Department of Electrical and Computer Engineering, Northeastern University, Boston, Massachusetts 02115, United States.
Department of Chemical Engineering, Northeastern University, Boston, Massachusetts 02115, United States.
ACS Synth Biol. 2020 Aug 21;9(8):2172-2187. doi: 10.1021/acssynbio.0c00228. Epub 2020 Jul 14.
Starting in the early 2000s, sophisticated technologies have been developed for the rational construction of synthetic genetic networks that implement specified logical functionalities. Despite impressive progress, however, the scaling necessary in order to achieve greater computational power has been hampered by many constraints, including repressor toxicity and the lack of large sets of mutually orthogonal repressors. As a consequence, a typical circuit contains no more than roughly seven repressor-based gates per cell. A possible way around this scalability problem is to distribute the computation among multiple cell types, each of which implements a small subcircuit, which communicate among themselves using diffusible small molecules (DSMs). Examples of DSMs are those employed by quorum sensing systems in bacteria. This paper focuses on systematic ways to implement this distributed approach, in the context of the evaluation of arbitrary Boolean functions. The unique characteristics of genetic circuits and the properties of DSMs require the development of new Boolean synthesis methods, distinct from those classically used in electronic circuit design. In this work, we propose a fast algorithm to synthesize distributed realizations for any Boolean function, under constraints on the number of gates per cell and the number of orthogonal DSMs. The method is based on an exact synthesis algorithm to find the minimal circuit per cell, which in turn allows us to build an extensive database of Boolean functions up to a given number of inputs. For concreteness, we will specifically focus on circuits of up to 4 inputs, which might represent, for example, two chemical inducers and two light inputs at different frequencies. Our method shows that, with a constraint of no more than seven gates per cell, the use of a single DSM increases the total number of realizable circuits by at least 7.58-fold compared to centralized computation. Moreover, when allowing two DSM's, one can realize 99.995% of all possible 4-input Boolean functions, still with at most 7 gates per cell. The methodology introduced here can be readily adapted to complement recent genetic circuit design automation software. A toolbox that uses the proposed algorithm was created and made available at https://github.com/sontaglab/DBC/.
从 21 世纪初开始，人们已经开发出了用于构建实现特定逻辑功能的合成遗传网络的复杂技术。尽管取得了令人印象深刻的进展，但为了实现更大的计算能力而进行的扩展受到了许多限制，包括抑制剂毒性和缺乏大量相互正交的抑制剂。因此，典型的电路在每个细胞中不包含超过大约七个基于抑制剂的门。解决这个可扩展性问题的一种可能方法是将计算分布在多个细胞类型中，每个细胞类型实现一个小的子电路，这些子电路通过可扩散的小分子 (DSMs) 相互通信。DSMs 的示例是细菌中群体感应系统使用的那些。本文重点介绍了在评估任意布尔函数的背景下，实现这种分布式方法的系统方法。遗传电路的独特特性和 DSM 的特性需要开发新的布尔综合方法，与经典的电子电路设计中使用的方法不同。在这项工作中，我们提出了一种快速算法，用于在每个细胞的门数和正交 DSM 数的约束下，为任何布尔函数合成分布式实现。该方法基于一种精确的综合算法来找到每个细胞的最小电路，这反过来又使我们能够构建一个广泛的布尔函数数据库，直到给定的输入数量。具体来说，我们将特别关注最多 4 个输入的电路，例如，两个化学诱导物和两个不同频率的光输入。我们的方法表明，在每个细胞不超过七个门的约束下，使用单个 DSM 将可实现的电路总数与集中计算相比至少增加了 7.58 倍。此外，当允许使用两个 DSM 时，可以实现所有可能的 4 输入布尔函数的 99.995%，仍然每个细胞最多 7 个门。这里介绍的方法可以很容易地适应最近的遗传电路设计自动化软件。创建了一个使用所提出算法的工具箱，并在 https://github.com/sontaglab/DBC/ 上提供。
