By simulating biological processes, computational models help researchers understand and predict how cells make decisions, how they work together in processes such as tissue development and adaptation, and how tissue organization affects function [1]. They can even estimate quantities that are difficult or impossible to measure experimentally, such as mechanical stresses within tissues, and thereby uncover how these factors drive regeneration.
In this way, computational models make in vitro and in vivo experiments more effective. They help design more efficient studies, reduce trial and error, and bridge multiple biological scales — from molecules and cells to tissues, organs, and entire systems to enable advanced analysis of experimental outcomes. There are many types of computational approaches, but they can be broadly divided into two categories: black box and white box models. Black box approaches, such as those based on artificial intelligence (AI), use data-driven methods to identify patterns and make predictions. White box approaches are based on fundamental principles — like physics and biology — to describe mechanisms and processes directly.
The research groups of both Aurélie Carlier (MERLN, Maastricht University) and Sandra Loerakker (TU/e) are mainly working on white box models. Within DRIVE-RM, Sandra and Aurélie are exploring collaborations where a PhD student will work in each institute and the projects are aligned to strengthen each other.
Within Aurélie’s research group at Maastricht University, the focus lies on ordinary and partial differential equation models and stochastic agent-based models. These are used to study how cells interact with each other and with their environment across different scales. At the subcellular level, the group has demonstrated how the size, distribution, and lifetime of cell adhesions influence YAP phosphorylation — a key regulator of cell behavior [2]. At the cellular level, they have shown that both the overall density and the local clustering of integrin ligands play a role in stem cell regulation [3]. At the tissue level, their work revealed how feedback mechanisms can help control the foreign body reaction and limit fibrosis [4]. To further refine their predictions, the group is now investigating the role of mechanical forces in these processes and is exploring collaborations with Sandra’s group to integrate experimental and computational insights.
Sandra’s research group at Eindhoven University of Technology develops mechanistic (white box) models to understand and predict how tissues grow and remodel in response to mechanical factors, and leverage these insights to improve regenerative strategies. The group particularly focuses on linking detailed mechanobiological processes at the subcellular level to tissue-scale growth and remodeling, with cardiovascular regenerative medicine as main application area. Using insights obtained from computational models, the group has identified a superior design for tissue-engineered heart valves, for which in vivo studies indeed demonstrated radically improved valve remodeling and functionality [5]. The group also pioneered the development of experimentally-informed computational models to analyze how cell-cell communication via Notch regulates cardiovascular tissue adaptation [6,7]. The group is currently developing computational methods to analyze and predict the transformation of biomaterials into living tissues, by integrating insights into mechanobiological processes into mechanistic models of mechano-regulated regeneration. The expertise of Aurélie’s group on modeling detailed intracellular processes may offer exciting opportunities for collaboration via DRIVE-RM.
The above examples illustrate the wide variety of computational techniques, and their huge potential to advance the regenerative medicine field, especially when integrated with experimental research. In the future, we envision that the AI-driven and white box techniques will be integrated into hybrid approaches, harnessing the best of both worlds to bring regenerative medicine to the next level and realize the full potential of digital twins to enable successful and predictable regeneration.
[1] Post, J.N., Loerakker, S., Merks, R.M.H., Carlier, A. (2022) Implementing computational modeling in tissue engineering: where disciplines meet. Tissue Engineering Part A, https://doi.org/10.1089/ten.TEA.2021.0215
[2] Jafarinia, H., Shi, L., Wolfenson, H., Carlier, A. (2024) YAP phosphorylation within integrin adhesions: insights from a computational model. Biophysical Journal, DOI: 10.1016/j.bpj.2024.09.002
[3] Zhang, X., Karagöz, Z., Swapnasrita, S., Habibovic, P., Carlier, A., van Rijt, S. (2023) Development of mesoporous silica nanoparticle-based films with tunable arginine-glycine-aspartate peptide global density and clustering levels to study stem cell adhesion and differentiation. ACS Appl Mater Interfaces, https://pubmed.ncbi.nlm.nih.gov/37527490/
[4] Marradi, M., van Griensven, M., Beijer, N., de Boer, J., Carlier, A. (2025) Towards predicting implant-induced fibrosis: A standardized network model of macrophage-fibroblast interactions. Computational and Structural Biotechnology Journal, https://doi.org/10.1016/j.csbj.2025.07.022
[5] Emmert M.Y., Schmitt B.A., Loerakker S., Sanders B., Spriestersbach H., Fioretta E.S., Bruder L., Brakmann K., Motta S.E., Lintas V., Dijkman P.E., Frese L., Berger F., Baaijens F.P.T., Hoerstrup S.P. (2018) Computational modeling guides tissue-engineered heart valve design for long-term in vivo performance in a translational sheep model. Science Translational Medicine, https://doi.org/10.1126/scitranslmed.aan4587
[6] Loerakker S, Stassen O.M.J.A., Ter Huurne F.M., Boareto M., Bouten C.V.C., Sahlgren C.M. (2018) Mechanosensitivity of Jagged-Notch signaling can induce a switch-type behavior in vascular homeostasis. PNAS, https://doi.org/10.1073/pnas.1715277115
[7] Van Asten J.G.M., Latorre M., Karakaya C., Baaijens F.P.T., Sahlgren C.M., Ristori T., Humphrey J.D., Loerakker S (2023) A multiscale computational model or arterial growth and remodeling including Notch signaling. Biomechanics and Modeling in Mechanobiology, https://doi.org/10.1007/s10237-023-01697-3
