Detecting limit cycles in stochastic time series
Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt
The emergence of oscillatory behaviour represents fundamental information about the interactions of the underlying system. In biological systems, oscillations have been observed in experimental data, but due to the significant level of noise, it is difficult to characterize whether observed dynamics based on time series, are truly limit cycles. Here, we present a simple three step method to identify the presence of limit cycles in stochastic systems. Considering input from one-dimensional time series, as are typically obtained in experiments, we propose statistical measures to detect the existence of limit cycles. This is tested on models from chemical networks, and we investigate how the underlying dynamics can be separated depending on the noise level and length of the series.
Originalsprog | Engelsk |
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Artikelnummer | 127917 |
Tidsskrift | Physica A: Statistical Mechanics and its Applications |
Vol/bind | 605 |
Antal sider | 10 |
ISSN | 0378-4371 |
DOI | |
Status | Udgivet - 17 feb. 2022 |
Bibliografisk note
Funding Information:
We are grateful to Namiko Mitarai for interesting discussions on the validity of the theoretical assumptions. All authors acknowledge support from the Danish Council for Independent Research and Danish National Research Foundation through StemPhys Center of Excellence , grant number DNRF116 . MLH acknowledge funding from the Lundbeck Foundation grant R347-2020-2250 . MHJ acknowledges support from the Independent Research Fund Denmark grant number 9040-00116B .
Publisher Copyright:
© 2022
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