Database design and development for the automated assembly of genetic circuits' models - Teo, Edwin Zaiyi (2015)
Synthetic biology brings together the fields of engineering and biology in an exciting quest to create that which does not exist naturally, using parts which exist naturally. Nonetheless, the seemingly endless possibilities must be streamlined to focus on feasible opportunities. Biological modeling presents an effective method to achieve this. It allows the prediction of experimental outcome, even during the process of experiment design. However, current modeling methods are tedious and time-consuming, a large part due to the retrieval of process parameters. Hence this project aims to develop a database for the storage of process parameters, and subsequently, develop a method to automatically assemble models for synthetic genetic circuits. The modeling platform was developed using the Python programming language, with the database managed using SQLite RDBMS. The database was designed, adapting the structure of the DICOM database, for simplicity and ease-of-use. The associated functions interacting with the database were similarly developed to maximize user-friendliness. The final step in this project involved integrating the database with the Python modeling platform for the automatic assembly and generation of genetic circuits’ models. After experimental testing, the results indicate that the Python modeling platform is indeed able to generate the same simulation outcomes with minimal input from the user. Thus, the Python modeling platform shows great potential in future modeling undertakings.