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Abstract：
Computational tools addressing various components of design-build-test-learn (DBTL) loops for the construction of synthetic genetic networks exist but do not generally cover the entire DBTL loop. This manuscript introduces an end-to-end sequence of tools that together form a DBTL loop called Design Assemble Round Trip (DART). DART provides rational selection and refinement of genetic parts to construct and test a circuit. Computational support for experimental process, metadata management, standardized data collection and reproducible data analysis is provided via the previously published Round Trip (RT) test-learn loop. The primary focus of this work is on the Design Assemble (DA) part of the tool chain, which improves on previous techniques by screening up to thousands of network topologies for robust performance using a novel robustness score derived from dynamical behavior based on circuit topology only. In addition, novel experimental support software is introduced for the assembly of genetic circuits. A complete design-through-analysis sequence is presented using several OR and NOR circuit designs, with and without structural redundancy, that are implemented in budding yeast. The execution of DART tested the predictions of the design tools, specifically with regard to robust and reproducible performance under different experimental conditions. The data analysis depended on a novel application of machine learning techniques to segment bimodal flow cytometry distributions. Evidence is presented that, in some cases, a more complex build may impart more robustness and reproducibility across experimental conditions. Graphical Abstract.
摘要：
存在解决用于构建合成遗传网络的设计-构建-测试-学习（DBTL）循环的各种组件的计算工具，但通常不覆盖整个DBTL循环。该手稿介绍了端到端工具序列，这些工具一起形成了称为设计组装往返（DART）的DBTL环路。DART提供了遗传部分的合理选择和完善，以构建和测试电路。对实验过程的计算支持，元数据管理，标准化的数据收集和可重复的数据分析是通过先前发布的RoundTrip（RT）测试学习循环提供的。这项工作的主要重点是工具链的设计装配（DA）部分，它通过筛选多达数千个网络拓扑来改进以前的技术，以使用仅基于电路拓扑的动态行为得出的新颖的鲁棒性得分来实现鲁棒性能。此外,引入了新型的实验支持软件，用于遗传电路的组装。使用几种OR和NOR电路设计，提出了一个完整的通过分析设计的序列，有和没有结构冗余，在萌芽酵母中实施。DART的执行测试了设计工具的预测，特别是关于不同实验条件下的鲁棒性和可重现性。数据分析依赖于机器学习技术的新颖应用来分割双峰流式细胞术分布。有证据表明,在某些情况下,更复杂的构建可以在实验条件下赋予更多的鲁棒性和可重复性。图形抽象。
