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，通过物理组织组件连接和空间组织来控制信息流。然而，尽管最近的技术进步使得全面绘制生物网络（例如，连接组）的布局成为可能，但研究人员仍然远远不能理解空间建筑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，通过非常有限的一组分子和细胞组件来实现复杂的信息处理。所提出的方法将导致对计算的生物物理学的新见解。而不是问信息处理如何发生在一个特定的，现有的生物系统，研究者问的是复杂的计算和信息处理的最低要求是什么，在一个合成的多细胞，空间组织系统。利用这一原理，他们认为几乎任意复杂的计算可以用非常小的一组工程细胞来实现。重要的是，与之前的工作不同，研究人员不需要设计新的分子成分或细胞类型来增加电路的复杂性，而是可以扩大电路在空间中的占地面积，以容纳额外的构建模块。研究人员将开发技术创新，以扩大使用最小分子部件的蜂窝计算和存储系统。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、细胞间通信和细胞存储容量。一个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。结果将是系统能够按需编程任意电路架构到多细胞酵母生物膜。为了实现这些目标，生物膜将由两种不同的酵母菌株组成，每种酵母菌株含有不同的电路元件。这两种类型的组件分别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 -因子的存在表示，逻辑“1”将由小分子生长素的存在编码。逻辑整合将由“门”细胞进行，它将包含一个细胞内转录NOR逻辑门。对于信号传播，将使用单个“线”细胞菌株通过主动信号沿着连接门细胞区域的空间定义路径传输布尔输出（α因子或生长素），并将门细胞输出到下游门细胞和/或电路输出细胞。电路输入细胞将有可能与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，而输出细胞可以与分子记忆系统进行编程，该系统可以记录并永久存储直接在细胞DNA中的瞬态电路输出（例如，使用基于crispr的分子记录器）。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。

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.