研究背景：系統生物學旨在以系統性的觀點研究生物體內的動態行為，生物的適應性以及體內平衡機制就屬於系統生物學的研究範疇。本研究在適應性及內平衡機制中分別選擇一個例子作為研究主題，分別為趨化性和血糖調控，以控制系統的觀點探討此二種機制的運作。此外，生物體內的分子交互作用也屬系統生物學之研究範疇。高通篩選的技術讓研究人員得以在同時間內分析多種分子間的交互作用。高通篩選搭配統計方法及影像處理，研究蛋白質間的交互作用為本研究第三個研究主題。
研究目標：本研究的目標有三，首先是分別以生物學及控制系統的觀點來探討大腸桿菌的趨化行為。再者，以控制系統的觀點探討動態補償並進一步說明其為一種適應系統，且能夠與控制方法結合。最後是設計一系列流程用以研究蛋白質之間的結合，以及比較兩種結合力的量化方法。
研究方法：在趨化行為的主題中，從化學反應式推導出微分方程。在血糖調控的主題中，首先將動態補償的模型簡化，並證明其恆定性，再結合回授線性化加以分析。在蛋白質交互作用的主題中，以中心定位及尋邊找出蛋白質微陣列影像中的訊號，之後以回歸分析的方法去除其誤差，最後以標準化均差分析結合效果。
研究結果：我們以生物學家較易理解的圖例來呈現大腸桿菌趨化性的說明圖示，加上控制系統觀點的說明來連結生物與控制。在動態補償的研究中，我們證明了兩條方程式便足以讓達成動態補償，而此種系統是可回授線性化的。最後是蛋白質交互作用的主題，中心定位以及尋邊能夠找出蛋白質訊號，再以標準化均差的數值作為結合力的判定標準；另一方面，蛋白質結合力也能透過計算耦合力來分析，我們利用濃度反應曲線藉以計算出結合後濃度來計算耦合力。
結論: 首先，大腸桿菌的趨化性能夠分別從生物與控制的觀點來解讀。再者，我們減低了動態補償模型的複雜度，以便未來研究。此外，動態補償也能夠結合控制理論進行研究。最後，我們提出為標的選取提出了系列流程方法，但仍有改進空間。

Background: Systems biology studies organisms from a systematic perspective with the focus on modeling the dynamical behavior such as adaptation and homeostasis. We select chemotaxis from adaptation and hormonal circuit regulation from homeostasis as the first and second research topics which we want to study the interaction inside the biological systems from a control perspective. Moreover, systems biology includes researching the molecular interactions within organisms and high-throughput screening (HTS) is an approach that allows researchers to study multiple molecular interactions at the same time. We, therefore, combine statistics and image processing with HTS to study protein interactions, which is the third research topic.
Integral feedback in bacterial chemotaxis
Aim: The aim is to present how the chemotaxis in Escherichia coli (E. coli) works from both of a control and a biological perspective.
Method: We derive an ordinary differential equation (ODE) model from chemical reaction equations. With the ODEs and the chemical reaction equations, showing that control and biology are able to have the connection.
Result: We present the chemotaxis with an illustrative figure using graphical notations that are familiar to biologists, as well as the ODE model which is a common expression of a system in the control theory.
An analysis of dynamical compensation (DC) from a control perspective
Aim: The aim is to show that a system with the DC property can be explained as an adaptive control system so that DC can be involved in designing a control strategy
Method: First we simplify the original DC model as well as include the control elements to derive a simpler model structure. The model is then shown to still be invariant to parameters using P-invariance. Furthermore, we study the DC property with a nonlinear control strategy which is feedback linearization.
Result: We show that only two equations are needed for a system to have the DC property. By adding control elements to the two-equation model, the system can still demonstrate the DC property with the proof of P-invariance. Additionally, the system with DC property is feedback linearizable.
Hit selection using strictly standardized mean deviation (SSMD)
Aim: The aim is to develop a pipeline method for hit selection, as well as comparing the ranking list of binding ability between the SSMD approach and the approach using dose-response curve normalization.
Method: First of all, we distinguish the protein spots on the protein microarray image by applying center finding and edge detection approach. Secondly, eliminate the systematic errors by regression analysis. Eventually, identify the hits and duds through the SSMD approach. On the other hand, we fit a dose-response curve for obtaining the concentration through normalizing the fluorescence signal. On the other hand, we obtain the estimated concentration of proteins by fitting a dose-response curve and normalizing the fluorescence signal.
Result: We demonstrate that most of the protein spots can be captured using the pipeline method. On the other hand, we normalize the fluorescence of protein chip from the dose-response curve and generate the binding ranking list, which is compared with the ranking list generated from SSMD.
Conclusions: First of all, by deriving the mathematical model of chemotaxis in E. coli, we are able to illustrate the same system from both of a control and biological perspectives. Secondly, the complexity of analyzing a system with DC property is been decreased and the DC property indicates that it is an adaptive control strategy. Furthermore, the pipeline method provides a novel way for hit selection although there still exist future works for improvement.

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