Abstract
BackgroundSystems Biology develops computational models in order to understand biological phenomena. The increasing number and complexity of such “bio-models” necessitate computer support for the overall modelling task. Computer-aided modelling has to be based on a formal semantic description of bio-models. But, even if computational bio-models themselves are represented precisely in terms of mathematical expressions their full meaning is not yet formally specified and only described in natural language.ResultsWe present a conceptual framework – the meaning facets – which can be used to rigorously specify the semantics of bio-models. A bio-model has a dual interpretation: On the one hand it is a mathematical expression which can be used in computational simulations (intrinsic meaning). On the other hand the model is related to the biological reality (extrinsic meaning). We show that in both cases this interpretation should be performed from three perspectives: the meaning of the model’s components (structure), the meaning of the model’s intended use (function), and the meaning of the model’s dynamics (behaviour). In order to demonstrate the strengths of the meaning facets framework we apply it to two semantically related models of the cell cycle. Thereby, we make use of existing approaches for computer representation of bio-models as much as possible and sketch the missing pieces.ConclusionsThe meaning facets framework provides a systematic in-depth approach to the semantics of bio-models. It can serve two important purposes: First, it specifies and structures the information which biologists have to take into account if they build, use and exchange models. Secondly, because it can be formalised, the framework is a solid foundation for any sort of computer support in bio-modelling. The proposed conceptual framework establishes a new methodology for modelling in Systems Biology and constitutes a basis for computer-aided collaborative research.
Highlights
Systems Biology develops computational models in order to understand biological phenomena
In order to understand the living nature Systems Biology develops computational models of biological systems. These models are computational in the sense, that the models are expressed in an appropriate formal language, like the Systems Biology Markup Language (SBML, [1]) and CellML [2], and can be used by computer programs in order to infer statements about its dynamical behaviour
The semantics of bio-models is a formal account of their meaning
Summary
Systems Biology develops computational models in order to understand biological phenomena. Even if computational bio-models themselves are represented precisely in terms of mathematical expressions their full meaning is not yet formally specified and only described in natural language. In order to understand the living nature Systems Biology develops computational models of biological systems. These models are computational in the sense, that the models are expressed in an appropriate formal language, like the Systems Biology Markup Language (SBML, [1]) and CellML [2], and can be used by computer programs in order to infer statements about its dynamical behaviour (either quantitative or qualitative). If the model has both the same performance (the behaviour) and the same competence (the mechanism) as the biological system, we can understand the living system by means of the model [4]
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