SHF: Small: Experimental and Theoretical Development of Error Correction and Digitalization Concepts for Multi-Enzyme Biomolecular Computing Networks

SHF：小型：多酶生物分子计算网络的纠错和数字化概念的实验和理论发展

• 批准号: 1015983
• 负责人: Evgeny Katz
• 金额: $ 45万
• 依托单位: Clarkson University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-09-01 至 2013-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1015983&HistoricalAwards=false
• 关键词: SHF; Small; Experimental; Theoretical; Development

Biocomputing systems are being researched for multi-stage information processing in applications ranging from novel biosensors, to signaling and fast decision making in cases of trauma/injury, implantable devices, biofuel cells, and ultimately, interfacing of living organisms with Si electronics. This research program aims at advancing experimental design and theoretical modeling/optimization of primarily enzyme-based logic gates, as well as developing a toolbox of non-Boolean and bio-inspired network elements. Approaches will be developed for realizing interconnected networks, as well as for interfacing biochemical logic with bioelectronic systems. Models will be devised for improving the performance of biochemical logic networks in terms of noise reduction, robustness, fault-tolerance, and scalability, for information processing applications. Thus, the present project will advance the basic science of scaling up information processing with biomolecules, primarily, with enzymes, beyond single gates to multi-element networks. The research plan involves both experimental investigations of enzyme-based biochemical gates and their networking as well as theoretical analysis of the concepts and results. Experimental aspects of the work will include the selection and networking of enzymatic systems performing logic processing of biochemical signals at various levels of complexity. The theoretical part will encompass optimization and computer simulation of the biocomputing systems to ensure their stable and fault-tolerant operation. We will specifically address noise reduction and robustness of biomolecular information processing systems. Biocomputing elements of even moderate complexity will allow effective interfacing between complex physiological systems and nanostructured materials and/or electronic devices. Thus, the broader impacts of this research will include the advancement of a new emerging technological paradigm: Networking of information processing stages that involve biochemical processes. We aim at taking out the wires and batteries, and reducing the overall need for electrical power supplies at those stages of information processing that are carried out on-site in implantable devices and other biotechnology applications, as well as contributing to the advancement of novel biosensor concepts. Significant educational impact will be achieved by training of postdocs and educating Ph.D. students, offering undergraduate research projects, K-12 outreach, and developing new coursework.

生物计算系统正在研究多阶段信息处理的应用范围从新型生物传感器，信号和快速决策的情况下，创伤/损伤，植入式设备，生物燃料电池，并最终，接口的生物有机体与硅电子。该研究计划旨在推进主要基于酶的逻辑门的实验设计和理论建模/优化，以及开发非布尔和生物启发网络元素的工具箱。将开发实现互连网络的方法，以及将生物化学逻辑与生物电子系统连接起来的方法。模型将被设计用于改善生物化学逻辑网络的性能，在降噪，鲁棒性，容错性和可扩展性方面，信息处理应用。因此，本项目将推进生物分子（主要是酶）信息处理的基础科学，超越单门到多元素网络。研究计划包括基于酶的生化门及其网络的实验研究以及概念和结果的理论分析。这项工作的实验方面将包括酶系统的选择和网络化，这些酶系统在各种复杂程度上对生化信号进行逻辑处理。理论部分将包括生物计算系统的优化和计算机模拟，以确保其稳定和容错操作。我们将专门讨论生物分子信息处理系统的降噪和鲁棒性。 即使是中等复杂性的生物计算元件也将允许复杂的生理系统与纳米结构材料和/或电子设备之间的有效接口。因此，这项研究的更广泛的影响将包括一个新的新兴技术范式的进步：涉及生物化学过程的信息处理阶段的网络化。我们的目标是取出电线和电池，并减少在植入式设备和其他生物技术应用中现场进行的信息处理阶段对电源的总体需求，并为新型生物传感器概念的发展做出贡献。通过培养博士后和培养博士生，将产生重大的教育影响。学生，提供本科研究项目，K-12推广，并开发新的课程。