Computational Methods in the Modeling of Scaffolds for Tissue Engineering - Computational Modeling in Tissue Engineering - page 115

Fig. 9 Influence of scaffold morphology [a, c Hexagonal Prism, porosity 70%; b, d Gyroid,
porosity 70%] in stimuli for tissue differentiation. a, b Optimization for conditions represented
through the percentage of stimuli that correspond with bone tissue differentiation. c, d phenotypes
representations in models distributed for fluid stimuli induced by 0.1 mm/s (modified from
Olivares et al. [ 5 ])
the finite element (tissue) were changed depending on the stimuli. The fluid phase
was the only phase to change (through a change of viscosity) to simulate the
growth of tissues within the pores. The discretization of the fluid phase and the
solid phase captured the discontinuity of mechanical stimuli affecting cells seeded
within a scaffold over time. Using regular scaffolds with ideal morphology,
Olivares et al. [ 5 ] concluded that the mechanoregulation diagram on these scaf-
folds was more sensitive to fluid flow changes than to solid strain changes. They
studied the influence of the experimental conditions for eight different scaffold
designs to create a bone formation optimization plot (Fig. 9 a, b). In the same
study, the authors related the scaffold design with the phenotype (Fig. 9 c, d) using
the mechano-regulation theory explained previously. For the same inlet fluid
stimuli and porosity a scaffold with a hexagonal prism leads to increased bone
formation areas compared with a gyroid structure.
5.2 Lattice Point Approach
The lattice formulation was introduced in a mechano-biological model by
Perez and Prendergast [ 38 ] in order to include individual behavior of cell. A lattice
is created within each finite element and is assumed with granulation tissue