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Chromar, a language of parameterised objects

Research output: Contribution to journalArticle

Original languageEnglish
Number of pages25
JournalTheoretical Computer Science
Early online date8 Aug 2017
DOIs
StateE-pub ahead of print - 8 Aug 2017

Abstract

Modelling in biology becomes necessary when systems are complex. However, the more complex the systems, the harder models become to read and write. The most common ways of writing models are by writing reactions on discrete, typed objects (e.g., molecules of different species), 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 is fixed, whereas biology often grows new compartments, which means new species and new reactions. Here we develop a modelling language Chromar that provides an extension to the representation of reactions in which 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 — which means that enumerating the dynamics of each agent is not necessary. This compact representation addresses the first problem. Having such 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 new species, thereby addressing the second problem. We have also developed an embedding of Chromar in the programming language Haskell and we 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.

Research areas

  • Rule-based modelling, Stochastic, Representation, Systems biology

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