In silico tools for regenerative medicine


One of the major challenges in improving medical devices and regenerative medicine strategies is understanding the exact interaction between the biomaterial and the human body. Our group strongly believes that an interdisciplinary approach which combines experimental with computational research is crucial to increase our fundamental knowledge, reduce the trial-and-error of experimental research and move towards more predictive cell-biomaterial interactions and improved medical devices. We focus on computational modelling of biological processes and cell-biomaterial interactions, using a range of data-driven to mechanistic modeling approaches, covering the intracellular and cellular scale as each in silico model system has its own benefits and limitations which determines the application for which it can be used. We are currently active in developing in silico models and machine learning algorithms to design advanced micropatterned and microfluidic high-throughput screening platforms, to gain fundamental understanding of cell-biomaterial interactions, to analyze high-throughput screening data and to inform the design and bioprocessing of regenerative medicine products. Importantly, to calibrate and validate the in silico predictions we closely work together with colleagues of the MERLN Institute, the MDR program and REGMEDXB.

Keywords: computational modeling, machine learning, Gene networks, cell-biomaterial interaction, mechanobiology

Dr. Aurélie Carlier

Maastricht University
MERLN Institute for Technology-Inspired Regenerative Medicine
Universiteitssingel 40 (Room C3.577)
6229 ER Maastricht
The Netherlands
Phone: +31 (0)6 39 60 21 38

E-mail: a.carlier(at)