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Noise in interacting biological systems

Research output: Contribution to conference - Without ISBN/ISSN Conference paperpeer-review

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Noise in interacting biological systems. / Stankovski, Tomislav; McClintock, Peter V. E.; Stefanovska, Aneta.

2019. Paper presented at 25th International Conference on Noise and Fluctuations, Neuchâtel, Switzerland.

Research output: Contribution to conference - Without ISBN/ISSN Conference paperpeer-review

Harvard

Stankovski, T, McClintock, PVE & Stefanovska, A 2019, 'Noise in interacting biological systems', Paper presented at 25th International Conference on Noise and Fluctuations, Neuchâtel, Switzerland, 18/06/19 - 21/06/19.

APA

Stankovski, T., McClintock, P. V. E., & Stefanovska, A. (2019). Noise in interacting biological systems. Paper presented at 25th International Conference on Noise and Fluctuations, Neuchâtel, Switzerland.

Vancouver

Stankovski T, McClintock PVE, Stefanovska A. Noise in interacting biological systems. 2019. Paper presented at 25th International Conference on Noise and Fluctuations, Neuchâtel, Switzerland.

Author

Stankovski, Tomislav ; McClintock, Peter V. E. ; Stefanovska, Aneta. / Noise in interacting biological systems. Paper presented at 25th International Conference on Noise and Fluctuations, Neuchâtel, Switzerland.

Bibtex

@conference{9789749d430c488da5f35f8097914ba1,
title = "Noise in interacting biological systems",
abstract = "Biological systems are never isolated, usually oscillatory, and invariably subject to noise and fluctuations that may be of either internal or external origin. We present a methodological framework for studying the deterministic interactions of biological oscillatory systems, while at the same time decomposing and evaluating the noise strength. Based on dynamical Bayesian inference, the method models coupled phase oscillators in the presence of dynamical noise. We demonstrate first the potential and the precision of the method on a predefined numerical system. Then we illustrate its usefulness in detecting how the noisestrengths from three human physiological systems – the heart, the lungs and the brain – are affected by general an{\ae}sthesia. The results demonstrate the potential of the method for detecting and quantifying noise from biological dynamical systems, quite generally.",
keywords = "dynamical Bayesian inference, noise, interactions, coupling functions, biological systems",
author = "Tomislav Stankovski and McClintock, {Peter V. E.} and Aneta Stefanovska",
year = "2019",
month = aug,
day = "23",
language = "English",
note = "25th International Conference on Noise and Fluctuations, ICNF 2019 ; Conference date: 18-06-2019 Through 21-06-2019",

}

RIS

TY - CONF

T1 - Noise in interacting biological systems

AU - Stankovski, Tomislav

AU - McClintock, Peter V. E.

AU - Stefanovska, Aneta

PY - 2019/8/23

Y1 - 2019/8/23

N2 - Biological systems are never isolated, usually oscillatory, and invariably subject to noise and fluctuations that may be of either internal or external origin. We present a methodological framework for studying the deterministic interactions of biological oscillatory systems, while at the same time decomposing and evaluating the noise strength. Based on dynamical Bayesian inference, the method models coupled phase oscillators in the presence of dynamical noise. We demonstrate first the potential and the precision of the method on a predefined numerical system. Then we illustrate its usefulness in detecting how the noisestrengths from three human physiological systems – the heart, the lungs and the brain – are affected by general anæsthesia. The results demonstrate the potential of the method for detecting and quantifying noise from biological dynamical systems, quite generally.

AB - Biological systems are never isolated, usually oscillatory, and invariably subject to noise and fluctuations that may be of either internal or external origin. We present a methodological framework for studying the deterministic interactions of biological oscillatory systems, while at the same time decomposing and evaluating the noise strength. Based on dynamical Bayesian inference, the method models coupled phase oscillators in the presence of dynamical noise. We demonstrate first the potential and the precision of the method on a predefined numerical system. Then we illustrate its usefulness in detecting how the noisestrengths from three human physiological systems – the heart, the lungs and the brain – are affected by general anæsthesia. The results demonstrate the potential of the method for detecting and quantifying noise from biological dynamical systems, quite generally.

KW - dynamical Bayesian inference

KW - noise

KW - interactions

KW - coupling functions

KW - biological systems

M3 - Conference paper

T2 - 25th International Conference on Noise and Fluctuations

Y2 - 18 June 2019 through 21 June 2019

ER -