SHF: Large: Collaborative Research: Molecular computing for the real world

SHF：大型：协作研究：现实世界的分子计算

• 批准号: 1832985
• 负责人: Sergei Rudchenko
• 金额: $ 39.61万
• 依托单位: Columbia University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-01-01 至 2020-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1832985&HistoricalAwards=false
• 关键词: SHF; Large; Collaborative; Research; Molecular

Molecular computing is a promising computational paradigm in which computational functions are evaluated at the nanoscale, with potential applications in smart molecular diagnostics and therapeutics. A molecular computing system comprises biomolecules, such as DNA strands, which have been designed to detect certain input molecules by binding to them and subsequently to undergo programmed sequences of chemical reactions that serve to compute a logical function based on the observed pattern of input molecules. For example, a molecular system that requires both of its two inputs to be present simultaneously in order to generate an output signal would be referred to as computing a logical "AND" function on the two inputs. However, despite recent advances in the field, prospects for direct application of these techniques to solve real-world problems are limited by the lack of robust interfaces between molecular computers and biological and chemical systems. This project will address this limitation by targeting two specific application domains: wide-spectrum chemical sensing and cell surface analysis using molecular logic cascades. The state of the art in molecular computer design, modeling, and implementation will be advanced by an interdisciplinary combination of research by computer scientists, bioengineers, chemists, and computer engineers, and successful completion of the proposed activity will be a significant step towards routine deployment of molecular computers to address real-world problems in chemical and biological sensing.In this project, molecular circuit architectures that process sensor inputs from chemical sensors and cell-surface analysis reactions will be designed, modeled, and implemented in the laboratory. This will require specific advances in the isolation of aptamers (DNA sequences that exhibit particular binding affinity to one or more target non-nucleic acid molecules) and in their integration into molecular computing systems. In this context, the aptamer will serve as an interface that allows a rationally-designed DNA-based molecular computing system to use small molecules as input signals. Furthermore, computational modeling and simulation will be used to predict and optimize interactions between DNA aptamers and a range of binding targets, and to choose optimal aptamer combinations to produce cross-reactive multi-sensor arrays capable of discriminating between target ligands by effectively projecting the signal into a multi-dimensional aptamer response space. Furthermore, advanced molecular circuit architectures capable of adaptive, bio-inspired behavior, such as dynamic learning and adaptation, will be designed, with a view to future experimental implementations of these features in large-scale molecular computers. This will include research on highly recurrent, bio-inspired information processing networks to extract meaningful responses from potentially non-specific aptamer-based sensors.

分子计算是一种很有前途的计算范式，其计算功能在纳米尺度上进行评估，在智能分子诊断和治疗方面具有潜在的应用前景。分子计算系统包括生物分子，如DNA链，它们被设计成通过与某些输入分子结合来检测它们，并随后经历编程的化学反应序列，该序列用于基于观察到的输入分子的模式计算逻辑函数。例如，一个需要两个输入同时存在以产生输出信号的分子系统将被称为在两个输入上计算逻辑与函数。然而，尽管该领域最近取得了进展，但由于分子计算机与生物和化学系统之间缺乏强大的接口，直接应用这些技术来解决现实世界问题的前景受到限制。该项目将针对两个特定的应用领域来解决这一限制：广谱化学传感和使用分子逻辑级联的细胞表面分析。计算机科学家、生物工程师、化学家和计算机工程师的跨学科研究将推动分子计算机设计、建模和实现的最新水平，而拟议活动的成功完成将是分子计算机常规部署的重要一步，以解决现实世界中的化学和生物传感问题。在这个项目中，处理来自化学传感器的传感器输入和细胞表面分析反应的分子电路体系结构将在实验室设计、建模和实现。这将需要在适配子(对一个或多个目标非核酸分子表现出特殊结合亲和力的DNA序列)的分离以及在将其整合到分子计算系统方面取得具体进展。在这种情况下，适体将作为一个接口，允许合理设计的基于DNA的分子计算系统使用小分子作为输入信号。此外，计算建模和模拟将用于预测和优化DNA适配子与一系列结合靶之间的相互作用，并选择最佳适配子组合，以通过将信号有效地投影到多维适配子响应空间来产生能够区分靶配体的交叉反应多传感器阵列。此外，还将设计能够自适应、受生物启发的行为的先进分子电路体系结构，如动态学习和适应，以期在未来的大型分子计算机中实现这些特征的实验实现。这将包括对高度重复的、生物启发的信息处理网络的研究，以从潜在的非特定适体传感器中提取有意义的反应。