title: Identification of models of heterogeneous cell populations from population snapshot data.
abstract: 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.
authors.affiliation: Institute for Systems Theory and Automatic Control, University of Stuttgart, Germany. Jan.Hasenauer@ist.uni-stuttgart.de
authors.forename: Jan
authors.initials: J
authors.lastname: Hasenauer
authors.forename: Steffen
authors.initials: S
authors.lastname: Waldherr
authors.forename: Malgorzata
authors.initials: M
authors.lastname: Doszczak
authors.forename: Nicole
authors.initials: N
authors.lastname: Radde
authors.forename: Peter
authors.initials: P
authors.lastname: Scheurich
authors.forename: Frank
authors.initials: F
authors.lastname: Allgöwer
mesh_terms: D001499:Bayes Theorem; D003584:Cytological Techniques; D008954:Models, Biological; D012044:Regression Analysis; D015398:Signal Transduction; D014409:Tumor Necrosis Factor-alpha / Q000378:metabolism
chemical_list: D014409:Tumor Necrosis Factor-alpha