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Multiscale Computational Engineering of Bones: State-of-the-Art Insights for the Future

  • Melissa L. Knothe Tate
Part of the Topics in Bone Biology book series (TBB, volume 3)

Abstract

Computational models provide a platform that is equivalent to an in vivo, in vitro, and in situ or ex vivo model platform. Indeed, the National Institutes of Health have made the development of predictive computational models a high priority of the “Roadmap for the Future” (http://nihroadmap.nih.gov/overview.asp; see especially “New Pathways to Discovery”). The power of computational models lies in their usefulness to predict which variables are most likely to influence a given result, simulation of the system response to changes in that variable, and optimization of system variables to achieve a desired bio logical effect. Typically, these models are computer representations of the actual system, based on experimentally determined parameters and system variables; increasingly these computer models are referred to as in silico models (Fig. 10.1).

Keywords

Pore Pressure Medullary Cavity Osteocyte Density Transverse Layer Pericellular Space 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Anderson EJ, Kaliyamoorthy S, Iwan J, Alexander D, Knothe Tate ML (2005) Nano-microscale models of periosteocytic flow show differences in stresses imparted to cell body and processes. Ann Biomed Eng 33:52–62.PubMedCrossRefGoogle Scholar
  2. 2.
    Anderson EJ, Savrin J, Cooke M, Dean D, Knothe Tate ML (2005) Evaluation and optimization of tissue engineering scaffolds using computational fluid dynamics. In: Annual Meeting of the Biomedical Engineering Society, Baltimore.Google Scholar
  3. 3.
    Bassett CAL (1966) Electromechanical factors regulating bone architecture. In: Fleisch H, Blackwood HJJ, Owen M, eds. Third European Symposium on Calcified Tissues. Springer Verlag, New York.Google Scholar
  4. 4.
    Biot M (1955) Theory of elasticity and consolidation for a porous anisotropic solid. J Appl Phys 26:182–185.CrossRefGoogle Scholar
  5. 5.
    Fernandez-Seara MA, Wehrli SL, Takahashi M, Wehrli FW (2004) Water content measured by protondeuteron exchange NMR predicts bone mineral density and mechanical properties. J Bone Miner Res 19:289–296.PubMedCrossRefGoogle Scholar
  6. 6.
    Knothe Tate ML (1994) Diffusive and convective transport in the osteon. M.S. thesis, Divisions of Applied Mechanics and Engineering Design, Department of Mechanical and Process Engineering, Institute of Biomedical Engineering and Medical Informatics, Swiss Federal Institute of Technology, Zurich.Google Scholar
  7. 7.
    Knothe Tate ML (1997) Theoretical and experimental study of load-induced fluid flow phenomena in compact bone. Ph.D. thesis, Mechanical and Biomedical Engineering, Swiss Federal Institute of Technology, Zurich.Google Scholar
  8. 8.
    Knothe Tate ML (2003) Whither flows the fluid in bone? An osteocyte’s perspective. J Biomech 36: 1409–1424.PubMedCrossRefGoogle Scholar
  9. 9.
    Knothe Tate ML, Knothe U (2000) An ex vivo model to study transport processes and fluid flow in loaded bone. J Biomech 33:247–254.PubMedCrossRefGoogle Scholar
  10. 10.
    Knothe Tate ML, Knothe U, Niederer P (1998) Experimental elucidation of mechanical load-induced fluid flow and its potential role in bone metabolism and functional adaptation. Am J Med Sci 316:189–195.PubMedCrossRefGoogle Scholar
  11. 11.
    Knothe Tate ML, Niederer P (1998) A theoretical FE-based model developed to predict the relative contribution of convective and diffusive transport mechanisms for the maintenance of local equilibria within cortical bone. Adv Heat Mass Transfer Biotechnol 40:133–142.Google Scholar
  12. 12.
    Knothe Tate ML, Steck R, Forwood MR, Niederer P (2000) In vivo demonstration of load-induced fluid flow in the rat tibia and its potential implications for processes associated with functional adaptation. J Exp Biol 203:2737–2745.PubMedGoogle Scholar
  13. 13.
    Lanyon L, Mosley J, Torrance A (1994) Effects of the viscoelastic behavior of the rat ulna loading model. Bone 25:383–384.Google Scholar
  14. 14.
    Maurer B, Lehmann C (2006), Die Statik von Knochen. In: Karl Culmann und die graphische Statik. Zeichnen, die Sprache des Ingenieurs. Ernst und Sohn, Berlin.Google Scholar
  15. 15.
    Meyers JJ, Liapis AI (1998) Network modeling of the intraparticle convection and diffusion of molecules in porous particles pack in a chromatographic column. J Chromatogr A 827:197–213.CrossRefGoogle Scholar
  16. 16.
    Mishra S, Knothe Tate ML (2003) Effect of lacunocanalicular architecture on hydraulic conductance in bone tissue: implications for bone health and evolution. Anat Rec A Discov Mol Cell Evol Biol 273: 752–762.PubMedCrossRefGoogle Scholar
  17. 17.
    Mishra S, Knothe Tate M (2004) Allometric scaling relationships in microarchitecture of mammalian cortical bone. 50th Annual Meeting of the Orthopaedic Research Society, San Francisco, 29:0401.Google Scholar
  18. 18.
    Niederer PF, Knothe Tate ML, Steck R, Boesiger P (2000) Some remarks on intravascular and extravascular transport and flow dynamics. Int J Cardiovasc Med Sci 3:21–31.Google Scholar
  19. 19.
    Piekarski K, Munro M (1977) Transport mechanism operating between blood supply and osteocytes in long bones. Nature 269:80–82.PubMedCrossRefGoogle Scholar
  20. 20.
    Qiu S, Rao DS, Palnitkar S, Parfitt AM (2002) Age and distance from the surface, but not menopause, reduce osteocyte density in human cancellous bone. Bone 31:313–318.PubMedCrossRefGoogle Scholar
  21. 21.
    Qiu S, Rao DS, Paltnitkar S, Parfitt AM (2002) Relationships between osteocyte density and bone formation rate in human cancellous bone. Bone 31: 709–711.PubMedCrossRefGoogle Scholar
  22. 22.
    Reich KM, Frangos JA (1991) Effect of flow on prostaglandin E2 and inositol trisphosphate levels in osteoblasts. Am J Physiol 261(3 Pt 1):C428–432.PubMedGoogle Scholar
  23. 23.
    Sikavitsas VI, Bancroft GN, Lemoine JJ, Liebschner MA, Dauner M, Mikos AG (2005) Flow perfusion enhances the calcified matrix deposition of marrow stromal cells in biodegradable nonwoven fiber mesh scaffolds. Ann Biomed Eng 33:63–70.PubMedCrossRefGoogle Scholar
  24. 24.
    Sidler H, Steck R, Knothe Tate ML (2006) Site-Specific Porosity and its Impact on Load-Induced Fluid Movement in Cortical Bone, 52nd Annual Meeting of the Orthopaedic Research Society, Chicago, 31:1591.Google Scholar
  25. 25.
    Steck R, Niederer P, Knothe Tate ML (2003) A finite element analysis for the prediction of load-induced fluid flow and mechanochemical transduction in bone. J Theor Biol 220:249–259.PubMedCrossRefGoogle Scholar
  26. 26.
    Steck R, Knothe Tate ML (2005) In silico stochastic network models that emulate the molecular sieving characteristics of bone. Ann Biomed Eng 33:87–94.PubMedCrossRefGoogle Scholar
  27. 27.
    Tami AE, Niederer P, Steck R, Knothe Tate ML (2003) New insights into mechanical loading behavior of the ulna-radius-interosseous membrane construct based on finite element analysis of the ulnar compression model. 49th Annual Meeting of the Orthopaedic Research Society, New Orleans. 28:1196.Google Scholar
  28. 28.
    Turner CH, Forwood MR, Rho JY, Yoshikawa T (1994) Mechanical loading thresholds for lamellar and woven bone formation. J Bone Miner Res 9: 87–97.PubMedCrossRefGoogle Scholar
  29. 29.
    Wolff J (1892) Das Gesetz der Transformation der Knochen. Berlin: Herschwald Verlag.Google Scholar

Copyright information

© Springer-Verlag London Limited 2007

Authors and Affiliations

  • Melissa L. Knothe Tate
    • 1
  1. 1.Department of Biomedical Engineering and Mechanical & Aerospace Engineering and Thinktank for Multiscale Computational Modeling of Biomedical and Bio-Inspired SystemsCase Western Reserve UniversityClevelandUSA

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