Computational Modeling of Tissue Engineering Scaffolds as Delivery Devices for Mechanical and Mechanically Modulated Signals - Computational Modeling in Tissue Engineering - page 130

Fig. 7 Stem cell morphology changes at the subcellular length scale as a consequence of seeding
at different densities and using different protocols to achieve density. a, b, c Nucleus shape
changes b and cell shape changes c are attributable to target cell seeding densities and means of
achieving density [ 12 ], used with permission. d, e, f The cytoskeleton components, including
actin (f) and tubulin (e), also change when exposed to dilatational and deviatoric stress induced
through different target densities and seeding protocols as well as exposure to fluid flow [ 46 ],
used with permission. Please refer to online version of chapter for color version of the figure
gene expression of differentiation markers associated with mesenchymal con-
densation [ 46 ] (Fig. 7 ). Although correlation does not equal causation, it will be
interesting to determine whether cell shape changes in response to mechanical
signals are more of a response to minimize energy demands, e.g., by streamlining
(laminar) flow to reduce losses associated with chaotic flow in boundary layers or
as an active means for a cell to adapt to its environment (perhaps concomitant to
reducing metabolic energy costs).
Computational methods can be applied to analyze cell deformations in near real
time under shear flow; computational methods can predict deformation based on a
given geometry and mechanical or chemical properties under external forces,
which is passive behavior [ 7 , 39 ]. On the other hand, active morphological
changes of cells can occur through biological changes such as gene expression and
reorientation of cytoskeletal filaments [ 10 , 12 , 31 ]. This active behavior can be
also analyzed by computational models based on a given results under forces,
which is an important approach to elucidate mechanisms of mechanoadaptation
[ 17 , 20 , 31 ].