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  1. NTU Theses and Dissertations Repository
  2. 理學院
  3. 物理學系
請用此 Handle URI 來引用此文件: http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/6709
完整後設資料紀錄
DC 欄位值語言
dc.contributor.advisor陳義裕(Yih-Yuh Chen),龐寧寧(Ning-Ning Pang)
dc.contributor.authorGeng-Ming Huen
dc.contributor.author胡耿銘zh_TW
dc.date.accessioned2021-05-17T09:16:41Z-
dc.date.available2012-08-01
dc.date.available2021-05-17T09:16:41Z-
dc.date.copyright2012-08-01
dc.date.issued2012
dc.date.submitted2012-07-31
dc.identifier.citation[1] Chang, H. H., Hemberg, M., Barahona, M., Ingber, D. E., Huang, S.(2008) Transcriptome-wide noise controls lineage choice in mammalian progenitor cells. Nature, 453 , 544-548.
[2] Sui Hung, Ingemar Ernberg, and Stuart Kauffman. (2009) Cancer attractors: A systems view of tumors from a gene network dynamics anddevelopmental perspective. Semin Cell Dev Biol. 20(7),869-876.
[3] Enmon, R., Yang, W. H., Ballangrud, A. M., Solit, D. B., Heller, G., Rosen, N., and Scher, H. I. et al. (2003) Combination Treatment with 17-N-Allylamino- 17-Demethoxy Geldanamycin and Acute Irradiation Produces Supra-Additive Growth Suppression in Human Prostate Carcinoma Spheroids. Cancer Res., 63, 8393-8399.
[4] Reya, T., Morrison, S. J., Clarke, M. F., and Weissman,I. L.(2001) Stem cells, cancer, and cancer stem cells. Nature ,414 , 105-111.
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[6] Gupta, P. B., Chaffer, C. L., and Weinberg, R.A.(2009) Cancer stem cells mirage or reality? Nat. Med.. 15, 1010-1012.
[7] Grivennikov, S. and Karin, M. (2008) Autocrine IL-6 signaling: a key event in tumorigenesis? CANCER CELL , 13 ,7-9.
[8] Wolkenhauer O, Auffray C, Baltrusch S, Bl‥uthgen N, Byrne H, Cascante M, and Ciliberto A, et al.(2010) Systems Biologists Seek Fuller Integration of Systems Biology Approaches in New Cancer Research Programs. Cancer Res., 70(1), 12-13.
[9] Monika Joanna Piotrowska, Heiko Enderling, Uwe an der Heiden, and Michael C. Mackey. (2008) Cancer and stem cells, chapter 2, Nova Science Publishers, Inc.
[10] Garner, A. L., Lau, Y. Y., Jordan, D. W., Uhler, M. D., and Gilgenbach, R. M.(2006) Implications of a simple mathematical model to cancer cell population dynamics. Cell Prolif. , 39, 15-28.
[11] Ganguly, R. and Puri, I. K.(2006) Mathematical model for the cancer stem cell hypothesis. Cell Prolif., 39, 3-14.
[12] Bajzer ˇZ., and Vuk-Pavlovi’c S.(2005) Modeling positive regulatory feedbacks in cell-cell interactions. Biosystems, 80, 1-10.
[13] Ghosh, S., Elankumaran, S., and Puri, I. K.(2011) Mathematical model of the role of intercellular signalling in intercellular cooperation during tumorigenesis. Cell Prolif., 44, 192-203.
[14] Swierniak, A., Kimmel, M., and Smieja, J.(2009) Mathematical modeling as a tool for planning anticancer therapy. Eur. J. Pharmacol., 625, 108-121.
[15] Tanaka, G., Hirata, Y., Goldenberg, S. L., Bruchovsky, N., and Aihara, K.(2010) Mathematical modelling of prostate cancer growth and its application to hormone therapy. Philos. Trans. R. Soc. A-Math. Phys. Eng. Sci., 368, 5029-5044.
[16] Leder, K., Holland, E. C., and Michor, F.(2010) The Therapeutic Implications of Plasticity of the Cancer Stem Cell Phenotype. PLOS ONE, 5, article number: e14366.
[17] Ganguly, R. and Puri, I. K.(2007) Mathematical model for chemotherapeutic drug efficacy in arresting tumour growth based on the cancer stem cell hypothesis. Cell Prolif., 40, 338-354.
[18] Usmani, S. Z., Bon a, R., and Li, Z. H.(2009) 17 AAG for HSP90 Inhibition in Cancer – From Bench to Bedside. Curr. Mol. Med., 9, 654-664.
[19] Hirata Y, Tanaka G, Bruchovsky N, Aihara K.(2012) Mathematically modelling and controlling prostate cancer under intermittent hormone therapy. Asian J Androl., 15, 270-277.
[20] Huggins, C. and Hodges, C. V.(1941) Studies on prostatic cancer: I. The effect of castration, of estrogen and of androgen injection on serum phosphatases in metastatic carcinoma of the prostate. Cancer Res., 1, 293-297.
[21] Chodak, G. W.(2005) Maximum androgen blockade: a clinical update. Rev. Urol., 7,(Suppl. 5) S13-S17.
[22] Noble RL.(1977) Hormonal control of growth and progression in tumors of Nb rats and a theory of action. Cancer Res., 37 , 82-94.
[23] Bruchovsky N., Rennie P.S., Coldman A.J., Goldenberg S.L., To M. et al.(1990) Effects of androgen withdrawal on the stem cell composition of the Shionogi carcinoma. Cancer Res., 50, 2275-2282.
[24] Akakura K., Bruchovsky N., Goldenberg S. L., Rennie P. S., Buckley A. R. et al.(1993) Effects of intermittent androgen suppression on androgen-dependent tumors: apoptosis and serum prostate-specific antigen. Cancer Res., 71, 2782-2790.
[25] Abrahamsson P.A.(2010) Potential benefits of intermittent androgen suppression therapy in the treatment of prostate cancer: a systematic review of the literature. Eur Urol, 57, 49-59.
[26] Jackson T.L.(2004) A mathematical investigation of the multiple pathways to recurrent prostate cancer: comparison with experimental data. Neoplasia, 6, 679-704.
[27] Jackson T.L.(2004) A mathematical model of prostate tumor growth and androgenindependent relapse. Disc Cont Dyn Syst- Ser. B , 4, 187-201.
[28] Guo Q, Tao Y, Aihara K.(2008) Mathematical modelling of prostate tumor growth under intermittent androgen suppression with partial differential equations. Int J Bifurcat Chaos , 18, 3789-3797.
[29] Shimada T. , and Aihara K. (2008) A nonlinear model with competition between prostate tumor cells and its application to intermittent androgen suppression therapy of prostate cancer. Math Biosci , 214, 134-139.
[30] Tanaka G, Hirata Y, Goldenberg SL, Bruchovsky N, Aihara K. (2010) Mathematical modelling of prostate cancer growth and its application to hormone therapy. Philos Trans R Soc A, 368, 5029-44.
[31] Hirata Y, Bruchovsky N, Aihara K. (2010) Development of a mathematical model that predicts the outcome of hormone therapy for prostate cancer. J Theor Biol, 264, 517-527.
[32] William H. Press, et al. Numerical recipes in fortran 77. Cambridge [England] ; New York : Cambridge University Press, 1999
[33] Bert Sakmann and Erwin Neher. Single-Channel Recording. New York : Plenum Press, c1995
[34] Brad S. Rothberg and Ricardo A. Bello et al (1997). Two-Dimensional Components and Hidden Dependencies Provide Insight into Ion Channel Gating Mechanisms. Biophys. J. 72 2524-2544
[35] William H. Press, et al. Numerical recipes in fortran 90. Cambridge [England] ; New York : Cambridge University Press, 1996
[36] A. L. Blatz and K. L. Magleby. (1986) Quantitative description of three modes of activity of fast chloride channels from rat skeletal muscle. Physiol, 378(1):141-174.
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dc.identifier.urihttp://tdr.lib.ntu.edu.tw/jspui/handle/123456789/6709-
dc.description.abstract具有異質性的族群是自然界中常見的一種複雜系統,而其中一種讓人感興趣的簡單模型就是僅有兩個子群但彼此會互相競爭且可互相轉換的系統。在本篇的工作中,我們發展了一套描述這樣子的二態成長且具有互相轉換性質系統的數學模型,並且應用這個模型對應到了前列腺癌症腫瘤球在治療之下的真實成長數據。zh_TW
dc.description.abstractHeterogeneous population is common among complex systems in nature. A simple heterogeneous growth model of interest is one with two states which not only compete with each other but exhibit transition phenomenon. In this work, we develop a mathematical model to describe such a system and apply it to fit the real growth data of the prostate cancer spheroid under treatments.en
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Previous issue date: 2012
en
dc.description.tableofcontentsI A two-state growth model with transition probability 1
1 Introduction 2
2 Mathematical model of two-state growth with transition
probability 6
2.1 The general Growth and transition model . . . . . . . . . . . . . . . 7
2.1.1 The time evolution equation of x1 and x2 . . . . . . . . . . . 7
2.1.2 Transforming x1 and x2 to xt and w1 . . . . . . . . . . . . . . 7
2.1.3 Some simplified cases. . . . . . . . . . . . . . . . . . . . . . . 8
2.2 The general two-state growth and transition model with auto/paracrine
signaling pathway . . . . . . . . . . . . . . . . . . . . . . . . . . 10
2.2.1 The auto/para-crine signaling pathway and it mathematical
form . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
2.2.2 The nullclines and fixed points in general autocrine transition
case . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
2.2.3 Jacobian matrix in general autocrine transition case. . . . . . 13
2.3 fi is a constant, the exponential growth model . . . . . . . . . . . . 16
2.3.1 Two-state exponential growth and autocrine transition model 16
2.3.2 The exact solution . . . . . . . . . . . . . . . . . . . . . . . . 16
2.4 When fi is a linear function- the general logistic growth model . . . 18
2.4.1 A general form of two-state logistic growth and autocrine transition
model. . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
i
2.4.2 The xt nullcline for the general logistic growth . . . . . . . . 19
2.4.3 w˜1(xt) in a general logistic growth and autocrine transition
model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
2.4.4 The fixed point formula in general logistic growth and autocrine
transition model . . . . . . . . . . . . . . . . . . . . . 28
2.4.5 The stability and possible spiral behavior in general logistic
growth and autocrine transition model . . . . . . . . . . . . . 40
2.5 The classification of logistic growth model. . . . . . . . . . . . . . . . 41
2.6 LH-0 model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42
2.6.1 The exact solution when r1 = r2 . . . . . . . . . . . . . . . . 43
2.6.2 The nullclines . . . . . . . . . . . . . . . . . . . . . . . . . . 44
2.6.3 The fixed point condition . . . . . . . . . . . . . . . . . . . . 45
2.6.4 The stability of the fixed point . . . . . . . . . . . . . . . . . 45
2.6.5 The phase portraits of LH-0 . . . . . . . . . . . . . . . . . . . 45
2.7 LH-A model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45
2.7.1 The nullclines . . . . . . . . . . . . . . . . . . . . . . . . . . 46
2.7.2 The fixed point condition . . . . . . . . . . . . . . . . . . . . 48
2.7.3 The stability of fixed point . . . . . . . . . . . . . . . . . . . 48
2.7.4 The phase portraits of LH-A . . . . . . . . . . . . . . . . . . 49
2.8 LH-B model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49
2.8.1 The nullclines . . . . . . . . . . . . . . . . . . . . . . . . . . 50
2.8.2 The fixed point condition . . . . . . . . . . . . . . . . . . . . 52
2.8.3 The stability of fixed point . . . . . . . . . . . . . . . . . . . 53
2.8.4 The phase portraits of LH-B . . . . . . . . . . . . . . . . . . 53
2.9 LH-C model. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53
2.9.1 The nullclines . . . . . . . . . . . . . . . . . . . . . . . . . . 54
2.9.2 The fixed point condition . . . . . . . . . . . . . . . . . . . . 57
2.9.3 The stability of fixed point . . . . . . . . . . . . . . . . . . . 58
2.9.4 The phase diagram and phase portraits of LH-C . . . . . . . 60
2.10 LH-D model. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60
ii
2.10.1 The phase diagram and phase portraits of LH-D . . . . . . . 60
3 Conclusion 61
II An application to cancer growth system and
treatment development 62
4 A simple introduction of carcinomatous process 63
5 A heterogeneous cancer growth with autocrine signaling
pathway and fitting the growth curves of prostate tumor spheriod 65
5.1 Fitting idea . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65
5.2 Result and discussion . . . . . . . . . . . . . . . . . . . . . . . . . . 68
5.2.1 Some simple properties of this model . . . . . . . . . . . . . . 68
5.2.2 A comparison of the numerical results and real data from tumor
growth with treatments . . . . . . . . . . . . . . . . . . . . . . 72
6 IAS treatment simulation by the mathematical model and the
planning of optimized therapy. 78
6.1 The clinical prostate cancer therapy of human- IAS (Intermittent Androgen
Suppression) therapy . . . . . . . . . . . . . . . . . . . . . . . 78
7 Conclusion 81
A Statistical methods to make out sub-state from a whole system. 82
A.1 Least squares method . . . . . . . . . . . . . . . . . . . . . . . . . . . 82
A.2 Maximum likelihood method . . . . . . . . . . . . . . . . . . . . . . . 84
A.2.1 Maximum Likelihood . . . . . . . . . . . . . . . . . . . . . . . 84
A.2.2 Logarithmic likelihood ratio test . . . . . . . . . . . . . . . . . 85
A.3 Numerical simulation method . . . . . . . . . . . . . . . . . . . . . . 87
iii
A.3.1 Generation of non-uniform random number . . . . . . . . . . . 87
A.3.2 Optimum method . . . . . . . . . . . . . . . . . . . . . . . . 92
A.4 Data analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94
A.4.1 1-D data set . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94
A.4.2 Application of 2-D data analysis . . . . . . . . . . . . . . . . . 111
B The phase portraits of two-state logistic growth and autocrine
transition model 120
B.1 The phase portraits of LH-0 . . . . . . . . . . . . . . . . . . . . . . . 120
B.2 The phase portraits of LH-A . . . . . . . . . . . . . . . . . . . . . . 133
B.3 The phase portraits of LH-B . . . . . . . . . . . . . . . . . . . . . . 179
B.4 The phase portraits of LH-C . . . . . . . . . . . . . . . . . . . . . . 183
B.5 The phase portraits of LH-D . . . . . . . . . . . . . . . . . . . . . . 232
C Simple analysis of the time evolution of the cancer system model 248
iv
dc.language.isoen
dc.title二態成長與躍遷模型及其在癌症成長系統及療法開發的應用zh_TW
dc.titleTwo-state growth with transition model and its application to cancer growth system and therapy developmenten
dc.typeThesis
dc.date.schoolyear100-2
dc.description.degree博士
dc.contributor.oralexamcommittee陳宣毅(Hsuan-Yi Chen),陳啟明(Chi-Ming Chen),李世炳(Sai-Ping Li)
dc.subject.keyword二態系統,成長與躍遷模型,異質性系統,癌症,療法,zh_TW
dc.subject.keywordtwo-state system,growth with transition model,heterogeneous system,cancer,therapy,en
dc.relation.page257
dc.rights.note同意授權(全球公開)
dc.date.accepted2012-08-01
dc.contributor.author-college理學院zh_TW
dc.contributor.author-dept物理研究所zh_TW
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