The Layer-Oriented Approach to Declarative Languages for Biological Modeling

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. Author Summary: The pursuit for understanding of neural function by computational modeling has produced a variety of software tools, with each tool targeting specific audiences and often requiring input in its own distinct language. Consequently, comprehending and communicating neuroscience models is a difficult and time-consuming task. In this paper we suggest a new approach towards designing biological modeling languages, which we call the layer-oriented approach. The approach stems from the observation that diverse biological phenomena are described using a small set of mathematical formalisms (e.g. differential equations), which are structured according to some biological principles. Our proposal is illustrated by means of a computer language for describing computational models of ionic currents. The language consists of rules for expressing mathematical equations as well as rules to organize these equations according to the specific terminology used by neuroscientists. The layer-oriented approach offers two chief advantages. First, it allows the flexible use of mathematical equations to represent many different kinds of biological models. Second, it restricts the language within a framework of biological concepts so that existing modeling software can be reused. The goal of the layer-oriented approach is to help define appropriate notations for computational biology while enabling interoperability of software for biological modeling.