FET: Small: Exploring the Computational Power of Stochastic Processes in Molecular Information Technology

FET：小型：探索分子信息技术中随机过程的计算能力

• 批准号: 2008589
• 负责人: Erik Winfree
• 金额: $ 45万
• 依托单位: California Institute of Technology
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-08-01 至 2024-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2008589&HistoricalAwards=false
• 关键词: FET; Small; Exploring; Computational; Power

As computing technology matures, it becomes possible to embed programmable computing devices into objects and materials where it was previously almost unthinkable: autonomous robots on Mars, “smart dust” femtosatellites and “smart paint” with embedded millimeter-scale electronic circuits, “smart” molecular therapeutics with embedded biochemical circuits, genetically engineered living cells with embedded genetic regulatory networks controlling their activity, and “smart” chemistry with programmable molecular robots that control the assembly and disassembly of molecular materials, for example. As miniaturization reaches the nanometer and molecular scale, both device fabrication and device operation become unreliable, ultimately dominated by stochastic effects. Despite decades of study, the theory of computation in the presence of high levels of stochasticity remains underdeveloped, and the practice of building stochastic computing systems is limited accordingly. While the majority of prior work has focused on error-tolerant designs that enable robust implementation of deterministic computation using unreliable and stochastic components, this project will investigate how the abundantly available stochastic operation of molecular devices can provide augmented computing power – going beyond what a deterministic implementation could achieve with the same resources. As such, it will help establish a rigorous computer-science foundation for molecular information technology. Long-term, programmable molecular information technology is poised to eventually impact industry and society broadly, as programmable chemistry will enable information-based responsive molecular materials, advanced biomedical therapeutics and diagnostics, sophisticated chemical synthesis and molecular-scale instruments, and other applications of molecular nanotechnology. The proposal includes education and outreach plans to train and prepare students with emphasis on recruiting students from women and minority groups.Initial investigations will consider models of computation that have been used in the rapidly developing fields of DNA nanotechnology and molecular programming: formal chemical-reaction networks, molecular tile self-assembly systems, polymer-reaction networks and reaction-diffusion systems. Recent work has shown that well-mixed chemical-reaction networks operating in small volumes can utilize their stochasticity to represent complex probability distributions, to perform information-processing tasks such as probabilistic inference, and to effectively search for solutions to complex combinatorial problems. This project aims to improve understanding of the benefits of stochastic molecular computation by building on these insights. First, it will establish a complexity theory for chemical-reaction networks that generate probability distributions. Second, it will explore how stochastic constraint satisfaction by chemical-reaction networks can lead to robust spatial pattern formation in self-organizing reaction-diffusion systems and other models that incorporate geometry. Third, it will develop an understanding of how stochastic self-assembly processes can augment the power of algorithmic self-assembly. A concrete outcome will be a demonstration of how the stochastic nucleation of self-assembled DNA structures can perform an information-processing task similar to pattern recognition by neural networks.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.

随着计算技术的成熟，将可编程计算设备嵌入到物体和材料中成为可能，这在以前几乎是不可想象的：火星上的自主机器人，嵌入毫米级电子电路的“智能尘埃”毫微微卫星和“智能涂料”，嵌入生化电路的“智能”分子疗法，嵌入基因调控网络控制其活动的基因工程活细胞，以及“智能”化学，例如，可编程分子机器人控制分子材料的组装和拆卸。随着微型化达到纳米和分子尺度，器件制造和器件操作都变得不可靠，最终由随机效应主导。尽管经过了几十年的研究，在高水平的随机性存在的计算理论仍然不发达，并建立随机计算系统的实践是有限的。虽然大多数先前的工作都集中在容错设计，使确定性计算的鲁棒实现使用不可靠和随机组件，这个项目将研究如何丰富的可用随机操作的分子设备可以提供增强的计算能力-超越确定性实现可以实现相同的资源。 因此，它将有助于为分子信息技术建立严格的计算机科学基础。长期的可编程分子信息技术将最终广泛影响工业和社会，因为可编程化学将使基于信息的响应分子材料，先进的生物医学治疗和诊断，复杂的化学合成和分子尺度仪器以及分子纳米技术的其他应用成为可能。该提案包括教育和推广计划，以培养和准备学生，重点是招收妇女和少数群体的学生，初步调查将考虑已被用于DNA纳米技术和分子编程的快速发展领域的计算模型：正式的化学反应网络，分子瓦自组装系统，聚合物反应网络和反应扩散系统。最近的研究表明，在小体积下运行的良好混合的化学反应网络可以利用其随机性来表示复杂的概率分布，执行信息处理任务，如概率推理，并有效地搜索复杂组合问题的解决方案。 该项目旨在通过建立这些见解来提高对随机分子计算益处的理解。首先，它将为产生概率分布的化学反应网络建立一个复杂性理论。其次，它将探讨如何随机约束满足化学反应网络可以导致强大的空间模式形成自组织反应扩散系统和其他模型，包括几何。第三，它将发展如何随机自组装过程可以增强算法自组装的力量的理解。 具体成果将是展示自组装DNA结构的随机成核如何执行类似于神经网络模式识别的信息处理任务。该奖项反映了NSF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。