Liesbet Geris Computational Bone Tissue Engineering: Virtual Models for Living Implants
Liesbet Geris - University of Li├Ęge & KU Leuven, Belgium
Session chair: Peter Sloot and Michael Amato

Abstract : Tissue Engineering is a field that combines the principles of engineering and biomedical sciences in order to develop living implants that can restore or replace tissues or organs. 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 mechanistic models (hypothesis-based) over gene network models to empirical models (data-driven), 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. In this talk I will give an overview of various application of in silico regenerative medicine that were developed to answer questions from the experimental researchers and clinicians in our Tissue Engineering platform. Models of intracellular signaling, biomaterial design, bioprocess design and in vivo regeneration under challenging conditions will be discussed. I will end with a discussion of a number of challenges we are facing in the in silico medicine community as a whole as it pertains to establishing credibility of our models and technologies.