高通量实验中的自动拉曼测量装置

IF 3.6 2区 生物学 Q2 BIOTECHNOLOGY & APPLIED MICROBIOLOGY
Christoph Lange, Simon Seidel, Madeline Altmann, Daniel Stors, Annina Kemmer, Linda Cai, Stefan Born, Peter Neubauer, M. Nicolas Cruz Bournazou
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引用次数: 0

摘要

高通量(HT)实验通过系统地探索复杂的多维实验条件,正在改变生物技术。然而,目前的分析方法往往无法处理高温工作流程中样品生成的快速步伐。本研究提出了一个集成的物理设备和软件系统,以自动化和加速高温设施中的拉曼光谱测量。该装置同时处理由移液机器人输送的8个平行L样品,在45秒内完成每个样品的测量,处理,清洁和浓度预测。我们引入了一个机器学习模型来预测来自拉曼光谱的代谢物浓度,在大肠杆菌培养过程中获得葡萄糖和乙酸的平均绝对误差。这种方法能够为发酵监测、校准和离线分析提供一致的高通量光谱数据收集,支持生成广泛的数据集,从而能够训练更强大、更通用的机器学习模型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Setup for Automatic Raman Measurements in High‐Throughput Experimentation
High‐throughput (HT) experimentation is transforming biotechnology by enabling systematic exploration of complex multi‐dimensional experimental conditions. However, current analytical methods are often unable to handle the rapid pace of sample generation in HT workflows. This study presents an integrated system of physical devices and software to automate and accelerate Raman spectral measurements in HT‐facilities. The setup simultaneously handles eight parallel L samples delivered by a pipetting robot, completing measurement, handling, cleaning, and concentration prediction within 45 s per sample. We introduce a machine learning model to predict metabolite concentrations from Raman spectra, achieving mean absolute errors of for glucose and for acetate during Escherichia coli cultivations. This approach enables consistent high‐throughput spectral data collection for fermentation monitoring, calibration, and offline analysis, supporting the generation of extensive datasets, enabling the training of more robust and generalizable machine learning models.
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来源期刊
Biotechnology and Bioengineering
Biotechnology and Bioengineering 工程技术-生物工程与应用微生物
CiteScore
7.90
自引率
5.30%
发文量
280
审稿时长
2.1 months
期刊介绍: Biotechnology & Bioengineering publishes Perspectives, Articles, Reviews, Mini-Reviews, and Communications to the Editor that embrace all aspects of biotechnology. These include: -Enzyme systems and their applications, including enzyme reactors, purification, and applied aspects of protein engineering -Animal-cell biotechnology, including media development -Applied aspects of cellular physiology, metabolism, and energetics -Biocatalysis and applied enzymology, including enzyme reactors, protein engineering, and nanobiotechnology -Biothermodynamics -Biofuels, including biomass and renewable resource engineering -Biomaterials, including delivery systems and materials for tissue engineering -Bioprocess engineering, including kinetics and modeling of biological systems, transport phenomena in bioreactors, bioreactor design, monitoring, and control -Biosensors and instrumentation -Computational and systems biology, including bioinformatics and genomic/proteomic studies -Environmental biotechnology, including biofilms, algal systems, and bioremediation -Metabolic and cellular engineering -Plant-cell biotechnology -Spectroscopic and other analytical techniques for biotechnological applications -Synthetic biology -Tissue engineering, stem-cell bioengineering, regenerative medicine, gene therapy and delivery systems The editors will consider papers for publication based on novelty, their immediate or future impact on biotechnological processes, and their contribution to the advancement of biochemical engineering science. Submission of papers dealing with routine aspects of bioprocessing, description of established equipment, and routine applications of established methodologies (e.g., control strategies, modeling, experimental methods) is discouraged. Theoretical papers will be judged based on the novelty of the approach and their potential impact, or on their novel capability to predict and elucidate experimental observations.
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