Uncovering Hierarchical Cellular Mechanisms: Linking Molecular Regulation and Biological Topology

Abstract

This dissertation investigates biological phenotypes across spatial scales, emphasizinghierarchical and topological features through computational physics and machine learning. At the molecular scale, coarse-grained simulations revealed diverse conformations of cofilin oligomers stabilized by disulfide bonds, which regulate actomyosin dynamics via redox-sensitive modifications. At the mesoscale, mechanochemical simulations and network theory uncovered topological transitions, or "avalanches," in branched actomyosin networks controlled by Arp2/3, with machine learning models predicting these events. At the cellular scale, the GRIP-Tomo 2.0 framework integrated synthetic cryo-electron tomography with graph-based learning, enabling robust protein classification through conserved topological fingerprints under limited data. Together, these studies demonstrate how topological insights across scales illuminate the physical principles underlying biological function, highlighting the power of physics-based computation in complex living systems.