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

当前的结构化和非结构化数据生成速度以及对实时数据分析的需求需要完全不同的计算方法，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，某些类别的计算问题，即组合优化问题，在许多现实世界的情况下，如故障诊断，调度，资源分配，甚至神经网络训练有着广泛的应用，从根本上难以解决使用布尔框架，我们目前的计算框架的骨干。该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作为集体计算结构的潜力，用于解决计算困难的问题，如优化，学习和推理。新的计算范式可以使用基于细胞的集体计算结构有效地解决计算困难的问题，这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了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。更广泛的影响将进一步实现通过跨学科的学生辅导和教育的PI与互补背景的生物学，工程和计算机科学，及时传播的主要研究成果，通过发表的论文和演讲，以及拟议的推广活动，包括一个讲习班，为中学女生提供实践活动在干，辅导高中生，以及每年为本科生和研究生举办的周末研讨会，名为“生物启发计算”。这些活动将有助于培养一批新的学生，以满足这一新兴领域的未来需求。通过参加PI所在机构的招生计划，将注意从代表性不足的群体中招收学生。目前探索生物计算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，这些单元本身就很慢（几小时到几天的速度）。使用可以在百赫兹范围内单独操作的电活性细胞，并且可以连接为网络以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制成的电耦合振荡器网络的潜力，以形成一个集体计算结构，用于解决计算困难的问题，如优化，学习和推理任务。心肌细胞是电活性成分，可以启动和中继电信号而不会丢失。更有趣的是，它们自发地以稳定的速度跳动（即振荡），当它们通过离子流相互耦合时，它们同步到锁定的稳定频率。在这项研究中，假设使用耦合的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成功能连续时间动态系统，以解决计算困难的问题。为此，将使用最先进的纳米生物制造方法来创建生物振荡器，并通过纳米多孔离子膜将它们双向耦合，以进行可编程计算。设计空间将探索的几何形状和大小的个人生物振荡器，纳米多孔膜设计的生物振荡器连接通过离子通量，以及网络拓扑结构和制造的可行性大型生物振荡器阵列。首先，一对耦合的生物振荡器将被研究和计算高效的紧凑型模型将被开发，准确地反映耦合的生物振荡器的连续时间动态。然后，该设计将被放大到更大的耦合生物振荡器网络，并进行可行性研究，以解决一个原型的硬优化任务，如图的顶点着色和图分区。除了它们固有的连接性、可扩展性和并行处理能力之外，这项研究中提出的基于肌肉细胞的方法需要最少的能量，主要是以糖的形式，因此，这将是一个低-能源替代目前的能源需求的传统计算方法，解决困难的优化问题，使用基于数学的数字计算技术。这个奖项反映了NSF的法定使命，并已被认为是值得的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来提供支持。

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.