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020 ▼a 9780438353534
035 ▼a (MiAaPQ)AAI10840589
035 ▼a (MiAaPQ)umn:19465
040 ▼a MiAaPQ ▼c MiAaPQ ▼d 248032
0491 ▼f DP
0820 ▼a 519
1001 ▼a Lindsay, Danika Gray.
24510 ▼a Applications of Evolutionary Modeling to the Study of Drug Resistance in Cancer.
260 ▼a [S.l.] : ▼b University of Minnesota., ▼c 2018
260 1 ▼a Ann Arbor : ▼b ProQuest Dissertations & Theses, ▼c 2018
300 ▼a 140 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 The evolution of resistance to therapy remains a significant challenge to the clinical treatment of cancer. As a tumor evolves, new genetic variants possessing a fitness advantage over normal cancer cells may be produced, thus leading to the dev
520 ▼a In the first project, we develop a stochastic model of a non-small cell lung tumor undergoing treatment with a combination of two drugs. One drug is the current standard therapy used in the clinic to treat this disease, which has proven to be in
520 ▼a The goal of the second project is to understand the impact of different resistance mechanisms on tumor recurrence. We define two separate branching process models to compare the case in which resistance arises via a single gene mutation with the
590 ▼a School code: 0130.
650 4 ▼a Applied mathematics.
690 ▼a 0364
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=T14999739 ▼n KERIS
980 ▼a 201812 ▼f 2019
990 ▼a 관리자 ▼b 관리자