Matches in SemOpenAlex for { <https://semopenalex.org/work/W4361283325> ?p ?o ?g. }
- W4361283325 endingPage "e0283548" @default.
- W4361283325 startingPage "e0283548" @default.
- W4361283325 abstract "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." @default.
- W4361283325 created "2023-03-31" @default.
- W4361283325 creator A5013689370 @default.
- W4361283325 creator A5018937724 @default.
- W4361283325 creator A5027494956 @default.
- W4361283325 creator A5035861712 @default.
- W4361283325 creator A5060891848 @default.
- W4361283325 creator A5066572638 @default.
- W4361283325 creator A5076179093 @default.
- W4361283325 creator A5079674519 @default.
- W4361283325 creator A5086772504 @default.
- W4361283325 date "2023-03-29" @default.
- W4361283325 modified "2023-09-30" @default.
- W4361283325 title "Precision engineering of biological function with large-scale measurements and machine learning" @default.
- W4361283325 cites W1495868618 @default.
- W4361283325 cites W1497340069 @default.
- W4361283325 cites W1520225081 @default.
- W4361283325 cites W1522916443 @default.
- W4361283325 cites W1979197349 @default.
- W4361283325 cites W2009217296 @default.
- W4361283325 cites W2011512765 @default.
- W4361283325 cites W2020366340 @default.
- W4361283325 cites W2038701138 @default.
- W4361283325 cites W2047360542 @default.
- W4361283325 cites W2051280575 @default.
- W4361283325 cites W2053738779 @default.
- W4361283325 cites W2062147399 @default.
- W4361283325 cites W2071965573 @default.
- W4361283325 cites W2075099666 @default.
- W4361283325 cites W2075334658 @default.
- W4361283325 cites W2090609904 @default.
- W4361283325 cites W2101893648 @default.
- W4361283325 cites W2108329085 @default.
- W4361283325 cites W2112500061 @default.
- W4361283325 cites W2118464978 @default.
- W4361283325 cites W2123467717 @default.
- W4361283325 cites W2124893589 @default.
- W4361283325 cites W2134220523 @default.
- W4361283325 cites W2152116863 @default.
- W4361283325 cites W2161304688 @default.
- W4361283325 cites W2230709708 @default.
- W4361283325 cites W2269732204 @default.
- W4361283325 cites W2273190468 @default.
- W4361283325 cites W2297327485 @default.
- W4361283325 cites W2342782366 @default.
- W4361283325 cites W2463492506 @default.
- W4361283325 cites W2468620764 @default.
- W4361283325 cites W2501356178 @default.
- W4361283325 cites W2515895007 @default.
- W4361283325 cites W2524362624 @default.
- W4361283325 cites W2556376536 @default.
- W4361283325 cites W2565717971 @default.
- W4361283325 cites W2577537660 @default.
- W4361283325 cites W2581468399 @default.
- W4361283325 cites W2740841182 @default.
- W4361283325 cites W2792271439 @default.
- W4361283325 cites W2792895905 @default.
- W4361283325 cites W2800740738 @default.
- W4361283325 cites W2807490385 @default.
- W4361283325 cites W2883421345 @default.
- W4361283325 cites W2900498998 @default.
- W4361283325 cites W2932346662 @default.
- W4361283325 cites W2946722611 @default.
- W4361283325 cites W2971141280 @default.
- W4361283325 cites W2989695108 @default.
- W4361283325 cites W2990316567 @default.
- W4361283325 cites W2990395719 @default.
- W4361283325 cites W3020873672 @default.
- W4361283325 cites W3091400119 @default.
- W4361283325 cites W3092434764 @default.
- W4361283325 cites W3092728598 @default.
- W4361283325 cites W3093355121 @default.
- W4361283325 cites W3101380508 @default.
- W4361283325 cites W3118661275 @default.
- W4361283325 cites W3145085575 @default.
- W4361283325 cites W3152965655 @default.
- W4361283325 cites W3203080888 @default.
- W4361283325 cites W4200586790 @default.
- W4361283325 cites W4206680700 @default.
- W4361283325 cites W4210426905 @default.
- W4361283325 cites W4214558685 @default.
- W4361283325 cites W4220746215 @default.
- W4361283325 cites W4224216253 @default.
- W4361283325 cites W4224317046 @default.
- W4361283325 cites W4225332093 @default.
- W4361283325 cites W4283331799 @default.
- W4361283325 cites W4294237795 @default.
- W4361283325 cites W847394227 @default.
- W4361283325 cites W89691187 @default.
- W4361283325 doi "https://doi.org/10.1371/journal.pone.0283548" @default.
- W4361283325 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/36989327" @default.
- W4361283325 hasPublicationYear "2023" @default.
- W4361283325 type Work @default.
- W4361283325 citedByCount "1" @default.
- W4361283325 countsByYear W43612833252023 @default.
- W4361283325 crossrefType "journal-article" @default.
- W4361283325 hasAuthorship W4361283325A5013689370 @default.
- W4361283325 hasAuthorship W4361283325A5018937724 @default.