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

SUBMITTER: Ma C 

PROVIDER: S-EPMC12707676 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature