在本研究中，為了利用已鑑別出的啟動子-核糖體結合位元件的強度來做系統化的基因電路設計，我們建立了三種啟動子-核糖體結合位庫，即連續表現型啟動子-核糖體結合位庫、抑制子調控型啟動子-核糖體結合位庫及促進子調控型啟動子-核糖體結合位庫。利用輸入/輸出的特性以及外加誘導物濃度的調控，基因電晶體可以被設計擁有基因放大以及基因開關的功能，並且基因電晶體的各種設計規格可以藉由替換不同的啟動子-核糖體結合位元件以及調整誘導物的濃度來獲得。根據基因電晶體的動態模型，我們發展了一套基因電晶體的設計方法，這設計方法是利用以基因演算法為基礎的搜尋演算法從所相對應的啟動子-核糖體結合位庫找尋一個啟動子-核糖體結合位元件及誘導物濃度的集合來達成已規定的基因電晶體的輸入/輸出特性。此外，根據不同種類基因電晶體設計規格，一個基因電晶體的搜尋表格可以被建立，藉由這搜尋表格我們可以簡單地選擇合適的啟動子-核糖體結合位元件及誘導物濃度的集合來建構我們所想要的基因電晶體，這樣子可以節省很多在無謂的實驗試驗上的時間。最後，我們藉由運用啟動子-核糖體結合位庫設計所想要的基因電晶體線性放大器以及開關的功能來展現我們方法的實用性。

In this study, three kinds of promoter-RBS libraries, i.e., constitutive promoter-RBS library, repressor-regulated promoter-RBS library and activator-regulated promoter-RBS library, are constructed for the systematic genetic circuit design by identifying the kinetic strengths of promoter-RBS components. Based on the input/output (I/O) characteristics and concentrations of external inducers, the genetic transistor can be designed for genetic amplification or genetic switching. By the different selections of promoter-RBS components and regulations of inducer concentrations, the different design specifications of genetic transistor can be obtained. According to the dynamic model of genetic transistor, a design methodology of genetic transistor via GA-based searching algorithm is developed to search for a set of promoter-RBS components and concentrations of inducers from the corresponding promoter-RBS libraries to achieve a prescribed I/O characteristic of genetic transistor. Furthermore, according to design specifications of different kind of genetic transistors, a look-up table is built for genetic transistor design, from which we could easily select an adequate set of promoter-RBS components and concentrations of inducer. This will save much time on trial-and-error in experimental procedure for a genetic transistor with a desired I/O characteristic. Finally, we demonstrate the applicability of our methodology by designing genetic transistors respectively for a desired linear amplifier and a desired switching I/O response via in silico simulation based on promoter-RBS libraries.

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