Peer-Reviewed Journal Details
Mandatory Fields
Stops, AJF,McMahon, LA,O'Mahoney, D,Prendergast, PJ,McHugh, PE
2008
December
Journal Of Biomechanical Engineering-Transactions Of The Asme
A Finite Element Prediction of Strain on Cells in a Highly Porous Collagen-Glycosaminoglycan Scaffold
Published
()
Optional Fields
cellular biophysics molecular biophysics porous materials proteins tissue engineering SMOOTH-MUSCLE ACTIN IN-VITRO MICRO-CT FIBROBLAST CONTRACTION TISSUE REGENERATION ARTICULAR-CARTILAGE ELASTIC PROPERTIES MECHANICAL STRAIN GAG SCAFFOLDS BONE
130
Tissue engineering often involves seeding cells into porous scaffolds and subjecting the scaffold to mechanical stimulation. Current experimental techniques have provided a plethora of data regarding cell responses within scaffolds, but the quantitative understanding of the load transfer process within a cell-seeded scaffold is still relatively unknown. The objective of this work was to develop a finite element representation of the transient and heterogeneous nature of a cell-seeded collagen-GAG-scaffold. By undertaking experimental investigation, characteristics such as scaffold architecture and shrinkage, cellular attachment patterns, and cellular dimensions were used to create a finite element model of a cell-seeded porous scaffold. The results demonstrate that a very wide range of microscopic strains act at the cellular level when a sample value of macroscopic (apparent) strain is applied to the collagen-GAG-scaffold. An external uniaxial strain of 10% generated a cellular strain as high as 49%, although the majority experienced less than similar to 5% strain. The finding that the strain on some cells could be higher than the macroscopic strain was unexpected and proves contrary to previous in vitro investigations. These findings indicate a complex system of biophysical stimuli created within the scaffolds and the difficulty of inducing the desired cellular responses from artificial environments. Future in vitro studies could also corroborate the results from this computational prediction to further explore mechanoregulatory mechanisms in tissue engineering.
ARTN 061001
Grant Details
Publication Themes