Engineered bacterial sensors for rapid acrylamide detection

Overview

This project develops genetically engineered bacterial sensors capable of detecting acrylamide in under 15 to 30 minutes at low parts-per-billion concentrations. Acrylamide is a probable carcinogen that forms in starchy foods during high-temperature cooking and is also an industrial contaminant of concern. The solution leverages synthetic biology to program bacteria to recognize acrylamide and produce a measurable signal, offering a faster and more affordable alternative to conventional laboratory-based detection methods.

The core value proposition is a living sensor that is self-replicating, inexpensive to deploy, and capable of long-term real-time monitoring without expensive equipment. The platform can be adapted to produce either a luminescent readout for ultra-low detection limits or a colorimetric readout for simple visual analysis in field settings. Potential applications include food safety screening, industrial wastewater monitoring, and environmental contamination assessment.

Technical specifications

Key features:

• Bacteria engineered to detect acrylamide at low ppb concentrations with response times of 15-30 minutes
• Luminescent readout option for ultra-low detection limits
• Colorimetric readout option for simple, equipment-free visual analysis
• Built on synthetic biology frameworks demonstrated for detecting explosives, nerve agents, heavy metals, and environmental contaminants
• Feedback signaling pathways engineered to amplify signal output for improved sensitivity
• Machine learning algorithms applied to optimize sensor performance and tune detection parameters
• Self-replicating living sensor eliminates need for frequent replacement or resupply
• Scalable to diverse environments without requiring expensive instrumentation

Technology readiness level

The research is in an early-to-mid stage of development. Prior published work has validated the broader approach of engineering bacteria to detect multiple classes of molecules, establishing proof-of-concept for the synthetic biology platform. Current and planned validation includes three specific aims: engineering bacteria with a luminescent acrylamide readout, measuring the lower limit of detection and speed of response, and optimizing sensor performance using machine learning combined with engineered feedback signaling pathways. Key parameters under evaluation include sensor selectivity, detection sensitivity, and response speed. The technology is not yet commercially available and requires further laboratory validation before deployment-ready status.