella, Matteo;Ruedt, Daniel T.;Silver, R. Angus
通讯作者：
Silver, R. Angus
影响因子：
5.8
作者：
Hucka, M;Finney, A;Wang, J
通讯作者：
Wang, J
影响因子：
3
作者：
Goodman, Dan F. M.
通讯作者：
Goodman, Dan F. M.