Abstract
BackgroundMost 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.ResultsIn 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.ConclusionsThe 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.
Highlights
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”
The main goals of research in systems biology are the development of quantitative models of intracellular pathways and the development of tools to support the modeling process
In order to understand the dynamical behavior of heterogeneous cell populations, it is crucial to develop cell population models, describing the whole population and a single individual [1,2,3,4]
Summary
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”. Thereby, most of the available methods and models consider only a single “typical cell” whereas most experimental data used to calibrate the models are obtained using cell population experiments, e.g. western blotting. This yields problems in particular if the studied population shows a large cell-to-cell variability. In order to understand the dynamical behavior of heterogeneous cell populations, it is crucial to develop cell population models, describing the whole population and a single individual [1,2,3,4] This has already been realized by several authors, and it has been shown that stochasticity in biochemical
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