Abstract
Rule-based languages such as Kappa excel in their support for handling the combinatorial complexities prevalent in many biological systems, including signalling pathways. But Kappa provides little structure for organising rules, and large models can therefore be hard to read and maintain. This paper introduces a high-level, modular extension of Kappa called LBS-κ. We demonstrate the constructs of the language through examples and three case studies: a chemotaxis switch ring, a MAPK cascade, and an insulin signalling pathway. We then provide a formal definition of LBS-κ through an abstract syntax and a translation to plain Kappa. The translation is implemented in a compiler tool which is available as a web application. We finally demonstrate how to increase the expressivity of LBS-κ through embedded scripts in a general-purpose programming language, a technique which we view as generally applicable to other domain specific languages.
Highlights
Mathematical modelling plays a key role in systems biology, facilitating the generation of knowledge through the cycle of model analysis, experimental testing of hypotheses, and model refinement
This has prompted the development of a range of new modelling formalisms inspired by computer science, allowing models to be written in a structured fashion and generally supporting multiple types of simulation and analysis based on a single model
We demonstrate through case studies of a chemotactic switch ring, a Mitogen-Activated Protein Kinase (MAPK) cascade, and an insulin signalling pathway, how the features of Language for Biochemical Systems (LBS)-κ give rise to improved readability, maintainability and reusability
Summary
Mathematical modelling plays a key role in systems biology, facilitating the generation of knowledge through the cycle of model analysis, experimental testing of hypotheses, and model refinement. Large models based on traditional mathematical formalisms such as ODEs are hard to develop and maintain since they are “flat” with no structure or modularity and since they bear a somewhat indirect relation to the corresponding biological phenomena. This has prompted the development of a range of new modelling formalisms inspired by computer science, allowing models to be written in a structured fashion and generally supporting multiple types of simulation and analysis based on a single model. These formalisms include process calculi such as the stochastic π-calculus [1, 2], the continuous π-calculus [3], Beta binders [4, 5], BlenX [6], PEPA [7] and BioPEPA [8]; and rule-based languages such as BIOCHAM [9], Kappa [10, 11], BioNetGen [12] and Stochastic Multilevel Multiset Rewriting [13].
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