Modelling in biology becomes necessary when systems are complex. However, the more complex the systems are, the harder models become to read and write. The most common ways of writing models are by writing reactions on species (discrete objects), or by writing rate equations for the populations of such species. One problem with such approaches is that the number of species is often so large that the model cannot be realistically enumerated. Another problem is that the number of species and reactions often fixed by default, whereas new variations of species and reactions are constantly being improvised by evolution in the long-term and moment to moment by the changing chemical environments and compartments living organisms inhabit and create within. Here we develop a modelling language Chromar that provides an extension to the representation of reactions, in which objects, called agents, carry attributes with associated types — for example, Leaf agents all have a mass attribute. Dynamics are given by stochastic rules defined on groups of agents — for example all agents of a specific type — and so enumerating the dynamics of each agent is not necessary. This compact representation addresses the first problem. Having a more compact representation can also help make models a tool for knowledge representation and exchange instead of just simulation. Further, if we think of agents as the analogue of species in reactions, then creating a new agent of some type effectively creates a new species, thereby addressing the second problem. We then develop an extension of Chromar equipped with deterministically changing time-dependent values (fluents) and aggregate values computed from the state of the system (observables). Fluents are useful for describing the context of the system, which is rarely static; observables are useful in abstracting away details of system behaviour and providing a way to observe the system during simulation. Finally, we develop an embedding of Chromar (and its extensions) in the programming language Haskell and demonstrate its applicability via two examples. Embedding Chromar in a general purpose programming language such as Haskell eases some of the constraints of modelling languages while still maintaining the naturalness of a domain-specific language.

MOTIVATION: Biological systems are complex and challenging to model and therefore model reuse is highly desirable. To promote model reuse, models should include both information about the specifics of simulations and the underlying biology in the form of metadata. The availability of computationally tractable metadata is especially important for the effective automated interpretation and processing of models. Metadata are typically represented as machine-readable annotations which enhance programmatic access to information about models. Rule-based languages have emerged as a modelling framework to represent the complexity of biological systems. Annotation approaches have been widely used for reaction-based formalisms such as SBML. However, rule-based languages still lack a rich annotation framework to add semantic information, such as machine-readable descriptions, to the components of a model. RESULTS: We present an annotation framework and guidelines for annotating rule-based models, encoded in the commonly used Kappa and BioNetGen languages. We adapt widely adopted annotation approaches to rule-based models. We initially propose a syntax to store machine-readable annotations and describe a mapping between rule-based modelling entities, such as agents and rules, and their annotations. We then describe an ontology to both annotate these models and capture the information contained therein, and demonstrate annotating these models using examples. Finally, we present a proof of concept tool for extracting annotations from a model that can be queried and analyzed in a uniform way. The uniform representation of the annotations can be used to facilitate the creation, analysis, reuse and visualization of rule-based models. Although examples are given, using specific implementations the proposed techniques can be applied to rule-based models in general. AVAILABILITY AND IMPLEMENTATION: The annotation ontology for rule-based models can be found at http://purl.org/rbm/rbmo The krdf tool and associated executable examples are available at http://purl.org/rbm/rbmo/krdf CONTACT: [email protected] or [email protected].