SHF: Small: Localized DNA Hybridization Computation

该项目的重点是计算中的一个新兴挑战：在纳米尺度上扩展对物质和现象的编程控制。利用基于DNA的反应的纳米系统是实现这一目标的一种有前途的技术，因为它们是可行的设计，模拟和实验测试。在过去的二十年里，DNA计算系统的复杂性不断增加。这些系统中的大多数涉及通过基于扩散的杂交化学相互作用的多条DNA链。虽然这种模式具有许多优点和优点，但基于扩散的DNA杂交计算存在根本限制，特别是由于更大规模的复杂性所增加的时间。这项工作旨在研究一种基于DNA杂交的计算的替代范式，该计算在基板上本地操作。由于反应物种的局部浓度增加，局部性允许反应以更高的速度进行-这种局部化可能会使基于DNA杂交的计算加速一个数量级。此外，由于每个局部反应路径彼此不干扰，因此还可以同时并行执行多个路径。这也允许在空间上分离的区域中重复使用DNA序列，从而增加反应的模块性和可扩展性。智力优势：研究工作涵盖理论和实验技术，包括开发生物物理数学模型，设计软件，计算模拟，小规模实验演示。特别是，这项工作将开发生物物理模型的本地化杂交，这将是模拟，并通过简单的动力学实验验证。这些实验提供了有关本地化分子的杂交化学中所涉及的速率常数的关键数据。模拟模型将根据实验数据进一步完善。作为这项工作的核心，一个主要的挑战是泄漏：导致纳米系统显着偏离其编程轨迹的意外反应，可能发生在局部杂交系统。多个泄漏模型将在实验室通过简单的实验进行测试。继续与Andrew菲利普斯博士（微软剑桥研究院）的持续合作，由微软内部资助，这项工作还将创建模拟局部杂交网络的软件系统。模拟软件的开发将与实验过程紧密结合，根据实验数据不断完善模拟模型和参数。最后，这项工作将通过实验实现一系列小到中等规模的局部杂交系统，以证明局部杂交反应的可行性和潜力。这项工作还将研究使用局部性来加速其他相关分子尺度计算过程的更广泛问题，包括使用酶的反应，或其他基于蛋白质的反应，以及DNA杂交反应。更广泛的影响：纳米科学、生物化学和化学将受到多学科的影响，这些学科将受益于引入来自主流计算机科学的关键方法，如数学建模、软件工程、算法和模块化设计方法。教育影响包括四名博士生的跨学科培训，认真监督的指导和本科生的暑期实习。

This project focuses on an emerging challenge in computation: to extend programmatic control over matter and phenomenon at the nanoscale. Nanosystems making use of DNA-based reactions are a promising technique to achieve this since they are feasible to design, simulate and test experimentally. DNA computation systems of increasing complexity have been demonstrated over the past two decades. Most of these systems involve multiple strands of DNA that interact with each other via diffusion based hybridization chemistry. While this paradigm has many advantages and merits, there are fundamental limits to diffusion based DNA hybridization computations, particularly due the increased time for larger-scales of complexity. This work seeks to study an alternate paradigm of DNA hybridization-based computations that operate locally on a substrate. Locality allows reactions to proceed at higher speed due to increased local concentration of reacting species - this localization could potentially speed up DNA hybridization-based computations by an order of magnitude. Also, since each of the local reaction pathways do not interfere with each other, it is also possible to simultaneously execute multiple pathways in parallel. This also allows one to reuse DNA sequences in spatially separated regions that increase the modularity and scalability of the reactions. Intellectual Merit: The research work spans both theory and experimental techniques, and includes development of biophysical mathematical models, design software, computational simulations, small-scale experimental demonstrations. In particular, the work will develop biophysical models of localized hybridization, which will be simulated, and also verified via simple kinetic experiments. The experiments provide crucial data about the rate constants involved in the hybridization chemistry of localized molecules. The simulation model will be further refined based on the experimental data,. A major challenge addressed as a center-piece of this effort is leaks: the unintended reactions that cause the nanosystem to significantly deviate from its programmed trajectory that might occur in localized hybridization systems. Multiple leak models will be tested in the lab via simple experiments. Continuing an on-going collaboration with Dr. Andrew Phillips (Microsoft Research Cambridge), funded internally by Microsoft, this work will also create software systems that will simulate localized hybridization networks. The simulation software development will be tightly coupled to the experimental progress by constantly refining the simulation models and parameters based on experimental data. Finally, this work will experimentally implement a series of small to moderate scale localized hybridization systems to demonstrate the feasibility and the potential of localized hybridization reactions. The work will also investigate the broader issues of the use of locality to speed-up other related molecular-scale computation processes, including reactions that make use of enzymes, or other protein-based reactions, in addition to DNA hybridization reactions. Broader Impact: There is substantial multidisciplinary impact to nanoscience, biochemistry and chemistry, which will profit from the introduction of key methodologies derived from mainstream computer science, such as mathematical modeling, software engineering, algorithms and modular design methodologies. Educational impact includes cross-disciplinary training of four PhD students, carefully supervised mentoring and summer internships for undergraduates.