SemiSynBio: Collaborative Research: YeastOns: Neural Networks Implemented in Communicating Yeast Cells

SemiSynBio：合作研究：YeastOns：在酵母细胞通讯中实现的神经网络

• 批准号: 1807132
• 负责人: Eric Klavins
• 金额: $ 33.75万
• 依托单位: University of Washington
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-08-01 至 2021-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1807132&HistoricalAwards=false
• 关键词: SemiSynBio; Collaborative; Research; YeastOns; Neural

Large, three-dimensional cell colonies grown inexpensively using simple raw materials could be made into cheap, energy-efficient computers. A fundamental challenge in using living cells for computing is that computation by cells is error prone, and cells divide, die and reorganize inside a cell culture, making it difficult to maintain a defined architecture. This research will explore the design of yeast cell-based computing systems inspired by how computing is performed by the animal brain cells. To develop new knowledge at the intersection of electronics, computing and biology will require a new generation of students familiar with each of these areas who can work in collaborative teams. Building on work with organizations including the Freshman Research Initiative at UT Austin and Women in Science and Engineering at JHU, the PIs will develop programs to allow groups of undergraduate researchers to engage in long term research programs in which students have the opportunity to perform independent investigations as part of collaborative, inter-university teams.This project will combine ideas from computer architecture and systems neuroscience with new tools from synthetic biology to develop yeastons - Saccharomyces cerevisiae cells that can collectively emulate a feedforward neural network through engineered cell-cell communication processes and programmable transcriptional logic. Crucially, yeaston networks will be designed to tolerate the inherent noisiness of single-cell biomolecular information processing and require no specific higher order spatial organization or patterning. The project members will build new protein receptors for small molecule signals and genetic logic systems that will enable single yeastons to emulate nodes in a feedforward neural network. The input-output behavior of single yeastons and yeaston networks will be quantitatively characterized, making it possible to evaluate the potential for scalable computation in yeaston systems. High-level models from neuroscience will be used to develop design principles for assembling robust yeaston networks and to derive scaling laws for yeaston computing.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.

使用简单的原材料廉价地生长的大型三维细胞菌落可以将其制成廉价，节能的计算机。使用活细胞进行计算的一个基本挑战是，细胞计算的计算是容易发生的，并且细胞在细胞培养中分裂，死亡和重组，从而难以维护定义的结构。这项研究将探讨受动物脑细胞如何执行计算方式启发的基于酵母细胞的计算系统的设计。为了在电子产品的交集中发展新知识，计算和生物学将需要新一代的学生熟悉这些领域的学生可以在协作团队中工作。在与组织的合作基础上，包括UT Austin的大一新生研究计划和JHU的科学与工程女性的工作，PI将开发计划，允许一群本科研究人员参与长期研究计划，学生有机会进行独立的调查，作为协作，间歇性团队的一部分，该项目将与New Systection New Sytormenty的Ideasty Intery Sytone Yearthy Toolsy合成合成工具，从可以通过工程的细胞 - 细胞通信过程和可编程的转录逻辑来集体模仿前馈神经网络的酿酒酵母细胞。至关重要的是，YEASTON网络将旨在耐受单细胞生物分子信息处理的固有噪音，并且不需要特定的高阶空间组织或模式。该项目成员将为小分子信号和遗传逻辑系统建立新的蛋白质受体，这些蛋白质受体将使单酵母在喂养神经网络中模仿节点。单酵母和Yeaston网络的投入输出行为将被定量表征，从而可以评估Yeaston Systems中可扩展计算的潜力。来自神经科学的高级模型将用于制定设计原理，以组装强大的YEASTON网络并得出Yeaston Computing的缩放定律。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛的影响评估的评估来进行评估的。