ArticlePLoS computational biology2012
The layer-oriented approach to declarative languages for biological modeling.
Article in PLoS computational biology, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed.
- SpineCreator: a Graphical User Interface for the Creation of Layered Neural Models.Neuroinformatics · 2017Article
- From model specification to simulation of biologically constrained networks of spiking neurons.Neuroinformatics · 2014Article
- Dendritic diameters affect the spatial variability of intracellular calcium dynamics in computer models.Frontiers in cellular neuroscience · 2014Article
- LEMS: a language for expressing complex biological models in concise and hierarchical form and its use in underpinning NeuroML 2.Frontiers in neuroinformatics · 2014Article
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2 authors.
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Abstract
We present a new approach to modeling languages for computational biology, which we call the layer-oriented approach. The approach stems from the observation that many diverse biological phenomena are described using a small set of mathematical formalisms (e.g. differential equations), while at the same time different domains and subdomains of computational biology require that models are structured according to the accepted terminology and classification of that domain. Our approach uses distinct semantic layers to represent the domain-specific biological concepts and the underlying mathematical formalisms. Additional functionality can be transparently added to the language by adding more layers. This approach is specifically concerned with declarative languages, and throughout the paper we note some of the limitations inherent to declarative approaches. The layer-oriented approach is a way to specify explicitly how high-level biological modeling concepts are mapped to a computational representation, while abstracting away details of particular programming languages and simulation environments. To illustrate this process, we define an example language for describing models of ionic currents, and use a general mathematical notation for semantic transformations to show how to generate model simulation code for various simulation environments. We use the example language to describe a Purkinje neuron model and demonstrate how the layer-oriented approach can be used for solving several practical issues of computational neuroscience model development. We discuss the advantages and limitations of the approach in comparison with other modeling language efforts in the domain of computational biology and outline some principles for extensible, flexible modeling language design. We conclude by describing in detail the semantic transformations defined for our language.
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