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Structure, function, and behaviour of computational models in systems biology.

Knüpfer C, Beckstein C, Dittrich P, Le Novère N - BMC Syst Biol (2013)

Bottom Line: Thereby, we make use of existing approaches for computer representation of bio-models as much as possible and sketch the missing pieces.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.

View Article: PubMed Central - HTML - PubMed

Affiliation: Artificial Intelligence Group, University of Jena, Ernst-Abbe-Platz 2, Jena, Germany. christian.knuepfer@uni-jena.de

ABSTRACT

Background: Systems 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.

Results: We 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.

Conclusions: The 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.

Show MeSH
Dual interpretation of bio-models. A model can be mathematically interpreted as a text in a formal language resulting in “formal semantics”. This intrinsic meaning is necessary for using the model in computations. In order to exploit the results of such computations for the explanation of biological phenomena the model needs also a biological interpretation: the model possesses an extrinsic meaning relating its structure, its functionality, and its behaviour to biological reality. Ultimately, modelling is about making appropriate computational representation of biological reality.
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Figure 2: Dual interpretation of bio-models. A model can be mathematically interpreted as a text in a formal language resulting in “formal semantics”. This intrinsic meaning is necessary for using the model in computations. In order to exploit the results of such computations for the explanation of biological phenomena the model needs also a biological interpretation: the model possesses an extrinsic meaning relating its structure, its functionality, and its behaviour to biological reality. Ultimately, modelling is about making appropriate computational representation of biological reality.

Mentions: A Bio-model has a dual interpretation: The mathematical expression bears meaning by itself without referring to the biological reality. It can be interpreted, analysed, and used in computational simulations without knowing what it represents. We call this interpretation the intrinsic meaning of the bio-model. However, a bio-model is more than a pure syntactical formal expression: it describes a piece of biological reality and thereby also exhibits an extrinsic meaning. Often, the extrinsic interpretation is referred to by the word “represents”: for example, we say that a variable xrepresents the concentration of a specific substance and that the oscillation shown in simulations represents variations in concentrations during the cell cycle. An explanatory bio-model establishes a mapping between the two conceptual sides, i.e. between the intrinsic and extrinsic meaning. Note that the biological interpretation has to be consistent with the usual conceptualisation made in biology. This ensures that modelling results represent biological phenomena in such a way that the (intrinsic interpreted) model can explain biological reality (cf. Figure 2).


Structure, function, and behaviour of computational models in systems biology.

Knüpfer C, Beckstein C, Dittrich P, Le Novère N - BMC Syst Biol (2013)

Dual interpretation of bio-models. A model can be mathematically interpreted as a text in a formal language resulting in “formal semantics”. This intrinsic meaning is necessary for using the model in computations. In order to exploit the results of such computations for the explanation of biological phenomena the model needs also a biological interpretation: the model possesses an extrinsic meaning relating its structure, its functionality, and its behaviour to biological reality. Ultimately, modelling is about making appropriate computational representation of biological reality.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3716781&req=5

Figure 2: Dual interpretation of bio-models. A model can be mathematically interpreted as a text in a formal language resulting in “formal semantics”. This intrinsic meaning is necessary for using the model in computations. In order to exploit the results of such computations for the explanation of biological phenomena the model needs also a biological interpretation: the model possesses an extrinsic meaning relating its structure, its functionality, and its behaviour to biological reality. Ultimately, modelling is about making appropriate computational representation of biological reality.
Mentions: A Bio-model has a dual interpretation: The mathematical expression bears meaning by itself without referring to the biological reality. It can be interpreted, analysed, and used in computational simulations without knowing what it represents. We call this interpretation the intrinsic meaning of the bio-model. However, a bio-model is more than a pure syntactical formal expression: it describes a piece of biological reality and thereby also exhibits an extrinsic meaning. Often, the extrinsic interpretation is referred to by the word “represents”: for example, we say that a variable xrepresents the concentration of a specific substance and that the oscillation shown in simulations represents variations in concentrations during the cell cycle. An explanatory bio-model establishes a mapping between the two conceptual sides, i.e. between the intrinsic and extrinsic meaning. Note that the biological interpretation has to be consistent with the usual conceptualisation made in biology. This ensures that modelling results represent biological phenomena in such a way that the (intrinsic interpreted) model can explain biological reality (cf. Figure 2).

Bottom Line: Thereby, we make use of existing approaches for computer representation of bio-models as much as possible and sketch the missing pieces.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.

View Article: PubMed Central - HTML - PubMed

Affiliation: Artificial Intelligence Group, University of Jena, Ernst-Abbe-Platz 2, Jena, Germany. christian.knuepfer@uni-jena.de

ABSTRACT

Background: Systems 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.

Results: We 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.

Conclusions: The 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.

Show MeSH