Browsing by Author "Belkhatir, Zehor"

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    Finite-time simultaneous estimation of aortic blood flow and differentiation order for fractional-order arterial Windkessel model calibration
    (Elsevier, 2021-11-02) Bahloul, Mohamed; Benencase, Marcelo; Belkhatir, Zehor; Laleg Kirat, Taous-Meriem
    A fractional-order vascular model representation for emulating arterial hemody-namics has been recently presented as an alternative to the well-known integer-order arterial Windkessel. The model uses a fractional-order capacitor (FOC) to describe the complex and frequency-dependent arterial compliance. This paper presents a two-stage algorithm based on modulating functions for finite-time simultaneous estimation of the model’s input and the fractional differentiation order. The proposed approach is validated using in-silico human data. Results show the prominent potential of this method for calibrating arterial models and enhancing cardiovascular mechanics research as well as clinical practice.
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    Long-term p21 and p53 trends regulate the frequency of mitosis events and cell cycle arrest
    (Preprint, 2021-08-19) Tran, Anh Phong; Tralie, Christopher; Moosmüller, Caroline; Belkhatir, Zehor; Reyes, José; Levine, Arnold; Deasy, Joseph; Tannenbaum, Allen
    Radiation exposure of healthy cells can halt cell cycle temporarily or permanently. In this work, two single cell datasets that monitored the time evolution of p21 and p53, one subjected to gamma irradiation and the other to x-ray irradiation, are analyzed to uncover the dynamics of this process. New insights into the biological mechanisms were found by decomposing the p53 and p21 signals into transient and oscillatory components. Through the use of dynamic time warping on the oscillatory components of the two signals, we found that p21 signaling lags behind its lead signal, p53, by about 3.5 hours with oscillation periods of around 6 hours. Additionally, through various quantification methods, we showed how p21 levels, and to a lesser extent p53 levels, dictate whether the cells are arrested in their cell cycle and how fast these cells divide depending on their long-term trend in these signals.
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    Pan-Cancer Prediction of Cell-Line Drug Sensitivity Using Network-Based Methods
    (MDPI, 2022-01-19) Pouryahya, Maryam; Oh, Jung Hun; Mathews, James; Belkhatir, Zehor; Moosmüller, Caroline; Deasy, Joseph; Tannenbaum, Allen
    The development of reliable predictive models for individual cancer cell lines to identify an optimal cancer drug is a crucial step to accelerate personalized medicine, but vast differences in cancer cell lines and drug characteristics make it quite challenging to develop predictive models that result in high predictive power and explain the similarity of cell lines or drugs. Our study proposes a novel network-based methodology that breaks the problem into smaller, more interpretable problems to improve the predictive power of anti-cancer drug responses in cell lines. For the drug-sensitivity study, we used the GDSC database for 915 cell lines and 200 drugs. The theory of optimal mass transport was first used to separately cluster cell lines and drugs, using gene-expression profiles and extensive cheminformatic drug features, represented in a form of data networks. To predict cell-line specific drug responses, random forest regression modeling was separately performed for each cell-line drug cluster pair. Post-modeling biological analysis was further performed to identify potential biological correlates associated with drug responses. The network-based clustering method resulted in 30 distinct cell-line drug cluster pairs. Predictive modeling on each cell-line-drug cluster outperformed alternative computational methods in predicting drug responses. We found that among the four drugs top-ranked with respect to prediction performance, three targeted the PI3K/mTOR signaling pathway. Predictive modeling on clustered subsets of cell lines and drugs improved the prediction accuracy of cell-line specific drug responses. Post-modeling analysis identified plausible biological processes associated with drug responses.
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    Parameter Sensitivity and Experimental Validation for Fractional-Order Dynamical Modeling of Neurovascular Coupling
    (Cold Spring Harbor Laboratory, 2021-10-20) Alhazmi, Fahd; Belkhatir, Zehor; Bahloul, Mohamed; Laleg-Kirati, Meriem Taous
    Goal: Neurovascular coupling is a fundamental mechanism linking neural activity to cerebral blood flow (CBF) response. Modeling this coupling is very important to understand brain functions, yet challenging due to the complexity of the involved phenomena. One key feature that different studies have reported is the time delay that is inherently present between the neural activity and cerebral blood flow, which has been described by adding a delay parameter in standard models. An alternative approach was recently proposed where the framework of fractional-order modeling is employed to characterize the complex phenomena underlying the neurovascular. Thanks to its nonlocal property, a fractional derivative is suitable for modeling delayed and power-law phenomena. Methods: In this study, we analyzed and validated an effective fractional-order for the effective modeling and characterization of the neurovascular coupling mechanism. To show the added value of the fractional order parameters of the proposed model, we perform a parameter sensitivity analysis of the fractional model compared to its integer counterpart. Moreover, the model was validated using neural activity-CBF data related to both event and block design experiments that were acquired using electrophysiology and laser Doppler flowmetry recordings, respectively. Results: The validation results show the aptitude and flexibility of the fractional-order paradigm in fitting a more comprehensive range of well-shaped CBF response behaviors while maintaining a low model complexity. Comparison with the standard integer-order models shows the added value of the fractional-order parameters in capturing various key determinants of the cerebral hemodynamic response, e.g., post-stimulus undershoot. Conclusions: This investigation authenticates the ability and adaptability of the fractional-order framework to characterize a wider range of wellshaped cerebral blood flow responses while preserving low model complexity through a series of unconstrained and constrained optimizations.
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    Periodicity Scoring of Time Series Encodes Dynamical Behavior of the Tumor Suppressor p53
    (Elsevier, 2021-07-16) Moosmüller, Caroline; Tralie, Christopher; Kooshkbaghi, Mahdi; Belkhatir, Zehor; Pouryahya, Maryam; Reyes, José; Deasy, Joseph; Tannenbaum, Allen; Kevrekidis, Ioannis
    In 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.