# Design of bacterial DNT sensors based on computational models

## TL;DR

Researchers designed a bacterial biosensor to detect DNT using computational models, significantly improving sensitivity and response time.

## Contribution

A novel integration of computational modeling and synthetic biology to enhance biosensor performance for DNT detection.

## Key findings

- 367 novel promoter variants were generated to improve biosensor performance.
- Biosensors showed up to a four-fold increase in signal intensity upon DNT exposure.
- DNA folding patterns and nucleotide motifs were identified as key contributors to improved detection.

## Abstract

Detecting explosive compounds, such as 2,4,6-trinitrotoluene and its volatile byproduct 2,4-dinitrotoluene (DNT), is paramount for public health and environmental safety. In this study, 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. The Escherichia coli-based biosensor harbors a plasmid-based fusion of a gene promoter, acting as the sensing element, to a microbial bioluminescence gene cassette as the reporter. By analyzing endogenous and heterologous promoter data under conditions of DNT exposure, a total of 367 novel variants were generated. The biosensors engineered with these modifications demonstrated a remarkable amplification of up to four-fold change in signal intensity upon exposure to 2,4-dinitrotoluene, compared to non-modified biosensors, accompanied by a decrease in the detection threshold and a shortening of the response times. 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. These computational insights guided the rational design of the biosensor, leading to significantly improved DNT detection capabilities compared to the original biosensor strain. Our results demonstrate the effectiveness of integrating computational modeling with synthetic biology techniques to develop advanced biosensors tailored for environmental monitoring applications. A similar approach may be applied to a wide array of ecological, industrial, and medical sensing endeavors.

Graphical Abstract