FET: Medium: Programming multi-cellular systems with spatially-defined computation

许多生物系统，最突出的是神经元网络，通过物理组织组成部分的连通性和空间组织来控制信息流。然而，尽管最近的技术进步使全面绘制生物网络(例如连接组)的布局成为可能，但研究人员仍然远未理解空间结构如何使信息处理成为可能并与之相关。该项目的研究人员建议采用工程方法来理解如何通过控制这些组件的空间组织来利用非常有限的分子和细胞组件集实现复杂的信息处理。提出的方法将为计算的生物物理学带来新的见解。研究人员不是问信息处理是如何在特定的、现有的生物系统中发生的，而是问在一个合成的多细胞、空间组织的系统中，复杂的计算和信息处理的最低要求是什么。利用这一原理，他们争辩说，用一组非常小的工程细胞就可以完成几乎任意复杂的计算。重要的是，与以前的工作不同，研究人员不需要设计新的分子组件或细胞类型来增加电路的复杂性，而是可以在空间中扩大电路足迹，以容纳更多的构建块。研究人员将开发技术创新，利用最少的分子部件集来扩大细胞计算和存储系统的规模。他们将通过克服多个领域的限制来实现这一目标，包括正交电路元件、单元间通信和单元存储容量。一项关键的创新是使用空间组织来实现分子信号和计算单元的系统重用。其结果将是能够按需将任意电路体系结构编程为多细胞酵母生物膜的系统。为了实现这些目标，生物膜将由两个不同的酵母菌株组成，每个菌株含有不同的电路成分。这两种类型的组件用于逻辑集成和信号传播。在该系统中，将使用双轨逻辑编码，其中逻辑‘0’将由化学信号分子α-因子的存在来表示，而逻辑‘1’将由小分子生长素的存在来编码。逻辑集成将由包含细胞内转录NOR逻辑门的“门”单元执行。对于信号传播，将使用单个“线”细胞株通过空间定义的路径传递布尔输出(α因子或生长素)，这些路径连接门单元的区域，并将门单元的输出连接到下游的门单元和/或电路输出单元。电路输入单元将有可能使用任意一组生物传感器(例如，基于电生氧化还原的传感器)进行编程，而输出单元可以使用分子存储系统进行编程，该系统可以直接记录并永久存储细胞DNA中的暂态电路输出(例如，使用基于CRISPR的分子记录器)。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Many biological systems, most prominently networks of neurons, control the flow of information by physically organizing component connectivity and spatial organization. However, although recent technological advancements make it possible to comprehensively map the layout of biological networks (e.g., the connectome), researchers are still far from understanding how spatial architecture enables and relates to information processing. Investigators of this project propose to take an engineering approach to understanding how complex information processing can be achieved with a very limited set of molecular and cellular components simply by controlling the spatial organization of these components. The presented approach will result in novel insights into the biophysics of computation. Rather than asking how information processing occurs in a specific, existing biological system, investigators ask what the minimal requirements are for complex computation and information processing in a synthetic multicellular, spatially organized system. Using this principle, they argue that almost arbitrarily complex computation can be achieved with a very small set of engineered cells. Importantly, and unlike prior work, investigators do not need to engineer new molecular components or cell types to increase circuit complexity but can expand the circuit footprint in space to accommodate additional building blocks.Investigators will develop technological innovations to scale up cellular computing and memory systems using a minimal set of molecular parts. They will achieve this goal by overcoming limitations in multiple domains, including orthogonal circuit components, cell-to-cell communication and cell storage capacities. A key innovation is the use of spatial organization to enable systematic reuse of molecular signaling and computation units. The result will be systems that enable on-demand programming of arbitrary circuit architectures into multicellular yeast biofilms. To accomplish these goals, the biofilms will be composed of two different yeast strains, each containing a different circuit component. The two types of components are for logic integration and signal propagation. In this system, a dual rail logic encoding will be used in which the logical '0' will be represented by the presence of the chemical signaling molecule alpha-factor, and the logical '1' will be encoded by the presence of the small molecule auxin. Logic integration will be carried out by ``gate" cells, which will contain an intracellular transcriptional NOR logic gate. For signal propagation, a single strain of ``wire" cells will be used to transmit the Boolean outputs (alpha-factor or auxin) via active signaling along spatially-defined paths that connect regions of gate cells, and gate cell outputs to downstream gate cells and/or circuit output cells. Circuit input cells will have the potential to be programmed with any arbitrary set of biosensors (e.g., electrogenetic redox-based sensors), while the output cells can be programmed with molecular memory systems that can record and permanently store transient circuit outputs directly in cellular DNA (e.g., using CRISPR-based molecular recorders).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.