Unique mechanical and structural properties arise from silk fibroin-based materials when these proteins are rendered water-insoluble through a process that results in the collapse of the linearized protein into beta sheet structures resulting in nanocrystalline domains within the tertiary structure. We are leveraging these mechanical and structural properties to create useful silk biomaterial-based culture platforms for investigating mechanisms of disease as well as developing implantable biomaterials for applications in rehabilitative engineering.
However, in the biomaterials community, it is often difficult to quantify the term “useful.” Thus, recent work has focused on improving predictive material design through kinetic modeling of silk biomaterial degradation in vitro as a fucntion of nanocrystaline domains and addition of secondary components, such as extracellular matrix, to the silk biomaterial formulation.
This work, coupled with the investigation of growth factor delivery and in vivo cell infiltration, sheds light on the influence of silk fibroin protein structure on in vitro and in vivo material performance based on intial material formulations. Our results aid in the reduction of biomaterial formulation optimiation using a “guess and check” strategy. On-going work by Stoppel Lab PhD student Julie Jameson, in collaboration with a PhD student in the Zare Lab in UF ECE, Joshua Peeples, takes this a step further using machine learning to evaluate key parameters of biomaterial performance following implantation, such as degradation rate and adipose tissue accumulation, as we aim to build tools and methods to quantitatively assess biomaterial performance in vivo.