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Autor(en): Hasenauer, Jan
Waldherr, Steffen
Doszczak, Malgorzata
Radde, Nicole
Scheurich, Peter
Allgöwer, Frank
Titel: Identification of models of heterogeneous cell populations from population snapshot data
Erscheinungsdatum: 2011
Dokumentart: Zeitschriftenartikel
Erschienen in: BMC bioinformatics 12 (2011), Nr. 125. URL
Zusammenfassung: Background: Most of the modeling performed in the area of systems biology aims at achieving a quantitative description of the intracellular pathways within a "typical cell". However, in many biologically important situations even clonal cell populations can show a heterogeneous response. These situations require study of cell-to-cell variability and the development of models for heterogeneous cell populations. Results: In this paper we consider cell populations in which the dynamics of every single cell is captured by a parameter dependent differential equation. Differences among cells are modeled by differences in parameters which are subject to a probability density. A novel Bayesian approach is presented to infer this probability density from population snapshot data, such as flow cytometric analysis, which do not provide single cell time series data. The presented approach can deal with sparse and noisy measurement data. Furthermore, it is appealing from an application point of view as in contrast to other methods the uncertainty of the resulting parameter distribution can directly be assessed. Conclusions: The proposed method is evaluated using artificial experimental data from a model of the tumor necrosis factor signaling network. We demonstrate that the methods are computationally efficient and yield good estimation result even for sparse data sets.
Enthalten in den Sammlungen:04 Fakultät Energie-, Verfahrens- und Biotechnik

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