Functional engineering of load-bearing tissues is challenging because the tissue engineered construct (TEC) must withstand the rigorous loads associated with the in vivo environments. Determining mechanical design parameters for TECs requires that mechanical' behavior and structure-function relationships of both native tissue and the TEC be given emphasis. Our goals are to quantify tissue structure-function relationships and identify the parameters most significant for the successful design of a TEC. We hypothesize that collagen fiber architecture is a critical variable for tissue mechanical function. Our preliminary work supports this contention and has led to early mathematical models providing insight into these relationships. Additionally, our preliminary work suggests that magnetic resonance imaging to non-invasively assess fiber architecture may provide input to our mathematical models to predict in vivo mechanics. Therefore, we propose a design paradigm that integrates sophisticated and rigorous mechanical testing, non-invasive tissue architectural measurements, and mathematical models of structure-function relationships, which, when combined with other biotechnologies, will advance the design of load-bearing TECs and lead to their successful use in clinical repair and replacement applications.
Aim 1. Measure mechanics, fiber architecture, and composition of native tissue.
Aim 2. Quantify structure-function relationships of native tissue using a mathematical model.
Aim 3. Quantify structure-function relationships of a tissue engineered construct. We hypothesize a functional relationship exists between collagen architecture and mechanical behavior in the intervertebral disc. We further hypothesize that, like native tissue, the mechanical behavior of a load-bearing TEC can be predicted using a structure-function model based upon collagen fiber architecture. This study will establish a design paradigm for functional TECs in load-bearing applications. Ultimately, clinical success of TEC function will be assessed non-invasively with structure-function models and MRI.
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