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

使用简单的原材料廉价培育出的大型三维细胞群可以制成廉价、节能的计算机。使用活细胞进行计算的一个基本挑战是，细胞的计算容易出错，并且细胞在细胞培养物中分裂、死亡和重组，因此难以维护已定义的体系结构。这项研究将探索基于酵母细胞的计算系统的设计，灵感来自于如何计算由动物脑细胞执行。要在电子学、计算机和生物学的交叉领域发展新知识，就需要新一代的学生熟悉这些领域，并能在协作团队中工作。在与UT Austin的新生研究计划和JHU的女性科学与工程组织合作的基础上，pi将开发项目，允许本科生研究人员团体参与长期研究项目，学生有机会作为合作的大学间团队的一部分进行独立调查。该项目将把计算机体系结构和系统神经科学的思想与合成生物学的新工具结合起来，开发酵母——酿酒酵母细胞，这些细胞可以通过工程化的细胞间通信过程和可编程的转录逻辑共同模拟前馈神经网络。至关重要的是，酵母网络将被设计成能够承受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模式。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，使单个酵母能够模拟前馈神经网络中的节点。单个酵母和酵母网络的输入-输出行为将被定量表征，从而有可能评估酵母系统中可扩展计算的潜力。来自神经科学的高级模型将用于开发组装健壮酵母网络的设计原则，并推导酵母计算的缩放定律。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。

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.