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.
Highlights
Scientists who construct computational models of biological processes often find it necessary to use several different software tools in order to carry out various forms of data analysis and model simulation
In this paper we suggest a new approach towards designing biological modeling languages, which we call the layeroriented approach
The approach stems from the observation that diverse biological phenomena are described using a small set of mathematical formalisms, which are structured according to some biological principles
Summary
Scientists who construct computational models of biological processes often find it necessary to use several different software tools in order to carry out various forms of data analysis and model simulation. In computational neuroscience, both the GENESIS [3] and NEURON [4] simulators provide a parameterized form of the Hodgkin-Huxley model [5] as a basic object for model construction, but with some important differences between their description languages. The Hodgkin-Huxley object that exists in the Genesis language allows the rate equations to be specified in functional form and it can express the standard formulation of the model, but a whole family of conductance-based models of ionic currents. The NEURON HOC language provides a Hodgkin-Huxley object, but its rate equations are fixed and it only allows different values for the parameters and initial states. The two simulators have very different assumptions about what is meant by a ‘‘Hodgkin-Huxley model’’
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