Advancing theory and tools for molecular programming

分子编程是设计携带信息的分子的过程，这些分子以有目的的方式做出反应或执行指令。核酸-DNA和RNA分子-编程很有趣，因为它们的数字序列和动态结构特性，以及因为它们通过自然地与“湿”生物系统相互作用而扩展了计算设备的覆盖范围。分子编程领域的研究人员正在应对令人兴奋的工程挑战，例如利用DNA和RNA序列构建逻辑电路、纳米结构，甚至分子行走机器人。在接下来的几十年里，这些技术可以带来巨大的回报，改变我们监测和调节细胞内分子动力学的方式，或者开发可以评估疾病指标浓度并做出相应反应的“智能药物”。*我的研究旨在通过对理论和经验模型的研究，发现新的更好的方法来对分子编程，以及分子编程和节能计算的局限性。*我研究的一个主要目标将是解决目前设计和分析分子程序的一个重大障碍，即缺乏良好的生物物理模型。尤其缺乏能够准确预测核链通过碱基对形成和断裂而相互作用的速率的动力学模型。现在有了一个取得进展的机会，通过使用分子程序员的湿实验室实验中的宝贵数据来学习模型参数。这项工作将在一定程度上涉及建立一个实验核动力学数据数据库，这是此类数据的第一次。此外，我的目标是通过考虑重要的能量特征(如同轴堆积和非对称多环惩罚)来进一步改进当前的热力学模型，这些特征不容易被纳入传统的动态规划二级结构预测算法中。这些贡献将支持分子程序员将当前的设计从数百条相互作用的链扩大到数千条，以及希望了解RNA构象动力学的生物学家。*我还想通过研究更高抽象水平的模型，即化学反应网络(CRN)和变体，来了解分子编程的基本能力和局限性。CRN提供了一种自然的方法来指定和推理高度分布的、异步的分子程序，并且通过对这些模型的研究，出现了一个关于什么可以用分子编程，什么不可以用分子编程的美丽理论。我计划为这一理论做出贡献，为领导人选举等关键问题开发可证明的快速算法，并开发类似CRN的模型，这些模型在设计和推理包含在单链中的程序时会很有用。**

Molecular programming is the process of designing information-carrying molecules that react, or execute instructions, in purposeful ways. Nucleic acids - DNA and RNA molecules - are interesting to program because of their digital sequences and dynamic structural properties, and because they extend the reach of computational devices by naturally interacting with "wet" biological systems. Researchers in the field of molecular programming are tackling exciting engineering challenges such as building logic circuits, nanoscale structures and even molecular walking robots, from DNA and RNA sequences. In the coming decades, these technologies can offer significant payoffs, in transforming the ways in which we monitor and mediate molecular dynamics within the cell, or in developing "smart drugs" that could assess concentrations of disease indicators and respond accordingly.******My research aims to uncover new and better ways to program molecules, as well as limits to molecular programming and energy-efficient computation, through the study of theoretical and empirical models.******A primary goal of my research will be to address a significant barrier currently to design and analysis of molecular programs, namely the lack of good biophysical models. Particularly lacking are kinetics models that can accurately predict the rates at which nucleic strands interact through base pair formation and breakage. There is an opportunity now to make progress, by learning model parameters using valuable data from wet lab experiments of molecular programmers. This work will in part involve the creation of a database of experimental nucleic kinetics data, the first of its kind. In addition, I aim to further improve current thermodynamics models by accounting for important energy features (such as coaxial stacking and asymmetric multi-loop penalties) that are not easily incorporated into traditional dynamic programming secondary structure prediction algorithms. These contributions will support molecular programmers in scaling up current designs from hundreds to thousands of interacting strands, as well as biologists who wish to understand RNA conformational dynamics.******I also want to understand the fundamental capabilities and limitations of molecular programming by studying models at higher levels of abstraction, namely chemical reaction networks (CRNs) and variants. CRNs provide a natural means for specifying and reasoning about highly distributed, asynchronous molecular programs, and a beautiful theory of what can and cannot be programmed with molecules is emerging from study of these models. I plan to contribute to this theory by developing provably fast algorithms for key problems such as leader election, and by developing models analogous to CRNs that can be useful when designing and reasoning about programs that are contained in single strands.**