摘要：The endogenous ligand TRAIL induces cell death and constitutes a promising molecule for cancer therapies. However, reasons for TRAIL-insensitivity of various tumor-based cancer cell lines remain unclear. In this paper, we introduce a complex individual-based model that captures the major effects of TRAIL in a heterogeneous cancer cell population. First, we adapted an existing TRAIL-signaling model to recent insights. The improved model was integrated into an established population framework. Next, we included a cell cycle-dependent upregulation of anti-apoptotic signaling proteins, such as Bcl-2. Afterwards, specific model parameters were adapted to fit physiological cell counts and death timing during TRAIL stimulation. With help of the adapted population model, we observed a phenotypical cell cycle-dependence of death kinetics. Cells died on average slightly faster and more efficiently when treated in the first half of the cell cycle. Lastly, we focused on changes in protein distributions during a TRAIL treatment. We predicted the anti-apoptotic protein XIAP and the pro-apoptotic protein Bid to undergo the highest changes on average. Surviving cells exhibited decreased amounts of XIAP whereas synthesis rates of XIAP increased. Initial flow cytometry experiments confirmed the predicted drop of XIAP qualitatively. After TRAIL wash out, XIAP amounts recovered fast, indicating a correct prediction of high synthesis rates. Overall, the developed model represents a versatile tool for gaining holistic insights into TRAIL-based cancer treatments.
