Using digital twins to design 3D printed implants for skeletal tissue engineering

Liesbet Geris

by Liesbet Geris, University of Liege

One of the major challenges in tissue engineering and an essential step towards successful clinical applications is the translation of biological knowledge on complex cell and tissue behavior into predictive and robust engineering processes. Computational modelling can contribute to this, among others because it allows to study the biological complexity in a more quantitative way. Computational tools can help in quantifying and optimizing micro-environmental signals to which cells and tissues are exposed and in understanding and predicting the biological response under different conditions.

A wide variety of model systems has been presented in the context of tissue engineering ranging from empirical models (data-driven) over gene network models to mechanistic models (hypothesis-based), targeting processes at the intracellular over the cellular up to the tissue level. Each model system has its own benefits and limitations which delineate the context in which it can be used. Whereas mechanistic models are used as in silico tools to design new therapeutic strategies and experiments, empirical models are used to identify, in large data sets, those in vitro parameters (biological, biomaterial, environmental) that are critical for the in vivo outcome.

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