Model of integrin binding as a function of light-induced variations in forces exerted on cells via RGD-terminated, photoswitchable azobenzene surfaces

Photo-responsive biomaterials are attractive because of the ability to non-invasively alter and control the material properties, thus allowing control over the cell response at the interface with the bioamaterial surface. While in silico mathematical models have been implemented for simulating single-cell force spectroscopy (SCFS) experiments on static and some dynamic biomaterials, these models have yet to be extended to light-responsive biointerfaces. So here, we develop a mathematical model that describes and predicts the strength of integrin-mediated cell adhesions to a photoswitchable biomaterial surface. The fluctuating biomaterial comprises photoswitchable azobenzene attached to a glass surface and terminated with peptide c(RGDfK). Upon irradiation with light at 530 nm, the azobenzenes rapidly fluctuate between an extended and contracted conformation, leading to a change in the length of the azobenzene that stimulates integrins bound to c(RGDfK). The mathematical model mimics the nascent adhesion and spatial fluctuations in the extra-cellular matrix (ECM) and is calibrated using single-cell force microscopy retraction curves. It relies on spring-based mechanics to describe the stretch and deformation of a cell and ensembles of integrins when applied to a fluctuating biomaterial. We use the model to simulate retraction curves and the proportion of bound integrins on the surface as the material fluctuates. Additionally, we use the model to predict SCFS retraction curves for varying experimental conditions. This includes the length of time the cell (attached to the tip of the atomic force microscopy cantilever) is kept in contact with the biomaterial before retraction, and for varying frequency of light-induced movement of the azobenzene conformations. These model outcomes provide an attractive route to integrate and rigorously control certain experimental variables, thus accelerating experimental design of the study of cell adhesion to light-responsive biomaterials. Furthermore the model complements experiments by providing estimates of variables that are experimentally inaccessible. Thus, the outcomes of the model provide a valuable resource to aid in the interpretation and design of light-responsive biointerface functionalities.