Periodicity Scoring of Time Series Encodes Dynamical Behavior of the Tumor Suppressor p53

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dc.cclicenceCC-BY-NC-NDen
dc.contributor.authorMoosmüller, Caroline
dc.contributor.authorTralie, Christopher
dc.contributor.authorKooshkbaghi, Mahdi
dc.contributor.authorBelkhatir, Zehor
dc.contributor.authorPouryahya, Maryam
dc.contributor.authorReyes, José
dc.contributor.authorDeasy, Joseph
dc.contributor.authorTannenbaum, Allen
dc.contributor.authorKevrekidis, Ioannis
dc.date.acceptance2021-07-01
dc.date.accessioned2021-09-01T10:03:10Z
dc.date.available2021-09-01T10:03:10Z
dc.date.issued2021-07-16
dc.description.abstractIn this paper we analyze the dynamical behavior of the tumor suppressor protein p53, an essential player in the cellular stress response, which prevents a cell from dividing if severe DNA damage is present. When this response system is malfunctioning, e.g. due to mutations in p53, uncontrolled cell proliferation may lead to the development of cancer. Understanding the behavior of p53 is thus crucial to prevent its failing. It has been shown in various experiments that periodicity of the p53 signal is one of the main descriptors of its dynamics, and that its pulsing behavior (regular vs. spontaneous) indicates the level and type of cellular stress. In the present work, we introduce an algorithm to score the local periodicity of a given time series (such as the p53 signal), which we call Detrended Autocorrelation Periodicity Scoring (DAPS). It applies pitch detection (via autocorrelation) on sliding windows of the entire time series to describe the overall periodicity by a distribution of localized pitch scores. We apply DAPS to the p53 time series obtained from single cell experiments and establish a correlation between the periodicity scoring of a cell’s p53 signal and the number of cell division events. In particular, we show that high periodicity scoring of p53 is correlated to a low number of cell divisions and vice versa. We show similar results with a more computationally intensive state-of-the-art periodicity scoring algorithm based on topology known as Sw1PerS. This correlation has two major implications: It demonstrates that periodicity scoring of the p53 signal is a good descriptor for cellular stress, and it connects the high variability of p53 periodicity observed in cell populations to the variability in the number of cell division events.en
dc.funderNo external funderen
dc.identifier.citationMoosmüller, C. et al. (2021) Periodicity Scoring of Time Series Encodes Dynamical Behavior of the Tumor Suppressor p53, IFAC-PapersOnLine, 54 (9), pp. 488-495en
dc.identifier.doihttps://doi.org/10.1016/j.ifacol.2021.06.106
dc.identifier.urihttps://dora.dmu.ac.uk/handle/2086/21215
dc.language.isoen_USen
dc.publisherElsevieren
dc.subjectSystems biologyen
dc.subjectcellular systemsen
dc.subjecttime series modelingen
dc.subjectbioinformaticsen
dc.subjectcomputational biologyen
dc.titlePeriodicity Scoring of Time Series Encodes Dynamical Behavior of the Tumor Suppressor p53en
dc.typeConferenceen

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