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with explanations of the rules combined with examples of their usage. Keywords: computational physiology; modularity; physiome project; reproducibility; reusability. *Corresponding author: David P. Nickerson, University of Auckland, Auckland, New Zealand, E-mail: d.nickerson@auckland.ac.nz. https://orcid.org/0000-0003-4667-9779 Michael Clerx: University of Oxford, Oxford, UK. https://orcid.org/0000-0003-4062-3061 Michael T. Cooling, Alan Garny, Keri Moyle, Poul M. F. Nielsen and Hugh Sorby: University of Auckland, Auckland, New Zealand. https://orcid.org/0000

. Bordas, J. Cooper, A. Corrias, Y. Davit, S.-J. Dunn, A. G. Fletcher, D. G. Harvey et al., Chaste: an open source C++ library for computational physiology and biology. PLoS Computational Biology 9 (2013), e1002970. 23516352 Mirams G. R. Arthurs C. J. Bernabeu M. O. Bordas R. Cooper J. Corrias A. Davit Y. Dunn S.-J. Fletcher A. G. Harvey D. G. et al., Chaste: an open source C++ library for computational physiology and biology. PLoS Computational Biology 9 2013 e1002970. [14] E. Schenone, A. Collin, and J.-F. Gerbeau, Numerical simulation of electrocardiograms for full

? What property or set of properties does a group of organs have in order to be considered as an organ system? The growth of our understanding of living organisms and the dramatic steps in the application of general systems theory to modern physiology and biology has revealed an increasingly complex dialogue within each living organism as a whole. The ‘omics’ revolution in the era of computational physiology and systems biology has generated a wealth of insight into the plurality of what were previously considered mono-functioning organs. For example, the

statistical power analysis program for the social, behavioural, and biomedical sciences. Behav Res Methods , 2007; 39(2): 175-191 10.3758/BF03193146 Faul F Erdfelder E Lang AG Buchner A G*Power 3: a flexible statistical power analysis program for the social, behavioural, and biomedical sciences Behav Res Methods 2007 39 2 175 191 Fonoberova M, Mezić I, Buckman JF, Fonoberov V, Mezić A, Vaschillo EG, Mun EY, Vaschillo B, Bates ME. A computational physiology approach to personalized treatment models: the beneficial effects of slow breathing on the human cardiovascular system

above, it is not surprising that the mathematical and computational techniques used for molecular-scale systems-modelling are less appropriate. Bioengineers have used well established techniques, such as Finite Element Analysis, to define cells or zones of tissue and their interactions. This is best exemplified by the Physiome Project (6). Boundary Element analysis is a similar modelling technique but pays specific attention to the interfaces between elements (1), and several computational physiology groups have applied Computational Fluid Dynamics to

. Sherlock, P. Spellman, C. Stoeckert, J. Aach, W. Ansorge, C.A. Ball, H.C. Causton, T. Gaaster- land, P. Glenisson, F.C. Holstege, I.F. Kim, V. Markowitz, J.C. Matese, H. Parkinson, A. Robinson, U. Sarkans, S. Schulze-Kremer, J. Stewart, R. Taylor, J. Vilo, and M. Vingron: Minimum information about a microarray experiment (MIAME)-toward standards for microarray data. In: Nature Genetics 29 (2001), pp. 365–71. [13] E. Crampin, M. Halstead, P. Hunter, P. Nielsen, D. Noble, N. Smith, and M. Tawhai: Computational physiology and the Physiome Project. In: Exp Physiol 89 (2004

.2202/1558-9544.1261 37. Docherty SJ, Davis OSP, Kovas Y, Meaburn EL, Dale PS, Petrill SA, Schalwyk LC, Plomin R. (2010). A genome-wide association study identifies multiple loci associated with mathematical ability and disability. Genes, Brain and Behavior, 9: 234 – 247. 38. Crampin EJ, Halstead M, Hunter P, Nielsen P, Noble D, Smith N, Tawhi M. (2004). Computational physiology and the physiome project. Experimental Physiology, 89:1 – 26 39. Leek JT, Storey JD. (2008). A general framework for multiple testing dependence. Proceedings of the National Academy of Sciences, 105

electrophysiology. Comput. Visual. Sci. 5 (2002), 215–239. [25] J. H. Metzen, T. Kröger, A. Schenk, S. Zidowitz, H.-O. Peitgen, and X. Jiang, Matching of anatomical tree structures for registration of medical images. Image Vision Comp. 27 (2009), 923–933. [26] G. R. Mirams, C. J. Arthurs, M. O. Bernabeu, R. Bordas, J. Cooper, A. Corrias, Y. Davit, S.-J. Dunn, A. G. Fletcher, D. G. Har- vey, M. E. Marsh, J. M. Osborne, P. Pathmanathan, J. Pitt-Francis, J. Southern, N. Zemzemi, and D. J. Gavaghan, Chaste: An open source C++ library for computational physiology and biology. PLoS

Computer modelling of the heart mechanics is a fast developing field of computational physiology. During the last two decades a number of electromechanical models of the whole heart or its left ventricle has been developed [ 26 ]. Such multiscale models usually combine several models that describe electrical and chemical processes at the level of a single cell with mechanical properties of cardiac muscle tissue. Generally, a model of the heart consists of a model of ionic currents in the cardiomyocytes, model of myocardial mechanics, model of blood circulation

a colloquium day starting with an overview of the history of COMBINE by Mike Hucka (California Institute of Technology, USA), one of the co-founders of COMBINE. Subsequently, Peter Hunter (University of Auckland, New Zealand) gave a keynote lecture in the HITS colloquium series. He showed recent developments in computational physiology with a focus on novel developments within the Physiome Project [ 19 ]. The Physiome Project is developing model and data encoding standards, web accessible databases and open source software for multiscale modeling ( http