EAGER: Closed-loop Silicon-biomolecular Systems with Integrated Synthesis-fluidics-nanopore Interfaces

鉴于摩尔定律带来的成本效益增长放缓，以及数字存储技术的可扩展性限制，生物分子是信息存储和处理的有吸引力的替代方案——尤其是DNA数据存储，由于其密度、耐用性和可行性，尤其令人感兴趣。与此同时，由于其高性能和可设计的能力，电子产品可能会继续成为计算系统的一个组成部分。因此，考虑混合生物分子-电子系统是很自然的。为此，本项目致力于建立一个完全集成的闭环系统，包括DNA合成、分子传感器（纳米孔）和流体技术，用于生物分子信息处理的新应用。如果成功，科学界将提供新的和易于使用的方法来加速分子数据存储-处理-计算和合成生物学实验。这将使分子计算和合成生物学的应用更容易为更广泛的社区所接受。将体内和体外生物分子组件与硅系统相结合，可以在健康诊断和治疗、新材料、食品和信息技术等一系列领域带来创新。研究人员将设计一个新的分子部件工具箱，用于存储和传输合成DNA和蛋白质的纳米孔可寻址分子条形码形式的信息。数字流体系统将采用计算机视觉技术来可靠地控制液滴运动。研究人员将开发机器学习技术来分析原始纳米孔传感器数据，以实现低成本和高通量的分子输出鉴定。作为该项目的一部分，这些组件在集成系统内的演示将展示推进分子信息处理能力的原理验证应用。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Given the slowing down of cost-performance gains from Moore's law and the scalability limits of digital storage technology, biomolecules are attractive alternatives for information storage and processing -- DNA data storage in particular is especially interesting due to its density, durability, and path to feasibility. At the same time, electronics will likely continue to be an integral part of computing systems, due to its high performance and the ability to be engineered. Hence, it is natural to consider hybrid biomolecular-electronic systems. Towards this end, this project is focused on building a fully integrated, closed-loop system that includes DNA synthesis, molecular sensors (nanopore), and fluidics for novel applications in biomolecular information processing. If successful, the scientific community will be provided with new and easy-to-use methods to accelerate molecular data storage-processing-computing and synthetic biology experimentation. This will make molecular computing and synthetic biology applications more accessible to the broader community. Integrating in-vivo and in-vitro biomolecular components with silicon systems can lead to innovations in a range of areas from health diagnostics and therapies, new materials, food, to information technology.The investigators will engineer a toolbox of new molecular parts that can be used to store and transmit information in the form of nanopore-addressable molecular barcoding of synthetic DNA and proteins. A digital fluidics system will employ computer vision techniques for reliable control of droplet movements. The investigators will develop machine learning techniques to analyze raw nanopore sensor data for low-cost and high-throughput identification of molecular outputs. Demonstration of these components within the integrated system to be developed as part of this project will show proof-of-principle applications that advance molecular information processing capabilities.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.