AssemblyTron:利用Opentrons OT-2实验室机器人实现DNA组装的灵活自动化。

IF 2.6 Q2 BIOCHEMICAL RESEARCH METHODS Synthetic biology (Oxford, England) Pub Date : 2023-01-01 DOI:10.1093/synbio/ysac032
John A Bryant, Mason Kellinger, Cameron Longmire, Ryan Miller, R Clay Wright
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引用次数: 2

摘要

作为工程领域的最新领域之一,合成生物学依靠一种反复试验的设计-构建-测试-学习(DBTL)方法来同时学习生物学中功能的编码方式并尝试对其进行工程设计。已经开发了许多软件和硬件平台来自动化、优化和算法执行DBTL周期的每个步骤。但是,用于自动化构建步骤的选项要少得多。构建通常涉及脱氧核糖核酸(DNA)组装,这在大多数情况下仍然是手工的,低通量和不可靠的,并且限制了我们推进生物学科学和工程的能力。在这里,我们介绍了AssemblyTron,这是一个开源的Python包,可以将j5 DNA组装设计软件输出与Opentrons液体处理机器人中的构建实现集成在一起,从而减少人为干预。我们通过几个无疤痕的多部分DNA组装演示了AssemblyTron的多功能性,从片段扩增开始。我们表明,通过使用最优退火温度梯度计算算法,AssemblyTron可以在片段长度和退火温度范围内进行聚合酶链反应。然后,我们通过同时构建四个四片段的染色蛋白报告表达质粒组装,证明了AssemblyTron可以执行金门和同源依赖的体内组装(IVAs),其保真度与手动组装相当。最后,我们使用AssemblyTron通过同源依赖的IVA进行定点诱变反应,并且通过测序评估获得了与手动组装相当的保真度。AssemblyTron可以减少与合成生物学相关的时间、培训、成本和浪费,再加上开源和负担得起的自动化,将进一步促进合成生物学的可及性,并加速生物研究和工程。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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AssemblyTron: flexible automation of DNA assembly with Opentrons OT-2 lab robots.

As one of the newest fields of engineering, synthetic biology relies upon a trial-and-error Design-Build-Test-Learn (DBTL) approach to simultaneously learn how a function is encoded in biology and attempt to engineer it. Many software and hardware platforms have been developed to automate, optimize and algorithmically perform each step of the DBTL cycle. However, there are many fewer options for automating the build step. Build typically involves deoxyribonucleic acid (DNA) assembly, which remains manual, low throughput and unreliable in most cases and limits our ability to advance the science and engineering of biology. Here, we present AssemblyTron, an open-source Python package to integrate j5 DNA assembly design software outputs with build implementation in Opentrons liquid handling robotics with minimal human intervention. We demonstrate the versatility of AssemblyTron through several scarless, multipart DNA assemblies, beginning from fragment amplification. We show that AssemblyTron can perform polymerase chain reactions across a range of fragment lengths and annealing temperatures by using an optimal annealing temperature gradient calculation algorithm. We then demonstrate that AssemblyTron can perform Golden Gate and homology-dependent in vivo assemblies (IVAs) with comparable fidelity to manual assemblies by simultaneously building four four-fragment assemblies of chromoprotein reporter expression plasmids. Finally, we used AssemblyTron to perform site-directed mutagenesis reactions via homology-dependent IVA also achieving comparable fidelity to manual assemblies as assessed by sequencing. AssemblyTron can reduce the time, training, costs and wastes associated with synthetic biology, which, along with open-source and affordable automation, will further foster the accessibility of synthetic biology and accelerate biological research and engineering.

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