Biotechnology Journal DOI 10.1002/biot.201100304 Biotechnol. J. 2012, 7, 958–972 958 © 2012 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim 1 Introduction The quantitative description of a complex biologi- cal system depends upon the formulation of math- ematical models. These models should, wherever possible, incorporate biophysically based mecha- nisms to maximise their predictive and explanatory power. One approach to modelling human physiol- ogy is to capture structure-function relations at the tissue and organ level with the inclusion of as much molecular detail as possible, usually by linking to systems biology models. The development of com- prehensive biophysically based integrative models is also becoming increasingly important in helping to understand how changes at the genetic level af- fect multiscale phenotype at the cell, tissue, organ and whole organism levels. For instance, one key area of application for this multiscale approach is the interpretation of genotype-to-phenotype stud- ies [1]. Formal approaches in knowledge representa- tion (KR), such as ontologies, provide an addition- al practical avenue for bridging molecular and bio- physical models. For example, the goal of linking quantitative descriptions of systems biology and physiology across multiple structural scales may be partly achieved through the integration of such models, as well as relevant data, on the basis of their ontological annotation. A central challenge in Perspective Integrating knowledge representation and quantitative modelling in physiology Bernard de Bono 1,2,3 and Peter Hunter 1,4 1 Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand 2 Centre for Health Informatics and Multiprofessional Education, University College London, London, UK 3 European Bioinformatics Institute, Hinxton, Cambridge, UK 4 Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford, UK 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 pres- ent 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 health- care decisions and contributing towards realising the goal of personalized medicine. Keywords: Computational physiology · Medical biotechnology · Multiscale modelling · Personalized medicine · Physiome project Correspondence: Prof. Peter Hunter, Bioengineering Institute, University of Auckland, 70 Symonds St., Auckland, New Zealand E-mail: p.hunter@auckland.ac.nz Abbreviations: ANP, atrial natriuretic peptide; ANPR, ANP receptor; DM, diabetes mellitus; EtOH, ethanol; FMA, Foundation Model of Anatomy; KR, knowledge representation; PK, pharmacokinetics; SBML, Systems Biol- ogy markup language; VPH, Virtual Physiological Human Received 12 MAR 2012 Revised 23 MAY 2012 Accepted 29 JUN 2012