Extracting Landscape Features From Single Particle Trajectories

The predictive power of dynamical models of cell signaling is often limited due to the difficulty in estimating the relevant kinetic parameters. Super-resolution microscopy techniques can provide in vivo trajectories of individual receptors, and serve as a direct source of quantitative information on molecular processes. Single particle tracking (SPT) has been used to extract reaction kinetic parameters such as dimer lifetimes and diffusion rates. However, signaling models aim to characterize kinetics relevant to the entire cell while SPT follows individual molecules in a small fraction of the cell. The gap in resolution can be bridged with spatial simulations of molecular movement, validated at SPT resolution, which are used to infer effective kinetics on larger spatial scales.Our focus is on processes that involve receptors bound to the cell membrane. Extrapolating kinetics observed at SPT resolution must take into account the spatial structures that interferes with the free movement of molecules of interest. This is reflected in patterns of movement that deviate from standard Brownian motion. Ideally, simulations at SPT resolution should reproduce observed movement patterns, which reflect the properties and transformation of the molecules as well as those of the underlying cell membrane.We first sought to identify general signatures of the underlying membrane landscape in jump size distributions extracted from SPT data. We found that Brownian motion simulations in the presence of a pattern of obstacles could provide a good qualitative match. The next step is to infer the underlying landscape structures. We discuss our method used to identify such structures from long single particle trajectories that are obtained at low density. Our approach is based on deviations from ideal Brownian motion and identifies likely regions that trap receptors. We discuss the details of the method in its current form and outline a framework aimed at refinement using simulated motion in a known landscape.