FET: SMALL: New Abstraction and Design Automation for Complex Computations with DNA Using Fractional Coding

FET：SMALL：使用分数编码进行 DNA 复杂计算的新抽象和设计自动化

• 批准号: 2103437
• 负责人: sayed ahmad salehi
• 金额: $ 39.94万
• 依托单位: University of Kentucky Research Foundation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-10-01 至 2025-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2103437&HistoricalAwards=false
• 关键词: FET; SMALL; New; Abstraction; Design

The use of DNA-based technologies in complex biomolecular circuits is a quickly developing field of synthetic biology that has the potential to disrupt how one designs smart drugs or delivers targeted treatments. Given their biological nature and in-vivo compatibility, other applications for synthesized DNA-based computational circuits that cannot be foreseen now will surely follow. Recent advances in biotechnology have shown the potential for implementing complex DNA-based circuits. However, as the complexity of these circuits increases, they become slower and more difficult to be designed efficiently. As shown in silicon-based technology, new design approaches and automation tools are essential for progress in the field. This project aims to develop a systematic design framework and new approaches for fast and complex molecular computing circuits using fractional coding and to investigate the use of this framework for the efficient and scalable implementation of artificial neural networks (ANNs). Along with the development of the design automation framework, a companion software, called FUNDNA, that maps mathematical FUNctions to DNA reactions will be produced. This freely available, open-source software with its tutorials and documentation will transform bio-design automation research by enabling efficient design of molecular circuits for complex computations with mathematical representation without requiring biological/chemical skills. The tool will make the creation of novel complex DNA computing circuits faster, cheaper, and more accessible. This project includes two major educational initiatives with the goal of broadening participation in STEM. First, students who are underrepresented in engineering fields will be recruited through the Kentucky-West Virginia LSAMP program, the University of Kentucky (UK) Student Chapter of the National Society of Black Engineers, and UK Student Support Services to be trained to work on the research project. A new course, Molecular Programming and DNA Computing Circuits, will be developed to integrate the knowledge and findings of this research and to help train students in the area of DNA computing. Second, an iGEM (International Genetically Engineered Machine) chapter, the first iGEM team in the state of Kentucky, will be established to share resources and compete in international synthetic biology competitions. This project will enable systematic design of efficient molecular circuits for fast computation of complex mathematical functions. The design of molecular computing circuits will be systematic because the project will develop a new level of design abstraction that accepts mathematical representation of desired computations. It will create an end-to-end compiler that transfers the mathematical representation to chemical reaction network, and then generates candidate DNA reactions and sequences. The developed circuits will be fast because they work based on fractional coding. While for traditional molecular encoding each input/output is represented by a molecular concentration, for fractional coding inputs/outputs are ratios of pairs of molecular concentrations. Molecular circuits based on traditional encoding need to wait for the final (at equilibrium) concentrations of molecules, whereas for circuits based on fractional coding, the ratios reach their final values much faster than molecular concentrations. This project will expand the computational power of molecular computing circuits to complex computations through two approaches: 1) it will map all electronic stochastic computing logic circuits to molecular circuits, 2) it will implement finite state Markov chains by molecular reactions. Finally, the project will develop new approaches for performing ANNs with molecular reactions. The goal is to demonstrate how fractional coding can improve molecular computation, and DNA computing in particular, through three objectives. 1) Development of a design automation framework and a companion software, called FUNDNA, that maps mathematical FUNctions to DNA reactions. 2) Investigation of complex computations such as neural networks based on fractional coding. 3) Experimental DNA implementation of basic molecular computing circuits using fractional coding. This project will have a significant impact on the field of bio-design automation because it will introduce and attempt to realize the idea of automated design of molecular circuits, starting from high level mathematical or algorithmic representation. While there is prior work in molecular computation of different sorts of mathematical functions, no systematic method has been proposed for computing complex general mathematical functions within a molecular circuit.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.

在复杂的生物分子回路中使用基于 DNA 的技术是合成生物学的一个快速发展的领域，它有可能颠覆人们设计智能药物或提供靶向治疗的方式。鉴于其生物学性质和体内兼容性，目前无法预见的基于合成 DNA 的计算电路的其他应用肯定会随之而来。生物技术的最新进展显示了实现复杂的基于 DNA 的电路的潜力。然而，随着这些电路的复杂性增加，它们变得更慢并且更难以有效地设计。正如硅基技术所示，新的设计方法和自动化工具对于该领域的进步至关重要。该项目旨在开发一个系统设计框架和新方法，用于使用分数编码的快速和复杂的分子计算电路，并研究使用该框架来高效和可扩展地实现人工神经网络（ANN）。随着设计自动化框架的发展，将产生一个名为 FUNDNA 的配套软件，它将数学函数映射到 DNA 反应。这款免费提供的开源软件及其教程和文档将通过无需生物/化学技能即可高效设计分子电路以进行复杂计算的数学表示来改变生物设计自动化研究。该工具将使新型复杂 DNA 计算电路的创建更快、更便宜、更容易实现。该项目包括两项主要的教育举措，旨在扩大 STEM 的参与范围。首先，工程领域代表性不足的学生将通过肯塔基州-西弗吉尼亚州 LSAMP 计划、肯塔基大学（英国）国家黑人工程师协会学生分会和英国学生支持服务机构招募，接受培训以从事以下工作：研究项目。将开发一门新课程“分子编程和 DNA 计算电路”，以整合这项研究的知识和发现，并帮助培训 DNA 计算领域的学生。其次，将成立iGEM（国际基因工程机器）分会，这是肯塔基州第一个iGEM团队，以共享资源并参加国际合成生物学竞赛。 该项目将实现高效分子电路的系统设计，以快速计算复杂的数学函数。分子计算电路的设计将是系统化的，因为该项目将开发一个新的设计抽象水平，接受所需计算的数学表示。它将创建一个端到端编译器，将数学表示转移到化学反应网络，然后生成候选 DNA 反应和序列。开发的电路速度很快，因为它们基于分数编码工作。对于传统的分子编码，每个输入/输出由分子浓度表示，而对于分数编码，输入/输出是分子浓度对的比率。基于传统编码的分子电路需要等待分子的最终（平衡）浓度，而对于基于分数编码的电路，比率达到其最终值的速度比分子浓度快得多。该项目将通过两种方法将分子计算电路的计算能力扩展到复杂计算：1）将所有电子随机计算逻辑电路映射到分子电路，2）通过分子反应实现有限状态马尔可夫链。最后，该项目将开发利用分子反应执行人工神经网络的新方法。目标是通过三个目标展示分数编码如何改进分子计算，特别是 DNA 计算。 1) 开发设计自动化框架和名为 FUNDNA 的配套软件，将数学函数映射到 DNA 反应。 2）研究复杂计算，例如基于分数编码的神经网络。 3) 使用分数编码的基本分子计算电路的实验 DNA 实现。该项目将对生物设计自动化领域产生重大影响，因为它将引入并尝试从高级数学或算法表示开始实现分子电路自动化设计的想法。虽然先前在不同类型数学函数的分子计算方面已有工作，但尚未提出用于计算分子电路内复杂的一般数学函数的系统方法。该奖项反映了 NSF 的法定使命，并通过使用基金会的评估进行评估，被认为值得支持。智力价值和更广泛的影响审查标准。