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

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 advanced in large language models in artificial intelligence (AI) trained on existing GPCRs to engineer new recognition proteins. High-through 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.

该项目寻求合成生物学，生物电子学和机器学习方法的融合，以提供使用点传感器，以评估水质，对公共卫生和环境保护具有广泛的影响。该活动的主要结果将是在整个水周期中评估水中化学物质的新的低成本传感器，包括废水治疗监测，饮用水监测，工业用水和纽约市最大的大都市地区之一的雨水排放。这项工作通过纽约市环境保护部以及大学和行业合作伙伴的参与来吸引公共部门。这些收敛研究活动的结果将是一个框架，用于快速开发基于微生物的生物传感器，以检测广泛的分析物，同时连接到互补的金属 - 氧化物 - 氧化物 - 氧化型读出设备。将对环境保护署（EPA）建立的良好质量指标进行评估。在重要的先前工作的基础上，该项目将采用酵母（酿酒酵母）作为工程感应微生物，这是一种表达真核性重组蛋白的强大底盘。该项目将主要使用工程G蛋白偶联受体（GPCR）作为酵母中的识别蛋白，使用对现有GPCR培训的最新高级语言模型（AI），以设计新的识别蛋白。高直接DNA合成和筛选方法将被雇用以快速评估候选蛋白。该项目将从这些传感器中开发“模拟”和“数字”读数，同时采用其他具有反馈的遗传控制系统，以在存在噪声和混杂因素的情况下提高感应鲁棒性。对于输出，该项目将使用酵母在灵敏度时使用酵母表达的光学绝对（颜料）和氧化还原活跃的胡椒体。这两种方法都使酵母很容易与低成本互补的金属氧化物 - 氧化物 - 副导体（CMOS）读出的设备进行接口，这将是这项工作的另一个收敛方面。该奖项反映了NSF的法定任务，并被认为是通过基金会的知识分子和宽广的影响来评估的支持，并被视为珍贵的支持。