SemiSynBio: Cardiac Muscle-Cell-Based Coupled Oscillator Networks for Collective Computing

当前结构化和非结构化数据生成的速度以及对实时数据分析的需求需要截然不同的计算方法，这些方法可以以大规模并行、可扩展和节能的方式运行。此外，某些类型的计算问题，即组合优化问题，在许多现实世界的情况下有广泛的应用，如故障诊断、调度、资源分配甚至神经网络训练，从根本上讲，很难使用布尔框架来解决，布尔框架是我们当前计算框架的支柱。本提案旨在探索由活心肌细胞或生物振荡器组成的耦合振荡器网络的潜力，作为解决优化、学习和推理等计算难题的集体计算结构。新的计算范式可以有效地解决计算难题，使用基于细胞的集体计算结构将改变合成生物计算电路的构建方式。此外，拟议的研究将导致更好地理解肌肉细胞网络中的电通信，影响从生物机器人到理解和治疗肌肉疾病的潜在未来应用。由具有生物学、工程学和计算机科学互补背景的pi提供跨学科的学生指导和教育，通过发表论文和演讲及时传播重要的研究成果，以及拟议的外展活动，包括为中学女生提供STEM实践活动的研讨会，为高中生提供指导，每年为本科生和研究生举办一个周末的研讨会，名为“生物学启发的计算”。这些活动将有助于培养一批新的学生骨干，以满足这一新兴领域未来的需要。通过参与pi所在机构的此类招生项目，将关注从代表性不足的群体中招募学生。目前探索生物计算的生物成分的设计利用了细胞的信息处理单元，如DNA、基因或蛋白质电路，这些电路固有地很慢（几小时到几天的速度）。使用可以在百赫兹频率下单独工作的电活性细胞，并且可以连接成网络来执行大规模并行任务，可以改变生物计算并导致节能信息处理的新方法。该项目的目标是探索由活心肌细胞组成的电耦合振荡器网络的潜力，以形成一个集体计算结构，用于解决计算难题，如优化、学习和推理任务。心肌细胞是电活性成分，可以启动和传递电信号而不会丢失。更有趣的是，它们以稳定的速度自发地跳动（即振荡），当它们通过离子通量相互耦合时，它们同步到一个锁定的、稳定的频率。在这项研究中，假设使用耦合心肌细胞振荡器或生物振荡器制造的可重构电路可以配置为功能连续时间动力系统，以解决计算难题。为此，最先进的纳米生物制造方法将被用于制造生物振荡器，并通过纳米多孔离子膜将它们双向耦合，用于可编程计算。设计空间将探索单个生物振荡器的几何形状和尺寸，通过离子通量进行生物振荡器连接的纳米孔膜设计，以及大型生物振荡器阵列的网络拓扑和制造可行性。首先，将研究一对耦合生物振荡器，并开发计算效率高的紧凑模型，以准确反映耦合生物振荡器的连续时间动力学。然后，将设计扩展到更大的耦合生物振荡器网络，并进行可行性研究，以解决图的顶点着色和图划分等原型硬优化任务。除了其固有的连通性，可扩展性和并行处理能力外，本研究中提出的基于肌肉细胞的方法需要的能量最小，主要以糖的形式存在，因此将成为当前需要能量的传统计算方法的低能量替代方案，用于使用基于启发式的数字计算技术解决困难的优化问题。该奖项反映了美国国家科学基金会的法定使命，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。

Current rate of structured and unstructured data generation and the need for real-time data analytics requires radically different computational approaches that can operate in a massively parallel, scalable and energy efficient manner. In addition, certain classes of computational problems, i.e. combinatorial optimization problems, which have extensive applications in many real-world situations such as fault diagnosis, scheduling, resource allocation and even neural network training are fundamentally difficult to solve using the Boolean framework, the backbone of our current computational framework. This proposal aims to explore the potential of coupled oscillator networks made of living heart muscle cells, or bio-oscillators, as a collective computing fabric for solving computationally hard problems such as optimization, learning and inferences. New computing paradigms that can solve computationally hard problems efficiently using cell-based collective computing fabrics will transform how synthetic biocomputing circuits are built. In addition, the proposed research will lead to a better understanding of the electrical communication in muscle cell networks impacting potential future applications ranging from biorobotics to understanding and treating muscle disorders. The broader impacts will further be achieved through interdisciplinary student mentoring and education by the PIs with complementary backgrounds in Biology, Engineering and Computer Science, timely dissemination of key research outcomes via published papers and presentations, as well as proposed outreach activities including a workshop for middle school girls providing hands-on activities in STEM, mentoring of high school students, and a weekend long workshop each year for undergraduate and graduate students, called 'Biology Inspired Computing'. These activities will contribute towards educating a new cadre of students that will meet the future need of this emerging field. Attention will be paid to student recruitment from under-represented groups by participating in such recruitment programs at the PIs' institution.Current designs that explore biological components for biocomputing leverages the information processing units of the cells, such as DNA, gene or protein circuitries, which are inherently slow (hours to days speed). Using electrically active cells that could individually operate in the hundred Hertz regime, and can be connected as networks to perform massively parallel tasks, can transform biocomputing and lead to novel ways of energy efficient information processing. The goal of this project is to explore the potential of electrically coupled oscillator networks made of living heart muscle cells to form a collective computing fabric for solving computationally hard problems such as optimization, learning and inference tasks. Heart muscle cells are electrically active components that can initiate and relay electrical signals without loss. More interestingly, they spontaneously beat (i.e. oscillate) at a stable pace, and when coupled with each other through ion fluxes, they synchronize to a locked, steady frequency. In this study, it is hypothesized that reconfigurable circuits fabricated using coupled heart muscle cell oscillators, or bio-oscillators, can be configured into functional continuous-time dynamical systems to solve computationally hard problems. Towards this end, state-of-art nanobiofabrication methods will be used to create bio-oscillators and to bi-directionally couple them through nanoporous ionic membranes for programmable computing. The design space will be explored for the geometry and size of the individual bio-oscillators, nanoporous membrane design for bio-oscillator connectivity through ion fluxes, as well as the network topology and fabrication feasibility of large bio-oscillator arrays. First, a pair of coupled bio-oscillators will be studied and computationally efficient compact models will be developed that accurately reflect the continuous time dynamics of the coupled bio-oscillators. Then, the design will be scaled up to larger network of coupled bio-oscillators and a feasibility study would be conducted for solving a prototypical hard optimization task such as vertex coloring of graph and graph partitioning. In addition to their inherent connectivity, scalability and parallel processing ability, the muscle cell-based approach proposed in this study requires minimal energy mostly in the form of sugar, and as such will be a low-energy alternative to current energy demanding traditional computing approaches for solving hard optimization problems using heuristics based digital computing techniques.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.