TRamWAy

TRamWAy infers spatio-temporal maps of diffusivity, drift, force, and potential energy from single-molecule trajectories to analyze biomolecular dynamics in living cells.

Key Features:

• Bayesian Framework: Employs a Bayesian approach with prior distributions to regularize the high-dimensional inference of spatio-temporal maps.
• Parameter Inference: Infers diffusivity, drift, force, and potential energy across spatial and temporal dimensions from recorded single-particle trajectories.
• Stochastic Optimization: Uses stochastic optimization based on local mini-batches and parsimonious gradient calculations to infer thousands of map parameters and improve convergence speed.
• Temporal Regularization and Alignment: Applies temporal regularization and aligns inferred potential fields across multiple time segments to produce coherent dynamic maps.
• High-Resolution Mapping of VLP Formation: Has been applied to map HIV-1 Gag protein dynamics during virus-like particle (VLP) formation on the plasma membrane of live T cells, resolving transient aggregation events.

Scientific Applications:

• Single-molecule dynamics: Quantifies spatially and temporally resolved molecular motion and interactions from single-particle tracking data.
• Protein aggregation and VLP formation: Characterizes transient aggregation events such as HIV-1 Gag-driven virus-like particle assembly on cell membranes.
• Membrane dynamics: Maps forces and potentials governing protein behavior on plasma membranes.
• Intracellular transport mechanisms: Infers drift and diffusivity landscapes relevant to intracellular transport processes.

Methodology:

Records single-molecule trajectories and uses a Bayesian inference framework combined with stochastic optimization (local mini-batches and parsimonious gradient calculations), temporal regularization, and alignment to infer spatio-temporal maps of diffusivity, drift, force, and potential energy.