Speaker
Description
In vitro experiments are an important tool in cancer research to understand the growth of cancer cells and their response to chemotherapeutic treatment. Mathematical models facilitate the analysis of the obtained experimental data and can increase our understanding of cancer cell dynamics.
Using confluence time series capturing the in vitro growth of pancreatic cancer cell lines treated with different doses of Gemcitabine, we have defined a system of ordinary differential equations to describe the logistic growth over time.
Distinguishing cell growth into birth and death processes and applying Bayesian inference, we have estimated the birth and death rates and their variability across different cell lines and drug concentrations.
We derived dose-response curves for the birth and death rates and found that Gemcitabine has a mainly cytostatic effect on Panc1 Parental cell lines, while it acts both cytostatic and cytotoxic in Panc89 Parental cell lines.
Our mathematical approach thus helps to analyze the experimental data, widens the understanding of how chemotherapeutic drugs affect the growth of different cancer cell lines, and also deepens our knowledge of the role of drug concentration.
