Quantitative design of prokaryotic promoters based on precise machine learning models
文献类型：会议论文
| 作者 | Meng, Hailin; He, Jingyu; Cui, Jinming; Wang, Yong; Liu, Chenli |
| 出版日期 | 2017 |
| 会议日期 | 2017 |
| 会议地点 | 上海 |
| 英文摘要 | The prediction of the prokaryotic promoter strength based on its sequence is of great importance not only in the fundamental research of life sciences but also in the applied aspect of synthetic biology. Much advance has been made to build quantitative models for strength prediction and sequence design. Here we developed precise predicting models for de novo and quantitative design of prokaryotic promoters based on two machine learning methods, including artificial neural network (ANN) and support vector machine (SVM). A library of one hundred promoter sequences (with relative strength ranged from 0 to 3.559) was constructed and used for model training and test. As a result, two well-trained models, NET90_19_576 of ANN and OptModel of SVM, were finally constructed with high correlation (R2 > 0.96) for both model training and test, which are much higher than that of the previous modeling methods. Next, sixteen artificial promoters were in silico designed using the ANN model as an example. All of them were proved to have good consistency between the measured strength and the designed strength. The functional reliability of the designed promoters was validated in two different genetic contexts. The designed promoters were successfully utilized to improve the expression of a peptide toxin BmK1 and fine-tune the deoxy-xylulose phosphate pathway in Escherichia coli. Our results demonstrate that the methodology based on precise machine learning models can de novo and quantitatively design promoters with desired strengths, which are of great importance for synthetic biology applications. |
| 语种 | 英语 |
| 源URL | [http://ir.siat.ac.cn:8080/handle/172644/11545]  |
| 专题 | 深圳先进技术研究院_南沙所 |
| 作者单位 | 2017 |
| 推荐引用方式 GB/T 7714 | Meng, Hailin,He, Jingyu,Cui, Jinming,et al. Quantitative design of prokaryotic promoters based on precise machine learning models[C]. 见:. 上海. 2017. |
入库方式： OAI收割
来源：深圳先进技术研究院
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