Challenges in the Integration of Flow Cytometry and Time-Lapse Live Cell Imaging Data Using a Cell Proliferation Model

Multicellular systems are currently studied both in vitro and in vivo using different platforms, providing high throughput data of different types. Mathematical modelling is now called to interpret this reality and has to face more and more with quantitative data. This requires a connection between the basic theoretical model and the data structures, taking account of the processes of measure. Working on the response to anticancer treatment, we considered the data provided by flow cytometry (FC) and time-lapse live cell imaging (TL) in time-course experiments in vitro with untreated and treated cell populations. We created a flexible cell cycle simulator including subsequent cell generations to achieve a full reconstruction in silico of the cell cycle progression under a variety of treatment effects. Unperturbed growth was modelled taking into account intercellular variability of G1,S and G2M transit times, quiescent cells and natural cell loss. The effect of treatment was modelled by “perturbation modules” associated to each cell cycle phase and cell generation, containing a submodel of the checkpoint activity in that phase. Upon input of a set of parameters associated to unperturbed growth and perturbation modules, the program reproduced the time course of cell cycling through subsequent generations, providing outputs comparable with both TL and FC measures. The challenges to fit the data of specific experiments were discussed, indicating a feasible procedure for model building and identification. This lead to a dynamic rendering of proliferation midway between the macroscopic data level and the underlying molecular processes.