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

非技术描述：感知特定分子对生命至关重要，是许多诊断技术的基础。通常，这是通过一个传感器-一个分析物的锁-键机制来实现的，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替代传感方法的材料。该系统包括一组从DNA/单壁碳纳米管（SWCNT）家族中选择的纳米传感器元件，并将在体液上工作。每个纳米传感器元件（特定的DNA/ swcnts组合）对分析物或生理状态只有弱特异性响应。然而，与机器学习（ML）技术和高通量实验询问相配合，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是，纳米传感器阵列的设计首先和主要是通过纳米传感器元件的选择，其次是通过机器学习算法的使用。由于纳米传感器阵列最初将被构建为与分析物无关的，因此优化设计的传感器阵列有可能成为能够诊断许多疾病的通用生物流体传感系统。技术描述：这个项目的主要目标是测试一个假设，即精心选择的纳米传感器阵列可以被发现并训练来检测各种分析物或水体液中的生理状态。提出了三个主要目标：开发一种基于标准氨基酸测试的DNA/单壁碳纳米管（SWCNT）家族中功能多样化的纳米传感器元件的选择方法；为了证明一个最初与分析物无关的纳米传感器元件池可以用作一个系统的元件，该系统可以被训练来感知特定的分析物或生理条件；并利用粗粒度（CG）和全原子（AA）分子模型建立分析物- dna - swcnts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，以实现材料基因组计划（MGI）的使命，以更快、更低的成本发现、制造和部署先进材料。这个多学科团队由来自三个机构的科学家领导，包括一位化学家、一位材料科学家、一位生物工程师和一位计算机科学家。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。

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.