We present the successful application of diverse computational and data analysis models toward developing a bacterial biosensor engineered to detect DNT with high sensitivity and specificity.
First-pass extracted concept
computational modeling-guided biosensor design
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Aliases
diverse computational and data analysis models, integrating computational modeling with synthetic biology techniques
Evidence Snippets
Supporting Sources
Linked Claims
Computational insights guided rational biosensor design that improved DNT detection capabilities compared with the original biosensor strain.
Quoted textsource-backed
These computational insights guided the rational design of the biosensor, leading to significantly improved DNT detection capabilities compared to the original biosensor strain.
Analysis of endogenous and heterologous promoter data under DNT exposure was used to generate 367 novel biosensor variants.
Quoted textsource-backed
By analyzing endogenous and heterologous promoter data under conditions of DNT exposure, a total of 367 novel variants were generated.
Computational and data analysis models were successfully applied to develop an E. coli bacterial biosensor for DNT detection with high sensitivity and specificity.
Quoted textsource-backed
we present the successful application of diverse computational and data analysis models toward developing a bacterial biosensor engineered to detect DNT with high sensitivity and specificity
DNA folding patterns and nucleotide motifs associated with DNT sensing were identified as sequence features with the highest contribution to biosensor performance.
Quoted textsource-backed
Our analysis suggests that the sequence features with the highest contribution to biosensor performance are DNA folding patterns and nucleotide motifs associated with DNT sensing.