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: 1518833
• 负责人: Christof Teuscher
• 金额: $ 26.55万
• 依托单位: Portland State University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-01 至 2022-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1518833&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.

分子计算是一种有希望的计算范式，在纳米级评估计算函数，并在智能分子诊断和治疗学中使用了潜在的应用。分子计算系统包含生物分子，例如DNA链，旨在通过与它们结合，然后通过观察到的输入分子的模式来计算逻辑功能来检测某些输入分子。例如，为了生成输出信号而同时存在两个输入中的两个输入中的两个分子系统将被称为计算两个输入上的逻辑“和”函数。然而，尽管该领域最近取得了进步，但直接应用这些技术来解决现实世界问题的前景受到分子计算机与生物学和化学系统之间缺乏强大界面的限制。该项目将通过针对两个特定的应用领域来解决这一限制：使用分子逻辑级联反应的宽光谱化学传感和细胞表面分析。分子计算机设计，建模和实施中的艺术状态将通过计算机科学家，生物工程师，化学家和计算机工程师的研究的跨学科研究组合以及成功完成拟议活动的重要一步，这将是迈向分子计算机的常规部署，以解决这种投射型和生物学圈子中的化学循环系统，以解决该变性。细胞表面分析反应将在实验室中设计，建模和实现。这将需要在适体分离（表现出与一个或多个目标非核酸分子具有特殊结合亲和力的DNA序列）以及将其整合到分子计算系统中的特定进步。在这种情况下，适体将用作界面，允许基于理性设计的基于DNA的分子计算系统使用小分子作为输入信号。 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.此外，将设计能够自适应，生物启发的行为（例如动态学习和适应）的高级分子电路体系结构，以期在大型分子计算机中对这些特征的未来实验实现。这将包括对高度复发的，受生物启发的信息处理网络的研究，以从潜在的非特异性适体传感器中提取有意义的响应。