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020 ▼a 9780438351264
035 ▼a (MiAaPQ)AAI10830016
035 ▼a (MiAaPQ)umn:19356
040 ▼a MiAaPQ ▼c MiAaPQ ▼d 248032
0820 ▼a 519
1001 ▼a Storey, Kathleen M.
24510 ▼a Stochastic Models of Epithelial Cancer Initiation and Glioblastoma Recurrence.
260 ▼a [S.l.] : ▼b University of Minnesota., ▼c 2018
260 1 ▼a Ann Arbor : ▼b ProQuest Dissertations & Theses, ▼c 2018
300 ▼a 167 p.
500 ▼a Source: Dissertation Abstracts International, Volume: 80-01(E), Section: B.
500 ▼a Adviser: Jasmine Foo.
5021 ▼a Thesis (Ph.D.)--University of Minnesota, 2018.
520 ▼a Cancer development involves the inherently stochastic accumulation of genetic mutations, conferring growth advantages to the cells affected by these mutations. Thus, stochastic modeling provides useful insight when studying the evolutionary proc
520 ▼a First we explore the temporal dynamics of spatial heterogeneity during the process of carcinogenesis from healthy tissue. We utilize a spatial stochastic model of mutation accumulation and clonal expansion to describe this process. Under a two-s
520 ▼a Next we study the propagation speed of a premalignant clone during carcinogenesis. We approximate a premalignant clone in epithelial tissue containing w layers of proliferating cells (referred to as a ``basal zone'') with a biased voter model o
520 ▼a Finally we develop a multi-type branching process model of the tumor progression and treatment response in glioblastoma multiforme (GBM). GBM recurrence is often attributed to acquired resistance to the standard chemotherapeutic agent temozolomi
590 ▼a School code: 0130.
650 4 ▼a Applied mathematics.
650 4 ▼a Biology.
690 ▼a 0364
690 ▼a 0306
71020 ▼a University of Minnesota. ▼b Mathematics.
7730 ▼t Dissertation Abstracts International ▼g 80-01B(E).
773 ▼t Dissertation Abstract International
790 ▼a 0130
791 ▼a Ph.D.
792 ▼a 2018
793 ▼a English
85640 ▼u http://www.riss.kr/pdu/ddodLink.do?id=T14999380 ▼n KERIS
980 ▼a 201812 ▼f 2019
990 ▼a 관리자