The research vision of the Buganza Lab is the development of predictive computational tools to increase our basic understanding of mechanobiological adaptation and soft matter mechanics, and to apply these tools in relevant clinical settings. These include but are not limited to tissue expansion, wound healing, and reconstructive surgery. Additionally, modeling and simulation of biological systems requires suitable numerical methods. For instance, we are interested in the development of new thin shell, multifield, and multiscale finite element formulations.
Skin is a living tissue, its ability to adapt to extreme conditions has lead to tissue expansion, a widely used technique in reconstructive surgery that grows skin in vivo to correct large defects. Applying the theory of finite growth we can model tissue expansion within a continuum mechanics framework. In collaboration with Dr. Arun K. Gosain, from Northwestern University, we carry out validation experiments on a porcine model. We are currently working on translating our method to the clinical setting of breast reconstruction after mastectomy.
Achieving perfect skin regeneration after wounding remains challenging because of the lack of fundamental understanding of the precise interplay among different cell types, complex cell signaling networks and mechanical feedback loops. We develop new theoretical and numerical tools to study the spatiotemporal dynamics of this system across scales.
Mechanical loading plays a crucial role in chronic and acute tissue response to excessive loads, including damage, rupture, and pathological growth and remodeling. However, despite this knowledge, surgical procedures have rarely been looked at from from a structural point of view before. We use computational mechanics to improve reconstructive surgery. We capture patient specific geometries with 3D photography and multi-view stereo pre-, intra-, and post-operatively. Tissue mechanical properties change with age, gender, and from one individual to another. We develop numerical methods to account for material uncertainty and propagate this variability through our surgical simulations.
Computational modeling of biological tissues and soft matter requires new computational tools. For example, we are interested in developing new formuations for thin, soft membranes.
Current shell formulations are mainly focused on linear isotropic materials, while soft and biological materials are characterized by nonlinear anisotropic stress-strain response and large deformations.
In addition, biological tissues have the ability to growth and remodel. These adaptive processes occur through the coupling of biological fields, across multiple spatial scales, requiring new multifield and multiscale finite element methods.
Finally, the material behavior of soft tissues change from one individual to another, demaning new formulations capable of uncertainty propagation and quantification.