請用此 Handle URI 來引用此文件：
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/55574完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 黃筱鈞(Huang, Hsiao-Chun) | |
| dc.contributor.author | Ying-Yu Jiang | en |
| dc.contributor.author | 江瑩育 | zh_TW |
| dc.date.accessioned | 2021-06-16T04:10:16Z | - |
| dc.date.available | 2014-09-03 | |
| dc.date.copyright | 2014-09-03 | |
| dc.date.issued | 2014 | |
| dc.date.submitted | 2014-08-20 | |
| dc.identifier.citation | Reference
1. Eldar, A. and M.B. Elowitz, Functional roles for noise in genetic circuits. Nature, 2010. 467(7312): p. 167-173. 2. Arriaga, E., Determining biological noise via single cell analysis. Analytical and Bioanalytical Chemistry, 2009. 393(1): p. 73-80. 3. Schleif, R., Regulation of the l-arabinose operon of Escherichia coli. Trends in Genetics, 2000. 16(12): p. 559-565. 4. Hamilton, E.P. and N. Lee, Three binding sites for AraC protein are required for autoregulation of araC in Escherichia coli. Proceedings of the National Academy of Sciences, 1988. 85(6): p. 1749-1753. 5. Lee, N., C. Francklyn, and E.P. Hamilton, Arabinose-induced binding of AraC protein to araI2 activates the araBAD operon promoter. Proceedings of the National Academy of Sciences, 1987. 84(24): p. 8814-8818. 6. Lutz, R. and H. Bujard, Independent and tight regulation of transcriptional units in Escherichia coli via the LacR/O, the TetR/O and AraC/I1-I2 regulatory elements. Nucleic Acids Res, 1997. 25(6): p. 1203-10. 7. Thattai, M. and A. van Oudenaarden, Intrinsic noise in gene regulatory networks. Proceedings of the National Academy of Sciences, 2001. 98(15): p. 8614-8619. 8. Kolodrubetz, D. and R. Schleif, Identification of araC protein and two-dimensional gels, its in vivo instability and normal level. J Mol Biol, 1981. 149(1): p. 133-9. 9. Bernstein, J.A., et al., Global analysis of Escherichia coli RNA degradosome function using DNA microarrays. Proceedings of the National Academy of Sciences of the United States of America, 2004. 101(9): p. 2758-2763. 10. Shetty, R., D. Endy, and T. Knight, Engineering BioBrick vectors from BioBrick parts. Journal of Biological Engineering, 2008. 2(1): p. 5. 11. Chen, Y.-J., et al., Characterization of 582 natural and synthetic terminators and quantification of their design constraints. Nat Meth, 2013. 10(7): p. 659-664. 12. Boye, E., H.B. Steen, and K. Skarstad, Flow cytometry of bacteria: a promising tool in experimental and clinical microbiology. J Gen Microbiol, 1983. 129(4): p. 973-80. 13. Steen, H.B., K. Skarstad, and E. Boye, Flow cytometry of bacteria: cell cycle kinetics and effects of antibiotics. Ann N Y Acad Sci, 1986. 468: p. 329-38. 14. Skinner, S.O., et al., Measuring mRNA copy number in individual Escherichia coli cells using single-molecule fluorescent in situ hybridization. Nat. Protocols, 2013. 8(6): p. 1100-1113. 15. Guzman, L.M., et al., Tight regulation, modulation, and high-level expression by vectors containing the arabinose PBAD promoter. J Bacteriol, 1995. 177(14): p. 4121-30. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/55574 | - |
| dc.description.abstract | 正回饋基因調控迴路與未調控基因在基因轉錄網絡中相較之下，
正回饋基因調控迴路具有噪音放大的特性，此特性會造成相同基因組成的細胞在基因表達量上產生更大的變異。而細胞間變異為此細胞族群在變化萬千的環境中提供更具優勢的生存機會。然而，正回饋基因調控迴路與未調控基因相比之下，同時也具有基因高表達量的特性，因此，為避免基因高表達量造成生存優勢上的主因，我們試圖調控正回饋基因調控迴路上之啟動子強弱，藉此降低正回饋基因表達量與未調控基因相同。在我們的研究項目當中，我們選擇在大腸桿菌中建構阿糖胞苷正回饋表達系統，首先，我們在阿糖胞苷啟動子上設計一系列與核醣核酸聚合酶較弱親和性之核酸序列，藉此減弱阿糖胞苷正回饋之強度達到降低整體基因表達量的目的，而此研究使用紅色螢光蛋白為同步報導阿糖胞苷表現之螢光蛋白。另外一方面，我們使用Gillespie 演算法模擬啟動子強度及阿糖胞苷的親和性和協同性不同參數預測正回饋基因與未調空基因在平均蛋白表達量的一致，接著，藉由模擬預測結果在大腸桿菌中設計並植入阿糖胞苷正回饋線路的核酸質體，最後由流式細胞儀及影像分析軟體透過紅色螢光報導基因分析阿糖胞苷之表達變異性。相信再過不久的將來，我們能夠替換紅色螢光報導基因，研究噪音放大效應對抗藥性變異特性等生存優勢之關係。 | zh_TW |
| dc.description.abstract | Compared to an unregulated gene, auto-regulation or positive-feedback loop (PFL) in a transcriptional network is known to have the characteris¬tic of noise amplification, leading to larger expression variations for cells with identical genetics. A high cell-cell variation potentially offers greater chance of survival for the population in a changing environment. PFL, however, also increases mean expression level, making it difficult to conclude the survival advantage comes from greater variation. To over¬come this, we aim to normalize the expression level of PFL to that of an unregulated gene. We chose AraC expression in E coli as our model system, and designed a series of AraC and RNA polymerase bind¬ing sites in AraC promoter to provide a spectrum of binding affinity and thus expression/feedback strengths. RFP was used as to report expres¬sion level. We also used Gillespie algorithm to predict which combina¬tions of -35 and/or -10 regions (according to the Anderson library) and AraC cooperativity/affinity that would most likely to generate same pro¬tein expression for unregulated gene and PFL. With these predictions, we constructed plasmids, transformed them into E coli, and analyzed RFP strength by flow cytometry, and will further confirm if these data agrees with our computational predictions. Our ultimate goal is to replace RFP with an antibiotic gene (e.g. ampicillin), and test if a larger expression of this resistance gene provides greater survival advantages with a fluctuat¬ing/higher ampicillin concentrations. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-16T04:10:16Z (GMT). No. of bitstreams: 1 ntu-103-R01b43025-1.pdf: 1192477 bytes, checksum: e168e0184d2c0aad4712b66bcd9a7195 (MD5) Previous issue date: 2014 | en |
| dc.description.tableofcontents | 論文口試委員審定書................................................................................i
致謝.............................................................................................ii 中文摘要..................................................................................................iii Abstract....................................................................................................iv Table of content…………………………………………………………vi List of Figures........................................................................................viii List of Tables............................................................................................ix List of Abbreviations................................................................................x Chapter 1 Introduction...............................................................................1 1. Cell-cell variation................................................................................1 1.1 Isogenic cell-cell variation............................................................1 1.2 Cellular noise quantification.........................................................1 2. Noise in gene circuits..........................................................................2 2.1 Simple regulation and Positive feedback loop..............................2 2.2 The unimodal and bimodal distribution in PFL............................2 3. pBAD induction system......................................................................2 3.1 AraC-PC-PBAD arabinose induction mechanism............................2 3.2 Synthetic ParaI promoter...............................................................3 3.3 PBAD single AraC protein binding..................................................3 4. Simulation ..........................................................................................3 4.1 Stochastic simulation.....................................................................4 5. Biobricks™..........................................................................................5 Chapter 2 Materials and Methods.............................................................6 1. Materials (E. coli, enzymes, compounds and equipment)…………...6 2. Construction and Cloning....................................................................7 2.1 PCR and DNA digestion.................................................................7 2.1.1 I23119 synthetic promoter............................................................7 2.1.2 I23119R and J23119R RFP reporter............................................8 2.1.3 PBAD-s-araC positive feedback loop..............................................9 2.2. Ligation, transformation and DNA sequencing..............................9 3. Flow cytometry analysis......................................................................10 3.1 Sample preparation......................................................................10 3.2 Flow cytometry setting................................................................11 3.3 Data analysis ...............................................................................11 4. Image analysis...................................................................................11 4.1 Sample preparation......................................................................11 4.2 Image acquisition……................................................................12 4.3 Data analysis ...............................................................................12 Chapter 3 Results.....................................................................................13 Chapter 4 Discussion...............................................................................15 Reference.................................................................................................17 Figures.....................................................................................................20 Tables......................................................................................................35 | |
| dc.language.iso | zh-TW | |
| dc.subject | 抗藥性變異 | zh_TW |
| dc.subject | 正回饋基因調控迴路 | zh_TW |
| dc.subject | 噪音放大 | zh_TW |
| dc.subject | 細胞間變異 | zh_TW |
| dc.subject | 阿糖胞? | zh_TW |
| dc.subject | Gillespie 演算法 | zh_TW |
| dc.subject | antibiotic gene | en |
| dc.subject | noise amplification | en |
| dc.subject | positive feedback loop | en |
| dc.subject | cell-cell variation | en |
| dc.title | AraC正迴饋線路促使細胞間變異性放大提供細胞族群之生存優勢 | zh_TW |
| dc.title | AraC Positive Feedback Loop amplifies Cell-Cell Variation to provide survival advantage to a clonal population | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 102-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 葉怡均(Yi-Chun Yeh),溫進德(Jin-Der Wen) | |
| dc.subject.keyword | 正回饋基因調控迴路,噪音放大,細胞間變異,阿糖胞?,Gillespie 演算法,抗藥性變異, | zh_TW |
| dc.subject.keyword | cell-cell variation,positive feedback loop,noise amplification,antibiotic gene, | en |
| dc.relation.page | 35 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2014-08-21 | |
| dc.contributor.author-college | 生命科學院 | zh_TW |
| dc.contributor.author-dept | 分子與細胞生物學研究所 | zh_TW |
| 顯示於系所單位： | 分子與細胞生物學研究所 | |
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