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NSFDirectorate for Technology, Innovation, and Partnerships

NSF Convergence Accelerator Track L: Engineered microbial sensors for assessing water quality

Completed
Institution
Columbia University, NY
Principal investigator
Virginia W Cornish
Funding
$650K
Duration
2024–2025
Open portfolio

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Abstract

The project seeks the convergence of synthetic biology, bioelectronics, and machine learning approaches to provide point-of-use sensors for assessing water quality with broad implications for public health and environmental protection. The primary results of this activity will be new low-cost sensing systems for assessing chemicals in water as applied throughout the water cycle, including waste-water treatment monitoring, drinking-water monitoring, industrial water use, and storm-water discharges in one of the largest metropolitan regions in the world, New York City. This work engages the public sector through the participation of the New York City Department of Environmental Protection as well as university and industry partners. The result of these convergence research activities will be a framework for quickly developing microbial-based biosensors to detect broad classes of analytes, while interfacing to complementary metal-oxide-semiconductor read-out devices. Sensors will be evaluated against well-established quality metrics established by the Environmental Protection Agency (EPA). Building on significant prior work, this project will employ yeast (Saccharomyces cerevisiae) as the engineered sensing microbe, a powerful chassis for the expression of eukaryotic recombinant proteins. The project will focus primarily on engineering G-protein-coupled receptors (GPCRs) as recognition proteins in yeast, using the latest advances in large language models in artificial intelligence (AI) trained on existing GPCRs to engineer new recognition proteins. High-throughput DNA synthesis and screening approaches will be employed to rapidly assess candidate proteins. The project will develop both “analog” and “digital” readout from these sensors, while employing other genetic control systems with feedback to improve sensing robustness in the presence of noise and confounders. For output, the project will use optical absorption (pigments) and redox-active peptides expressed by the yeast upon sensing. Both of these approaches allow the yeast to be easily interfaced with low-cost complementary metal-oxide-semiconductor (CMOS) read-out devices, which will be another convergent aspect of this effort. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

Research team

  • Virginia W CornishPI
  • Kenneth L ShepardCo-PI
  • Kartik ChandranCo-PI
  • Mohammed AlquraishiCo-PI
  • Alex RosenthalCo-PI

LOADING MOONBASE SCORE

Directorate for Technology, Innovation, and PartnershipsConvergence Accelerator Resrchsensorsconvergencewater qualityenvironmental protectionyeastyork cityproteins projectgpcrs

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