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Combinatorics and Cancer

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It is difficult to follow the evolution of a cancer over large numbers of cell divisions. Instead of using direct measurements, it is possible to infer features of this evolution from indirect measurements such as patterns of mutations in the tumour. I will give an overview of two stochastic processes that arise in understanding tumour heterogeneity, the spatial structure of mutations in tumour cells. Time allowing, I will describe how Approximate Bayesian Computation can be used for inference in this setting. Biological context will be provided!

This talk is part of the Trinity Mathematical Society series.

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