Stochastic population models: Theory and applications in Cancer Research




Stochastic population models include classical processes of population genetics such as Wright-Fisher or Moran model, models of proliferation such as branching and birth and death processes, as well more involved Markov chains, diffusion and spatial models. Application of such models in cancer research has been facilitated by a recent explosion of DNA sequencing and other molecular-level techniques. We will address both methodological mathematical problems such as identifiability of partly observed birth and death processes (Brandon Legried) and dynamics of countable-type branching processes describing a range of selection modes in proliferating cell populations (Ren-Yi Wang), as well as more more applied issues. One problem of current importance is estimation of dynamics of mutant clones evolving in healthy bone marrow over lifetime. This so-called Clonal Hematopoiesis (CH) is implicated in transition to leukemias and other adverse health outcomes (Xiaochen Long). Another issue is retrospective estimation of growth and mutation rates in surgically excised tumors, which may have prognostic importance (Marek Kimmel). These two talks will be illustrated with biological data.

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