Mathematical Modeling for Multi-Input-Sensing Promoters

• Main Authors: Chiu, I-Hsuan, 邱譯玄
• Other Authors: Ho, Shinn-Ying
• Format: Others
• Language: zh-TW
• Published: 2013
• Online Access: http://ndltd.ncl.edu.tw/handle/00302372500104632605

碩士 === 國立交通大學 === 生物資訊及系統生物研究所 === 101 === Synthetic biology is a hot researching field recently. By using gene recombination techniques, synthetic biologists can implant pre-designed genetic circuits into bacterial cells, and manipulate the cells to perform specific tasks. Gene circuits are composed of some basic biological parts (or BioBricks), including promoters, ribosome binding sites, protein coding sequences, terminators and so on. Among these parts, promoters are necessary for gene transcription. Depends on the needs, synthetic biologists may choose a promoter which contains multiple transcription factor binding sites (TFBSs). This type of promoters can integrate multiple transcription factor (TF) concentration inputs, and output a gene expression at a specific rate. We defined this type of promoters as “multi-input sensing promoters”. Owing to the fact that fine-tuning of gene expressions is an important topic for synthetic biologists, using a mathematical model to describe the relationship between TF (or TF’s inducer) concentrations inputs and gene expression rates is needed. There are some researches using modeling and experimental validation methods. However, these researches only focused on the co-regulation by two TFs, and still not concerned about the co-regulation by three or more TFs. Based on the model, these researches can be divided by two classes: The first class is simple, general model; the second class is complex, problem-dependent model. For experimental validations, these researches optimized the parameters for best fitting; however, these researches did not perform independent tests; therefore, for newly generated expression rate outputs, whether these models reach the same performances are not confirmed. In this research, we proposed a reformed mathematical model which can describe the relationship between three inducer inputs and gene expression rate outputs. This model contains 14 biological meaning parameters which can reflect the expression rates under different TF binding states and the features of Hill function curves. In order to validate that our model can generate a robust set of parameter solution to a set of gene expression data, we performed two numerical experiments before biological experiments. In the first numerical experiment, we assigned 64 inducer concentration combinations and 14 target parameter values in our model to generate 64 simulated gene expression rate data. Assuming we knew all the information given above except 14 target parameter values, we used a parameter optimization tool to solve these values. Our result showed that among 30 runs, most of the solutions were robust and close to target parameter values. The fitting error of the top one solution was 1.29E-07. We also used the top one solution for independent test, and the fitting error was 5.67E-07. In the second numerical experiment, we aimed to simulate the deviation which generated from biological experiments and test the parameter solving performances under these deviations. To achive this goal, we added noises in a degree within 5% to the gene expression rate data in the first numerical experiment. The result showed that among 30 runs, most of the solutions were still robust and close to target parameter values. The fitting error of the top one solution was 3.00E-03, and the fitting error of top one solution in independent tests was 5.89E-03. In order to validate that our model can precisely simulate the real expression data, we used BioBricks from iGEM (International Genetically Engineered Machine) to construct a promoter which contains TFBSs for three kinds of TFs. By using 64 sets of inducer concentration combinations, we acquired 64 GFP expression rates. After parameter optimization, 11 parameters were robust among 30 runs. The fitting error of top one solution is 4.88E-03, and the fitting error of top one solution in independent tests was 1.78E-02. In conclusion, our mathematical model could fit the real expression data and predict newly generated outputs. We prospect that our model would contribute to synthetic biologists. For example, helps synthetic biologists designing the multi-input sensing promoters, and modeling the input-output relationships of these promoters. Furthermore, we also prospect that the extensive usage of multi-input sensing promoters would enlarge the applications in synthetic biology.