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Chromar, a Rule-based Language of Parameterised Objects

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    Rights statement: © 2018 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

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    Licence: CC BY

Original languageEnglish
Pages (from-to)49-66
Number of pages18
JournalElectronic Notes in Theoretical Computer Science
StatePublished - 10 Apr 2018


Modelling in biology becomes necessary when systems are complex but the more complex the systems are the harder the models become to read. The most common ways of writing models are by writing reactions on discrete, typed objects (e.g. molecules of different species), or writing rate equations for the populations of such species. One problem (1) with those approaches is that the number of species and reactions is often so large that the model cannot be realistically enumerated. Another problem (2) is that the number of species and reactions is fixed, whereas biology often grows new compartments which means new reactions and species. Here we develop an extension to the representation of reactions where the objects carry variables that are defined by their type (for example objects of type Leaf all have a Mass variable). The dynamics are defined by rules about types, which means they work for all objects of that type. This compact representation solves problem 1. If we think of the object variables as the analogue of reaction/rate equation species, creating a new object of some type means we are also creating new species (solving problem 2). We also developed an embedding of Chromar in the programming language Haskell and showed its applicability to two examples. Having a more compact representation can help make models a tool for knowledge representation and exchange instead of just a simulation input. Embedding Chromar in a general purpose programming language lifts some of the constraints of modelling languages while still maintaining the naturalness of a domain-specific language.

    Research areas

  • representation, rule-based modelling, stochastic, systems biology

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