Inferring Spatially Heterogeneous Cytoplasmic Mechanics from Anomalous Transport

The interior of a living cell is not a uniform liquid. It is an organized, changing material that helps control how molecules move, how reactions occur, and how proteins assemble. Many experiments can now record the motion of tiny particles inside cells, but those movies do not automatically reveal what physical environment caused the motion. This project will create a new way to turn particle-motion measurements into quantitative maps of how the cell interior is organized and how that organization changes under stress. The research combines mathematical modeling, controlled live-cell imaging, and data-driven computation to determine which mechanical features can be reliably inferred from noisy biological data and which cannot. The resulting tools may support biotechnology by improving the ability to measure and predict cell states associated with stress responses, aging, and disease. The project will also train students in an interdisciplinary area that connects mathematics, physics, computation, machine learning, and cell biology. This project addresses the inverse problem of determining what measurements of molecular motion inside living cells reliably reveal about the spatially heterogeneous mechanical environment that produced them. The work establishes the mathematical, statistical, and identifiability foundations of intracellular microrheology by coupling stochastic transport models with controlled experiments. The modeling component formulates inference-ready continuous-time random walk and mobile-immobile fractional transport models with spatially varying, uncertainty-aware coefficients capturing diffusivity, trapping, viscoelastic memory, and relaxation. The inference component determines which mechanical features are identifiable from finite, noisy trajectories. Experimentally, the project uses genetically encoded multimeric nanoparticles of tunable size, alongside cytoskeletal and metabolic perturbations, to generate particle-tracking datasets designed to constrain the transport models. Machine-learning-based surrogate models are deployed to accelerate uncertainty-aware inference and model comparison. Mechanistic interpretation connects inferred transport signatures to molecular network connectivity within a phase-separation-coupled-with-percolation framework, distinguishing droplet-localized slowing, diffuse-network effects, and global crowding. The ultimate outcome is a principled pipeline for converting intracellular transport measurements into spatially resolved, testable statements about cytoplasmic mechanical organization, driving innovative capabilities in predictive biotechnology. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.