Modeling and parameter estimation for heterogeneous cell populations
Jan Hasenauer
Cite this publication as
Jan Hasenauer, Modeling and parameter estimation for heterogeneous cell populations (2013), Logos Verlag, Berlin, ISBN: 9783832591571
Description / Abstract
Most of the modeling performed in biology aims at achieving a quantitative description and understanding of the intracellular signaling pathways within a ``typical cell''. However, in many biologically important situations even genetically identical cell populations show a heterogeneous response. This means that individual members of the cell population behave differently. Such situations require the study of cell-to-cell variability and the development of models for heterogeneous cell populations.
The main contribution of this thesis is the development of unifying modeling frameworks for signal transduction and proliferation processes in heterogeneous cell populations. These modeling frameworks allow for the detailed description of individual cells as well as differences between them. In contrast to many existing modeling approaches, the proposed frameworks allow for a direct comparison of model predictions with available data. Beyond this, the proposed population models can be simulated efficiently and, by exploiting the model structures, we are able to develop model-tailored Bayesian parameter estimation methods. These methods enable the calculation of the optimal parameter estimates, as well as the evaluation of the parameter and prediction uncertainties.
The proposed tools allow for novel insights in population dynamics, in particular the model-based characterization of population heterogeneity and cellular subgroups. This is illustrated for two different application examples: pro- and anti-apoptotic signaling, which is interesting in the context of cancer therapy, and immune cell proliferation.
The main contribution of this thesis is the development of unifying modeling frameworks for signal transduction and proliferation processes in heterogeneous cell populations. These modeling frameworks allow for the detailed description of individual cells as well as differences between them. In contrast to many existing modeling approaches, the proposed frameworks allow for a direct comparison of model predictions with available data. Beyond this, the proposed population models can be simulated efficiently and, by exploiting the model structures, we are able to develop model-tailored Bayesian parameter estimation methods. These methods enable the calculation of the optimal parameter estimates, as well as the evaluation of the parameter and prediction uncertainties.
The proposed tools allow for novel insights in population dynamics, in particular the model-based characterization of population heterogeneity and cellular subgroups. This is illustrated for two different application examples: pro- and anti-apoptotic signaling, which is interesting in the context of cancer therapy, and immune cell proliferation.
Table of content
- BEGINN
- 1 Introduction
- 1.1 Research motivation
- 1.2 Research topic overview
- 1.3 Contribution of this thesis
- 1.4 Outline of this thesis
- 2 Background
- 2.1 Single cells models
- 2.2 Cell population models
- 2.3 Bayesian parameter estimation in a nutshell
- 3 Signal transduction in heterogeneous cell populations
- 3.1 Introduction and problem statement
- 3.2 Modeling of signal transduction in heterogeneous cell populations
- 3.3 Bayesian estimation and uncertainty analysis of population heterogeneity
- 3.4 Example: Apoptotic signaling in cancer cell populations
- 3.5 Summary and discussion
- 4 Proliferation of heterogeneous cell populations
- 4.1 Introduction and problem statement
- 4.2 Modeling the proliferation of heterogeneous cell populations
- 4.3 Bayesian estimation and uncertainty analysis of proliferation dynamics
- 4.4 Application example: Proliferation of T lymphocytes
- 4.5 Summary and discussion
- 5 Conclusion
- 5.1 Summary and conclusions
- 5.2 Outlook
- Appendix