Probabilistic modeling of cell cycle dynamics in response to cell cycle targeting chemotherapy drugs to guide treatment strategies.

Abstract

Understanding how chemotherapy perturbs cell cycle dynamics is critical for advancing cancer treatment. We develop a probabilistic, multi-generational framework based on a Bellman-Harris branching process to quantify treatment-induced shifts in tumor cell dynamics. The model incorporates key drug-responsive behaviors, including checkpoint activation, apoptosis, and checkpoint adaptation that propagates inherited DNA damage, enabling the characterization of heterogeneous survival outcomes after treatment. Biological parameters map directly onto DNA repair fidelity and cell-fate decisions, providing mechanistic insights beyond what is accessible from experiments alone. Dose-dependent extensions further allow exploration of treatment-induced perturbations. Model parameters were calibrated to empirical cell cycle measurements using the robust adaptive Metropolis algorithm. Global sensitivity analysis shows that scale parameters governing unfaithful DNA repair under G2/M- and S phase-specific agents exert major influence on model predictions, particularly at later time points. Across three chemotherapies, the framework reveals consistent dose-dependent alterations in cell cycle dynamics, with higher doses driving pronounced disruptions. Together, these results demonstrate how model-informed analyses can provide quantitative insight into treatment-induced cell cycle perturbations and support the refinement of therapeutic strategies.