Identification of models of heterogeneous cell populations from population snapshot data

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

Oncogenic Stress Induced by Acute Hyper-Activation of Bcr-Abl Leads to Cell Death upon Induction of Excessive Aerobic Glycolysis

In response to deregulated oncogene activation, mammalian cells activate disposal programs such as programmed cell death. To investigate the mechanisms behind this oncogenic stress response we used Bcr-Abl over-expressing cells cultivated in presence of imatinib. Imatinib deprivation led to rapid induction of Bcr-Abl activity and over-stimulation of PI3K/Akt-, Ras/MAPK-, and JAK/STAT pathways. This resulted in a delayed necrosis-like cell death starting not before 48 hours after imatinib withdrawal. Cell death was preceded by enhanced glycolysis, glutaminolysis, and amino acid metabolism leading to elevated ATP and protein levels. This enhanced metabolism could be linked to induction of cell death as inhibition of glycolysis or glutaminolysis was sufficient to sustain cell viability. Therefore, these data provide first evidence that metabolic changes induced by Bcr-Abl hyper-activation are important mediators of oncogenic stress-induced cell death