In Silico Tools for Regenerative Medicine

cBITE Department

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 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 figure on the right. 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
  • van Gastel, N., Stegen, S., Eelen, G., Schoors, S., Carlier, A., Daniëls, V., Baryawno, N., Przybylzki, D., Depypere, M., Stiers, P-J., Lambrechts, D., Van Looveren, R., Torrekens, S., Sharda, A., Agostinis, P., Lambrechts, D., Maes, F., Swinnen, J., Geris, L., Van Oosterwyck, H., Thienpont, B., Carmeliet P., Scadden, D., Carmeliet, G. (2020) Lipid availability determines fate of skeletal progenitor cells via SOX9. Nature, 579:111-7, https://doi.org/10.1038/s41586-020-2050-1
  • Vassey, M., Figueredo, G.P., Scurr, P., Vasilevich, A., Vermeulen, S., Carlier, A., Luckett, J., Beijer, N., Williams, P., Winkler, D., de Boer, J., Ghaemmaghami, A.M., Alexander, M.R. (2020) Immune modulation by design: using topography to control human monocyte attachment and macrophage differentiation. Advanced Science, 7:1903392 https://doi.org/10.1002/advs.201903392

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