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.

使用简单原材料廉价生长的大型三维细胞集落可以制成廉价、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容易出错，并且细胞在细胞培养物内分裂、死亡和重组，使得维持定义的架构变得困难。这项研究将探索基于酵母细胞的计算系统的设计，其灵感来自于动物脑细胞如何执行计算。为了开发电子、计算和生物学交叉领域的新知识，需要熟悉这些领域并能够在协作团队中工作的新一代学生。在与德克萨斯大学奥斯汀分校的新生研究计划和约翰霍普金斯大学科学与工程领域的女性等组织合作的基础上，PI 将开发项目，允许本科生研究人员小组参与长期研究项目，使学生有机会进行独立调查作为大学间合作团队的一部分。该项目将把计算机体系结构和系统神经科学的想法与合成生物学的新工具结合起来，开发酵母菌 - 酿酒酵母细胞，这些细胞可以通过工程设计共同模拟前馈神经网络细胞间通信过程和可编程转录逻辑。至关重要的是，酵母网络的设计能够容忍单细胞生物分子信息处理的固有噪声，并且不需要特定的高阶空间组织或图案。该项目成员将为小分子信号和遗传逻辑系统构建新的蛋白质受体，使单个酵母菌能够模拟前馈神经网络中的节点。单个酵母菌和酵母菌网络的输入输出行为将被定量表征，从而可以评估酵母菌系统中可扩展计算的潜力。神经科学的高级模型将用于开发组装强大的酵母网络的设计原则，并推导出酵母计算的缩放法则。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响进行评估，被认为值得支持审查标准。