Identification of models of heterogeneous cell populations from population snapshot data

BMC Bioinformatics

Methodology Article

Nicole Radde¹  Jan Hasenauer¹  Frank Allgöwer¹  Steffen Waldherr¹  Malgorzata Doszczak²  Peter Scheurich² 

[1] Institute for Systems Theory and Automatic Control, University of Stuttgart, Germany;Institute of Cell Biology and Immunology, University of Stuttgart, Germany;

关键词: Posterior Probability;    Conditional Probability;    Parameter Density;    Markov Chain Monte Carlo Sampling;    Prediction Uncertainty;   

DOI  :  10.1186/1471-2105-12-125

 received in 2010-09-30, accepted in 2011-04-28,  发布年份 2011

来源: Springer

【 摘 要 】

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.

【 授权许可】

CC BY   
© Hasenauer et al; licensee BioMed Central Ltd. 2011