In Silico Tools for Regenerative Medicine

cBITE Department

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 and tissue engineered products. 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,

as schematically shown in the left side of figure 1. Importantly, to calibrate and validate the in silico predictions we closely work together with colleagues of the MERLN Institute, the MDR program and REGMEDXB.

Selected publications

  • Geris, L., Lambrechts, T., Carlier, A., Papantoniou, I. (2018) The future is digital: in silico tissue engineering. Current Opinion in Biomedical Engeering, https://doi.org/10.1016/j.cobme.2018.04.001
  • Hebels, D.G.A.J, Carlier, A., Coonen, M.L.J., Theunissen, D.H., de Boer, J. (2017) cBiT: a transcriptomics database for innovative biomaterial engineering. Biomaterials, https://doi.org/10.1016/j.biomaterials.2017.10.008
  • Carlier, A., Vasilevich, A., Marechal, M., de Boer, J., Geris, L. (2018) In silico clinical trials for pediatric orphan diseases. Scientific Reports, 6;8(1):2465, https://doi.org/10.1038/s41598-018-20737-y
  • Carlier, A., Akdeniz Skvortsov, G., Hafezi, F., Ferraris, E., Patterson, J., Koc, B., Van Oosterwyck, H. (2016) Computational model-informed design and bioprinting of cell-patterned constructs for bone tissue engineering. Biofabrication, 8:2, http://dx.doi.org/10.1088/1758-5090/8/2/025009
  • Carlier, A., van Gastel, N., Geris, L., Carmeliet, G., Van Oosterwyck, H. (2014). Size does matter: an integrative in vivo-in silico approach for the treatment of critical size bone defects. PLoS Comput Biol, 10(11), e1003888; https://doi.org/10.1371/journal.pcbi.1003888

Image

Schematic representation of the tissue engineering research and development process (horizontally) and the computer model classification (vertically).  Figure is taken from: Geris, L., Lambrechts, T., Carlier, A., Papantoniou, I. (2018) The future is digital: in silico tissue engineering. Current Opinion in Biomedical Engeering,https://doi.org/10.1016/j.cobme.2018.04.001Schematic representation of the tissue engineering research and development process (horizontally) and the computer model classification (vertically). Figure is taken from: Geris, L., Lambrechts, T., Carlier, A., Papantoniou, I. (2018) The future is digital: in silico tissue engineering. Current Opinion in Biomedical Engeering,https://doi.org/10.1016/j.cobme.2018.04.001