Speaker
Description
The mitotic cell cycle governs DNA replication and cell division. Efficacy of radiotherapy depends on cell-cycle position and so accurate mathematical models of the cell cycle are essential for understanding and predicting treatment response. Mathematical modellers often face a lack of available, sufficiently resolved data for parametrising models. We consider how the ability to collate summary data across the literature affects identifiability of parameters for a cell cycle model.
Initially synchronised cell populations desynchronise over successive cycles, converging to balanced exponential growth (BEG), characterised by exponential population growth and steady, time-independent phase proportions. These proportions can be obtained from flow cytometry data. The increasing use of the Fluorescent Ubiquitination-based Cell Cycle Indicator (FUCCI) provides higher-resolution information on phase dynamics, such as minimum phase durations and variability.
We present an age-structured PDE model in which cell-cycle phase progression follows a delayed gamma distribution. We derive analytical expressions for BEG phase proportions and other observable quantities, and use them to assess how data availability influences parameter identifiability. When parameters are not uniquely identifiable, we determine identifiable parameter groupings, thereby determining the minimum amount of data that must be available for successfully fitting structured population models of the cell cycle.
