Identifying non-equilibrium fluctuations in Intracellular Motion Using Recurrent Neural Networks

Abstract

Distinguishing active from passive dynamics is a fundamental challenge in understanding the motion of living cells and other active matter systems. Here, we introduce a framework that combines physical modeling, analytical theory, and machine learning to identify and characterize active fluctuations from trajectory data. We train a long short-term memory (LSTM) neural network on synthetic trajectories generated from well-defined stochastic models of active particles, enabling it to classify motion as passive or active and to infer the underlying active process. Applied to experimental trajectories of a tracer in the cytoplasm of a living cell, the method robustly identifies actively driven motion and selects an Ornstein-Uhlenbeck active noise model as the best description. Crucially, the classifier's performance on simulated data approaches the theoretical optimum that we derive, and it also yields accurate estimates of the active diffusion coefficient. This integrated approach opens a powerful route to quantify non-equilibrium fluctuations in complex biological systems from limited data.