Skip Navigation

Quantitative Biosciences, Inc.

Superfund Research Program

A Customizable Real-Time Biosensor for Continuous Monitoring of Water Contaminants

Project Leader: Scott W. Cookson
Grant Number: R43ES028993
Funding Period: Phase I: September 2018 - August 2019

Connect with the Grant Recipients

Visit the grantee's eNewsletter page


Ensuring access to clean water for generations to come will involve developing novel approaches to determine the safety and composition of potable water that are practical and affordable. Arsenic, mercury, and cadmium are three of the top priorities among hazardous substances commonly found at Superfund sites, as they are linked to health problems in people exposed to them in drinking water, yet the current real-time monitoring methods for these and other contaminants are either extremely costly or nonexistent, making it difficult to monitor water quality with high spatial or temporal resolution.

Quantitative Biosciences (QBiSci) is developing a biosensor that uses synthetic microbial sensor strains that fluoresce in response to specific toxins to continuously monitor water for contamination. The platform will substantially improve upon currently available technologies for toxin detection, making monitoring more affordable, continuous, and field-deployable.

As part of this project, QBiSci aims to:

  • Fully characterize three synthetic E. coli strains that specifically detect arsenic, mercury, and cadmium in a continuous water stream. For a real-time sensor to be maximally effective, it must be able to report accurate toxin concentrations in real-time. Focusing on three of the highest priority contaminants as a proof of feasibility, comprehensive data is being acquired to train a machine learning algorithm to be able classify real-world samples in real-time.
  • Develop and train a classification algorithm to recognize the type and amount of each contaminant present in a continuous water stream. The ability to analyze and interpret data in real-time from a constantly fluctuating water source will require an extensive classification training effort. QBiSci's existing machine learning framework is being trained and tested using many contamination induction scenarios, ranging from sudden pulses to subtly varying concentrations.
  • Develop a microfluidic cartridge system that reduces device complexity and enables sensor deployment with minimal intervention. QBiSci is developing a swappable cartridge system using devices that are pre-loaded with biologically-stable strains and can simply be “plugged in” to the sensor platform to achieve repeatable results in a user-friendly manner. The development of a method for thermoplastic device fabrication will enable the more precise connections required for a cartridge clamping system that will require little operational expertise.

The research team is working to develop a biosensor capable of real-time quantification of arsenic, mercury, and cadmium in a continuous water input. Further research will focus on real-world performance evaluations of these sensors via deployment in areas of concern and comparison of their results to standard techniques as well as an expansion of the platform to detect other contaminants quantitatively and continuously.

Access to clean, reliable water supplies is critical to our quality of life and our economy, yet across the country over 100,000 hazardous waste sites are so heavily contaminated that the underlying groundwater doesn't meet drinking water standards. QBiSci is developing a customizable real-time biosensor that will enable contamination monitoring to become more affordable, continuous, and eld-deployable and will facilitate improved management decisions aimed at reducing toxin concentrations in the environment, tracking the progression of contamination plums, and targeting investments in remediation efforts.

to Top