| Abstract Scope |
Biological filaments such as deoxyribonucleic acid (DNA) derive function from nonlinear mechanics across length scales, where bending, twisting, stretching, intrinsic curvature, and their couplings can strongly influence shape, stability, and interaction with surrounding molecular or material systems. This talk presents a computational framework for learning effective constitutive behavior of slender biological filaments from deformation data. Using geometrically exact rod theory, forward simulations, and inverse modeling, we connect observed filament shapes and boundary-value responses to underlying mechanical laws that are difficult to measure directly. I will discuss how specific features of the constitutive law can produce qualitatively different mechanical behavior relevant to DNA deformation, looping, and confinement, and how these insights motivate inverse approaches for discovering filament mechanics. The broader goal is to transform biological form and motion into quantitative mechanical understanding for biological materials science, bioinspired mechanics, and bioenabled material systems. |