Soares' Simulation Shows How to Grow Better Engineered Tissue

Mechanical stimulation of tissue during in vitro incubation and early tissue development are an increasingly important tool in bioengineering. It exposes engineered tissues to physical forces similar to those experienced in a living body by mechanically pulling and flexing the scaffolds where they’re grown.

Joao Soares, a postdoctoral researcher working in the ICES Center for Cardiovascular Simulation (CCS), and Michael Sacks have developed a novel theoretical framework for engineered tissues that simulates the mechanisms that control cell and tissue growth under mechanical training.

Their research was published in the journal Biomechanics and Modeling in Mechanobiology, one of the top impact factor journals in biomedical engineering and biomechanics.

Compared to cells grown in static environments, such as petri dishes, mechanically conditioned tissues are more robust. They’re better at absorbing nutrients and oxygen, growing, and adhering to the extracellular matrix, the network of proteins and sugars that acts as scaffolding to connect cells in a tissue together. Most importantly, mechanical stimulation is widely established to induce better responses from the cells and lead to better outcomes for the engineered tissue/implant.

Having a source for robust cells could lead to lab-grown tissue grafts to help heal damaged organs, and eventually lab-grown organs. However, experiments have not been successful at understanding the mechanisms that influence tissue growth—a piece of information that could scientists more precisely influence and improve tissue engineering.

Their framework helps shed a light on the mechanisms that govern cell and tissue growth and behavior. His simulation digitally recreates simplified models of tissue flexure experiments that enable researchers to understand the relationship between tissue components, cell behavior, and implant mechanics.

“There is a need within the field to add some rationality to the experimentation, people can only do so much by trial and error,” Soares said. “It’s great to have a predictable, theoretical model that systematizes what we know so far, and can help give us the next experimental conditions and explain what we were not thinking about beforehand.”

The data that the framework describes/predicts were based on previous extensive experimental studies of engineered heart valve tissues. The primary purpose of these wet-lab experiments was to measure how flexure, a common natural heart valve leaflet deformation, affected the development of the extracellular matrix secreted from vascular smooth muscle cells.

The model also looked at how flexure impacted the 3D formation of the extracellular matrix. Their approach relied on the simplification of the phenomena by focusing only on the interactions between three key parts: dissolved nutrients, which the model treated as oxygen; cells; and the extracellular matrix.

The specific mechanisms described within the framework included: the convection, diffusion, and consumption of oxygen by cells; the movement of cells and their rate of growth and death; and the rate of extracellular matrix production.

Although the initial model was a simplified instance of an idealized experiment, the results it produced were in line with what was observed in the lab—repeated flexure over time causes an improvement in cellular synthetic activity, and stiffening of the extracellular matrix, or put simply, a better tissue.

Soares says this shows the value of simplified models in understanding the basic underlying forces that govern complex systems, such as tissues.

“The idea is to keep things as simple as possible in order to isolate the effects that we’re interested in,” Soares said. “And there are some things that you can’t even conceive or think about by looking at the experiments, but the model can bring it into play.”

The framework’s simplified nature is also a benefit when it comes to applying it to other scenarios. Their approach reproduced experiments on heart valve tissue, but the underlying assumptions are based on principles and laws that govern tissue mechanics in general. Tissue engineers of all kinds could model their mechanically conditioned tissue engineering experiments using this framework, Soares said.

“We have created a framework that can be applied to many other problems in tissue engineering,” Soares said. “The methodology is sufficiently general such that you could apply the framework to your specific system.”