Precision engineering of biological function with large-scale measurements and machine learning.
Precision engineering of biological function with large-scale measurements and machine learning.
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DOI：
10.1371/journal.pone.0283548
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发表时间：
2023
期刊：
影响因子：
3.7
通讯作者：
中科院分区：
文献类型：
--
作者：
As synthetic biology expands and accelerates into real-world applications, methods for quantitatively and precisely engineering biological function become increasingly relevant. This is particularly true for applications that require programmed sensing to dynamically regulate gene expression in response to stimuli. However, few methods have been described that can engineer biological sensing with any level of quantitative precision. Here, we present two complementary methods for precision engineering of genetic sensors: in silico selection and machine-learning-enabled forward engineering. Both methods use a large-scale genotype-phenotype dataset to identify DNA sequences that encode sensors with quantitatively specified dose response. First, we show that in silico selection can be used to engineer sensors with a wide range of dose-response curves. To demonstrate in silico selection for precise, multi-objective engineering, we simultaneously tune a genetic sensor’s sensitivity (EC50) and saturating output to meet quantitative specifications. In addition, we engineer sensors with inverted dose-response and specified EC50. Second, we demonstrate a machine-learning-enabled approach to predictively engineer genetic sensors with mutation combinations that are not present in the large-scale dataset. We show that the interpretable machine learning results can be combined with a biophysical model to engineer sensors with improved inverted dose-response curves.
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影响因子：
14.9
作者：
Espah Borujeni A;Mishler DM;Wang J;Huso W;Salis HM
通讯作者：
Salis HM
影响因子：
2.7
作者：
Sochor MA
通讯作者：
Sochor MA
DOI：
10.1073/pnas.1907869116
发表时间：
2019-09-10
影响因子：
11.1
作者：
Chure, Griffin;Razo-Mejia, Manuel;Phillips, Rob
通讯作者：
Phillips, Rob
影响因子：
4.7
作者：
Beal, Jacob;Teague, Brian;Weiss, Ron
通讯作者：
Weiss, Ron
影响因子：
64.8
作者：
Faure, Andre J.;Domingo, Julia;Lehner, Ben
通讯作者：
Lehner, Ben