Variability in the timing of G(1)/S transition

DNA-structured mathematical model of cell-cycle progression in cyclic hypoxia

New experimental data have shown how the periodic exposure of cells to low oxygen levels (i.e., cyclic hypoxia) impacts their progress through the cell-cycle. Cyclic hypoxia has been detected in tumours and linked to poor prognosis and treatment failure. While fluctuating oxygen environments can be reproduced in vitro, the range of oxygen cycles that can be tested is limited. By contrast, mathematical models can be used to predict the response to a wide range of cyclic dynamics. Accordingly, in this paper we develop a mechanistic model of the cell-cycle that can be combined with in vitro experiments, to better understand the link between cyclic hypoxia and cell-cycle dysregulation. A distinguishing feature of our model is the inclusion of impaired DNA synthesis and cell-cycle arrest due to periodic exposure to severely low oxygen levels. Our model decomposes the cell population into five compartments and a time-dependent delay accounts for the variability in the duration of the S phase which increases in severe hypoxia due to reduced rates of DNA synthesis. We calibrate our model against experimental data and show that it recapitulates the observed cell-cycle dynamics. We use the calibrated model to investigate the response of cells to oxygen cycles not yet tested experimentally. When the re-oxygenation phase is sufficiently long, our model predicts that cyclic hypoxia simply slows cell proliferation since cells spend more time in the S phase. On the contrary, cycles with short periods of re-oxygenation are predicted to lead to inhibition of proliferation, with cells arresting from the cell-cycle in the G2 phase. While model predictions on short time scales (about a day) are fairly accurate (i.e, confidence intervals are small), the predictions become more uncertain over longer periods. Hence, we use our model to inform experimental design that can lead to improved model parameter estimates and validate model predictions.

Using a mammalian cell cycle simulation to interpret differential kinase inhibition in anti-tumour pharmaceutical development

Systems biology needs to show practical relevance to commercial biological challenges such as those of pharmaceutical development. The aim of this work is to design and validate some applications in anti-cancer therapeutic development. The test system was a group of novel cyclin-dependent kinase (CDK) inhibitors synthesised by Cyclacel Ltd. The measured in vitro IC50s of each compound were used as input data to a proprietary cell cycle model developed by Physiomics plc. The model was able to predict over three orders of magnitude the cytotoxicity of each compound without model adaptation to specific cancer cell types. This pattern matched the experimentally determined data. One class of compounds was predicted to cause an increase of the cell cycle length with a non-linear dose-response curve. Further work will use apoptosis and DNA replication simulations to look at overall cell effects.