Computational Modeling of Tissue Engineering Scaffolds as Delivery Devices for Mechanical and Mechanically Modulated Signals - Computational Modeling in Tissue Engineering - page 123

1 Introduction
During physiological activity, external loads in dynamic environments get transduced
via the musculoskeletal system to the cells which build, maintain, and remodel
musculoskeletal tissues. This loading of poroelastic, viscoelastic, and hyperelastic
fluid-imbibed solid elements and complex fluids transduces dilatational mechanical
stresses, which induce volume without shape changes, and deviatoric mechanical
stresses, which induces shape without volume changes. In this way, ground forces
transduced via the muscles, ligaments, tendons and bones are experienced as stresses at
the tissue length scale (e.g., cortical or trabecular bone, tendon) and cellular length
scale (e.g., osteocytes in the pericellular lacunocanalicular system, or tenocytes),
respectively [ 6 , 24 , 33 ]. (Fig. 1 ) Osteocytes and tenocytes have a crucial role in sensing
these mechanical signals through a putative feedback system that enables maintenance
and remodeling of bone, respectively tendon, tissue structure and function in dynamic
environments [ 9 , 23 ].
Taking into account these typical examples of mechanoadaptation as a means to
maintain structure-function relationships in tissues exposed to spatiotemporally
dynamic mechanobiological environments, new strategies for engineering and man-
ufacture of replacement tissues are incorporating biomimicry approaches to harness
nature's smart biomaterial paradigms. The design and engineering of tissue engi-
neering scaffolds has entered a new era, where such scaffolds are considered as much as
delivery devices as structural and functional tissue replacements [ 2 , 26 ]. To harness
nature's paradigms, we aim to drive structure-function relationships at the tissue and
organ length scales by delivering appropriate mechanical and chemical cues to cells.
One approach to optimize scaffolds as delivery devices is to use predictive computa-
tional modeling as a powerful tool that ''…help[s] us to prioritize which variables exert
dominant effects on system behavior and thus which experiments are key to test
predictions. [As such] predictive computational model[s] allow for the study of [tis-
sue's smart, multiscale properties] without the imperative to carry out thousands of
experiments,'' as summed up in a recent publication [ 25 ]. In this chapter, we review
computational modeling of tissue engineering scaffolds as delivery devices for
mechanical and mechanically modulated (biological and chemical) signals.
Computational models can predict and simulate the role of mechanical forces in
cell differentiation, motility, adhesion, proliferation, and secretion of extracellular
matrix proteins within tissue engineering scaffold environments. The computa-
tional method even helps to unravel the most enigmatic problems whose solutions
are stymied by experimental or technological limitations. For example, experi-
mental mechanical testing of the femur can elucidate boundary stresses and
strains; in contrast, computational models can predict intrinsic mechanical loading
distributions of the structure after experimental validation. With a given tissue
engineering scaffold geometry, computational models can be used to control and
optimize parameters to deliver mechanical stimuli to cells seeded within, in order
to maximize the probability of achieving the targeted tissue manufacture and
integration. (Fig. 2 )[ 2 ].