Abstract
We present a framework to assist the diagrammatic modelling of complex biological systems using the unified modelling language (UML). The framework comprises three levels of modelling, ranging in scope from the dynamics of individual model entities to system-level emergent properties. By way of an immunological case study of the mouse disease experimental autoimmune encephalomyelitis, we show how the framework can be used to produce models that capture and communicate the biological system, detailing how biological entities, interactions and behaviours lead to higher-level emergent properties observed in the real world. We demonstrate how the UML can be successfully applied within our framework, and provide a critique of UML's ability to capture concepts fundamental to immunology and biology more generally. We show how specialized, well-explained diagrams with less formal semantics can be used where no suitable UML formalism exists. We highlight UML's lack of expressive ability concerning cyclic feedbacks in cellular networks, and the compounding concurrency arising from huge numbers of stochastic, interacting agents. To compensate for this, we propose several additional relationships for expressing these concepts in UML's activity diagram. We also demonstrate the ambiguous nature of class diagrams when applied to complex biology, and question their utility in modelling such dynamic systems. Models created through our framework are non-executable, and expressly free of simulation implementation concerns. They are a valuable complement and precursor to simulation specifications and implementations, focusing purely on thoroughly exploring the biology, recording hypotheses and assumptions, and serve as a communication medium detailing exactly how a simulation relates to the real biology.
Highlights
Computational modelling and simulation methods are increasingly employed as a complement to traditional wet-laboratory techniques in exploring biological processes such as those of the immune system [1]
This diagram provides an immediate overview of the biology being modelled, highlighting the components hypothesized to play a significant role in the real-world phenomena and the system-level behaviours that result from their interactions
To attempt to address the ambiguity of biological entity multiplicity, we have examined the use of unified modelling language (UML) activity diagram expansion regions for depicting ‘compounding concurrency’ in cell populations: the snow-balling effect of ever increasing numbers of cells engaging in some activity, and populations of which exhibit positive and negative feedbacks on one another
Summary
Computational modelling and simulation methods are increasingly employed as a complement to traditional wet-laboratory techniques in exploring biological processes such as those of the immune system [1]. Networks of cells and molecules comprise numerous positive and negative feedback pathways, which can result in either stable system-level behaviours, or frequent switches Both the EAE domain model and its subsequent simulation are published in full, as open access, elsewhere [4]. The UML is chosen here for its ease of abstraction that coincides with the multiple layers of abstraction in our domain modelling framework, and its successful record in representing multicellular multi-spatial biological systems [20,24,25,26,27,28] Integrated technologies, such as IBM’s rational rhapsody, the play engine and live sequence charts [29], can facilitate the diagrammatic specification and implementation of computer programs. We discuss the utility of the UML for modelling complex biological systems
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