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.

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 therapy with embedded biochemical circuits, genetically engineered living cells with embedded genetic regulatory networks例如，使用可编程的分子机器人来控制其活性和“智能”化学，例如控制分子材料的组装和拆卸。随着微型化到达纳米尺度和分子尺度，设备制造和设备操作变得不可靠，最终由随机效应主导。尽管进行了数十年的研究，但在高水平的随机性存在下的计算理论仍然欠发达，并且构建随机计算系统的实践受到相应的限制。虽然大多数先前的工作都集中在容易耐受性的设计上，这些设计能够使用不可靠和随机组件来确定性计算的强大实施，但该项目将调查分子设备的绝对可用随机操作如何提供增强的计算能力 - 超出确定性实施可以实现相同资源的方法。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。长期可编程的分子信息技术被毒化，最终会影响行业和社会，因为可编程化学将使基于信息的响应式分子材料，先进的生物医学疗法和诊断，复杂的化学化学合成和分子尺度仪器以及其他分子纳米技术的应用。该提案包括教育和宣传计划，培训和准备学生重点是招募妇女和少数群体的学生。Initial调查将考虑在DNA纳米技术和分子编程的快速发展领域中使用的计算模型：形式的化学反应网络，分子瓷砖自动化系统，分子自我组装系统，Polymer-fection网络 - 反应系统和反应系统。最近的工作表明，用少量混合的化学反应网络可以利用其随机性来代表复杂的概率分布，执行信息处理任务，例如概率推断，并有效地寻找解决方案以进行复杂的组合问题。该项目旨在通过构建这些见解来提高对随机分子计算的好处的理解。首先，它将为产生概率分布的化学反应网络建立复杂性理论。其次，它将探索化学反应网络的随机约束满意度如何导致自组织反应扩散系统和其他结合几何形状的模型中强大的空间模式形成。第三，它将对随机自组装过程如何增强算法自组装的力量有所了解。具体的结果将证明自组装DNA结构的随机核系统如何执行与神经网络的模式识别类似的信息处理任务。该奖项反映了NSF的法定任务，并被认为是通过基金会的知识分子优点和更广泛的影响审查标准来通过评估来支持的。