A triphasic constrained mixture model of engineered tissue formation under in vitro dynamic mechanical conditioning
Joao S Soares1, Michael S Sacks2
1Center for Cardiovascular Simulation, Institute for Computational Engineering and Sciences (ICES), Department of Biomedical Engineering, The University of Texas at Austin, 201 East 24th Street, Austin, TX, 78712-1129, USA.
Biomechanics and Modeling in Mechanobiology
|June 10, 2015
Summary
Mechanical signals drive engineered tissue growth, but mechanisms are unclear. This study uses mixture theory and multi-scale methods to model nutrient transport and extracellular matrix development, offering a rational approach to optimize tissue engineering protocols.
Area of Science:
- Biomedical Engineering
- Mechanobiology
- Tissue Engineering
Background:
- Mechanical signals are known to influence in vitro engineered tissue formation.
- The precise mechanisms driving extracellular matrix (ECM) development under mechanical stimuli are not well understood.
- Optimizing ECM development has largely relied on empirical methods.
Purpose of the Study:
- To develop a mechanistic understanding of dense connective tissue growth under mechanical stimulation.
- To create a theoretical framework for optimizing tissue engineering (TE) conditioning protocols.
Main Methods:
- Utilized mixture theory to model the engineered construct as a nutrient-cell-ECM triphasic system.
- Employed multi-scale methods to couple cellular proliferation and ECM synthesis with dynamic conditioning protocols (around 1 Hz).
- Integrated enhanced nutrient transport due to pore fluid advection into the growth model.
Main Results:
- Simulation results showed favorable comparison with existing experimental data.
- The model successfully distinguished between static and dynamic conditioning regimes.
- Spatially dependent ECM distribution was used to describe evolving poroelastic characteristics.
Conclusions:
- The developed theoretical framework provides a mechanistic basis for understanding mechanically conditioned TE.
- This approach allows for the formulation of informed hypotheses regarding TE growth and development.
- Enables rational exploration and optimization of conditioning protocols for improved tissue engineering outcomes.
