Physics-Based and Data-Driven Multiscale Modeling of the Structural Formation in Macromolecular Systems

In order to improve knowledge on macromolecular structural formation and self-assembly, this work proposes a physics-based and data-driven multiscale modeling framework capable of describing structural formation on micro-meter and milli-second scales near molecular-level precision. The framework abstracts macromolecules as anisotropic unit objects and models the interactions and environment using data-driven approaches. The models are parameterized in a bottom-up fashion and validated top-down by comparison with literature and collaborator data for self-assembly of three model system: alginate gelation, hepatitis B virus capsids, and the pyruvate dehydrogenase complex.