In wound healing applications, platelet-like particles (PLPs) are engineered biomaterials that aim to mimic the behavior of natural platelets. Platelets play an essential role in the successful formation of the extracellular fibrin matrix in blood clots, aiding in both fibrin polymerization and clot retraction. We consider techniques for data driven mathematical and computational modeling of such biomimetic systems. These approaches enable investigation of relationships between tunable system properties and functional outcomes. We present models for two applications. The first problem considers the effects of tunable PLP design properties on the polymerization of fibrinogen into fibrin matrix using thrombin. The second problem addresses the integration of acoustic force-based ultrasound to promote dynamic interactions between PLPs and fibrin matrix to enhance clot retraction. Based on data from experiments, we develop approaches combining parameter estimation and local sensitivity-based identifiability analysis to enable future work on parameter subset selection. Parameter subset selection algorithms permit reduction of the number of parameters being estimated, allowing for more efficient model optimization and robust parameter estimation.