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Mathematical Models for Ovarian Cancer

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서명/저자사항Mathematical Models for Ovarian Cancer.
개인저자Botesteanu, Dana-Adriana.
단체저자명University of Maryland, College Park. Applied Mathematics and Scientific Computation.
발행사항[S.l.]: University of Maryland, College Park., 2017.
발행사항Ann Arbor: ProQuest Dissertations & Theses, 2017.
형태사항175 p.
기본자료 저록Dissertation Abstracts International 79-07B(E).
Dissertation Abstract International
ISBN9780355628968
학위논문주기Thesis (Ph.D.)--University of Maryland, College Park, 2017.
일반주기 Source: Dissertation Abstracts International, Volume: 79-07(E), Section: B.
Adviser: Doron Levy.
이용제한사항This item is not available from ProQuest Dissertations & Theses.
요약Ovarian cancer is the most fatal cancer of the female reproductive system. High-grade serous ovarian cancer (HGSOC) represent the majority of ovarian cancers and accounts for the largest proportion of deaths from the disease. From a clinical pe
요약Studying the growth, progression, and dynamic response to treatment of ovarian cancers in an integrated systems biology/mathematical framework offers an innovative tool at the disposal of the oncological community to further exploit readily avai
요약As a first step, we developed a mathematical model for a quantitative explanation why transvaginal ultrasound-based (TVU) screening fails to improve low-volume detectability and overall survival (OS) of HGSOC. This mathematical model can accurat
요약At the cell population level, we have quantitatively investigated the role of cell heterogeneity emerging from variations in cell-cycle parameters and cell-death. Many commonly used chemotherapeutic agents in treating ovarian cancers target only
요약At the single cell level, we developed a mathematical model to explain the emerging heterogeneity in individual cancer cell responses to drugs targeting the cell-cycle, which have a broad spectrum of anti-tumor activity in ovarian cancers. This
요약The model incorporates an intrinsic form of heterogeneity via the duration of time single cells spend in mitosis. It uses published single cell in vitro experimental data for calibration. Herein, the goal is to better understand why, within a d
요약Studying the natural history, growth, and progression of ovarian cancers in an integrated systems biology/mathematical framework represents a complementary tool that can be used to provide valuable insights into the treatment of HGSOC.
요약My work focuses on developing and applying quantitative, integrated mathematical modeling frameworks to pre-clinical and clinical data, in order to better understand ovarian cancer dynamics and develop new therapeutics.
일반주제명Applied mathematics.
Oncology.
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