Computational Modeling of Tissue Engineering Scaffolds as Delivery Devices for Mechanical and Mechanically Modulated Signals

  • Min Jae Song
  • David Dean
  • Melissa L. Knothe Tate
Part of the Studies in Mechanobiology, Tissue Engineering and Biomaterials book series (SMTEB, volume 10)


In this chapter, we outline the use of computational modeling and novel experimental methods to develop tissue engineering scaffolds as delivery devices for exogenous and endogenous cues, including biochemical and mechanical signals, to drive the fate of mesenchymal stem cells (MSCs) seeded within. Tissue regeneration in mature organisms recapitulates de novo tissue generation during organismal development. This gave us the impetus to develop tissue engineering scaffolds that deliver mechanical and chemical cues intrinsic to the environment of cells during mesenchymal condensation, which marks the initiation of skeletogenesis during development. Cell seeding density and mode of achieving density (protocol) have been shown to effect dilatational (volume changing) stresses on stem cells and deviatoric (shape changing) stresses on their nuclei. Shear flow provides a practical means to deliver mechanical forces within scaffolds, resulting in both dilatational and deviatoric stresses on cell surfaces. Both spatiotemporal mechanical cue delivery and mechanically modulated biochemical gradients can be further honed through optimization of scaffold geometry and mechanical properties. We use computational fluid dynamics (CFD) coupled with finite element analysis (FEA) modeling to predict flow regimes within the scaffolds and optimize flow rates to simulate seeded cells. This chapter outlines to major advantages of using computational modeling to design and optimize tissue engineering scaffold geometry, material behavior, and tissue ingrowth over time.


Computational Fluid Dynamic Particle Image Velocimetry Wall Shear Stress Computational Fluid Dynamic Model Delivery Device 
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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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Min Jae Song
    • 1
  • David Dean
    • 1
  • Melissa L. Knothe Tate
    • 1
  1. 1.Case Western Reserve UniversityClevelandUSA

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