Abstract :
[en] The creation of man-made living implants is the holy grail of tissue engineering (TE). As basic science advances, one of the major challenges in TE is the translation of the increasing biological knowledge on complex cell and tissue behavior into a predictive and robust engineering process. Mastering this complexity is an essential step towards clinical applications of TE. Computational modeling allows to study the biological complexity in a more integrative and quantitative way. Specifically, computational tools can help in quantifying and optimizing the TE product and process but also in assessing the influence of the in vivo environment on the behavior of the TE product after implantation.
In this talk, I will use the example of bone tissue engineering to demonstrate how computational modeling can contribute in all aspects of the TE product development cycle: cells, carriers, culture conditions and clinics (figure 1 and 2). Depending on the specific question that needs to be answered the optimal model systems can vary from single scale to multiscale. Furthermore, depending on the available information, model systems can be purely data-driven or more hypothesis-driven in nature. The talk makes the case for in silico models receiving proper recognition, besides the in vitro and in vivo work in the TE field.
Figure 1: overview of the 4 important components in bone tissue engineering: cells, carriers, culture and clinics.
Figure 2: overview of in silico contributions to the 4 important components in bone tissue engineering: cells [1], carriers, culture [3] and clinics [4].
Acknowledgements
This work presented in this talk is part of Prometheus, the KU Leuven R&D division for skeletal tissue engineering. http://www.kuleuven.be/prometheus. The research leading to these results has received funding from the European Research Council under the European Union's Seventh Framework Programme (FP/2007-2013) / ERC Grant Agreements 279100; from the Research Programme of the Research Foundation - Flanders (FWO, grant n. G.0982.11), from the Belgian National Fund for Scientific Research (FNRS) and from the special research fund of the KU Leuven (GOA/13/016)
References
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