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Abstract:
Pancreatic cancer has a very high mortality rate and today available treatments show only limited effects. Since pancreatic cancer cells also have a high heterogeneity, this leads to the question, whether the treatments have different effects on distinct cell types and what the impact of the treatment dose is. In this project, confluence time series data of Panc1 and Panc89 pancreatic cancer cell lines are investigated, in order to estimate the birth and death rates in dependence on the cell type and increasing doses of the chemotherapeutic drug Gemcitabine. For this purpose, a logistic growth model, which is able to distinguish between birth and death rate, will be introduced. Considering the mathematical background of Bayesian inference, allows to define a model in the probabilistic programming language Stan, to evaluate its convergence and hence to infer posterior distributions for the birth and death rates. Comparing the distributions for birth and death rates of various cell lines and increasing
drug concentrations enables to draw conclusions on the effect of the drug dose and to derive a cytostatic or cytotoxic effect on Panc1 and Panc89 cell lines. The extended understanding of the treatment effect may help to improve future therapies.
