Robust calibration of hierarchical population models for heterogeneous cell populations

JOURNAL OF THEORETICAL BIOLOGY

卷:488

Article

Loos, Carolin^1,2  Hasenauer, Jan^1,2,3 

[1] Helmholtz Zentrum Munchen German Res Ctr Environm, Inst Computat Biol, D-85764 Neuherberg, Germany

[2] Tech Univ Munich, Ctr Math, Chair Math Modeling Biol Syst, D-85748 Garching, Germany

[3] Univ Bonn, Fac Math & Nat Sci, D-53115 Bonn, Germany

关键词: Single-cell data;    Heterogeneity;    Population model;    Skew normal distribution;    Student's t distribution;    Mechanistic Modeling;    Dynamic Modeling;   

DOI  :  10.1016/j.jtbi.2019.110118

来源: Elsevier

【 摘 要 】

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.

【 授权许可】

Free