Bioreactors are devices used for the growth of tissues in a laboratory environment. They exist in many different forms, each designed to enable the production of high-quality tissues. The dynamic environment within bioreactors is known to significantly affect the growth and development of the tissue. Chondrocytes, the building blocks of articular cartilage, for example, are stimulated by mechanical stresses such as shear, as compared with those in tissues grown under static incubation conditions. On the other hand, high shear can damage cells. Consequently the shear-stress level has to be controlled in order to optimize the design and the operating conditions of bioreactors.
Spinner flasks have been used for the production of articular cartilage in vitro. Assuming the existence of a relation between the cellular glycosaminoglycan (GAG) synthesis and the local shear stresses on the construct surfaces, this research focuses on the development of a model for cartilage growth in such devices. The flow produced in a model spinner flask is characterized experimentally using particle-image velocimetry (PIV). A computational fluid dynamic (CFD) model validated with respect to the laboratory measurements is constructed in order to predict the local shear stresses on the construct surfaces. Tissue growth experiments conducted in the prototype bioreactor permit construct histologies and GAG contents to be analyzed and then correlated with the shear-stress predictions. The integration of this relation into the CFD model enables the prediction of GAG synthesis through convective effects. Coupling this convective model to an existing diffusive model produces a complete cartilage-growth model for use in aiding the optimization of existing bioreactors, and in the design of new ones.
| Identifer | oai:union.ndltd.org:GATECH/oai:smartech.gatech.edu:1853/6875 |
| Date | 30 March 2005 |
| Creators | Sucosky, Philippe |
| Publisher | Georgia Institute of Technology |
| Source Sets | Georgia Tech Electronic Thesis and Dissertation Archive |
| Language | en_US |
| Detected Language | English |
| Type | Dissertation |
| Format | 6908087 bytes, application/pdf |
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