臺灣博碩士論文加值系統

中文摘要
合成生物學是一門新興的學科領域，該領域的主要目的為利用工程方法來使生命產生特定的功能及表現模式。目前的合成生物學家主要藉由人工合成具特定功能的基因電路，並將其轉殖入細胞中來使細胞能執行特定的功能。因此麻省理工學院建立了標準生物元件資料庫，提供不同特性的標準生物元件讓合成生物學家能組裝設計出不同的基因電路。然而，因為我們對於生物元件及基因電路的定量性質所知仍比其定性性質所知還要來的少，而有些定量性質也很難利用分子生物學實驗方法來測得，所以要達成使基因電路具有符合我們需求的表現模式仍需要更多研究上的努力。因此，為了要能夠取得任何我們有興趣的基因電路的量化性質，我們稱一個只包含四個生物元件(啟動子、核醣體結合位、編碼基因、終止子)的最小基因電路為單位基因電路，並將此單位基因電路的量化性質建成一個可運算的數學模型，如此我們便可利用此單位基因電路模型來組裝出任何我們有興趣的基因電路模型並了解其量化性質。為了產生建模所需的資料，我們基於大腸桿菌內的乳糖操縱子系統，用標準生物元件在大腸桿菌中組裝了人工合成的基因電路，並用此基因電路來偵測蛋白質表現及調控等量化性質。接著，我們根據轉譯的前後過程，將單位基因電路模型分成兩個子模型-前轉譯模型和後轉譯模型，並個別描述基因調控因子在彼此結合達到穩定狀態時的情況，以及蛋白質在細胞中濃度的動態變化。然而因為實驗資料所能提供的資訊有限，所以後轉譯模型中的參數主要只能藉由文獻搜尋得到，而對於前轉譯模型中的參數，我們發展了一個稱作S形曲線化的輔助參數鑑定方法，來獲得前轉譯模型中的參數。最後，透過預測基因電路的量化性質表現，我們可以去驗證單位基因電路模型及其中的參數是否能夠完整的代表基因電路所具有的量化性質。最後的結果顯示前轉譯模型有好的表現結果，但後轉譯模型則可能因為文獻搜尋所得到的參數不夠精確而使表現結果不好。儘管如此，我們的研究成果對於合成生物學上所需的基因電路表現模式量化設計仍有一定的貢獻與幫助。

Abstract
The emerging discipline of synthetic biology is dedicated to engineer biological processes with specific functions and desired behaviors for practical use. In order to engineer the functions of cells, synthetic biologists transform engineered gene circuits with specific function into them. Therefore, Massachusetts Institute of Technology established The Registry of Standard Biological Parts (BioBricks) which provides the needed standard biological parts with specific characteristics for synthetic gene circuit design. However, extensive research works are required for engineering synthetic gene circuits with desired behaviors, since the quantitative characteristics of gene circuits are less recognized compared to the qualitative information. Moreover, some of them are difficult to be identified by experiments only. Hence, in order to characterize the quantitative features of each gene circuit we are interested in, in this study, we define the unit gene circuit as the simplest gene circuit including four biological parts: a promoter, a ribosome binding site, a coding gene and a terminator. A mathematically computable unit gene circuit model is then constructed to represent the quantitative features of a unit gene circuit. Furthermore, to generate the data for modeling, we construct synthetic gene circuits by standard biological parts based on lac operon system in E. coli to detect the protein expression and regulation process of gene circuits. Subsequently, the unit gene circuit model is separated into two parts, pre-translational model and post-translational model, to represent the quantitative features of protein expression process before and after gene translation, respectively. Specifically, the binding process between gene regulation factors is described in steady state in the pre-translational model and the dynamic concentration change of gene products in cell is modeled in the post-translational model. Based on the constructed models and experimental data, the parameters in each model are identified using our proposed parameter identification method to make the model predictable. In the process of parameter identification, the parameters in post-translational model are obtained by literature survey and calculation rather than training since the information of experimental data is not sufficient for model training. On the other hand, the parameters in pre-translational model are identified by our proposed method, called Sigmoidization. Finally, the unit gene circuit model with identified parameters are validated by predicting the quantitative features of gene circuit. The results show that the pre-translational model performs well, however, the post-translational model does not due to the imprecision of the surveyed parameters. Despite that the performance of our constructed model may not be fully satisfied, we still make a progress in engineering or designing biological processes by quantitative gene circuit design.

Contents
中文摘要.......i
Abstract.......ii
Contents.......iv
List of Figures.......vi
List of Tables.......vii
Chapter 1 Introduction.......1
1.1 Gene Circuit and Gene Expression.......1
1.2 lac Operon.......3
1.3 Motivation and Specific Aim.......5
Chapter 2 Materials and Methods.......8
2.1 Overview of the Modeling Process.......8
2.2 Experiment.......9
2.2.1 Experimental Materials.......9
2.2.2 Growth Conditions and Measurements.......13
2.3 Data Preprocessing.......14
2.3.1 Growth State Selection and Time Calibration.......14
2.3.2 Smoothing.......16
2.3.3 Outlier Deletion.......17
2.4 Unit Gene Circuit Model for Protein Expression.......19
2.4.1 Dynamic Post-Translational Model.......19
2.4.2 Static Pre-Translational Model.......21
2.4.3 Model Presumption.......24
2.5 Parameter Identification in Pre-Translational Model.......25
2.5.1 Preparation for Parameter Identification.......25
2.5.2 Unbound Factor Concentration Estimation.......27
2.5.3 Factor Concentration Simulation and Solving.......31
2.5.4 Estimation Parameter, α and β, Optimization.......32
2.5.5 Endogenous and Exogenous Factor Concentration Correction.......34
2.5.6 Summary of Parameter training in Pre-Translational Model.......36
2.6 Validation.......37
Chapter 3 Results.......39
3.1 Smoothing Results.......39
3.2 Parameter Identification Results.......40
3.3 Estimation, Simulation and Validation Results.......43
Chapter 4 Discussions.......55
Chapter 5 Conclusions.......58
References.......60
Appendix.......63

List of Figures
Figure 1.1-1. The gene expression of gene circuit.......3
Figure 1.1-2. The regulation of lac operon.......5
Figure 2.1-1. Modeling flowchart.......9
Figure 2.2-1. Experimental process - growth conditions and measurements.......13
Figure 2.3-1. The time calibration process.......16
Figure 2.3-2. The smoothing process.......17
Figure 2.4-1. The influencing factors of GFP.......21
Figure 2.4-2. Binding process of promoter, repressor and inducer.......23
Figure 2.5-1. Preparation for parameter identification.......26
Figure 2.5-2. The double training method.......31
Figure 2.5-3. Flowchart of parameter training in pre-translational model.......37
Figure 3.1-1. Smoothing result.......39
Figure 3.3-1. αP and βP combination distribution.......44
Figure 3.3-2. αI and βI combination distribution.......45
Figure 3.3-3. Comparison of Sigmoidization and model solving by R ratio.......46
Figure 3.3-4. Comparison of Sigmoidization and model solving by I ratio.......47
Figure 3.3-5. The behavior of the pre-translational model for promoter.......48
Figure 3.3-6. The behavior of the pre-translational model for inducer with Sigmoidization input.......50
Figure 3.3-7. The behavior of the pre-translational model for inducer with simulated input.......51
Figure 3.3-8. Pre-translational model validation by simulation.......52
Figure 3.3-9. Pre-translational model validation by Sigmoidization.......53
Figure 3.3-10. Post-translational model validation by simulation.......54

List of Tables
Table 2.2-1. Experimental materials.......12
Table 2.3-1. An example for outlier deletion.......18
Table 3.2-1. Calculation results.......43

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