FET: Small: A closed-loop electronically automated platform for debugging synthetic biology

该项目旨在增加合成生物学调试系统的可扩展性，这些系统可以在医学和农业中扩展基于细胞的传感器和执行器的能力，以及在分子生物学研究中增加新知识的产生。这将通过建立一个端到端的生物分子-硅杂化系统来实现。广泛地说，由此产生的系统将：(1)探索通过将生物分子存储和计算与电子学相结合来构建混合生物分子硅计算机系统的新方法，以及(2)自动化闭环合成生物学设计-构建-测试-学习过程。重要的是，该项目还将培养生物学和计算交叉领域的学生和专业人员，这是一个非常有前途的科学和经济发展新领域。这项工作的成果还将被整合到嵌入式计算/流体学、分子生物学和机器学习的新课程教材中，目标是训练学生在分子计算/合成生物学中快速构建想法的原型。研究人员将在一个闭环系统上设计和构建可扩展的生物分子计算和调试工具，该闭环系统集成了用于生物分子到数字读出(DNA和蛋白质)的纳米孔传感器阵列和用于数字到生物分子接口的DNA合成器，通过数字/液滴微流控系统和集成编程模型(PurpleDrop+Puddle)实现自动化。数字流体系统将使用计算机视觉技术来可靠地控制液滴的运动。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，以低成本和高通量识别分子输出。具体地说，该系统将实现两个主要目标。第一个目标是将DNA合成和数字微流控技术结合起来，用于组装生物部件，以实现自动化克隆，并与基于DNA测序的质量控制保持一致。第二个目标是转录电路自动调谐，它将使用新的基因报告蛋白质库对设计的体内转录电路进行自动表征和并行组件调试/调谐。对于每个目标，纳米孔传感器阵列将读出分子结果(DNA或蛋白质)；然后控制系统(Puddle)将解释数据并确定下一步的流体操纵行动以及生产额外的合成DNA以指导设计或发现过程。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

This project aims to increase the scalability of synthetic-biology debugging systems that could have broad applications in extending capabilities for cell-based sensors and actuators in medicine and agriculture, as well as for addition to new knowledge generation in molecular-biology research. This will be accomplished by building an end-to-end biomolecular-silicon hybrid system. Broadly, the resulting system will: (1) explore new ways to build hybrid bio-molecular silicon computer systems by integrating bio-molecular storage and computing with electronics, and (2) automate closed-loop synthetic biology design-build-test-learn processes. Importantly, this project will also train students and professionals in the intersection of biology and computing, which is a very promising new area of scientific and economic development. Results from this work will also be incorporated into new course materials at the intersection of embedded computing/fluidics, molecular biology and machine learning, with the goal of training students to quickly prototype ideas in molecular computing/synthetic biology.The investigators will design and build scalable bio-molecular computing and debugging tools on top of a closed-loop system that integrates a nanopore sensor array for biomolecule-to-digital read-out (DNA and protein) with a DNA synthesizer for digital-to-biomolecule interface, automated with a digital/droplet microfluidic system and an integrated programming model (PurpleDrop+Puddle). The 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. Specifically, the system will achieve two main objectives. The first objective will combine DNA synthesis and digital microfluidics for assembly of biological parts to implement automated cloning, in-line with DNA sequencing-based quality control. The second objective is transcriptional circuit auto-tuning, which will carry out automated characterization and parallel component debugging/tuning of designed in-vivo transcriptional circuits using a library of new genetic reporter proteins. For each objective, the nanopore sensor array will read-out the molecular results (DNA or protein); the control system (Puddle) will then interpret the data and determine the next fluidic manipulation actions as well as the production of additional synthetic DNA to guide the design or discovery process.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.