Stochastic Force Inference

Brownian dynamics is ubiquitous in biophysics, from the motion of single molecules and cytoskeletal filaments to effective models for cell and small animal behavior. We propose a principled framework, Stochastic Force Inference, for the inverse problem of Brownian dynamics: reconstruct spatially dependent force and diffusion fields from individual trajectories. It consists in a linear regression of these fields with a basis of smooth functions. We show that it is data efficient and successfully addresses the many challenges associated to real biological data: localization error, high dimensionality of phase space, out-of-equilibrium currents, multiplicative noise, complex force fields.

Reference: Frishman and Ronceray, arXiv:1809.09650.