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Mathematical Modeling of Cancer Stem...

Youssefpour, Hamed.

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Mathematical Modeling of Cancer Stem Cells and Therapeutic Intervention Methods.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Mathematical Modeling of Cancer Stem Cells and Therapeutic Intervention Methods./
Author:	Youssefpour, Hamed.
Description:	152 p.
Notes:	Source: Dissertation Abstracts International, Volume: 74-07(E), Section: B.
Contained By:	Dissertation Abstracts International74-07B(E).
Subject:	Applied Mathematics. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3557204
ISBN:	9781303001093

Mathematical Modeling of Cancer Stem Cells and Therapeutic Intervention Methods.
Youssefpour, Hamed.

Mathematical Modeling of Cancer Stem Cells and Therapeutic Intervention Methods. - 152 p.

Source: Dissertation Abstracts International, Volume: 74-07(E), Section: B.

Thesis (Ph.D.)--University of California, Irvine, 2013.

We develop a multispecies continuum model to simulate the spatiotemporal dynamics of cell lineages in solid tumors is discussed. The model accounts for protein signaling factors produced by cells in lineages, and nutrients supplied by the microenvironment. We find that the combination therapy involving differentiation promoters and radiotherapy is very effective in eradicating such a tumor. We investigate the effect of production of various feedback factors by healthy tissue on tumor morphologies. Our simulation results show that the larger production rate of the negative feedback factor by healthy tissue surrounding the tumor, in general lead to smaller, more compact and more circular tumor shapes. However, the increase in the concentration of these feedback factors may have non-monotone effect on the tumor morphologies. We investigate the effect of initial shape on therapy effectiveness. The results from the simulations show that the initial tumor geometry might play an important role in tumor prognostic and the effectiveness of a specific treatment. We observe that the therapy is more effective on tumors that still respond to the signals received from the healthy tissue in comparison with the ones that do not respond to signaling factors (in this case differentiation signals) by stromal tissue or healthy tissue surrounding the tumor. It is shown that the tumors with larger shape factors and smaller areas (more elongated and thinner) respond better to treatment, and the combination therapy is more successful on tumors with such characteristics. We applied mathematical modeling of radiotherapy using experimental data provided from our collaborative work with radiational oncology department of University of California, Los Angeles. Our investigations show that in order to match the experimental results with the simulations, the dedifferentiation rate of non-stem cells should be increased as a function of radiation dose. It is also observed that the population of induced stem cells followed such exponential relationship with respect to therapy dose. The results from simulations and the analysis of the equations suggest that in order for the simulation results to match with the experimental data, the original stem cells and the induced stem cells may undergo direct differentiation.

ISBN: 9781303001093Subjects--Topical Terms:

1669109
Applied Mathematics.

Mathematical Modeling of Cancer Stem Cells and Therapeutic Intervention Methods.
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100 1 $a Youssefpour, Hamed. $3 2099925
245 1 0 $a Mathematical Modeling of Cancer Stem Cells and Therapeutic Intervention Methods.
300 $a 152 p.
500 $a Source: Dissertation Abstracts International, Volume: 74-07(E), Section: B.
500 $a Adviser: John S. Lowengrub.
502 $a Thesis (Ph.D.)--University of California, Irvine, 2013.
520 $a We develop a multispecies continuum model to simulate the spatiotemporal dynamics of cell lineages in solid tumors is discussed. The model accounts for protein signaling factors produced by cells in lineages, and nutrients supplied by the microenvironment. We find that the combination therapy involving differentiation promoters and radiotherapy is very effective in eradicating such a tumor. We investigate the effect of production of various feedback factors by healthy tissue on tumor morphologies. Our simulation results show that the larger production rate of the negative feedback factor by healthy tissue surrounding the tumor, in general lead to smaller, more compact and more circular tumor shapes. However, the increase in the concentration of these feedback factors may have non-monotone effect on the tumor morphologies. We investigate the effect of initial shape on therapy effectiveness. The results from the simulations show that the initial tumor geometry might play an important role in tumor prognostic and the effectiveness of a specific treatment. We observe that the therapy is more effective on tumors that still respond to the signals received from the healthy tissue in comparison with the ones that do not respond to signaling factors (in this case differentiation signals) by stromal tissue or healthy tissue surrounding the tumor. It is shown that the tumors with larger shape factors and smaller areas (more elongated and thinner) respond better to treatment, and the combination therapy is more successful on tumors with such characteristics. We applied mathematical modeling of radiotherapy using experimental data provided from our collaborative work with radiational oncology department of University of California, Los Angeles. Our investigations show that in order to match the experimental results with the simulations, the dedifferentiation rate of non-stem cells should be increased as a function of radiation dose. It is also observed that the population of induced stem cells followed such exponential relationship with respect to therapy dose. The results from simulations and the analysis of the equations suggest that in order for the simulation results to match with the experimental data, the original stem cells and the induced stem cells may undergo direct differentiation.
590 $a School code: 0030.
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650 4 $a Engineering, Biomedical. $3 1017684
650 4 $a Biology, Cell. $3 1017686
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