Cummins Breschine, Vrana Justin, Moseley Robert C, Eramian Hamed, Deckard Anastasia, Fontanarrosa Pedro, Bryce Daniel, Weston Mark, Zheng George, Nowak Joshua, Motta Francis C, Eslami Mohammed, Johnson Kara Layne, Goldman Robert P, Myers Chris J, Johnson Tessa, Vaughn Matthew W, Gaffney Niall, Urrutia Joshua, Gopaulakrishnan Shweta, Biggers Vanessa, Higa Trissha R, Mosqueda Lorraine A, Gameiro Marcio, Gedeon Tomáš, Mischaikow Konstantin, Beal Jacob, Bartley Bryan, Mitchell Tom, Nguyen Tramy T, Roehner Nicholas, Haase Steven B
Department of Mathematical Sciences, Montana State University, Bozeman, MT, USA.
UW BIOFAB, Seattle, WA, USA.
Synth Biol (Oxf). 2023 Mar 28;8(1):ysad005. doi: 10.1093/synbio/ysad005. eCollection 2023.
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. .
用于构建合成遗传网络的设计-构建-测试-学习（DBTL）循环各个组件的计算工具已经存在，但通常不能涵盖整个DBTL循环。本文介绍了一系列端到端的工具，这些工具共同构成了一个名为“设计组装往返”（DART）的DBTL循环。DART提供了对遗传元件的合理选择和优化，以构建和测试电路。通过之前发布的“往返”（RT）测试-学习循环，为实验过程、元数据管理、标准化数据收集和可重复数据分析提供了计算支持。这项工作的主要重点是工具链的设计组装（DA）部分，它通过使用仅基于电路拓扑的动态行为得出的新颖稳健性分数，筛选多达数千种网络拓扑以实现稳健性能，从而改进了先前的技术。此外，还引入了用于组装遗传电路的新型实验支持软件。使用几种有或没有结构冗余的或门和或非门电路设计，展示了一个完整的从设计到分析的流程，这些设计在芽殖酵母中得以实现。DART的执行验证了设计工具的预测，特别是在不同实验条件下的稳健性和可重复性方面。数据分析依赖于机器学习技术的一种新颖应用，用于对双峰流式细胞术分布进行分段。有证据表明，在某些情况下，更复杂的构建可能会在不同实验条件下赋予更高的稳健性和可重复性。
