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Cells interact with the extracellular matrix (ECM) via cell–ECM adhesions. These physical interactions are transduced into biochemical signals inside the cell which influence cell behaviour. Although cell–ECM interactions have been studied extensively, it is not completely understood how immature (nascent) adhesions develop into mature (focal) adhesions and how mechanical forces influence this process. Given the small size, dynamic nature and short lifetimes of nascent adhesions, studying them using conventional microscopic and experimental techniques is challenging. Computational modelling provides a valuable resource for simulating and exploring various “what if?” scenarios in silico and identifying key molecular components and mechanisms for further investigation. Here, we present a simplified mechano-chemical model based on ordinary differential equations with three major proteins involved in adhesions: integrins, talin and vinculin. Additionally, we incorporate a hypothetical signal molecule that influences adhesion (dis)assembly rates. We find that assembly and disassembly rates need to vary dynamically to limit maturation of nascent adhesions. The model predicts biphasic variation of actin retrograde velocity and maturation fraction with substrate stiffness, with maturation fractions between 18–35%, optimal stiffness of ∼1 pN/nm, and a mechanosensitive range of 1-100 pN/nm, all corresponding to key experimental findings. Sensitivity analyses show robustness of outcomes to small changes in parameter values, allowing model tuning to reflect specific cell types and signaling cascades. The model proposes that signal-dependent disassembly rate variations play an underappreciated role in maturation fraction regulation, which should be investigated further. We also provide predictions on the changes in traction force generation under increased/decreased vinculin concentrations, complementing previous vinculin overexpression/knockout experiments in different cell typ

In vivo cells reside in a complex extracellular matrix (ECM) that presents spatially distributed biochemical and ‑physical cues at the nano- to micrometer scales. Chemical micropatterning is successfully used to generate adhesive islands to control where and how cells attach and restore cues of the ECM in vitro. Although chemical micropatterning has become a powerful tool to study cell–material interactions, only a fraction of the possible micropattern designs was covered so far, leaving many other possible designs still unexplored. Here, a high-throughput screening platform called “Galapagos chip” is developed. It contains a library of 2176 distinct subcellular chemical patterns created using mathematical algorithms and a straightforward UV-induced two-step surface modification. This approach enables the immobilization of ligands in geometrically defined regions onto cell culture substrates. To validate the system, binary RGD/polyethylene glycol patterns are prepared on which human mesenchymal stem cells are cultured, and the authors observe how different patterns affect cell and organelle morphology. As proof of concept, the cells are stained for the mechanosensitive YAP protein, and, using a machine-learning algorithm, it is demonstrated that cell shape and YAP nuclear translocation correlate. It is concluded that the Galapagos chip is a versatile platform to screen geometrical aspects of cell–ECM interaction.

In recent years, the mathematical and computational sciences have developed novel methodologies and insights that can aid in designing advanced bioreactors, microfluidic setups or organ-on-chip devices, in optimizing culture conditions, or predicting long-term behavior of engineered tissues in vivo. In this review, we introduce the concept of computational models and how they can be integrated in an interdisciplinary workflow for Tissue Engineering and Regenerative Medicine (TERM). We specifically aim this review of general concepts and examples at experimental scientists with little or no computational modeling experience. We also describe the contribution of computational models in understanding TERM processes and in advancing the TERM field by providing novel insights. Although in recent years the use of mathematical and computational sciences has increased in the Tissue Engineering and Regenerative Medicine (TERM) field, we believe that a further integration of experimental and computational approaches has a huge potential for advancing the field due to the ability of models to explain and predict experimental results and efficiently optimize TERM product and process designs. By providing an overview of existing computational models, how they have contributed to the field, as well as a future perspective, this review represents an important step to help realize TERM's ultimate goal: a cure instead of care.

In recent years, the mathematical and computational sciences have developed novel methodologies and insights that can aid in designing advanced bioreactors, microfluidic set-ups or organ-on-chip devices, in optimizing culture conditions, or predicting long-term behavior of engineered tissues in vivo. In this review, we introduce the concept of computational models and how they can be integrated in an interdisciplinary workflow for Tissue Engineering and Regenerative Medicine (TERM). We specifically aim this review of general concepts and examples at experimental scientists with little or no computational modeling experience. We also describe the contribution of computational models in understanding TERM processes and in advancing the TERM field by providing novel insights.

A comparatively straightforward approach to accomplish more physiological realism in organ-on-a-chip (OoC) models is through substrate geometry. There is increasing evidence that the strongly, microscale curved surfaces that epithelial or endothelial cells experience when lining small body lumens, such as the alveoli or blood vessels, impact their behavior. However, the most commonly used cell culture substrates for modeling of these human tissue barriers in OoCs, ion track-etched porous membranes, provide only flat surfaces. Here, we propose a more realistic culture environment for alveolar cells based on biomimetically microcurved track-etched membranes. They recreate the mainly spherical geometry of the cells' native microenvironment. In this feasibility study, the membranes were given the shape of hexagonally arrayed hemispherical microwells by an innovative combination of three-dimensional (3D) microfilm (thermo)forming and ion track technology. Integrated in microfluidic chips, they separated a top from a bottom cell culture chamber. The microcurved membranes were seeded by infusion with primary human alveolar epithelial cells. Despite the pronounced topology, the cells fully lined the alveoli-like microwell structures on the membranes' top side. The confluent curved epithelial cell monolayers could be cultured successfully at the air-liquid interface for 14 days. Similarly, the top and bottom sides of the microcurved membranes were seeded with cells from the Calu-3 lung epithelial cell line and human lung microvascular endothelial cells, respectively. Thereby, the latter lined the interalveolar septum-like interspace between the microwells in a network-type fashion, as in the natural counterpart. The coculture was maintained for 11 days. The presented 3D lung-on-a-chip model might set the stage for other (micro)anatomically inspired membrane-based OoCs in the future.