Nobias: analyzing anomalous diffusion in single-molecule tracks with nonparametric Bayesian inference

Abstract

Single-molecule imaging techniques and single-particle tracking (SPT) of molecules inside living cells investigate biomolecular dynamics at nanometer spatial resolution and millisecond temporal resolution. However, the complexity of living cells requires rich statistical approaches to deal with heterogeneous dynamics and complex diffusion models. To achieve these goals, we have developed a new SPT analysis framework: NOBIAS (Nonparametric Bayesian Inference for Anomalous Diffusion in Single-Molecule Tracking), which combines nonparametric Bayesian statistics and recurrent neural network (RNN) methods to deal with SPT datasets that contain heterogeneous dynamics with a mixture of unknown number of diffusive states, even within a single trajectory, asymmetrical motion of target biomolecules, and non-Brownian anomalous diffusion.