Real-Time Detection of State Transitions in Stochastic Signals from Biological Systems

Robust analysis of signals from stochastic biomolecular processes is critical for understanding the dynamics of biological systems. Measured signals typically show multiple states with heterogeneities and a wide range of state lifetimes. Here, we present an algorithm for robust detection of state transitions in experimental time traces where the properties of the underlying states are a priori unknown. The method implements a maximum-likelihood approach to fit models in neighboring windows of data points. Multiple windows are combined to achieve a high sensitivity for state transitions with a wide range of lifetimes. The proposed maximum-likelihood multiple-windows change point detection (MM-CPD) algorithm is computationally extremely efficient and enables real-time signal analysis. By analyzing both simulated and experimental data, we demonstrate that the algorithm provides accurate change point detection in time traces with multiple heterogeneous states that are a priori unknown. A high sensitivity for a wide range of state lifetimes is achieved.