机器人培养平台的实验工作流程管理。

IF 2.5 4区 医学 Q3 BIOCHEMICAL RESEARCH METHODS SLAS Technology Pub Date : 2024-10-30 DOI:10.1016/j.slast.2024.100214
Lucas Kaspersetz , Britta Englert , Fabian Krah , Ernesto C. Martinez , Peter Neubauer , M. Nicolas Cruz Bournazou
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引用次数: 0

摘要

在过去几十年里,机器人培养设施与自动执行工作流程相结合,大大提高了生物技术研究的速度。在这项工作中,我们介绍了机器人培养平台数字基础设施的设计和部署。我们在开源平台 Apache Airflow 的基础上,使用有向无环图实施了一个工作流管理系统,以提高实验的可追溯性和自动化执行。我们在实验室环境中演示了实验工作流的集成和自动化,实验室环境中的异构设备包括液体处理站、并行培养系统和移动机器人。在生产不同弹性蛋白的葡萄糖振荡并行大肠杆菌分批进行培养的过程中,对我们方法的可行性进行了评估。我们的研究表明,在机器人培养平台中使用工作流管理系统可以提高实验数据的自动化、稳健性和可追溯性。
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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Management of experimental workflows in robotic cultivation platforms
In the last decades, robotic cultivation facilities combined with automated execution of workflows have drastically increased the speed of research in biotechnology. In this work, we present the design and deployment of a digital infrastructure for robotic cultivation platforms. We implement a Workflow Management System, using Directed Acyclic Graphs, based on the open-source platform Apache Airflow to increase traceability and the automated execution of experiments. We demonstrate the integration and automation of experimental workflows in a laboratory environment with a heterogeneous device landscape including liquid handling stations, parallel cultivation systems, and mobile robots. The feasibility of our approach is assessed in parallel E. coli fed-batch cultivations with glucose oscillations in which different elastin-like proteins are produced. We show that the use of workflow management systems in robotic cultivation platforms increases automation, robustness and traceability of experimental data.
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来源期刊
SLAS Technology
SLAS Technology Computer Science-Computer Science Applications
CiteScore
6.30
自引率
7.40%
发文量
47
审稿时长
106 days
期刊介绍: SLAS Technology emphasizes scientific and technical advances that enable and improve life sciences research and development; drug-delivery; diagnostics; biomedical and molecular imaging; and personalized and precision medicine. This includes high-throughput and other laboratory automation technologies; micro/nanotechnologies; analytical, separation and quantitative techniques; synthetic chemistry and biology; informatics (data analysis, statistics, bio, genomic and chemoinformatics); and more.
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