| Summary: | 碩士 === 國立陽明大學 === 生物醫學資訊研究所 === 104 === 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.
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