In:
PLOS ONE, Public Library of Science (PLoS), Vol. 18, No. 3 ( 2023-3-29), p. e0283548-
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 ( EC 50 ) and saturating output to meet quantitative specifications. In addition, we engineer sensors with inverted dose-response and specified EC 50 . 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.
Type of Medium:
Online Resource
ISSN:
1932-6203
DOI:
10.1371/journal.pone.0283548
DOI:
10.1371/journal.pone.0283548.g001
DOI:
10.1371/journal.pone.0283548.g002
DOI:
10.1371/journal.pone.0283548.g003
DOI:
10.1371/journal.pone.0283548.g004
DOI:
10.1371/journal.pone.0283548.g005
DOI:
10.1371/journal.pone.0283548.g006
DOI:
10.1371/journal.pone.0283548.g007
DOI:
10.1371/journal.pone.0283548.g008
DOI:
10.1371/journal.pone.0283548.g009
DOI:
10.1371/journal.pone.0283548.s001
DOI:
10.1371/journal.pone.0283548.s002
DOI:
10.1371/journal.pone.0283548.s003
DOI:
10.1371/journal.pone.0283548.s004
DOI:
10.1371/journal.pone.0283548.s005
DOI:
10.1371/journal.pone.0283548.r001
DOI:
10.1371/journal.pone.0283548.r002
DOI:
10.1371/journal.pone.0283548.r003
DOI:
10.1371/journal.pone.0283548.r004
DOI:
10.1371/journal.pone.0283548.r005
Language:
English
Publisher:
Public Library of Science (PLoS)
Publication Date:
2023
detail.hit.zdb_id:
2267670-3
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