Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Innovation
  • Published:

Integration from proteins to organs: the Physiome Project

Abstract

The Physiome Project will provide a framework for modelling the human body, using computational methods that incorporate biochemical, biophysical and anatomical information on cells, tissues and organs. The main project goals are to use computational modelling to analyse integrative biological function and to provide a system for hypothesis testing.

This is a preview of subscription content, access via your institution

Access options

Buy this article

Prices may be subject to local taxes which are calculated during checkout

Figure 1: Illustration of the relationship between the physiome and other areas of biological organization.
Figure 2: Linking molecular and cellular events with physiological function must deal with wide ranges of length scales and timescales.
Figure 3: Accessing information at the various spatial scales using ontologies and web databases that contain models encoded in markup languages.
Figure 4: The 12 organ systems of the body with illustrations of some of the anatomical models.

Similar content being viewed by others

References

  1. International Human Genome Mapping Consortium. A physical map of the human genome. Nature 409, 934–941 (2001).

  2. Venter, C. et al. The sequence of the human genome. Science 291, 1304–1351 (2001).

    Article  CAS  Google Scholar 

  3. Noble, D. Biological computation. Encyclopedia of Life Sciences [online], (DOI 10.1038/npg.els.0003433), http://www.els/net (2002).

    Google Scholar 

  4. Noble, D. The rise of computational biology. Nature Rev. Mol. Cell Biol. 3, 460–463 (2002).

    Article  CAS  Google Scholar 

  5. Kitano, H. Systems biology: a brief overview. Science 295, 1662–1664 (2002).

    Article  CAS  Google Scholar 

  6. Kitano, H. Computational systems biology. Nature 420, 206–210 (2002).

    Article  CAS  Google Scholar 

  7. Rao, C. V., Wolf, D. M. & Arkin, A. P. Control, exploitation and tolerance of intracellular noise. Nature 420, 231–237 (2002).

    Article  CAS  Google Scholar 

  8. Goldbeter, A. Computational approaches to cellular rhythms. Nature 420, 238–245 (2002).

    Article  CAS  Google Scholar 

  9. Edelstein-Keshet, L. Mathematical Models in Biology (Random House, New York, 1988).

    Google Scholar 

  10. Keener, J. & Sneyd, J. Mathematical Physiology (Springer, New York, 1998).

    Google Scholar 

  11. Bower, J. M. & Bolouri, H. (eds). Computational Modeling of Genetic and Biochemical Networks (MIT Press, Cambridge, Massachusetts, 2001).

    Google Scholar 

  12. Fall, C. P., Marland, E. S., Wagner, J. M. & Tyson, J. J. Computational Cell Biology (Springer, New York, 2002).

    Google Scholar 

  13. Christie, G. R., Bullivant, D. P., Blackett, S. A. & Hunter, P. J. Modelling and visualising the heart. Computing. Vis. Sci. 4, 227–235 (2002).

    Article  Google Scholar 

  14. Kohl, P., Noble, D. & Hunter, P. J. (eds). The integrated heart: modelling cardiac structure and function. Phil. Trans. R. Soc. A 359 (2001).

  15. Smith, N. P. et al. Mathematical modelling of the heart: cell to organ. Chaos, Solitons Fractals 13, 1613–1621 (2001).

    Article  Google Scholar 

  16. Smith, N. P., Pullan, A. J. & Hunter, P. J. An anatomically based model of transient coronary blood flow in the heart. SIAM J. Appl. Math. 62, 990–1018 2001).

    Article  Google Scholar 

  17. Barth, T. J., Chan, T. & Haimes, R. (eds). Multiscale and Multiresolution Methods. Lecture Notes in Computational Science and Engineering (Springer, Berlin, 2002).

    Book  Google Scholar 

  18. Antzelovitch, C. et al. Influence of transmural gradients on the electrophysiology and pharmacology of ventricular myocardium. Cellular basis for the Brugada and long-QT syndromes. Phil. Trans. R. Soc. A 359, 1201–1216 (2001).

    Article  Google Scholar 

  19. Noble, D. Unraveling the genetics and mechanisms of cardiac arrhythmia. Proc. Natl Acad. Sci. USA 99, 5755–5756 (2002).

    Article  CAS  Google Scholar 

  20. Hedley, W. J. H., Nelson, M. R., Bullivant, D. P. & Nielsen, P. F. A short introduction to CellML. Phil. Trans. R. Soc. A 359, 1073–1089 (2001).

    Article  Google Scholar 

  21. Bock, G. R. & Goode, J. A. (eds). The limits of reductionism. Novartis Foundation Symp. 213 (John Wiley, London, 1998).

  22. Bock, G. R. & Goode, J. A. (eds). Complexity in biological information processing. Novartis Found. Symp. 239 (John Wiley, London, 2001).

  23. Bock, G. & Goode, J. (eds) In silico simulation of biological processes. Novartis Found. Symp. 247 (John Wiley, London, 2002).

  24. Kitano, H. in Foundations of Systems Biology (ed. Kitano, H) 1–36 (MIT Press, Cambridge, Massachusetts, 2002).

    Google Scholar 

  25. Kohl, P., Noble, D., Winslow, R. L. & Hunter, P. J. Computational modelling of biological systems: tools and visions. Phil. Trans. R. Soc. A 358, 579–610 (2000).

    Article  CAS  Google Scholar 

  26. Bassingthwaighte, J. B. Strategies for the Physiome Project. Ann. Biomed. Eng. 28, 1043–1058 (2000).

    Article  CAS  Google Scholar 

  27. LeGrice, I. J., Hunter, P. J. & Smaill, B. H. Laminar structure of the heart: a mathematical model. J. Physiol. 272, H2466–H2476 (1997).

    CAS  Google Scholar 

  28. LeGrice, I. J., Hunter, P. J., Young, A. A. & Smaill, B. H. The architecture of the heart: a data-based model. Phil. Trans. R. Soc. A 359, 1217–1232 (2001).

    Article  Google Scholar 

  29. Luo, C. & Rudy, Y. A Dynamic model of the cardiac ventricular action potential- simulations of ionic currents and concentration changes. Circ. Res. 74, 1071–1097 (1994).

    Article  CAS  Google Scholar 

  30. Noble, D., Varghese, A., Kohl, P. & Noble, P. Improved guinea-pig ventricular cell model incorporating a diadic space, IKr and IKs, and length- and tension-dependent processes. Can. J. Cardiol. 14, 123–134 (1998).

    CAS  PubMed  Google Scholar 

  31. Noble, D. Modelling the heart: from genes to cells to the whole organ. Science 295, 1678–1682 (2002).

    Article  CAS  Google Scholar 

  32. Hunter, P. J. & Smaill, B. H. in Cardiac Electrophysiology: from cell to bedside 3rd edn Vol. 32 (eds Zipes, D. P. & Jalife, J.) 277–283 (W. B. Saunders, Philadelphia, 2000).

    Google Scholar 

  33. Tomlinson, K. A., Hunter, P. J. & Pullan, A. J. A finite element method for an eikonal equation model of myocardial excitation wavefront propagation. SIAM J. Appl. Math. 63, 324–350 (2002).

    Article  Google Scholar 

  34. Hunter, P. J., McCulloch, A. D. & ter Keurs, H. E. D. J. Modeling the mechanical properties of cardiac muscle. Prog. Biophys. Mol. Biol. 69, 289–331 (1998).

    Article  CAS  Google Scholar 

  35. Nash, M. P. & Hunter, P. J. Computational mechanics of the heart. J. Elast. 61, 113–141 (2001).

    Article  Google Scholar 

  36. Kohl, P., Hunter, P. J. & Noble, D. Stretch-induced changes in heart rate and rhythm: clinical observations, experiments and mathematical models. Prog. Biophys. Mol. Biol. 71, 91–138 (1999).

    Article  CAS  Google Scholar 

  37. Smith, N. P., Pullan A. J. & Hunter, P. J. Generation of an anatomically based geometric coronary model. Ann. Biomed. Eng. 28, 14–25 (2000).

    Article  CAS  Google Scholar 

  38. Bradley, C. P., Pullan, A. J. & Hunter, P. J. Geometric modeling of the human torso using cubic hermite elements. Ann. Biomed. Eng. 25, 96–111 (1997).

    Article  CAS  Google Scholar 

  39. Hunter, P. J., Robbins P. & Noble, D. The IUPS Human Physiome Project. Pflugers Arch. Eur. J. Physiol. 445, 1–9 (2002).

    Article  CAS  Google Scholar 

  40. Tawhai, M., Pullan, A. J. & Hunter, P. J. Generation of an anatomically based three-dimensional model of the conducting airways. Ann. Biomed. Eng. 28, 793–802 (2000).

    Article  Google Scholar 

  41. Sagar, M. A., Bullivant, D. P., Mallinson, G. D., Hunter, P. J. & Hunter, I. W. A virtual environment and model of the eye for surgical simulation. Compute. Graph. (ACM) 205–212 (Siggraph, ACM, Addison Wesley, Ontario, 1994).

Download references

Acknowledgements

The authors gratefully acknowledge the contributions from members of the Bioengineering Institute at the University of Auckland, New Zealand. P.J.H. acknowledges the support of the New Zealand Foundation for Research, Science and Technology, the New Zealand Health Research Council and the Wellcome Trust. He is also grateful for the discussions on the Physiome Project, over many years, with D. Noble (Oxford University, UK), J. Bassingthwaighte (University of Washington in Seattle, USA) and A. McCulloch (University of California at San Diego, USA).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Peter J. Hunter.

Related links

Related links

FURTHER INFORMATION

AnatML

BioPSE

CellML

CMISS

Continuity

E-Cell project

Gene Ontology Consortium

Gepasi

Global open biological ontologies (GOBO)

The Bioengineering Institute

The IUPS Physiome Project

The Microcirculation Physiome Project

The National Resource for Cell Analysis and Modeling

The World Wide Web Consortium

Systems Biology Workbench

Virtual Cell Project

Rights and permissions

Reprints and permissions

About this article

Cite this article

Hunter, P., Borg, T. Integration from proteins to organs: the Physiome Project. Nat Rev Mol Cell Biol 4, 237–243 (2003). https://doi.org/10.1038/nrm1054

Download citation

  • Issue Date:

  • DOI: https://doi.org/10.1038/nrm1054

This article is cited by

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing