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Robust calibration of hierarchical population models for heterogeneous cell populations.

J. Theor. Biol. 488:110118 (2020)
Postprint DOI
Open Access Green
Cellular heterogeneity is known to have important effects on signal processing and cellular decision making. To understand these processes, multiple classes of mathematical models have been introduced. The hierarchical population model builds a novel class which allows for the mechanistic description of heterogeneity and explicitly takes into account subpopulation structures. However, this model requires a parametric distribution assumption for the cell population and, so far, only the normal distribution has been employed. Here, we incorporate alternative distribution assumptions into the model, assess their robustness against outliers and evaluate their influence on the performance of model calibration in a simulation study and a real-world application example. We found that alternative distributions provide reliable parameter estimates even in the presence of outliers, and can in fact increase the convergence of model calibration. (C) 2019 Elsevier Ltd. All rights reserved.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Schlagwörter Single-cell Data ; Heterogeneity ; Population Model ; Skew Normal Distribution ; Student's T Distribution ; Mechanistic Modeling ; Dynamic Modeling; Parameter-estimation; Distributions; Noise
ISSN (print) / ISBN 0022-5193
e-ISSN 1095-8541
Quellenangaben Band: 488, Heft: , Seiten: , Artikelnummer: 110118 Supplement: ,
Verlag Elsevier
Verlagsort 24-28 Oval Rd, London Nw1 7dx, England
Begutachtungsstatus Peer reviewed