SHF: Small: Advanced Digital Signal Processing with DNA

该项目将开发用于实现一般计算的技术，特别是先进的数字信号处理操作，使用一般的分子反应，特别是基于DNA的反应。 正如电子系统根据电压（每单位电荷的能量）实现计算一样，人们可以设想根据化学浓度（每单位体积的分子）进行计算的分子系统。这个建议将探讨实现各种计算结构，如逻辑，内存，算术和信号处理的技术。一种称为DNA链置换的技术是目标实验底盘。这项研究的动力不是计算本身。分子计算永远无法与硅集成电路制成的传统计算机在数字运算等任务上竞争。化学系统本来就缓慢而混乱，需要几分钟甚至几个小时才能完成，并产生支离破碎的结果。相反，其目标是创造“嵌入式控制器”--病毒和细菌被设计成在需要的地方进行有用的分子计算，例如在药物输送和生物化学传感应用中。数字电路设计社区拥有独特的专业知识，可以用来解决合成生物学中遇到的具有挑战性的设计问题。反过来，生物学中的应用在算法开发中提供了大量有趣的问题。随着其跨学科的重点，这个项目将带来新的视角，这两个领域。 如果成功的话，拟议中的研究将改变诸如药物输送系统的基因工程等学科。目前，普遍采用的是一种代价高昂、效率低下的临时办法。随着用于实现诸如数字信号处理之类的操作的鲁棒技术的出现，将会开发出更加有效的系统。该项目的一个重要目标是向广大受众传达跨学科研究的动力。基于目前的研究工作，包括女学生，代表性不足的学生将被招募到该项目。这一建议将建立在以前的工作的成功，探索离散时间和数字信号处理应用与DNA的复杂信号处理功能的实现。 该项目将开发用于信号处理功能的分子实现的合成技术，例如有限脉冲响应（FIR）和无限脉冲响应（IIR）数字滤波器，快速傅立叶变换（FFT）和功率谱密度（PSD）计算。 本计画的一个主要组成部分是研究如何利用分子反应来实现类比至数位（A/D）及数位至类比（D/A）转换。将在离散时间信号处理和数字信号处理之间进行区分。虽然在两个系统中周期性地对信号进行采样，但是在前者中信号被表示为模拟值，而在后者中信号被量化为数字值。各有各的优点。离散时间信号处理系统类似于采样数据系统，需要较低的分子浓度;然而，分辨率无法精确控制。数字系统更精确，但需要更高的分子浓度。具体的研究重点如下。首先，将展示一个完整的数字信号处理系统。这样的系统将包含A/D和D/A转换器，并将执行复杂操作的全部指令。其次，该项目将开发离散时间和数字信号处理系统的更快实现。在先前的离散时间信号处理实现中的主要瓶颈是速度。与电子系统的速度受电荷变化的限制不同，分子系统的速度受分子浓度变化的限制，而分子浓度的变化本来就很慢。该项目将开发新的调度方法，将多个计算映射到不同的传输阶段。计算将是同步的，分子转移由一个“时钟”同步，通过持续的化学振荡实现。新的调度方法将允许并行输出的计算，而不增加延迟转移反应的数量。将目前可实现的采样周期从40-80小时减少到4-8小时将使得能够使用DNA对一些示例信号处理功能进行实验演示。最后，该项目将研究信号处理功能的离散时间和数字实现在速度，准确性和鲁棒性方面的权衡。将对系统特性和行为进行详细研究，例如，分辨率如何与变化的分子浓度相关，以及设计对参数变化的稳健性如何。

This project will develop techniques for implementing computation in general, and advanced digital signal processing operations in particular, using molecular reactions in general, and DNA-based reactions in particular. Just as electronic systems implement computation in terms of voltage (energy per unit charge), one can conceive of molecular systems that compute in terms of chemical concentrations (molecules per unit volume). This proposal will explore techniques for implementing a variety of computational constructs such as logic, memory, arithmetic, and signal processing. A technique called DNA strand displacement is the target experimental chassis. The impetus for this research is not computation per se. Molecular computation will never compete with conventional computers made of silicon integrated circuits for tasks such as number crunching. Chemical systems are inherently slow and messy, taking minutes or even hours to finish, and producing fragmented results. Rather, the goal is to create "embedded controllers" - viruses and bacteria that are engineered to perform useful molecular computation in situ where it is needed, for instance in drug delivery and biochemical sensing applications.The digital circuit design community has unique expertise that can be brought to bear on the challenging design problems encountered in synthetic biology. Applications in biology, in turn, offer a wealth of interesting problems in algorithmic development. With its cross-disciplinary emphasis, this project will bring new perspectives to both fields. If successful, the proposed research will transform disciplines such as genetic engineering of drug-delivery systems. Currently, a costly and ineffective ad-hoc approach prevails. With robust techniques for implementing operations such as digital signal processing, much more effective systems will be developed. An important goal of the project is to communicate the impetus for interdisciplinary research to a wide audience. Building upon current research efforts that include female students, underrepresented students will be recruited into the project.This proposal will build on the success of prior work, exploring the implementation of complex signal processing functions for both discrete-time and digital signal processing applications with DNA. The project will develop synthesis techniques for molecular implementations of signal processing functions such as finite-impulse response (FIR) and infinite impulse response (IIR) digital filters, fast Fourier transforms (FFT), and power spectral density (PSD) computations. A major component of this project is to study how to implement analog-to-digital (A/D) and digital-to-analog (D/A) conversion with molecular reactions. A distinction will be made between discrete-time signal processing and digital signal processing. While signals are sampled periodically in both systems, the signal is represented as an analog value in the former while the signal is quantized to a digital value in the latter. Each has its advantages. Discrete-time signal processing systems are similar to sampled data systems and require lower molecular concentrations; however, the resolution cannot be precisely controlled. Digital systems are more precise, but require higher molecular concentrations.Specific research thrusts are as follows. Firstly, a complete digital signal processing system will be demonstrated. Such a system will contain A/D and D/A converters and will implement a full repertoire of complex operations. Secondly, the project will develop faster implementations of both discrete-time and digital signal processing systems. The main bottleneck in prior discrete-time signal processing implementations has been speed. In contrast to electronic systems, where the speed is limited by changes in electric charge, the speed in molecular systems is limited by changes in molecular concentrations, which are inherently slow. The project will develop new scheduling approaches where multiple computations are mapped to different phases of transfer. The computation will be synchronous, with molecular transfers synchronized by a "clock", implemented through sustained chemical oscillations. The new scheduling approaches will allow computation of parallel outputs without increasing the number of delay transfer reactions. Reducing currently achievable sample periods from 40-80 hours to 4-8 hours will enable experimental demonstration of some example signal processing functions using DNA. Finally, the project will investigate tradeoffs in discrete-time and digital implementations of signal processing functions with respect to speed, accuracy, and robustness. Detailed studies of the system properties and behaviors will be performed, e.g., how the resolution correlates with changing molecular concentrations and how robust the designs are to parametric variations.