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MiS Preprint
58/2004
Modelling the effect of deregulated proliferation and apoptosis on the growth dynamics of epithelial cell populations in vitro
Jörg Galle, Markus Loeffler and Dirk Drasdo
Abstract
We present a 3d individual cell based, biophysical model to study the effect of normal and malfunctioning growth regulation and control on the spatial-temporal organization of growing cell populations in vitro. The model includes explicit representations of typical epithelial cell growth regulation and control mechanisms, namely (i) a cell-cell contact mediated form of growth inhibition, (ii) cell-substrate contact dependent cell-cycle arrest, and (iii) cell-substrate contact dependent programmed cell death (anoikis). The model cells are characterized by experimentally accessible biomechanical and cell-biological parameters.
First, we study by variation of these cell-specific parameters which of them affect the macroscopic morphology and growth kinetics of a cell population within the initial expanding phase. Second, we apply selective knock-outs of growth regulation and control mechanisms to investigate how the different mechanisms collectively act together. Thereby our simulation studies cover the growth behaviour of epithelial cell populations ranging from undifferentiated stem cell populations via transformed variants up to tumour cell lines in vitro. We find that the cell-specific parameters, and in particular the strength of the cell-substrate anchorage, have a significant impact on the population morphology. Furthermore they control the efficacy of the growth regulation and control mechanisms and consequently tune the transition from controlled to uncontrolled growth induced by failures of these mechanisms. In contrast we find that the qualitative and quantitative growth kinetics is remarkably robust against variations of cell-specific parameters. We compare our simulation results with experimental findings on a number of epithelial and tumour cell populations and suggest in vitro experiments to test our model predictions.