title: Integrating knowledge representation and quantitative modelling in physiology.
abstract: A wealth of potentially shareable resources, such as data and models, is being generated through the study of physiology by computational means. Although in principle the resources generated are reusable, in practice, few can currently be shared. A key reason for this disparity stems from the lack of consistent cataloguing and annotation of these resources in a standardised manner. Here, we outline our vision for applying community-based modelling standards in support of an automated integration of models across physiological systems and scales. Two key initiatives, the Physiome Project and the European contribution - the Virtual Phsysiological Human Project, have emerged to support this multiscale model integration, and we focus on the role played by two key components of these frameworks, model encoding and semantic metadata annotation. We present examples of biomedical modelling scenarios (the endocrine effect of atrial natriuretic peptide, and the implications of alcohol and glucose toxicity) to illustrate the role that encoding standards and knowledge representation approaches, such as ontologies, could play in the management, searching and visualisation of physiology models, and thus in providing a rational basis for healthcare decisions and contributing towards realising the goal of of personalized medicine.
authors.affiliation: Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand.
authors.forename: Bernard
authors.initials: B
authors.lastname: de Bono
authors.forename: Peter
authors.initials: P
authors.lastname: Hunter
mesh_terms: D001185:Artificial Intelligence; D001709:Biotechnology; D019295:Computational Biology; D003196:Computer Graphics; D006801:Humans; D008954:Models, Biological; D010827:Physiology / Q000379:methods*; D057285:Precision Medicine