Statistical Inference on Chemotherapy Effects from Flow Cytometry Data- Our preliminary studies have resulted in a stochastic model of clonal growth of oligodendrocyte progenitor cells in vitro. The model has been applied to several sets of experimental data and validated in independent time-lapse experiments. New estimation techniques built on this model allow analysis of the underlying processes of cell proliferation and differentiation in terms of biologically meaningful parameters. However, the ability of practitioners to benefit from the proposed methodology is limited because clonal analyses are very laborious and difficult to conduct in an automated fashion. By contrast, in vitro experiments with DNA, protein, or cell surface markers are readily amenable to automatization by resorting to flow cytometry. Some labeling techniques (BrdU) provide the needed information on the structure of cell cycle under in vivo conditions. These practical considerations motivate an in-depth study of cell kinetics using flow cytometry data.
The specific aims of this project are: (1) To develop a stochastic framework for modeling cell kinetics during flow cytometry experiments;(2) To design new methods of statistical inference for the quantitative analysis of flow cytometry data by building on the proposed stochastic models;(3) To validate the proposed methods in specially designed biological experiments;(4) To assess the utility of these methods to study effects of chemotherapeutic drugs on normal and neoplastic tissues. Specific applications will be focused on responses of oligodendrocyte precursors the myelin-forming cells of the central nervous system - and leukemic progenitor cells to chemotherapeutic drugs used for leukemia treatment.
This project is concerned with the effects of chemotherapy on normal and neoplastic tissues, which has obvious clinical and public health implications for oncology in general and for leukemia treatment in particular.
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