An individual-based simulation framework for dynamic, heterogeneous cell populations during extrinsic stimulations
Authored by Dirke Imig, Nadine Pollak, Timm Strecker, Peter Scheurich, Frank Allgoewer, Steffen Waldherr
Date Published: 2015
DOI: 10.1166/jcsmd.2015.1072
Sponsors:
German Federal Ministry of Education and Research (BMBF)
German Research Foundation (Deutsche Forschungsgemeinschaft, DFG)
Platforms:
Python
Model Documentation:
Other Narrative
Flow charts
Mathematical description
Model Code URLs:
Model code not found
Abstract
As experimental measurements consider cell populations rather than
individual cells and because of the awarness of the role of
heterogeneity in cellular systems, mathematical modeling of populations
became a systems biological focus in recent years. When investigating
the response behavior of cells to extrinsic stimulations, processes on
the level of the population such as directional selection and
inheritance must be taken into account. In this work, an
individual-based modeling framework for the efficient analysis and
simulation of heterogeneous cell populations is presented. With help of
prior knowledge about stationary population characteristics, initial
conditions of model states and parameters are derived. As cell division, cell death and inheritance are considered realistically, short-term as
well as long-term responses of cell populations can be analyzed and
predicted. The model provides information about population dynamics and, furthermore, about states in individual cells, also with respect to
different cell cycle phases. The framework is applied to the
biologically relevant scenarios of ligand-induced apoptosis and cell
differentiation. Within short simulation times, a representative number
of cells can be simulated so that a comprehensive analysis of the model
is possible. For future applications, other single cell models of
interest can easily be included into the population framework, allowing
the investigation of effects caused by cell heterogeneity.
Tags
differentiation
Model
Origins
Variability
Gene-expression
Identification
Bistability
Single
cells
Caspase activation
Cd95-induced apoptosis