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Open Access Research article

Eliciting candidate anatomical routes for protein interactions: a scenario from endocrine physiology

Pierre Grenon1* and Bernard de Bono23

Author Affiliations

1 EMBL‐EBI, Wellcome Trust Genome Campus, Hinxton, Cambridgeshire, CB10 1SD, UK

2 ABI, University of Auckland, Symonds Street, Auckland 1010, New Zealand

3 CHIME Institute, Archway Campus, University College London, London UK

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BMC Bioinformatics 2013, 14:131  doi:10.1186/1471-2105-14-131

Published: 16 April 2013

Abstract

Background

In this paper, we use: i) formalised anatomical knowledge of connectivity between body structures and ii) a formal theory of physiological transport between fluid compartments in order to define and make explicit the routes followed by proteins to a site of interaction. The underlying processes are the objects of mathematical models of physiology and, therefore, the motivation for the approach can be understood as using knowledge representation and reasoning methods to propose concrete candidate routes corresponding to correlations between variables in mathematical models of physiology. In so doing, the approach projects physiology models onto a representation of the anatomical and physiological reality which underpins them.

Results

The paper presents a method based on knowledge representation and reasoning for eliciting physiological communication routes. In doing so, the paper presents the core knowledge representation and algorithms using it in the application of the method. These are illustrated through the description of a prototype implementation and the treatment of a simple endocrine scenario whereby a candidate route of communication between ANP and its receptors on the external membrane of smooth muscle cells in renal arterioles is elicited. The potential of further development of the approach is illustrated through the informal discussion of a more complex scenario.

Conclusions

The work presented in this paper supports research in intercellular communication by enabling knowledge‐based inference on physiologically‐related biomedical data and models.