DMREF: DNA-Nanocarbon Hybrid Materials for Perception-Based, Analyte-Agnostic Sensing

DMREF：用于基于感知、与分析物无关的传感的 DNA-纳米碳混合材料

• 批准号: 2323759
• 负责人: Anand Jagota
• 金额: $ 199.56万
• 依托单位: Lehigh University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-10-01 至 2027-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2323759&HistoricalAwards=false
• 关键词: DMREF; DNA; Nanocarbon; Hybrid; Materials

Non-technical Description: Sensing specific molecules is essential for life and forms the basis of many diagnostic technologies. Often, this is achieved by one sensor-one analyte lock-and-key mechanisms, e.g., by antibody-antigen binding. This project develops materials for an alternative approach to sensing based on an artificial perception system. This system comprises a set of nanosensor elements chosen from the family of DNA/Single-Walled Carbon Nanotubes (SWCNT) and will work on bodily fluids. Each nanosensor element (a particular DNA/SWCNT combination) will have only a weakly specific response to an analyte or physiological state. However, acting in concert with machine learning (ML) techniques and high throughput experimental interrogation, a suitably designed nanosensor array can accurately detect or measure multiple analytes or physiological states in biofluids. A compelling feature of this approach will be the ability for a nanosensor array to be designed first and primarily by the choice of nanosensor elements and secondarily by the use of machine learning algorithms. Because the nanosensor array will be built as initially analyte-agnostic, the optimally designed sensor array has the potential to be a universal biofluid sensing system capable of diagnosing many diseases. Technical Description: The principal goal of this project will be to test the hypothesis that well-chosen nanosensor arrays can be found and trained to detect a variety of analytes or physiological states in aqueous bodily fluids. Three principal aims are proposed: to develop a method for selection of a functionally diverse set of nanosensor elements from the family of DNA/Single-Walled Carbon Nanotubes (SWCNT) based on a standard amino acid test; to demonstrate that an initially analyte-agnostic pool of nanosensor elements can be used as elements of a system that can be trained to sense specific analytes or physiological conditions; and to establish the structural and physical basis for analyte-DNA-SWCNT interactions using Coarse-Grained (CG) and All-Atom (AA) molecular models. The project integrates high-throughput experimentation, machine learning (ML), and physical modeling in order to accomplish the Materials Genome Initiative (MGI) mission to discover, manufacture, and deploy advanced materials much more quickly and at a much-reduced cost. The multidisciplinary team is led by scientists from three institutions, including a chemist, a materials scientist, a bioengineer, and a computer scientist.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.

非技术描述：感知特定分子对生命至关重要，并构成许多诊断技术的基础。通常，这通过一个传感器一个分析物的锁和钥匙机制来实现，例如，通过抗体-抗原结合。该项目开发了一种基于人工感知系统的传感替代方法的材料。该系统包括一组选自DNA/单壁碳纳米管（SWCNT）家族的纳米传感器元件，并将对体液起作用。每个纳米传感器元件（特定的DNA/SWCNT组合）将仅对分析物或生理状态具有弱特异性响应。然而，与机器学习（ML）技术和高通量实验询问相结合，适当设计的纳米传感器阵列可以准确地检测或测量生物流体中的多种分析物或生理状态。这种方法的一个引人注目的特点将是纳米传感器阵列的设计能力，首先主要是通过选择纳米传感器元件，其次是通过使用机器学习算法。由于纳米传感器阵列将被构建为初始分析物不可知的，因此优化设计的传感器阵列具有成为能够诊断许多疾病的通用生物流体感测系统的潜力。技术说明：该项目的主要目标将是测试的假设，精心挑选的纳米传感器阵列可以被发现和训练，以检测各种分析物或生理状态的含水体液。提出了三个主要目标：开发一种基于标准氨基酸测试从DNA/单壁碳纳米管（SWCNT）家族中选择功能多样的纳米传感器元件集合的方法;证明纳米传感器元件的初始分析物不可知池可以用作可以被训练以感测特定分析物或生理条件的系统的元件;并使用粗粒（CG）和全原子（AA）分子模型建立分析物-DNA-SWCNT相互作用的结构和物理基础。该项目集成了高通量实验、机器学习（ML）和物理建模，以完成材料基因组计划（MGI）的使命，即更快地发现、制造和部署先进材料，并大大降低成本。该多学科团队由来自三个机构的科学家领导，包括化学家，材料科学家，生物工程师和计算机科学家。该奖项反映了NSF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。