Unknown

Dataset Information

0

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


ABSTRACT: 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.

SUBMITTER: Bergkamp MH 

PROVIDER: S-EPMC8280633 | biostudies-literature |

REPOSITORIES: biostudies-literature

Similar Datasets

| S-EPMC6778254 | biostudies-literature
| S-EPMC4995396 | biostudies-literature
| S-EPMC3489938 | biostudies-literature
| S-EPMC5478290 | biostudies-literature
| S-EPMC7946427 | biostudies-literature
| S-EPMC3505197 | biostudies-other
| S-EPMC10774284 | biostudies-literature
| S-EPMC5563804 | biostudies-literature
| S-EPMC5444049 | biostudies-literature
| S-EPMC1609169 | biostudies-literature