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Robust calibration of hierarchical population models for heterogeneous cell populations.
J. Theor. Biol. 488:110118 (2020)
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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Publication type
Article: Journal article
Document type
Scientific Article
Keywords
Single-cell Data ; Heterogeneity ; Population Model ; Skew Normal Distribution ; Student's T Distribution ; Mechanistic Modeling ; Dynamic Modeling; Parameter-estimation; Distributions; Noise
Language
english
Publication Year
2020
Prepublished in Year
2019
HGF-reported in Year
2019
ISSN (print) / ISBN
0022-5193
e-ISSN
1095-8541
Journal
Journal of Theoretical Biology
Quellenangaben
Volume: 488,
Article Number: 110118
Publisher
Elsevier
Publishing Place
24-28 Oval Rd, London Nw1 7dx, England
Reviewing status
Peer reviewed
Institute(s)
Institute of Computational Biology (ICB)
POF-Topic(s)
30205 - Bioengineering and Digital Health
Research field(s)
Enabling and Novel Technologies
PSP Element(s)
G-553800-001
WOS ID
WOS:000512482500004
Scopus ID
85077344258
Erfassungsdatum
2020-01-22