A coupled diffusion-fluid pressure model to predict cell density distribution for cells encapsulated in a porous hydrogel scaffold under mechanical loading. (1st October 2017)

• Record Type: Journal Article
• Authors: Zhao, Feihu; Vaughan, Ted J.; Mc Garrigle, Myles J.; McNamara, Laoise M.
• Is Part Of: Computers in biology and medicine. Volume 89(2017)
• Page Start: 181
• Page End: 189
• Publication Date: 2017-10-01
• Subjects: Coupled thermal-pore pressure -- Biphasic poroelasticity -- Cell transport -- Mechanical stimulation; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2017.08.003
• Languages: English
• ISSNs: 0010-4825

Abstract

Abstract: Tissue formation within tissue engineering (TE) scaffolds is preceded by growth of the cells throughout the scaffold volume and attachment of cells to the scaffold substrate. It is known that mechanical stimulation, in the form of fluid perfusion or mechanical strain, enhances cell differentiation and overall tissue formation. However, due to the complex multi-physics environment of cells within TE scaffolds, cell transport under mechanical stimulation is not fully understood. Therefore, in this study, we have developed a coupled multiphysics model to predict cell density distribution in a TE scaffold. In this model, cell transport is modelled as a thermal conduction process, which is driven by the pore fluid pressure under applied loading. As a case study, the model is investigated to predict the cell density patterns of pre-osteoblasts MC3T3-e1 cells under a range of different loading regimes, to obtain an understanding of desirable mechanical stimulation that will enhance cell density distribution within TE scaffolds. The results of this study have demonstrated that fluid perfusion can result in a higher cell density in the scaffold region closed to the outlet, while cell density distribution under mechanical compression was similar with static condition. More importantly, the study provides a novel computational approach to predict cell distribution in TE scaffolds under mechanical loading. Graphical abstract: