Open AccessReal-Time Detection of State Transitions in Stochastic Signals from Biological Systems
Author(s) -
Max H. Bergkamp,
L.J. van IJzendoorn,
Mwj Menno Prins
Publication year - 2021
Publication title -
acs omega
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.779
H-Index - 40
ISSN - 2470-1343
DOI - 10.1021/acsomega.1c02498
Subject(s) - a priori and a posteriori , sensitivity (control systems) , algorithm , computer science , range (aeronautics) , state (computer science) , change detection , signal (programming language) , point (geometry) , biological system , mathematics , artificial intelligence , engineering , electronic engineering , philosophy , geometry , epistemology , biology , programming language , aerospace engineering
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.